diff --git a/.gitignore b/.gitignore
index fa279f9071b..28f2aca854b 100644
--- a/.gitignore
+++ b/.gitignore
@@ -26,21 +26,29 @@
 # Compiled MATLAB
 *.mex*
 
-# build, distribute, and bins
-build
-.build_debug/*
-.build_release/*
-distribute/*
-*.testbin
-*.bin
-python/caffe/proto/
+# IPython notebook checkpoints
+.ipynb_checkpoints
 
 # Editor temporaries
 *.swp
 *~
 
-# IPython notebook checkpoints
-.ipynb_checkpoints
+# Sublime Text settings
+*.sublime-workspace
+*.sublime-project
+
+# Eclipse Project settings
+*.*project
+.settings
+
+# QtCreator files
+*.user
+
+# PyCharm files
+.idea
+
+# OSX dir files
+.DS_Store
 
 ## Caffe
 
@@ -58,13 +66,16 @@ models/*
 *leveldb
 *lmdb
 
-# LevelDB files
-*.sst
-*.ldb
-LOCK
-LOG*
-CURRENT
-MANIFEST-*
+# build, distribute, and bins (+ python proto bindings)
+build
+.build_debug/*
+.build_release/*
+distribute/*
+*.testbin
+*.bin
+python/caffe/proto/
+cmake_build
+.cmake_build
 
 # Generated documentation
 docs/_site
@@ -73,12 +84,10 @@ _site
 doxygen
 docs/dev
 
-# Sublime Text settings
-*.sublime-workspace
-*.sublime-project
-
-# Eclipse Project settings
-*.*project
-
-# CMake generated files
-*.gen.cmake
+# LevelDB files
+*.sst
+*.ldb
+LOCK
+LOG*
+CURRENT
+MANIFEST-*
diff --git a/.travis.yml b/.travis.yml
index 3deb45a2f0c..69285dfb57a 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -2,15 +2,16 @@
 # one using CMake, and one using make.
 env:
   matrix:
-    - WITH_CUDA=false WITH_CMAKE=false
     - WITH_CUDA=false WITH_CMAKE=true
-    - WITH_CUDA=true WITH_CMAKE=false
     - WITH_CUDA=true WITH_CMAKE=true
 
 language: cpp
 
 # Cache Ubuntu apt packages.
-cache: apt
+cache:
+  apt: true
+  directories:
+    - /home/travis/miniconda
 
 compiler: gcc
 
@@ -22,7 +23,8 @@ install:
   - sudo -E $SCRIPTS/travis_install.sh
 
 before_script:
-  - export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/lib
+  - export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/lib:/usr/local/cuda/lib64
+  - export PATH=/home/travis/miniconda/bin:$PATH
   - if ! $WITH_CMAKE; then $SCRIPTS/travis_setup_makefile_config.sh; fi
 
 script: $SCRIPTS/travis_build_and_test.sh
@@ -34,7 +36,7 @@ notifications:
 # your Caffe fork.  To configure your git email address, use:
 #     git config --global user.email me@example.com
   email:
-    on_success: always
+    on_success: change
     on_failure: always
 
 # IRC notifications disabled by default.
diff --git a/CHANGELOG.md b/CHANGELOG.md
new file mode 100644
index 00000000000..7fd0944de09
--- /dev/null
+++ b/CHANGELOG.md
@@ -0,0 +1,53 @@
+## Apr 27, 2016
+
+Features:
+
+  - Supported cuDNN v5
+  - Use the cuDNN's BatchNormalization implementation as the default engine for BN layer
+  - BN layer will now store running variance in its fourth blob.
+  - the script `python/bn_convert_style.py` is added to help converting the bn style forth and back.
+
+## Dec 23, 2015
+
+Features:
+
+  - Implemented a planning algorithm to globally optimize the cudnn workspace consumption and speed trade-off.
+  - Now `richness` parameter specifies the total memory in MBs available to cudnn for convolution workspaces.
+  - Now the framework will try to find the best convolution algorithm combinations under memory limit.
+  
+## Dec 17, 2015
+
+Features:
+
+  - cuDNN v4 support
+  - 20% overall speed gain with faster convolution and batch normalization
+  - the native batch normalization is changed to comply with cuDNN. Use the script `python/bn_var_to_inv_std.py` to upgrade your models.
+  
+## Nov 22, 2015
+
+Features:
+  - python layer can expose a prefetch() method, which will be run in parallel with network processing.
+  
+## Oct 13, 2015
+
+Features:
+  - Improved cuDNN wrapper to use less GPU memory. 
+  - Now there is a new parameter `richness` which controls the limit of workspace for cuDNN.
+  
+## Sep 30, 2015
+
+Features:
+  - Support for cuDNN v3.
+  
+## Sep. 7, 2015
+
+Features:
+  - New mechanism for parallel comminucation reduced parallel overhead.
+  - Batch normalization, courtesy of @Cysu.
+  
+## Jul, 2015
+
+Features:
+  - Action recognition tools, scripts, and examples.
+  - Basic parallel training support
+  - Various extra data augmentations
diff --git a/CMakeLists.txt b/CMakeLists.txt
index 3cb7d583504..3082d77fd05 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -1,90 +1,71 @@
-cmake_minimum_required(VERSION 2.8.8)
-project( Caffe )
+cmake_minimum_required(VERSION 2.8.7)
 
-###    Build Options     ##########################################################################
+# ---[ Caffe project
+project(Caffe C CXX)
 
-option(CPU_ONLY "Build Caffe without GPU support" OFF)
-option(BUILD_PYTHON "Build Python wrapper" OFF)
-option(BUILD_MATLAB "Build Matlab wrapper" OFF)
-option(BUILD_EXAMPLES "Build examples" ON)
-option(BUILD_SHARED_LIBS "Build SHARED libs if ON and STATIC otherwise" OFF)
+# ---[ Using cmake scripts and modules
+list(APPEND CMAKE_MODULE_PATH ${PROJECT_SOURCE_DIR}/cmake/Modules)
 
-if(NOT BLAS)
-    set(BLAS atlas)
-endif()
+include(ExternalProject)
 
-if(NOT CUDA_TEST_DEVICE)
-    set(CUDA_TEST_DEVICE -1)
-endif()
+include(cmake/Utils.cmake)
+include(cmake/Targets.cmake)
+include(cmake/Misc.cmake)
+include(cmake/Summary.cmake)
+include(cmake/ConfigGen.cmake)
 
-#    Install Prefix
-if (CMAKE_INSTALL_PREFIX_INITIALIZED_TO_DEFAULT)
-    set (CMAKE_INSTALL_PREFIX "${CMAKE_BINARY_DIR}/install" CACHE PATH "Default install path" FORCE )
-endif()
+# ---[ Options
+caffe_option(CPU_ONLY  "Build Caffe without CUDA support" OFF) # TODO: rename to USE_CUDA
+caffe_option(USE_CUDNN "Build Caffe with cuDNN libary support" ON IF NOT CPU_ONLY)
+caffe_option(BUILD_SHARED_LIBS "Build shared libraries" ON)
+caffe_option(BUILD_python "Build Python wrapper" ON)
+set(python_version "2" CACHE STRING "Specify which python version to use")
+caffe_option(BUILD_matlab "Build Matlab wrapper" OFF IF UNIX OR APPLE)
+caffe_option(BUILD_docs   "Build documentation" ON IF UNIX OR APPLE)
+caffe_option(BUILD_python_layer "Build the Caffe python layer" ON)
+
+caffe_option(USE_MPI "whether to include MPI parallelization" OFF) #Used to switch on and off MPI
 
-###    Configuration    ###########################################################################
-#    Compiler Flags
-set(CMAKE_CXX_COMPILER_FLAGS ${CMAKE_CXX_COMPILER_FLAGS} -Wall)
-set(CMAKE_CXX_FLAGS ${CMAKE_CXX_FLAGS} -fPIC) # set global flags
-set(CMAKE_CXX_FLAGS_DEBUG ${CMAKE_CXX_FLAGS_DEBUG}) # set debug flags
-set(CMAKE_CXX_FLAGS_RELEASE ${CMAKE_CXX_FLAGS_RELEASE}) # set release flags
+# ---[ Dependencies
+include(cmake/Dependencies.cmake)
 
-#    Global Definitions
-if(CPU_ONLY)
-    add_definitions(-DCPU_ONLY)
+# ---[ Flags
+if(UNIX OR APPLE)
+  set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fPIC -Wall")
 endif()
 
-#    Include Directories
-set(${PROJECT_NAME}_INCLUDE_DIRS ${CMAKE_SOURCE_DIR}/include)
-include_directories(${${PROJECT_NAME}_INCLUDE_DIRS})
-include_directories(${CMAKE_SOURCE_DIR}/src)
+if(USE_libstdcpp)
+  set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -stdlib=libstdc++")
+  message("-- Warning: forcing libstdc++ (controlled by USE_libstdcpp option in cmake)")
+endif()
 
-#    CMake Scripts dir
-set(CMAKE_SCRIPT_DIR ${CMAKE_SOURCE_DIR}/CMakeScripts)
+add_definitions(-DGTEST_USE_OWN_TR1_TUPLE)
 
-#	CMake module path for custom module finding
-set( CMAKE_MODULE_PATH ${CMAKE_MODULE_PATH} ${CMAKE_SCRIPT_DIR})
+# ---[ Warnings
+caffe_warnings_disable(CMAKE_CXX_FLAGS -Wno-sign-compare -Wno-uninitialized)
 
-#    CUDA is required globally
-if(NOT CPU_ONLY)
-    find_package(CUDA 5.5 REQUIRED)
-    include_directories(${CUDA_INCLUDE_DIRS})
-endif()
+# ---[ Config generation
+configure_file(cmake/Templates/caffe_config.h.in "${PROJECT_BINARY_DIR}/caffe_config.h")
 
-###    Subdirectories    ##########################################################################
+# ---[ Includes
+set(Caffe_INCLUDE_DIR ${PROJECT_SOURCE_DIR}/include)
+include_directories(${Caffe_INCLUDE_DIR} ${PROJECT_BINARY_DIR})
+include_directories(BEFORE src) # This is needed for gtest.
 
+# ---[ Subdirectories
 add_subdirectory(src/gtest)
 add_subdirectory(src/caffe)
 add_subdirectory(tools)
+add_subdirectory(examples)
+add_subdirectory(python)
+add_subdirectory(matlab)
+add_subdirectory(docs)
 
-if(BUILD_EXAMPLES)
-    message(STATUS "Examples enabled")
-    add_subdirectory(examples)
-endif()
-
-if(BUILD_PYTHON)
-    message(STATUS "Python enabled")
-    add_subdirectory(python)
-endif()
-
-if(BUILD_MATLAB)
-    message(STATUS "Matlab enabled")
-    add_subdirectory(matlab)
-endif()
-
-###    Lint Target Setup    ##########################################################################
-
-set(LINT_TARGET lint)
-set(LINT_SCRIPT ${CMAKE_SCRIPT_DIR}/lint.cmake)
-add_custom_target(
-    ${LINT_TARGET} 
-    COMMAND ${CMAKE_COMMAND} -P ${LINT_SCRIPT}
-)
-
-###    Install    #################################################################################
-
-#    Install Includes
-file(GLOB folders ${${PROJECT_NAME}_INCLUDE_DIRS}/*)
-install(DIRECTORY ${folders} DESTINATION include)
+# ---[ Linter target
+add_custom_target(lint COMMAND ${CMAKE_COMMAND} -P ${PROJECT_SOURCE_DIR}/cmake/lint.cmake)
 
+# ---[ Configuration summary
+caffe_print_configuration_summary()
 
+# ---[ Export configs generation
+caffe_generate_export_configs()
diff --git a/CMakeScripts/FindLMDB.cmake b/CMakeScripts/FindLMDB.cmake
deleted file mode 100644
index e615f542335..00000000000
--- a/CMakeScripts/FindLMDB.cmake
+++ /dev/null
@@ -1,28 +0,0 @@
-# Try to find the LMBD libraries and headers
-#  LMDB_FOUND - system has LMDB lib
-#  LMDB_INCLUDE_DIR - the LMDB include directory
-#  LMDB_LIBRARIES - Libraries needed to use LMDB
-
-# FindCWD based on FindGMP by:
-# Copyright (c) 2006, Laurent Montel, <montel@kde.org>
-#
-# Redistribution and use is allowed according to the terms of the BSD license.
-
-# Adapted from FindCWD by:
-# Copyright 2013 Conrad Steenberg <conrad.steenberg@gmail.com>
-# Aug 31, 2013
-
-if (LMDB_INCLUDE_DIR AND LMDB_LIBRARIES)
-  # Already in cache, be silent
-  set(LMDB_FIND_QUIETLY TRUE)
-endif (LMDB_INCLUDE_DIR AND LMDB_LIBRARIES)
-
-find_path(LMDB_INCLUDE_DIR NAMES "lmdb.h" HINTS "$ENV{LMDB_DIR}/include")
-find_library(LMDB_LIBRARIES NAMES lmdb HINTS $ENV{LMDB_DIR}/lib )
-MESSAGE(STATUS "LMDB lib: " ${LMDB_LIBRARIES} )
-MESSAGE(STATUS "LMDB include: " ${LMDB_INCLUDE} )
-
-include(FindPackageHandleStandardArgs)
-FIND_PACKAGE_HANDLE_STANDARD_ARGS(LMDB DEFAULT_MSG LMDB_INCLUDE_DIR LMDB_LIBRARIES)
-
-mark_as_advanced(LMDB_INCLUDE_DIR LMDB_LIBRARIES)
diff --git a/CMakeScripts/FindLevelDB.cmake b/CMakeScripts/FindLevelDB.cmake
deleted file mode 100644
index f3386f26dbf..00000000000
--- a/CMakeScripts/FindLevelDB.cmake
+++ /dev/null
@@ -1,37 +0,0 @@
-# - Find LevelDB
-#
-#  LEVELDB_INCLUDE - Where to find leveldb/db.h
-#  LEVELDB_LIBS    - List of libraries when using LevelDB.
-#  LEVELDB_FOUND   - True if LevelDB found.
-
-get_filename_component(module_file_path ${CMAKE_CURRENT_LIST_FILE} PATH)
-
-# Look for the header file.
-find_path(LEVELDB_INCLUDE NAMES leveldb/db.h PATHS $ENV{LEVELDB_ROOT}/include /opt/local/include /usr/local/include /usr/include DOC "Path in which the file leveldb/db.h is located." )
-mark_as_advanced(LEVELDB_INCLUDE)
-
-# Look for the library.
-# Does this work on UNIX systems? (LINUX)
-find_library(LEVELDB_LIBS NAMES leveldb PATHS /usr/lib $ENV{LEVELDB_ROOT}/lib DOC "Path to leveldb library." )
-mark_as_advanced(LEVELDB_LIBS)
-
-# Copy the results to the output variables.
-if (LEVELDB_INCLUDE AND LEVELDB_LIBS)
-  message(STATUS "Found leveldb in ${LEVELDB_INCLUDE} ${LEVELDB_LIBS}")
-  set(LEVELDB_FOUND 1)
-  include(CheckCXXSourceCompiles)
-  set(CMAKE_REQUIRED_LIBRARY ${LEVELDB_LIBS} pthread)
-  set(CMAKE_REQUIRED_INCLUDES ${LEVELDB_INCLUDE})
- else ()
-   set(LEVELDB_FOUND 0)
- endif ()
-
- # Report the results.
- if (NOT LEVELDB_FOUND)
-   set(LEVELDB_DIR_MESSAGE "LEVELDB was not found. Make sure LEVELDB_LIBS and LEVELDB_INCLUDE are set.")
-   if (LEVELDB_FIND_REQUIRED)
-     message(FATAL_ERROR "${LEVELDB_DIR_MESSAGE}")
-   elseif (NOT LEVELDB_FIND_QUIETLY)
-     message(STATUS "${LEVELDB_DIR_MESSAGE}")
-   endif ()
- endif ()
\ No newline at end of file
diff --git a/CMakeScripts/FindMKL.cmake b/CMakeScripts/FindMKL.cmake
deleted file mode 100644
index eb2d9f8868b..00000000000
--- a/CMakeScripts/FindMKL.cmake
+++ /dev/null
@@ -1,113 +0,0 @@
-# - Find Intel MKL
-# Find the MKL libraries
-#
-# Options:
-#
-#   MKL_STATAIC       :   use static linking
-#   MKL_MULTI_THREADED:   use multi-threading
-#   MKL_SDL           :   Single Dynamic Library interface
-#
-# This module defines the following variables:
-#
-#   MKL_FOUND            : True if MKL_INCLUDE_DIR are found
-#   MKL_INCLUDE_DIR      : where to find mkl.h, etc.
-#   MKL_INCLUDE_DIRS     : set when MKL_INCLUDE_DIR found
-#   MKL_LIBRARIES        : the library to link against.
-
-
-include(FindPackageHandleStandardArgs)
-
-set(INTEL_ROOT "/opt/intel" CACHE PATH "Folder contains intel libs")
-set(MKL_ROOT ${INTEL_ROOT}/mkl CACHE PATH "Folder contains MKL")
-
-# Find include dir
-find_path(MKL_INCLUDE_DIR mkl.h
-    PATHS ${MKL_ROOT}/include)
-
-# Find include directory
-#  There is no include folder under linux
-if(WIN32)
-    find_path(INTEL_INCLUDE_DIR omp.h
-        PATHS ${INTEL_ROOT}/include)
-    set(MKL_INCLUDE_DIR ${MKL_INCLUDE_DIR} ${INTEL_INCLUDE_DIR})
-endif()
-
-# Find libraries
-
-# Handle suffix
-set(_MKL_ORIG_CMAKE_FIND_LIBRARY_SUFFIXES ${CMAKE_FIND_LIBRARY_SUFFIXES})
-
-if(WIN32)
-    if(MKL_STATAIC)
-        set(CMAKE_FIND_LIBRARY_SUFFIXES .lib)
-    else()
-        set(CMAKE_FIND_LIBRARY_SUFFIXES _dll.lib)
-    endif()
-else()
-    if(MKL_STATAIC)
-        set(CMAKE_FIND_LIBRARY_SUFFIXES .a)
-    else()
-        set(CMAKE_FIND_LIBRARY_SUFFIXES .so)
-    endif()
-endif()
-
-
-# MKL is composed by four layers: Interface, Threading, Computational and RTL
-
-if(MKL_SDL)
-    find_library(MKL_LIBRARY mkl_rt
-        PATHS ${MKL_ROOT}/lib/ia32/)
-
-    set(MKL_MINIMAL_LIBRARY ${MKL_LIBRARY})
-else()
-    ######################### Interface layer #######################
-    if(WIN32)
-        set(MKL_INTERFACE_LIBNAME mkl_intel_c)
-    else()
-        set(MKL_INTERFACE_LIBNAME mkl_intel)
-    endif()
-
-    find_library(MKL_INTERFACE_LIBRARY ${MKL_INTERFACE_LIBNAME}
-        PATHS ${MKL_ROOT}/lib/ia32/)
-
-    ######################## Threading layer ########################
-    if(MKL_MULTI_THREADED)
-        set(MKL_THREADING_LIBNAME mkl_intel_thread)
-    else()
-        set(MKL_THREADING_LIBNAME mkl_sequential)
-    endif()
-
-    find_library(MKL_THREADING_LIBRARY ${MKL_THREADING_LIBNAME}
-        PATHS ${MKL_ROOT}/lib/ia32/)
-
-    ####################### Computational layer #####################
-    find_library(MKL_CORE_LIBRARY mkl_core
-        PATHS ${MKL_ROOT}/lib/ia32/)
-    find_library(MKL_FFT_LIBRARY mkl_cdft_core
-        PATHS ${MKL_ROOT}/lib/ia32/)
-    find_library(MKL_SCALAPACK_LIBRARY mkl_scalapack_core
-        PATHS ${MKL_ROOT}/lib/ia32/)
-
-    ############################ RTL layer ##########################
-    if(WIN32)
-        set(MKL_RTL_LIBNAME libiomp5md)
-    else()
-        set(MKL_RTL_LIBNAME libiomp5)
-    endif()
-    find_library(MKL_RTL_LIBRARY ${MKL_RTL_LIBNAME}
-        PATHS ${INTEL_RTL_ROOT}/lib)
-
-    set(MKL_LIBRARY ${MKL_INTERFACE_LIBRARY} ${MKL_THREADING_LIBRARY} ${MKL_CORE_LIBRARY} ${MKL_FFT_LIBRARY} ${MKL_SCALAPACK_LIBRARY} ${MKL_RTL_LIBRARY})
-    set(MKL_MINIMAL_LIBRARY ${MKL_INTERFACE_LIBRARY} ${MKL_THREADING_LIBRARY} ${MKL_CORE_LIBRARY} ${MKL_RTL_LIBRARY})
-endif()
-
-set(CMAKE_FIND_LIBRARY_SUFFIXES ${_MKL_ORIG_CMAKE_FIND_LIBRARY_SUFFIXES})
-
-find_package_handle_standard_args(MKL DEFAULT_MSG
-    MKL_INCLUDE_DIR MKL_LIBRARY MKL_MINIMAL_LIBRARY)
-
-if(MKL_FOUND)
-    set(MKL_INCLUDE_DIRS ${MKL_INCLUDE_DIR})
-    set(MKL_LIBRARIES ${MKL_LIBRARY})
-    set(MKL_MINIMAL_LIBRARIES ${MKL_LIBRARY})
-endif()
diff --git a/CMakeScripts/FindSnappy.cmake b/CMakeScripts/FindSnappy.cmake
deleted file mode 100644
index d769b442812..00000000000
--- a/CMakeScripts/FindSnappy.cmake
+++ /dev/null
@@ -1,33 +0,0 @@
-# Find the Snappy libraries
-#
-# The following variables are optionally searched for defaults
-#  Snappy_ROOT_DIR:    Base directory where all Snappy components are found
-#
-# The following are set after configuration is done:
-#  Snappy_FOUND
-#  Snappy_INCLUDE_DIRS
-#  Snappy_LIBS
-
-find_path(SNAPPY_INCLUDE_DIR
-    NAMES snappy.h
-    HINTS ${SNAPPY_ROOT_DIR}
-          ${SNAPPY_ROOT_DIR}/include
-)
-
-find_library(SNAPPY_LIBS
-    NAMES snappy
-    HINTS ${SNAPPY_ROOT_DIR}
-          ${SNAPPY_ROOT_DIR}/lib
-)
-
-include(FindPackageHandleStandardArgs)
-find_package_handle_standard_args(Snappy
-    DEFAULT_MSG
-    SNAPPY_LIBS
-    SNAPPY_INCLUDE_DIR
-)
-
-mark_as_advanced(
-    SNAPPY_LIBS
-    SNAPPY_INCLUDE_DIR
-)
diff --git a/LICENSE b/LICENSE
index efcc5c5b6b0..d69d16f5bc7 100644
--- a/LICENSE
+++ b/LICENSE
@@ -1,11 +1,11 @@
 COPYRIGHT
 
 All contributions by the University of California:
-Copyright (c) 2014, The Regents of the University of California (Regents)
+Copyright (c) 2014, 2015, The Regents of the University of California (Regents)
 All rights reserved.
 
 All other contributions:
-Copyright (c) 2014, the respective contributors
+Copyright (c) 2014, 2015, the respective contributors
 All rights reserved.
 
 Caffe uses a shared copyright model: each contributor holds copyright over
diff --git a/Makefile b/Makefile
index 6a660f5e0f2..e4e66dfd138 100644
--- a/Makefile
+++ b/Makefile
@@ -1,12 +1,19 @@
-# The makefile for caffe. Pretty hacky.
 PROJECT := caffe
 
 CONFIG_FILE := Makefile.config
+# Explicitly check for the config file, otherwise make -k will proceed anyway.
+ifeq ($(wildcard $(CONFIG_FILE)),)
+$(error $(CONFIG_FILE) not found. See $(CONFIG_FILE).example.)
+endif
 include $(CONFIG_FILE)
 
 BUILD_DIR_LINK := $(BUILD_DIR)
-RELEASE_BUILD_DIR := .$(BUILD_DIR)_release
-DEBUG_BUILD_DIR := .$(BUILD_DIR)_debug
+ifeq ($(RELEASE_BUILD_DIR),)
+	RELEASE_BUILD_DIR := .$(BUILD_DIR)_release
+endif
+ifeq ($(DEBUG_BUILD_DIR),)
+	DEBUG_BUILD_DIR := .$(BUILD_DIR)_debug
+endif
 
 DEBUG ?= 0
 ifeq ($(DEBUG), 1)
@@ -17,18 +24,20 @@ else
 	OTHER_BUILD_DIR := $(DEBUG_BUILD_DIR)
 endif
 
-# The target shared library and static library name
+# All of the directories containing code.
+SRC_DIRS := $(shell find * -type d -exec bash -c "find {} -maxdepth 1 \
+	\( -name '*.cpp' -o -name '*.proto' \) | grep -q ." \; -print)
+
+# The target shared library name
 LIB_BUILD_DIR := $(BUILD_DIR)/lib
-NAME := $(LIB_BUILD_DIR)/lib$(PROJECT).so
 STATIC_NAME := $(LIB_BUILD_DIR)/lib$(PROJECT).a
+DYNAMIC_NAME := $(LIB_BUILD_DIR)/lib$(PROJECT).so
 
 ##############################
 # Get all source files
 ##############################
 # CXX_SRCS are the source files excluding the test ones.
 CXX_SRCS := $(shell find src/$(PROJECT) ! -name "test_*.cpp" -name "*.cpp")
-# HXX_SRCS are the header files
-HXX_SRCS := $(shell find include/$(PROJECT) ! -name "test_*.hpp" -name "*.hpp")
 # CU_SRCS are the cuda source files
 CU_SRCS := $(shell find src/$(PROJECT) ! -name "test_*.cu" -name "*.cu")
 # TEST_SRCS are the test source files
@@ -37,8 +46,6 @@ TEST_SRCS := $(shell find src/$(PROJECT) -name "test_*.cpp")
 TEST_SRCS := $(filter-out $(TEST_MAIN_SRC), $(TEST_SRCS))
 TEST_CU_SRCS := $(shell find src/$(PROJECT) -name "test_*.cu")
 GTEST_SRC := src/gtest/gtest-all.cpp
-# TEST_HXX_SRCS are the test header files
-TEST_HXX_SRCS := $(shell find include/$(PROJECT) -name "test_*.hpp")
 # TOOL_SRCS are the source files for the tool binaries
 TOOL_SRCS := $(shell find tools -name "*.cpp")
 # EXAMPLE_SRCS are the source files for the example binaries
@@ -58,7 +65,7 @@ NONGEN_CXX_SRCS := $(shell find \
 	src/$(PROJECT) \
 	include/$(PROJECT) \
 	python/$(PROJECT) \
-	matlab/$(PROJECT) \
+	matlab/+$(PROJECT)/private \
 	examples \
 	tools \
 	-name "*.cpp" -or -name "*.hpp" -or -name "*.cu" -or -name "*.cuh")
@@ -70,14 +77,14 @@ EMPTY_LINT_REPORT := $(BUILD_DIR)/.$(LINT_EXT)
 NONEMPTY_LINT_REPORT := $(BUILD_DIR)/$(LINT_EXT)
 # PY$(PROJECT)_SRC is the python wrapper for $(PROJECT)
 PY$(PROJECT)_SRC := python/$(PROJECT)/_$(PROJECT).cpp
-PY$(PROJECT)_HXX_SRC := python/$(PROJECT)/_$(PROJECT).hpp
 PY$(PROJECT)_SO := python/$(PROJECT)/_$(PROJECT).so
-# MAT$(PROJECT)_SRC is the matlab wrapper for $(PROJECT)
-MAT$(PROJECT)_SRC := matlab/$(PROJECT)/mat$(PROJECT).cpp
+PY$(PROJECT)_HXX := include/$(PROJECT)/python_layer.hpp
+# MAT$(PROJECT)_SRC is the mex entrance point of matlab package for $(PROJECT)
+MAT$(PROJECT)_SRC := matlab/+$(PROJECT)/private/$(PROJECT)_.cpp
 ifneq ($(MATLAB_DIR),)
 	MAT_SO_EXT := $(shell $(MATLAB_DIR)/bin/mexext)
 endif
-MAT$(PROJECT)_SO := matlab/$(PROJECT)/$(PROJECT).$(MAT_SO_EXT)
+MAT$(PROJECT)_SO := matlab/+$(PROJECT)/private/$(PROJECT)_.$(MAT_SO_EXT)
 
 ##############################
 # Derive generated files
@@ -87,7 +94,6 @@ PROTO_GEN_HEADER_SRCS := $(addprefix $(PROTO_BUILD_DIR)/, \
 		$(notdir ${PROTO_SRCS:.proto=.pb.h}))
 PROTO_GEN_HEADER := $(addprefix $(PROTO_BUILD_INCLUDE_DIR)/, \
 		$(notdir ${PROTO_SRCS:.proto=.pb.h}))
-HXX_SRCS += $(PROTO_GEN_HEADER)
 PROTO_GEN_CC := $(addprefix $(BUILD_DIR)/, ${PROTO_SRCS:.proto=.pb.cc})
 PY_PROTO_BUILD_DIR := python/$(PROJECT)/proto
 PY_PROTO_INIT := python/$(PROJECT)/proto/__init__.py
@@ -97,25 +103,22 @@ PROTO_GEN_PY := $(foreach file,${PROTO_SRCS:.proto=_pb2.py}, \
 # These objects will be linked into the final shared library, so we
 # exclude the tool, example, and test objects.
 CXX_OBJS := $(addprefix $(BUILD_DIR)/, ${CXX_SRCS:.cpp=.o})
-CU_OBJS := $(addprefix $(BUILD_DIR)/, ${CU_SRCS:.cu=.cuo})
+CU_OBJS := $(addprefix $(BUILD_DIR)/cuda/, ${CU_SRCS:.cu=.o})
 PROTO_OBJS := ${PROTO_GEN_CC:.cc=.o}
-OBJ_BUILD_DIR := $(BUILD_DIR)/src/$(PROJECT)
-LAYER_BUILD_DIR := $(OBJ_BUILD_DIR)/layers
-UTIL_BUILD_DIR := $(OBJ_BUILD_DIR)/util
 OBJS := $(PROTO_OBJS) $(CXX_OBJS) $(CU_OBJS)
 # tool, example, and test objects
 TOOL_OBJS := $(addprefix $(BUILD_DIR)/, ${TOOL_SRCS:.cpp=.o})
 TOOL_BUILD_DIR := $(BUILD_DIR)/tools
-TEST_BUILD_DIR := $(BUILD_DIR)/src/$(PROJECT)/test
+TEST_CXX_BUILD_DIR := $(BUILD_DIR)/src/$(PROJECT)/test
+TEST_CU_BUILD_DIR := $(BUILD_DIR)/cuda/src/$(PROJECT)/test
 TEST_CXX_OBJS := $(addprefix $(BUILD_DIR)/, ${TEST_SRCS:.cpp=.o})
-TEST_CU_OBJS := $(addprefix $(BUILD_DIR)/, ${TEST_CU_SRCS:.cu=.cuo})
+TEST_CU_OBJS := $(addprefix $(BUILD_DIR)/cuda/, ${TEST_CU_SRCS:.cu=.o})
 TEST_OBJS := $(TEST_CXX_OBJS) $(TEST_CU_OBJS)
 GTEST_OBJ := $(addprefix $(BUILD_DIR)/, ${GTEST_SRC:.cpp=.o})
-GTEST_BUILD_DIR := $(dir $(GTEST_OBJ))
 EXAMPLE_OBJS := $(addprefix $(BUILD_DIR)/, ${EXAMPLE_SRCS:.cpp=.o})
-EXAMPLE_BUILD_DIR := $(BUILD_DIR)/examples
-EXAMPLE_BUILD_DIRS := $(EXAMPLE_BUILD_DIR)
-EXAMPLE_BUILD_DIRS += $(foreach obj,$(EXAMPLE_OBJS),$(dir $(obj)))
+# Output files for automatic dependency generation
+DEPS := ${CXX_OBJS:.o=.d} ${CU_OBJS:.o=.d} ${TEST_CXX_OBJS:.o=.d} \
+	${TEST_CU_OBJS:.o=.d} $(BUILD_DIR)/${MAT$(PROJECT)_SO:.$(MAT_SO_EXT)=.d}
 # tool, example, and test bins
 TOOL_BINS := ${TOOL_OBJS:.o=.bin}
 EXAMPLE_BINS := ${EXAMPLE_OBJS:.o=.bin}
@@ -128,6 +131,7 @@ TEST_CU_BINS := $(addsuffix .testbin,$(addprefix $(TEST_BIN_DIR)/, \
 TEST_CXX_BINS := $(addsuffix .testbin,$(addprefix $(TEST_BIN_DIR)/, \
 		$(foreach obj,$(TEST_CXX_OBJS),$(basename $(notdir $(obj))))))
 TEST_BINS := $(TEST_CXX_BINS) $(TEST_CU_BINS)
+# TEST_ALL_BIN is the test binary that links caffe dynamically.
 TEST_ALL_BIN := $(TEST_BIN_DIR)/test_all.testbin
 
 ##############################
@@ -135,11 +139,11 @@ TEST_ALL_BIN := $(TEST_BIN_DIR)/test_all.testbin
 ##############################
 WARNS_EXT := warnings.txt
 CXX_WARNS := $(addprefix $(BUILD_DIR)/, ${CXX_SRCS:.cpp=.o.$(WARNS_EXT)})
-CU_WARNS := $(addprefix $(BUILD_DIR)/, ${CU_SRCS:.cu=.cuo.$(WARNS_EXT)})
+CU_WARNS := $(addprefix $(BUILD_DIR)/cuda/, ${CU_SRCS:.cu=.o.$(WARNS_EXT)})
 TOOL_WARNS := $(addprefix $(BUILD_DIR)/, ${TOOL_SRCS:.cpp=.o.$(WARNS_EXT)})
 EXAMPLE_WARNS := $(addprefix $(BUILD_DIR)/, ${EXAMPLE_SRCS:.cpp=.o.$(WARNS_EXT)})
 TEST_WARNS := $(addprefix $(BUILD_DIR)/, ${TEST_SRCS:.cpp=.o.$(WARNS_EXT)})
-TEST_CU_WARNS := $(addprefix $(BUILD_DIR)/, ${TEST_CU_SRCS:.cu=.cuo.$(WARNS_EXT)})
+TEST_CU_WARNS := $(addprefix $(BUILD_DIR)/cuda/, ${TEST_CU_SRCS:.cu=.o.$(WARNS_EXT)})
 ALL_CXX_WARNS := $(CXX_WARNS) $(TOOL_WARNS) $(EXAMPLE_WARNS) $(TEST_WARNS)
 ALL_CU_WARNS := $(CU_WARNS) $(TEST_CU_WARNS)
 ALL_WARNS := $(ALL_CXX_WARNS) $(ALL_CU_WARNS)
@@ -166,10 +170,8 @@ ifneq ($(CPU_ONLY), 1)
 	LIBRARIES := cudart cublas curand
 endif
 LIBRARIES += glog gflags protobuf leveldb snappy \
-	lmdb \
-	boost_system \
-	hdf5_hl hdf5 \
-	opencv_core opencv_highgui opencv_imgproc pthread
+	lmdb boost_system hdf5_hl hdf5 m \
+	opencv_core opencv_highgui opencv_imgproc
 PYTHON_LIBRARIES := boost_python python2.7
 WARNINGS := -Wall -Wno-sign-compare
 
@@ -177,20 +179,17 @@ WARNINGS := -Wall -Wno-sign-compare
 # Set build directories
 ##############################
 
+DISTRIBUTE_DIR ?= distribute
 DISTRIBUTE_SUBDIRS := $(DISTRIBUTE_DIR)/bin $(DISTRIBUTE_DIR)/lib
 DIST_ALIASES := dist
 ifneq ($(strip $(DISTRIBUTE_DIR)),distribute)
 		DIST_ALIASES += distribute
 endif
 
-ALL_BUILD_DIRS := $(sort \
-		$(BUILD_DIR) $(LIB_BUILD_DIR) $(OBJ_BUILD_DIR) \
-		$(LAYER_BUILD_DIR) $(UTIL_BUILD_DIR) $(TOOL_BUILD_DIR) \
-		$(TEST_BUILD_DIR) $(TEST_BIN_DIR) $(GTEST_BUILD_DIR) \
-		$(EXAMPLE_BUILD_DIRS) \
-		$(LINT_OUTPUT_DIR) \
-		$(PROTO_BUILD_DIR) $(PROTO_BUILD_INCLUDE_DIR) $(PY_PROTO_BUILD_DIR) \
-		$(DISTRIBUTE_SUBDIRS))
+ALL_BUILD_DIRS := $(sort $(BUILD_DIR) $(addprefix $(BUILD_DIR)/, $(SRC_DIRS)) \
+	$(addprefix $(BUILD_DIR)/cuda/, $(SRC_DIRS)) \
+	$(LIB_BUILD_DIR) $(TEST_BIN_DIR) $(PY_PROTO_BUILD_DIR) $(LINT_OUTPUT_DIR) \
+	$(DISTRIBUTE_SUBDIRS) $(PROTO_BUILD_INCLUDE_DIR))
 
 ##############################
 # Set directory for Doxygen-generated documentation
@@ -224,6 +223,7 @@ else ifeq ($(UNAME), Darwin)
 	OSX := 1
 endif
 
+# Linux
 ifeq ($(LINUX), 1)
 	CXX ?= /usr/bin/g++
 	GCCVERSION := $(shell $(CXX) -dumpversion | cut -f1,2 -d.)
@@ -232,22 +232,33 @@ ifeq ($(LINUX), 1)
 		WARNINGS += -Wno-uninitialized
 	endif
 	# boost::thread is reasonably called boost_thread (compare OS X)
-	LIBRARIES += boost_thread
+	# We will also explicitly add stdc++ to the link target.
+	LIBRARIES += boost_thread stdc++
 endif
 
 # OS X:
 # clang++ instead of g++
-# libstdc++ instead of libc++ for CUDA compatibility on 10.9
+# libstdc++ for NVCC compatibility on OS X >= 10.9 with CUDA < 7.0
 ifeq ($(OSX), 1)
 	CXX := /usr/bin/clang++
-	# clang throws this warning for cuda headers
-	WARNINGS += -Wno-unneeded-internal-declaration
-	ifneq ($(findstring 10.9, $(shell sw_vers -productVersion)),)
-		CXXFLAGS += -stdlib=libstdc++
-		LINKFLAGS += -stdlib=libstdc++
+	ifneq ($(CPU_ONLY), 1)
+		CUDA_VERSION := $(shell $(CUDA_DIR)/bin/nvcc -V | grep -o 'release \d' | grep -o '\d')
+		ifeq ($(shell echo $(CUDA_VERSION) \< 7.0 | bc), 1)
+			CXXFLAGS += -stdlib=libstdc++
+			LINKFLAGS += -stdlib=libstdc++
+		endif
+		# clang throws this warning for cuda headers
+		WARNINGS += -Wno-unneeded-internal-declaration
 	endif
+	# gtest needs to use its own tuple to not conflict with clang
+	COMMON_FLAGS += -DGTEST_USE_OWN_TR1_TUPLE=1
 	# boost::thread is called boost_thread-mt to mark multithreading on OS X
 	LIBRARIES += boost_thread-mt
+	# we need to explicitly ask for the rpath to be obeyed
+	DYNAMIC_FLAGS := -install_name @rpath/libcaffe.so
+	ORIGIN := @loader_path
+else
+	ORIGIN := \$$ORIGIN
 endif
 
 # Custom compiler
@@ -255,6 +266,16 @@ ifdef CUSTOM_CXX
 	CXX := $(CUSTOM_CXX)
 endif
 
+# Static linking
+ifneq (,$(findstring clang++,$(CXX)))
+	STATIC_LINK_COMMAND := -Wl,-force_load $(STATIC_NAME)
+else ifneq (,$(findstring g++,$(CXX)))
+	STATIC_LINK_COMMAND := -Wl,--whole-archive $(STATIC_NAME) -Wl,--no-whole-archive
+else
+  # The following line must not be indented with a tab, since we are not inside a target
+  $(error Cannot static link with the $(CXX) compiler)
+endif
+
 # Debugging
 ifeq ($(DEBUG), 1)
 	COMMON_FLAGS += -DDEBUG -g -O0
@@ -279,6 +300,12 @@ ifeq ($(CPU_ONLY), 1)
 	COMMON_FLAGS += -DCPU_ONLY
 endif
 
+# Python layer support
+ifeq ($(WITH_PYTHON_LAYER), 1)
+	COMMON_FLAGS += -DWITH_PYTHON_LAYER
+	LIBRARIES += $(PYTHON_LIBRARIES)
+endif
+
 # BLAS configuration (default = ATLAS)
 BLAS ?= atlas
 ifeq ($(BLAS), mkl)
@@ -300,22 +327,41 @@ else
 		endif
 	else ifeq ($(OSX), 1)
 		# OS X packages atlas as the vecLib framework
-		BLAS_INCLUDE ?= /System/Library/Frameworks/vecLib.framework/Versions/Current/Headers/
 		LIBRARIES += cblas
-		LDFLAGS += -framework vecLib
+		# 10.10 has accelerate while 10.9 has veclib
+		XCODE_CLT_VER := $(shell pkgutil --pkg-info=com.apple.pkg.CLTools_Executables | grep -o 'version: 6')
+		ifneq (,$(findstring version: 6,$(XCODE_CLT_VER)))
+			BLAS_INCLUDE ?= /System/Library/Frameworks/Accelerate.framework/Versions/Current/Frameworks/vecLib.framework/Headers/
+			LDFLAGS += -framework Accelerate
+		else
+			BLAS_INCLUDE ?= /System/Library/Frameworks/vecLib.framework/Versions/Current/Headers/
+			LDFLAGS += -framework vecLib
+		endif
 	endif
 endif
 INCLUDE_DIRS += $(BLAS_INCLUDE)
 LIBRARY_DIRS += $(BLAS_LIB)
 
+LIBRARY_DIRS += $(LIB_BUILD_DIR)
+
+# Automatic dependency generation (nvcc is handled separately)
+CXXFLAGS += -MMD -MP
+
 # Complete build flags.
 COMMON_FLAGS += $(foreach includedir,$(INCLUDE_DIRS),-I$(includedir))
 CXXFLAGS += -pthread -fPIC $(COMMON_FLAGS) $(WARNINGS)
 NVCCFLAGS += -ccbin=$(CXX) -Xcompiler -fPIC $(COMMON_FLAGS)
 # mex may invoke an older gcc that is too liberal with -Wuninitalized
 MATLAB_CXXFLAGS := $(CXXFLAGS) -Wno-uninitialized
-LINKFLAGS += -fPIC $(COMMON_FLAGS) $(WARNINGS)
-LDFLAGS += $(foreach librarydir,$(LIBRARY_DIRS),-L$(librarydir)) \
+LINKFLAGS += -pthread -fPIC $(COMMON_FLAGS) $(WARNINGS)
+
+USE_PKG_CONFIG ?= 0
+ifeq ($(USE_PKG_CONFIG), 1)
+	PKG_CONFIG := $(shell pkg-config opencv --libs)
+else
+	PKG_CONFIG :=
+endif
+LDFLAGS += $(foreach librarydir,$(LIBRARY_DIRS),-L$(librarydir)) $(PKG_CONFIG) \
 		$(foreach library,$(LIBRARIES),-l$(library))
 PYTHON_LDFLAGS := $(LDFLAGS) $(foreach library,$(PYTHON_LIBRARIES),-l$(library))
 
@@ -330,6 +376,13 @@ PYTHON_LDFLAGS := $(LDFLAGS) $(foreach library,$(PYTHON_LIBRARIES),-l$(library))
 # default behavior of 'find'.
 SUPERCLEAN_EXTS := .so .a .o .bin .testbin .pb.cc .pb.h _pb2.py .cuo
 
+# Set the sub-targets of the 'everything' target.
+EVERYTHING_TARGETS := all py$(PROJECT) test warn lint
+# Only build matcaffe as part of "everything" if MATLAB_DIR is specified.
+ifneq ($(MATLAB_DIR),)
+	EVERYTHING_TARGETS += mat$(PROJECT)
+endif
+
 ##############################
 # Define build targets
 ##############################
@@ -337,9 +390,9 @@ SUPERCLEAN_EXTS := .so .a .o .bin .testbin .pb.cc .pb.h _pb2.py .cuo
 	py mat py$(PROJECT) mat$(PROJECT) proto runtest \
 	superclean supercleanlist supercleanfiles warn everything
 
-all: $(NAME) $(STATIC_NAME) tools examples
+all: $(STATIC_NAME) $(DYNAMIC_NAME) tools examples
 
-everything: all py$(PROJECT) mat$(PROJECT) test warn lint runtest
+everything: $(EVERYTHING_TARGETS)
 
 linecount:
 	cloc --read-lang-def=$(PROJECT).cloc \
@@ -376,7 +429,7 @@ $(LINT_OUTPUTS): $(LINT_OUTPUT_DIR)/%.lint.txt : % $(LINT_SCRIPT) | $(LINT_OUTPU
 		> $@ \
 		|| true
 
-test: $(TEST_ALL_BIN) $(TEST_BINS)
+test: $(TEST_ALL_BIN) $(TEST_ALL_DYNLINK_BIN) $(TEST_BINS)
 
 tools: $(TOOL_BINS) $(TOOL_BIN_LINKS)
 
@@ -386,10 +439,11 @@ py$(PROJECT): py
 
 py: $(PY$(PROJECT)_SO) $(PROTO_GEN_PY)
 
-$(PY$(PROJECT)_SO): $(STATIC_NAME) $(PY$(PROJECT)_SRC) $(PY$(PROJECT)_HXX_SRC)
-	$(CXX) -shared -o $@ $(PY$(PROJECT)_SRC) \
-		$(STATIC_NAME) $(LINKFLAGS) $(PYTHON_LDFLAGS)
-	@ echo
+$(PY$(PROJECT)_SO): $(PY$(PROJECT)_SRC) $(PY$(PROJECT)_HXX) | $(DYNAMIC_NAME)
+	@ echo CXX/LD -o $@ $<
+	$(Q)$(CXX) -shared -o $@ $(PY$(PROJECT)_SRC) \
+		-o $@ $(LINKFLAGS) -l$(PROJECT) $(PYTHON_LDFLAGS) \
+		-Wl,-rpath,$(ORIGIN)/../../build/lib
 
 mat$(PROJECT): mat
 
@@ -401,15 +455,25 @@ $(MAT$(PROJECT)_SO): $(MAT$(PROJECT)_SRC) $(STATIC_NAME)
 			"to build mat$(PROJECT)."; \
 		exit 1; \
 	fi
-	$(MATLAB_DIR)/bin/mex $(MAT$(PROJECT)_SRC) \
+	@ echo MEX $<
+	$(Q)$(MATLAB_DIR)/bin/mex $(MAT$(PROJECT)_SRC) \
 			CXX="$(CXX)" \
 			CXXFLAGS="\$$CXXFLAGS $(MATLAB_CXXFLAGS)" \
-			CXXLIBS="\$$CXXLIBS $(STATIC_NAME) $(LDFLAGS)" -output $@
-	@ echo
+			CXXLIBS="\$$CXXLIBS $(STATIC_LINK_COMMAND) $(LDFLAGS)" -output $@
+	@ if [ -f "$(PROJECT)_.d" ]; then \
+		mv -f $(PROJECT)_.d $(BUILD_DIR)/${MAT$(PROJECT)_SO:.$(MAT_SO_EXT)=.d}; \
+	fi
 
 runtest: $(TEST_ALL_BIN)
+	$(TOOL_BUILD_DIR)/caffe
 	$(TEST_ALL_BIN) $(TEST_GPUID) --gtest_shuffle $(TEST_FILTER)
 
+pytest: py
+	cd python; python -m unittest discover -s caffe/test
+	
+mattest: mat
+	cd matlab; $(MATLAB_DIR)/bin/matlab -nodisplay -r 'caffe.run_tests(), exit()'
+
 warn: $(EMPTY_WARN_REPORT)
 
 $(EMPTY_WARN_REPORT): $(ALL_WARNS) | $(BUILD_DIR)
@@ -423,9 +487,7 @@ $(EMPTY_WARN_REPORT): $(ALL_WARNS) | $(BUILD_DIR)
 	  $(RM) $(NONEMPTY_WARN_REPORT); \
 	  echo "No compiler warnings!";
 
-$(ALL_CXX_WARNS): %.o.$(WARNS_EXT) : %.o
-
-$(ALL_CU_WARNS): %.cuo.$(WARNS_EXT) : %.cuo
+$(ALL_WARNS): %.o.$(WARNS_EXT) : %.o
 
 $(BUILD_DIR_LINK): $(BUILD_DIR)/.linked
 
@@ -442,128 +504,79 @@ $(BUILD_DIR)/.linked:
 $(ALL_BUILD_DIRS): | $(BUILD_DIR_LINK)
 	@ mkdir -p $@
 
-$(NAME): $(PROTO_OBJS) $(OBJS) | $(LIB_BUILD_DIR)
-	$(CXX) -shared -o $@ $(OBJS) $(LINKFLAGS) $(LDFLAGS)
-	@ echo
-
-$(STATIC_NAME): $(PROTO_OBJS) $(OBJS) | $(LIB_BUILD_DIR)
-	ar rcs $@ $(PROTO_OBJS) $(OBJS)
-	@ echo
+$(DYNAMIC_NAME): $(OBJS) | $(LIB_BUILD_DIR)
+	@ echo LD -o $@
+	$(Q)$(CXX) -shared -o $@ $(OBJS) $(LINKFLAGS) $(LDFLAGS) $(DYNAMIC_FLAGS)
 
-$(TEST_BUILD_DIR)/%.o: src/$(PROJECT)/test/%.cpp $(HXX_SRCS) $(TEST_HXX_SRCS) \
-		| $(TEST_BUILD_DIR)
-	$(CXX) $< $(CXXFLAGS) -c -o $@ 2> $@.$(WARNS_EXT) \
-		|| (cat $@.$(WARNS_EXT); exit 1)
-	@ cat $@.$(WARNS_EXT)
-	@ echo
+$(STATIC_NAME): $(OBJS) | $(LIB_BUILD_DIR)
+	@ echo AR -o $@
+	$(Q)ar rcs $@ $(OBJS)
 
-$(TEST_BUILD_DIR)/%.cuo: src/$(PROJECT)/test/%.cu $(HXX_SRCS) $(TEST_HXX_SRCS) \
-		| $(TEST_BUILD_DIR)
-	$(CUDA_DIR)/bin/nvcc $(NVCCFLAGS) $(CUDA_ARCH) -c $< -o $@ 2> $@.$(WARNS_EXT) \
+$(BUILD_DIR)/%.o: %.cpp | $(ALL_BUILD_DIRS)
+	@ echo CXX $<
+	$(Q)$(CXX) $< $(CXXFLAGS) -c -o $@ 2> $@.$(WARNS_EXT) \
 		|| (cat $@.$(WARNS_EXT); exit 1)
 	@ cat $@.$(WARNS_EXT)
-	@ echo
-
-$(TEST_ALL_BIN): $(TEST_MAIN_SRC) $(TEST_OBJS) $(GTEST_OBJ) $(STATIC_NAME) \
-		| $(TEST_BIN_DIR)
-	$(CXX) $(TEST_MAIN_SRC) $(TEST_OBJS) $(GTEST_OBJ) $(STATIC_NAME) \
-		-o $@ $(LINKFLAGS) $(LDFLAGS)
-	@ echo
-
-$(TEST_CU_BINS): $(TEST_BIN_DIR)/%.testbin: $(TEST_BUILD_DIR)/%.cuo $(GTEST_OBJ) $(STATIC_NAME) \
-		| $(TEST_BIN_DIR)
-	$(CXX) $(TEST_MAIN_SRC) $< $(GTEST_OBJ) $(STATIC_NAME) \
-		-o $@ $(LINKFLAGS) $(LDFLAGS)
-	@ echo
-
-$(TEST_CXX_BINS): $(TEST_BIN_DIR)/%.testbin: $(TEST_BUILD_DIR)/%.o $(GTEST_OBJ) $(STATIC_NAME) \
-		| $(TEST_BIN_DIR)
-	$(CXX) $(TEST_MAIN_SRC) $< $(GTEST_OBJ) $(STATIC_NAME) \
-		-o $@ $(LINKFLAGS) $(LDFLAGS)
-	@ echo
-
-# Target for extension-less symlinks to tool binaries with extension '*.bin'.
-$(TOOL_BUILD_DIR)/%: $(TOOL_BUILD_DIR)/%.bin | $(TOOL_BUILD_DIR)
-	@ $(RM) $@
-	@ ln -s $(abspath $<) $@
-
-$(TOOL_BINS): %.bin : %.o $(STATIC_NAME)
-	$(CXX) $< $(STATIC_NAME) -o $@ $(LINKFLAGS) $(LDFLAGS)
-	@ echo
-
-$(EXAMPLE_BINS): %.bin : %.o $(STATIC_NAME)
-	$(CXX) $< $(STATIC_NAME) -o $@ $(LINKFLAGS) $(LDFLAGS)
-	@ echo
-
-$(LAYER_BUILD_DIR)/%.o: src/$(PROJECT)/layers/%.cpp $(HXX_SRCS) \
-		| $(LAYER_BUILD_DIR)
-	$(CXX) $< $(CXXFLAGS) -c -o $@ 2> $@.$(WARNS_EXT) \
-		|| (cat $@.$(WARNS_EXT); exit 1)
-	@ cat $@.$(WARNS_EXT)
-	@ echo
 
 $(PROTO_BUILD_DIR)/%.pb.o: $(PROTO_BUILD_DIR)/%.pb.cc $(PROTO_GEN_HEADER) \
 		| $(PROTO_BUILD_DIR)
-	$(CXX) $< $(CXXFLAGS) -c -o $@ 2> $@.$(WARNS_EXT) \
+	@ echo CXX $<
+	$(Q)$(CXX) $< $(CXXFLAGS) -c -o $@ 2> $@.$(WARNS_EXT) \
 		|| (cat $@.$(WARNS_EXT); exit 1)
 	@ cat $@.$(WARNS_EXT)
-	@ echo
 
-$(UTIL_BUILD_DIR)/%.o: src/$(PROJECT)/util/%.cpp $(HXX_SRCS) | $(UTIL_BUILD_DIR)
-	$(CXX) $< $(CXXFLAGS) -c -o $@ 2> $@.$(WARNS_EXT) \
+$(BUILD_DIR)/cuda/%.o: %.cu | $(ALL_BUILD_DIRS)
+	@ echo NVCC $<
+	$(Q)$(CUDA_DIR)/bin/nvcc $(NVCCFLAGS) $(CUDA_ARCH) -M $< -o ${@:.o=.d} \
+		-odir $(@D)
+	$(Q)$(CUDA_DIR)/bin/nvcc $(NVCCFLAGS) $(CUDA_ARCH) -c $< -o $@ 2> $@.$(WARNS_EXT) \
 		|| (cat $@.$(WARNS_EXT); exit 1)
 	@ cat $@.$(WARNS_EXT)
-	@ echo
 
-$(GTEST_OBJ): $(GTEST_SRC) | $(GTEST_BUILD_DIR)
-	$(CXX) $< $(CXXFLAGS) -c -o $@ 2> $@.$(WARNS_EXT) \
-		|| (cat $@.$(WARNS_EXT); exit 1)
-	@ cat $@.$(WARNS_EXT)
-	@ echo
+$(TEST_ALL_BIN): $(TEST_MAIN_SRC) $(TEST_OBJS) $(GTEST_OBJ) \
+		| $(DYNAMIC_NAME) $(TEST_BIN_DIR)
+	@ echo CXX/LD -o $@ $<
+	$(Q)$(CXX) $(TEST_MAIN_SRC) $(TEST_OBJS) $(GTEST_OBJ) \
+		-o $@ $(LINKFLAGS) $(LDFLAGS) -l$(PROJECT) -Wl,-rpath,$(ORIGIN)/../lib
 
-$(LAYER_BUILD_DIR)/%.cuo: src/$(PROJECT)/layers/%.cu $(HXX_SRCS) \
-		| $(LAYER_BUILD_DIR)
-	$(CUDA_DIR)/bin/nvcc $(NVCCFLAGS) $(CUDA_ARCH) -c $< -o $@ 2> $@.$(WARNS_EXT) \
-		|| (cat $@.$(WARNS_EXT); exit 1)
-	@ cat $@.$(WARNS_EXT)
-	@ echo
+$(TEST_CU_BINS): $(TEST_BIN_DIR)/%.testbin: $(TEST_CU_BUILD_DIR)/%.o \
+	$(GTEST_OBJ) | $(DYNAMIC_NAME) $(TEST_BIN_DIR)
+	@ echo LD $<
+	$(Q)$(CXX) $(TEST_MAIN_SRC) $< $(GTEST_OBJ) \
+		-o $@ $(LINKFLAGS) $(LDFLAGS) -l$(PROJECT) -Wl,-rpath,$(ORIGIN)/../lib
 
-$(UTIL_BUILD_DIR)/%.cuo: src/$(PROJECT)/util/%.cu | $(UTIL_BUILD_DIR)
-	$(CUDA_DIR)/bin/nvcc $(NVCCFLAGS) $(CUDA_ARCH) -c $< -o $@ 2> $@.$(WARNS_EXT) \
-		|| (cat $@.$(WARNS_EXT); exit 1)
-	@ cat $@.$(WARNS_EXT)
-	@ echo
+$(TEST_CXX_BINS): $(TEST_BIN_DIR)/%.testbin: $(TEST_CXX_BUILD_DIR)/%.o \
+	$(GTEST_OBJ) | $(DYNAMIC_NAME) $(TEST_BIN_DIR)
+	@ echo LD $<
+	$(Q)$(CXX) $(TEST_MAIN_SRC) $< $(GTEST_OBJ) \
+		-o $@ $(LINKFLAGS) $(LDFLAGS) -l$(PROJECT) -Wl,-rpath,$(ORIGIN)/../lib
 
-$(TOOL_BUILD_DIR)/%.o: tools/%.cpp $(PROTO_GEN_HEADER) | $(TOOL_BUILD_DIR)
-	$(CXX) $< $(CXXFLAGS) -c -o $@ 2> $@.$(WARNS_EXT) \
-		|| (cat $@.$(WARNS_EXT); exit 1)
-	@ cat $@.$(WARNS_EXT)
-	@ echo
+# Target for extension-less symlinks to tool binaries with extension '*.bin'.
+$(TOOL_BUILD_DIR)/%: $(TOOL_BUILD_DIR)/%.bin | $(TOOL_BUILD_DIR)
+	@ $(RM) $@
+	@ ln -s $(abspath $<) $@
 
-$(EXAMPLE_BUILD_DIR)/%.o: examples/%.cpp $(PROTO_GEN_HEADER) \
-		| $(EXAMPLE_BUILD_DIRS)
-	$(CXX) $< $(CXXFLAGS) -c -o $@ 2> $@.$(WARNS_EXT) \
-		|| (cat $@.$(WARNS_EXT); exit 1)
-	@ cat $@.$(WARNS_EXT)
-	@ echo
+$(TOOL_BINS): %.bin : %.o | $(DYNAMIC_NAME)
+	@ echo CXX/LD -o $@
+	$(Q)$(CXX) $< -o $@ $(LINKFLAGS) -l$(PROJECT) $(LDFLAGS) \
+		-Wl,-rpath,$(ORIGIN)/../lib
 
-$(BUILD_DIR)/src/$(PROJECT)/%.o: src/$(PROJECT)/%.cpp $(HXX_SRCS)
-	$(CXX) $< $(CXXFLAGS) -c -o $@ 2> $@.$(WARNS_EXT) \
-		|| (cat $@.$(WARNS_EXT); exit 1)
-	@ cat $@.$(WARNS_EXT)
-	@ echo
+$(EXAMPLE_BINS): %.bin : %.o | $(DYNAMIC_NAME)
+	@ echo CXX/LD -o $@
+	$(Q)$(CXX) $< -o $@ $(LINKFLAGS) -l$(PROJECT) $(LDFLAGS) \
+		-Wl,-rpath,$(ORIGIN)/../../lib
 
 proto: $(PROTO_GEN_CC) $(PROTO_GEN_HEADER)
 
 $(PROTO_BUILD_DIR)/%.pb.cc $(PROTO_BUILD_DIR)/%.pb.h : \
 		$(PROTO_SRC_DIR)/%.proto | $(PROTO_BUILD_DIR)
-	protoc --proto_path=$(PROTO_SRC_DIR) --cpp_out=$(PROTO_BUILD_DIR) $<
-	@ echo
+	@ echo PROTOC $<
+	$(Q)protoc --proto_path=$(PROTO_SRC_DIR) --cpp_out=$(PROTO_BUILD_DIR) $<
 
 $(PY_PROTO_BUILD_DIR)/%_pb2.py : $(PROTO_SRC_DIR)/%.proto \
 		$(PY_PROTO_INIT) | $(PY_PROTO_BUILD_DIR)
-	protoc --proto_path=$(PROTO_SRC_DIR) --python_out=$(PY_PROTO_BUILD_DIR) $<
-	@ echo
+	@ echo PROTOC \(python\) $<
+	$(Q)protoc --proto_path=$(PROTO_SRC_DIR) --python_out=$(PY_PROTO_BUILD_DIR) $<
 
 $(PY_PROTO_INIT): | $(PY_PROTO_BUILD_DIR)
 	touch $(PY_PROTO_INIT)
@@ -601,7 +614,7 @@ superclean: clean supercleanfiles
 
 $(DIST_ALIASES): $(DISTRIBUTE_DIR)
 
-$(DISTRIBUTE_DIR): all py $(HXX_SRCS) | $(DISTRIBUTE_SUBDIRS)
+$(DISTRIBUTE_DIR): all py | $(DISTRIBUTE_SUBDIRS)
 	# add include
 	cp -r include $(DISTRIBUTE_DIR)/
 	mkdir -p $(DISTRIBUTE_DIR)/include/caffe/proto
@@ -610,7 +623,9 @@ $(DISTRIBUTE_DIR): all py $(HXX_SRCS) | $(DISTRIBUTE_SUBDIRS)
 	cp $(TOOL_BINS) $(DISTRIBUTE_DIR)/bin
 	cp $(EXAMPLE_BINS) $(DISTRIBUTE_DIR)/bin
 	# add libraries
-	cp $(NAME) $(DISTRIBUTE_DIR)/lib
 	cp $(STATIC_NAME) $(DISTRIBUTE_DIR)/lib
+	cp $(DYNAMIC_NAME) $(DISTRIBUTE_DIR)/lib
 	# add python - it's not the standard way, indeed...
 	cp -r python $(DISTRIBUTE_DIR)/python
+
+-include $(DEPS)
diff --git a/Makefile.config.example b/Makefile.config.example
index 5cb0b243aca..a873502559f 100644
--- a/Makefile.config.example
+++ b/Makefile.config.example
@@ -17,15 +17,14 @@ CUDA_DIR := /usr/local/cuda
 # "sudo apt-get install nvidia-cuda-toolkit" then use this instead:
 # CUDA_DIR := /usr
 
-# CUDA architecture setting: going with all of them (up to CUDA 5.5 compatible).
-# For the latest architecture, you need to install CUDA >= 6.0 and uncomment
-# the *_50 lines below.
+# CUDA architecture setting: going with all of them.
+# For CUDA < 6.0, comment the *_50 lines for compatibility.
 CUDA_ARCH := -gencode arch=compute_20,code=sm_20 \
 		-gencode arch=compute_20,code=sm_21 \
 		-gencode arch=compute_30,code=sm_30 \
 		-gencode arch=compute_35,code=sm_35 \
-		#-gencode arch=compute_50,code=sm_50 \
-		#-gencode arch=compute_50,code=compute_50
+		-gencode arch=compute_50,code=sm_50 \
+		-gencode arch=compute_50,code=compute_50
 
 # BLAS choice:
 # atlas for ATLAS (default)
@@ -38,6 +37,10 @@ BLAS := atlas
 # BLAS_INCLUDE := /path/to/your/blas
 # BLAS_LIB := /path/to/your/blas
 
+# Homebrew puts openblas in a directory that is not on the standard search path
+# BLAS_INCLUDE := $(shell brew --prefix openblas)/include
+# BLAS_LIB := $(shell brew --prefix openblas)/lib
+
 # This is required only if you will compile the matlab interface.
 # MATLAB directory should contain the mex binary in /bin.
 # MATLAB_DIR := /usr/local
@@ -48,18 +51,35 @@ BLAS := atlas
 PYTHON_INCLUDE := /usr/include/python2.7 \
 		/usr/lib/python2.7/dist-packages/numpy/core/include
 # Anaconda Python distribution is quite popular. Include path:
-# PYTHON_INCLUDE := $(HOME)/anaconda/include \
-		# $(HOME)/anaconda/include/python2.7 \
-		# $(HOME)/anaconda/lib/python2.7/site-packages/numpy/core/include
+# Verify anaconda location, sometimes it's in root.
+# ANACONDA_HOME := $(HOME)/anaconda
+# PYTHON_INCLUDE := $(ANACONDA_HOME)/include \
+		# $(ANACONDA_HOME)/include/python2.7 \
+		# $(ANACONDA_HOME)/lib/python2.7/site-packages/numpy/core/include \
 
 # We need to be able to find libpythonX.X.so or .dylib.
 PYTHON_LIB := /usr/lib
-# PYTHON_LIB := $(HOME)/anaconda/lib
+# PYTHON_LIB := $(ANACONDA_HOME)/lib
+
+# Homebrew installs numpy in a non standard path (keg only)
+# PYTHON_INCLUDE += $(dir $(shell python -c 'import numpy.core; print(numpy.core.__file__)'))/include
+# PYTHON_LIB += $(shell brew --prefix numpy)/lib
+
+# Uncomment to support layers written in Python (will link against Python libs)
+# WITH_PYTHON_LAYER := 1
 
 # Whatever else you find you need goes here.
 INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include
 LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib
 
+# If Homebrew is installed at a non standard location (for example your home directory) and you use it for general dependencies
+# INCLUDE_DIRS += $(shell brew --prefix)/include
+# LIBRARY_DIRS += $(shell brew --prefix)/lib
+
+# Uncomment to use `pkg-config` to specify OpenCV library paths.
+# (Usually not necessary -- OpenCV libraries are normally installed in one of the above $LIBRARY_DIRS.)
+# USE_PKG_CONFIG := 1
+
 BUILD_DIR := build
 DISTRIBUTE_DIR := distribute
 
@@ -68,3 +88,6 @@ DISTRIBUTE_DIR := distribute
 
 # The ID of the GPU that 'make runtest' will use to run unit tests.
 TEST_GPUID := 0
+
+# enable pretty build (comment to see full commands)
+Q ?= @
diff --git a/README.md b/README.md
index 0e3c833e8fb..3a83a670774 100644
--- a/README.md
+++ b/README.md
@@ -1,4 +1,160 @@
+# Action Recognition with Deep Learning
+
+[![Build Status](https://travis-ci.org/yjxiong/caffe.svg?branch=action_recog)](https://travis-ci.org/yjxiong/caffe)
+
+This branch hosts the code for the technical report ["Towards Good Practices for Very Deep Two-stream ConvNets"](http://arxiv.org/abs/1507.02159), and more.
+
+* [Updates](#updates)
+* [Features](#features)
+* [Usage](#usage)
+* [Working Examples](#working-examples)
+* [Extension](#extension)
+* [Questions](#questions)
+* [Citation](#citation)
+
+
+### Updates
+- Jul 16, 2016
+  * New working sample: "Real-time Action Recognition with Enhanced Motion Vector CNNs" on CVPR 2016. 
+- Apr 27, 2016
+  * cuDNN v5 support, featuring the super fast WINOGrad Convolution and cuDNN implementation of BatchNormalization.
+- Dec 23, 2015
+  * Refactored cudnn wrapper to control overall memory consumption. Will automatically find the best algorithm combination under memory constraint.
+- Dec 17, 2015
+  * cuDNN v4 support: faster convolution and batch normalization (around 20% performance gain).
+- Nov 22, 2015
+  * Now python layer can expose a `prefetch()` method, which will be run in parallel with network processing.
+
+[Full Change Log](CHANGELOG.md)
+
+### Features
+- `VideoDataLayer` for inputing video data.
+- Training on optical flow data. 
+- Data augmentation with fixed corner cropping and multi-scale cropping.
+- Parallel training with multiple GPUs.
+- Newest cuDNN integration.
+- Slim memory footprints in both training and testing,
+
+### Usage
+
+#### *See more in* [Wiki](https://github.com/yjxiong/caffe/wiki).
+
+Generally it's the same as the original caffe. Please see the original README. 
+Please see following instruction for accessing features above. More detailed documentation is on the way.
+
+- Video/optic flow data
+  - First use the [optical flow extraction tool](https://github.com/wanglimin/dense_flow) to convert videos to RGB images and opitcal flow images.
+  - A new data layer called `VideoDataLayer` has been added to support multi-frame input. See the UCF101 sample for how to use it.
+  - **Note:** The `VideoDataLayer` can only input the optical-flow images generated by the tool listed above.
+- Fixed corner cropping augmentation
+  - Set `fix_crop` to `true` in `tranform_param` of network's protocol buffer definition.
+- "Multi-scale" cropping augmentation
+  - Set `multi_scale` to `true` in `transform_param`
+  - In `transform_param`, specify `scale_ratios` as a list of floats smaller than one, default is `[1, .875, .75, .65]`
+  - In `transform_param`, specify `max_distort` to an integer, which will limit the aspect ratio distortion, default to `1`
+- cuDNN v5
+ - The cuDNN v5 wrapper has optimized engines for convolution and batch normalization.
+ - The solver protobuf config has a parameter `richness` which specifies the total GPU memory in MBs available to the cudnn convolution engine as workspaces. Default `richness` is 300 (300MB). Using this parameter you can control the GPU memory consumption of training, the system will find the best setup under the memory limit for you.
+- Training with multiple GPUs
+  - Requires OpenMPI > 1.7.4 ([Why?](https://www.open-mpi.org/faq/?category=runcuda)). **Remember to compile your OpenMPI with option `--with-cuda`**
+  - Specify list of GPU IDs to be used for training, in the solver protocol buffer definition, like `device_id: [0,1,2,3]`
+  - Compile using cmake and use `mpirun` to launch caffe executable, like 
+```bash
+mkdir build && cd build
+cmake .. -DUSE_MPI=ON
+make && make install
+mpirun -np 4 ./install/bin/caffe train --solver=<Your Solver File> [--weights=<Pretrained caffemodel>]
+```
+**Note**: actual batch_size will be `num_device` times `batch_size` specified in network's prototxt.
+- Runtime memory optimization
+  - Memory optimization drastically reduces memory usage (half for training and almost all for testing) by
+  safely sharing underlying storage of a series of blobs.
+  Note in this case, the Python/Matlab interfaces can no longer retrieve correct contents of affected blobs.
+  - Training time memory optimization is automatically enabled.
+  - To adjust memory optimization setting, add 'optimize_mem' option to the network prototxt. It can be set to
+  `TRAIN_ONLY` (default), `ALL_OPTIM`, and `NO_OPTIM`.
+  - Testing time optimization is disabled by default. To enable testing time optimization, set `optimize_mem` to `NO_OPTIM`
+  - To disable memory optimization, set `optimize_mem` to `NO_OPTIM`. This may be useful when working with intermediate blobs.
+
+### Working Examples
+- Actionness Estimation Using Hybrid FCNs
+  - [CVPR 2016 paper](http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Wang_Actionness_Estimation_Using_CVPR_2016_paper.pdf)
+  - [Project Site](https://github.com/wanglimin/Actionness-Estimation)
+- Real-time Action Recognition with Enhanced Motion Vector CNNs
+  - [CVPR 2016 paper](https://wanglimin.github.io/papers/ZhangWWQW_CVPR16.pdf)
+  - [Project Site](http://zbwglory.github.io/MV-CNN/index.html)
+- Action recognition on UCF101
+  - [Project Site](http://personal.ie.cuhk.edu.hk/~xy012/others/action_recog/)
+  - [Caffe Model Files](https://github.com/yjxiong/caffe/tree/action_recog/models/action_recognition)
+  - [Training scripts and data files examples](https://github.com/yjxiong/caffe/tree/action_recog/examples/action_recognition)
+  - [Optical Flow Data](http://mmlab.siat.ac.cn/very_deep_two_stream_model/ucf101_flow_img_tvl1_gpu.zip)
+- Scene recognition on Places205
+  - [Model Files](https://github.com/wanglimin/Places205-VGGNet)
+  - [Technical Report](http://wanglimin.github.io/papers/WangGHQ15.pdf)
+
+### Extension
+Currently all existing data layers sub-classed from `BasePrefetchingDataLayer` support parallel training. If you have newly added layer which is also sub-classed from `BasePrefetchingDataLayer`, simply implement the virtual method 
+```C++
+inline virtual void advance_cursor();
+```
+Its function should be forwarding the "data cursor" in your data layer for one step. Then your new layer will be able to provide support for parallel training.
+
+### Questions
+Contact 
+- [Limin Wang](http://wanglimin.github.io/)
+- [Yuanjun Xiong](http://personal.ie.cuhk.edu.hk/~xy012/)
+
+### Citation
+You are encouraged to also cite the following report if you find this repo helpful
+
+```
+@article{MultiGPUCaffe2015,
+  author    = {Limin Wang and
+               Yuanjun Xiong and
+               Zhe Wang and
+               Yu Qiao},
+  title     = {Towards Good Practices for Very Deep Two-Stream ConvNets},
+  journal   = {CoRR},
+  volume    = {abs/1507.02159},
+  year      = {2015},
+  url       = {http://arxiv.org/abs/1507.02159},
+}
+```
+
+----
+Following is the original README of Caffe.
+
 # Caffe
 
-Caffe is a deep learning framework developed with cleanliness, readability, and speed in mind.<br />
-Consult the [project website](http://caffe.berkeleyvision.org) for all documentation.
+Caffe is a deep learning framework made with expression, speed, and modularity in mind.
+It is developed by the Berkeley Vision and Learning Center ([BVLC](http://bvlc.eecs.berkeley.edu)) and community contributors.
+
+Check out the [project site](http://caffe.berkeleyvision.org) for all the details like
+
+- [DIY Deep Learning for Vision with Caffe](https://docs.google.com/presentation/d/1UeKXVgRvvxg9OUdh_UiC5G71UMscNPlvArsWER41PsU/edit#slide=id.p)
+- [Tutorial Documentation](http://caffe.berkeleyvision.org/tutorial/)
+- [BVLC reference models](http://caffe.berkeleyvision.org/model_zoo.html) and the [community model zoo](https://github.com/BVLC/caffe/wiki/Model-Zoo)
+- [Installation instructions](http://caffe.berkeleyvision.org/installation.html)
+
+and step-by-step examples.
+
+[![Join the chat at https://gitter.im/BVLC/caffe](https://badges.gitter.im/Join%20Chat.svg)](https://gitter.im/BVLC/caffe?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)
+
+Please join the [caffe-users group](https://groups.google.com/forum/#!forum/caffe-users) or [gitter chat](https://gitter.im/BVLC/caffe) to ask questions and talk about methods and models.
+Framework development discussions and thorough bug reports are collected on [Issues](https://github.com/BVLC/caffe/issues).
+
+Happy brewing!
+
+## License and Citation
+
+Caffe is released under the [BSD 2-Clause license](https://github.com/BVLC/caffe/blob/master/LICENSE).
+The BVLC reference models are released for unrestricted use.
+
+Please cite Caffe in your publications if it helps your research:
+
+    @article{jia2014caffe,
+      Author = {Jia, Yangqing and Shelhamer, Evan and Donahue, Jeff and Karayev, Sergey and Long, Jonathan and Girshick, Ross and Guadarrama, Sergio and Darrell, Trevor},
+      Journal = {arXiv preprint arXiv:1408.5093},
+      Title = {Caffe: Convolutional Architecture for Fast Feature Embedding},
+      Year = {2014}
+    }
diff --git a/action_matlab/VideoSpatialPrediction.m b/action_matlab/VideoSpatialPrediction.m
new file mode 100644
index 00000000000..d60b4b17d26
--- /dev/null
+++ b/action_matlab/VideoSpatialPrediction.m
@@ -0,0 +1,48 @@
+function prediction = VideoSpatialPrediction(vid_name, mean_file, net)
+num=25;
+vidObj = VideoReader(vid_name);
+duration = vidObj.NumberOfFrames;
+step = floor((duration-1)/(num-1));
+rgb = zeros(256,340,3,num,'single');
+rgb_flip = zeros(256,340,3,num,'single');
+
+
+for i = 1:num
+    img = single(imresize(read(vidObj,(i-1)*step+1),[256,340]));
+    rgb(:,:,:,i) = img;
+    rgb_flip(:,:,:,i) = img(:,end:-1:1,:);
+end
+
+rgb_1 = rgb(1:224,1:224,:,:);
+rgb_2 = rgb(1:224,end-223:end,:,:);
+rgb_3 = rgb(16:16+223,60:60+223,:,:);
+rgb_4 = rgb(end-223:end,1:224,:,:);
+rgb_5 = rgb(end-223:end,end-223:end,:,:);
+rgb_f_1 = rgb_flip(1:224,1:224,:,:);
+rgb_f_2 = rgb_flip(1:224,end-223:end,:,:);
+rgb_f_3 = rgb_flip(16:16+223,60:60+223,:,:);
+rgb_f_4 = rgb_flip(end-223:end,1:224,:,:);
+rgb_f_5 = rgb_flip(end-223:end,end-223:end,:,:);
+
+rgb = cat(4,rgb_1,rgb_2,rgb_3,rgb_4,rgb_5,rgb_f_1,rgb_f_2,rgb_f_3,rgb_f_4,rgb_f_5);
+d = load(mean_file);
+IMAGE_MEAN = single(d.image_mean);
+rgb = bsxfun(@minus,rgb(:,:,[3,2,1],:),IMAGE_MEAN);
+rgb = permute(rgb,[2,1,3,4]);
+
+
+
+prediction = zeros(101,size(rgb,4));
+batch_size = 50;
+num_batches = ceil(size(rgb,4)/batch_size);
+rgbs = zeros(224,224,3,batch_size,'single');
+
+for bb = 1:num_batches
+	range = 1 + batch_size*(bb-1): min(size(rgb,4),batch_size*bb);
+	rgbs(:,:,:,mod(range-1,batch_size)+1) = single(rgb(:,:,:,range));
+	out_put = net.forward({rgbs});
+	out_put = squeeze(out_put{1});
+	prediction(:,range) = out_put(:,mod(range-1,batch_size)+1);
+end
+
+end
\ No newline at end of file
diff --git a/action_matlab/VideoTemporalPrediction.m b/action_matlab/VideoTemporalPrediction.m
new file mode 100644
index 00000000000..31becb66cdd
--- /dev/null
+++ b/action_matlab/VideoTemporalPrediction.m
@@ -0,0 +1,56 @@
+function prediction = VideoTemporalPrediction(flow_path, mean_file, net)
+num=25;
+imglist = dir([flow_path,'/*flow_x*.jpg']);
+duration = length(imglist);
+L = 10;
+
+% selection
+step = floor((duration-L+1)/num);
+flow = zeros(256,340,L*2,num,'single');
+flow_flip =  zeros(256,340,L*2,num,'single');
+for i = 1:num
+	for j = 1:L
+		img_x = single(imread(sprintf('%s/flow_x_%04d.jpg',flow_path,(i-1)*step+j)));
+		img_y = single(imread(sprintf('%s/flow_y_%04d.jpg',flow_path,(i-1)*step+j)));
+		flow(:,:,(j-1)*2+1,i) = img_x;
+		flow(:,:,(j-1)*2+2,i) = img_y;
+		flow_flip(:,:,(j-1)*2+1,i) = 255-img_x(:,end:-1:1);
+		flow_flip(:,:,(j-1)*2+2,i) = img_y(:,end:-1:1);
+	end
+end
+
+% crop
+flow_1 = flow(1:224,1:224,:,:);
+flow_2 = flow(1:224,end-223:end,:,:);
+flow_3 = flow(16:16+223,60:60+223,:,:);
+flow_4 = flow(end-223:end,1:224,:,:);
+flow_5 = flow(end-223:end,end-223:end,:,:);
+flow_f_1 = flow_flip(1:224,1:224,:,:);
+flow_f_2 = flow_flip(1:224,end-223:end,:,:);
+flow_f_3 = flow_flip(16:16+223,60:60+223,:,:);
+flow_f_4 = flow_flip(end-223:end,1:224,:,:);
+flow_f_5 = flow_flip(end-223:end,end-223:end,:,:);
+
+flow= cat(4,flow_1,flow_2,flow_3,flow_4,flow_5,flow_f_1,flow_f_2,flow_f_3,flow_f_4,flow_f_5);
+
+% substract mean
+d = load(mean_file);
+FLOW_MEAN = d.image_mean;
+flow = bsxfun(@minus,flow,FLOW_MEAN);
+flow = permute(flow,[2,1,3,4]);
+
+% test
+prediction = zeros(101,size(flow,4));
+batch_size = 50;
+num_batches = ceil(size(flow,4)/batch_size);
+flows = zeros(224,224,2*L,batch_size,'single');
+
+for bb = 1:num_batches
+	range = 1 + batch_size*(bb-1): min(size(flow,4),batch_size*bb);
+	flows(:,:,:,mod(range-1,batch_size)+1) = single(flow(:,:,:,range));
+	out_put = net.forward({flows});
+	out_put = squeeze(out_put{1});
+	prediction(:,range) = out_put(:,mod(range-1,batch_size)+1);
+end
+
+end
\ No newline at end of file
diff --git a/action_matlab/demo.m b/action_matlab/demo.m
new file mode 100644
index 00000000000..02e9f6c784c
--- /dev/null
+++ b/action_matlab/demo.m
@@ -0,0 +1,34 @@
+% test video and its optical flow field
+video_name = 'test.avi';
+video_flow = 'test/';
+
+% spatial prediction
+model_def_file = 'cuhk_action_spatial_vgg_16_deploy.prototxt';
+model_file = 'cuhk_action_spatial_vgg_16_split1.caffemodel';
+mean_file = 'rgb_mean.mat';
+gpu_id = 0;
+
+caffe.reset_all();
+caffe.set_mode_gpu();
+caffe.set_device(gpu_id);
+net = caffe.Net(model_def_file, model_file, 'test');
+
+spatial_prediction = VideoSpatialPrediction(video_name, mean_file, net);
+
+caffe.reset_all();
+
+
+% temporal prediction
+model_def_file = 'cuhk_action_temporal_vgg_16_deploy.prototxt';
+model_file = 'cuhk_action_temporal_vgg_16_split1.caffemodel';
+mean_file = 'flow_mean.mat';
+gpu_id = 0;
+
+caffe.reset_all();
+caffe.set_mode_gpu();
+caffe.set_device(gpu_id);
+net = caffe.Net(model_def_file, model_file, 'test');
+
+temporal_prediction = VideoTemporalPrediction(video_flow, mean_file, net);
+
+caffe.reset_all();
\ No newline at end of file
diff --git a/action_matlab/flow_mean.mat b/action_matlab/flow_mean.mat
new file mode 100644
index 00000000000..013d2c704a6
Binary files /dev/null and b/action_matlab/flow_mean.mat differ
diff --git a/action_matlab/rgb_mean.mat b/action_matlab/rgb_mean.mat
new file mode 100644
index 00000000000..ab480bd6e93
Binary files /dev/null and b/action_matlab/rgb_mean.mat differ
diff --git a/action_python/VideoSpatialPrediction.py b/action_python/VideoSpatialPrediction.py
new file mode 100755
index 00000000000..e769677a804
--- /dev/null
+++ b/action_python/VideoSpatialPrediction.py
@@ -0,0 +1,76 @@
+'''
+A sample function for classification using spatial network
+Customize as needed:
+e.g. num_categories, layer for feature extraction, batch_size
+'''
+
+import glob
+import os
+import numpy as np
+import math
+import cv2
+import scipy.io as sio
+
+def VideoSpatialPrediction(
+        vid_name,
+        mean_file,
+        net,
+        num_categories,
+        feature_layer,
+        start_frame=0,
+        num_frames=0,
+        num_samples=25
+        ):
+
+    if num_frames == 0:
+        imglist = glob.glob(os.path.join(vid_name, '*image_*.jpg'))
+        duration = len(imglist)
+    else:
+        duration = num_frames
+
+    # selection
+    step = int(math.floor((duration-1)/(num_samples-1)))
+    dims = (256,340,3,num_samples)
+    rgb = np.zeros(shape=dims, dtype=np.float64)
+    rgb_flip = np.zeros(shape=dims, dtype=np.float64)
+
+    for i in range(num_samples):
+        img_file = os.path.join(vid_name, 'image_{0:05d}.jpg'.format(i*step+1))
+        img = cv2.imread(img_file, cv2.IMREAD_UNCHANGED)
+        img = cv2.resize(img, dims[1::-1])
+        rgb[:,:,:,i] = img
+        rgb_flip[:,:,:,i] = img[:,::-1,:]
+
+    # crop
+    rgb_1 = rgb[:224, :224, :,:]
+    rgb_2 = rgb[:224, -224:, :,:]
+    rgb_3 = rgb[16:240, 60:284, :,:]
+    rgb_4 = rgb[-224:, :224, :,:]
+    rgb_5 = rgb[-224:, -224:, :,:]
+    rgb_f_1 = rgb_flip[:224, :224, :,:]
+    rgb_f_2 = rgb_flip[:224, -224:, :,:]
+    rgb_f_3 = rgb_flip[16:240, 60:284, :,:]
+    rgb_f_4 = rgb_flip[-224:, :224, :,:]
+    rgb_f_5 = rgb_flip[-224:, -224:, :,:]
+
+    rgb = np.concatenate((rgb_1,rgb_2,rgb_3,rgb_4,rgb_5,rgb_f_1,rgb_f_2,rgb_f_3,rgb_f_4,rgb_f_5), axis=3)
+
+    # substract mean
+    d = sio.loadmat(mean_file)
+    image_mean = d['image_mean']
+
+    rgb = rgb[...] - np.tile(image_mean[...,np.newaxis], (1, 1, 1, rgb.shape[3]))
+    rgb = np.transpose(rgb, (1,0,2,3))
+
+    # test
+    batch_size = 50
+    prediction = np.zeros((num_categories,rgb.shape[3]))
+    num_batches = int(math.ceil(float(rgb.shape[3])/batch_size))
+
+    for bb in range(num_batches):
+        span = range(batch_size*bb, min(rgb.shape[3],batch_size*(bb+1)))
+        net.blobs['data'].data[...] = np.transpose(rgb[:,:,:,span], (3,2,1,0))
+        output = net.forward()
+        prediction[:, span] = np.transpose(output[feature_layer])
+
+    return prediction
diff --git a/action_python/VideoTemporalPrediction.py b/action_python/VideoTemporalPrediction.py
new file mode 100755
index 00000000000..685afe7d7cf
--- /dev/null
+++ b/action_python/VideoTemporalPrediction.py
@@ -0,0 +1,87 @@
+'''
+A sample function for classification using temporal network
+Customize as needed:
+e.g. num_categories, layer for feature extraction, batch_size
+'''
+
+import glob
+import os
+import numpy as np
+import math
+import cv2
+import scipy.io as sio
+
+def VideoTemporalPrediction(
+        vid_name,
+        mean_file,
+        net,
+        num_categories,
+        feature_layer,
+        start_frame=0,
+        num_frames=0,
+        num_samples=25,
+        optical_flow_frames=10
+        ):
+
+    if num_frames == 0:
+        imglist = glob.glob(os.path.join(vid_name, '*flow_x*.jpg'))
+        duration = len(imglist)
+    else:
+        duration = num_frames
+
+    # selection
+    step = int(math.floor((duration-optical_flow_frames+1)/num_samples))
+    dims = (256,340,optical_flow_frames*2,num_samples)
+    flow = np.zeros(shape=dims, dtype=np.float64)
+    flow_flip = np.zeros(shape=dims, dtype=np.float64)
+
+    for i in range(num_samples):
+        for j in range(optical_flow_frames):
+            flow_x_file = os.path.join(vid_name, 'flow_x_{0:05d}.jpg'.format(i*step+j+1 + start_frame))
+            flow_y_file = os.path.join(vid_name, 'flow_y_{0:05d}.jpg'.format(i*step+j+1 + start_frame))
+            img_x = cv2.imread(flow_x_file, cv2.IMREAD_GRAYSCALE)
+            img_y = cv2.imread(flow_y_file, cv2.IMREAD_GRAYSCALE)
+            img_x = cv2.resize(img_x, dims[1::-1])
+            img_y = cv2.resize(img_y, dims[1::-1])
+
+            flow[:,:,j*2  ,i] = img_x
+            flow[:,:,j*2+1,i] = img_y
+
+            flow_flip[:,:,j*2  ,i] = 255 - img_x[:, ::-1]
+            flow_flip[:,:,j*2+1,i] = img_y[:, ::-1]
+
+    # crop
+    flow_1 = flow[:224, :224, :,:]
+    flow_2 = flow[:224, -224:, :,:]
+    flow_3 = flow[16:240, 60:284, :,:]
+    flow_4 = flow[-224:, :224, :,:]
+    flow_5 = flow[-224:, -224:, :,:]
+    flow_f_1 = flow_flip[:224, :224, :,:]
+    flow_f_2 = flow_flip[:224, -224:, :,:]
+    flow_f_3 = flow_flip[16:240, 60:284, :,:]
+    flow_f_4 = flow_flip[-224:, :224, :,:]
+    flow_f_5 = flow_flip[-224:, -224:, :,:]
+
+    flow = np.concatenate((flow_1,flow_2,flow_3,flow_4,flow_5,flow_f_1,flow_f_2,flow_f_3,flow_f_4,flow_f_5), axis=3)
+
+    # substract mean
+    d = sio.loadmat(mean_file)
+    flow_mean = d['image_mean']
+
+    flow = flow - np.tile(flow_mean[...,np.newaxis], (1, 1, 1, flow.shape[3]))
+    flow = np.transpose(flow, (1,0,2,3))
+
+    # test
+    batch_size = 50
+    prediction = np.zeros((num_categories,flow.shape[3]))
+    num_batches = int(math.ceil(float(flow.shape[3])/batch_size))
+
+    for bb in range(num_batches):
+        span = range(batch_size*bb, min(flow.shape[3],batch_size*(bb+1)))
+
+        net.blobs['data'].data[...] = np.transpose(flow[:,:,:,span], (3,2,1,0))
+        output = net.forward()
+
+        prediction[:, span] = np.transpose(output[feature_layer])
+
+    return prediction
diff --git a/action_python/demo.py b/action_python/demo.py
new file mode 100755
index 00000000000..15d20b91bfb
--- /dev/null
+++ b/action_python/demo.py
@@ -0,0 +1,74 @@
+#!/usr/bin/env python
+
+'''
+A sample script to run classificition using both spatial/temporal nets.
+Modify this script as needed.
+'''
+
+import numpy as np
+import caffe
+import math
+
+from VideoSpatialPrediction import VideoSpatialPrediction
+from VideoTemporalPrediction import VideoTemporalPrediction
+
+def softmax(x):
+    y = [math.exp(k) for k in x]
+    sum_y = math.fsum(y)
+    z = [k/sum_y for k in y]
+
+    return z
+
+def main():
+
+    # caffe init
+    gpu_id = 0
+    caffe.set_device(gpu_id)
+    caffe.set_mode_gpu()
+
+    # spatial prediction
+    model_def_file = '../models/action_recognition/dextro_spatial.prototxt'
+    model_file = '../dextro_benchmark_rgb_iter_48000.caffemodel'
+    spatial_net = caffe.Net(model_def_file, model_file, caffe.TEST)
+
+    # temporal prediction
+    model_def_file = '../models/action_recognition/dextro_temporal.prototxt'
+    model_file = '../dextro_benchmark_flow_iter_39000.caffemodel'
+    temporal_net = caffe.Net(model_def_file, model_file, caffe.TEST)
+
+    # input video (containing image_*.jpg and flow_*.jpg) and some settings
+    input_video_dir = 'video/'
+    start_frame = 0
+    num_categories = 131
+    feature_layer = 'fc8-2'
+
+    # temporal net prediction
+    temporal_mean_file = 'flow_mean.mat'
+    temporal_prediction = VideoTemporalPrediction(
+            input_video_dir,
+            temporal_mean_file,
+            temporal_net,
+            num_categories,
+            feature_layer,
+            start_frame)
+    avg_temporal_pred_fc8 = np.mean(temporal_prediction, axis=1)
+    avg_temporal_pred = softmax(avg_temporal_pred_fc8)
+
+    # spatial net prediction
+    spatial_mean_file = 'rgb_mean.mat'
+    spatial_prediction = VideoSpatialPrediction(
+            input_video_dir,
+            spatial_mean_file,
+            spatial_net,
+            num_categories,
+            feature_layer,
+            start_frame)
+    avg_spatial_pred_fc8 = np.mean(spatial_prediction, axis=1)
+    avg_spatial_pred = softmax(avg_spatial_pred_fc8)
+
+    # fused prediction (temporal:spatial = 2:1)
+    fused_pred = np.array(avg_temporal_pred) * 2./3 + \
+                 np.array(avg_spatial_pred) * 1./3
+
+if __name__ == "__main__":
+    main()
diff --git a/cmake/ConfigGen.cmake b/cmake/ConfigGen.cmake
new file mode 100644
index 00000000000..566d6ca0aa7
--- /dev/null
+++ b/cmake/ConfigGen.cmake
@@ -0,0 +1,104 @@
+
+################################################################################################
+# Helper function to fetch caffe includes which will be passed to dependent projects
+# Usage:
+#   caffe_get_current_includes(<includes_list_variable>)
+function(caffe_get_current_includes includes_variable)
+  get_property(current_includes DIRECTORY PROPERTY INCLUDE_DIRECTORIES)
+  caffe_convert_absolute_paths(current_includes)
+
+  # remove at most one ${PROJECT_BINARY_DIR} include added for caffe_config.h
+  list(FIND current_includes ${PROJECT_BINARY_DIR} __index)
+  list(REMOVE_AT current_includes ${__index})
+
+  # removing numpy includes (since not required for client libs)
+  set(__toremove "")
+  foreach(__i ${current_includes})
+    if(${__i} MATCHES "python")
+      list(APPEND __toremove ${__i})
+    endif()
+  endforeach()
+  if(__toremove)
+    list(REMOVE_ITEM current_includes ${__toremove})
+  endif()
+
+  caffe_list_unique(current_includes)
+  set(${includes_variable} ${current_includes} PARENT_SCOPE)
+endfunction()
+
+################################################################################################
+# Helper function to get all list items that begin with given prefix
+# Usage:
+#   caffe_get_items_with_prefix(<prefix> <list_variable> <output_variable>)
+function(caffe_get_items_with_prefix prefix list_variable output_variable)
+  set(__result "")
+  foreach(__e ${${list_variable}})
+    if(__e MATCHES "^${prefix}.*")
+      list(APPEND __result ${__e})
+    endif()
+  endforeach()
+  set(${output_variable} ${__result} PARENT_SCOPE)
+endfunction()
+
+################################################################################################
+# Function for generation Caffe build- and install- tree export config files
+# Usage:
+#  caffe_generate_export_configs()
+function(caffe_generate_export_configs)
+  set(install_cmake_suffix "share/Caffe")
+
+  # ---[ Configure build-tree CaffeConfig.cmake file ]---
+  caffe_get_current_includes(Caffe_INCLUDE_DIRS)
+
+  set(Caffe_DEFINITIONS "")
+  if(NOT HAVE_CUDA)
+    set(HAVE_CUDA FALSE)
+    list(APPEND Caffe_DEFINITIONS -DCPU_ONLY)
+  endif()
+
+  if(NOT HAVE_CUDNN)
+    set(HAVE_CUDNN FALSE)
+  else()
+    list(APPEND DEFINITIONS -DUSE_CUDNN)
+  endif()
+
+  if(BLAS STREQUAL "MKL" OR BLAS STREQUAL "mkl")
+    list(APPEND Caffe_DEFINITIONS -DUSE_MKL)
+  endif()
+
+  configure_file("cmake/Templates/CaffeConfig.cmake.in" "${PROJECT_BINARY_DIR}/CaffeConfig.cmake" @ONLY)
+
+  # Add targets to the build-tree export set
+  export(TARGETS caffe proto FILE "${PROJECT_BINARY_DIR}/CaffeTargets.cmake")
+  export(PACKAGE Caffe)
+
+  # ---[ Configure install-tree CaffeConfig.cmake file ]---
+
+  # remove source and build dir includes
+  caffe_get_items_with_prefix(${PROJECT_SOURCE_DIR} Caffe_INCLUDE_DIRS __insource)
+  caffe_get_items_with_prefix(${PROJECT_BINARY_DIR} Caffe_INCLUDE_DIRS __inbinary)
+  list(REMOVE_ITEM Caffe_INCLUDE_DIRS ${__insource} ${__inbinary})
+
+  # add `install` include folder
+  set(lines
+     "get_filename_component(__caffe_include \"\${Caffe_CMAKE_DIR}/../../include\" ABSOLUTE)\n"
+     "list(APPEND Caffe_INCLUDE_DIRS \${__caffe_include})\n"
+     "unset(__caffe_include)\n")
+  string(REPLACE ";" "" Caffe_INSTALL_INCLUDE_DIR_APPEND_COMMAND ${lines})
+
+  configure_file("cmake/Templates/CaffeConfig.cmake.in" "${PROJECT_BINARY_DIR}/cmake/CaffeConfig.cmake" @ONLY)
+
+  # Install the CaffeConfig.cmake and export set to use with install-tree
+  install(FILES "${PROJECT_BINARY_DIR}/cmake/CaffeConfig.cmake" DESTINATION ${install_cmake_suffix})
+  install(EXPORT CaffeTargets DESTINATION ${install_cmake_suffix})
+
+  # ---[ Configure and install version file ]---
+
+  # TODO: Lines below are commented because Caffe does't declare its version in headers.
+  # When the declarations are added, modify `caffe_extract_caffe_version()` macro and uncomment
+
+  # configure_file(cmake/Templates/CaffeConfigVersion.cmake.in "${PROJECT_BINARY_DIR}/CaffeConfigVersion.cmake" @ONLY)
+  # install(FILES "${PROJECT_BINARY_DIR}/CaffeConfigVersion.cmake" DESTINATION ${install_cmake_suffix})
+endfunction()
+
+
diff --git a/cmake/Cuda.cmake b/cmake/Cuda.cmake
new file mode 100644
index 00000000000..ff58d31c166
--- /dev/null
+++ b/cmake/Cuda.cmake
@@ -0,0 +1,254 @@
+if(CPU_ONLY)
+  return()
+endif()
+
+# Known NVIDIA GPU achitectures Caffe can be compiled for.
+# This list will be used for CUDA_ARCH_NAME = All option
+set(Caffe_known_gpu_archs "20 21(20) 30 35 50")
+
+################################################################################################
+# A function for automatic detection of GPUs installed  (if autodetection is enabled)
+# Usage:
+#   caffe_detect_installed_gpus(out_variable)
+function(caffe_detect_installed_gpus out_variable)
+  if(NOT CUDA_gpu_detect_output)
+    set(__cufile ${PROJECT_BINARY_DIR}/detect_cuda_archs.cu)
+
+    file(WRITE ${__cufile} ""
+      "#include <cstdio>\n"
+      "int main()\n"
+      "{\n"
+      "  int count = 0;\n"
+      "  if (cudaSuccess != cudaGetDeviceCount(&count)) return -1;\n"
+      "  if (count == 0) return -1;\n"
+      "  for (int device = 0; device < count; ++device)\n"
+      "  {\n"
+      "    cudaDeviceProp prop;\n"
+      "    if (cudaSuccess == cudaGetDeviceProperties(&prop, device))\n"
+      "      std::printf(\"%d.%d \", prop.major, prop.minor);\n"
+      "  }\n"
+      "  return 0;\n"
+      "}\n")
+
+    execute_process(COMMAND "${CUDA_NVCC_EXECUTABLE}" "--run" "${__cufile}"
+                    WORKING_DIRECTORY "${PROJECT_BINARY_DIR}/CMakeFiles/"
+                    RESULT_VARIABLE __nvcc_res OUTPUT_VARIABLE __nvcc_out
+                    ERROR_QUIET OUTPUT_STRIP_TRAILING_WHITESPACE)
+
+    if(__nvcc_res EQUAL 0)
+      string(REPLACE "2.1" "2.1(2.0)" __nvcc_out "${__nvcc_out}")
+      set(CUDA_gpu_detect_output ${__nvcc_out} CACHE INTERNAL "Returned GPU architetures from caffe_detect_gpus tool" FORCE)
+    endif()
+  endif()
+
+  if(NOT CUDA_gpu_detect_output)
+    message(STATUS "Automatic GPU detection failed. Building for all known architectures.")
+    set(${out_variable} ${Caffe_known_gpu_archs} PARENT_SCOPE)
+  else()
+    set(${out_variable} ${CUDA_gpu_detect_output} PARENT_SCOPE)
+  endif()
+endfunction()
+
+
+################################################################################################
+# Function for selecting GPU arch flags for nvcc based on CUDA_ARCH_NAME
+# Usage:
+#   caffe_select_nvcc_arch_flags(out_variable)
+function(caffe_select_nvcc_arch_flags out_variable)
+  # List of arch names
+  set(__archs_names "Fermi" "Kepler" "Maxwell" "All" "Manual")
+  set(__archs_name_default "All")
+  if(NOT CMAKE_CROSSCOMPILING)
+    list(APPEND __archs_names "Auto")
+    set(__archs_name_default "Auto")
+  endif()
+
+  # set CUDA_ARCH_NAME strings (so it will be seen as dropbox in CMake-Gui)
+  set(CUDA_ARCH_NAME ${__archs_name_default} CACHE STRING "Select target NVIDIA GPU achitecture.")
+  set_property( CACHE CUDA_ARCH_NAME PROPERTY STRINGS "" ${__archs_names} )
+  mark_as_advanced(CUDA_ARCH_NAME)
+
+  # verify CUDA_ARCH_NAME value
+  if(NOT ";${__archs_names};" MATCHES ";${CUDA_ARCH_NAME};")
+    string(REPLACE ";" ", " __archs_names "${__archs_names}")
+    message(FATAL_ERROR "Only ${__archs_names} architeture names are supported.")
+  endif()
+
+  if(${CUDA_ARCH_NAME} STREQUAL "Manual")
+    set(CUDA_ARCH_BIN ${Caffe_known_gpu_archs} CACHE STRING "Specify 'real' GPU architectures to build binaries for, BIN(PTX) format is supported")
+    set(CUDA_ARCH_PTX "50"                     CACHE STRING "Specify 'virtual' PTX architectures to build PTX intermediate code for")
+    mark_as_advanced(CUDA_ARCH_BIN CUDA_ARCH_PTX)
+  else()
+    unset(CUDA_ARCH_BIN CACHE)
+    unset(CUDA_ARCH_PTX CACHE)
+  endif()
+
+  if(${CUDA_ARCH_NAME} STREQUAL "Fermi")
+    set(__cuda_arch_bin "20 21(20)")
+  elseif(${CUDA_ARCH_NAME} STREQUAL "Kepler")
+    set(__cuda_arch_bin "30 35")
+  elseif(${CUDA_ARCH_NAME} STREQUAL "Maxwell")
+    set(__cuda_arch_bin "50")
+  elseif(${CUDA_ARCH_NAME} STREQUAL "All")
+    set(__cuda_arch_bin ${Caffe_known_gpu_archs})
+  elseif(${CUDA_ARCH_NAME} STREQUAL "Auto")
+    caffe_detect_installed_gpus(__cuda_arch_bin)
+  else()  # (${CUDA_ARCH_NAME} STREQUAL "Manual")
+    set(__cuda_arch_bin ${CUDA_ARCH_BIN})
+  endif()
+
+  # remove dots and convert to lists
+  string(REGEX REPLACE "\\." "" __cuda_arch_bin "${__cuda_arch_bin}")
+  string(REGEX REPLACE "\\." "" __cuda_arch_ptx "${CUDA_ARCH_PTX}")
+  string(REGEX MATCHALL "[0-9()]+" __cuda_arch_bin "${__cuda_arch_bin}")
+  string(REGEX MATCHALL "[0-9]+"   __cuda_arch_ptx "${__cuda_arch_ptx}")
+  caffe_list_unique(__cuda_arch_bin __cuda_arch_ptx)
+
+  set(__nvcc_flags "")
+  set(__nvcc_archs_readable "")
+
+  # Tell NVCC to add binaries for the specified GPUs
+  foreach(__arch ${__cuda_arch_bin})
+    if(__arch MATCHES "([0-9]+)\\(([0-9]+)\\)")
+      # User explicitly specified PTX for the concrete BIN
+      list(APPEND __nvcc_flags -gencode arch=compute_${CMAKE_MATCH_2},code=sm_${CMAKE_MATCH_1})
+      list(APPEND __nvcc_archs_readable sm_${CMAKE_MATCH_1})
+    else()
+      # User didn't explicitly specify PTX for the concrete BIN, we assume PTX=BIN
+      list(APPEND __nvcc_flags -gencode arch=compute_${__arch},code=sm_${__arch})
+      list(APPEND __nvcc_archs_readable sm_${__arch})
+    endif()
+  endforeach()
+
+  # Tell NVCC to add PTX intermediate code for the specified architectures
+  foreach(__arch ${__cuda_arch_ptx})
+    list(APPEND __nvcc_flags -gencode arch=compute_${__arch},code=compute_${__arch})
+    list(APPEND __nvcc_archs_readable compute_${__arch})
+  endforeach()
+
+  string(REPLACE ";" " " __nvcc_archs_readable "${__nvcc_archs_readable}")
+  set(${out_variable}          ${__nvcc_flags}          PARENT_SCOPE)
+  set(${out_variable}_readable ${__nvcc_archs_readable} PARENT_SCOPE)
+endfunction()
+
+################################################################################################
+# Short command for cuda comnpilation
+# Usage:
+#   caffe_cuda_compile(<objlist_variable> <cuda_files>)
+macro(caffe_cuda_compile objlist_variable)
+  foreach(var CMAKE_CXX_FLAGS CMAKE_CXX_FLAGS_RELEASE CMAKE_CXX_FLAGS_DEBUG)
+    set(${var}_backup_in_cuda_compile_ "${${var}}")
+
+    # we remove /EHa as it generates warnings under windows
+    string(REPLACE "/EHa" "" ${var} "${${var}}")
+
+  endforeach()
+
+  if(UNIX OR APPLE)
+    list(APPEND CUDA_NVCC_FLAGS -Xcompiler -fPIC)
+  endif()
+
+  if(APPLE)
+    list(APPEND CUDA_NVCC_FLAGS -Xcompiler -Wno-unused-function)
+  endif()
+
+  cuda_compile(cuda_objcs ${ARGN})
+
+  foreach(var CMAKE_CXX_FLAGS CMAKE_CXX_FLAGS_RELEASE CMAKE_CXX_FLAGS_DEBUG)
+    set(${var} "${${var}_backup_in_cuda_compile_}")
+    unset(${var}_backup_in_cuda_compile_)
+  endforeach()
+
+  set(${objlist_variable} ${cuda_objcs})
+endmacro()
+
+################################################################################################
+# Short command for cuDNN detection. Believe it soon will be a part of CUDA toolkit distribution.
+# That's why not FindcuDNN.cmake file, but just the macro
+# Usage:
+#   detect_cuDNN()
+function(detect_cuDNN)
+  set(CUDNN_ROOT "" CACHE PATH "CUDNN root folder")
+
+  find_path(CUDNN_INCLUDE cudnn.h
+            PATHS ${CUDNN_ROOT} $ENV{CUDNN_ROOT} ${CUDA_TOOLKIT_INCLUDE}
+            DOC "Path to cuDNN include directory." )
+
+  get_filename_component(__libpath_hist ${CUDA_CUDART_LIBRARY} PATH)
+  find_library(CUDNN_LIBRARY NAMES libcudnn.so # libcudnn_static.a
+                             PATHS ${CUDNN_ROOT} $ENV{CUDNN_ROOT} ${CUDNN_INCLUDE} ${__libpath_hist}
+                             DOC "Path to cuDNN library.")
+
+  if(CUDNN_INCLUDE AND CUDNN_LIBRARY)
+    set(HAVE_CUDNN  TRUE PARENT_SCOPE)
+    set(CUDNN_FOUND TRUE PARENT_SCOPE)
+
+    mark_as_advanced(CUDNN_INCLUDE CUDNN_LIBRARY CUDNN_ROOT)
+    message(STATUS "Found cuDNN (include: ${CUDNN_INCLUDE}, library: ${CUDNN_LIBRARY})")
+  endif()
+endfunction()
+
+
+################################################################################################
+###  Non macro section
+################################################################################################
+
+find_package(CUDA 5.5 QUIET)
+find_cuda_helper_libs(curand)  # cmake 2.8.7 compartibility which doesn't search for curand
+
+if(NOT CUDA_FOUND)
+  return()
+endif()
+
+set(HAVE_CUDA TRUE)
+message(STATUS "CUDA detected: " ${CUDA_VERSION})
+include_directories(SYSTEM ${CUDA_INCLUDE_DIRS})
+list(APPEND Caffe_LINKER_LIBS ${CUDA_CUDART_LIBRARY}
+                              ${CUDA_curand_LIBRARY} ${CUDA_CUBLAS_LIBRARIES})
+
+# cudnn detection
+if(USE_CUDNN)
+  detect_cuDNN()
+  if(HAVE_CUDNN)
+    add_definitions(-DUSE_CUDNN)
+    include_directories(SYSTEM ${CUDNN_INCLUDE})
+    list(APPEND Caffe_LINKER_LIBS ${CUDNN_LIBRARY})
+  endif()
+endif()
+
+# setting nvcc arch flags
+caffe_select_nvcc_arch_flags(NVCC_FLAGS_EXTRA)
+list(APPEND CUDA_NVCC_FLAGS ${NVCC_FLAGS_EXTRA})
+message(STATUS "Added CUDA NVCC flags for: ${NVCC_FLAGS_EXTRA_readable}")
+
+# Boost 1.55 workaround, see https://svn.boost.org/trac/boost/ticket/9392 or
+# https://github.com/ComputationalRadiationPhysics/picongpu/blob/master/src/picongpu/CMakeLists.txt
+if(Boost_VERSION EQUAL 105500)
+  message(STATUS "Cuda + Boost 1.55: Applying noinline work around")
+  # avoid warning for CMake >= 2.8.12
+  set(CUDA_NVCC_FLAGS "${CUDA_NVCC_FLAGS} \"-DBOOST_NOINLINE=__attribute__((noinline))\" ")
+endif()
+
+# disable some nvcc diagnostic that apears in boost, glog, glags, opencv, etc.
+foreach(diag cc_clobber_ignored integer_sign_change useless_using_declaration set_but_not_used)
+  list(APPEND CUDA_NVCC_FLAGS -Xcudafe --diag_suppress=${diag})
+endforeach()
+
+# setting default testing device
+if(NOT CUDA_TEST_DEVICE)
+  set(CUDA_TEST_DEVICE -1)
+endif()
+
+mark_as_advanced(CUDA_BUILD_CUBIN CUDA_BUILD_EMULATION CUDA_VERBOSE_BUILD)
+mark_as_advanced(CUDA_SDK_ROOT_DIR CUDA_SEPARABLE_COMPILATION)
+
+# Handle clang/libc++ issue
+if(APPLE)
+  caffe_detect_darwin_version(OSX_VERSION)
+
+  # OSX 10.9 and higher uses clang/libc++ by default which is incompartible with old CUDA toolkits
+  if(OSX_VERSION VERSION_GREATER 10.8)
+    # enabled by default if and only if CUDA version is less than 7.0
+    caffe_option(USE_libstdcpp "Use libstdc++ instead of libc++" (CUDA_VERSION VERSION_LESS 7.0))
+  endif()
+endif()
diff --git a/cmake/Dependencies.cmake b/cmake/Dependencies.cmake
new file mode 100644
index 00000000000..757d9685ec2
--- /dev/null
+++ b/cmake/Dependencies.cmake
@@ -0,0 +1,166 @@
+# This list is required for static linking and exported to CaffeConfig.cmake
+set(Caffe_LINKER_LIBS "")
+
+# ---[ Boost
+find_package(Boost 1.46 REQUIRED COMPONENTS system thread)
+include_directories(SYSTEM ${Boost_INCLUDE_DIR})
+list(APPEND Caffe_LINKER_LIBS ${Boost_LIBRARIES})
+
+# ---[ Threads
+find_package(Threads REQUIRED)
+list(APPEND Caffe_LINKER_LIBS ${CMAKE_THREAD_LIBS_INIT})
+
+# ---[ Google-glog
+include("cmake/External/glog.cmake")
+include_directories(SYSTEM ${GLOG_INCLUDE_DIRS})
+list(APPEND Caffe_LINKER_LIBS ${GLOG_LIBRARIES})
+
+# ---[ Google-gflags
+include("cmake/External/gflags.cmake")
+include_directories(SYSTEM ${GFLAGS_INCLUDE_DIRS})
+list(APPEND Caffe_LINKER_LIBS ${GFLAGS_LIBRARIES})
+
+# ---[ Google-protobuf
+include(cmake/ProtoBuf.cmake)
+
+# ---[ HDF5
+find_package(HDF5 COMPONENTS HL REQUIRED)
+include_directories(SYSTEM ${HDF5_INCLUDE_DIRS} ${HDF5_HL_INCLUDE_DIR})
+list(APPEND Caffe_LINKER_LIBS ${HDF5_LIBRARIES})
+
+# ---[ LMDB
+find_package(LMDB REQUIRED)
+include_directories(SYSTEM ${LMDB_INCLUDE_DIR})
+list(APPEND Caffe_LINKER_LIBS ${LMDB_LIBRARIES})
+
+# ---[ LevelDB
+find_package(LevelDB REQUIRED)
+include_directories(SYSTEM ${LevelDB_INCLUDE})
+list(APPEND Caffe_LINKER_LIBS ${LevelDB_LIBRARIES})
+
+# ---[ Snappy
+find_package(Snappy REQUIRED)
+include_directories(SYSTEM ${Snappy_INCLUDE_DIR})
+list(APPEND Caffe_LINKER_LIBS ${Snappy_LIBRARIES})
+
+# ---[ CUDA
+include(cmake/Cuda.cmake)
+if(NOT HAVE_CUDA)
+  if(CPU_ONLY)
+    message("-- CUDA is disabled. Building without it...")
+  else()
+    message("-- CUDA is not detected by cmake. Building without it...")
+  endif()
+
+  # TODO: remove this not cross platform define in future. Use caffe_config.h instead.
+  add_definitions(-DCPU_ONLY)
+endif()
+
+# ---[ OpenCV
+find_package(OpenCV QUIET COMPONENTS core highgui imgproc imgcodecs)
+if(NOT OpenCV_FOUND) # if not OpenCV 3.x, then imgcodecs are not found
+  find_package(OpenCV REQUIRED COMPONENTS core highgui imgproc)
+endif()
+include_directories(SYSTEM ${OpenCV_INCLUDE_DIRS})
+list(APPEND Caffe_LINKER_LIBS ${OpenCV_LIBS})
+message(STATUS "OpenCV found (${OpenCV_CONFIG_PATH})")
+
+# ---[ BLAS
+if(NOT APPLE)
+  set(BLAS "Atlas" CACHE STRING "Selected BLAS library")
+  set_property(CACHE BLAS PROPERTY STRINGS "Atlas;Open;MKL")
+
+  if(BLAS STREQUAL "Atlas" OR BLAS STREQUAL "atlas")
+    find_package(Atlas REQUIRED)
+    include_directories(SYSTEM ${Atlas_INCLUDE_DIR})
+    list(APPEND Caffe_LINKER_LIBS ${Atlas_LIBRARIES})
+  elseif(BLAS STREQUAL "Open" OR BLAS STREQUAL "open")
+    find_package(OpenBLAS REQUIRED)
+    include_directories(SYSTEM ${OpenBLAS_INCLUDE_DIR})
+    list(APPEND Caffe_LINKER_LIBS ${OpenBLAS_LIB})
+  elseif(BLAS STREQUAL "MKL" OR BLAS STREQUAL "mkl")
+    find_package(MKL REQUIRED)
+    include_directories(SYSTEM ${MKL_INCLUDE_DIR})
+    list(APPEND Caffe_LINKER_LIBS ${MKL_LIBRARIES})
+    add_definitions(-DUSE_MKL)
+  endif()
+elseif(APPLE)
+  find_package(vecLib REQUIRED)
+  include_directories(SYSTEM ${vecLib_INCLUDE_DIR})
+  list(APPEND Caffe_LINKER_LIBS ${vecLib_LINKER_LIBS})
+endif()
+
+# ---[ Python
+if(BUILD_python)
+  if(NOT "${python_version}" VERSION_LESS "3.0.0")
+    # use python3
+    find_package(PythonInterp 3.0)
+    find_package(PythonLibs 3.0)
+    find_package(NumPy 1.7.1)
+    # Find the matching boost python implementation
+    set(version ${PYTHONLIBS_VERSION_STRING})
+    
+    STRING( REPLACE "." "" boost_py_version ${version} )
+    find_package(Boost 1.55 COMPONENTS "python-py${boost_py_version}")
+    set(Boost_PYTHON_FOUND ${Boost_PYTHON-PY${boost_py_version}_FOUND})
+    
+    while(NOT "${version}" STREQUAL "" AND NOT Boost_PYTHON_FOUND)
+      STRING( REGEX REPLACE "([0-9.]+).[0-9]+" "\\1" version ${version} )
+      STRING( REGEX MATCHALL "([0-9.]+).[0-9]+" has_more_version ${version} )
+      if("${has_more_version}" STREQUAL "")
+        break()
+      endif()
+      
+      STRING( REPLACE "." "" boost_py_version ${version} )
+      find_package(Boost 1.55 COMPONENTS "python-py${boost_py_version}")
+      set(Boost_PYTHON_FOUND ${Boost_PYTHON-PY${boost_py_version}_FOUND})
+    endwhile()
+    if(NOT Boost_PYTHON_FOUND)
+      find_package(Boost 1.55 COMPONENTS python)
+    endif()
+  else()
+    # disable Python 3 search
+    find_package(PythonInterp 2.7)
+    find_package(PythonLibs 2.7)
+    find_package(NumPy 1.7.1)
+    find_package(Boost 1.55 COMPONENTS python)
+  endif()
+  if(PYTHONLIBS_FOUND AND NUMPY_FOUND AND Boost_PYTHON_FOUND)
+    set(HAVE_PYTHON TRUE)
+    if(BUILD_python_layer)
+      add_definitions(-DWITH_PYTHON_LAYER)
+      include_directories(SYSTEM ${PYTHON_INCLUDE_DIRS} ${NUMPY_INCLUDE_DIR} ${Boost_INCLUDE_DIRS})
+      list(APPEND Caffe_LINKER_LIBS ${PYTHON_LIBRARIES} ${Boost_LIBRARIES})
+    endif()
+  endif()
+endif()
+
+# ---[ Matlab
+if(BUILD_matlab)
+  find_package(MatlabMex)
+  if(MATLABMEX_FOUND)
+    set(HAVE_MATLAB TRUE)
+  endif()
+
+  # sudo apt-get install liboctave-dev
+  find_program(Octave_compiler NAMES mkoctfile DOC "Octave C++ compiler")
+
+  if(HAVE_MATLAB AND Octave_compiler)
+    set(Matlab_build_mex_using "Matlab" CACHE STRING "Select Matlab or Octave if both detected")
+    set_property(CACHE Matlab_build_mex_using PROPERTY STRINGS "Matlab;Octave")
+  endif()
+endif()
+
+# ---[ Doxygen
+if(BUILD_docs)
+  find_package(Doxygen)
+endif()
+
+if (USE_MPI)
+  set(MPIEXEC "/usr/local/openmpi/bin/mpiexec")
+  find_package(MPI)
+  include_directories(SYSTEM ${MPI_CXX_INCLUDE_PATH})
+  list(APPEND Caffe_LINKER_LIBS ${MPI_CXX_LIBRARIES})
+  add_definitions(-DUSE_MPI)
+  set(CMAKE_EXE_LINKER_FLAGS "${CMAKE_SHARED_LINKER_FLAGS} ${MPI_CXX_LINK_FLAGS}")
+endif()
diff --git a/cmake/External/gflags.cmake b/cmake/External/gflags.cmake
new file mode 100644
index 00000000000..e3dba04f33f
--- /dev/null
+++ b/cmake/External/gflags.cmake
@@ -0,0 +1,56 @@
+if (NOT __GFLAGS_INCLUDED) # guard against multiple includes
+  set(__GFLAGS_INCLUDED TRUE)
+
+  # use the system-wide gflags if present
+  find_package(GFlags)
+  if (GFLAGS_FOUND)
+    set(GFLAGS_EXTERNAL FALSE)
+  else()
+    # gflags will use pthreads if it's available in the system, so we must link with it
+    find_package(Threads)
+
+    # build directory
+    set(gflags_PREFIX ${CMAKE_BINARY_DIR}/external/gflags-prefix)
+    # install directory
+    set(gflags_INSTALL ${CMAKE_BINARY_DIR}/external/gflags-install)
+
+    # we build gflags statically, but want to link it into the caffe shared library
+    # this requires position-independent code
+    if (UNIX)
+        set(GFLAGS_EXTRA_COMPILER_FLAGS "-fPIC")
+    endif()
+
+    set(GFLAGS_CXX_FLAGS ${CMAKE_CXX_FLAGS} ${GFLAGS_EXTRA_COMPILER_FLAGS})
+    set(GFLAGS_C_FLAGS ${CMAKE_C_FLAGS} ${GFLAGS_EXTRA_COMPILER_FLAGS})
+
+    ExternalProject_Add(gflags
+      PREFIX ${gflags_PREFIX}
+      GIT_REPOSITORY "https://github.com/gflags/gflags.git"
+      GIT_TAG "v2.1.2"
+      UPDATE_COMMAND ""
+      INSTALL_DIR ${gflags_INSTALL}
+      CMAKE_ARGS -DCMAKE_BUILD_TYPE=${CMAKE_BUILD_TYPE}
+                 -DCMAKE_INSTALL_PREFIX=${gflags_INSTALL}
+                 -DBUILD_SHARED_LIBS=OFF
+                 -DBUILD_STATIC_LIBS=ON
+                 -DBUILD_PACKAGING=OFF
+                 -DBUILD_TESTING=OFF
+                 -DBUILD_NC_TESTS=OFF
+                 -BUILD_CONFIG_TESTS=OFF
+                 -DINSTALL_HEADERS=ON
+                 -DCMAKE_C_FLAGS=${GFLAGS_C_FLAGS}
+                 -DCMAKE_CXX_FLAGS=${GFLAGS_CXX_FLAGS}
+      LOG_DOWNLOAD 1
+      LOG_INSTALL 1
+      )
+
+    set(GFLAGS_FOUND TRUE)
+    set(GFLAGS_INCLUDE_DIRS ${gflags_INSTALL}/include)
+    set(GFLAGS_LIBRARIES ${gflags_INSTALL}/lib/libgflags.a ${CMAKE_THREAD_LIBS_INIT})
+    set(GFLAGS_LIBRARY_DIRS ${gflags_INSTALL}/lib)
+    set(GFLAGS_EXTERNAL TRUE)
+
+    list(APPEND external_project_dependencies gflags)
+  endif()
+
+endif()
diff --git a/cmake/External/glog.cmake b/cmake/External/glog.cmake
new file mode 100644
index 00000000000..a44672f2753
--- /dev/null
+++ b/cmake/External/glog.cmake
@@ -0,0 +1,56 @@
+# glog depends on gflags
+include("cmake/External/gflags.cmake")
+
+if (NOT __GLOG_INCLUDED)
+  set(__GLOG_INCLUDED TRUE)
+
+  # try the system-wide glog first
+  find_package(Glog)
+  if (GLOG_FOUND)
+      set(GLOG_EXTERNAL FALSE)
+  else()
+    # fetch and build glog from github
+
+    # build directory
+    set(glog_PREFIX ${CMAKE_BINARY_DIR}/external/glog-prefix)
+    # install directory
+    set(glog_INSTALL ${CMAKE_BINARY_DIR}/external/glog-install)
+
+    # we build glog statically, but want to link it into the caffe shared library
+    # this requires position-independent code
+    if (UNIX)
+      set(GLOG_EXTRA_COMPILER_FLAGS "-fPIC")
+    endif()
+
+    set(GLOG_CXX_FLAGS ${CMAKE_CXX_FLAGS} ${GLOG_EXTRA_COMPILER_FLAGS})
+    set(GLOG_C_FLAGS ${CMAKE_C_FLAGS} ${GLOG_EXTRA_COMPILER_FLAGS})
+
+    # depend on gflags if we're also building it
+    if (GFLAGS_EXTERNAL)
+      set(GLOG_DEPENDS gflags)
+    endif()
+
+    ExternalProject_Add(glog
+      DEPENDS ${GLOG_DEPENDS}
+      PREFIX ${glog_PREFIX}
+      GIT_REPOSITORY "https://github.com/google/glog"
+      GIT_TAG "v0.3.4"
+      UPDATE_COMMAND ""
+      INSTALL_DIR ${gflags_INSTALL}
+      CONFIGURE_COMMAND env "CFLAGS=${GLOG_C_FLAGS}" "CXXFLAGS=${GLOG_CXX_FLAGS}" ${glog_PREFIX}/src/glog/configure --prefix=${glog_INSTALL} --enable-shared=no --enable-static=yes --with-gflags=${GFLAGS_LIBRARY_DIRS}/..
+      LOG_DOWNLOAD 1
+      LOG_CONFIGURE 1
+      LOG_INSTALL 1
+      )
+
+    set(GLOG_FOUND TRUE)
+    set(GLOG_INCLUDE_DIRS ${glog_INSTALL}/include)
+    set(GLOG_LIBRARIES ${GFLAGS_LIBRARIES} ${glog_INSTALL}/lib/libglog.a)
+    set(GLOG_LIBRARY_DIRS ${glog_INSTALL}/lib)
+    set(GLOG_EXTERNAL TRUE)
+
+    list(APPEND external_project_dependencies glog)
+  endif()
+
+endif()
+
diff --git a/cmake/Misc.cmake b/cmake/Misc.cmake
new file mode 100644
index 00000000000..7676754fe04
--- /dev/null
+++ b/cmake/Misc.cmake
@@ -0,0 +1,52 @@
+# ---[ Configuration types
+set(CMAKE_CONFIGURATION_TYPES "Debug;Release" CACHE STRING "Possible configurations" FORCE)
+mark_as_advanced(CMAKE_CONFIGURATION_TYPES)
+
+if(DEFINED CMAKE_BUILD_TYPE)
+  set_property(CACHE CMAKE_BUILD_TYPE PROPERTY STRINGS ${CMAKE_CONFIGURATION_TYPES})
+endif()
+
+# --[ If user doesn't specify build type then assume release
+if("${CMAKE_BUILD_TYPE}" STREQUAL "")
+  set(CMAKE_BUILD_TYPE Release)
+endif()
+
+if("${CMAKE_CXX_COMPILER_ID}" STREQUAL "Clang")
+  set(CMAKE_COMPILER_IS_CLANGXX TRUE)
+endif()
+
+# ---[ Solution folders
+caffe_option(USE_PROJECT_FOLDERS "IDE Solution folders" (MSVC_IDE OR CMAKE_GENERATOR MATCHES Xcode) )
+
+if(USE_PROJECT_FOLDERS)
+  set_property(GLOBAL PROPERTY USE_FOLDERS ON)
+  set_property(GLOBAL PROPERTY PREDEFINED_TARGETS_FOLDER "CMakeTargets")
+endif()
+
+# ---[ Install options
+if(CMAKE_INSTALL_PREFIX_INITIALIZED_TO_DEFAULT)
+  set(CMAKE_INSTALL_PREFIX "${PROJECT_BINARY_DIR}/install" CACHE PATH "Default install path" FORCE)
+endif()
+
+# ---[ RPATH settings
+set(CMAKE_INSTALL_RPATH_USE_LINK_PATH TRUE CACHE BOOLEAN "Use link paths for shared library rpath")
+set(CMAKE_MACOSX_RPATH TRUE)
+
+list(FIND CMAKE_PLATFORM_IMPLICIT_LINK_DIRECTORIES ${CMAKE_INSTALL_PREFIX}/lib __is_systtem_dir)
+if(${__is_systtem_dir} STREQUAL -1)
+  set(CMAKE_INSTALL_RPATH ${CMAKE_INSTALL_PREFIX}/lib)
+endif()
+
+# ---[ Funny target
+if(UNIX OR APPLE)
+  add_custom_target(symlink_to_build COMMAND "ln" "-sf" "${PROJECT_BINARY_DIR}" "${PROJECT_SOURCE_DIR}/build"
+                                     COMMENT "Adding symlink: <caffe_root>/build -> ${PROJECT_BINARY_DIR}" )
+endif()
+
+# ---[ Set debug postfix
+set(Caffe_DEBUG_POSTFIX "-d")
+
+set(CAffe_POSTFIX "")
+if(CMAKE_BUILD_TYPE MATCHES "Debug")
+  set(CAffe_POSTFIX ${Caffe_DEBUG_POSTFIX})
+endif()
diff --git a/CMakeScripts/FindAtlas.cmake b/cmake/Modules/FindAtlas.cmake
similarity index 63%
rename from CMakeScripts/FindAtlas.cmake
rename to cmake/Modules/FindAtlas.cmake
index 27657a6c7d7..6e1564351c7 100644
--- a/CMakeScripts/FindAtlas.cmake
+++ b/cmake/Modules/FindAtlas.cmake
@@ -23,14 +23,14 @@ set(Atlas_LIB_SEARCH_PATHS
   $ENV{Atlas_ROOT_DIR}/lib
 )
 
-find_path(Atlas_CBLAS_INCLUDE_DIR NAMES cblas.h PATHS ${Atlas_INCLUDE_SEARCH_PATHS})
+find_path(Atlas_CBLAS_INCLUDE_DIR   NAMES cblas.h   PATHS ${Atlas_INCLUDE_SEARCH_PATHS})
 find_path(Atlas_CLAPACK_INCLUDE_DIR NAMES clapack.h PATHS ${Atlas_INCLUDE_SEARCH_PATHS})
-find_library(Atlas_CBLAS_LIBRARY NAMES ptcblas_r ptcblas cblas_r cblas PATHS ${Atlas_LIB_SEARCH_PATHS})
-find_library(Atlas_BLAS_LIBRARY NAMES atlas_r atlas PATHS ${Atlas_LIB_SEARCH_PATHS})
-find_library(Atlas_LAPACK_LIBRARY NAMES alapack_r alapack lapack_atlas PATHS ${Atlas_LIB_SEARCH_PATHS})
 
-set(LOOKED_FOR
+find_library(Atlas_CBLAS_LIBRARY NAMES  ptcblas_r ptcblas cblas_r cblas PATHS ${Atlas_LIB_SEARCH_PATHS})
+find_library(Atlas_BLAS_LIBRARY NAMES   atlas_r   atlas                 PATHS ${Atlas_LIB_SEARCH_PATHS})
+find_library(Atlas_LAPACK_LIBRARY NAMES alapack_r alapack lapack_atlas  PATHS ${Atlas_LIB_SEARCH_PATHS})
 
+set(LOOKED_FOR
   Atlas_CBLAS_INCLUDE_DIR
   Atlas_CLAPACK_INCLUDE_DIR
 
@@ -43,19 +43,10 @@ include(FindPackageHandleStandardArgs)
 find_package_handle_standard_args(Atlas DEFAULT_MSG ${LOOKED_FOR})
 
 if(ATLAS_FOUND)
-
+  set(Atlas_INCLUDE_DIR ${Atlas_CBLAS_INCLUDE_DIR} ${Atlas_CLAPACK_INCLUDE_DIR})
+  set(Atlas_LIBRARIES ${Atlas_LAPACK_LIBRARY} ${Atlas_CBLAS_LIBRARY} ${Atlas_BLAS_LIBRARY})
   mark_as_advanced(${LOOKED_FOR})
 
-  set(Atlas_INCLUDE_DIR
-    ${Atlas_CBLAS_INCLUDE_DIR}
-    ${Atlas_CLAPACK_INCLUDE_DIR}
-  )
-
-  set(Atlas_LIBRARIES
-    ${Atlas_LAPACK_LIBRARY}
-    ${Atlas_CBLAS_LIBRARY}
-    ${Atlas_BLAS_LIBRARY}
-  )
-
+  message(STATUS "Found Atlas (include: ${Atlas_CBLAS_INCLUDE_DIR}, library: ${Atlas_BLAS_LIBRARY})")
 endif(ATLAS_FOUND)
 
diff --git a/CMakeScripts/FindGFlags.cmake b/cmake/Modules/FindGFlags.cmake
similarity index 79%
rename from CMakeScripts/FindGFlags.cmake
rename to cmake/Modules/FindGFlags.cmake
index f93c57136a1..29b60f05037 100644
--- a/CMakeScripts/FindGFlags.cmake
+++ b/cmake/Modules/FindGFlags.cmake
@@ -38,11 +38,13 @@ else()
     find_library(GFLAGS_LIBRARY gflags)
 endif()
 
-find_package_handle_standard_args(GFLAGS DEFAULT_MSG
-    GFLAGS_INCLUDE_DIR GFLAGS_LIBRARY)
+find_package_handle_standard_args(GFlags DEFAULT_MSG GFLAGS_INCLUDE_DIR GFLAGS_LIBRARY)
 
 
 if(GFLAGS_FOUND)
     set(GFLAGS_INCLUDE_DIRS ${GFLAGS_INCLUDE_DIR})
     set(GFLAGS_LIBRARIES ${GFLAGS_LIBRARY})
+    message(STATUS "Found gflags  (include: ${GFLAGS_INCLUDE_DIR}, library: ${GFLAGS_LIBRARY})")
+    mark_as_advanced(GFLAGS_LIBRARY_DEBUG GFLAGS_LIBRARY_RELEASE
+                     GFLAGS_LIBRARY GFLAGS_INCLUDE_DIR GFLAGS_ROOT_DIR)
 endif()
diff --git a/CMakeScripts/FindGlog.cmake b/cmake/Modules/FindGlog.cmake
similarity index 70%
rename from CMakeScripts/FindGlog.cmake
rename to cmake/Modules/FindGlog.cmake
index 0dc30abdbf5..99abbe478a0 100644
--- a/CMakeScripts/FindGlog.cmake
+++ b/cmake/Modules/FindGlog.cmake
@@ -34,15 +34,15 @@ if(MSVC)
 else()
     find_library(GLOG_LIBRARY glog
         PATHS ${GLOG_ROOT_DIR}
-        PATH_SUFFIXES
-            lib
-            lib64)
+        PATH_SUFFIXES lib lib64)
 endif()
 
-find_package_handle_standard_args(GLOG DEFAULT_MSG
-    GLOG_INCLUDE_DIR GLOG_LIBRARY)
+find_package_handle_standard_args(Glog DEFAULT_MSG GLOG_INCLUDE_DIR GLOG_LIBRARY)
 
 if(GLOG_FOUND)
-    set(GLOG_INCLUDE_DIRS ${GLOG_INCLUDE_DIR})
-    set(GLOG_LIBRARIES ${GLOG_LIBRARY})
+  set(GLOG_INCLUDE_DIRS ${GLOG_INCLUDE_DIR})
+  set(GLOG_LIBRARIES ${GLOG_LIBRARY})
+  message(STATUS "Found glog    (include: ${GLOG_INCLUDE_DIR}, library: ${GLOG_LIBRARY})")
+  mark_as_advanced(GLOG_ROOT_DIR GLOG_LIBRARY_RELEASE GLOG_LIBRARY_DEBUG
+                                 GLOG_LIBRARY GLOG_INCLUDE_DIR)
 endif()
diff --git a/CMakeScripts/FindLAPACK.cmake b/cmake/Modules/FindLAPACK.cmake
similarity index 100%
rename from CMakeScripts/FindLAPACK.cmake
rename to cmake/Modules/FindLAPACK.cmake
diff --git a/cmake/Modules/FindLMDB.cmake b/cmake/Modules/FindLMDB.cmake
new file mode 100644
index 00000000000..8a817fd6f10
--- /dev/null
+++ b/cmake/Modules/FindLMDB.cmake
@@ -0,0 +1,28 @@
+# Try to find the LMBD libraries and headers
+#  LMDB_FOUND - system has LMDB lib
+#  LMDB_INCLUDE_DIR - the LMDB include directory
+#  LMDB_LIBRARIES - Libraries needed to use LMDB
+
+# FindCWD based on FindGMP by:
+# Copyright (c) 2006, Laurent Montel, <montel@kde.org>
+#
+# Redistribution and use is allowed according to the terms of the BSD license.
+
+# Adapted from FindCWD by:
+# Copyright 2013 Conrad Steenberg <conrad.steenberg@gmail.com>
+# Aug 31, 2013
+
+find_path(LMDB_INCLUDE_DIR NAMES  lmdb.h PATHS "$ENV{LMDB_DIR}/include")
+find_library(LMDB_LIBRARIES NAMES lmdb   PATHS "$ENV{LMDB_DIR}/lib" )
+
+include(FindPackageHandleStandardArgs)
+find_package_handle_standard_args(LMDB DEFAULT_MSG LMDB_INCLUDE_DIR LMDB_LIBRARIES)
+
+if(LMDB_FOUND)
+  message(STATUS "Found lmdb    (include: ${LMDB_INCLUDE_DIR}, library: ${LMDB_LIBRARIES})")
+  mark_as_advanced(LMDB_INCLUDE_DIR LMDB_LIBRARIES)
+
+  caffe_parse_header(${LMDB_INCLUDE_DIR}/lmdb.h
+                     LMDB_VERSION_LINES MDB_VERSION_MAJOR MDB_VERSION_MINOR MDB_VERSION_PATCH)
+  set(LMDB_VERSION "${MDB_VERSION_MAJOR}.${MDB_VERSION_MINOR}.${MDB_VERSION_PATCH}")
+endif()
diff --git a/cmake/Modules/FindLevelDB.cmake b/cmake/Modules/FindLevelDB.cmake
new file mode 100644
index 00000000000..97f08ac9349
--- /dev/null
+++ b/cmake/Modules/FindLevelDB.cmake
@@ -0,0 +1,44 @@
+# - Find LevelDB
+#
+#  LevelDB_INCLUDES  - List of LevelDB includes
+#  LevelDB_LIBRARIES - List of libraries when using LevelDB.
+#  LevelDB_FOUND     - True if LevelDB found.
+
+# Look for the header file.
+find_path(LevelDB_INCLUDE NAMES leveldb/db.h
+                          PATHS $ENV{LEVELDB_ROOT}/include /opt/local/include /usr/local/include /usr/include
+                          DOC "Path in which the file leveldb/db.h is located." )
+
+# Look for the library.
+find_library(LevelDB_LIBRARY NAMES leveldb
+                             PATHS /usr/lib $ENV{LEVELDB_ROOT}/lib
+                             DOC "Path to leveldb library." )
+
+include(FindPackageHandleStandardArgs)
+find_package_handle_standard_args(LevelDB DEFAULT_MSG LevelDB_INCLUDE LevelDB_LIBRARY)
+
+if(LEVELDB_FOUND)
+  message(STATUS "Found LevelDB (include: ${LevelDB_INCLUDE}, library: ${LevelDB_LIBRARY})")
+  set(LevelDB_INCLUDES ${LevelDB_INCLUDE})
+  set(LevelDB_LIBRARIES ${LevelDB_LIBRARY})
+  mark_as_advanced(LevelDB_INCLUDE LevelDB_LIBRARY)
+
+  if(EXISTS "${LevelDB_INCLUDE}/leveldb/db.h")
+    file(STRINGS "${LevelDB_INCLUDE}/leveldb/db.h" __version_lines
+           REGEX "static const int k[^V]+Version[ \t]+=[ \t]+[0-9]+;")
+
+    foreach(__line ${__version_lines})
+      if(__line MATCHES "[^k]+kMajorVersion[ \t]+=[ \t]+([0-9]+);")
+        set(LEVELDB_VERSION_MAJOR ${CMAKE_MATCH_1})
+      elseif(__line MATCHES "[^k]+kMinorVersion[ \t]+=[ \t]+([0-9]+);")
+        set(LEVELDB_VERSION_MINOR ${CMAKE_MATCH_1})
+      endif()
+    endforeach()
+
+    if(LEVELDB_VERSION_MAJOR AND LEVELDB_VERSION_MINOR)
+      set(LEVELDB_VERSION "${LEVELDB_VERSION_MAJOR}.${LEVELDB_VERSION_MINOR}")
+    endif()
+
+    caffe_clear_vars(__line __version_lines)
+  endif()
+endif()
diff --git a/cmake/Modules/FindMKL.cmake b/cmake/Modules/FindMKL.cmake
new file mode 100644
index 00000000000..d2012db579a
--- /dev/null
+++ b/cmake/Modules/FindMKL.cmake
@@ -0,0 +1,110 @@
+# Find the MKL libraries
+#
+# Options:
+#
+#   MKL_USE_SINGLE_DYNAMIC_LIBRARY  : use single dynamic library interface
+#   MKL_USE_STATIC_LIBS             : use static libraries
+#   MKL_MULTI_THREADED              : use multi-threading
+#
+# This module defines the following variables:
+#
+#   MKL_FOUND            : True mkl is found
+#   MKL_INCLUDE_DIR      : unclude directory
+#   MKL_LIBRARIES        : the libraries to link against.
+
+
+# ---[ Options
+caffe_option(MKL_USE_SINGLE_DYNAMIC_LIBRARY "Use single dynamic library interface" ON)
+caffe_option(MKL_USE_STATIC_LIBS "Use static libraries" OFF IF NOT MKL_USE_SINGLE_DYNAMIC_LIBRARY)
+caffe_option(MKL_MULTI_THREADED  "Use multi-threading"   ON IF NOT MKL_USE_SINGLE_DYNAMIC_LIBRARY)
+
+# ---[ Root folders
+set(INTEL_ROOT "/opt/intel" CACHE PATH "Folder contains intel libs")
+find_path(MKL_ROOT include/mkl.h PATHS $ENV{MKL_ROOT} ${INTEL_ROOT}/mkl
+                                   DOC "Folder contains MKL")
+
+# ---[ Find include dir
+find_path(MKL_INCLUDE_DIR mkl.h PATHS ${MKL_ROOT} PATH_SUFFIXES include)
+set(__looked_for MKL_INCLUDE_DIR)
+
+# ---[ Find libraries
+if(CMAKE_SIZEOF_VOID_P EQUAL 4)
+  set(__path_suffixes lib lib/ia32)
+else()
+  set(__path_suffixes lib lib/intel64)
+endif()
+
+set(__mkl_libs "")
+if(MKL_USE_SINGLE_DYNAMIC_LIBRARY)
+  list(APPEND __mkl_libs rt)
+else()
+  if(CMAKE_SIZEOF_VOID_P EQUAL 4)
+    if(WIN32)
+      list(APPEND __mkl_libs intel_c)
+    else()
+      list(APPEND __mkl_libs intel gf)
+    endif()
+  else()
+    list(APPEND __mkl_libs intel_lp64 gf_lp64)
+  endif()
+
+  if(MKL_MULTI_THREADED)
+    list(APPEND __mkl_libs intel_thread)
+  else()
+     list(APPEND __mkl_libs sequential)
+  endif()
+
+  list(APPEND __mkl_libs core cdft_core)
+endif()
+
+
+foreach (__lib ${__mkl_libs})
+  set(__mkl_lib "mkl_${__lib}")
+  string(TOUPPER ${__mkl_lib} __mkl_lib_upper)
+
+  if(MKL_USE_STATIC_LIBS)
+    set(__mkl_lib "lib${__mkl_lib}.a")
+  endif()
+
+  find_library(${__mkl_lib_upper}_LIBRARY
+        NAMES ${__mkl_lib}
+        PATHS ${MKL_ROOT} "${MKL_INCLUDE_DIR}/.."
+        PATH_SUFFIXES ${__path_suffixes}
+        DOC "The path to Intel(R) MKL ${__mkl_lib} library")
+  mark_as_advanced(${__mkl_lib_upper}_LIBRARY)
+
+  list(APPEND __looked_for ${__mkl_lib_upper}_LIBRARY)
+  list(APPEND MKL_LIBRARIES ${${__mkl_lib_upper}_LIBRARY})
+endforeach()
+
+
+if(NOT MKL_USE_SINGLE_DYNAMIC_LIBRARY)
+  if (MKL_USE_STATIC_LIBS)
+    set(__iomp5_libs iomp5 libiomp5mt.lib)
+  else()
+    set(__iomp5_libs iomp5 libiomp5md.lib)
+  endif()
+
+  if(WIN32)
+    find_path(INTEL_INCLUDE_DIR omp.h PATHS ${INTEL_ROOT} PATH_SUFFIXES include)
+    list(APPEND __looked_for INTEL_INCLUDE_DIR)
+  endif()
+
+  find_library(MKL_RTL_LIBRARY ${__iomp5_libs}
+     PATHS ${INTEL_RTL_ROOT} ${INTEL_ROOT}/compiler ${MKL_ROOT}/.. ${MKL_ROOT}/../compiler
+     PATH_SUFFIXES ${__path_suffixes}
+     DOC "Path to Path to OpenMP runtime library")
+
+  list(APPEND __looked_for MKL_RTL_LIBRARY)
+  list(APPEND MKL_LIBRARIES ${MKL_RTL_LIBRARY})
+endif()
+
+
+include(FindPackageHandleStandardArgs)
+find_package_handle_standard_args(MKL DEFAULT_MSG ${__looked_for})
+
+if(MKL_FOUND)
+  message(STATUS "Found MKL (include: ${MKL_INCLUDE_DIR}, lib: ${MKL_LIBRARIES}")
+endif()
+
+caffe_clear_vars(__looked_for __mkl_libs __path_suffixes __lib_suffix __iomp5_libs)
diff --git a/cmake/Modules/FindMatlabMex.cmake b/cmake/Modules/FindMatlabMex.cmake
new file mode 100644
index 00000000000..28ae65e7cbb
--- /dev/null
+++ b/cmake/Modules/FindMatlabMex.cmake
@@ -0,0 +1,48 @@
+# This module looks for MatlabMex compiler
+# Defines variables:
+#    Matlab_DIR    - Matlab root dir
+#    Matlab_mex    - path to mex compiler
+#    Matlab_mexext - path to mexext
+
+if(MSVC)
+  foreach(__ver "9.30" "7.14" "7.11" "7.10" "7.9" "7.8" "7.7")
+    get_filename_component(__matlab_root "[HKEY_LOCAL_MACHINE\\SOFTWARE\\MathWorks\\MATLAB\\${__ver};MATLABROOT]" ABSOLUTE)
+    if(__matlab_root)
+      break()
+    endif()
+  endforeach()
+endif()
+
+if(APPLE)
+  foreach(__ver "R2014b" "R2014a" "R2013b" "R2013a" "R2012b" "R2012a" "R2011b" "R2011a" "R2010b" "R2010a")
+    if(EXISTS /Applications/MATLAB_${__ver}.app)
+      set(__matlab_root /Applications/MATLAB_${__ver}.app)
+      break()
+    endif()
+  endforeach()
+endif()
+
+if(UNIX)
+   execute_process(COMMAND which matlab OUTPUT_STRIP_TRAILING_WHITESPACE
+                   OUTPUT_VARIABLE __out RESULT_VARIABLE __res)
+
+   if(__res MATCHES 0) # Suppress `readlink` warning if `which` returned nothing
+     execute_process(COMMAND which matlab  COMMAND xargs readlink
+                     COMMAND xargs dirname COMMAND xargs dirname COMMAND xargs echo -n
+                     OUTPUT_VARIABLE __matlab_root OUTPUT_STRIP_TRAILING_WHITESPACE)
+   endif()
+endif()
+
+
+find_path(Matlab_DIR NAMES bin/mex bin/mexext PATHS ${__matlab_root}
+                     DOC "Matlab directory" NO_DEFAULT_PATH)
+
+find_program(Matlab_mex    NAMES mex    mex.bat    HINTS ${Matlab_DIR} PATH_SUFFIXES bin NO_DEFAULT_PATH)
+find_program(Matlab_mexext NAMES mexext mexext.bat HINTS ${Matlab_DIR} PATH_SUFFIXES bin NO_DEFAULT_PATH)
+
+include(FindPackageHandleStandardArgs)
+find_package_handle_standard_args(MatlabMex DEFAULT_MSG Matlab_mex Matlab_mexext)
+
+if(MATLABMEX_FOUND)
+  mark_as_advanced(Matlab_mex Matlab_mexext)
+endif()
diff --git a/cmake/Modules/FindNumPy.cmake b/cmake/Modules/FindNumPy.cmake
new file mode 100644
index 00000000000..a671494caba
--- /dev/null
+++ b/cmake/Modules/FindNumPy.cmake
@@ -0,0 +1,58 @@
+# - Find the NumPy libraries
+# This module finds if NumPy is installed, and sets the following variables
+# indicating where it is.
+#
+# TODO: Update to provide the libraries and paths for linking npymath lib.
+#
+#  NUMPY_FOUND               - was NumPy found
+#  NUMPY_VERSION             - the version of NumPy found as a string
+#  NUMPY_VERSION_MAJOR       - the major version number of NumPy
+#  NUMPY_VERSION_MINOR       - the minor version number of NumPy
+#  NUMPY_VERSION_PATCH       - the patch version number of NumPy
+#  NUMPY_VERSION_DECIMAL     - e.g. version 1.6.1 is 10601
+#  NUMPY_INCLUDE_DIR         - path to the NumPy include files
+
+unset(NUMPY_VERSION)
+unset(NUMPY_INCLUDE_DIR)
+
+if(PYTHONINTERP_FOUND)
+  execute_process(COMMAND "${PYTHON_EXECUTABLE}" "-c"
+    "import numpy as n; print(n.__version__); print(n.get_include());"
+    RESULT_VARIABLE __result
+    OUTPUT_VARIABLE __output
+    OUTPUT_STRIP_TRAILING_WHITESPACE)
+
+  if(__result MATCHES 0)
+    string(REGEX REPLACE ";" "\\\\;" __values ${__output})
+    string(REGEX REPLACE "\r?\n" ";"    __values ${__values})
+    list(GET __values 0 NUMPY_VERSION)
+    list(GET __values 1 NUMPY_INCLUDE_DIR)
+
+    string(REGEX MATCH "^([0-9])+\\.([0-9])+\\.([0-9])+" __ver_check "${NUMPY_VERSION}")
+    if(NOT "${__ver_check}" STREQUAL "")
+      set(NUMPY_VERSION_MAJOR ${CMAKE_MATCH_1})
+      set(NUMPY_VERSION_MINOR ${CMAKE_MATCH_2})
+      set(NUMPY_VERSION_PATCH ${CMAKE_MATCH_3})
+      math(EXPR NUMPY_VERSION_DECIMAL
+        "(${NUMPY_VERSION_MAJOR} * 10000) + (${NUMPY_VERSION_MINOR} * 100) + ${NUMPY_VERSION_PATCH}")
+      string(REGEX REPLACE "\\\\" "/"  NUMPY_INCLUDE_DIR ${NUMPY_INCLUDE_DIR})
+    else()
+     unset(NUMPY_VERSION)
+     unset(NUMPY_INCLUDE_DIR)
+     message(STATUS "Requested NumPy version and include path, but got instead:\n${__output}\n")
+    endif()
+  endif()
+else()
+  message(STATUS "To find NumPy Python interpretator is required to be found.")
+endif()
+
+include(FindPackageHandleStandardArgs)
+find_package_handle_standard_args(NumPy REQUIRED_VARS NUMPY_INCLUDE_DIR NUMPY_VERSION
+                                        VERSION_VAR   NUMPY_VERSION)
+
+if(NUMPY_FOUND)
+  message(STATUS "NumPy ver. ${NUMPY_VERSION} found (include: ${NUMPY_INCLUDE_DIR})")
+endif()
+
+caffe_clear_vars(__result __output __error_value __values __ver_check __error_value)
+
diff --git a/CMakeScripts/FindOpenBLAS.cmake b/cmake/Modules/FindOpenBLAS.cmake
similarity index 100%
rename from CMakeScripts/FindOpenBLAS.cmake
rename to cmake/Modules/FindOpenBLAS.cmake
diff --git a/cmake/Modules/FindSnappy.cmake b/cmake/Modules/FindSnappy.cmake
new file mode 100644
index 00000000000..eff2a864a7b
--- /dev/null
+++ b/cmake/Modules/FindSnappy.cmake
@@ -0,0 +1,28 @@
+# Find the Snappy libraries
+#
+# The following variables are optionally searched for defaults
+#  Snappy_ROOT_DIR:    Base directory where all Snappy components are found
+#
+# The following are set after configuration is done:
+#  SNAPPY_FOUND
+#  Snappy_INCLUDE_DIR
+#  Snappy_LIBRARIES
+
+find_path(Snappy_INCLUDE_DIR NAMES snappy.h
+                             PATHS ${SNAPPY_ROOT_DIR} ${SNAPPY_ROOT_DIR}/include)
+
+find_library(Snappy_LIBRARIES NAMES snappy
+                              PATHS ${SNAPPY_ROOT_DIR} ${SNAPPY_ROOT_DIR}/lib)
+
+include(FindPackageHandleStandardArgs)
+find_package_handle_standard_args(Snappy DEFAULT_MSG Snappy_INCLUDE_DIR Snappy_LIBRARIES)
+
+if(SNAPPY_FOUND)
+  message(STATUS "Found Snappy  (include: ${Snappy_INCLUDE_DIR}, library: ${Snappy_LIBRARIES})")
+  mark_as_advanced(Snappy_INCLUDE_DIR Snappy_LIBRARIES)
+
+  caffe_parse_header(${Snappy_INCLUDE_DIR}/snappy-stubs-public.h
+                     SNAPPY_VERION_LINES SNAPPY_MAJOR SNAPPY_MINOR SNAPPY_PATCHLEVEL)
+  set(Snappy_VERSION "${SNAPPY_MAJOR}.${SNAPPY_MINOR}.${SNAPPY_PATCHLEVEL}")
+endif()
+
diff --git a/cmake/Modules/FindvecLib.cmake b/cmake/Modules/FindvecLib.cmake
new file mode 100644
index 00000000000..9600da43647
--- /dev/null
+++ b/cmake/Modules/FindvecLib.cmake
@@ -0,0 +1,34 @@
+# Find the vecLib libraries as part of Accelerate.framework or as standalon framework
+#
+# The following are set after configuration is done:
+#  VECLIB_FOUND
+#  vecLib_INCLUDE_DIR
+#  vecLib_LINKER_LIBS
+
+
+if(NOT APPLE)
+  return()
+endif()
+
+set(__veclib_include_suffix "Frameworks/vecLib.framework/Versions/Current/Headers")
+
+find_path(vecLib_INCLUDE_DIR vecLib.h
+          DOC "vecLib include directory"
+          PATHS /System/Library/${__veclib_include_suffix}
+                /System/Library/Frameworks/Accelerate.framework/Versions/Current/${__veclib_include_suffix}
+                /Applications/Xcode.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX10.9.sdk/System/Library/Frameworks/Accelerate.framework/Versions/Current/Frameworks/vecLib.framework/Headers/)
+
+include(FindPackageHandleStandardArgs)
+find_package_handle_standard_args(vecLib DEFAULT_MSG vecLib_INCLUDE_DIR)
+
+if(VECLIB_FOUND)
+  if(vecLib_INCLUDE_DIR MATCHES "^/System/Library/Frameworks/vecLib.framework.*")
+    set(vecLib_LINKER_LIBS -lcblas "-framework vecLib")
+    message(STATUS "Found standalone vecLib.framework")
+  else()
+    set(vecLib_LINKER_LIBS -lcblas "-framework Accelerate")
+    message(STATUS "Found vecLib as part of Accelerate.framework")
+  endif()
+
+  mark_as_advanced(vecLib_INCLUDE_DIR)
+endif()
diff --git a/cmake/ProtoBuf.cmake b/cmake/ProtoBuf.cmake
new file mode 100644
index 00000000000..fc799bd3906
--- /dev/null
+++ b/cmake/ProtoBuf.cmake
@@ -0,0 +1,90 @@
+# Finds Google Protocol Buffers library and compilers and extends
+# the standard cmake script with version and python generation support
+
+find_package( Protobuf REQUIRED )
+include_directories(SYSTEM ${PROTOBUF_INCLUDE_DIR})
+list(APPEND Caffe_LINKER_LIBS ${PROTOBUF_LIBRARIES})
+
+# As of Ubuntu 14.04 protoc is no longer a part of libprotobuf-dev package
+# and should be installed separately as in: sudo apt-get install protobuf-compiler
+if(EXISTS ${PROTOBUF_PROTOC_EXECUTABLE})
+  message(STATUS "Found PROTOBUF Compiler: ${PROTOBUF_PROTOC_EXECUTABLE}")
+else()
+  message(FATAL_ERROR "Could not find PROTOBUF Compiler")
+endif()
+
+if(PROTOBUF_FOUND)
+  # fetches protobuf version
+  caffe_parse_header(${PROTOBUF_INCLUDE_DIR}/google/protobuf/stubs/common.h VERION_LINE GOOGLE_PROTOBUF_VERSION)
+  string(REGEX MATCH "([0-9])00([0-9])00([0-9])" PROTOBUF_VERSION ${GOOGLE_PROTOBUF_VERSION})
+  set(PROTOBUF_VERSION "${CMAKE_MATCH_1}.${CMAKE_MATCH_2}.${CMAKE_MATCH_3}")
+  unset(GOOGLE_PROTOBUF_VERSION)
+endif()
+
+# place where to generate protobuf sources
+set(proto_gen_folder "${PROJECT_BINARY_DIR}/include/caffe/proto")
+include_directories(SYSTEM "${PROJECT_BINARY_DIR}/include")
+
+set(PROTOBUF_GENERATE_CPP_APPEND_PATH TRUE)
+
+################################################################################################
+# Modification of standard 'protobuf_generate_cpp()' with output dir parameter and python support
+# Usage:
+#   caffe_protobuf_generate_cpp_py(<output_dir> <srcs_var> <hdrs_var> <python_var> <proto_files>)
+function(caffe_protobuf_generate_cpp_py output_dir srcs_var hdrs_var python_var)
+  if(NOT ARGN)
+    message(SEND_ERROR "Error: caffe_protobuf_generate_cpp_py() called without any proto files")
+    return()
+  endif()
+
+  if(PROTOBUF_GENERATE_CPP_APPEND_PATH)
+    # Create an include path for each file specified
+    foreach(fil ${ARGN})
+      get_filename_component(abs_fil ${fil} ABSOLUTE)
+      get_filename_component(abs_path ${abs_fil} PATH)
+      list(FIND _protoc_include ${abs_path} _contains_already)
+      if(${_contains_already} EQUAL -1)
+        list(APPEND _protoc_include -I ${abs_path})
+      endif()
+    endforeach()
+  else()
+    set(_protoc_include -I ${CMAKE_CURRENT_SOURCE_DIR})
+  endif()
+
+  if(DEFINED PROTOBUF_IMPORT_DIRS)
+    foreach(dir ${PROTOBUF_IMPORT_DIRS})
+      get_filename_component(abs_path ${dir} ABSOLUTE)
+      list(FIND _protoc_include ${abs_path} _contains_already)
+      if(${_contains_already} EQUAL -1)
+        list(APPEND _protoc_include -I ${abs_path})
+      endif()
+    endforeach()
+  endif()
+
+  set(${srcs_var})
+  set(${hdrs_var})
+  set(${python_var})
+  foreach(fil ${ARGN})
+    get_filename_component(abs_fil ${fil} ABSOLUTE)
+    get_filename_component(fil_we ${fil} NAME_WE)
+
+    list(APPEND ${srcs_var} "${output_dir}/${fil_we}.pb.cc")
+    list(APPEND ${hdrs_var} "${output_dir}/${fil_we}.pb.h")
+    list(APPEND ${python_var} "${output_dir}/${fil_we}_pb2.py")
+
+    add_custom_command(
+      OUTPUT "${output_dir}/${fil_we}.pb.cc"
+             "${output_dir}/${fil_we}.pb.h"
+             "${output_dir}/${fil_we}_pb2.py"
+      COMMAND ${CMAKE_COMMAND} -E make_directory "${output_dir}"
+      COMMAND ${PROTOBUF_PROTOC_EXECUTABLE} --cpp_out    ${output_dir} ${_protoc_include} ${abs_fil}
+      COMMAND ${PROTOBUF_PROTOC_EXECUTABLE} --python_out ${output_dir} ${_protoc_include} ${abs_fil}
+      DEPENDS ${abs_fil}
+      COMMENT "Running C++/Python protocol buffer compiler on ${fil}" VERBATIM )
+  endforeach()
+
+  set_source_files_properties(${${srcs_var}} ${${hdrs_var}} ${${python_var}} PROPERTIES GENERATED TRUE)
+  set(${srcs_var} ${${srcs_var}} PARENT_SCOPE)
+  set(${hdrs_var} ${${hdrs_var}} PARENT_SCOPE)
+  set(${python_var} ${${python_var}} PARENT_SCOPE)
+endfunction()
diff --git a/cmake/Summary.cmake b/cmake/Summary.cmake
new file mode 100644
index 00000000000..e094ac0040e
--- /dev/null
+++ b/cmake/Summary.cmake
@@ -0,0 +1,168 @@
+################################################################################################
+# Caffe status report function.
+# Automatically align right column and selects text based on condition.
+# Usage:
+#   caffe_status(<text>)
+#   caffe_status(<heading> <value1> [<value2> ...])
+#   caffe_status(<heading> <condition> THEN <text for TRUE> ELSE <text for FALSE> )
+function(caffe_status text)
+  set(status_cond)
+  set(status_then)
+  set(status_else)
+
+  set(status_current_name "cond")
+  foreach(arg ${ARGN})
+    if(arg STREQUAL "THEN")
+      set(status_current_name "then")
+    elseif(arg STREQUAL "ELSE")
+      set(status_current_name "else")
+    else()
+      list(APPEND status_${status_current_name} ${arg})
+    endif()
+  endforeach()
+
+  if(DEFINED status_cond)
+    set(status_placeholder_length 23)
+    string(RANDOM LENGTH ${status_placeholder_length} ALPHABET " " status_placeholder)
+    string(LENGTH "${text}" status_text_length)
+    if(status_text_length LESS status_placeholder_length)
+      string(SUBSTRING "${text}${status_placeholder}" 0 ${status_placeholder_length} status_text)
+    elseif(DEFINED status_then OR DEFINED status_else)
+      message(STATUS "${text}")
+      set(status_text "${status_placeholder}")
+    else()
+      set(status_text "${text}")
+    endif()
+
+    if(DEFINED status_then OR DEFINED status_else)
+      if(${status_cond})
+        string(REPLACE ";" " " status_then "${status_then}")
+        string(REGEX REPLACE "^[ \t]+" "" status_then "${status_then}")
+        message(STATUS "${status_text} ${status_then}")
+      else()
+        string(REPLACE ";" " " status_else "${status_else}")
+        string(REGEX REPLACE "^[ \t]+" "" status_else "${status_else}")
+        message(STATUS "${status_text} ${status_else}")
+      endif()
+    else()
+      string(REPLACE ";" " " status_cond "${status_cond}")
+      string(REGEX REPLACE "^[ \t]+" "" status_cond "${status_cond}")
+      message(STATUS "${status_text} ${status_cond}")
+    endif()
+  else()
+    message(STATUS "${text}")
+  endif()
+endfunction()
+
+
+################################################################################################
+# Function for fetching Caffe version from git and headers
+# Usage:
+#   caffe_extract_caffe_version()
+function(caffe_extract_caffe_version)
+  set(Caffe_GIT_VERSION "unknown")
+  find_package(Git)
+  if(GIT_FOUND)
+    execute_process(COMMAND ${GIT_EXECUTABLE} describe --tags --always --dirty
+                    ERROR_QUIET OUTPUT_STRIP_TRAILING_WHITESPACE
+                    WORKING_DIRECTORY "${PROJECT_SOURCE_DIR}"
+                    OUTPUT_VARIABLE Caffe_GIT_VERSION
+                    RESULT_VARIABLE __git_result)
+    if(NOT ${__git_result} EQUAL 0)
+      set(Caffe_GIT_VERSION "unknown")
+    endif()
+  endif()
+
+  set(Caffe_GIT_VERSION ${Caffe_GIT_VERSION} PARENT_SCOPE)
+  set(Caffe_VERSION "<TODO> (Caffe doesn't declare its version in headers)" PARENT_SCOPE)
+
+  # caffe_parse_header(${Caffe_INCLUDE_DIR}/caffe/version.hpp Caffe_VERSION_LINES CAFFE_MAJOR CAFFE_MINOR CAFFE_PATCH)
+  # set(Caffe_VERSION "${CAFFE_MAJOR}.${CAFFE_MINOR}.${CAFFE_PATCH}" PARENT_SCOPE)
+
+  # or for #define Caffe_VERSION "x.x.x"
+  # caffe_parse_header_single_define(Caffe ${Caffe_INCLUDE_DIR}/caffe/version.hpp Caffe_VERSION)
+  # set(Caffe_VERSION ${Caffe_VERSION_STRING} PARENT_SCOPE)
+
+endfunction()
+
+
+################################################################################################
+# Prints accumulated caffe configuration summary
+# Usage:
+#   caffe_print_configuration_summary()
+
+function(caffe_print_configuration_summary)
+  caffe_extract_caffe_version()
+  set(Caffe_VERSION ${Caffe_VERSION} PARENT_SCOPE)
+
+  caffe_merge_flag_lists(__flags_rel CMAKE_CXX_FLAGS_RELEASE CMAKE_CXX_FLAGS)
+  caffe_merge_flag_lists(__flags_deb CMAKE_CXX_FLAGS_DEBUG   CMAKE_CXX_FLAGS)
+
+  caffe_status("")
+  caffe_status("******************* Caffe Configuration Summary *******************")
+  caffe_status("General:")
+  caffe_status("  Version           :   ${Caffe_VERSION}")
+  caffe_status("  Git               :   ${Caffe_GIT_VERSION}")
+  caffe_status("  System            :   ${CMAKE_SYSTEM_NAME}")
+  caffe_status("  C++ compiler      :   ${CMAKE_CXX_COMPILER}")
+  caffe_status("  Release CXX flags :   ${__flags_rel}")
+  caffe_status("  Debug CXX flags   :   ${__flags_deb}")
+  caffe_status("  Build type        :   ${CMAKE_BUILD_TYPE}")
+  caffe_status("")
+  caffe_status("  BUILD_SHARED_LIBS :   ${BUILD_SHARED_LIBS}")
+  caffe_status("  BUILD_python      :   ${BUILD_python}")
+  caffe_status("  BUILD_matlab      :   ${BUILD_matlab}")
+  caffe_status("  BUILD_docs        :   ${BUILD_docs}")
+  caffe_status("  CPU_ONLY          :   ${CPU_ONLY}")
+  caffe_status("")
+  caffe_status("Dependencies:")
+  caffe_status("  BLAS              : " APPLE THEN "Yes (vecLib)" ELSE "Yes (${BLAS})")
+  caffe_status("  Boost             :   Yes (ver. ${Boost_MAJOR_VERSION}.${Boost_MINOR_VERSION})")
+  caffe_status("  glog              :   Yes")
+  caffe_status("  gflags            :   Yes")
+  caffe_status("  protobuf          : " PROTOBUF_FOUND THEN "Yes (ver. ${PROTOBUF_VERSION})" ELSE "No" )
+  caffe_status("  lmdb              : " LMDB_FOUND THEN "Yes (ver. ${LMDB_VERSION})" ELSE "No")
+  caffe_status("  Snappy            : " SNAPPY_FOUND THEN "Yes (ver. ${Snappy_VERSION})" ELSE "No" )
+  caffe_status("  LevelDB           : " LEVELDB_FOUND THEN  "Yes (ver. ${LEVELDB_VERSION})" ELSE "No")
+  caffe_status("  OpenCV            :   Yes (ver. ${OpenCV_VERSION})")
+  caffe_status("  CUDA              : " HAVE_CUDA THEN "Yes (ver. ${CUDA_VERSION})" ELSE "No" )
+  caffe_status("")
+  if(HAVE_CUDA)
+    caffe_status("NVIDIA CUDA:")
+    caffe_status("  Target GPU(s)     :   ${CUDA_ARCH_NAME}" )
+    caffe_status("  GPU arch(s)       :   ${NVCC_FLAGS_EXTRA_readable}")
+    if(USE_CUDNN)
+      caffe_status("  cuDNN             : " HAVE_CUDNN THEN "Yes" ELSE "Not found")
+    else()
+      caffe_status("  cuDNN             :   Disabled")
+    endif()
+    caffe_status("")
+  endif()
+  if(HAVE_PYTHON)
+    caffe_status("Python:")
+    caffe_status("  Interpreter       :" PYTHON_EXECUTABLE THEN "${PYTHON_EXECUTABLE} (ver. ${PYTHON_VERSION_STRING})" ELSE "No")
+    caffe_status("  Libraries         :" PYTHONLIBS_FOUND  THEN "${PYTHON_LIBRARIES} (ver ${PYTHONLIBS_VERSION_STRING})" ELSE "No")
+    caffe_status("  NumPy             :" NUMPY_FOUND  THEN "${NUMPY_INCLUDE_DIR} (ver ${NUMPY_VERSION})" ELSE "No")
+    caffe_status("")
+  endif()
+  if(BUILD_matlab)
+    caffe_status("Matlab:")
+    caffe_status("  Matlab            :" HAVE_MATLAB THEN "Yes (${Matlab_mex}, ${Matlab_mexext}" ELSE "No")
+    caffe_status("  Octave            :" Octave_compiler THEN  "Yes (${Octave_compiler})" ELSE "No")
+    if(HAVE_MATLAB AND Octave_compiler)
+      caffe_status("  Build mex using   :   ${Matlab_build_mex_using}")
+    endif()
+    caffe_status("")
+  endif()
+  if(BUILD_docs)
+    caffe_status("Documentaion:")
+    caffe_status("  Doxygen           :" DOXYGEN_FOUND THEN "${DOXYGEN_EXECUTABLE} (${DOXYGEN_VERSION})" ELSE "No")
+    caffe_status("  config_file       :   ${DOXYGEN_config_file}")
+
+    caffe_status("")
+  endif()
+  caffe_status("Install:")
+  caffe_status("  Install path      :   ${CMAKE_INSTALL_PREFIX}")
+  caffe_status("")
+endfunction()
+
diff --git a/cmake/Targets.cmake b/cmake/Targets.cmake
new file mode 100644
index 00000000000..2401f252e93
--- /dev/null
+++ b/cmake/Targets.cmake
@@ -0,0 +1,173 @@
+################################################################################################
+# Defines global Caffe_LINK flag, This flag is required to prevent linker from excluding
+# some objects which are not addressed directly but are registered via static constructors
+if(BUILD_SHARED_LIBS)
+  set(Caffe_LINK caffe)
+else()
+  if("${CMAKE_CXX_COMPILER_ID}" STREQUAL "Clang")
+    set(Caffe_LINK -Wl,-force_load caffe)
+  elseif("${CMAKE_CXX_COMPILER_ID}" STREQUAL "GNU")
+    set(Caffe_LINK -Wl,--whole-archive caffe -Wl,--no-whole-archive)
+  endif()
+endif()
+
+################################################################################################
+# Convenient command to setup source group for IDEs that support this feature (VS, XCode)
+# Usage:
+#   caffe_source_group(<group> GLOB[_RECURSE] <globbing_expression>)
+function(caffe_source_group group)
+  cmake_parse_arguments(CAFFE_SOURCE_GROUP "" "" "GLOB;GLOB_RECURSE" ${ARGN})
+  if(CAFFE_SOURCE_GROUP_GLOB)
+    file(GLOB srcs1 ${CAFFE_SOURCE_GROUP_GLOB})
+    source_group(${group} FILES ${srcs1})
+  endif()
+
+  if(CAFFE_SOURCE_GROUP_GLOB_RECURSE)
+    file(GLOB_RECURSE srcs2 ${CAFFE_SOURCE_GROUP_GLOB_RECURSE})
+    source_group(${group} FILES ${srcs2})
+  endif()
+endfunction()
+
+################################################################################################
+# Collecting sources from globbing and appending to output list variable
+# Usage:
+#   caffe_collect_sources(<output_variable> GLOB[_RECURSE] <globbing_expression>)
+function(caffe_collect_sources variable)
+  cmake_parse_arguments(CAFFE_COLLECT_SOURCES "" "" "GLOB;GLOB_RECURSE" ${ARGN})
+  if(CAFFE_COLLECT_SOURCES_GLOB)
+    file(GLOB srcs1 ${CAFFE_COLLECT_SOURCES_GLOB})
+    set(${variable} ${variable} ${srcs1})
+  endif()
+
+  if(CAFFE_COLLECT_SOURCES_GLOB_RECURSE)
+    file(GLOB_RECURSE srcs2 ${CAFFE_COLLECT_SOURCES_GLOB_RECURSE})
+    set(${variable} ${variable} ${srcs2})
+  endif()
+endfunction()
+
+################################################################################################
+# Short command getting caffe sources (assuming standard Caffe code tree)
+# Usage:
+#   caffe_pickup_caffe_sources(<root>)
+function(caffe_pickup_caffe_sources root)
+  # put all files in source groups (visible as subfolder in many IDEs)
+  caffe_source_group("Include"        GLOB "${root}/include/caffe/*.h*")
+  caffe_source_group("Include\\Util"  GLOB "${root}/include/caffe/util/*.h*")
+  caffe_source_group("Include"        GLOB "${PROJECT_BINARY_DIR}/caffe_config.h*")
+  caffe_source_group("Source"         GLOB "${root}/src/caffe/*.cpp")
+  caffe_source_group("Source\\Util"   GLOB "${root}/src/caffe/util/*.cpp")
+  caffe_source_group("Source\\Layers" GLOB "${root}/src/caffe/layers/*.cpp")
+  caffe_source_group("Source\\Cuda"   GLOB "${root}/src/caffe/layers/*.cu")
+  caffe_source_group("Source\\Cuda"   GLOB "${root}/src/caffe/util/*.cu")
+  caffe_source_group("Source\\Proto"  GLOB "${root}/src/caffe/proto/*.proto")
+
+  # source groups for test target
+  caffe_source_group("Include"      GLOB "${root}/include/caffe/test/test_*.h*")
+  caffe_source_group("Source"       GLOB "${root}/src/caffe/test/test_*.cpp")
+  caffe_source_group("Source\\Cuda" GLOB "${root}/src/caffe/test/test_*.cu")
+
+  # collect files
+  file(GLOB test_hdrs    ${root}/include/caffe/test/test_*.h*)
+  file(GLOB test_srcs    ${root}/src/caffe/test/test_*.cpp)
+  file(GLOB_RECURSE hdrs ${root}/include/caffe/*.h*)
+  file(GLOB_RECURSE srcs ${root}/src/caffe/*.cpp)
+  list(REMOVE_ITEM  hdrs ${test_hdrs})
+  list(REMOVE_ITEM  srcs ${test_srcs})
+
+  # adding headers to make the visible in some IDEs (Qt, VS, Xcode)
+  list(APPEND srcs ${hdrs} ${PROJECT_BINARY_DIR}/caffe_config.h)
+  list(APPEND test_srcs ${test_hdrs})
+
+  # collect cuda files
+  file(GLOB    test_cuda ${root}/src/caffe/test/test_*.cu)
+  file(GLOB_RECURSE cuda ${root}/src/caffe/*.cu)
+  list(REMOVE_ITEM  cuda ${test_cuda})
+
+  # add proto to make them editable in IDEs too
+  file(GLOB_RECURSE proto_files ${root}/src/caffe/*.proto)
+  list(APPEND srcs ${proto_files})
+
+  # convet to absolute paths
+  caffe_convert_absolute_paths(srcs)
+  caffe_convert_absolute_paths(cuda)
+  caffe_convert_absolute_paths(test_srcs)
+  caffe_convert_absolute_paths(test_cuda)
+
+  # propogate to parent scope
+  set(srcs ${srcs} PARENT_SCOPE)
+  set(cuda ${cuda} PARENT_SCOPE)
+  set(test_srcs ${test_srcs} PARENT_SCOPE)
+  set(test_cuda ${test_cuda} PARENT_SCOPE)
+endfunction()
+
+################################################################################################
+# Short command for setting defeault target properties
+# Usage:
+#   caffe_default_properties(<target>)
+function(caffe_default_properties target)
+  set_target_properties(${target} PROPERTIES
+    DEBUG_POSTFIX ${Caffe_DEBUG_POSTFIX}
+    ARCHIVE_OUTPUT_DIRECTORY "${PROJECT_BINARY_DIR}/lib"
+    LIBRARY_OUTPUT_DIRECTORY "${PROJECT_BINARY_DIR}/lib"
+    RUNTIME_OUTPUT_DIRECTORY "${PROJECT_BINARY_DIR}/bin")
+  # make sure we build all external depepdencies first
+  if (DEFINED external_project_dependencies)
+    add_dependencies(${target} ${external_project_dependencies})
+  endif()
+endfunction()
+
+################################################################################################
+# Short command for setting runtime directory for build target
+# Usage:
+#   caffe_set_runtime_directory(<target> <dir>)
+function(caffe_set_runtime_directory target dir)
+  set_target_properties(${target} PROPERTIES
+    RUNTIME_OUTPUT_DIRECTORY "${dir}")
+endfunction()
+
+################################################################################################
+# Short command for setting solution folder property for target
+# Usage:
+#   caffe_set_solution_folder(<target> <folder>)
+function(caffe_set_solution_folder target folder)
+  if(USE_PROJECT_FOLDERS)
+    set_target_properties(${target} PROPERTIES FOLDER "${folder}")
+  endif()
+endfunction()
+
+################################################################################################
+# Reads lines from input file, prepends source directory to each line and writes to output file
+# Usage:
+#   caffe_configure_testdatafile(<testdatafile>)
+function(caffe_configure_testdatafile file)
+  file(STRINGS ${file} __lines)
+  set(result "")
+  foreach(line ${__lines})
+    set(result "${result}${PROJECT_SOURCE_DIR}/${line}\n")
+  endforeach()
+  file(WRITE ${file}.gen.cmake ${result})
+endfunction()
+
+################################################################################################
+# Filter out all files that are not included in selected list
+# Usage:
+#   caffe_leave_only_selected_tests(<filelist_variable> <selected_list>)
+function(caffe_leave_only_selected_tests file_list)
+  if(NOT ARGN)
+    return() # blank list means leave all
+  endif()
+  string(REPLACE "," ";" __selected ${ARGN})
+  list(APPEND __selected caffe_main)
+
+  set(result "")
+  foreach(f ${${file_list}})
+    get_filename_component(name ${f} NAME_WE)
+    string(REGEX REPLACE "^test_" "" name ${name})
+    list(FIND __selected ${name} __index)
+    if(NOT __index EQUAL -1)
+      list(APPEND result ${f})
+    endif()
+  endforeach()
+  set(${file_list} ${result} PARENT_SCOPE)
+endfunction()
+
diff --git a/cmake/Templates/CaffeConfig.cmake.in b/cmake/Templates/CaffeConfig.cmake.in
new file mode 100644
index 00000000000..8f23742e52e
--- /dev/null
+++ b/cmake/Templates/CaffeConfig.cmake.in
@@ -0,0 +1,58 @@
+# Config file for the Caffe package.
+#
+# Note:
+#   Caffe and this config file depends on opencv,
+#   so put `find_package(OpenCV)` before searching Caffe
+#   via `find_package(Caffe)`. All other lib/includes
+#   dependencies are hard coded in the file
+#
+# After successful configuration the following variables
+# will be defined:
+#
+#   Caffe_INCLUDE_DIRS - Caffe include directories
+#   Caffe_LIBRARIES    - libraries to link against
+#   Caffe_DEFINITIONS  - a list of definitions to pass to compiler
+#
+#   Caffe_HAVE_CUDA    - signals about CUDA support
+#   Caffe_HAVE_CUDNN   - signals about cuDNN support
+
+
+# OpenCV dependency
+
+if(NOT OpenCV_FOUND)
+  set(Caffe_OpenCV_CONFIG_PATH "@OpenCV_CONFIG_PATH@")
+  if(Caffe_OpenCV_CONFIG_PATH)
+    get_filename_component(Caffe_OpenCV_CONFIG_PATH ${Caffe_OpenCV_CONFIG_PATH} ABSOLUTE)
+
+    if(EXISTS ${Caffe_OpenCV_CONFIG_PATH} AND NOT TARGET opencv_core)
+      message(STATUS "Caffe: using OpenCV config from ${Caffe_OpenCV_CONFIG_PATH}")
+      include(${Caffe_OpenCV_CONFIG_PATH}/OpenCVModules.cmake)
+    endif()
+
+  else()
+    find_package(OpenCV REQUIRED)
+  endif()
+  unset(Caffe_OpenCV_CONFIG_PATH)
+endif()
+
+# Compute paths
+get_filename_component(Caffe_CMAKE_DIR "${CMAKE_CURRENT_LIST_FILE}" PATH)
+set(Caffe_INCLUDE_DIRS "@Caffe_INCLUDE_DIRS@")
+
+@Caffe_INSTALL_INCLUDE_DIR_APPEND_COMMAND@
+
+# Our library dependencies
+if(NOT TARGET caffe AND NOT caffe_BINARY_DIR)
+  include("${Caffe_CMAKE_DIR}/CaffeTargets.cmake")
+endif()
+
+# List of IMPORTED libs created by CaffeTargets.cmake
+set(Caffe_LIBRARIES caffe)
+
+# Definitions
+set(Caffe_DEFINITIONS "@Caffe_DEFINITIONS@")
+
+# Cuda support variables
+set(Caffe_CPU_ONLY @CPU_ONLY@)
+set(Caffe_HAVE_CUDA @HAVE_CUDA@)
+set(Caffe_HAVE_CUDNN @HAVE_CUDNN@)
diff --git a/cmake/Templates/CaffeConfigVersion.cmake.in b/cmake/Templates/CaffeConfigVersion.cmake.in
new file mode 100644
index 00000000000..19f85309a5f
--- /dev/null
+++ b/cmake/Templates/CaffeConfigVersion.cmake.in
@@ -0,0 +1,11 @@
+set(PACKAGE_VERSION "@Caffe_VERSION@")
+
+# Check whether the requested PACKAGE_FIND_VERSION is compatible
+if("${PACKAGE_VERSION}" VERSION_LESS "${PACKAGE_FIND_VERSION}")
+  set(PACKAGE_VERSION_COMPATIBLE FALSE)
+else()
+  set(PACKAGE_VERSION_COMPATIBLE TRUE)
+  if ("${PACKAGE_VERSION}" VERSION_EQUAL "${PACKAGE_FIND_VERSION}")
+    set(PACKAGE_VERSION_EXACT TRUE)
+  endif()
+endif()
diff --git a/cmake/Templates/caffe_config.h.in b/cmake/Templates/caffe_config.h.in
new file mode 100644
index 00000000000..6039e8f6b21
--- /dev/null
+++ b/cmake/Templates/caffe_config.h.in
@@ -0,0 +1,32 @@
+/* Sources directory */
+#define SOURCE_FOLDER "${PROJECT_SOURCE_DIR}"
+
+/* Binaries directory */
+#define BINARY_FOLDER "${PROJECT_BINARY_DIR}"
+
+/* NVIDA Cuda */
+#cmakedefine HAVE_CUDA
+
+/* NVIDA cuDNN */
+#cmakedefine HAVE_CUDNN
+#cmakedefine USE_CUDNN
+
+/* NVIDA cuDNN */
+#cmakedefine CPU_ONLY
+
+/* Test device */
+#define CUDA_TEST_DEVICE ${CUDA_TEST_DEVICE}
+
+/* Temporary (TODO: remove) */
+#if 1
+  #define CMAKE_SOURCE_DIR SOURCE_FOLDER "/src/"
+  #define EXAMPLES_SOURCE_DIR BINARY_FOLDER "/examples/"
+  #define CMAKE_EXT ".gen.cmake"
+#else
+  #define CMAKE_SOURCE_DIR "src/"
+  #define EXAMPLES_SOURCE_DIR "examples/"
+  #define CMAKE_EXT ""
+#endif
+
+/* Matlab */
+#cmakedefine HAVE_MATLAB
diff --git a/cmake/Utils.cmake b/cmake/Utils.cmake
new file mode 100644
index 00000000000..a1bde1ae95b
--- /dev/null
+++ b/cmake/Utils.cmake
@@ -0,0 +1,381 @@
+################################################################################################
+# Command alias for debugging messages
+# Usage:
+#   dmsg(<message>)
+function(dmsg)
+  message(STATUS ${ARGN})
+endfunction()
+
+################################################################################################
+# Removes duplicates from list(s)
+# Usage:
+#   caffe_list_unique(<list_variable> [<list_variable>] [...])
+macro(caffe_list_unique)
+  foreach(__lst ${ARGN})
+    if(${__lst})
+      list(REMOVE_DUPLICATES ${__lst})
+    endif()
+  endforeach()
+endmacro()
+
+################################################################################################
+# Clears variables from list
+# Usage:
+#   caffe_clear_vars(<variables_list>)
+macro(caffe_clear_vars)
+  foreach(_var ${ARGN})
+    unset(${_var})
+  endforeach()
+endmacro()
+
+################################################################################################
+# Removes duplicates from string
+# Usage:
+#   caffe_string_unique(<string_variable>)
+function(caffe_string_unique __string)
+  if(${__string})
+    set(__list ${${__string}})
+    separate_arguments(__list)
+    list(REMOVE_DUPLICATES __list)
+    foreach(__e ${__list})
+      set(__str "${__str} ${__e}")
+    endforeach()
+    set(${__string} ${__str} PARENT_SCOPE)
+  endif()
+endfunction()
+
+################################################################################################
+# Prints list element per line
+# Usage:
+#   caffe_print_list(<list>)
+function(caffe_print_list)
+  foreach(e ${ARGN})
+    message(STATUS ${e})
+  endforeach()
+endfunction()
+
+################################################################################################
+# Function merging lists of compiler flags to single string.
+# Usage:
+#   caffe_merge_flag_lists(out_variable <list1> [<list2>] [<list3>] ...)
+function(caffe_merge_flag_lists out_var)
+  set(__result "")
+  foreach(__list ${ARGN})
+    foreach(__flag ${${__list}})
+      string(STRIP ${__flag} __flag)
+      set(__result "${__result} ${__flag}")
+    endforeach()
+  endforeach()
+  string(STRIP ${__result} __result)
+  set(${out_var} ${__result} PARENT_SCOPE)
+endfunction()
+
+################################################################################################
+# Converts all paths in list to absolute
+# Usage:
+#   caffe_convert_absolute_paths(<list_variable>)
+function(caffe_convert_absolute_paths variable)
+  set(__dlist "")
+  foreach(__s ${${variable}})
+    get_filename_component(__abspath ${__s} ABSOLUTE)
+    list(APPEND __list ${__abspath})
+  endforeach()
+  set(${variable} ${__list} PARENT_SCOPE)
+endfunction()
+
+################################################################################################
+# Reads set of version defines from the header file
+# Usage:
+#   caffe_parse_header(<file> <define1> <define2> <define3> ..)
+macro(caffe_parse_header FILENAME FILE_VAR)
+  set(vars_regex "")
+  set(__parnet_scope OFF)
+  set(__add_cache OFF)
+  foreach(name ${ARGN})
+    if("${name}" STREQUAL "PARENT_SCOPE")
+      set(__parnet_scope ON)
+    elseif("${name}" STREQUAL "CACHE")
+      set(__add_cache ON)
+    elseif(vars_regex)
+      set(vars_regex "${vars_regex}|${name}")
+    else()
+      set(vars_regex "${name}")
+    endif()
+  endforeach()
+  if(EXISTS "${FILENAME}")
+    file(STRINGS "${FILENAME}" ${FILE_VAR} REGEX "#define[ \t]+(${vars_regex})[ \t]+[0-9]+" )
+  else()
+    unset(${FILE_VAR})
+  endif()
+  foreach(name ${ARGN})
+    if(NOT "${name}" STREQUAL "PARENT_SCOPE" AND NOT "${name}" STREQUAL "CACHE")
+      if(${FILE_VAR})
+        if(${FILE_VAR} MATCHES ".+[ \t]${name}[ \t]+([0-9]+).*")
+          string(REGEX REPLACE ".+[ \t]${name}[ \t]+([0-9]+).*" "\\1" ${name} "${${FILE_VAR}}")
+        else()
+          set(${name} "")
+        endif()
+        if(__add_cache)
+          set(${name} ${${name}} CACHE INTERNAL "${name} parsed from ${FILENAME}" FORCE)
+        elseif(__parnet_scope)
+          set(${name} "${${name}}" PARENT_SCOPE)
+        endif()
+      else()
+        unset(${name} CACHE)
+      endif()
+    endif()
+  endforeach()
+endmacro()
+
+################################################################################################
+# Reads single version define from the header file and parses it
+# Usage:
+#   caffe_parse_header_single_define(<library_name> <file> <define_name>)
+function(caffe_parse_header_single_define LIBNAME HDR_PATH VARNAME)
+  set(${LIBNAME}_H "")
+  if(EXISTS "${HDR_PATH}")
+    file(STRINGS "${HDR_PATH}" ${LIBNAME}_H REGEX "^#define[ \t]+${VARNAME}[ \t]+\"[^\"]*\".*$" LIMIT_COUNT 1)
+  endif()
+
+  if(${LIBNAME}_H)
+    string(REGEX REPLACE "^.*[ \t]${VARNAME}[ \t]+\"([0-9]+).*$" "\\1" ${LIBNAME}_VERSION_MAJOR "${${LIBNAME}_H}")
+    string(REGEX REPLACE "^.*[ \t]${VARNAME}[ \t]+\"[0-9]+\\.([0-9]+).*$" "\\1" ${LIBNAME}_VERSION_MINOR  "${${LIBNAME}_H}")
+    string(REGEX REPLACE "^.*[ \t]${VARNAME}[ \t]+\"[0-9]+\\.[0-9]+\\.([0-9]+).*$" "\\1" ${LIBNAME}_VERSION_PATCH "${${LIBNAME}_H}")
+    set(${LIBNAME}_VERSION_MAJOR ${${LIBNAME}_VERSION_MAJOR} ${ARGN} PARENT_SCOPE)
+    set(${LIBNAME}_VERSION_MINOR ${${LIBNAME}_VERSION_MINOR} ${ARGN} PARENT_SCOPE)
+    set(${LIBNAME}_VERSION_PATCH ${${LIBNAME}_VERSION_PATCH} ${ARGN} PARENT_SCOPE)
+    set(${LIBNAME}_VERSION_STRING "${${LIBNAME}_VERSION_MAJOR}.${${LIBNAME}_VERSION_MINOR}.${${LIBNAME}_VERSION_PATCH}" PARENT_SCOPE)
+
+    # append a TWEAK version if it exists:
+    set(${LIBNAME}_VERSION_TWEAK "")
+    if("${${LIBNAME}_H}" MATCHES "^.*[ \t]${VARNAME}[ \t]+\"[0-9]+\\.[0-9]+\\.[0-9]+\\.([0-9]+).*$")
+      set(${LIBNAME}_VERSION_TWEAK "${CMAKE_MATCH_1}" ${ARGN} PARENT_SCOPE)
+    endif()
+    if(${LIBNAME}_VERSION_TWEAK)
+      set(${LIBNAME}_VERSION_STRING "${${LIBNAME}_VERSION_STRING}.${${LIBNAME}_VERSION_TWEAK}" ${ARGN} PARENT_SCOPE)
+    else()
+      set(${LIBNAME}_VERSION_STRING "${${LIBNAME}_VERSION_STRING}" ${ARGN} PARENT_SCOPE)
+    endif()
+  endif()
+endfunction()
+
+########################################################################################################
+# An option that the user can select. Can accept condition to control when option is available for user.
+# Usage:
+#   caffe_option(<option_variable> "doc string" <initial value or boolean expression> [IF <condition>])
+function(caffe_option variable description value)
+  set(__value ${value})
+  set(__condition "")
+  set(__varname "__value")
+  foreach(arg ${ARGN})
+    if(arg STREQUAL "IF" OR arg STREQUAL "if")
+      set(__varname "__condition")
+    else()
+      list(APPEND ${__varname} ${arg})
+    endif()
+  endforeach()
+  unset(__varname)
+  if("${__condition}" STREQUAL "")
+    set(__condition 2 GREATER 1)
+  endif()
+
+  if(${__condition})
+    if("${__value}" MATCHES ";")
+      if(${__value})
+        option(${variable} "${description}" ON)
+      else()
+        option(${variable} "${description}" OFF)
+      endif()
+    elseif(DEFINED ${__value})
+      if(${__value})
+        option(${variable} "${description}" ON)
+      else()
+        option(${variable} "${description}" OFF)
+      endif()
+    else()
+      option(${variable} "${description}" ${__value})
+    endif()
+  else()
+    unset(${variable} CACHE)
+  endif()
+endfunction()
+
+################################################################################################
+# Utility macro for comparing two lists. Used for CMake debugging purposes
+# Usage:
+#   caffe_compare_lists(<list_variable> <list2_variable> [description])
+function(caffe_compare_lists list1 list2 desc)
+  set(__list1 ${${list1}})
+  set(__list2 ${${list2}})
+  list(SORT __list1)
+  list(SORT __list2)
+  list(LENGTH __list1 __len1)
+  list(LENGTH __list2 __len2)
+
+  if(NOT ${__len1} EQUAL ${__len2})
+    message(FATAL_ERROR "Lists are not equal. ${__len1} != ${__len2}. ${desc}")
+  endif()
+
+  foreach(__i RANGE 1 ${__len1})
+    math(EXPR __index "${__i}- 1")
+    list(GET __list1 ${__index} __item1)
+    list(GET __list2 ${__index} __item2)
+    if(NOT ${__item1} STREQUAL ${__item2})
+      message(FATAL_ERROR "Lists are not equal. Differ at element ${__index}. ${desc}")
+    endif()
+  endforeach()
+endfunction()
+
+################################################################################################
+# Command for disabling warnings for different platforms (see below for gcc and VisualStudio)
+# Usage:
+#   caffe_warnings_disable(<CMAKE_[C|CXX]_FLAGS[_CONFIGURATION]> -Wshadow /wd4996 ..,)
+macro(caffe_warnings_disable)
+  set(_flag_vars "")
+  set(_msvc_warnings "")
+  set(_gxx_warnings "")
+
+  foreach(arg ${ARGN})
+    if(arg MATCHES "^CMAKE_")
+      list(APPEND _flag_vars ${arg})
+    elseif(arg MATCHES "^/wd")
+      list(APPEND _msvc_warnings ${arg})
+    elseif(arg MATCHES "^-W")
+      list(APPEND _gxx_warnings ${arg})
+    endif()
+  endforeach()
+
+  if(NOT _flag_vars)
+    set(_flag_vars CMAKE_C_FLAGS CMAKE_CXX_FLAGS)
+  endif()
+
+  if(MSVC AND _msvc_warnings)
+    foreach(var ${_flag_vars})
+      foreach(warning ${_msvc_warnings})
+        set(${var} "${${var}} ${warning}")
+      endforeach()
+    endforeach()
+  elseif((CMAKE_COMPILER_IS_GNUCXX OR CMAKE_COMPILER_IS_CLANGXX) AND _gxx_warnings)
+    foreach(var ${_flag_vars})
+      foreach(warning ${_gxx_warnings})
+        if(NOT warning MATCHES "^-Wno-")
+          string(REPLACE "${warning}" "" ${var} "${${var}}")
+          string(REPLACE "-W" "-Wno-" warning "${warning}")
+        endif()
+        set(${var} "${${var}} ${warning}")
+      endforeach()
+    endforeach()
+  endif()
+  caffe_clear_vars(_flag_vars _msvc_warnings _gxx_warnings)
+endmacro()
+
+################################################################################################
+# Helper function get current definitions
+# Usage:
+#   caffe_get_current_definitions(<definitions_variable>)
+function(caffe_get_current_definitions definitions_var)
+  get_property(current_definitions DIRECTORY PROPERTY COMPILE_DEFINITIONS)
+  set(result "")
+
+  foreach(d ${current_definitions})
+    list(APPEND result -D${d})
+  endforeach()
+
+  caffe_list_unique(result)
+  set(${definitions_var} ${result} PARENT_SCOPE)
+endfunction()
+
+################################################################################################
+# Helper function get current includes/definitions
+# Usage:
+#   caffe_get_current_cflags(<cflagslist_variable>)
+function(caffe_get_current_cflags cflags_var)
+  get_property(current_includes DIRECTORY PROPERTY INCLUDE_DIRECTORIES)
+  caffe_convert_absolute_paths(current_includes)
+  caffe_get_current_definitions(cflags)
+
+  foreach(i ${current_includes})
+    list(APPEND cflags "-I${i}")
+  endforeach()
+
+  caffe_list_unique(cflags)
+  set(${cflags_var} ${cflags} PARENT_SCOPE)
+endfunction()
+
+################################################################################################
+# Helper function to parse current linker libs into link directories, libflags and osx frameworks
+# Usage:
+#   caffe_parse_linker_libs(<Caffe_LINKER_LIBS_var> <directories_var> <libflags_var> <frameworks_var>)
+function(caffe_parse_linker_libs Caffe_LINKER_LIBS_variable folders_var flags_var frameworks_var)
+
+  set(__unspec "")
+  set(__debug "")
+  set(__optimized "")
+  set(__framework "")
+  set(__varname "__unspec")
+
+  # split libs into debug, optimized, unspecified and frameworks
+  foreach(list_elem ${${Caffe_LINKER_LIBS_variable}})
+    if(list_elem STREQUAL "debug")
+      set(__varname "__debug")
+    elseif(list_elem STREQUAL "optimized")
+      set(__varname "__optimized")
+    elseif(list_elem MATCHES "^-framework[ \t]+([^ \t].*)")
+      list(APPEND __framework -framework ${CMAKE_MATCH_1})
+    else()
+      list(APPEND ${__varname} ${list_elem})
+      set(__varname "__unspec")
+    endif()
+  endforeach()
+
+  # attach debug or optimized libs to unspecified according to current configuration
+  if(CMAKE_BUILD_TYPE MATCHES "Debug")
+    set(__libs ${__unspec} ${__debug})
+  else()
+    set(__libs ${__unspec} ${__optimized})
+  endif()
+
+  set(libflags "")
+  set(folders "")
+
+  # convert linker libraries list to link flags
+  foreach(lib ${__libs})
+    if(TARGET ${lib})
+      list(APPEND folders $<TARGET_LINKER_FILE_DIR:${lib}>)
+      list(APPEND libflags -l${lib})
+    elseif(lib MATCHES "^-l.*")
+      list(APPEND libflags ${lib})
+    elseif(IS_ABSOLUTE ${lib})
+      get_filename_component(name_we ${lib} NAME_WE)
+      get_filename_component(folder  ${lib} PATH)
+
+      string(REGEX MATCH "^lib(.*)" __match ${name_we})
+      list(APPEND libflags -l${CMAKE_MATCH_1})
+      list(APPEND folders    ${folder})
+    else()
+      message(FATAL_ERROR "Logic error. Need to update cmake script")
+    endif()
+  endforeach()
+
+  caffe_list_unique(libflags folders)
+
+  set(${folders_var} ${folders} PARENT_SCOPE)
+  set(${flags_var} ${libflags} PARENT_SCOPE)
+  set(${frameworks_var} ${__framework} PARENT_SCOPE)
+endfunction()
+
+################################################################################################
+# Helper function to detect Darwin version, i.e. 10.8, 10.9, 10.10, ....
+# Usage:
+#   caffe_detect_darwin_version(<version_variable>)
+function(caffe_detect_darwin_version output_var)
+  if(APPLE)
+    execute_process(COMMAND /usr/bin/sw_vers -productVersion
+                    RESULT_VARIABLE __sw_vers OUTPUT_VARIABLE __sw_vers_out
+                    ERROR_QUIET OUTPUT_STRIP_TRAILING_WHITESPACE)
+
+    set(${output_var} ${__sw_vers_out} PARENT_SCOPE)
+  else()
+    set(${output_var} "" PARENT_SCOPE)
+  endif()
+endfunction()
diff --git a/CMakeScripts/lint.cmake b/cmake/lint.cmake
similarity index 92%
rename from CMakeScripts/lint.cmake
rename to cmake/lint.cmake
index 04df3409e84..70a006572bb 100644
--- a/CMakeScripts/lint.cmake
+++ b/cmake/lint.cmake
@@ -1,10 +1,12 @@
 
-set(CMAKE_SOURCE_DIR ../)
+set(CMAKE_SOURCE_DIR ..)
 set(LINT_COMMAND ${CMAKE_SOURCE_DIR}/scripts/cpp_lint.py)
 set(SRC_FILE_EXTENSIONS h hpp hu c cpp cu cc)
 set(EXCLUDE_FILE_EXTENSTIONS pb.h pb.cc)
 set(LINT_DIRS include src/caffe examples tools python matlab)
 
+cmake_policy(SET CMP0009 NEW)  # suppress cmake warning
+
 # find all files of interest
 foreach(ext ${SRC_FILE_EXTENSIONS})
     foreach(dir ${LINT_DIRS})
@@ -24,7 +26,7 @@ list(REMOVE_ITEM LINT_SOURCES ${EXCLUDED_FILES})
 
 execute_process(
     COMMAND ${LINT_COMMAND} ${LINT_SOURCES}
-    ERROR_VARIABLE LINT_OUTPUT 
+    ERROR_VARIABLE LINT_OUTPUT
     ERROR_STRIP_TRAILING_WHITESPACE
 )
 
diff --git a/docs/CMakeLists.txt b/docs/CMakeLists.txt
new file mode 100644
index 00000000000..ae47e461736
--- /dev/null
+++ b/docs/CMakeLists.txt
@@ -0,0 +1,106 @@
+# Building docs script
+# Requirements:
+#   sudo apt-get install doxygen texlive ruby-dev
+#   sudo gem install jekyll execjs therubyracer
+
+if(NOT BUILD_docs OR NOT DOXYGEN_FOUND)
+  return()
+endif()
+
+#################################################################################################
+# Gather docs from <root>/examples/**/readme.md
+function(gather_readmes_as_prebuild_cmd target gathered_dir root)
+  set(full_gathered_dir ${root}/${gathered_dir})
+
+  file(GLOB_RECURSE readmes ${root}/examples/readme.md ${root}/examples/README.md)
+  foreach(file ${readmes})
+    # Only use file if it is to be included in docs.
+    file(STRINGS ${file} file_lines REGEX "include_in_docs: true")
+
+    if(file_lines)
+      # Since everything is called readme.md, rename it by its dirname.
+      file(RELATIVE_PATH file ${root} ${file})
+      get_filename_component(folder ${file} PATH)
+      set(new_filename ${full_gathered_dir}/${folder}.md)
+
+      # folder value might be like <subfolder>/readme.md. That's why make directory.
+      get_filename_component(new_folder ${new_filename} PATH)
+      add_custom_command(TARGET ${target} PRE_BUILD
+        COMMAND ${CMAKE_COMMAND} -E make_directory ${new_folder}
+        COMMAND ln -sf ${root}/${file} ${new_filename}
+        COMMENT "Creating symlink ${new_filename} -> ${root}/${file}"
+        WORKING_DIRECTORY ${root} VERBATIM)
+    endif()
+  endforeach()
+endfunction()
+
+################################################################################################
+# Gather docs from examples/*.ipynb and add YAML front-matter.
+function(gather_notebooks_as_prebuild_cmd target gathered_dir root)
+  set(full_gathered_dir ${root}/${gathered_dir})
+
+  if(NOT PYTHON_EXECUTABLE)
+    message(STATUS "Python interpeter is not found. Can't include *.ipynb files in docs. Skipping...")
+    return()
+  endif()
+
+  file(GLOB_RECURSE notebooks ${root}/examples/*.ipynb)
+  foreach(file ${notebooks})
+    file(RELATIVE_PATH file ${root} ${file})
+    set(new_filename ${full_gathered_dir}/${file})
+
+    get_filename_component(new_folder ${new_filename} PATH)
+    add_custom_command(TARGET ${target} PRE_BUILD
+      COMMAND ${CMAKE_COMMAND} -E make_directory ${new_folder}
+      COMMAND ${PYTHON_EXECUTABLE} scripts/copy_notebook.py ${file} ${new_filename}
+      COMMENT "Copying notebook ${file} to ${new_filename}"
+      WORKING_DIRECTORY ${root} VERBATIM)
+  endforeach()
+
+  set(${outputs_var} ${outputs} PARENT_SCOPE)
+endfunction()
+
+################################################################################################
+########################## [ Non macro part ] ##################################################
+
+# Gathering is done at each 'make doc'
+file(REMOVE_RECURSE ${PROJECT_SOURCE_DIR}/docs/gathered)
+
+# Doxygen config file path
+set(DOXYGEN_config_file ${PROJECT_SOURCE_DIR}/.Doxyfile CACHE FILEPATH "Doxygen config file")
+
+# Adding docs target
+add_custom_target(docs COMMAND ${DOXYGEN_EXECUTABLE} ${DOXYGEN_config_file}
+                       WORKING_DIRECTORY ${PROJECT_SOURCE_DIR}
+                       COMMENT "Launching doxygen..." VERBATIM)
+
+# Gathering examples into docs subfolder
+gather_notebooks_as_prebuild_cmd(docs docs/gathered ${PROJECT_SOURCE_DIR})
+gather_readmes_as_prebuild_cmd(docs docs/gathered  ${PROJECT_SOURCE_DIR})
+
+# Auto detect output directory
+file(STRINGS ${DOXYGEN_config_file} config_line REGEX "OUTPUT_DIRECTORY[ \t]+=[^=].*")
+if(config_line)
+  string(REGEX MATCH "OUTPUT_DIRECTORY[ \t]+=([^=].*)" __ver_check "${config_line}")
+  string(STRIP ${CMAKE_MATCH_1} output_dir)
+  message(STATUS "Detected Doxygen OUTPUT_DIRECTORY: ${output_dir}")
+else()
+  set(output_dir ./doxygen/)
+  message(STATUS "Can't find OUTPUT_DIRECTORY in doxygen config file. Try to use default: ${output_dir}")
+endif()
+
+if(NOT IS_ABSOLUTE ${output_dir})
+  set(output_dir ${PROJECT_SOURCE_DIR}/${output_dir})
+  get_filename_component(output_dir ${output_dir} ABSOLUTE)
+endif()
+
+# creates symlink in docs subfolder to code documentation built by doxygen
+add_custom_command(TARGET docs POST_BUILD VERBATIM
+                   COMMAND ln -sfn "${output_dir}/html" doxygen
+                   WORKING_DIRECTORY ${PROJECT_SOURCE_DIR}/docs
+                   COMMENT "Creating symlink ${PROJECT_SOURCE_DIR}/docs/doxygen -> ${output_dir}/html")
+
+# for quick launch of jekyll
+add_custom_target(jekyll COMMAND jekyll serve -w -s . -d _site --port=4000
+                         WORKING_DIRECTORY ${PROJECT_SOURCE_DIR}/docs
+                         COMMENT "Launching jekyll..." VERBATIM)
diff --git a/docs/_layouts/default.html b/docs/_layouts/default.html
index 73c6d5873d3..b8efe60bc3b 100644
--- a/docs/_layouts/default.html
+++ b/docs/_layouts/default.html
@@ -11,6 +11,8 @@
       Caffe {% if page contains 'title' %}| {{ page.title }}{% endif %}
     </title>
 
+    <link rel="icon" type="image/png" href="/images/caffeine-icon.png">
+
     <link rel="stylesheet" href="/stylesheets/reset.css">
     <link rel="stylesheet" href="/stylesheets/styles.css">
     <link rel="stylesheet" href="/stylesheets/pygment_trac.css">
@@ -34,8 +36,16 @@
       <header>
         <h1 class="header"><a href="/">Caffe</a></h1>
         <p class="header">
-          Deep learning framework developed by <a class="header name" href="http://daggerfs.com/">Yangqing Jia</a> / <a class="header name" href="http://bvlc.eecs.berkeley.edu/">BVLC</a>
+          Deep learning framework by the <a class="header name" href="http://bvlc.eecs.berkeley.edu/">BVLC</a>
         </p>
+        <p class="header">
+          Created by
+          <br>
+          <a class="header name" href="http://daggerfs.com/">Yangqing Jia</a>
+          <br>
+          Lead Developer
+          <br>
+          <a class="header name" href="http://imaginarynumber.net/">Evan Shelhamer</a>
         <ul>
           <li>
             <a class="buttons github" href="https://github.com/BVLC/caffe">View On GitHub</a>
diff --git a/docs/development.md b/docs/development.md
index dfed3308eeb..107c2c3b281 100644
--- a/docs/development.md
+++ b/docs/development.md
@@ -1,10 +1,10 @@
 ---
 title: Developing and Contributing
 ---
-# Development
+# Development and Contributing
 
 Caffe is developed with active participation of the community.<br>
-The [BVLC](http://bvlc.eecs.berkeley.edu/) maintainers welcome all contributions!
+The [BVLC](http://bvlc.eecs.berkeley.edu/) brewers welcome all contributions!
 
 The exact details of contributions are recorded by versioning and cited in our [acknowledgements](http://caffe.berkeleyvision.org/#acknowledgements).
 This method is impartial and always up-to-date.
@@ -21,7 +21,7 @@ If a contributor wants to further mark their specific copyright on a particular
 
 ### Documentation
 
-This website, written with [Jekyll](http://jekyllrb.com/), functions as the official Caffe documentation -- simply run `scripts/build_docs.sh` and view the website at `http://0.0.0.0:4000`.
+This website, written with [Jekyll](http://jekyllrb.com/), acts as the official Caffe documentation -- simply run `scripts/build_docs.sh` and view the website at `http://0.0.0.0:4000`.
 
 We prefer tutorials and examples to be documented close to where they live, in `readme.md` files.
 The `build_docs.sh` script gathers all `examples/**/readme.md` and `examples/*.ipynb` files, and makes a table of contents.
@@ -30,82 +30,77 @@ Similarly for IPython notebooks: simply include `"include_in_docs": true` in the
 
 Other docs, such as installation guides, are written in the `docs` directory and manually linked to from the `index.md` page.
 
-We strive to provide provide lots of usage examples, and to document all code in docstrings.
+We strive to provide lots of usage examples, and to document all code in docstrings.
 We absolutely appreciate any contribution to this effort!
 
-### The release cycle
+### Versioning
 
-- The `dev` branch receives all new development, including community contributions.
-We aim to keep it in a functional state, but large changes do occur, and things do get broken every now and then.
-Use only if you want the "bleeding edge".
-- BVLC maintainers will periodically update the `master` branch with changes from `dev`, giving it a release tag ([releases so far](https://github.com/BVLC/caffe/releases)).
-Use this if you want more stability.
+The `master` branch receives all new development including community contributions.
+We try to keep it in a reliable state, but it is the bleeding edge, and things do get broken every now and then.
+BVLC maintainers will periodically make releases by marking stable checkpoints as tags and maintenance branches. [Past releases](https://github.com/BVLC/caffe/releases) are catalogued online.
 
-### Issues & Pull Request Protocol
+#### Issues & Pull Request Protocol
 
-Use Github Issues to report [bugs], propose features, and ask development [questions].
-Large-scale development work is guided by [milestones], which are sets of Issues selected for concurrent release (integration from `dev` to `master`).
+Post [Issues](https://github.com/BVLC/caffe/issues) to propose features, report [bugs], and discuss framework code.
+Large-scale development work is guided by [milestones], which are sets of Issues selected for bundling as releases.
 
 Please note that since the core developers are largely researchers, we may work on a feature in isolation for some time before releasing it to the community, so as to claim honest academic contribution.
 We do release things as soon as a reasonable technical report may be written, and we still aim to inform the community of ongoing development through Github Issues.
 
-When you are ready to start developing your feature or fixing a bug, follow this protocol:
+**When you are ready to develop a feature or fixing a bug, follow this protocol**:
 
-- Develop in [feature branches] with descriptive names.
-    - For new development branch off `dev`.
-    - For documentation and fixes for `master` branch off `master`.
-- Bring your work up-to-date by [rebasing] onto the latest `dev` / `master`.
-(Polish your changes by [interactive rebase], if you'd like.)
-- [Pull request] your contribution to `BVLC/caffe`'s `dev` / `master` branch for discussion and review.
+- Develop in [feature branches] with descriptive names. Branch off of the latest `master`.
+- Bring your work up-to-date by [rebasing] onto the latest `master` when done.
+(Groom your changes by [interactive rebase], if you'd like.)
+- [Pull request] your contribution to `BVLC/caffe`'s `master` branch for discussion and review.
   - Make PRs *as soon as development begins*, to let discussion guide development.
   - A PR is only ready for merge review when it is a fast-forward merge, and all code is documented, linted, and tested -- that means your PR must include tests!
 - When the PR satisfies the above properties, use comments to request maintainer review.
 
-Below is a poetic presentation of the protocol in code form.
+The following is a poetic presentation of the protocol in code form.
 
 #### [Shelhamer's](https://github.com/shelhamer) “life of a branch in four acts”
 
-Make the `feature` branch off of the latest `bvlc/dev`
-```
-git checkout dev
-git pull upstream dev
-git checkout -b feature
-# do your work, make commits
-```
-
-Prepare to merge by rebasing your branch on the latest `bvlc/dev`
-```
-# make sure dev is fresh
-git checkout dev
-git pull upstream dev
-# rebase your branch on the tip of dev
-git checkout feature
-git rebase dev
-```
-
-Push your branch to pull request it into `dev`
-```
-git push origin feature
-# ...make pull request to dev...
-```
-
-Now make a pull request! You can do this from the command line (`git pull-request -b dev`) if you install [hub](https://github.com/github/hub).
-
-The pull request of `feature` into `dev` will be a clean merge. Applause.
+Make the `feature` branch off of the latest `bvlc/master`
+
+    git checkout master
+    git pull upstream master
+    git checkout -b feature
+    # do your work, make commits
+
+Prepare to merge by rebasing your branch on the latest `bvlc/master`
+
+    # make sure master is fresh
+    git checkout master
+    git pull upstream master
+    # rebase your branch on the tip of master
+    git checkout feature
+    git rebase master
+
+Push your branch to pull request it into `BVLC/caffe:master`
+
+    git push origin feature
+    # ...make pull request to master...
+
+Now make a pull request! You can do this from the command line (`git pull-request -b master`) if you install [hub](https://github.com/github/hub). Hub has many other magical uses.
+
+The pull request of `feature` into `master` will be a clean merge. Applause.
 
 [bugs]: https://github.com/BVLC/caffe/issues?labels=bug&page=1&state=open
-[questions]: https://github.com/BVLC/caffe/issues?labels=question&page=1&state=open
 [milestones]: https://github.com/BVLC/caffe/issues?milestone=1
 [Pull request]: https://help.github.com/articles/using-pull-requests
 [interactive rebase]: https://help.github.com/articles/interactive-rebase
 [rebasing]: http://git-scm.com/book/en/Git-Branching-Rebasing
 [feature branches]: https://www.atlassian.com/git/workflows#!workflow-feature-branch
 
+**Historical note**: Caffe once relied on a two branch `master` and `dev` workflow.
+PRs from this time are still open but these will be merged into `master` or closed.
+
 ### Testing
 
 Run `make runtest` to check the project tests. New code requires new tests. Pull requests that fail tests will not be accepted.
 
-The `googletest` framework we use provides many additional options, which you can access by running the test binaries directly. One of the more useful options is `--gtest_filter`, which allows you to filter tests by name:
+The `gtest` framework we use provides many additional options, which you can access by running the test binaries directly. One of the more useful options is `--gtest_filter`, which allows you to filter tests by name:
 
     # run all tests with CPU in the name
     build/test/test_all.testbin --gtest_filter='*CPU*'
@@ -119,7 +114,7 @@ To get a list of all options `googletest` provides, simply pass the `--help` fla
 
 ### Style
 
-- Follow [Google C++ style](http://google-styleguide.googlecode.com/svn/trunk/cppguide.xml) and [Google python style](http://google-styleguide.googlecode.com/svn/trunk/pyguide.html) + [PEP 8](http://legacy.python.org/dev/peps/pep-0008/).
+- **Run `make lint` to check C++ code.**
 - Wrap lines at 80 chars.
+- Follow [Google C++ style](http://google-styleguide.googlecode.com/svn/trunk/cppguide.xml) and [Google python style](http://google-styleguide.googlecode.com/svn/trunk/pyguide.html) + [PEP 8](http://legacy.python.org/dev/peps/pep-0008/).
 - Remember that “a foolish consistency is the hobgoblin of little minds,” so use your best judgement to write the clearest code for your particular case.
-- **Run `make lint` to check C++ code.**
diff --git a/docs/images/caffeine-icon.png b/docs/images/caffeine-icon.png
new file mode 100644
index 00000000000..88b4a002bb0
Binary files /dev/null and b/docs/images/caffeine-icon.png differ
diff --git a/docs/index.md b/docs/index.md
index ccc8f750eef..932b3b58d1d 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -4,31 +4,33 @@ title: Deep Learning Framework
 
 # Caffe
 
-Caffe is a deep learning framework developed with cleanliness, readability, and speed in mind.
-It was created by [Yangqing Jia](http://daggerfs.com) during his PhD at UC Berkeley, and is in active development by the Berkeley Vision and Learning Center ([BVLC](http://bvlc.eecs.berkeley.edu)) and by community contributors.
+Caffe is a deep learning framework made with expression, speed, and modularity in mind.
+It is developed by the Berkeley Vision and Learning Center ([BVLC](http://bvlc.eecs.berkeley.edu)) and by community contributors.
+[Yangqing Jia](http://daggerfs.com) created the project during his PhD at UC Berkeley.
 Caffe is released under the [BSD 2-Clause license](https://github.com/BVLC/caffe/blob/master/LICENSE).
 
 Check out our web image classification [demo](http://demo.caffe.berkeleyvision.org)!
 
-## Why use Caffe?
+## Why Caffe?
 
-**Clean architecture** enables rapid deployment.
-Networks are specified in simple config files, with no hard-coded parameters in the code.
-Switching between CPU and GPU is as simple as setting a flag -- so models can be trained on a GPU machine, and then used on commodity clusters.
+**Expressive architecture** encourages application and innovation.
+Models and optimization are defined by configuration without hard-coding.
+Switch between CPU and GPU by setting a single flag to train on a GPU machine then deploy to commodity clusters or mobile devices.
 
-**Readable & modifiable implementation** fosters active development.
-In Caffe's first year, it has been forked by over 600 developers on Github, and many have pushed significant changes.
+**Extensible code** fosters active development.
+In Caffe's first year, it has been forked by over 1,000 developers and had many significant changes contributed back.
+Thanks to these contributors the framework tracks the state-of-the-art in both code and models.
 
-**Speed** makes Caffe perfect for industry use.
-Caffe can process over **40M images per day** with a single NVIDIA K40 or Titan GPU\*.
-That's 5 ms/image in training, and 2 ms/image in test.
-We believe that Caffe is the fastest CNN implementation available.
+**Speed** makes Caffe perfect for research experiments and industry deployment.
+Caffe can process **over 60M images per day** with a single NVIDIA K40 GPU\*.
+That's 1 ms/image for inference and 4 ms/image for learning.
+We believe that Caffe is the fastest convnet implementation available.
 
 **Community**: Caffe already powers academic research projects, startup prototypes, and even large-scale industrial applications in vision, speech, and multimedia.
-There is an active discussion and support community on [Github](https://github.com/BVLC/caffe/issues).
+Join our community of brewers on the [caffe-users group](https://groups.google.com/forum/#!forum/caffe-users) and [Github](https://github.com/BVLC/caffe/).
 
 <p class="footnote" markdown="1">
-\* When files are properly cached, and using the ILSVRC2012-winning [SuperVision](http://www.image-net.org/challenges/LSVRC/2012/supervision.pdf) model.
+\* With the ILSVRC2012-winning [SuperVision](http://www.image-net.org/challenges/LSVRC/2012/supervision.pdf) model and caching IO.
 Consult performance [details](/performance_hardware.html).
 </p>
 
@@ -46,7 +48,7 @@ Tested on Ubuntu, Red Hat, OS X.
 BVLC suggests a standard distribution format for Caffe models, and provides trained models.
 * [Developing & Contributing](/development.html)<br>
 Guidelines for development and contributing to Caffe.
-* [API Documentation](/doxygen/)<br>
+* [API Documentation](/doxygen/annotated.html)<br>
 Developer documentation automagically generated from code comments.
 
 ### Examples
@@ -56,7 +58,7 @@ Developer documentation automagically generated from code comments.
 - <div><a href="{{page.url}}">{{page.title}}</a><br>{{page.description}}</div>
 {% endfor %}
 
-### Notebook examples
+### Notebook Examples
 
 {% assign notebooks = site.pages | where:'category','notebook' | sort: 'priority' %}
 {% for page in notebooks %}
@@ -77,6 +79,17 @@ Please cite Caffe in your publications if it helps your research:
 If you do publish a paper where Caffe helped your research, we encourage you to update the [publications wiki](https://github.com/BVLC/caffe/wiki/Publications).
 Citations are also tracked automatically by [Google Scholar](http://scholar.google.com/scholar?oi=bibs&hl=en&cites=17333247995453974016).
 
+## Contacting Us
+
+Join the [caffe-users group](https://groups.google.com/forum/#!forum/caffe-users) to ask questions and discuss methods and models. This is where we talk about usage, installation, and applications.
+
+Framework development discussions and thorough bug reports are collected on [Issues](https://github.com/BVLC/caffe/issues).
+
+Contact [caffe-dev](mailto:caffe-dev@googlegroups.com) if you have a confidential proposal for the framework *and the ability to act on it*.
+Requests for features, explanations, or personal help will be ignored; post to [caffe-users](https://groups.google.com/forum/#!forum/caffe-users) instead.
+
+The core Caffe developers offer [consulting services](mailto:caffe-coldpress@googlegroups.com) for appropriate projects.
+
 ## Acknowledgements
 
 The BVLC Caffe developers would like to thank NVIDIA for GPU donation, A9 and Amazon Web Services for a research grant in support of Caffe development and reproducible research in deep learning, and BVLC PI [Trevor Darrell](http://www.eecs.berkeley.edu/~trevor/) for guidance.
@@ -85,20 +98,9 @@ The BVLC members who have contributed to Caffe are (alphabetical by first name):
 [Eric Tzeng](https://github.com/erictzeng), [Evan Shelhamer](http://imaginarynumber.net/), [Jeff Donahue](http://jeffdonahue.com/), [Jon Long](https://github.com/longjon), [Ross Girshick](http://www.cs.berkeley.edu/~rbg/), [Sergey Karayev](http://sergeykarayev.com/), [Sergio Guadarrama](http://www.eecs.berkeley.edu/~sguada/), and [Yangqing Jia](http://daggerfs.com/).
 
 The open-source community plays an important and growing role in Caffe's development.
-Check out the Github [project pulse](https://github.com/BVLC/caffe/pulse) for recent activity, and the [contributors](https://github.com/BVLC/caffe/graphs/contributors) for a sorted list.
+Check out the Github [project pulse](https://github.com/BVLC/caffe/pulse) for recent activity and the [contributors](https://github.com/BVLC/caffe/graphs/contributors) for the full list.
 
 We sincerely appreciate your interest and contributions!
 If you'd like to contribute, please read the [developing & contributing](development.html) guide.
 
 Yangqing would like to give a personal thanks to the NVIDIA Academic program for providing GPUs, [Oriol Vinyals](http://www1.icsi.berkeley.edu/~vinyals/) for discussions along the journey, and BVLC PI [Trevor Darrell](http://www.eecs.berkeley.edu/~trevor/) for advice.
-
-## Contacting us
-
-All questions about usage, installation, code, and applications should be searched for and asked on the [caffe-users mailing list](https://groups.google.com/forum/#!forum/caffe-users).
-
-All development discussion should be carried out at [GitHub Issues](https://github.com/BVLC/caffe/issues).
-
-If you have a proposal that may not be suited for public discussion *and an ability to act on it*, please email us [directly](mailto:caffe-dev@googlegroups.com).
-Requests for features, explanations, or personal help will be ignored; post such matters publicly as issues.
-
-The core Caffe developers may be able to provide [consulting services](mailto:caffe-coldpress@googlegroups.com) for appropriate projects.
diff --git a/docs/install_apt.md b/docs/install_apt.md
new file mode 100644
index 00000000000..f588b74dcdb
--- /dev/null
+++ b/docs/install_apt.md
@@ -0,0 +1,50 @@
+---
+title: Installation: Ubuntu
+---
+
+# Ubuntu Installation
+
+**General dependencies**
+
+    sudo apt-get install libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libhdf5-serial-dev protobuf-compiler
+    sudo apt-get install --no-install-recommends libboost-all-dev
+
+**CUDA**: Install via the NVIDIA package instead of `apt-get` to be certain of the library and driver versions.
+Install the library and latest driver separately; the driver bundled with the library is usually out-of-date.
+This can be skipped for CPU-only installation.
+
+**BLAS**: install ATLAS by `sudo apt-get install libatlas-base-dev` or install OpenBLAS or MKL for better CPU performance.
+
+**Python** (optional): if you use the default Python you will need to `sudo apt-get install` the `python-dev` package to have the Python headers for building the pycaffe interface.
+
+**Remaining dependencies, 14.04**
+
+Everything is packaged in 14.04.
+
+    sudo apt-get install libgflags-dev libgoogle-glog-dev liblmdb-dev
+
+**Remaining dependencies, 12.04**
+
+These dependencies need manual installation in 12.04.
+
+    # glog
+    wget https://google-glog.googlecode.com/files/glog-0.3.3.tar.gz
+    tar zxvf glog-0.3.3.tar.gz
+    cd glog-0.3.3
+    ./configure
+    make && make install
+    # gflags
+    wget https://github.com/schuhschuh/gflags/archive/master.zip
+    unzip master.zip
+    cd gflags-master
+    mkdir build && cd build
+    export CXXFLAGS="-fPIC" && cmake .. && make VERBOSE=1
+    make && make install
+    # lmdb
+    git clone https://gitorious.org/mdb/mdb.git
+    cd mdb/libraries/liblmdb
+    make && make install
+
+Note that glog does not compile with the most recent gflags version (2.1), so before that is resolved you will need to build with glog first.
+
+Continue with [compilation](installation.html#compilation).
diff --git a/docs/install_osx.md b/docs/install_osx.md
new file mode 100644
index 00000000000..6405d8ad046
--- /dev/null
+++ b/docs/install_osx.md
@@ -0,0 +1,128 @@
+---
+title: Installation: OS X
+---
+
+# OS X Installation
+
+We highly recommend using the [Homebrew](http://brew.sh/) package manager.
+Ideally you could start from a clean `/usr/local` to avoid conflicts.
+In the following, we assume that you're using Anaconda Python and Homebrew.
+
+**CUDA**: Install via the NVIDIA package that includes both CUDA and the bundled driver. **CUDA 7 is strongly suggested.** Older CUDA require `libstdc++` while clang++ is the default compiler and `libc++` the default standard library on OS X 10.9+. This disagreement makes it necessary to change the compilation settings for each of the dependencies. This is prone to error.
+
+**Library Path**: We find that everything compiles successfully if `$LD_LIBRARY_PATH` is not set at all, and `$DYLD_FALLBACK_LIBRARY_PATH` is set to provide CUDA, Python, and other relevant libraries (e.g. `/usr/local/cuda/lib:$HOME/anaconda/lib:/usr/local/lib:/usr/lib`).
+In other `ENV` settings, things may not work as expected.
+
+**General dependencies**
+
+    brew install -vd snappy leveldb gflags glog szip lmdb
+    # need the homebrew science source for OpenCV and hdf5
+    brew tap homebrew/science
+    brew install hdf5 opencv
+
+If using Anaconda Python, a modification to the OpenCV formula might be needed
+Do `brew edit opencv` and change the lines that look like the two lines below to exactly the two lines below.
+
+      -DPYTHON_LIBRARY=#{py_prefix}/lib/libpython2.7.dylib
+      -DPYTHON_INCLUDE_DIR=#{py_prefix}/include/python2.7
+
+If using Anaconda Python, HDF5 is bundled and the `hdf5` formula can be skipped.
+
+**Remaining dependencies, with / without Python**
+
+    # with Python pycaffe needs dependencies built from source
+    brew install --build-from-source --with-python -vd protobuf
+    brew install --build-from-source -vd boost boost-python
+    # without Python the usual installation suffices
+    brew install protobuf boost
+
+**BLAS**: already installed as the [Accelerate / vecLib Framework](https://developer.apple.com/library/mac/documentation/Darwin/Reference/ManPages/man7/Accelerate.7.html). OpenBLAS and MKL are alternatives for faster CPU computation.
+
+**Python** (optional): Anaconda is the preferred Python.
+If you decide against it, please use Homebrew.
+Check that Caffe and dependencies are linking against the same, desired Python.
+
+Continue with [compilation](installation.html#compilation).
+
+## libstdc++ installation
+
+This route is not for the faint of heart.
+For OS X 10.10 and 10.9 you should install CUDA 7 and follow the instructions above.
+If that is not an option, take a deep breath and carry on.
+
+In OS X 10.9+, clang++ is the default C++ compiler and uses `libc++` as the standard library.
+However, NVIDIA CUDA (even version 6.0) currently links only with `libstdc++`.
+This makes it necessary to change the compilation settings for each of the dependencies.
+
+We do this by modifying the Homebrew formulae before installing any packages.
+Make sure that Homebrew doesn't install any software dependencies in the background; all packages must be linked to `libstdc++`.
+
+The prerequisite Homebrew formulae are
+
+    boost snappy leveldb protobuf gflags glog szip lmdb homebrew/science/opencv
+
+For each of these formulas, `brew edit FORMULA`, and add the ENV definitions as shown:
+
+      def install
+          # ADD THE FOLLOWING:
+          ENV.append "CXXFLAGS", "-stdlib=libstdc++"
+          ENV.append "CFLAGS", "-stdlib=libstdc++"
+          ENV.append "LDFLAGS", "-stdlib=libstdc++ -lstdc++"
+          # The following is necessary because libtool likes to strip LDFLAGS:
+          ENV["CXX"] = "/usr/bin/clang++ -stdlib=libstdc++"
+          ...
+
+To edit the formulae in turn, run
+
+    for x in snappy leveldb protobuf gflags glog szip boost boost-python lmdb homebrew/science/opencv; do brew edit $x; done
+
+After this, run
+
+    for x in snappy leveldb gflags glog szip lmdb homebrew/science/opencv; do brew uninstall $x; brew install --build-from-source -vd $x; done
+    brew uninstall protobuf; brew install --build-from-source --with-python -vd protobuf
+    brew install --build-from-source -vd boost boost-python
+
+If this is not done exactly right then linking errors will trouble you.
+
+**Homebrew versioning** that Homebrew maintains itself as a separate git repository and making the above `brew edit FORMULA` changes will change files in your local copy of homebrew's master branch. By default, this will prevent you from updating Homebrew using `brew update`, as you will get an error message like the following:
+
+    $ brew update
+    error: Your local changes to the following files would be overwritten by merge:
+      Library/Formula/lmdb.rb
+    Please, commit your changes or stash them before you can merge.
+    Aborting
+    Error: Failure while executing: git pull -q origin refs/heads/master:refs/remotes/origin/master
+
+One solution is to commit your changes to a separate Homebrew branch, run `brew update`, and rebase your changes onto the updated master. You'll have to do this both for the main Homebrew repository in `/usr/local/` and the Homebrew science repository that contains OpenCV in  `/usr/local/Library/Taps/homebrew/homebrew-science`, as follows:
+
+    cd /usr/local
+    git checkout -b caffe
+    git add .
+    git commit -m "Update Caffe dependencies to use libstdc++"
+    cd /usr/local/Library/Taps/homebrew/homebrew-science
+    git checkout -b caffe
+    git add .
+    git commit -m "Update Caffe dependencies"
+
+Then, whenever you want to update homebrew, switch back to the master branches, do the update, rebase the caffe branches onto master and fix any conflicts:
+
+    # Switch batch to homebrew master branches
+    cd /usr/local
+    git checkout master
+    cd /usr/local/Library/Taps/homebrew/homebrew-science
+    git checkout master
+
+    # Update homebrew; hopefully this works without errors!
+    brew update
+
+    # Switch back to the caffe branches with the formulae that you modified earlier
+    cd /usr/local
+    git rebase master caffe
+    # Fix any merge conflicts and commit to caffe branch
+    cd /usr/local/Library/Taps/homebrew/homebrew-science
+    git rebase master caffe
+    # Fix any merge conflicts and commit to caffe branch
+
+    # Done!
+
+At this point, you should be running the latest Homebrew packages and your Caffe-related modifications will remain in place.
diff --git a/docs/install_yum.md b/docs/install_yum.md
new file mode 100644
index 00000000000..478e7d952cc
--- /dev/null
+++ b/docs/install_yum.md
@@ -0,0 +1,45 @@
+---
+title: Installation: RHEL / Fedora / CentOS
+---
+
+# RHEL / Fedora / CentOS Installation
+
+**General dependencies**
+
+    sudo yum install protobuf-devel leveldb-devel snappy-devel opencv-devel boost-devel hdf5-devel
+
+**Remaining dependencies, recent OS**
+
+    sudo yum install gflags-devel glog-devel lmdb-devel
+
+**Remaining dependencies, if not found**
+
+    # glog
+    wget https://google-glog.googlecode.com/files/glog-0.3.3.tar.gz
+    tar zxvf glog-0.3.3.tar.gz
+    cd glog-0.3.3
+    ./configure
+    make && make install
+    # gflags
+    wget https://github.com/schuhschuh/gflags/archive/master.zip
+    unzip master.zip
+    cd gflags-master
+    mkdir build && cd build
+    export CXXFLAGS="-fPIC" && cmake .. && make VERBOSE=1
+    make && make install
+    # lmdb
+    git clone git://gitorious.org/mdb/mdb.git
+    cd mdb/libraries/liblmdb
+    make && make install
+
+Note that glog does not compile with the most recent gflags version (2.1), so before that is resolved you will need to build with glog first.
+
+**CUDA**: Install via the NVIDIA package instead of `yum` to be certain of the library and driver versions.
+Install the library and latest driver separately; the driver bundled with the library is usually out-of-date.
+    + CentOS/RHEL/Fedora:
+
+**BLAS**: install ATLAS by `sudo yum install atlas-devel` or install OpenBLAS or MKL for better CPU performance. For the Makefile build, uncomment and set `BLAS_LIB` accordingly as ATLAS is usually installed under `/usr/lib[64]/atlas`).
+
+**Python** (optional): if you use the default Python you will need to `sudo yum install` the `python-devel` package to have the Python headers for building the pycaffe wrapper.
+
+Continue with [compilation](installation.html#compilation).
diff --git a/docs/installation.md b/docs/installation.md
index 207e17d1539..144e6a34f67 100644
--- a/docs/installation.md
+++ b/docs/installation.md
@@ -4,28 +4,36 @@ title: Installation
 
 # Installation
 
-Prior to installing, it is best to read through this guide and take note of the details for your platform.
-We have installed Caffe on Ubuntu 14.04, Ubuntu 12.04, OS X 10.9, and OS X 10.8.
+Prior to installing, have a glance through this guide and take note of the details for your platform.
+We install and run Caffe on Ubuntu 14.04 and 12.04, OS X 10.10 / 10.9 / 10.8, and AWS.
+The official Makefile and `Makefile.config` build are complemented by an automatic CMake build from the community.
 
 - [Prerequisites](#prerequisites)
 - [Compilation](#compilation)
-- [Hardware questions](#hardware_questions)
+- [Hardware](#hardware)
+- Platforms: [Ubuntu guide](install_apt.html), [OS X guide](install_osx.html), and [RHEL / CentOS / Fedora guide](install_yum.html)
+
+When updating Caffe, it's best to `make clean` before re-compiling.
 
 ## Prerequisites
 
-Caffe depends on several software packages.
+Caffe has several dependencies.
+
+* [CUDA](https://developer.nvidia.com/cuda-zone) is required for GPU mode.
+    * library version 7.0 and the latest driver version are recommended, but 6.* is fine too
+    * 5.5, and 5.0 are compatible but considered legacy
+* [BLAS](http://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms) via ATLAS, MKL, or OpenBLAS.
+* [Boost](http://www.boost.org/) >= 1.55
+* [OpenCV](http://opencv.org/) >= 2.4 including 3.0
+* `protobuf`, `glog`, `gflags`
+* IO libraries `hdf5`, `leveldb`, `snappy`, `lmdb`
 
-* [CUDA](https://developer.nvidia.com/cuda-zone) library version 6.5 (recommended), 6.0, 5.5, or 5.0 and the latest driver version for CUDA 6 or 319.* for CUDA 5 (and NOT 331.*)
-* [BLAS](http://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms) (provided via ATLAS, MKL, or OpenBLAS).
-* [OpenCV](http://opencv.org/).
-* [Boost](http://www.boost.org/) (>= 1.55, although only 1.55 and 1.56 are tested)
-* `glog`, `gflags`, `protobuf`, `leveldb`, `snappy`, `hdf5`, `lmdb`
-* For the Python wrapper
-    * `Python 2.7`, `numpy (>= 1.7)`, boost-provided `boost.python`
-* For the MATLAB wrapper
-    * MATLAB with the `mex` compiler.
+Pycaffe and Matcaffe interfaces have their own natural needs.
 
-**cuDNN Caffe**: for fastest operation Caffe is accelerated by drop-in integration of [NVIDIA cuDNN](https://developer.nvidia.com/cudnn). To speed up your Caffe models, install cuDNN then uncomment the `USE_CUDNN := 1` flag in `Makefile.config` when installing Caffe. Acceleration is automatic.
+* For Python Caffe:  `Python 2.7` or `Python 3.3+`, `numpy (>= 1.7)`, boost-provided `boost.python`
+* For MATLAB Caffe: MATLAB with the `mex` compiler.
+
+**cuDNN Caffe**: for fastest operation Caffe is accelerated by drop-in integration of [NVIDIA cuDNN](https://developer.nvidia.com/cudnn). To speed up your Caffe models, install cuDNN then uncomment the `USE_CUDNN := 1` flag in `Makefile.config` when installing Caffe. Acceleration is automatic. For now cuDNN v1 is integrated but see [PR #1731](https://github.com/BVLC/caffe/pull/1731) for v2.
 
 **CPU-only Caffe**: for cold-brewed CPU-only Caffe uncomment the `CPU_ONLY := 1` flag in `Makefile.config` to configure and build Caffe without CUDA. This is helpful for cloud or cluster deployment.
 
@@ -37,13 +45,9 @@ To install CUDA, go to the [NVIDIA CUDA website](https://developer.nvidia.com/cu
 For best performance, Caffe can be accelerated by [NVIDIA cuDNN](https://developer.nvidia.com/cudnn). Register for free at the cuDNN site, install it, then continue with these installation instructions. To compile with cuDNN set the `USE_CUDNN := 1` flag set in your `Makefile.config`.
 
 Caffe requires BLAS as the backend of its matrix and vector computations.
-There are several implementations of this library.
-The choice is yours:
+There are several implementations of this library. The choice is yours:
 
 * [ATLAS](http://math-atlas.sourceforge.net/): free, open source, and so the default for Caffe.
-    + Ubuntu: `sudo apt-get install libatlas-base-dev`
-    + CentOS/RHEL/Fedora: `sudo yum install atlas-devel`
-    + OS X: already installed as the [Accelerate / vecLib Framework](https://developer.apple.com/library/mac/documentation/Darwin/Reference/ManPages/man7/Accelerate.7.html).
 * [Intel MKL](http://software.intel.com/en-us/intel-mkl): commercial and optimized for Intel CPUs, with a free trial and [student](http://software.intel.com/en-us/intel-education-offerings) licenses.
     1. Install MKL.
     2. Set `BLAS := mkl` in `Makefile.config`
@@ -51,7 +55,7 @@ The choice is yours:
     1. Install OpenBLAS
     2. Set `BLAS := open` in `Makefile.config`
 
-### Python and/or MATLAB wrappers (optional)
+### Python and/or MATLAB Caffe (optional)
 
 #### Python
 
@@ -59,177 +63,19 @@ The main requirements are `numpy` and `boost.python` (provided by boost). `panda
 
 You can install the dependencies with
 
-    for req in $(cat requirements.txt); do sudo pip install $req; done
-
-but we highly recommend first installing the [Anaconda](https://store.continuum.io/cshop/anaconda/) Python distribution, which provides most of the necessary packages, as well as the `hdf5` library dependency.
+    for req in $(cat requirements.txt); do pip install $req; done
 
-For **Ubuntu**, if you use the default Python you will need to `sudo apt-get install` the `python-dev` package to have the Python headers for building the wrapper.
-
-For **Fedora**, if you use the default Python you will need to `sudo yum install` the `python-devel` package to have the Python headers for building the wrapper.
-
-For **OS X**, Anaconda is the preferred Python. If you decide against it, please use Homebrew -- but beware of potential linking errors!
+but we suggest first installing the [Anaconda](https://store.continuum.io/cshop/anaconda/) Python distribution, which provides most of the necessary packages, as well as the `hdf5` library dependency.
 
 To import the `caffe` Python module after completing the installation, add the module directory to your `$PYTHONPATH` by `export PYTHONPATH=/path/to/caffe/python:$PYTHONPATH` or the like. You should not import the module in the `caffe/python/caffe` directory!
 
-*Caffe's Python interface works with Python 2.7. Python 3 or earlier Pythons are your own adventure.*
+*Caffe's Python interface works with Python 2.7. Python 3.3+ should work out of the box without protobuf support. For protobuf support please install protobuf 3.0 alpha (https://developers.google.com/protocol-buffers/). Earlier Pythons are your own adventure.*
 
 #### MATLAB
 
 Install MATLAB, and make sure that its `mex` is in your `$PATH`.
 
-*Caffe's MATLAB interface works with versions 2012b, 2013a/b, and 2014a.*
-
-### The rest of the dependencies
-
-#### Linux
-
-On **Ubuntu**, most of the dependencies can be installed with
-
-    sudo apt-get install libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libboost-all-dev libhdf5-serial-dev
-
-and for **Ubuntu 14.04** the rest of the dependencies can be installed with
-
-    sudo apt-get install libgflags-dev libgoogle-glog-dev liblmdb-dev protobuf-compiler
-
-Keep reading to find out how to manually build and install the Google flags library, Google logging library and LMDB on **Ubuntu 12.04**.
-
-On **CentOS / RHEL / Fedora**, most of the dependencies can be installed with
-
-    sudo yum install protobuf-devel leveldb-devel snappy-devel opencv-devel boost-devel hdf5-devel
-
-The Google flags library, Google logging library and LMDB already made their ways into newer versions of **CentOS / RHEL / Fedora** so it is better to first attempt to install them using `yum`
-
-    sudo yum install gflags-devel glog-devel lmdb-devel
-
-**Finally** in case you couldn't find those extra libraries mentioned above in your distribution's repositories, here are the instructions to follow for manually building and installing them on **Ubuntu 12.04 / CentOS / RHEL / Fedora** (or practically on any Linux distribution)
-
-    # glog
-    wget https://google-glog.googlecode.com/files/glog-0.3.3.tar.gz
-    tar zxvf glog-0.3.3.tar.gz
-    cd glog-0.3.3
-    ./configure
-    make && make install
-    # gflags
-    wget https://github.com/schuhschuh/gflags/archive/master.zip
-    unzip master.zip
-    cd gflags-master
-    mkdir build && cd build
-    export CXXFLAGS="-fPIC" && cmake .. && make VERBOSE=1
-    make && make install
-    # lmdb
-    git clone git://gitorious.org/mdb/mdb.git
-    cd mdb/libraries/liblmdb
-    make && make install
-
-Note that glog does not compile with the most recent gflags version (2.1), so before that is resolved you will need to build with glog first.
-
-#### OS X
-
-On **OS X**, we highly recommend using the [Homebrew](http://brew.sh/) package manager, and ideally starting from a clean install of the OS (or from a wiped `/usr/local`) to avoid conflicts.
-In the following, we assume that you're using Anaconda Python and Homebrew.
-
-To install the OpenCV dependency, we'll need to provide an additional source for Homebrew:
-
-    brew tap homebrew/science
-
-If using Anaconda Python, a modification is required to the OpenCV formula.
-Do `brew edit opencv` and change the lines that look like the two lines below to exactly the two lines below.
-
-      -DPYTHON_LIBRARY=#{py_prefix}/lib/libpython2.7.dylib
-      -DPYTHON_INCLUDE_DIR=#{py_prefix}/include/python2.7
-
-**NOTE**: We find that everything compiles successfully if `$LD_LIBRARY_PATH` is not set at all, and `$DYLD_FALLBACK_LIBRARY_PATH` is set to to provide CUDA, Python, and other relevant libraries (e.g. `/usr/local/cuda/lib:$HOME/anaconda/lib:/usr/local/lib:/usr/lib`).
-In other `ENV` settings, things may not work as expected.
-
-**NOTE**: There is currently a conflict between boost 1.56 and CUDA in some configurations. Check the [conflict description](https://github.com/BVLC/caffe/issues/1193#issuecomment-57491906) and try downgrading to 1.55.
-
-#### 10.8-specific Instructions
-
-Simply run the following:
-
-    brew install --build-from-source boost boost-python
-    brew install --with-python protobuf
-    for x in snappy leveldb gflags glog szip lmdb homebrew/science/opencv; do brew install $x; done
-
-Building boost from source is needed to link against your local Python (exceptions might be raised during some OS X installs, but **ignore** these and continue). If you do not need the Python wrapper, simply doing `brew install boost` is fine.
-
-**Note** that the HDF5 dependency is provided by Anaconda Python in this case.
-If you're not using Anaconda, include `hdf5` in the list above.
-
-#### 10.9-specific Instructions
-
-In OS X 10.9, clang++ is the default C++ compiler and uses `libc++` as the standard library.
-However, NVIDIA CUDA (even version 6.0) currently links only with `libstdc++`.
-This makes it necessary to change the compilation settings for each of the dependencies.
-
-We do this by modifying the Homebrew formulae before installing any packages.
-Make sure that Homebrew doesn't install any software dependencies in the background; all packages must be linked to `libstdc++`.
-
-The prerequisite Homebrew formulae are
-
-    boost snappy leveldb protobuf gflags glog szip lmdb homebrew/science/opencv
-
-For each of these formulas, `brew edit FORMULA`, and add the ENV definitions as shown:
-
-      def install
-          # ADD THE FOLLOWING:
-          ENV.append "CXXFLAGS", "-stdlib=libstdc++"
-          ENV.append "CFLAGS", "-stdlib=libstdc++"
-          ENV.append "LDFLAGS", "-stdlib=libstdc++ -lstdc++"
-          # The following is necessary because libtool likes to strip LDFLAGS:
-          ENV["CXX"] = "/usr/bin/clang++ -stdlib=libstdc++"
-          ...
-
-To edit the formulae in turn, run
-
-    for x in snappy leveldb protobuf gflags glog szip boost boost-python lmdb homebrew/science/opencv; do brew edit $x; done
-
-After this, run
-
-    for x in snappy leveldb gflags glog szip lmdb homebrew/science/opencv; do brew uninstall $x; brew install --build-from-source --fresh -vd $x; done
-    brew uninstall protobuf; brew install --build-from-source --with-python --fresh -vd protobuf
-    brew install --build-from-source --fresh -vd boost boost-python
-
-**Note** that `brew install --build-from-source --fresh -vd boost` is fine if you do not need the Caffe Python wrapper.
-
-**Note** that the HDF5 dependency is provided by Anaconda Python in this case.
-If you're not using Anaconda, include `hdf5` in the list above.
-
-**Note** that in order to build the Caffe Python wrappers you must install `boost` and `boost-python`:
-
-    brew install --build-from-source --fresh -vd boost boost-python
-
-**Note** that Homebrew maintains itself as a separate git repository and making the above `brew edit FORMULA` changes will change files in your local copy of homebrew's master branch. By default, this will prevent you from updating Homebrew using `brew update`, as you will get an error message like the following:
-
-    $ brew update
-    error: Your local changes to the following files would be overwritten by merge:
-      Library/Formula/lmdb.rb
-    Please, commit your changes or stash them before you can merge.
-    Aborting
-    Error: Failure while executing: git pull -q origin refs/heads/master:refs/remotes/origin/master
-
-One solution is to commit your changes to a separate Homebrew branch, run `brew update`, and rebase your changes onto the updated master, as follows:
-
-    cd /usr/local
-    git checkout -b caffe
-    git add .
-    git commit -m "Update Caffe dependencies to use libstdc++"
-    git checkout master
-    brew update
-    git rebase master caffe
-    # Resolve any merge conflicts here
-    git checkout caffe
-
-At this point, you should be running the latest Homebrew packages and your Caffe-related modifications will remain in place. You may still get the following error:
-
-    $ brew update
-    error: Your local changes to the following files would be overwritten by merge:
-	opencv.rb
-    Please, commit your changes or stash them before you can merge.
-    Aborting
-    Error: Failed to update tap: homebrew/science
-
-but non-OpenCV packages will still update as expected.
+*Caffe's MATLAB interface works with versions 2014a/b, 2013a/b, and 2012b.*
 
 #### Windows
 
@@ -237,8 +83,7 @@ There is an unofficial Windows port of Caffe at [niuzhiheng/caffe:windows](https
 
 ## Compilation
 
-Now that you have the prerequisites, edit your `Makefile.config` to change the paths for your setup (you should especially uncomment and set `BLAS_LIB` accordingly on distributions like **CentOS / RHEL / Fedora** where ATLAS is installed under `/usr/lib[64]/atlas`)
-The defaults should work, but uncomment the relevant lines if using Anaconda Python.
+Now that you have the prerequisites, edit your `Makefile.config` to change the paths for your setup The defaults should work, but uncomment the relevant lines if using Anaconda Python.
 
     cp Makefile.config.example Makefile.config
     # Adjust Makefile.config (for example, if using Anaconda Python)
@@ -246,24 +91,22 @@ The defaults should work, but uncomment the relevant lines if using Anaconda Pyt
     make test
     make runtest
 
-To compile with cuDNN acceleration, you should uncomment the `USE_CUDNN := 1` switch in `Makefile.config`.
-
-If there is no GPU in your machine, you should switch to CPU-only Caffe by uncommenting `CPU_ONLY := 1` in `Makefile.config`.
+- For cuDNN acceleration, you should uncomment the `USE_CUDNN := 1` switch in `Makefile.config`.
+- For CPU-only Caffe, uncomment `CPU_ONLY := 1` in `Makefile.config`.
 
 To compile the Python and MATLAB wrappers do `make pycaffe` and `make matcaffe` respectively.
 Be sure to set your MATLAB and Python paths in `Makefile.config` first!
 
-*Distribution*: run `make distribute` to create a `distribute` directory with all the Caffe headers, compiled libraries, binaries, etc. needed for distribution to other machines.
+**Distribution**: run `make distribute` to create a `distribute` directory with all the Caffe headers, compiled libraries, binaries, etc. needed for distribution to other machines.
 
-*Speed*: for a faster build, compile in parallel by doing `make all -j8` where 8 is the number of parallel threads for compilation (a good choice for the number of threads is the number of cores in your machine).
+**Speed**: for a faster build, compile in parallel by doing `make all -j8` where 8 is the number of parallel threads for compilation (a good choice for the number of threads is the number of cores in your machine).
 
 Now that you have installed Caffe, check out the [MNIST tutorial](gathered/examples/mnist.html) and the [reference ImageNet model tutorial](gathered/examples/imagenet.html).
 
-### Compilation using CMake (beta)
+### CMake Compilation
 
-In lieu of manually editing `Makefile.config` to tell Caffe where dependencies are located, Caffe also provides a CMake-based build system (currently in "beta").
-It requires CMake version >= 2.8.8.
-The basic installation steps are as follows:
+In lieu of manually editing `Makefile.config` to configure the build, Caffe offers an unofficial CMake build thanks to @Nerei, @akosiorek, and other members of the community. It requires CMake version >= 2.8.7.
+The basic steps are as follows:
 
     mkdir build
     cd build
@@ -271,21 +114,14 @@ The basic installation steps are as follows:
     make all
     make runtest
 
-#### Ubuntu 12.04
-
-Note that in Ubuntu 12.04, Aptitude will install version CMake 2.8.7 by default, which is not supported by Caffe's CMake build (requires at least 2.8.8).
-As a workaround, if you are using Ubuntu 12.04 you can try the following steps to install (or upgrade to) CMake 2.8.9:
-
-    sudo add-apt-repository ppa:ubuntu-sdk-team/ppa -y
-    sudo apt-get -y update
-    sudo apt-get install cmake
+See [PR #1667](https://github.com/BVLC/caffe/pull/1667) for options and details.
 
-## Hardware Questions
+## Hardware
 
-**Laboratory Tested Hardware**: Berkeley Vision runs Caffe with K40s, K20s, and Titans including models at ImageNet/ILSVRC scale. We also run on GTX series cards and GPU-equipped MacBook Pros. We have not encountered any trouble in-house with devices with CUDA capability >= 3.0. All reported hardware issues thus-far have been due to GPU configuration, overheating, and the like.
+**Laboratory Tested Hardware**: Berkeley Vision runs Caffe with K40s, K20s, and Titans including models at ImageNet/ILSVRC scale. We also run on GTX series cards (980s and 770s) and GPU-equipped MacBook Pros. We have not encountered any trouble in-house with devices with CUDA capability >= 3.0. All reported hardware issues thus-far have been due to GPU configuration, overheating, and the like.
 
 **CUDA compute capability**: devices with compute capability <= 2.0 may have to reduce CUDA thread numbers and batch sizes due to hardware constraints. Your mileage may vary.
 
 Once installed, check your times against our [reference performance numbers](performance_hardware.html) to make sure everything is configured properly.
 
-Refer to the project's issue tracker for [hardware/compatibility](https://github.com/BVLC/caffe/issues?labels=hardware%2Fcompatibility&page=1&state=open).
+Ask hardware questions on the [caffe-users group](https://groups.google.com/forum/#!forum/caffe-users).
diff --git a/docs/model_zoo.md b/docs/model_zoo.md
index 330850cbbd6..06dc0a49ec7 100644
--- a/docs/model_zoo.md
+++ b/docs/model_zoo.md
@@ -3,8 +3,10 @@ title: Model Zoo
 ---
 # Caffe Model Zoo
 
-Lots of people have used Caffe to train models of different architectures and applied to different problems, ranging from simple regression to AlexNet-alikes to Siamese networks for image similarity to speech applications.
-To lower the friction of sharing these models, we introduce the model zoo framework:
+Lots of researchers and engineers have made Caffe models for different tasks with all kinds of architectures and data.
+These models are learned and applied for problems ranging from simple regression, to large-scale visual classification, to Siamese networks for image similarity, to speech and robotics applications.
+
+To help share these models, we introduce the model zoo framework:
 
 - A standard format for packaging Caffe model info.
 - Tools to upload/download model info to/from Github Gists, and to download trained `.caffemodel` binaries.
@@ -12,14 +14,19 @@ To lower the friction of sharing these models, we introduce the model zoo framew
 
 ## Where to get trained models
 
-First of all, we provide some trained models out of the box.
+First of all, we bundle BVLC-trained models for unrestricted, out of the box use.
+<br>
+See the [BVLC model license](#bvlc-model-license) for details.
 Each one of these can be downloaded by running `scripts/download_model_binary.py <dirname>` where `<dirname>` is specified below:
 
-- **BVLC Reference CaffeNet** in `models/bvlc_reference_caffenet`: AlexNet trained on ILSVRC 2012, with a minor variation from the version as described in the NIPS 2012 paper.
-- **BVLC AlexNet** in `models/bvlc_alexnet`: AlexNet trained on ILSVRC 2012, almost exactly as described in NIPS 2012.
-- **BVLC Reference R-CNN ILSVRC-2013** in `models/bvlc_reference_rcnn_ilsvrc13`: pure Caffe implementation of [R-CNN](https://github.com/rbgirshick/rcnn).
+- **BVLC Reference CaffeNet** in `models/bvlc_reference_caffenet`: AlexNet trained on ILSVRC 2012, with a minor variation from the version as described in [ImageNet classification with deep convolutional neural networks](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks) by Krizhevsky et al. in NIPS 2012. (Trained by Jeff Donahue @jeffdonahue)
+- **BVLC AlexNet** in `models/bvlc_alexnet`: AlexNet trained on ILSVRC 2012, almost exactly as described in [ImageNet classification with deep convolutional neural networks](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks) by Krizhevsky et al. in NIPS 2012. (Trained by Evan Shelhamer @shelhamer)
+- **BVLC Reference R-CNN ILSVRC-2013** in `models/bvlc_reference_rcnn_ilsvrc13`: pure Caffe implementation of [R-CNN](https://github.com/rbgirshick/rcnn) as described by Girshick et al. in CVPR 2014. (Trained by Ross Girshick @rbgirshick)
+- **BVLC GoogLeNet** in `models/bvlc_googlenet`: GoogLeNet trained on ILSVRC 2012, almost exactly as described in [Going Deeper with Convolutions](http://arxiv.org/abs/1409.4842) by Szegedy et al. in ILSVRC 2014. (Trained by Sergio Guadarrama @sguada)
 
-User-provided models are posted to a public-editable [wiki page](https://github.com/BVLC/caffe/wiki/Model-Zoo).
+**Community models** made by Caffe users are posted to a publicly editable [wiki page](https://github.com/BVLC/caffe/wiki/Model-Zoo).
+These models are subject to conditions of their respective authors such as citation and license.
+Thank you for sharing your models!
 
 ## Model info format
 
@@ -35,11 +42,11 @@ A caffe model is distributed as a directory containing:
     - License information.
 - [optional] Other helpful scripts.
 
-## Hosting model info
+### Hosting model info
 
 Github Gist is a good format for model info distribution because it can contain multiple files, is versionable, and has in-browser syntax highlighting and markdown rendering.
 
-- `scripts/upload_model_to_gist.sh <dirname>`: uploads non-binary files in the model directory as a Github Gist and prints the Gist ID. If `gist_id` is already part of the `<dirname>/readme.md` frontmatter, then updates existing Gist.
+`scripts/upload_model_to_gist.sh <dirname>` uploads non-binary files in the model directory as a Github Gist and prints the Gist ID. If `gist_id` is already part of the `<dirname>/readme.md` frontmatter, then updates existing Gist.
 
 Try doing `scripts/upload_model_to_gist.sh models/bvlc_alexnet` to test the uploading (don't forget to delete the uploaded gist afterward).
 
@@ -51,4 +58,13 @@ It is up to the user where to host the `.caffemodel` file.
 We host our BVLC-provided models on our own server.
 Dropbox also works fine (tip: make sure that `?dl=1` is appended to the end of the URL).
 
-- `scripts/download_model_binary.py <dirname>`: downloads the `.caffemodel` from the URL specified in the `<dirname>/readme.md` frontmatter and confirms SHA1.
+`scripts/download_model_binary.py <dirname>` downloads the `.caffemodel` from the URL specified in the `<dirname>/readme.md` frontmatter and confirms SHA1.
+
+## BVLC model license
+
+The Caffe models bundled by the BVLC are released for unrestricted use.
+
+These models are trained on data from the [ImageNet project](http://www.image-net.org/) and training data includes internet photos that may be subject to copyright.
+
+Our present understanding as researchers is that there is no restriction placed on the open release of these learned model weights, since none of the original images are distributed in whole or in part.
+To the extent that the interpretation arises that weights are derivative works of the original copyright holder and they assert such a copyright, UC Berkeley makes no representations as to what use is allowed other than to consider our present release in the spirit of fair use in the academic mission of the university to disseminate knowledge and tools as broadly as possible without restriction.
diff --git a/docs/performance_hardware.md b/docs/performance_hardware.md
index b35246feabd..cdd4b361dea 100644
--- a/docs/performance_hardware.md
+++ b/docs/performance_hardware.md
@@ -48,7 +48,7 @@ and then set the clock speed with
 
     sudo nvidia-smi -i 0 -ac 3004,875    # repeat with -i x for each GPU ID
 
-but note that this configuration resets across driver reloading / rebooting. Include these commands in a boot script to intialize these settings. For a simple fix, add these commands to `/etc/rc.local` (on Ubuntu).
+but note that this configuration resets across driver reloading / rebooting. Include these commands in a boot script to initialize these settings. For a simple fix, add these commands to `/etc/rc.local` (on Ubuntu).
 
 ## NVIDIA Titan
 
diff --git a/docs/tutorial/data.md b/docs/tutorial/data.md
index 40605f7cd73..3bf7d932eda 100644
--- a/docs/tutorial/data.md
+++ b/docs/tutorial/data.md
@@ -10,15 +10,15 @@ New input types are supported by developing a new data layer -- the rest of the
 
 This data layer definition
 
-    layers {
+    layer {
       name: "mnist"
-      # DATA layer loads leveldb or lmdb storage DBs for high-throughput.
-      type: DATA
+      # Data layer loads leveldb or lmdb storage DBs for high-throughput.
+      type: "Data"
       # the 1st top is the data itself: the name is only convention
       top: "data"
       # the 2nd top is the ground truth: the name is only convention
       top: "label"
-      # the DATA layer configuration
+      # the Data layer configuration
       data_param {
         # path to the DB
         source: "examples/mnist/mnist_train_lmdb"
@@ -46,9 +46,9 @@ The (data, label) pairing is a convenience for classification models.
 
 **Transformations**: data preprocessing is parametrized by transformation messages within the data layer definition.
 
-    layers {
+    layer {
       name: "data"
-      type: DATA
+      type: "Data"
       [...]
       transform_param {
         scale: 0.1
diff --git a/docs/tutorial/forward_backward.md b/docs/tutorial/forward_backward.md
index f58b9cac19e..a645f002f61 100644
--- a/docs/tutorial/forward_backward.md
+++ b/docs/tutorial/forward_backward.md
@@ -15,7 +15,7 @@ This pass goes from bottom to top.
 
 <img src="fig/forward.jpg" alt="Forward pass" width="320">
 
-The data $x$ is passed through an inner product layer for $g(x)$ then through a softmax for $h(g(x))$ and softmax loss to give $f_W(x)$.
+The data $$x$$ is passed through an inner product layer for $$g(x)$$ then through a softmax for $$h(g(x))$$ and softmax loss to give $$f_W(x)$$.
 
 The **backward** pass computes the gradient given the loss for learning.
 In backward Caffe reverse-composes the gradient of each layer to compute the gradient of the whole model by automatic differentiation.
@@ -24,7 +24,7 @@ This pass goes from top to bottom.
 
 <img src="fig/backward.jpg" alt="Backward pass" width="320">
 
-The backward pass begins with the loss and computes the gradient with respect to the output $\frac{\partial f_W}{\partial h}$. The gradient with respect to the rest of the model is computed layer-by-layer through the chain rule. Layers with parameters, like the `INNER_PRODUCT` layer, compute the gradient with respect to their parameters $\frac{\partial f_W}{\partial W_{\text{ip}}}$ during the backward step.
+The backward pass begins with the loss and computes the gradient with respect to the output $$\frac{\partial f_W}{\partial h}$$. The gradient with respect to the rest of the model is computed layer-by-layer through the chain rule. Layers with parameters, like the `INNER_PRODUCT` layer, compute the gradient with respect to their parameters $$\frac{\partial f_W}{\partial W_{\text{ip}}}$$ during the backward step.
 
 These computations follow immediately from defining the model: Caffe plans and carries out the forward and backward passes for you.
 
diff --git a/docs/tutorial/interfaces.md b/docs/tutorial/interfaces.md
index 6b0ec347dfe..12963318485 100644
--- a/docs/tutorial/interfaces.md
+++ b/docs/tutorial/interfaces.md
@@ -9,7 +9,13 @@ Caffe has command line, Python, and MATLAB interfaces for day-to-day usage, inte
 
 The command line interface -- cmdcaffe -- is the `caffe` tool for model training, scoring, and diagnostics. Run `caffe` without any arguments for help. This tool and others are found in caffe/build/tools. (The following example calls require completing the LeNet / MNIST example first.)
 
-**Training**: `caffe train` learns models from scratch, resumes learning from saved snapshots, and fine-tunes models to new data and tasks. All training requires a solver configuration through the `-solver solver.prototxt` argument. Resuming requires the `-snapshot model_iter_1000.solverstate` argument to load the solver snapshot. Fine-tuning requires the `-weights model.caffemodel` argument for the model initialization.
+**Training**: `caffe train` learns models from scratch, resumes learning from saved snapshots, and fine-tunes models to new data and tasks:
+
+* All training requires a solver configuration through the `-solver solver.prototxt` argument. 
+* Resuming requires the `-snapshot model_iter_1000.solverstate` argument to load the solver snapshot. 
+* Fine-tuning requires the `-weights model.caffemodel` argument for the model initialization.
+
+For example, you can run:
 
     # train LeNet
     caffe train -solver examples/mnist/lenet_solver.prototxt
@@ -26,17 +32,19 @@ For a full example of fine-tuning, see examples/finetuning_on_flickr_style, but
 **Testing**: `caffe test` scores models by running them in the test phase and reports the net output as its score. The net architecture must be properly defined to output an accuracy measure or loss as its output. The per-batch score is reported and then the grand average is reported last.
 
     #
-    # score the learned LeNet model on the validation set as defined in the model architeture lenet_train_test.prototxt
-    caffe test -model examples/mnist/lenet_train_test.prototxt -weights examples/mnist/lenet_iter_10000 -gpu 0 -iterations 100
+    # score the learned LeNet model on the validation set as defined in the 
+    # model architeture lenet_train_test.prototxt
+    caffe test -model examples/mnist/lenet_train_test.prototxt -weights examples/mnist/lenet_iter_10000.caffemodel -gpu 0 -iterations 100
 
 **Benchmarking**: `caffe time` benchmarks model execution layer-by-layer through timing and synchronization. This is useful to check system performance and measure relative execution times for models.
 
     # (These example calls require you complete the LeNet / MNIST example first.)
     # time LeNet training on CPU for 10 iterations
     caffe time -model examples/mnist/lenet_train_test.prototxt -iterations 10
-    # time a model architecture with the given weights on the first GPU for 10 iterations
     # time LeNet training on GPU for the default 50 iterations
     caffe time -model examples/mnist/lenet_train_test.prototxt -gpu 0
+    # time a model architecture with the given weights on the first GPU for 10 iterations
+    caffe time -model examples/mnist/lenet_train_test.prototxt -weights examples/mnist/lenet_iter_10000.caffemodel -gpu 0 -iterations 10
 
 **Diagnostics**: `caffe device_query` reports GPU details for reference and checking device ordinals for running on a given device in multi-GPU machines.
 
@@ -59,10 +67,213 @@ Compile pycaffe by `make pycaffe`. The module dir caffe/python/caffe should be i
 
 ## MATLAB
 
-The MATLAB interface -- matcaffe -- is the `caffe` mex and its helper m-files in caffe/matlab. Load models, do forward and backward, extract output and read-only model weights, and load the binaryproto format mean as a matrix.
+The MATLAB interface -- matcaffe -- is the `caffe` package in caffe/matlab in which you can integrate Caffe in your Matlab code.
+
+In MatCaffe, you can
+
+* Creating multiple Nets in Matlab
+* Do forward and backward computation
+* Access any layer within a network, and any parameter blob in a layer
+* Get and set data or diff to any blob within a network, not restricting to input blobs or output blobs
+* Save a network's parameters to file, and load parameters from file
+* Reshape a blob and reshape a network
+* Edit network parameter and do network surgery
+* Create multiple Solvers in Matlab for training
+* Resume training from solver snapshots
+* Access train net and test nets in a solver
+* Run for a certain number of iterations and give back control to Matlab
+* Intermingle arbitrary Matlab code with gradient steps
+
+An ILSVRC image classification demo is in caffe/matlab/demo/classification_demo.m (you need to download BVLC CaffeNet from [Model Zoo](http://caffe.berkeleyvision.org/model_zoo.html) to run it).
+
+### Build MatCaffe
+
+Build MatCaffe with `make all matcaffe`. After that, you may test it using `make mattest`.
+
+Common issue: if you run into error messages like `libstdc++.so.6:version 'GLIBCXX_3.4.15' not found` during `make mattest`, then it usually means that your Matlab's runtime libraries do not match your compile-time libraries. You may need to do the following before you start Matlab:
+
+    export LD_LIBRARY_PATH=/opt/intel/mkl/lib/intel64:/usr/local/cuda/lib64
+    export LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libstdc++.so.6
+
+Or the equivalent based on where things are installed on your system, and do `make mattest` again to see if the issue is fixed. Note: this issue is sometimes more complicated since during its startup Matlab may overwrite your `LD_LIBRARY_PATH` environment variable. You can run `!ldd ./matlab/+caffe/private/caffe_.mexa64` (the mex extension may differ on your system) in Matlab to see its runtime libraries, and preload your compile-time libraries by exporting them to your `LD_PRELOAD` environment variable.
+
+After successful building and testing, add this package to Matlab search PATH by starting `matlab` from caffe root folder and running the following commands in Matlab command window.
+
+    addpath ./matlab
+
+You can save your Matlab search PATH by running `savepath` so that you don't have to run the command above again every time you use MatCaffe.
+
+### Use MatCaffe
+
+MatCaffe is very similar to PyCaffe in usage.
+
+Examples below shows detailed usages and assumes you have downloaded BVLC CaffeNet from [Model Zoo](http://caffe.berkeleyvision.org/model_zoo.html) and started `matlab` from caffe root folder.
+
+    model = './models/bvlc_reference_caffenet/deploy.prototxt';
+    weights = './models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel';
+
+#### Set mode and device
+
+**Mode and device should always be set BEFORE you create a net or a solver.**
+
+Use CPU:
+
+    caffe.set_mode_cpu();
+
+Use GPU and specify its gpu_id:
+
+    caffe.set_mode_gpu();
+    caffe.set_device(gpu_id);
+
+#### Create a network and access its layers and blobs
+
+Create a network:
+
+    net = caffe.Net(model, weights, 'test'); % create net and load weights
+
+Or
+
+    net = caffe.Net(model, 'test'); % create net but not load weights
+    net.copy_from(weights); % load weights
+
+which creates `net` object as
+
+      Net with properties:
+
+               layer_vec: [1x23 caffe.Layer]
+                blob_vec: [1x15 caffe.Blob]
+                  inputs: {'data'}
+                 outputs: {'prob'}
+        name2layer_index: [23x1 containers.Map]
+         name2blob_index: [15x1 containers.Map]
+             layer_names: {23x1 cell}
+              blob_names: {15x1 cell}
+
+The two `containers.Map` objects are useful to find the index of a layer or a blob by its name.
+
+You have access to every blob in this network. To fill blob 'data' with all ones:
+
+    net.blobs('data').set_data(ones(net.blobs('data').shape));
+
+To multiply all values in blob 'data' by 10:
+
+    net.blobs('data').set_data(net.blobs('data').get_data() * 10);
+
+**Be aware that since Matlab is 1-indexed and column-major, the usual 4 blob dimensions in Matlab are `[width, height, channels, num]`, and `width` is the fastest dimension. Also be aware that images are in BGR channels.** Also, Caffe uses single-precision float data. If your data is not single, `set_data` will automatically convert it to single.
+
+You also have access to every layer, so you can do network surgery. For example, to multiply conv1 parameters by 10:
+
+    net.params('conv1', 1).set_data(net.params('conv1', 1).get_data() * 10); % set weights
+    net.params('conv1', 2).set_data(net.params('conv1', 2).get_data() * 10); % set bias
+
+Alternatively, you can use
+
+    net.layers('conv1').params(1).set_data(net.layers('conv1').params(1).get_data() * 10);
+    net.layers('conv1').params(2).set_data(net.layers('conv1').params(2).get_data() * 10);
+
+To save the network you just modified:
+
+    net.save('my_net.caffemodel');
+
+To get a layer's type (string):
+
+    layer_type = net.layers('conv1').type;
+
+#### Forward and backward
+
+Forward pass can be done using `net.forward` or `net.forward_prefilled`. Function `net.forward` takes in a cell array of N-D arrays containing data of input blob(s) and outputs a cell array containing data from output blob(s). Function `net.forward_prefilled` uses existing data in input blob(s) during forward pass, takes no input and produces no output. After creating some data for input blobs like `data = rand(net.blobs('data').shape);` you can run
+    
+    res = net.forward({data});
+    prob = res{1};
+
+Or
+
+    net.blobs('data').set_data(data);
+    net.forward_prefilled();
+    prob = net.blobs('prob').get_data();
+
+Backward is similar using `net.backward` or `net.backward_prefilled` and replacing `get_data` and `set_data` with `get_diff` and `set_diff`. After creating some gradients for output blobs like `prob_diff = rand(net.blobs('prob').shape);` you can run
+
+    res = net.backward({prob_diff});
+    data_diff = res{1};
+
+Or
+
+    net.blobs('prob').set_diff(prob_diff);
+    net.backward_prefilled();
+    data_diff = net.blobs('data').get_diff();
+    
+**However, the backward computation above doesn't get correct results, because Caffe decides that the network does not need backward computation. To get correct backward results, you need to set `'force_backward: true'` in your network prototxt.**
+
+After performing forward or backward pass, you can also get the data or diff in internal blobs. For example, to extract pool5 features after forward pass:
+
+    pool5_feat = net.blobs('pool5').get_data();
+
+#### Reshape
+
+Assume you want to run 1 image at a time instead of 10:
+
+    net.blobs('data').reshape([227 227 3 1]); % reshape blob 'data'
+    net.reshape();
+
+Then the whole network is reshaped, and now `net.blobs('prob').shape` should be `[1000 1]`;
+
+#### Training
+
+Assume you have created training and validation lmdbs following our [ImageNET Tutorial](http://caffe.berkeleyvision.org/gathered/examples/imagenet.html), to create a solver and train on ILSVRC 2012 classification dataset:
+
+    solver = caffe.Solver('./models/bvlc_reference_caffenet/solver.prototxt');
+
+which creates `solver` object as
+
+      Solver with properties:
+
+              net: [1x1 caffe.Net]
+        test_nets: [1x1 caffe.Net]
+
+To train:
+
+    solver.solve();
+
+Or train for only 1000 iterations (so that you can do something to its net before training more iterations)
+
+    solver.step(1000);
+
+To get iteration number:
+
+    iter = solver.iter();
+
+To get its network:
+
+    train_net = solver.net;
+    test_net = solver.test_nets(1);
+
+To resume from a snapshot "your_snapshot.solverstate":
+
+    solver.restore('your_snapshot.solverstate');
+
+#### Input and output
+
+`caffe.io` class provides basic input functions `load_image` and `read_mean`. For example, to read ILSVRC 2012 mean file (assume you have downloaded imagenet example auxiliary files by running `./data/ilsvrc12/get_ilsvrc_aux.sh`):
+
+    mean_data = caffe.io.read_mean('./data/ilsvrc12/imagenet_mean.binaryproto');
+
+To read Caffe's example image and resize to `[width, height]` and suppose we want `width = 256; height = 256;`
+
+    im_data = caffe.io.load_image('./examples/images/cat.jpg');
+    im_data = imresize(im_data, [width, height]); % resize using Matlab's imresize
+
+**Keep in mind that `width` is the fastest dimension and channels are BGR, which is different from the usual way that Matlab stores an image.** If you don't want to use `caffe.io.load_image` and prefer to load an image by yourself, you can do
+
+    im_data = imread('./examples/images/cat.jpg'); % read image
+    im_data = im_data(:, :, [3, 2, 1]); % convert from RGB to BGR
+    im_data = permute(im_data, [2, 1, 3]); % permute width and height
+    im_data = single(im_data); % convert to single precision
+
+Also, you may take a look at caffe/matlab/demo/classification_demo.m to see how to prepare input by taking crops from an image.
 
-A MATLAB demo is in caffe/matlab/caffe/matcaffe_demo.m
+We show in caffe/matlab/hdf5creation how to read and write HDF5 data with Matlab. We do not provide extra functions for data output as Matlab itself is already quite powerful in output.
 
-Note that MATLAB matrices and memory are in column-major layout counter to Caffe's row-major layout! Double-check your work accordingly.
+#### Clear nets and solvers
 
-Compile matcaffe by `make matcaffe`.
+Call `caffe.reset_all()` to clear all solvers and stand-alone nets you have created.
diff --git a/docs/tutorial/layers.md b/docs/tutorial/layers.md
index 5f8f519cdc4..806374e3f93 100644
--- a/docs/tutorial/layers.md
+++ b/docs/tutorial/layers.md
@@ -5,9 +5,7 @@ title: Layer Catalogue
 
 To create a Caffe model you need to define the model architecture in a protocol buffer definition file (prototxt).
 
-Caffe layers and their parameters are defined in the protocol buffer definitions for the project in [caffe.proto](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto). The latest definitions are in the [dev caffe.proto](https://github.com/BVLC/caffe/blob/dev/src/caffe/proto/caffe.proto).
-
-TODO complete list of layers linking to headings
+Caffe layers and their parameters are defined in the protocol buffer definitions for the project in [caffe.proto](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto).
 
 ### Vision Layers
 
@@ -23,7 +21,7 @@ In contrast, other layers (with few exceptions) ignore the spatial structure of
 
 #### Convolution
 
-* LayerType: `CONVOLUTION`
+* Layer type: `Convolution`
 * CPU implementation: `./src/caffe/layers/convolution_layer.cpp`
 * CUDA GPU implementation: `./src/caffe/layers/convolution_layer.cu`
 * Parameters (`ConvolutionParameter convolution_param`)
@@ -43,15 +41,15 @@ In contrast, other layers (with few exceptions) ignore the spatial structure of
     - `n * c_o * h_o * w_o`, where `h_o = (h_i + 2 * pad_h - kernel_h) / stride_h + 1` and `w_o` likewise.
 * Sample (as seen in `./examples/imagenet/imagenet_train_val.prototxt`)
 
-      layers {
+      layer {
         name: "conv1"
-        type: CONVOLUTION
+        type: "Convolution"
         bottom: "data"
         top: "conv1"
-        blobs_lr: 1          # learning rate multiplier for the filters
-        blobs_lr: 2          # learning rate multiplier for the biases
-        weight_decay: 1      # weight decay multiplier for the filters
-        weight_decay: 0      # weight decay multiplier for the biases
+        # learning rate and decay multipliers for the filters
+        param { lr_mult: 1 decay_mult: 1 }
+        # learning rate and decay multipliers for the biases
+        param { lr_mult: 2 decay_mult: 0 }
         convolution_param {
           num_output: 96     # learn 96 filters
           kernel_size: 11    # each filter is 11x11
@@ -67,11 +65,11 @@ In contrast, other layers (with few exceptions) ignore the spatial structure of
         }
       }
 
-The `CONVOLUTION` layer convolves the input image with a set of learnable filters, each producing one feature map in the output image.
+The `Convolution` layer convolves the input image with a set of learnable filters, each producing one feature map in the output image.
 
 #### Pooling
 
-* LayerType: `POOLING`
+* Layer type: `Pooling`
 * CPU implementation: `./src/caffe/layers/pooling_layer.cpp`
 * CUDA GPU implementation: `./src/caffe/layers/pooling_layer.cu`
 * Parameters (`PoolingParameter pooling_param`)
@@ -87,9 +85,9 @@ The `CONVOLUTION` layer convolves the input image with a set of learnable filter
     - `n * c * h_o * w_o`, where h_o and w_o are computed in the same way as convolution.
 * Sample (as seen in `./examples/imagenet/imagenet_train_val.prototxt`)
 
-      layers {
+      layer {
         name: "pool1"
-        type: POOLING
+        type: "Pooling"
         bottom: "conv1"
         top: "pool1"
         pooling_param {
@@ -101,7 +99,7 @@ The `CONVOLUTION` layer convolves the input image with a set of learnable filter
 
 #### Local Response Normalization (LRN)
 
-* LayerType: `LRN`
+* Layer type: `LRN`
 * CPU Implementation: `./src/caffe/layers/lrn_layer.cpp`
 * CUDA GPU Implementation: `./src/caffe/layers/lrn_layer.cu`
 * Parameters (`LRNParameter lrn_param`)
@@ -115,7 +113,7 @@ The local response normalization layer performs a kind of "lateral inhibition" b
 
 #### im2col
 
-`IM2COL` is a helper for doing the image-to-column transformation that you most likely do not need to know about. This is used in Caffe's original convolution to do matrix multiplication by laying out all patches into a matrix.
+`Im2col` is a helper for doing the image-to-column transformation that you most likely do not need to know about. This is used in Caffe's original convolution to do matrix multiplication by laying out all patches into a matrix.
 
 ### Loss Layers
 
@@ -123,19 +121,19 @@ Loss drives learning by comparing an output to a target and assigning cost to mi
 
 #### Softmax
 
-* LayerType: `SOFTMAX_LOSS`
+* Layer type: `SoftmaxWithLoss`
 
 The softmax loss layer computes the multinomial logistic loss of the softmax of its inputs. It's conceptually identical to a softmax layer followed by a multinomial logistic loss layer, but provides a more numerically stable gradient.
 
 #### Sum-of-Squares / Euclidean
 
-* LayerType: `EUCLIDEAN_LOSS`
+* Layer type: `EuclideanLoss`
 
 The Euclidean loss layer computes the sum of squares of differences of its two inputs, $$\frac 1 {2N} \sum_{i=1}^N \| x^1_i - x^2_i \|_2^2$$.
 
 #### Hinge / Margin
 
-* LayerType: `HINGE_LOSS`
+* Layer type: `HingeLoss`
 * CPU implementation: `./src/caffe/layers/hinge_loss_layer.cpp`
 * CUDA GPU implementation: none yet
 * Parameters (`HingeLossParameter hinge_loss_param`)
@@ -149,17 +147,17 @@ The Euclidean loss layer computes the sum of squares of differences of its two i
 * Samples
 
       # L1 Norm
-      layers {
+      layer {
         name: "loss"
-        type: HINGE_LOSS
+        type: "HingeLoss"
         bottom: "pred"
         bottom: "label"
       }
 
       # L2 Norm
-      layers {
+      layer {
         name: "loss"
-        type: HINGE_LOSS
+        type: "HingeLoss"
         bottom: "pred"
         bottom: "label"
         top: "loss"
@@ -172,15 +170,15 @@ The hinge loss layer computes a one-vs-all hinge or squared hinge loss.
 
 #### Sigmoid Cross-Entropy
 
-`SIGMOID_CROSS_ENTROPY_LOSS`
+`SigmoidCrossEntropyLoss`
 
 #### Infogain
 
-`INFOGAIN_LOSS`
+`InfogainLoss`
 
 #### Accuracy and Top-k
 
-`ACCURACY` scores the output as the accuracy of output with respect to target -- it is not actually a loss and has no backward step.
+`Accuracy` scores the output as the accuracy of output with respect to target -- it is not actually a loss and has no backward step.
 
 ### Activation / Neuron Layers
 
@@ -193,7 +191,7 @@ In general, activation / Neuron layers are element-wise operators, taking one bo
 
 #### ReLU / Rectified-Linear and Leaky-ReLU
 
-* LayerType: `RELU`
+* Layer type: `ReLU`
 * CPU implementation: `./src/caffe/layers/relu_layer.cpp`
 * CUDA GPU implementation: `./src/caffe/layers/relu_layer.cu`
 * Parameters (`ReLUParameter relu_param`)
@@ -201,66 +199,66 @@ In general, activation / Neuron layers are element-wise operators, taking one bo
         - `negative_slope` [default 0]: specifies whether to leak the negative part by multiplying it with the slope value rather than setting it to 0.
 * Sample (as seen in `./examples/imagenet/imagenet_train_val.prototxt`)
 
-      layers {
+      layer {
         name: "relu1"
-        type: RELU
+        type: "ReLU"
         bottom: "conv1"
         top: "conv1"
       }
 
-Given an input value x, The `RELU` layer computes the output as x if x > 0 and negative_slope * x if x <= 0. When the negative slope parameter is not set, it is equivalent to the standard ReLU function of taking max(x, 0). It also supports in-place computation, meaning that the bottom and the top blob could be the same to preserve memory consumption.
+Given an input value x, The `ReLU` layer computes the output as x if x > 0 and negative_slope * x if x <= 0. When the negative slope parameter is not set, it is equivalent to the standard ReLU function of taking max(x, 0). It also supports in-place computation, meaning that the bottom and the top blob could be the same to preserve memory consumption.
 
 #### Sigmoid
 
-* LayerType: `SIGMOID`
+* Layer type: `Sigmoid`
 * CPU implementation: `./src/caffe/layers/sigmoid_layer.cpp`
 * CUDA GPU implementation: `./src/caffe/layers/sigmoid_layer.cu`
 * Sample (as seen in `./examples/imagenet/mnist_autoencoder.prototxt`)
 
-      layers {
+      layer {
         name: "encode1neuron"
         bottom: "encode1"
         top: "encode1neuron"
-        type: SIGMOID
+        type: "Sigmoid"
       }
 
-The `SIGMOID` layer computes the output as sigmoid(x) for each input element x.
+The `Sigmoid` layer computes the output as sigmoid(x) for each input element x.
 
 #### TanH / Hyperbolic Tangent
 
-* LayerType: `TANH`
+* Layer type: `TanH`
 * CPU implementation: `./src/caffe/layers/tanh_layer.cpp`
 * CUDA GPU implementation: `./src/caffe/layers/tanh_layer.cu`
 * Sample
 
-      layers {
+      layer {
         name: "layer"
         bottom: "in"
         top: "out"
-        type: TANH
+        type: "TanH"
       }
 
-The `TANH` layer computes the output as tanh(x) for each input element x.
+The `TanH` layer computes the output as tanh(x) for each input element x.
 
 #### Absolute Value
 
-* LayerType: `ABSVAL`
+* Layer type: `AbsVal`
 * CPU implementation: `./src/caffe/layers/absval_layer.cpp`
 * CUDA GPU implementation: `./src/caffe/layers/absval_layer.cu`
 * Sample
 
-      layers {
+      layer {
         name: "layer"
         bottom: "in"
         top: "out"
-        type: ABSVAL
+        type: "AbsVal"
       }
 
-The `ABSVAL` layer computes the output as abs(x) for each input element x.
+The `AbsVal` layer computes the output as abs(x) for each input element x.
 
 #### Power
 
-* LayerType: `POWER`
+* Layer type: `Power`
 * CPU implementation: `./src/caffe/layers/power_layer.cpp`
 * CUDA GPU implementation: `./src/caffe/layers/power_layer.cu`
 * Parameters (`PowerParameter power_param`)
@@ -270,11 +268,11 @@ The `ABSVAL` layer computes the output as abs(x) for each input element x.
         - `shift` [default 0]
 * Sample
 
-      layers {
+      layer {
         name: "layer"
         bottom: "in"
         top: "out"
-        type: POWER
+        type: "Power"
         power_param {
           power: 1
           scale: 1
@@ -282,16 +280,16 @@ The `ABSVAL` layer computes the output as abs(x) for each input element x.
         }
       }
 
-The `POWER` layer computes the output as (shift + scale * x) ^ power for each input element x.
+The `Power` layer computes the output as (shift + scale * x) ^ power for each input element x.
 
 #### BNLL
 
-* LayerType: `BNLL`
+* Layer type: `BNLL`
 * CPU implementation: `./src/caffe/layers/bnll_layer.cpp`
 * CUDA GPU implementation: `./src/caffe/layers/bnll_layer.cu`
 * Sample
 
-      layers {
+      layer {
         name: "layer"
         bottom: "in"
         top: "out"
@@ -309,7 +307,7 @@ Common input preprocessing (mean subtraction, scaling, random cropping, and mirr
 
 #### Database
 
-* LayerType: `DATA`
+* Layer type: `Data`
 * Parameters
     - Required
         - `source`: the name of the directory containing the database
@@ -322,7 +320,7 @@ Common input preprocessing (mean subtraction, scaling, random cropping, and mirr
 
 #### In-Memory
 
-* LayerType: `MEMORY_DATA`
+* Layer type: `MemoryData`
 * Parameters
     - Required
         - `batch_size`, `channels`, `height`, `width`: specify the size of input chunks to read from memory
@@ -331,7 +329,7 @@ The memory data layer reads data directly from memory, without copying it. In or
 
 #### HDF5 Input
 
-* LayerType: `HDF5_DATA`
+* Layer type: `HDF5Data`
 * Parameters
     - Required
         - `source`: the name of the file to read from
@@ -339,7 +337,7 @@ The memory data layer reads data directly from memory, without copying it. In or
 
 #### HDF5 Output
 
-* LayerType: `HDF5_OUTPUT`
+* Layer type: `HDF5Output`
 * Parameters
     - Required
         - `file_name`: name of file to write to
@@ -348,7 +346,7 @@ The HDF5 output layer performs the opposite function of the other layers in this
 
 #### Images
 
-* LayerType: `IMAGE_DATA`
+* Layer type: `ImageData`
 * Parameters
     - Required
         - `source`: name of a text file, with each line giving an image filename and label
@@ -360,17 +358,17 @@ The HDF5 output layer performs the opposite function of the other layers in this
 
 #### Windows
 
-`WINDOW_DATA`
+`WindowData`
 
 #### Dummy
 
-`DUMMY_DATA` is for development and debugging. See `DummyDataParameter`.
+`DummyData` is for development and debugging. See `DummyDataParameter`.
 
 ### Common Layers
 
 #### Inner Product
 
-* LayerType: `INNER_PRODUCT`
+* Layer type: `InnerProduct`
 * CPU implementation: `./src/caffe/layers/inner_product_layer.cpp`
 * CUDA GPU implementation: `./src/caffe/layers/inner_product_layer.cu`
 * Parameters (`InnerProductParameter inner_product_param`)
@@ -387,13 +385,13 @@ The HDF5 output layer performs the opposite function of the other layers in this
     - `n * c_o * 1 * 1`
 * Sample
 
-      layers {
+      layer {
         name: "fc8"
-        type: INNER_PRODUCT
-        blobs_lr: 1          # learning rate multiplier for the filters
-        blobs_lr: 2          # learning rate multiplier for the biases
-        weight_decay: 1      # weight decay multiplier for the filters
-        weight_decay: 0      # weight decay multiplier for the biases
+        type: "InnerProduct"
+        # learning rate and decay multipliers for the weights
+        param { lr_mult: 1 decay_mult: 1 }
+        # learning rate and decay multipliers for the biases
+        param { lr_mult: 2 decay_mult: 0 }
         inner_product_param {
           num_output: 1000
           weight_filler {
@@ -409,59 +407,118 @@ The HDF5 output layer performs the opposite function of the other layers in this
         top: "fc8"
       }
 
-The `INNER_PRODUCT` layer (also usually referred to as the fully connected layer) treats the input as a simple vector and produces an output in the form of a single vector (with the blob's height and width set to 1).
+The `InnerProduct` layer (also usually referred to as the fully connected layer) treats the input as a simple vector and produces an output in the form of a single vector (with the blob's height and width set to 1).
 
 #### Splitting
 
-The `SPLIT` layer is a utility layer that splits an input blob to multiple output blobs. This is used when a blob is fed into multiple output layers.
+The `Split` layer is a utility layer that splits an input blob to multiple output blobs. This is used when a blob is fed into multiple output layers.
 
 #### Flattening
 
-The `FLATTEN` layer is a utility layer that flattens an input of shape `n * c * h * w` to a simple vector output of shape `n * (c*h*w) * 1 * 1`.
+The `Flatten` layer is a utility layer that flattens an input of shape `n * c * h * w` to a simple vector output of shape `n * (c*h*w)`
+
+#### Reshape
+
+* Layer type: `Reshape`
+* Implementation: `./src/caffe/layers/reshape_layer.cpp`
+* Parameters (`ReshapeParameter reshape_param`)
+    - Optional: (also see detailed description below)
+        - `shape`
+
+* Input
+    - a single blob with arbitrary dimensions
+* Output
+    - the same blob, with modified dimensions, as specified by `reshape_param`
+
+* Sample
+
+        layer {
+          name: "reshape"
+          type: "Reshape"
+          bottom: "input"
+          top: "output"
+          reshape_param {
+            shape {
+              dim: 0  # copy the dimension from below
+              dim: 2
+              dim: 3
+              dim: -1 # infer it from the other dimensions
+            }
+          }
+        }
+
+The `Reshape` layer can be used to change the dimensions of its input, without changing its data. Just like the `Flatten` layer, only the dimensions are changed; no data is copied in the process.
+
+Output dimensions are specified by the `ReshapeParam` proto. Positive numbers are used directly, setting the corresponding dimension of the output blob. In addition, two special values are accepted for any of the target dimension values:
+
+* **0** means "copy the respective dimension of the bottom layer". That is, if the bottom has 2 as its 1st dimension, the top will have 2 as its 1st dimension as well, given `dim: 0` as the 1st target dimension.
+* **-1** stands for "infer this from the other dimensions". This behavior is similar to that of -1 in *numpy*'s or `[]` for *MATLAB*'s reshape: this dimension is calculated to keep the overall element count the same as in the bottom layer. At most one -1 can be used in a reshape operation.
+
+As another example, specifying `reshape_param { shape { dim: 0 dim: -1 } }` makes the layer behave in exactly the same way as the `Flatten` layer.
 
 #### Concatenation
 
-* LayerType: `CONCAT`
+* Layer type: `Concat`
 * CPU implementation: `./src/caffe/layers/concat_layer.cpp`
 * CUDA GPU implementation: `./src/caffe/layers/concat_layer.cu`
 * Parameters (`ConcatParameter concat_param`)
     - Optional
-        - `concat_dim` [default 1]: 0 for concatenation along num and 1 for channels.
+        - `axis` [default 1]: 0 for concatenation along num and 1 for channels.
 * Input
     - `n_i * c_i * h * w` for each input blob i from 1 to K.
 * Output
-    - if `concat_dim = 0`: `(n_1 + n_2 + ... + n_K) * c_1 * h * w`, and all input `c_i` should be the same.
-    - if `concat_dim = 1`: `n_1 * (c_1 + c_2 + ... + c_K) * h * w`, and all input `n_i` should be the same.
+    - if `axis = 0`: `(n_1 + n_2 + ... + n_K) * c_1 * h * w`, and all input `c_i` should be the same.
+    - if `axis = 1`: `n_1 * (c_1 + c_2 + ... + c_K) * h * w`, and all input `n_i` should be the same.
 * Sample
 
-      layers {
+      layer {
         name: "concat"
         bottom: "in1"
         bottom: "in2"
         top: "out"
-        type: CONCAT
+        type: "Concat"
         concat_param {
-          concat_dim: 1
+          axis: 1
         }
       }
 
-The `CONCAT` layer is a utility layer that concatenates its multiple input blobs to one single output blob. Currently, the layer supports concatenation along num or channels only.
+The `Concat` layer is a utility layer that concatenates its multiple input blobs to one single output blob.
 
 #### Slicing
 
-The `SLICE` layer is a utility layer that slices an input layer to multiple output layers along a given dimension (currently num or channel only) with given slice indices.
+The `Slice` layer is a utility layer that slices an input layer to multiple output layers along a given dimension (currently num or channel only) with given slice indices.
+
+* Sample
+
+      layer {
+        name: "slicer_label"
+        type: "Slice"
+        bottom: "label"
+        ## Example of label with a shape N x 3 x 1 x 1
+        top: "label1"
+        top: "label2"
+        top: "label3"
+        slice_param {
+          axis: 1
+          slice_point: 1
+          slice_point: 2
+        }
+      }
+
+`axis` indicates the target axis; `slice_point` indicates indexes in the selected dimension (the number of indices must be equal to the number of top blobs minus one).
+
 
 #### Elementwise Operations
 
-`ELTWISE`
+`Eltwise`
 
 #### Argmax
 
-`ARGMAX`
+`ArgMax`
 
 #### Softmax
 
-`SOFTMAX`
+`Softmax`
 
 #### Mean-Variance Normalization
 
diff --git a/docs/tutorial/loss.md b/docs/tutorial/loss.md
index aac561774bb..d2d0e77fbed 100644
--- a/docs/tutorial/loss.md
+++ b/docs/tutorial/loss.md
@@ -10,30 +10,30 @@ Hence, the goal of learning is to find a setting of the weights that *minimizes*
 The loss in Caffe is computed by the Forward pass of the network.
 Each layer takes a set of input (`bottom`) blobs and produces a set of output (`top`) blobs.
 Some of these layers' outputs may be used in the loss function.
-A typical choice of loss function for one-versus-all classification tasks is the `SOFTMAX_LOSS` function, used in a network definition as follows, for example:
+A typical choice of loss function for one-versus-all classification tasks is the `SoftmaxWithLoss` function, used in a network definition as follows, for example:
 
-    layers {
+    layer {
       name: "loss"
-      type: SOFTMAX_LOSS
+      type: "SoftmaxWithLoss"
       bottom: "pred"
       bottom: "label"
       top: "loss"
     }
 
-In a `SOFTMAX_LOSS` function, the `top` blob is a scalar (dimensions $$1 \times 1 \times 1 \times 1$$) which averages the loss (computed from predicted labels `pred` and actuals labels `label`) over the entire mini-batch.
+In a `SoftmaxWithLoss` function, the `top` blob is a scalar (empty shape) which averages the loss (computed from predicted labels `pred` and actuals labels `label`) over the entire mini-batch.
 
 ### Loss weights
 
-For nets with multiple layers producing a loss (e.g., a network that both classifies the input using a `SOFTMAX_LOSS` layer and reconstructs it using a `EUCLIDEAN_LOSS` layer), *loss weights* can be used to specify their relative importance.
+For nets with multiple layers producing a loss (e.g., a network that both classifies the input using a `SoftmaxWithLoss` layer and reconstructs it using a `EuclideanLoss` layer), *loss weights* can be used to specify their relative importance.
 
-By convention, Caffe layer types with the suffix `_LOSS` contribute to the loss function, but other layers are assumed to be purely used for intermediate computations.
+By convention, Caffe layer types with the suffix `Loss` contribute to the loss function, but other layers are assumed to be purely used for intermediate computations.
 However, any layer can be used as a loss by adding a field `loss_weight: <float>` to a layer definition for each `top` blob produced by the layer.
-Layers with the suffix `_LOSS` have an implicit `loss_weight: 1` for the first `top` blob (and `loss_weight: 0` for any additional `top`s); other layers have an implicit `loss_weight: 0` for all `top`s.
-So, the above `SOFTMAX_LOSS` layer could be equivalently written as:
+Layers with the suffix `Loss` have an implicit `loss_weight: 1` for the first `top` blob (and `loss_weight: 0` for any additional `top`s); other layers have an implicit `loss_weight: 0` for all `top`s.
+So, the above `SoftmaxWithLoss` layer could be equivalently written as:
 
-    layers {
+    layer {
       name: "loss"
-      type: SOFTMAX_LOSS
+      type: "SoftmaxWithLoss"
       bottom: "pred"
       bottom: "label"
       top: "loss"
diff --git a/docs/tutorial/net_layer_blob.md b/docs/tutorial/net_layer_blob.md
index 1f0966f88a4..e8b7bd316a9 100644
--- a/docs/tutorial/net_layer_blob.md
+++ b/docs/tutorial/net_layer_blob.md
@@ -11,22 +11,20 @@ We will go over the details of these components in more detail.
 
 ## Blob storage and communication
 
-A Blob is a wrapper over the actual data being processed and passed along by Caffe, and also under the hood provides synchronization capability between the CPU and the GPU. Mathematically, a blob is a 4-dimensional array that stores things in the order of (Num, Channels, Height and Width), from major to minor, and stored in a C-contiguous fashion.  The main reason for putting Num (the name is due to legacy reasons, and is equivalent to the notation of "batch" as in minibatch SGD).
+A Blob is a wrapper over the actual data being processed and passed along by Caffe, and also under the hood provides synchronization capability between the CPU and the GPU. Mathematically, a blob is an N-dimensional array stored in a C-contiguous fashion.
 
-Caffe stores and communicates data in 4-dimensional arrays called blobs. Blobs provide a unified memory interface, holding data e.g. batches of images, model parameters, and derivatives for optimization.
+Caffe stores and communicates data using blobs. Blobs provide a unified memory interface holding data; e.g., batches of images, model parameters, and derivatives for optimization.
 
 Blobs conceal the computational and mental overhead of mixed CPU/GPU operation by synchronizing from the CPU host to the GPU device as needed. Memory on the host and device is allocated on demand (lazily) for efficient memory usage.
 
-The conventional blob dimensions for data are number N x channel K x height H x width W. Blob memory is row-major in layout so the last / rightmost dimension changes fastest. For example, the value at index (n, k, h, w) is physically located at index ((n * K + k) * H + h) * W + w.
+The conventional blob dimensions for batches of image data are number N x channel K x height H x width W. Blob memory is row-major in layout, so the last / rightmost dimension changes fastest. For example, in a 4D blob, the value at index (n, k, h, w) is physically located at index ((n * K + k) * H + h) * W + w.
 
 - Number / N is the batch size of the data. Batch processing achieves better throughput for communication and device processing. For an ImageNet training batch of 256 images B = 256.
 - Channel / K is the feature dimension e.g. for RGB images K = 3.
 
-Note that although we have designed blobs with its dimensions corresponding to image applications, they are named purely for notational purpose and it is totally valid for you to do non-image applications. For example, if you simply need fully-connected networks like the conventional multi-layer perceptron, use blobs of dimensions (Num, Channels, 1, 1) and call the InnerProductLayer (which we will cover soon).
+Note that although many blobs in Caffe examples are 4D with axes for image applications, it is totally valid to use blobs for non-image applications. For example, if you simply need fully-connected networks like the conventional multi-layer perceptron, use 2D blobs (shape (N, D)) and call the InnerProductLayer (which we will cover soon).
 
-Caffe operations are general with respect to the channel dimension / K. Grayscale and hyperspectral imagery are fine. Caffe can likewise model and process arbitrary vectors in blobs with singleton. That is, the shape of blob holding 1000 vectors of 16 feature dimensions is 1000 x 16 x 1 x 1.
-
-Parameter blob dimensions vary according to the type and configuration of the layer. For a convolution layer with 96 filters of 11 x 11 spatial dimension and 3 inputs the blob is 96 x 3 x 11 x 11. For an inner product / fully-connected layer with 1000 output channels and 1024 input channels the parameter blob is 1 x 1 x 1000 x 1024.
+Parameter blob dimensions vary according to the type and configuration of the layer. For a convolution layer with 96 filters of 11 x 11 spatial dimension and 3 inputs the blob is 96 x 3 x 11 x 11. For an inner product / fully-connected layer with 1000 output channels and 1024 input channels the parameter blob is 1000 x 1024.
 
 For custom data it may be necessary to hack your own input preparation tool or data layer. However once your data is in your job is done. The modularity of layers accomplishes the rest of the work for you.
 
@@ -95,9 +93,9 @@ A simple logistic regression classifier
 is defined by
 
     name: "LogReg"
-    layers {
+    layer {
       name: "mnist"
-      type: DATA
+      type: "Data"
       top: "data"
       top: "label"
       data_param {
@@ -105,18 +103,18 @@ is defined by
         batch_size: 64
       }
     }
-    layers {
+    layer {
       name: "ip"
-      type: INNER_PRODUCT
+      type: "InnerProduct"
       bottom: "data"
       top: "ip"
       inner_product_param {
         num_output: 2
       }
     }
-    layers {
+    layer {
       name: "loss"
-      type: SOFTMAX_LOSS
+      type: "SoftmaxWithLoss"
       bottom: "ip"
       bottom: "label"
       top: "loss"
@@ -135,19 +133,19 @@ Model initialization is handled by `Net::Init()`. The initialization mainly does
     I0902 22:52:17.935807 2079114000 data_layer.cpp:135] Opening leveldb input_leveldb
     I0902 22:52:17.937155 2079114000 data_layer.cpp:195] output data size: 64,1,28,28
     I0902 22:52:17.938570 2079114000 net.cpp:103] Top shape: 64 1 28 28 (50176)
-    I0902 22:52:17.938593 2079114000 net.cpp:103] Top shape: 64 1 1 1 (64)
+    I0902 22:52:17.938593 2079114000 net.cpp:103] Top shape: 64 (64)
     I0902 22:52:17.938611 2079114000 net.cpp:67] Creating Layer ip
     I0902 22:52:17.938617 2079114000 net.cpp:394] ip <- data
     I0902 22:52:17.939177 2079114000 net.cpp:356] ip -> ip
     I0902 22:52:17.939196 2079114000 net.cpp:96] Setting up ip
-    I0902 22:52:17.940289 2079114000 net.cpp:103] Top shape: 64 2 1 1 (128)
+    I0902 22:52:17.940289 2079114000 net.cpp:103] Top shape: 64 2 (128)
     I0902 22:52:17.941270 2079114000 net.cpp:67] Creating Layer loss
     I0902 22:52:17.941305 2079114000 net.cpp:394] loss <- ip
     I0902 22:52:17.941314 2079114000 net.cpp:394] loss <- label
     I0902 22:52:17.941323 2079114000 net.cpp:356] loss -> loss
     # set up the loss and configure the backward pass
     I0902 22:52:17.941328 2079114000 net.cpp:96] Setting up loss
-    I0902 22:52:17.941328 2079114000 net.cpp:103] Top shape: 1 1 1 1 (1)
+    I0902 22:52:17.941328 2079114000 net.cpp:103] Top shape: (1)
     I0902 22:52:17.941329 2079114000 net.cpp:109]     with loss weight 1
     I0902 22:52:17.941779 2079114000 net.cpp:170] loss needs backward computation.
     I0902 22:52:17.941787 2079114000 net.cpp:170] ip needs backward computation.
diff --git a/docs/tutorial/solver.md b/docs/tutorial/solver.md
index 8884ea0e1e8..17f793ef778 100644
--- a/docs/tutorial/solver.md
+++ b/docs/tutorial/solver.md
@@ -6,7 +6,7 @@ title: Solver / Model Optimization
 The solver orchestrates model optimization by coordinating the network's forward inference and backward gradients to form parameter updates that attempt to improve the loss.
 The responsibilities of learning are divided between the Solver for overseeing the optimization and generating parameter updates and the Net for yielding loss and gradients.
 
-The Caffe solvers are Stochastic Gradient Descent (SGD), Adaptive Gradient (ADAGRAD), and Nesterov's Accelerated Gradient (NAG).
+The Caffe solvers are Stochastic Gradient Descent (SGD), Adaptive Gradient (ADAGRAD), and Nesterov's Accelerated Gradient (NESTEROV).
 
 The solver
 
@@ -126,7 +126,7 @@ Note that in practice, for weights $$ W \in \mathcal{R}^d $$, AdaGrad implementa
 
 ### NAG
 
-**Nesterov's accelerated gradient** (`solver_type: NAG`) was proposed by Nesterov [1] as an "optimal" method of convex optimization, achieving a convergence rate of $$ \mathcal{O}(1/t^2) $$ rather than the $$ \mathcal{O}(1/t) $$.
+**Nesterov's accelerated gradient** (`solver_type: NESTEROV`) was proposed by Nesterov [1] as an "optimal" method of convex optimization, achieving a convergence rate of $$ \mathcal{O}(1/t^2) $$ rather than the $$ \mathcal{O}(1/t) $$.
 Though the required assumptions to achieve the $$ \mathcal{O}(1/t^2) $$ convergence typically will not hold for deep networks trained with Caffe (e.g., due to non-smoothness and non-convexity), in practice NAG can be a very effective method for optimizing certain types of deep learning architectures, as demonstrated for deep MNIST autoencoders by Sutskever et al. [2].
 
 The weight update formulas look very similar to the SGD updates given above:
diff --git a/examples/00-classification.ipynb b/examples/00-classification.ipynb
new file mode 100644
index 00000000000..46bbb193fe7
--- /dev/null
+++ b/examples/00-classification.ipynb
@@ -0,0 +1,13187 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Instant Recognition with Caffe\n",
+    "\n",
+    "In this example we'll classify an image with the bundled CaffeNet model based on the network architecture of Krizhevsky et al. for ImageNet. We'll compare CPU and GPU operation then reach into the model to inspect features and the output.\n",
+    "\n",
+    "(These feature visualizations follow the DeCAF visualizations originally by Yangqing Jia.)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First, import required modules, set plotting parameters, and run `./scripts/download_model_binary.py models/bvlc_reference_caffenet` to get the pretrained CaffeNet model if it hasn't already been fetched."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "# Make sure that caffe is on the python path:\n",
+    "caffe_root = '../'  # this file is expected to be in {caffe_root}/examples\n",
+    "import sys\n",
+    "sys.path.insert(0, caffe_root + 'python')\n",
+    "\n",
+    "import caffe\n",
+    "\n",
+    "plt.rcParams['figure.figsize'] = (10, 10)\n",
+    "plt.rcParams['image.interpolation'] = 'nearest'\n",
+    "plt.rcParams['image.cmap'] = 'gray'\n",
+    "\n",
+    "import os\n",
+    "if not os.path.isfile(caffe_root + 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel'):\n",
+    "    print(\"Downloading pre-trained CaffeNet model...\")\n",
+    "    !../scripts/download_model_binary.py ../models/bvlc_reference_caffenet"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Set Caffe to CPU mode, load the net in the test phase for inference, and configure input preprocessing."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "caffe.set_mode_cpu()\n",
+    "net = caffe.Net(caffe_root + 'models/bvlc_reference_caffenet/deploy.prototxt',\n",
+    "                caffe_root + 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel',\n",
+    "                caffe.TEST)\n",
+    "\n",
+    "# input preprocessing: 'data' is the name of the input blob == net.inputs[0]\n",
+    "transformer = caffe.io.Transformer({'data': net.blobs['data'].data.shape})\n",
+    "transformer.set_transpose('data', (2,0,1))\n",
+    "transformer.set_mean('data', np.load(caffe_root + 'python/caffe/imagenet/ilsvrc_2012_mean.npy').mean(1).mean(1)) # mean pixel\n",
+    "transformer.set_raw_scale('data', 255)  # the reference model operates on images in [0,255] range instead of [0,1]\n",
+    "transformer.set_channel_swap('data', (2,1,0))  # the reference model has channels in BGR order instead of RGB"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's start with a simple classification. We'll set a batch of 50 to demonstrate batch processing, even though we'll only be classifying one image. (Note that the batch size can also be changed on-the-fly.)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# set net to batch size of 50\n",
+    "net.blobs['data'].reshape(50,3,227,227)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Feed in the image (with some preprocessing) and classify with a forward pass."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Predicted class is #281.\n"
+     ]
+    }
+   ],
+   "source": [
+    "net.blobs['data'].data[...] = transformer.preprocess('data', caffe.io.load_image(caffe_root + 'examples/images/cat.jpg'))\n",
+    "out = net.forward()\n",
+    "print(\"Predicted class is #{}.\".format(out['prob'].argmax()))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "What did the input look like?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7f665b02ae90>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": [
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+       "u1hkBdaTvtN7EsqyMwsV+78SNz77NctlDOMapXAlSMNVd1+BeZfJoIxjN9MxhjmJcV82MtYyXktc\n",
+       "q/icK90E9csmnc90I/r971u6++RZEMduKMtl/E0Z56RPykgZMDHUJZPJxqVF3Z10AVe1uODR3lpo\n",
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+       "xjkh03mcQJ6vhLy/CfesrBs/DghThWu0rZLISSyXHfSJbfFqxhs7Qx+4S9fxBNRzNvPQ7yC2T6Of\n",
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+       "+ObuVwgcQuw1tt8C8Asi0i4iowDGAVxqQv0CHxD0YWr4IvLaBmKsDzUkJ+R8BcDVlNKf0Vexth8T\n",
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+       "fBLALwF4U0Qua9lXEGv7sREpYgKBQCAQCAT2iR9r7YdAIBAIBAKB/w/iIBUIBAKBQCCwT8RBKhAI\n",
+       "BAKBQGCfiINUIBAIBAKBwD4RB6lAIBAIBAKBfSIOUoFAIBAIBAL7RBykAoFAIBAIBPaJOEgFAoFA\n",
+       "IBAI7BP/B0EEnTIvM42+AAAAAElFTkSuQmCC\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f66a46f8a10>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.imshow(transformer.deprocess('data', net.blobs['data'].data[0]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Adorable, but was our classification correct?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['n02123045 tabby, tabby cat' 'n02123159 tiger cat'\n",
+      " 'n02124075 Egyptian cat' 'n02119022 red fox, Vulpes vulpes'\n",
+      " 'n02127052 lynx, catamount']\n"
+     ]
+    }
+   ],
+   "source": [
+    "# load labels\n",
+    "imagenet_labels_filename = caffe_root + 'data/ilsvrc12/synset_words.txt'\n",
+    "try:\n",
+    "    labels = np.loadtxt(imagenet_labels_filename, str, delimiter='\\t')\n",
+    "except:\n",
+    "    !../data/ilsvrc12/get_ilsvrc_aux.sh\n",
+    "    labels = np.loadtxt(imagenet_labels_filename, str, delimiter='\\t')\n",
+    "\n",
+    "# sort top k predictions from softmax output\n",
+    "top_k = net.blobs['prob'].data[0].flatten().argsort()[-1:-6:-1]\n",
+    "print labels[top_k]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Indeed! But how long did it take?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1 loops, best of 3: 7.14 s per loop\n"
+     ]
+    }
+   ],
+   "source": [
+    "# CPU mode\n",
+    "net.forward()  # call once for allocation\n",
+    "%timeit net.forward()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "That's a while, even for a batch size of 50 images. Let's switch to GPU mode."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "10 loops, best of 3: 90.9 ms per loop\n"
+     ]
+    }
+   ],
+   "source": [
+    "# GPU mode\n",
+    "caffe.set_device(0)\n",
+    "caffe.set_mode_gpu()\n",
+    "net.forward()  # call once for allocation\n",
+    "%timeit net.forward()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Much better. Now let's look at the net in more detail.\n",
+    "\n",
+    "First, the layer features and their shapes (1 is the batch size, corresponding to the single input image in this example)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[('data', (50, 3, 227, 227)),\n",
+       " ('conv1', (50, 96, 55, 55)),\n",
+       " ('pool1', (50, 96, 27, 27)),\n",
+       " ('norm1', (50, 96, 27, 27)),\n",
+       " ('conv2', (50, 256, 27, 27)),\n",
+       " ('pool2', (50, 256, 13, 13)),\n",
+       " ('norm2', (50, 256, 13, 13)),\n",
+       " ('conv3', (50, 384, 13, 13)),\n",
+       " ('conv4', (50, 384, 13, 13)),\n",
+       " ('conv5', (50, 256, 13, 13)),\n",
+       " ('pool5', (50, 256, 6, 6)),\n",
+       " ('fc6', (50, 4096)),\n",
+       " ('fc7', (50, 4096)),\n",
+       " ('fc8', (50, 1000)),\n",
+       " ('prob', (50, 1000))]"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "[(k, v.data.shape) for k, v in net.blobs.items()]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The parameters and their shapes. The parameters are `net.params['name'][0]` while biases are `net.params['name'][1]`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[('conv1', (96, 3, 11, 11)),\n",
+       " ('conv2', (256, 48, 5, 5)),\n",
+       " ('conv3', (384, 256, 3, 3)),\n",
+       " ('conv4', (384, 192, 3, 3)),\n",
+       " ('conv5', (256, 192, 3, 3)),\n",
+       " ('fc6', (4096, 9216)),\n",
+       " ('fc7', (4096, 4096)),\n",
+       " ('fc8', (1000, 4096))]"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "[(k, v[0].data.shape) for k, v in net.params.items()]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Helper functions for visualization"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# take an array of shape (n, height, width) or (n, height, width, channels)\n",
+    "# and visualize each (height, width) thing in a grid of size approx. sqrt(n) by sqrt(n)\n",
+    "def vis_square(data, padsize=1, padval=0):\n",
+    "    data -= data.min()\n",
+    "    data /= data.max()\n",
+    "    \n",
+    "    # force the number of filters to be square\n",
+    "    n = int(np.ceil(np.sqrt(data.shape[0])))\n",
+    "    padding = ((0, n ** 2 - data.shape[0]), (0, padsize), (0, padsize)) + ((0, 0),) * (data.ndim - 3)\n",
+    "    data = np.pad(data, padding, mode='constant', constant_values=(padval, padval))\n",
+    "    \n",
+    "    # tile the filters into an image\n",
+    "    data = data.reshape((n, n) + data.shape[1:]).transpose((0, 2, 1, 3) + tuple(range(4, data.ndim + 1)))\n",
+    "    data = data.reshape((n * data.shape[1], n * data.shape[3]) + data.shape[4:])\n",
+    "    \n",
+    "    plt.imshow(data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The input image"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The first layer filters, `conv1`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": [
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+       "EXCWLhlLmgDeAhYpyzc+b6Hs+05u+xIRERFRQ64jEREREVFDmqiIiIiIGtJERURERNSQJioiIiKi\n",
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+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7ffaf3877710>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# the parameters are a list of [weights, biases]\n",
+    "filters = net.params['conv1'][0].data\n",
+    "vis_square(filters.transpose(0, 2, 3, 1))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The first layer output, `conv1` (rectified responses of the filters above, first 36 only)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
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+       "PSJupCIiIiIiIiIi9oi4kYqIiIiIiIiI2CPiRioiIiIiIiIiYo+IG6mIiIiIiIiIiD0ibqQiIiIi\n",
+       "IiIiIvaI/w/CAMCOMj3yxQAAAABJRU5ErkJggg==\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7ffb01f952d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "feat = net.blobs['conv1'].data[0, :36]\n",
+    "vis_square(feat, padval=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The second layer filters, `conv2`\n",
+    "\n",
+    "There are 256 filters, each of which has dimension 5 x 5 x 48. We show only the first 48 filters, with each channel shown separately, so that each filter is a row."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
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+       "KBQKhUJhg6jK5oVCoVAoFAobRB2kCoVCoVAoFDaIOkgVCoVCoVAobBB1kCoUCoVCoVDYIOogVSgU\n",
+       "CoVCobBB1EGqUCgUCoVCYYOog1ShUCgUCoXCBlEHqUKhUCgUCoUN4v8BaJ+sNGqpC6EAAAAASUVO\n",
+       "RK5CYII=\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7ffb01f42290>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "filters = net.params['conv2'][0].data\n",
+    "vis_square(filters[:48].reshape(48**2, 5, 5))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The second layer output, `conv2` (rectified, only the first 36 of 256 channels)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
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+       "MQ8HvQAAAABJRU5ErkJggg==\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7ffb01f42310>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "feat = net.blobs['conv2'].data[0, :36]\n",
+    "vis_square(feat, padval=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The third layer output, `conv3` (rectified, all 384 channels)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
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+       "S7i+ZxWrD2q4WAf6h+T6mDtd13WSrpP0cN/3n1jzkfvIgsnqwv3jYBa6aXHf9//Sdd27Jf1PST8h\n",
+       "6TpH7C2cn5F0w4E+on8l6b/3fX9z13X3SvrrrusulfSEDkRmmBnTdd1fStoh6d92XfcPkv6rpI9p\n",
+       "nbLv+/7hruv+WtLDkv5F0m/03opgpqxTH1dJelXXdafrwLLEfkm/Lrk+FsR2Se+QtLfruvtX37tS\n",
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+       "iZQxxhhjTCOeSBljjDHGNOKJlDHGGGNMI55IGWOMMcY08v8BGKNoaFbqcjsAAAAASUVORK5CYII=\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7ffb01dfb3d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "feat = net.blobs['conv3'].data[0]\n",
+    "vis_square(feat, padval=0.5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The fourth layer output, `conv4` (rectified, all 384 channels)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
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+       "ZZ9z3pNS+o6kPZL+JOkj2VsRjJVl6uM6SRemlN6s+WWJfZI+LLk+psTbJV0uaXdK6ZHBe5+S+8gs\n",
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+       "xhhjTCOeSBljjDHGNOKJlDHGGGNMI55IGWOMMcY08v87vVmfz9SwBgAAAABJRU5ErkJggg==\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7ffb01cf9110>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "feat = net.blobs['conv4'].data[0]\n",
+    "vis_square(feat, padval=0.5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The fifth layer output, `conv5` (rectified, all 256 channels)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
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+       "ugq8BbxDixhHxIKkk8AC0ABeCi+rUCst4v02cEDSXpJbOL8BL4Lj3SP2A88BFyTNp23HcI5v4iVi\n",
+       "zMzMzArqizkezMzMzLrBhZSZmZlZQS6kzMzMzApyIWVmZmZWkAspMzMzs4JcSJmZmZkV5ELKzMzM\n",
+       "rCAXUmZmZmYF/Q0kXnLFpKb3UQAAAABJRU5ErkJggg==\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7ffb01c2f0d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "feat = net.blobs['conv5'].data[0]\n",
+    "vis_square(feat, padval=0.5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The fifth layer after pooling, `pool5`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
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+       "qtqw+r51v1x+AIPzka/13p9+KCJfUNVaVb1ORG4UkZdzyC8zuXzti6r+TyLy53L5SYu/cs79b5kn\n",
+       "sYGp6sdF5CcickT+5VbrI3L5Yn9SRPaISJ+IfM45N5ZHjhuRqt4nIl91zj2oqh3CuciFqn5ILjf5\n",
+       "14rIL0Xk9+TyexXnIweq+rBc/qBeEZHXRORfi0iLcD4yoapPiMh9IrJFLvc//QcR+YGsc/xV9U9E\n",
+       "5F+JyJJcbhX5cQ5pZ4bvzgMAAIhQ0fMbAAAASoUiCgAAIAJFFAAAQASKKAAAgAgUUQAAABEoogAA\n",
+       "ACJQRAEAAET4/wEtHY5P7rJ1tgAAAABJRU5ErkJggg==\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7ffb01bbe590>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "feat = net.blobs['pool5'].data[0]\n",
+    "vis_square(feat, padval=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The first fully connected layer, `fc6` (rectified)\n",
+    "\n",
+    "We show the output values and the histogram of the positive values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
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+       "gCRLtA9Nz0zLVlU9TnIeMx8uyTJwv6pWzXycJIu0p1bQXl09raoNMx8ryRXaQI4Z4Atwj9ZbumTu\n",
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+       "/QRSxC44KICduwAAAABJRU5ErkJggg==\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7ffb01ae5a90>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "feat = net.blobs['fc6'].data[0]\n",
+    "plt.subplot(2, 1, 1)\n",
+    "plt.plot(feat.flat)\n",
+    "plt.subplot(2, 1, 2)\n",
+    "_ = plt.hist(feat.flat[feat.flat > 0], bins=100)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The second fully connected layer, `fc7` (rectified)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
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+       "ppQukPRMLapmnzh1QJiHtPhFDtccrPN2SedJOl/S7ZLePG04yIntkyW9V9JrU0qfK3/W5/oyRHL1\n",
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+       "K6V0RfF2yPVliOTqw5IeYPtc28dJ+hZJVw6wHGwB2yfaPqV4fZKkp0u6cf1c2HFXSrq4eH2xpCvW\n",
+       "TIsdV9wgl54nri+QZNuSLpX08ZTSW0sfhVxfBhnnyvYzJb1Vdw8w+tPhC8FWsH2eFrVV0mJQ23dz\n",
+       "vGDJ9nskPUnS6Vr0f/hRSf+vpF+V9BWSbpH0gpTS30wVI/JRcbxcImm/Fk2CSdLNkl5R6lODHWX7\n",
+       "CZL+UNINurvp7w1aPHGm9/WFQUQBAAACDTKIKAAAwK4iuQIAAAhEcgUAABCI5AoAACAQyRUAAEAg\n",
+       "kisAAIBAJFcAAACBSK4AAAAC/f9A40wIgLpJlQAAAABJRU5ErkJggg==\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7ffb01953f90>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "feat = net.blobs['fc7'].data[0]\n",
+    "plt.subplot(2, 1, 1)\n",
+    "plt.plot(feat.flat)\n",
+    "plt.subplot(2, 1, 2)\n",
+    "_ = plt.hist(feat.flat[feat.flat > 0], bins=100)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The final probability output, `prob`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7ffb01f12a50>]"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
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+       "r1y/Zn93/S7mSvJDG4/Z17BfDGsx11PhZUl+MMnHquoj6+vekuR/TPKPqur1Se5L8v1J0t0fr6p/\n",
+       "lNU3m4eT/MW2EN1S7OwH++70+NEk71j/kPpvs1oI+0zsv8Xr7o9W1T/IarDhkSS/muSnkjw59t/i\n",
+       "VNXPZfXrFr+qqu5P8uO5uO+VfzHJ38vqd6S+p7v/2davbT8DAJw865AAAAwgwgAABhBhAAADiDAA\n",
+       "gAFEGADAACIMAGAAEQYAMIAIAwAY4P8Hf+iH2xY5ngUAAAAASUVORK5CYII=\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7ffb019b9dd0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "feat = net.blobs['prob'].data[0]\n",
+    "plt.plot(feat.flat)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's see the top 5 predicted labels."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['n02123045 tabby, tabby cat' 'n02123159 tiger cat'\n",
+      " 'n02124075 Egyptian cat' 'n02119022 red fox, Vulpes vulpes'\n",
+      " 'n02127052 lynx, catamount']\n"
+     ]
+    }
+   ],
+   "source": [
+    "# load labels\n",
+    "imagenet_labels_filename = caffe_root + 'data/ilsvrc12/synset_words.txt'\n",
+    "try:\n",
+    "    labels = np.loadtxt(imagenet_labels_filename, str, delimiter='\\t')\n",
+    "except:\n",
+    "    !../data/ilsvrc12/get_ilsvrc_aux.sh\n",
+    "    labels = np.loadtxt(imagenet_labels_filename, str, delimiter='\\t')\n",
+    "\n",
+    "# sort top k predictions from softmax output\n",
+    "top_k = net.blobs['prob'].data[0].flatten().argsort()[-1:-6:-1]\n",
+    "print labels[top_k]"
+   ]
+  }
+ ],
+ "metadata": {
+  "description": "Instant recognition with a pre-trained model and a tour of the net interface for visualizing features and parameters layer-by-layer.",
+  "example_name": "Image Classification and Filter Visualization",
+  "include_in_docs": true,
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.9"
+  },
+  "priority": 1
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/examples/01-learning-lenet.ipynb b/examples/01-learning-lenet.ipynb
new file mode 100644
index 00000000000..3562c7adaf2
--- /dev/null
+++ b/examples/01-learning-lenet.ipynb
@@ -0,0 +1,5196 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Python solving with LeNet\n",
+    "\n",
+    "In this example, we'll explore learning with Caffe in Python, using the fully-exposed `Solver` interface."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "os.chdir('..')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "sys.path.insert(0, './python')\n",
+    "import caffe\n",
+    "\n",
+    "from pylab import *\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We'll be running the provided LeNet example (make sure you've downloaded the data and created the databases, as below)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Downloading...\n",
+      "--2015-06-30 14:41:56--  http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n",
+      "Resolving yann.lecun.com... 128.122.47.89\n",
+      "Connecting to yann.lecun.com|128.122.47.89|:80... connected.\n",
+      "HTTP request sent, awaiting response... 200 OK\n",
+      "Length: 9912422 (9.5M) [application/x-gzip]\n",
+      "Saving to: 'train-images-idx3-ubyte.gz'\n",
+      "\n",
+      "train-images-idx3-u 100%[=====================>]   9.45M   146KB/s   in 57s    \n",
+      "\n",
+      "2015-06-30 14:42:53 (171 KB/s) - 'train-images-idx3-ubyte.gz' saved [9912422/9912422]\n",
+      "\n",
+      "--2015-06-30 14:42:53--  http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n",
+      "Resolving yann.lecun.com... 128.122.47.89\n",
+      "Connecting to yann.lecun.com|128.122.47.89|:80... connected.\n",
+      "HTTP request sent, awaiting response... 200 OK\n",
+      "Length: 28881 (28K) [application/x-gzip]\n",
+      "Saving to: 'train-labels-idx1-ubyte.gz'\n",
+      "\n",
+      "train-labels-idx1-u 100%[=====================>]  28.20K   107KB/s   in 0.3s   \n",
+      "\n",
+      "2015-06-30 14:42:53 (107 KB/s) - 'train-labels-idx1-ubyte.gz' saved [28881/28881]\n",
+      "\n",
+      "--2015-06-30 14:42:53--  http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\n",
+      "Resolving yann.lecun.com... 128.122.47.89\n",
+      "Connecting to yann.lecun.com|128.122.47.89|:80... connected.\n",
+      "HTTP request sent, awaiting response... 200 OK\n",
+      "Length: 1648877 (1.6M) [application/x-gzip]\n",
+      "Saving to: 't10k-images-idx3-ubyte.gz'\n",
+      "\n",
+      "t10k-images-idx3-ub 100%[=====================>]   1.57M   205KB/s   in 8.2s   \n",
+      "\n",
+      "2015-06-30 14:43:02 (197 KB/s) - 't10k-images-idx3-ubyte.gz' saved [1648877/1648877]\n",
+      "\n",
+      "--2015-06-30 14:43:02--  http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n",
+      "Resolving yann.lecun.com... 128.122.47.89\n",
+      "Connecting to yann.lecun.com|128.122.47.89|:80... connected.\n",
+      "HTTP request sent, awaiting response... 200 OK\n",
+      "Length: 4542 (4.4K) [application/x-gzip]\n",
+      "Saving to: 't10k-labels-idx1-ubyte.gz'\n",
+      "\n",
+      "t10k-labels-idx1-ub 100%[=====================>]   4.44K  26.9KB/s   in 0.2s   \n",
+      "\n",
+      "2015-06-30 14:43:02 (26.9 KB/s) - 't10k-labels-idx1-ubyte.gz' saved [4542/4542]\n",
+      "\n",
+      "Unzipping...\n",
+      "Done.\n",
+      "Creating lmdb...\n",
+      "Done.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Download and prepare data\n",
+    "!data/mnist/get_mnist.sh\n",
+    "!examples/mnist/create_mnist.sh"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We need two external files to help out:\n",
+    "* the net prototxt, defining the architecture and pointing to the train/test data\n",
+    "* the solver prototxt, defining the learning parameters\n",
+    "\n",
+    "We start with the net. We'll write the net in a succinct and natural way as Python code that serializes to Caffe's protobuf model format.\n",
+    "\n",
+    "This network expects to read from pregenerated LMDBs, but reading directly from `ndarray`s is also possible using `MemoryDataLayer`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from caffe import layers as L\n",
+    "from caffe import params as P\n",
+    "\n",
+    "def lenet(lmdb, batch_size):\n",
+    "    # our version of LeNet: a series of linear and simple nonlinear transformations\n",
+    "    n = caffe.NetSpec()\n",
+    "    n.data, n.label = L.Data(batch_size=batch_size, backend=P.Data.LMDB, source=lmdb,\n",
+    "                             transform_param=dict(scale=1./255), ntop=2)\n",
+    "    n.conv1 = L.Convolution(n.data, kernel_size=5, num_output=20, weight_filler=dict(type='xavier'))\n",
+    "    n.pool1 = L.Pooling(n.conv1, kernel_size=2, stride=2, pool=P.Pooling.MAX)\n",
+    "    n.conv2 = L.Convolution(n.pool1, kernel_size=5, num_output=50, weight_filler=dict(type='xavier'))\n",
+    "    n.pool2 = L.Pooling(n.conv2, kernel_size=2, stride=2, pool=P.Pooling.MAX)\n",
+    "    n.ip1 = L.InnerProduct(n.pool2, num_output=500, weight_filler=dict(type='xavier'))\n",
+    "    n.relu1 = L.ReLU(n.ip1, in_place=True)\n",
+    "    n.ip2 = L.InnerProduct(n.relu1, num_output=10, weight_filler=dict(type='xavier'))\n",
+    "    n.loss = L.SoftmaxWithLoss(n.ip2, n.label)\n",
+    "    return n.to_proto()\n",
+    "    \n",
+    "with open('examples/mnist/lenet_auto_train.prototxt', 'w') as f:\n",
+    "    f.write(str(lenet('examples/mnist/mnist_train_lmdb', 64)))\n",
+    "    \n",
+    "with open('examples/mnist/lenet_auto_test.prototxt', 'w') as f:\n",
+    "    f.write(str(lenet('examples/mnist/mnist_test_lmdb', 100)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The net has been written to disk in more verbose but human-readable serialization format using Google's protobuf library. You can read, write, and modify this description directly. Let's take a look at the train net."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "layer {\r\n",
+      "  name: \"data\"\r\n",
+      "  type: \"Data\"\r\n",
+      "  top: \"data\"\r\n",
+      "  top: \"label\"\r\n",
+      "  transform_param {\r\n",
+      "    scale: 0.00392156862745\r\n",
+      "  }\r\n",
+      "  data_param {\r\n",
+      "    source: \"examples/mnist/mnist_train_lmdb\"\r\n",
+      "    batch_size: 64\r\n",
+      "    backend: LMDB\r\n",
+      "  }\r\n",
+      "}\r\n",
+      "layer {\r\n",
+      "  name: \"conv1\"\r\n",
+      "  type: \"Convolution\"\r\n",
+      "  bottom: \"data\"\r\n",
+      "  top: \"conv1\"\r\n",
+      "  convolution_param {\r\n",
+      "    num_output: 20\r\n",
+      "    kernel_size: 5\r\n",
+      "    weight_filler {\r\n",
+      "      type: \"xavier\"\r\n",
+      "    }\r\n",
+      "  }\r\n",
+      "}\r\n",
+      "layer {\r\n",
+      "  name: \"pool1\"\r\n",
+      "  type: \"Pooling\"\r\n",
+      "  bottom: \"conv1\"\r\n",
+      "  top: \"pool1\"\r\n",
+      "  pooling_param {\r\n",
+      "    pool: MAX\r\n",
+      "    kernel_size: 2\r\n",
+      "    stride: 2\r\n",
+      "  }\r\n",
+      "}\r\n",
+      "layer {\r\n",
+      "  name: \"conv2\"\r\n",
+      "  type: \"Convolution\"\r\n",
+      "  bottom: \"pool1\"\r\n",
+      "  top: \"conv2\"\r\n",
+      "  convolution_param {\r\n",
+      "    num_output: 50\r\n",
+      "    kernel_size: 5\r\n",
+      "    weight_filler {\r\n",
+      "      type: \"xavier\"\r\n",
+      "    }\r\n",
+      "  }\r\n",
+      "}\r\n",
+      "layer {\r\n",
+      "  name: \"pool2\"\r\n",
+      "  type: \"Pooling\"\r\n",
+      "  bottom: \"conv2\"\r\n",
+      "  top: \"pool2\"\r\n",
+      "  pooling_param {\r\n",
+      "    pool: MAX\r\n",
+      "    kernel_size: 2\r\n",
+      "    stride: 2\r\n",
+      "  }\r\n",
+      "}\r\n",
+      "layer {\r\n",
+      "  name: \"ip1\"\r\n",
+      "  type: \"InnerProduct\"\r\n",
+      "  bottom: \"pool2\"\r\n",
+      "  top: \"ip1\"\r\n",
+      "  inner_product_param {\r\n",
+      "    num_output: 500\r\n",
+      "    weight_filler {\r\n",
+      "      type: \"xavier\"\r\n",
+      "    }\r\n",
+      "  }\r\n",
+      "}\r\n",
+      "layer {\r\n",
+      "  name: \"relu1\"\r\n",
+      "  type: \"ReLU\"\r\n",
+      "  bottom: \"ip1\"\r\n",
+      "  top: \"ip1\"\r\n",
+      "}\r\n",
+      "layer {\r\n",
+      "  name: \"ip2\"\r\n",
+      "  type: \"InnerProduct\"\r\n",
+      "  bottom: \"ip1\"\r\n",
+      "  top: \"ip2\"\r\n",
+      "  inner_product_param {\r\n",
+      "    num_output: 10\r\n",
+      "    weight_filler {\r\n",
+      "      type: \"xavier\"\r\n",
+      "    }\r\n",
+      "  }\r\n",
+      "}\r\n",
+      "layer {\r\n",
+      "  name: \"loss\"\r\n",
+      "  type: \"SoftmaxWithLoss\"\r\n",
+      "  bottom: \"ip2\"\r\n",
+      "  bottom: \"label\"\r\n",
+      "  top: \"loss\"\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat examples/mnist/lenet_auto_train.prototxt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now let's see the learning parameters, which are also written as a `prototxt` file. We're using SGD with momentum, weight decay, and a specific learning rate schedule."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "# The train/test net protocol buffer definition\r\n",
+      "train_net: \"examples/mnist/lenet_auto_train.prototxt\"\r\n",
+      "test_net: \"examples/mnist/lenet_auto_test.prototxt\"\r\n",
+      "# test_iter specifies how many forward passes the test should carry out.\r\n",
+      "# In the case of MNIST, we have test batch size 100 and 100 test iterations,\r\n",
+      "# covering the full 10,000 testing images.\r\n",
+      "test_iter: 100\r\n",
+      "# Carry out testing every 500 training iterations.\r\n",
+      "test_interval: 500\r\n",
+      "# The base learning rate, momentum and the weight decay of the network.\r\n",
+      "base_lr: 0.01\r\n",
+      "momentum: 0.9\r\n",
+      "weight_decay: 0.0005\r\n",
+      "# The learning rate policy\r\n",
+      "lr_policy: \"inv\"\r\n",
+      "gamma: 0.0001\r\n",
+      "power: 0.75\r\n",
+      "# Display every 100 iterations\r\n",
+      "display: 100\r\n",
+      "# The maximum number of iterations\r\n",
+      "max_iter: 10000\r\n",
+      "# snapshot intermediate results\r\n",
+      "snapshot: 5000\r\n",
+      "snapshot_prefix: \"examples/mnist/lenet\"\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat examples/mnist/lenet_auto_solver.prototxt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's pick a device and load the solver. We'll use SGD (with momentum), but Adagrad and Nesterov's accelerated gradient are also available."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "caffe.set_device(0)\n",
+    "caffe.set_mode_gpu()\n",
+    "solver = caffe.SGDSolver('examples/mnist/lenet_auto_solver.prototxt')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To get an idea of the architecture of our net, we can check the dimensions of the intermediate features (blobs) and parameters (these will also be useful to refer to when manipulating data later)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[('data', (64, 1, 28, 28)),\n",
+       " ('label', (64,)),\n",
+       " ('conv1', (64, 20, 24, 24)),\n",
+       " ('pool1', (64, 20, 12, 12)),\n",
+       " ('conv2', (64, 50, 8, 8)),\n",
+       " ('pool2', (64, 50, 4, 4)),\n",
+       " ('ip1', (64, 500)),\n",
+       " ('ip2', (64, 10)),\n",
+       " ('loss', ())]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# each output is (batch size, feature dim, spatial dim)\n",
+    "[(k, v.data.shape) for k, v in solver.net.blobs.items()]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[('conv1', (20, 1, 5, 5)),\n",
+       " ('conv2', (50, 20, 5, 5)),\n",
+       " ('ip1', (500, 800)),\n",
+       " ('ip2', (10, 500))]"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# just print the weight sizes (not biases)\n",
+    "[(k, v[0].data.shape) for k, v in solver.net.params.items()]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Before taking off, let's check that everything is loaded as we expect. We'll run a forward pass on the train and test nets and check that they contain our data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'loss': array(2.301163673400879, dtype=float32)}"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "solver.net.forward()  # train net\n",
+    "solver.test_nets[0].forward()  # test net (there can be more than one)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 5.  0.  4.  1.  9.  2.  1.  3.]\n"
+     ]
+    },
+    {
+     "data": {
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+       "40F9c+84y1w99q1FkiSvtArw3/jdUv5b3VcPq027+v/nYlzFymDfBAqFEDlCCD3wfVbqQH7rEUKY\n",
+       "hBCW1cdm4CgruVjkOpkQZZ3M32Me1DcfAj8QQuiFELlAIXB9C9r3zLBqeGTW5vn51vbVY9Smhedk\n",
+       "XD12PuwnQZKkRSHEPwLOsKIY+WtJku7E4lzPIWnAqdWyWHHA30qS9JkQ4iZPWSfz9wEhxN8BB4AU\n",
+       "IcQQ8G95QA1RSZLahRC/AtqBReAfrs4svxXcp6/+HfCSEGJdnh/41vfVE9WmfZb7Sg1NV1FRUXlO\n",
+       "UCMdVVRUVJ4TVIOtoqKi8pygGmwVFRWV5wTVYKuoqKg8J6gGW0VFReU5QTXYKioqKs8JqsFWUVFR\n",
+       "eU5QDbaKiorKc8L/DzAr6bE92WeRAAAAAElFTkSuQmCC\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f7939901710>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# we use a little trick to tile the first eight images\n",
+    "imshow(solver.net.blobs['data'].data[:8, 0].transpose(1, 0, 2).reshape(28, 8*28), cmap='gray')\n",
+    "print solver.net.blobs['label'].data[:8]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 7.  2.  1.  0.  4.  1.  4.  9.]\n"
+     ]
+    },
+    {
+     "data": {
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+       "AAAASUVORK5CYII=\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f7939901490>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "imshow(solver.test_nets[0].blobs['data'].data[:8, 0].transpose(1, 0, 2).reshape(28, 8*28), cmap='gray')\n",
+    "print solver.test_nets[0].blobs['label'].data[:8]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Both train and test nets seem to be loading data, and to have correct labels.\n",
+    "\n",
+    "Let's take one step of (minibatch) SGD and see what happens."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "solver.step(1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Do we have gradients propagating through our filters? Let's see the updates to the first layer, shown here as a $4 \\times 5$ grid of $5 \\times 5$ filters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7f79383819d0>"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
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+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f7939901850>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "imshow(solver.net.params['conv1'][0].diff[:, 0].reshape(4, 5, 5, 5)\n",
+    "       .transpose(0, 2, 1, 3).reshape(4*5, 5*5), cmap='gray')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Something is happening. Let's run the net for a while, keeping track of a few things as it goes.\n",
+    "Note that this process will be the same as if training through the `caffe` binary. In particular:\n",
+    "* logging will continue to happen as normal\n",
+    "* snapshots will be taken at the interval specified in the solver prototxt (here, every 5000 iterations)\n",
+    "* testing will happen at the interval specified (here, every 500 iterations)\n",
+    "\n",
+    "Since we have control of the loop in Python, we're free to compute additional things as we go, as we show below. We can do many other things as well, for example:\n",
+    "* write a custom stopping criterion\n",
+    "* change the solving process by updating the net in the loop"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration 0 testing...\n",
+      "Iteration 25 testing...\n",
+      "Iteration 50 testing...\n",
+      "Iteration 75 testing...\n",
+      "Iteration 100 testing...\n",
+      "Iteration 125 testing...\n",
+      "Iteration 150 testing...\n",
+      "Iteration 175 testing...\n",
+      "CPU times: user 12.3 s, sys: 3.96 s, total: 16.2 s\n",
+      "Wall time: 15.7 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "niter = 200\n",
+    "test_interval = 25\n",
+    "# losses will also be stored in the log\n",
+    "train_loss = zeros(niter)\n",
+    "test_acc = zeros(int(np.ceil(niter / test_interval)))\n",
+    "output = zeros((niter, 8, 10))\n",
+    "\n",
+    "# the main solver loop\n",
+    "for it in range(niter):\n",
+    "    solver.step(1)  # SGD by Caffe\n",
+    "    \n",
+    "    # store the train loss\n",
+    "    train_loss[it] = solver.net.blobs['loss'].data\n",
+    "    \n",
+    "    # store the output on the first test batch\n",
+    "    # (start the forward pass at conv1 to avoid loading new data)\n",
+    "    solver.test_nets[0].forward(start='conv1')\n",
+    "    output[it] = solver.test_nets[0].blobs['ip2'].data[:8]\n",
+    "    \n",
+    "    # run a full test every so often\n",
+    "    # (Caffe can also do this for us and write to a log, but we show here\n",
+    "    #  how to do it directly in Python, where more complicated things are easier.)\n",
+    "    if it % test_interval == 0:\n",
+    "        print 'Iteration', it, 'testing...'\n",
+    "        correct = 0\n",
+    "        for test_it in range(100):\n",
+    "            solver.test_nets[0].forward()\n",
+    "            correct += sum(solver.test_nets[0].blobs['ip2'].data.argmax(1)\n",
+    "                           == solver.test_nets[0].blobs['label'].data)\n",
+    "        test_acc[it // test_interval] = correct / 1e4"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's plot the train loss and test accuracy."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.text.Text at 0x7f793878f490>"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
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+       "oCOEAAVBEAQdIQQoCIIg6AghQEEQBEFHCAEKgiAIOkIIUBAEQdARQoCCIAiCjvD/AXFRJnS871y9\n",
+       "AAAAAElFTkSuQmCC\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f79387025d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "_, ax1 = subplots()\n",
+    "ax2 = ax1.twinx()\n",
+    "ax1.plot(arange(niter), train_loss)\n",
+    "ax2.plot(test_interval * arange(len(test_acc)), test_acc, 'r')\n",
+    "ax1.set_xlabel('iteration')\n",
+    "ax1.set_ylabel('train loss')\n",
+    "ax2.set_ylabel('test accuracy')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The loss seems to have dropped quickly and coverged (except for stochasticity), while the accuracy rose correspondingly. Hooray!\n",
+    "\n",
+    "Since we saved the results on the first test batch, we can watch how our prediction scores evolved. We'll plot time on the $x$ axis and each possible label on the $y$, with lightness indicating confidence."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
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+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f7938797710>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
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+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f7938323190>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
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+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f7938233050>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
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+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f793811c290>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
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+       "YII=\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f79380a2550>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
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+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7d792bc15650>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
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+       "SUVORK5CYII=\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7d792ba9ddd0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
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+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7d792b9b9550>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
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+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7d792b97ec50>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
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+       "FQAAQEMkVAAAAA11ZUL1k5/8pNNNQAMPP/xwp5uAE7R27dpONwENrFu3rtNNwAlauXJlp5uAhkio\n",
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+       "kXS1pPMlXWf7eZ1tFY7j02r11VA3SloaEedJ+q/2NLrToKQbIuICSRdLekf7eKMPu1xE7Jd0ZUS8\n",
+       "QNLzJV1p+2Wi73rN9ZI2SjpyMzP918O6KqGS9BJJj0TEpogYlHSHpNd0uE0YRUSskLRz2OxrJX22\n",
+       "/fqzkl47ro1CsYh4PCLWtl8PSHpQ0rNFH/aEiNjbfjlZ0kS1jkX6rkfYXijpGkmfknTkqTH6r4d1\n",
+       "W0L1bEmbh0xvac9D71gQEVvbr7dKWtDJxqCM7cWSLpS0SvRhT7A9wfZatfpoWUQ8IPqul3xU0nsl\n",
+       "PTVkHv3Xw7otoWIMh1NItMbkoE+7nO0Zkr4s6fqI2D30Pfqwe0XEU+1LfgslXW77ymHv03ddyvar\n",
+       "JG2LiDX6+dmpp6H/ek+3JVQ/k9Q3ZLpPrbNU6B1bbZ8pSbbPkrStw+3Bcdg+Ta1k6vaIuLM9mz7s\n",
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+       "5ZLu188vLbxf0mrRh13N9hK1blqe0P7v9oj4iO1niL7rKbavkPTuiLiW/uttXZdQAQAA9Jpuu+QH\n",
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+       "ZGIcKgBdw/bL1Rrk8NWJz0yKiEPHeX93RMw8Ge0DgNFwhgpAx9keaL/8kKTLbK+xfb3tCbY/Ynu1\n",
+       "7XW2/6Qd/3LbK2x/VdKG9rw7bd9re4Ptt7XnfUjStPbybh+6Lrd8xPZ62/fbfv2QZd9l+19tP2j7\n",
+       "c+O7NQD0okmdbgAA6Oelb94n6T1HzlC1E6hdEfES21Mkfdf2t9uxF0q6ICL+pz391ojY2a5tt9r2\n",
+       "lyLiRtvviIgLR1jXb0v6VUnPl/RMST+wvbz93gsknS/pMUnfs/3SiOBSIYBRcYYKQDfxsOnfkPRm\n",
+       "22skfV/SMyT9Uvu91UOSKUm63vZaSfdI6pP03DHW9TJJX4iWbZLulvRitRKu1RHxaLTuiVgraXGD\n",
+       "7wTgFwBnqAB0u3dGxNKhM9r3Wu0ZNv0KSRdHxH7byyRNHWO5oWMTuCNnrw4MmXdY/FYCGANnqAB0\n",
+       "k92Sht5A/i1Jb7c9SZJsn2d7+gifmyVpZzuZ+hVJFw95b/DI54dZIekN7fu0ninpckmrdWySBQBj\n",
+       "4l9dALrBkTND6yQdbl+6+7Skj6t1ue2Hti1pm6TfascPfUT5m5L+1PZGST9W67LfEZ+UdL/t+yLi\n",
+       "TUc+FxH/ZvuS9jpD0nsjYpvt5w1btkaYBoCnYdgEAACAhrjkBwAA0BAJFQAAQEMkVAAAAA2RUAEA\n",
+       "ADREQgUAANAQCRUAAEBDJFQAAAANkVABAAA09P8W4xDCBDf4RgAAAABJRU5ErkJggg==\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7d792b91c3d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for i in range(8):\n",
+    "    figure(figsize=(2, 2))\n",
+    "    imshow(solver.test_nets[0].blobs['data'].data[i, 0], cmap='gray')\n",
+    "    figure(figsize=(10, 2))\n",
+    "    imshow(output[:50, i].T, interpolation='nearest', cmap='gray')\n",
+    "    xlabel('iteration')\n",
+    "    ylabel('label')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We started with little idea about any of these digits, and ended up with correct classifications for each. If you've been following along, you'll see the last digit is the most difficult, a slanted \"9\" that's (understandably) most confused with \"4\".\n",
+    "\n",
+    "Note that these are the \"raw\" output scores rather than the softmax-computed probability vectors. The latter, shown below, make it easier to see the confidence of our net (but harder to see the scores for less likely digits)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": [
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+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7d792b44f290>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
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+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7d792b2b79d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
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+       "YII=\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7d792b23ec90>"
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+     "metadata": {},
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+       "YII=\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7d792b1bd290>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
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+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7d792b1419d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
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+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7d792b127d90>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
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+       "SUVORK5CYII=\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7d792af4a550>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
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+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7d792aed8c90>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
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+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7d792baffb50>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for i in range(8):\n",
+    "    figure(figsize=(2, 2))\n",
+    "    imshow(solver.test_nets[0].blobs['data'].data[i, 0], cmap='gray')\n",
+    "    figure(figsize=(10, 2))\n",
+    "    imshow(exp(output[:50, i].T) / exp(output[:50, i].T).sum(0), interpolation='nearest', cmap='gray')\n",
+    "    xlabel('iteration')\n",
+    "    ylabel('label')"
+   ]
+  }
+ ],
+ "metadata": {
+  "description": "Define, train, and test the classic LeNet with the Python interface.",
+  "example_name": "Learning LeNet",
+  "include_in_docs": true,
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.9"
+  },
+  "priority": 2
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/examples/02-brewing-logreg.ipynb b/examples/02-brewing-logreg.ipynb
new file mode 100644
index 00000000000..d36871fcdfd
--- /dev/null
+++ b/examples/02-brewing-logreg.ipynb
@@ -0,0 +1,5771 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Brewing Logistic Regression then Going Deeper\n",
+    "\n",
+    "While Caffe is made for deep networks it can likewise represent \"shallow\" models like logistic regression for classification. We'll do simple logistic regression on synthetic data that we'll generate and save to HDF5 to feed vectors to Caffe. Once that model is done, we'll add layers to improve accuracy. That's what Caffe is about: define a model, experiment, and then deploy."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "import os\n",
+    "os.chdir('..')\n",
+    "\n",
+    "import sys\n",
+    "sys.path.insert(0, './python')\n",
+    "import caffe\n",
+    "\n",
+    "\n",
+    "import os\n",
+    "import h5py\n",
+    "import shutil\n",
+    "import tempfile\n",
+    "\n",
+    "import sklearn\n",
+    "import sklearn.datasets\n",
+    "import sklearn.linear_model\n",
+    "\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Synthesize a dataset of 10,000 4-vectors for binary classification with 2 informative features and 2 noise features."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": [
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+       "FhYWFhYWFjnl/wPBByFp6Gp27QAAAABJRU5ErkJggg==\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11dbd0090>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "X, y = sklearn.datasets.make_classification(\n",
+    "    n_samples=10000, n_features=4, n_redundant=0, n_informative=2, \n",
+    "    n_clusters_per_class=2, hypercube=False, random_state=0\n",
+    ")\n",
+    "\n",
+    "# Split into train and test\n",
+    "X, Xt, y, yt = sklearn.cross_validation.train_test_split(X, y)\n",
+    "\n",
+    "# Visualize sample of the data\n",
+    "ind = np.random.permutation(X.shape[0])[:1000]\n",
+    "df = pd.DataFrame(X[ind])\n",
+    "_ = pd.scatter_matrix(df, figsize=(9, 9), diagonal='kde', marker='o', s=40, alpha=.4, c=y[ind])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Learn and evaluate scikit-learn's logistic regression with stochastic gradient descent (SGD) training. Time and check the classifier's accuracy."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy: 0.783\n",
+      "Accuracy: 0.783\n",
+      "Accuracy: 0.783\n",
+      "Accuracy: 0.783\n",
+      "1 loops, best of 3: 508 ms per loop\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%timeit\n",
+    "# Train and test the scikit-learn SGD logistic regression.\n",
+    "clf = sklearn.linear_model.SGDClassifier(\n",
+    "    loss='log', n_iter=1000, penalty='l2', alpha=1e-3, class_weight='auto')\n",
+    "\n",
+    "clf.fit(X, y)\n",
+    "yt_pred = clf.predict(Xt)\n",
+    "print('Accuracy: {:.3f}'.format(sklearn.metrics.accuracy_score(yt, yt_pred)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Save the dataset to HDF5 for loading in Caffe."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Write out the data to HDF5 files in a temp directory.\n",
+    "# This file is assumed to be caffe_root/examples/hdf5_classification.ipynb\n",
+    "dirname = os.path.abspath('./examples/hdf5_classification/data')\n",
+    "if not os.path.exists(dirname):\n",
+    "    os.makedirs(dirname)\n",
+    "\n",
+    "train_filename = os.path.join(dirname, 'train.h5')\n",
+    "test_filename = os.path.join(dirname, 'test.h5')\n",
+    "\n",
+    "# HDF5DataLayer source should be a file containing a list of HDF5 filenames.\n",
+    "# To show this off, we'll list the same data file twice.\n",
+    "with h5py.File(train_filename, 'w') as f:\n",
+    "    f['data'] = X\n",
+    "    f['label'] = y.astype(np.float32)\n",
+    "with open(os.path.join(dirname, 'train.txt'), 'w') as f:\n",
+    "    f.write(train_filename + '\\n')\n",
+    "    f.write(train_filename + '\\n')\n",
+    "    \n",
+    "# HDF5 is pretty efficient, but can be further compressed.\n",
+    "comp_kwargs = {'compression': 'gzip', 'compression_opts': 1}\n",
+    "with h5py.File(test_filename, 'w') as f:\n",
+    "    f.create_dataset('data', data=Xt, **comp_kwargs)\n",
+    "    f.create_dataset('label', data=yt.astype(np.float32), **comp_kwargs)\n",
+    "with open(os.path.join(dirname, 'test.txt'), 'w') as f:\n",
+    "    f.write(test_filename + '\\n')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's define logistic regression in Caffe through Python net specification. This is a quick and natural way to define nets that sidesteps manually editing the protobuf model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from caffe import layers as L\n",
+    "from caffe import params as P\n",
+    "\n",
+    "def logreg(hdf5, batch_size):\n",
+    "    # logistic regression: data, matrix multiplication, and 2-class softmax loss\n",
+    "    n = caffe.NetSpec()\n",
+    "    n.data, n.label = L.HDF5Data(batch_size=batch_size, source=hdf5, ntop=2)\n",
+    "    n.ip1 = L.InnerProduct(n.data, num_output=2, weight_filler=dict(type='xavier'))\n",
+    "    n.accuracy = L.Accuracy(n.ip1, n.label)\n",
+    "    n.loss = L.SoftmaxWithLoss(n.ip1, n.label)\n",
+    "    return n.to_proto()\n",
+    "    \n",
+    "with open('examples/hdf5_classification/logreg_auto_train.prototxt', 'w') as f:\n",
+    "    f.write(str(logreg('examples/hdf5_classification/data/train.txt', 10)))\n",
+    "    \n",
+    "with open('examples/hdf5_classification/logreg_auto_test.prototxt', 'w') as f:\n",
+    "    f.write(str(logreg('examples/hdf5_classification/data/test.txt', 10)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Time to learn and evaluate our Caffeinated logistic regression in Python."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy: 0.782\n",
+      "Accuracy: 0.782\n",
+      "Accuracy: 0.782\n",
+      "Accuracy: 0.782\n",
+      "1 loops, best of 3: 287 ms per loop\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%timeit\n",
+    "caffe.set_mode_cpu()\n",
+    "solver = caffe.get_solver('examples/hdf5_classification/solver.prototxt')\n",
+    "solver.solve()\n",
+    "\n",
+    "accuracy = 0\n",
+    "batch_size = solver.test_nets[0].blobs['data'].num\n",
+    "test_iters = int(len(Xt) / batch_size)\n",
+    "for i in range(test_iters):\n",
+    "    solver.test_nets[0].forward()\n",
+    "    accuracy += solver.test_nets[0].blobs['accuracy'].data\n",
+    "accuracy /= test_iters\n",
+    "\n",
+    "print(\"Accuracy: {:.3f}\".format(accuracy))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Do the same through the command line interface for detailed output on the model and solving."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "I0318 00:58:32.322571 2013098752 caffe.cpp:117] Use CPU.\n",
+      "I0318 00:58:32.643163 2013098752 caffe.cpp:121] Starting Optimization\n",
+      "I0318 00:58:32.643229 2013098752 solver.cpp:32] Initializing solver from parameters: \n",
+      "train_net: \"examples/hdf5_classification/logreg_auto_train.prototxt\"\n",
+      "test_net: \"examples/hdf5_classification/logreg_auto_test.prototxt\"\n",
+      "test_iter: 250\n",
+      "test_interval: 1000\n",
+      "base_lr: 0.01\n",
+      "display: 1000\n",
+      "max_iter: 10000\n",
+      "lr_policy: \"step\"\n",
+      "gamma: 0.1\n",
+      "momentum: 0.9\n",
+      "weight_decay: 0.0005\n",
+      "stepsize: 5000\n",
+      "snapshot: 10000\n",
+      "snapshot_prefix: \"examples/hdf5_classification/data/train\"\n",
+      "solver_mode: CPU\n",
+      "I0318 00:58:32.643333 2013098752 solver.cpp:61] Creating training net from train_net file: examples/hdf5_classification/logreg_auto_train.prototxt\n",
+      "I0318 00:58:32.643465 2013098752 net.cpp:42] Initializing net from parameters: \n",
+      "state {\n",
+      "  phase: TRAIN\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"data\"\n",
+      "  type: \"HDF5Data\"\n",
+      "  top: \"data\"\n",
+      "  top: \"label\"\n",
+      "  hdf5_data_param {\n",
+      "    source: \"examples/hdf5_classification/data/train.txt\"\n",
+      "    batch_size: 10\n",
+      "  }\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"ip1\"\n",
+      "  type: \"InnerProduct\"\n",
+      "  bottom: \"data\"\n",
+      "  top: \"ip1\"\n",
+      "  inner_product_param {\n",
+      "    num_output: 2\n",
+      "    weight_filler {\n",
+      "      type: \"xavier\"\n",
+      "    }\n",
+      "  }\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"accuracy\"\n",
+      "  type: \"Accuracy\"\n",
+      "  bottom: \"ip1\"\n",
+      "  bottom: \"label\"\n",
+      "  top: \"accuracy\"\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"loss\"\n",
+      "  type: \"SoftmaxWithLoss\"\n",
+      "  bottom: \"ip1\"\n",
+      "  bottom: \"label\"\n",
+      "  top: \"loss\"\n",
+      "}\n",
+      "I0318 00:58:32.644197 2013098752 layer_factory.hpp:74] Creating layer data\n",
+      "I0318 00:58:32.644219 2013098752 net.cpp:84] Creating Layer data\n",
+      "I0318 00:58:32.644230 2013098752 net.cpp:338] data -> data\n",
+      "I0318 00:58:32.644256 2013098752 net.cpp:338] data -> label\n",
+      "I0318 00:58:32.644269 2013098752 net.cpp:113] Setting up data\n",
+      "I0318 00:58:32.644278 2013098752 hdf5_data_layer.cpp:66] Loading list of HDF5 filenames from: examples/hdf5_classification/data/train.txt\n",
+      "I0318 00:58:32.644327 2013098752 hdf5_data_layer.cpp:80] Number of HDF5 files: 2\n",
+      "I0318 00:58:32.646458 2013098752 net.cpp:120] Top shape: 10 4 (40)\n",
+      "I0318 00:58:32.646502 2013098752 net.cpp:120] Top shape: 10 (10)\n",
+      "I0318 00:58:32.646518 2013098752 layer_factory.hpp:74] Creating layer label_data_1_split\n",
+      "I0318 00:58:32.646538 2013098752 net.cpp:84] Creating Layer label_data_1_split\n",
+      "I0318 00:58:32.646546 2013098752 net.cpp:380] label_data_1_split <- label\n",
+      "I0318 00:58:32.646556 2013098752 net.cpp:338] label_data_1_split -> label_data_1_split_0\n",
+      "I0318 00:58:32.646569 2013098752 net.cpp:338] label_data_1_split -> label_data_1_split_1\n",
+      "I0318 00:58:32.646579 2013098752 net.cpp:113] Setting up label_data_1_split\n",
+      "I0318 00:58:32.646586 2013098752 net.cpp:120] Top shape: 10 (10)\n",
+      "I0318 00:58:32.646595 2013098752 net.cpp:120] Top shape: 10 (10)\n",
+      "I0318 00:58:32.646601 2013098752 layer_factory.hpp:74] Creating layer ip1\n",
+      "I0318 00:58:32.646615 2013098752 net.cpp:84] Creating Layer ip1\n",
+      "I0318 00:58:32.646622 2013098752 net.cpp:380] ip1 <- data\n",
+      "I0318 00:58:32.646664 2013098752 net.cpp:338] ip1 -> ip1\n",
+      "I0318 00:58:32.646689 2013098752 net.cpp:113] Setting up ip1\n",
+      "I0318 00:58:32.652330 2013098752 net.cpp:120] Top shape: 10 2 (20)\n",
+      "I0318 00:58:32.652371 2013098752 layer_factory.hpp:74] Creating layer ip1_ip1_0_split\n",
+      "I0318 00:58:32.652393 2013098752 net.cpp:84] Creating Layer ip1_ip1_0_split\n",
+      "I0318 00:58:32.652407 2013098752 net.cpp:380] ip1_ip1_0_split <- ip1\n",
+      "I0318 00:58:32.652421 2013098752 net.cpp:338] ip1_ip1_0_split -> ip1_ip1_0_split_0\n",
+      "I0318 00:58:32.652467 2013098752 net.cpp:338] ip1_ip1_0_split -> ip1_ip1_0_split_1\n",
+      "I0318 00:58:32.652480 2013098752 net.cpp:113] Setting up ip1_ip1_0_split\n",
+      "I0318 00:58:32.652489 2013098752 net.cpp:120] Top shape: 10 2 (20)\n",
+      "I0318 00:58:32.652498 2013098752 net.cpp:120] Top shape: 10 2 (20)\n",
+      "I0318 00:58:32.652505 2013098752 layer_factory.hpp:74] Creating layer accuracy\n",
+      "I0318 00:58:32.652521 2013098752 net.cpp:84] Creating Layer accuracy\n",
+      "I0318 00:58:32.652534 2013098752 net.cpp:380] accuracy <- ip1_ip1_0_split_0\n",
+      "I0318 00:58:32.652545 2013098752 net.cpp:380] accuracy <- label_data_1_split_0\n",
+      "I0318 00:58:32.652562 2013098752 net.cpp:338] accuracy -> accuracy\n",
+      "I0318 00:58:32.652577 2013098752 net.cpp:113] Setting up accuracy\n",
+      "I0318 00:58:32.652590 2013098752 net.cpp:120] Top shape: (1)\n",
+      "I0318 00:58:32.652642 2013098752 layer_factory.hpp:74] Creating layer loss\n",
+      "I0318 00:58:32.652655 2013098752 net.cpp:84] Creating Layer loss\n",
+      "I0318 00:58:32.652663 2013098752 net.cpp:380] loss <- ip1_ip1_0_split_1\n",
+      "I0318 00:58:32.652672 2013098752 net.cpp:380] loss <- label_data_1_split_1\n",
+      "I0318 00:58:32.652679 2013098752 net.cpp:338] loss -> loss\n",
+      "I0318 00:58:32.652689 2013098752 net.cpp:113] Setting up loss\n",
+      "I0318 00:58:32.652701 2013098752 layer_factory.hpp:74] Creating layer loss\n",
+      "I0318 00:58:32.652716 2013098752 net.cpp:120] Top shape: (1)\n",
+      "I0318 00:58:32.652724 2013098752 net.cpp:122]     with loss weight 1\n",
+      "I0318 00:58:32.652740 2013098752 net.cpp:167] loss needs backward computation.\n",
+      "I0318 00:58:32.652746 2013098752 net.cpp:169] accuracy does not need backward computation.\n",
+      "I0318 00:58:32.652753 2013098752 net.cpp:167] ip1_ip1_0_split needs backward computation.\n",
+      "I0318 00:58:32.652760 2013098752 net.cpp:167] ip1 needs backward computation.\n",
+      "I0318 00:58:32.652786 2013098752 net.cpp:169] label_data_1_split does not need backward computation.\n",
+      "I0318 00:58:32.652801 2013098752 net.cpp:169] data does not need backward computation.\n",
+      "I0318 00:58:32.652808 2013098752 net.cpp:205] This network produces output accuracy\n",
+      "I0318 00:58:32.652815 2013098752 net.cpp:205] This network produces output loss\n",
+      "I0318 00:58:32.652825 2013098752 net.cpp:447] Collecting Learning Rate and Weight Decay.\n",
+      "I0318 00:58:32.652833 2013098752 net.cpp:217] Network initialization done.\n",
+      "I0318 00:58:32.652839 2013098752 net.cpp:218] Memory required for data: 528\n",
+      "I0318 00:58:32.652964 2013098752 solver.cpp:154] Creating test net (#0) specified by test_net file: examples/hdf5_classification/logreg_auto_test.prototxt\n",
+      "I0318 00:58:32.652986 2013098752 net.cpp:42] Initializing net from parameters: \n",
+      "state {\n",
+      "  phase: TEST\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"data\"\n",
+      "  type: \"HDF5Data\"\n",
+      "  top: \"data\"\n",
+      "  top: \"label\"\n",
+      "  hdf5_data_param {\n",
+      "    source: \"examples/hdf5_classification/data/test.txt\"\n",
+      "    batch_size: 10\n",
+      "  }\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"ip1\"\n",
+      "  type: \"InnerProduct\"\n",
+      "  bottom: \"data\"\n",
+      "  top: \"ip1\"\n",
+      "  inner_product_param {\n",
+      "    num_output: 2\n",
+      "    weight_filler {\n",
+      "      type: \"xavier\"\n",
+      "    }\n",
+      "  }\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"accuracy\"\n",
+      "  type: \"Accuracy\"\n",
+      "  bottom: \"ip1\"\n",
+      "  bottom: \"label\"\n",
+      "  top: \"accuracy\"\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"loss\"\n",
+      "  type: \"SoftmaxWithLoss\"\n",
+      "  bottom: \"ip1\"\n",
+      "  bottom: \"label\"\n",
+      "  top: \"loss\"\n",
+      "}\n",
+      "I0318 00:58:32.653069 2013098752 layer_factory.hpp:74] Creating layer data\n",
+      "I0318 00:58:32.653080 2013098752 net.cpp:84] Creating Layer data\n",
+      "I0318 00:58:32.653090 2013098752 net.cpp:338] data -> data\n",
+      "I0318 00:58:32.653128 2013098752 net.cpp:338] data -> label\n",
+      "I0318 00:58:32.653146 2013098752 net.cpp:113] Setting up data\n",
+      "I0318 00:58:32.653154 2013098752 hdf5_data_layer.cpp:66] Loading list of HDF5 filenames from: examples/hdf5_classification/data/test.txt\n",
+      "I0318 00:58:32.653192 2013098752 hdf5_data_layer.cpp:80] Number of HDF5 files: 1\n",
+      "I0318 00:58:32.654850 2013098752 net.cpp:120] Top shape: 10 4 (40)\n",
+      "I0318 00:58:32.654897 2013098752 net.cpp:120] Top shape: 10 (10)\n",
+      "I0318 00:58:32.654914 2013098752 layer_factory.hpp:74] Creating layer label_data_1_split\n",
+      "I0318 00:58:32.654933 2013098752 net.cpp:84] Creating Layer label_data_1_split\n",
+      "I0318 00:58:32.654943 2013098752 net.cpp:380] label_data_1_split <- label\n",
+      "I0318 00:58:32.654953 2013098752 net.cpp:338] label_data_1_split -> label_data_1_split_0\n",
+      "I0318 00:58:32.654966 2013098752 net.cpp:338] label_data_1_split -> label_data_1_split_1\n",
+      "I0318 00:58:32.654976 2013098752 net.cpp:113] Setting up label_data_1_split\n",
+      "I0318 00:58:32.654985 2013098752 net.cpp:120] Top shape: 10 (10)\n",
+      "I0318 00:58:32.654992 2013098752 net.cpp:120] Top shape: 10 (10)\n",
+      "I0318 00:58:32.655000 2013098752 layer_factory.hpp:74] Creating layer ip1\n",
+      "I0318 00:58:32.655010 2013098752 net.cpp:84] Creating Layer ip1\n",
+      "I0318 00:58:32.655017 2013098752 net.cpp:380] ip1 <- data\n",
+      "I0318 00:58:32.655030 2013098752 net.cpp:338] ip1 -> ip1\n",
+      "I0318 00:58:32.655041 2013098752 net.cpp:113] Setting up ip1\n",
+      "I0318 00:58:32.655061 2013098752 net.cpp:120] Top shape: 10 2 (20)\n",
+      "I0318 00:58:32.655072 2013098752 layer_factory.hpp:74] Creating layer ip1_ip1_0_split\n",
+      "I0318 00:58:32.655148 2013098752 net.cpp:84] Creating Layer ip1_ip1_0_split\n",
+      "I0318 00:58:32.655159 2013098752 net.cpp:380] ip1_ip1_0_split <- ip1\n",
+      "I0318 00:58:32.655170 2013098752 net.cpp:338] ip1_ip1_0_split -> ip1_ip1_0_split_0\n",
+      "I0318 00:58:32.655180 2013098752 net.cpp:338] ip1_ip1_0_split -> ip1_ip1_0_split_1\n",
+      "I0318 00:58:32.655190 2013098752 net.cpp:113] Setting up ip1_ip1_0_split\n",
+      "I0318 00:58:32.655199 2013098752 net.cpp:120] Top shape: 10 2 (20)\n",
+      "I0318 00:58:32.655206 2013098752 net.cpp:120] Top shape: 10 2 (20)\n",
+      "I0318 00:58:32.655213 2013098752 layer_factory.hpp:74] Creating layer accuracy\n",
+      "I0318 00:58:32.655223 2013098752 net.cpp:84] Creating Layer accuracy\n",
+      "I0318 00:58:32.655230 2013098752 net.cpp:380] accuracy <- ip1_ip1_0_split_0\n",
+      "I0318 00:58:32.655237 2013098752 net.cpp:380] accuracy <- label_data_1_split_0\n",
+      "I0318 00:58:32.655251 2013098752 net.cpp:338] accuracy -> accuracy\n",
+      "I0318 00:58:32.655259 2013098752 net.cpp:113] Setting up accuracy\n",
+      "I0318 00:58:32.655267 2013098752 net.cpp:120] Top shape: (1)\n",
+      "I0318 00:58:32.655340 2013098752 layer_factory.hpp:74] Creating layer loss\n",
+      "I0318 00:58:32.655354 2013098752 net.cpp:84] Creating Layer loss\n",
+      "I0318 00:58:32.655361 2013098752 net.cpp:380] loss <- ip1_ip1_0_split_1\n",
+      "I0318 00:58:32.655369 2013098752 net.cpp:380] loss <- label_data_1_split_1\n",
+      "I0318 00:58:32.655378 2013098752 net.cpp:338] loss -> loss\n",
+      "I0318 00:58:32.655388 2013098752 net.cpp:113] Setting up loss\n",
+      "I0318 00:58:32.655397 2013098752 layer_factory.hpp:74] Creating layer loss\n",
+      "I0318 00:58:32.655414 2013098752 net.cpp:120] Top shape: (1)\n",
+      "I0318 00:58:32.655422 2013098752 net.cpp:122]     with loss weight 1\n",
+      "I0318 00:58:32.655438 2013098752 net.cpp:167] loss needs backward computation.\n",
+      "I0318 00:58:32.655446 2013098752 net.cpp:169] accuracy does not need backward computation.\n",
+      "I0318 00:58:32.655455 2013098752 net.cpp:167] ip1_ip1_0_split needs backward computation.\n",
+      "I0318 00:58:32.655462 2013098752 net.cpp:167] ip1 needs backward computation.\n",
+      "I0318 00:58:32.655469 2013098752 net.cpp:169] label_data_1_split does not need backward computation.\n",
+      "I0318 00:58:32.655477 2013098752 net.cpp:169] data does not need backward computation.\n",
+      "I0318 00:58:32.655483 2013098752 net.cpp:205] This network produces output accuracy\n",
+      "I0318 00:58:32.655489 2013098752 net.cpp:205] This network produces output loss\n",
+      "I0318 00:58:32.655503 2013098752 net.cpp:447] Collecting Learning Rate and Weight Decay.\n",
+      "I0318 00:58:32.655511 2013098752 net.cpp:217] Network initialization done.\n",
+      "I0318 00:58:32.655517 2013098752 net.cpp:218] Memory required for data: 528\n",
+      "I0318 00:58:32.655547 2013098752 solver.cpp:42] Solver scaffolding done.\n",
+      "I0318 00:58:32.655567 2013098752 solver.cpp:222] Solving \n",
+      "I0318 00:58:32.655575 2013098752 solver.cpp:223] Learning Rate Policy: step\n",
+      "I0318 00:58:32.655583 2013098752 solver.cpp:266] Iteration 0, Testing net (#0)\n",
+      "I0318 00:58:32.683643 2013098752 solver.cpp:315]     Test net output #0: accuracy = 0.3736\n",
+      "I0318 00:58:32.683686 2013098752 solver.cpp:315]     Test net output #1: loss = 1.00555 (* 1 = 1.00555 loss)\n",
+      "I0318 00:58:32.683846 2013098752 solver.cpp:189] Iteration 0, loss = 0.869394\n",
+      "I0318 00:58:32.683861 2013098752 solver.cpp:204]     Train net output #0: accuracy = 0.3\n",
+      "I0318 00:58:32.683871 2013098752 solver.cpp:204]     Train net output #1: loss = 0.869394 (* 1 = 0.869394 loss)\n",
+      "I0318 00:58:32.683883 2013098752 solver.cpp:464] Iteration 0, lr = 0.01\n",
+      "I0318 00:58:32.698721 2013098752 solver.cpp:266] Iteration 1000, Testing net (#0)\n",
+      "I0318 00:58:32.701917 2013098752 solver.cpp:315]     Test net output #0: accuracy = 0.7848\n",
+      "I0318 00:58:32.701961 2013098752 solver.cpp:315]     Test net output #1: loss = 0.590972 (* 1 = 0.590972 loss)\n",
+      "I0318 00:58:32.702014 2013098752 solver.cpp:189] Iteration 1000, loss = 0.54742\n",
+      "I0318 00:58:32.702029 2013098752 solver.cpp:204]     Train net output #0: accuracy = 0.7\n",
+      "I0318 00:58:32.702041 2013098752 solver.cpp:204]     Train net output #1: loss = 0.54742 (* 1 = 0.54742 loss)\n",
+      "I0318 00:58:32.702051 2013098752 solver.cpp:464] Iteration 1000, lr = 0.01\n",
+      "I0318 00:58:32.718360 2013098752 solver.cpp:266] Iteration 2000, Testing net (#0)\n",
+      "I0318 00:58:32.721529 2013098752 solver.cpp:315]     Test net output #0: accuracy = 0.7696\n",
+      "I0318 00:58:32.721562 2013098752 solver.cpp:315]     Test net output #1: loss = 0.593946 (* 1 = 0.593946 loss)\n",
+      "I0318 00:58:32.721593 2013098752 solver.cpp:189] Iteration 2000, loss = 0.729569\n",
+      "I0318 00:58:32.721603 2013098752 solver.cpp:204]     Train net output #0: accuracy = 0.5\n",
+      "I0318 00:58:32.721613 2013098752 solver.cpp:204]     Train net output #1: loss = 0.729569 (* 1 = 0.729569 loss)\n",
+      "I0318 00:58:32.721622 2013098752 solver.cpp:464] Iteration 2000, lr = 0.01\n",
+      "I0318 00:58:32.740182 2013098752 solver.cpp:266] Iteration 3000, Testing net (#0)\n",
+      "I0318 00:58:32.743494 2013098752 solver.cpp:315]     Test net output #0: accuracy = 0.77\n",
+      "I0318 00:58:32.743544 2013098752 solver.cpp:315]     Test net output #1: loss = 0.591229 (* 1 = 0.591229 loss)\n",
+      "I0318 00:58:32.744209 2013098752 solver.cpp:189] Iteration 3000, loss = 0.406097\n",
+      "I0318 00:58:32.744231 2013098752 solver.cpp:204]     Train net output #0: accuracy = 0.8\n",
+      "I0318 00:58:32.744249 2013098752 solver.cpp:204]     Train net output #1: loss = 0.406096 (* 1 = 0.406096 loss)\n",
+      "I0318 00:58:32.744266 2013098752 solver.cpp:464] Iteration 3000, lr = 0.01\n",
+      "I0318 00:58:32.764135 2013098752 solver.cpp:266] Iteration 4000, Testing net (#0)\n",
+      "I0318 00:58:32.769110 2013098752 solver.cpp:315]     Test net output #0: accuracy = 0.7848\n",
+      "I0318 00:58:32.769170 2013098752 solver.cpp:315]     Test net output #1: loss = 0.590972 (* 1 = 0.590972 loss)\n",
+      "I0318 00:58:32.769223 2013098752 solver.cpp:189] Iteration 4000, loss = 0.54742\n",
+      "I0318 00:58:32.769242 2013098752 solver.cpp:204]     Train net output #0: accuracy = 0.7\n",
+      "I0318 00:58:32.769255 2013098752 solver.cpp:204]     Train net output #1: loss = 0.54742 (* 1 = 0.54742 loss)\n",
+      "I0318 00:58:32.769265 2013098752 solver.cpp:464] Iteration 4000, lr = 0.01\n",
+      "I0318 00:58:32.785846 2013098752 solver.cpp:266] Iteration 5000, Testing net (#0)\n",
+      "I0318 00:58:32.788722 2013098752 solver.cpp:315]     Test net output #0: accuracy = 0.7696\n",
+      "I0318 00:58:32.788751 2013098752 solver.cpp:315]     Test net output #1: loss = 0.593946 (* 1 = 0.593946 loss)\n",
+      "I0318 00:58:32.788811 2013098752 solver.cpp:189] Iteration 5000, loss = 0.72957\n",
+      "I0318 00:58:32.788833 2013098752 solver.cpp:204]     Train net output #0: accuracy = 0.5\n",
+      "I0318 00:58:32.788846 2013098752 solver.cpp:204]     Train net output #1: loss = 0.729569 (* 1 = 0.729569 loss)\n",
+      "I0318 00:58:32.788856 2013098752 solver.cpp:464] Iteration 5000, lr = 0.001\n",
+      "I0318 00:58:32.804762 2013098752 solver.cpp:266] Iteration 6000, Testing net (#0)\n",
+      "I0318 00:58:32.808061 2013098752 solver.cpp:315]     Test net output #0: accuracy = 0.7856\n",
+      "I0318 00:58:32.808112 2013098752 solver.cpp:315]     Test net output #1: loss = 0.59028 (* 1 = 0.59028 loss)\n",
+      "I0318 00:58:32.808732 2013098752 solver.cpp:189] Iteration 6000, loss = 0.415444\n",
+      "I0318 00:58:32.808753 2013098752 solver.cpp:204]     Train net output #0: accuracy = 0.9\n",
+      "I0318 00:58:32.808773 2013098752 solver.cpp:204]     Train net output #1: loss = 0.415444 (* 1 = 0.415444 loss)\n",
+      "I0318 00:58:32.808786 2013098752 solver.cpp:464] Iteration 6000, lr = 0.001\n",
+      "I0318 00:58:32.827118 2013098752 solver.cpp:266] Iteration 7000, Testing net (#0)\n",
+      "I0318 00:58:32.831614 2013098752 solver.cpp:315]     Test net output #0: accuracy = 0.7848\n",
+      "I0318 00:58:32.831657 2013098752 solver.cpp:315]     Test net output #1: loss = 0.589454 (* 1 = 0.589454 loss)\n",
+      "I0318 00:58:32.831707 2013098752 solver.cpp:189] Iteration 7000, loss = 0.538038\n",
+      "I0318 00:58:32.831728 2013098752 solver.cpp:204]     Train net output #0: accuracy = 0.8\n",
+      "I0318 00:58:32.831745 2013098752 solver.cpp:204]     Train net output #1: loss = 0.538037 (* 1 = 0.538037 loss)\n",
+      "I0318 00:58:32.831759 2013098752 solver.cpp:464] Iteration 7000, lr = 0.001\n",
+      "I0318 00:58:32.849634 2013098752 solver.cpp:266] Iteration 8000, Testing net (#0)\n",
+      "I0318 00:58:32.852712 2013098752 solver.cpp:315]     Test net output #0: accuracy = 0.7796\n",
+      "I0318 00:58:32.852748 2013098752 solver.cpp:315]     Test net output #1: loss = 0.589365 (* 1 = 0.589365 loss)\n",
+      "I0318 00:58:32.852792 2013098752 solver.cpp:189] Iteration 8000, loss = 0.684219\n",
+      "I0318 00:58:32.852840 2013098752 solver.cpp:204]     Train net output #0: accuracy = 0.5\n",
+      "I0318 00:58:32.852852 2013098752 solver.cpp:204]     Train net output #1: loss = 0.684219 (* 1 = 0.684219 loss)\n",
+      "I0318 00:58:32.852861 2013098752 solver.cpp:464] Iteration 8000, lr = 0.001\n",
+      "I0318 00:58:32.868440 2013098752 solver.cpp:266] Iteration 9000, Testing net (#0)\n",
+      "I0318 00:58:32.871438 2013098752 solver.cpp:315]     Test net output #0: accuracy = 0.7816\n",
+      "I0318 00:58:32.871461 2013098752 solver.cpp:315]     Test net output #1: loss = 0.589656 (* 1 = 0.589656 loss)\n",
+      "I0318 00:58:32.872109 2013098752 solver.cpp:189] Iteration 9000, loss = 0.421879\n",
+      "I0318 00:58:32.872131 2013098752 solver.cpp:204]     Train net output #0: accuracy = 0.9\n",
+      "I0318 00:58:32.872143 2013098752 solver.cpp:204]     Train net output #1: loss = 0.421879 (* 1 = 0.421879 loss)\n",
+      "I0318 00:58:32.872153 2013098752 solver.cpp:464] Iteration 9000, lr = 0.001\n",
+      "I0318 00:58:32.889981 2013098752 solver.cpp:334] Snapshotting to examples/hdf5_classification/data/train_iter_10000.caffemodel\n",
+      "I0318 00:58:32.890224 2013098752 solver.cpp:342] Snapshotting solver state to examples/hdf5_classification/data/train_iter_10000.solverstate\n",
+      "I0318 00:58:32.890362 2013098752 solver.cpp:248] Iteration 10000, loss = 0.538933\n",
+      "I0318 00:58:32.890380 2013098752 solver.cpp:266] Iteration 10000, Testing net (#0)\n",
+      "I0318 00:58:32.893728 2013098752 solver.cpp:315]     Test net output #0: accuracy = 0.782\n",
+      "I0318 00:58:32.893757 2013098752 solver.cpp:315]     Test net output #1: loss = 0.589366 (* 1 = 0.589366 loss)\n",
+      "I0318 00:58:32.893775 2013098752 solver.cpp:253] Optimization Done.\n",
+      "I0318 00:58:32.893786 2013098752 caffe.cpp:134] Optimization Done.\n"
+     ]
+    }
+   ],
+   "source": [
+    "!./build/tools/caffe train -solver examples/hdf5_classification/solver.prototxt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you look at output or the `logreg_auto_train.prototxt`, you'll see that the model is simple logistic regression.\n",
+    "We can make it a little more advanced by introducing a non-linearity between weights that take the input and weights that give the output -- now we have a two-layer network.\n",
+    "That network is given in `nonlinear_auto_train.prototxt`, and that's the only change made in `nonlinear_solver.prototxt` which we will now use.\n",
+    "\n",
+    "The final accuracy of the new network should be higher than logistic regression!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from caffe import layers as L\n",
+    "from caffe import params as P\n",
+    "\n",
+    "def nonlinear_net(hdf5, batch_size):\n",
+    "    # one small nonlinearity, one leap for model kind\n",
+    "    n = caffe.NetSpec()\n",
+    "    n.data, n.label = L.HDF5Data(batch_size=batch_size, source=hdf5, ntop=2)\n",
+    "    # define a hidden layer of dimension 40\n",
+    "    n.ip1 = L.InnerProduct(n.data, num_output=40, weight_filler=dict(type='xavier'))\n",
+    "    # transform the output through the ReLU (rectified linear) non-linearity\n",
+    "    n.relu1 = L.ReLU(n.ip1, in_place=True)\n",
+    "    # score the (now non-linear) features\n",
+    "    n.ip2 = L.InnerProduct(n.ip1, num_output=2, weight_filler=dict(type='xavier'))\n",
+    "    # same accuracy and loss as before\n",
+    "    n.accuracy = L.Accuracy(n.ip2, n.label)\n",
+    "    n.loss = L.SoftmaxWithLoss(n.ip2, n.label)\n",
+    "    return n.to_proto()\n",
+    "    \n",
+    "with open('examples/hdf5_classification/nonlinear_auto_train.prototxt', 'w') as f:\n",
+    "    f.write(str(nonlinear_net('examples/hdf5_classification/data/train.txt', 10)))\n",
+    "    \n",
+    "with open('examples/hdf5_classification/nonlinear_auto_test.prototxt', 'w') as f:\n",
+    "    f.write(str(nonlinear_net('examples/hdf5_classification/data/test.txt', 10)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy: 0.832\n",
+      "Accuracy: 0.832\n",
+      "Accuracy: 0.832\n",
+      "Accuracy: 0.831\n",
+      "1 loops, best of 3: 386 ms per loop\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%timeit\n",
+    "caffe.set_mode_cpu()\n",
+    "solver = caffe.get_solver('examples/hdf5_classification/nonlinear_solver.prototxt')\n",
+    "solver.solve()\n",
+    "\n",
+    "accuracy = 0\n",
+    "batch_size = solver.test_nets[0].blobs['data'].num\n",
+    "test_iters = int(len(Xt) / batch_size)\n",
+    "for i in range(test_iters):\n",
+    "    solver.test_nets[0].forward()\n",
+    "    accuracy += solver.test_nets[0].blobs['accuracy'].data\n",
+    "accuracy /= test_iters\n",
+    "\n",
+    "print(\"Accuracy: {:.3f}\".format(accuracy))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Do the same through the command line interface for detailed output on the model and solving."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "I0318 00:58:43.336922 2013098752 caffe.cpp:117] Use CPU.\n",
+      "I0318 00:58:43.654698 2013098752 caffe.cpp:121] Starting Optimization\n",
+      "I0318 00:58:43.654747 2013098752 solver.cpp:32] Initializing solver from parameters: \n",
+      "train_net: \"examples/hdf5_classification/nonlinear_auto_train.prototxt\"\n",
+      "test_net: \"examples/hdf5_classification/nonlinear_auto_test.prototxt\"\n",
+      "test_iter: 250\n",
+      "test_interval: 1000\n",
+      "base_lr: 0.01\n",
+      "display: 1000\n",
+      "max_iter: 10000\n",
+      "lr_policy: \"step\"\n",
+      "gamma: 0.1\n",
+      "momentum: 0.9\n",
+      "weight_decay: 0.0005\n",
+      "stepsize: 5000\n",
+      "snapshot: 10000\n",
+      "snapshot_prefix: \"examples/hdf5_classification/data/train\"\n",
+      "solver_mode: CPU\n",
+      "I0318 00:58:43.654855 2013098752 solver.cpp:61] Creating training net from train_net file: examples/hdf5_classification/nonlinear_auto_train.prototxt\n",
+      "I0318 00:58:43.655004 2013098752 net.cpp:42] Initializing net from parameters: \n",
+      "state {\n",
+      "  phase: TRAIN\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"data\"\n",
+      "  type: \"HDF5Data\"\n",
+      "  top: \"data\"\n",
+      "  top: \"label\"\n",
+      "  hdf5_data_param {\n",
+      "    source: \"examples/hdf5_classification/data/train.txt\"\n",
+      "    batch_size: 10\n",
+      "  }\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"ip1\"\n",
+      "  type: \"InnerProduct\"\n",
+      "  bottom: \"data\"\n",
+      "  top: \"ip1\"\n",
+      "  inner_product_param {\n",
+      "    num_output: 40\n",
+      "    weight_filler {\n",
+      "      type: \"xavier\"\n",
+      "    }\n",
+      "  }\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"relu1\"\n",
+      "  type: \"ReLU\"\n",
+      "  bottom: \"ip1\"\n",
+      "  top: \"ip1\"\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"ip2\"\n",
+      "  type: \"InnerProduct\"\n",
+      "  bottom: \"ip1\"\n",
+      "  top: \"ip2\"\n",
+      "  inner_product_param {\n",
+      "    num_output: 2\n",
+      "    weight_filler {\n",
+      "      type: \"xavier\"\n",
+      "    }\n",
+      "  }\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"accuracy\"\n",
+      "  type: \"Accuracy\"\n",
+      "  bottom: \"ip2\"\n",
+      "  bottom: \"label\"\n",
+      "  top: \"accuracy\"\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"loss\"\n",
+      "  type: \"SoftmaxWithLoss\"\n",
+      "  bottom: \"ip2\"\n",
+      "  bottom: \"label\"\n",
+      "  top: \"loss\"\n",
+      "}\n",
+      "I0318 00:58:43.655120 2013098752 layer_factory.hpp:74] Creating layer data\n",
+      "I0318 00:58:43.655139 2013098752 net.cpp:84] Creating Layer data\n",
+      "I0318 00:58:43.655264 2013098752 net.cpp:338] data -> data\n",
+      "I0318 00:58:43.655297 2013098752 net.cpp:338] data -> label\n",
+      "I0318 00:58:43.655310 2013098752 net.cpp:113] Setting up data\n",
+      "I0318 00:58:43.655318 2013098752 hdf5_data_layer.cpp:66] Loading list of HDF5 filenames from: examples/hdf5_classification/data/train.txt\n",
+      "I0318 00:58:43.655365 2013098752 hdf5_data_layer.cpp:80] Number of HDF5 files: 2\n",
+      "I0318 00:58:43.657317 2013098752 net.cpp:120] Top shape: 10 4 (40)\n",
+      "I0318 00:58:43.657342 2013098752 net.cpp:120] Top shape: 10 (10)\n",
+      "I0318 00:58:43.657356 2013098752 layer_factory.hpp:74] Creating layer label_data_1_split\n",
+      "I0318 00:58:43.657373 2013098752 net.cpp:84] Creating Layer label_data_1_split\n",
+      "I0318 00:58:43.657384 2013098752 net.cpp:380] label_data_1_split <- label\n",
+      "I0318 00:58:43.657395 2013098752 net.cpp:338] label_data_1_split -> label_data_1_split_0\n",
+      "I0318 00:58:43.657407 2013098752 net.cpp:338] label_data_1_split -> label_data_1_split_1\n",
+      "I0318 00:58:43.657418 2013098752 net.cpp:113] Setting up label_data_1_split\n",
+      "I0318 00:58:43.657426 2013098752 net.cpp:120] Top shape: 10 (10)\n",
+      "I0318 00:58:43.657433 2013098752 net.cpp:120] Top shape: 10 (10)\n",
+      "I0318 00:58:43.657441 2013098752 layer_factory.hpp:74] Creating layer ip1\n",
+      "I0318 00:58:43.657451 2013098752 net.cpp:84] Creating Layer ip1\n",
+      "I0318 00:58:43.657459 2013098752 net.cpp:380] ip1 <- data\n",
+      "I0318 00:58:43.657467 2013098752 net.cpp:338] ip1 -> ip1\n",
+      "I0318 00:58:43.657479 2013098752 net.cpp:113] Setting up ip1\n",
+      "I0318 00:58:43.662454 2013098752 net.cpp:120] Top shape: 10 40 (400)\n",
+      "I0318 00:58:43.662477 2013098752 layer_factory.hpp:74] Creating layer relu1\n",
+      "I0318 00:58:43.662497 2013098752 net.cpp:84] Creating Layer relu1\n",
+      "I0318 00:58:43.662508 2013098752 net.cpp:380] relu1 <- ip1\n",
+      "I0318 00:58:43.662520 2013098752 net.cpp:327] relu1 -> ip1 (in-place)\n",
+      "I0318 00:58:43.662530 2013098752 net.cpp:113] Setting up relu1\n",
+      "I0318 00:58:43.662539 2013098752 net.cpp:120] Top shape: 10 40 (400)\n",
+      "I0318 00:58:43.662546 2013098752 layer_factory.hpp:74] Creating layer ip2\n",
+      "I0318 00:58:43.662555 2013098752 net.cpp:84] Creating Layer ip2\n",
+      "I0318 00:58:43.662562 2013098752 net.cpp:380] ip2 <- ip1\n",
+      "I0318 00:58:43.662571 2013098752 net.cpp:338] ip2 -> ip2\n",
+      "I0318 00:58:43.662580 2013098752 net.cpp:113] Setting up ip2\n",
+      "I0318 00:58:43.662595 2013098752 net.cpp:120] Top shape: 10 2 (20)\n",
+      "I0318 00:58:43.662606 2013098752 layer_factory.hpp:74] Creating layer ip2_ip2_0_split\n",
+      "I0318 00:58:43.662654 2013098752 net.cpp:84] Creating Layer ip2_ip2_0_split\n",
+      "I0318 00:58:43.662665 2013098752 net.cpp:380] ip2_ip2_0_split <- ip2\n",
+      "I0318 00:58:43.662678 2013098752 net.cpp:338] ip2_ip2_0_split -> ip2_ip2_0_split_0\n",
+      "I0318 00:58:43.662689 2013098752 net.cpp:338] ip2_ip2_0_split -> ip2_ip2_0_split_1\n",
+      "I0318 00:58:43.662698 2013098752 net.cpp:113] Setting up ip2_ip2_0_split\n",
+      "I0318 00:58:43.662706 2013098752 net.cpp:120] Top shape: 10 2 (20)\n",
+      "I0318 00:58:43.662714 2013098752 net.cpp:120] Top shape: 10 2 (20)\n",
+      "I0318 00:58:43.662722 2013098752 layer_factory.hpp:74] Creating layer accuracy\n",
+      "I0318 00:58:43.662734 2013098752 net.cpp:84] Creating Layer accuracy\n",
+      "I0318 00:58:43.662740 2013098752 net.cpp:380] accuracy <- ip2_ip2_0_split_0\n",
+      "I0318 00:58:43.662749 2013098752 net.cpp:380] accuracy <- label_data_1_split_0\n",
+      "I0318 00:58:43.662756 2013098752 net.cpp:338] accuracy -> accuracy\n",
+      "I0318 00:58:43.662766 2013098752 net.cpp:113] Setting up accuracy\n",
+      "I0318 00:58:43.662818 2013098752 net.cpp:120] Top shape: (1)\n",
+      "I0318 00:58:43.662827 2013098752 layer_factory.hpp:74] Creating layer loss\n",
+      "I0318 00:58:43.662839 2013098752 net.cpp:84] Creating Layer loss\n",
+      "I0318 00:58:43.662847 2013098752 net.cpp:380] loss <- ip2_ip2_0_split_1\n",
+      "I0318 00:58:43.662854 2013098752 net.cpp:380] loss <- label_data_1_split_1\n",
+      "I0318 00:58:43.662863 2013098752 net.cpp:338] loss -> loss\n",
+      "I0318 00:58:43.662873 2013098752 net.cpp:113] Setting up loss\n",
+      "I0318 00:58:43.662883 2013098752 layer_factory.hpp:74] Creating layer loss\n",
+      "I0318 00:58:43.662901 2013098752 net.cpp:120] Top shape: (1)\n",
+      "I0318 00:58:43.662909 2013098752 net.cpp:122]     with loss weight 1\n",
+      "I0318 00:58:43.662922 2013098752 net.cpp:167] loss needs backward computation.\n",
+      "I0318 00:58:43.662930 2013098752 net.cpp:169] accuracy does not need backward computation.\n",
+      "I0318 00:58:43.662936 2013098752 net.cpp:167] ip2_ip2_0_split needs backward computation.\n",
+      "I0318 00:58:43.662942 2013098752 net.cpp:167] ip2 needs backward computation.\n",
+      "I0318 00:58:43.662976 2013098752 net.cpp:167] relu1 needs backward computation.\n",
+      "I0318 00:58:43.662988 2013098752 net.cpp:167] ip1 needs backward computation.\n",
+      "I0318 00:58:43.662997 2013098752 net.cpp:169] label_data_1_split does not need backward computation.\n",
+      "I0318 00:58:43.663003 2013098752 net.cpp:169] data does not need backward computation.\n",
+      "I0318 00:58:43.663009 2013098752 net.cpp:205] This network produces output accuracy\n",
+      "I0318 00:58:43.663017 2013098752 net.cpp:205] This network produces output loss\n",
+      "I0318 00:58:43.663028 2013098752 net.cpp:447] Collecting Learning Rate and Weight Decay.\n",
+      "I0318 00:58:43.663035 2013098752 net.cpp:217] Network initialization done.\n",
+      "I0318 00:58:43.663041 2013098752 net.cpp:218] Memory required for data: 3728\n",
+      "I0318 00:58:43.663158 2013098752 solver.cpp:154] Creating test net (#0) specified by test_net file: examples/hdf5_classification/nonlinear_auto_test.prototxt\n",
+      "I0318 00:58:43.663179 2013098752 net.cpp:42] Initializing net from parameters: \n",
+      "state {\n",
+      "  phase: TEST\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"data\"\n",
+      "  type: \"HDF5Data\"\n",
+      "  top: \"data\"\n",
+      "  top: \"label\"\n",
+      "  hdf5_data_param {\n",
+      "    source: \"examples/hdf5_classification/data/test.txt\"\n",
+      "    batch_size: 10\n",
+      "  }\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"ip1\"\n",
+      "  type: \"InnerProduct\"\n",
+      "  bottom: \"data\"\n",
+      "  top: \"ip1\"\n",
+      "  inner_product_param {\n",
+      "    num_output: 40\n",
+      "    weight_filler {\n",
+      "      type: \"xavier\"\n",
+      "    }\n",
+      "  }\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"relu1\"\n",
+      "  type: \"ReLU\"\n",
+      "  bottom: \"ip1\"\n",
+      "  top: \"ip1\"\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"ip2\"\n",
+      "  type: \"InnerProduct\"\n",
+      "  bottom: \"ip1\"\n",
+      "  top: \"ip2\"\n",
+      "  inner_product_param {\n",
+      "    num_output: 2\n",
+      "    weight_filler {\n",
+      "      type: \"xavier\"\n",
+      "    }\n",
+      "  }\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"accuracy\"\n",
+      "  type: \"Accuracy\"\n",
+      "  bottom: \"ip2\"\n",
+      "  bottom: \"label\"\n",
+      "  top: \"accuracy\"\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"loss\"\n",
+      "  type: \"SoftmaxWithLoss\"\n",
+      "  bottom: \"ip2\"\n",
+      "  bottom: \"label\"\n",
+      "  top: \"loss\"\n",
+      "}\n",
+      "I0318 00:58:43.663349 2013098752 layer_factory.hpp:74] Creating layer data\n",
+      "I0318 00:58:43.663365 2013098752 net.cpp:84] Creating Layer data\n",
+      "I0318 00:58:43.663373 2013098752 net.cpp:338] data -> data\n",
+      "I0318 00:58:43.663385 2013098752 net.cpp:338] data -> label\n",
+      "I0318 00:58:43.663396 2013098752 net.cpp:113] Setting up data\n",
+      "I0318 00:58:43.663422 2013098752 hdf5_data_layer.cpp:66] Loading list of HDF5 filenames from: examples/hdf5_classification/data/test.txt\n",
+      "I0318 00:58:43.663457 2013098752 hdf5_data_layer.cpp:80] Number of HDF5 files: 1\n",
+      "I0318 00:58:43.664719 2013098752 net.cpp:120] Top shape: 10 4 (40)\n",
+      "I0318 00:58:43.664739 2013098752 net.cpp:120] Top shape: 10 (10)\n",
+      "I0318 00:58:43.664754 2013098752 layer_factory.hpp:74] Creating layer label_data_1_split\n",
+      "I0318 00:58:43.664772 2013098752 net.cpp:84] Creating Layer label_data_1_split\n",
+      "I0318 00:58:43.664783 2013098752 net.cpp:380] label_data_1_split <- label\n",
+      "I0318 00:58:43.664791 2013098752 net.cpp:338] label_data_1_split -> label_data_1_split_0\n",
+      "I0318 00:58:43.664803 2013098752 net.cpp:338] label_data_1_split -> label_data_1_split_1\n",
+      "I0318 00:58:43.664813 2013098752 net.cpp:113] Setting up label_data_1_split\n",
+      "I0318 00:58:43.664822 2013098752 net.cpp:120] Top shape: 10 (10)\n",
+      "I0318 00:58:43.664829 2013098752 net.cpp:120] Top shape: 10 (10)\n",
+      "I0318 00:58:43.664837 2013098752 layer_factory.hpp:74] Creating layer ip1\n",
+      "I0318 00:58:43.664846 2013098752 net.cpp:84] Creating Layer ip1\n",
+      "I0318 00:58:43.664854 2013098752 net.cpp:380] ip1 <- data\n",
+      "I0318 00:58:43.664862 2013098752 net.cpp:338] ip1 -> ip1\n",
+      "I0318 00:58:43.664875 2013098752 net.cpp:113] Setting up ip1\n",
+      "I0318 00:58:43.664901 2013098752 net.cpp:120] Top shape: 10 40 (400)\n",
+      "I0318 00:58:43.664924 2013098752 layer_factory.hpp:74] Creating layer relu1\n",
+      "I0318 00:58:43.664945 2013098752 net.cpp:84] Creating Layer relu1\n",
+      "I0318 00:58:43.664958 2013098752 net.cpp:380] relu1 <- ip1\n",
+      "I0318 00:58:43.664966 2013098752 net.cpp:327] relu1 -> ip1 (in-place)\n",
+      "I0318 00:58:43.664975 2013098752 net.cpp:113] Setting up relu1\n",
+      "I0318 00:58:43.664983 2013098752 net.cpp:120] Top shape: 10 40 (400)\n",
+      "I0318 00:58:43.664990 2013098752 layer_factory.hpp:74] Creating layer ip2\n",
+      "I0318 00:58:43.665000 2013098752 net.cpp:84] Creating Layer ip2\n",
+      "I0318 00:58:43.665006 2013098752 net.cpp:380] ip2 <- ip1\n",
+      "I0318 00:58:43.665015 2013098752 net.cpp:338] ip2 -> ip2\n",
+      "I0318 00:58:43.665030 2013098752 net.cpp:113] Setting up ip2\n",
+      "I0318 00:58:43.665052 2013098752 net.cpp:120] Top shape: 10 2 (20)\n",
+      "I0318 00:58:43.665066 2013098752 layer_factory.hpp:74] Creating layer ip2_ip2_0_split\n",
+      "I0318 00:58:43.665077 2013098752 net.cpp:84] Creating Layer ip2_ip2_0_split\n",
+      "I0318 00:58:43.665086 2013098752 net.cpp:380] ip2_ip2_0_split <- ip2\n",
+      "I0318 00:58:43.665093 2013098752 net.cpp:338] ip2_ip2_0_split -> ip2_ip2_0_split_0\n",
+      "I0318 00:58:43.665103 2013098752 net.cpp:338] ip2_ip2_0_split -> ip2_ip2_0_split_1\n",
+      "I0318 00:58:43.665113 2013098752 net.cpp:113] Setting up ip2_ip2_0_split\n",
+      "I0318 00:58:43.665122 2013098752 net.cpp:120] Top shape: 10 2 (20)\n",
+      "I0318 00:58:43.665128 2013098752 net.cpp:120] Top shape: 10 2 (20)\n",
+      "I0318 00:58:43.665137 2013098752 layer_factory.hpp:74] Creating layer accuracy\n",
+      "I0318 00:58:43.665144 2013098752 net.cpp:84] Creating Layer accuracy\n",
+      "I0318 00:58:43.665153 2013098752 net.cpp:380] accuracy <- ip2_ip2_0_split_0\n",
+      "I0318 00:58:43.665168 2013098752 net.cpp:380] accuracy <- label_data_1_split_0\n",
+      "I0318 00:58:43.665180 2013098752 net.cpp:338] accuracy -> accuracy\n",
+      "I0318 00:58:43.665192 2013098752 net.cpp:113] Setting up accuracy\n",
+      "I0318 00:58:43.665200 2013098752 net.cpp:120] Top shape: (1)\n",
+      "I0318 00:58:43.665207 2013098752 layer_factory.hpp:74] Creating layer loss\n",
+      "I0318 00:58:43.665216 2013098752 net.cpp:84] Creating Layer loss\n",
+      "I0318 00:58:43.665223 2013098752 net.cpp:380] loss <- ip2_ip2_0_split_1\n",
+      "I0318 00:58:43.665230 2013098752 net.cpp:380] loss <- label_data_1_split_1\n",
+      "I0318 00:58:43.665241 2013098752 net.cpp:338] loss -> loss\n",
+      "I0318 00:58:43.665251 2013098752 net.cpp:113] Setting up loss\n",
+      "I0318 00:58:43.665259 2013098752 layer_factory.hpp:74] Creating layer loss\n",
+      "I0318 00:58:43.665273 2013098752 net.cpp:120] Top shape: (1)\n",
+      "I0318 00:58:43.665282 2013098752 net.cpp:122]     with loss weight 1\n",
+      "I0318 00:58:43.665290 2013098752 net.cpp:167] loss needs backward computation.\n",
+      "I0318 00:58:43.665338 2013098752 net.cpp:169] accuracy does not need backward computation.\n",
+      "I0318 00:58:43.665351 2013098752 net.cpp:167] ip2_ip2_0_split needs backward computation.\n",
+      "I0318 00:58:43.665380 2013098752 net.cpp:167] ip2 needs backward computation.\n",
+      "I0318 00:58:43.665387 2013098752 net.cpp:167] relu1 needs backward computation.\n",
+      "I0318 00:58:43.665393 2013098752 net.cpp:167] ip1 needs backward computation.\n",
+      "I0318 00:58:43.665400 2013098752 net.cpp:169] label_data_1_split does not need backward computation.\n",
+      "I0318 00:58:43.665407 2013098752 net.cpp:169] data does not need backward computation.\n",
+      "I0318 00:58:43.665415 2013098752 net.cpp:205] This network produces output accuracy\n",
+      "I0318 00:58:43.665421 2013098752 net.cpp:205] This network produces output loss\n",
+      "I0318 00:58:43.665431 2013098752 net.cpp:447] Collecting Learning Rate and Weight Decay.\n",
+      "I0318 00:58:43.665441 2013098752 net.cpp:217] Network initialization done.\n",
+      "I0318 00:58:43.665446 2013098752 net.cpp:218] Memory required for data: 3728\n",
+      "I0318 00:58:43.665534 2013098752 solver.cpp:42] Solver scaffolding done.\n",
+      "I0318 00:58:43.665568 2013098752 solver.cpp:222] Solving \n",
+      "I0318 00:58:43.665577 2013098752 solver.cpp:223] Learning Rate Policy: step\n",
+      "I0318 00:58:43.665586 2013098752 solver.cpp:266] Iteration 0, Testing net (#0)\n",
+      "I0318 00:58:43.683938 2013098752 solver.cpp:315]     Test net output #0: accuracy = 0.5184\n",
+      "I0318 00:58:43.683981 2013098752 solver.cpp:315]     Test net output #1: loss = 0.716141 (* 1 = 0.716141 loss)\n",
+      "I0318 00:58:43.684236 2013098752 solver.cpp:189] Iteration 0, loss = 0.764954\n",
+      "I0318 00:58:43.684267 2013098752 solver.cpp:204]     Train net output #0: accuracy = 0.5\n",
+      "I0318 00:58:43.684285 2013098752 solver.cpp:204]     Train net output #1: loss = 0.764954 (* 1 = 0.764954 loss)\n",
+      "I0318 00:58:43.684305 2013098752 solver.cpp:464] Iteration 0, lr = 0.01\n",
+      "I0318 00:58:43.714700 2013098752 solver.cpp:266] Iteration 1000, Testing net (#0)\n",
+      "I0318 00:58:43.721762 2013098752 solver.cpp:315]     Test net output #0: accuracy = 0.8168\n",
+      "I0318 00:58:43.721818 2013098752 solver.cpp:315]     Test net output #1: loss = 0.434918 (* 1 = 0.434918 loss)\n",
+      "I0318 00:58:43.721899 2013098752 solver.cpp:189] Iteration 1000, loss = 0.282425\n",
+      "I0318 00:58:43.721917 2013098752 solver.cpp:204]     Train net output #0: accuracy = 0.9\n",
+      "I0318 00:58:43.721932 2013098752 solver.cpp:204]     Train net output #1: loss = 0.282426 (* 1 = 0.282426 loss)\n",
+      "I0318 00:58:43.721942 2013098752 solver.cpp:464] Iteration 1000, lr = 0.01\n",
+      "I0318 00:58:43.750509 2013098752 solver.cpp:266] Iteration 2000, Testing net (#0)\n",
+      "I0318 00:58:43.754590 2013098752 solver.cpp:315]     Test net output #0: accuracy = 0.8224\n",
+      "I0318 00:58:43.754621 2013098752 solver.cpp:315]     Test net output #1: loss = 0.416874 (* 1 = 0.416874 loss)\n",
+      "I0318 00:58:43.754660 2013098752 solver.cpp:189] Iteration 2000, loss = 0.51988\n",
+      "I0318 00:58:43.754672 2013098752 solver.cpp:204]     Train net output #0: accuracy = 0.7\n",
+      "I0318 00:58:43.754683 2013098752 solver.cpp:204]     Train net output #1: loss = 0.51988 (* 1 = 0.51988 loss)\n",
+      "I0318 00:58:43.754690 2013098752 solver.cpp:464] Iteration 2000, lr = 0.01\n",
+      "I0318 00:58:43.782609 2013098752 solver.cpp:266] Iteration 3000, Testing net (#0)\n",
+      "I0318 00:58:43.789728 2013098752 solver.cpp:315]     Test net output #0: accuracy = 0.8176\n",
+      "I0318 00:58:43.789777 2013098752 solver.cpp:315]     Test net output #1: loss = 0.415907 (* 1 = 0.415907 loss)\n",
+      "I0318 00:58:43.790487 2013098752 solver.cpp:189] Iteration 3000, loss = 0.5093\n",
+      "I0318 00:58:43.790510 2013098752 solver.cpp:204]     Train net output #0: accuracy = 0.7\n",
+      "I0318 00:58:43.790530 2013098752 solver.cpp:204]     Train net output #1: loss = 0.509301 (* 1 = 0.509301 loss)\n",
+      "I0318 00:58:43.790544 2013098752 solver.cpp:464] Iteration 3000, lr = 0.01\n",
+      "I0318 00:58:43.817451 2013098752 solver.cpp:266] Iteration 4000, Testing net (#0)\n",
+      "I0318 00:58:43.821740 2013098752 solver.cpp:315]     Test net output #0: accuracy = 0.8252\n",
+      "I0318 00:58:43.821770 2013098752 solver.cpp:315]     Test net output #1: loss = 0.409124 (* 1 = 0.409124 loss)\n",
+      "I0318 00:58:43.821822 2013098752 solver.cpp:189] Iteration 4000, loss = 0.284815\n",
+      "I0318 00:58:43.821835 2013098752 solver.cpp:204]     Train net output #0: accuracy = 0.9\n",
+      "I0318 00:58:43.821846 2013098752 solver.cpp:204]     Train net output #1: loss = 0.284815 (* 1 = 0.284815 loss)\n",
+      "I0318 00:58:43.821890 2013098752 solver.cpp:464] Iteration 4000, lr = 0.01\n",
+      "I0318 00:58:43.847015 2013098752 solver.cpp:266] Iteration 5000, Testing net (#0)\n",
+      "I0318 00:58:43.852102 2013098752 solver.cpp:315]     Test net output #0: accuracy = 0.8256\n",
+      "I0318 00:58:43.852145 2013098752 solver.cpp:315]     Test net output #1: loss = 0.404445 (* 1 = 0.404445 loss)\n",
+      "I0318 00:58:43.852188 2013098752 solver.cpp:189] Iteration 5000, loss = 0.511566\n",
+      "I0318 00:58:43.852200 2013098752 solver.cpp:204]     Train net output #0: accuracy = 0.7\n",
+      "I0318 00:58:43.852210 2013098752 solver.cpp:204]     Train net output #1: loss = 0.511566 (* 1 = 0.511566 loss)\n",
+      "I0318 00:58:43.852219 2013098752 solver.cpp:464] Iteration 5000, lr = 0.001\n",
+      "I0318 00:58:43.876060 2013098752 solver.cpp:266] Iteration 6000, Testing net (#0)\n",
+      "I0318 00:58:43.880080 2013098752 solver.cpp:315]     Test net output #0: accuracy = 0.8328\n",
+      "I0318 00:58:43.880105 2013098752 solver.cpp:315]     Test net output #1: loss = 0.396847 (* 1 = 0.396847 loss)\n",
+      "I0318 00:58:43.880700 2013098752 solver.cpp:189] Iteration 6000, loss = 0.397858\n",
+      "I0318 00:58:43.880718 2013098752 solver.cpp:204]     Train net output #0: accuracy = 0.9\n",
+      "I0318 00:58:43.880729 2013098752 solver.cpp:204]     Train net output #1: loss = 0.397858 (* 1 = 0.397858 loss)\n",
+      "I0318 00:58:43.880738 2013098752 solver.cpp:464] Iteration 6000, lr = 0.001\n",
+      "I0318 00:58:43.913795 2013098752 solver.cpp:266] Iteration 7000, Testing net (#0)\n",
+      "I0318 00:58:43.917851 2013098752 solver.cpp:315]     Test net output #0: accuracy = 0.8316\n",
+      "I0318 00:58:43.917876 2013098752 solver.cpp:315]     Test net output #1: loss = 0.398135 (* 1 = 0.398135 loss)\n",
+      "I0318 00:58:43.917956 2013098752 solver.cpp:189] Iteration 7000, loss = 0.243849\n",
+      "I0318 00:58:43.917971 2013098752 solver.cpp:204]     Train net output #0: accuracy = 0.9\n",
+      "I0318 00:58:43.917989 2013098752 solver.cpp:204]     Train net output #1: loss = 0.243849 (* 1 = 0.243849 loss)\n",
+      "I0318 00:58:43.918002 2013098752 solver.cpp:464] Iteration 7000, lr = 0.001\n",
+      "I0318 00:58:43.943681 2013098752 solver.cpp:266] Iteration 8000, Testing net (#0)\n",
+      "I0318 00:58:43.947589 2013098752 solver.cpp:315]     Test net output #0: accuracy = 0.8312\n",
+      "I0318 00:58:43.947615 2013098752 solver.cpp:315]     Test net output #1: loss = 0.394763 (* 1 = 0.394763 loss)\n",
+      "I0318 00:58:43.947651 2013098752 solver.cpp:189] Iteration 8000, loss = 0.513399\n",
+      "I0318 00:58:43.947664 2013098752 solver.cpp:204]     Train net output #0: accuracy = 0.7\n",
+      "I0318 00:58:43.947674 2013098752 solver.cpp:204]     Train net output #1: loss = 0.513399 (* 1 = 0.513399 loss)\n",
+      "I0318 00:58:43.947682 2013098752 solver.cpp:464] Iteration 8000, lr = 0.001\n",
+      "I0318 00:58:43.973080 2013098752 solver.cpp:266] Iteration 9000, Testing net (#0)\n",
+      "I0318 00:58:43.977033 2013098752 solver.cpp:315]     Test net output #0: accuracy = 0.834\n",
+      "I0318 00:58:43.977056 2013098752 solver.cpp:315]     Test net output #1: loss = 0.395663 (* 1 = 0.395663 loss)\n",
+      "I0318 00:58:43.977710 2013098752 solver.cpp:189] Iteration 9000, loss = 0.399341\n",
+      "I0318 00:58:43.977735 2013098752 solver.cpp:204]     Train net output #0: accuracy = 0.9\n",
+      "I0318 00:58:43.977746 2013098752 solver.cpp:204]     Train net output #1: loss = 0.399342 (* 1 = 0.399342 loss)\n",
+      "I0318 00:58:43.977756 2013098752 solver.cpp:464] Iteration 9000, lr = 0.001\n",
+      "I0318 00:58:44.003437 2013098752 solver.cpp:334] Snapshotting to examples/hdf5_classification/data/train_iter_10000.caffemodel\n",
+      "I0318 00:58:44.003702 2013098752 solver.cpp:342] Snapshotting solver state to examples/hdf5_classification/data/train_iter_10000.solverstate\n",
+      "I0318 00:58:44.003850 2013098752 solver.cpp:248] Iteration 10000, loss = 0.244639\n",
+      "I0318 00:58:44.003871 2013098752 solver.cpp:266] Iteration 10000, Testing net (#0)\n",
+      "I0318 00:58:44.008216 2013098752 solver.cpp:315]     Test net output #0: accuracy = 0.8308\n",
+      "I0318 00:58:44.008252 2013098752 solver.cpp:315]     Test net output #1: loss = 0.397291 (* 1 = 0.397291 loss)\n",
+      "I0318 00:58:44.008262 2013098752 solver.cpp:253] Optimization Done.\n",
+      "I0318 00:58:44.008270 2013098752 caffe.cpp:134] Optimization Done.\n"
+     ]
+    }
+   ],
+   "source": [
+    "!./build/tools/caffe train -solver examples/hdf5_classification/nonlinear_solver.prototxt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Clean up (comment this out if you want to examine the hdf5_classification/data directory).\n",
+    "shutil.rmtree(dirname)"
+   ]
+  }
+ ],
+ "metadata": {
+  "description": "Use Caffe as a generic SGD optimizer to train logistic regression on non-image HDF5 data.",
+  "example_name": "Off-the-shelf SGD for classification",
+  "include_in_docs": true,
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.9"
+  },
+  "priority": 3
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/examples/03-fine-tuning.ipynb b/examples/03-fine-tuning.ipynb
new file mode 100644
index 00000000000..cc90b16bbfa
--- /dev/null
+++ b/examples/03-fine-tuning.ipynb
@@ -0,0 +1,947 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Fine-tuning a Pretrained Network for Style Recognition\n",
+    "\n",
+    "In this example, we'll explore a common approach that is particularly useful in real-world applications: take a pre-trained Caffe network and fine-tune the parameters on your custom data.\n",
+    "\n",
+    "The upside of such approach is that, since pre-trained networks are learned on a large set of images, the intermediate layers capture the \"semantics\" of the general visual appearance. Think of it as a very powerful feature that you can treat as a black box. On top of that, only a few layers will be needed to obtain a very good performance of the data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First, we will need to prepare the data. This involves the following parts:\n",
+    "(1) Get the ImageNet ilsvrc pretrained model with the provided shell scripts.\n",
+    "(2) Download a subset of the overall Flickr style dataset for this demo.\n",
+    "(3) Compile the downloaded Flickr dataset into a database that Caffe can then consume."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "os.chdir('..')\n",
+    "import sys\n",
+    "sys.path.insert(0, './python')\n",
+    "\n",
+    "import caffe\n",
+    "import numpy as np\n",
+    "from pylab import *\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# This downloads the ilsvrc auxiliary data (mean file, etc),\n",
+    "# and a subset of 2000 images for the style recognition task.\n",
+    "!data/ilsvrc12/get_ilsvrc_aux.sh\n",
+    "!scripts/download_model_binary.py models/bvlc_reference_caffenet\n",
+    "!python examples/finetune_flickr_style/assemble_data.py \\\n",
+    "    --workers=-1 --images=2000 --seed=1701 --label=5"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's show what is the difference between the fine-tuning network and the original caffe model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1c1\r\n",
+      "< name: \"CaffeNet\"\r\n",
+      "---\r\n",
+      "> name: \"FlickrStyleCaffeNet\"\r\n",
+      "4c4\r\n",
+      "<   type: \"Data\"\r\n",
+      "---\r\n",
+      ">   type: \"ImageData\"\r\n",
+      "15,26c15,19\r\n",
+      "< # mean pixel / channel-wise mean instead of mean image\r\n",
+      "< #  transform_param {\r\n",
+      "< #    crop_size: 227\r\n",
+      "< #    mean_value: 104\r\n",
+      "< #    mean_value: 117\r\n",
+      "< #    mean_value: 123\r\n",
+      "< #    mirror: true\r\n",
+      "< #  }\r\n",
+      "<   data_param {\r\n",
+      "<     source: \"examples/imagenet/ilsvrc12_train_lmdb\"\r\n",
+      "<     batch_size: 256\r\n",
+      "<     backend: LMDB\r\n",
+      "---\r\n",
+      ">   image_data_param {\r\n",
+      ">     source: \"data/flickr_style/train.txt\"\r\n",
+      ">     batch_size: 50\r\n",
+      ">     new_height: 256\r\n",
+      ">     new_width: 256\r\n",
+      "31c24\r\n",
+      "<   type: \"Data\"\r\n",
+      "---\r\n",
+      ">   type: \"ImageData\"\r\n",
+      "42,51c35,36\r\n",
+      "< # mean pixel / channel-wise mean instead of mean image\r\n",
+      "< #  transform_param {\r\n",
+      "< #    crop_size: 227\r\n",
+      "< #    mean_value: 104\r\n",
+      "< #    mean_value: 117\r\n",
+      "< #    mean_value: 123\r\n",
+      "< #    mirror: true\r\n",
+      "< #  }\r\n",
+      "<   data_param {\r\n",
+      "<     source: \"examples/imagenet/ilsvrc12_val_lmdb\"\r\n",
+      "---\r\n",
+      ">   image_data_param {\r\n",
+      ">     source: \"data/flickr_style/test.txt\"\r\n",
+      "53c38,39\r\n",
+      "<     backend: LMDB\r\n",
+      "---\r\n",
+      ">     new_height: 256\r\n",
+      ">     new_width: 256\r\n",
+      "323a310\r\n",
+      ">   # Note that lr_mult can be set to 0 to disable any fine-tuning of this, and any other, layer\r\n",
+      "360c347\r\n",
+      "<   name: \"fc8\"\r\n",
+      "---\r\n",
+      ">   name: \"fc8_flickr\"\r\n",
+      "363c350,351\r\n",
+      "<   top: \"fc8\"\r\n",
+      "---\r\n",
+      ">   top: \"fc8_flickr\"\r\n",
+      ">   # lr_mult is set to higher than for other layers, because this layer is starting from random while the others are already trained\r\n",
+      "365c353\r\n",
+      "<     lr_mult: 1\r\n",
+      "---\r\n",
+      ">     lr_mult: 10\r\n",
+      "369c357\r\n",
+      "<     lr_mult: 2\r\n",
+      "---\r\n",
+      ">     lr_mult: 20\r\n",
+      "373c361\r\n",
+      "<     num_output: 1000\r\n",
+      "---\r\n",
+      ">     num_output: 20\r\n",
+      "384a373,379\r\n",
+      ">   name: \"loss\"\r\n",
+      ">   type: \"SoftmaxWithLoss\"\r\n",
+      ">   bottom: \"fc8_flickr\"\r\n",
+      ">   bottom: \"label\"\r\n",
+      ">   top: \"loss\"\r\n",
+      "> }\r\n",
+      "> layer {\r\n",
+      "387c382\r\n",
+      "<   bottom: \"fc8\"\r\n",
+      "---\r\n",
+      ">   bottom: \"fc8_flickr\"\r\n",
+      "393,399d387\r\n",
+      "< }\r\n",
+      "< layer {\r\n",
+      "<   name: \"loss\"\r\n",
+      "<   type: \"SoftmaxWithLoss\"\r\n",
+      "<   bottom: \"fc8\"\r\n",
+      "<   bottom: \"label\"\r\n",
+      "<   top: \"loss\"\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!diff models/bvlc_reference_caffenet/train_val.prototxt models/finetune_flickr_style/train_val.prototxt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For your record, if you want to train the network in pure C++ tools, here is the command:\n",
+    "\n",
+    "<code>\n",
+    "build/tools/caffe train \\\n",
+    "    -solver models/finetune_flickr_style/solver.prototxt \\\n",
+    "    -weights models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel \\\n",
+    "    -gpu 0\n",
+    "</code>\n",
+    "\n",
+    "However, we will train using Python in this example."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "iter 0, finetune_loss=3.360094, scratch_loss=3.136188\n",
+      "iter 10, finetune_loss=2.672608, scratch_loss=9.736364\n",
+      "iter 20, finetune_loss=2.071996, scratch_loss=2.250404\n",
+      "iter 30, finetune_loss=1.758295, scratch_loss=2.049553\n",
+      "iter 40, finetune_loss=1.533391, scratch_loss=1.941318\n",
+      "iter 50, finetune_loss=1.561658, scratch_loss=1.839706\n",
+      "iter 60, finetune_loss=1.461696, scratch_loss=1.880035\n",
+      "iter 70, finetune_loss=1.267941, scratch_loss=1.719161\n",
+      "iter 80, finetune_loss=1.192778, scratch_loss=1.627453\n",
+      "iter 90, finetune_loss=1.541176, scratch_loss=1.822061\n",
+      "iter 100, finetune_loss=1.029039, scratch_loss=1.654087\n",
+      "iter 110, finetune_loss=1.138547, scratch_loss=1.735837\n",
+      "iter 120, finetune_loss=0.917412, scratch_loss=1.851918\n",
+      "iter 130, finetune_loss=0.971519, scratch_loss=1.801927\n",
+      "iter 140, finetune_loss=0.868252, scratch_loss=1.745545\n",
+      "iter 150, finetune_loss=0.790020, scratch_loss=1.844925\n",
+      "iter 160, finetune_loss=1.092668, scratch_loss=1.695591\n",
+      "iter 170, finetune_loss=1.055344, scratch_loss=1.661715\n",
+      "iter 180, finetune_loss=0.969769, scratch_loss=1.823639\n",
+      "iter 190, finetune_loss=0.780566, scratch_loss=1.820862\n",
+      "done\n"
+     ]
+    }
+   ],
+   "source": [
+    "niter = 200\n",
+    "# losses will also be stored in the log\n",
+    "train_loss = np.zeros(niter)\n",
+    "scratch_train_loss = np.zeros(niter)\n",
+    "\n",
+    "caffe.set_device(0)\n",
+    "caffe.set_mode_gpu()\n",
+    "# We create a solver that fine-tunes from a previously trained network.\n",
+    "solver = caffe.SGDSolver('models/finetune_flickr_style/solver.prototxt')\n",
+    "solver.net.copy_from('models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel')\n",
+    "# For reference, we also create a solver that does no finetuning.\n",
+    "scratch_solver = caffe.SGDSolver('models/finetune_flickr_style/solver.prototxt')\n",
+    "\n",
+    "# We run the solver for niter times, and record the training loss.\n",
+    "for it in range(niter):\n",
+    "    solver.step(1)  # SGD by Caffe\n",
+    "    scratch_solver.step(1)\n",
+    "    # store the train loss\n",
+    "    train_loss[it] = solver.net.blobs['loss'].data\n",
+    "    scratch_train_loss[it] = scratch_solver.net.blobs['loss'].data\n",
+    "    if it % 10 == 0:\n",
+    "        print 'iter %d, finetune_loss=%f, scratch_loss=%f' % (it, train_loss[it], scratch_train_loss[it])\n",
+    "print 'done'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's look at the training loss produced by the two training procedures respectively."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7fbb36f0ad50>,\n",
+       " <matplotlib.lines.Line2D at 0x7fbb36f0afd0>]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": [
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+       "UlvRigAAAABJRU5ErkJggg==\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fbb37f20990>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot(np.vstack([train_loss, scratch_train_loss]).T)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notice how the fine-tuning procedure produces a more smooth loss function change, and ends up at a better loss. A closer look at small values, clipping to avoid showing too large loss during training:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7fbb347a8310>,\n",
+       " <matplotlib.lines.Line2D at 0x7fbb347a8590>]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
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+       "asoJ3sysppzgzcxq6j+vUsbacqJa4gAAAABJRU5ErkJggg==\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fbb37f207d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot(np.vstack([train_loss, scratch_train_loss]).clip(0, 4).T)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's take a look at the testing accuracy after running 200 iterations. Note that we are running a classification task of 5 classes, thus a chance accuracy is 20%. As we will reasonably expect, the finetuning result will be much better than the one from training from scratch. Let's see."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy for fine-tuning: 0.570000001788\n",
+      "Accuracy for training from scratch: 0.224000000954\n"
+     ]
+    }
+   ],
+   "source": [
+    "test_iters = 10\n",
+    "accuracy = 0\n",
+    "scratch_accuracy = 0\n",
+    "for it in arange(test_iters):\n",
+    "    solver.test_nets[0].forward()\n",
+    "    accuracy += solver.test_nets[0].blobs['accuracy'].data\n",
+    "    scratch_solver.test_nets[0].forward()\n",
+    "    scratch_accuracy += scratch_solver.test_nets[0].blobs['accuracy'].data\n",
+    "accuracy /= test_iters\n",
+    "scratch_accuracy /= test_iters\n",
+    "print 'Accuracy for fine-tuning:', accuracy\n",
+    "print 'Accuracy for training from scratch:', scratch_accuracy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Huzzah! So we did finetuning and it is awesome. Let's take a look at what kind of results we are able to get with a longer, more complete run of the style recognition dataset. Note: the below URL might be occassionally down because it is run on a research machine.\n",
+    "\n",
+    "http://demo.vislab.berkeleyvision.org/"
+   ]
+  }
+ ],
+ "metadata": {
+  "description": "Fine-tune the ImageNet-trained CaffeNet on new data.",
+  "example_name": "Fine-tuning for Style Recognition",
+  "include_in_docs": true,
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.9"
+  },
+  "priority": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt
index 055f4ef0d35..f29fc7e5522 100644
--- a/examples/CMakeLists.txt
+++ b/examples/CMakeLists.txt
@@ -1,16 +1,31 @@
-project( Examples )
+file(GLOB_RECURSE examples_srcs "${PROJECT_SOURCE_DIR}/examples/*.cpp")
 
-file(GLOB_RECURSE EXAMPLES_SOURCES ${CMAKE_CURRENT_SOURCE_DIR}/*.cpp)
-
-foreach(source ${EXAMPLES_SOURCES})
-    #    get file name
-    get_filename_component(name ${source} NAME_WE)  
+foreach(source_file ${examples_srcs})
+  # get file name
+  get_filename_component(name ${source_file} NAME_WE)
     
-    #get folder name
-    get_filename_component(path ${source} PATH)      
-    get_filename_component(folder ${path} NAME_WE)      
+  # get folder name
+  get_filename_component(path ${source_file} PATH)
+  get_filename_component(folder ${path} NAME_WE)
     
-    add_executable(${name} ${source})
-    target_link_libraries(${name} caffe)
-    set_target_properties(${name} PROPERTIES RUNTIME_OUTPUT_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}/${folder})
-endforeach(source)
+  add_executable(${name} ${source_file})
+  target_link_libraries(${name} ${Caffe_LINK})
+  caffe_default_properties(${name})
+
+  # set back RUNTIME_OUTPUT_DIRECTORY
+  set_target_properties(${name} PROPERTIES
+    RUNTIME_OUTPUT_DIRECTORY "${PROJECT_BINARY_DIR}/examples/${folder}")
+
+  caffe_set_solution_folder(${name} examples)
+
+  # install
+  install(TARGETS ${name} DESTINATION bin)
+
+  if(UNIX OR APPLE)
+    # Funny command to make tutorials work
+    # TODO: remove in future as soon as naming is standartaized everywhere
+    set(__outname ${PROJECT_BINARY_DIR}/examples/${folder}/${name}${CAffe_POSTFIX})
+    add_custom_command(TARGET ${name} POST_BUILD
+                       COMMAND ln -sf "${__outname}" "${__outname}.bin")
+  endif()
+endforeach()
diff --git a/examples/action_recognition/dataset_file_examples/train_flow_split1.txt b/examples/action_recognition/dataset_file_examples/train_flow_split1.txt
new file mode 100644
index 00000000000..767eff29785
--- /dev/null
+++ b/examples/action_recognition/dataset_file_examples/train_flow_split1.txt
@@ -0,0 +1,9537 @@
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+ucf101_flow_img_tvl1_gpu/StillRings/v_StillRings_g18_c02 192 85
+ucf101_flow_img_tvl1_gpu/IceDancing/v_IceDancing_g19_c06 251 43
+ucf101_flow_img_tvl1_gpu/Skijet/v_Skijet_g08_c04 208 81
+ucf101_flow_img_tvl1_gpu/Lunges/v_Lunges_g13_c01 266 51
+ucf101_flow_img_tvl1_gpu/CleanAndJerk/v_CleanAndJerk_g16_c04 105 20
+ucf101_flow_img_tvl1_gpu/RopeClimbing/v_RopeClimbing_g13_c04 88 74
+ucf101_flow_img_tvl1_gpu/BaseballPitch/v_BaseballPitch_g14_c01 103 6
+ucf101_flow_img_tvl1_gpu/CuttingInKitchen/v_CuttingInKitchen_g16_c02 197 24
+ucf101_flow_img_tvl1_gpu/SkateBoarding/v_SkateBoarding_g22_c04 85 79
+ucf101_flow_img_tvl1_gpu/RockClimbingIndoor/v_RockClimbingIndoor_g18_c02 168 73
+ucf101_flow_img_tvl1_gpu/FloorGymnastics/v_FloorGymnastics_g24_c01 174 29
+ucf101_flow_img_tvl1_gpu/LongJump/v_LongJump_g16_c03 134 50
+ucf101_flow_img_tvl1_gpu/BalanceBeam/v_BalanceBeam_g23_c04 106 4
+ucf101_flow_img_tvl1_gpu/BoxingPunchingBag/v_BoxingPunchingBag_g12_c05 231 16
+ucf101_flow_img_tvl1_gpu/Bowling/v_Bowling_g20_c02 133 15
+ucf101_flow_img_tvl1_gpu/ThrowDiscus/v_ThrowDiscus_g13_c03 113 92
+ucf101_flow_img_tvl1_gpu/Mixing/v_Mixing_g19_c03 101 53
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+ucf101_flow_img_tvl1_gpu/Diving/v_Diving_g25_c03 145 25
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+ucf101_flow_img_tvl1_gpu/PlayingSitar/v_PlayingSitar_g16_c02 412 64
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+ucf101_flow_img_tvl1_gpu/FrisbeeCatch/v_FrisbeeCatch_g09_c03 68 30
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+ucf101_flow_img_tvl1_gpu/JavelinThrow/v_JavelinThrow_g08_c02 95 44
+ucf101_flow_img_tvl1_gpu/TableTennisShot/v_TableTennisShot_g08_c02 50 89
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+ucf101_flow_img_tvl1_gpu/VolleyballSpiking/v_VolleyballSpiking_g08_c04 116 96
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+ucf101_flow_img_tvl1_gpu/Diving/v_Diving_g25_c04 148 25
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+ucf101_flow_img_tvl1_gpu/PlayingDaf/v_PlayingDaf_g14_c06 292 59
+ucf101_flow_img_tvl1_gpu/VolleyballSpiking/v_VolleyballSpiking_g10_c02 168 96
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+ucf101_flow_img_tvl1_gpu/MilitaryParade/v_MilitaryParade_g14_c01 150 52
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+ucf101_flow_img_tvl1_gpu/JugglingBalls/v_JugglingBalls_g20_c01 240 45
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diff --git a/examples/action_recognition/dataset_file_examples/train_rgb_split1.txt b/examples/action_recognition/dataset_file_examples/train_rgb_split1.txt
new file mode 100644
index 00000000000..eb92e0266a4
--- /dev/null
+++ b/examples/action_recognition/dataset_file_examples/train_rgb_split1.txt
@@ -0,0 +1,9537 @@
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diff --git a/examples/action_recognition/dataset_file_examples/val_flow_split1.txt b/examples/action_recognition/dataset_file_examples/val_flow_split1.txt
new file mode 100644
index 00000000000..292d72d8944
--- /dev/null
+++ b/examples/action_recognition/dataset_file_examples/val_flow_split1.txt
@@ -0,0 +1,3783 @@
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diff --git a/examples/action_recognition/dataset_file_examples/val_rgb_split1.txt b/examples/action_recognition/dataset_file_examples/val_rgb_split1.txt
new file mode 100644
index 00000000000..f91a9280fc3
--- /dev/null
+++ b/examples/action_recognition/dataset_file_examples/val_rgb_split1.txt
@@ -0,0 +1,3783 @@
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+ucf101_rgb_img/WalkingWithDog/v_WalkingWithDog_g05_c01 239 97
+ucf101_rgb_img/JavelinThrow/v_JavelinThrow_g07_c05 71 44
+ucf101_rgb_img/Archery/v_Archery_g06_c02 115 2
+ucf101_rgb_img/BoxingSpeedBag/v_BoxingSpeedBag_g04_c02 131 17
+ucf101_rgb_img/PlayingSitar/v_PlayingSitar_g05_c06 433 64
+ucf101_rgb_img/LongJump/v_LongJump_g04_c03 140 50
+ucf101_rgb_img/FieldHockeyPenalty/v_FieldHockeyPenalty_g05_c07 106 28
+ucf101_rgb_img/YoYo/v_YoYo_g05_c02 178 100
+ucf101_rgb_img/HulaHoop/v_HulaHoop_g02_c02 120 42
+ucf101_rgb_img/BabyCrawling/v_BabyCrawling_g07_c05 140 3
+ucf101_rgb_img/JumpingJack/v_JumpingJack_g01_c06 87 46
+ucf101_rgb_img/Diving/v_Diving_g06_c06 218 25
+ucf101_rgb_img/ThrowDiscus/v_ThrowDiscus_g02_c06 89 92
+ucf101_rgb_img/IceDancing/v_IceDancing_g03_c03 246 43
+ucf101_rgb_img/BoxingSpeedBag/v_BoxingSpeedBag_g04_c01 110 17
+ucf101_rgb_img/SoccerJuggling/v_SoccerJuggling_g07_c02 300 83
+ucf101_rgb_img/Swing/v_Swing_g05_c05 200 88
+ucf101_rgb_img/CricketShot/v_CricketShot_g06_c05 121 23
+ucf101_rgb_img/BrushingTeeth/v_BrushingTeeth_g02_c02 731 19
+ucf101_rgb_img/ParallelBars/v_ParallelBars_g03_c04 157 56
+ucf101_rgb_img/Typing/v_Typing_g02_c02 249 94
+ucf101_rgb_img/FrisbeeCatch/v_FrisbeeCatch_g06_c01 73 30
+ucf101_rgb_img/ThrowDiscus/v_ThrowDiscus_g03_c01 118 92
+ucf101_rgb_img/ApplyLipstick/v_ApplyLipstick_g01_c01 148 1
+ucf101_rgb_img/CricketShot/v_CricketShot_g02_c03 96 23
+ucf101_rgb_img/BodyWeightSquats/v_BodyWeightSquats_g01_c02 251 14
+ucf101_rgb_img/MoppingFloor/v_MoppingFloor_g06_c03 222 54
+ucf101_rgb_img/PoleVault/v_PoleVault_g01_c01 195 67
+ucf101_rgb_img/SalsaSpin/v_SalsaSpin_g05_c01 199 76
+ucf101_rgb_img/BoxingSpeedBag/v_BoxingSpeedBag_g07_c03 450 17
+ucf101_rgb_img/ThrowDiscus/v_ThrowDiscus_g06_c02 61 92
+ucf101_rgb_img/SkateBoarding/v_SkateBoarding_g07_c02 198 79
+ucf101_rgb_img/BabyCrawling/v_BabyCrawling_g05_c01 220 3
+ucf101_rgb_img/SoccerJuggling/v_SoccerJuggling_g01_c01 899 83
+ucf101_rgb_img/Skiing/v_Skiing_g06_c02 299 80
+ucf101_rgb_img/Punch/v_Punch_g04_c05 197 70
+ucf101_rgb_img/YoYo/v_YoYo_g04_c05 179 100
+ucf101_rgb_img/BodyWeightSquats/v_BodyWeightSquats_g03_c03 47 14
+ucf101_rgb_img/SalsaSpin/v_SalsaSpin_g05_c04 199 76
+ucf101_rgb_img/Lunges/v_Lunges_g06_c07 261 51
+ucf101_rgb_img/Hammering/v_Hammering_g05_c02 123 34
+ucf101_rgb_img/Knitting/v_Knitting_g06_c04 109 49
+ucf101_rgb_img/JavelinThrow/v_JavelinThrow_g07_c04 89 44
+ucf101_rgb_img/MilitaryParade/v_MilitaryParade_g01_c01 127 52
+ucf101_rgb_img/Rowing/v_Rowing_g06_c01 324 75
+ucf101_rgb_img/Surfing/v_Surfing_g01_c03 219 87
+ucf101_rgb_img/Drumming/v_Drumming_g01_c07 299 26
+ucf101_rgb_img/JumpingJack/v_JumpingJack_g04_c04 84 46
+ucf101_rgb_img/LongJump/v_LongJump_g02_c01 109 50
+ucf101_rgb_img/BandMarching/v_BandMarching_g05_c02 83 5
+ucf101_rgb_img/Bowling/v_Bowling_g01_c07 130 15
+ucf101_rgb_img/Archery/v_Archery_g05_c03 487 2
+ucf101_rgb_img/Typing/v_Typing_g07_c05 128 94
+ucf101_rgb_img/ShavingBeard/v_ShavingBeard_g06_c01 180 77
+ucf101_rgb_img/Diving/v_Diving_g02_c02 150 25
+ucf101_rgb_img/PlayingGuitar/v_PlayingGuitar_g03_c06 249 62
+ucf101_rgb_img/RopeClimbing/v_RopeClimbing_g02_c06 185 74
+ucf101_rgb_img/Rowing/v_Rowing_g05_c03 413 75
+ucf101_rgb_img/VolleyballSpiking/v_VolleyballSpiking_g03_c03 122 96
+ucf101_rgb_img/CricketShot/v_CricketShot_g06_c03 117 23
+ucf101_rgb_img/Punch/v_Punch_g01_c05 70 70
+ucf101_rgb_img/PlayingGuitar/v_PlayingGuitar_g02_c04 199 62
+ucf101_rgb_img/BoxingPunchingBag/v_BoxingPunchingBag_g07_c01 251 16
+ucf101_rgb_img/FrisbeeCatch/v_FrisbeeCatch_g07_c05 78 30
+ucf101_rgb_img/Drumming/v_Drumming_g06_c01 299 26
+ucf101_rgb_img/CricketShot/v_CricketShot_g02_c06 95 23
+ucf101_rgb_img/WallPushups/v_WallPushups_g05_c04 85 98
+ucf101_rgb_img/Skijet/v_Skijet_g03_c02 315 81
+ucf101_rgb_img/Typing/v_Typing_g05_c03 249 94
+ucf101_rgb_img/FieldHockeyPenalty/v_FieldHockeyPenalty_g05_c03 111 28
+ucf101_rgb_img/PushUps/v_PushUps_g02_c01 66 71
+ucf101_rgb_img/Fencing/v_Fencing_g04_c05 109 27
+ucf101_rgb_img/Skijet/v_Skijet_g01_c03 415 81
+ucf101_rgb_img/BandMarching/v_BandMarching_g07_c04 131 5
+ucf101_rgb_img/Haircut/v_Haircut_g01_c03 109 33
+ucf101_rgb_img/Kayaking/v_Kayaking_g02_c03 180 48
+ucf101_rgb_img/BandMarching/v_BandMarching_g02_c05 122 5
+ucf101_rgb_img/SoccerPenalty/v_SoccerPenalty_g06_c03 64 84
+ucf101_rgb_img/PlayingGuitar/v_PlayingGuitar_g07_c04 249 62
+ucf101_rgb_img/PlayingDhol/v_PlayingDhol_g04_c05 424 60
+ucf101_rgb_img/Bowling/v_Bowling_g06_c05 129 15
+ucf101_rgb_img/BreastStroke/v_BreastStroke_g01_c02 95 18
+ucf101_rgb_img/BoxingPunchingBag/v_BoxingPunchingBag_g04_c01 244 16
+ucf101_rgb_img/CliffDiving/v_CliffDiving_g06_c06 71 21
+ucf101_rgb_img/Nunchucks/v_Nunchucks_g04_c05 267 55
+ucf101_rgb_img/BoxingSpeedBag/v_BoxingSpeedBag_g04_c05 223 17
+ucf101_rgb_img/BlowingCandles/v_BlowingCandles_g07_c03 124 13
+ucf101_rgb_img/BoxingPunchingBag/v_BoxingPunchingBag_g05_c04 237 16
+ucf101_rgb_img/SkateBoarding/v_SkateBoarding_g03_c03 125 79
+ucf101_rgb_img/Lunges/v_Lunges_g02_c01 164 51
+ucf101_rgb_img/FrontCrawl/v_FrontCrawl_g06_c03 99 31
+ucf101_rgb_img/Lunges/v_Lunges_g04_c01 261 51
+ucf101_rgb_img/WallPushups/v_WallPushups_g05_c01 88 98
+ucf101_rgb_img/VolleyballSpiking/v_VolleyballSpiking_g04_c06 109 96
+ucf101_rgb_img/GolfSwing/v_GolfSwing_g01_c01 171 32
+ucf101_rgb_img/SoccerJuggling/v_SoccerJuggling_g01_c04 704 83
+ucf101_rgb_img/FloorGymnastics/v_FloorGymnastics_g01_c03 185 29
diff --git a/examples/action_recognition/train_action_recognition_flow.sh b/examples/action_recognition/train_action_recognition_flow.sh
new file mode 100755
index 00000000000..a2397b45357
--- /dev/null
+++ b/examples/action_recognition/train_action_recognition_flow.sh
@@ -0,0 +1,8 @@
+#!/usr/bin/env sh
+
+GOOGLE_LOG_DIR=models/action_recognition/log \
+    mpirun -np 4 \
+    cmake_build/install/bin/caffe train \
+    --solver=models/action_recognition/vgg_16_flow_solver.prototxt \
+    --weights=vgg_16_action_flow_pretrain.caffemodel
+
diff --git a/examples/action_recognition/train_action_recognition_rgb.sh b/examples/action_recognition/train_action_recognition_rgb.sh
new file mode 100755
index 00000000000..b38857e3041
--- /dev/null
+++ b/examples/action_recognition/train_action_recognition_rgb.sh
@@ -0,0 +1,8 @@
+#!/usr/bin/env sh
+
+GOOGLE_LOG_DIR=models/action_recognition/log \
+    mpirun -np 4 \
+    cmake_build/install/bin/caffe train \
+    --solver=models/action_recognition/vgg_16_rgb_solver.prototxt \
+    --weights=vgg_16_action_rgb_pretrain.caffemodel
+
diff --git a/examples/cifar10/cifar10_full.prototxt b/examples/cifar10/cifar10_full.prototxt
index 8bbd30004fd..c16f7dca49f 100644
--- a/examples/cifar10/cifar10_full.prototxt
+++ b/examples/cifar10/cifar10_full.prototxt
@@ -6,13 +6,17 @@ input_dim: 1
 input_dim: 3
 input_dim: 32
 input_dim: 32
-layers {
+layer {
   name: "conv1"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "data"
   top: "conv1"
-  blobs_lr: 1
-  blobs_lr: 2
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
   convolution_param {
     num_output: 32
     pad: 2
@@ -20,9 +24,9 @@ layers {
     stride: 1
   }
 }
-layers {
+layer {
   name: "pool1"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv1"
   top: "pool1"
   pooling_param {
@@ -31,31 +35,35 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "relu1"
-  type: RELU
+  type: "ReLU"
   bottom: "pool1"
   top: "pool1"
 }
-layers {
+layer {
   name: "norm1"
-  type: LRN
+  type: "LRN"
   bottom: "pool1"
   top: "norm1"
   lrn_param {
-    norm_region: WITHIN_CHANNEL
     local_size: 3
     alpha: 5e-05
     beta: 0.75
+    norm_region: WITHIN_CHANNEL
   }
 }
-layers {
+layer {
   name: "conv2"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "norm1"
   top: "conv2"
-  blobs_lr: 1
-  blobs_lr: 2
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
   convolution_param {
     num_output: 32
     pad: 2
@@ -63,15 +71,15 @@ layers {
     stride: 1
   }
 }
-layers {
+layer {
   name: "relu2"
-  type: RELU
+  type: "ReLU"
   bottom: "conv2"
   top: "conv2"
 }
-layers {
+layer {
   name: "pool2"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv2"
   top: "pool2"
   pooling_param {
@@ -80,21 +88,21 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "norm2"
-  type: LRN
+  type: "LRN"
   bottom: "pool2"
   top: "norm2"
   lrn_param {
-    norm_region: WITHIN_CHANNEL
     local_size: 3
     alpha: 5e-05
     beta: 0.75
+    norm_region: WITHIN_CHANNEL
   }
 }
-layers {
+layer {
   name: "conv3"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "norm2"
   top: "conv3"
   convolution_param {
@@ -104,15 +112,15 @@ layers {
     stride: 1
   }
 }
-layers {
+layer {
   name: "relu3"
-  type: RELU
+  type: "ReLU"
   bottom: "conv3"
   top: "conv3"
 }
-layers {
+layer {
   name: "pool3"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv3"
   top: "pool3"
   pooling_param {
@@ -121,22 +129,26 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "ip1"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "pool3"
   top: "ip1"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 250
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 250
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   inner_product_param {
     num_output: 10
   }
 }
-layers {
+layer {
   name: "prob"
-  type: SOFTMAX
+  type: "Softmax"
   bottom: "ip1"
   top: "prob"
 }
diff --git a/examples/cifar10/cifar10_full_train_test.prototxt b/examples/cifar10/cifar10_full_train_test.prototxt
index 8fde19f046e..d45fc61e120 100644
--- a/examples/cifar10/cifar10_full_train_test.prototxt
+++ b/examples/cifar10/cifar10_full_train_test.prototxt
@@ -1,39 +1,49 @@
 name: "CIFAR10_full"
-layers {
+layer {
   name: "cifar"
-  type: DATA
+  type: "Data"
   top: "data"
   top: "label"
-  data_param {
-    source: "examples/cifar10/cifar10_train_leveldb"
-    batch_size: 100
+  include {
+    phase: TRAIN
   }
   transform_param {
     mean_file: "examples/cifar10/mean.binaryproto"
   }
-  include: { phase: TRAIN }
+  data_param {
+    source: "examples/cifar10/cifar10_train_lmdb"
+    batch_size: 100
+    backend: LMDB
+  }
 }
-layers {
+layer {
   name: "cifar"
-  type: DATA
+  type: "Data"
   top: "data"
   top: "label"
-  data_param {
-    source: "examples/cifar10/cifar10_test_leveldb"
-    batch_size: 100
+  include {
+    phase: TEST
   }
   transform_param {
     mean_file: "examples/cifar10/mean.binaryproto"
   }
-  include: { phase: TEST }
+  data_param {
+    source: "examples/cifar10/cifar10_test_lmdb"
+    batch_size: 100
+    backend: LMDB
+  }
 }
-layers {
+layer {
   name: "conv1"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "data"
   top: "conv1"
-  blobs_lr: 1
-  blobs_lr: 2
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
   convolution_param {
     num_output: 32
     pad: 2
@@ -48,9 +58,9 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "pool1"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv1"
   top: "pool1"
   pooling_param {
@@ -59,31 +69,35 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "relu1"
-  type: RELU
+  type: "ReLU"
   bottom: "pool1"
   top: "pool1"
 }
-layers {
+layer {
   name: "norm1"
-  type: LRN
+  type: "LRN"
   bottom: "pool1"
   top: "norm1"
   lrn_param {
-    norm_region: WITHIN_CHANNEL
     local_size: 3
     alpha: 5e-05
     beta: 0.75
+    norm_region: WITHIN_CHANNEL
   }
 }
-layers {
+layer {
   name: "conv2"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "norm1"
   top: "conv2"
-  blobs_lr: 1
-  blobs_lr: 2
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
   convolution_param {
     num_output: 32
     pad: 2
@@ -98,15 +112,15 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu2"
-  type: RELU
+  type: "ReLU"
   bottom: "conv2"
   top: "conv2"
 }
-layers {
+layer {
   name: "pool2"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv2"
   top: "pool2"
   pooling_param {
@@ -115,21 +129,21 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "norm2"
-  type: LRN
+  type: "LRN"
   bottom: "pool2"
   top: "norm2"
   lrn_param {
-    norm_region: WITHIN_CHANNEL
     local_size: 3
     alpha: 5e-05
     beta: 0.75
+    norm_region: WITHIN_CHANNEL
   }
 }
-layers {
+layer {
   name: "conv3"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "norm2"
   top: "conv3"
   convolution_param {
@@ -146,15 +160,15 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu3"
-  type: RELU
+  type: "ReLU"
   bottom: "conv3"
   top: "conv3"
 }
-layers {
+layer {
   name: "pool3"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv3"
   top: "pool3"
   pooling_param {
@@ -163,15 +177,19 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "ip1"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "pool3"
   top: "ip1"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 250
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 250
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   inner_product_param {
     num_output: 10
     weight_filler {
@@ -183,17 +201,19 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "accuracy"
-  type: ACCURACY
+  type: "Accuracy"
   bottom: "ip1"
   bottom: "label"
   top: "accuracy"
-  include: { phase: TEST }
+  include {
+    phase: TEST
+  }
 }
-layers {
+layer {
   name: "loss"
-  type: SOFTMAX_LOSS
+  type: "SoftmaxWithLoss"
   bottom: "ip1"
   bottom: "label"
   top: "loss"
diff --git a/examples/cifar10/cifar10_quick.prototxt b/examples/cifar10/cifar10_quick.prototxt
index 505158f7a34..1ad190e185f 100644
--- a/examples/cifar10/cifar10_quick.prototxt
+++ b/examples/cifar10/cifar10_quick.prototxt
@@ -4,13 +4,17 @@ input_dim: 1
 input_dim: 3
 input_dim: 32
 input_dim: 32
-layers {
+layer {
   name: "conv1"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "data"
   top: "conv1"
-  blobs_lr: 1
-  blobs_lr: 2
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
   convolution_param {
     num_output: 32
     pad: 2
@@ -18,9 +22,9 @@ layers {
     stride: 1
   }
 }
-layers {
+layer {
   name: "pool1"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv1"
   top: "pool1"
   pooling_param {
@@ -29,19 +33,23 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "relu1"
-  type: RELU
+  type: "ReLU"
   bottom: "pool1"
   top: "pool1"
 }
-layers {
+layer {
   name: "conv2"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "pool1"
   top: "conv2"
-  blobs_lr: 1
-  blobs_lr: 2
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
   convolution_param {
     num_output: 32
     pad: 2
@@ -49,15 +57,15 @@ layers {
     stride: 1
   }
 }
-layers {
+layer {
   name: "relu2"
-  type: RELU
+  type: "ReLU"
   bottom: "conv2"
   top: "conv2"
 }
-layers {
+layer {
   name: "pool2"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv2"
   top: "pool2"
   pooling_param {
@@ -66,13 +74,17 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "conv3"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "pool2"
   top: "conv3"
-  blobs_lr: 1
-  blobs_lr: 2
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
   convolution_param {
     num_output: 64
     pad: 2
@@ -80,15 +92,15 @@ layers {
     stride: 1
   }
 }
-layers {
+layer {
   name: "relu3"
-  type: RELU
+  type: "ReLU"
   bottom: "conv3"
   top: "conv3"
 }
-layers {
+layer {
   name: "pool3"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv3"
   top: "pool3"
   pooling_param {
@@ -97,31 +109,39 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "ip1"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "pool3"
   top: "ip1"
-  blobs_lr: 1
-  blobs_lr: 2
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
   inner_product_param {
     num_output: 64
   }
 }
-layers {
+layer {
   name: "ip2"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "ip1"
   top: "ip2"
-  blobs_lr: 1
-  blobs_lr: 2
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
   inner_product_param {
     num_output: 10
   }
 }
-layers {
+layer {
   name: "prob"
-  type: SOFTMAX
+  type: "Softmax"
   bottom: "ip2"
   top: "prob"
 }
diff --git a/examples/cifar10/cifar10_quick_train_test.prototxt b/examples/cifar10/cifar10_quick_train_test.prototxt
index 409cfe809f4..2317739353e 100644
--- a/examples/cifar10/cifar10_quick_train_test.prototxt
+++ b/examples/cifar10/cifar10_quick_train_test.prototxt
@@ -1,39 +1,49 @@
 name: "CIFAR10_quick"
-layers {
+layer {
   name: "cifar"
-  type: DATA
+  type: "Data"
   top: "data"
   top: "label"
-  data_param {
-    source: "examples/cifar10/cifar10_train_leveldb"
-    batch_size: 100
+  include {
+    phase: TRAIN
   }
   transform_param {
     mean_file: "examples/cifar10/mean.binaryproto"
   }
-  include: { phase: TRAIN }
+  data_param {
+    source: "examples/cifar10/cifar10_train_lmdb"
+    batch_size: 100
+    backend: LMDB
+  }
 }
-layers {
+layer {
   name: "cifar"
-  type: DATA
+  type: "Data"
   top: "data"
   top: "label"
-  data_param {
-    source: "examples/cifar10/cifar10_test_leveldb"
-    batch_size: 100
+  include {
+    phase: TEST
   }
   transform_param {
     mean_file: "examples/cifar10/mean.binaryproto"
   }
-  include: { phase: TEST }
+  data_param {
+    source: "examples/cifar10/cifar10_test_lmdb"
+    batch_size: 100
+    backend: LMDB
+  }
 }
-layers {
+layer {
   name: "conv1"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "data"
   top: "conv1"
-  blobs_lr: 1
-  blobs_lr: 2
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
   convolution_param {
     num_output: 32
     pad: 2
@@ -48,9 +58,9 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "pool1"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv1"
   top: "pool1"
   pooling_param {
@@ -59,19 +69,23 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "relu1"
-  type: RELU
+  type: "ReLU"
   bottom: "pool1"
   top: "pool1"
 }
-layers {
+layer {
   name: "conv2"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "pool1"
   top: "conv2"
-  blobs_lr: 1
-  blobs_lr: 2
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
   convolution_param {
     num_output: 32
     pad: 2
@@ -86,15 +100,15 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu2"
-  type: RELU
+  type: "ReLU"
   bottom: "conv2"
   top: "conv2"
 }
-layers {
+layer {
   name: "pool2"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv2"
   top: "pool2"
   pooling_param {
@@ -103,13 +117,17 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "conv3"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "pool2"
   top: "conv3"
-  blobs_lr: 1
-  blobs_lr: 2
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
   convolution_param {
     num_output: 64
     pad: 2
@@ -124,15 +142,15 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu3"
-  type: RELU
+  type: "ReLU"
   bottom: "conv3"
   top: "conv3"
 }
-layers {
+layer {
   name: "pool3"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv3"
   top: "pool3"
   pooling_param {
@@ -141,13 +159,17 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "ip1"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "pool3"
   top: "ip1"
-  blobs_lr: 1
-  blobs_lr: 2
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
   inner_product_param {
     num_output: 64
     weight_filler {
@@ -159,13 +181,17 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "ip2"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "ip1"
   top: "ip2"
-  blobs_lr: 1
-  blobs_lr: 2
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
   inner_product_param {
     num_output: 10
     weight_filler {
@@ -177,17 +203,19 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "accuracy"
-  type: ACCURACY
+  type: "Accuracy"
   bottom: "ip2"
   bottom: "label"
   top: "accuracy"
-  include: { phase: TEST }
+  include {
+    phase: TEST
+  }
 }
-layers {
+layer {
   name: "loss"
-  type: SOFTMAX_LOSS
+  type: "SoftmaxWithLoss"
   bottom: "ip2"
   bottom: "label"
   top: "loss"
diff --git a/examples/cifar10/convert_cifar_data.cpp b/examples/cifar10/convert_cifar_data.cpp
index 90ecb6d9a88..f4c42e4d2e7 100644
--- a/examples/cifar10/convert_cifar_data.cpp
+++ b/examples/cifar10/convert_cifar_data.cpp
@@ -9,14 +9,18 @@
 #include <fstream>  // NOLINT(readability/streams)
 #include <string>
 
+#include "boost/scoped_ptr.hpp"
 #include "glog/logging.h"
 #include "google/protobuf/text_format.h"
-#include "leveldb/db.h"
 #include "stdint.h"
 
 #include "caffe/proto/caffe.pb.h"
+#include "caffe/util/db.hpp"
 
+using caffe::Datum;
+using boost::scoped_ptr;
 using std::string;
+namespace db = caffe::db;
 
 const int kCIFARSize = 32;
 const int kCIFARImageNBytes = 3072;
@@ -31,26 +35,20 @@ void read_image(std::ifstream* file, int* label, char* buffer) {
   return;
 }
 
-void convert_dataset(const string& input_folder, const string& output_folder) {
-  // Leveldb options
-  leveldb::Options options;
-  options.create_if_missing = true;
-  options.error_if_exists = true;
+void convert_dataset(const string& input_folder, const string& output_folder,
+    const string& db_type) {
+  scoped_ptr<db::DB> train_db(db::GetDB(db_type));
+  train_db->Open(output_folder + "/cifar10_train_" + db_type, db::NEW);
+  scoped_ptr<db::Transaction> txn(train_db->NewTransaction());
   // Data buffer
   int label;
   char str_buffer[kCIFARImageNBytes];
-  string value;
-  caffe::Datum datum;
+  Datum datum;
   datum.set_channels(3);
   datum.set_height(kCIFARSize);
   datum.set_width(kCIFARSize);
 
   LOG(INFO) << "Writing Training data";
-  leveldb::DB* train_db;
-  leveldb::Status status;
-  status = leveldb::DB::Open(options, output_folder + "/cifar10_train_leveldb",
-      &train_db);
-  CHECK(status.ok()) << "Failed to open leveldb.";
   for (int fileid = 0; fileid < kCIFARTrainBatches; ++fileid) {
     // Open files
     LOG(INFO) << "Training Batch " << fileid + 1;
@@ -62,17 +60,20 @@ void convert_dataset(const string& input_folder, const string& output_folder) {
       read_image(&data_file, &label, str_buffer);
       datum.set_label(label);
       datum.set_data(str_buffer, kCIFARImageNBytes);
-      datum.SerializeToString(&value);
-      snprintf(str_buffer, kCIFARImageNBytes, "%05d",
+      int length = snprintf(str_buffer, kCIFARImageNBytes, "%05d",
           fileid * kCIFARBatchSize + itemid);
-      train_db->Put(leveldb::WriteOptions(), string(str_buffer), value);
+      string out;
+      CHECK(datum.SerializeToString(&out));
+      txn->Put(string(str_buffer, length), out);
     }
   }
+  txn->Commit();
+  train_db->Close();
 
   LOG(INFO) << "Writing Testing data";
-  leveldb::DB* test_db;
-  CHECK(leveldb::DB::Open(options, output_folder + "/cifar10_test_leveldb",
-      &test_db).ok()) << "Failed to open leveldb.";
+  scoped_ptr<db::DB> test_db(db::GetDB(db_type));
+  test_db->Open(output_folder + "/cifar10_test_" + db_type, db::NEW);
+  txn.reset(test_db->NewTransaction());
   // Open files
   std::ifstream data_file((input_folder + "/test_batch.bin").c_str(),
       std::ios::in | std::ios::binary);
@@ -81,28 +82,28 @@ void convert_dataset(const string& input_folder, const string& output_folder) {
     read_image(&data_file, &label, str_buffer);
     datum.set_label(label);
     datum.set_data(str_buffer, kCIFARImageNBytes);
-    datum.SerializeToString(&value);
-    snprintf(str_buffer, kCIFARImageNBytes, "%05d", itemid);
-    test_db->Put(leveldb::WriteOptions(), string(str_buffer), value);
+    int length = snprintf(str_buffer, kCIFARImageNBytes, "%05d", itemid);
+    string out;
+    CHECK(datum.SerializeToString(&out));
+    txn->Put(string(str_buffer, length), out);
   }
-
-  delete train_db;
-  delete test_db;
+  txn->Commit();
+  test_db->Close();
 }
 
 int main(int argc, char** argv) {
-  if (argc != 3) {
+  if (argc != 4) {
     printf("This script converts the CIFAR dataset to the leveldb format used\n"
            "by caffe to perform classification.\n"
            "Usage:\n"
-           "    convert_cifar_data input_folder output_folder\n"
+           "    convert_cifar_data input_folder output_folder db_type\n"
            "Where the input folder should contain the binary batch files.\n"
            "The CIFAR dataset could be downloaded at\n"
            "    http://www.cs.toronto.edu/~kriz/cifar.html\n"
            "You should gunzip them after downloading.\n");
   } else {
     google::InitGoogleLogging(argv[0]);
-    convert_dataset(string(argv[1]), string(argv[2]));
+    convert_dataset(string(argv[1]), string(argv[2]), string(argv[3]));
   }
   return 0;
 }
diff --git a/examples/cifar10/create_cifar10.sh b/examples/cifar10/create_cifar10.sh
index dfba7cca48a..a42725cb610 100755
--- a/examples/cifar10/create_cifar10.sh
+++ b/examples/cifar10/create_cifar10.sh
@@ -3,16 +3,17 @@
 
 EXAMPLE=examples/cifar10
 DATA=data/cifar10
+DBTYPE=lmdb
 
-echo "Creating leveldb..."
+echo "Creating $DBTYPE..."
 
-rm -rf $EXAMPLE/cifar10_train_leveldb $EXAMPLE/cifar10_test_leveldb
+rm -rf $EXAMPLE/cifar10_train_$DBTYPE $EXAMPLE/cifar10_test_$DBTYPE
 
-./build/examples/cifar10/convert_cifar_data.bin $DATA $EXAMPLE
+./build/examples/cifar10/convert_cifar_data.bin $DATA $EXAMPLE $DBTYPE
 
 echo "Computing image mean..."
 
-./build/tools/compute_image_mean $EXAMPLE/cifar10_train_leveldb \
-  $EXAMPLE/mean.binaryproto leveldb
+./build/tools/compute_image_mean -backend=$DBTYPE \
+  $EXAMPLE/cifar10_train_$DBTYPE $EXAMPLE/mean.binaryproto
 
 echo "Done."
diff --git a/examples/cifar10/readme.md b/examples/cifar10/readme.md
index a329b49c5ea..4a95cee9e8f 100644
--- a/examples/cifar10/readme.md
+++ b/examples/cifar10/readme.md
@@ -24,8 +24,8 @@ You will first need to download and convert the data format from the [CIFAR-10 w
 
     cd $CAFFE_ROOT/data/cifar10
     ./get_cifar10.sh
-    cd $CAFFE_ROOT/examples/cifar10
-    ./create_cifar10.sh
+    cd $CAFFE_ROOT
+    ./examples/cifar10/create_cifar10.sh
 
 If it complains that `wget` or `gunzip` are not installed, you need to install them respectively. After running the script there should be the dataset, `./cifar10-leveldb`, and the data set image mean `./mean.binaryproto`.
 
@@ -39,8 +39,8 @@ Training and Testing the "Quick" Model
 
 Training the model is simple after you have written the network definition protobuf and solver protobuf files (refer to [MNIST Tutorial](../examples/mnist.html)). Simply run `train_quick.sh`, or the following command directly:
 
-    cd $CAFFE_ROOT/examples/cifar10
-    ./train_quick.sh
+    cd $CAFFE_ROOT
+    ./examples/cifar10/train_quick.sh
 
 `train_quick.sh` is a simple script, so have a look inside. The main tool for training is `caffe` with the `train` action, and the solver protobuf text file as its argument.
 
diff --git a/examples/classification.ipynb b/examples/classification.ipynb
deleted file mode 100644
index fb44da8958f..00000000000
--- a/examples/classification.ipynb
+++ /dev/null
@@ -1,407 +0,0 @@
-{
- "metadata": {
-  "description": "Use the pre-trained ImageNet model to classify images with the Python interface.",
-  "example_name": "ImageNet classification",
-  "include_in_docs": true,
-  "priority": 1
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
-  {
-   "cells": [
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Classifying ImageNet: the instant Caffe way\n",
-      "===========================================\n",
-      "\n",
-      "Caffe provides a general Python interface for models with `caffe.Net` in `python/caffe/pycaffe.py`, but to make off-the-shelf classification easy we provide a `caffe.Classifier` class and `classify.py` script. Both Python and MATLAB wrappers are provided. However, the Python wrapper has more features so we will describe it here. For MATLAB, refer to `matlab/caffe/matcaffe_demo.m`.\n",
-      "\n",
-      "Before we begin, you must compile Caffe and install the python wrapper by setting your `PYTHONPATH`. If you haven't yet done so, please refer to the [installation instructions](installation.html). This example uses our pre-trained CaffeNet model, an ILSVRC12 image classifier. You can download it by running `./scripts/download_model_binary.py models/bvlc_reference_caffenet`. Note that this pre-trained model is licensed for academic research / non-commercial use only.\n",
-      "\n",
-      "Ready? Let's start."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import numpy as np\n",
-      "import matplotlib.pyplot as plt\n",
-      "%matplotlib inline\n",
-      "\n",
-      "# Make sure that caffe is on the python path:\n",
-      "caffe_root = '../'  # this file is expected to be in {caffe_root}/examples\n",
-      "import sys\n",
-      "sys.path.insert(0, caffe_root + 'python')\n",
-      "\n",
-      "import caffe\n",
-      "\n",
-      "# Set the right path to your model definition file, pretrained model weights,\n",
-      "# and the image you would like to classify.\n",
-      "MODEL_FILE = '../models/bvlc_reference_caffenet/deploy.prototxt'\n",
-      "PRETRAINED = '../models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel'\n",
-      "IMAGE_FILE = 'images/cat.jpg'"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 1
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Loading a network is easy. `caffe.Classifier` takes care of everything. Note the arguments for configuring input preprocessing: mean subtraction switched on by giving a mean array, input channel swapping takes care of mapping RGB into the reference ImageNet model's BGR order, and raw scaling multiplies the feature scale from the input [0,1] to the ImageNet model's [0,255]."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "net = caffe.Classifier(MODEL_FILE, PRETRAINED,\n",
-      "                       mean=np.load(caffe_root + 'python/caffe/imagenet/ilsvrc_2012_mean.npy'),\n",
-      "                       channel_swap=(2,1,0),\n",
-      "                       raw_scale=255,\n",
-      "                       image_dims=(256, 256))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 2
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "We will set the phase to test since we are doing testing, and will first use CPU for the computation."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "net.set_phase_test()\n",
-      "net.set_mode_cpu()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 3
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Let's take a look at our example image with Caffe's image loading helper."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "input_image = caffe.io.load_image(IMAGE_FILE)\n",
-      "plt.imshow(input_image)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 4,
-       "text": [
-        "<matplotlib.image.AxesImage at 0x7fda204c0e10>"
-       ]
-      },
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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/+dP/NH/oX/73OL56j5/7E/8pt+v71GJ4AZGaz0b0iV+8dpUsa+F4WLBiBDui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71Uxl70yVqVOiQqaGuxTtFYg28yxgW8BAhgTal9SbknQjWuaCXT+5ypVklRfIAMcUVHm9FWdNZt\n6zikMKhUcM3/D3EXDx8+5OnTpzx69IgPP/yQBw8eAPD666/z/vvvH7/u+9//Pq+//voPfI67Jzu5\nkQv4padZ+WNVqJR4pMtBi6mUWKuSohjJDT+wLYUTqOsELtdS/vCoerAiE7dSEmSxWMaDyOkwCSTX\naaRGm7ojK4NqZGGLIZNDwWpH04rbxji5oLTWhH0kpULT1J/dlOqSSKSkK1XFCkjDaKxxWA1RCdx4\nDjPZiIsppsA0z4R5QhXJadZV0xqSLIpZH/BdhjxLdaoVwg9VwjQsRqAK0uYQOUaJMG1hZ8sRlkKU\ndoJRGp09UUsomJrBny5oncbNMyUXxjjKz+IMOUZK1sxB+JA5JxZ9j9aG09NzciyElLj/+E3G3a1c\nI2+8idKW1Xotv/+m5VPvfI4Pnnyf9959j+1mplt6qbZjYIozTnesml7iPTDs9jte++TbkuGtIiGk\n6nDxbLY3tG0HSvPaozdZn5+TgaZtGO+u2Q43MLeYvuN285KLizOM6/nekw/RKtEvBd3m3IJhGJjD\ngHWFdXeKxkPWdI2rA5FQY09mXGMw3nJ9ec3uZstut8PogrcZ7x0lK+ZRMspt79jfbXjzE4bu8Rvo\nuwXv/9XfEM2MQYYyRVX5TDKkPImLTReMqqJvJQM+U8BhsEqBCoJGVGKWMCmiVBRrsU4UHcnKCjZP\nF7GwKtBZFnqdIM+RZORkpuosoOTD0KmQgmQCkWXwKTi2cqziZHZQsFpegzGWFBKN90zzWJGQMuxx\nrcG3nq5vsQYxY1hwthzbVd45lNXi7KvidK1r642KStRCGZujYOw+coYk5CiRv0pjjJWN4OgwFGtp\nIvLdv3nGu3/94Q+dwRwe/6JF85d+6Zf46le/ym/+5m/y1a9+lX/7b//t8eO//Mu/zG/8xm/w5MkT\nvv3tb/MzP/MzP/A5Vo9XVfIjFSdksV8ddySEr1elA3Jw1cRa7hcRdtUjuyx6xoBSEimQ84E8pKpt\nsFbFQFQfHRgdUG4zrmkkOVfN8sYoA0p6J9Za5jmjdcQ6L5ZEK0d+ozI5S3pmSQWxIZQ6SZTpPFQ/\nrlYi7jXifGjblrlMYu8bdhIfkITunUuufmbpR+aUyVNCZU2pvvNcokicauUcVUI5och7a4gFQpBc\nl5w1WhfiPjGbgioOFQNlNhhvcDrR+syyPeXUtnzm4ac471bEcU9OmWke8VaxWq8YpxHvHFMlehsj\nnFBrLM43pPgq42XYb3nw6BNobbi9veVs1RGjZg6RaR65f++Cvuu4vLzhO999n2gG3rh4yJwT2jSg\nDs9ZKKowTBPOedanp3zn77/FovEMw0DXLTg7O2fYT7z+xuvc3m7Q1lHIzPOM1g5t13jvmXNitTzD\nG8vlZsv5w8dcf/A9bm7uWCwXjONIyTNKJRq/JIaZGGa58ULAupZs9pLd03eUEthubpiGPdfXLwDY\n3N2hSsE1vQw2w0xSDq8aSky0fYvq1ijjoFhSgXGaiDGRYiAVIfhIQF9B61ytgoLnO3jWXRECu0Na\nBU6rWv8dHG6vrkmrjBynDQKoyAe8oa3uPNBRKkGlDEY1zLGQ54N9OAtLU2oZ6acai/fNkfcgdU71\nhVPo2p4UM646lUB6srp19AtH0xisAWUkQlergFJgncMYhVUahRGJYs6kOZLTLImWSlxHWsnwNs6B\nYowMyZATZizglUdXRUkusR7PxVmlMbz+2TNe/+yZIPKA/+t//Ma/fNH80pe+xNe//nUuLy958803\n+e3f/m1+67d+iy9+8Yv83u/9Hm+99RZ/+Id/CMDnP/95vvjFL/L5z38eay2/+7u/+08ez4+aqrrg\nCZC4UrLR9XhZ5UPVN3rYXcm8gnqkmuOti6TU1STHksrxWB5zPvZCAUzJpFLnMiVJRdA29F2H8VF6\nL0XIJ1YbYgAz136rivi24BuFtkUC7XN9jdnKwqZleFUq+T2XGetkd1RGKuoYM41r8N7RdR3DduDl\ndMU0BvKcpbF+yIuKEFKQDPQ5k6JCKMmHY7ipAyC56o3NtI2jWyhiKYxzJk6GaS/oOa0tYaMoKRLH\nTJgKrs94VyBqmjLJZFt15HlgHG5J0xZrPVq3oqdcLLi7vhVkWJQWgvdtrU6ksg4xSnWQC3fbLYvl\nik++9QmmcWAeBm5vXnL18il/8xd7Ht2/z+rsdX7ycz/J//F//weefO97/MSn3yYXR9c2BDTDFGhM\npm1blsueYRj51Cc+zbe+9Q26rkFpw6Jfc3v9HsZYuq6TSW5p0CkyDAFtehIR5zy+6VEYXn/rPsN2\nR86FRWvZ3lwS4oz1lkW3JMRE36+Y9B6tYRp25JKwbc+6OSWVSNt2xDBDEpr/9vaGEiaWp/fI2rHd\n3rFcNCRr0M5ycn7CYrkmF0mYNMpxd33Hbh4Yx5FhGgU0nKJkJ6ksiNxKJwopShtKS3WIl7gMp01t\nNRVUyWJ4MNXiSyKpLHpiFKUkZG4p75EwvVW1Hh6SDV5N0UtSpMoAUFEwN8pomsZhjaGUuinHVPkL\nGu8NzmSckX+fi8J4g3GGoDLFCM/TaIO38u/RPVZXPkSWe/GwDSgtAYJWVYedE3ScqcPWOeXaz5Sh\ncs4JlQrTQX+qFBCgCHoxxIBxogeVGBppt/2wxz+7aP7+7//+D/z4n/7pn/7Aj3/5y1/my1/+8j/3\ntK8m4NUNJJVgkXlLERqRcDJTnWAXstIiydE1bkIBTkg9Tafw3pCTkaEHWY45RSo0rZVcZIXK0xSC\ndcoK1zgan3FNxncKimc/BrKKQELbhG0NeiygDcoUrAdlCphMyV7CqGIkZ7l4UxFWoSKhTcaYXPuV\nGaOl6mwbg/eeohS+MSSVuHz6kjkEAoKK0y6jYqmVhGY8QDqqXEtVRpNUmiLlaHvD6tSgfcZrw1It\n2N4kGq2YhpEwZfJYyFp+8XGXKXNBLRznbcujfoFDs58msNJ3RmucteL7nQMvty/pu4btMBJDBZUo\n6R0ZbZhjlSkpzzSMoLdYlfi7Zx9wfXXN9dUVz5+/YDdIFev1t1mfLvmpf/Vf89/963/Dv/v3/ytP\nnl/x+MHbqG7BTEJPhbaXSt16h1WGzdUl/aKl5IjSjpgi5xcXtL6RIUYWC2lKgawbUt4wT3usbWkX\nltPzB6QUMe3E1jtuNldYW+i6nqZfoopBm1xRfAVdijhknFjvYpDB11hGwhS5fnGFsZrVyZI5QL84\n5W7Y0PYNy/Ua27W0y5azi1M2u0su5k9Cu+bDZ8+5vb5miJFxHIhzYB4nWShVPYtpAzqRmBA8oEIX\ny36YaJzCIu6hkhPikbAEXcSh5DWq9RyQlyrVe00rmXLPAUxDVkoWBV1zgIqjxICYLsU0obMs2IqC\njBI0WjtSFoShdUokczpjncbbhpIj1ovTx7aerDIlTqLFTEWye3BY29SvzxIFkyX+IicFSuzBRTli\nmtHKUOZCiZF5NkyzZbubGMIEWgkNPgoj8pCDbg6U/hRBlZqoIL1Nd6DXp/BD164fKbCjFJATq6qN\nYXH/qIM48iPi9EMCpVGCqJIcHzmqd31D24qPtWTBuY17GbikRKWdxOoBp5LeqagqhfbQLh2ujWhT\n8N4ypkn6NrqQdMImi7UyDFJolI4YrchESpnluZMcC3IpGCQVUlPQJgoCDukjde0Kpx1N4+m6Duct\ny9jSOokOuLqNbG4mlAuYVgLGhF6taZaWMCa5iSszS+kqyDUR7TX9PY9fB5yDtumJsyLuExaNMZ69\nlmCvRjmWbY9y4lo67dc8XN3jnj9l3ZyQ1MQ4RlSINLaVI3ISLWSeZ7Z3W5rGUUqmaXrZrV1DKYqm\nkdbFOAa0dQy7kWnO7KbEbtS8++Gev/+HW242G5JS+Kbh9QeRy9s/5zNvveCX/vv/gf/5j/8nFosJ\nvRtYeMPJYsFmuCMn8MUxM4ht1cL+LvLo0QV977FuSbdeMyvNarmsCAnNy+vn5JK52+559PiEZtET\nEAvmzfVLtrdXLFYdXq0oWjPWSXDTL9hvZeEjwzQWYgr4GJiTbP7TfgtoFqsWf7Hg2Ycfcu/8TbZb\nQdidP3zEEGZWi56mb5hzJk2JtL/FLs9p+zXDuGUcAvshUKZACAGlCr4xKGelX6ml5RGTBOOFLEfd\nORWsKSiJepTrIWvJO9eqWl9FjWKdwIbLMSZIevmpJKEcYY60olgzkkKYyLmtw9ODdTKitUPZQirz\nQbRHqRFfh7Ay14ipxBhF03is0+zDhEkVcJPF2lmqy+tQpYaQmWcxoqQgVLE4R9om0fUwjwOBgRIT\nm6DZD4qbzcR2lKm9DKk1TjdgJbxwt79lLodKNGKUEkvtosd7h/WWQ2DcP/X4EaLhxOJU6gtQSoYg\nRx9UFWaWckj9Oeg2D+HvgCo0XmOdZrHw1TJoCbGQs0Skmiy8yBSrDZNSM3KAotCpoHShWQqCX9si\nu/ZeMY0HEbkSYbCDFKQDqo1ESxircFaO7UXJMUOha09FYbwFLYi2WApeC2jDGk2/6GjblsY5jDGs\nlyswCd0WpjxQVEA1IkBnjLgg0o12YSp9SZMr5i3lgDbQriz9RWa9drKDZssImObwW1XYAF2/oG8b\n2qbHWEdUkd0w8e6T99mtbnltfcp+85I3Lh7RGw/aMM0zaZoYdgMlzOQc0GaBd43QhXJExZ0MF6xw\nCr1zTMEw0/L0ww03+4Fv/8O79CcnPP7cZ7kIgeeXz7nd3vHdZ5F333/C1d3ELibefucdJh1ZLk7J\naSag2Q8z99Yrht2OptFYrQXs3HXMY8CcONq2JQOnJ+eEPGHs/8fcu/xalt33fZ/13o9z7rOqu7r6\nzSbFpkhaEvVwYDgJLAhOAliInVGcQQaZZJSBPbATJAYyyT8RBHk7GWaQIDCSSHAUKxLsmBIpUXw0\nm81mV3W97+vcc/be65nBb1eLA4sZBc0DFLpRqH7ce89Ze631+34/n446RXy/5fLFQ6wLhG7A+kBM\nC8SCwfDqvftkJUoNcsXTOOwP5BhxWqC6ymmJPOVMLjfg5DAcQicDSdMxT3tev3+PB59c0ppiczRS\nNRyfHXN6fkyuQvLqug7jHMvNA3708fdY9o04F7FeVsixYL2mZWimop2Q4i2GyILSYJQBU1lqojMO\nlSt1VaqY1jAWYlshwkbaSM0UrLXSaRe1IyCDRWHBriWQJJAVsT2CUQLDkNhbxTmDcxalxYrpjbjU\nBXotvp+YIsGDs4JIpCVaVXTGkVWVmrRePzfr1UBeCkY5TLWYWiglU1IiJfkstlUJ01lJ2sScoDkU\nVhbYtVmkjZH1oxayyRIRsxtUymIgiEmmGX1mmaUGq435jIvyF70+Z+95+wz1Jt/sP0fSr2A1WAO3\nL3+3rTEGVYUFaWzFdwkb5GjYlILcWIpoIIwHlREqi1pJSUbqjTlXtBZPugtip2tmRgeD6RRlFiCG\nImH9+u9vhqZWYruTqaU1gWEDeYESMzQn1VBTwRaMl6M0LUl7Ind4FwjB0XeeEHqckxxZVpFsMvt6\nzX56IZrcJhi6MkFXGjaA7WXHnRbQLpBKoSroNjCeKIaNXXFfmRw1rpNFrJqMpxG6wPHJOcZoAhrr\nHXOb8V3h7Oics/GEwVgOseF0QetITYllEk4lREqOtEPDHVmuby6I6YZBC6GpkbHe4t0R2zvvEY7P\neXH7Kd/5o9/nS1/9Oq7r6LrAl97/Bh/88EN+7/f+T6q/pMUT/tn3f0CMld/8rbukktDWc/f8LhfX\nj1HKkMtEHzakODN0HfvrG4KznN09Wz8omhgnNuOI2XakRUAkS70lFs27b71F6I642k2c3DkjNU0f\nerQylPl2zQg3pts9mgNohesH5mlh3AZOjqXiSU1y3FuD2j5Yxr6nHvU8fnZN1QtDOKK0iA+GO+db\nqJFgFJvgKB7wgYcffI9Hz58zTTccrpMsGlrR9T2bTc849CxlJppJdoDak+uNkOQlWAbVUFpAN71i\n2xTegY+VXAyKitMQ1ysipatMrLXsxqgNlWSw+nJ+3JIwF1KR4zamAQml1kSH0aVrHpcAACAASURB\nVDQysEK1Jf8n12vNkHNB6cjiKnZYJ/LKUldNtWT0oLRK1U1qlsViq5XcZpVKcq4Fo8UXXzLoVtF2\nwVuPxYKpGJWwFJwq5GWPVgMUS6kJ1Qq4Jn6kYggaxjuvEHPh8nrHHPekXHFertLszyu5vRSZZsqd\nnFrzjGv+8qf65K29zFiugVOlXraqcM4SuoIPDe0LXS8Edpc1cTbSDW8F443UCpu8QawxEjlSBpct\n2qR198jKnWwMfaAsipYk+F5awwaZbDvv8J2RWFJrGF0Yj0V0P++gpor1iuDXi3AnU9+cI2mKtGqF\nPpMS7ugE5y2hF6PkZjzmPBcu98+xfqbmRM1CoWlORFXdoOm2kJthmjW5REZnJKtoCmFIaCeu65wz\nph8IW1gOGaMrfefx1tBvDMEFOhdECZIKy7JnKntS66H5FYispBJXJINKa8Rc0cpRauPFxTNKXUgp\n0XIkp0I/BgZruXvnNV778td5elV4dPVdXvvSe5QK3/uT7/MLv/g1fvO3/hX+n29+k0+eP2feXfDF\nd9/gS1/9Ot/842/y5a98iXffe4/gPcZaxn5kOeyYpz2b7QmHQyIue8btiJHhMNvxiMOyMHYdZjOy\nVIfpNOWQOTo/5o3XX2XaXbHbz6S4MN08xPiG1h7vLJ0ZBUZmJV429gPTYcfN7oZuMzDFBa0R1umc\nMK1huw7f9YzjBmM83gXcsGXcnnB9cYVVA3fvnNIocmVUwXjH0d17tJz40z/9No8+/ZQ6FTau43ba\nE7xn4wc6O7DpRo7tKfs6cR2fktSy8hKURI0I2CLXlcZUnMpY5VEYKWGohrEO48R/o53BrNhV+TA2\nuQ992chTRsRkGmhaej9KUG2yG9QYJ5pqsSQUOm9RiIJXK0k+a2TXmLxCKScnQZWoTYFyEhlMDWoW\nGEhNtCKAYqMqfRjwrseYnkPckXMirnCUkjLRRqq2FCZy1fL3bYZWmKY9Rsl132YY6EMnAyOrcXZA\nawhdx9vH97mYdlzuXnCz7NGe1UH1F78+t0XTB0uKbUVArcfml0H5NWb1Mhz9EtaxBieAFcNvFNo1\njAcbGtrJMKm1l80HjTfSxilajJXyTzeslrYBTVD31qywXmtQBkKviRMsrdCK7DiNK7heoY3wMp11\nOCPH+1RkCOObJk0N4xXOKZxhrbjJlDtHxcX+lqCPmDcLpUas6yROYaDre7p54OhoQ1ZX5JhQTiqY\nrgvUMdN1DhsMyji6ZJnnjHFS5TQ6Y8MeY0UP0GxbFRsQ9QE68MbjHdig2Pa9ELYNdN7QdVuUlye/\nVnL9L1GOSM6JZZqwSuGsW5tLkcN+x9D1UPt1Ejzhui1Hd+9zcv9dZj3w8NGPuLy65v57r/P8yVN+\n8vDH/LNv/Qm/+3/9IRcXz4nLRIqJHz98yF/+pd/g6M5jHj+/4c37C1pprHM4a7hOM1pD7yzZW5zZ\n4BxY5dgenzIvB45OR7qjc4w/wVbZNfphw4nd8L1vf4snj55x8eIBaVmY9gec9Yxjz9mrp7z9hV9g\nu93Shy37NGP7wKA1lcQ8J+xKj3LayIOpCSHcOIu1gVIKSxLL5J27W8bRUXOlqYRRFqu05Hi1wjjH\nzcVTPnn0Y9699xp3230+fPCIfdszhi3eeoJRBKNxztIY2ecOtMLrmUVsgVgqqgnqzVu56pG2TRVz\n6Vqtc1atPhwBXb9s4em25pVfTsl5GRtS6+S9odVab24SxdOr2kVc5w1ri4TKtSGmJHnjWslVeAcp\nN7QpKGPXBl6FaohTZlkSk4ocbccVGCKxQVVlUKysIlRH6yx6Fh9nqolQJSPaVERVRa0RrfLKh5AB\n0tiPOBRBWbquxxmHVQFnnQC1vWc7HnP/9BUe3zzmYvecpuLPXLs+t0Xz+GTgdrcQUxZPzcsLYP4c\n4stP4d4AUNKAQAvFxHsJmSstbQZlGs4aSqmr7mE9NllLzXKx/vKeFORSnGbQZgAq3stCDOA9uN6x\nRHnymlYwfjUIBolGeBPwfkEZTSoG1wA9Y7zF6x5tLM4K0FVlJT/sotk927HtDoybA7eHK4btQMuS\nc6ytYD30fSAsHTATtIcoH8yqG8PoQVXG/oT9fpYpu+mpLZFLxnmLUwtNW2ZdiWbGhICNkmY2TsCv\n1ii8tZIosApvBJhrqyKTOJQdTlUWBsosQXtnDLFkAoZaktT7FEzTgjEdyjnunG157d67vPruF1Fu\nwyFNPH36BNf1nB+f8Gff/CapVWqFBx//CDRMhwP/xt/4bf77/+a/4O//nb+P/tqv8emTD5jmSQZh\nRhofxjd06Zj3B4bBc31xJQCCznF1mDg/GdFO0Z28giagUmF/mLlzdpd/8r/9z3z3ex8w2Fe5nF7h\n2fMLUq4M1nKeNM+fP+LTjx/wi7/yPl94+8sMR1tKzizTAaUNNU/CvQS095ycbjikPc5YOmuI87S+\nNzPOBQwNezRQqyLGiDWWuCSq0WxPT9A1UQ/XfOHNt3iy3/HRRz8GBdtuQ0sVf9TjjAwyfRBlrrcG\npwNRdygTsdqgkLxuLprm9FposCglXAXVBD7dWhH+6hrRAbWqRwQFh6prAw10s2gEhahsxdoKpf4U\n4Ud2nKnW9YQmdtVUM1o1Ui3EJlnjlCsxa5wzlMZaCknYplC50BJMJaN1wnuPMnq9nKsoVait4K1e\nDaWN62VHrQs0TWsGihFmZs2oJnGiVioVjcngbQC1kIvm6PiIwQ60FuQUZhUai3eee+4eY7BcTU9+\n5tr1uS2awxhw3nB7e2Besjy5SgEr942q/XQUSa1h1NXDoxrOOYxLEqh2Gucb1jWMyWhb6HsheiuE\n7qK9J1QJJmtt0cqwLGnNZymMqWgjfnOaHKe74IheqC66GZQyhKBxTmMs1LZQjYAEjG+4FUCsek/n\nA7WsrMyWASWiq1gpB3j2+BofDJvjgO93bMfGNJUVUxXX+1rDoAZc8TQnaDnnLMFo+o28uZRqzKkJ\n7q4KZ9BqhcLKw6hmQYaYgg8V5QzKVpyWIz9mvUe2BauVuMbNiCuyyz6khKoTtUZYEt4Y+q4jRdGu\nxtW7XltjGMQPNIyn2G6gKcPp6Qm7h8+RDnLj+cUNMSmuLm9Ad1gteVrnO37040+otfLbf/O3+S//\nq3/IvDT6oxP6IIMZqx01JU7PXkF3hpxuGTZbWs5sjo4YTu5Q5z0ta1rTlNDTmOi6jo++/wGPH+15\n8Awe3XzE6at3GO69SscJ0xL54GKHSZ63tOKjP31AjI53v/AOqlZiqmjX04+NmtOqa5CHYHCOUiqH\naY8PPb0fUUrG0k0BtcoO0BliWghDx3B8ynByxhIXfvDwh3zrwz/l+z/5EbXA6XjKrB03h2uMKhjr\ncc5InK5W+uRYioAtVDWopjE6YDGonDBNTlROGwmSe0VulUknNJGsKraBWjcO3ggVXcwGMjh+WVZQ\nVUyT3stASSNRvxgzVgVKE06s0w2vqzBZdaUoyKWJ3tk6yVhmwcm95HXINimhrDT6jLaUbFiiIRgH\n2kjqELk6qLqimlwnbetWhIntIImRKjXOYA2da4x9YZrAKQFuKz1Lxls1DtMz+uO36J0nzU4aVl6W\nwT4m1HAE7ed0pzkMjloNobdcXx2YJiGfNNYIUv3puFFba1NiO3ReM246fC8cPa0kS6hVA93oe7+6\nYNS6YKg1qwc+dNJ9rQ3tNM6vE3IjrR9RCwhAQmmBvCrVoAkbsh86hlGhzUKtiVI02hmCl6GUKwaa\nwakGbV2EWeuUOdJW588yRfa3M5eXN4S+R9xRilwjSxF3SrBeYhtao5RH6Q5tDM42uiDTz1wNpiTJ\npc0FRZEPRJOprjOO1OSoHYJFd4amFQ6L0o1qRNeqkF2MqpB0o3eWPjt80Zgsu4hq5ft2OEw4q+XX\nZkPKGW89tcF2M2CsYhwD/bDBe8/gLGfnW27+7Pu8+dY79F1PMJacG0XpzwLhD370ff7hf/s/UpeF\n/e6W3X5iszlhHHqJjljD8WaLMdIKGq3nZnfFyXaD7TrKelS2fkS7Hpwn7W9Jh8R3v/MB3/zuAw6+\n45VffIXT8Q0ePnzEhx//kDvHI+//4teY9ws/+PBDju/cY7drPH1yyenJiHOOuExy4nAKq+Glg0bK\nZdLgUbVQc8UHUdDqpigUfDCklCQE3nm2x6coHXjw4Xf5J//093mRbtlYj9EBZTs6Z2m6kNNM7TW1\nRGo1KJ3pQ0edQStHkCIdpq4+cmXQzQg7QSm8C/SdwwWDs6BD5Ho+UNNCSUmKB3ZVVFQls0tlaUVh\nrBF7pWmEFSv30lOkqqKkLKcjYzG6rgYDOS1ar+mzo2QrNWaiXMGpTKNQysusk7h9jLXY1qNVYL9L\nGDLWBjkJqoquoFzFAjkVgnb04QTtDU3vaU0iYFopnFV0vWYzGgyWcXC4oPBBiGW57Hmx+4Q7x2/R\nhwFT22rWNPTeAp7iNz9z7frcFs0QAs7JHZA1jt3uwP5WlJ4vgRq8LEz+1G7TOE0/eMaNoRuMuG90\nlvsMI6FV6xq+M4RexPQheIyR6FLJFec8VnvsnFhUpLTMuDnB+0xT6bOKmLEK6y25zdSoscFiOnBB\ng7LoamRjnApKa7rBQ9MrLagILNYoaq7ElKipsCyQdWF0jjYbbq8X9ts9ZOh6y7RMxJrIRIzSWNdj\nSqWgsKbjM5J17VE+YXUBZkqWuIi1A9YYalpoTeGV9G2TW9bduZfKXLQYxI1u7LojFiqKzE+zplMO\nry3WK/SsiCtCrFE47G8pSViK25MTmmr0vYUSUXrDxdPnvPb2O8R5z52zM774ZuEfpT9kOSTefvct\nnt0850++/REgWUCjNF/+4mt8+4/+Kbe7iR9+8AO2RkMRpe322HP1/AZlBpZS6b1DqUzfeWJtnNhA\nt9mQ9w1/ciJUpDljbceHP/4JnzzZ83yuvPHOa/zb/87f5s75u/zdv/ef8uD5Nd/7zg958viCv/pX\nfo2v/Pqv8sff+hP+tfvf4Oj4dUKXSdMlU1zojUFjMEoTYxSCjvNQM1o5nFuHe84SvAw68pLRSpw0\ng+9x/YDfbEna8L2PvkOsezpVOD8/58X1Itlb69mwYZ+ek8oVcx6wBRoJrxVZ289UFMY0MhHVEk5t\n2dhO+tvOSZSu8/ihw3lFNzju5sJuOnB9c8M0H1jqAd0ke9yZDucttQh/0nmFdRmnEzDjjBEWrPLE\nJpVKhUK3QqsZYy1Gd+jScH2PqWsN2hoMk7zPXKOUSM2Wisf5AVqAOhDchsZCLpXbaUEph/ca1Uv+\n1JoV6OPXarQB6zqimmipUqjYDlzW9J0g5cxQ5RRq/jx7OcULPn66cP/8C5wNJ+haSXkBJVdb3f/H\nsvi5LZr96HHWYe1I3/d0/gZjrjgcFva3cpyV1qNM1Guta/WtEbqCcQ0XLMFLmF0ZiSLVlkFlQteT\nJsVcpW4odxfSQddKjpTOy1SxNodzDesqqA6aYNtKzryknqPAGkfXebqukss6BHDI/asKYntUbYUr\nyNepSGtv+eUxVuIYxgcRryWIU8GwQC0scWYpM9XIXalZwR6sldAubISEvfqCFAbdMt6IgtWoRrCB\nhiYhcBOQnacLgeAHjLYktABQKAIidoWiBdXkzIitDpUUMSWhINGIKUFp8vWUyjBs6LQllyo9YR3I\nOTJNM9lm/vD3f4e3vvB1+u3rHA2av/Vb/yr/0//xj/nSL7zDF997B4PnweNP6fuee/fu8fWvfZm4\nND768SdcPH/GN/7KN0QzXKHGSrCOpDRdCDjdqPNMS4mTk3OUlxplVQbjN+QqErDDTeGTTx7zB9/+\ngK/9yi/zw598n//g7/wndP2WDz78gPmwZ9kv/PG3v8PdOye880XFe3/p1/nkOvKV8R7kSyyXONWo\nK2A6z2IB9V7urdMyEXzHkma0giXKdYVzDmsstam10VPo77yKPrtHTjNHJ+ecbk7YPf2Evh+4q0dq\nysyt4sII80TmgPEFpSeMlpZbDYqQDaZVgtOkIgMmr4S63nsrv8a1mugMOnR0uSemzHg0cLQ95mZ3\ny+72ksN+xqAI1tE5h/cb4U96hQuZVtedbo0yU6iRqhsZvaZHDJBBFbQu9DqQGthNL8PCatA64taY\nk1rvPIPVVKNEj1ECPnj6LqCryNqmZaJZx3So9J0G3SCArQplRThnnUJbQ1MLyiqqUvRViXe+WZyr\neC9XC0Z7sSboyrwkLnaP8KYRXAdqkayzbjjf/oVr1svX57doBo93HqOt/KCcpZmInNYa8yFRqxT1\nGy/955m+9/iu4AM43wh9FcJJlcXVGPlzWmV8F8jr4sI6IUc1ako45yXvWcBqTT+IoldhMFqOKJOW\naZy1ilzEL2StxgVoy8vJPqu9z4mveY1zxJZQupFLpNSFnCulKKmDNcU0HTg5O2PTDeikaLYxlURZ\nnT/OOrQR7p/SBqUqS5oZx9O1RVUxqqBbXZtKUmFzyqErNGVRukDVaDwGg9U9gx9AVYJ2kOVOx6zD\nBvneZ3JeiMXR2gAKcs2oZaLEQlwWnDUMw4ANFopmGAYhJDXQ1jDf7lG9ZRy3PPrJj3n9vQ7dMr/0\npdeY6q/zO7/7+7z/9S/T9Rve/fJbbLqO09Nznj6LfP+7P+BHH36fr73/Dr/6K18n51tyilxfLKS4\nwzuN9YY4z6Igrl4yltZhXKCyx2yOKMnQlsxH3/shf/BH3+Z6aSjfSMvEd7/7LYoCVw3T7pr/6B/8\nA/7yb3yD//jv/V3unt/n9LhwnW451MqQG9fPP2W6ekxOC40qtdtuJMaBzck5J9tTrm8uqLWseVvx\nJ2ljsM4Anlwiwzigjo6hH6BERhc48h3v3X+dw1KYOs9+Mgza8Dwe8FnkX239MMtubyQXRauJ4Axj\nCDTA6kbJDeclVmadwXeBzjuM6/BqJCdPypUua4au0XUbhnFk2S+keY+qRQoRVkl+uFMoU6lVMH5W\nJw77HSXLsIcqiw9V0Q2FlAvbsRfcm7XEWeP9Fu97Urui8ARDouodxkqluet65grKVrypbMcNXncU\nVdlNF+ymW1TQtAjKKqwuWCdYN6011q8AHyWcz1YtGs8QPIqwxheVXH3QQTFUlfEqYIrh+uoJ52d3\nZPixXh8Y83O6aG76NdCtjMBya+Ps7JRSILW8VtMEgMrK66ssoJxEebyiD4ouFHI2tKLWO03kiWcq\nNiRMFOWDkG6KtBVoxDThrCcEjzYNZwvBO5yR3JsmsPSJeZopRdOMHBWcl0CwIPEtygvNR9zQYgAs\nVLIppCyRomqjPA0nYWi6AK1Gzk+2nJ6dQoNgDM0UplZoqaGrwuHIaiZXycwZnal1xuhe4AhK0Fah\nOaHeNCTIi7ixFW6twQkyK1hH7+SqokSLsT2siC7B6FWyBdMaNWVu0h4VG33z2LiQp0XUqTkx7TOh\neHyQgZeyipgKeUlSIa2ezThgmfjuP/89su94/c0v8o333+W0s3zvhx9i0gFXMjePdlx88phSKu/e\nGfg3//2/zZ2796hzIS3XPHvygPv3z6EWtF6PxkZTS2McB2JrdLkQdzs2J2eUacH4nsvHL7g+FL71\ng4+49+Z7pCWTpkqnFRGDMpqlwNnZKb/x67+GdT2tNQ7zhI6iCDk6GZkuO/YVbndX+NCxnw6ELrM9\n0jSuSV0ip8SyHDCbYzKRmCaC79kenUjxAiM7nZRQOTLtrki7K0almWohLTuGcIwfey7iHmsUm2HD\nbZJjbQWsHtf7+cIYLE1B54VUbnXF9p5SZ5TJglA0Ct91hG5L1R0QSKWyRMc0z2C0gDP8TIkdqlZp\neWFEEREktldKlWBTjTgPSjvB9qVGikAViI21DaWSzBSsx9uOo82rWN1YUqDZHuV2LDlwe9iRUsS6\nxmazoWaNN06aV53cnw4qkNrMVdqhqsNGjeoq1RacrWuDp6xDngoqY7TDmxWajNz9irDN0srq7LIj\nNWmUFUndFK8Zh1O0scQ0r3nxv/j1uS2azkv9EJCFrhmiMvSDIyxGNBGqME9xJZJL9bC0ggua0AnP\n0jrJTc77RKmyEzS2p7UolkhdaMWTM7TqSEkupYU7WNCu4L2T+xEb8N6jlYdWONqMlBS5vsnUrNc7\nGcRKycrkM+IX0drjjBezo67YmlnSLYKWU7hQiVPEeU1Vmlfuvspmu2Wz3UgWU1W013L5r2X4Y1cS\nea1if8bIrk+3BCVjVlOnLprcDlhnKRHaOgQCjXUeE2cKUXD/1mF1IxuDNR1UK7peDlSiBPo1JNUo\nLdE7j60e7TOuNlIsLPNMP46oYiTuYySvp1sl1cIUE/vDnsvbK95++13Oj04wXeDFowdcXt5w92TD\nX/3Vr5BiY5lneWNrRW+hlJllitxMz+jdBlPAuca0v8Fby3xYON4OTIcDvQEzeEw/cJgPbE7OmA4H\nNqFnuYxc3+y4uV0wdsvN7S3O3+fOK3fwXaDOGVrl5PiEf/S//y6qFbbjBtMbjIIWNcErNnd72vwm\nh90Fp0bx/OICq2V09vzqkm4/8corr6KUIoRBuKJNpsTBD2I+VY3edeKaqmKodNMtm03HJm6YLg+0\nznOxXBH1wpwjyhp6pVB0JCf1Ya07uk6MAlMLUgv0hqYrugmk2DmLd4qSIzkq2mmPHR3OBJoeyAXc\ntKp7lVx5RQ25M1AarcgDSWthJgiLM0kkqVYMCquyBMerPLjKSidzToNKGKcYuzOcOYNqMTqBKiTE\nVOq9JseCUgspX7AdTmjFrWH0mboOX63PuNQYlELVSFLSdRqURtsqR3KdsG6FgWtDUgqlsmxkEIkh\nhJUUbyhZfOxOW1rVkppRjlIP9H0AJbi4n/X63BZNay1o8XDXLOpaXRpdZ+iCZc4T4ybgTGY/JUqu\nn4WtG/mzPndTQv0wVgvWScslMQqUroReYkq1WLHplSKZtYroWNVA6BogObDgB5qydA1s66gloWjk\ncrv+wCvaK3QV1YQAYQPBBLwPQKNUsM4yqI45TqRUsUHTDYZ50vih4/U3XmXcdDgnVTitGlUlDI4Q\neqxX8jU2yZW+pNekdMBqh9LSNzduHZTVdadtFZS6LoiyaNZOo7XBG9lRvsTkybXCEcE3GpXU5Ii3\nnwoB2PqROkWKlu+BtZYUM5vtlu7oiFI0ZmUeqpZJSYYBtVVMU3QedjfXvPH6mfiqtz1LSVw8fc4Y\nArlmjjcdoe9oxmH9wO7qGTomTscNNzfXHPY3TFNhfOcdrm9v2Y6BGjOaJpPU0IOSae50u2N7fEpe\ndjz7dMfuUBmC4rXzI77zk4+pX32P19865+3X3uDPfvADjDZYY/jHv/O/8qMPvsmXv/T+ykrt8LbS\nO8twukFdn/Puu1/kxbNPyc1y2F3QmmHTBZncpnlttOn1IW6wrqMBuSRc8uAaug/i2Zl25NsLnLFY\n29OFnpu0A5XAaLabgaUWmWprRykL3ajpjaLvT8FodLhhng4YD2gLGeIc6Xyj1SJd8fWBbrzH2YDv\nB1KqGH1YYduW/X597yyALXKPnw3WeJSVHKMpUJIE0xMOoyPBGg5UdHEol0Qlo8D5QOMa609lk5My\nzjqqahiV0NoQ281q2fTE2XGYLjg5fo08Z1ItECVxYK1m7B3WOZYYiSWitFkD+3+u2VDKMvQD2Ypu\nJtlF1gttaFlgzlKXl79mPa951baeLOW9NC+3Mu3/C3CWn61d/7+ujD/jJeShArVKuV9brLUYI/Gd\nWgRY0CqMyrHEBdUMVpfVDCnys0bEuoG+98zzywnZOn1fp2GtGuaDApU+oyvVqqkolpToq5aGh9Vg\nwCiH9oZgBmo9xVmDdSO17QleKCulVlQuOLOh5p7gPFYHlrJfw/iWvpcAcs6WohphmznGcbo55+ho\nKzY8rTFBKEwtKbSy+H5DbQmMIscZVIUKMU0S3DcOVQET6WwGnagpoZR72Q+hqroi/leVr1I4o3DW\nUlvGBo1Vspt1K2gipQOqNI5tj6odaoLebzAEbEhUnfG6k2+xNozjSKvSvrDaYpwjl4LzgZpmctEs\nS+Lxo4+5c/9NSoHTYWTjE8PRlvHkFaiF4zt3GY/OuX5xzWF3QUkHOle4uXpIKYUvv/8+F8+eoI3m\nbDvScsUqOT7Pc6Y3iZZmbueJrvM43TPd3FJj4Pz8Lr/9r/81Hv7X/wOf/OQpb75zl9/4l36F2A48\nePCYWitf+8oXefvdN7h//11OTs958vED/sYv/wqnr56gN0foo552Bcenp9wcJqyONDRTWtAYrq6v\nGccNm80RxmmUshgbcM5RFcRW6Y0FF1Bec3j8kHLYM4YNZ5tESbc4/SrbfmYphbkVDrUxk6nOoHVP\n6HsGd4T1HSEE+nHDzf6S/byjUuQIrysxzjSV0M6QSkGpQG4d1sppKlgPJlNVBAW6Wpy1JGeIy56s\nIDPLe7guKDVjdKSqeY0IaawRipX3mezteo+/R2vppVciqT2nFU3n7pFrwXlDbR5lNeDJfYerFas9\nKWWW+QpvNkJKapF5Fv5lsOBdwCpgiUIpq41aHcF7vDOSteQW5w+0ujraW6ZmRdN23fgYAXYrTTBO\nOunKgPIS09OVWhXBO8Eu/ozX57Zozsse5yw5JeYkk6/WCtosuFDJ1aKbQHwVZsVUZZkYa6FOC3rK\nShTBNVLSq2BNtBbSNRU8lbXSHqhVtBTOGErJtJYpRa9g4EwLUtnT1kF0bLfH9P2GTZeJ5YqmL3B+\nljC5bTjbQzkWt7KKBNfT5pnSFlALzje6wZBTo1o4GjtxWgeDcVEqnNaSSmWeD1SV0aoHbWUDUQw5\nHagkSjtIUDoIoqxNGUwht5lSo0Bei+wuYVWAVAEBWwOKjKLInSOCwKMqtHZYs8VnMG5gNANdf0IY\nHFY1HBB3EzlNLHEGVium0lhvMc4yH3bM+1tKkhjQ2HfEqdF3R9jecXl9ydtvvkONlXG7pdSMbxMm\nbHj+6QOunj1h2d+yu3hM33mePXuMVpnt8cDFi6csy8LdO2cYldjvd1itCDA5DgAAIABJREFUSVKE\n58WzJ3ivWeaFw/U1tw+fkPaNkgqb7RFvnAX+s//w3+U//+/+Fy4edrz11Xv89d/8a3zy8BOMabxy\n9zXeuP8Wu/mWxx9/yq+/8zavvL3l7ntvENOO2nYMznN58wJvNH7csp8TNUJTijgnXGgY19H1PbUm\njBH7YdNKHtzGY+xIvd1hlz1VGXql2SpPsyNJOYJzXM97VFogGNCS7w1e0/UndO6YMHhMcxx5y2Zz\nwtNnn3K9e7bGrzr2+8g8LWgTaNVBtXInj2NOGWOk6tj1jlqiYNycpnnDrUoc8oy1CUUBFamp4UxG\nWVjyAWtlAGt9wfdmtRnI/6tICQu0Qk4HtNtRWo9xQfxUdU+rC5WJ0BlKMXgzUqsmR6lXagO5zhgD\nNUds8Cg0g3e05qh5Amsx1WOUxZse50CZDbncktUe40TMlpug9MraFnK+k2poquJEbwrvRNmiKqCN\n6GzG7meuXZ/bohnTRENyeDHvqVl0F8ZIRKBVTQSUEuhtwGJcJ9Uwm0W25gWwga4Yi1BMELxbKcKv\nbKVgrFu9IhKmhZdUTCONBiv5u5yjkL6LvOmtCxhtmKcDfhhRJlAIxPKcqsTD4swGyga0Jq/k6sqB\nJc1CNNcW7wAS2jtG7wlmwYcJpXpimah4puWWm9tLjo+PBebrLNpAyh5SpSk5VueSPzP61VaIS0Fb\nQ4pK9BLrG8Q4g2qS16str+0kyHmRlpSuWGMw1mOtxXvFIWWWekWOL6h6ppYOE4XJaIG8zITQybHW\nu7WTr0lZuJnKDAyuEuc9Lw4HIIPNDGUglUaKH/HK2V0ePPgJJRceeYPxnWRdafTbY4KRxS9XqMby\n6MULKJU7Z2fs9rd4pWQ40aDvB168eI6zQrSquZCnmZsXF1w+u+L8lXvoAo7Ckd3w7/1b/zIfPXzO\ndz++4MDM66+9QWuJki0vntywzJf80uv3eP+9u/zCL71NPFxx8/wB5uIFF7tLpsOtRNfMgFUJ1xQl\nSvsspsjzi6cMw8B2M6JS46AawTt8Bbc9po0D+dFT2rJnOUxM0wGdIyddx20x7KaCcQXtoOkMpTAE\nT7YNZQSC4s2Gznfkkuk2W5zqiNPCNF/LvbjW1GaISyYtkcM8448hN6l/UiKtIVc7KpHVhFGe3MQT\n1DnPNEUaiaZEC6Faoa4blqqU+MZ7zdHW08pMZkGpRllBzbktUCAXzUSiFXmAh07T8i3GZUInylxl\nDa14kjKUJBuhbtys5soElFWeqBlcRyHijRPvUtbgBVVndQfN0/meXK8peVo3DQVjwtqZBqc0tS7r\n51+WP2tlSFeLlEN0/TnNaWpdxcNCJpcDiYhuDioSKUBBlQZQ5wXIoHWguYnQW6yptDavaCtR7zpr\nKElo1sZ7UrkGEs1KnSsXoVlrLWR4FwasT8LXxK6K30xpkRorw3CXlGTh6e0GZQaU3bCbHXMWIIc3\n5wKqMI1UHCndoE3GFE2pGZrC2gBKpuzGWvlv+j0tWWJaiAV2tztyjSgtu1aJT4H3niUp6ewa4Wc2\nGtqtDqLaKDHJhL0sco9U5B7JWCs6Y9NWe6a8d4wWZmBrmZxn0E52ACoxc8tSr9lPC12+w5Ee2FgP\nRaqDu5uduFmsGPuU0kLsBrR1HOYbye3pxnSY6XoRWXnvSDFyfXPNq/ff5uLyOcZa+n4kx4VaFqZp\nYd7PlFqIy57aDK0FmorEVLnjRpwfscGzpETOMHQdtUZKnEkx8uR2xnUd6Jk0PacuYMKWzXZkON9i\ndeH9t+/zycOnPH52w9WhcXQUMK3yyhtv8PZbb/De19+FJVGvP2L+9IfcPnvMze4KZzsyho0fGLU4\nbOIhk2Jmmm9wzhC8kJ+stoSuF6Tg0TH0IzlFvNZEJ2HzlGZSVVANWhX60DMDUWuKiuhciTagVGJJ\nt+SaODs6EgbAygTt/REUePjoA/aHF3inIMuCU/ItOU8sKWKa0NM9AmRuFQFYl4m4FEoVgLczA3bs\niHHHkjMxXeNMxNpMKQalPdZHhtZDVZSmOExaNhQ5SUi8jRSVoO1FkFYDBkNrBq0DqlkUhuDFclow\n9ENPjpZaBHitnZfvT9mjtJPscZ05LAeRB7aCMnI6Va2X+0oEOqJVh9FJ2lrKgupQKtBqWIEhhVoj\nOcvGwPuOlj2tgGoR6s9pjdIYQ4xNIjlNpms0yShaYylruNYag1UGmmgLjAsYK64YZwMtTWhrsM1h\n6ClaiRLaQjOe3MS0aB2kHCEJpspYjXWGfpRBhjaGSmFJB3ptSWni+vYnbPu36foBimIcenAa48+5\nvp2lNaS9YK4UKGeobQdzQrsiCdOyUJvBWJFgaVfASrUs1SuZlKpGinvAgJqxrhCzaEyd2WKtIbVI\n6KRKp8xCa2LYqxUKhiVK5lTZgpzFtag0qoK4oLVYFGO6JeaE1zMJLcO1FigtAguaIkcU1zHYjhob\n+3TAZkF4lZro1EDLhXlZqLWSSuRmd71mPRVD13G8GTi9c87N1SUX1zvSsuf46JScMod55gvvfYmH\nDx7S9QOvvfE6F8+eMh92jGPP1e4GbTXLbqbrNjjb04eRrj+nWSdA6QpVa3I1tNS4vdkxzwvOGXot\nQNlGoWsVPzpivOH8zffp7r3Ch3/wf/PmGz3vvH2OMj26t2yPTzk+7qmxsH/xEy4fP+D5xx/R5oiy\nim7jsb6nU4Z5lnB/qzCOPQd1YIry4LDWMo69UNBpnBydoc5fgeNT1IsnlFzXHZ0jayO6Z20INnDU\nJIJH0nhvOajKbCpHpmeXPLc3V5wd3af3G4nJBYPzPefxNTbdEdfXT/n44T9HuwPGKmq9JOdLynJC\nPHhaH4C0CtosVXXM2UFVWBVANXKFli2dOcYbj2k9MT2RoUqTzLJKgeIK/WCJ2RJCR8x7piVR5obu\nBEJMS9Qyr0kTvy66cqIzSWGNk567ttRk8bYHq0lpwdgkLFnVr0fpjPEBuItyO4LvX24eSWXCak2t\nmSVONGZKlbt/o3vJLFcDyqKUwfYj8yIAk1QWYmr04QyyktPvT2FJ/kWvz23RrFVQ96kUytr91LD6\naCq6VZzXaOWwTdzlxoAz0ot1Roldztj13sauHDwLWix8unqsTqSa0EYyYbWKMdGg6JyROp6uq3St\nUsokd6M6MMdLunCK1SdY72kCCsQYQxf6z3KYRksFsdZFyDMeWoaildgoyYD4VKqKNO0pTWAM2lRy\n3mO7Ri1Q24RSSTwprWCto+sGCjuSSrgmKlKFIdcijd9myFmm56U1nFoJ1qXgXUCrgRQTpR7k+AfU\nektplkqgpkLThUZEo3AtYJrDNkcXPDpWdC0UK62l2+tLUYW0yjJn5uWAptCyoTs6ocVIOizslkjJ\n9bOd79X1FVrf0lSltIWvfuWXePH0Mc8fTZyennB21HF5c8upOyU9ayydZZonjk7vcPfVN+m6EyE9\nxYQvWSbyXjHlmYahtURqBpcFUG2NZnd7wRv37tKsZrl8gj67yxd/+S9xuLzi6vKSZX7GWE+YLw/c\nPJ2J88J8uEEtM94o2mA45Mh8dWAzCHyjOsGMeec53EykHOmHHtXg5uqSVhPD0YZNv8EcnaKOz2go\n9IpMW3JiOczUIj+LluVr6Y2hGUtVPddKCEO6ZpK1wr9Mt1wfPsX61wl6xChH5yz2aOSw71HV0+5n\nrvZ/xpye0uxMXq5ZDs9xfSBnoYLV6oQ1mxUwUtIt1AVTe+mFV9FFGGMZ/AmhGFK5IOUdjUxDtMJK\nK/ou0ErF6A6rDSVngZqstCSjG0pVai4sU0S1tVefK615tArCRFTSW6daWhMyu3cG321Qpch9fkkE\n7cjVoKoUOlByoqux0SjUFslFct3OeKwNsoM0DZqE31EapZw8BBzMywGaJfhjVPOU+HOqu1BK3tgq\nG7SWjJbVegUFQ6wCcVCARQvJ2YppUhuDMgVp8ldas9SqpTutxZHeapbpWDNYW6muEhdhIWoT8Xoj\nYjM0xitqFQ+RapWUrlFuizKB6/1Djke5K22x4buXwidFaYWSK1EpDJXKxFInrO8BMd9pZYl5QmHJ\ndaaWiZID2lnG0Et3NlnmQ+JwuCbVayrnaNzafy90HJPrgaYupfOOXrFdkuAVG6UciZuWqJUCSiwU\n5I1idKCxrLU+QW61lqAmStPkKJpglS0dhqNui8uOkjLKqhXkkIVNSqRMkTlm5qmQcyKVxGZ7wsWz\n5xjdOPy/zL1brG7pVab3jO8wD/9pnfehdh1N2RhDuZ1uh0MIJCg0EUlDK4mEZBQhYaEkROLG3FkC\ngRLBFTcQISEBUhMpBDURQQpK0lHSdtJpCQeBSbnLUGW7ylV7V9U+rb0O///POb9jLsasouk2Jgpp\nwX9Vqlq1dtVac47vG2O87/N6FEmGChmmcSLGxOZgzfrgiKOjM3IqvPDCs+zGHavNIeN+INQ9MSVy\ndVRnOL15BLXh+nLEhIFbTx0TnaNkwTlHJmDWJ7hcMXGvpoNqGKdrbPGsVyfUccIddEzbC8y45fLd\nu9im4Wx1wGVxXDy5R9e07C6vKbVyfLghL1vefecBl+f3WbcGfKdAZtcTayElyFMk5kSqM6jD6R1l\nHAb65ZLF+pDatdRwjc2VGgdKSnjXEGVPDpGcktKlqMQQMLnQW0ssFpGWnIWpFLzvWYpl2l1zWb9M\nv3ma3i1orMMsFhgimZHt2HNkn2efWoZ4nxICDDvSfqs6xqDvW04VyQZrOpAd15fnODZYezyjGS3W\nOhpr1RSSRyKJmCeEiOBAHH3vyTFgRaliOSudLMakl5hqscZjrUKRY1R9dUwT02Rp/UL5VzWSwg6K\nx+BJAQyVVa+yqZoMtRQlgBkDZSDGgWI9Xgy5VlLVDbjUhUJzzCGWDtPuNEkzKXlKELzvNBa7BITM\nMD1SWZ1Z657k63z+GjOC5kB5r7nWmsEjs/hc3t9mOycqZzFKpX5PD2fm63jKf5ZoGYPyojMN1im5\nyNCSMlAz1hS6pgeqwoaNqP3OVEQmqmQwmVInYk4IC6RmLq/e4fDgBuDIRXT+VxI5JVLKpLxTgrYU\nckq6RHJqgWxblcCUAsUtqDVibY+zepO0pqPvT3F+S2RLLDtiuqDvnJ7oWelKIgbnvMaZlkipmSI6\nhzIUWtNoBAcWqnIUS6nkpA+JGEFEN/zvKSpSiZgSKAmN8ciVGDO5JCTvWNPgm4a0D6SQsKUQxokw\nBmpK7Pd7chZNZDSW3TjirKPdrBXskRMAXbfA9ZGr6y3FdmxObhFKg+1WnO8musUhX757ztnRMXee\nv8F2u6e0D7n7ypc5f3BFzg/ZtI6lF+69fsTJ2W0WywPEeprFEu97olmS3AlLH/C+4DZrSgTEsNs/\n4cbpDTha4oZAazsePHqXEgL7aUByxswz477teP0rX4YaadqOg8MjnDEKl0HIJCgRb1pa17NceIZB\n/eW1JqRUGu9ZLdeYtgNjMPstNU6YUqjWEseJrmuZRk8qkRgnjDgWtqERy7YEVhha47G1YlIilIqn\nY5smhu05sY4smobGPo3FIRSkOPpuwRguWDVHyJSZ0iU5XRPHLUVavFgsaY6/zqSUNVnSV/bjE0wa\n6Zo1OYGd419qjXMn0yLonF55CwqVWy5WDDISgyoWUlE6OrViTYt3DWBneDGAI8WRfd1SS4NrWkpu\ndOEU1a5sciGlwJY96/VqhtG8N1vPxKBxwKVkcoFMBJOpMlFKxbuOXFXor/G/aoKJaU9JmZh02VWq\ndqG1JKb0LtFczDXiL/78pUXzk5/8JL/3e7/HjRs3ePnllwH4mZ/5GX71V3+Vs7MzAH7u536O7//+\n7wfg53/+5/n1X/91rLX84i/+It/3fd/3Nb+viC4nKt3s305gC4JSjpwR8pzFY5v3tt/yXnKQMi9R\niY1YzctxzlKyzEVmxsph3kd62QlKcTNuTdmUzluMVep0ISNNpERDzZYiW2qqlNKw3cJqfYgkUYAB\nmVK19ZzSSI5q/apkataxQtuojtE53a6mFKnG4Eyjsakl4myHcUZD19qWFCdiCdi0V2xXcmAyRty8\n7WcmQFlynAnbiJ7mTuVWxugIoyQV8DurDqsc0dmxoEWcQs2JWjzTOGhJKBliofUecR1UQ9s21DxQ\ncqBpO+IwEXJksdxoiJWx5GJ0tmxVuTDlQtf2iAhXY8BSOTy9ycFmzWpzwLMf+AZOT4/BONq25+Do\nNk8eP6bajnbTsYqJk5MbvPbwVcouIl1ldbpg2j3h9Qdv0fmWbrlieXQD3y9JtmNxeIy3idZOVGNY\nr9dIqSwWjmF/xfL4iJAM3fqYdYpc73f4pufsxjHbJxeEkNltH9F2HbZ2NL7BOa83yRw1frbRW9A4\nbnWGPRSsc7TekSZVKKyWG3yrQOZsG8yUYHdNiokcJ1IMTMM1UGkbjbMVa8gxYhH6WlVbWwpL4/E4\nkghXFIZiCCYTtw94bJbUpaN1C3JRrKAmpToEza7KeUFMetMEVYDEOqn0BiFlCOMEJErdM8VrQrmi\n9z01Cg36zNSayblSSppvmZqoYK3yI9qq7X+pou191SDDvl9iMPqezrNCTYwVfXem/ZyfBbVUcnZI\njhoYR2W/nelIbn7sJapZZU6xtGJIUUdVxhWwGiUtoo4nIwHnNEUzJV30pvnSAQXrtBuyAqWOlDoS\nyvBXK5o/+qM/yk/8xE/wIz/yI/9cwRM+9alP8alPferPfe0rr7zCb/3Wb/HKK69w7949vvd7v5dX\nX311nhf++U+tVbl/ttHYUCNQC9Zape0wkqvVxDqCbnhJNKI3rxjTzOkDmQuKVNHi957uigrGYygq\nircg1WGKx2LmRL74fia6sqwFcZaCJeWJWjOpDkyx0BeDoyekRAwjSCKTKZIJYaTxnoJGY3jjqGKU\nvJPVt15KoWSjVvr5T7M2Yp0QksH6AC4wpiv9b8yNZsGkNLunWphzZkSMivQFUi6zrEpUJ0dRmRGG\nmCKlWGy0+FZjQnJVelOuFakRi1KsS43EMuKlIcZMcYWmXcJYsDZQ2w5iQdaJ5bLjYjtQykTTAzmz\n3+6IKekhUISLqyd60zLCcnmDao84e+oZPvrSN9H6lkcXW/bjyFO3b5Oy4YN/62N4t+Dy+gorHmc3\nXFxPfOXz1/zR73+BF24t+JaPPoUlMeFZtisuL3c0IXCVPM+uDtlNe45vbog5YFLEdZ4qlrbrKX1H\nd/sG5tYzHN+/x/jqFxjHS3Z3H9N4w+1bSx7e23G12zHFxNAf0blC23bQ9fSLHus62sYxjYFxGui6\nnlwy26vHQCRknQ93scDVpb7o/QozteTrc0ouWCKLpiVadfE470i50jjtUDo8aTfSkjFVD9nRGLwJ\n9HhibsnumsvdVynZ4lhi7IYadSkSoiY/1mxxtOSSiEOiykBtqjqPqnYvOWWmKZB2O4QRa4WQ99Ti\n6KXHu1ap/m1FopAmsFKJRWN/a7FIY3BY2oUF02GmaY4BcTRe4d1TCKoksT0xB8RaSgnEOmKDByIl\ntYTksSlTcTS+o5TI5dU7aqSQgSlcKlzD6OVKRMhlrzKq7HAiiK1zLIe+B1PYIXTknBATZ+Kk0tGQ\npJbMKjjjYUYy/5WK5nd913fxxhtvfM2i9y9+fvd3f5dPfOITeO95/vnnefHFF/nc5z7Ht3/7t/9L\nX+uNvvRGBGyjt0OTKSXibEPOAbFgiuipUKNqKv0KQTFkMSdy0V90KoWU1VObS6amjPVG0+oEIoHW\nVlIxiFiM6OnnPVQ0LIuZ0QeGkPIcE6wLErHCMF2oDSvpD1tT60S3/jWr+DZ7sjiMc6S5ODnX6QLK\nFVItTONE13dUKYS8x+aEMZX16pgQd+z3e8ZwjrMLjGm0fTZRRxjG6MNahWqFWNRDrbkvGp0rkrUN\nyUKIELnCGk9xhhIVaZfShHULRNOz5t9pIZcJTMDWR+yCpaSJlVvhmoZp3BFKQpwCpFdHvYKSjbbx\n65UaAXKBmAFnODg8ZsqFwyO9lbZOKDRELP36iKlccf/xjt12z9uPLrHOcfvOM7THt7l9dJMPXW11\nsRUmDpuJ633keNVzevMp/uS1h0yp5dadO9x57gOYruWp0yOOT9bUFNhuBxKVg6NbNLc+CLYjhx5q\nojt8hoPlQ3Yh8nAI7Act+vSHLLtDzfrZXrMb9Sa0OTymGkOqajtslits15NToml6DmxHGa8xs20x\nHqxpjtb6uxq35HHPFBJTmFMTjZBzVZwgWfOcaiVXzdVp51a2lsw+XVMEnC3Y+Z9FK0S27MI9pK7I\n6SGmNNRkQTKd8zizYAhxfi4jebzUpY+gJK8qlCKkOBJzoHEeEPVgl4lq9qSyxUmL8Zpe0JVeo28L\nSrSXgLEVIVHKpLduseSUEaPgnJzUpGKtJeegkjkRGt+QkzCFa4yxMxW/o+L0WYwTvrHkEgjxShe2\n0mO9R2zByBFIZgyXhDhhTNUcsBn9WEqdF1JCjhM5F0rN5CqkOlAp2NohUtQdZRW/WP+qRfMv+vzS\nL/0Sv/Ebv8HHP/5xfuEXfoHDw0PefvvtP1cgn376ae7du/e1/2BrEeOYYp2lKrrFVqlExTlLa3ty\nLlTRjG3bqlfbmo4ULMYmzWQmYavoILqEORTKo1kpKmpXrFuFbGakm26gQUnUqSZsowNwEYuxBUmW\nlANWGp2fJiHEHSXb2YqZlTCPLmNKhpw1j2WUQWODEaxJc6H2GDMRAux3ezAR5zO+eLpuTetPWPVP\nI/Uu17sLYtjibY9U+/4c0tiZYiX6M8xZPfgiyjW01qqEi0KpGjuQc9B4WrF0c3ZMqUIJ2pZUSaQ8\nIaK3zVoSsXqkOyVLItSMsR0HN26zuLNApsqwv+TJxQPyZLi4esiUIvshsGgdq4NjlssjusWK6/GC\nguXeW4nOt9y8ecaqc3T9jBBrMuv1Ac5fc3LrBl3bcb0duNzuaUzhw9/yEgeHJ7z9lS/TDQ8o2XP+\neMCvRl76+Hdw69mPcbU9x/vM8e2Ws9MD2s0B5xeX1NxTs2WwRyAdvjsF72jMligThy++yMM/PCeb\nx4y7rVoYt5eEMbA4WOvopqpDZXf+gCJCzJnVoqftOnWN5cQ021R3uyusF9YHN5HgmLYT1idSSXTW\n0y/WWDNAmZT0Po+EUoogRu2BSaOYqcpisLXi502xKZVDJzgMe3Fkk9QcURMxOnIQvKxxzpCix/ke\n7xK77agtcBj0MRJQu4Ih10gKATu3rwY7O+garNXCGeuEbxtyTjOQxkLRGbkYQwwR6wKYmRFh1IZc\na0Yo1JSQrIsakYRrdLRmnWoxU94CDlPXWNeSY9ZuMaFJmX03L5YMYj1iFT5uZgxkLxus25LLJTBS\n1OxDSAHnwcqKWiGGogCcGpWKxHzbzrPppWgumYrq/38umj/+4z/OT//0TwPwUz/1U/zkT/4kv/Zr\nv/Y1v/a9ON5/8WOtnTfoWa/IxmJMM5vwA9Zq6h3WzqFLHm86naFIA85i6grJlVx3FBEgUquhcUul\nPjuLdZPawWrFiH7PnMBZg2+8puxVbaVTAcl+ZmpWHVybCERCKO+HUzm7JEwRbCTmQsxRs31CJJWi\nPvbq8Y1FJLHdbWmadhbPR20p5jYup0grG7xd422Pk451/xTDkNlPO6Tm2e/bAQNGkpKTcPP2XDeA\n1ugiyljBiNWFTp6dV8XqwshZoo0qLZJWgckm6AbaZowpNFkYh8LyYM1ivWDV3uTQnTJdDVxtdzwJ\n19Rhy7i9Zhqu2F0N3H76aZquo2B4/O4lj999xL38FhXP089+AyenJxyenUJNXA7XHIVDLt58AycL\nDo6OwRoODk8Yc2bTb7i9OeH+w3dxVuUvB0enfM/f+wH+19/+B7QhUcdATi37sRDjRCpPONxsOOpb\njOtwfsnpc6cQoaaM+DXVOmhWWFGL7vnddxievMO7b9+ltwmXA5dPHrLslvjNCuMdxjX43lCqYYoj\nfb/AWa8EoahMz8P1ijQGas2s12uOjs/YnJzSHB6Abagxk7ePmcIEUeVhecqUaWAqI63oZrmWqA6w\nmHR05bRDMqniqtfDvC20thKz5To4aDydh33Ql9xKo6qJohCcVM3srMmkvFfCUoGUE1QN1CsykpNV\nEnzJWlBQ+pFoQAHWRGqNtIueahLRWBgsMQmNW2BcYZwG7cAwlBowRlvjVDKlCGMcEGuxThF072mM\nMQWLoRSZY208+KhxKJPDUNiPmvNkrcyLJDNnqKs7jllPgniqKKshhAnnM/tpoG+1jmAMgsfZFskt\nxuiFQsShO0tl5FL9161//5+K5o0bN97/6x/7sR/jB37gBwC4c+cOb7311vv/7O7du9y5c+drfo/P\n/eM3qRVSKTz1wiHPvniqEI85O8S5npJVY2ktONfg3FLnelUJPYYGpKGEgEiac350Xul9jzGC9wbj\nG6wplDqR7Lxln617OWVyiSTUSeRqg/MK381moFpLiorGSjGTkm4TEV2ipAKIRlpQIcailBSXyEVb\nIWcs03SN80IuFZlDnlKsOK9F3JoVtRpqHbVlMw2gkASZ2xVrPDGN81zGUYujSsYaQ6lxPvk1SraW\nqENzdHhvRJcDuWj2UZ3lSrlEKhNIZtGsOexucvr8HeLQIKFnex6ZhndZ9g0GtaTWdkUuHmkOufW0\n5+LinDe/9Ab73QXtwnF4+BQf/eC/wWp9xPVuz8PHjxjjE1YHa77xQ99MHPfce/iQTX/E4+2W7sGC\nWoVu1fL2vXcxxnHj7AbX045xu2WzOebkzvP87b/793n5s58hJE9ZnnL81B3cBm4cHrJZLDHec/7o\nios3H2C7Hu8cTbfm8LShXy+xNVGNxTcd6/URnD/i6GTNW19+nX7V4w9vsQ8DZRppqeQpEKdJRxC+\nYbvdUYoWx265pm87ckos+p6LS4UQu25JuzwhWdF5bs74/oAigWQm0u4aMZaQi3ZEVrshXcyAGKFx\nDbUUMELrhBQqYy6klJFSaMTSySG1joQ8gBkoVZ041rYgfoZXN0C/ZgpTAAAgAElEQVRPrU6f8zxh\nrGbYl3ylMr3iKRSyUR4BFWTUILWVayg5YN1E4zMljJjOcdgvCM4x7D0pZKztsPaQGJ+Q8ohIIZdM\nKhFqQy5CrIkaGjwJYytNM8dSe0cMy5leNsfVWIupomyErP77lNQs0EkD0VKsxo4IhlQmSt2T614V\nE1ZZm3lWRUzhCmpLpdP8I2kwTuO8pS5IKfD6K+e89eql3lL/VbTn77zzDrdv3wbgd37nd3jppZcA\n+MEf/EF++Id/mE996lPcu3eP1157jW/91m/9mt/j3/73v4mSLcXO5vqU5jmhQ2hwVsiibWlMSUXa\n1QCt+k6LI5WCE0vI+u9b22Do1IZFo6mRjSeXHV1vqDkxEkGqnlJJiKkorTwXXGmg7xDrtOA2hTJp\nBnhKkZQLuQamXBTDZpMSocVQUO0mYhlDJKSIiQXn3ay3TDTGULLKqaYIJWsfcV0u6PsVy/6UGHVT\naXH0fkkpdT5FC7mIbrxLmhdeWbfr8wbdSEVqwdmebMCZTOM6hrgn1R25Nljmr6/Ky6ypIibxkQ+/\nhPWW+++8zZdff4XjzS18OWLhT1m2h5hs6FxiGAeSeI5vHZND4atv/CmXjx7gTOVbv+M7uXX7w7zz\nzlf5Pz7zP/L4yQNOb93k3/t7/yG2P+Ltu2/xv/zP/4iTm2f87Y+9RO8UwzeNE+MYWB+uMMayu97x\nR3/4RxwfH2Oq4dE7j7nc7tlsTnBHN3HrAxY3bmAWHuMNi/6IkkaGYSCFhHGVcfeY1K7Adjx5/ISQ\nLKuasZ3HtC3d8U18SSQbwXekNFFCIFahwRCmHVUycRjJYSKHiHcObx0xRMKoSyAaj3HCwWbNen1K\nvzmgmozPLRJH0sUjdtdXpLDHOEPI0DUdp7efZ7jest8/JoWB1aLDWIMJ6EHpHCYF4n5AaqUzKp0x\nxutMtQoRyxBVZ1tzJadJl57WIuqkwJSG3h0Spy2IJrkaosbCFKEkQ1YzsxobrCXs37MROtYH2pF4\nV0gZSjaKL2w6pLYkFwnRYUxHKZDynpIdvtHn2xqdMUIllqQ/R++UkWsV2+adI9UIWCV/Va85WFY7\nz6XrVQsctuR8Tb8y1AnaWikUxEKOI5hKiAlXHdU1eL8ghsxuesyyb+etv1f6mVOQdywJsYYXPnLE\ncx8+QKowhC3/9H9462vWrf9XRfMTn/gEn/3sZ3n06BHPPPMMP/uzP8tnPvMZPv/5zyMivPDCC/zK\nr/wKAB/5yEf4oR/6IT7ykY/gnOOXf/mX/8L2vMgcipSTesffw9I7BeVConEdORmkRkqqiPuz+SEG\nlRsUwDjEZ1KqWNOQq0EsGiJmCkjA+kh1FY/mm9TsEAc1CTm1GjcqC1JQ6Ib3YEW0fS8wBdVUZqNy\nhZgHyBVvq1ows84PU1BtJKIRHW1u8M08O6oVisbLhqjxqCkZELi8ejzHbLznoAFXe4SemPbEFEgV\nclVBv3W6jSxzWqcK2i0UQ5oSJcssU6q0zZKuB3GaLe3m2zaSuPnUs3hOePfuu2SuEWs4O7kFucEY\nmPKWkgJtWugJbVsOT3qur7ZcPH7EnTsv8JFveYm7b36Jr7z+FV794hd58OQRd24/w3f/O/8uu/0V\n/+Sz/5TqLN572u6YF198kTdef5M7d55mHCecbbHWE6fEOG6ZJpVJxRC4utpzujnA24Htbou4hoPN\nAY/P90z5Psv+CTfONrQu4UzFeej7jpOTm7SLNdV1rFanGNPpIq0IaT9Q6khdLlie3eHZzSnj9RXD\n9SXjuGcct6zmm0owHaNcEseRadKQvkW7YBq2pDDSL1YU69gcbug2HTXuyUPBNAM5QWk6+o0hx54Y\nRmydSNOOy7CjGEO3OqJpn4IcidMOjCPnqEaDKrRNgxTLEAdKyowpgTesxBOqILXDDpkxXjDFJ4xD\nZLV4RouuiUryMSsOl08zxUsqo8qCbCCTSLEyxVEXN2gGFrVh3KksTySyMUvw4FwkxURI+3kZOy9x\n65JMT24qIYAQSHFAjM5HmRUvalLR/QWS0YztoiYXo4QjY4TO95QIgiHUSs1C4zqkVqZ4xeXlfayD\nfqnvqpgAkqi54F0z15AeZ1qa3lFYoFJ5g7hCpcWZTsHNTdSkBCI5q/ba/SVV8S8tmr/5m7/5L/29\nT37yk3/h13/605/m05/+9F/2bYmlYpyGlcU0v/RZaSapZhppMM6xWDjGFEghKKjABYpzGKObOKmG\nmqxawqrOZJxfKonHWdVyWYc1hmyCYuQmIU5A1gF7qoacHGGyODQxrzoza9EADM6uqLVqtojrSbkh\n1nN1Jc3a0VqKbuxKVsmFE1KpaOx5hFgBSwwZZmiBtyr83Q9bUglslgsMjW7NrcXiqWVFyjvCNFBw\niNtTUNKRcVlvAKUSo2K6StGM8YrON7vG03pH1zvE7hjGa5aLDacnL/LmW6+T6+ucrI+QamjoFb5Q\nITPhbYtNPUaWNNbRNR3biz3Wej70Td/MeL3j5Ve+QOcMfdfx4OKa7/7u78G1K/7P//2zxLjj1s2n\nGYqwWJzxw//xJ/iHv/UP+Q/+o7/P7/9ff8hzT9/h6upaSThiSSkSQqDrOq6vr2lcwzaO7Ic9YRh5\n67WvIN/wHIfPnCHeEXPmajeR88CN40Me3H+A9x0HRx7rJ+7cfkZZpCXAJNjFzLdKUJuO9vgU2e51\nE2w8hkfE7SXTtKdQcVI5ONpALJQc5meqkFNlCnqYnR2fIdXQr49w/ZI0RcZxS9uv8G0PNTKeP2bY\nbRl3V0jNNK3DiiOnSIgjWI/3K4pMLG0l5UoIIyEZhW+UTK2FphqmkiEbGtPT5USolTjt2A2JWi5w\n5gDX9+/bcL1dUoP63UuN1NqyD+eITIhMM6NSI3Bz0flmKY5hp++HlBYrmb53FPTZLjlhaHS2Xg2m\ndLTmlFXbMsaH5CqUule1B0LfeyIoC1Z0ZiuE+R0TBcjkQJGJUrdYv0RqZWF7alpQyTR+iY0dU3lI\nSFt2+y2+KRgbEWOwRm2/zneA0o8QMKihxXghpkmBOkbf1ThumR0gmmprdIn69T5/fY6gbCgygTQz\n8NSSqTTV4n3LNAVaa2jcir4tXG4fkSUgU5mlB41uHnMC9AdQcsa4oNxNsYzDiPMC1ulySToKAeej\nUtCz/rKcF6YxE2Mh2Y4YIxjNQwHVvJms1rOUIq1vlTifVSjvnCFRkFyBMoOOtYDlEqBYEL0RxhjI\nsRCniHWGtikgSn6JEhhMZdEssEbHECnOA/PiKWlkShHxgabNiFFRu2TVfOZcGadA0/zZdtM3LV4E\nZwYaByI9d57/Zu69fY+vfPWPqUUwsmLcQ99lSh4JZYtzKxwdhkRM51AHSlpTa+Xm7UN22z1fffNN\nJBuevfUcFxfnLI6f4sN/69v4/B98jjhds1lYmsVtrvaBb/nYt/Hxj38b/+V/8TP8Z//pT/Bf/4P/\nhm//ju/k1Ve/hFA5O7vJbruj6zpqrTx++Ihpmtjt9ioFipk4BA6Ojhl2E/HeWzz97B265YIQlOCe\nMRyenOHbJbVaFosNF5dbQqpgLSdnB8RBA+TEClbAJGidw5wcY8eBtm+RWhkuH9BZy7Db0TjPjds9\n1xdPiGEkpYiVwmrhNUveGbp2Qc0CpsUedjAtVYQ97tmdPyHur4lBpW2N8aQQKTON3wZlH9RuQSmV\nUHSTLUZwXU+I4LKng3mmLjqvNJ79NJHjBTV7pqnHSmHYD3Re7YZ91+ozbB1hnB11pcFWxaxZVzEp\n46rSqqCS0pZaW0qx+MYTbeHySdQZek2EMJIHi6kTzjQYY7TdnaCxPTUdkmwhSyHViJVWDRadJ6Wi\nIXwqpHx/UWWtxmbkvCWRcK2nMQdI7WgWPYIlhKDQ8nSEMiYs5J3alSnMvkCcPaDooBhrdQyQUyZE\n3WVQCqkGQkiMYSKFLaVWXGPUVPCvSnL0V/3EkAjTiJvjGnIqhJqQMeNNi/MNVcA1SxZFmEJhzBfs\npi3WRxq7whg/pz0mck6UbAgM1HpB9Su8OIzpwSpk2MSilBMTSJIR4/QWmg3eiQq6KeyHPb0bITYq\nLaoTvuk059k1FCrGFHzV+AJbzfxjLv9cu/xeXLBF5ozolLOCC8bAOBS865CaMWbS7BVvmaaEYWTR\nGMQKxjRYBx5Dmx1TZn7QoFSwaJ5QxpCLbktJSrfPuSLGzeR5jxg4Ojzi0ePXCGGHtR0xGYpkprRD\nksG6hIREi6Pxa8R6nGuoudL3K45Wxzx6cI/d9YC3DYt2yZQTdz7wQbZXF7z8xy/T9Au805z4YYQP\nffgFnrn9AX7lV/4r/pP//Mf5zP/2Wb773/wuXv3Sq2w2a8ZhQIxmT5ch8cbrX+XR/UecnpxinMfk\nQq2Zg4M17vSMkBLjbse7b7/D4eaQk9MDrFjeuX/F0dGGzvQcbNa0y56K0K06uq5lNw50bUsOgabt\nmcaBEAM5BFLcUYYtIcPqqedYnN7m4s1XGcdHnD95GxPBt7phjiFQbWbReY4Pb3P7+Q/DZoOzjuni\nMTlc0EjLlDP7ccRkQxj25BQZ91uysbTeYrzRXJ9mgTiPEXBujlV2lpgScRqQFOhqxSBEgUollUDK\nE7lGTA7kvJs5sC0lwn63xVqPkYauW1Cyw9kFOQ+kkIkBqjXkojALg1DS7PgRg9CQkyWFJc1yiacy\nbHcKxk6BEAakXNJ6S9ecaLyGabEkvPWk7Ei1YJ06gxrjKEYPd71ZJvLM6FRaewVUG13rRIxbjFuz\nXh2wENW7jrKnZqWB7YbCNE2IK7iawaV5plkwztN3R/pnWYHaIpKodZiXXwZhAvSgiOqzxiSVfRlr\nv27t+usrmiUyjiNtSTTek0tmCDtSzIjNLBdHONtpzo/vWC832LFwNV2TUsDUESNxFr4Gcs7EGDUT\nGWGfEr3vMTYjOKwpeJkzU7zHWkimYm2msUJpHWY+bXOZaHKkOnUNWDMvWholqk/TqCOB2a6prUhB\nyTCGnCI5B1LqcdbTti2lTKScybXMG+1CzoaUMzU7oq2UKdFgKJyrUNc26lX3lloNrfMsuzW7MBHT\nVmlKUjRUSixFmBcCajfNRU/YvtlQSsuN0zPOn9wF+5i2OSCMnpIHjEtghXGaMCVi8h5rWqbc4Rdr\n4li4cfQ8ve/5kz/9v7l5csZ6fUzXrrj31bf5pg9/I6+9/iW8t6yOj5CSkXrAoycP+ZaX/g7eH/Df\n/fe/yd/9vn+L3/nt3+a5Zz/A5/7g9/nGb/wQr7zyxXnG+VVCCIQQuLq65GCjMN/N5pCcEturK6RU\nuuWas9WCxw8ekuLEw0cPuLw+5/T0iNMbZ+yCoV16Lq52iLUa5RA9uSRWBxv2+z2b9SExRFzXcbHb\nUcdL7t9/h+ODNY3AG1/8PDHD3/m27+H83Xe4eueLnJ/fI11fQplYrU91+WcdpUSeXL3DwapB/A3a\nBUAi7LfUkDhoWmKBq8d79rtLdcE5S0Xm5aVlGAZsm+maDubBijGWFEdqyph5dj2OW6Y8gZup/Bly\nmHAZGlEHulRhypk07Fl2C/bD1fsCb+sqOVtisgyhII0+j84pyMK5TIoTxmgIXLvo53QFo4J3YzFS\n2E/3lXNZLLV6apm100kLaqma1aU3Ce2GfNO+L7w3tkKNeN+r7ZmsSapSFa5dKk3TUstECFes+mPl\nJFhd+jhf8dkwDjJv3IWUIyYXXFfYDXexLuHsmpiEPMdo5+TJcUnbWmU3lEDXGZxtKNkwTbrYDePf\n0GC1qVyDKeQ6ErNHxFIZGdOAnzKr5SHOepyt2OrJ3qggPHliHLFmIr0XOlaqRlcUodSorbLRdMqU\nLVAwrqG6pNnec0vPHMokBhonSIvaIst7zoyBRkPGtSUznnGaCCniHag4VnN+SBZKpWajPmURaq4U\nKmlSx4/MWlFrLN5rzChZZkBJYdpPVNyMk1Oh/LI5wpleTz9vSaWlNGumEig5YlwFU6FWpBiqVIyp\n1Fmom9LAdl947rlnefvRG1h2UNfEIroYSoYqhSkN5DzRsMTnlkkCrrnk4vGWF5/+19hdXbCLV5yc\nHENtmabI44d3ufXUU7z8z15muV5QK3T9gtZ1XFxc8tI3/+vEEHn5C5/l27/zW7n/8DE3zm6SU+aD\nH/wgX/zin/Dss89y9+5d2lZVCKvVCu8skirGZEoZOTo80JgFI7RNT6Vy+/YJVMPl9SVXF1ecn1/g\nugWb444QE32ni5zlYgFU+r7Hzo1XihN2sSCnyNFiwa4OrPsFD+6/RdjuONtsuLx4zD/5n/5bbjz1\nFL6D06eeY7g8Z395TuM965MbHB6esD68oQsnMileUfFE14GfGK63jPsd18MexLJaHjAN16qysGa2\nA8d5vqejHZm35KUW+kVHLiqvm8aBputY5ol9HNmWwqGz7GImEVnWSmggYohVw/rGcYvIUpkHBUQs\nMUWG8Zox7hC2tF1WCdqcJeVQqVzruzkmV0h1UoC3/vRm91zB2EAM19QSadwRpQqBnapMZMJaNY/k\nEjDFYEw/R1+XOVl2whqoRa2kahax6ihLic57pjFwUe+x6I8pEsEkMgmxMrflnpQCY87UMtKIp6Vw\nffkO3j/COqvx1tki9YCaOu0ygVwTMQ/EGGdjiiNGKPFv6E2TrPDdnLK6dSx0nWcaEyFOxHLNwqxZ\nNGcEM5CLpfEL7ORJcUtyI8Z4YswouF6jbpGqyXw1YcqIsR6pBZ8d2WScq+SsLoycDGAVBlDnM74k\njAipVqQkBZnalpwrw7BlioFiMsVokdKMZIOVBc4bTAbJjlwi3gtQsEXtcM5aUlG8mjbWKlMSGrpm\nllmFcbZ/JShVXTOdwxl1LZkUqVkfrpLVe2uZ/we8JUwqSUIEyYa0h8PTY9746p+yPBgxlHnjuJqh\nKYUsdXYyaeZOqZ4k1+z3hg/c/jj7/Y4ahb5Zs9+NeoueLM8/8wyvfenLrJYLhv2OzcEhi37Fdrfl\n2eeeo1a4/85dTk5usOg3vPbaGzz3zHNA4Qv/7GWevnOHR48eaWyyMSz7Fftxh9TKwcGGtm2pAuOw\npXVC03gODtbst9c69jCew5M7PHzQcvXkknHcsswrqJ6+W9I0DavVipwTMSg9q+97Lq4uOHAG41qe\n7C6RGJUcHo9ADE/Oz+lax7O3Trm8eEBtW+g8x2c3uXHnWcIQOD48YLM+wK6PMAfHlKEgjOzTRLs+\nY3d3R9pPbC8fkqo6srx1WLGUnCmmQPmzMMGma2dhesEaozKckqhZC4hm2SuN3GZoMiQRjmeaUBTh\nyiVSjTQoKWiaRkIccLadZ/4z19UaTGGW2k10Tm3MRipZQEqmMlLZk0ulaRZshx3rZTN3UKoTLlVB\nGjls2UehWkcou7mbmt5HKBZJ1JIVeGJ7PeDJOKOHRapBUw1MS6094oLO/8OEAa53T8jZqgqFHanu\ndHmGKlVymRe5sUFqxVLIdk9KQuN7jHOIqCQql4EyKjFMjCGXSEwDIKRkoQo1t1+3dP01RviaeQ7S\nKKKJSOs7pCyZpoHtOHKwajGuxVGxblLXkBVSEUJIWFtBVBDsrCOZjFRtGWKcEDNSR1FakCt0uRJT\nwMyc1VLyvEEUbYExpAQlZ2pW8lE2GtAmaOpiipHqC756jNGtvJEFIk4hqbbBeI+RhHOVkPbEnBQt\nVrRtLwXiBN50c/FToXrjlpi2ZUwDMURGChd5i0jLctHiOvXimwIkBSqn6LDOg2hL5LzeXlLOTIPn\nQ89/lIeP32KzPsSWazADtVQsFRFN4as1YlymJH0kjLVIgeeeeZHHF/eRMLGuZ7iiAWteOk6P7vDG\nV17n5umZio77BdUIpcLpjTO22z3GgnUNH3vp4/zjz/wjPvyhb+T8/AnWF87Ojjh/8gjB0nXKDh3G\nHcO4Q1JkHK8IQfM5bhydslouefLkCSenx5yeHXF9ecli4agi3Lp5yGbdIc5iuobVao1vGpbLJdaq\n1CnnTAiBtmlZbw6o0x6RzOlzL/LojS/QWsvh+gM8fniPrvWMwxUYy53100zTiPctzjiadsPJnZsI\nhdy2uNWhFrrGEEbP6mBDyBPrm8+zDEBJXFzen2d9FeySXMZ55q2diogwjRpYJyIU+54CQsEsJRUQ\nS6lFlyZiaEUo1tOkQtpFsjP0qwMIV4SUMaaS9iMhTCwXB4AlxEiuI0imbR1RHE2j89Guaahp0pC/\nmonhgiJWOZXFYnCEKeC9YQrTLMszOBOgRGIKZFpiyGT03ZOis1GRoss3FC9XSsJURwoyRz7vKGlC\nTMWagtARoyOlMPNfJ652d/HhPUyiBriJcdQYiThSEVrnkVygeFKdKKZipSWWALMOVYojlUCpCesK\nlIyYNM//PWHc4+RvaEZQygPWtojxWHFAIcWCwWKko5YFpQqZCUQzTTRgScXoZHW3eN9oVKlRkWxO\n7/nCYRyjwoSqo+yBRnBZ/d4xJmqpqiEzBUyedZuaUEmGUova10WF4GIgprmY+qQRoq6j5E7BHKK8\nzda2GKcLJOfNvL1PupSxLcU6ohUEjzcLGtvSOE/jIeRA53pijcSQcWLYb/e0jcE0dt6oW8Y556Wm\nhjKT76sxQAAKYaqcnT7H/XffxjaBxi0QseRiqSYhcxiW85USVf1qrCK0ah443HyA7W7HdvcWB+55\nakmkOCI4bp09y8NHjzg9OSGmeZBuhOVmw9XVBSKWRb/g3r03uXXrDn/0x3/A8y88z+XlltPTGzx4\ncE+p9zljrTCMe7xvFAdWBWsqzgklRxrXYGaKlKGw357jDg9ZrRfUkvGNY7064OD4kLZfINbRtgsW\niwVd24ERmn6hpgmjywZrPbUBKRH2W47ufIAHb75C3T5hc/MWw2WDua6EZHCupV2fUtLI4vAUsiEN\nI4uT27THx6TthLhMjpPCda+22q3gKd2SxcGxAnfHa6ZYKPmarms0d9xYpVNZR9M2egAblbqJiOIE\nQ8F6LSDGGozRg6LWwhgnplKYxFKrw7mGRWtpmqK5QK4S4kBMHdBQyUxhwHswNapzJyWaRjFtSmjT\nAmd9VdK/tUxBLyPjvtIvFH6d0h7nEoUM3s2Yt5E4WZIMNK2mBxhTVLeZBExHqRNWhDpbPWtOqtU2\nEVOVzCTFkoIup8I0qYun0bGadQYMiOkQM5JKUbaCabAuYUwmx4TxLbZWUkiMMWJNpvUVI3OBbCwp\nR1KNs+e/zHlaUMr+69auv772vCZS7sBA5xtsFUKs1GKhqHDdmlZBGCXjnNJ93nMPCS3VFQwJsLPM\nx82g3YSxnpwLY5gwNlOLV1hxVp5aLUp+0ZO9Yo3CE4ypuuWOCduo9KMQlQotAjMcQzX7HlNaqB7v\nLUUqJe6wzilphULXtGAt++lKmZvGaaywEUqGxnm8bVl2a7rWM8UdU4o0pmFXB8JUaAlM4zWtXSkd\nvG/ZB8MYKiU2GmIlBicNzlViyNw4O2F7caXpgbaB4mjajlAKyE7J5+8jtBrILY3bYMQxDomD9YZ3\n732VVb+BFJgkkPMa7AGPLh9TpXJ5dUFIyg04OT3j6uqKp59+hkcPLxmnLauVjgCaxlJroW09T548\nnscPlff0raoxDVTn8NbjDHRtw/XVSLaW/bDn8KCja73696vaOa+udtw6WrFYHFMEuuUK27T0i15F\n1IAY8G2H9w0pjzSLjriLtIs1aRwo4xYxS25+4KNs33qVOI3cfP5DPL7b0qYBI57FcoWjoS48aZ9o\nxOCmQLm6wm6OKTkzXu4pV/dZmEx69JBtrSzXR0zDgDE6R+X/Ye7NlW1J0zLN55/dfQ17n32miCQg\ns4ukDVrowgwBRASSG0DAUEBDROQWSCQMLgBk0OAOUFpAwdq6O7FKoMkhpjPsca3l7v/cwufnVFV3\nVQqUtWW62bYIizixYw/uv3/D+z5v1zhnWNeIsQYTmtzjenvRd3HRhBBEC9nZjBOdnGXkYrRDaUta\nnphTpGnPYdhJHO5gWZJYIbtRuNWQk2VeT9D9ds+KesPRKFo4Ca3KLDU3SX5sTaMZaF2itoUvq2gt\nc1kru92IcYXaTjgnBY+2jTqLz7wiJDBrO62tKG2ofaW1E96MgIauaaWQcyVRML6gmhzmtIzuEymD\nJK9a6F46w54Fa7hNYbGGXkT/2VWXOOLSpaBQss+Iq4Je6H7BDxJG2FKhfUjcrJ1WNIogyy31MzrT\nbBvqTXVFjhqtPYpCqVWsV6nTcqVuqYuyUXMYNciNg6c3yFFKcKUyylqWRbblWoEJltI1ikqtmbhK\nlk9JbWuL5O2OUpIE3iHnCCB6RzdAkSiCtiG7lO503clFMziPUpJzYqwn94w1Ha0LxjlqCyhdBEpg\nFLordJUMdmsVpWu6FvJ5SR2722HtDrWcRRxtoaGovZETOCcthTWGYZjIbaZWsPVIo1BVRgH7/RXn\n+cycCzv/EmcCtI7WAa+vyF0R88Jgd9TWJPXFBLx9jlaBX/z5l3z+w39hMoGaKr2dUfWKYbrmG598\nk68/f0OZV7qWyInr6xsen554/vIF3//+P/Pi+Svubu/55V/+Ff71X/+VX/qlX+KLL77g6uqKGFcR\nHOsPQmdFSkkywjuEwaM6nE4nhmHk6fTENE6s64W0rLw8TtRaxZpqBLJ82DesGxiHEesDKSamacI5\nR6kF6x1ohTEO2oAZO117TAi0oKhLwWXN/tU36U93XO4fufrGLxMvt9S4+f9DwAwHFBk7OXGAXRa8\nuUfvj+xfvCRbg56fCFeK+ct/4uuv/o3p+jmPj3d46+RB3iRp1li0spRc0ErjtNogNop1XaUwUBIs\n2BVoNdHxLGmGXrk+7FF5x12qtH5mbZDTTK+Sj6V6YZomtAqcTpF1jZSaBeirNXGJmMFQomzhhQon\nM3rNjpREwtc7Qh0qAoQhN6xZGLXfOq8L2ngheqlGzI3aJZxMdSf604o4cVqm1PMm+2lbThiAIkWH\ncQ3NIvdq06QkdDBjhMerlNsWNtszvum7lb6IDhrp3owR+/AbxAAAACAASURBVHWvAtxOuYg7q1XW\nMqNtwdoslXU1lGLpzVFLxRoveUI/4fqpHZp+2AnduUHJjaajBJRtwnA5SCJzF/pJ2fBmwe+oXMki\npxvZVHvxoIPB2RG1zYOs8wQcqFXmOUXkFtrtKC1vURqajuQ61y7SnlIaOVfOc2S/G2TOojpm433S\npVItLuOdxikv0onuWOKZ1i5MUwOdgYIEQDVJ3WsrdIv3AarYR60bKdmS5sJuvyPYRjaR0mQTXkpk\nXTXeR6CjlRV3jpM3sO576CsKodeHwfJ4XnHO01VGmRFl7AaI8Oi6hy62UWc/6FjLlkJ5zeUpMgwT\n1BGfO0EPOG7Y2WfcvbkjXy5oFKV0xmnk9HRiHCd6E6dHCIFp2vHu3TumaSLGyDAMzPMsdjpjhWfa\nt5ZIa4knbmKpM9oTnCHGlWEnkN+YVnqTQC4Gxel8wRhFro1GYwgB50Xb+2Gx1HrDh4FaZcxhg6Om\nhvaetm2Te9cYP9DWSB8CanrJLs3M64ndzWfky4kWn1hrweeIP1yRY0aHgJue0R8+h3c/og0HejpT\n0z3r03v8/gbTFKf7222LLCaJRscZs7045FBkMz2AYAx7b4I+0x9iXRrSTWn2g8jg1iR4udorFcNd\nbbSSQXcqmVoiRg9Mg4OaKOmBGCtLWrFGqEWmNmzQ+CAVGXTK5k8vRdGKEeZml46sYzBaM8+yXFIK\nDFXmpOww2tFLISZFKxYVBkxQqCwyI6UaKS0SZ6GEHlWoEmWhBzoC/daA6uJb781RUsEZxRB2tB5F\nYtg6ujesK3QlBQTNCpRZO4FwN9GE9mYpBVFjULEqg4o4ZSjZkbOmpUZXmpYLdvgZrTQHfy0WyOwo\nKFqPeOPQxrCmyBwv3J9umYYj0KXKo1ArKDWBmjddl7Tv3QaqlhmXqp3et4fTGIFxaKEP9cK2HVOA\nEKJbU9sDJDeGNQH6hbgW9vuOG/Q2MO7krIix0bUixogzCWf3eOvQ2rDbR5Y509uKdaLZbVU4oSkn\neVEgeUcuKHEfYaA7atEiLfEGHQ0oRUVuonVNKLsyDQO78cBoNc1pqvZYHEp3Yl1xRnOJD0yjJUdP\nK4VuMqUqbHc4rTFtxFkwBHTb4d0gVVksHMOOy9OZgz3SVJMOIBse5hPXV6+5e7zDKYGlDMOIQuGd\nYxwG3r95SxhHvn7zJS9evODh4Y6XL19zf/+Ic4ac8ybR2iJHtmiTEAK5iK+70+jaUir43Z7eKrU1\nNIpxN1FqZZ4XEf47jzKB1BqmV1IpDONI2WQ8y7Kg0HKYaygRsHqbl1W6CqjuZAY5WEzLtGGCDr4/\nkk7vsYcrWl6xqXJ+/46bb+ywuwnSSp9n7KtvMv/433DvvxT4SF55eHrPQOHF8xvm+T09RpQL4hLb\nwgFr6+Q1Mg4GoyzeSpT0B8j1sqx4v8G5FYCSw1MV0TJjMaax844lZ56Ne0qurOXCmmZCcGizE5vj\nYNFHg+meLx++IFKElFY7YevKaq7kmqQj0pnWRDrnnYwP6GKfrE0zhhsRoVdIpQsQGLEGD25giYW4\nyjxTbMgdnTb+ZWvbjsCgrZDFvDV4Z2isxHihtYzqBZA5bvCBcdhht0iX3gxWw5wixhQqQgRbo8w2\nU0bAHKiNd2vRulFrxgfRoxqzjRyylo8mlXHpmVx/RoPVDJ5gDmgr8bOlLJS0bjnnlkzhfLkXa6SW\nqE6lNVa5LREvMPmA1ppS1y0ULWP1jGk7ehW3g9YWp8IWwCRZLKAwymHUxs3rRqjtqm5JjVKtSFUp\n6C5rJZxsqEbiR0uh5UYunao0zhq8t7Q+SDVZJXWwdkPOiVaykJJKlcTLFElRlk1392959dwyL9Ji\nlGaxdkRl2R5qFDFLtrgzA9UZhvAcEwaWdKavHW0c2npifmQYnLTqXaHsHoPCWaFYm+oxRuOqpleL\ncTvAcnV8xXxeOJ9OKBI1GxSNuD6h88Cr5z+HKo79eKCuM2prgWrrxLTgvRx8g9lJfksVa2PvneO2\nICqlbGF5cttp7bYqSmaYzmvJvbcOY6VNMgZoFacNgx+2vCiDHwd8mBj2V3Q8rXUenx4BmRvO84zS\nipQiznviUjHe4ZTjclkIVtFVx45OmKR1E4H1SLcKFTVNr8S7hJ5uaJcTo3W8/+G/8uyTz6RdX26J\nvRBePKf+4McYgO45fOOXePt//m88vPkRLz77D8RZtIDaGMmX6h3nPGGaCEHGCKlmgfkag9EWPzr8\n9r0C1JIxppGy8FNjSlIEKC2Fxnrm8fw1l5pAG5TzaGeFgNQUjoCzEiq3JOEjYDU5Su66OHGCaBiV\nsD2tVXS1opQjZzAmiPssy8Go9wGlB1pdSB9g3KajjYjVY0qYZlBNQdViR9ZCendWGJqqK1T7z+kB\ntQticc0XtG5Y17m6eo1zA7VsYyozoLTicr5I4eQTJSlS7SyXLM/MpuQAaF1jtMI6hTENpbYqtgac\nOdKLoSB2Za0lhO0nXT+1Q9OpPaYZtAk4N5BxzLVTW0aj8S6wxJU1nTFesQs7DAeUdgz+CGklaIcP\nGnTeAqFWrE54HajFoaISnJIWR4XzGt0967yKda1rdGvycGpopQq0wDS0lQNatQ2nZbbMEe+wRpGy\nYl0TzhfWPuO8wdFxrjMER1qlUmTTZNLlZumqySKqI5lGrXE5X1j2F1Qw9BYoOVNNZxpGYlwJYUTF\nzGVRlLyjIVCC4Ce8c5yLAGC1dfQu0QnBj2QMrUhOumhNxbERgsfoAaUHvN2DgnXtWBfI/kRaErZp\nVK2YlnFqx9AmYpzJdUVr0EozrwnrPNdXVzydTgzTtOU0qY9SH2OkwvwA1wUIIXC5XND6gzlAMQyB\nTsVaK2OYMCBZ9wpUZxwG7BYlEfwgIW7DhDKe1BoqJva7A5fLiePxKG1u65QqyYveWWiOukS8NeT5\ngp08ywK+5S0pUkGXl/MyZ3pMrOnCZB3T1TPq0z0vvvENyuUkP7PcCfmO0s7oZ695/Ofvoawldc3u\nk5/n4cf/F+fLLdevXvD27VtqlReGdyKxqimTlHjZldabg8nLyEgrwiBZ6jFK0FeMK7VVtLWMNnBO\nkXldeZjPrCWjTGAwikstzPPC3h7FnUijI1tmo7SMl/qm70UOwdYzyjSqACkJZuMo0FG1MbgBqyUX\nvRdDKZr728TVcRIYNkUqxNZxWuawpUpFV0qDnnCILrX3iDMNYw3oSm0rusmIqNVVKkDElaONQemM\ns0estqIGSBIVrIxke3kDuED1nRihlLqlvYo0ySiZaTsnWVdKO5Qa2U0vyGaQxaI21CpuQO1+Rmea\nkk1TofdN+DvhfWNNT+KdNhbvR56Wd7QS8UZt9HPwbsSbidzWTfsFzg60okS64B5BDbQaKLFTtAhs\na9XC9nNQKDg1UreHxRtDaxlvLao5isloFN5orO6iXELhtPxdR0Lq1+XCeB1o3dFaANWxQZOrge1r\ns8pRlMAEjJYb1XoHraKLcAPn+ZGbqyNaiW6y5YK2kjSosRwOB5ENaYPVO4awxxiLVp2zuqepRFwi\nKkTZK5pRkhNVp7VOR6RVrWdSrih2eDOizIDVgbwmrG1sCmBUy9hoUHqgl443UHLlMI3E1GTUgMSs\nPj4+YKwlx4Q10qpba7m7u+Pq6or7+/tNXiRV04dq80Ni6DhOrOuCUshh2TshDKSUtj8HtSn5mRlL\nVYq4JmrjowbTasPlcmEcR5ZlYRwlCbM2eeiXMjOZgU6j5UaKhbXeY4Zn1HVBlRk3TvQMl/nEOO2p\nrGgF8f6O6Rf+Fx7fvWF8e8f0/IrqwexeU8pKffsVJhb218+5/fF/4urFS+IYKDevWc5PtKoJw7At\nsDYiVu8EL9G7wcsipSGc0941Tlvymj62s9YY3OFI7o35MpNSpG0pjmG346palqfKaX3LEmfS2jFq\nQuuBVC4ilLcJ6zp7Y+lNwYevpXaMlTmgNisKAfH2DrZ7yT1SAaqHaqjZU3OlVsNJty0tcoPGoDG6\nC1bRCFXJmwFtG0rLi1ObSiqPBBPoutMpxGRoFEHf1YZSWaJtbKerFW36xh4SF19vRbSuVlIWgtUk\nJ+T7WjspCsy406FLuF+viao6VlmG6RlKTRgUe/bkbOmtkcojVSV+0vVTOzTnfMtgrtC9ojbCiTay\n6OlV0uEmt8d5x93Tl6QU8fpMV54YFVO4olQoKeOHQK1P8mYhSQSu3WEHCTYrZZD5YK2gIygJs+pd\n5BklNoqRXObRB1RqLMwYZSQqootz6YMsROkuVBVTqIjrInjJk9EKEd8a+eUaJzeSUoa9tvQy05um\nakOqCJCiG3qT4Ks+jiKf8ZZUErvdDaqPeD9ymDQlCxLO6B3GgGEH1jEvd0DDtSQRAz0RwohWipY2\nao6pgCGuBWMWaCe0GnBmwJgglrhe0LYQ14WmRlyxeKXpaRbKTNfkUknrSk4JlMFqLfkq2hBjZH91\n5O27dx8rypQS3nuW5cJ+v8d7z+l0YrfbkXMmhMDT04mXL19QqyxBlFIfK9UPWUygoAnspdZCrY3d\nbk/vjXmetxmgVLmC5pMo5bxmlviIdo7BD6TSucQFc04ML8SJlWOjR3lpOmeIcaHWxM4NnO8e+fL7\n/8jNy5+jvPu/adFhwp5iQD9F3PGKy/07rFIMw8jdm7ccryZGF2DY0Uve7k2E1xiCAChixDtHTAml\nKy5Im26bPOTGGKyXWeCHq5ZKCAFjLct6lphcPOt2pOSaxXFE493T14zuSuI5aqT0SDcL1hT0Rifq\nvWGUp9OpathqywtOG6ySzkwrRy2WViRZNadG75rWDJfTgh3k0LReSRFkOj4I1xZjsMbibGdwndpW\nYl3pXCQpVgVSziyLQG5ayWggBCEUDZOXhV95IkVLTk30oCmBVRgVaFay2IfSUWNl1Zp1rdRuMC0I\n0awIXm/cG6zaY9WO1py08mHCWUdKUSKA3f9g7vn/X9fp8ojZH7AUUJ2GJsaMtRrndtSS5Bdn9lyN\nrzjPb6jB0FslxotkjWi2llMeql5Fv9W7QrmIUhZMxaKgaBHL90JXmVTYHsRN/4lCtYZz8suy24NX\nSmOZq1Sw6C0TqFK1QmNoPdF0Yq0z2IrtssyATteaMHi6lh9zp6KVIy6Fbi2WQi8Kqy0WR68LuimU\n8YKUQ1My3Fw/Y5qODM6RYmEpi1RX1kNrWHvAu3vOl4WqFAfrqClS1IK1E3FplJjItUhueis059E6\nMS93aDRTONBKw3gjej0ndkzTPWVNzDYyhonSqryI6CJaTgqFpQdNbBmLxj+/kQwkYJ5nhmHYZEVB\nsmeU4ng8CrAlBJZlEbjvtP+4LFJKMQyDuKmMJ6XEOE7kmGhSkxFC4P7+npcvX7LOy391GNecOVwd\nuL99x/X1M5ZLZH8s3J4fuLm6RiEba7eeMeFImAZaKuS84rUhq0Zm4PHrH3E87Lj/8l94Gi07N5Lm\ni1TTJZL2Gn54z3R9ze0XP6CkwmE3cT4tPLs6kNJM12L0Lbl9nOHK+CJsml8ZZWhtsWic3WZ8XcDS\nvUs2t1bS6Sjr6EoxuoGzi1we77mfH0jlLAdu0ZRUeYgnZp/QGmrNaJU+OsCUg94dk79CYcglYowm\n10rvHqMLRjmc3knhUA0tKzEzZNFK9l5IJZPRTIMB3bHO43SVPYF10D3WC1KOrWPTBVIrQi3qhpw6\nKRnohl6rdHuDB1Up+QzuyBobcQ7kLMvTulmTp2AkGkQVlC0EZbFOobUjzhLlba2Xdp5Or2DMQO8G\n7wa0ChQKKINRgg9T7mfUEbSmR5Z1ZBeeb5KFCWcCKZ9EzqC32IduOQ5HtIrkItqqSuXh8pbReUqJ\nuA7eKazZ0zr0ulJUxqpCGAbWtRC0I66NUjvNJNCN2CKaCY8sLGT5M6OtQRtLWiK0DbRhJVI150Ip\nmdaEValUY00n8XPTaVoOM60VvWe6dttG0kur0RtUqaR072S1Bb51IcBIbnmhNJHMzOsjn4ZfwJsg\nEAVT0dkwr7eEEBj8Adt3jO45hMjcIKcFZTqxrTgzC4k7RnICrRONhUHdYFyj5ZleHshpQetCbRWL\nx7qGriOmj0yHPd5fk5cVb7WI6WnUkhmcRKo2YAoDVhtxFXVZDGj9n6EUtVa8DwJX6bKkG4aBN2/e\ncHPzDLdBG5xzWGs+VozeuY9V0RpXrLFY53DOMY7j9nk9zm2LpdZQVmJkj8cDJSd24w4qjONEuTxy\nOFyRtUF1ETsv8yODdZS4ELPEn4TjDdEZ7h/uOYwTt1/8E/rqG4zGkG8L1Ir95FPKFFCnB24++ZSv\nHt/TrQHVuL29YzcdOF2eRGvYJc9J5nqiN9RagxYW67AtyHLOoJBFYt0E7dYAGmUaVhuK8HtRKdNa\nY24rl7qSeiOiaMoxmj2xLJS6oFXHqI61nW7M9nU4jHVYHNZ5lnRCa0/vGqUiJQtxyfQAzWB6p6pl\nG9VnecF1gav0UtFG461k+5QGWNH/OqOoTZ4HYz2mRGoyxFxBJWqFWgaBzKgmErhWoFl6zazpTMsT\nOWvWdQP0tI6ycD4XdqPGeEXTG5WsN3ZTwDTHujRUUzhjaShsC+ju6AhRiarRtpOWZdufVMxPXp7/\nFMXtbSHmO5x1uBSw2tONgTwyL3dMwyizlS5zsv3wnKfLV+QCtVXWtLBGAXRMm4TFeS2zzKagOpqR\nOIphkAfXDdCzJrfN2VPESmfVgGl92+LJNlVLUgxKd2qVhUXpUQSwKGJt5FwYxrAh5SCXBmRUQ97o\nCJ/QOgvdoLB4pyj5JAN322QY35QwGlUndYEs126xztPqSswzV4dPUKbLzFBrni7vWdcz03jFoK9J\nJRF0pPREzFluhtqJbUYDS8ykKNlI1ncos2zSlWdeH+h1xFnFNDq8HWlREkDDMKGyaAlRnZIbtYm7\nx9gdcXbUZkTq5RzLecHN80dLoLyIGikl9nuh37dtrbnbTayb5zqEAVDs9zu0tqS0fmzRx2nClSJt\n/MMTGD6K1/f7PTFGpnHcBPId7zxdS8Rxb3A47jmfThjAaIV3lrXJdj+WiGqVwVlakQz5h9uvIK3M\n737M1WffZtDCn0zv7qnXL1DK4lJh6RH/byfc//xt3v7v/8LNp8/ZjYHzwwO7aeTd4z2n8+NGtZKv\nrVUxcAzDAMh8d11npv2O3iraeozzBC9RtV0Zuh8lCKw2lLHk0wMqZ7w2vLh+jgk79DDA3UiNtzy1\ne7QCbyy9CjUJIroVvHIYv3EKrAZkL6CUbO178xQWcfg0JH6lCRWsNhlx5RIFbdiqIOBwsvRp0KqM\nvLTptB7Fc648Vn769FpRykN15GI3ZoKl1UBvHTcYjG04L0WKRnOZ35PmHTmOxLXJjNJ6dDM0ldFt\n4MrvQHkST7Re8V6j9xrVNfMaZYNPk2Vr0ZhmqGmFFilkqi5gsgC/8/oTz66fno1Sw5qfcG6P1TuU\nndBqZPCKFBtrOuN0gKowoWOdZXI75rISa6SRUKVuIAArc0qt6RgoFmM7kMi5YCwonbFuFGlFtuKx\nNUJ/Nmhx62jZXtPWbVEVRCjcEyVv4VUtUaumFiOtUnUEtxPykM7EtG1szULXDVKTA6FqdA+ovsOH\nziU+UVSkmYyzshkuOXOJDesbxgbAYJzh9uErXtz8Aik7CcXScnA9nS54E6BKK616geoobQLdsE1J\n+FpbKa0TS8FqGfw7Mq0t6C6ynlI1hgnTHbrBLniBJ8dEqQrVEzXLR1eKmhOldvHWu471Qt4P4yia\nOrulHFY57D4Qh0TgvmXRa9Ee7vd7Wus4J3ZUrS0xLh9b9BAGrG0Mg8zyhmHAWMM0TRhjOBwO0PqW\nFLknWEspBeckisFay36/E0xYl9gKYzzn85PELFdLywpNpZXG6Acent7gW+bu/Z6XQ8COBz55/T9x\n+/4d7ue+RX16ZBhhiSf8j/6F558+Z/76nZDCVSYuhW/+h1/k/dfvyXnevOaaopRoDXunlYK2hnHc\nIaFpSuRgLqB1oPeCsQpKomYhX5Wt01FKScSDMRzGEW0dqnu8GegEPn94y5wWUm9oHRjchCJhTaG3\nTKcIPamL+kRCVWXT3XWjVqlMW51lkdgtrYkoX+J3RWHSexZwRt/o80qE93wQ8BuRC1qv6N1Qm0fr\ninEDKmd6DeI9R+GcYRccIXSsEUhJbiJfyzWSqwLlaV2UAKObMHovFKeicf451u4p5URKYqQIo6fS\nSKlvTkKDVp6SK2x5W1VLBr21kjjb1c/q9hxPa4o1NbwqGApD6Fg0IYw8rrd4XXDW0EvE64Hgr2n9\nkdzF8dN6wiiFqSIPao0tpElRyirC9p5lnmM6Sq14H7DWU0plXcVFYXyTz7NxCFs3KJFnUkoTkbeS\nGWjJ4pUuWYkLyUo1MI47Sl1ZyoXYErpLoJtE5jaCuqYWkT7RA9ZN9HShq0JuCxpLN3KTqKbopaD0\ngm6OmO+5vXvLfnwFOpG2lus8v6e3GWsmYl7prOJe6hatnQibVREUl1KbW8cwjNB1pLVE61oAI7Xj\ntcY0jQ0QlxnXxARQc2W/M3g9osJILpm4gVOUaXRdNqsr2CBkoVIETXZ1dSClzDAMIjbfYj92uz3r\nulJrY5p2HA5Hdrsd1op97oOzyFpxT8nmvTOOAyGEjx/ee7z3lJSZlxnrLd44qe6MwbmBjmyrVRea\nVV1FD2lNJ81n3NghK+L6QFsj3hnCbiQ/PpKfvuTHj4Gfe31D1pnnXlH6SmkrRCT1MxcuD2/ZP7+m\nrhee3bzmdP+WXBIvPvuM09svyFXo4GEMpCTQbBMsHwTYWlsJBTMGyII81HK4am3khY5Ceydqg2XG\nKcNazqytMNdE7wXbO/tp4lW/4evHewGJWIt1Fm+D6BSRDmRNM4oi4nVlqB2sGSkUcswbxEbRS2aZ\nE7oPtN7QSqGNJrWI851OpKlOTBk/aOZ4gSyide0SPWUGdcCa8JEnG+wONUzM55WUZ5zpjMYzmhFv\nK86Lrri2TlyQbqUWepOK3VrhPxz2V+ieiWuCZgnhiLKNeX4AEtYcCWOnd0OvAzl3es843/G+ir1T\nic+/94rRSqKMf8L10zs0FXSlSG0m1hljLwxKAp2s8wx1YIknum/YNkGS+IeKwZqBsXsqia4KBkVb\nKm70wqjUGtUqpURJOcwFctvym4Novxjxe8e79480FqnscmfQRuhDNlDsQoqZ0jTBAV1EuqWA5I47\n0bg5D21i0CNJi2zKtoaxmtwzSwLvj2gl4WdrljjgXhW5JKE86UbDbfNAjbV+i+NtoDR3t29wrweM\ncaScSOWRtXzF093KtH9G7hdyi1g/4LqW7BZlZX5qOmaUWZPqnXGwdO1QNWJUQbWC1RqrKipXWulY\n5xDnu2VSE9bA6XLmcpGFizGKaTdSK4T9kdOcGIYgUJMudspRDdQqsQRirZy2KgkeHx8BmKYdu518\neO8JwZHzmXEcGcdRljq1E0JgXdeP9CJrRfP3ca5pHdNuJOWIUrCuM8PwjN6bzFFLFluldbC5t4Zx\nZD6f6CZzefcVe1+ZT48s85lvvP4mLdywdM903HP75T/xySefcvf5VxjT2SnHl1+95XD9guiaZK6/\n+RHP9s95eHfL1fXI/d0bJr+yuzqIRKoWtNGUUkgpg24E5+RFirTqc4p47whWMoDEe23Q1goDUll6\nq3gNy3Jh5wOJjitS3HVVBa24nDFGMypP6xVrDc4OaIwkduoXxBC5f3zL+SyONeUs1lusgmwqS75g\nnXQDFZjjLFwIoHUlraxydH0RVQmG03oSY4RRYCq6bpK3dmIc5eVllMW3IyWDqQrdheVpjRH+rZIx\nilKVZY7QPb2JySPVKPCVloglsafSssbg0X2AakAdaNVj3Ap6wSAb/NI6KXZyUpTSNi9+xgdJjnDb\nDN2Z6SeeXT89R5Br6C4zpFge0XiMsiI5oqGapavOvC5iGUTLJrE0oTAri3OKXuWfobbEQatRAUFz\n9coaIygoPTHqCa0cznmaHtBq5LNPb7jMicvyQAiNMN7gCEBhnI7kAvG0sPaM7o3S1QYXUZQU2Y2f\nYNgLIkvDGI5c1kRKJ2yvKKMoqfOQ37OfOqVaaqqkXIm5bjT5gtEHBBIv+jnVtWg6G4xDwKiOplNz\nkr+WzLw8kvoT69OdiMC3MYN1OwYT6JQta0baMR8MLYPVFuMHgSakAmSUMmhd8d7hzEAps+DwSmJe\nH+mpU3vn2fNrOUq1IZWKKo3z+ZEQjlgn/u9hCDyezqRU8Erx/PkL9vud5JJvEQ/DMG4H3n7TWgpE\npWSZFV9dHZimHcZotG7sdjvWdcUHoRjZTY5kjCGEQNUGpRBrZ63sDgeeTiee37zg4f4e7w0lixV3\nNw6cLxd0a0yDJ7aMSjO3T3eoBn1duP36x5jBUO1I5omrqxuens7cvP4Gn7/7Avv8E477Z8QUqTVz\nPF5xONzw7t3XKNu5vb0n+EA3iqfTheD8RqeXw7H3TsuZ0jYtgPak3rHaMPgRawPaOLrpIkvTjtaF\nlqQ3yrpC4Yzj5d4z+MBaklSrStHpzOs9RTfGcKR3wzAcGcKEphH8EWMGPrkpLJeVd7dvWeo9RhWC\nfYbe7L8xztgtHdVaoEv2eU6ZXCF4jZ1E7dF6kQhfJfNhpQR47Wyj1UxbMt5NtLrdq2iMGpBcjErX\nCrSltUYpIvPLqZPTB/aDRPaiOlY5es88Pb3n+fFTDB7VusiRasWaG7R+BCWfW0LTJNYiZ7VV8Uqe\nmyaYR8MHXefwE8+un3ho/vjHP+b3f//3efv2LUop/vAP/5A/+qM/4u7ujt/93d/lhz/8Id/61rf4\nm7/5G66vrwH4kz/5E/7yL/8SYwx/8Rd/wW//9m//Nz+36orWq0Rp1kwqC75OqCIOELxFN0uqmiVG\nnNFQQVdDa1pwZ13TKjirqemM1pq4dHTtTOOAM0bkQaUzhAOtSvrdECzDOEKfCPaKF892XJYH3t99\nTm4LNIdwKR3jMDLPE2s8YY2nZkuwHqcNKRXScsF5i1ZY7wAAIABJREFUaVm1FjeLVY5zFL+u84Za\nt/xwFE6PLHGhFkWpHaONbMxVxLoBowLOOnSWUQF0equMe0uKZ4z2oLqANnSllSjJkxpkiORQ2tN1\nwiAb+a4auWSRfmhp/yydpkT034hUHKlHzukJy1lSohs4PH4I7PwBeqOguLl+xjxfYF3J6cJu3OHD\nhA97wuS5vX+idTgcrlCqc331nDWeWNcVrcXtI9g4mMYjp9PTBmfQNKWYxon9/kAInpTyVmEpnj17\nxt1dxWwV5gexfGsN7yXV1DpZOdQqmTvaWnKOzJcLtSR208Td+4XdOJDnMzUvDMcXvHn4isMhcHla\nuTruWZeFyd6gwkA4HHn/wx9w9ewZb96+55PjC+o0YlYIKnH77i21FA5XR6ZxR4ozTVvWmBmN5zAd\nRGtKYxxHcimiojDSfovovaOVAFdKzegP35/RMnvf7gatRRpH7/hR6FV3T4903dlPI4dhz36c2ccT\nT0mTU6GVClYq/nG6Yh+uOeyu2E/PUN0SS+QXPvslvrr9IXePn5PiBW/2NFuJMZNVkQWa9xKGaHe0\nZkQutBZx9li9LZY6zu2gdlqJ5JrQo4y41lzw2aKVkxl5k1lo8I4Q5EBsVSSDtRbJHMqWmkFbISdZ\n3alNXjoyW83McUT3HdRKZyG2jPONro0I6mkYW+ktCwxcNaHLl04plVITpRb2uyucPQhE5d97aDrn\n+LM/+zN+9Vd/lfP5zK/92q/xne98h7/6q7/iO9/5Dn/8x3/Mn/7pn/Ld736X7373u3zve9/jr//6\nr/ne977HF198wW/91m/x/e9/X2QV/6/LYKh903O1TOHCZVU0taertJGPGk4HcpZUvkakRdj5a47h\nhaD2XSLHldgiS5rx4UBchAu42+252u2J8YFORjtDK5116YRjYAzXeHvEENiFa6Zw5P3jj2g9QhE6\nUisNbwaKXVBN4Y3HmkYwBpUr8+kdfhzpeBQrzhvCMFCqIpVHEbmXSm6Juj6gmKk0YumorkmLgdGg\nbRHvevEYHRh2I70kIIJaSOVBbHVaAA678RXH/beID6t45muWoXZNGL2QS6WrA8ZWlJlRJBqFcdhR\nY4Ju0L3QcpbwKazE2jrPNO5RpTEazeCuoVr53sPAYRhJRUKMWmvsD3uUCyjlURYRwANXV1fUWhhH\nyS9XunwUsj9//nwTsEtK5ziO22adDYzsNreQHIYfDsZpmnj7NhPCFa21TbrTpYI1MgPNKWO8/Pdh\nHDDOc7i6Iq+GWiJ5XbG9sTzcUtcz58d77GXB9Mz8eGE/HokloYPjtF7o68qL0ePGgct84ebFCy7v\n3uF3Ab0fiY8Lz25umC8X7u/uuDkcCKMXPWw3hOA30IYs+h7u77cZK/Qm1fUHhUHPldzAB0tKCaU9\n1riPv9vWq+h4lf6IMCylQJdIjF4aow84RN6kdUeZTiORkmY5F6bxGXaaOB6vuT6+4rB7xhIX3rz5\nkqvpGSVfeGgXNAGln9GaY0l3aFXQrmLM5rxTMkpSJtFKQiNqBR8CTnmcG8TH3RIlz0JLV52CAKZ1\nlRli8I7RGYxr9J7oWVGsGNNK06I4CRI3Y4wHMqUs1NzQxmOM5f3jO3bDjGpGzBy9YbSn1jMKYUl8\nyO2yXuM0lFRQ6kOsTCHVC+c5c9wNeHf49x+an3zyCZ988gkA+/2eX/mVX+GLL77g7/7u7/j7v/97\nAP7gD/6A3/zN3+S73/0uf/u3f8vv/d7v4ZzjW9/6Ft/+9rf5h3/4B37jN37j//O5JzvSWyK1TGli\nV2tVUZe66e0qTYkQWGlFJYmoto1b+h3spmthL/LAU5xJtdNyYhoOkBWmeXb7ieA8a7yAltzndZ15\n7E8ML59htcFbQ8kGo0fGcENuj/QegSeabTiXULEzBI9XFt0VrgutPPaV27efc7x5QQsabSfGMDG5\na86LYcm3lO1Gbz3JlhsvM8MipCFqh6rJCcJHaYoCZUFVnPHkvLIsDxwPn6AqTGHPZy/+IyVV7i//\nSeZH6oMkI0qchfXshh25LqIAKKvQopqnFIM10JDo5KoSXg+UXrisMwOW0h2Pl3u83qO8pqyJyzLT\nmiZ4s0UDS9SALN+EAfnq1SvKVi30bQm1LAvGWH7+s2+RS6Q3LZG4RnM8HolReI+dzm7aM4SBXArW\nKEoteO+ptQqCbjuwpZ3PMtPMZSNGyU2ttKLUJhZdOo+nE5BklEEhzRfIkV4SukpIXXCOuKzowaGt\nYzCOUg2X04x3CrosnMabK+7e36L2N3TVySUz7Sdomdoy9/f3XB8PInPDMM8Xrp+/YM0rOWWBEpdK\niuvHEYPzAsPQxklr7iy1ZtoqG3bjLEZ7So7kKtG3Wmv8BufI85lh0EwEhtMD5tFChTVGmqlMo0MZ\ny/3jV7y4fkWvhiHsuLk5EOwLnu0nvv+jhbm845Q0eVnoNELwKP2MuDyilUU7K9k6WgZmgl+zdCUW\nUa8HfAuophl0ABWYlSL2JMnkCUyX58E6w+Ac1gMqkVKlFU3NTYwozWH1bkt2kJbc2iAsU06UdkH3\nhNFB8orMnpKFiNbmFT/UjZMg64jSIjU3UAPaKNKa0FYJPV+tdHVmXj/HaPfvPzT/y+sHP/gB//iP\n/8iv//qv8+bNG16/fg3A69evefPmDQBffvnlf3VAfvbZZ3zxxRf/7f+xcijlqBRSTRhjqO2JSiRV\nv7Vkkhipbcf0Tm4ZqsU5jfeGw/4oMAtreVgf2NsdMSe0ajizoxdNTYohHHB2YM1nUok4P/F0eqTz\nBd94MdA9aCuzxNYqKWeULliF/HmXGaxjcCNOd0wV0EfsGaM763xPf+y8fPmK1mBwE6PbswuB20fF\npT6hraLrKHxLFGMYKWuGbih0cqwSiDZIjk1FeI9KDTQ0qJllecNuv8e7a4bg2ZmBb776X0lfPvC4\n/hCtZEPbe2I3DpvMoxHcHq3gcjkB201eC1V1lJfwq2VZqRmmkBn7me6P1DJw5SdUU+RaGQbL/d0D\nx+MzTqcneu8Mg8P6A4+PD9y8es311TWXNRLCSK0S6XB7e0drleurl2LnxEiQW1NcXV1tNB/NZS6k\nlDDabAu8ytaJopTaFkoj3gfmOdM2J9jp8Ynj8Sgb/FqZt4N0WVactWhVmLzhy8+/YHm4Z/LCIKgx\n4vcTu9GRfMCYAT1o1rTQc0Gnxv7qOQ3QXWOMIzjPeHAYF7h9eKQvJ3opXB33LJcTZRj49NNPuX+4\n58XrV8znM7tJ9KjDuBdjREnkFDc1gWKaJjoKHwLBObQJUmEqUYP4wYMSwjqqSmLqFq/SNnG+dxPv\nL4/MpdCCpVlLSsLPXGtDK0/wisuy8vmX/we7ceI4Hyl5x/PjDU5rUv9FLvXE+9uvUf1CJ1GaZGgN\nfkdKCa278E+9kpyhJFEukUyMib0RuLfqMkoz2uF1xemVljO6D2Iv9g7nxfDhQgXtaAykBj0tpJKh\nBUzwjG5P02xyIcewv+Hkbrms70At4uFvidwu9A65J9GLFoX9oFelkjfKliLJfkQrIZ/Z7RD3HcqF\n8/zP/+OH5vl85nd+53f48z//c9HE/RfXB8vbf+/67/673ii1UrYYn1pWKo1mVxnO9gGjPNrIW0Yp\nacdKO+NDYhiOsmEd9pSW2T0deZxPaKM45/eEyZHrHr1WyUr2Ft0HbBP/uHMr727fEHPjsDswjBMG\njzOdOTZifYR2odPxg8W5PaYNIpnohbJErG20WMmt0taF+fLEzfUrCXejE8KB/f6GckpUpWndbGgs\nJe2TVdSkaNVSeqF3Ta9ZiNeuYLRDY+gqU2sh1RN3p5Gff30jLhljGMKB11f/kVo7T+vnQKeUwjTu\naF3SDZ11OH2F1VUwWcpJHISKWOeYumfyE2WttFpRekQj8Rg5NXQrTNOe8/mJw37PMl9wfmC3P3B3\n/57RFl6+ekFXmmVd0MayrCvOWR4fH8URZQYOhz3DMHK5nJnnCyE8p5SM924jGhlp0+ms64pzbnNg\nlc1hJXKlEAKlZHIpuG2+ef/wQPCe3ThRUmJdVuhwe/cWXSNadW5uXjJrRTzdk0vDGst8ufD/MPcu\nrZZ1973eM+7zttbat6q3pFeWZNmcY78kJibELUO+gTAY1DC45S9g3LHRB3HPDYG/RMAEQo4gB9xw\nGrId2ZYlvZe67NqXdZtzjnsaY1WdkNg6EHOQV6vYULuovdcac4zx//2eZ+yv2dy8gKLbzkRr1uVE\nUorT4T2ygpu65iHPjsfHI8O04+52y/qUOe+fmU97XNfaSe/u75nGDfN+RilNFokiFMFHFAbtDErp\nj4H3Vsxw9F3rPAtZGwbxwxVGlQgtEQiUbci3dtfdSESyNpr/Vt3w/v0jMUk0msE5+tz66j7MgEJp\neL//Cv3F36CtwnaSimDqtwzdlpdX3+Dh7jWff/6IjzPCtFaesQYjRiiFGtvcIfrWfhNIUI4cBefD\ngt45VNHMOWGNolcdur7gKZ9ZRWlZ6iqa+cC0jVHJ7T2eC6RcCD4w2h55qTJb07cCTLUgKy+mDdvp\nijl8SSoeZEURyLJiddvFWqeRMreadS2XO1Ao5YxSQwMeqwqioK1AiBXnRkr+N4bbY4z8/u//Pn/4\nh3/I7/3e7wFtd/nmzRtevXrF69evefnyJQCffvopn3/++ce/+8UXX/Dpp5/+i9/3h//rW2JNxJK4\n+9Ty8lcEVEMthZgEVUeEVReat7oEsFeUrszr/jItBmU007Bju7nllN5SCVijOK73TE6SF0WuLdxs\npCXXgJKVWh1VrNw//YSzH9gNtw1cIQ1CJEQphBRQyrDbXiHERF41JVQonkWciKLhvKw1CFVZjgdi\nWNogR0iMNjjtMKZvRCOhkUpSLvoNUQFRkKK0BlOBVFocxcgOaL/QIvPlcnxl9s8clxOje4EWLW7j\nzBUvdr9ByoXT8p5KIMTIqGWjVYs2IMrZYoRDyg5YqUJgNFg9oMUARmPLRE3QiQEdO6ZhwKoOsmEY\nd7iuY3vlGKaev/+7v2e3u8ZYx7ysjNuOEAPr6Uw/NobmujRYx+3tLc51CFF4fHzPOLZp+jRNbXep\nFKZ29H1HLpHT6ch2uyOlALWyrh5rLc/Pz0xTc5nHED6S32spl7ZKQCuFjw3m3BnN4WlPCSs5htbb\nlgWRweieYbzhvMz0zmKcJJemIckVRIwMux1P9+/x6wnbdWAT4zBy3O8JMWBy4Wq343n/RCmF1c9M\n0w6hNUoYYggNM2gsOa9kUdoA0XtC9CjVfFG1aKiZrnOkmiklI7NoaZAKorQKrtIGkE1r4VQ7pudE\nDgETNbdXdyzWsT8f2S8PjKnjED2lrgg1gGjYti/f/iNSKazrSXnlbnuHEYZBbunMSLWW6CObItGm\n5XWFlKQkkFmRL3lNShvgODmQtSbkxNPxPb3Z4OiQ2aJ1h7MSJXr2cSaWBvsuQtJbjWZkCXsomVJm\ncqio3CGqJa2ROezJNtL3V1gzIWTGdgNOOUa34RBfk+LCGmeKqJc6skBpUFISfCalSow0r3pq7xep\na8PAicRX/7Ty1U+W1s//r6yJv3DRrLXyR3/0R3z22Wf88R//8cevf/e73+UHP/gBf/qnf8oPfvCD\nj4vpd7/7Xf7gD/6AP/mTP+HLL7/kH/7hH/id3/mdf/F7/4//8zfJpRBrJuWFXFdSWdCqxYtibHcb\nQrYJtNL1cjdROPs9S36mzxMmdWip2W22PCwjtcyoAD6vPB/fMdoNPoJQ7TghadNUAeTY4B3ePxGl\nQVjPcmEAlizQ3HF3c8fY3TVwh/QEAqufqVpSrKFmz0brtkusijfvf87XXv065yAoqtnu+q6HdabU\n5mePRTQdgdJIIxEytyhEahPU6FdWAcM4UoS89LgFBcUaZ87+ieN8ze3uZfPKSIUSjk9ufpV8Hzmu\nX+L9kSC3qOwoJVy4ghaKpaa2g7fGUvWpJROKpDcj4dAmlbJUdtOG1XtOpwd0sYgL0GEzGX70ox8x\njgPDONIPIz4lfPCICs5ZYlp5enqk70fu7l4yjVukhOPpCaUk0zQB7RSz27XBTtf1tLB3y69WEss6\ns9lseHh44Pb2FiEEj4+P1NwWy+PxyMuXLy809ERYoSpNDGfC0hQpndGsK5yWGSdVU+4qWmSnc6zL\nyuG4x6UOpzegHNYO+POeZVnZXd8yn54pue2Au2FouUc0i5+RznB9cwOykfZP85nOGPq7G8QaiOcj\nKfiW8kiSkPMlydFskENvWdelmQDMCduPdH2mG9rPTMomA8Q6Pqh8MYUaEuG8UksAZZpHvCaS9yhn\nwQqqKaAFKSZCWDG6Q0mNUvD5F/+M667oht+hlzOdcYxW851Xv8H+/Mhxfk+qnmnYYK1hWTzeJ2Ke\nCUu7vza6R4hCyjQYi5asy8LRn+m3GwSSXAtaaqZuRPc9SyxU1zXOaxFICVZMnMuKXzPRV2qWrCVi\nRLrIFCO+FHZXDis7qgAtBmy3xfY3nJY31PPn+HJoGWSRiaGgOgsIcmr5YWhai0qDfEAipsCr7zi+\n+R8HlJCImvk//pf9/79F84c//CF/+Zd/yW/91m/x27/920CLFP3Zn/0Z3/ve9/iLv/iLj5EjgM8+\n+4zvfe97fPbZZ2it+fM///N/9XjuQ4czFikKCkepM+ckiGmhkknRIGjw35wT2l7qZ7EQi+fLx58w\nbW953qt2SS8zfW8ISyHXuf2Q54AQCSU7nvaK3Wak73vO54hfT0gSqkxIHS7cHIGolXUNdGbkV7/1\nm/TDruUAQ+V1fssy+5bDTBXdSaSwlLDgtKJURxWS4/Kewd0Qz8fWuKitoytLRVfRKoNSkWnKYmpB\nmBWnHSUbEIXKSqVVz1IGrSRGj8zeU2rgsL6j74fLfe1jO2IIy4urXyU/FaI/c8jP1G5Cp4qQBSkc\n2hhyKtSqMGXCygGlM1Jl4joTpKIsgo2zHI9PreVhHUo259I4DLx9+0UjqovK8/MDX73+OV0/sd18\ngh07UinUXBjGka+/+pSrq2uMdhxPe0KI9EN/OXpHaq3EGNntdh8BHfMcLsdzTd/37bQh5UdO5vG4\nJ/rQdtnOMc8zwziwzDNaN5htColaEufTPTfjhtP+EeUUFMnQb4j+jDaKsAREbazW+XAg65V+c0Pt\nBnyKxGVFhMD26go/e7TRLbtoNLl4csnMMXO3vWVdA/2wxcT29efHR8ZpS5LtM9Cwd5okFbM/o4yk\n5EqKsXm7bX8ZDLUHpQ8eJ1U7ndRMDhklaJ4JKRrwVwm8j6SQ8FWSlwUrC8ZoNtMVaz7g18ISPSmH\nVnfEYo1GyMI//uSv2W6uGL/1PyDoMJ3gdvc1/uM3/idKFnz19GMEI+Nwze1uYH/Yc1LvEOKE92fW\n/ICzFzygvOw8naLkzNN8pNs4pDQNENJptLPoc6Fae7kyS42BGUQLpqfmBQo+tCsrY1DWscaIkCfm\n+8+53n7ClXFoO2DcyMYILA5ZBI/hn6mybT5yDm2hDyshFqQwH2lRSsumRFY09XcpiNq0M/LfIlb7\n3d/93Y8oq//366/+6q/+xa9///vf5/vf//4v/EcBQig47dpCVUCJgiiJnCIV0RSvpd3N5JCIPmN0\nm4zFUnm/f83d0xsm93XmpVUmye0NFXIlpkSshf3ZM3Y3lKgJtsNpxdhtmJdnUjzgOo2SrvnSjUPb\nHiETV7trxqHj+uqWq90OqiNEeH3/jjXNxDxf2hodZmgEHlEFQsHZH1DCkmtFCY8VmlLSxXxZESRq\nURin6VyrF8pSqEVSs0QbgXWCnFZaorhN86SwGJM4nZ64+frXOPnnRjLvO+pBIqTG6Q1fu/5Nnvfv\nOK9foeUjgxyaAKt4Fu/ph5coeYWqE05YtPOs8YksV6TusK5HSIVUltubHRKFMztC8CynM1bDUtpA\nx/uFnDxT/xKt4HTaE1LhfPZ859e+Q98PCCEoNeD9Quf6jz30+/t7hGhd664bPvbF5/kdp+MMyI8E\n+PP5TM4fCEj6Y0BcKdWwc0rizzPdVraf46XYsBkG5vnEMA6cTvtWTJCCHFuGWYgmrjPKMA098zyz\nvv+Sq5sbbq5v2L/9CttZ5uPMze0tx+MRZzTOdsQYCV6xzCt5J+j79m/UonheVl7cjPjz8UJNL5ew\n+Bk/z1TRrlza/90QQuMoKGVw1l52Z1ByJoX2IPDzjFatPYMAbR3KGjrbcz4eeD6d8aWRiaZ+yznc\nEvPCMkfWJTYfuJIUmQkRlKoUEj/6ux+yGya+/fX/jhIVWldeXL8kpP+erhs47U84c03fD+yuvsHx\n4Ve4f/gJ++NXHM6PeB9wncbYDi070twUFN4vHOOxDXykRlbVwNG6UnXFWNG87qLVRMmKkgRaSZIE\nWSrXV3dkekKa8XFhvz4xrzPIntF9gmFA5IXBbhCinQjW/Eyi5WLX1V/uvzPOfsgzf1BsV5QuiNbQ\nJaRMZ3uQ/17RcOfMaGwjc9PYeLV4cu4oVUIW5MQHrCA5Z3yBVNtRu8SZr97/mE9fCKiJimKNz4S0\nkGvB5+bXyyWR88zN9R2iWPws0dYyjS9ZQrvDsMqhVU8uht6OSJcY+h1Kdk06prbM80zvJl7dvuJ0\nfI8SO3JMUCOhNMGbkvJSiSss8XyZ/C50ykINjbaUF9AOoVvOTSrDbtfjw8p5XcEU9AWYrKSCojC2\nw2iHVBUZKynOlOxRYuR0PDQajNSE6HGuVe/udt9i6Eaej1+iZGQcBFIYivCs65HN+ALBgLFbjI4U\nLGt6BBEQUrGWGajUOWDUwHwukD2H4x4pC1c3Lxj7Dc+lMPYtP/ru/Vuej0du716243e5AIFpQWVj\n2uJvreX9+/eXhoy41DIVKSViiHRdj9IS7/3HAHvXtUWqlIyzjsfHR/SkLsf6jhQjSkgO+0e6ziFq\nRgvB6Ximc5pcazPQxhN+nRnHER884FHVU0MgKUvXD6zzidPTezCK65s7jvsjt3fXxBSZthPrPJPi\nmb4f2U4bttOWdfWYouiHphARQjCfT6ScCMljRcsQCpp5UymLD5HFR2a/MriOrhuoJbdrBaVJYgGT\nkVXj3Ii5urqAMhQN1NVsrKob2AjJQmV92rPMR1IJ7Rje9YzTyLIE4hIRNAB3aSU6lNaczvf89d/8\nb5Ss+PrLb7d2jFDcXO2o5lusV4HT6YC+xKF22xsqM6kcUVqwhiM+r0S/UomoqrDSgk7s/RGNxSnX\n4LcopJaI4gk+UoQghJYr5jKEoxS6fuRq801GewuiEFLi7I+cl4hfV758/VOuxyt0jUQhkSqRksLq\nTdP81pmQJRdv8uVqr/XV1eWar7ZdFsj2NakUVStk+TfsNP9bvvbHGacDV9cdRjmKTAwyEQ+V85KQ\nuQnlc1kuR1RNSp5UC9oUtJY8Hx7oup/Qu5EqAnPZs/gjJTUFcC0OqWCNCz7OTHaklsQ6F5TpGPot\nPh7QemDsb7B2aMeldCDGxHz21BvFYb/ncDxxPjcV8Nh1aFGIoZBqpXHnWzNCqMqy+Eu8pznbawl0\ntt1lxRIhFXrXQ8qkNaJ6h9EO1zUZWi2KnNs9kawNrzapAbSgUz3n5Z4YjigGCisCjZaegKfi0Moi\nq2BSX7vcC74hpUpne4QYWVeNjwnrBDUbZOlRCHpjWOQjiQI54LRh9YE1eHq1peZGHdpuBlAdb+/f\n4OxEjoJ3h7ec5hM3n3wNJRWd69vRNCWmqT10Qkht0v30TN+3qpq79MihkY989BcknL6I6Nrfcc5d\noNARqSQ3Nzc83L/Hdc0XFGNAKcm6BnJccVpTSiCXiPft3rBc7Jc5R87zM5vNFcEXVKcbbtCvdB2N\nNKQkKWdCDOzubgn5kqm0FpcrNRfOxxkloO97hqHHh4WwJmTXkUtmu9sRU2L1C4f9kZrzZYCi0VLg\nOn1JBWTIzTJpO4nqDLU2JqUulSpbYFt+EH/FSK6Vai/RsdwIUVPXU64Fh1LZ79+jRaXTGzbjgq6K\n02HF+4gVklASWVaUMdhcuH//M/7PH/0nfPZ848W3ccqhtWHbXzEOhdENHA/PZALaaLQZGadrQj5j\n0SjRsXhPyAUtJKVUlNLUKnhaTxTl6I2mzw6pLqSitSnMvPctDlQTQgtyaMbLu5tvoUXXShI0OEp9\ntvTdhLWCh+efoeQrSslYZ5CyIIzF6S2pKPyyknIml4y1HUqJy7C1zQjgQyIjooxDCkWOYH5xTPOX\niIZLkfl8YDM5hrEDmdAqYa4gh6bjNMYSM/gwk2rGmB6n5kbctiPGdZyWGaUkQq2kegbaNNoZSy2O\nabJ4v/D+6QuGr18ha22MzFoROLRyl1qkoTcTZuh4OmWO85lKZv4nz83mU06nBUSl5hWlc0OvCYPK\niVxFIwApLioKhdKSdW6KjBAXIpWYC0JLYjpTo8SwQxSDjAnjGii1ArkmBBJRNGRLCZViKlp0F43F\nwNPhLS+vu0akl0Cd0SWha0SpnpBadbLvJrT+hFoDUg50/Q6hAotfGIis89owZFJSssO5a4QplCUQ\n14BxPeM4YVNPiJXr6Q5VC8fZ8yvf+hXevXnP+XRqLY0cWY5n3O2IVpV1OaOt5enpkZQCSld+/vN/\n5Pq6NYKstYxj25WllFiWGWiLkD3b1pqp5eNdJ4BSbSciEfSuw88LSrQdU83LJXDdVCZaSaTRzOcj\ncTkxdo41ZpSybUcXPO0wa3Fdh5Aa70/EGMi+0ZdKicTk2YwDawjEktsdWC1oVck5cpwPOGcxylKF\npdT2sDydz4zDyDhtCD4RvWc5zIS13dtvNiOqSoxqdk1pNNurO0w3kLIn54rWDmUciDa4pMS2U0WS\nlxVBJWQ4nw48HA68ftrzbj7gkyeIRDG5/TwVOGOZz4G4JChdyzOGhNCOodO8efszUg6sv1759ovv\n0HcO63S75pi2pOh53D8gnUF2EpcGtO/I3iNyRFJxolWXhbokGWwPUfO4PLJhRxKgZcQo00AePrGS\nCNG3WddgqFIy9Fu2VxMSxTpLUo2EJ09vOzrSWSKjAAAgAElEQVTnmlCxZp4Ob6gioYPCWkcVCi47\n6RAj0Ue0tXTdBqvatU7MzQrbGnQRoSD6hBQSK3qK+sXz81/aoqmlhFqZTytXu2us1cTSnMtXW8Vj\ngZwCAtOo56WpGly3oTjT1AxjwVrTvNZGokoDq9bSlBVC1Qt6bCLlhbeHn7Ht76Cohk5T9UIbtyhp\n2yW5qFg3cP/wnv3hnnX9O17efpMcaFRp2eqISjXcfwVUvdxVivYhp2Ss0qjBEmLEuYlcPRWIaUbr\nZtWTukns269BoXTziecUSRG0cKRcWVLAJok0AlEiJSV8mdmfvsLWgXTZhZTk8Su4XiOwkAWlOIxq\nx0YhLEoqpiHyuDzy7t2XTOOZJVq6YUPJDZXXOUlSDWnXS0mMLdw6biZiSuyPDxg7cnw68vz8gKzt\nvrTvNtxeX/O8f0Ybiw0Lm92WL7/6nOvra16//ooQPafzkevrW8Zx+phVTKl5fz6oMT5YLJ1rmubz\n+dx2Bfm/MDq9Wojec8yBcTNhjcTatpNNutIZTY4eZzRpXTkdnrGuQ0nVMqy5otRF7LVmlFGM44jo\nR5JfiSXhpMJeIk1Om1ZZzAnjDMopSrEcjnuO55mrjWn0nDQzbTacj0eSDyBFu7pRsu0wNxtiipxO\nJ6Z+YOgmXNdhhrEtkFKjPmgpmga16XoL7YGQMyBJoYW4s9BUJEIalNYIWQnLypJX/OTprEJ3XfOM\na8WiI/EUKKugSHFRv2is1ty/e8sc/xM5BL756tdQoX2m5vMJaRVCS/aHR/pBtkrszTeYl4Gnxy8x\nKlFqBFlBZ6SthLDgbEUlSUpnghUYYREUSszUlFAUkvck2XgBrncM49DsnLLHq4CfF47HAzkVrO7o\nrEFRKTUT80yh2TWUdK0EUAs1GbTaojAUb9D9gFEFLSvndGxBeB8oGVAtIlaVYKn/ThfNHI9gtoQQ\nCCEwTA7NhoRnmjwRzfPDSsmghUAqS28t1gq0Nvi4UHxBd2CUwilATCQ/E3K41MzEx12M0gahInN6\ni6k9m34HF8dPygZtRLt7y56aKyEk9qcHUjmy//k7jDKY3jDaHVaNzMcVqWjVthTaUSSLFh8qqWXY\nZGTsNaVWau2QqhJjZQkLvTYYs9CytVuCr9jSpFSyGIiBUDxCKM4pkTkzpAPWmuY5qonj/LYRmYrF\nKoftDPO6EE8RUUeCV6hOI9DkpNqOUUpKjNxsFW/ev+Hx8AZ1MvT9ytiPaFnwteLcgLUghENpg8qw\nPzwRvCfFE7d24Hw4YmQzgcYsub75GjFHpmni1atPmFfPmy9+xm5q5HTvW9TLdR1StshN8JFxMjw9\nPV3yl+31AcDxYfhTSpOnTcPA6XxmK1ts6f79PUZK9scnbqeJnAO3V9e8ffMFXiRkbeFvZyRa0abm\nWrc8roSUVrpeUzIICjlGejsyXG9Z4kIJTbFbasEaw4VbRhQVp027q1OSw+GJ0/HIq699yuFw4N3b\nt83t5DoO5yMhBtLlYaC0ph8GnGs09hhbttQiSDEg6oVxWVV7L1mHVB0oi46+pS1qq64GH0ihDTu2\n04RPiYf5QCclOkXWOVOkQbu28NNVdKwImcA051b2hSpohW/gq89/wrwEYg584+V30KpQiKyp7Ww7\nCeG0UI3E6R7Z31J3iXV9wi8ngo+X5Ac032ZsgjVdUbJgVNOMiOSpBQwtkJ5JpBwbxNrIS98+YK2k\nzvmikPas3uC6DRh5GZY1sHEunkqhpEoMmZqb6loWSVojUQiU0hQRm0yvKkTdkM4BZXqKEFQ84r9y\nPv+lLZr90OP9E30/8PhwYhhU252oDauakST6YUD0hvPhhFGGrhsuDp2INY6YAn71dKYnZUsuLfUQ\nfSHGjNECa8XHAHGpoETFGoEPp8tgInFa3qOtonNXH2VrStoW1PYNlNt3CWU8dph4dfdNBBM/+/Jv\nebf/ClmbyTKn0o7tSuC0xGhL13Ut2hJjy0ZagekkoiYkFS1qo7NUQwoNuV+rAjp88uTmfOIQTvS9\npB8dne2ggNAaH32DHaeKQ1KF5zwfSXFPyZqBLcpZQgItJML2lJwxquP2+hPun97wcHzGnheuNjcM\n00iXHZ3u6KcemTVhXolxJYVMSTC4HTEG1nDk+uaalGDrOsZp4t27NyiT+ed/+DGuH7HWYTrL/PiA\nURqjWz70eDxeXEqS8zk3/mXfcH5KN9ybMYZ1XS84OUEtGUTFOcu6rvRdj7aanBJSVR73z5hSwcgW\nGM9ND5piJPuVvu8uAFt58XFnapXE+YyUCu16ZLdp/p5S6bqR3Ar6SGVJJaJMg0+XUvDBo7qO67tX\nDNsrnt+84Xm/Z+x7us7h13AxcepmKTWSYwyksJKjp+s143aLVh1VgpECaRRVqubp0QqhJBUDCIQz\n0PeNy3BxJclcyXElicLTwz0n38oRpWac6YhCQinEWkFJiImqE9ppyuovVdW20DQQh0QKx+P7e/7m\nb/8zPp24Gq+RSiAu71XlWjIkIVC0qJAsDqc2SC0QKbLGGZGb6ydnqFUgVaW3rS1XayXlBn5JJSFk\nxSrNEpvXKuWZlFekVvjYGkGf3L7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lN7sbnh6+QNTCZnvNZrtlXRe0s5TcFBBGyubsLhm/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\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-       "text": [
-        "<matplotlib.figure.Figure at 0x7fda517da210>"
-       ]
-      }
-     ],
-     "prompt_number": 4
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Time to classify. The default is to actually do 10 predictions, cropping the center and corners of the image as well as their mirrored versions, and average over the predictions:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "prediction = net.predict([input_image])  # predict takes any number of images, and formats them for the Caffe net automatically\n",
-      "print 'prediction shape:', prediction[0].shape\n",
-      "plt.plot(prediction[0])\n",
-      "print 'predicted class:', prediction[0].argmax()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "prediction shape: (1000,)\n",
-        "predicted class: 281\n"
-       ]
-      },
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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-       "text": [
-        "<matplotlib.figure.Figure at 0x7fda20473950>"
-       ]
-      }
-     ],
-     "prompt_number": 5
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "You can see that the prediction is 1000-dimensional, and is pretty sparse.\n",
-      "\n",
-      "The predicted class 281 is \"Tabby cat.\" Our pretrained model uses the synset ID ordering of the classes, as listed in `../data/ilsvrc12/synset_words.txt` if you fetch the auxiliary imagenet data by `../data/ilsvrc12/get_ilsvrc_aux.sh`. If you look at the top indices that maximize the prediction score, they are cats, foxes, and other cute mammals. Not unreasonable predictions, right?\n",
-      "\n",
-      "Now let's classify by the center crop alone by turning off oversampling. Note that this makes a single input, although if you inspect the model definition prototxt you'll see the network has a batch size of 10. The python wrapper handles batching and padding for you!"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "prediction = net.predict([input_image], oversample=False)\n",
-      "print 'prediction shape:', prediction[0].shape\n",
-      "plt.plot(prediction[0])\n",
-      "print 'predicted class:', prediction[0].argmax()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "prediction shape: (1000,)\n",
-        "predicted class: 281\n"
-       ]
-      },
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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iLd0g8Pv9sFqtsFgsMBqNqK6uRmtra1yZrKwsOJ1OGI3GKdelmccgICIt3SAIhULIycmJ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\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0x7fda1abd8e10>"
-       ]
-      }
-     ],
-     "prompt_number": 6
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "\n",
-      "\n",
-      "Now, why don't we see how long it takes to perform the classification end to end? This result is run from an Intel i5 CPU, so you may observe some performance differences."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%timeit net.predict([input_image])"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "1 loops, best of 3: 355 ms per loop\n"
-       ]
-      }
-     ],
-     "prompt_number": 7
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "It may look a little slow, but note that time is spent on cropping, python interfacing, and running 10 images. For performance, if you really want to make prediction fast, you can optionally code in C++ and pipeline operations better. For experimenting and prototyping the current speed is fine.\n",
-      "\n",
-      "Let's time classifying a single image with input preprocessed:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# Resize the image to the standard (256, 256) and oversample net input sized crops.\n",
-      "input_oversampled = caffe.io.oversample([caffe.io.resize_image(input_image, net.image_dims)], net.crop_dims)\n",
-      "# 'data' is the input blob name in the model definition, so we preprocess for that input.\n",
-      "caffe_input = np.asarray([net.preprocess('data', in_) for in_ in input_oversampled])\n",
-      "# forward() takes keyword args for the input blobs with preprocessed input arrays.\n",
-      "%timeit net.forward(data=caffe_input)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "1 loops, best of 3: 210 ms per loop\n"
-       ]
-      }
-     ],
-     "prompt_number": 8
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "OK, so how about GPU? it is actually pretty easy:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "net.set_mode_gpu()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 9
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Voila! Now we are in GPU mode. Let's see if the code gives the same result:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "prediction = net.predict([input_image])\n",
-      "print 'prediction shape:', prediction[0].shape\n",
-      "plt.plot(prediction[0])"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "prediction shape: (1000,)\n"
-       ]
-      },
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 10,
-       "text": [
-        "[<matplotlib.lines.Line2D at 0x7fda1ac309d0>]"
-       ]
-      },
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEACAYAAAC+gnFaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIAAAyxEEAGA5ggAALEcQAIDl/h+B3FfVQOwSzQAAAABJRU5ErkJggg==\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0x7fda1ab53650>"
-       ]
-      }
-     ],
-     "prompt_number": 10
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Good, everything is the same. And how about time consumption? The following benchmark is obtained on the same machine with a GTX 770 GPU:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# Full pipeline timing.\n",
-      "%timeit net.predict([input_image])"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "10 loops, best of 3: 174 ms per loop\n"
-       ]
-      }
-     ],
-     "prompt_number": 11
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# Forward pass timing.\n",
-      "%timeit net.forward(data=caffe_input)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "10 loops, best of 3: 34.2 ms per loop\n"
-       ]
-      }
-     ],
-     "prompt_number": 12
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Pretty fast right? Not as fast as you expected? Indeed, in this python demo you are seeing only 4 times speedup. But remember - the GPU code is actually very fast, and the data loading, transformation and interfacing actually start to take **more** time than the actual conv. net computation itself!\n",
-      "\n",
-      "To fully utilize the power of GPUs, you really want to:\n",
-      "\n",
-      "* Use larger batches, and minimize python call and data transfer overheads.\n",
-      "* Pipeline data load operations, like using a subprocess.\n",
-      "* Code in C++. A little inconvenient, but maybe worth it if your dataset is really, really large."
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Parting Words\n",
-      "-------------\n",
-      "\n",
-      "So this is python! We hope the interface is easy enough for one to use. The python wrapper is interfaced with boost::python, and source code can be found at `python/caffe` with the main interface in `pycaffe.py` and the classification wrapper in `classifier.py`. If you have customizations to make, start there! Do let us know if you make improvements by sending a pull request!"
-     ]
-    }
-   ],
-   "metadata": {}
-  }
- ]
-}
diff --git a/examples/cpp_classification/classification.cpp b/examples/cpp_classification/classification.cpp
new file mode 100644
index 00000000000..1c6371e382b
--- /dev/null
+++ b/examples/cpp_classification/classification.cpp
@@ -0,0 +1,255 @@
+#include <caffe/caffe.hpp>
+#include <opencv2/core/core.hpp>
+#include <opencv2/highgui/highgui.hpp>
+#include <opencv2/imgproc/imgproc.hpp>
+#include <iosfwd>
+#include <memory>
+#include <string>
+#include <utility>
+#include <vector>
+
+using namespace caffe;  // NOLINT(build/namespaces)
+using std::string;
+
+/* Pair (label, confidence) representing a prediction. */
+typedef std::pair<string, float> Prediction;
+
+class Classifier {
+ public:
+  Classifier(const string& model_file,
+             const string& trained_file,
+             const string& mean_file,
+             const string& label_file);
+
+  std::vector<Prediction> Classify(const cv::Mat& img, int N = 5);
+
+ private:
+  void SetMean(const string& mean_file);
+
+  std::vector<float> Predict(const cv::Mat& img);
+
+  void WrapInputLayer(std::vector<cv::Mat>* input_channels);
+
+  void Preprocess(const cv::Mat& img,
+                  std::vector<cv::Mat>* input_channels);
+
+ private:
+  shared_ptr<Net<float> > net_;
+  cv::Size input_geometry_;
+  int num_channels_;
+  cv::Mat mean_;
+  std::vector<string> labels_;
+};
+
+Classifier::Classifier(const string& model_file,
+                       const string& trained_file,
+                       const string& mean_file,
+                       const string& label_file) {
+#ifdef CPU_ONLY
+  Caffe::set_mode(Caffe::CPU);
+#else
+  Caffe::set_mode(Caffe::GPU);
+#endif
+
+  /* Load the network. */
+  net_.reset(new Net<float>(model_file, TEST));
+  net_->CopyTrainedLayersFrom(trained_file);
+
+  CHECK_EQ(net_->num_inputs(), 1) << "Network should have exactly one input.";
+  CHECK_EQ(net_->num_outputs(), 1) << "Network should have exactly one output.";
+
+  Blob<float>* input_layer = net_->input_blobs()[0];
+  num_channels_ = input_layer->channels();
+  CHECK(num_channels_ == 3 || num_channels_ == 1)
+    << "Input layer should have 1 or 3 channels.";
+  input_geometry_ = cv::Size(input_layer->width(), input_layer->height());
+
+  /* Load the binaryproto mean file. */
+  SetMean(mean_file);
+
+  /* Load labels. */
+  std::ifstream labels(label_file.c_str());
+  CHECK(labels) << "Unable to open labels file " << label_file;
+  string line;
+  while (std::getline(labels, line))
+    labels_.push_back(string(line));
+
+  Blob<float>* output_layer = net_->output_blobs()[0];
+  CHECK_EQ(labels_.size(), output_layer->channels())
+    << "Number of labels is different from the output layer dimension.";
+}
+
+static bool PairCompare(const std::pair<float, int>& lhs,
+                        const std::pair<float, int>& rhs) {
+  return lhs.first > rhs.first;
+}
+
+/* Return the indices of the top N values of vector v. */
+static std::vector<int> Argmax(const std::vector<float>& v, int N) {
+  std::vector<std::pair<float, int> > pairs;
+  for (size_t i = 0; i < v.size(); ++i)
+    pairs.push_back(std::make_pair(v[i], i));
+  std::partial_sort(pairs.begin(), pairs.begin() + N, pairs.end(), PairCompare);
+
+  std::vector<int> result;
+  for (int i = 0; i < N; ++i)
+    result.push_back(pairs[i].second);
+  return result;
+}
+
+/* Return the top N predictions. */
+std::vector<Prediction> Classifier::Classify(const cv::Mat& img, int N) {
+  std::vector<float> output = Predict(img);
+
+  std::vector<int> maxN = Argmax(output, N);
+  std::vector<Prediction> predictions;
+  for (int i = 0; i < N; ++i) {
+    int idx = maxN[i];
+    predictions.push_back(std::make_pair(labels_[idx], output[idx]));
+  }
+
+  return predictions;
+}
+
+/* Load the mean file in binaryproto format. */
+void Classifier::SetMean(const string& mean_file) {
+  BlobProto blob_proto;
+  ReadProtoFromBinaryFileOrDie(mean_file.c_str(), &blob_proto);
+
+  /* Convert from BlobProto to Blob<float> */
+  Blob<float> mean_blob;
+  mean_blob.FromProto(blob_proto);
+  CHECK_EQ(mean_blob.channels(), num_channels_)
+    << "Number of channels of mean file doesn't match input layer.";
+
+  /* The format of the mean file is planar 32-bit float BGR or grayscale. */
+  std::vector<cv::Mat> channels;
+  float* data = mean_blob.mutable_cpu_data();
+  for (int i = 0; i < num_channels_; ++i) {
+    /* Extract an individual channel. */
+    cv::Mat channel(mean_blob.height(), mean_blob.width(), CV_32FC1, data);
+    channels.push_back(channel);
+    data += mean_blob.height() * mean_blob.width();
+  }
+
+  /* Merge the separate channels into a single image. */
+  cv::Mat mean;
+  cv::merge(channels, mean);
+
+  /* Compute the global mean pixel value and create a mean image
+   * filled with this value. */
+  cv::Scalar channel_mean = cv::mean(mean);
+  mean_ = cv::Mat(input_geometry_, mean.type(), channel_mean);
+}
+
+std::vector<float> Classifier::Predict(const cv::Mat& img) {
+  Blob<float>* input_layer = net_->input_blobs()[0];
+  input_layer->Reshape(1, num_channels_,
+                       input_geometry_.height, input_geometry_.width);
+  /* Forward dimension change to all layers. */
+  net_->Reshape();
+
+  std::vector<cv::Mat> input_channels;
+  WrapInputLayer(&input_channels);
+
+  Preprocess(img, &input_channels);
+
+  net_->ForwardPrefilled();
+
+  /* Copy the output layer to a std::vector */
+  Blob<float>* output_layer = net_->output_blobs()[0];
+  const float* begin = output_layer->cpu_data();
+  const float* end = begin + output_layer->channels();
+  return std::vector<float>(begin, end);
+}
+
+/* Wrap the input layer of the network in separate cv::Mat objects
+ * (one per channel). This way we save one memcpy operation and we
+ * don't need to rely on cudaMemcpy2D. The last preprocessing
+ * operation will write the separate channels directly to the input
+ * layer. */
+void Classifier::WrapInputLayer(std::vector<cv::Mat>* input_channels) {
+  Blob<float>* input_layer = net_->input_blobs()[0];
+
+  int width = input_layer->width();
+  int height = input_layer->height();
+  float* input_data = input_layer->mutable_cpu_data();
+  for (int i = 0; i < input_layer->channels(); ++i) {
+    cv::Mat channel(height, width, CV_32FC1, input_data);
+    input_channels->push_back(channel);
+    input_data += width * height;
+  }
+}
+
+void Classifier::Preprocess(const cv::Mat& img,
+                            std::vector<cv::Mat>* input_channels) {
+  /* Convert the input image to the input image format of the network. */
+  cv::Mat sample;
+  if (img.channels() == 3 && num_channels_ == 1)
+    cv::cvtColor(img, sample, CV_BGR2GRAY);
+  else if (img.channels() == 4 && num_channels_ == 1)
+    cv::cvtColor(img, sample, CV_BGRA2GRAY);
+  else if (img.channels() == 4 && num_channels_ == 3)
+    cv::cvtColor(img, sample, CV_BGRA2BGR);
+  else if (img.channels() == 1 && num_channels_ == 3)
+    cv::cvtColor(img, sample, CV_GRAY2BGR);
+  else
+    sample = img;
+
+  cv::Mat sample_resized;
+  if (sample.size() != input_geometry_)
+    cv::resize(sample, sample_resized, input_geometry_);
+  else
+    sample_resized = sample;
+
+  cv::Mat sample_float;
+  if (num_channels_ == 3)
+    sample_resized.convertTo(sample_float, CV_32FC3);
+  else
+    sample_resized.convertTo(sample_float, CV_32FC1);
+
+  cv::Mat sample_normalized;
+  cv::subtract(sample_float, mean_, sample_normalized);
+
+  /* This operation will write the separate BGR planes directly to the
+   * input layer of the network because it is wrapped by the cv::Mat
+   * objects in input_channels. */
+  cv::split(sample_normalized, *input_channels);
+
+  CHECK(reinterpret_cast<float*>(input_channels->at(0).data)
+        == net_->input_blobs()[0]->cpu_data())
+    << "Input channels are not wrapping the input layer of the network.";
+}
+
+int main(int argc, char** argv) {
+  if (argc != 6) {
+    std::cerr << "Usage: " << argv[0]
+              << " deploy.prototxt network.caffemodel"
+              << " mean.binaryproto labels.txt img.jpg" << std::endl;
+    return 1;
+  }
+
+  ::google::InitGoogleLogging(argv[0]);
+
+  string model_file   = argv[1];
+  string trained_file = argv[2];
+  string mean_file    = argv[3];
+  string label_file   = argv[4];
+  Classifier classifier(model_file, trained_file, mean_file, label_file);
+
+  string file = argv[5];
+
+  std::cout << "---------- Prediction for "
+            << file << " ----------" << std::endl;
+
+  cv::Mat img = cv::imread(file, -1);
+  CHECK(!img.empty()) << "Unable to decode image " << file;
+  std::vector<Prediction> predictions = classifier.Classify(img);
+
+  /* Print the top N predictions. */
+  for (size_t i = 0; i < predictions.size(); ++i) {
+    Prediction p = predictions[i];
+    std::cout << std::fixed << std::setprecision(4) << p.second << " - \""
+              << p.first << "\"" << std::endl;
+  }
+}
diff --git a/examples/cpp_classification/readme.md b/examples/cpp_classification/readme.md
new file mode 100644
index 00000000000..a086db1a035
--- /dev/null
+++ b/examples/cpp_classification/readme.md
@@ -0,0 +1,77 @@
+---
+title: CaffeNet C++ Classification example
+description: A simple example performing image classification using the low-level C++ API.
+category: example
+include_in_docs: true
+priority: 10
+---
+
+# Classifying ImageNet: using the C++ API
+
+Caffe, at its core, is written in C++. It is possible to use the C++
+API of Caffe to implement an image classification application similar
+to the Python code presented in one of the Notebook example. To look
+at a more general-purpose example of the Caffe C++ API, you should
+study the source code of the command line tool `caffe` in `tools/caffe.cpp`.
+
+## Presentation
+
+A simple C++ code is proposed in
+`examples/cpp_classification/classification.cpp`. For the sake of
+simplicity, this example does not support oversampling of a single
+sample nor batching of multiple independant samples. This example is
+not trying to reach the maximum possible classification throughput on
+a system, but special care was given to avoid unnecessary
+pessimization while keeping the code readable.
+
+## Compiling
+
+The C++ example is built automatically when compiling Caffe. To
+compile Caffe you should follow the documented instructions. The
+classification example will be built as `examples/classification.bin`
+in your build directory.
+
+## Usage
+
+To use the pre-trained CaffeNet model with the classification example,
+you need to download it from the "Model Zoo" using the following
+script:
+```
+./scripts/download_model_binary.py models/bvlc_reference_caffenet
+```
+The ImageNet labels file (also called the *synset file*) is also
+required in order to map a prediction to the name of the class:
+```
+./data/ilsvrc12/get_ilsvrc_aux.sh.
+```
+Using the files that were downloaded, we can classify the provided cat
+image (`examples/images/cat.jpg`) using this command:
+```
+./build/examples/cpp_classification/classification.bin \
+  models/bvlc_reference_caffenet/deploy.prototxt \
+  models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel \
+  data/ilsvrc12/imagenet_mean.binaryproto \
+  data/ilsvrc12/synset_words.txt \
+  examples/images/cat.jpg
+```
+The output should look like this:
+```
+---------- Prediction for examples/images/cat.jpg ----------
+0.3134 - "n02123045 tabby, tabby cat"
+0.2380 - "n02123159 tiger cat"
+0.1235 - "n02124075 Egyptian cat"
+0.1003 - "n02119022 red fox, Vulpes vulpes"
+0.0715 - "n02127052 lynx, catamount"
+```
+
+## Improving Performance
+
+To further improve performance, you will need to leverage the GPU
+more, here are some guidelines:
+
+* Move the data on the GPU early and perform all preprocessing
+operations there.
+* If you have many images to classify simultaneously, you should use
+batching (independent images are classified in a single forward pass).
+* Use multiple classification threads to ensure the GPU is always fully
+utilized and not waiting for an I/O blocked CPU thread.
diff --git a/examples/detection.ipynb b/examples/detection.ipynb
index d05c0c22052..6a03c996245 100644
--- a/examples/detection.ipynb
+++ b/examples/detection.ipynb
@@ -1,845 +1,8392 @@
 {
- "metadata": {
-  "description": "Run a pretrained model as a detector in Python.",
-  "example_name": "R-CNN detection",
-  "include_in_docs": true,
-  "priority": 3
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
+ "cells": [
   {
-   "cells": [
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[R-CNN](https://github.com/rbgirshick/rcnn) is a state-of-the-art detector that classifies region proposals by a finetuned Caffe model. For the full details of the R-CNN system and model, refer to its project site and the paper:\n",
-      "\n",
-      "> *Rich feature hierarchies for accurate object detection and semantic segmentation*. Ross Girshick, Jeff Donahue, Trevor Darrell, Jitendra Malik. CVPR 2014. [Arxiv 2013](http://arxiv.org/abs/1311.2524).\n",
-      "\n",
-      "In this example, we do detection by a pure Caffe edition of the R-CNN model for ImageNet. The R-CNN detector outputs class scores for the 200 detection classes of ILSVRC13. Keep in mind that these are raw one vs. all SVM scores, so they are not probabilistically calibrated or exactly comparable across classes. Note that this off-the-shelf model is simply for convenience, and is not the full R-CNN model.\n",
-      "\n",
-      "Let's run detection on an image of a bicyclist riding a fish bike in the desert (from the ImageNet challenge\u2014no joke).\n",
-      "\n",
-      "First, we'll need region proposals and the Caffe R-CNN ImageNet model:\n",
-      "\n",
-      "- [Selective Search](http://koen.me/research/selectivesearch/) is the region proposer used by R-CNN. The [selective_search_ijcv_with_python](https://github.com/sergeyk/selective_search_ijcv_with_python) Python module takes care of extracting proposals through the selective search MATLAB implementation. To install it, download the module and name its directory `selective_search_ijcv_with_python`, run the demo in MATLAB to compile the necessary functions, then add it to your `PYTHONPATH` for importing. (If you have your own region proposals prepared, or would rather not bother with this step, [detect.py](https://github.com/BVLC/caffe/blob/master/python/detect.py) accepts a list of images and bounding boxes as CSV.)\n",
-      "\n",
-      "-Run `./scripts/download_model_binary.py models/bvlc_reference_caffenet` to get the Caffe R-CNN ImageNet model.\n",
-      "\n",
-      "With that done, we'll call the bundled `detect.py` to generate the region proposals and run the network. For an explanation of the arguments, do `./detect.py --help`."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "!mkdir -p _temp\n",
-      "!echo `pwd`/images/fish-bike.jpg > _temp/det_input.txt\n",
-      "!../python/detect.py --crop_mode=selective_search --pretrained_model=models/bvlc_reference_rcnn_ilsvrc13/bvlc_reference_rcnn_ilsvrc13.caffemodel --model_def=models/bvlc_reference_rcnn_ilsvrc13/deploy.prototxt --gpu --raw_scale=255 _temp/det_input.txt _temp/det_output.h5"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "WARNING: Logging before InitGoogleLogging() is written to STDERR\r\n",
-        "I0610 10:12:49.299607 25530 net.cpp:36] Initializing net from parameters: \r\n",
-        "name: \"R-CNN-ilsvrc13\"\r\n",
-        "layers {\r\n",
-        "  bottom: \"data\"\r\n",
-        "  top: \"conv1\"\r\n",
-        "  name: \"conv1\"\r\n",
-        "  type: CONVOLUTION\r\n",
-        "  convolution_param {\r\n",
-        "    num_output: 96\r\n",
-        "    kernel_size: 11\r\n",
-        "    stride: 4\r\n",
-        "  }\r\n",
-        "}\r\n",
-        "layers {\r\n",
-        "  bottom: \"conv1\"\r\n",
-        "  top: \"conv1\"\r\n",
-        "  name: \"relu1\"\r\n",
-        "  type: RELU\r\n",
-        "}\r\n",
-        "layers {\r\n",
-        "  bottom: \"conv1\"\r\n",
-        "  top: \"pool1\"\r\n",
-        "  name: \"pool1\"\r\n",
-        "  type: POOLING\r\n",
-        "  pooling_param {\r\n",
-        "    pool: MAX\r\n",
-        "    kernel_size: 3\r\n",
-        "    stride: 2\r\n",
-        "  }\r\n",
-        "}\r\n",
-        "layers {\r\n",
-        "  bottom: \"pool1\"\r\n",
-        "  top: \"norm1\"\r\n",
-        "  name: \"norm1\"\r\n",
-        "  type: LRN\r\n",
-        "  lrn_param {\r\n",
-        "    local_size: 5\r\n",
-        "    alpha: 0.0001\r\n",
-        "    beta: 0.75\r\n",
-        "  }\r\n",
-        "}\r\n",
-        "layers {\r\n",
-        "  bottom: \"norm1\"\r\n",
-        "  top: \"conv2\"\r\n",
-        "  name: \"conv2\"\r\n",
-        "  type: CONVOLUTION\r\n",
-        "  convolution_param {\r\n",
-        "    num_output: 256\r\n",
-        "    pad: 2\r\n",
-        "    kernel_size: 5\r\n",
-        "    group: 2\r\n",
-        "  }\r\n",
-        "}\r\n",
-        "layers {\r\n",
-        "  bottom: \"conv2\"\r\n",
-        "  top: \"conv2\"\r\n",
-        "  name: \"relu2\"\r\n",
-        "  type: RELU\r\n",
-        "}\r\n",
-        "layers {\r\n",
-        "  bottom: \"conv2\"\r\n",
-        "  top: \"pool2\"\r\n",
-        "  name: \"pool2\"\r\n",
-        "  type: POOLING\r\n",
-        "  pooling_param {\r\n",
-        "    pool: MAX\r\n",
-        "    kernel_size: 3\r\n",
-        "    stride: 2\r\n",
-        "  }\r\n",
-        "}\r\n",
-        "layers {\r\n",
-        "  bottom: \"pool2\"\r\n",
-        "  top: \"norm2\"\r\n",
-        "  name: \"norm2\"\r\n",
-        "  type: LRN\r\n",
-        "  lrn_param {\r\n",
-        "    local_size: 5\r\n",
-        "    alpha: 0.0001\r\n",
-        "    beta: 0.75\r\n",
-        "  }\r\n",
-        "}\r\n",
-        "layers {\r\n",
-        "  bottom: \"norm2\"\r\n",
-        "  top: \"conv3\"\r\n",
-        "  name: \"conv3\"\r\n",
-        "  type: CONVOLUTION\r\n",
-        "  convolution_param {\r\n",
-        "    num_output: 384\r\n",
-        "    pad: 1\r\n",
-        "    kernel_size: 3\r\n",
-        "  }\r\n",
-        "}\r\n",
-        "layers {\r\n",
-        "  bottom: \"conv3\"\r\n",
-        "  top: \"conv3\"\r\n",
-        "  name: \"relu3\"\r\n",
-        "  type: RELU\r\n",
-        "}\r\n",
-        "layers {\r\n",
-        "  bottom: \"conv3\"\r\n",
-        "  top: \"conv4\"\r\n",
-        "  name: \"conv4\"\r\n",
-        "  type: CONVOLUTION\r\n",
-        "  convolution_param {\r\n",
-        "    num_output: 384\r\n",
-        "    pad: 1\r\n",
-        "    kernel_size: 3\r\n",
-        "    group: 2\r\n",
-        "  }\r\n",
-        "}\r\n",
-        "layers {\r\n",
-        "  bottom: \"conv4\"\r\n",
-        "  top: \"conv4\"\r\n",
-        "  name: \"relu4\"\r\n",
-        "  type: RELU\r\n",
-        "}\r\n",
-        "layers {\r\n",
-        "  bottom: \"conv4\"\r\n",
-        "  top: \"conv5\"\r\n",
-        "  name: \"conv5\"\r\n",
-        "  type: CONVOLUTION\r\n",
-        "  convolution_param {\r\n",
-        "    num_output: 256\r\n",
-        "    pad: 1\r\n",
-        "    kernel_size: 3\r\n",
-        "    group: 2\r\n",
-        "  }\r\n",
-        "}\r\n",
-        "layers {\r\n",
-        "  bottom: \"conv5\"\r\n",
-        "  top: \"conv5\"\r\n",
-        "  name: \"relu5\"\r\n",
-        "  type: RELU\r\n",
-        "}\r\n",
-        "layers {\r\n",
-        "  bottom: \"conv5\"\r\n",
-        "  top: \"pool5\"\r\n",
-        "  name: \"pool5\"\r\n",
-        "  type: POOLING\r\n",
-        "  pooling_param {\r\n",
-        "    pool: MAX\r\n",
-        "    kernel_size: 3\r\n",
-        "    stride: 2\r\n",
-        "  }\r\n",
-        "}\r\n",
-        "layers {\r\n",
-        "  bottom: \"pool5\"\r\n",
-        "  top: \"fc6\"\r\n",
-        "  name: \"fc6\"\r\n",
-        "  type: INNER_PRODUCT\r\n",
-        "  inner_product_param {\r\n",
-        "    num_output: 4096\r\n",
-        "  }\r\n",
-        "}\r\n",
-        "layers {\r\n",
-        "  bottom: \"fc6\"\r\n",
-        "  top: \"fc6\"\r\n",
-        "  name: \"relu6\"\r\n",
-        "  type: RELU\r\n",
-        "}\r\n",
-        "layers {\r\n",
-        "  bottom: \"fc6\"\r\n",
-        "  top: \"fc6\"\r\n",
-        "  name: \"drop6\"\r\n",
-        "  type: DROPOUT\r\n",
-        "  dropout_param {\r\n",
-        "    dropout_ratio: 0.5\r\n",
-        "  }\r\n",
-        "}\r\n",
-        "layers {\r\n",
-        "  bottom: \"fc6\"\r\n",
-        "  top: \"fc7\"\r\n",
-        "  name: \"fc7\"\r\n",
-        "  type: INNER_PRODUCT\r\n",
-        "  inner_product_param {\r\n",
-        "    num_output: 4096\r\n",
-        "  }\r\n",
-        "}\r\n",
-        "layers {\r\n",
-        "  bottom: \"fc7\"\r\n",
-        "  top: \"fc7\"\r\n",
-        "  name: \"relu7\"\r\n",
-        "  type: RELU\r\n",
-        "}\r\n",
-        "layers {\r\n",
-        "  bottom: \"fc7\"\r\n",
-        "  top: \"fc7\"\r\n",
-        "  name: \"drop7\"\r\n",
-        "  type: DROPOUT\r\n",
-        "  dropout_param {\r\n",
-        "    dropout_ratio: 0.5\r\n",
-        "  }\r\n",
-        "}\r\n",
-        "layers {\r\n",
-        "  bottom: \"fc7\"\r\n",
-        "  top: \"fc-rcnn\"\r\n",
-        "  name: \"fc-rcnn\"\r\n",
-        "  type: INNER_PRODUCT\r\n",
-        "  inner_product_param {\r\n",
-        "    num_output: 200\r\n",
-        "  }\r\n",
-        "}\r\n",
-        "input: \"data\"\r\n",
-        "input_dim: 10\r\n",
-        "input_dim: 3\r\n",
-        "input_dim: 227\r\n",
-        "input_dim: 227\r\n",
-        "I0610 10:12:49.300204 25530 net.cpp:77] Creating Layer conv1\r\n",
-        "I0610 10:12:49.300214 25530 net.cpp:87] conv1 <- data\r\n",
-        "I0610 10:12:49.300220 25530 net.cpp:113] conv1 -> conv1\r\n",
-        "I0610 10:12:49.300283 25530 net.cpp:128] Top shape: 10 96 55 55 (2904000)\r\n",
-        "I0610 10:12:49.300294 25530 net.cpp:154] conv1 needs backward computation.\r\n",
-        "I0610 10:12:49.300302 25530 net.cpp:77] Creating Layer relu1\r\n",
-        "I0610 10:12:49.300308 25530 net.cpp:87] relu1 <- conv1\r\n",
-        "I0610 10:12:49.300314 25530 net.cpp:101] relu1 -> conv1 (in-place)\r\n",
-        "I0610 10:12:49.300323 25530 net.cpp:128] Top shape: 10 96 55 55 (2904000)\r\n",
-        "I0610 10:12:49.300328 25530 net.cpp:154] relu1 needs backward computation.\r\n",
-        "I0610 10:12:49.300335 25530 net.cpp:77] Creating Layer pool1\r\n",
-        "I0610 10:12:49.300341 25530 net.cpp:87] pool1 <- conv1\r\n",
-        "I0610 10:12:49.300348 25530 net.cpp:113] pool1 -> pool1\r\n",
-        "I0610 10:12:49.300357 25530 net.cpp:128] Top shape: 10 96 27 27 (699840)\r\n",
-        "I0610 10:12:49.300365 25530 net.cpp:154] pool1 needs backward computation.\r\n",
-        "I0610 10:12:49.300372 25530 net.cpp:77] Creating Layer norm1\r\n",
-        "I0610 10:12:49.300379 25530 net.cpp:87] norm1 <- pool1\r\n",
-        "I0610 10:12:49.300384 25530 net.cpp:113] norm1 -> norm1\r\n",
-        "I0610 10:12:49.300393 25530 net.cpp:128] Top shape: 10 96 27 27 (699840)\r\n",
-        "I0610 10:12:49.300400 25530 net.cpp:154] norm1 needs backward computation.\r\n",
-        "I0610 10:12:49.300406 25530 net.cpp:77] Creating Layer conv2\r\n",
-        "I0610 10:12:49.300412 25530 net.cpp:87] conv2 <- norm1\r\n",
-        "I0610 10:12:49.300420 25530 net.cpp:113] conv2 -> conv2\r\n",
-        "I0610 10:12:49.300925 25530 net.cpp:128] Top shape: 10 256 27 27 (1866240)\r\n",
-        "I0610 10:12:49.300935 25530 net.cpp:154] conv2 needs backward computation.\r\n",
-        "I0610 10:12:49.300941 25530 net.cpp:77] Creating Layer relu2\r\n",
-        "I0610 10:12:49.300947 25530 net.cpp:87] relu2 <- conv2\r\n",
-        "I0610 10:12:49.300954 25530 net.cpp:101] relu2 -> conv2 (in-place)\r\n",
-        "I0610 10:12:49.300961 25530 net.cpp:128] Top shape: 10 256 27 27 (1866240)\r\n",
-        "I0610 10:12:49.300967 25530 net.cpp:154] relu2 needs backward computation.\r\n",
-        "I0610 10:12:49.300974 25530 net.cpp:77] Creating Layer pool2\r\n",
-        "I0610 10:12:49.300981 25530 net.cpp:87] pool2 <- conv2\r\n",
-        "I0610 10:12:49.300987 25530 net.cpp:113] pool2 -> pool2\r\n",
-        "I0610 10:12:49.300994 25530 net.cpp:128] Top shape: 10 256 13 13 (432640)\r\n",
-        "I0610 10:12:49.301000 25530 net.cpp:154] pool2 needs backward computation.\r\n",
-        "I0610 10:12:49.301007 25530 net.cpp:77] Creating Layer norm2\r\n",
-        "I0610 10:12:49.301013 25530 net.cpp:87] norm2 <- pool2\r\n",
-        "I0610 10:12:49.301019 25530 net.cpp:113] norm2 -> norm2\r\n",
-        "I0610 10:12:49.301026 25530 net.cpp:128] Top shape: 10 256 13 13 (432640)\r\n",
-        "I0610 10:12:49.301033 25530 net.cpp:154] norm2 needs backward computation.\r\n",
-        "I0610 10:12:49.301041 25530 net.cpp:77] Creating Layer conv3\r\n",
-        "I0610 10:12:49.301048 25530 net.cpp:87] conv3 <- norm2\r\n",
-        "I0610 10:12:49.301054 25530 net.cpp:113] conv3 -> conv3\r\n",
-        "I0610 10:12:49.302455 25530 net.cpp:128] Top shape: 10 384 13 13 (648960)\r\n",
-        "I0610 10:12:49.302467 25530 net.cpp:154] conv3 needs backward computation.\r\n",
-        "I0610 10:12:49.302477 25530 net.cpp:77] Creating Layer relu3\r\n",
-        "I0610 10:12:49.302484 25530 net.cpp:87] relu3 <- conv3\r\n",
-        "I0610 10:12:49.302490 25530 net.cpp:101] relu3 -> conv3 (in-place)\r\n",
-        "I0610 10:12:49.302496 25530 net.cpp:128] Top shape: 10 384 13 13 (648960)\r\n",
-        "I0610 10:12:49.302503 25530 net.cpp:154] relu3 needs backward computation.\r\n",
-        "I0610 10:12:49.302510 25530 net.cpp:77] Creating Layer conv4\r\n",
-        "I0610 10:12:49.302515 25530 net.cpp:87] conv4 <- conv3\r\n",
-        "I0610 10:12:49.302521 25530 net.cpp:113] conv4 -> conv4\r\n",
-        "I0610 10:12:49.303639 25530 net.cpp:128] Top shape: 10 384 13 13 (648960)\r\n",
-        "I0610 10:12:49.303650 25530 net.cpp:154] conv4 needs backward computation.\r\n",
-        "I0610 10:12:49.303658 25530 net.cpp:77] Creating Layer relu4\r\n",
-        "I0610 10:12:49.303663 25530 net.cpp:87] relu4 <- conv4\r\n",
-        "I0610 10:12:49.303670 25530 net.cpp:101] relu4 -> conv4 (in-place)\r\n",
-        "I0610 10:12:49.303676 25530 net.cpp:128] Top shape: 10 384 13 13 (648960)\r\n",
-        "I0610 10:12:49.303683 25530 net.cpp:154] relu4 needs backward computation.\r\n",
-        "I0610 10:12:49.303691 25530 net.cpp:77] Creating Layer conv5\r\n",
-        "I0610 10:12:49.303697 25530 net.cpp:87] conv5 <- conv4\r\n",
-        "I0610 10:12:49.303704 25530 net.cpp:113] conv5 -> conv5\r\n",
-        "I0610 10:12:49.304410 25530 net.cpp:128] Top shape: 10 256 13 13 (432640)\r\n",
-        "I0610 10:12:49.304420 25530 net.cpp:154] conv5 needs backward computation.\r\n",
-        "I0610 10:12:49.304427 25530 net.cpp:77] Creating Layer relu5\r\n",
-        "I0610 10:12:49.304433 25530 net.cpp:87] relu5 <- conv5\r\n",
-        "I0610 10:12:49.304440 25530 net.cpp:101] relu5 -> conv5 (in-place)\r\n",
-        "I0610 10:12:49.304446 25530 net.cpp:128] Top shape: 10 256 13 13 (432640)\r\n",
-        "I0610 10:12:49.304471 25530 net.cpp:154] relu5 needs backward computation.\r\n",
-        "I0610 10:12:49.304478 25530 net.cpp:77] Creating Layer pool5\r\n",
-        "I0610 10:12:49.304484 25530 net.cpp:87] pool5 <- conv5\r\n",
-        "I0610 10:12:49.304491 25530 net.cpp:113] pool5 -> pool5\r\n",
-        "I0610 10:12:49.304498 25530 net.cpp:128] Top shape: 10 256 6 6 (92160)\r\n",
-        "I0610 10:12:49.304504 25530 net.cpp:154] pool5 needs backward computation.\r\n",
-        "I0610 10:12:49.304512 25530 net.cpp:77] Creating Layer fc6\r\n",
-        "I0610 10:12:49.304517 25530 net.cpp:87] fc6 <- pool5\r\n",
-        "I0610 10:12:49.304523 25530 net.cpp:113] fc6 -> fc6\r\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "I0610 10:12:49.364333 25530 net.cpp:128] Top shape: 10 4096 1 1 (40960)\r\n",
-        "I0610 10:12:49.364372 25530 net.cpp:154] fc6 needs backward computation.\r\n",
-        "I0610 10:12:49.364387 25530 net.cpp:77] Creating Layer relu6\r\n",
-        "I0610 10:12:49.364420 25530 net.cpp:87] relu6 <- fc6\r\n",
-        "I0610 10:12:49.364429 25530 net.cpp:101] relu6 -> fc6 (in-place)\r\n",
-        "I0610 10:12:49.364437 25530 net.cpp:128] Top shape: 10 4096 1 1 (40960)\r\n",
-        "I0610 10:12:49.364444 25530 net.cpp:154] relu6 needs backward computation.\r\n",
-        "I0610 10:12:49.364455 25530 net.cpp:77] Creating Layer drop6\r\n",
-        "I0610 10:12:49.364461 25530 net.cpp:87] drop6 <- fc6\r\n",
-        "I0610 10:12:49.364467 25530 net.cpp:101] drop6 -> fc6 (in-place)\r\n",
-        "I0610 10:12:49.364480 25530 net.cpp:128] Top shape: 10 4096 1 1 (40960)\r\n",
-        "I0610 10:12:49.364487 25530 net.cpp:154] drop6 needs backward computation.\r\n",
-        "I0610 10:12:49.364495 25530 net.cpp:77] Creating Layer fc7\r\n",
-        "I0610 10:12:49.364501 25530 net.cpp:87] fc7 <- fc6\r\n",
-        "I0610 10:12:49.364507 25530 net.cpp:113] fc7 -> fc7\r\n",
-        "I0610 10:12:49.391316 25530 net.cpp:128] Top shape: 10 4096 1 1 (40960)\r\n",
-        "I0610 10:12:49.391350 25530 net.cpp:154] fc7 needs backward computation.\r\n",
-        "I0610 10:12:49.391361 25530 net.cpp:77] Creating Layer relu7\r\n",
-        "I0610 10:12:49.391369 25530 net.cpp:87] relu7 <- fc7\r\n",
-        "I0610 10:12:49.391377 25530 net.cpp:101] relu7 -> fc7 (in-place)\r\n",
-        "I0610 10:12:49.391384 25530 net.cpp:128] Top shape: 10 4096 1 1 (40960)\r\n",
-        "I0610 10:12:49.391391 25530 net.cpp:154] relu7 needs backward computation.\r\n",
-        "I0610 10:12:49.391398 25530 net.cpp:77] Creating Layer drop7\r\n",
-        "I0610 10:12:49.391427 25530 net.cpp:87] drop7 <- fc7\r\n",
-        "I0610 10:12:49.391433 25530 net.cpp:101] drop7 -> fc7 (in-place)\r\n",
-        "I0610 10:12:49.391440 25530 net.cpp:128] Top shape: 10 4096 1 1 (40960)\r\n",
-        "I0610 10:12:49.391446 25530 net.cpp:154] drop7 needs backward computation.\r\n",
-        "I0610 10:12:49.391454 25530 net.cpp:77] Creating Layer fc-rcnn\r\n",
-        "I0610 10:12:49.391459 25530 net.cpp:87] fc-rcnn <- fc7\r\n",
-        "I0610 10:12:49.391466 25530 net.cpp:113] fc-rcnn -> fc-rcnn\r\n",
-        "I0610 10:12:49.392812 25530 net.cpp:128] Top shape: 10 200 1 1 (2000)\r\n",
-        "I0610 10:12:49.392823 25530 net.cpp:154] fc-rcnn needs backward computation.\r\n",
-        "I0610 10:12:49.392829 25530 net.cpp:165] This network produces output fc-rcnn\r\n",
-        "I0610 10:12:49.392850 25530 net.cpp:183] Collecting Learning Rate and Weight Decay.\r\n",
-        "I0610 10:12:49.392868 25530 net.cpp:176] Network initialization done.\r\n",
-        "I0610 10:12:49.392875 25530 net.cpp:177] Memory required for Data 41950840\r\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "GPU mode\r\n",
-        "Loading input...\r\n",
-        "selective_search_rcnn({'/home/shelhamer/caffe/examples/images/fish-bike.jpg'}, '/tmp/tmpo7yOum.mat')\r\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Processed 1570 windows in 35.012 s.\r\n",
-        "/home/shelhamer/anaconda/lib/python2.7/site-packages/pandas/io/pytables.py:2446: PerformanceWarning: \r\n",
-        "your performance may suffer as PyTables will pickle object types that it cannot\r\n",
-        "map directly to c-types [inferred_type->mixed,key->block1_values] [items->['prediction']]\r\n",
-        "\r\n",
-        "  warnings.warn(ws, PerformanceWarning)\r\n",
-        "Saved to _temp/det_output.h5 in 0.035 s.\r\n"
-       ]
-      }
-     ],
-     "prompt_number": 1
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "This run was in GPU mode. For CPU mode detection, call `detect.py` without the `--gpu` argument.\n",
-      "\n",
-      "Running this outputs a DataFrame with the filenames, selected windows, and their detection scores to an HDF5 file.\n",
-      "(We only ran on one image, so the filenames will all be the same.)"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import numpy as np\n",
-      "import pandas as pd\n",
-      "import matplotlib.pyplot as plt\n",
-      "%matplotlib inline\n",
-      "\n",
-      "df = pd.read_hdf('_temp/det_output.h5', 'df')\n",
-      "print(df.shape)\n",
-      "print(df.iloc[0])"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "(1570, 5)\n",
-        "prediction    [-2.64547, -2.88455, -2.85903, -3.17038, -1.92...\n",
-        "ymin                                                     79.846\n",
-        "xmin                                                       9.62\n",
-        "ymax                                                     246.31\n",
-        "xmax                                                    339.624\n",
-        "Name: /home/shelhamer/caffe/examples/images/fish-bike.jpg, dtype: object\n"
-       ]
-      }
-     ],
-     "prompt_number": 2
-    },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[R-CNN](https://github.com/rbgirshick/rcnn) is a state-of-the-art detector that classifies region proposals by a finetuned Caffe model. For the full details of the R-CNN system and model, refer to its project site and the paper:\n",
+    "\n",
+    "> *Rich feature hierarchies for accurate object detection and semantic segmentation*. Ross Girshick, Jeff Donahue, Trevor Darrell, Jitendra Malik. CVPR 2014. [Arxiv 2013](http://arxiv.org/abs/1311.2524).\n",
+    "\n",
+    "In this example, we do detection by a pure Caffe edition of the R-CNN model for ImageNet. The R-CNN detector outputs class scores for the 200 detection classes of ILSVRC13. Keep in mind that these are raw one vs. all SVM scores, so they are not probabilistically calibrated or exactly comparable across classes. Note that this off-the-shelf model is simply for convenience, and is not the full R-CNN model.\n",
+    "\n",
+    "Let's run detection on an image of a bicyclist riding a fish bike in the desert (from the ImageNet challenge—no joke).\n",
+    "\n",
+    "First, we'll need region proposals and the Caffe R-CNN ImageNet model:\n",
+    "\n",
+    "- [Selective Search](http://koen.me/research/selectivesearch/) is the region proposer used by R-CNN. The [selective_search_ijcv_with_python](https://github.com/sergeyk/selective_search_ijcv_with_python) Python module takes care of extracting proposals through the selective search MATLAB implementation. To install it, download the module and name its directory `selective_search_ijcv_with_python`, run the demo in MATLAB to compile the necessary functions, then add it to your `PYTHONPATH` for importing. (If you have your own region proposals prepared, or would rather not bother with this step, [detect.py](https://github.com/BVLC/caffe/blob/master/python/detect.py) accepts a list of images and bounding boxes as CSV.)\n",
+    "\n",
+    "-Run `./scripts/download_model_binary.py models/bvlc_reference_rcnn_ilsvrc13` to get the Caffe R-CNN ImageNet model.\n",
+    "\n",
+    "With that done, we'll call the bundled `detect.py` to generate the region proposals and run the network. For an explanation of the arguments, do `./detect.py --help`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "1570 regions were proposed with the R-CNN configuration of selective search. The number of proposals will vary from image to image based on its contents and size -- selective search isn't scale invariant.\n",
-      "\n",
-      "In general, `detect.py` is most efficient when running on a lot of images: it first extracts window proposals for all of them, batches the windows for efficient GPU processing, and then outputs the results.\n",
-      "Simply list an image per line in the `images_file`, and it will process all of them.\n",
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING: Logging before InitGoogleLogging() is written to STDERR\n",
+      "I0218 20:43:25.383932 2099749632 net.cpp:42] Initializing net from parameters: \n",
+      "name: \"R-CNN-ilsvrc13\"\n",
+      "input: \"data\"\n",
+      "input_dim: 10\n",
+      "input_dim: 3\n",
+      "input_dim: 227\n",
+      "input_dim: 227\n",
+      "state {\n",
+      "  phase: TEST\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"conv1\"\n",
+      "  type: \"Convolution\"\n",
+      "  bottom: \"data\"\n",
+      "  top: \"conv1\"\n",
+      "  convolution_param {\n",
+      "    num_output: 96\n",
+      "    kernel_size: 11\n",
+      "    stride: 4\n",
+      "  }\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"relu1\"\n",
+      "  type: \"ReLU\"\n",
+      "  bottom: \"conv1\"\n",
+      "  top: \"conv1\"\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"pool1\"\n",
+      "  type: \"Pooling\"\n",
+      "  bottom: \"conv1\"\n",
+      "  top: \"pool1\"\n",
+      "  pooling_param {\n",
+      "    pool: MAX\n",
+      "    kernel_size: 3\n",
+      "    stride: 2\n",
+      "  }\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"norm1\"\n",
+      "  type: \"LRN\"\n",
+      "  bottom: \"pool1\"\n",
+      "  top: \"norm1\"\n",
+      "  lrn_param {\n",
+      "    local_size: 5\n",
+      "    alpha: 0.0001\n",
+      "    beta: 0.75\n",
+      "  }\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"conv2\"\n",
+      "  type: \"Convolution\"\n",
+      "  bottom: \"norm1\"\n",
+      "  top: \"conv2\"\n",
+      "  convolution_param {\n",
+      "    num_output: 256\n",
+      "    pad: 2\n",
+      "    kernel_size: 5\n",
+      "    group: 2\n",
+      "  }\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"relu2\"\n",
+      "  type: \"ReLU\"\n",
+      "  bottom: \"conv2\"\n",
+      "  top: \"conv2\"\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"pool2\"\n",
+      "  type: \"Pooling\"\n",
+      "  bottom: \"conv2\"\n",
+      "  top: \"pool2\"\n",
+      "  pooling_param {\n",
+      "    pool: MAX\n",
+      "    kernel_size: 3\n",
+      "    stride: 2\n",
+      "  }\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"norm2\"\n",
+      "  type: \"LRN\"\n",
+      "  bottom: \"pool2\"\n",
+      "  top: \"norm2\"\n",
+      "  lrn_param {\n",
+      "    local_size: 5\n",
+      "    alpha: 0.0001\n",
+      "    beta: 0.75\n",
+      "  }\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"conv3\"\n",
+      "  type: \"Convolution\"\n",
+      "  bottom: \"norm2\"\n",
+      "  top: \"conv3\"\n",
+      "  convolution_param {\n",
+      "    num_output: 384\n",
+      "    pad: 1\n",
+      "    kernel_size: 3\n",
+      "  }\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"relu3\"\n",
+      "  type: \"ReLU\"\n",
+      "  bottom: \"conv3\"\n",
+      "  top: \"conv3\"\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"conv4\"\n",
+      "  type: \"Convolution\"\n",
+      "  bottom: \"conv3\"\n",
+      "  top: \"conv4\"\n",
+      "  convolution_param {\n",
+      "    num_output: 384\n",
+      "    pad: 1\n",
+      "    kernel_size: 3\n",
+      "    group: 2\n",
+      "  }\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"relu4\"\n",
+      "  type: \"ReLU\"\n",
+      "  bottom: \"conv4\"\n",
+      "  top: \"conv4\"\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"conv5\"\n",
+      "  type: \"Convolution\"\n",
+      "  bottom: \"conv4\"\n",
+      "  top: \"conv5\"\n",
+      "  convolution_param {\n",
+      "    num_output: 256\n",
+      "    pad: 1\n",
+      "    kernel_size: 3\n",
+      "    group: 2\n",
+      "  }\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"relu5\"\n",
+      "  type: \"ReLU\"\n",
+      "  bottom: \"conv5\"\n",
+      "  top: \"conv5\"\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"pool5\"\n",
+      "  type: \"Pooling\"\n",
+      "  bottom: \"conv5\"\n",
+      "  top: \"pool5\"\n",
+      "  pooling_param {\n",
+      "    pool: MAX\n",
+      "    kernel_size: 3\n",
+      "    stride: 2\n",
+      "  }\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"fc6\"\n",
+      "  type: \"InnerProduct\"\n",
+      "  bottom: \"pool5\"\n",
+      "  top: \"fc6\"\n",
+      "  inner_product_param {\n",
+      "    num_output: 4096\n",
+      "  }\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"relu6\"\n",
+      "  type: \"ReLU\"\n",
+      "  bottom: \"fc6\"\n",
+      "  top: \"fc6\"\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"drop6\"\n",
+      "  type: \"Dropout\"\n",
+      "  bottom: \"fc6\"\n",
+      "  top: \"fc6\"\n",
+      "  dropout_param {\n",
+      "    dropout_ratio: 0.5\n",
+      "  }\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"fc7\"\n",
+      "  type: \"InnerProduct\"\n",
+      "  bottom: \"fc6\"\n",
+      "  top: \"fc7\"\n",
+      "  inner_product_param {\n",
+      "    num_output: 4096\n",
+      "  }\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"relu7\"\n",
+      "  type: \"ReLU\"\n",
+      "  bottom: \"fc7\"\n",
+      "  top: \"fc7\"\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"drop7\"\n",
+      "  type: \"Dropout\"\n",
+      "  bottom: \"fc7\"\n",
+      "  top: \"fc7\"\n",
+      "  dropout_param {\n",
+      "    dropout_ratio: 0.5\n",
+      "  }\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"fc-rcnn\"\n",
+      "  type: \"InnerProduct\"\n",
+      "  bottom: \"fc7\"\n",
+      "  top: \"fc-rcnn\"\n",
+      "  inner_product_param {\n",
+      "    num_output: 200\n",
+      "  }\n",
+      "}\n",
+      "I0218 20:43:25.385720 2099749632 net.cpp:336] Input 0 -> data\n",
+      "I0218 20:43:25.385769 2099749632 layer_factory.hpp:74] Creating layer conv1\n",
+      "I0218 20:43:25.385783 2099749632 net.cpp:76] Creating Layer conv1\n",
+      "I0218 20:43:25.385790 2099749632 net.cpp:372] conv1 <- data\n",
+      "I0218 20:43:25.385802 2099749632 net.cpp:334] conv1 -> conv1\n",
+      "I0218 20:43:25.385815 2099749632 net.cpp:105] Setting up conv1\n",
+      "I0218 20:43:25.386574 2099749632 net.cpp:112] Top shape: 10 96 55 55 (2904000)\n",
+      "I0218 20:43:25.386610 2099749632 layer_factory.hpp:74] Creating layer relu1\n",
+      "I0218 20:43:25.386625 2099749632 net.cpp:76] Creating Layer relu1\n",
+      "I0218 20:43:25.386631 2099749632 net.cpp:372] relu1 <- conv1\n",
+      "I0218 20:43:25.386641 2099749632 net.cpp:323] relu1 -> conv1 (in-place)\n",
+      "I0218 20:43:25.386649 2099749632 net.cpp:105] Setting up relu1\n",
+      "I0218 20:43:25.386656 2099749632 net.cpp:112] Top shape: 10 96 55 55 (2904000)\n",
+      "I0218 20:43:25.386663 2099749632 layer_factory.hpp:74] Creating layer pool1\n",
+      "I0218 20:43:25.386675 2099749632 net.cpp:76] Creating Layer pool1\n",
+      "I0218 20:43:25.386682 2099749632 net.cpp:372] pool1 <- conv1\n",
+      "I0218 20:43:25.386690 2099749632 net.cpp:334] pool1 -> pool1\n",
+      "I0218 20:43:25.386699 2099749632 net.cpp:105] Setting up pool1\n",
+      "I0218 20:43:25.386716 2099749632 net.cpp:112] Top shape: 10 96 27 27 (699840)\n",
+      "I0218 20:43:25.386725 2099749632 layer_factory.hpp:74] Creating layer norm1\n",
+      "I0218 20:43:25.386736 2099749632 net.cpp:76] Creating Layer norm1\n",
+      "I0218 20:43:25.386744 2099749632 net.cpp:372] norm1 <- pool1\n",
+      "I0218 20:43:25.386803 2099749632 net.cpp:334] norm1 -> norm1\n",
+      "I0218 20:43:25.386819 2099749632 net.cpp:105] Setting up norm1\n",
+      "I0218 20:43:25.386832 2099749632 net.cpp:112] Top shape: 10 96 27 27 (699840)\n",
+      "I0218 20:43:25.386842 2099749632 layer_factory.hpp:74] Creating layer conv2\n",
+      "I0218 20:43:25.386852 2099749632 net.cpp:76] Creating Layer conv2\n",
+      "I0218 20:43:25.386865 2099749632 net.cpp:372] conv2 <- norm1\n",
+      "I0218 20:43:25.386878 2099749632 net.cpp:334] conv2 -> conv2\n",
+      "I0218 20:43:25.386899 2099749632 net.cpp:105] Setting up conv2\n",
+      "I0218 20:43:25.387024 2099749632 net.cpp:112] Top shape: 10 256 27 27 (1866240)\n",
+      "I0218 20:43:25.387042 2099749632 layer_factory.hpp:74] Creating layer relu2\n",
+      "I0218 20:43:25.387050 2099749632 net.cpp:76] Creating Layer relu2\n",
+      "I0218 20:43:25.387058 2099749632 net.cpp:372] relu2 <- conv2\n",
+      "I0218 20:43:25.387066 2099749632 net.cpp:323] relu2 -> conv2 (in-place)\n",
+      "I0218 20:43:25.387075 2099749632 net.cpp:105] Setting up relu2\n",
+      "I0218 20:43:25.387081 2099749632 net.cpp:112] Top shape: 10 256 27 27 (1866240)\n",
+      "I0218 20:43:25.387089 2099749632 layer_factory.hpp:74] Creating layer pool2\n",
+      "I0218 20:43:25.387097 2099749632 net.cpp:76] Creating Layer pool2\n",
+      "I0218 20:43:25.387104 2099749632 net.cpp:372] pool2 <- conv2\n",
+      "I0218 20:43:25.387112 2099749632 net.cpp:334] pool2 -> pool2\n",
+      "I0218 20:43:25.387121 2099749632 net.cpp:105] Setting up pool2\n",
+      "I0218 20:43:25.387130 2099749632 net.cpp:112] Top shape: 10 256 13 13 (432640)\n",
+      "I0218 20:43:25.387137 2099749632 layer_factory.hpp:74] Creating layer norm2\n",
+      "I0218 20:43:25.387145 2099749632 net.cpp:76] Creating Layer norm2\n",
+      "I0218 20:43:25.387152 2099749632 net.cpp:372] norm2 <- pool2\n",
+      "I0218 20:43:25.387161 2099749632 net.cpp:334] norm2 -> norm2\n",
+      "I0218 20:43:25.387168 2099749632 net.cpp:105] Setting up norm2\n",
+      "I0218 20:43:25.387176 2099749632 net.cpp:112] Top shape: 10 256 13 13 (432640)\n",
+      "I0218 20:43:25.387228 2099749632 layer_factory.hpp:74] Creating layer conv3\n",
+      "I0218 20:43:25.387249 2099749632 net.cpp:76] Creating Layer conv3\n",
+      "I0218 20:43:25.387258 2099749632 net.cpp:372] conv3 <- norm2\n",
+      "I0218 20:43:25.387266 2099749632 net.cpp:334] conv3 -> conv3\n",
+      "I0218 20:43:25.387276 2099749632 net.cpp:105] Setting up conv3\n",
+      "I0218 20:43:25.389375 2099749632 net.cpp:112] Top shape: 10 384 13 13 (648960)\n",
+      "I0218 20:43:25.389408 2099749632 layer_factory.hpp:74] Creating layer relu3\n",
+      "I0218 20:43:25.389421 2099749632 net.cpp:76] Creating Layer relu3\n",
+      "I0218 20:43:25.389430 2099749632 net.cpp:372] relu3 <- conv3\n",
+      "I0218 20:43:25.389438 2099749632 net.cpp:323] relu3 -> conv3 (in-place)\n",
+      "I0218 20:43:25.389447 2099749632 net.cpp:105] Setting up relu3\n",
+      "I0218 20:43:25.389456 2099749632 net.cpp:112] Top shape: 10 384 13 13 (648960)\n",
+      "I0218 20:43:25.389462 2099749632 layer_factory.hpp:74] Creating layer conv4\n",
+      "I0218 20:43:25.389472 2099749632 net.cpp:76] Creating Layer conv4\n",
+      "I0218 20:43:25.389478 2099749632 net.cpp:372] conv4 <- conv3\n",
+      "I0218 20:43:25.389487 2099749632 net.cpp:334] conv4 -> conv4\n",
+      "I0218 20:43:25.389497 2099749632 net.cpp:105] Setting up conv4\n",
+      "I0218 20:43:25.391810 2099749632 net.cpp:112] Top shape: 10 384 13 13 (648960)\n",
+      "I0218 20:43:25.391856 2099749632 layer_factory.hpp:74] Creating layer relu4\n",
+      "I0218 20:43:25.391871 2099749632 net.cpp:76] Creating Layer relu4\n",
+      "I0218 20:43:25.391880 2099749632 net.cpp:372] relu4 <- conv4\n",
+      "I0218 20:43:25.391888 2099749632 net.cpp:323] relu4 -> conv4 (in-place)\n",
+      "I0218 20:43:25.391898 2099749632 net.cpp:105] Setting up relu4\n",
+      "I0218 20:43:25.391906 2099749632 net.cpp:112] Top shape: 10 384 13 13 (648960)\n",
+      "I0218 20:43:25.391913 2099749632 layer_factory.hpp:74] Creating layer conv5\n",
+      "I0218 20:43:25.391923 2099749632 net.cpp:76] Creating Layer conv5\n",
+      "I0218 20:43:25.391929 2099749632 net.cpp:372] conv5 <- conv4\n",
+      "I0218 20:43:25.391937 2099749632 net.cpp:334] conv5 -> conv5\n",
+      "I0218 20:43:25.391947 2099749632 net.cpp:105] Setting up conv5\n",
+      "I0218 20:43:25.393072 2099749632 net.cpp:112] Top shape: 10 256 13 13 (432640)\n",
+      "I0218 20:43:25.393108 2099749632 layer_factory.hpp:74] Creating layer relu5\n",
+      "I0218 20:43:25.393122 2099749632 net.cpp:76] Creating Layer relu5\n",
+      "I0218 20:43:25.393129 2099749632 net.cpp:372] relu5 <- conv5\n",
+      "I0218 20:43:25.393138 2099749632 net.cpp:323] relu5 -> conv5 (in-place)\n",
+      "I0218 20:43:25.393148 2099749632 net.cpp:105] Setting up relu5\n",
+      "I0218 20:43:25.393157 2099749632 net.cpp:112] Top shape: 10 256 13 13 (432640)\n",
+      "I0218 20:43:25.393167 2099749632 layer_factory.hpp:74] Creating layer pool5\n",
+      "I0218 20:43:25.393175 2099749632 net.cpp:76] Creating Layer pool5\n",
+      "I0218 20:43:25.393182 2099749632 net.cpp:372] pool5 <- conv5\n",
+      "I0218 20:43:25.393190 2099749632 net.cpp:334] pool5 -> pool5\n",
+      "I0218 20:43:25.393199 2099749632 net.cpp:105] Setting up pool5\n",
+      "I0218 20:43:25.393209 2099749632 net.cpp:112] Top shape: 10 256 6 6 (92160)\n",
+      "I0218 20:43:25.393218 2099749632 layer_factory.hpp:74] Creating layer fc6\n",
+      "I0218 20:43:25.393226 2099749632 net.cpp:76] Creating Layer fc6\n",
+      "I0218 20:43:25.393232 2099749632 net.cpp:372] fc6 <- pool5\n",
+      "I0218 20:43:25.393240 2099749632 net.cpp:334] fc6 -> fc6\n",
+      "I0218 20:43:25.393249 2099749632 net.cpp:105] Setting up fc6\n",
+      "I0218 20:43:25.516396 2099749632 net.cpp:112] Top shape: 10 4096 1 1 (40960)\n",
+      "I0218 20:43:25.516445 2099749632 layer_factory.hpp:74] Creating layer relu6\n",
+      "I0218 20:43:25.516463 2099749632 net.cpp:76] Creating Layer relu6\n",
+      "I0218 20:43:25.516470 2099749632 net.cpp:372] relu6 <- fc6\n",
+      "I0218 20:43:25.516480 2099749632 net.cpp:323] relu6 -> fc6 (in-place)\n",
+      "I0218 20:43:25.516490 2099749632 net.cpp:105] Setting up relu6\n",
+      "I0218 20:43:25.516497 2099749632 net.cpp:112] Top shape: 10 4096 1 1 (40960)\n",
+      "I0218 20:43:25.516505 2099749632 layer_factory.hpp:74] Creating layer drop6\n",
+      "I0218 20:43:25.516515 2099749632 net.cpp:76] Creating Layer drop6\n",
+      "I0218 20:43:25.516521 2099749632 net.cpp:372] drop6 <- fc6\n",
+      "I0218 20:43:25.516530 2099749632 net.cpp:323] drop6 -> fc6 (in-place)\n",
+      "I0218 20:43:25.516538 2099749632 net.cpp:105] Setting up drop6\n",
+      "I0218 20:43:25.516557 2099749632 net.cpp:112] Top shape: 10 4096 1 1 (40960)\n",
+      "I0218 20:43:25.516566 2099749632 layer_factory.hpp:74] Creating layer fc7\n",
+      "I0218 20:43:25.516576 2099749632 net.cpp:76] Creating Layer fc7\n",
+      "I0218 20:43:25.516582 2099749632 net.cpp:372] fc7 <- fc6\n",
+      "I0218 20:43:25.516589 2099749632 net.cpp:334] fc7 -> fc7\n",
+      "I0218 20:43:25.516599 2099749632 net.cpp:105] Setting up fc7\n",
+      "I0218 20:43:25.604786 2099749632 net.cpp:112] Top shape: 10 4096 1 1 (40960)\n",
+      "I0218 20:43:25.604838 2099749632 layer_factory.hpp:74] Creating layer relu7\n",
+      "I0218 20:43:25.604852 2099749632 net.cpp:76] Creating Layer relu7\n",
+      "I0218 20:43:25.604859 2099749632 net.cpp:372] relu7 <- fc7\n",
+      "I0218 20:43:25.604868 2099749632 net.cpp:323] relu7 -> fc7 (in-place)\n",
+      "I0218 20:43:25.604878 2099749632 net.cpp:105] Setting up relu7\n",
+      "I0218 20:43:25.604885 2099749632 net.cpp:112] Top shape: 10 4096 1 1 (40960)\n",
+      "I0218 20:43:25.604893 2099749632 layer_factory.hpp:74] Creating layer drop7\n",
+      "I0218 20:43:25.604902 2099749632 net.cpp:76] Creating Layer drop7\n",
+      "I0218 20:43:25.604908 2099749632 net.cpp:372] drop7 <- fc7\n",
+      "I0218 20:43:25.604917 2099749632 net.cpp:323] drop7 -> fc7 (in-place)\n",
+      "I0218 20:43:25.604924 2099749632 net.cpp:105] Setting up drop7\n",
+      "I0218 20:43:25.604933 2099749632 net.cpp:112] Top shape: 10 4096 1 1 (40960)\n",
+      "I0218 20:43:25.604939 2099749632 layer_factory.hpp:74] Creating layer fc-rcnn\n",
+      "I0218 20:43:25.604948 2099749632 net.cpp:76] Creating Layer fc-rcnn\n",
+      "I0218 20:43:25.604954 2099749632 net.cpp:372] fc-rcnn <- fc7\n",
+      "I0218 20:43:25.604962 2099749632 net.cpp:334] fc-rcnn -> fc-rcnn\n",
+      "I0218 20:43:25.604971 2099749632 net.cpp:105] Setting up fc-rcnn\n",
+      "I0218 20:43:25.606878 2099749632 net.cpp:112] Top shape: 10 200 1 1 (2000)\n",
+      "I0218 20:43:25.606904 2099749632 net.cpp:165] fc-rcnn does not need backward computation.\n",
+      "I0218 20:43:25.606909 2099749632 net.cpp:165] drop7 does not need backward computation.\n",
+      "I0218 20:43:25.606916 2099749632 net.cpp:165] relu7 does not need backward computation.\n",
+      "I0218 20:43:25.606922 2099749632 net.cpp:165] fc7 does not need backward computation.\n",
+      "I0218 20:43:25.606928 2099749632 net.cpp:165] drop6 does not need backward computation.\n",
+      "I0218 20:43:25.606935 2099749632 net.cpp:165] relu6 does not need backward computation.\n",
+      "I0218 20:43:25.606940 2099749632 net.cpp:165] fc6 does not need backward computation.\n",
+      "I0218 20:43:25.606946 2099749632 net.cpp:165] pool5 does not need backward computation.\n",
+      "I0218 20:43:25.606952 2099749632 net.cpp:165] relu5 does not need backward computation.\n",
+      "I0218 20:43:25.606958 2099749632 net.cpp:165] conv5 does not need backward computation.\n",
+      "I0218 20:43:25.606964 2099749632 net.cpp:165] relu4 does not need backward computation.\n",
+      "I0218 20:43:25.606971 2099749632 net.cpp:165] conv4 does not need backward computation.\n",
+      "I0218 20:43:25.606976 2099749632 net.cpp:165] relu3 does not need backward computation.\n",
+      "I0218 20:43:25.606982 2099749632 net.cpp:165] conv3 does not need backward computation.\n",
+      "I0218 20:43:25.606988 2099749632 net.cpp:165] norm2 does not need backward computation.\n",
+      "I0218 20:43:25.606995 2099749632 net.cpp:165] pool2 does not need backward computation.\n",
+      "I0218 20:43:25.607002 2099749632 net.cpp:165] relu2 does not need backward computation.\n",
+      "I0218 20:43:25.607007 2099749632 net.cpp:165] conv2 does not need backward computation.\n",
+      "I0218 20:43:25.607013 2099749632 net.cpp:165] norm1 does not need backward computation.\n",
+      "I0218 20:43:25.607199 2099749632 net.cpp:165] pool1 does not need backward computation.\n",
+      "I0218 20:43:25.607213 2099749632 net.cpp:165] relu1 does not need backward computation.\n",
+      "I0218 20:43:25.607219 2099749632 net.cpp:165] conv1 does not need backward computation.\n",
+      "I0218 20:43:25.607225 2099749632 net.cpp:201] This network produces output fc-rcnn\n",
+      "I0218 20:43:25.607239 2099749632 net.cpp:446] Collecting Learning Rate and Weight Decay.\n",
+      "I0218 20:43:25.607255 2099749632 net.cpp:213] Network initialization done.\n",
+      "I0218 20:43:25.607262 2099749632 net.cpp:214] Memory required for data: 62425920\n",
+      "E0218 20:43:26.388214 2099749632 upgrade_proto.cpp:618] Attempting to upgrade input file specified using deprecated V1LayerParameter: ../models/bvlc_reference_rcnn_ilsvrc13/bvlc_reference_rcnn_ilsvrc13.caffemodel\n",
+      "I0218 20:43:27.089423 2099749632 upgrade_proto.cpp:626] Successfully upgraded file specified using deprecated V1LayerParameter\n",
+      "GPU mode\n",
+      "Loading input...\n",
+      "selective_search_rcnn({'/Users/shelhamer/h/desk/caffe/caffe-dev/examples/images/fish-bike.jpg'}, '/var/folders/bk/dtkn5qjd11bd17b2j36zplyw0000gp/T/tmpakaRLL.mat')\n",
+      "Processed 1570 windows in 102.895 s.\n",
+      "/Users/shelhamer/anaconda/lib/python2.7/site-packages/pandas/io/pytables.py:2453: PerformanceWarning: \n",
+      "your performance may suffer as PyTables will pickle object types that it cannot\n",
+      "map directly to c-types [inferred_type->mixed,key->block1_values] [items->['prediction']]\n",
       "\n",
-      "Although this guide gives an example of R-CNN ImageNet detection, `detect.py` is clever enough to adapt to different Caffe models\u2019 input dimensions, batch size, and output categories. You can switch the model definition and pretrained model as desired. Refer to `python detect.py --help` for the parameters to describe your data set. There's no need for hardcoding.\n",
-      "\n",
-      "Anyway, let's now load the ILSVRC13 detection class names and make a DataFrame of the predictions. Note you'll need the auxiliary ilsvrc2012 data fetched by `data/ilsvrc12/get_ilsvrc12_aux.sh`."
+      "  warnings.warn(ws, PerformanceWarning)\n",
+      "Saved to _temp/det_output.h5 in 0.298 s.\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "with open('../data/ilsvrc12/det_synset_words.txt') as f:\n",
-      "    labels_df = pd.DataFrame([\n",
-      "        {\n",
-      "            'synset_id': l.strip().split(' ')[0],\n",
-      "            'name': ' '.join(l.strip().split(' ')[1:]).split(',')[0]\n",
-      "        }\n",
-      "        for l in f.readlines()\n",
-      "    ])\n",
-      "labels_df.sort('synset_id')\n",
-      "predictions_df = pd.DataFrame(np.vstack(df.prediction.values), columns=labels_df['name'])\n",
-      "print(predictions_df.iloc[0])"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "name\n",
-        "accordion      -2.645470\n",
-        "airplane       -2.884554\n",
-        "ant            -2.859026\n",
-        "antelope       -3.170383\n",
-        "apple          -1.924201\n",
-        "armadillo      -2.493925\n",
-        "artichoke      -2.235427\n",
-        "axe            -2.378177\n",
-        "baby bed       -2.757855\n",
-        "backpack       -2.160120\n",
-        "bagel          -2.715738\n",
-        "balance beam   -2.716172\n",
-        "banana         -2.418939\n",
-        "band aid       -1.604563\n",
-        "banjo          -2.329196\n",
-        "...\n",
-        "trombone        -2.531519\n",
-        "trumpet         -2.382109\n",
-        "turtle          -2.378510\n",
-        "tv or monitor   -2.777433\n",
-        "unicycle        -2.263807\n",
-        "vacuum          -1.894700\n",
-        "violin          -2.797967\n",
-        "volleyball      -2.807812\n",
-        "waffle iron     -2.418155\n",
-        "washer          -2.429423\n",
-        "water bottle    -2.163465\n",
-        "watercraft      -2.803971\n",
-        "whale           -3.094172\n",
-        "wine bottle     -2.830827\n",
-        "zebra           -2.791829\n",
-        "Name: 0, Length: 200, dtype: float32\n"
-       ]
-      }
-     ],
-     "prompt_number": 3
-    },
+    }
+   ],
+   "source": [
+    "!mkdir -p _temp\n",
+    "!echo `pwd`/images/fish-bike.jpg > _temp/det_input.txt\n",
+    "!../python/detect.py --crop_mode=selective_search --pretrained_model=../models/bvlc_reference_rcnn_ilsvrc13/bvlc_reference_rcnn_ilsvrc13.caffemodel --model_def=../models/bvlc_reference_rcnn_ilsvrc13/deploy.prototxt --gpu --raw_scale=255 _temp/det_input.txt _temp/det_output.h5"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This run was in GPU mode. For CPU mode detection, call `detect.py` without the `--gpu` argument.\n",
+    "\n",
+    "Running this outputs a DataFrame with the filenames, selected windows, and their detection scores to an HDF5 file.\n",
+    "(We only ran on one image, so the filenames will all be the same.)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Let's look at the activations."
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(1570, 5)\n",
+      "prediction    [-2.62247, -2.84579, -2.85122, -3.20838, -1.94...\n",
+      "ymin                                                     79.846\n",
+      "xmin                                                       9.62\n",
+      "ymax                                                     246.31\n",
+      "xmax                                                    339.624\n",
+      "Name: /Users/shelhamer/h/desk/caffe/caffe-dev/examples/images/fish-bike.jpg, dtype: object\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "plt.gray()\n",
-      "plt.matshow(predictions_df.values)\n",
-      "plt.xlabel('Classes')\n",
-      "plt.ylabel('Windows')"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 4,
-       "text": [
-        "<matplotlib.text.Text at 0x4e2c090>"
-       ]
-      },
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "text": [
-        "<matplotlib.figure.Figure at 0x4d008d0>"
-       ]
-      },
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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ymTAej+FwOKDrOiwWC6xWK4xGI4xGo/wOc3VGLKfTCafTicXFRdjtdoRCIUkpXC4XHA4H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+9rdtp/vggw90d3env/zLv9Rv/uZv/oc/A89ggmDAc0EHgOSoAdEJXl/fpzoBRWFFRVpU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\nZp1t5rPVM+s/AIIOk5coA2g1AAAAAElFTkSuQmCC\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0x4d00a10>"
-       ]
-      }
-     ],
-     "prompt_number": 4
-    },
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "df = pd.read_hdf('_temp/det_output.h5', 'df')\n",
+    "print(df.shape)\n",
+    "print(df.iloc[0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "1570 regions were proposed with the R-CNN configuration of selective search. The number of proposals will vary from image to image based on its contents and size -- selective search isn't scale invariant.\n",
+    "\n",
+    "In general, `detect.py` is most efficient when running on a lot of images: it first extracts window proposals for all of them, batches the windows for efficient GPU processing, and then outputs the results.\n",
+    "Simply list an image per line in the `images_file`, and it will process all of them.\n",
+    "\n",
+    "Although this guide gives an example of R-CNN ImageNet detection, `detect.py` is clever enough to adapt to different Caffe models’ input dimensions, batch size, and output categories. You can switch the model definition and pretrained model as desired. Refer to `python detect.py --help` for the parameters to describe your data set. There's no need for hardcoding.\n",
+    "\n",
+    "Anyway, let's now load the ILSVRC13 detection class names and make a DataFrame of the predictions. Note you'll need the auxiliary ilsvrc2012 data fetched by `data/ilsvrc12/get_ilsvrc12_aux.sh`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Now let's take max across all windows and plot the top classes."
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "name\n",
+      "accordion      -2.622471\n",
+      "airplane       -2.845788\n",
+      "ant            -2.851219\n",
+      "antelope       -3.208377\n",
+      "apple          -1.949950\n",
+      "armadillo      -2.472935\n",
+      "artichoke      -2.201684\n",
+      "axe            -2.327404\n",
+      "baby bed       -2.737925\n",
+      "backpack       -2.176763\n",
+      "bagel          -2.681061\n",
+      "balance beam   -2.722538\n",
+      "banana         -2.390628\n",
+      "band aid       -1.598909\n",
+      "banjo          -2.298197\n",
+      "...\n",
+      "trombone        -2.582361\n",
+      "trumpet         -2.352853\n",
+      "turtle          -2.360859\n",
+      "tv or monitor   -2.761043\n",
+      "unicycle        -2.218467\n",
+      "vacuum          -1.907717\n",
+      "violin          -2.757079\n",
+      "volleyball      -2.723689\n",
+      "waffle iron     -2.418540\n",
+      "washer          -2.408994\n",
+      "water bottle    -2.174899\n",
+      "watercraft      -2.837425\n",
+      "whale           -3.120338\n",
+      "wine bottle     -2.772960\n",
+      "zebra           -2.742913\n",
+      "Name: 0, Length: 200, dtype: float32\n"
      ]
-    },
+    }
+   ],
+   "source": [
+    "with open('../data/ilsvrc12/det_synset_words.txt') as f:\n",
+    "    labels_df = pd.DataFrame([\n",
+    "        {\n",
+    "            'synset_id': l.strip().split(' ')[0],\n",
+    "            'name': ' '.join(l.strip().split(' ')[1:]).split(',')[0]\n",
+    "        }\n",
+    "        for l in f.readlines()\n",
+    "    ])\n",
+    "labels_df.sort('synset_id')\n",
+    "predictions_df = pd.DataFrame(np.vstack(df.prediction.values), columns=labels_df['name'])\n",
+    "print(predictions_df.iloc[0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's look at the activations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "max_s = predictions_df.max(0)\n",
-      "max_s.sort(ascending=False)\n",
-      "print(max_s[:10])"
-     ],
-     "language": "python",
+     "data": {
+      "text/plain": [
+       "<matplotlib.text.Text at 0x114f15f90>"
+      ]
+     },
+     "execution_count": 4,
      "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "name\n",
-        "person          1.883164\n",
-        "bicycle         0.936994\n",
-        "unicycle        0.016907\n",
-        "banjo           0.013019\n",
-        "motorcycle     -0.024704\n",
-        "electric fan   -0.193420\n",
-        "turtle         -0.243857\n",
-        "cart           -0.289637\n",
-        "lizard         -0.307945\n",
-        "baby bed       -0.582180\n",
-        "dtype: float32\n"
-       ]
-      }
-     ],
-     "prompt_number": 5
+     "output_type": "execute_result"
     },
     {
-     "cell_type": "markdown",
+     "data": {
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x114254b50>"
+      ]
+     },
      "metadata": {},
-     "source": [
-      "The top detections are in fact a person and bicycle.\n",
-      "Picking good localizations is a work in progress; we pick the top-scoring person and bicycle detections."
-     ]
+     "output_type": "display_data"
     },
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# Find, print, and display the top detections: person and bicycle.\n",
-      "i = predictions_df['person'].argmax()\n",
-      "j = predictions_df['bicycle'].argmax()\n",
-      "\n",
-      "# Show top predictions for top detection.\n",
-      "f = pd.Series(df['prediction'].iloc[i], index=labels_df['name'])\n",
-      "print('Top detection:')\n",
-      "print(f.order(ascending=False)[:5])\n",
-      "print('')\n",
-      "\n",
-      "# Show top predictions for second-best detection.\n",
-      "f = pd.Series(df['prediction'].iloc[j], index=labels_df['name'])\n",
-      "print('Second-best detection:')\n",
-      "print(f.order(ascending=False)[:5])\n",
-      "\n",
-      "# Show top detection in red, second-best top detection in blue.\n",
-      "im = plt.imread('images/fish-bike.jpg')\n",
-      "plt.imshow(im)\n",
-      "currentAxis = plt.gca()\n",
-      "\n",
-      "det = df.iloc[i]\n",
-      "coords = (det['xmin'], det['ymin']), det['xmax'] - det['xmin'], det['ymax'] - det['ymin']\n",
-      "currentAxis.add_patch(plt.Rectangle(*coords, fill=False, edgecolor='r', linewidth=5))\n",
-      "\n",
-      "det = df.iloc[j]\n",
-      "coords = (det['xmin'], det['ymin']), det['xmax'] - det['xmin'], det['ymax'] - det['ymin']\n",
-      "currentAxis.add_patch(plt.Rectangle(*coords, fill=False, edgecolor='b', linewidth=5))"
-     ],
-     "language": "python",
+     "data": {
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+       "kpLGXNf9Q12XEN9wXbcnadNxnL988/ecm+8hx3Eeua77M9d1/6mkhqTiV/jj/lrXw2b+1ay/JOl3\n",
+       "bqC5LyT9M0lVfVmC/K6kv+U4znNJy5II9/htSc8dx/lE0l+V9C8lFST9T8dxPpX07yX9o5vP/YGk\n",
+       "v33zNb6Q9BdvPv4vbhrFVUk/cV3386/yB/11rgduxsMamfVwMj+skVkPm/lhjcx62MwPa2TWw2Z+\n",
+       "WCOzHjbzwxqZ9bCZH9bIrIfN/LBGZj1s5oc1Muv/AuHZAPr9VeA9AAAAAElFTkSuQmCC\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x114254ad0>"
+      ]
+     },
      "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Top detection:\n",
-        "name\n",
-        "person             1.883164\n",
-        "swimming trunks   -1.136701\n",
-        "rubber eraser     -1.251888\n",
-        "plastic bag       -1.286928\n",
-        "snowmobile        -1.304962\n",
-        "dtype: float32\n",
-        "\n",
-        "Second-best detection:\n",
-        "name\n",
-        "bicycle     0.936994\n",
-        "unicycle   -0.372841\n",
-        "scorpion   -0.812350\n",
-        "lobster    -1.041506\n",
-        "lamp       -1.118889\n",
-        "dtype: float32\n"
-       ]
-      },
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 6,
-       "text": [
-        "<matplotlib.patches.Rectangle at 0x4f59f10>"
-       ]
-      },
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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7bG1tAaCUZDwep7ApMC1nTGczqqokz3NGL11FG7NI1Bw8O2Q2nzMoMoTStOWMPM+4eePl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\n8upRmOoyHX+bg6qLbyvaU7fTu36Gq5efQkjBzOxssdvTOXEc0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FhGAe9973uI4pI0bcji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pNGwkyzBgeP0INMnickpWVJiWRafb5ehgnyLLiOOYe/dOKWso6wbb8Rnt7tM0FWkaE4YnLGYL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/iex9mNTcIw5Pe/+Ht89KMf1fS7wRIHd/bwPJ84zvEDn/FojB8GONh0goAkiamq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\njRz4yEY2spG9Q+3/AULZeneOI2OUAAAAAElFTkSuQmCC\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0x3f5b2d0>"
-       ]
-      }
-     ],
-     "prompt_number": 6
-    },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.gray()\n",
+    "plt.matshow(predictions_df.values)\n",
+    "plt.xlabel('Classes')\n",
+    "plt.ylabel('Windows')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now let's take max across all windows and plot the top classes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "That's cool. Let's take all 'bicycle' detections and NMS them to get rid of overlapping windows."
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "name\n",
+      "person          1.835771\n",
+      "bicycle         0.866110\n",
+      "unicycle        0.057080\n",
+      "motorcycle     -0.006122\n",
+      "banjo          -0.028209\n",
+      "turtle         -0.189831\n",
+      "electric fan   -0.206788\n",
+      "cart           -0.214235\n",
+      "lizard         -0.393519\n",
+      "helmet         -0.477942\n",
+      "dtype: float32\n"
      ]
-    },
+    }
+   ],
+   "source": [
+    "max_s = predictions_df.max(0)\n",
+    "max_s.sort(ascending=False)\n",
+    "print(max_s[:10])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The top detections are in fact a person and bicycle.\n",
+    "Picking good localizations is a work in progress; we pick the top-scoring person and bicycle detections."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def nms_detections(dets, overlap=0.3):\n",
-      "    \"\"\"\n",
-      "    Non-maximum suppression: Greedily select high-scoring detections and\n",
-      "    skip detections that are significantly covered by a previously\n",
-      "    selected detection.\n",
-      "\n",
-      "    This version is translated from Matlab code by Tomasz Malisiewicz,\n",
-      "    who sped up Pedro Felzenszwalb's code.\n",
-      "\n",
-      "    Parameters\n",
-      "    ----------\n",
-      "    dets: ndarray\n",
-      "        each row is ['xmin', 'ymin', 'xmax', 'ymax', 'score']\n",
-      "    overlap: float\n",
-      "        minimum overlap ratio (0.3 default)\n",
-      "\n",
-      "    Output\n",
-      "    ------\n",
-      "    dets: ndarray\n",
-      "        remaining after suppression.\n",
-      "    \"\"\"\n",
-      "    x1 = dets[:, 0]\n",
-      "    y1 = dets[:, 1]\n",
-      "    x2 = dets[:, 2]\n",
-      "    y2 = dets[:, 3]\n",
-      "    ind = np.argsort(dets[:, 4])\n",
-      "\n",
-      "    w = x2 - x1\n",
-      "    h = y2 - y1\n",
-      "    area = (w * h).astype(float)\n",
-      "\n",
-      "    pick = []\n",
-      "    while len(ind) > 0:\n",
-      "        i = ind[-1]\n",
-      "        pick.append(i)\n",
-      "        ind = ind[:-1]\n",
-      "\n",
-      "        xx1 = np.maximum(x1[i], x1[ind])\n",
-      "        yy1 = np.maximum(y1[i], y1[ind])\n",
-      "        xx2 = np.minimum(x2[i], x2[ind])\n",
-      "        yy2 = np.minimum(y2[i], y2[ind])\n",
-      "\n",
-      "        w = np.maximum(0., xx2 - xx1)\n",
-      "        h = np.maximum(0., yy2 - yy1)\n",
-      "\n",
-      "        wh = w * h\n",
-      "        o = wh / (area[i] + area[ind] - wh)\n",
-      "\n",
-      "        ind = ind[np.nonzero(o <= overlap)[0]]\n",
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Top detection:\n",
+      "name\n",
+      "person             1.835771\n",
+      "swimming trunks   -1.150371\n",
+      "rubber eraser     -1.231106\n",
+      "turtle            -1.266037\n",
+      "plastic bag       -1.303265\n",
+      "dtype: float32\n",
       "\n",
-      "    return dets[pick, :]"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 7
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "scores = predictions_df['bicycle']\n",
-      "windows = df[['xmin', 'ymin', 'xmax', 'ymax']].values\n",
-      "dets = np.hstack((windows, scores[:, np.newaxis]))\n",
-      "nms_dets = nms_detections(dets)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 8
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Show top 3 NMS'd detections for 'bicycle' in the image and note the gap between the top scoring box (red) and the remaining boxes."
+      "Second-best detection:\n",
+      "name\n",
+      "bicycle     0.866110\n",
+      "unicycle   -0.359139\n",
+      "scorpion   -0.811621\n",
+      "lobster    -0.982891\n",
+      "lamp       -1.096808\n",
+      "dtype: float32\n"
      ]
     },
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "plt.imshow(im)\n",
-      "currentAxis = plt.gca()\n",
-      "colors = ['r', 'b', 'y']\n",
-      "for c, det in zip(colors, nms_dets[:3]):\n",
-      "    currentAxis.add_patch(\n",
-      "        plt.Rectangle((det[0], det[1]), det[2]-det[0], det[3]-det[1],\n",
-      "        fill=False, edgecolor=c, linewidth=5)\n",
-      "    )\n",
-      "print 'scores:', nms_dets[:3, 4]"
-     ],
-     "language": "python",
+     "data": {
+      "text/plain": [
+       "<matplotlib.patches.Rectangle at 0x118576a90>"
+      ]
+     },
+     "execution_count": 6,
      "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "scores: [ 0.93699419 -0.65612102 -1.32907355]\n"
-       ]
-      },
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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CCEzGE6bTCafnCz78+COUUoyGkcseDYdMJlOapsZ7R2YMdV0zGo8wmWY4GHLw5AAfHFJJ\ntNAxgDsYxMmoFNY7qrJCKU0xLLAuuemAkJrz2YJFVdK0LYtFRdM0NK1jNCwI1vVKSQiVkIpEKI23\nHi0jzaO1xltL3fieNlBKIVTidBW0zvYImV450Cvlzi1VSvV0hXcu4tWEsKy3CN8h86gQXWcURERT\nJFwbZHL3IS1aYuSKp2JmCCAzUak652ibaJClymDNXYe42KuqSh5dFoNtWqZnXt0HBKxzCB+56e5v\n4+K2uOAiD5/4VkI3vobgWwaFYbE453vf/w5vvv1DysWS4bDANg0hODKj0UqiFFR1BBbz83N++IPv\nM5lOyYoM5yI9dHDwCOctg2LMK1/MyPMhWa6xPlDXNdqYxIEHnI28t0iBTbp5kmxQVO4ucuqJDulo\nLB+iR+G1WgUB+xhAUvZiLbU4cQfaxKA6BLzzkR9HRkomxQyFEChjnqJIOvoEVp5Qp+AljtXbT36O\n8BHNIFbKHIEyMgUwAyLdr++oGylJ0Ress70h74xDH0RNBrjzRqRSiV7x/bN349U9j1YSNL1nIPza\nXE0Po/pgucALkQzls+VzU+DvvfM++WBA0zRMJmO0MsxnS6Q8o8gLdra2kEpz7/BhRG1CkBUZGxvT\ndAaB1hlSRDpiuazZ2RwzXyz6oFXwHm10iuKHyLkJQZFltHXNZDSiyDJs0zDa32d7exsApSTn5+fJ\nbQrMywXzxYKqKsnznMnNW2hj+kDNwZNDFssloyJDKE1bLsjzjJdfeK7nfpfnZ8zPTlguS05Mxvb2\nNtbD4/v3GU/GbE3uRAXkWmrbYrSmsTEbZns64Wx2Rl0LZmcnOBuzcZqmxYfA44MjfAjYEJhsbDAe\nDsgyjVKKumkYFIOURaOYL0uOT2c8PjxAGc10MiWECkJIHpCnLquYiZO8CRcsKvjoVSSIU4ic4FLQ\nMSFWHxTOJx6KmKUSA7ARlfo0QQMe6xzOeaSQGJ1FzjUFqL0LT4dwRAy0qZSV0wfIuvP3nOyK0ugX\n+FpUP2Ys9P9ERa0lmS7QWlM1EWUHH5GPS8E+QsC2FqVEH2DqFmj0GExC05YuIN3dU9va/l5sSME8\nISLtoyR1XZNlOVorzk6Pee+dt6gWM4LznJ+Wkd5zDmN0pBWB6XTMjevXyfIa21iaukJrye7OFmfz\nc+q6pCwXzM7PqeuaO3deYGdnH61yyDRKS9o20nlRK3W2zkJC1yFl5MQMlmhgu+eQkGgziwseqyIf\nHEIA13lAHX8rnorHCS3x1ifFLuN8SEFtJcyKMmHFA6/iF/SezDrSF0LQOBd9uRQnEjICBeEFISnR\nTsHyVBZKQPgEMkLMiIrvxlAMNKFZp2s8LvHUncfovKOx0asyepQCntHg+KTko05RfUKDtRapZCRK\nkue/TqO5ZMWEiIHRHyWfmwJXSMaDMTaLfPLm1pgmnDOvG07mSx4dn7C/v8/poorpgloiy4qzsmK5\nXKKkxlnHxuYWzsYAy+nJMc7HwOVgMEDJOGgmM3hrKcsmIaeWsFgihaCqKs5nM8r5nJPDQ8bjMcPh\nEGk0TdNQFAXX9vYZ5OdIKSmKAtu2ZFlGMRjgvWc6HLFYLiNlkmXMzs8py5KjoyeMx1P2NzeiwvKO\nbG+XumpihgqC/d0tlFKcHh4gpMA2NXt7u2xujjk8nONsw/nxDJPnHBwdsbe7R6EN3nmMFBwcHFPX\nDcYY2rphIecYKRC5pHWCsqypmhrnYHN7G6E0xXjEpg80rcU6zzjLmI7GZMpgfUOeGTIdAz+T8YT5\nMr4DvAetUEKtKIC2TSg0pg1aZxFKYBNitc5Sp6wOYwzOCpx1kX6QkfoK1TLyr3KVShYXYHItQ0RE\nDh+xlFRpccRJ3yOdzgCEAAm4yI6STRSHSH65d47KtilVMAb4smIQs0tkVBQmeQUx6Nn2WTPOOYSM\nHHZdx+fMsgytV3GOTnl57yBERKWkQkpSloajqSNaczbgneAv/+J7HB8epuwnT9s0cQxDDIIaU6C1\nxJg8omUEddXGOdk03L9/H60l+dBTLmdk+RDvW374w+/zxS++xo1rt8iMwbY1EtAmBVylQBH5ZUnM\nYmq8i7SS9zgX00ojiozjIqXEGIMKHikCUsWAXKdRojJP76JHuSnuEd9wUnQp7bTn3KNLE7wHKfBC\nRr65C/IlI+pcSskkov8gO9Qb8CFRNKqjNwTBRwARPAjley5dJO9ch5gpJLpUwoS4Q0Lc8bLpGsH1\nxlobTSZjWnFr19IKA4BE6zXaJqWlxowtC0RjvkqNXOPYfUhc/d9QBP7VL3+Vo5NjzuZzNja2QCpu\n3L7D1tYWB0eHvPrqq2xvbfP+Rx9zenqCUpKmqRKtMk4DFVjWLW+8+RbXr11nb3MST1611I1LLz0i\ngo6/9i4uOENMe8rznNxkPVKvyjIG+1IK3vzsnCBioKZDVcYYJpMJZVn2aCvPDEoVDPMcFRzD3HB2\nfMgw1+RFxmg0QmsDwNHRMcYY8vGYbDhiNBry1ptvcHp6ys7eNmcnx/imZDgY8OHH93ny+DFeCmwI\n5NrQlBVlWSOlZr4oufP880w2tpgvljTWMhwMyAtBuSyB6DZbB7b1nM/mzBYlVV0jjcFkGVpYykWJ\nrRq0iqx0nmWRximGuCZmNUxGI+rgGOSRFwzO00qZsko6xzMi6bZtUr5wzLtdoVWJCx4ZYnaB847W\ntkwmxVNoS9G5yGEF4pIiWEfgEBVz6NMxE+JOC1qFp9GaDJF3NZmO7nGiO2JOsUg8po9UjlxxmLnJ\noqIrCrzzCK0YDAZrQajQ8/sr9z7GLrxrsdYDbU9BdPfpvWexaDk7O+MHP/h+DLjbCBDaZklTL2Jm\njEz51K0nLCvgFNs6dnd3Iz1iG6bTEVluODk7ixk4w6gM6sry3jtvo6Xmxo0bKJnUf3AoQaIR0noR\nkq7+oEOSQkmkiHQT6TlDym1GgFSrjAkZJAiBFpIggWQUAjGW5LXvUTNED2l9vESE3qBk/Oroi9Dx\nPPEde6V6IykESFGwSntc5Y/HawiCAhHid6V9zKNPCpU0R2PGrKftMpUyg3S6Y2Ho8riVMmijU3ZT\niNQVAe9i9pZSq9RaIMVYQo/OY93JKl4AAe8tXUaUTpQUkCicZ8vnpsDzqeH2xk1G5+e89tqXWZQ1\nQmrOZjPeeec9jo/P2Nu7wc1JwfPbzyGUpCzLuIBD4PTknLb1DK5NePjJQ3KR89JLt3HOxXTBtu0V\ni3OOsixp2kgALMsFUhmkVCzLVYpZlwbWueV9iliXVZHylYVtOS9PqMqKpm3JTYE2JmGHFi1iMGJr\n5zpGa5QwSK+ozpdsTKfsTjbBBwbFiK3tXZQSHH58j9e/9Br7e3t89+Db3BnvUC6XZPOW/+kf/Y/8\nJ//xr+NkwB4fRqPiAns7e5wfPqJWinHTsp0XfHLvAZPtKSb31LOa+uQcJw3O5BS7t9goJpSzisXB\nCbtb2xw/OmA+1ujNAQ0O6paXbj+PlRlHWB77ipPyhOvDIVsEhM5oFwuGWjHdnBJ2p9TeIi3UiwZt\ntlgK+OHdB2xvjRiPR2TDDOkcNliKvGA4KNBS4YXEVTF/fCElqn6MoMHKCTrfJw+WurwPhWCpMoQv\nyKxCGWjrGtfaSJPlGSrLY3FNoltMiGlcFYbgYjqiToUZ0fiQCjEkVduglaGVS5Y256BcMGueoL1j\nkwG3pznbA0FVasxoj3O3wNtholtj0ZXSIGTAtk3ilDVSaKTUZMOMmGMds4q0iAlm8/k8paXCt37/\n9zg5O0aYDC8k9aJCiSwqDReVh1QxPVOEQHVe0gbLvF6S5wbfVAznBdPxkExJlmennB2fYAYDDo9P\nefELL/Hg4cdcv74XU/qEiTn9SoEgZTu1EVmiyF1HAbXRc/Ke1tbEh3bE/PxUoNJ2mTQBVExFdT56\nXUKuApMqBZZ9T73FQi/nXF9cI6VEpXReb9ukLURPygsRC5xkUBBWGTw6tOmwmNTgBT0a9inI3nHN\nwcXU46BWxXJdIVXrSIYjUkVCp1oJuwpgOuKxHaDrAEQQTc+HA71esdFViNcmGiqVHmkFLuLtxQD3\nqv5A/JhuJ595JeZfRUSXanQlV3IlV/L/sWhtefmVB/z7/8H/xWRSrwKuiX5bIfuop5xbATxYKebO\nMSAeTRNcCmqulO46yu4oKVilpX76uM4LXAVB/6v/4j/jWWr6akeeK7mSK/mpEms1b735HP/Lb/0S\nRiu0EmipUEJitCYzhsxojFbk2pBrTWEMhTFkSmGkJFOaXGsyJcmUREsoTIaRGpky2IJ14GKVLS70\n/xf+6eD7+lenvDuF3Xkqz5LPjUK5kiu5kiv5POXdd24gQ8z/FsTipS7YHUIXc4lB+hhojJlDSq0q\nRgMxwOq9x9LFXmLAVLBC2Z/KqFnLjlpH10LqXml3qbY/Sq4U+JVcyZX8VIq1mtAHD2MQvu/hFQAR\n8M7GRl1BpspiiVIiBrI7BS1j7xiZ+ph0qawXG1MFYqwBQHiPv4C24/dV7vg6bfMsuVLgV3IlV/JT\nK30edixeiKmQqXpUhFhgKlNBmHcOH2Qq5On6ca5y0VVKUxUpeyZdoa/87fLCQ4iKfD3XvUu+sc6i\n5KrS+MfJ567A/9f/+b8DYs5vtGyur86TUtKsHSvWYrIiDXpXtSSlwIlVBom8kD4WC0rWyrHl5Zbt\nMovn+/A1TxclpDQzsRaNliG2zpGpvWZXBu7DWjOmkIpcUrqc66u6RKwxSKladVUjpeTs7IwAjMcT\nTo6POTo6ZDKZ8Mndu2itsd5zNl/QtJatnV3wgep8wf2PPsTgGeWa7Z0dbn/hBZbW8YO33sUFyUuv\nfInM5BweP+b//uH3uHXnNs/fvI1d1szOS8x0QuVbgm1YPHnMVpHTtseMsjF/+xf/Dn/53ofMhaCY\nbPCVl7/Io3ff5ejRPa7dusayWbLJBjs72xS5IcsUZblgMCiYTCdIHcvpT45POZ/PaZymGI35w++/\nzbtngiafMsDx2u19tnf2efvjuxS55Qu3pgzrQ85PT3j04CF3bt+icS1BSGzqxJjnQ2ztcK1FIRmO\nhmxubqbmSTFL4pNPPmZZLiEEiqJgdn7O4+Oa2moqV6Ly2Ibh+Wsv4coz9veGOKcYjDdxckEhhikf\nPTXI8qumYk3TYLKY4dG2LaMsIziHyTKk1LTWUrcNznmKfICUkvfee48333wzVRl7GhurN4WIfKq0\nUYEEIWiFoHSW7e1tZCo6evELzzMoct55+y3apsV7we3bz/H40QH/4X/06yAzppu7bG1O+fPv/ClK\nBbQE72ILVJl67oSuDFPEHHVCVD7Bx3Q9rWL9BanNgPOuL1CJLn/XUEyv5ZHHPiRSCKRetWhWyjyl\npEKIedddQaWMzRxSKmfMIvGpz0xXASuFwHmPFqZP0YQQi8dSR9GuL81/+g//wSXrfe27WDWq6nWH\nFH1rXZFSYEUIKJ1aS3bjFSA4ByK2F7iYpiGJed1aKaQQNJDy0v0qVz6Oarx+omn8j1Hin7sC7yQE\nh/Ndlz76nF7ZR3XFBeUaYn9ssf4SVgp6/bn7kuaumEOIvnS2+7vu/11O6uo8IU60p+51bdLFsDO9\nk+Qd+NT6dO14lZr4CCH68mIpZOo+1hV5SDqvzFrFeDSO6YhbW1gX24Hu7u3yCq/gveOlV16iLCuk\n1pg844dvvEU+GNJUDffre7z2+pdZnJ7EImKtaHxgY2uTnZ1tFmXN+ckRhcnYzgt81TDOM7IQUNax\nubVJJQWNVnzh+S/y5p/MmD9+gs1rFkfnfPLh+xgpCbbl9PAJ9zLNm3/xPTZHBW++dcS9x/f4pa/9\nMmPfxvajakBtW3zpWVSx78fmzjbDyZR8NGKoJhwen/L+D96i2biF2crIQsv+aMxzN5/nz7//LnNV\ns7U54KWtTdSi4vV/7V+nKHIeHzxh3jQs6hpdDDk8OmVYjNDCcDyfMd7cZXf/NrPZDCEEzz3/HHt7\n1zk7OeK9d9/l/OQYJQTX93ZZLFp0MSLoc4aDmwzNHtLvUJanKGnIzAinWqplnJtKS4IKtK7G5Bqj\nFLIxseBFZRgjkR5AxV43uaJuY+GV0pAPCmazGb/7rd8DYH62RGWxCZUQkEmJbC0b+YCmrMjGY5Yi\nYKYTHj9hQFvLAAAgAElEQVR+yHQwJATPRx98wPbuVlRyAgajYaySDJbXv/I6Tw5OqK3g+PiYEGK7\n14BlMpngnKVu26h0if3ohQQfWgSJ803z0juXuiXGua6loHVNdPcxqSOmTum7PnZ4XLUQJGiJcF3u\nfSrQEV1vka4CNxb9WB+LXfI8wzbJmCWFGtJa8jE1PDWkW8sW6XOwZV8Mdpn0raZhrWhGdjp5BdK6\nGgO/4rw7/eA8fYdMwtMgcNU7pQOTsUBOidS3J6W3+hDvJTeqL2bza/UCz5LPXYEHQerBvmoIk7R2\nQtypJ7AAgexTdELoyma7N+OTIk/mm5Vy7hL8Y6giXffC5/39XFDs8Xe+v64UApEa1/eJP2vHS0Hf\nMzqBhL47Q3eukHg2t56HRHy5AhkbyYtYVCCkpGxriqLoXawQYle3oR4wnoyp6oogFK+++goHB4fs\nbW9R6BgpP3hYIIVgMBow3Jwymkz4xb1dRIhGo1lUvP/2+1zb2ORLz79AhkQPJ2TZgNNyiRoY3NkJ\n7fyMQa4Y37hBfVZy8OghOzfvsDseY4XE25rbt64zGhQcnh3x9Rd/kdZoDtsludDMyoaAp5CCtmmY\nLRxHtqJpWpQxbA88dfBYEVhUZ2yEnCITvPfWn3N8No9dFgcZTxZz5GjARj5gcXTKzVdeZv/VPZZt\ny9F8zl++/R6uAZFnFIMhQ6HQ2ZDZoqJqIp95fjbj/t2P+OKLX2C2dYRbzBkWGd5AKGsmwzG6mCCE\noVwsOD4+4qOP3sVZTTGakA1a6iZ6fZNJwcbmkMXyFEJDpg2j0ZjNjW1GwxFnZzXlsmY4HMVe013f\nFyFQWnP3/j1+95u/iw/QNg3j6YTWtqhgwFlU27Bpcl67/Rx5lvHu3Y85n59T+4rgBdXxCSbTzE8l\nRwePUFoxGo1xruWTjz+myAccHx7FugcPZd3Q2Ca2SG4tJ6dPYjGNyVHeIJVmMMjRmerRs21aQKKE\noPWxHSxpjiulCMIklN3BloC1LZE+6Jpipf0+rOxzqmW3ptYLXxKPHBWiTkUxqdVUCIgQg4Rd61hj\nDF2fewh0m2XE63WwKvTr76I0bd3B79VaTl41gLWrBlpxmceEbSFFun8ZG3SlFgwr3d31YAqsA0vv\nXf9Z17JXG4NMn8W2xKmgytOP0bPkc1fgJjNEx6Frt7mquhNSIlKb0Y6i6BSp7weme7mrhkd9eW5H\nnUiV/n4NPYvLewx0aP0ptO9j8YIQq2qwjr+COHV6Ky6ILtcaqu+KGDpZp3bCmoX1IaReD6l1JY6q\njUGWej5HaRXRTPBIF0u+q7JMih+KPGc6GXN4eMj+/jYvvfgix4fHNE0bm1c5izCKOzeugXPgHEpq\nBtMpk60xf+v1L9O0DRtbW5ycnXMLwenRIQ/ufkg20PzsV3+WoBX33vuIpqy5c2uf47pm+/o17n70\nMVkWO/dt7m6zd/smi3lDbWNDq9PzBYJAZjKC9+TW0abNNvI853xZcnx4issDr33lRcww5+j9N3nx\n9gucHj3B2cDtO8+zvadpzh9wcHSEAEaPHzPd2oQ85/s/fIMPHzxhMNxgac+Zzx/z4OAeL774BYbD\nIbmOTcmq+Yz97U3auuTlF57HuIrXX/sS/9vv/GMGepvdwTYik3z80UO2t+5waivq5TFKb7A8ldz7\n5BFOKvb3tzDGwdmcs7PHaOWpqxLben7uqz/Pr/7dv4tRBY8fHfPk4JCqqpiXFbPFksxklHXNP/+j\nf8nZfA4hFuqU9TIieykQvmF/MuFn7jyPO1+wMx4yfuUl5N0P+Oj0iPFggqsdhdYMhgXWWXa3d/j4\nk7ts7+zRVC2ZNLz/7tu88MqXcQhMpvHexS6WGgwaBORFwbK21HWJCw6/bPsydNvE7pdFVnQTeA2I\nxCZeq3m9KpHXfSuCrqlX7Isto+McS9eFpKrqVdMmkXqHAFoEJJ62bdHd+k/UiDGpuRYidqNM1Env\nfadK0K5C9llItvPEQ/CphH6F1qP3rZ5ar93PsRe+6hW5EALhupbIq4ZUsLaLk1/tyqSU7O9XpCIv\nEbrq0KdB5Y+Sz12Bx/ScpGB9ekVC9pa7Iy9ilFj2irfrQQcxArweve048E7WKZKu+1g819NIe/3Y\ndeSuRGr639E4fRXV6hhSZzIvZGqeE55Szt1L7AyJ7LYWk6J3B/GekHYBCggckBX52v2FGEzxsfS2\naSxaqdSXQQGe7e0txqMBuZEcHR/EhacL1CCj8S52YlOB4CxKBRCOvedvcefmNUTTEjQsVWB6ew+3\nqNjfnnJ9Z8LzL96gsjXXNvcQtWNrusHG/h77uUFkGbvTKbL1LKuS48WMwdaUa9t5nKz4yMvaFhE6\no6Nir25lUGgWvmW8Meb1179Em0nq6pSbOyN2BoaN0R4zKo4ePiHPxzw+O+bx2TGTwZAHRwccNyXB\nZIjREPKck6ZmsZixmC9RCJ4cHLKYz5iMxyxnc/Y2Jvztr/8cN3Ze5ODhfb77p9/m6PEDFmf3+MVf\n+BlOT2se3LvPvY/us/kz+2CXSCoG2SZtrQk1kAcGgwK8pS4rRgPNzZs77O1sc3RwQnAVh4/uMxpu\nsL29w3S6wf6NGzx8/IQfvvEG737wIe+88y6PHj2KG0Z40aNJrQSDTLGzu8Gt8RTRNtzZ3eHxo8ds\n3b5OLjyjXCGco1Ca6WBIVsSqzUwqtiYTZqcnDAZjyuWCf/Y7/5R/8A9foXWCtlpSFBltNadqawix\nd5C1LaQeLk1T0WmxrmlT8HFbQpLT2JW3BwQ6j3PUWotPsYCuLNx7j3UWY+IOT7jkYCMIzsX2uFKQ\nyTx6pc4mBafQWsa2FkKkXazSVmX9Rgqktq+WbJD1NIe1liDX8qxlh54vERHL6DtKU3R0jo8eu090\nTmcAopKOTIB3Ftwqp1ulDU4uevXdZh+wAm9dYDR6GysQGI/xKwqHZ914lM9dgeNSMFLKnisTArTu\nUHAe0fQaKl6nOVZpOCtEHZsWPY14V+MZFXC3ccDFoKWUa8qSEHtrC9kTMyQF7ELXfvJpZC/XX6BY\n/V6lZvRdUx63FsHu3pEUCnx6uR1V5J7u+wEryknrLFpyt2rp6m1LkedxDIXu0YIEciHiRgWzsh8n\nKR2iPWPZNWBq4lj7po1N7pVktL/P5Pp1IKCVZu/OizR13Y9HhMKJw2tGbLEbG325Vc/rPqqeXKxV\n75H4O+sdUmpefm7Bsq7xDsplxXwxZ6AXvOLO2bm2i1IN7WDAxk5sNXA6nzP0Mcy8nWWMb+2xKJds\nvHwDqTWi0gQpuP/gPl4Kjo3kZtpke1l7PnlwxMLmvP/JKY3dxKkxrTjn6LTBhpxHT55wcloBWwSZ\ns7AzGgJCl1hfIssAdcPsbMZeu8vR3Rmnp0tcKDm9s2Rwa4fZIvD+xw/4Nza/yPb+Nnx0SBgf8t7j\nj3GDQFu1DNUY1RY4I7ChZCQUz40GXM8Vsm45mR+R7U1pxznj/R3c+0cUwyHaG65vbXJzcwqh5ej8\nhK2b2/zFBx9xVp4TZE5zcMifv/E9fvZrX+Xg8QOWi0PyXGM9OJ3RKKhmZ0wGBZmB2la0WkALA5+R\nmZxlU1K1M4rxgKZuyKRBtLHHt1OxhLyuG4aDEW3d4F2gqkqUUuSDASHEPSOd9kgn0WiUVGljkYAX\ndeoTDsJJsI4mcfVa69g0KjdIZbAhNq9rGovSiuFkRNOco0IEA5nIYh8W5aj8Ei9BhctVnbUNSIGW\nqdEVApl23VFBYHQGUuF83DcXkfYbdS3CJ88/GZZmIFKwM27f5mykVHyTkjJU7HfsQ6CwIipwEZBe\nErs3xh17YouAiBI7A/os+dwV+HqT9PXKpL51Zae419y0i8HH9b+7eEx33LpctJCXyVN/I1Z2sL+v\nC9dYD34+65qXXTvSPU8f22WkrI/DZTmh6yW/61/xHCu3r+vvchEFrF/vwon7JjouNZhfF611H/zp\ndi3pft/db2YMTspPXas770UDbLyP26xpw2ZI/Sx8oG5asjzrd29p24ZqXHDrxo2Itpo2ZYH4nrZq\nnaWqKhblknbuuf3cHbZ2XsOHwMHxIZtbWyip+PiTDzg9O2Zza8JoOMIFzaODA6plgzF5zDQQnqap\nY9BRRLde4JmaCbKWjMYDTk5LwPDRJ/epywXDfMhgNODo4AE7u1Pe/ugT/sk3/wCztcGXXv8qTx6f\n8p0/+T7zo5rlwZzt4YT2vKTIVkUc48GA7cmUHJBInhw+pDw958ZkjLeWjfGExbJiOMwZ5BqtwAjD\nxnDI4WLOV157lffvP+bwbEG1POWP/8X/ydHhfa7t7kSFqDNsUyFFfMfDQQYhUDcWoWOATRJ7oUih\n0EqxOS6o2goVYlaFdQ6lM7yP+8MqJWjaioBH6tjHP4Q4fjIVyTjbxsZqTqVt30A4CCJtrJI2bJEI\nkHHXJyEjEreujX3pnYv7Wsq4Z2tZtVhbkYm4kUmkJgM2tDhsBFLPgODGaFCgu/no4ibHEL1861d7\n7kKMw3XgbD3AGULcJ9f7VTpi3IdVx+ZaLnYj7NZ0tyG7FLElbccgCNFtNh3pl7/xQcyu2qhTBl21\nUqfYO/djnYK4qMzWK5cuyyhZV4jdZ7rbmDesIsqwchk/jfBXctFQ/Kh7WfWqdk/TKOLpXM+Lkev1\n41Z0zqfvY73kdr3a6zJl37uBF/h5vdY2df3z9bG5eH8dn9d1Brw4Fl1Hx4tjdtHY9AYnudV96zfi\nprxdOmmRj6mqiizTTMcxMGvbuCu6MYZMG5q2pbVtTPdK7UhlG7McqqaOnRcHcH4+Y9m2KATTjQHB\nVywXZ1y/tUPbOk7Pzzg5O+bw8AlVtcBkGiMDCs/+7iY725txkftAu7BUi4Z6uWQ0yLEehhsjinHB\n49kTHn77ER8czJlVJwzHQ77z3e/xh3/wrzBs8PILP0OoPLOjJ0zHA8rFGVrFMu6t4YjCZIS6pm0d\no8mUwmQM84Lt8Qbn53PUQHBtZ8J0VKAIaAHDLGPqC05OTnjp1g2++pVtppu7ZHnOxvYWg8GI2XyJ\n97HXvXYOfEtbWiwanQ3BerQMKGmwITajcs7RziqM1gjnEcphjMK6FiUEZblECIkxsZWwtW3vnflg\nkWHV4VCk9EXvLDpRf32PbhxOaHzQBNemQF8Uo3KapupjRbapY495rTGZJlhP01RIoVFK0IYaJ+O+\nncGtJySvxPnY8M6HlCwsQCiBlhKFonYRRUsZ+3Q7HzeJkVKnYGeMw4UQMEEgZOxI6F1MD5TOxc2T\nE08vZNyVSBmdkHun61LsTXbGIrIGFztvXpTPXYF36BCeVoydXI4ufa8Qn/V5CKHfnmhdkXSKptuQ\nd/0a69e8DPleVIrrhqFTgJcpqovXvqgsL577MuW7fj/r519Xyt1xz0LaF6X7G7fG3V12DDztYQgh\nyLLsqfu7OB7dO7p4zPq5nhqbbkdxZL/npckUuTRpMwWFGg3idVKxgxgUKYDlca1DG0WWG7odKmP2\nnkVpjcriXovXR/tcu7aHUgrbtFTLkrosqcoKdMCojNFowny+ZHd3m43NMbPZOYvlkt2dTV56+RXm\n85KPH97FNp56tuTVl19mczLlu9/5Lm3ToosNXv7K6+TTAQ8PHnHrxS1ufeFl/uiP/4gPPzpksXQc\nHp2wuVmwsXeHYCRPHn/CZKvAnS3YH+8wNBm2aVGAJfDKl17l3pNDyrKinC0ohCIfFmyNcwwW4cDI\nHKNznPG0LqC94+zRQ/7+v/MN3vvgA4SCcrGkbQClIGiEb9HWUuQZjTMENcK1Ncq5iG6bljYEcqXI\nlcSVcYu3qm7xypMVA0K7ar9c13H/19SuPG03J7De4W3sHQ4y9eeO71KEgE9bhaS9eRBYJCnNL2WA\nlNWi718eQox7aSXRyrNYlmhpkCIi3rq2eOEwefQEntWWtWkrsAABmegQIUS/ZaAXsXzeGINQAue6\nMvukP/pgmyeTOs39gEvbGpLQulKJFoln7dvztrbp4wWrvLYOWKlPefYX5XNX4BepAnh6sa8j4g75\ndWi9O3ZdiV0857qCWv9sXdmsBxnWt41aR7jdudZ/Xv+77l4uQ9IAeddDe+3rryJ9psuaklx/jmcZ\nl+4eLzvfRYV7Metm3TB05+k+X1f2F8dn3YjA5d7Ms56rdS2dAu92+A4hENzTG8x65wgavEvN8FPg\nSWuJ0rFnt0uL3BNSm1mPF9Gt9+n+QghgFBsbE8z2FovFApkb2qZle3uPV15+haoqMTqmSC6rJVIo\ninzE8ek5z728w/lswfKsIhMDPvnoIV/+2i8wXzacLRf87h99n3xc4JVg79oWjx9/yOzMkmXbCKHZ\nnG4yPz9GIBiYEc/feYEnB/e5sbPD1nCMDLCsSoTRyNxwcHqGzDPKpmE5LxlkOVkOG8MC7QNGKKSP\nnO32IGM4HLO0LT/71Z/BLmdMiwydDzl/fM6H9w5orOcLt/d57oXrVItjlM744N4xR2WDdI5rYwGD\nAWY4QWqNa1vcfM5AapRXqRe8YLlYMNVx8xOlFdoYWmdpmjZumr0Wi5JJMcaQ1WrvUBfamFwg6L+E\niF1dg/NY76IhkKCVxLYW6+qEnCVlU6GLjOA8ztcxXz9XtC5uvBB3FbqcQvHe9TGZmECQvE4ft1vz\nUkRvJcSNz51N311M+YvUUVSjMeVQpr0sU9l82ulHyG69xCClNhqpRAp1rfbWbGyLD3EjEGstP05N\nfO4KHOgR8Tqv2j3QRQ7oRymui0rssp/XEeWPUizrsk5LrCueixTIupJ/lmJ9lqG6eL3Lfl5Xjp1c\nhiw6hXcZ5XJxvC7zEi6LIVxE4hefY90AXvSQnnXtp/7ffR56bxopJE1lYz6wWO1O4n3a5ixEoKeU\nRgRobJtQUbpXrfAujlmW5WR5RtO2Pcc4Go5o6xolJBsbG3gdaZtmWTMcDtkKG7R2yXLmKQZTvIMs\nGzAajsiHDUFIWgwtQ176uud41vLf/w//mMWiIrSB5pNT8J43eQelFdXSsjGecX664Otf+zphpLFt\nhWTKyckT9DSwkQmKTGOdY2kbnFORIpAVrfcsq5bd3V2qeomSltC0IAwkSqTrpjcdZLhM8Lde/TKz\ntmRnc4PaaZybce36HaTJuXNzl739AfVMc//hY54cnrAIEzIh2fvCbfKtDe6dnHB0es6N7S22J9ss\nDp4wKIb4zOJF3Pjjhd09ZrMZp+dntLYlBBWpCW1SpkgsyFFGp37skqBiVooQgUwoRELgTgiC0HHj\nZVuTZTG7xPmulD2gjEppeKSUQk2T6kCC8FhX463HB+IerV7FjoCXiExV0pGrt7FSJNCDAxviRsR9\nQkAq2On498a1CJ9oxNr2O9gDOC/7Tb2FlKsCHQcImZS6T9vudWwESNVtHbdKm36WfO4KvENpWutP\nUR2X8a8XqYTud+sIcP3zy5TGs3hYWNue6xmy/jcXj73YvGbds1gP1q6f6zKU/Cwq47Lfrxu4TwVH\nL6FiLrvOj1P0P+7eekTL6v10vHp3/ovv5VP30LePWKVsEjxZqhOIrHg07l52W1axCv/60P/UFW14\nbxFO9QUTbVvSebUhBNrKxjJtkUqyRUT+g+EACEjvyfIR25uThIh82i1cgqtxAYIpeO/uI37vj/6M\nN977iGVlGRUjZvNjlPVkQqGrIVpJJsJTHh8x0Zrv/PHvsbO7h9SK7b0d5q1nunubxeMPUb5hlGsG\nmcEpwbJt+i26bHAE68mLnExoZBDkWY4JmoEaQJDsTLcYb26ydWuP06MzauUJSjFv4Z33P2BmM2rr\nePDJkMnf+RojY3jjjXd4dFgSBoJmWfJe7phev86ZbTifL3n1xVf4+lde58/+xb9AScnhw/u89ckn\neOD+eMz+/j571/YJQnB4fMIH77/HdDqNhnBjk7ZpaFrPsMho2pT2KhVSeKTzEBxVXSNMhhOWfDDE\nuoq6qRMS9eRZhveePM8xuY6BStuSGY20FiFC3GXKmNiKwlqE1gQX2E/73V4UpaNX4H3AKBVL29OW\nfwDC+1hdKuJGDaUtMamWIfYJX+0fqnLTZ8t1m0EjwTpPYYqYLioiz13VizRfAy411KJOSVoibnj9\nExHE7ORZaPQiX7qupNc/vxiovAylryufLjiwbhC6oOll8qP44Wfd/zpyX+1QvqJ0ut2CLnvmy5Dr\nZQr34v1eZrguPvPFzy7zcp71Ptbv5yLNtH7Ms+SyVgXQKez4v0BArO0F2J+v/1z2/S1kn4WT3O+w\n6jnjCf1uSuvXXE/vDCHELd+EoKlqSGlkEYFBCJaz85KiKDAmB5kRfIDJbbwLfOt3v8W3v/3nHJ+c\n0ZzMcHXDozLmWldNxd4Lz3Nt+Bx3P/qYqj0nm+TM6jM2b005W55x+/aLnJUV4519yrLCG0NQgYaA\nCiEGZluHQhBQyZA5Qoj7LYogqZuWwWgYRyNIdnf3GUxGBAdt61n4ilZANtnk57/2c4h8g2XdMB1F\n42h9YFk3kdIInpe+8ByvfflFfvD2O9w/OKBsa769qDh/+ICt0QiU4v69h2S6IIjAfFbyxS/ucHoy\n48233+L49JwbN25w68ZznByd8MZfvE25LCnyjOPjJ5hiyL/5y7/Mu+9/wNHhY8bG8MKdm1zf32dR\nV9w7OGC6JdicDDg7O2M4HFEtS4q0laIQguPTc6SMa0gajW8tEHusLJYzrPXovGC5KKnLlnExunQ+\nZlkGQrCYlyitCSoWEKqEfFWaX9ZanHfkxuDaJqU3Zn2KrDQGK2IGi0joXaqYlaWySOcFFYOmznpy\npdfo3xUNrDNDneZhBB1/w4OYnVzkVJ/l7sOFophnueWsQgKXKZj1INzFz9blWUj+WfTL08G+/pOn\nfv90dsbT1+/Q4WXK8eLP69e7aLQuU8DGdK/70wr2Ijf/V6F3uvu5jIKB9Zx6ei5v3VNanSvgvej/\nfzGY059DrMVLunz6RKfE3P9YHNXxjUIA/hID3j1rPxoCBORoBBKCTFytA6EYmBFCKMrGIYTj5HTB\nn/zZezy8+4C//PZ3ccfn6LLl52/dYTIZUoX/h7k3D7btuus7P2vt6Yz3njsPb7pPepplSbYkPGFs\nsCRDDAbnD1yENE7SAToQKklXF3R1VYdUuhNEUZ1KaNJNdxoKAz1gCGCDCRjwEOMBWciaZenpvac3\n3Ond8cxnT2v1H/usc9fZd5/7BKRbWaqrc94+e6+9ht/6rt/vt35DSF8mDNwUrx5QmTnFOx64ky99\n7U+4un2J6rTPYecmJafElSuXuf3UbZS0pB5UaQdVtm6+weriLEiHJIxwPQ+lNa4rSdMYR3pIRxNF\nUZaIOTuPRAjwHY/LV19nefU0slImmGvgKS9L8Ov5xM1Dmnv7SClZapzhYHeXfr/DwuICiW6hUOxv\nX+dF2szNLVKtlTk8aDLbmCLwPT77p3+MVopSpcrM7AKbm5ssLi+yuLjIf/jDPySKExzpsLWxjSt8\ndrZu4krJ6cVVVJLiuQ6tXp/LV67SG/TROts4X//ma1w4t8Y3nnuejd1dzt0BKiozOztLGIaUynX6\nYcru7i6lUhnfD4iSmNnGDP3BgKmgSqdzSJrGBL4HpKSpRgqPjY1rzNanC+l5f28fzy+RKoUWw1gv\ngHIUjpA4MiUMY1wnU424noca6r8zudAdMhMC6TACdOFIEAqNMS88WtN+4JGE0cjRLwv1lJkZJkkW\n5dCYIDpOMU6Z8tcC8LW1NaampkbK/Keeeor9/X0+9rGPcfXqVdbW1vjkJz9Jo9GYWIe9uGxRvEiM\nvxW42gebwy/HQM58mlPzItO6fP3HXOuZHGx9Uj22ymX8QLSYW80fdE7iiotUOPnrpqRpMTAWbYwn\nAfhJG5h9qDzezvE25ut3cDHhOUEO7YKt90oBOnOocuUwXoTWo2cy4wajQhnr9XDzMCmwjnwO8idE\n0ug+nWEmcz0UaV2HKE4p1ab4p//0X3L27O305BQvfeNZ5oMKdz54Hq83IDxs8sDaGqlIeX3jGvv9\nFkkyoFHTtG7u8K333c/n9tdpHTSZrk+hIoFMJfub2zz64Nu5dukSp0+t0urcJEmHEf2EgwoTpO/h\nSYnULp4jcUTmZKaFQjgCnBTpeaQqYv36BgftJrpc5r0f+hCzSwukUiMcyanVVbRS9Pt9BuEAv1El\nrpdxXZdTK6cJuyHaERBIUAJXCRZPrbB6epUwGVCueFTKFVqtDs3tbU7PzlGrVdjcXEcITZrGCDIn\nnm67zaDXY2F2nunqNM3mIYN+j/39A5xalQRNpVxiKvBp+B6rS4u0Dw+ZbUyzceM67qnTzM0ucvbM\nKt3+gG6vx+ycw431DVZXTyHwSJTD2x58hNvm59jd2WRr+zo3d28SOC5+ZYqXXnmdnd1DKg9PFdKz\nkIIwDPFLAaVSmXa7hUpSpAOlUokkTjKpR8JgEBKGQ5+AYRLvwPPQGsIwJPD8zJ1waNxgznb8oDQy\nuQ3DcBhLSSOcYTwYYeKmMLR9d0hTTZyEZNY2k8tfC8CFEHzhC19g1tIvPfnkkzz++OP85E/+JD/7\nsz/Lk08+yZNPPnliHfbnX7UdQhyZCuU3hSJVythCpli3XsR12wB2EugX9Se/wdgbVlGf7M98vZPe\nO0mtYf+W76ety89vhieVfP8NgOctdorakW9DlqVeDAE504Obw0YtjILcHHQKxmUsU+/45qM1SGGC\nnI1aPVRjxcfHFE061IdK6WbvEA69Xp/aVINESX7kv/ox/uzLT/GV3/g9Ti+vcHZxBqV6qBJ84COP\nkXYHBDj8jQ9+iN2tHdavXqd3o4+WAb1uj3sefoKnLr7As1cuoSs1WlGIFwT8wZ9/jne/+1s42Nti\ndeUU/eY+rnBxgzKR7mfqjVRRLQVk7ssKP/ABjeNLemGPKOxRdkqcOrPEvfc/xLWb+1TqVXAkiUrQ\nUYRUCYHQVGs+ac1noDXSD1heXsaXDoEQHLSbHKYxNa9Cv9XDFS79bpuD9gFT0zUcx+F0ZYlGZZpB\nf5RuJqQAACAASURBVIB2BJ1um7Xz59jd26fT6VEulSiVAqZq9Syo2vZNms1DlJswNz+DEJpKuUzS\n6xJUS0zVa8SDAefPnmar1WKm3KDV6vDccy9w+sxZBlHIwsISS8unODjs8I3nXmAwyCJxtjoDKg++\njWq1xMLcAhcvXSSMU8p1zYsvvgxacOXKG4W0WC5VCKOYw8Mm7XaHSqVMfaqeOQulMY4eWpNozdzc\nHJknpaJcKpMkCXGUEMUxpVKJKOwNE6FnUR2F4QBkFrUxKJWzhNuOS+wrwEGpZLT+XNclTmOEdLLD\nVSUyKfCE8tdWoeQX6ac//Wm++MUvAvDxj3+cD3zgAycCOBy3MR77HDKpYvifdWMmuojsqlZ6xMye\ndJg3iavPc/7jQDAO6jbHWsQp58tx1cpkyaLIguUkjjhfh7k3f8B6kn6/6Poku1nzjvw4FLW5SAVk\nt2fskFMdAXempHYYTi8ZtDKkA1A2UUvQWg1jK+sR15PpITPnCwP85i+LWGe3IeOAwqCNjh3cJMBL\nPVx8lFCUSh6DOOWlV1/h5//nX2Rnr8V33f8+br/tdsr1Cte3N5lfWaY3P09T7vO2tQt85cXn8Q6a\n1FLFnWcqVBtTHHS7+Hjc7VZRvYQvdTdJp33oh8won2effxlJzKP33EPU7VMtV4i6BwyiLiLwiTWU\nHBfSzMrHL5fxpAt4RCpl8fx59lst3veh72J+eYUz0gXHBZk5quhh0CStTIiDLDqkUhpVUqRJikRQ\ndxp4YQgKyrNTCAE1VWJhqcHZU0tDu3yXclBCA7FK6PX6pCrl/OkFDvYPMi9N6RBHswz6A1qtFjJw\nWN8acHrpLHOLC1x8/XV8z+Wg1cQLfJ65+BqqUmZ1eorZublhbBWFcFwWKwskqWZze5Ner59F08SD\nEK68cpmNV59mdnaO8xduJ1QuO/tNBpsHoFKk0Owf3iyk5e2NHV6/vkEvjmnMLTBVTaiVUxbm5lhe\nOodTa7K0uMrmtS16rZC4H9OYmua+e+7l9OlVUq34i2ee5blnn0PqHp7noF03O+hEoJVgbm6F6cYi\naSLZudlkZmYeGaQMwj6OI9i5uZGdmQw66G4LqVNcT4IHcRRPXIfwn4ADf+yxx3Achx/90R/lh3/4\nh9ne3mZpaQmApaUltre3T6yjyAY7/448OBQBBmRBaYpKEddsA1SRLjdfd/5aETdf9Jt5/0kZNiZJ\nHydx0ycVs5uftBHl35P/fpIEMHb4KMTQSqB447GLrXbKA30WW2yyFGb3wZXj1kpaC3DG25apx7Ld\n35bE8pJWdr+pN8iCIDkSkSpSYrTK4j2vb27xC//m3zLoKR68+wFOnz1Ds9WkH/dJo4hyqcT6+gZn\nTp9hdW0NdzDA2duDdpubnT3Sfh+/UmG+XOGelbfxgarmuc9+iuYgRCcurhvgKYESkn4YUavXM9v0\nJKFWr6KlIBGCfr+PIwSB6xEniqDiIh2PMAy5/a67eeLue+hGEd0kzjwgZXaQlpnyHUmdjuPgSKfY\nW3bIKplAUcaCKo6zODvGQU7rLOFA4PjUqpXRGJ85tUocx2NzMRgMCMOIfjez9/YDn2rZp9vrcdhq\nUatXSVXK9tYW3W6XmdkZlpYWWFpaHsbKjqgEFcLBgMO9HRq1Ct60jyNcBoMBjqyQpJKd7RadVsLV\nqztZSFpK9Lttttf3Cunq4msXEeUqjnTpd7oszM0zNT3N3ffcw0y9jjfXQaQO19OUV159lUZlmuZB\nk/m5OTzfo1QuUanUWFxaoR8f0G63KdfrNKYbxHEWTTT1PdpxxPr1bdbW7uDKletc37zGOx5+B2Gv\nz/zKGkIoairG8wTohO3tDabqVVrt1sQ1AX9NAP/yl7/MysoKOzs7PP7449x9991jvxepGI7KPwPg\nV37tJR564D4eevC+Qg89aXHdeZC0D/syjqtYX5QHCyHEcX05x/W79nX7nfnPPKAV9d2Y1eUBrOgQ\n8C9TJnH8ttXLrTaBSfFW7D7kx9oGwLwq6KTv+Q0g34ei8ZtER0Ubf16NUzQuk6QoNchc0h2hkW4K\nJCghKJdqPPkzP4eTejz+3m8F5dDtt+n3Oty8vMXC3DyXn3+BU+fW2H7jDX7rpVeYCXxuX1rg9jvP\n0/dOc9hpkSYpUbVBtdbgYw98lDe2bvAnT32NnqPoyggRO3he5nXoui4iFQRBCU2SOb4MY4I4rpsF\nL0MwSFLKZYlfrjA9M4NwHErlUnYAmmiMHbEzPOA92tzUUJWUjo0FHNlAmzE2ts3lcvk48YgsrrXJ\nRGQsnYwpn6H3IAgys7spQAjCKGLqwm0IJ0tBpsns+C+cP0MSJ2hB5hbvyMw6R2n64YC40+Psyjyu\n49JutZidmeahB7LEHkGpzPMvvMz2xh6Dbh+BoNfvUa/W6Q0DuOXL/ffdQ7sfEiO4ubPPzuY6N9ev\nEXaa7O7usHbPNI2pWfa2mwSuk4U5mJ7h619/ii9/+UucOXuWUqnC5cuXaazMMDW/RKvb5ea1dXq9\nPufOrTHQKa32ProMpVmPBTVDR3fxa2WE7/A7/+EP0Frz/m/7Np579hsIlbC9sc709DT9fq+w3ab8\ntQB8ZWUFgIWFBT760Y/y1FNPsbS0xNbWFsvLy9kJ9eLihKf/GQD/5d/57WNcnilCZDaat+LsTLmV\n22kRII3/Xmzmlnf3z9fxZkDXXjQGxIz9elEbJ4FgUZ+KruXVNieVSeOaB9a8d2YeEIvmz7T/pD7k\nN8j8NfM+ux2TQDh/r4l5A8fjxeTbFiTTCBmhZR8l+sQ6JtUeSRTx0//0f+SPPvXHlFWZihewtf8G\n25vrVFyPweEu9foMzmCAkA733Xc3l159la4jeObqJeKK5J677+Rt997Py998hUtbW6weVvj4u97P\n05//EwblgNj3mavUWd9YZ3OqxIMXztHe7hHHEamKQTsMkgTXCxCOn6ku0MRRiKOr/MN/8o/pRzFh\nEiNdF3SWopChczrDJCH22OZpzB6L1BorUwaDwegAzz6QN5y37/uUy+UxBsmmGaUUUW9AqlKiMMpc\n7+NoeMCXSQz1kkdpuoY3dOCJ04QwzKIOap21YWg8ikp0ZoGUdlFpxObGOm9cfoHBoMXiXGV4sOih\nVI+V5WIrlGopICiXOHNmja2bNzl/+210Om0cR3K41CBSN9FxxFS5TI8uTz/1Nd7x4NvZ3dmh2+sQ\nJzH33vcA9Xqdmlvjs7//x3z4u7+b6dVpKpVKJjE5ks32JlMln7/42he47757WTu3xMbGZS6+dpF3\nvfMdlMtVpPSYbszi+wHnL9xPtVpDSMlnfuffT1w7f2UA7/V6pGlKfSjqffazn+Wnf/qn+chHPsIn\nPvEJfuqnfopPfOITfN/3fd+J9dhBnkyxF74jnWNAki+jxXwLVYT5bl+z//L3TlIf2PXm67F/y5e8\na7nNodh12xvGsT4W9K2oFHmavpmNZtIzNkjnnaby7TtprPP3mz+j0so/Yz5Hqa90sVfspD4kBdEU\n7b7Y7XLxEEKhHJkFwpIuSpfwnAavvvIyZbdOtN+k09qgm+5SdWG64hP3BkTdJpvrV5HVGs+/+hLV\nWo2b+zeplXwGnTZbL13mjecu8oHv/Hb0oA/rTSrNDv/i7/wEP/LL/4rILdPzAsqVTBUzEzjUXcV0\nrU5/0MEpBYTdHtqRJMOUXuVKiVK1THVmhoN2m6BSBZVmIUnJvB9HGaw4stgxRWs9RpP2OOU9ebXW\n+L4/WqtGPw2MMSZxHI+kv/x8SylxvSxqYq1WJo4z/a6Jbqm0DfaZaWkcx0ReRJZ/NKVeLtHv9/F9\nn8FgQLVSpd3uECUHNBoeH/yOR0lTTbvdodPtsre3x6DfYzAo5mQrJY+dnT0+90efYfnUKTavXybR\nKbdfuI1Spcz5UxfodUIOkw7nz57h3MoZ4jii223T7jRZWlzMkhCnKTdee521xVVeffZF4iih0ZjB\n81z2D3aZmZtmulHjvvNrLE1VcVST1QurPHjnWTrtHlPTM6QKLr7YptGo027tsf7G5f/vHHm2t7f5\n6Ec/OprMH/zBH+SJJ57gkUce4fu///v5pV/6JdbWMjPCk4rtoTgJoIrUE3kwBEZBjPKlyMKk6OAu\n/y57oU8CvzyQGcLND7wNfHkOOQ/8hZLIhPGZNMG3sqrJ15//vWjzKno+v/kWzZdjmVUVjaMBknG9\ndHEERLtNk9RX+b4VbUZF0onjaLSr0UIRa0GsPaRT46tfeRGtywRumZ2DyyTtXZwgoeS5yCTCISWK\ne5xduZOecNl64zLvvusOpioVziwv89X/+1PM3XY7f/F7n+X5p/+cd77nER6YWeWZl1/kkXe9m7/x\n6Hv4vcsv0k9DatNTbF2/Sn8Qsrq6gEp7/Df/7U+xuHqK65ubXHnjGlcuvp5Zt4Q9nHKZBx95hMb8\nAoetJtJxERwFTsoyoSeZZYNzXNLNvAMnW1WZ8RIiO+swYS4ya4tx5qqI5kd20cP7BkM1S7mUmda5\nrksSRaP1YM43hNQjBtE8HwTBUJ+f0ut3kLJBFMeUKz6pqtA8bCMcQRTFVEslSBPiSoXbzp4hCHx+\n4RePdY0Lt69x14XbUepRWp0WbsknjEIOWk2m62WWZucQsx5fvvpVXr94lfm5RW677TYQy5TKWYjh\nVvOQWrVCreSyvLTC9PQM7U6f6cYsu7t7LLQXcD147rlnuPj6N4mSECeJ6XX7BEFAqVKl2epSrzdw\nPJ8bnUNa7S7vfe/7CIKAXzne7KO50X9Zpet/gpJNZvbaz//RbxX8dlRc6R1b0HmQHC1IeZzTNiLe\nSUBWBNr2vUWcpl3nrUAk/458XXmgKuIYJ20ib0a9kpcg8u0tAjNbLZHfYPPqilttbnmp5q/aD3Nf\nkYXNpJIHb9OWwj4nDqmMCSou/STGK83wpT97getvHLBQX+Tay89zcO2byKTFVJBlsQl8l07Yp7aw\nwOyZs5QXl3FLVYQWrM4tcOWbr3K/O8X1V1/l/ocf4IWrr3J95zr3rV3g7au38cXPfo50rsFnvvk8\nL7d3qU/NsLu+ydmlGe6//Qx337HGd3/Ph4lQKOkghIMvXXzHIUxCYhRSCpI0s3d3hok9TLYoyBQO\nQ1ubY+N1K3q178mrrk56vmheNJp06CWL1iMv2syMbGhyZ9GWYhhKGjF0zmJ4OKqOIvUN9fqe8EdO\nXY7jkSQJURhlDCIaxxG874nj2oA/+J1fH9p6ZyasvX6PFIXjuXT7XQJP4DkBApckyg7Mp6ZqnL/9\nPAjB3v4hL774Cp3ugMTt0+72aEzN4bo+WjtUyjXa3Q5h2Kc+VaVaKxMO+pSjzFFo//CAg8Mshky3\nH3L1+nXq09O0O50s8UOpwm//5m9PpPG33BPTcF6Tdn/0OJHYollRYKe8uZ7hAsy/8+J/keh4rAkT\nAMcGkiJnn/z9k7gbu632+/IAbD/zZhZOHuRs56VJTkj5Ooo2Kvv9t4odk6+36N/2taI22Ny7UgrP\n88aetVUrRSUfL8YETsuXRAxwHYdmq4+UJT79u7+PIxu4yuWN118nGXSZm59mf3uPXldSKnn0BiHl\nWpX55UUurV/lobNnSAVsb27jJrCzvcNfHF5iaX6OF6++xuraac7edztPPfM0j37H+/lbD/4j/s3P\n/WvONOZoB5KdbsjK6mlU0mNhaYUnvvO7cHwPoVK0yLKXh2mSBY0SGi3JVCbCHMoL4CjwF2SSqRDC\n8Exv6mykaKMt2sDtecrPSf45gSBLnzh8RgzTCQ7/l+F35hGJ1gg3C1KWbT4gVBbtT4gs5rgeBrfS\naBKTl0yApzSQBbkyAaKY0OX5+TnSJAtYpbRiOq0TJRFJmlCtZKkJk1jhOCXSJFs35XKJZnMfBQz6\nA+ZmG9TrKRFNTq8uI6VHkmjCMKZeLyN0SugI0lhRK9WoBFWqImBnZ4fpuVXO3HYPvbBPq90hFJkN\nuVutU6qUOWw2T5yntxzAYdy6I8+V6vRkvfcYyOnjqgqbU7d1dUWWF0ZMhPHkEJPuneTS/2YJ2gbE\n/II4aQHZ7zhJFVQEnKZfefNNu34hjptZ2mN3kjqkqOTHraicpOuzN8u8FPFmuEFjAWQsLvIbwGiD\ncjRJnLIws8wzX3+RKiXKQZWXXvsmncNDqr4mqHjMLiyydeMAR2Yu7ufWzrPf6eAi2d2+yR33PsjN\nzZuUqxXKU1Ps7W3Qa4YsLi/TSyJO1Rf5+z/242wdHnBzZ4MP/+0fQJZL/OA/+XGoz+CXS3Tahzz3\n/Iv8wx//UdrdZubdJ2UWY0NppAKkGGq1j4Np3qv5zYCuNZBkjHF2CGpdHj47qiWrtyDSn/2+o7nN\nAoeZNHoj/w4pjoKTyaM4N3rYDvQw7LaUw0PpLISCIrP/B3CIh4y8JjXJGwzz5xR7TcPQ9NiRSFfg\napBOmarIIig6TpaRKU1B6yxSpbGnj+KIVCUolTA9VSGKEkgzz8o4TinXqvTlAEcrZuZnabZa+H4Z\nkUCn00c1qvilGofNQw7am2gBg3jA7PwijuuQao3n+5w6fZbfnDxTbz2A2/bRNvgWBXSyuSyjizNF\na43v+oXg6I7EyuMEnAfFIiuUIs4jf/1WAGUXGwjz7utFXK7d3vy/bwV8dpkUejbfZnMIZffH3gj/\nMn0tuvckDrzoWn7jzVuTFB2amWIkj3x/i1QEwnGpeXU+90ef55vPXYTYoVrZp5R2ccsQR33SuIQU\nNdw5h8N+j3svXKDV7pPGCXVZQnVC6qUyC7NzDNKE0vQUu8TE/R7tK222rl1n78ZNFpdOcee99/K5\n7T9jea5B1S/zd/7Wf8H/+QefpdvrE5TKaA1f+vKXue/Be7P8lWi0SLOMLqlCK4HJnZr1/yj+zPjc\na4bRYgpVaPmitB4l+oUjWkxycfUxDlIFZ095eh6tW2E2BT3aEXSqhty3HgEyMAzBmqV2G1G5ksPN\nRSPRjGJ5K2MenAH/qHohsgPQCedjqTJcPGilsjyfSNIoAQ3KVXheQJpqXNfDcbNQv4HnIaWH0OA6\nJeI4hUGE53mE4SBLyBxkSb2F41Jyp0lTzaAfZ7lpoxZTFUnJqzMIQxzPQ7qztDtdavUa0vVot9tZ\naroTylsO4LYdal63CkcceNHBST72R1Gs7bwYnufK8wBzkirDXLsV9zkJSN7svSdZ5dgqGygG8FuJ\nvyfdP0m1ZN5ZNCa3AvKTwMKU/CGm/WxeZXPSQXFR38zGlTc3zM+9FpI3Ll/ny1/8KucWzjLTmObS\n6xfp9lrMzc/guoJ+P6LkV/BnK8xXTjFIIkScUHEDokSRdPpErS43rt9ABj69cECruYfqdqn4FaKB\npn9jn603Nvn7//y/572PPcaN7S1ee+mbPHj/A3z2K89wEHYRg5Rz52/nsNnC8wIGcT9LpE3GrUqZ\nOSBh6HEYMhcBqU6tfmZqFikkjjjutFMYzhiyoF4F9+bVaUafPamYdem6bgaIhomw15RjWcrY9Qpl\nImhn5sQAKksHro80QqRCIygTpyqLRaL1WIQhJbMwDUXFcb0sv6dSWfRAJK4QCC9AIuirECmcUSyT\nNE1QOiZJFAiFThRSDEBLXKdCkqbgeaQOOH5AqrMQxfXpadIEGtIlSTUybh+Nm5AMwpj+IGShNpvl\ngS15NPwGyYQ45qa85QAO44GdhMjm1iTllcOJT7QijY8yV4xEvCEBOVKQimA0KEIc3aOVHh2CyFFM\nDYb35SfWVs2YBW6qNe7edh2C8Spsr84hp4F5Xlv3ZPWm6Xg6JXOflEftyAPOpHOAPPBNAqpRS0eg\nftRH05bsvdn37DNbMUWJTYzn3rEixz1cT9pEtM5vGHpEA0difNaW/OZ7cnHJsn+rEeeWicHZgVQG\nShKEpCTqfPkPf5OVqSVK5QpUJEq1qNJFhhJZmeJwENPA5+xdd7EwXeXi01+j7kl02CPpDejs7XL5\n6aepDCKiwxazgUc7FTTKdSqOR31pmrgzIGru8b/9zL/kh/67/5pqENDb3mR70GNxZY6ZZIb1G69z\n/0P38/aH30aqUnycLDbGsA/ZuA7na8i5agNcwmZgyNLOCU0qisMjTJL0Rte1cY46Qs0sCNNwTkQx\nyBRx4GMzPGz7iFljnNZH3+22YnTiJpiZRg4/tVGp6KHxwmiRc8yEctSGNMvI42iGkkRKqobvEQJv\nmG3H8ZzR+4QIhutFZQgqBAJJFMej1kVRAkN9vpRZyF+ts41RCIFHFtLAGaqEXN9hujyF1lC18ONW\n5xVvOYCPqw3M1aMFPy62ZT8pPdz5pEQMn1NaooZ2pba4LUSmY0NngJCadEiui5BiJHLaIt8RQRs1\nhQ3IWfuMRGDeN2r5BLAsisJ39M7j45JaGawz7lRhMl7nnSOKgNn+s9+VH3ssW2G7GjMfRyA+3FA5\nLi0xsm8YLzasnxQLBrJ0VPmlajbI/DVxAmDkixphVpamymR+cTwHKR3QkiRJ8VyPT/76b7CzucuZ\n1TU6vR6vX/kmjcDFSQU67CFK5cyNvVZlbXGFS6+8wP76OspJ8VWURXtUThagyfNJkoTA9VHSod1s\nU6nXCXyXBx9+ENGMuXa4zxd+63d59wcfo3TYwQkE9cCjlQ5YXVniV3/tE3zvR/4vdndv4ns+Woth\nHC+N0kfqDZ3jL4+piU5g4oocsUaqxLG1MKxngirPvNeUSVZaY3M05BiEuTcn/Y2esd939Lbh/7P+\nu07mbXpMdWSwYJKUoK0YOnYRWQxvaamdIPNSzfTs9plQFudbcBTn3/fcUR+MKaW9XhJFFnFymBA5\njuNjSVDeTPnPAsBNpwt3mxFwMBoMKeVw58pEMp0qSFUWAAYztRmnJRFIcVS3lvoobrQ1afn0aLbI\nnnd2sEV4GyDNtSKinjQhRdx0npPOnFGOm+ydpHvPb0ZFKpHsemGzJrbFEcf7Mqlvtj7zVtYqUjIE\nZ3sjLb636HWT+pcdlkky6SkL3YnKFm0Yhvh+mThMuXHtKjvru8ytnqIZ9vEUuN2YXtJhplEmiWOi\nwxbveuhbqSye5saLL7D12ks43U6WONlzKPkBSkF70GJxaY39m11m56aYWprLaDdK2NvY4vVuyNvP\n3s2p8hSJ8Nh5+RVm3YDf+X9+i8OZBtfah9xxx1keefjtDMI+1WqZJBmqgYSDFlmcFynkyOV9Ekjm\nr+XvmxQGoWi+dG4+8+Nvr5+ihNaT1JzmN/tzUilSDZp3m/oNTti/T6rX9tQtkhjM9Xwu2CLsKFLt\n2u0d9wPJPFfN7/4w21C+LbcqbzmAG532EVCJMccPKeUIvM3AZpHUrAEd6tDCUThHDylklvZIZ4Hv\nXc8duQFn2aWPe/7BOBDk3wlHXmNpmlIqlcYmHSboFK3JLgJU87wJCmXr+49+V4ZhGSuTwNRwqbfa\n0bMxPk7cReoXIQRodYyIJxKaHndXz9eZb8dJCy1/75stjivJUHw4LyL7qhVM1es0D9ssLa7w+7/3\nh9SrDc7ecSeJ0nz5U5+hGsb4rsNhu0W1VMaPU6LWIcHcPO32TeK4Q5z0iLSiXqqSpjG+4xF123ha\nM2i3GfT7BOUSC2fOMVjfZGtnEz2I+MrGAX5tilNLj/DIh74d2Qn5rd/8TYhj0mjAxo1rfOJX/x07\nNzfw/MwqIZPHxVBJbUuEx5mOIporKnlP1Ty92Ooq+3DbPFcUDqIIZO3f8r+bd5h6jHPfSSGNb9U/\n4/BzxCUXj4G9bs05TJ5eRxJJrg/H1ZvFa802czV9TdOjzdNm/ux18GZCOr/lAG5KXiUxIhylR3as\nY39SDpmrIQFpcJ2jk3alkhFIe54PiKPIaoBWtj692KHGtMt85onAeLHZljNFgFpEQKaPhjjsCVNK\nZS7EE7zk7O9jqonsR0ZawtxiKSLiTHIt0kkf5+ABJEeEeysgnUT0Rc+laTK8J//L5M3lzZRsIzN/\ngM7iaLiuS68bMl2f4dmnn2P/5gEP3vdOBmj2Dw6Ym59D7u6jVJc4TOmmfdJ+ysXXXqXc79KYrzGI\nGuynLZRK6auEWrlKEmlEnBK2upSkR2+vTak6zesvvsIjZ8/zyHvehULj9lP8cpWdmRIvt3eJW23e\n/9GPcHF7i5f/6PcplRRR2MfzHaI4RA7jn2g9lCCMKlEdn9f83Nljdmw+C+i6aNMXQoyFeCiynnqz\nc1QEcJPWX77k23qSlGAzgpNo1bTfjlVkrhuLJ9t3JD+O43Q97vxm7s0zi0IIKzvWUbsnpUc8qbzl\nAJ4nNBsQARzpZu7A2uSWHMZ4kDI7uBCClExAdnWKcSF2hBzFbpAyC4SjxVCQFmKY6PbonTZh5wnX\nuPzaOSxtkcfm1PMgW7Sbm+9GbWT/Nuq35eBkj1XRd3tTy4txt+JqhWA0ZpNKkYRh92uSCV9iSUn5\n+orbUQTyRfeeHHXSLtk4WAA+LGEYUy3XSWPNM19/lvmZBa7ubDM1Pc2NN64h05S5hTnS0MWJHHb2\ndhHSpVzzqVVLeAJqlTJbYYzQKa4Cx0nQjs9Bu0U9jig15ijVp2icW8RNNDs7h4jtPe68904aXpVw\nEKNn61y46276hy12v/IKu1tbxNGAf/QTP0W3fYjjO0jHyVzOU4Z0OwSCoTF1nk7y6q/8+ctJ3Osk\nycvQss0dm3/bUrRNJ7faWGy6z4Pnmy3599nrN7/uikp+I8ubrObHyqb7/P3GeSqPJ6NYL0PNgblu\nx/cx6+hWsU/y5S0H8DwgmIGD4SHmMJmo1oZjHp4Vp2lmFytBuh5RkoCQKJXi+z5xGONqY80yDH40\nBHDD7QqOi3d2sYk3771nBt9ub1HJH3QWAa+9+2utR3q5Iu4hv1DsqHC2iGsfBJt8fPnxNs85zuRw\nsKYcLTBTv1lsRiddtOiyTDrGmiSzDDq+YY2LkOObX5HFSebN9+YcifIHnlprEA6OhDhOePprz7C3\nu8f99zzAQeDSublP0mxS8Vza6YCZqWnoCJZOV+hJqC4ss7u3x7QnWZ6fZ6c8TffwgHYY4XgVXSci\n0gAAIABJREFU3MBj/swZTt97F6LeYOPwEHd+AWduj95ei51mk82vfI1TjQUeve8dlJ0a8iBiIZjm\n3lO38+9/73d54vEP8sjDj9DqZDGsfT9LDuAKidBOltBC6MyckGIwztObEMdDShiJx1zKnjfgevzQ\n3aZNc90+eIPj6/n4fBSrO4qeuRVN5kue+72Vd7T9TN6LW+tMpWlC6Zpi1oEN3GYDs7H3aPwzZsGM\nrTPMrWnaZ9dbNBa36vdbDuBwnOs21+AoLrLSKUIMHRUEKJV19rXLV7hw59145QqD7iHloITjeggc\n0iiiUioThWFmfiUEjhGH0jQ7zIQx3Xv+/fnrdjHciPm0OYj8wBtCmETAtv4tz32bybZtvw0B5Q9h\n7I3AHlezGdqR/I5A8zi3lZ+P0eJX41yU+a2oX8aWOD8u9vgcbQzjMb2LysmgXjy2jjBOHBlYCZHZ\nUmshcZB84+ln0LGiuX9I/cIpdvZ2mK+USXRCXyl2200CLUhcl9sfehunzq/x/J99nf7+DoedHtWZ\necJY46Lwq3Uac7OErkN/MKA649KPErqDiMR16GmFk2pSFDeae8xt3uDhM2eYDWp0wh5TS4t8x+OP\n8dC3v5PEpOlKY9I0JShVMklJM8yYniKHYVWLgCvPfWZjMy7i58f2aAz1CIxsWsrPo8082DRRJMUW\nzaENnnkaNHSVL7eiDfv7mwHBItovqte0Mw+0dhk/syre1G5V7LU0KeSFXd5yAJ800aYkSQbcJomo\nlALpOFnOOgR//vWv87/8H7/M+77t/Tz+He9HxymplpAoSn6JMIqzQZFDQhPGFM0di6xmBr3IOSQP\naDbQ2u0uAqZJIpxNbPnDk6IT7yjKAvM40sFY0BhQFkMHjgyYjgdrKlpsdhvyHpr5xTACXsB1jjiw\nk6QXAOmMOxvZdRW1r2iM7boNx5i/Jy+52UWpoe3uSDARoLPIdq+9dJFep8vq0hkO9w64tHUZDrvM\n+WVc38F1KxyGh0Sppj67QH1umXYvZvXsGvPvfIjNjQ1W7rmTF5/+BjJKSHptPMdFpIrB7h4Li6cI\nBgmlRBP6Pm3PxZcC5WsiV/Da/jrTV19HXp5j9u41xB1LnG3dzczMHFJq4gRK5Qqe79HpDrLmZx0H\nNEIeJWzIz589j0ec9XE9sz2WR79l6yw/vvZzRevCfr9NP0XtsqXM/FwXrZWiduTfWdQ3e7MoKkXn\nNHadeebM9LfIq7mIGTL1Fn0Wlbxj43/2AG44Uxtk4GiyM/EEEJnhu9bZgkyUwitX8IISDzz4EG4Q\n8Muf+HVOnVrlOz/4ODNTddI4tkBwOCBk3l0OR55/juOMuNMiLtx214ajgTUxNYomyAYUm9O1Nwqb\nyOwJz59GC5FxXpk55LhJo2Z8oZhrBiRNO0wgK7s9NlGadt9qceZNpiYRMxgd7bieskhSyf4tRnlN\ndfYQCGNJPhwDYVQgt+ZkTNFKgXkPmQQhUKg05fOf/wJzM3Ps7+4x25ij+/o1fA2Hjku5Uka7HtVy\nla4SLJ6/jXCQpbqqlctUdUriSpbPnePS629wcOMGnlJ0W02CwEf1u9Rdh5WZKcqejyiXaPkSej06\n/R5NV9NLXf788wfgCO6se8jpCqkLyyurHDR3SIUiTRJSFeP7QXaII/TQsVyilEbp4+BVtI6K1Ff2\nRpqf+zyw2nUZkLElwkmgVMTpGyYlzyHnjQqKznEmcdTH+1vk13G8FG0wRZz7+GbIGNNlfi8C25OY\nwqJiZ+4qsvA5dv+Jv/7/UOI4HjstzpdMd8dw0WY23GES02g0aPX71Gp1+vEhs/MLzM3Pc+XyZf7V\nz/883/3Eh3jXw4/gShedJqAzLqxIJ5oHmOy94th1eyKklKOA9Dbh5cUoe5MoAnj7PlsiMCW/OMxG\nYxaAneZqRLxS4DjjgffNhlPEFeSlAQPK+U0ra9DxBTdpUWmVHltQ9uZh9811PFTGXto1jAHNqA5Z\nLGoX0Y8ms2IyXnpKa1zh8vwLL7BxY5071u7Aqbl0mi3mUsGABBxBe2cXjYTZObzVU3hTU+hIkPRS\nZtdW2N+4wgvfeJZpp8zppRX62zdxVEK7fUhtagmv4hLqAZ2kT3ujTRh22d3ZoLzfRAWCQRnq1Kj1\nUqIXLqJuO4s8t8S7v+WdPPvss5w+s5JlOy8HtHsdPC8YnsVmXqWSFKSLkMelwGJQArMRFklE489k\nf3bJ15/fxA2N2CA/mgOL1gxNua57TOIydGr+iuZ4EqDZ6zX/3pN04UWbg93Hk57J033+vMse56KN\nsKi8mU3HLm85gKvhAeXx4FTZpysUsdKkaYJIUoQrcH2fqNnB8xxmzy5xdW+bBccj0pp33HkPD95x\nFy++9BIXL73Od33oQ8w0pnClRKQpnuui0xSZxFmscaWO4iA47nDwhrbDOTC39V+26sUuJpqhea6I\nm8jnx8xPWJHXpvm3ebfN/dt/tupGOg6OM+TChRxTzRhwNIva9DEfjTEP9DrVQ89WRpngh70d9s9u\n73EuP8/Zj4g9LdZra7I4H2KoBdBKgR5vl93e/LhJkdGYmQPXCej3FZ/706+yvHyOTrNDo1xmf3+T\naqnMoNdE6BjHTUiUoNna4fT503Rbhwxafc6eXkJEffZeu0TQG/DMF7/Ahz/83ezN1Em7gtiFwSCh\nOdhBB6/SimLC1oDA83BSiXZ8yq5HHPXRekCIYG93m97uAXKmQqXSYPvGBsvnTyETCVGCVw5IBPgp\nuMoZxvbILKuMx6698R0HVzPfR+Nrz0fRuMHx+cjHHrLvz9PqrRgZmwnJg5VhJOw6beuNomJLr38Z\nALRVFvm68tJx0Xoco7UCXbfNgNgbWBGXb+6fNOZF5S0HcKNrS5Jxbtb8pVFM5GbieOC4ILPF7kYg\nHIdT58/yx//xCwRhTLlS5rDVInUclpaXqc80+He/9qu8593v5jsff4xes4WHxHUcpE5xJSTaxGkT\nw3gS2bu0SjMvT2tHdIbmXIZ7MHbaUHwIUjSRBkjyYigwxnmYDcJsbGmqSOJkrC7IJBgzVqZd9sK2\nJY4kjUmSZAjSGscx3P7ReJt358XjjJDE6B1FQaW0NlYNQ1DWx21oJxFkkiRj7xfCtuk/4saUVkg9\nLimZNhSpDrzAIe5HzM3N0mx2iaOU7e0D0tQDGVCrBHT3bxK294m1pFwqgQrpqohOFDG9tEJZK9Yv\nXaTd6VH1YWdrg3jzJkuNKTrRgJdeeIZarcTW3i6dww5pfIDreZSCCo3ZabYGfRzfJ6jWSBKoNmpM\nu5BEPXqdkKQsKVerpH4Z3y3hxgopHXzpQRqiPYjJohC6ykEjSIcWWUZWy/c7T4PZ+B9X2xWrIyCv\nLzf0bx+snSTiH+fqj1Qnk+7PS5tF0l3edLdICrAZnVuVfP35zczuc/59+fptmrT7XJTWL8/EmWIz\nWaYfJ5W3HMDtUK95ESxJEmQqSQDXcYa6TEWqUlIlSFPJ3OwsSRjRj0Pq7gxLq9MElQqraUo3HPDt\nH3iMl196kZ/5i5/jb37P93DnhQv0uh1Kjks4zMQhHEmcxtkJv5tFJBOOAxYXYYNzNEwBZas6jJmh\nAdz8BPu+D4zr5vIAn5/MMXESMdJ/28WObX00jqNaR4BqiKFcLltckFGTjHNFSmUZxk05AlVG3qIG\nLItS2+X7kK/H7pv5tz2Wtghqv8v+rQjAzXV7M221OlQqJa5dW6dSrhOUqjzzjS9w/9seZPvaOiKJ\nuLG5gYvAA1AQJymV6jSyojh15jz7nR69ZpeluTl2r1/n2qWLVNOERMW4lRLrN24wOzNDu90ClRL1\nB0gp6TQPmV+ax6mXkFMlKskM7SRCC1hamqd3cIBIXeJ2h+e+8ue8feW7UUnKzvV1rj3zIhfuv5O+\nEnixRngOys0i6zlaooemnyLX3/zY2J/Fh7zHN9Rsbia7oufPgopKXmo9STVg6jeMQR4A7Tm36cd+\n1qYd2zrsVpx4fozyddnrAo7oNE/X5v6i+g1Y25JF/s/cmz+DK6rTLm85gOcP0/L5E108vGGfRHZi\ngys8hNY4UuIjmZma5trWBvfOn2X/sIts9+lHEZVaFc8JePThd6G14pO//Tu8+53v5P3f9j6iOEIG\nPkJrQCFlFopSqWyDAAEyOzRM0wQpjuK1GNA0OQJt21hb52y7G0dRNHZAY3O8RcUmCqUUKlUjYM4T\nWf6w0ly3iTvrVzLGBUnp4DjjsdLNM7Z54pgqKB3X39ttzC92m3ux57RIrWKD+6jPOa7HXAvD8Bjh\nu657TK0EEJTLtFptZucWSWLNn/3Hr7K7s0d5dYq182tsXblMbWqWQeuANOwRqgSFpt3qMrWwiOdX\ncMM+jZokUJrN9ev4UYQjBZ6UlIOASKWZy3zgo1KNLJcIAp+wP8jUdUqRhANcRxJFYZaAWGhII/r7\nh+h+ynqcsPWnAe9597fynY89wb/4H/45P/dr/yuHNztUhYOTQM+DSGj8VGU+EGjQxzfP/FwW/Zan\nsxz1FVor2QdrNsd8UhYrm+ahOCZ9/n67XUXgmO+DAX7z3VbT5NfBsZ5a9eeZK9O3vDrRZhry12za\nG5MccxKrzfjkJYBbqU3s8pYDuN1x4FhnpHTAGe54kixLvRYIqYlUipNIvvWd7+bysy/R7LTJ4vK6\nTFUCKqUKiVYcHO4TJSHv+9b388yzT7O+vclHvud7Kfk+QiV4InOSUNEAbygRIFyE44KQwyD0OV2w\n1qNDQcN9p2k68ti0OVTXdUe6ZZsTKSJOe7INMRq1iFZHHE0Rtw5HHHJefIWjOC7mHgP+trhoiMxI\nGfk2SjHOKeeJ3n5vko6rUMyhr11GqrKc16apy+Ya7YPiPJeZ1z+axTWIB5QrVfq9kGiQ8udfe5qz\nZy+gUkUYRWzevEm5ViNwJeW4zP7BAb1IIUpVanPLbO93aHd7nF1dRUYDRDgg0ClCCXqdNp2ohww8\ntNKcXlqmqw5J0MMwiCmdw0NklJIkOjOrTBQ6ha2Nmzz+oQ9y9eIlSCWDSkCwOMeN5h7n7ngbd952\nB5VShVKlgowTPCmJHU0iskNqb2g66jAZ4CapB25VsvuOSzye541x1bYHZr5MtEw6gQM37ykC7zw9\n5+ux6cSmiSLO2i6mT/nxM3+2+sPua956x76WP7wsGh+bQSn6/VactylvOYAXGb/bn4lOSYcBeu1w\nkY7rIFGUkJxbPc1Xv/AlHl9eodlqUfaD7Pk4Jh4MqHo+lcBnoAY89NBDbG1v8eT/9K/5vo98hEff\n8SC95h6OSqiVA+IozEDCNZk8sgUjEMcmLQ9c+ZN1I3rZBzD5ibF1imZBmChl45wow3PV8QnPO0EU\ntREy4jLEaC9E+1DFbpOpL79IVXokTmpd7DU66r8eX2y2+JkXQc2mZydfsDkW+2ygaGFMEk8d3yGN\nFb5T4uIbV2lMzzI/O4dK4drly/R6XYQUTFVruAlM+y70I8qNWZzqFHsbm0zX62idcuXSRaSKCQKX\nMEpQacrhQYtYaHzfZ2Fmlkq1SjuMCQchbjmg22pTmllgu91idnWZqeUV3M6AiuOwfGqNc2dvx5U+\nL924zl1vf5jrmxtEvuRtjz6MCCr0ByGlep2k3UEoUD4MkJSUQGhIC3Apz8X+ZUE8G7vj9+ZB2aaT\nfDE0dlTfcQbtpPefpD4oktaKLMjeTF/zoGu3wWa0xqTQIZ3mz4nyTMWk9xvatjl4m/Gw+3Cr8pYD\neJra6pNx3bEBJxc5zKU37KjWRFrjBj4i0azMLaBLLkJHkEYkkUYCvuczv7xEs9NG+hIvmKU96LI4\nP8+Fex7iM7//exwe7PHB970HX2j6vU7m5aY0aZzgDA82s2DvghQ1Bg5BEAx7YQFqMgwQP+S8bW7g\nOCgXn2oPBoORPh3Gs9DkuVK3QC9uOKg0TYciNmSu15nnngkN4sihtKDTQhNDw5GPE6jKsphYYnQ+\nFsaIkNVxz0kDtJ7njY3JKEywFWNjUsTIIq48TVNiy+5/1Bal0YnAk3Dl4hVOLa4w6HSZnZ2l3dxH\n6pR+b4AvNJ6TEgpJeXaWC/c/SDdKmEkUnlY0W4dEUR8dRXi+i+f59JNouDFGlDyPWKWQgh8EuK5L\nq9Nmf0/x6Le8i9b2Biv33oMuVWhfWkeFCc89/zLnTp1ifnqW+y7cxUJtjoVHTnHl6hXues+jXP7m\nq/iBx+bBPtOOi4xBC4jclFKSZdjR7vGDNFvCuhWYFKk/smQi4y7z+ZJXFeTvta2x7HnK01jRZmxo\npqjtt+JYzTsmHRJO6kdRG+zf8zRXxHga3fukttkltnIX2J9v5uDVLm85gJuBKNIHC5ElQXUQmTmY\nAKEFEkEiMi9NXzhEKsWrVdi5uUW9VicOQ4JShV6vS7PZJCiXqHk1modNOr0OSEHslPiO93+A1159\nhV/4hV/kb//A97O8OI8joN/tEJRKDMJBBjauN9RBj++wRYQrhMjM7BjnPuM4HuMi7edsjsNWvUw6\n5Tb3GPAs4pjtZ0z9qUqP1asZD3Npg2zRnNgL0fxmFmsRt2IDqt1uMx550La5dfM97w1n/3uM48+N\ng+u6xCqiXC4jlcvl1y5xfu0O+r0e7f1dpI5YmK2jBz79doeODomQnDlznsrMLO29fe68927KjuC5\nr3wJpVP8ckAYxZR9iYo1binAiTS1xhRhmtBpdal5AaUgYNp18IIAKSSrq6cJgdQPOOwNUEpz5cY6\nzVaLRx58iJIria9c4fTb7yPxXDbbB9x79jb29m8SBuUsK0+iUHFCLCBNVJYKLJdu3Yxvni7zAGeP\nbb4IMQxZwXGAsUHVppU8AE4CzjzjUqQaKZIc8v3Il0nxx2/FydrvM6o8+3q+H0UbThHoTmq7DdRF\nUsmk75PKWw7gZjHahyOmCCHAlaCG5oaOwFUCqcUwD54gUJLUdZg/vcrW1haLDywSRhG7hweEg4hq\nrUaKZmfvgEEYUq3VqNXKHPR77B40uevCHbjyTv7tL/7v/PiP/QNKnsvKyiLtwwOmpxtEg37mQm7t\n6CcNcqoUOj3uSWmDTBFQ22BpOMm8CGfGJ8/Vm3oniY5HbRaFMcdtPaANknlvuXx7TP8NN10EDna/\nTTtsDsbUYTYjWyy19fn2OJmD4jyt2IemYRhmi89RqFDzlc9/npnpGV5+7gXOnjnNa5dewXM0TqXE\nbHWK+uwM1zv7LM0vc+Hue9nt9Gj1+iwuzNHavkEcDVhYnKdz0GQQpaT9ATguvbBPpdFgbmmZw5u7\neEFAuVKnMTWN53nsHR6w9colFu6+wO5Bm7LjEQQ+TpKSkNAj4uruBqvlsyzOVpmdbdDoNiGM2bm2\nTqVeZb/Twqv6aEfiCEkgJdKFNDkexS8POkUgYNPaJJA4Smd3VGz6sj+L6rBp1G5L0TqaxP0WSQ1/\nGUnC1HWSKsVug20VZRiKfKwh0wZ7Y7BpPn/vpGt58C6Sxt9MecsB3OiNTbGBQkpJokHoTOo32aOV\nVgjXxRMS0c/M4E7fdo4XP/mnnF1bI0pTqo0GM0EZ6bg4jkuaKGaGdsXSEZxaqLI0O8Pu7j5hkvCO\nh9/Jb3/qMzz89oeo1ut4QYkwHBBHA0peOcu9Z+lkizhAyA5ZsbjavPmR4SyLOOX8qXl+wvOcrilC\niDFzQqPSOUqVNuTW5bjJplIqS8LKOOEaULbrL5KOTDFx1vPXbTAxbcxz+qaP+QVoc3amzab/ZsMo\nAi9DNyPdvJsiYo8/+sM/5OH7v4X5xgzdwwOcNKbfbeGGPr3dHerlKmmpzOLyKqVylb03blCrV4gG\nfa5duYyKQ1zPp1ypEUWKQb+JdlOE67C0usIgiVGOJCgF7BwckMQJKysrKK1pb2xx/o7bCQOX6UqD\nZHGOwd4eipR+3OP1q6/ywivPcufGJb53YZ6646MErF+/zGylyvTcFJGSdIkQArwwZWBUDOrW3F8e\nxGxutRjcMpvxIuajqP68ekQIMTpvsVUhdh3mPtunwJ7HSe2cxNnnpVr7uVtx4HYfbHrKc9ZF7yhS\nv+SLbY2Sb1e+fbfafPPllgD+9/7e3+Mzn/kMi4uLvPDCCwDs7+/zsY99jKtXr7K2tsYnP/lJGo0G\nAD/zMz/DL//yL+M4Dj//8z/PE088cctGGCcOOz62GUApHbxUkwhFwvDwQEEikixBaArCldx25x18\n5tqv0O73KVfqSL+EU6nguj7r1zfptjtIIaiVKzSmppmu+QjPYemeu9lv9VhYPsXS6hm+8KXPU64E\nXDh3GpKQki+Jk0z5aKsClFIj226wD45ADAHEnhzzu0nUkD8AyjseGOI3Zot5dQIccam2+WVWNNlX\nhR3/IgzDscWWplme0FLZP8bl25HX7IVpv8fWD+ZBwrQ/nw/QttyxNytjIVMkqtvct+HAJ3GCtkml\nEIIo6bGxsUG9UsVzXNxymdcvXkXKBEcn+K5PFIb0WjERHlI6rK9vUC6VWJibZ3f9DVr7+xBHhEox\nMzNHuTLNoeuw2zpkutFAOFm88KlKjUptimsXr9DtdKlOTYGQBGVJKlKcwCcmYa+5S/fmJo7UKNXH\nSxIcBS/9xy9y6dI6P/pj/5il+TnqF+7kj3/n03z4+76HQzfhQIdUNDiDmI5McTwXN9WFwGJvkHnJ\n7M2I6UKMM1PjNF4MOnmGxP40v9n6efNbEASFZzxF9U4yvZ1k9VLUXrvkzfnyUmKRvbfpV/5cy56H\nSWM9aTM0n/lwvbcqtwTwv/t3/y4/8RM/wQ/90A+Nrj355JM8/vjj/ORP/iQ/+7M/y5NPPsmTTz7J\nyy+/zG/8xm/w8ssvs76+zmOPPcZrr712S8V8/rDOLkqlqKEFSMlxcV0NioxDAJSXJRRdCWo0zi1z\n0G7i+QHdfpeNzS0qXoVUOjQWF3CSlJKQHDYP2NtvIaQkThWOV6JSr6OV5r3v/Ta++rWnSZKEe++5\nA+0IdC8EpUglxEPvQsd10EpnnnAi0yVrR4BW2Mm/7YkxAGS4W3uSizhcA372gUeeOIoIQkqzCDJn\nHXOPH/jDBK4gpIPrCdA6ixaSW1S2tGFzYrYpoA2ygqOj3LzKxV74tpRh35fnUop0mqN3jfSzw2sa\npMgcjbJolYI0jVFK4/sVXn7uZeJBQm16iudeeQovjfAdTW2qDlGEq6CXJtSDIMsqv7HJqfNrxF2f\n1v4OgZT45ToiVfR7IdL1mFldRU7XmVqYpzXoEXgVKkGF7c0tvJKLJmH/cJfV06eI4oiX1i/S23Gp\nOGWivQOqqUSnCVJ6BDggFO00ZLff5hOf+BXuPnuWb//wh3jg0bfjDRIqgY+nPVIUSoLrcBTlJU3R\nKTiOS6LB8V2UyKKxO4AzDHqVMC4xTuICDSlml/RInaL10D/CzIPIiD+LJ0+u6FFd2Rxn9GmYHnvz\nzlsl2cVua15St4scSr7Dp6w+TFbHjLU2x2jBuFoy/86izWw8OfjRWjoye9Wjz0kSg33tPwkH/r73\nvY833nhj7NqnP/1pvvjFLwLw8Y9/nA984AM8+eSTfOpTn+IHfuAH8DyPtbU1Lly4wFNPPcW73vWu\nifXb3FReZINh5hsBKI1KEvSw485wQvtuikAw24r5tr/5OK984evcdf52Wv2Q2UqNmiyzH/bZ3t9m\n2vWYqk6xvDJLaeosSZSBW6fTYfvmDp1Oh1RoFlZOs7Hf5ou/9kl+9B/8CPX4EF9AKDOXf68U4KRA\nlECSBUmKUcQCfM/By1mGSClHLu9G52wmyD7YNGAWRRGe52WHcHE82u2hWJ1hm9llddmZfo4cLWxP\n0ey5oywhturBfkeey7DfmQf7PIEXWRLYm4T9abj+/AIyv9ttSlSKEJk/AGRnIjgyiz7pSqI4Jkkj\nSiUfdIXXXrzMXRfu46DZpN9ps+gKGNq5uylo4eJWfOZrVeL2IW7aI+7s8sr/S92bB1uWXeWdv73P\nfMd375sz82VlVlbWXDnUpEISEgYJCSGEBG0JySHTDI0NhoiGdje47SCwuxv14KAdHWZQ29AIy2aS\nADFUgYXQLFGSSlLNVco58+Wbhzuf+ez+47zz7r7nvSwRDncX3hE33rv3nnvOPmevvfa3vrX2Wk9f\n48alK8zWp6i4NZI0IYpj0iAirFnMnzrF0SPHef6556l5DtFwxKDbQ2UxwjLwwx7dnkPcDxhkCe70\nNG5zmlajRifukiiBmUBFWZhZSqIyRrbFudc8zMtf/jK9zGfugdOsfuMmRlalMdVgM9rFtC0qiSKL\nY4RjI5I8P6Ftu3i2yzAKyMhApYg0RaYpSoj9ncWHWX+FfBTvdYVYODSLFAyHWUnlpi/Wxfuyj+tv\n2vSFXpfHyWvpn40Xj/H/hytwXS6L56JHNhXf6RbNYRRVfi4dfI6R/LjPGePatmJi0ftmyPxW7T+J\nA19fX2d+fh6A+fl51tfXAVhZWZlQ1seOHePmzZuveK6yWV6mA4odjMWDK6ILysKQJAmPnD3Lk3/2\nl2x2NkhicEQFVa0w15plvurimQbdtXWCfp+Vjc180DPF9PQMty2dQBoSYUgGwRA/Ctja2uLDv/lh\n3vv2NzNV9ZAILJVCkCAMienaoPKHaKs8X3aUJocOgm6eFZ+VY5qLey7CE3Xv/mEKsHheBQVRVqQ6\n766UmgjtKpyUQoj94sy6g7W8sUa/H33sis915V4o78NaOa5cp4PKPoDD4rqzLAMpc3i4N0GFyDlX\ny7aJooA0SahWqqRpxp9+7AkWbr8DM5WEOx0MpQhUgisUZpKiDAMcg6XFRaaPHOMbly/Sak1DlrG9\nugphSKi62LUatrQI05R+EKJMSb3doheMMDyP2flZLj77LEmSYCqBSBXJKGR3bYt2ajNnmrhpXtcy\nm6mzOdjGySRumiFMm1picUdthmu9lOe/8mUeeP1j1BotsF06JriRTxCnMF1htL3NsdoUYegTK0Vo\ngOFahCol8/tYSuzXLk0NSWDkYbAmB1GjHh11mGWky9thwKGYk+Xvy8CksCTzoZuUad2ihIe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Qmmqk1kxWQ32OTm9QucvL2Fa1foDnyYrrFRE6iNFWTosrsZYnsW//CHfozHH/84RxcWeN1D59gY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UUnmRd4ZNdVWoOL4sQzctLicTojjj2edVuHSel3iujaDF0ARNpWh7bSuVa6RuECcpjmXw+//8\nt/m7n/0pQCpVrtRAmOQVxGkKBrieRVXXuIZLXTVKvNadHG3b3sTLNZ3ISgKaoeO67qbrLItq42/i\nBwGarjjjaZpi6xqHh0ecn19i2hYvf+bvvOsz0R8Mmc3n9HyTqioZjUa4nsfl5SWz2QwNQRh4PHjw\nAHi3kAdadndHXFxcsLu7u6HxXVycKb/56QrX9bBtkzwvO3ZPq2iCTdvJ/VMAAlfpPdqqQZiP49i9\nsM/Nm09w+9ZbeI79GA1xmyK9bTHxf2a97wV8DYWs19r0BTppqRQITaNFUne82TVGZJomslHJHWpi\nLNHLBs2xmBYJPdkgk4rY11kEOv/jb/w237//FD/6ib9F6eiI7viySlNSI8cIbBzHwrZDkiRhMplQ\nRQWm6TMa7hDu7XYFdKrEOGWBbdscHA5xXJv5fMZynjCdrjBNi6efeIE4zYiiGNs7Js0iTicRjlNj\nhyP64x7jvV3iJMYyLco8ZX9v9z1jlf76a/+Wtm3xfZ+syHnzzTd5/vkPMxqNGI/HtG3Lzs4Oq9WS\nXjikqipcx8Z2DJJkxSB0uHv/Pk899TSLxQKJpMUkWUUYusGqrLr5g/I4ny3mfOELX+DG9Sc4OTnB\n0S08R3UQtZBkWcQ6+k4XnXVnlZNlK6Ug1PxOoNLZY5omVV0hpUYDNLVKMXnhwx/Fc1raRrJarVhF\nGZcXl0ihcefu29x44jqj0YCd/WPyMqMslcDHMk0QJcOe2fGEFR/ZsW3OL05ZrVZMr1KiaIXvKVra\n/niXy/MLdke7jPwe/bBPWdYsl0sWcdxR3yqCIGS8t49pmkynM4WBNi3jvT1836dtC/qDkCJPiKIF\nWpWrgObulLh/cIRhOkqU1bQM+iFVVVAUBWWjZOVvvfEmX/va1/jsZ/8ddF1DEzl7wx7IFMMw0f1H\niS1pkmP3QtIsZb5YYhiqoAg0ylJhuE2XflSWBV/5ypf5+Z//Oa5bFrreoMkaJ9C7cIWCpCiwbFeJ\nuTQd23PV5uvY+H2fi8UVbr9HmmZczJfKAhidwXDEYD17TAAAIABJREFUyeiIoihYLiOqvKYWKxzH\nYTQabSi/s9mMqnzUiPV7PkGgDLfSaKV48rajbHptmyTPiKKI+WxBEAT4vo8feJxfnSN0weXl+Xf8\nTMi64Gh/hzLPsG2bs4cPlPOhoTMYDHjrrbeQsuHGU099x99vWg2JwXA05t79B4z3xmiGzvR8wSpO\nGPRHzBZTFvM516/foNfzuXv3Ab1ej34/ZL5cga4T+AFnFxf0+33VzJQ1mqKdYBg6VdUwHh/Sthp3\n797FdTpHwrbFMpTLY1WWWJatNr22RdO+d3n+rjTC+/fv8wu/8AsbC9Rf+qVf4pd/+ZeZzWb87M/+\nLHfv3uXmzZv83u/9HoPBAIBf+ZVf4Td/8zfRdZ1f//Vf5yd/8iff/Ue3aIRf+bM/3HhkbCfobAxh\nOi637NRLaI/8vQ1dp60bhARd0yjzHGEZiAZMXafWNDIdJqsFv/97/zMnh9f4/pc+RjlbUeoaumEw\nGAywOoMeJRypSTt1oGxbXM8jSwuyLN+Q6y3LIgg85bHRqBDZNeRj2z5to6bSRVGzjBI0TceyLaq6\nBK0hjldUdUno2Qx6AQJJPwwIA588S9E0jR/5yb//ruv2p3/0L7oAYCUCKcuS+XzOzs4umqbk5lVV\no2sadVV2goeGOE24e/cuWZZh2haf/OSnMEwTy7RYLpdd2kilBjII2lZy//4DXNdlZ2dMVVYURUma\npbiuQRAE5HmuTjYdXLVtHgZKeCFbA7oTUVGVagbQtpRlRVFUNC2o5BUTDVWMbNvGsj0kgrws8TyP\n6WxC06nZ0ARC2JSVUiyOd3eoirLzwlBiFEM3aJsG13WwXZeiUAKhNInxfQ/PcUnTFNmobMPVImJn\nZxccfcufQjEO4ljZ/J6cKH56kqj309A7daUucRwLXRdourp2ddUQpylCM1mtIiytRSAJwwAhJL1e\nyOXVBRdn53zyk5+i7Dwy2lZi6KBYaYJWPAoFaGp17+mGUuiuVpGipVrORpymGzrL5YKzs4cURcGL\nL34YkBsRkKZpiouMQDadXYFQKkI6mm1Vt1im4F/+/j/nZ/79/4CqqhkMRuRZgabpZFmOablUVU3b\nSsIwpCiyTeTZ+lS8dgtdd5NrrNd2PEBlUbat4rRLKWmRaLqO5yosen0iX5taSQE//ROfftdn4g//\nzRdBCELX3lBwk0RxyXu9HlJKJpMJw+GQj3/kuXf9/uXljCRJlQmXEGR53tkiWB2hwu5UtGYn5xeM\nRspGVzN0dMOk6AgIopboumLNKW64wPWcjd9509RI2XLv3j3i1RlStmgCmrrCte3N7K5tW6q6RtOM\n7yml/64l3jRNfu3Xfo2PfexjxHHMJz7xCV5++WV+67d+i5dffpnPfe5z/Oqv/iqf//zn+fznP89r\nr73G7/7u7/Laa6/x8OFDfuInfoI33njjuzpqbYcwbBfu9QsumxoDRW5HVzfgmi8ppKStW2UkKMFy\nXZZ5Qt/2kWnJQpasAoPf+Wf/jJvOiB/6yCdwdwbUrkdVSeIk4e23b2NZJkdHxwwGPTRNYNtZF49l\ns5hHDIdKAZamGWmakud5N/DoY1k2rutvZMPR6gpNN7Bt5YC3vz8milOm0ymtbLAcA9f1MGuDrMxZ\n3nvIteNjsqLizt3X2R/v8h3CswF44403NtCDEIKLiwsWiwWf/exnCcOwEzyY2JZJ0yiq0je+8TrD\nwQ6f/P5PIrtNs25qyqIhSdRwJ0kiXNdTxd8wuHP3Dh966unuJjTRNJhMLzsDqhbP8zBNUxXBbh6R\npilhGOJ5HlEUqa4qzZl3dEvD0Oj1+miaZDAISBJ1jcNen7pp0IWjVIR5QZaXZHmJaZksFnOOrx1T\n1SWLxaK7uUuml1fsjEaYuoGwYO/aNaLVgrJUARGraMVsMUdoQpl1/R/svVmwZdd53/fba+15n/nc\nsSc0GgMxEQMnQSTFURRBu+ywChaZiFapSqYtyybzYFXESiWp6EnUm8LoMVWqkl2pVCKbiWRKskQN\npghzACkSIEECaDQaPd3uO59xz3vtlYe1z+6GuiEycbmoxNxVqK4GcO+595y9v/V9/+8/+C4bW5sN\n1FAipMCyLfwgYDgesbOzg6NcVG2K5NraGnYOWaaJpEtVxriORbQ+xBKCsjBQ1bwJHQ59D9ux8fwA\n1/XYiPpM53M6UZfp0S7CggsXLrC7ex2lDHT1Dz/xCTOdeT51bd5HdI2qSkCjxc3FvWWZhXwYhcxm\n00bw4ZJlKWmW8L3vfQ+ABx98EMdxOH36NJ5nimiv5yKERa2qFnfV0kA4cbzED0Ic10HYklBKet3I\nTEOTKbbrMZ1OsaVLtxvS7w/Js5KjyYQ4TkiShMGgQ6djciuLJsfy4OCAMAzp9XqNL75Z0CeJsfwt\nyoqgkf8rpZjNZpRFwf4iptPp0Ov1SJKs6UJFC2389SvNC06dPkW+MMKh7e1t5vM53W6XGzduAPDm\nNz/CtWs7d/z6xWzBqVPbfPe73ycMQ7a2N+l0unzvhe+xubWJdD0WRxPCIGBjYw20UUivb25SVhVH\nx8ecOr3NZDrHqmo63S7T+Zww8LFdn8OjI9bWxtRaU2ujTj1x6hQvfGcHz7EBTa/bIUvNoVM19dBY\ni1hvSH9cXf+PhDwf/ehH+dSnPsWnPvUpvvSlL7G5ucnu7i7ve9/7eOmll/jsZz+LEILPfOYzADz1\n1FP82q/9Gk8++eTrX/SWDvwv/vhftwKd1fgJtwQcq6YzvgVLFUKAqnFs2ziJNdmLGRpL1aA0fr/L\nYbbkC//ujygWKX/v/R+ijDMGa+vsL6f0vKjFyW+qBY9xHJd+v0dRFEhpmzzFPMZ1nQbXMziXZVnM\nZvMG41NtGrkljIFWUZiggLq2EI2RVFmWlFVOUWRUtcKxJYvZnDheEAY+w36XKPRxbMnf/eg/vO39\n/ye/+DRbW1vM53POnTvLxsYGly9fRgjBT/7kk8znc1NA5wsC12M2m7G+vt66Eq46mjAMjSLUajrG\nqiJNU4IgwPcDsqxo4Jew7WaSJGG5jLm2c4WHH34YsHBsh263S13XJEnSpu10Ol3TNaLp9btIKYmb\nZPGamtl0jtaawWBEkiSAoKo0gR+YLtT1qKoaadtYQjCZTCjKnDAK8TyPqqyJotC8npQkcYKua2zb\nbR/0TqcDQK4Uda2YTieoqkDrCmEJpBQ4tm2SkrzAKA+luSfnc/O5Gk9x6PW6TdOgbgrLhNPg5jY0\no7BSiulsxnS2YDKdobVkNl+wMerS7YQMRwOqqmQ6nVDXiq3NDcIwosgNbqzUzQ5ca1PAW5GbcNq9\nRVGVTKdTrl83BerEiROMRmaHUJZ5K8mOoojFYtHAlBXCMrxms2uyqWuNFDY1mvnSJBjVtaHEfeNr\nX+Vtb3s7QRAgHY88X+kvBHlWGjuIMDJuh7oyRbkoTOMiZZt6lGV5u2tZ0YNNHqxNUZSkWd4a0Akh\n8HzfTD1ZRuAGVNrEq6la8ZEPv/+2Z+ILX/xLVK04u7XZ1o4VCaLT6Ziwj9DcKx956gO3ff1Xnnm2\nWUwWrK+vc+3aNbrdnvF5cRwuXb/Gvfee4/hwAlrT6/WaQI0UaRvTvOl8RhiF+FISxyme7zdTR9ns\ncIydgePYaK2Qtk26nLC3e4M8TSiLDEvXzb3ZKDHbhtbizW99/3+8F8qlS5d473vfywsvvMCZM2eY\nTCaAudFGoxGTyYRPf/rTPPnkk3ziE58A4JOf/CQf+chHePrpp1//orcU8K9/+Q9fB9qvoJTVWJ5X\nN5WY7da2iZpymqBcgTmxSiwCYVO4gn2d83u/+3ncacY73/UunG7EemeAXWl2iyVlkmBbDoNBn7Ks\nGgK9bXDTo2NGoxGrKLSakjheMpvNmkIXEYYhYdih2+lRFLkJSFgska6FkIIo7DAarzGZzJnNFggp\nmyJeNDcrzOYLI/kucqTQlEVGmiwYDfv8k1/+b277DP7X3/kch4cHN7sordnY2KCqzMO1vr5uFjG2\nQ5ZkKKUYDodkWdaOs6v3tta69ZlOUoMfLpdLvva1r/PUU081SjHVOOmZEf3o6BgpBfv7+5w8eZIs\ny/A8n36v3zjOOWhtkecmFSYtTdfsuLLJJnUboy2nLS6rxaDx5TcjbA10uz18z8eSoj1giqIgTmIG\n/QFxHHPy5EmqSrWj+Gy2aPxNslYc5nf61HVFEPgURU6Rp1RVgdY1y+USS2vG4zWyrGA+OeDs2bP4\ngY+0BPPZzBwi3LQ9ns1mWEKQZCVpkuA45pB3hFnOTWYzvv/9l1nfMNJ323HJkin33XsPeZoQdUL2\n9/c4efIEVWG8Y8IwxPN80jgBfYTl/RaWfAHL+jGV9McXvP/9d04Lgh9yiblcLnn66af53Oc+R7fb\nfd1/e52Z1B2uH0SDuTWxeTU6rL5OSom4JS+vpdzom74mt25x+7gsdMVcaJ795re4sXOdf/Cen2EQ\nREgv4GgxIwpCIsfD3wgRQhjDnKqkUsbOtNvpEUUnkNIhyzIWizkIM+51u93G1rTg6OiIyfExO9d2\nkNJmPF7j1KnTFCpF1SVZVvDiSy9i2y79/gDH9lC1wve7LBdz8sKkka+tb1GVOYv5jMp1cD2b3b29\nO75XOzvXWFtbY29vj29+85sMBgNOnTrReE0EXL16la2tLc6/fJ5O0OG/+Pt/n8lkitb1LZ4Sxncm\nyxJsxyVJMuJkwflXXiKOUx555GHyPGveV9nSPC0Ler2I5XLJ2bNnODg4AuC1116j3xvQ7xv/cRCs\nzPcn8wWuaywH4mSJZcF0MmExn/Otb32bX/7lf2ZGbksQhH1836fb67FMYuq6YnfvhukIbZtup0O/\n3+fkyZPMphOicJ293V3yomCxiFlfXycIPLrdPmVV0e12mc3m7O0eotFYlm67U8uqmRwfNctVwXde\neBnbduiHNi+88H3KLOdtb3sbo9EQ19VYAtI0RQjBskliXxuNsDc3qGtFkWYNrXSXg4NDHn30MYqy\nJghCpONQFWtgQRhFHOwfEMcJx8fHuLZDv99nuVwyn83ohB1wfwthf/uHeSx/fP34+sEFvCxLnn76\naX7+53+ej370owAtdLK1tcWNGzfY2DCJHSdPnjTm5s117do1Tp48+Qbf+dcA+O3feZm3v/Vx3vH2\nJ9q8RM/z2hAHzzPqRaUUtIEMGq9ZvK060drS1IWiimz+w9e/ygtf+wbvePQJCldQqgovzhkOOpS+\ng7XIKIqMWtdgQbcXkmcFVVWwjOctpGNgkw5FmaO1YjZd4Dgunuuzub5BmmaMRxZxnFAWOQkgXfPz\nGGMcG6U0qiqaAFQPakVZFNhCopXm2pWr+L5DGHpEkctiXuN5d8b7VgIn27Y5ceIEg8GA++9/AMdx\nWCwWHB4e8vzzz9PvDXA9n50bu6yNx/ieR15kbZe8WMypqpIbN26QpAnjtTVOnz6N6xoPicVigS2d\nluK5OoRXWHme5wyHfSzLWJ2maQYaptMpQRBx/fp1kxTjd/EDH8eRbG6uM5vNOH3qLJ0o4sknfwqA\nfs80BMeTmbGc1ZowCrEsq52OiqKkKIqmY0/xPZfFYkG/30fVNcPhkMUiJs/zZnfhUzVh18PhGMsS\nZFlCLm2uX7+G57kIy6Xb7bK5sck997yJMAgo0yUAR4cH2FJy6dJVwjDk4YcfQmDjOJIoiMjznMnk\nCNtxCBoW1aqxCYKw8fm20dQs5nM810iu86pifXODQdHHsjS+66Lqim6ng9OEPWj7hR/0SP74+v/5\n9dxz5p8f5vobIRStNb/wC7/AeDzmN3/zN9t//6u/+quMx2M+85nP8Bu/8RtMp9N2iflzP/dzPPvs\ns+0S88KFC7d14bdCKF/+s/+r7aThpmfBKpVCNJJVoRvOuHVziel5XpMbaEbcOK+YZkv+5e/8S979\njneyfeokTiekmiwoD2bERYa3PmA4GrE27KJ1TZpmzKZTwiik1x20v/cK01t5qgwGA6Kog1I1R0dH\nFPnNCLetrRNNGsgutaVI8xRhScIoaixFffI8Z2fnehsR1+108PwI23Ub+lnJ8dEB+wc3CHyXf/pP\nb4dQfus3/3vCsNPuC5Ik4eLFi4RhhNY1o9GIK1eucNeZs2gFRVly9uxZbGkRhiGj0YgkWWLfkn40\nHg8pqwo/CNC6bt3uOp0Oly9fNu+zGwCY7NCG+5qmOfP5nAsXLnLhwgWksDk6OuLs2XO89a1v5dFH\nH8WyDVwgbYOB/+mf/Snv/Mkn8T0fpTQHBwcMBkM2NjaQnkm0MQtQl+PjYyxLGI9r10VKg6Hu3tgn\nzZNW9HDPPfc0Cz27dagzGLb5/lnhmRQcVVDkGViaNz/yMBpQVcVkMmHZ+KX3QzOVDUcDPNcl9AOE\n0Ewmx3TCkOPjYzzf4eTJkxSqYrFcspjNybMM27Y5Pj5mfX3D+LI7xkWzKBVZmlAUKXlu2EOqzLl2\n7Srv+al3GS58bhavvV6fUvydH+7J/fH1n831N0Eof2MBf+aZZ3jPe95jHsamCH/2s5/lHe94Bx/7\n2Me4cuXKbTTCX//1X+e3f/u3sW2bz33uc3z4wx++/UVvKeBf+uLngZvxQrdGq63EHFKalGvLsgxF\nbCW/d2xKddPwKu6H/O7//L9wojfkgSceo3YErpBI26YXRoi4YLlYcCk5xioqhv2BMcrnpuvhyoXs\nVlFRWRl6VJIkuK5ZZBrqoyl2FhaWsAy+6xpBy4oNYewmDXfbbONNRqUUkqIywQ1a1ASBz6UrF7Es\njUXNL/3Sr972vv3W//Q/oLVmsViY4BRtkaUpBwdHPPTww0wnE+IkMeKUJpFbCkG326MThdS14p5z\nd1PXivl8xl1nznB4dNQWw6oqW1VrlmX0B72WbZMkCVEY4XkBcWxoicPhmLo2GPKN67t4nsfhoVka\nDQYDai1ZWzc+4mVZNH4TfWptoWsjgInjlNlijuPaBi93HM6du/tm8kyeozWkccZ4PDavn6csl8vW\nfnhlhEbj+e75HkVeIIUgzmiEIUvCKCQIvMZi1dDqVmITIYShIzZ0tzxPERbUqsJ1JKPRkHi5MH7W\nZYnte1iALSVS2KRpyqsXLnDvffdhWcZj3pKN/bE297tj26RpjOfaLOZTpBCsDYemISkrdF2Ti7/7\nwz7XP77+M7n+Xxfw/1TXrQX8L//0/2w9S6ZTQ+IfjUZNYoeFVjfx21rVhsfabOfTPMNy7EYkYvOv\n/vgPKK8e8I9+9ufIUOahKEt2Z8eUNnS1zVZniLs+oCwU+3t77O/vU5YlGxsbbG5u4jQKqjzPKYrC\nhKQ2nPEVtDOfG+FCGIZtkpDWmul0ynyR0u128T0PKUwxXC6XhklRmAXmqptf4cwvv/Iik/mU8doQ\n15UcHu7z6U//j7e9b//df/uPiaIIy7IaGl7B0eERnU6vCaMt6EQ9lFKkadqKIY6Pj+j3emxtbrKz\nc41XL17gU7/8z3Ac459toqmcNhps5XG8EobE8bI1DPv+977P1as7fPCDH8RxPKqq4saNPcqiYmtr\nC88zHGvzGQum0yl5kaF0I9ayBG4zlRSVYrmMiaIOy+WcU6dOcenSJZIkRjdL10cffZTRcIRScOHC\nq7z66kVOnTvbThS+a3jxeZawvr5usiyns/Z9l57xeD5xYhvVqFyXy2V7v5Wlag5sl25vgO97bG6s\nG4YLmiRdcrC3i9aK7e1tRoM+lmWxSDMWiwWzyYQsy+g1XtqnTp9isVygtMHuJ9MJruOTZhlR4AOa\nfi9CNMpJ13aIwpAiN0k0cf3UbZ+7a/0JeZG2/Oj+YABWjesEqHIVgKvb+3GxnDX3yIJOp2M4/soi\nT0vKqqKqFTUmU7bb7RgXzKJk0OsZla2u+fzv/Rve9773EccJyzhhPB7T6XSYzxcMh0aWXgNxkuB7\nYdPQmHum1grHsQ3bqqoATZYZ1W2VV62jqKprlDasEdd16ff7jMdjul3DXNLSYzKdtpPZP/+lf37b\ne/Pv/+IvyPMcz5ctu2UVczebGcFTEPj0ej0effMTt339c889z3RqSAurZsDc99LoLSrTpHU7nZt5\nvLXF+vo6SZayjGPGa2MOj47wHRPeHTdK383NTW7cuEG/32/zDnzfTOPYhupbpkt0XfDqhe8T+BJL\nqwZpkEjhsDt979/uAv7Mn/9eu8BcsStujVeTllmKmYgvU7hVXZOXJv8waYr4X337Wzz7tW/ygY98\nmNNb2wSFJrAklu9SSSgFKAEqK/DnObntYzeZfUWRk2UptTa8WK3rhjJoDoea2vh3O4bKtfJtMXJ+\nU5ha4ZHlorVoqHfLlj5l1FglNMVxlTu4WC7QVo3nu0hH8urF83S6HT79qdsL+DN/+W948cWXGgHP\njCzNqWsj7Z7N5kRhB62h1ho/9NF1TVkUhIFPrWuK3ORaWsJQ49bHa4AmbJJs4KZHsed5bRGvGn/j\n5XLJYDDg7W//CaqyIk1zer0+vh+QZ0WTxuO3E9Sg30MISa0NtzorClStKYqSJCvMAjVOSLOM+cwU\nXSOvL6krI4EvS4Xv+QRBh3PnznHu3L1cuXGDNE3o9XvMZ3M81whpVBPvVtcVr732GoN+nxNnzhjM\nW1hkmaFKlmWJ7TjYtoPjuG3G5zI2MV+1VqDNLsJCE/gelq4py5z5fEYUhrhBB1va+J5HrWp0XTOf\nz+l0O1RK4XgOtmNTVhW2NPYMQkCZ51jUlGWKI23KPCPwAyOHx2JR3T6xOvxxG6gbx7GhI6oKCxut\nbqa45HlKnmf4gUeem99luVyyvb1FlhQIHJrBDYR53tI0NQn1rkuRpgjLROx94Y9+n4997GPkuaH5\nVUoBBr7M87xNc/LDCKVoyAfGKdQSZmlsiAbaTGCNF38e51iNMteWDo7rkmRpS9M0v0dBkqZov2uC\nMYKQqiz56Edun06+8If/DtdxqUpDofV9k3bked7r7mnbtnnvez5429f/5Z//RbPkN6EeSinDFHMN\nYyopSyzM5J/nOWvDkalDllmAZ3lB2InQaMq8aBs7IQSLxYKNjU2yzIiVpJREkeHLl7WmqnKSeIa0\nKg72d+iELnYjVLS0BVpyuPzAfzwL5T/1tZKOrzDlFW0sDENScnpBBxuoswrbdanQaMcBzyzdDnb3\nuPraNf7eBz/MqL+GXdY4nk9Z19RVRTyLcT0X1/fo+h1q4aGqmEqlLJMFgR8wHBsFWBgNWS6W0ERu\nBYFveNy2w2w+5XD/ANdxCMKQfreH48i2K9daEwQOtrSxbEnUGbFYxEymR60abTgY4HhGSbhYzvAD\nGyEdsizl2tVdPvCe9zcLsdsLeL835smfeDdKVTz//He4dOkSVVXxxBOPcvnyFbIsM4IU10ZWplg6\nngNSsb+3b25E20VoyY29Q5Ks4q67znDPgw9gHr4KtCYKI9IkYXdnl29961u8653v5L577mfn2g79\nXofI83B7PSaTCWk8Y2/3GkHYIQwjyrpgMTM83mW2bCPGwjBAY5kCWiim0wXr65vkRYmPhZA+jm2z\n7Qe4nst4tIa0TZrS3t4+Fy5cYLLI+ObzL7C2uU1nEGE7LmsbA9I05ejoiCxPODw8YDabkmUpIuqR\nvnqBxx57jCgKcRzDxU+SjKoyB7XvB3Q6HcLAZ31tgzRJUA03fjqdkMQJExZgYeT86yexHZu8XCJ9\nh4oKhTmg3DAgywuSOGW5jOn3BobRE3qUVUUn7KB1TehHOE6A79rYosCyTGiI1vWdn0irJl6a3EjH\nsdGVhyOMn51aPfBYdCIPW5gHPfIjaiBJFRcuXOP0mTM4vkdRFpR5gdAC23HpeT5CSFSl6AwChCXQ\nKLAk0/mMOF6ysb6B6/lYtcRxPAJXo3RJnqWkSUqpDCxYVYZHbtsrZz1B4AfYjm0OO9/HF2afIqUE\ny7xuL/SMTYatKSuFIwJcGVBbnoEUK7C5M5vNsyWgcL0ASzgUZY0f9c1E2cCnUkiSPL3j17u90KQf\nCYvID7h2bYetM3c1qUgBjowQ0kCkabpkMjmm3+8RJzGWEMjMNDFZlrOxsUaWZWilcRzJ2toIpUy4\n9NraGlmSQq1QZUGhUqqyoBO5XL60Q+SHOEKgmz2TsCyE+MG99Y+8A//ql75wRyrhijFR2TUqK+i4\nAUJpQFBYGivwyZXJSfzKXz6DZ9u85eFH285xtYi0G7EG0Bro5HkOomrYDobyRiMh933z/9rSaeXB\ny6URdEDdjlmtd6+w8H2vpT8WDV5uBAwKvyH1r0xqVpzsNE3p9zvM5pOGvRHw2GOPtbj7Y2+9XXTw\n3ee+1ApxVpTA6XTKxYsXWV9f5+DggCtXrqDqCkRtzIMswd7uPkVhDJRWRvRVpXAdh/HamMn0gDzL\nuOfc3a3owhY2URixtblJkZf0ur0mjNh4vsxnUyxgOBwyWhsbwylV44UhUpoDbvV7o2EZL1nlXZoC\nPmc4HCOlQ6fbQ2lzXxRFwXy+oKpWWHTOYDCi2+lQa02WZjhRhyuXr5AkCYeHx80OAk6c2GY47OP7\nXmPpmZNMD40BVnNfRVHULpJXU5Nhr2SoamXSbw66VZCIboQ1WZZQVWXTQWWkaYIjbdbX15nPF8yn\nMzbWN1gbjZHSpshyhBRkqqBSijwrsG2PPM2aTq/EFoYhI4RFXStOnPnF2z734/3fIQhcBt0ufuBR\n5Cm+76FxQNgmKq8wdgJGY2CTFSVxklKU5l67en2Hbq/L+voGnuM090BFWZZkmZHJy0ZPYQkIAkmt\nK4IgMFNpLcjTCimMBN5xJb7vYAkLt3k/bxox1aBv5mOWZdm+VllW1KrGtp0mtNnGasYLx3EbUZwx\ne6pqw94xy3XFBz50uy3HH/3hHwIgHaMNEVIipLGWNtOz1S79P/Dud9329c987Wtm77bKSE1TLEzw\n9nA0whGmk9/a2kQ6grIqEOKmoLAojUlYWSlCz4jKVjVoddm2bcy5XN9Aw4ATSI6PDhmPB1zfuUY/\nCrF0o9Wwbmb9Hi0/9Le7A1/xvVe/8K2uhHVEqrQ1AAAgAElEQVRdk8YpoeuZGy3J8MMQ4XtUtaJU\nFa+8fJ6rly/zkQ992EjFhYUjTWRZnWfEqRnRe72eSUJ3HdzcJS/TJhjVyMVd19AVp5Mpy2WM7xs8\nKwwN/ztN03acXC2/pJTEccx0YiTclgWuH+J6Hr1er03UrqrKBPiGPpPJEVHUYTDo8bWvf5WjowM+\n/vGPG0rcLV4id3yvAGFZ5pRvQi18z+Oec+dMcQpDTmxvs4yX5EXG7u4uWZZxYvtEaxSVZYZd0+kZ\n2ftsOqEqC2qluHjxNcqy5OGHHmY4GrNcLDl/4aJJGrHMSPjmRx4CBPfc9yZsKahrxcHBIVmWsbm1\njS0ks9ncLHCN2Yk58LBImjT6w/1rbG+dBCzKUnFj5xq2b1SU5tAYk+WZUQnWNa+99hr7exXdXo/h\nYECVZRwd7LG+vkH3zCmy3PjU+I5tqKZaY9XKBOZ6XptonyTG0mDlG7MaucMwbIq8pCgKppM5RRmj\nalPgPTcg6gRI6Rj6qio5PCiRVsSVy1d46aXXcKRgMOizv/893vPud4GqKesMV9oEjqCyNGtb68Rx\nSjcKyLIShCTPCrA9qqqkKoo7fu5ZllNrzWw658SJbWpVoWqB40qC0CXPU6RtwqyVpvHI11hCsrt3\nHUvYrI3XcTwTehwvkoYy6iGEZDgcGZVnXjQqTpssj5vnwGMwGGAhkZaHro1HTKUKlDKFWUN777oN\nLGlZNzUgtm3ftL9YNWqIVixWQ5thCpAkS6pK4XmBCR63aPyzb79kE5hc5gb2yNLUEA1cj7IocLyA\noqGX3ulyHQ9dazzXFN1ed2gUzUEXVWqyYoFlaWbzKUkac+rkCarahIXXdY3nGfVyWcSkSWGKevPM\nO47TNm1RGJJlKVKa32d39zppkuC6ktD3DUQrLFzXwZZGhfkGGRSvu37kBbwsbzIfVn4ot7JA/NBH\nr6iFtotwHSpMruXhjes899y3eeKxx5kfT+iejlphji1tU0gHA2wpSdKUV1+7SFEUBL5P1FmpKYMm\nu9LgVJ1Ol8Fg2GJY+/v7ALiuS6/Xa3nSKyvUfr/P+roRdAghmC2WlGXFjRs3qKqKU6dO4boujiO5\nfv16K/H+gz/4AmdOn+IX/8WvNLxrm0rT4sB3ukwBN2OWaA64JMuMO2GStu+Z67p4nse95+4jSRIu\nXbrULDgjisKIkFYn/fraiJoeliWZTCacOXOG3RsHnD9/sfVnD4LQKDeF4LnvvMATTzzBiy+d5+jw\ngEHfiJvW19dbG9+1tbFh6iiFqitqXRt+tmszOT5ia2sTdIXnGmOp7a1N0tJI8ZNkyt7+Es/1EVLS\n63V5/PGHqXVNmuS8+uqr9IdbPPTg/eY9sSTCEvT6fSwLkiQmjudGgFRXSKXQlSLyAzbXDBd9MBhQ\nVcaEbDmbtzCPdB083yPqBoRhFzOVQbxMODw4Ji+LFp6hdtna2uRN9z+CEBbxco7WFdvbW+zt7eH7\nDqPRkKLMKJKCIi84PjzC9QKqGmotsGyXo+MpcZKwjGPWNzbv+Lk/+NAjjc1ESbxYomqBUJLp0ZR1\n4bT+IxYatNmtzBcxV65cpdPr0+316feHzbSRUjWS+SzN0Zj4QYAsSRrKpimy8TJhe3uzsUhwsOoU\nKU0Qt+s6SGmokpY0B+JqmknTlCJPW7bYatJZmatBYy8sTXxY6HtUarWMtaiVSdFCr+IRK/P3O1zC\nMqU9Co0HTSYM4cD1jJOjsASOtI0G4w5XXZlJPU3MxG+Rk6eZmdKEBKui1pqjo308z2UyOSaOY1zP\nw3Fd5vM5+3uHBtsuEvr9flu8Xdfh6PDATPGLmXGclALXcRn0+zhSmuwCa5W1KUiTBNuWLcT0g64f\neQFfbXZXC8zVn206faUoioogisgpKFE4fsjFy5d4/tvP8dgjb2YQdthc22DZ3DRKKdLGFnQlhw+C\ngNN3nWmXHGVWkiYZWZpjSqO5gSaTGcYxrofneaytrVGWBZrmg05Noex2u00RMMu4lb1tFHYbK0hJ\nksYcHBxQ12Z87HY77B/s89xz3+bpp59mY22tcQE0FMUoit6weAMIjLXuapmEkLjSpmoYBGlqXPeE\ntrC0RZEVONLhkYceMj9rUZBnOXG85IUXvovW0Ol20FbN0dExJ0+eYjqZk+UFtuvi+AFaQ1lrLGHR\n6w9Ilkv+8E++yMmtTWOx2wlxXYeDgyMuX7lGWZb0en22tk+wsT6mauigErClYH08MjAOktlsQhR1\nOYqXaFvjSAtpSYYntlnGMUVesLdzjWo8bj3Hex0fVWUI2+V4MsFzPTzX5/piynA4BGoCz2XQ71CV\nBVmcUKmKK1evsDYe0+11mc1neK4LlsbzXIIwQNc1mSooq5I0XbBYLACJ6/rMZ0vmywVBEHDq1GnD\n1qndRgHrU5QZrm3juJJlPCVNltS10Sl0OiG1dEwc3zhiMY+RlmCZ5MyPjrhy9Rr33Hc/m9vbdP6a\nynl1xamR/hd5Qac3RJUmbX4cRpSqRqw8fUpjBpZlGa9efI2Tp04RhB2ElBgsAjwvJPDF64ytyqow\nTA7PaxabGbUuW/VpEAQIYeNKH6VoPPoVVVWjmwV/lmXtZBqGIZ0oMt7y1G3RNv4nRduRr6i4RWto\nJ0nTxLgkao20TGCwFDZYb5CoszJoKwrKPMd2HMomI1cKG2zbwJ9vIAhXqiJoog4tDKtmOBpQ5Dll\nkYNVtodQnqVkWYrn+eRZhqpq0Oa+XjVISZK05IHJcdJColHkk8QJUdjBsSWWbZuiLixcxzHLbUfi\nuA5oc6jqN5g6br1+5AV8xZW+VZK/+rOua2xLGkN5aaFrhWXbTBdz8jznu9/5Dp/4+H/FRn9EmRet\n2Y8QAt/36XQ6uK4RiMxmMy5evIjWmiiKOLl9ml5vgOu6zOfT1nJz5U+cpjGTyaT1K1857WWZkU0n\nSUJdG3Vkt2u8sPM8Z+9gD8f1iKIA2zH2nkmSYduSV199lfOvvMR//alPk8QxvudxeHjYuBqaBZvB\nHOM7vlerB2p13KxuDtd1jaWtZeG4LkJKsqzA8ZyWIy+tEtd2EIDvOXzkqacMPHHpNQ6PDxmPxhSl\ngZPiOGH7xCmu7lzD8zw8y2IxXXBjd88wQqKIyWyGkMaL+vBgv4Waer0em5ubuI7Diy++yGg0YnNz\no1WXFnlBrTS93oDt++9nsVhS15q4SDg8PDQYaq2xhWS8vYXvG4ZMHMfs7OwYLxerJgps7r37cfLc\nYOYrOOvo6KBhmRiDpO3tbRzHodPpUFUVy+US0JRVaaa1xq5UCMFwzXDUx+MxliXZvbHP0dEheVa2\nB/qpU6fMkj3OKMuSw6MpZVkQhQEg2do8Qa/fYTmbAZq9vX3CbhfbDVGWTWc44PDguDkgah5/9GFO\nnz7DfLEgCiMWi9s/d9UUhE6nx87O9fZ5KaqcssrRyrCnpJScu/tuwqjDaDzizNmz5LlxdlwulxS5\n4dVXRdl0eaKB7DRr62Pq0tDcXNehqgocx2Vv7wDLMsrnWKU4TuMx43sIgWkoLEGeZyRJ3E6nqjRw\nSaVKYxzWUIFXU93K/M3zvAaqAtuWhGFws5GjbqAETa3uXMxWxdeQGm9m69YYUzmjQajeOF9SG1vc\nRWP7HAUB6AIhFFqqBqosG6+gmDNnzhjLY9dnvlgAgrIw0FMSx81BZDVMNjNxBL6HrhVh4JJnCV7j\nJX7p0kXGw4E5qFzTyPqe2b3VjZEVb9zPmVr5o15ifumLn28XICv4ZBVcbNuNt7fvk9UVFTUVmp2d\nHT7/u/+an/nAT7O1to5vu6hKYbmmk1/5c6/Unb7vt2rP1QKxLm8aZ5nzwjAPjEGT13TUdnuwZFlG\n0fhTr62tGVe4omwPjJVJFFJSViWTycT4Q1cGL3vllfOcu+duPvzTP00cL5puxUWIm+EVKzhJa80T\nT96+sHnxW19qsPbXB56uEk1WP4uF6dKNofzq9zRwlR94wGqpasKeaUQt0+mS48mEJM25em2H6XxG\nXddMZzNm8ylhaJSl0+mU0bCPpWs82+auM6fpdXuEQUBeFuztHbCYL6ga75EsS+l0ovaBfddPvpsk\nSQ0XPC9wHQ/h2C31a7XoraqKMAxb/mw7XRVmKaiqGs/zKYqSKIyazwpsx24SfIwzYKfTuZkA03yG\nxsfa/Hy9nuHOC8ek7qRpRp5XJEnGaLQOSIPZorEaUyvXaZbYeqXcNTS5ovG4SZKEMAiI4wTLd1nE\nMdPJMa5tk6ZLTm4ZTrnrOqb2NNTUXN++vLbtL5EmKbq+mWw0m80oq5y8THEdG2EJ5vMZ165epT8Y\n8Nhjj5OXBY7jobFwbBddNz9rluM4NnmeNc+Khe1IUHX7d9d1iJM5vX6E5xk7WFXW2NLDuEeW5rmx\noG6gPyklg0GfqiqxhWz53pawyLPMUB+tm0pnpRSe6yOb4r7ae7XeR5iwEN83iT/v/dDtNMJn/8OX\nDaGgNBi3xmDtqq6RjoNSNVLa2I7DO9/5ztu+/qtfeYaqWjV/Cs9zQBsobmXvaozMTAF3GtO7FaNq\nBfeaxasJRMlzY6YWLxcUedpMHQLXc/E9kzq1ffIUL734Iv1elzxLTUpP8/6YfQJgCY6XH/zbvcRc\n8alXHMlV8W4dCi3BZD4l6PcoiwIvDHjmy1/m7W95K/efu4e66eClI1kmaeuEt4JPbNtuU1bquiYI\nAobDIRLTsa28q8PQ+G+sIJb5fI7n+S3bY2trG8uyuHHjBi+88D3yPOfEiROcPn0arTVHRwfkecF0\nMUPY5gZcX1/nO88/z7f+6q/45D/+JPfcfZblYmFGsNx08sYvRRFFUWu3uRLN/PWrqEpqNF6TB7oK\nUajROJ6Lbj58x5HUlYFxTGe6RCkz0teqNsKaZukkpcSRDnGcMhj0cVwPW7qcvfscruvyzFe+jGXV\nFGWK1hWlsghCjzhesrmxxonNLXZ3b3D12hVUpZG2ZGtrG8/3efSxx/irb32D+x94gNMnT7GxscbR\n4TFlWTbQVIW0BMfHx1R6FYjh0et16XQM395xHJOWM5224qTR+gjLMkswVWksS7K/v99Y02p832Dy\nJ7a3uPvcObNonk4py5I8T1ksZhwemqlhY8OIt7RWJMs5eVEQ+R2KbE6306EsUjY2TtCJOkgpWSbm\ne+1PDs2OxgvMod4f4HkGbkmSDCkdprMlaZqSzObEaQK1sZvtd0LO3HUK2xJYtaKuSlRZQZne8Yk8\nPjpoF1rXd3Z54YUXeMtb3oKQgiA0odhGb2CCLFzHwfPcJqDBhHGoSqFrY7O7Kt6O4zAcDimrnOVy\nief7bTOgtUUYGt8X33cZjUZYWiAsB7N8LilLA9ekeWHMyyYTjo+PKYocx5ZIIRsYqYPvB+3zeCsu\nLoRA1YqiXFnq3qQUrybxLM3Jizs7M1ZaUZclHd9vFdCrvNxK1ziOC9Yb4CeA1grXs6lVicDkC5ju\nXxnPd9s0c2hYX19DCrtlf6kip1RmiWtZFklipuosNUIeAycFgMZxbJLlEte22d/bJ+p06HU7SCFw\nbZOAtcoB8H1DXVaqhuUb/ujA34ICDjQudjc32atxa3WSukGI1XS3v/97/5ajvQPe87YnUUWJUhX7\nh4cE3Q4dN8BvPCgQNZEftD7YfmiCgsuyZHJ4hMn667K9vdmMfQlVVXFwcNAminQ6HYqiAATTyaJZ\njCnuuedehDB0xMuXX8N1XcLQSNV7/R5KG97un/zJH7O9tc1nP/vrYGmq4ua4HkVd9C1JRHVdQ0O7\nWrE3/vq18vNeUSFXE8Vqall976Io8N1GvJMVYFn4gY/WUJRFQ5f0G/yzQpUVju1Qlcr4f0iHcRTx\nxT/7U/79n/8Fw/GA9Y0RN27coChLhoM+AiNRPzo+AGGww/F4jeFwRBB2iNMDLr72Gvff9wBhGHBl\n5xqvXLhAr9tla3MLIWyq0lAFB/0Btm+YCzcLg1FzmvAISRgOyPOArFHIpmlKWVbkWcl4vEa3220O\nQbvtDo+Oj6jrI4qiNBBKM0oXeYGwLOLlku/s7oI2z7jrwf7+IdvbJ1hf38S2NH4QsZxPWcyngEYK\nSeC59PsnjBNhUVGpguPjw8Z0q2Tn2g2CMEJYgvF4nQjFXeFJpLDQdYlWFVpVJFkGtcmntIWEN5jy\n19bGXLlyhfPnL/DAAw/yD372adIkbWBtjW0LVFXhOQ5+w4CqytKYZWlNEBjvGavxri+rHI0iSXMm\n06P2/tN+0ByaBubY2blE2DEFbDqdEHghqsooS0Mv1NrssIJGVbyxsdFi3LpWVK+jD64KsEWeZ2aa\n8zyyLMN2jO+773sNXVc306gw6UxaE7wBhDKZHAMwO1ZYQrQdsRCSGhCN7/mtsY23XqosEdivS8PR\n2tge1E0KlFKKIslQjdDIbjIEsEAKM6krpRj2O8zn88bN08L3HYLAI00NLr62tsZyuaTb7TI5PjYd\nfmMHga7xfa99r3Tjvf6Drh95AV99sO3SElMYVhCK7bloKTieTDg4PuJrX/kqH/ngh6iLiixOsB2H\nU6dPk+kKv5bMZ3PDSxUCVVZEgQkvqJUyHUFjUjRbTtm5vsNsNmM8GtPpdOl2OyTJHsfHxw3vN+Ps\n2bvpdYcMBiOiqMPh4QFFURIEHmEYEIY+09mEaztXTfcbeiRZxvPf/jb/5cc/zsmTp1gs5/ieh8Ci\n1tqgR82fTuOP4thOM1J6TSd5+3UrLLSCUVab/RX8oJRq8EkL1zYe2KtO3WDkTit7rrXGdc0Datk2\nRa4Yj8ccH0/5/Oc/zysXXubsXadYxnMmB/t0Ao9pnHF4tEdd1aiq4PHHHiXLEoIwwHYdDo+P8ZKU\n02dOo4qK3f198oZGNRz1iaKIF196mTzLeddPvsvsN5Sm0iVVpRBS0O90QUOWZ6TpgjRLm+lEEIYB\nda1a0UilSl599dUGX/UZj4cNVOPT0TWu47W+Kc8/9xxvetOb6EQR/X6/5fK3cnOdcu+5e8jyAsuS\njIYDg382nPY0S0nThCyLsWKr3Yususo0yTl//jxr6wM6HWOnoOqaQLo4joWua6Jeh+VsjucIfMeE\nOdR1Tak0qqjAv/1zv3D+JW7sHfCe97y7yesMSNMY27FblbJGG4ZTrVDafDZxmuN6hn2x6hJdz8GJ\ngoa1FAEapUzBVEXZOjpKaeP7Aa5rFo9RFBH6AbWysKxV+EqBqg28uIpGM5CHjyNtbFu0mokVPGL2\nQS5xnDBrGEANT7CVm68akpUiWDouYdOA/fVrMBxQlCWOZcKTLUtQKYWQEqvWqCYQ4o22mFLY6Brq\n+tb/w0I6NmizbxLSRvg2jmM33wvshipZqgKlKuJkzmw2QWvTkBpqdEm8XDAej1llxy4WC6bTqUlM\ncl2qMqcqK2pVkSSmoRFCIG2HFcz8N10/8gLe4ra3fGirf6SUZLXxVtg8sc3/9n/877zjHe/gkQcf\nQuclk8NDagHxToHTCenhMhgMWix5NQ4OmsQNpRSqUhSqoN/vMxoZumCem437weEBUkruuusuRqMR\naZqxXMbcuLHL5ctXGpphxGg0bGS6NUdHh0hbcPr0SebzOX/1/HMgLT75yX9kFI1pgut5FIXpiCxL\n4NhuIx7SrW3ArR3CG0Eola6pLRMzV6iKsvFKdxyHRbJaoAiKqsRqPCZWByHQwjOr1zMjrKTWNWWa\n4rkhBwcHfP3r3+CV8+cZDgcsllNC36VSFbYjiSKzVHSkZGtzk0opbNeh44VYQlJWCWe27mbn+nW0\nsqhVyUMPPYQQgsP9fc7fuMhoMGBrc4uXX36ZMDQBtidOb+J6JvllsZgBBvcLQo9uL8QSpsus65p4\nmTEcjqhrbYRY6Wpslka8kyS88sorjTeHcY/Msoy3vOUtLUxXVbd0V41HTa0EbhgYo7G8ZLGYIaVL\nPpu0VNe10YAgCMgrRVlVzKYLKlWRZQmTyYTt7W3G41FTiIywxCo1rmvEHMlsQp4lCKvHcmm45kI4\nCOFiec4dH9nd3V0eevBBsizGdX12dq4a+4I8x6pNhy1dl9lk0sSamXQZPwqpyrrBwp2mgC7NYq45\nOEwXqIyScLzWMFHA93ySdMYiPiYMRxwdHXGsj9Fa4DoBWpuwXtcze40gCFrzubo2jJnV873KcDXN\nRYQQEsexcByXbtc2auHm/lzdr3Vt2DVCCObzJUly523ebD5HCJCuh9Y1laobmwSboqywLdHYRt+5\nm7Vtt+Wlm6WueS4sJJalqasSYRmhT5EbIdKtxm9Kl1iWJk6W+L7X3FcmZGQ0GOJ5hnYcBAHT6ZQi\nz81GRViNjYFEWBZ+GLb3YVVVjeXF/wdohH7kQwMJWFjURYXnuKiqJC0LiiDEdz2+8uVnyOYx43MD\npsczhr0Bp8/eixaCQlUkeUaVptSWRAuYLVOS5Jg8L3HdI/r9HqPRCEeajuHgaGrw1m6Xqoao0ycI\njbm+JW12buxi2w6Dfp/eYEicxJSFwdJLXXB8bDIubSnJq4LnX/hukzD+d3j0zW8GQBUlri2plWqK\nt4XvGbaJHxoqUt6IF2ptuK9K10j7zh+c73rtElO4NK6IphhLS7b0LIWgrmocx2seyNXJfsuCWBqP\nEtCoUjU3f8mz3/wmz33vOwzXh8bEyndBKxytqUuFWiZsbqyztbXdJHAHDMdrfPFP/xzpuHhBxHdf\nfAmtNd2wx/ramMtXdyibLvxN996P67lcuvQaly9fpsiNWnHVuZw+fZIzZ87Q6/Woa3O4Z1lGVRYG\nIpKCXr+LUmbEXKWxSxuKwjxUnY7JNlwsFnz72We5664zbKyvk+c5rmjCgSUkSYrv2OgqJ06XCKmw\nhNmNDPoDhHSoGodDVVcUVY0oLUpVIpqIM7fXQ2tIE49smSI0xNM5AEJYeL6P77hkcUYv6pBKG0cK\nA6XUJVZtPEwsaVMUFeEdmIT9XsRwYF7HEpLtzS0TDJ3FTRG2KEqF47rGeM2xOTw8ZGtrA5QiUzme\n7+EGIZm0qVVN7dXt4SVti6DnMp9P0LXGcV3idEkY+ly5ep27z93LoE+LBStlmEJJklIrRaWTFvpc\nwY+2bWTsjm0TRp2Gb64oq8J0zNJFY6ZtVSiyBn5ybBfHcSkqxbBnPPWjyKRC3eka9tdMnF2eYguJ\n55ouvswyXM/AqUorNHeGUGoqUDWqLrEsYbyKtELXxjHTEoZJIyyjwiwKA0nWSjXFvja5AbZFhSnE\nvh/g2i55UVGWCb4foKqS2fSIIs85dXKbKDSqTK01tmNTa6iaOEjbthHSep2a842uH3kBz7KMxivW\nLIXciCLLAUEUhsjAZefaNf7t7/8+7/+p9/HAfW8Cpdm5cYMir7CkpNPtEHY6eL7x28iLEtfzGI7G\nrax1Pp/z2qXLJMmS9fV1trY3Wix5Pl9QFhV2E/UFFuPxGkopdvf2CDtmfOv2uwRBwEsvv0gYBgbb\nsuCrX/sqtm3zL37lV/AdF9VMFXYjI5fNSbrq+vI8x1aGbWFe7+Z/gzfeOBdNyMVKYq5rs8BbeWHk\nTb6geT0L3wsoK7NgMqIlv+WxU9dURYHnutjCBil44fsv8Y1vfYPReI28LFC6xrYEqlRIy2I+X3D2\nrrNsb29z5sxd3Hff/Vy5eo3+cMTHfvZjfPu732Xv4ADfD5jNZjhDl6PjCUWW4NkOnuvy2qVLjTNd\nyV133cXdd59taH5dlkuz9Nvd3eXZZ59lOBzwwJvup6oqTpzYJstSsjSjqE3Ooi1dXMehrmG5XJiJ\nwrKIk5S9/T0WiwUPP/IgYRgSJwme67KMTUZkURZIWxAEN20QtFVR1xAEQUM5XJBkGa5rltyO67cZ\nqlVVtpTSWimu71znzJlTrA1HBr5rOk4pJGVZYQlBmmccHh5iWeah7XU7VFVNaNGmn6d32NWdPn0K\nzCPCfDozVFEhjeeNMM9NVVZYtsQSBnYoioLj42NG4yEyM2EcyTJp1I9GGOc4Rr2rlLE1DgPfSPqB\nslZ4rsuFCxf4ibe/jeVySRR1GphBE4ZRg2P7FA3ObUyuShaLOXVtTKxWWgnTmSt83wWBiZHzfWxp\nYUmbtV4PtEWamR2N60uOjw/bKEClFD9xh2fi6OjAfD6OR1kY+wrLMs+DhaHzOdJ+Q02M1gohNK40\neZVVWaLRZtLNCzzfJcsS6trsmlb2GqtJw0QFDojCAO0Y8sTqgLJtB2FBlqUcHx5gS8Hm6ZN0ooCs\nmUrgZv6vaCIilapbNOIHXT/yAt4NI2NtKSR5XmAJSdBYpq68Oy6cv8Bjjz5OJ+ri+T7pYsnZs2cp\ny4plHJPlOXmetYyEIjeChr29vdb3wtD/xqRpgKbm/PnzrTfGcDjE9wOKvGxphkmStBt0LNMdHxwc\nNKOx6RwMI+UFnnrqZ3j00UdbPHL1gaysLVfj982H/2Zoxa30wRVj5o2uW+mVvu83r+G0i1C/YRFY\nWIZmV1ettHm1+Fu9prRtfBGiauOZUuUlX/361xgMBiSJ8eYI/YBkOYO6plQlDz7wIOPxGo8//ji+\nH7BcxmysrXPpylXuue9NPPLAg1y9epUiSTh1YtvYn6qaU6dO0e90SJOYxSzn5MkTrbJ1Z+c6CEEU\nBGxubjbe3RlPPPEE/V4PyzJwx4ULrzIej+h0OgSWjZSCg4NjXnzllaYomsmKhmmRpgmPPPJmbN9h\nnhgoKU9yOlGHrDQ0OiEE2jIdmqoUtS4RwtAZV5+f6zhYlqFimgKxoo1Ker2Qfi9iZ2cHzzPxaPPZ\nrKWXrT5jq4HJlDIBG2mWECcxKtFozK7Ftl0jHb/DZZR9EiEk3V6E6/hGgq5LoEapyli4WhJpO43z\nX42wbC5euMBgOCAMIhMGImUbYIFWVCpHWIK8SBDW/03dmwdZdtV3np+73/v2l3tWZVbWotJSUpUk\n0IbEJoTAMAgwcrMYB7bbDkd4osdByDHT0+6ww9HG2BEeTHudcQR2GwYbe0wYg9vCCxgBliUQQmtJ\nqirVXplZub79vbueM3+ce26+krKA6Np/g/4AACAASURBVIgeeq5CoVJlvvXe+zu/8/19FxMjN8my\nbAtkwp75Gc6dP8uhQwdVt2+7yNxiIMsESTogE0l+/TlYllcUIl14C3hQSkaDkRIlxTHdYQuJ2n0G\nfgnXU9CMZTq4tsXk5GTxHVytmPm+p3QEg6Gi8I0i5T5pqzqgXQ6vVguzNCYWKQihoHipvfwVDDkY\nKmK+gjzV4q5tNMZ3HIZhKudJ21Ye8ZaJ6znITNBuD4jjmJmZKRzHyectVkGX1A2bho7GoeTvd/zQ\nC3i7pfBF1/GV8c9whFcKyKRAIAmHgn999DHe+SM/wt65efq9HkkYEYYK96w3aky7HkIKlpcvKu5w\nUGFpabagmq2trbGxsaYohiWPvXv3sn//Eu12m9XVVU6dOoVhqKHUoUOHsGwDU1hKJru+zuLiXgxT\nbZtMQ03z//Hv/4GFhb187D/9GlvbG8T5EKdRq+Vyd1nYfGqBkT7p2nNY0/j0/wshaDQaV5fS56Id\nvfXSrBPdFWiesza80mnyep5gGDsMH22yNcqDiX/rE59gamaaURhiWWr30O22aVSrtLc2ObR/PyW/\nRK3WYHZ2XglD4hQsk5uPHuM73/0u1x85wgcefJA//tP/QslzieOQbrfL6mrK2TDEMS1uvfkWxQTq\ndDl99hwTExOU/QDTsHju2efoD/ocO3aMZnOSF55/jpdeeomp6QnuvvvunLUQ0et36Ha7pGnMrbfe\nzGg0olGrk8QJvX6PJMdfe70eru+x1douFi5fpAzD4Y7/uTSwsBAIykFAmmYMBoO8OxKUSyVKlUqx\nUOpIuzAc0euO8mQol8Ggy/Rkk62tVjE01rBCnNvXGqbJVruF5zn4uTeL7ThMTs0Qx4rmGe0i5PEd\nl2TMkhSR4rtKnRglYX5uzYKeJ6RBEFQRWcKePfMcP36c7373KbrdPtdddx379u1TVFrLys3CGoXh\nm5GLX7IsYdjrc9OR6/m7v3uYg/uXlM9IzcVyHIQwMCyUsZOlh5opo9z3Wx96vqUH8LWySpRKhfIJ\n135Bw9GQXq+jTOCSjExk2K5bNDZXE+JUSipxyTQUhtyYaBLHEYNenyDwsGwdC3iVAmQoFlCSCaJR\nSJqqABmRZQwHQ0xb3VNhqOYueses2S5gYlm5YE4K+vncqVarYZmG2jHmMwnXdej3+/kw1C+olHBl\nEdcN4A9y/NALeKNWV4A9yt4SMyXJMnBtTMviT37/Dzi0/yCmYRGNIgLfo+wHO+5+wwFb25vYts3c\n3CxZpgYy3W6bJBeCNCdqLCzMFxhWGI7Y2twiSVP27l1g3759DHIVVStfUEzTxHZMpmcm6XSVvF5K\nyYkTJ3Achx/90fdy0003KZOqoEwYKY5ymiRFAY9zcyIdDqFXVM/zCnqjHqQpubJ5xe+98tA3QRAE\nRbeu3NvcYgDkuq6yucxTjPTPtDezpiEOhkP6wwF+EPDtxx/DdtQAbTQaqUViNCTwfFYuXeL6w4c5\nsH8/d95xJ/0o4fjzL7J//368HGPv9/ocOniAfqdDuVbhwfe8m5dOnGCztUkp8DAsRV8sVWu0Om1c\nx8WybRYWFjAMRd3a2t6mVCpz4MBB4jjhK1/5CoHv89rbbmNqcpLVlcu02238wKNWn8RxLCYnZ2lt\nb+O5Hltbm9iWhW0Z+NWAUlDCc0xOnHyRhYUFtftJM0bDIaWSsq5VoikQaQJSMhik+U2l9ABqIJjS\n63bzG9YBw8j9pS2kEHiuQxInTE40OHHipZwSqwaqhVI2l6iT6wyEyDBtk0xKWhub1GoKz+8Phli7\nzK8F5JoBtZOMY+U3YtpKXCTljh+9FBLbskiTGJmlrCxfYvnied5y75tYWjqQi5rSXMRk5tTNNIcI\nTAxD5p2ooD7TJE2rXDh/BtNSis0oCskEqN2/gWM7mFZaME0q5WpRMDUlUMcSCiEQoVLBKgV2ShxH\nCJFhmQYTjVpeyBRfXBgWZm6XezVYMQoH6trNzd0GA+Xt7bgWmchIwhiDq8MRcRgShiMykeHkIi3D\ngAxBtVZGkNFo1pWhm6k7ZgV3GIaloCKhRO+e7+G6LlJCEse080DxWqVMs1kniiImJibVjmkMktfv\nTX/Gq1Eedzt+6AXcsW1kLNRFjsTzAhIDOuGAbz35BBtrG7zvPT+qLtzhiFarxaDbY2JigmazSb1e\nw/UcMino9bo59Sig0WgUGGWrtU2328F1XWZnpymXA9JEErY7XF5do1T2C+pPvV5nMBiwvr6eC0dK\nuK7KO3z44Yd53/vexz333K0Ga2ma46UJ5bJKbFdBtVlBUQsCxa3V6k7dwenORC9Eems+GAyKgNxX\nHmEYFouLfpwu0Ppf/bqD4RDXVvxUKQSu4xKnKnknkwo2KZXLxEnCyVOnCMolur1e/r2NsC2LzbU1\nbjl6FEPCzUdvYXtrG2G7XHv9Dbz0wgu85pZbuHz5MqVSCaTN8sYyjUaV6YkJlt56H8++/BJPPfld\nTp8+q2holQrdwYBKoMwpfL+UwyyZYhw4NsuXV9ne3mZmZpZ9+xa5fPkyj3/727S2t3nd6+5iYWGB\n5ZUVGo0GrVaLaqWC69mkicy5vLC9uU0735lUSgHt7S2iKGLvnj2INKbcrJPEMWQKM08zBQF4filn\nJUWFt0ep5DMcjjBNmyhUqrpypUISpUxMNBECEttkfn4Ow1BMhjgOiaJcGYtBIhTRvFItUa5UlJBq\npFJr6vUGvX6fenOCq/g1UalWcCyb1ZUVej3lo14uV4jTGCGVYMs0cyouauaSZQn9QY8zZ05zxx13\nsGfPnpwDP8xZFgLLzK9HCzLAsrQ/iRKDZUmM53tUyj6tzU0ak1M4todhOmSZpNfrMxj0iaOwEMqp\nxkcpQ/WO03GVR75pmsrrw1CCrwwrZ7P4iEyxesKRCvlQ70tBgsqYavdSVa2WEULQ7Y2wbBNDSDxP\nKUWTOFFmV1KCvAoPXKRUy6WcA54VSkikgiozqXxnFFNFw2KaTqjsPRSrxmGU2wRr47EsUxYc1Uq5\n2JErGNUuIBl97+uirXckatD+/wMWymg4wpAmURwxihOcckCMIE4SHn/8W9z7pjfR63ZJowjbMlla\nWCyw4CgKabcHeZfjUq3WFb9ZZGxsrCkaWhAwMzNVQA3r6+sIIbFMl0qlomTUItlxUYtjfN/DdW08\nr0aSJJx5+WWyLOWX/+MvMTMzQ7/fp5bLs+M8+i1NM4Lcx7johnK6WpHhmW+NwjAsCq/eyuuBiB6o\n7XZoaqQWHCis1ii6Zg2hWLaF69nITBAEyi4ziWN8N8CwTDVFR2LaFt956hk63R7lcgnTMBiNhiRR\nhEhTlvYt4vs+N990lNXVVZb2LdGNdraTFy9eLNzXAA4cWOLEiRMsLCzQbkVMTUxxww03cu+99/Hk\nk0+BhNEwpN5sKte8VKW8YJAPe1ThrFbrmI7D0889z9bmJnOzM9z62oNstdq8dPIUQeCz34C5uXkG\n/T6dblcNTW0L2zKZm5uj1WozMz1BPwzZ2trCsSw6rTbTU1OM+oMiW9VxbFzLxswH2hr20JQwFW4d\n0W6vEYYhnXaHfr9Hmiqc9LrrrmNycop2u8Piwj6yVHmnj6whluVgWCaGZSkaZJYQRYozPxgMc3uG\nhMFgwMVLy3S7fV73arU3p069zHA4QOd4docjqjnPvFwp511zbs+apCCzQmi2b98+Dhw4QJrGxHFY\nfD6AOA6LRsJxrCtsIZCi2K0dPXaUc+fOctjzybKe4kybiift5Gk+GhfWBUnvLNM0ZdBXkJRmWqmu\nXInwDGS+CKnCqIqjgx94OIazo9pMd58N9TottcMVkmq1gpQZg+EIW7szYmBa5tW8rCh5bgEZiZw/\nbuZQpSraGla6kuKc5Qlhlq0WoeFwSCLSYtcFUCp5BJ5DksQouruhnAzz70vvmsebsjC3XNZ/39/d\nYbg4fugFXOY+NJmUBJUyUc4rfvGpp5menOb2174GKVT00+rqKiuXlvFcl6mpKarVCs3mZME1TUVU\nkP+llIxGI3q9XpGRNzExyeTkNFmW0W51czzKwbLVxa62lRSm/V/+8peZmZnhHT/ydhYXFuj1egXW\nrHP89IWqT6zjWMXqqT2QtcBBXwj6ZI1jpeNbRA29vPLQJ12feC1e0QVdP6++6C0355TmdqNJkpBG\nmcIwDQmZydce+RoHDx5ie3s7t7IdINMU33Op12rcdeedzM3OEQ6GPPPss9x462swTZN6vcb5s2eZ\nmpoqFp3BaMji4iKD4ZBDhw6x1u3S7Z7g3ntvZHurw2//9m8zMzNLFCXMz+8hiRMl+DBtMCUmUPUD\nKtUyvbbyYSlVyrS6XS5vrJNlGQcPHqTZqDEYjXj477/Mnvk5SqWggEkkcH5lmSzN2Ght5cIpyeTE\nBEEQ5L7sJSzDJJUp4VCJhCzXxRsbIpmmwcrKMs8/f5x+v4fvqe/5hhtu4NrDh5ieniTJPVmCXMou\nZEa/18fzXBWhZpn5UCyB3B/bc12ikdIAqGi8Fqsra8zMzDI5MbHreU/SDEyLYaRk8a2zFwuYYnpy\nAs/zmZmZUV22beN7ZXq9NkGgfHt0MXAdq2BSYBg4tpkbZe1cm4bjFFRBMAjDmMOHruXc+fPcaFvE\nMqNcKtFudShVqiAzDOkQh3FOrcvdATNFsfUcF99RiTtCZNhlhySOkKihoYkEQyJEBtIoILUkSbEA\nKTTv5SrqZMsA10YkEZaZYdm5HbVlkeWDSSefEex2mIbyTzEsS+XtsgNHZVmGaRtIofjiioprFTOt\nLBPYtpFTzNVuTuTQULVczgtximnaO6wkyyJNBWkaFt+52oXYxY68mGX8AFDKD72Apzkn2bJswjhW\n9J1+xKPf+CZvue9+up1WYUm6Z3YmxxCh2+1y7tz53O0roFQKMJ18oGGaJElWMDCmp6dz29cRly6t\nIAW5o5ib059CMFRh3dzcYG1tDSEyPvCB93PkyBEQGUkcMz01mefbCWrVKsOc1WEaBrajuoUkjopi\nqldtncmnxRTjpl26Qx8/YVcT8oxPrfUqr5VbGjqJ4xjTMjFt1XU41g77xfM8LKmK4jAK+fI//AON\nRpPtrW2yNEEKU3GWc976/qUlZufm6HV7eI7LgUOHWF+/zMzMDCBZXNrHqVOnWFxcJJNKFCKwGbXa\nvHTqZWb3LtLr9vnYr/06zzz3HI3mJFGc8NQzz1Kp1Aj8MkIaWKZBlCbFgHa0uU0SR7hBiebUFJub\nG2xubyPSlPPnL3D+fMr2dotGo0Gcpdxw8CBnz56l2+sWw91arcbk1DTNZp1+r8eF8xdUR7qwgBcE\n2LaJxU5wSBTHDPp9ZUI0GHDx4kW2traYn59j3+LteRB1mV6vR5IkDPp91UnZNq5j06zXyKTE9yeB\nDD9wsEyDNE1ysZTCkNNUmUUNez1OnThJrd7g+uuvZdAfMujv7kK5ur6hhtNpbszk+iSJor+tb22z\nvd1GZM9x8MABDh44gGUZtLY3OXnyJQ7sX6JWr+F7DpZJEWCdJImC8owd7/0sU9e576sCXS6XieOI\n6elZHnvscdbW1pif34vvukxONkCaCq4RAHYxKNxpSPIM2yxFpilpGrO+fJkkTfBsG893UbL5nP5q\nmKSZJMt3dKXcw1sIkWsWXn2YhtrJlX0fLx/QmzmUpV1kszQjzdJdH6/1l6ahDPaSvOEyTQMrl+Gn\naUq57OUUzJw7b1lYlpHTJMN8d62eq1wu581dlu9wZOH8aOVDjiyH7fT1p5KRwqIG6Ebv+x0/9AJu\nOTaZhGE4olqvE0UJ//LIN7CwWJibo2xb+eAxIs1UZ2oYRnFDkW8boyjOlXmSOE7z7ruLEFkuea5g\nWTaVcpUkyUiSqEjOSPJ8yk6nw9TUBHfeeSeLiwsFi8O1TKRQE2bbtpFZRi/H1A0JQqhEECEEAoFl\nmQVjQbmTRYXLoJa7j0Ml4wVZb2l3O/QWH3bglPFVWvlTBxiWAabEMmyy3G9c7xaSJKHb7RGmMRcu\nXsQPAgbdPo7jIkVGGI5o1KrITAkUtra2qJQrSGkoQYZlcvr0aW684YgKbZ2bJRU5rm8rU6l9S0tc\nuHiRP/j9P+Ty+jpgMj+3l+1Wi1q9wfTkNJfX1jl8zWGsvOMyTJNUCIXPK9s9wihk48IF+r0uYRwx\nNTlFUCrhuzAx2aTb63H23Dm6/QGjSE37lTLUo1Hv8+xLJ6iUHGZmZpifmydNUrZ7HbY6LWampvHz\nFKY4jAgCn+npaVqtVrEoHj58GCefVwyHQ8JwRL1WAyGIRiNkmuG4Dpubm4rX7Knsy1a7RaPRREiJ\nYUocO5d3m8p+N80yzrx8ksXFBZoTk7RbXbIsIfDdXc/7M88+R6M5wfrGFhIISlXq9QZbG5cxZFa4\nPL504iTnL1ygFATceOR6Hnj3exBZwoWLy1x/7SGSnLGi/UrGfXS0Ra3runz1q1+jVGrieY5qiCyI\nwpjRIGTt8gqmqSh65VIFISSYFqZlFtekZVqFiEZkSqPueiZ+UKJU9UlFgl1AEQm2VSeKYqSQBEEF\nkWX0ez2qNeXfg9QD0Vcfga9zPAWmzLtp1GNE7uqnbAR2L4ZpsrMD1t+BgpBUxKIKq/AJQ1V7KpWK\n8kQqPM71MFOQpYJms45pKbhMCKOoA+peBSFi7NyoSlMtx+93fV5U3Jz5Pz6EkkmJkBBUyoyiiO2t\nFi8cP869b7oXU8DW5gau51GtVnO5s8yTWxRdy/cCyuUKzaZLlClj+dFIMTyWlvbjujarq5e5dOkS\nURgTBJW8w1Wr3smTJ2m329x09AhvfOPrOXjwIGrarlSXnq/yIft5yLJeNbUgB8yi6OotUJalu26P\ngIJGCFwx+BmfRF+d8+oXWzGguDj0n7WhvuVYReSViUEmBe12m3K5qrZ/UqhdhpQIKZWtLYIoUWyM\nUlDitttu5aYjN7G5ucXpM6dZXFgiExlB4HHs2E28ePwF9uzZi+M4nD59moPXHCIcjZiameUb//Io\nn/3zP6dWnaRSranBkJS8+d63FirQrY1NnnjiCSabCtooNWr5e3eVraZpUrIMxVKQkkSkdHtdqrUq\nw2EPISV3ve51HDh4CM/3+eR//s+Yls3m9ja249DudRkMhyATTl+4SKVcxjItKqUSE40mYZQyPT1N\ntVIhBdr9IcvLl/Nd2Cb79+9nc3OTiWZTeXs4FsPBgFMnTyp9QKlMKZePO47LdrulxC7Vct6VjYpd\nj8hZCqZpUSr7bGwo2uni3j0MhyFTUypD88knn2TPvlef948+9JAKYhYS2/FYXlnDsh1kGlLybPr9\nPqsrq5w5c5r1jU081+HihfM8/fQ0N998lLnZGc5fuMDS4h6Agu2ktvPKI304HLK2tsaZM2d4+9vf\nhWVWSNIo5zNb7Jmb5/jx55mdu4FKqVxgtZ7vQw7hpWmW7yYFhqEqr2XtePcYBhgCXNMFQyDzGUSa\nZlRLpZxpFmMZBrNT04TpoLgfrtaNajsA3RBprD1NFQRkmJaCSeTu95TMFF1Z31f6HtKdd1AuI0TG\n5OREAcfq19RiHnVfW3iOo4RbcYyQKZZjgSGoViuKM59kjEYRoyzE8+zisyknySuDbV7ZnF3t+KEX\ncJGmYNsMhwOiOOXZZ59hcmKCPfOzpElCqVTBNA36ufF/EAR5XqWdT3wl3X5HDaEyg2qtipMpu8et\nrW1F/wOCUoDnq6Ffq7XBudOnqVYrHD68n1tvuUU9zrGJwxGWbeV2mCpoNkrCghmi+Z+ajSCkYj9I\nUGwP1yHLdmhP45j8OD9WXyzj5P3xTmC3I80Lvsjl3YYJUmRqW22rm7HXHapuyLaQmcDIDbNqtTpJ\nPmCyXIdTJ09Rdn36w4HqWUyDKBwx0ahRLZdZ3LNQOKfV6nX6gxFCSjbWNwmHIfN75tna3mJhcZHr\nbryey+sbGKbNn/7ZX3Dy1MsE5QZ2UKc5PcfkxCSe7xJHEUEpACFZ2ref6669Nt+CC3pRh9b2FpfX\n1oniiFq9pqiAgcvmygV6vTYGgvpkmXfd+zZmZ2dxNONHZLzuttfw6GOPM1Wv0h/0GbRbyvAqyxh1\nevzup3+bi+fPMxwM+Oev/jOPPfsc0jAoVSrMzc9RqpRp2C579i7A9jZnL1xg39ISpmVxYXWFNI0I\nXI/ZPbPKKzuOWOlukEQplmFQq1SZmZ7GNk1GgyGd1U0GXeVFbpYDas0GhpQkccLW+ha3v/Z2hgOV\nINXvt8gyQbNe2/W8x/0+jmFhmya2aXBwYQ5ME8MQmAjiuM7S4l7uuftOQHL58mU67Rarqyv86+OP\nc3l1lQMHlrj2mkPcfPMx5uZm2d7eVHRGy8B2bdbPr2NaJu945ztyKLCPbVtAShKDHygVa6PeUFRF\nxyUolXM6oxJW2baFECZ5z6sKtqGDiXP72Fx1rRc1MLBsW8nIybA8hdPHIs4tFlRM3FVtnfLZQpqz\nP8wcQiUzwFA0SyHyNn63h1sGpgTDdFQYMmBayjI3y8gHxAbtdgcpBb7vF7TgnaFtosIb0pTBcIBl\nmWpHmapiPBpFeaNl5Bxwil26hkrGdwA/iIBHHz/0Al6pVEgyCUZKvTHB0089zdvuvx9knlA+6NNs\nNqnVlOVsGIZ5mgljnGj1XyLBiy++gOs6qmN3FSYWRiOEVPDAo48+iud5fODBB1lY2FusdnE0Qoo8\niX6UFV+olDI3ssk9S8RY6HIcvSrn75X0ID141Di4HjjqIeZ48dZdxtXcCLN0R/yjn8tz1XMjlU+4\n73lgkAtybEwhQaiLWDEhUqqlMs8/9zzNeh3PdjAci26nQ61eo9ftsnj77blv+BDLipW/uWli2w4z\n0zP0ej1ePvMyx44d49SZl1lY3EciJP/xl/4D111/I36lznXX3oBhl6jVanR7bQLbx7dURqHt2WBA\nkkoy1DY+8H3smRn27d/P88dfoNvrsbaxQbXkc3l9lQ++/8e44brDxHHIXHlK7YCGQ1zXVo6Qm5s4\nBogkIh4q1oPrOEjL5uz5C6wvr1D1AqZrDV56/jiYJpVGjW6/T7c/YKPT5lsnT2GaFvVmk5tvfQ0n\nT59m5fIqE80GM9PT1CabGLbFheWLmIOESqmMb1rMzM3R6XQJRUS71+Wb33yUUrVCp9dVMFy7QzQa\nUalUueee1+P7PtMz0/R6PSUu85vqmkl33y9XfKVQHEURUX+E63sYtsVwOED7vOvrybZtJiebNJt1\nDhw8QKl0Py+//DJff+Rr/Mu/Ps4/fuWr7Ftc4MMf/iCWaZImMU888QS+7/O6u+4CCYPBUAUbsLM7\nFELypje9keFomN93ynJC49xmHi7sefYVthBXcpwNpfTUjI6xOZHp7BRYXdplpoaaukjvehhKzerk\nwef6/sry4aEsXvsqh4lqYKQK7jbybEopUbucHMMRQgu4wqIR086L9XrjivmWrgeOvXPva6qhvuf1\ne03TtOD46/SpcUrw9zt+6Ik8//r1LzGMYoJKlb/6/Odpt9q85d778B0vH6i4hWBFDwL1dkn/vfLg\njbBtr/A+kVIwHA5ZWV1hMOghpWBpaYnrb7iegwcOEI9GBfUpjuOiGx4n09u2rTrk3MVwHOpwXZc0\nN4nSgzOdiqPx5vE/a9tcDa3o1xh3YtzxRDC5/uY3vOp7O/6drwE71rvak2Gc1ZKmaR5zpTI0rfwm\nywBhGggDuv0Bn/3MZyj5SmJvegorN6XghmsPK9Opw9eoLiJTHP1Kva5EGzm9zLQsLi2vEKUZp06f\n5Ytf+q+Uq00aE9PMzS/g+SWwFHQkhcDMs/9KgUqoF5nIMw/VbiERMYpelhLGYc6lHtDe3qRWKfOB\nf/Mgg36HSrnMaLuDk7vg9Xo9HE/J8j/5O7/D2vpaIWTq9ftEocJ7b735FvbtXQAp+c6TTyKVTyiG\no4bnzclJFmYnefGFEyRpSqVaRxqwZ88eJicn8214Rr/TVl46fo1yqUQqBOvb2wgkgzhkGEc0p6bA\nNvFLJXr9PkGWsba8wuzsLJ7ns7W5ya23vkYN96QSDul0IKfykVeddyv+ojLVMk0lfTQkmVTfqRap\naFhk/LrSBUIXyVarxflzZ+l02ly6dI79S/uYnGgyNTnFNQcPkua0P9uy8zgyWVxXSZJiGCatVouJ\nCZVrmqUZtXq9CFLQuodxuh3sWECo59tpWorgFiWof/XnNnZYIQB3vPG9r/qd7/zLl/L7aoczvQND\nKpaN3tG+7k3vetXjv/nVv0ZKzQZTMJe2CjBME9OURb3R36u+j7USWkOqGpK6wg57DNrRjxuHffTz\nKYfDtJibaeLBZvfe//ZEnosXL/KRj3yE9fV1DMPg537u5/iFX/gFfvVXf5VPfepTTE9PA/Dxj3+c\nd7zjHQD8xm/8Bn/yJ3+CZVn87u/+Lm9726ujwcaPJBNIQw3Gnn76Gd52/9uwbZXCoj6M8imxbYcw\nDNnebjMYKDhFhxKo4V2Jfn+AYYAQKWfPnuXSpUvs27fIT3z4w5TKautTqVSIooh6XSmjdIHVBVFf\nAPrPURQh8s7XzaW92r/EtK0iZEHL3/Xz6RMaRVFxMvXJ0QIHPdwcP8FSyqKLf+UxviprwYCWxusV\nXUoJQuF6tu0WrxtHMaZjk5kGjz3+WM4hl/iBT4ogSlM8x6bdbnP/ffdhIEmzDIli2Gxutzh/4TzV\nwOPaa68Hw2T/wWv427/7Ml995JuYToWFxcOYTsDUzD6WV1aYmFYBwrMzswx6PbygSpSkiDTLTfDz\noU0CtlcnSSJGYZd6tcnZjRPMTDU5/eILLM3fzDf/+RFuPnqUmb0zBDPz9Ad9nnv+eTKR8cjXv46V\n+87EuY0AhkFjosnWRpt6rcalSyu87f63Y0m47y33E5RLPPzlh3nyqaeIRiH9ToevHn+Gu+95A6dP\nn+alkydoNpsMo5iTp17Gcx0s02R6epJzF59m6ZabMEYtpicnSMs2FT/g0MRBls9dYL45xcbyKkY/\noRQlbEcdJmenqE/USeOE6Zkp8DN2ywAAIABJREFUVteW2TO/l3CkdQRKgbsb/8iyDQyh8iczRB5N\nZuLaNjJ3oxRpholRnG+FbQ+KOLlut0ejUSe47jrSLOG6wwd5/LFHaVSrLO3bp+Y5maBRr7O5vU1Q\n3vHfVteyqURLOYxn2zaep1SLTt6plkqloqG52vHKn2np/m7HOKRwtW50vDPXtNziXkL5KX0vV78s\nUx22eo08z9JUxAQpMxxHGYNp/ry+/w3DyEMtdnYaGj/XPO5xLch43kFBUcxrjsrXFUVBV9qD+Kp0\n4vHjexZwx3H45Cc/yS233EK/3+e1r30t999/P4Zh8NBDD/HQQw9d8fsvvPACf/mXf8kLL7zA8vIy\nb33rWzl58uT33AqEUUxzaorHHv8Wx44dQyJZWV1BCkmtXCm2F0mSMByNyNIUx3EL/KnT7aqh5mBA\nNBwQBD6u53H9tYf58Ic+QK1WU1LZLKPk+cg0wTEN+v0+pmkyGo2KQcI4jUfDJJZlYflKNj0OX0gp\nSbK0SJUfN6rSgojxk6Y7fMMwiszHcQ64vliFEKoA7XLo9zbesevHjftFGKYyaZKpFmMooytpGjiu\nzfnz57EMpTj0fJ84iYiTmKWFvfQ7bQxDZRgKIZE5vlir1Th27BhGptzWLi5f5NHHv803/uVb1Cfm\nOHLTDTSnZnDdMq3OiNnZ/fTjLqVKg94wwg+q9IYJrm1j2g4YFtKWpFLNDuK+wLQcbMsnSVJmp2fY\nXL9ENBpAltDaaPNXn/tLWlvb7L9GWR8EQUC5WmHv3r3M79nDay2LL/3Xv2U4HCFNNcCqVKuEYcRL\nZ19iZWWF2197G67tYEh43Z13cebMWcIwZKJSw9q3j2efeYajx44RxynbrRaWpbbGg94AkWV0Ol1u\nuP56+ttd4jjhxLPHEWnG3NQMyxcvUq9UuObgQSzTUJFp0mDfoQNYtgq/7Q77JLFKQRJCKOm5aRUd\n3W5Hf9ClXm8SRiGGaeF7LmmWEY1CDMDSql4M0pwNZZoGJc/Hyu/jytws260WpVKJ4bCv5guez3XX\nXoshZK5g9Ol3ejSqdYZ5nun4lj4IfMrlUgEdRJGKYqvVGzktUQ3wNU+avIDmCSaQ49Hj8x7NB981\ncGHs3rhaEd6Ba8zifhin4mFcndUFO5RdrUDd2R0oL3ldfMc99avVajEH0122fh/jO4tX4tnjGo5x\nKLVcLheP0YuDhlviq3vbAd+ngM/NzTE3NwcorPqGG25geXl558t5xfHFL36RD33oQziOw/79+7nm\nmmv49re/zV133XXV1wj8Eq7rcu7cOW46epQsE4oWZEqGuTJSr2K2bZNmGauXL3PmzBmyLGNqaoqF\nhQWOHDnCwvwUnpd7EVs2Ugq2t7dwXQfTVLCNELLI/NMFWYtcdGHWHbLuNhDqs+ovV+PeWZxdUVR1\nQR7fNukLRBfu8aKrv0P9GL0oXW3B01u18W2bPtn6eaIoIhMZhmmowoByexMiw7CdIrLJ9nws21aJ\n2qZBNAxxXJe7775bsVLyC80w1TY0k8p/PEoSgkqFS6tr/OvjTzIzv8Ti0mHKtQksp0qUSvxyg81W\nB69qq9eMYzJspATHq5DGCRkGWZxg2yZIC9t2EVlEEJQQWY80CTFkykMf/XdkUUSz1sBCiX78ul8Y\neKUio93ucnl9jbPnzzMcjkjSjMFwSLPZxLRcpJHQaEzwmc98lnte93qyJObZZ5/llltu4cMf/BAv\nvvQST3znCSwMJuo1nn3qKY4cuZGXoph+q6UG2EIR1C5fWuVdP/JO0n7IVx7/KnfceSd/9hd/zh0/\n+zMMOj2ee/F5vvLoNzl4zUH2HVgiiiP808eRcYJtmkxMTpLGqiicOX2WN7/5zVTKAaNRyKA3pLqL\nIaHn2QgRA1nusyMwDfA9RWMdjUbFDjFLExWplmVooDKOlee6iggMcSwTw3OZyE2sRJYReD5RGOFY\nNlEYYrn5QG9swBZFEaWSEkOVyyqtRxdg2zZz9gnsFGzy+04XcuOKIochrkIR3DF2Gi/2ux26QKoF\nQ+0SCgYYYOde80Ls/vidhUHm71Un+whsG5w8vEJ33+NaDA0XafhLd9TjM63x+qJrhaYJ6k58HF4d\nrzs/yDDzBx5injt3jqeeeoq77rqLRx99lN/7vd/jM5/5DLfddhuf+MQnaDQarKysXFGsFxYWioJ/\ntcNxHWzDZHtrC5kJsiQhFRGVSpUkTojCKKdlRfR6yvN5YmKC977nASYnJ5mYmNjB+hIlUjBNRT+z\nHYtqRfltRHF6BYyBYRUWq/ri1/JZ3VXrwiryLDzdIWlb1vGiq7me45JifVJ04dcnZRzH1936+Nbz\naherHpSMn1i91dKdA4BpW7iOi8wkIklze1AbTJNooHYctuMwDEMc26Y/7FOtVmhvb3PyxAlc22Fu\ndgbf9wr/BymEEjnYDokw+Ou/+VsOXXsTQaWJX2mS4kAikYaNKQxqE1MkWZ80FXh+mTQTVCr1nMuu\nLrygUiEJQyQCKVOkVJ3paNjh3JmT/PRP/ji+bdCJI7Y3N+h1+mSpwCo5lEpl/KBEs9lkcnKK2fk9\n3HX3PRx//kWCSpn19Q06nQ5BqayGcqWA9laLj/36r3Ng6SCB7zA1NcXnPvc5Pvaxj5GlKV/+ysOI\nLGOy3uDS+XO8/q47eeqpp+h0uorpY1ks7tnD2soqX//W47RaLcRzT/GO976bz//NF/jJj3yE1eWL\njDo9/pef/FmO3nQTSMn61jpCSmqVKo1Gk2Ffhd4++9RTfOvx73Bw/0Gmp2fYWG9RnXn1eY8iBbPY\nlo1hohLYTUtJ4aXy9ZZCgsyUx3gSYdk2cRRj24q9Muj3lU2zVNFr3U6b6669jjRJCMrKXK0UlBSk\nY5mIXJ2ZQdEdmqYye/IcpSatVCqQL9CGYSglo1SsLMbwc11Mi44eiWmorlnm/6j7Y2dIL4Qomrdx\nG4pXHjszKDe/53aEcvZYQ2TbV0nkya1v9bAyikLVAOav5zhW7nMuqVQaBRqgFKPpmMIaoii9woF0\n/D4e7+DH4RV9aAhFF/PvZWp3xfv/vr8B9Pt9fuzHfozf+Z3foVKp8PM///P8yq/8CgC//Mu/zC/+\n4i/yx3/8x7s+9vu9iTSJ+cPf/wOi0Yhnn3maSkUlNfu+z8zMLHv2zOfyVVUYK5WqunByKXIcq4Ig\npUCkEZjgGOpCx1AZlKZlYaE6WJHjxYapCqFOo09TFbCgfUn0+47jmHJQKjoQXbjHC+b4haqGqXlA\nQL4A6E5C5wPqx+oFQK/E349GOK661O/vlUIMIQSpyAijSGGiprqBU9RCoruFLOdaZ/ni45fKiEzw\nkY98hM31DbIkJhqFJJlgc3uLqekZSuUKlh3wn37tN7HdCtX6FF6lgWF5mJaPaSvOs5AS17JIhUG5\nVFFMApUhgXZJFJnqoMlVaiIZEgQOw36fzc3L/Nt/+5M4psC2DGbnZrANm9EwwrVdhGWqbtFzsSyb\nDEkUpWBENJqTJFnG1NQMFy5cYmu7jRCCWqVKfzjkwsVLvPNd72b14gV+67f+D5aXL/Hxj3+cN7zh\nDWxvbHDwwAHOn7/A9NQkF86e5vV33cnZc2c5/fIZAtdDGgILwdFbjvGNb36T8xcvUKmo6Lz21hY3\n33ATF0+d5RMf+02OHT3Khz7wfqozUyRZhshg7fI6pmHiux533nk3N95wjHK5zPnz55m/dQ+9XQId\nVNhvVsA5Waac8wLXU3mKaVb4a+jrLw7V7CXJf79cCrBskzT3BalUyviey3DQx7EcXNtRIcC2g+u5\nZGNDxXF2RRQrG4DNrQ1My8h1DQGDQR8pHVQ4hFIfanOogoFiqEKq6b9X4scGr+Rqjw8Pr9bUjMMm\nO7+TS+HTnY73aocW5ZgWeZKQne8kjKJY67g4IZSlh/LrqRaduWalxPEO60zPqPT705/TMIyCgjg+\n2ATt+74D6fwg/JLvW8CTJOHBBx/kJ37iJ3jve9UUWEmp1fGzP/uzPPDAAwDs3buXixcvFj+7dOkS\ne/fuvcoz/yoAf/wnL/Ga1xzjp37qIzmjwi6y9QBkziixLLvojMnTMTzHKbZoqhDuQAmZkIz6fQaD\nIUHgF1+mZVlkGEUWoD4J5XIZ7USnmR2apjjsD15F/8myDMMyiyGiNsKq1+tFmn2v1ytwde3Prbda\nGjeL47gIxtWq0KthoeVyuXDK0yu2XvH1ReR5Ho7pksgMch64YVm4lkVn0KedD4c15qrnA0GphG0Z\nBa7nmCqx3nZN9u3bR5ykvPjiSzzz4lkGo5T919xAbWIGYbhYXplOp0fVC+j12kxNTbC6ssw1h5fY\n3mrhez5ZkuHauYeyyLAMG5lF2JaB7Zg4rs362jLbm6s8+L4HmGxM0u1sEscZlqeGr8IwwXZUwfKU\nf/xoFGHaKoXcMCyazUke+fo3ADAMW7FfXBvH8ajWapw+e4b/6//8A47eeCPVRp3b9+7h+PHjaggX\nBEgkSRqyf/8i586d5fhz3+XmW26hVi1x5sw5Nre2OHPmJPv2HuCG2b288OKLnH3+OA++43/ii1/4\na970pjdx/S1HOXnqJF9/6ts8+vx3+d9/8X/lxutvUAV2MMRyHBzLZjgY4vsqM3FxcVHx+FuvPu/D\n4ZBK7nZpGMrFUWKQiYxS2S8GYZlQW+9hr0+5XMbCGoMhQKQZnuOSpOr6dh2X0LLo5Dtb21aFnNEQ\niSh2k/qeclybkl9hMBgwPT1Nu91mdnaOs2fPsH///iLtKcvSohBq6EJ3qbo71gVMNSUGQhjFIqGb\nGSPHr8cHk6889PznlX8npZLUG4ZZFNPdDtd1sWxVQJV9sAos1wXbNHbYPHqXrQJDRldYXugGSnfO\nV2LrO4Vc1xVNetBNnH6NkydPsnJ5gxMnz6im8WoOAPnxPQu4lJKf+Zmf4ciRI3z0ox8t/n51dZX5\n+XkAvvCFL3A0z4B897vfzY//+I/z0EMPsby8zKlTp7jjjjuu8uy/CsDP/fQXFZ6ZxJR8JUVOkwjL\nUhiuMMDzFAxSzgeUCq/S3gLqgonjGMfVZk8mpVKlGCrqi0UXUL0tG426xZZFY1HaI2W8mOvHjvNt\nkyRRsWM5vbFcLuN5XgHJeLl6VG8FB4NBocDUTBMNieitou7KB4PdPTE6nU5BMdIXhg5CVWpBddGE\nUYRpm4oOlqnhjIQiH9PI1XEi/8wGagdSCSpqcbEsJDm311BCCwyTo0eP8tVHn8MOakxOz5NkBo7v\nEUUJ1UaTKAqZnGwQRUMOHVqi1+kqu+BM4DoOnuMg8ptbiIgoirE9myxNeOn4d9ncXCeJQ77+ta8h\nsxjPc8iSNFfzmYVqNMq5yAYGpq0yF5XrnM3G5jYLC/uI44S19XU836HTaTMxNYlt2xy96SZefvll\nnnvxBZr1BpMTHu/50fcihWR14wIZGc2JBputdY6/+CyHDh3i6ee+yzXXHOaW197EhUvLrKyssrxy\nib1755memUIaUKqW+MAH3s8zzz1PGKkBsO04ZCLjN3/9Y7zx9a/nQx/6ELVqTX2GcIhhQJqG+L6T\nMzF277iCPHyjWi4rZgXKDc+yLTq9rjKq8hWN1XZs6qWgoPUZlpnDFgZJnGHaWompjNvqjQbnzp1n\nMBhw6dIytu1w31veUtivjneB+jlB2zb4LC9fYmJyilarVQRGj1+fumDrBkg/5/juVWe2AmP3tfJq\n1/fb91JijjPIrnh+Q5CmomiYdjviJIRkp5Cq5skkDCP1efJmTy8q442d/i7057OsnUI/3oHrGqFr\njWab6PekG1PDMDh8+DCHDh3iDffcpfxRxGf59Kd3fevqua7+I3j00Uf57Gc/y7Fjx7j11lsBRRn8\n3Oc+x9NPP41hGBw4cIA/+qM/AuDIkSO8//3KAMq2bf7wD//w+0Iotm2xvb1Fw2ow0rxYyCfCksD3\n6XQGlEolkjRiMOxd4bEdRaOCb90bjna643xl1sZWQghSqbclBtWScmqDKzmaeoCgT04QBDgVuxj+\n6W1OuVymbFaK7idNU6pVlZmpFw7Y8TbxPO8KN8Fxzq7Gscc5oLsdGqMfpyP1ej0cx6Hb7aow3kaD\noFwizpQrGiLnlxu5zL74ThKi4QgwIFeY9ft9PvWpT7F3bp6S72FZNvXmBEGlRL3RJBUGj3/7Sd7z\no/8GaTkYlkOcChpTM5w7d5bZ2RmiUAUmLK9coF6qUS6XGQ5HOJ7PcDjAc2wMWyncgsBlbXWZJ7/7\nBL4Vs7G2jkhTHtneYv3ySrET830fw1R+3ZgGizNTBQc+TjM63W6u5DNwg4B+b4Dj6Z1JAlItxv1B\nnxMnT5GmCcNBTLfb5bnnn+Wv/vrzVEtl/JrNm9/8RgRV4iTmrntex4Vz56jUFvjuM09y+Npr2Xdw\nkVItYNBJubB2iYmpSebn5+kNVZLQ0888QxRGBLkpVqVcoVwxeOJbj/HkE9/i3/9v/55jx26m1epS\nrVbzcymI4gG+v3ukWrlcJssyej01AyhVKgpO8mx83813ciG2bTMc9gGVOSmlShUyTei2u9RrTYRM\n6XQ7TEw06XTabGxs4AU+j33rcRzb44EHHkBIiW1e6ZiZJCoBS8OIw6FS6WZZxubmJktLS/T7fYIg\nKAyzxg9dsPXweUcyvlP09LW9A18mBcT4veAEIUSxE9ULhxrAKy64vsd2O7Q9hn5+HYHYbDZVUTV3\nfM31+9eNlm6etra2mJycJE0FrVYL0zTZu1eJBPv9Pp1ORylyx7D8cabKK6FUDeVkWUbYvurHBv4H\nEPL8w5f+b6ampuh2u8WH0PFOQghEJnO/aLN4DIYSgpiW2gYWk2rTwPO9onvO0gz0EBHlN0z+3yxX\nvdmWBYYyazetHY41qA7VdlTiivK1cHOrzQzXc5VZEWpAKLLc73hsMdAnRlGxzGInsMNEUa/heZ7K\npUx2bF+P3XH/q7635779VTWQtKzC3cw0raI7UAVAEkYhpmVgYiojfMMkyRQ2fn55hb97+GEsI+8I\nhGDQ7zI3O0Pg2vzCv/uf6bY7WAaKgmVZ9IcjBCZ/8f98npcu9jh0+Hqq9RkMp8RgmOB4LmE4wnFM\nyr7qkKuVMmmmXOGSOMa1VVqOZUhkFrO9vc7a5UucO3+GMBqyvXkZ0zBwLINRf0izXqPf7xUD10wK\ntQiMRjjJSC3yWYZpOUrKbTukWYptq2R2w7QYhSFROCTwA0bhAJkJPNtmNBpRCjws2yYoBxw4eCDv\nUlWyjpKM17h08SKWY7F/aYk0S9nY2qLeaGCaFv12xOLCIt9+4gkqlQp33HUXf/ulv+X+t7+df/qn\nfyryEcvlCp4hcPNiuLW1xT2vfz1vu/9+JicniSIVLK271dX2fa867zL6M7IkoVKpom1eo0jtUJIs\nxspVkEqq7ebOhSnlUplROGKg+eBphmFIXM+l3+9TKgXEUcyzzz3H+Qvnuf+tb6NaqyoYQCp7VKTE\ntRWUol03h6MBfilgMBoSRTG9vip6s7MqEatUKhedddFN5wiGNofdoRHuiNrkmATfNC1kmrNYDEU3\nvO31rxbyPPbPX1DGVQVmL4tuX8noRXH//sh7fvJVj//6Vz6PYcBwoK6pRq1GtVpTO/fcnmKcmqjr\ng140NH3QNE2qVZUoFMdxkW3reV7BoEsSFSO309xZKL8Y6wpsXNcMKSWhePd/u5Dn/4ujVqvR6/WK\n4q1pNLojTZMdWp2TW7ZqbioApR0zmDiNiEY7XbjmWZq5WY1lqg7bcF3SJFZhDhqHsywG/V4xxNSv\nhxQ4jpUPkcwCRvE8j36/nyvXTIShtkaRnsiPY4eOjWnaBUSjaZGe6+Q4ocAyTWTejSTJ7t2CnUNE\nsNPZC5GnGQlZvLbnekip8PRMpAgjHyDaNqUgUE5rEsAmivOg4zRDOhbdXp8sS5CGwWAU4vkBAgNp\nmKysrVFtzOOXK0jDZtiP8UtVRqMhE40J+t0WhpD4lotruHRGfSqVMpVKGdcyiEY9PMfia994BGTC\nxuZl2p02vV6HUiVQwcJxQpxErF5eybv0gPWtdWqNGpudLYajkFKakAlBUAoYjiL1+fNh9eTUFNVK\nLb9GhOo4O9s0ajX6nY6SQicJ/TgilSlBWAHLZG5+nprnEnglTrx4kvnb9pLEcODQYaI4wbI8ZmeV\nJ7xpCqr1Oi+ePIkXBEjD4OzZsyzuW2RzbQ3bMCjlkJpt21iIIiSgWqvxyCOP8PRTT3Hvvffyzne+\nUzkeYtDpdHY973GSKDsEqdLsbUupBbe3t+n1uwRBwNzcPFJKhsMRvh/geWUur12mWq0SRgmel1Iq\neVfsTvXiYQI//sEPFhivZRqYlkWv3UbEKUHVYdhRaVdJmlKuVojCGNf1qdYaNBoJURyzlZuADQZD\nsrzbVCG/NtLMrZXj5AqsW0hZhCIIKfJwBxNDCIzYJDGUR4pgdwzcQtkzRHKEZVpgUAwf9X1l+eYV\nBXj8MG2Tc+fOMTExxYF9S2ouhkEmJINuH8syitnZzv28wzQbJzSsrCzjum7eQVu582cnb9x2KMqG\noY3oRL7b3yFMKIGUhwqZyHalx19RE773j//7Hxon0ls1PaDTRTLw7Ss4lZq2o+ER3W2rQIekgCjG\niz1cibsZhkGtVivCazXGpbdJulPWK6FehTUGOA6P6M+gsXU9nBgX8CiRUHzFQEO/7zRNioKvOeZ6\nB/LKw7aVH0ma7kA0erCqDK2UUCgTGVLsRLdhGKRSYlo2nusSDkeKUZKn88SJst/1XEcNb0yDS5cu\nMT2tbF/jVLK4/wDb2y0Wpg/mBT/Fsh36/XZOS9uiUvaxLWU8lImIesnANiPSUY+1zXU2N9Y4e/oE\nUqS021tgSiQpE5N1fM/FNg2qpTI3HbmO215zq8qbTFLanQ6lWoV6o4Hr+9hRgl8KCIKAUagyGkvl\nElEc81/+9NOMwlBljrounmOTpSnt7RYfeOgh3vyGNxKGIZVahcGwr5z0pOTL//gPfP0rX6YpJpib\nnyeKYw5cc0hRUMNIxZ416khpkgnJxbVlNjc3CXyfBx54gIcffpj77ruPr/zTPxVQg4L4IgLXAYNC\n+bu4uMjmxgZ///d/zxe/+EV83+fB972PN77hjbDL+MNxHGxTsxYMBoMRcRiTpoJGYyqHLVIs08a2\nPPr9Ea4nCIIyUhpMTk4rOlwcKiOu4QADNfhutVpgmvT7AxxHNSB6l+j6PnZgEgtBUFedec2rEScR\nUTjCQ3l3OLZFGsdkacqpkyeZmppS7CMMRJohMIr7x7MdxU83d8KOsyxDjsEnBWYO2AZgiNxP/dWH\nFAmJEHgl5e2vkujVbt0wdmh740Zy48dgMOD22+9UqulRiMwj6lzXzVXbo2IxGNd4aE2HbqiklMzM\nzFzB9BqfmY3XCD201MVf1wlNXtAwTZIksLvD8E5N+N4//u9/jA8GxkN+NV0vCqN8ZTYQ0kJKseM1\nLFOVI2gYuJ6Dk3+ccf7ouBpqvEiP8lizV9L2xrv98b8f9zUYx8z01kcvGLrD1q+/g2nHhQVlMWkX\nWU6fksXwU5/w3Q6dMK/+tYpptl6xw3CEZdl5kICic8n8c6dS4vkBtWpVJZE3J2i3e/nFZBCnKeVy\nmU9/+tPcc/fdzM5MMxyNmJicRBoWtmXRrNcQWYJlCuJoQCZtGtUGpYrNwDCxjJhKqaS42iZsXF7h\nO098B8c2GPZ7tNst0iRScIunBqyW6wAJaRgzGI34iQ/+PEeuvY5OaxvXtdm/bw/9wYB2t0MWD9ns\nbLNnYpZwpOLBwjjGtl3WLq9RbzbodrsYponrecRJjOeqIXcpCCiXy1y8eBHXdeh0WnilgDCOCMpl\n/uZv/gbHzBiEI+b27mEwGGKZFmkmOH32HKZpMRpGTExMUK1WmZyy8XzlIX7uwgVuueUWlpeXyfJC\nNTE5gZ0rLG1DEEUjRqMwz+B0mNsziyxk3CbD0YBM7l5k9Dm3bYd+f4DrekgShDCxLR/XKZFmKVGs\n9BK1Wk2pNvOB/nAwIghcRuEInaTeH/ZobW8zHI0YDIdEcUypXMbM0sIsrt/vY5gmM7Mz9Ptq1uJY\nEIbK82QwGBDHEZ7r5HOhgLW1jSLdSd0TFmmm8iFFloHj5PatKVJwJTxhWnnR1VCpglhEDv/t/uVk\n2JZBr9cpvifFatlRfSqjqN3VzTMzs1y+fFn52uSYe5ruCAgrlVJRDzQZQTeQ42Es+l7UehJNOx5n\ntGnK4fisbZy8oN+/JlGYpkn4fRxlf+gFXNNu9HBQv3nYWeV8R7E39AdXSkurMLMycvWXlsWPXxT6\nucaN0lXxFAyjiG63O7baqot/nPeqL0T9HvVzaRMs3/cLCmEUKQEF7GBW+kKoVusMh8MrpummZSGy\ntBjIjifn7HZoeEldlOYV3HJNSZRSeTh77o5RjmEYOKZFfzCg3pjANGEw6GMYEsd1sYwKcaSGYOEo\nZnHfPqJwVNC4hFCK1msPH2azn2DLhFrJQ+KAHLJ68SKLe2fp9zusXlrmzMunGQ2GVHzBa48e5IEH\n3sVoFPJLv/QfEGlMreoRJRFBuUQYRYDD1ESDn/6pj1ItB0SjPpVKwKDf4+zZMywtLVGulFDGipJz\nL59lz54FwjCkVq/j+h6VSoXjL75AuVRic3ubcrlMo9FgNBjg2A7rq2scPHAg72TVzROFIaZlsr62\nxp23385zLzxNIgTbnQ7loMT21jalUplr/l/q3jxIs+s87/udc/dv/3qd7ulBTwODhSBAcIMIbiJF\ncbdKNJVIMuVIVDlOXEqpnJSTqPJP1rJNMnbsklzOH0lFsSSXJdJ2IrFUskKJokTR4k4CA2CAGcw+\n07P0+u13Pyd/nHtufw3OEK5yOXBO1dQ0Bt3f9/W997znfZ/3eZ73zCMIJN12p9YKFBim0RNPPMH2\n9jaNMOR73/sea2trDAYDJpNJzRduBkYu3e12j1VxnusRBD5JkvLP/8U/58UXX+Snf/4H7/t8kuG6\nHrNpjOcFLC628YOQJElkJxz3AAAgAElEQVSYTsf4vke322UyHdfNwTzLcD2Xw+GAaTzhxZdfMpVn\nntHtdphMjNsnUrB9+xbNRoMwikjiGMd1mSQzJknMZGZ8sKNkRhQEJJlhaQgBAk2j2WR/b4+TG+sM\nDg/ZvrVNv9en3+9TFAa2aXVaZLkZ3KC0RqEQ2piZIZxKlannxqiZIS2l1gjuzUJJ04nB6SMz/EEp\nTVEWlEVJGEZVNX0cw55fjuOydXqLwcEBUWBew1pPWJLEdDqt93673a6RABtk5yvx+Yakfc+je+ce\nS1gtr9xU0E6Nn9s9HUURyb0JafV63QO4hSvsL2lghSPr1TiO64BsGwIWYplXLtrOt22CzsMnFnax\nXytlaG1aa3q9HnCUncfV4AYbvE1nf3ZMXWnf056mtfLLdZFzGfSRCuzIN8H+jPn86tjnsjzwHybk\nmYeEzGv79fWwXHatlVG7zdGrpIBuu41A8OSTT3L+/EUKpYiciDw13zuezqDM0RoazRZJkjCbzTgc\njGg2W/zI02/jN3778wwPdhmPExpRm+FkTBh6xIMeC/0Ok9GIH//Rt/Hs95/lP/qZT9JqtSjygsHk\nkL/2Cz/HpctXUFoRtppMZzM0cOHiK+g05Td/8zdJkylPPfkEZVEgheD555/nDY8/bibxRA26vR6h\n67Ozu8sjjzzKzu4uD2w+QLff5sKFC+zv7xn8cjYlK3KkKozbZSOg0QgZDof4rofreQSuw81bt/jS\nH/+RmZXZbHFwcACAfyJg9cQaqtD89E//DJcuXqIsSg4PBwyHQ/YG+2xtbbGyssJzzz3H3Tt3ePzx\nx7l48aKZRN5uG6vQ6bTqSWieeuopiqLgxo0bHBwcIB1JWgXYM2ceprfQv+d9N9WmZjQa43sh7XaH\ng4NDSj0jCht4nosf+BU8MsRxJAcHu4xGY/IsY319nU6nxeLyApubm3UAGg2HPH/2WSPrv3yZoijY\n2dmh1+vhOQ5Rs8E0S7l7uI/reXQ6HXJdMotntMKI/d09XNchTk2AD4KAaTwDR1IWOTv7eySZSWoE\nEMwCcpUz7xwohZ1XWQl5qqrBWLxOyHOF2Q7381MyFrZJYUeUubiugMovCURFsb0PFqF1ZfXbqgVG\nZuJQSlkUFKWh/ll/onmkwIqq5huMVkg135S0MWLeV2aezDAf0Dsd4wlvk9fXWq97ALcNAZt521/O\nQhHzfgMWorDB1AYxG6ClMI2QssjAcRBV89JzJY4EPwqqSeCZmWA+ly3b4DjPFc+yjEk1+xCo38v6\ndduDw3Xdmj44f7jYGyaFAIXZZJUfiRTSMCsqGMdCNvO42KtXWRRopSoPdLfusBuc21DmbGe/KLIK\nzzNUKtd1QTgIt+CpJ5/khRdfolBV1YKhKMZxzINbW1y6cpUHt7a4efMmp06dYnFphSAImM2m/Orn\n/jZIjzxTTKaxsUJw4cIrL+E4moP9A4SG//pv/g2KPKUstBlJV2Q88dgbCH2fd77nvcRpguP5/A//\n0/8IGqJmi7u3b9GIIsbThIcfPsOJ1WU+/hM/wWA44cIrr3D16lWuXb/F1YsXcFyXfr/P7bs7aGB1\n9QQHhwNa7XZtYDSdTimTGb7vs7CwwKVLl5jNjHjmwoULfOMb3yAvCzxb9lbP3XAw4uaNm2yeOk0Q\nBHz969/gYx/5KCBYX1/npZde4vP/8rc5ODjgn/3Tf8rS0hKf+MmfpNPp0O12OXv2LL1Ol2bUoNNq\nI5S5b2ceeoTHHnsMMIf7hQsXuHXrFtevX0ejefvbnwZ+8wfu+3g4or+4QOhEHBwMkNKh2WoRhBGe\n7xtoSmUUs5hZPOHmzRsMh0Zk8/DDD+G6HmsnT+L6Qb2XXNeFjQ3e9OQbOXfunNEBtFqcOnUKRzrM\n4hm7e3t87/nnuHL5Kj/24x/gT/7kT5hNZ7z9rW/lQGm6rTZOKRGeA67Di+dfJs9zrl27xo0b2yil\neOyxx3j66afpdruUeUqJwvdNQ9/3A+I0xXN9ikLhuh5aafPvcULTtRYWIXl2bwhlNB5RFBntxSWD\nr0tZVY4SPzDaCM8N7k9DVGZws4VFjfWxnT+rEMIhCLw6Rhh8O6tjlYF6Ld0yr+OCEVyFdRyYV2EX\nRVFb0c4LDG1yKoSg2WzeF7efX697ALenFFAHy3lJsP2lagOpYxzSo5PcnIpHYh37PZY7bYNyGIaV\nDeRRlnyMdYI5/bQ+8keZN46y8wdtoM7zvH5P25ywuJelGZmbpurs2p7cQkAYBcdu7Hyj5NWr0TAD\nna3s33VdYzUqBFLMOaZJEOKIXlkWBRqJ5wcoLThz5gxlWRCGDZRWeFKiMAKgOEm4dPkKaFhdXUVp\nGA0PmcUJnXaL0UHKcDKjGXW4desOUSNkFo85dfIE129codsKUIVGFQl5bn3UHcPeEC7ra+uMh0OE\n4xAEIUsLC+wfHBLPZvQXlynLgpvbt7l+4wZSSrqdDq7j4gchWW4OxFOnTuEHxr9ja+s0RWkOwqjR\nZDAc0O11yfOCLM/ptI0t6t7du/yjf/RrVaM8JYoilpaqwRBJwkK/z1J7ESkkk4UpWinW19eRQvDK\n+fPs3LlbeeckpHGC9DXD0ZB3PvMMJ09u8OY3P0WSJDzx+BvRn/o5dnd2uHH9Bp7nMRkPKfKcxcWl\nCurKAc3q6glWT5zgHc88YyCdLGV4j1keNfaKpNNpI6WLEJI0S0nzjDxP2du/y9Vrl5mMhviVEdWb\n3vQEJ1ZXKXJFkqbs7B3QbDYMW2UypVNVCg+c2iTNEpIk4fat2/R6XVzPo93p0O106XXNYfTYo49x\n6eJFFnp9JqMRjWaTsizwIp/TDz5If3ERNPzkJz5JGIbs7Oxw+fJlFhaXmM1mBEKC55CXBVlR1IKk\nPItxXa8OZkWRc+XKZSb71ylLTZoVKAUfuceeiJotBBrPiwCB7wdIaZ4tg0Vr4xNzn2UTRaWMsZbr\nupU3jw3CwQ8oOS2uPm+TMe9GOE+6sM1MW13bZd1QgZpnXhTFsV7Yv8l63QP4PHhvyweLL1kfApt9\nz2fINuB5nkej0ag3hsW5bSOhlt9DHXQNbGH/cOyC288zm81quex8YJ23mbXqKoth2Spins1SK7B0\nXjdNbdMzCHxjTjQng3ccp2ayvHoZOMmoEed7BWmakmdJfR2N4X9uxqk5bqUuDRiOxzSaHQ4PDnjL\nm9/MufMXKPOSZsvg+JPZlJ3dXTY3N9l4YJOlhZ550KpufpomTAZjoqjFhZfOsbl5Gjdw2dhY5oUX\nzhIGZmJPluYUWUGpBaLynxmORzz62OMkScLgcECn1yWNYx45c4ZvfevbhL7PxsZJfvmXf5mDgz3K\nsuTUxgbD4RCtNePRlH5/gUajwZWLL7G4tFQP1UA4fPs73+EP/uAPaLXM6DvP92iKBm3PQHT//X/7\n39VlcLvdMVzuyYTRaFTL2fcO99Ba0+l0+PM//xqdThfHceqhxTdu3GBwcMDb3/52FpZ6/NR/8FPs\n7OyYe5zlpNOY1DEwXLvZ4o2PP15tfEMjnE6n7Ozs1j72SHPopxUufr+SeXd3l26/h1IgBIzHQzzP\np1CaK9eu8fL5cwwH+ywvL9BsNhDVBPd2q8VsFuNIl0bYQArPCHI8j/bKCkII7ty5w507d3hwa5OH\nHzoDwPatW4xnRnzywnNnObG+hi5KZKk4tbbOdDwhz3J2K9pgXub87v/9e7zhDW9gaWmJS5cuM5vN\n2N/f58wZ85r9fp+o0cAPAxqNBmEYmgQoM70AKQRZesSm0kXJ2cObSGAYDxHy3qHq2vVtTp/ewnGM\nUtoImMxelcIFochVzn2K2iNKcsV711pTVsrQoijQSVYHVN/3j1lezEMiJo6UxwQ5NlGz1E2b4Gmt\n6XaPJoxZe4w8z1leXq6TToDxzXt/brte9wA+r5Cal7LbzHw8HlYXxUptqXnSNusuS9PVttN4jjwW\nZN28tLCLDexWoWWXPUXNe8i65LGNUxvkLeZlS575huO8urL2862wb0d6NbnfBv00M7Sl+aaHxf/v\ntY446senmkgpiRouUhj5sdKlYY5IpxYejccT0CbYCylYW1/jhXMv1eZeeZ4TNprs7u7S6XS4ceMG\nRZ4a7NJ3q/FdpSltO4s8sPUgzXab3Z273L5zC41gde0kw+EE3w1JC02qDXSQJUkFbSguXnqFJ594\nEgXE8YT3/eh7KIqUrVNb5EVGGk/QRY4r4dzZZ02T0gvxBKTTCZPDQxYW+mxvb9Pv9w23vdXihRde\nMIewa0Q/fsUa0Frx2GOP1noDpRS7u3u1F47v+wyH5hnbPHmK6XSGlA4f+/BH6/7BO3/kHUAlQLPS\ndFVyuLePJx1CPyBNEvr9Plma4jpmsC0Yr26EwPE9ut1+fY+FMFxrw1X28DyT8d0rA9/a2uLy5ctI\nKem0uwwGQ5Isw3F8nj37PEoV1Wtp0jTDdyoBWa5xXck0TimKGbJKKLI0r0VjvudxenOTw4MBt2/f\nYTIeEzZChOdx7tw5PvLhD/P8c2c5//yLNKKIpChZ6Bn/7yvXrhLHMQ+fOcPbnnoLT735zQwOB6ws\nmNF96+87aVSxFUwqhCBOY6bTGfFoZvoc8shq1RHGxkEgOLh7h8AJyKncC+W9I/DSyhphs02v08Xz\nzN4uVQnaIc9tVnx/Uz2toSwLM/1HGAW45igDNrbUJgG01fS88dw8EpCmybG5AK8e7mLVw8dgLKir\nfMtcmUwmdbx5rfW6B/BX86W11jUzxEy+DuoLYXFf+8sKIWprV5u5WnzYDk2wfErrOmgDsucF9XvP\n3wwbdO0paDmd9gGYfxBspvzqhqo9JCz2dVQamoBv8XIpRRXEzfs3Go36YLjXmvdOMNWHX0NL5pA5\nauQYhotCK43nugS+Q16U5KVRj25ubuK6xuvE+sUIIcgKxf7+Pl/96lf56//xX2Nvd4fLl27w0INb\ntJpNpkkHpIvSmu9+/zk6nTaj0YAHH9xiOJ5x5+4+D249zO7eIW5L0mo0SScZG5sbXLl2mfWNEwhH\nGz8TIUlmCe9/77tRWUmjGfH1r/4Z73v/+xiPRyz0u5SFYjo6ZGlhmbwo8KQZkvvQQw9y7dp1rt24\nzovnzhkWA8Za1QGK6tAuy4zTW5sMBgdobf04tPESqTIi3/dxpCSexLjSYTgcsbS8hC4VWZ5RFFk1\nvm1KiaLZbFFmGc1Go+Y3+75fD/0IPL9+blzHRSOIZ2n1jBi1YaPVrAK5g+u55GV+30bbk08+yebW\nafb29pjNZpx+cIv+wiJ7+0O+/u3v0YwCVFZidB8OrU6P2WTCdBrTbHiEQRO/5aF0yWAwqPaIUzlB\nOoZ54gecPXuWhYU+axvrpKrg5NoaaRxz8sQaL597ie7JJgutLp50OJwc8p3vfJcwCDi1ukaj0eDL\nf/jHvOc97zH0u26fvds7BEFIHOeEUchwMCRsRHi4NKOIJIlRqqTTaSKqQCowszIXOz32bl8jT5OK\n6XJvDPzMI29AlVYBbSY9OY6FXnUdwMvyfhRNM4xZ6CONBnNc9Cwv6iajzcJt4jSfRNkKysKh1mjO\nZt/zPHLHcRgMBscSNftz9j1sAvpa63UP4H4QGomvFOR5ZmYluuYmdDrmJmeZMVyyQdJ1zdRozwuM\ndFpI4iTBdYWBUZRC22aiqLIypSmVwlEaLaShkNlpHdJ4GQt55B88nymVc46IZjiEmcLtOPIYu0RW\nXF2LgUdhSFGahyqrDpQMTZpUHHIpUBVubyTxBUkS37fhEsfGynP+ILKDKOyDZXmwxn7AqfE/VZS4\nrofjSjOw23VZWVnmytVrOEFAUZYgJP3+Aju7+7zxscf4nd/5Au96x4/w8ENnSOIpU61xGg3ytGB/\n/6BqyGlWV5doNELu7uyxtr5BUWqWllYoHSNPHg2HbD6wybM3brCxscGN69c5uXHSHGiO6Xlcu77N\nqQdOsfnAaa5cuUq/36cRNSpvabh+4zrNZotut8t0MmU6TVheXiHNCy5cvMzu7r6ZJi6MXarnCDzH\nQycFjz/2OKPxmCiMkEJW0nX7LDkkcUKuTAmd5Rntdos4njGLYxYX+0YJCfR6XcrKT8fxfJI4QToO\nvh8ggPF4TLvVrhhT5uD2fI+yUEQNI2BRynh0zGazGh5UVJNl7lPmHx4O8IOA5aUV07PwfYxtq2nU\n2+rRdd2ah+55Pgv9BQQOjnQq7r3H0tIijuMyHA7IixzPcbn4yiXWT57kzW9+C0EYMhwPabdbDA8H\nBEHI0z/yIzSiiIsXL7K2vo4f+PT7fZYWFzmxukqn3SHLch4+c4bzL7/M8vISWilajSZJkiCEZDoZ\n0+93iZOK3SGMWV0UBAgpkNZ5XkOpCiaTEVmpSPIC6bhI594Z9HQyxZEerWZIkpgK11bKlnwAR1S+\nVy9Hmr1kFN8C13Oxk3mEMFOIDENGVZ4tR7CJDcpCWIqvwoqTbM/NsuPmA3JRFHWyNp883otd91rr\ndQ/gruOjVWbwJ8fHDz3ywiiqXAd8z9hlpllGUWaEYUBWFLiOh0oS8ysIRZZpgsBkZ55jfYQNmd/1\nfKTr4kphpLKikpsrbbyWqaAFR+BwdFGLCopxHaemPhk/gyOoRADakchqcrURHJXVmBHD886q0z/w\nXZP5VD+rhTCNxaosk1LMGdPfaxncft5L2XymfC54SyMp1roKZNXQCTBqUDRIges6fOyjH+Qf/sNf\nNSWgkAjHYxZnSCfkxXOv8La3PMXS0gqh7+IK8FyH7Z07hGFIux0gnZJb27c5sbpKnhUks5heu4cq\nCjzpovKSmzdusL66iu84REFIIzQCkCI/wnylkDQ7PcJGm4Ulp+LGaqbacGJb3RAvajKdTrl0/Tpr\nK8ugoSg033/2ecJGl+Fkm2azZe63NHh9lib81Cc+ThhGZspQhT+a3oZbKUpzPO/IiyJsGEhMa0nX\nN2ZNUgiKPKecwzwFEildpJBkqQnwnhegNAhZld/SoSzNWDpLGZWund5+5DvvCIFw3Nqt8tXLDxpV\nsJD4nnluhYZmFOJIAVTZo+PgeqYJWGQZeZEQ+QGeI5CRhxKCGzevGz53FNHt9NAaHnn0ser5cxmN\njFDIQRCFoVHZjkZEnRYf/NhHuHLlCmmWkSYJD2xssLu7y+FkyFve8haklPSW+0wmE65tX8efC1wG\nMvDwXJ+oEdGUTcMQiYIjKq6ELElpdzpcvnYN5QaU0qdAIfW9A3joBzjSJUmOrGyL4shf/9V6kFev\nrLKSyIu82gdHe6ssSyaVi6iZRtSoq1ULrxkXSVGJhcJjakobvOc9kWxSOA/FzLPv7Axd4L5Q6vx6\n3QP4ZDyqH2i0AqEJQx/XNeVInpmTNGoElYoxpyiNmKcoC9A5UrpV9p3heW6VlZvRSGVZUGQZRVZN\nJ3E9fD+gzApKrUgqS1ilFKHnklUT17U2QpsSTV5qHClwHFPeaWW41lGjYRgeuqTUpjTOC4UWDkKa\n7N8PzKYsqw5UnFWNGumg1BGbxHopW+rkvdarpbhCHLe6tA+sMeZyKPKctMhxS2PP3+/3mc5mKKFJ\ni5wTyys8cuYMOwdDhsMxnlfJ+LUgyXMunL8AZc7HP/oh8jRjOs1rvG5hYYHtm9ssLfYrDFCzsNDD\n9STSd3EcQZEYaprjOFy5coWVlRXyPOf06dNGYl5BSbPZzAydzmKC0GN3b5cHTj3A3bt3CcKghoE6\n7Q4rKytcePkCSysrXL1xg/3BgHMvn6fX69NoGMfDOE4RQoNWPPPMM+zv7x+Dwuz7wlETy5auFjab\nV8TOc++hophpieMcibrm2Qj2XtV/C2Nv8GqmgqWS1Q2z+2RcFi+1993aTXh+WD03rZr/X5YgA4dm\nq2lk8t0+GRkHwyGtToeNjY26rM+yHNc5GvmXZWbot+MKssJwo8fjMS+//DLvfPe7+eMvfYk4jtna\n2mI2m/HGN76Rd73rXXznO9/hxo0bRFHE8vIyUkpObWyQ5zmtVqum1JlezLSuGoui4NatWyYOVKyM\ntBpEMplO8ZsNlNJVJn2/pMZApEofbxTWcIi9X/dZlklm7aDTNCVN05oCaCcJATX5wD47ZrqUrgkF\nSimGw2FNdph/JuyzZ22p5+/3fOMTjphxrusyG9/3owP/HgTwVqt1rEmYZemxEiRqGi/wLM9BQNRs\noKvM1zQddFXaKKLqIYlnExBUwdo0UGazmKJQ2DFIQdAEIUnTAulIpOMxjdPKWMcEXAkYP2HHGDpp\ngVTV9GphFF4agZAeQpsNRJUBl0pRVNCPaapoXMc1nFS0mc6uDGQSBCFFofA8uwnvr5+dV33ZEm1e\ncWkDT6mMk6KpCsxDMhqNzKR5UUEH0ymf/MRf5h//779eDaHIERgvcIRkksTsDwZ85at/zuapDc6c\neZCSgulkwp1bt8mzrA6GOzs7rK6eoCwL3CAwJlpKsbKyQqPRYDweUxRFPd3EiqMajYahyY2n5FmK\nQHPixDL7B7ucWFvh9u3bhGFIv7/AcDgkSWNW1tb5+je/wd29PQajsTGOCgPiJMZw36HMc/7Sxz/K\nrVu36ves5cnVoTmPWU6n03rT2M9p13wWZ6ErA0sktUeHNQSLosjYmGqMBURZosVRr8W+jlXyzgvB\n7tf7aDSi6nA2boJRZIJL2GxRFHn9/mma0W43KYuSZtTg5Zde5pl3PAOOw4nVE+SqPCYuCcMARx4F\nJM9zKcsc6bj1offKK6/w0EMPsbezwwc+8AG2t7fZ3Nxkd9dI5re3t1lfXzeUU6UYDAa1qjnPcsbj\nMePxmEcffbTu/xzxp48GkqyvrxPHcX2wf+Nb30RrRZ5nuJ6R2N9rua6L8BzSdH4QxFEvyxIZ7ndt\n57Nta6o3//32s9r+nPX1t9fMPlvWj2m+J2Wvs62M7bNms2z7/+b535bBZkkQr7VeGyX/d7yms4Qs\nLxFSGhc1PyIMGvh+hMAhzw1NLAwbOI5HmmYkSUpRlqiqIedIie+5dbnmeZ6x2FSKJE4YDoZmTFsQ\nEIURC/2FWq3ZbBnJdRiGNKuNorWqN7jjOHiVnaauONhFbmY3JokZSpBleQV/BDSbLRqNBo1GRNRo\nEEQhQWj+aK3JcjOHMy/MSDPXPfJQAWH4y9k95mphMgDbsbflFhzRFeezcj8IyMsCpTW6MlLSlERh\nSJok6FKhC0UjCHnyySfZ3dk192M6pShLGu0WQrq8/Moldg8HBI0WaaEIg4C1tTWEEKysrJBUnjLj\n8ZgsSwHFdDqmKHIGg0FNw1tcXDTmU3HMaDSqs0o757RURc3gQCtu3rxOksR1YBiNR/iBT7PZYJJm\n7A1H3Lx1m8PDQzNcRJsDMJ5NCHyHZhTy9NvfWjevrVGaXZYRMF/RNJtNoiiqaX6W4mUzZau29TyP\nLM9wXIdms0EUhURRSKMZGSZNmpDlKUqVOK7xmrHmbJZlZTM0+36tVotms3nP+24w2bJmbEwmYzMn\nE6pJT0bpqyvFYlppE8bTKa12B6WhqJ4ZSwSwtLXDwb75nI5TB+yDgwPe+MY38sILL/DUU0/xyCOP\nEFb+5pYW+93vfpfHH3/ceM8IwV/8xV8cIw14roEO1tfXefrppzl37hxnz57l6tWr9TMLRx5DVoXY\nbDa5desWYRThOEcZtP36XnvCDKc4cg20X1v2i/2977Xsfc2yrCY+KKWYzWaMRiOA+sBvNBrHqMJh\nGNaGYIeHh/XvYl/D+iBZyqT9fPY97ZqvytI0PTaT97XW656BP/v8i3S7nbr8cj0XXTUiHSlxSkle\nZOS5OYXDoIUQmrzI6uYimDJGV1aSLpapYfzAwyhEII5tWCEt1SqtzfA9z6MXtI3151w5pUpzIU0D\nU0JltqNKBbo0NCVtxr/F0wl55SXsuWZGYJkXVenkgdYEvmFxoMGLzNQWe9J3OsGxQDO/5k3rbUfb\nZgY28wBzyJSqBAz+XzdUquyr3TRlrfQkudZ89MMf4bnvP4dSJUWZ43luzYEPm01euXSVWZzyxONv\n4B1PP8mFF16sxuoZYdPt27fZ2DiJGV9mIJbd3V2Wlpbqh3E8Hte2BJ1Oh+3t7foQ1VrTajXZ292r\nvCZcnnjiidoatdlqMZlMGQ5HjCcTvvbts2R5xngyqxWkhSpIRzNcKdjb3eE//+VfJk1n5HlZ27qG\nYVirdm0pbA99W9baDTZPfbPl+DF1bZVNzZe9NlDM9yOklGasnTjymj/CT4+mvduNf69lP1eSJDXU\nAzAajXj/+9/Pv/7XX6sw1tIMfGg00RgGTl4YpanRHETHKjeL644no0pA53NibZXZbFoPMF9eXq5Z\nYbaxf/78eX70R3+UyWTC6uqqGVzRaqGUqoc8oAyrand3l8PDQ970pjext7cHwlAM9/ZMthv4PkUF\nIQVBQH9hgb2DQ2ZJQhCakYk/DAs2vQyPosiOieFslmzv9f1gFPs8CCFqDrllhNlAPH/v5xuP9j5H\nlVGa/Z75Csv+93zAns/ybdVnD79aYV0xzV5rve4B/Bvf/j5KV4bnWYZ0HE6ePMnb3/42Tq6fxJU+\nnhfSbDYYjUaMxglCaqQUeNLDdR1DvFcS4Woc10cDutQ4vosrZJ0VuFVWUOQ5AoMV54WV4ZtgnsRF\nfaNc1/g0H22+jCI3bAVDPzNsGKWU4VwDge+RZCYzUFhcumA6juuGhoU88sL4PNiNbBsh8j6iBZsB\n2gzRZpHmsx0Fj6LMkY5ElSVJOkMKE+jb7TZlUZjyXmlQEHoeiRD8V//l3+Lv/N3P0Agb5GVhmmVo\ngigijRNefuUS7W6Pq1cv8N53v4dWu2dk6Y5LlhubUztBRSnFrVu36PUWmU7jOZjHZhk5vd4CZWnu\nY56XNMKA0WjM6a0tZnGM74UU5ZQ4yYwPR7/P3sGQr/zpn0PU4crV63T7HQLfYzwx/iZCQBLHfOqv\n/AyLi33iyjbXbgilVE3rtLisnRBjy23riWM3o4VPLD4+/7OTyYTJZHIsq4bjGZX9fq1M4A98F60N\n08F+r/IctCoJA4I2OEMAACAASURBVA8mP3jfvWqDe9WBXWfSSD70wQ/xp3/6p1UQM9S58XhCo9HE\n8wIO9gdsPbhpGsCvvFL3INrtdi1Ss5nvhcoKNo5hMhyzfmKNweGhGYgxNVOndu7cpdvt4jnVZ3Jc\nbt++zZvf/GYuXbrEQw89ZK5TNW92YWGBODaGUItLi2jg9vYtWq0WjUaTMi/xXI3fDUiznG9+89uc\nO/8ySZajKBCVs+b9+kL2WlgqsQ2ENgmygXteBfnqn5/nZfu+saW12XWcJDQajdrE7ODggF6vV2fi\ntrLL85zDw8Oq+m4cOxhsoK/H3Akzqcd+5n6/X8cGSz22qkz+bWZi/n+xTp4yneyiKHA8I2u9ub3N\nzu4uWZaxumgUY+vr67Rb7ZpbKx2JFOD6PmFo5O15maDRtJqtahiDefjDIMALTLdbaY3jeRR5XGPf\nqlTMpil5niGlKXlVUVLmVXO1qt4c4SDcI/VmmpqmKQoKXeJIp844gyAwA1GFxG206htZFoWZ9OO4\nRKGLF0S1sisIDD3sfpmYxV/n7XLneeb23zzp1/REE0TMATbMc4PFSzNdRSIoygIRhASu5EMf/ABf\n+MK/oLvQr09/pQzO12y1eeHcS2ysLfGt75/lnX5Eu91iMJriBw3AIQh8RqMxWZbzWMVs6PV6XLt2\njY2NDUajEUmS0Ov1UEpx/vx53vSmNwFwMBjQbLdJkoJZnKHxmExigqjNYDTl/PkLfOd73wPg7s1t\nWu0OKDMgwHc9kniKJwUPP7TFo488gipLw9PWeu5gPHKOM6KkI8WrzaCsBajNvu2BND9ExGZKFv6w\nG9kqiOd1AzbjtV/bctxuWPt6dqzevdZ0Oq2bX/OZZJaluL5Hq9VkNBrjOCFZZoJdluW0ej0uXbnM\naDzixIlVHn300boCsBnqYHAICA4ODlhaWmQ2m9DrdVleXqqEQ50a3rCH3MmTJ+vnzfM8nnnmGb78\n5S+ztbXFhQsX2NraMjS+6rBpNBocHBwwGAzo9ns022Y6UJakNYSYZRmOdNk68xAvvPwSzVaLeDY4\nZq1xrxWGYW3RPA8j2gx23s71XsvSb21Ariv0OUjGGuwBdWY9P1rNJntra2t1UL4XDDLfv2o2m8fs\npbMsq6E6a+D3b4KBv+4B/AM/9n5D4amaS4eHh9y4cYOdnTvMpmO2t6/j+wGDwQEIByldIyOuglxe\nFIbmJwWOX43vCqrTMTcZie+6hmUqoBE1WFxYoMimlGVBt9fl0UceZXV1FemFRGFkKHhVgzTPc/Ik\nr8pqt+KLG954keekmZFJO7Z0lh5oSakUQkpKDWVqIA3HMdPTzYgzDaUizad1oChLhdbivnay9mGw\nmWBRFLWlri29ytJw6rWFdqQVJUgE84b5hi8tNORJQpplvPMdT5MkM/7VH/4RYaOJdFxKrYiaDeJZ\ngut63N0fUODypT/5Ko89+jC9bof11RXDd85SFhdW2dvbodvpE6fJnPL1SHFmObpLS0t1ifmFL/xL\nfvEXf5FLV65xemuLLC9YXFnjd3/3dxlPZ8bnZDQlzwuiZouyyNHVFCIpFLoo6a8s8gs///Mk8YzQ\nN1CUFvKYXYNdtrS2TSQbfC2EYoVXthqzn9te69lsVh+c82yHWnk7FwRs9u95Xt30NSK1qL6nddPq\nHqwDO0DXqvRsuV+WJZPJhK2t01y5cs04KQpJEBhOdJYVHB4O+PjHP87Bwf6x63Dz5k1c16XTadcN\nbiFgeXmZy5cvs7i4WA/ltp7z3/72t9na2qqrFiHMCMPRaMTm5iZSSt71rnfxwgsv0G13atw/jmN6\nvV49O1ZKiR96FFnOZDKl0+lQqJJev8vXv/0t8qKg224xGe/XcNH99kSep4YRVl0jW0HZa2sz7/sF\nQ/t72Ax53trVNkDt/rQwzzxsCdTvOy9KtH/s4TxvdGefD3vI2PexlUOe57X18Gut1z2AR75D5BlP\n4FbkcWK5x5OPP0KjYUom6Uj29w949tnnubOzx+BwTJrnxrHJccysTMdBOg6ZKpB+g7QoQSukNLzQ\nUmv8KnMZjGYcDqek+QwpBeX1W3z9O2cRFX6ulSb0fVrNFo1mA6+atWh9FsLQwDlRIyKqbqbxXTDz\nCIU0qi9jjlMxFISsyjCzO6MoIvB9wsDDFaI+cT3fw3EkcXxvE2Cb6c2f5PNGO0dlpq4zecM3r5o1\nSLQ2KsWyyNBlidAgPR/fkag84y997GPcvbvD988+j+N6eH7EYGC8N/IsRziSG7d2WFla4Kt//nVO\nnlyn/d5347o+7U6fnbt38dyQLDXX4Pr166ytrdVZhvUjKcuShYWF2o96Mo3NkOSsRDgeu7d3eeHc\nOe7sGgXlyxcuG1l9ECGUVTvOKPMcIaHbafM3/vp/QpHneK6L1sbFTs9lwvPCLDji2drN9Gr80cJV\n87i1/ZkoOjJBskHAdSVCWFqeHdYAURRSlkcH7rwQa541cb+mleGpm+8LgiM8N5A+eaF46MGHuHr1\nuoGLtPHPLgrzecfTGd/61rc5vXmK3fEIpYzSdnNzs27c2UAhpeTWrVucOrVBWR5NkAmCgL29Pd77\n3vdy/vx5ptMpDz30kMHRE+PrPp1OiaKI8+fPs7S0xMHefv3z/X6fwWBAu91m93CPKIiIgpDA8+l2\ne9y8uU2n1+PmrW2efe45wmaTq9dusNDxaTQaxlum3b5vDDHfk9ZDVeb7EZZvfz9thT0YbBC1Fa19\nZqieFwuRCSHqwxuOjK0CW+HPvZd9ZubhlvlKbr7pa/e0rThms9n/P3jgIk9xPQeV5witEJiBw+PE\nZMgEhtZ24uQyQSMgSS+TDFMzYb4wOLcC0iylUALfN5u3LIp6Q6V5QZpWDoGuiyMdZCApyoJClbhR\nhF/d/LIoENJhnCpGybgqoYt6s9omiZAalZtBx+bGaRqNCClFfRjkmTGkDwLfmNgr42luIB4XqUp6\n7RYLC31WVpZZP7lu/v0+5Z7Ntuc72nBka2t5qVopXKfKth2JqDr4WZ4icFBlWQ9jRmt0mROnGY7n\ncVDmfPKTn6DRavFHX/4K7a6DVoI4Tuj2euTaCEf2Dkd4juDWrTv8/u//KzqtJgu9DqHn8d73vBsw\n2efOzg7r6+s1t9b63WRZRqvVYjAYUBQFD2w+SJoVXLtxk+899zxaSG7fucNgOKQsob+wRJIZiptW\nBUmWoMoCoTV5lvNL/8XfJAwCZrMJoR+S58aLIq+Uc3bzAXUGZTcQULMrbLY9D3vYzTcvyJgPevOW\nDHbTzlM6i6KsN/m8cZntDdjs+344rfXVsO9pA3+hjEZhfX3d+Jk3mkxjk3m3my3u7uywtLjIs2ef\nJ/A9er0OruvywAMPIIQw/uFTE/h6vR77+/uVkEzXvGzrS91sNjl//jztdps7d+7ULCiL92ptDJoO\nDw9rgzk74d2yV5Ikod/rkyYJ49GYVrNJUZScOnWKJM352te/TrfbJSlKHM+onWso4j6OgjZrLsuj\n+2nvk712r7VeDZHNJ0kWbrLMFBuwbSJi+yTzrqTz8It9jfnGtnUptffeCn7mm+Odjhm28VrrdQ/g\nZZ4gtVM5RBhlU14UxjGt2WSUT1Glot/rsL62zsmTG4wnCS+ff4U7OztkhYEKPN8hm2bkmcZ1PbQw\nFzfwAwLX4OGOrDJQpSi0wAubhFJiZ0wKJUF4gES6EoEmzYxQCKlBgu+Hldy3xPW0aYJimCmTJMd3\njbxZCPO58rxgOstqrC3NNeOpCcSeUNzC0BaVLk2DUcDq6gp/9x7X6mAwIgxCXFegyhxdyeo916ux\nXtdzkYAjTRZWlqo2DVKlAmEOPikl0jUiE0Mjk2aWZq5wkXzyL/8kaZrxtb/4Bp3uAhrJaDzB8SK6\nnQ7T6RitJTu7e+zcVawsLTIajomnEwODbG6ydWaD0STm7u4BShkjsb3dXZaXlymKgulsn929QwbD\nCePpjH/8v/5vuJ5Hq93he9//Pv2FBaR06fV7jKvJ6kmaIsrEWA6okoVem7/ztz/Hwf4eszih2WwR\nzwzOniQJZVYesUGqjHBeoRcEwRE1s2aYCBzHSN8NROfie2bTlYXCdVyEdCucch7aknXpPv8edSal\njTbB/ozlDJtD4v4ZeBRFTCZTgzlXTChHSrKZaYyf3DjJ6dObXL+xjeu5zOIZjShElWUdjHf393no\nwS0mU9N0VWXJeDJBVX2KJI7J0pQHNk3D8+TJdbLUPLdXrlxhfX2dzc1N4nhGv99DCMHy8jK7uzv0\nFhZMgzgIGI9GdDodet1uXXFIxyEMA5qtJmmWEYURk2zMZDIhajTY2d1hOou5efMmsyTFjyL63R6e\nTJlOJrQ7HaLo3iPRLAbuuUZ7YWXwYD32TRVk7Dd+cFlYo9ZPWEhlDv+eZ5fYe2ssAo4EYPbn4Ogg\nt8+dreBsZWDZRHklDBJS1GwkG8Dn5xD8sPW6B3A39Miq8nU6i4miBqUQKMdhmma4late6DhQFiw0\nAxZbEY+dfh/T6ZQ4TZhMYqPoKzWDw0MGwzG7e3vs7x2QFjM8PzRjmjyf3Ja7vkDpjCKv/AwcUW0s\nEK4wn8lx8RshuiwrgxurktIIt3ImdAXSOTrxVZEjlJHrowVC+sbSUgs0AoURCmkh0NIhtuwRSkpp\nMrXt4b0bNr/1+T+EShwiheG+B75Ht9cjCANjBeBIJIpAlnQ6HTodY526dmINjcav8EHL3d7Z3aXd\na9JqRLQq0dN4PGa8t8tHP/BjPHLmEf7Jb/02Wppxd045YZzH5tr4DZxGm1LD7jDmzsGEIGxwmM+4\nvPMivQvXCIKA5y/u1FCTKksccbGGVLIsYzabMZ6NTYO2mOGPp6yc2sBxBGEQMhwe0Go0iGcHZGlC\n5LmoPObpp5/mgx/8IPv7hwaiyAvywoy/Kg4HlEVBsxEeC6bzwo15nFspVTFEdFWhGK8OV7gILSkz\nBVoitURnGuVoSm2YTJbrbTaurr/W2GrMRZVm1Jd0rFrQeP8IocnzrBJw3GdyuuPR6fbqSsvi8Z7n\nUJQZZZnyH/70T/GZz/3P+CLC9SLiLKPTanNnd4/HHn2Yi5eu8La3vI0waBB4Pnd2t+m02zT6iwDc\nGU5YXFxlNs1x3ZDJyChzd3Z3OLm+TpomoH1cxzHj9xzJZDykEUWUeYErDSWw3+ubpuXhAYuLi3ie\nx92dHTztGS/cuCRohuSeT1pkxFnM8skV/uR3vsAsnrCwsMzgcEjk+pRFThSENMOIMw9u3fPalLnC\nlR5+ZRss3bDOeA3l1xqY3fvaWuZJmmVmGITWxjZBleg8Q6jjz858ljxvZmX/tt9rm5J2fsA81Km1\nBl3iSOtfBPFsQpKmBEFUB/77NW6Pff7X/I5/x2swGFTldPtYl92ccjOCwMPzfKbTGZ7v02w2ybL8\naJivAN8PUAqWfZ+NtRW0hjCI8LyA0WTCc8+d5crlqwzHY9AQRg00iqJUCFUNKVZGpel7PpQ5kWss\nbIs0w3FN9iqkxJMuSZmbh9bz8ByvMiOqjKWw8mo74UPiChddbeqiLM3h4Hk489kbDp4na1n9vVam\nFI4Gx/URKOI0I81yhlMDN7mei+t5oAryxGRs0pEkcUIY+MxmMX7gV1zwLlJKDg73EbLyaZGStdVl\nHnnkEdbW12m1Ozzxxsf4b37lb/EPfvXXUCqlFBLXc8iLlL3DpBpX5RGGHlq7JFliGrtScmP7wGCD\n+mjqts1GyqIwpkZFwSyOabZahGHI6mLPzBjNE9CSXGR40uH29i16vTau4zLY3+UXfuEXeMc73sFs\nNmM4OKDRCIkCk2WVeYpEIRzBaDSoIZN5Pq6l0BWFCZ5WR1BWB7fSJVmaV9inrCu4ojDKXc/zKUvX\nNNM1qLKiZgKlsNbHsmImVc0oYX2ic+TcbE5LS7zfhh0Oh8f6B0dlfBOtS9Isp9vt8M53PMP3nz3L\naDRCSodhOSIMA7Zv3sZ3Hf7Zb/82v/jpT3Pl6jWWlxbwfcMSMRCHQyMKcVyP0XDE4uISL730EidP\nnqTVMnzueGYsc5utDmmW41Z9EakFvh+SZSWbm1vs7++TJGb4xZ07d1lbW+P27dv4yz4LS4sMh0OC\nKEKnklkS80/+z99gb++QpeVF9vZ2aLe7FGWGK01Fs7CwwNbWvQO4DZ6OIypl9pHLpFEkq7qJeK9V\ns47mGryyCuRu6EN5NNF+nl5qm982qNu45ThOrUS17JX5/oZV30ahfyw79zwPx3VRinqfSCm5j6av\nXq97AO/3+9WJiZFyC1nTimwDKc8r3+264WfsQO0F8Cuhhu86CGG8R1SpUSqj1/R57zNv48fe8wyq\n1Ozt77F3cECaFVWjR6OUoWqNRqP6T6GE8QWWjqEWChDCQasCX2hEaEajKQrAsFL8RmAw5mpoqxaA\no1EYyEVKB+m6OBZPV8pM3cYGcW1Kdv/e5Z7rB5R5YRguWoN0zHsIjcYl04IsM94snm9K+VJrom6D\neDbDCZtI16PUmv3R2AQz4YEWzBLDx33p/GVubN/igc0HiJMZh8MRwnXpdULu7OyS4DJNZ4Shwfuz\nPKFURza2vufU1q5h2K7l5kIIZvEUXQVzjUZ6Dq1GRLffQ2tzLQaDAa7n4DrGy2WazIziMAoYD0a8\n/a1v5Sc+9p+yuLBIGk+QwMm1FcbjMaJyCmw3worRo2thid04QI1nWhGFpWVSHbqZFV8ASRZDBp7r\nkydZNX4uIE2PD6t1XOMHQ3WvValAgdJg3CHN9PkjmMQE4SiKaqbH/RptURjSajZNhldhvGVZosuS\nsixoNhpMZgnvfOadvPTSBYRwSZMUAoHr+gzGUxZ6PbTw+L9+94u8/0ffi+OagRBaK8azGaurK4Bm\nOh0RBB7Xrt5gdWWNMIi4e3cP3wvYPxyyuLzC0tIKcTyreMslWru4jo8jPYpckaUFG+unGAwGrK2e\nJIsz+p1FhgcjksB4w2d5TrPd58XzF5nOcjrdHoPhCIVGCEW706ZIDdSwsbFBmt6bkWGrESFB66Ph\n6I7jkFVsGQuR3GvVcFee15g9VDNy56iJ9t/m8Wwb0IH6EJ5OpzXH25pfla96Hc/z0Ko49rpgKM9+\nENX9ECkl03/fA7gtLXw/JIoatbeyJc9brqQ9oUxJEjIcjqqufIhGs7u7y2K7VZWlosIajQS3EUX4\ngfEFEbrFUr9JUZrsyOJe1u/AXrgwbNTMgXEyZTydcPnyFV4+f57xbAqOpNlooYVAKSirWZxKOHh+\nUDe7yioj8JBzlKUKJyty8iIDaRsv5nbcr/GihHE+LPLSGPJTqcekQOgjPwUhXHKlEdL8bpNpguuF\nNIOQJE3MyDXfBQ0CSV4oJIpJPAMk0zjn+889T6/f5cmn3kS31+PMww9zc/sW/8+ffo2zZ8/izqYs\nLyySVo21ukuvjzyWTbkgKj68CSZ1J16KqoGTMB6PCcOATqeD6whmE3O4aFUilCKJZ0zHY/7B3//7\n9Pt90tkhuvL0FmgO9vcNFc0aBGmQCFzfJS2OT1N6NU5pg3u32zU2u5WoA21sGvI8w3Fc0qzyrm4b\n/5EiV/UhYfxIdN3MqkvtKma4nqGe2kPOZGTGY+fw8LBmStwPA7d2BZ7nUWQZszQ1wiLH0GZNr0Oz\ntLhIr9vl8tXr+H5ISo7rFnS7XYbjCd1Wm7u7B1y8cp0nH38Mz5WUpeLkyQ3C0Ay2aDYajEcTepW4\nJysKppMZJx/d4OLly2it6XS75JUlalmWZEleN4EBM1lLQeiFbF/f5sSJE4bFFTa4u3eXRrsDwuPi\npSt86UtfIWqEdDptVAnNZoTjC3Z3b7O6tMrKyiqbm6fv29g/YgKZwy/PTXVuWURBJWO/3+H4akn/\n/D1wXZfpdFpP+DLxJqg1BK9mk1iSwrzK0saWecpvEATkWVLHnHmvnv0DMzS71+uxvLzM/r+NmVWS\nJLzvfe+ry4xPfOITfOYzn+Hg4ICf/dmf5dq1a5w+fZovfOEL9XT3z3zmM/z6r/86juPwa7/2a3z4\nwx/+4Z+AI3qN67o1kD/PArATo63kVqm4LndcR6K0Znl5GU+VNJsNkiTF+PaqyjPblKb2gksp8L2o\nKmGrGZS6IIlThJRI4ZDE46r54dJthnRbEWurS3zox99HXpRcv3GT6zduMpnFzKYzkjQjy3OyvCRJ\nM5LUlGW+62FGLYFnhm+jHGOM5TVaqNI8fGVZQGmaMKh7l3sKbQYjV6wWy2xVWiGFZ/BxrVGIyhNd\n43sBgeujy5JZYSh6Wunava0sFY7nIT3H0NVUQVlmeI7LcJry1a990/CkyxLfDygcl1MPbDIejbmz\nu2sm9ng+vm8OWVceufgJf24SS2mETqijeYN5YUx9gsinLAoODw9qTFBrjS4LJLC2eoKPffrTXLp4\nkdAPQJZoZWYYep6HcMC5s4NbWbIuLvTNKDFXgiOqDZORF2Wl3FVm3Jzn4fvm4MyyvKoctBmOWwVZ\nr2pAZllceUUrY7olQ1OVYVkoGqGMElgIkNJYP5SlIi1mOELiSA+lTOVnDtsjnnFR+bXfa9ksfTqd\n1rS+oihAWVMoA2GVWvCf/dIv8ff+3v/CeBaTJAbiimPDJJkmCZ7r853vPUtZlpx56DRZMgP6xotf\nKxxHkqQxvXafUpWMRkOWl5eYTEcYCYNgMhmzt2ea0Xbqux/4uK5Tze4UNCt5ua12bADsLSxw+co1\nbt/d4ctf/gqLyytEjYjxeEAUhfR6TabTMUtLC6ysrPLWt761qpDuHT0sXFKW+TF2iFKmrrXwyf0a\nglLK2ljNfxXl0MK0aZoym81qKMTK5+cV1PaPfT+rH4AjN8l5xa4UrTquWoUmFXyzvLyM1pqdnZ17\n/9Jz64cG8DAM+cpXvlKXAu95z3v42te+xhe/+EU+9KEP8Su/8it87nOf47Of/Syf/exnOXfuHJ//\n/Oc5d+4c29vbfPCDH+TChQv3PT3hKKgWRVGPvLLlre/7oCX7e7fNA+K5OE5Ou90iTWOkdHA817i/\nAUI7CEfgBQGBjLCTV1zXdO3rZ0CbQanSqQQtlWhHOoY9kuVZ9TBEJIkttT00gmRS4Hg+66tLbKyv\nkeU5Glnjb0WFleeZkdbeuXOHnd1ddnd2zXgp3zfqTcABEFDqDCnMNG4hgzpo/MDDJiBJZni1AZDh\newNI1zX852oKvdICXWWXnuchPd8Id6TJTm2GoLQmzlK06dGZh84xftCO3wRhHBlDS7vDyOY73T7d\nbs8wHYqSPDMmY3ExA4zfeBEf0fBqc7BKQPTqhpBbqViNcs5M5W62WgitybKcL37x98nTBM9xKJ3K\nMbG6Jo7jVCZYxuYgaoQUZVFR91yCIGTtxCqBH9Bf6PPQgw8RhiGHA/O8tdutKkNsmUOlNI3iojQe\n7QLQJXieT5aklEWJJkUIM9RDVT7t0jVQmsn4qtF7Ajqh2axFnuM6Pn5V4sezhCxJ0b7Gc02D8F7L\nZnGWzmbL/jwv8ANzIGdlBkLiIvirf/Xn+I3f+C2yrDCN/jhhYWGBrPKo7nW7fPM73+X2ndt86q/8\nNGkSo8sC33W4ceMm/X4fLRTC0biepNE0xmhFkRrPEWv7HBnKoVI5YeiRpFMWl3rs7u4xnRkWRbMV\nsbu7i9KGChm4DS5cvMizz52lt7hEEIYMBoc0mxFRw2MymdJshnQ6HU5tnDKNY+FW3j4/uGxzPCvS\nY1TfsqwcQD0PRx4fn3hsTzmOgXRSM2hinu9tA2yr2awSjgKqfaAsy6R6hrVSqEoMZMU6tu8yL8Sq\nq3COfHQsBl4UJW41Fu6HqUfn12tCKPZEsydLv9/ni1/8In/2Z38GwKc//Wne//7389nPfpbf+73f\n41Of+hSe53H69GnOnDnDt771LZ555pn7vr6uSm7b9Hu1iq3IVU0Jc12P0WhksqgKelFo0qzyb9YS\nGUuUKgmjyOCohVHGGftZs8HAzI0UUpoNKqn418YHgYrWE6cJSqsqwFee23iUeWYk6BVWWlPxNPie\noRQG0qV5YonN9ZXag0Upxd7eHleuXOHOnbvEdvCvaNXNEfv3vVavHZHEptGmc0O1VJgBAoUqzFQR\nxwwaULnJDAJXosocYYNDCWnlee77RqQUVddc5SAQNTPHkZ454BxtNpKEktgEOFVZ+rogQwtLGPGS\nzWJSldbBRlfwgiOO/CNqc/vSNGe9wK/KSa+uDlRZkmNGqSkNUgtyLeuMR0qBAvL6umniUYJ12Mv2\n93Fclzt3d8myHFVtFikE/X6ftRPGWXEyGdPwIzqdDo1GhOM4NJsRnW6bRiOiGTXIc4WUvqFcYnnH\n4LseZVGQzNKj+YbVRpVSEMemtxMGZmK6UiVFbvjFjUYTIeQxMcerl5Xpz5uZgeHxl4WqskCXoizI\n0oylhR6PPvow3z971rheimralVJEUYPheITv+Vy8co3/49d/g49+5EMs9Hvs7e/RbHerXkvGLI7R\nQpFWFeyJEydwHEkzajIeD4njKe7/2963xth1VWl+e+/zuo96uFx2+ZWQ4AfBiXFVEpJ5CKGQDnR3\nJp5oMp1JMh2lBfxB8wfQoIj8GPgxBAJiRqBpRhoJ1BbdM0Ej9SRpJqQJIiHpYaQEJ3GA9IBhbGKX\n7UrsetzXee2z9/xYe+17K3Y5SENs3HWWsIKryvee2vectdde63sEIYw2CEOFsiTRsunpjVhYWMBk\nNIUwDDA5tYHw/EWB7/6Pv8Yvf3kEG6c3QUiJXm8FgMHExBiqMsN4ewxxEmB6agpXX73Tzy7WGkL2\n+z2qsJPII4q8BIK15Lw1gsl+a/CJn6t0a60XcuOv+fe3QwE53iS4U1CWJQpXeDI5Z3RwyQxcz+jV\n5Gkweh1VVaHUQ57HWr/zaLxtAjfG4Prrr8evf/1rfOITn8C1116LhYUFzMzMAABmZmawsLAAADh5\n8uSqZL1jxw7Mz89f8PXDIHLHUOHdMBjgzoNMAJ6UsmPHDsKKlzTI6acDj4FWbmFVGKDb76HSZCgA\nzXrOgiB8nhbEAwAAIABJREFU1iIvCZ/KVY+Qgiy1nCkEk1xkEEBUhFIX1OClG0Q4cSZToaro4aDB\nnkGW5wgCBRUoFHkKVCUUYhhdYbLdxE3Xz9KNUmhAUWIPoxDNRhNLS0RswX/8xjlrtfPKrVhaWkLa\nHyBNacJdFpQkgyhEEieorIExJQJFGxMPd5QS0KZylTYpPQaCcOECllAbkSIn70hASoU8L2ENEEdk\nuUa4YAUZUmK3xvrjIn1GilQZXdIOhPKVtm/3cAVZDVsp1lqErCMDWnP2jgyDiNpKlvTUAQEVJa7S\ncolMEo+gqgz12yVgrERVAWFMCnPdtEASxkgSSt6wxMpdWfkVDckjsuZjI+QwCKhNIwzGxsbRaMQY\nHx/D5OQEtm/bhu1XbkOj2UDoer+VAcIoofaMIAafhQAEtVICFaDSFrnmTc0gigTy/Fzy0FuD+8ss\nkMSkGOn8NE1OpxZUGmWZQymJ2//4I0jTAX7y0suY2riJIH8tsoprNBoYpKT0uNJP8d//+nHsvWYP\ntsxsxnt27UJRZBBSYmlpCZs2TVPxYiwajSYCFaIsSsAKNBvUIhHO6BnQCMMYWVagP8ixYSpAZQGh\nIhx66VW8/vrrWOn2sGnTJuQlKSW2200kSQxdEK6/2RjHu664Avvf9z7oCojjhjcLPl/ECW1qgTvV\nrGI8Yqgzs1Z4DDgoCTcaDTQajVXM3OFsaWhjyNIInJtarRbaIygnRqBwsmfSkz95Cuuhgvy8xHGM\nUg9t1cIwRPY2SMK3TeBSSrzyyitYWVnBRz7yETzzzDOrvv9WjYm3xtrf+zwA4L9866eYfd9ezO6/\n1tOEB4OBv1m7GcmQLi0tYXxywifYsiSc5djYuKuSSdWu3SZRona7BaMrLC+xiSvZX8FYSCGhVAQV\nKGc666pGaxCGsUskjqRhKtiK3XDIU5NNcPmGscYgyzPkgz5kQJZWSsZQYUiT5TD2DLVQhcjKAawF\n4kYMbTWkNDCFxnLaw1h7DEVx/jX7gw/+UyipEIcRtK5Qao24keDosd/g/x49hsXlJXR7XWSDFNZo\nAORzGcUxBv0MVUls1KRBCbksM0SBghJEZhJCQsiSdBqrCo1QQKoQ1hoEyqLVSJBnfYDsLahdFQSA\nGxDr0qCsKpLKFUASNfw9YM2Qmi1cAjWmIqciC1hdIYoUsbkcm5XvP2ssYEhzxhiDTBN6JA6HHoOw\ngFQRkXCkANc20p3s4iCBtUBeVP5EoKTT6A4S9Hp9hEkEFcWABbJKQzlVyDeXe1C9Pk6fXYHWx6CC\nn8HoDAIW27Zvx9zsHLbMzDgGrEIUEnKJBqECoVLQUjv1B+mG1dbDDnlN1jrmG10hkMMeqy7oJEOo\nC0MnRljSvE9iaJ0jgMWf/Mm/gLEVXnrlVSRJA91OiVa7jTy3SJIWeoMBAqUwPjaGH/3dj7FtZiuE\nDLB921YIAIOsRL9fQAiqIpM4RpoSYqcRt9BbGbjWTgPGCFgTwBqJuJFg06YtmD/1Bvr9AZ56+m8B\nSzaGU1PTOHP2DbTGW2hGIYLQQqBAGIUYbxEBaPeuPdCFQVowe1evmUe4IKjM0I+Sh4PWYbpHh9dr\n5afA/TyLXzFCabSa5tcnDfbQF5paa6ysrDgnryGJyxjjW5hsTTesrA1iN5j2LOAggFTAiz95BS8e\nOkxovLcpwn9rFMrExARuv/12HDp0CDMzMzh9+jS2bNmCU6dOOW1oYPv27Th+/Lj/NydOnMD27dvX\neMXPAwD+9J6/dIOhElmWArAeXE8KZTG0NiR8ZCr/y8YxiRU1223HCrRI4gRlkSIKQ+eGA2zYMD4y\n8DO+rxmICKgAY62rXqidE8UhhCVGTxiEsNKgsM6eyZLGhTGAsBZZ1qfNQIIIKpIqVxsKQEgUuXYC\n7QZtp6SoZOArwLxMIaXDJ0sgVAqD7jIN+84TospRFRa9fo/QOnGEXj7ApslxzNw4ByiByljosoAw\nBaKQEDpZXuL1Eycwf3IBRVkiywv0ewNYAUxNTGHbxmmUFQ2RjQQAASOATrfj3JAKrHRXMBh0gaok\nwgMkYKl9YQVZ0kaxQgCa+JuKfq/IidrzcbAsS8CRI4QbWAZhQARYAa/lwf+fSFY0XxSwUBZohkO9\nC2KiBoCwhNV/y3OaFjQIHQoNkUSwFUTI6vcz9yAm0FKgdMNF5XRUdF4gTJqAsChKjSBqOHZmAliD\nkyfP4NTJHzhH+opMQ+IETVedh2GIdjMBLEktTExMot1uotVqYfPMZueCo33f83wx6ljFkDStNXm7\nJjEAJpZQ3z9q0LVneYp/fuCfQUqJw6++CgiJleUS45NTMNkASoUotUan28fG6Rm8eXYJjz32PxHH\nIWZmNmLnu9+NTZu2ksDVr36F6Q0T6Pf7gLUoS4s4Spx5Cmn6LHa7UEGJk7/4NY4e+w2OvX6c3KDC\npjdw7vfpBNBIIpQ6Q6iAohhgamILtm/bihvmbgRMAFsZNBqBl3ZdKxjhESYxbZjuBFiWpT/1jYIi\nznmmxFAllAsyYwwacbJK/pW/zkNHPgmNilRxMudWMLdD+fPjCrwoaOb1Voy4qSpUBth33TV43773\nQkqJtPqvOHhwzV//wgn8zJkzCIIAk5NErnj66afxuc99DgcOHMDBgwfx4IMP4uDBg7jzzjsBAAcO\nHMB9992HT3/605ifn8eRI0dw0003XegtEIaR71dKlUC545i1ZB0VBk0nADTU/CAJT4b59GixjEWg\nBMimrIStqC+olELo2hRRRI48oVQorULp3ocW3zhMq/Cbh6fYq4A0OKz1g8Gq1IhiSgaMIAmiECIY\nOvlUlUVDDvUzmnHiIE5D/LEQlev5A0nSxFirTQ/JeUIXOYqsRByGSEKFJIqQa41Cl7DCIAwTwpfC\nQAkLXaRQKkAzDrHzXVdiz+7diOMGLATKikxspQVML6XTR0wGz0YYWAlkRQ4ZCIRxSOp/EAiDBo4d\nO4YjR36F06cXkOU5+mkGCwUhSGtGCAELIO2ncI5f7sgZIFQKypkI04PmbMUMwf+sAEIpAbma8cbD\nKSUVIhnQqYs6WnBy7VSNGfqMSI3RwsYJpKT1l5Z/zkIKBRUIqFihyHMYK5Dm2g2PAF1UgKTKjuGG\nAkCakRhVZCr6fULS3hEg02cYi7ICer0MRtOcJW3H6HZWMEgHJJcAYGyMPCtJMMlifHwMjUYDd957\n7uc+PT2NTqcDpQKk6QDGkG+qscQ3qHQFYzSM24QHgwFUEGC500EQRvjX994NYwxeOnwYAhJLS2fQ\nak8gihqIogTGWCwtrlBPVgBKhXjttV/gN6/P44c//BGssZjeOIV/fNNN2Lx5M8ZabaT9PowG+ibD\nifl5/OIXv8DR3/wGYZxQSzOKACg0Ww0UZYWFNxfJrV5YNJpN9PsrmJxqoaoKXHnFDgRKYc/u3SjL\nEll3gM2bNuONlTPeUWnN04mD7mVsnyaGiptVRXBbHiquFV6FUgzlg4UlEEc7Glt1D/LPcoukKArf\nTknT1G+0PBtjHRluu3Dih61WeW8SnLV08GfhVTzTwQXT54UT+KlTp/DAAw/4i7///vtx6623Ym5u\nDnfffTe++c1vehghAOzduxd333039u7diyAI8I1vfOOC7RUAKDUJrxcl9d04oQ4/MDL7pQqZKrdu\nd8UjGzBC6slSloKk3pm1VDVBKBRFiazUfkC6qgVimWlH72gNPDU3jgJUhth9vNMmSQLVUkOcJ5zu\nthCw2iJwmuEKAhXgB1RZkaOsSO9EKKDZGiNEg6s0+/0Sg8EQfnRuRFChAoIAaZ6jn3dh4YYvxiBz\nm1kzbgC26XtwRVGgmSQodIkiHfiptxAleoM+ooRgiZUufBWorMR44vqOFTFKy7JEKArsuXI7du3Y\n6h8sPl4WFVUbi4uLyLIMg4LaPLos0ev1oKsKK8vL6PX7nnWodUbG0DJxbS66uU1pYGTgHwLrjuDG\nWlS5k3iVZOZhrWPPgZzNhXKu38YghKahdhS4n6XeulKKPAxlBNFQgAWaFW0YSg4f5EoKuiGczZ51\niKcoCpFlGSV0RXLGUpOombWGYG8ukSxmfdKADxJAgSj1iGGbk04fJUDPGqx0zz+8/ref/XeY3rgR\n27ZsxfTUFFqtloO4SiJCTUxg44YJGEns3jgKUJY5tm7YAAgg7yziXx74Y8xs3Igf/d3/Qj9L0e8v\nIytSRAkxlsMkghGkl9/JM7Qmp6mCNCVkoLCw1MPf/OBHq7DNVFkaBCHBJm0UI801kkbLKUtmyPsd\nFEWG8YkWev03MNGOMUj7aDQTpP0C27ZuxdVX7sIVO3b4xFhJjYWlk5AygC7zVcPCt4b3k4SB0RYy\nSZCmg1WYex4cni/CiJnf9Ee7U50lmSCvxT568mGziCAIiD/AWieSYcoShR7yMroDwpKXZsjQDVWI\nIAR0Rc9OXmg0Gk0ww/NCm9ZoXDCB79u3Dy85Ef3RmJqawg9+8IPz/puHHnoIDz300Nu+MQfvNFJK\nj8McTeKLi4sYGxvzO+iosDowBNGzYhu/3qi4/mBA2xhN4Rt+F2XYIoPvSQifBh58DCqKAkIOZUdZ\nNpKPUfwevBsz5rXZbIKFbvga2G2Dd+vuYEBGx45ANDq0PV8wXpWr0bGxMc+C5GMjDbek38B4k+K1\n4mvjCqXVasEKC1NqGLfzW2vR7/V8zy6KIkRh5N1KqpEbixNxmqaQgcLY+Dg2bdrkKprIHy0Zwzw2\nNuYZb+xqI6XEsflTyLMMCwtv4MT8Cax0VqDLwim10QlISIVYKZiY9M6ltAAIOy8QIFRk16VLTZt4\nFKI0ZIRNqAESpxIAtC4QBwqhEoiiGJ3OCg0cJWm5G66k3HqrgGzw+D6DkA5d5HwXjYF2vpVCBH5T\nVkrBaiZ6OB0UK5BmOaIoQVVpGiEbiyBYA6usInS6A5w8eRhJFDpp38LpqjhHKCXRajYxvXGKJAm2\nbMLMlhlMTU0hakQQUPjDP/wj7HnvtfhPf/7nKLICxggsL3XQao+h3SaZAiUkJWNXzYTu/u12u9iw\nYQNghlZiVAQAlclRVTwbsiiLDNZopOkAcRIicdDZKGphZfmMl1PeNL0J18/NYWrDBlJTTOienZyY\nQJ7lUGG0pkIjBydJ9swcNeTI88KT49Z6HSbmlK648xBEYFXrhJ9z9rlkWWA5UgiWWnshviqgWVDo\npDGUEIidIFcUR6h05c29AXjfzFFS0G8Tl5yJycpcHl7jdkBO1FNO6YyrXwbRjzrfjKq6MS2ad2wG\n1POOxq/L8CL+OuPPefLMG0YQBI4OrVbt5JzovOa2HfrwseVZt9v1HzAnTf7dCKbWghRDZxPG+a5V\nLYxKWHp5UTHsBfMGZg0rEQ6pvwS1ir0pAAC3mbEaofK/h9baH/t4c0izFLlbL07KgZMNBYB2m6CQ\nxikkZlmGRkTwtcJVRHEcocgGRGiCQW9l2Qlcabx7x1aUZYn3vPtdiKJ/grLU1M/X1HMfDAZYXlqG\n1galrbC0uESVkCBv0qIsYQVtJGma0gbd6SDtZagUkKXEZpTW0ZpBFX2apoCSmBxrIy2H99jogzR6\n7/HgCppgljQQpZZMzBR3EHxQCqf2qARp1Dj0uhLU99ZV5RI3/VywBtKiAglqNVrjkI5diyCCdqj8\nIIqhK4ulXoaV/ilYY/CzX/4aRVlAKImpqQ1oNVoQxmLbjivwr+6+D0/+7VNYeONNQi4VJTrLywgV\n3YNhRNX46PPTbLVQuCTIZiaMLrJCkPVZVdGw0hViYaiIWTvootdbQqORYOMECazt2b0be6+9FsLS\n7CIKCB1U5gVSSUxKaYaiVGYNHLiU9IeTLEsCxw6yqfXa1TsAvLFAZBmq1okvwpt35HranLiLovCk\nnLcyepk4JIRA6f6rhCBseRhCuhOLlBKDXh+5K9b4ngIwZJyveQI/Ny55AufFzfLVLQo+prBiXK/X\nQ7PZJEZZv++dT0axmuwlx1oqowwpTpK8E/NNNprcuVeWpqmnOLdaLUgZnLMx8JCC//AHMSrYxGyt\nPM+9cH4URd54oXKIFt5UuNpdCwfOPbbRfl4UD010/bFLCM/W63ZJ03xiYgJZQdUy3+QkZJQiSWJf\nRfBmyEdk3kx4sMyVAWFWhwaumfMONFpDQBB7Ni2QxJE/5ZRaE8vUVaZKAqGSyMocQlSIJJDnGdIu\nWduFUQRUFWAEImmxeXoSrWbLkbcktKM0E4STNs9urwvt3q+RJDh18gziOMYgHUCXGnmRw1qa73S7\nHbyx8Aa63Q61lcam6CheWQdbVIRMUW3quQOE/ilLSEWzATi2JfEJiMBFqoNDVx8oiSAcniCllIiS\n2G8So0PJ84UBUFSEwrElrR+5PVkYbZFrDRhL/fjKwliDqqwQRAkggNNnV2DKRbSTBCffOOtEkwQm\nJ6ewvLzskySkRTZIYQCoJFlFaoEbwkkhoKUcSu8KASl5KE0tpDQbQAmg1DmKwiBQROwKFXDD3Bxm\nZ2epBeqKon6/j2ZCQ05GjCkhETYil7ytAzicL3+Qi5W1o7K+wq2zRZblXhFwjRfwSDIL+sxhDHRZ\nwpqhRs2oFjyfrEZt05j402g0fN+dT9qsFR6GIZ2c5NCliS32hozcoQ/rhTYejkuewEd3oWpkWMUJ\niUXU2+02giBAp9PxtkbM7uOks7y8DCkl2u32qoQ2ShDiSf6bb77pE5sa2R3ZaIC9DrXWZJcG+OqL\nXxMYmqry4qdpilar5TU2+Pfj3ylNU6/xYh1Rxf/dDvUUzhf/6JYDF/GTqeNSxLN/eu7X/uov/9vF\nv5B3Kv7zd36nL8fP12iu48JOSoUosh6/fb5ot9uOpEggBw4u/DiXjKoGSklGF6MDSykl4jCCsHQi\nLHUFXZQeMWeMQebkDeI4RuhUQZn4xS1ZJhaNtj4vFJc8gXc6HSdU5bCzLjhRUptB+MVqt1pDSjGA\nwlW3SkoSNCpLxC6hB0r5XdRrWpQFOp0uwb1cP9wY43WWeQfUWmMwoKN/EFLfsdVqORTA0OU8ikIU\nBWHEW60WxsfGSNdFBQ79YtFqtpBlhAiJowhwACftXLdVGCCIiM1nBQ256qijjrePYQuWk/YocmmY\nYNcCU4xqh/NJnTcFgDw36YRFfrxE5Cmg3AmLGNoV6dtoA62FbzMKQeTBPM8QRcSnUIrmN7rMkaWV\nlwEpioJmEAHZOlYwpGj5NnHJE7ivjp2QFQuhcwWcOWWx0aM6H13YhigMhg7hnU6HkrE7pmitsXFq\nanhMBDC9cdq/N4vUGGPQ6XTQaDQ8QoFdoi2IdNLv91Z51fHuzUeo06dPo90ag4DwvWRuq3DPXGCk\n1WIMwbbc6aBiVqgQMFEI+XZiwHXUsY7DRENTaoANNaoRAIPyp/S12JhDbe/Vw0PeBMJQ+ZYH5SqF\nOI6Q5zmiKAQQenYmM0bpRA6fwFmkLAhIHVUIARkoKDWcUZHsR4kkif11/TZ2cJc8gWutsbi4iMCJ\n6nNSZrnGykHh2E+RBwq8W/IHJKXE2bNnvXIb44zjOPaVNB9VGILIR6M4jrFhwwbfN+f+NffFGXXB\nmM7RHvRQk8O1crRBHMX+ZuIe+ygsiAkCPPAb7a2zX2T3hllM/O8XL/4HUkcdl0kM3j+HIAj9EH90\nFkR96tUt1POFh82q1R6a/FxzvuD2CftXWmu98QoPTQM5dLPnzWB0ID7awhV2iFfn+RJvBAA8mAK/\nKybmOxWMMsnzHHDwPK5WrSV6cBzHePPNN2Gt9QYQVVVhMBj4ZK61xtTUFLIsQ57n2LJlCwaDAdI0\n9a0S7mFz34kRIyxly4OwZrPpF78sS6iR4Qj/O/4+s6vKskSz2cSgNxS04Q+HdRUAStCc3C0spBru\n8DzobDQaOPH5B4HPfQljhw5Dvg2Uqo461lOYKERnbh+OPvhvEFVD0+E8zz3wgPRQCB7okUPniTzN\nHIJFocgKPyszukJZFQgi5VumPNQdHeYDI+JioQNVOAnaLM3861W6QqWHRCDOYVyIEnGx4WdnXBAO\nzj+79XHJE3iv1xtCcjBEdzC6xFTEWJqcnPS/FDtlc7UOwKMuuAXS7XaJ/eiSMTB6XNJ+9ySLtmLV\nwrFSnl9cYBXWnCF7oyiUZrOJwWCApNEALPyR7a0sMK4WgiBAmmXQeY52u43DP30NN8y9b6hyNjmB\nX/2Hfw8pSfAmiWMICL9xcJ9eKqzq8TFRBXaIYYVbV3anHz1qAhahlMgLp4XuBreRkypotVpkShEG\nqLRGokJPuAijyDkNDf8dVxhSKdKeAYtbwa9HlqV+szSWZA6EZeErAtsJCbx46FVcP7vXzwQ8SqfU\nvkKRbs7BhB+AZghskZX2B/5Yy6/BSAKGhvFmqysDaw2iOCZIW6m9BniW0gwjcWbBVUH3R9JIkLkk\nwEmirIYtPylJQpcG1YGHPDL5SkjhW24QAujccs4z8tqLTztBpAyHX/17vP+G/YRDVhKFLiCFRCNJ\nHO5ZESNYCij3+aSOG9CKGvQzwhHXlEISx84xijRVKkuYd9InpzVL4gShQ8ow8mQIsSRNlyAkga0g\nJAs/KyyMGQ74syxDkedQoGeF1fdYapUjThJYS2uZpUNdEj55+2SpFJrumez3U7x46GXc/P45FEXh\nmcxBEPr7bi2bwna77fIHrd/oiVgp5RjOVHFnWeavhfMDF3PcFuX8YYzx0OHzQQ9Hg9slnBv4Gfqd\nqBG+08HiVc1mE3okWQsh0Ov1kIwgMhjSw1jwURw3P4xSylXwPZ5IA1gFPWTHn1HBd140XsxRDQ9+\nGNkFm782pMHSexd5Tg+6n44Pj1Lci2NYUhRFUIZughdefBnvv2EWocNWDwYDItlYi1arhUG/7zct\n/oDZDJnXgJOykgpBEK3CmFpLZr2j2HDrkqc22jMC+YFhM4CVlRU6pRiiJVtJ9nb9fh/jSqFwZJvx\n8fFVBCpjDGJnMCsVQca01iRfGzdgKwtpJXjObqzxOtnGuQv95NDLuOnGfauOwlnaRxg1kBeFd6ph\niU+lFIwAlBwpCGKH1weJmBlYaEsPV5EXKCqNRpKQdKqlf5dmuXMLl+j1BxgMBv5Ed3ZxCWNjY4hD\nImPoXHsmsHRH6CRKSEQL9HCWWU56PEIiEAqJu3epOHCOMrpiR7ZzIg5jBDJAM0nws78/gj+45QNe\n9thKgoUGShGTT0r0+j0EylG73X2KyqBw91Ack3MSq0wOBn10uz0EcQIVKCTNJnRVIlKkNihh0e2s\nOMYjQEN4y/+DkLQpC2FBRGMLWOOLHIAKLFiLQAw5FExH52fNwCLLM184mGoI0RvF4/PXGKonhMCr\nP/0/uPWWD/iECcD5lppVz8Fbg6+PoL65b6VwwQZpPTKECy/OCfwc8nWcPHkSmzdv9nkmjmN0Oh1f\nLHCO4c2LNwB+3VFy4IUEuEZD2N8GbPg7Drqwi/62v+fxebDAVx0cn8d6W5NnnrnwQ/sXfwH82Z9d\nlEu5rOIf8rrccsvamPC3BxrWUUcdddTxexmXpAKfnZ3F4cOHL/bb1lFHHXVcdvHBD34Qzz777Hm/\nd0kSeB111FFHHf//UbdQ6qijjjou06gTeB111FHHZRoXPYE/9dRTuOaaa7B792488sgjF/vtL1l8\n9KMfxczMDPbt2+e/tri4iNtuuw179uzBhz/8YSwvL/vvffGLX8Tu3btxzTXX4Pvf//6luOR3PI4f\nP45bbrkF1157La677jp8/etfB7C+1yXLMtx8882YnZ3F3r178dnPfhbA+l4TjqqqMDc3hzvuuANA\nvSYAAHsRQ2ttd+7caY8ePWqLorD79++3r7322sW8hEsWzz33nH3ppZfsdddd57/2mc98xj7yyCPW\nWmu/9KUv2QcffNBaa+3Pf/5zu3//flsUhT169KjduXOnrarqklz3OxmnTp2yL7/8srXW2m63a/fs\n2WNfe+21db8u/X7fWmttWZb25ptvts8///y6XxNrrf3qV79q77vvPnvHHXdYa+vnx1prL2oF/sIL\nL2DXrl246qqrEIYh7rnnHjz++OMX8xIuWXzgAx8gR5OReOKJJ/DAAw8AAB544AE89thjAIDHH38c\n9957L8IwxFVXXYVdu3bhhRdeuOjX/E7Hli1bMDs7C4AYce9973sxPz+/7teFTTKYZLZhw4Z1vyYn\nTpzAk08+iY9//OMeE73e1wS4yC2U+fl5XHHFFf7vO3bswPz8/MW8hN+rWFhYwMzMDABgZmYGCwsL\nAICTJ09ix44d/ufWwzodO3YML7/8Mm6++eZ1vy7GGMzOzmJmZsa3mNb7mnzqU5/CV77ylVWMyvW+\nJsBFTuC/DTV0vQYrk13o+/9Qo9fr4a677sLXvvY1jI2NrfreelwXKSVeeeUVnDhxAs899xyeeeaZ\nVd9fb2vy3e9+F5s3b8bc3NyajMT1tiYcFzWBb9++HcePH/d/P378+Kqdcr3FzMwMTp8+DQA4deoU\nNm/eDODcdTpx4gS2b99+Sa7xnY6yLHHXXXfh/vvvx5133gmgXheOiYkJ3H777Th06NC6XpMf//jH\neOKJJ3D11Vfj3nvvxQ9/+EPcf//963pNOC5qAr/xxhtx5MgRHDt2DEVR4Dvf+Q4OHFi/NmEHDhzA\nwYMHAQAHDx70CezAgQN49NFHURQFjh49iiNHjuCmm266lJf6joS1Fh/72Mewd+9efPKTn/RfX8/r\ncubMGY+mSNMUTz/9NObm5tb1mjz88MM4fvw4jh49ikcffRQf+tCH8O1vf3tdr4mPiz01ffLJJ+2e\nPXvszp077cMPP3yx3/6SxT333GO3bt1qwzC0O3bssN/61rfs2bNn7a233mp3795tb7vtNru0tOR/\n/gtf+ILduXOnfc973mOfeuqpS3jl71w8//zzVghh9+/fb2dnZ+3s7Kz93ve+t67X5dVXX7Vzc3N2\n//79dt++ffbLX/6ytdau6zUZjWeffdajUOo1sbam0tdRRx11XKZRMzHrqKOOOi7TqBN4HXXUUcdl\nGnW3i9jGAAAAP0lEQVQCr6OOOuq4TKNO4HXUUUcdl2nUCbyOOuqo4zKNOoHXUUcddVymUSfwOuqo\no47LNOoEXkcdddRxmcb/AwKQdm5yYaZEAAAAAElFTkSuQmCC\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0x5a3a290>"
-       ]
-      }
-     ],
-     "prompt_number": 10
+     "output_type": "execute_result"
     },
     {
-     "cell_type": "markdown",
+     "data": {
+      "image/png": [
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+       "wNe2trWt7QVq/w/Uvjt8hhUJzgAAAABJRU5ErkJggg==\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x116bcb210>"
+      ]
+     },
      "metadata": {},
-     "source": [
-      "This was an easy instance for bicycle as it was in the class's training set. However, the person result is a true detection since this was not in the set for that class.\n",
-      "\n",
-      "You should try out detection on an image of your own next!"
-     ]
-    },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Find, print, and display the top detections: person and bicycle.\n",
+    "i = predictions_df['person'].argmax()\n",
+    "j = predictions_df['bicycle'].argmax()\n",
+    "\n",
+    "# Show top predictions for top detection.\n",
+    "f = pd.Series(df['prediction'].iloc[i], index=labels_df['name'])\n",
+    "print('Top detection:')\n",
+    "print(f.order(ascending=False)[:5])\n",
+    "print('')\n",
+    "\n",
+    "# Show top predictions for second-best detection.\n",
+    "f = pd.Series(df['prediction'].iloc[j], index=labels_df['name'])\n",
+    "print('Second-best detection:')\n",
+    "print(f.order(ascending=False)[:5])\n",
+    "\n",
+    "# Show top detection in red, second-best top detection in blue.\n",
+    "im = plt.imread('images/fish-bike.jpg')\n",
+    "plt.imshow(im)\n",
+    "currentAxis = plt.gca()\n",
+    "\n",
+    "det = df.iloc[i]\n",
+    "coords = (det['xmin'], det['ymin']), det['xmax'] - det['xmin'], det['ymax'] - det['ymin']\n",
+    "currentAxis.add_patch(plt.Rectangle(*coords, fill=False, edgecolor='r', linewidth=5))\n",
+    "\n",
+    "det = df.iloc[j]\n",
+    "coords = (det['xmin'], det['ymin']), det['xmax'] - det['xmin'], det['ymax'] - det['ymin']\n",
+    "currentAxis.add_patch(plt.Rectangle(*coords, fill=False, edgecolor='b', linewidth=5))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "That's cool. Let's take all 'bicycle' detections and NMS them to get rid of overlapping windows."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "def nms_detections(dets, overlap=0.3):\n",
+    "    \"\"\"\n",
+    "    Non-maximum suppression: Greedily select high-scoring detections and\n",
+    "    skip detections that are significantly covered by a previously\n",
+    "    selected detection.\n",
+    "\n",
+    "    This version is translated from Matlab code by Tomasz Malisiewicz,\n",
+    "    who sped up Pedro Felzenszwalb's code.\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    dets: ndarray\n",
+    "        each row is ['xmin', 'ymin', 'xmax', 'ymax', 'score']\n",
+    "    overlap: float\n",
+    "        minimum overlap ratio (0.3 default)\n",
+    "\n",
+    "    Output\n",
+    "    ------\n",
+    "    dets: ndarray\n",
+    "        remaining after suppression.\n",
+    "    \"\"\"\n",
+    "    x1 = dets[:, 0]\n",
+    "    y1 = dets[:, 1]\n",
+    "    x2 = dets[:, 2]\n",
+    "    y2 = dets[:, 3]\n",
+    "    ind = np.argsort(dets[:, 4])\n",
+    "\n",
+    "    w = x2 - x1\n",
+    "    h = y2 - y1\n",
+    "    area = (w * h).astype(float)\n",
+    "\n",
+    "    pick = []\n",
+    "    while len(ind) > 0:\n",
+    "        i = ind[-1]\n",
+    "        pick.append(i)\n",
+    "        ind = ind[:-1]\n",
+    "\n",
+    "        xx1 = np.maximum(x1[i], x1[ind])\n",
+    "        yy1 = np.maximum(y1[i], y1[ind])\n",
+    "        xx2 = np.minimum(x2[i], x2[ind])\n",
+    "        yy2 = np.minimum(y2[i], y2[ind])\n",
+    "\n",
+    "        w = np.maximum(0., xx2 - xx1)\n",
+    "        h = np.maximum(0., yy2 - yy1)\n",
+    "\n",
+    "        wh = w * h\n",
+    "        o = wh / (area[i] + area[ind] - wh)\n",
+    "\n",
+    "        ind = ind[np.nonzero(o <= overlap)[0]]\n",
+    "\n",
+    "    return dets[pick, :]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "scores = predictions_df['bicycle']\n",
+    "windows = df[['xmin', 'ymin', 'xmax', 'ymax']].values\n",
+    "dets = np.hstack((windows, scores[:, np.newaxis]))\n",
+    "nms_dets = nms_detections(dets)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Show top 3 NMS'd detections for 'bicycle' in the image and note the gap between the top scoring box (red) and the remaining boxes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "(Remove the temp directory to clean up, and we're done.)"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "scores: [ 0.86610985 -0.70051557 -1.34796357]\n"
      ]
     },
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "!rm -rf _temp"
-     ],
-     "language": "python",
+     "data": {
+      "image/png": [
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+       "JAnLsqStKmazGQ8/+hj3n39jj0N96Iuf7mt6VVVR5DmGvQM0Iu9FX+MD7cQbCAOGlbiuxu1VT1cV\n",
+       "9NRaqjpyocfGbZbnlGUpF4P3cnK2LYVL+0m0NMui0tDwdxphWOeEewYlt6Jfj7Jc9pulKs+YoMRX\n",
+       "ArYzFr781Ud487n7+p5Aj9KJI+3ee+GfiMgbjabaru0lspbzRZ/W6msokkAvat1s284TgifLc6wx\n",
+       "NI3I1HnvKZfSwyiiWHBXy/lRTArKZdlP4wE03VDys1YodKVRnfSQxyzLZGzfmr7khpFeSj/IhERo\n",
+       "hhAJkUoefuQbvPX+NwkO2VnqtsYay6QoIu7ZyUSwNbj4/SzjbMA0m8hzjNBCOOco8jwqRgmnShcE\n",
+       "8y785LJmRV6QRqTMGB6ndWbvA0kq1BZJaimXpcwW+KHBX5YldVXhkGtF2fcUMqiWFwUhyFqWy4GX\n",
+       "RDNv3vRSHzKfL/nyVx/k7W89T13X/SRzkqT9ebdcLvu5Bx/rynoNi/+Q9RtnxEJJ4PuIuyzL/ljU\n",
+       "P4wH9bTkqs/TeYsx9HC4RgfTcsl4tH7cNL2Z7bsDd07Iq9bW1mhHztoYw+7uLsUIkaGQHmNMH1Wr\n",
+       "M9OL0Vq7B76nzQpgD/RQFX/GhO+6aLqYY0ymXoxFUexxzuoM9b3rqpILPTZCxqmU1uIUlpRlGc7L\n",
+       "SfClLz/IW+8/1w8XLBYLGbIJgel0yiJiyrUL3k8GMuDm1Sk760iSbC82PAjvx7hxE6LzbL2QQWk5\n",
+       "SuFNRVGwtbUlWYqXseRgRd5uPp+z4Rx1HLbZ2NjYM0DlvSePArPWCWSsbVuhr80nhC5gg0V77D74\n",
+       "nifbR3Whr3z1Qd72ljfsSYXL5Zw0m1DFmqZGRZqJeQPOjgKCXC7SLuKqPYE2yMVVVzV11zIpCqFO\n",
+       "jVDbZVlFtXDL7nzBYrHoM7oXr1xlfX2dPJVhjLZqUXk/G1PoIiuERAu5OJuyEj4eY0mMo4jnrgQH\n",
+       "cbq4FW3NJI2iD1FyS51oYhPWioKvfeNbvOfd7+xpj4MVWGjiHJPJWjzmXRIXR7vjeUrnqeM5lOfC\n",
+       "PaMsk4vFnJ2dXZK8wCWOYm2NtmvInLANWgI721txU4HIMqL/MFY2ZWMCXaucNf46B5kLNNMMjlPH\n",
+       "0fVa8wTKquwDB98NEL2bjZWvr6/zyKPf5Mff/c7eYQJRt3S49pbLpWT4ietBEmoC9R1KKxqwYffO\n",
+       "QIz9iV6HChl87rnnOHHiRO9n8jxne3u7DxbUx+jmpRuAvu54OFAd/iuVU/fdgX/gz/3V/T6EV4l9\n",
+       "nn/9b9613wfxKrPf5l/9yo/v90G8yuy3+Re//I79PohXlVWVOGbtgUEcg2/mvfMXfLnF+5aqGQUw\n",
+       "GuSEjnyS7YmSg/GILKPrgy4Jwirh3/cdnkAX+YhOnz4NDNUApWrQCF0DxnFVYIyC0WG/8RDZKwEa\n",
+       "9t2Br2xlK1vZH8aU6kAd7XgqU7NkpbIYo9f0b7UkqtF7OwJO1HW7h/sIJEtyDJO5ILDnLo7jj6ef\n",
+       "+8eui9rHZc+xw9bj1Y3llSbl93GUfmUrW9nKVvad2MuN0u+LA1/Zyla2spX94e3mc5orW9nKVray\n",
+       "V62tHPjKVrayld2mdssduDHmfcaYbxpjvmWM+flb/f77ZcaYXzXGXDLGPDq6b9MY82ljzO8ZY37T\n",
+       "GHN49NgvxDX6pjHmJ/fnqL+7Zoy5yxjzWWPM140xXzPG/PV4/4FdF2NMYYz5ojHmIWPMY8aYfxjv\n",
+       "P7BromaMccaYB40xvxF/P/BrsmcY5bv9H3DAE8BrgRR4CHjdrTyG/foPvBM4Dzw6uu8fA38r3v55\n",
+       "4Bfj7fvi2qRxrZ4A7H5/hu/CmpwCzsXbM+Bx4HWrdWEt/kyALwDvOOhrEj/r3wT+I/Dx+PuBX5Nb\n",
+       "HYG/DXgihHAhhNAA/xn46Vt8DPtiIYTfAa5ed/cHEck64s8/E2//NPDREEITQriAnIBvuxXHeSst\n",
+       "hHAxhPBQvL0LfAPRUj3o66IEGCIsKefNgV4TY8ydwPuBf4vSeR7wNYFbX0K5A/j90e/PxPsOqp0M\n",
+       "IVyKty8BJ+PtM8jaqH3Pr5Mx5rVIhvJFDvi6GGOsMeYh5LN/NoTwdQ74mgD/DPg5YDyOedDX5JY7\n",
+       "8BVm8WUsSO53s/X5nl07Y8wM+O+ICPbO+LGDuC4hBB9COAfcCfxJY8y7r3v8QK2JMeZPA5dDCA8y\n",
+       "RN977KCtidqtduDPAneNfr+LvTvlQbNLxphTAMaY08DleP/163RnvO97zowxKeK8PxJC+Fi8+8Cv\n",
+       "C0AIYQv438D9HOw1+WHgg8aYJ4GPAj9mjPkIB3tNgFvvwL8CnDXGvNYYkwF/Afj4LT6GV5N9HPhQ\n",
+       "vP0h4GOj+3/WGJMZY74fOAt8aR+O77tqRmaJ/x3wWAjhn48eOrDrYow5pmgKY8wE+AngQQ7wmoQQ\n",
+       "PhxCuCuE8P3AzwKfCSH8ZQ7wmvS2D53kn0LQBk8Av7DfXdxb+Lk/CjwH1Egf4K8Am8BvAb8H/CZw\n",
+       "ePT8D8c1+ibw3v0+/u/SmrwDqWk+hDipB4H3HeR1Ad4APBDX5BHg5+L9B3ZNrlufH2VAoRz4NVmN\n",
+       "0q9sZStb2W1qq0nMla1sZSu7TW3lwFe2spWt7Da1lQNf2cpWtrLb1FYOfGUrW9nKblNbOfCVrWxl\n",
+       "K7tNbeXAV7ayla3sNrWVA1/Zyla2stvUVg58ZStb2cpuU/t/6S2bnP6vZqYAAAAASUVORK5CYII=\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x114f15d10>"
+      ]
+     },
      "metadata": {},
-     "outputs": [],
-     "prompt_number": 10
+     "output_type": "display_data"
     }
    ],
-   "metadata": {}
+   "source": [
+    "plt.imshow(im)\n",
+    "currentAxis = plt.gca()\n",
+    "colors = ['r', 'b', 'y']\n",
+    "for c, det in zip(colors, nms_dets[:3]):\n",
+    "    currentAxis.add_patch(\n",
+    "        plt.Rectangle((det[0], det[1]), det[2]-det[0], det[3]-det[1],\n",
+    "        fill=False, edgecolor=c, linewidth=5)\n",
+    "    )\n",
+    "print 'scores:', nms_dets[:3, 4]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This was an easy instance for bicycle as it was in the class's training set. However, the person result is a true detection since this was not in the set for that class.\n",
+    "\n",
+    "You should try out detection on an image of your own next!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "(Remove the temp directory to clean up, and we're done.)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "!rm -rf _temp"
+   ]
   }
- ]
+ ],
+ "metadata": {
+  "description": "Run a pretrained model as a detector in Python.",
+  "example_name": "R-CNN detection",
+  "include_in_docs": true,
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.9"
+  },
+  "priority": 6
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
 }
diff --git a/examples/feature_extraction/imagenet_val.prototxt b/examples/feature_extraction/imagenet_val.prototxt
index 83fe8c1a08d..b0a1cefa00e 100644
--- a/examples/feature_extraction/imagenet_val.prototxt
+++ b/examples/feature_extraction/imagenet_val.prototxt
@@ -1,24 +1,24 @@
 name: "CaffeNet"
-layers {
+layer {
   name: "data"
-  type: IMAGE_DATA
+  type: "ImageData"
   top: "data"
   top: "label"
+  transform_param {
+    mirror: false
+    crop_size: 227
+    mean_file: "data/ilsvrc12/imagenet_mean.binaryproto"
+  }
   image_data_param {
     source: "examples/_temp/file_list.txt"
     batch_size: 50
     new_height: 256
     new_width: 256
   }
-  transform_param {
-    crop_size: 227
-    mean_file: "data/ilsvrc12/imagenet_mean.binaryproto"
-    mirror: false
-  }
 }
-layers {
+layer {
   name: "conv1"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "data"
   top: "conv1"
   convolution_param {
@@ -27,15 +27,15 @@ layers {
     stride: 4
   }
 }
-layers {
+layer {
   name: "relu1"
-  type: RELU
+  type: "ReLU"
   bottom: "conv1"
   top: "conv1"
 }
-layers {
+layer {
   name: "pool1"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv1"
   top: "pool1"
   pooling_param {
@@ -44,9 +44,9 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "norm1"
-  type: LRN
+  type: "LRN"
   bottom: "pool1"
   top: "norm1"
   lrn_param {
@@ -55,9 +55,9 @@ layers {
     beta: 0.75
   }
 }
-layers {
+layer {
   name: "conv2"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "norm1"
   top: "conv2"
   convolution_param {
@@ -67,15 +67,15 @@ layers {
     group: 2
   }
 }
-layers {
+layer {
   name: "relu2"
-  type: RELU
+  type: "ReLU"
   bottom: "conv2"
   top: "conv2"
 }
-layers {
+layer {
   name: "pool2"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv2"
   top: "pool2"
   pooling_param {
@@ -84,9 +84,9 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "norm2"
-  type: LRN
+  type: "LRN"
   bottom: "pool2"
   top: "norm2"
   lrn_param {
@@ -95,9 +95,9 @@ layers {
     beta: 0.75
   }
 }
-layers {
+layer {
   name: "conv3"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "norm2"
   top: "conv3"
   convolution_param {
@@ -106,15 +106,15 @@ layers {
     kernel_size: 3
   }
 }
-layers {
+layer {
   name: "relu3"
-  type: RELU
+  type: "ReLU"
   bottom: "conv3"
   top: "conv3"
 }
-layers {
+layer {
   name: "conv4"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "conv3"
   top: "conv4"
   convolution_param {
@@ -124,15 +124,15 @@ layers {
     group: 2
   }
 }
-layers {
+layer {
   name: "relu4"
-  type: RELU
+  type: "ReLU"
   bottom: "conv4"
   top: "conv4"
 }
-layers {
+layer {
   name: "conv5"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "conv4"
   top: "conv5"
   convolution_param {
@@ -142,15 +142,15 @@ layers {
     group: 2
   }
 }
-layers {
+layer {
   name: "relu5"
-  type: RELU
+  type: "ReLU"
   bottom: "conv5"
   top: "conv5"
 }
-layers {
+layer {
   name: "pool5"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv5"
   top: "pool5"
   pooling_param {
@@ -159,79 +159,79 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "fc6"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "pool5"
   top: "fc6"
   inner_product_param {
     num_output: 4096
   }
 }
-layers {
+layer {
   name: "relu6"
-  type: RELU
+  type: "ReLU"
   bottom: "fc6"
   top: "fc6"
 }
-layers {
+layer {
   name: "drop6"
-  type: DROPOUT
+  type: "Dropout"
   bottom: "fc6"
   top: "fc6"
   dropout_param {
     dropout_ratio: 0.5
   }
 }
-layers {
+layer {
   name: "fc7"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "fc6"
   top: "fc7"
   inner_product_param {
     num_output: 4096
   }
 }
-layers {
+layer {
   name: "relu7"
-  type: RELU
+  type: "ReLU"
   bottom: "fc7"
   top: "fc7"
 }
-layers {
+layer {
   name: "drop7"
-  type: DROPOUT
+  type: "Dropout"
   bottom: "fc7"
   top: "fc7"
   dropout_param {
     dropout_ratio: 0.5
   }
 }
-layers {
+layer {
   name: "fc8"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "fc7"
   top: "fc8"
   inner_product_param {
     num_output: 1000
   }
 }
-layers {
+layer {
   name: "prob"
-  type: SOFTMAX
+  type: "Softmax"
   bottom: "fc8"
   top: "prob"
 }
-layers {
+layer {
   name: "accuracy"
-  type: ACCURACY
+  type: "Accuracy"
   bottom: "prob"
   bottom: "label"
   top: "accuracy"
 }
-layers {
+layer {
   name: "loss"
-  type: SOFTMAX_LOSS
+  type: "SoftmaxWithLoss"
   bottom: "fc8"
   bottom: "label"
   top: "loss"
diff --git a/examples/feature_extraction/readme.md b/examples/feature_extraction/readme.md
index c325ed482e5..6c8917e27e1 100644
--- a/examples/feature_extraction/readme.md
+++ b/examples/feature_extraction/readme.md
@@ -51,7 +51,7 @@ Extract Features
 
 Now everything necessary is in place.
 
-    ./build/tools/extract_features.bin models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel examples/_temp/imagenet_val.prototxt fc7 examples/_temp/features 10
+    ./build/tools/extract_features.bin models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel examples/_temp/imagenet_val.prototxt fc7 examples/_temp/features 10 lmdb
 
 The name of feature blob that you extract is `fc7`, which represents the highest level feature of the reference model.
 We can use any other layer, as well, such as `conv5` or `pool3`.
diff --git a/examples/filter_visualization.ipynb b/examples/filter_visualization.ipynb
deleted file mode 100644
index 09b416fb4ca..00000000000
--- a/examples/filter_visualization.ipynb
+++ /dev/null
@@ -1,601 +0,0 @@
-{
- "metadata": {
-  "description": "Extracting features and visualizing trained filters with an example image, viewed layer-by-layer.",
-  "example_name": "Filter visualization",
-  "include_in_docs": true,
-  "priority": 2
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
-  {
-   "cells": [
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Here we visualize filters and outputs using the network architecture proposed by Krizhevsky et al. for ImageNet and implemented in `caffe`.\n",
-      "\n",
-      "(This page follows DeCAF visualizations originally by Yangqing Jia.)"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "First, import required modules and set plotting parameters"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import numpy as np\n",
-      "import matplotlib.pyplot as plt\n",
-      "%matplotlib inline\n",
-      "\n",
-      "# Make sure that caffe is on the python path:\n",
-      "caffe_root = '../'  # this file is expected to be in {caffe_root}/examples\n",
-      "import sys\n",
-      "sys.path.insert(0, caffe_root + 'python')\n",
-      "\n",
-      "import caffe\n",
-      "\n",
-      "plt.rcParams['figure.figsize'] = (10, 10)\n",
-      "plt.rcParams['image.interpolation'] = 'nearest'\n",
-      "plt.rcParams['image.cmap'] = 'gray'"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 1
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Run `./scripts/download_model_binary.py models/bvlc_reference_caffenet` to get the pretrained CaffeNet model, load the net, specify test phase and CPU mode, and configure input preprocessing."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "net = caffe.Classifier(caffe_root + 'models/bvlc_reference_caffenet/deploy.prototxt',\n",
-      "                       caffe_root + 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel')\n",
-      "net.set_phase_test()\n",
-      "net.set_mode_cpu()\n",
-      "# input preprocessing: 'data' is the name of the input blob == net.inputs[0]\n",
-      "net.set_mean('data', np.load(caffe_root + 'python/caffe/imagenet/ilsvrc_2012_mean.npy'))  # ImageNet mean\n",
-      "net.set_raw_scale('data', 255)  # the reference model operates on images in [0,255] range instead of [0,1]\n",
-      "net.set_channel_swap('data', (2,1,0))  # the reference model has channels in BGR order instead of RGB"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 2
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Run a classification pass"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "scores = net.predict([caffe.io.load_image(caffe_root + 'examples/images/cat.jpg')])"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 3
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The layer features and their shapes (10 is the batch size, corresponding to the the ten subcrops used by Krizhevsky et al.)"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "[(k, v.data.shape) for k, v in net.blobs.items()]"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 4,
-       "text": [
-        "[('data', (10, 3, 227, 227)),\n",
-        " ('conv1', (10, 96, 55, 55)),\n",
-        " ('pool1', (10, 96, 27, 27)),\n",
-        " ('norm1', (10, 96, 27, 27)),\n",
-        " ('conv2', (10, 256, 27, 27)),\n",
-        " ('pool2', (10, 256, 13, 13)),\n",
-        " ('norm2', (10, 256, 13, 13)),\n",
-        " ('conv3', (10, 384, 13, 13)),\n",
-        " ('conv4', (10, 384, 13, 13)),\n",
-        " ('conv5', (10, 256, 13, 13)),\n",
-        " ('pool5', (10, 256, 6, 6)),\n",
-        " ('fc6', (10, 4096, 1, 1)),\n",
-        " ('fc7', (10, 4096, 1, 1)),\n",
-        " ('fc8', (10, 1000, 1, 1)),\n",
-        " ('prob', (10, 1000, 1, 1))]"
-       ]
-      }
-     ],
-     "prompt_number": 4
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The parameters and their shapes (each of these layers also has biases which are omitted here)"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "[(k, v[0].data.shape) for k, v in net.params.items()]"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 5,
-       "text": [
-        "[('conv1', (96, 3, 11, 11)),\n",
-        " ('conv2', (256, 48, 5, 5)),\n",
-        " ('conv3', (384, 256, 3, 3)),\n",
-        " ('conv4', (384, 192, 3, 3)),\n",
-        " ('conv5', (256, 192, 3, 3)),\n",
-        " ('fc6', (1, 1, 4096, 9216)),\n",
-        " ('fc7', (1, 1, 4096, 4096)),\n",
-        " ('fc8', (1, 1, 1000, 4096))]"
-       ]
-      }
-     ],
-     "prompt_number": 5
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Helper functions for visualization"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# take an array of shape (n, height, width) or (n, height, width, channels)\n",
-      "#  and visualize each (height, width) thing in a grid of size approx. sqrt(n) by sqrt(n)\n",
-      "def vis_square(data, padsize=1, padval=0):\n",
-      "    data -= data.min()\n",
-      "    data /= data.max()\n",
-      "    \n",
-      "    # force the number of filters to be square\n",
-      "    n = int(np.ceil(np.sqrt(data.shape[0])))\n",
-      "    padding = ((0, n ** 2 - data.shape[0]), (0, padsize), (0, padsize)) + ((0, 0),) * (data.ndim - 3)\n",
-      "    data = np.pad(data, padding, mode='constant', constant_values=(padval, padval))\n",
-      "    \n",
-      "    # tile the filters into an image\n",
-      "    data = data.reshape((n, n) + data.shape[1:]).transpose((0, 2, 1, 3) + tuple(range(4, data.ndim + 1)))\n",
-      "    data = data.reshape((n * data.shape[1], n * data.shape[3]) + data.shape[4:])\n",
-      "    \n",
-      "    plt.imshow(data)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 6
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The input image"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# index four is the center crop\n",
-      "plt.imshow(net.deprocess('data', net.blobs['data'].data[4]))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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jJc8ze0QrcgYroKgalNIAuaKERauINI7vdv78W6Hf1xKA0Jcn5nOsu97EEmGOMiYYk5Pc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zz12uoZORjd9bOyd5k6RErREhFTry5yfn93K41a5SKoDVfwWBxnO8/9yV1DbQsi0Wr+nhCR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\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7N24EL67Fm0KZgwKrrCQUce72vsMi6EOZi5ncQd+jpy5fKz47PAptc3ri44pzx2Dg/Y+U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\nAABJRU5ErkJggg==\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0x7fd6306e1cd0>"
-       ]
-      }
-     ],
-     "prompt_number": 7
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The first layer filters, `conv1`"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# the parameters are a list of [weights, biases]\n",
-      "filters = net.params['conv1'][0].data\n",
-      "vis_square(filters.transpose(0, 2, 3, 1))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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HsnuPT6BhpP/ARhMFuOtruPHh1MmwX3/7sWOQp1rDDRtjzpC3VCAbERyjo0ToTZ5v1Jly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\nRK5CYII=\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0x7fd63064b6d0>"
-       ]
-      }
-     ],
-     "prompt_number": 8
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The first layer output, `conv1` (rectified responses of the filters above, first 36 only)"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "feat = net.blobs['conv1'].data[4, :36]\n",
-      "vis_square(feat, padval=1)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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ms2YoOpgSpqZhCsFvW62WPg9sBU7MjEwmo30ERsrKK5fP5z2z0cjIiMlIueZ5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wGNgv//Iv22uvvWb/83/+T/u1X/s1+6M/+iP77Gc/az/6oz9qX/va1+xHfuRH7LOf/ayZmb3+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dxu119/vW6kvD73MssD3mkyjxIf9gQk3w+IS9I8DIrForYvK3Czmw/PQNtw0l+0KasFY8OD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\n8lJsiPg+dMyicuh8ku+UxXQm+WFt1ieM1+LiopdYNGldEYnXhExKZ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-       "text": [
-        "<matplotlib.figure.Figure at 0x7fd621d91150>"
-       ]
-      }
-     ],
-     "prompt_number": 9
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The second layer filters, `conv2`\n",
-      "\n",
-      "There are 256 filters, each of which has dimension 5 x 5 x 48. We show only the first 48 filters, with each channel shown separately, so that each filter is a row."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "filters = net.params['conv2'][0].data\n",
-      "vis_square(filters[:48].reshape(48**2, 5, 5))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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0vzuvmqNWAfPO3EFL6ws5BltgaNfyy/LKGnOdQ8BnrH9o3PTtiEPGRv98b9eu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\nZsxo8/gdQWq+jUUreVymxRalLOrMsuefbf+Ots2sAAB+YKnms61ChvM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NAAAg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\n1k/7BsAeRj8v6JfrgWVZePb8OqPQWYG7du0ayyKM6HsB22sjRnPK9dhaT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dZbb+3kdrwB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\nxcV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c3/Gb9+s973lPRIy/53ekP5xi5p9yhg+A9wtOIb9/t9fMvG9TU1O92CV+gyy07dgO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7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\ni8T/A2aklexSCttuAAAAAElFTkSuQmCC\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0x7fd630658d90>"
-       ]
-      }
-     ],
-     "prompt_number": 10
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The second layer output, `conv2` (rectified, only the first 36 of 256 channels)"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "feat = net.blobs['conv2'].data[4, :36]\n",
-      "vis_square(feat, padval=1)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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44sWLqd6jk/OahTXn6zYWLlwYeU2rRmu6rL29PbLeq6++6uJDhw4Nux//99rc\n3OxiHa6fdiJcv6L14sWLXbxz585U70tbBbsKak3f+caMqfafl9AJkc+ePTvi92gpkyqm8/S70omj\ndYJws+iE5nQ9eEfwVd7R0WHTpk2z22+/3caOHWvr16+3wcFB+8M//EM7cOCAdXR02A9/+MObanEA\nAAA0guB0XlNTk7300ku2efNmW79+vZmZPffcc/b4449bV1eXPfroo/bcc89ldqAAAABVEjwB8eLF\ni23jxo2RUQednZ22du1aa2lpsf7+fvvgBz94U7M3EyfmZ8WKFS7etm2biznnxch6AmJ/dKhKm2rW\nbaR9j45yO3bsWOx6mj5euXJl5LW9e/em2katmPS5eGknw9X0sVn6FHLVaZr4yJEjhewz7wmINQWt\nFdn9e5Cup6l9fxRfo8h1AuKmpiZ77LHHbNWqVfatb33LzN7JqQ8Nj2xpack8xw4AAFAVwX2iXnnl\nFWttbbWjR4/a448/bp2dnZHXm5qa+J8hAABoWMEPUUOF1ObOnWuf+MQnbP369S6NN2/ePOvr64s0\newIAANSLZ5999pbrBPWJOn/+vF27ds2mTp1q586dsyeeeMK++tWv2osvvmizZ8+2L3/5y/bcc8/Z\nyZMnb+pcTutUfu69914Xv/HGGy7mnN9w3333uViHyp87dy6yXlyO3x+yrsOBT5486WK/NMDq1auH\n3ZffT+m1116LPXa8Y968eS7WKthc58UI7Z8zbtw4F2s5i6rz+/vpse/fv9/FefYLSjrneq/xyzZc\nu3Ytt2MaDdL0iQpqiRoYGLBPfOITZmZ29epV+/SnP21PPPGErVq1yp566in7zne+40ocAAAANKKg\nh6jFixfbli1bbvr3WbNm2YsvvljzQQEAAFRdtUvKYkS0aRnv8Iu9alrtnnvucXF3d3dkPa06f/Xq\nVRf7E53Onz9/2P1qms/M7Gc/+1m6A66Y97///ZHlV155paQjuaGsGQFQmyqk8LQC94ULF2LXW7p0\nqYv91PzGjRtdrGUcxo4dG1nv/Pnzwcd5K0uWLBn2GE6cOJHbPjE85s4DAAAIwEMUAABAANJ5DSRk\nYsyZM2e6WJu6Dx8+HHQMkyZNcnGezdlp+aPfurq6XKyjaXp6eiLr6Ui7BQsWuNgfnacV+5WfHozj\np/1CJ0WNo6N4QiYnyDt9p1XP/RGSqD86QW0WI8NCJs1OkpTCU9oNwL8udRuBE37UTGcE0CriZR3P\naEZLFADlD3hmAAAgAElEQVQAQAAeogAAAALwEAUAABCg1D5R/rQw2ocmpH9PFrSPUNJw0ba2Nhdr\nXyIzsz179mR/YCn4w/mH6Kzc/nmdO3eui/X78Kv0aq5dh/zPmTMnsp7O+t3b2+vizZs3R9bTPkd5\n0pIGZtGhwdpv6e67746sp+ds0aJFLj548GBkPT1/yp/BXod3ax+GLPpAab8q3bZZ/DXsnxf93tau\nXRu7L50jUz+jP5u99ofRod967ZiFlSvwZ5ZHuRYuXOjiadOmuXjMmOifF+2PmNRfUu/BKqlP1IoV\nK1y8ffv2+IMVfukC/e3ofdIvzTB16lQX+yVPyuD308ySfz/RqvNZ9FFLS68lvYf71fL1mNL2S60V\nLVEAAAABeIgCAAAIEDQBcU07TJjIDwAAoEqSnltoiQIAAAjAQxQAAECAUkbn+T3qkQ2trH3o0CEX\nZ3G+Ozo6XHzgwAEXp03N/tM//VNk+ec//7mL16xZU9vBJZg3b15kudbJa5MqjOu5yPsa15GY+hl3\n7tyZ6v06isosbJSRjpjxR90VpaxzrtXuR0Olda0s74/w1RFbWrHc/63oejp6078WdeSzjuz0f7s6\nckxHivqjvpcvX+5iHRXoT9iuv+Wka1tH7ir/Xqij+rTKuT+6VK+fY8eOufi3fuu3XPyf//mfkffw\nN7QYaf6+0RIFAAAQgIcoAACAADxEAQAABCi1Yjmydc8994z4Pdp/QPs6+BWFa63++sUvfjGy/Lu/\n+7up3qd9MUL6nvj9KLT6uPZT8Cuo+1WKh/gVxuOqxGtfDrNoH5oQfnVl/a527do14u35leZD+kSV\n1Q+qLFopeevWrSUeSfGSfnvab0QrRvsVrbUfj/aP8q8j/a3o76unpyeynvYf0m3rb9wseq3r/UT7\nZfnHoZ/pzJkzFkfvi37/Ge2npb+vSZMmxe5XZ3mI63uFaqElCgAAIAAPUQAAAAFI5zWQkHSeP3Fs\nUX784x+nWk/TCKtWrXLxli1bIuvpJNBJze9ankH5w441JRD3HrP4iZRrTd/5/Mlc49KNae3bt6+m\n948GfkpWly9dulT04VSWpsiSJtTWdFdLS4uL/cm/BwcHXawpQT/9FrdtTfOZRScNv/POO1189OjR\nyHp79+51sd5P/Anm9d6gaXX/XqDXiL8vpek9vcbiJg/Pwr333htZfvPNN3PbV6OjJQoAACAAD1EA\nAAABSOc1EL/ZuV78xV/8hYu/8Y1vxK63cePG2Nc0haepubRpF389TeHpSKJa02gjoVXis04P4tb8\n9FFc6rZR6e8oqUK2jqCLq9JtFq1mriPX9Do3i45K01Tc7NmzI+tp6kv35Y+Se/nll4c9Bv9+6R9v\nHJ0NYvHixS7W+4SZ2YYNG1JtT0dC6znXc5S3ou5xmsY1MxsYGMhtX2m9//3vd3HIiGNaogAAAALw\nEAUAABCAhygAAIAA9IlqIP6s6VnS/LxfibhW3/rWt1z8j//4j5HXtF/KX/3VX6XanvZvuu+++yKv\nvfHGGyM+vpA+AqEVy7W6clwl4yJpWQkzs507d7pYh3cn0SHhU6dOjbyWtvK39mVJGuqepevXr0eW\n0/aZaRRp+xPqta59XvxyB/ob0Nf84f/aV0l/D36ZD63if/jw4djj00rnv/71r108f/78yHq6L60w\n7vex0uteP7vfxyqpH1kcPed+2ZU0pk2bFlnWe4j2vSqrpEGRfaD0u0m6/77yyis17YeWKAAAgAA8\nRAEAAAQgnddA4tJOWaRCsk7hxW37mWeeiV1vwYIFLvYnI40Tkr7LQtr0nZ+C1bRp0mfUKsf+ZNFZ\n2rRpU2TZT23EaW9vd7EeX1L19yRFpfCS+Ok93EzTef5EwPodakkSf4YB/e3o0Hv/2tPK5nrva21t\njaynvyNNxXV1dcV8imgqra2tLfKafkbdr59S1HSSHpOfJtXPoXbs2BF7fHGefPLJyLKmFP/93/99\nxNtLa+nSpZFl/U6LTOHp/VOrsj/++OOR9f72b/82s33SEgUAABCAhygAAIAApPMaiE7Wq7QZ3R/h\nElKhNURodWCVNoVXRZqW0IrMftN+2s+YZwpPpU3f+bSqcxIdYaXiUhxlSvuZRpvbbrvxf3G91/i/\neU2DHT9+3MV9fX2R9XQbWhHcv+b1fXof8+9xOkGyHqt/fDoZu/4um5ubI+vpNavpI39EbtqK4zqi\nrru728Uhv71XX301shwyKX2IPXv2FLKfW9GuIevXr3exPyl1lmiJAgAACMBDFAAAQAAeogAAAALQ\nJ6qBLFu27JbrZNEH6oEHHnDx66+/nuo9eZZIqCKtfmwW7QOSRX+fPCvI50lLM5hF+7/4Fa5RHUnV\ntxcuXOhi7Rfk/wZ0Pa1cf/Lkych62vdMywv4fZO0/5D2B02qXq6lN5L6RCm/3572b9R4xowZkfW0\nb5b2AfX7gGXp4MGDicuNTs+5/q3bt29fbvukJQoAACAAD1EAAAABSOc1kNmzZxeyH03h+RNe6sSd\nKnSofNb0eDVFEZpK8tNTQ/IcUmtWXyk8VVRpBmQr6fervyNNM/sTVOvk1Zqa6+joiKyn14j+Lv2S\nAWlLCCithp62krZ/zfqTaA/xP69WuNf37N69O7Je3L0n7t6CeEWV7FG0RAEAAATgIQoAACAA6bwG\nEjfqSytk+03OSifDTZveikvfZUVH0IRUOffp8Wr14lB+deRaFTXqbv78+ZFlHSFFyg0joSkyTZf7\n1fi1qrWmu/3Rb3H3IT8NFjKxbX9/f6r1dNJ2fz9639DRef79V0fk6mfUyY397SmttI7qoiUKAAAg\nAA9RAAAAAXiIAgAACECfqAaiOXiV1A9KVbFidJ5DVnUIsj+cOG2/oLg+YX515bhqyL4FCxa4WCsb\na0XmLCRVdQZGQn9HJ06ccLH/G1Baldy/trXqufY5yvo3kET7kfr77erqGnY93+XLl12sFbO1v5W/\nDb3vZPF5s+5TWjX++U/7ty5LtEQBAAAE4CEKAAAgAOm8BqLN4PXKnxS0qCborIf1p03f+TQlW2T6\nAgilaStNE/uVvbV8h6a0/N+KVkfX+4FfoTzP1I2WIfBLEmgKX2O/qrumIvW8+J9j7ty5LtZ7eFxl\n9JH44Ac/6OINGza4OO8ZFYriT3JNOg8AAKBO8BAFAAAQgHReHdOJP80aY/RF0mcIqaheb3R0E1AP\ndAStpsWvXbsWWU9HUmnaSiuem0UreGuqr8j0tu7XH02ny5rO0/SdmVlbW5uLdQTj0aNHI+vp549L\nZYb62c9+VvM20vCrzut3mGfqsLu7O7dtp0VLFAAAQAAeogAAAALwEAUAABCAPlF1zB9S2+iy6AdV\nVAVff2Z2Xc6zCnvW/OHdly5dGvE29LP7ZThCtodqGRgYcLH292lpaYmsp0P7x4y58afH/x36fYuG\n+NdO2t/vzJkzXRzS59Dvi6W/icHBwdj36fB7nYnAL3Ggv4HJkye72J9FocqSzkOjoyUKAAAgAA9R\nAAAAAUjnNZBam3+1Qu6ZM2dqPZxKunjxootXrVrl4o0bN2a6Hx3SPNxylc2bN8/F/f39NW9PPzvp\nu8ajpQz093XgwIHIegsXLnSxprT8EgdadVqHx6edkWHatGmRZd2XVgfXYzWLpgc15R43yfitaOpw\n0aJFLvbv0/oZtfp7Pd0z0vJLIWj6Uielrie0RAEAAATgIQoAACAA6bw6piNczG5uKh2pRk3hKR3R\nqCk8HcFjln4Uj181Pg1NN4SmCtIKSdEy8TFGIm60aW9vb2RZf1N6Xfqj1TSlFVLt2h9RqsuaEvRH\n0CpNKYaOgtZRi5ra9O/Tev40pdjc3By036rRkYn+iMp6TeEpWqIAAAAC8BAFAAAQgIcoAACAAPSJ\nqmN+X4TRXDW2ViGVjM3C+kvk3Q9Kad+OtEZD3zhkR0scKL+chS7rdTllypTIerfffruLtd9n2kr/\nfskE3YbGWlHczKyrq8vFWc8Gof3DDh48GHkt7n4we/bsVNueMWOGi0+ePBlwdNnTqvP6Xed9fNrf\nrKi/h7REAQAABOAhCgAAIADpvAaS54S6GJ42pVfRaJukGsULue9oyi1tKt0v6aLpPS1doJOMm0VT\nS/oef3vnz59PdRxp6b40jZX2N5n2eKqSwlNa+qXI4yujSwstUQAAAAF4iAIAAAhAOq+B6KiWeuVP\nMuqPtEF986tJ6yTQx48fd/HOnTsLO6aq8Staa1rHnzR3NPGvHZ3UV0f++VXO9R6iqT2/Yrm+lvV5\nDkmrVzFNl9ZommicligAAIAAPEQBAAAE4CEKAAAgAH2iGkgj5KHrrQ9UPfdbKMPkyZMjy3v37nVx\nf39/0YdTSX7fxqr3g8q6NID2i9T7gV/ZXNfTqtj++dLh9lrWwK/mP2nSpNhtlMHvs4V8aL/My5cv\nj/j9fEsAAAABeIgCAAAIQDqvjiUN0UV2dCi1P3z63Llzme7r4YcfdvG2bdti95s1TXNoyu3UqVOZ\n7odJsm/t6NGjZR9CqeJS+v7E2Jp60XuhP4uATuSrKUD/WtQyBJoCLKvqf0hqqSz+ZM7ataTqE5pv\n3LixpvfTEgUAABCAhygAAIAApPPq2PXr1yPLBw8eLOlIGtuBAwdc/Cd/8ieR1w4fPpzpvtauXZvp\n9tLSyVInTJjg4qzTeUAofxSgLuukw5p+NzNrbW11sV7bmuYzM+vp6Rl2v3466tq1ay7OM9WXxUjl\nmTNnuli7HmSdKvS7G7S3t7v4ve99r4tfeOGFTPdbBbREAQAABOAhCgAAIAAPUQAAAAHoE9VA/Iq+\nQ7QCsubzq2jatGmR5dOnT2e6fR2Kq9XGW1paIutp9Ww9Z9/73vdit9cosq5AnZZ+90nfe3NzcxGH\ngzpy4cIFF/vXjt4XtZ+Rfx0dP3582NgvJaP9B8uifZ1OnDgRu17Sa3k6dOjQsHEjoiUKAAAgAA9R\nAAAAAUjnNZC2trZh/33+/Pku9ofkp03vaUXrPJuzs07f+eIqf/f29ma6vSSaHvDLVNRq6tSpkWWd\nSDXtkOmyKgyn/e71ekYx6mkyXP8eFzeTgz8BsV73mtLO+jeahbLSdI1IZ2gImYGifn4ZAAAAFcJD\nFAAAQADSeQ1k0qRJw/57yOiIvEfJqeXLl7u4q6srt/1URdbpAZ1wVUccZkEnbDXLpopyrbL+jLg1\nTXnUm+7u7rIPoRJ0RJ9OEGxW3ojcKqh1EnlaogAAAALwEAUAABCAhygAAIAA9IkqkJYJMIuWCtCq\nulp91yy+DMH9998fWc5y6HfepQZUnv2g/HPS19fn4jxnYE+yYMECF8fNHO9L6puUZx+hKvSB8tEn\nqnhVn+kAt0ZZhHzQEgUAABCAhygAAIAApaTzHnnkETMze+ihhyL/PnfuXBefOnXKxUlpqqTh4po+\n0+ZoPx2gqavW1lYX79u3L7Kevm/ixIku9ssBaCXcpqYmF/uT1eoxzZ4928Xjx4+PrKdpJ03r+MOO\nH330USva9OnTI8s6jFYrBS9cuDCynp7zTZs2uVjPq1m0/MHrr78eexz6HWia1K9erNu7fPmyi/Me\nBv3YY4+5WNN5/jW2ZcsWF+s5yjqt1tnZGVnW7/G1116Lfd/73vc+F//mN78Z8X7nzZsXWdZr5L77\n7nPxW2+9FVlPv58iU814hz8kHuV6z3ve42L9e/GrX/2qjMOxj370o5HlxYsXu/gb3/hG7Pv+4A/+\nwMV6L9S/CXnTv01pu1coWqIAAAAC8BAFAAAQoOntgocoNTU1lTYqCgAAYCSSnltoiQIAAAjAQxQA\nAEAAHqIAAAAClFLiQIf9Ix+av9XSCn7l6yNHjrg4qVxEnKQSB4sWLXLxoUOHIuv5Q/uHaOV2f/s6\n27ZfVkKX9bP7VXr9kgdxJk2a5OKpU6e6+MyZM5H1brvttmFfq/o17pfRmDBhgotvv/12F/v9ALT0\nSMj14tMyHRcvXozddlx/BP33qp/zqtDzlNQ/NW49/z2c9/z551zv6cePHy/6cEaNNP23aYkCAAAI\nwEMUAABAACYgHgU0daNpKjOz/v7+mrad1Mys+4pL3/m02vtwy0M09WMWrXytzduhk26eP39+2Nin\nqb564legLqsitaZos6bpZP2e/IrnBw8erGk/msI2S3/NPfzwwy7Wavx+yjhkwuokmtLXqv36G/Jf\nQ7X43xXKQ0sUAABAAB6iAAAAApSSzlu6dKmZ3Txiq9aUwqxZsyLLOuIo7aisRqQTHWfdDKyT/ZpF\nUyU6ci1rfjpvcHDQxXmmiHx+6gXVceDAARfrde+PKK1V2vSdTjJuZrZ27dpU74tL4d1///2RZZ28\nOklcmo70Xf3Q0ch6D4rr/oD80BIFAAAQgIcoAACAAKWk88aMeWe3fvN2SMptxYoVLh5KEw63vdGc\nztPRZX76LW3hvTh+amRgYMDFaUfkhVi1alVk+Y477nDxm2++6eKdO3fmdgyoH1pANG1xwoULF0aW\nJ06c6OJdu3aN+BiS9qv3rj179qTaHqmb0UtHWOrIU38kcRYFcZGMligAAIAAPEQBAAAE4CEKAAAg\nQCl9oob63viVfrU/jQ7LT7J9+3YX+0ON+/r6Qg+xLjU3Nw/773ous56s0v+erly54mK//1WWlixZ\nElletmyZi7VPVBItgeGXTEhrxowZw/67P9GznhcU78KFCyN+j07OnWSoj+eQzs5OF2/dujXVNtL2\ngwp5j3+Nnjx5csT7QrXobBB6n/XLymhf4JA+r7g1WqIAAAAC8BAFAAAQoJR03tBQYa26amb2yU9+\n0sU6NPN///d/I+vFpaTyTB/lbcqUKS72Uz9pK7mnTT9kyd+nlkzIWltbm4tbWloir+k1kbasQRbH\nGreNBx98MLK8Y8cOF586darm/WaRikSytOfVL4WQZ6X+tDo6OlysEzGbpa+UrrS8A8qn15hOMO+X\nNNAuMzqrQ5Jay96MNuX/2gEAAOoQD1EAAAABSknnDVXa9dNvOoFwa2uri/2JNn/xi18Mu12/2Vqb\nPHXkX1n0M5lFP69W/vYn0D169KiLQyqv63n2JyDOetLRrJt/tTn6/e9/v4u1ArWZ2UsvvTTibYeM\n2PLFpXw03ZYHUnj50JRxf39/5LW4EcN5VuZPoik7M7Pu7m4X6/1O/30kdIRpFr8VZEfvs9r9wx95\nGVLVnhTeyNASBQAAEICHKAAAgAA8RAEAAAQopU/UwYMHzezm/jha3VeH/OsQziQbN27M4Oiypf0K\n/GH5mr/Waut+f5e0Q1Pj6PDkuXPnRl7TvkUhM9On5Vd1TluOQvu5aR+3DRs2RNZLWxk6TtI1ptep\n318grq+I308mi7IGyJ9W9+7t7a15e1rqYtOmTTVvTyX1dcqiD1N7e7uLh+7ZqAbtn6clCfyZEnQ5\n6/6veActUQAAAAF4iAIAAAhQSjovTbOiDs0MGaZZFTr8dMuWLaUcg1Y891ODRVVXDq0mf+DAARdr\n+sKvzBvHr7Qcl+ZIWxU+LdIf1bVq1arIsqbttm3bVvP2P/vZz7rYL5MQ5x//8R9d/Mwzz9R8DGlp\nGttPad95550uDimtgvxo94/Tp0+7+MyZM5H1KIWSP1qiAAAAAvAQBQAAEKDp7YLLk+Y5QS1u0K91\nNJxzHf1X1kTUo+2cV0HIOZ88eXJkWVO8adPEWfj85z/v4rvvvtvFX/ziF1O9P6lieZ78Pxlc6/nz\nz/m8efNcXIXZOBrV0HlvamqKreROSxQAAEAAHqIAAAAC8BAFAAAQgD5RDapq/XO0Ar1ZdDj18ePH\nR7w9rbTu02q+RaraOR8NqnDOJ02aFFk+f/78sOv5JQSyLqtRFPpEFc8/51pZn9kQ8kOfKAAAgJzw\nEAUAABCglIrltZo+fbqLacqsrtWrV7tYh+SaRVMeL7zwwoi3XVbKDvD56TudBUBLJvgp7XpN56F8\nRZbiQDJaogAAAALwEAUAABCgLtN5jZjCa29vd/HRo0cjr6WdRNIfJVQ2Hc3Q1tYWeU2bo5cuXeri\nPXv21LxfHS1U8OBTVIiOhktKnTU3N7v4yJEjNe83LtUSMgoVGA73teqgJQoAACAAD1EAAAABeIgC\nAAAIQMXyCvL7D2l17mPHjrnYH1o9ZsyNLm5XrlxxcRXOufZ7MjN74IEHXKyX4Ouvvx5Zb+/evfke\n2Aj5VacvX77sYu0LU4VzPhokVSyfO3eui/1+hghHxfLi+edc+79euHCh6MMZNahYDgAAkBMeogAA\nAAKUUuKgtbXVzMz6+vrK2H3l9fb2Br3v6tWrGR9Jdg4ePBhZvueee1ysTdMzZ86MrDdx4kQXV6HZ\nmirT9SPrqs5xlciBonH9VQctUQAAAAF4iAIAAAhQSjpPR45hdPCrqetIwv7+fhf7o/GqkMJDfTp7\n9mym22v0FMr8+fMjy4cPHy7pSHArdCuojsSWqM997nPW0tIS6b8yODhojz/+uC1fvtyeeOIJO3ny\npHvt61//ui1btsw6OzvthRdeyO+oAQAASpb4EPXZz37W1qxZE/m35557zh5//HHr6uqyRx991J57\n7jkzM9u+fbv94Ac/sO3bt9uaNWvsC1/4QsP/zw0AAIxeiQ9RH/jAB24aLfXf//3f9pnPfMbMzD7z\nmc/Yj3/8YzMze/755+3pp5+2sWPHWkdHhy1dutTWr1+f02EDAACUa8R9ogYGBqylpcXMzFpaWmxg\nYMDM3smfr1692q23YMGC2KH6VR6Kj3xo2tfM7NChQy4eHBx08YkTJwo7JjQ27TeiVf9nzZoVWa8R\nq5lPmzbNxadPn071HvpAASNX0+i8pqamxJL/TAcAAAAa1YhbolpaWqy/v9/mzZtnfX191tzcbGbv\nzPemrQs9PT03zQE3hBFXAACgyp599tlbrnPLCYi7u7vtYx/7mL311ltmZvalL33JZs+ebV/+8pft\nueees5MnT9pzzz1n27dvt0996lO2fv166+3ttccee8z27NlzU2tUU1OTzZ4928zMjh8/HvjRcCtJ\nE7NWgU5IrOUPduzYEVmvnsphVP2cN6K051xfK3jO9YbDBMTF45yXI80ExIktUU8//bStXbvWjh07\nZu3t7fY3f/M39td//df21FNP2Xe+8x3r6OiwH/7wh2ZmtmLFCnvqqadsxYoVNmbMGPvmN7/JFw0A\nABrWLVuiMt8hLVGFqHqrCC1RyAItUcWjVaR4nPNy1NwSlZeiHp50wlC96K5du1bI/hFvwoQJLp4y\nZYqLly1bFllv3759Lr548WL+B/Z/qjbxMW5t0aJFkeUDBw64mAenxjZ58mQXnzt3rsQjwWjD3HkA\nAAABeIgCAAAIwEMUAABAgFI6liOZVlc2i+/D1dHREVmeM2eOizds2ODiKp7zqVOnuri1tdXF2snc\nLFrp/OzZsy72K5vX2s+tvb09sqzHsWvXrlTboGN58TjntdHr/Pz586neQyfn4nHOy5GmYzktUQAA\nAAF4iAIAAAhQSjpvqPTA9evXi9y146fLtBRCkXWJhuplmb0zYfMQHa5rZrZ7924XJ02WOm7cOBfr\n5KtVb/rV8zB37tzIa+PHj3fxwYMHXZzFRMWa/vTTgSHbJ7VUPM558fw/Gfr71cnEUZuxY8e6+PLl\ny5HXuNaLQToPAAAgJzxEAQAABCglnffQQw+ZmVlPT0/kNa00q6Oy/BSbVruePn26iwcGBjI91npW\nT2mOMWNuFM6/evVqzdvTEUczZ86MvKbXjqbsskhD1NM5bxR6zrXKvFmxFe5HE/9PxpIlS1ysMwwg\nO4zOKwfpPAAAgJzwEAUAABCAhygAAIAAY269SvaGhrG3tLRE/l1LD7zxxhsu9itGa18H+j3Uv7T5\n/RkzZrhY+8yZRYcDJ1Vh7u3tDTlE1IGFCxdGlru6ulK9T0uKaL9MpNPX11f2IQCloSUKAAAgAA9R\nAAAAAUpJ5+3fv9/MzLZs2VLG7lEBd999t4u1WrhfpkKrySdVGNcyGMeOHcvsOFE/QlNx9ZrC04m7\nzcpLq124cKGU/QJVQEsUAABAAB6iAAAAApRSsRz5S1s9W0dE+imyPGml+VOnTqV6T3t7u4uPHz8e\nec0fhVcGKpYXbzSfc3+i8qLSklTPLh7nvBxULAcAAMgJD1EAAAABeIgCAAAIUEqJA5Rn3rx5keXZ\ns2e7WPsZ9ff353ocaftBqcHBQRdXoQ+UmdnEiRPLPgRUzKxZs1y8bNkyF7/22ms1b3vChAkurtfS\nDEAjoSUKAAAgAA9RAAAAAUpJ540fP97MzC5duhT0fm3STjsBsU5Qq1WwfdOmTXOxP+Rf3zdmzI1T\n5w991FRV1hMk6yS8mjYwM7t+/fqw79HKxlop3OzGZNBm0c9x4sSJyHpaSfzQoUMu9ksN5EnLMeRN\nr7EpU6bErtfc3FzE4aCOaNo561kZipxwfebMmS727wcA3kFLFAAAQAAeogAAAAKUUrG84F0CAAAE\noWI5AABAxniIAgAACMBDFAAAQIBSShyMdAbqSZMmRZZrrVbtz37e2dnpYh3O3tvbG1mvr6/PxSHV\ngh966KHIsg7Z17IB/rD5TZs2ufjKlSsu1mrjZmZz5sxx8c6dO13MjN/F0Jw557wYWZxz/R1duHDB\nxVlXxQ+9j40bN27Y+OzZsyN+v5nZ5cuXXbxw4UIX+9X3d+3aNez2/L4hXOv588+5lp84efJkqm1M\nnz7dxSEzRphFryW9jhqFlrYxi94P4tASBQAAEICHKAAAgAB1MQFx1s3qfipO02VpLV++3MVdXV2p\n3vPyyy+nWs+vPK7V1jWd51cLr8qkvMg+BY1b02r+ZunTHDpJsP721q9fn82B/Z/Qa0BTKDrjgF9F\nXO9Dev0l7VdnjfBnQNDUhs7QgPLp95v2Og9N4Sn9jR05cqTm7VVNyIwAtEQBAAAE4CEKAAAgAG20\ngdKm8EJ0d3cHvS/NSIKhyZ+H6Mikw4cPj3ifS5YsiSzrCEZNI/gjf9Ica950Qum4yZtD3XHHHZFl\nTSo+yQwAACAASURBVIfs37/fxVk0seMdK1eujCzrKNwDBw64WEeumpmtW7fOxS0tLTkd3c1Wr17t\n4j179rj42LFjkfX0t6MpfH/0sEqbOtSJxTU2M1u1apWLSedViz+KrCgf/vCHXfy9732vsP0uXbrU\nxfpbyZr+TUj9nhyOAwAAoOHxEAUAABCAhygAAIAAJLo98+bNc7HfP+KNN96oadsdHR2R5dC+TyOl\n+XO/GvrBgwdHvD3NT6cts5C2D9SiRYsiy7r9tBWa08qiH5Q/rH6I3/dMvwO9xrRqvVm0hIV+3rgZ\nxHHDjh07Ist33XWXiz/5yU+6+Cc/+UlkPf1d+/2C8qT9jJKuxWvXrrk4i/4gbW1tLk7qV9XT0zPs\nMaB8ZX0fZfWN09IeefaJCvmbQEsUAABAAB6iAAAAAjRUOm/q1KmRZU2NpK1E2t/fP2xsFp3AMW7Y\nsb9fVVT6znffffe5OG5S0VvRUghaqfb06dPhBzYMHYo+EjqRqn5vfvOzVn9OW+lX6TWQtA1/v9Om\nTXOxpvD8dJ5W09cJPrWyNIbn/w51hoD29nYXVyU1mnYGA72v6fWWlHpYsWKFi/0JiDXNuWHDBhf7\naRL//ofqiPsbk7ek9G+WOjs7I8t+df68hJSOoCUKAAAgAA9RAAAAARoqnXfmzJlct6/Vpeup0rSm\nOfxJRvVzaLVWTd+ZVW+ySb869datW4ddT1NiZtHRg/p5k0bJKZ0M2sysqalp2PUOHToUWdY0nY5U\n9NOBekxlNdk3oldffdXFWsncLPodpk316fWS90gpvXbSjh7S9MfVq1djXwtNn6NcOgFx2msx5Dr3\n7du3L+h9I3XPPfdEll9//fVC9ssExAAAAAXhIQoAACAAD1EAAAABGqpPlN/fZ3BwMNX7FixY4GKt\n0lt1frVsHeavDh8+7GK/ovjy5cuHfU/aWeDT8r8bXU5bgVbLSsT1gRoJ7V/i94mK62dw7NixVNvW\ncz7cMoqVVF7kwQcfdPHmzZtdnNT/SPuk5N0XM+01p/r6+nI4ElSF9mlK2ycvi9IeeVYLV1OmTIks\np53xogy0RAEAAATgIQoAACBApdJ5Wpk3ZFhvaDqv1hSeppnMimt69IfHx1XPTkrNdXV1ZXpMcfzv\nIu13o9KeV01R+uckrlQD5QRGr71797o47b0m7xReGbS8hllyuhvlam1tdbGWsAmZhaEe6PNA1dAS\nBQAAEICHKAAAgACVSudpE7lWzPbTOHHpqaJGDviySN/phLV+hWGMjDZvV7kZGNVQ9RSITooaUlE5\nraRUZt5V2TEyM2fOdHHVU63z5s1zcdpJrf37dpVnCKElCgAAIAAPUQAAAAF4iAIAAAhQqT5RSvu1\nzJkzJ/Ja1tW0lfbF0mPIG/2gslPk96bV7oE8pL03tLW1uViHwG/cuDHzY0K59G9i1furhfTjq/pn\nUrREAQAABOAhCgAAIEBl03kqZALOUEWmgoqildz9Ssu1VurWKvPDbb9eafVmbTr3K57HDS8OrZ6P\nfHR0dLj40KFDkdeqnjrQdJ6m7Hp7eyPr6W+5yhO2onbNzc0urnqJjpDj86/tKqMlCgAAIAAPUQAA\nAAHqIp2H2qRNJWllWT/NpyMiNYVXZPpu7ty5Lj569GjsetrUHTfh8K1oReCkbRw4cGDYf+/s7Iws\n6yTXBw8eDDqmRuBPcjtu3DgXjx071sVawd/M7MSJEzXtV1N4VU/fJfHT50qv09DrHqgCvV9WHS1R\nAAAAAXiIAgAACMBDFAAAQIC67BPV1NTk4rffftvFkydPjqyny/QRuLW0M2xnMXxaK8Mrv8SEfodJ\n/aBUFt91raUutGK0WfSaTRryq32EdObyWktRVMX169cjy1rNWPtE+SUi9JprlArI+nm1VEbS5+vq\n6sr1mFAf0l7PSSUxqsyfpaTKfaRoiQIAAAjAQxQAAECASqXzVq1a5eKkSTM1hafOnTsXWb506VKq\n/WpTug5tz7tSug5X1nRNSLoirenTp0eWNWWUp/Hjx0eWtblWU3Z+mi/P9IV+1/7xpU1txpk2bVpk\nWatO62v+ZNq6nqYARwNN9fm/Af1+8vx95Mn/Pn/v937Pxd3d3S7esGFDZD09L346FKPT2bNnU613\n+vTpnI8EtEQBAAAE4CEKAAAgQKXSeUkpvBA60knTJD4d6VDkZMdFVfueP3++iw8fPlzIPs2iaTo/\n1bpr166atj1hwoTIsqbI0o7Oq7UKdhK/MreOxJo0aVLsepqC1tTN5cuXsz7ESvM/byOMTly2bFlk\nedu2bS7eunXriLen15RZY5wjpON3F4hTrxPC19P9jpYoAACAADxEAQAABOAhCgAAIECl+kSllbaP\njz98fLQqsh+U8vtB1WrixIku1v5uZtWrSO/3ddJh+Zrv1zILZvGlLvzt6XpZn+eyaFVyv89bI/T3\n8X+HaYepx9G+dWbFlStB+apefVzvVyFlOdL2+aoCWqIAAAAC8BAFAAAQoC7TeWWlp6rOn4C5CFrt\n3az2iV797S1ZssTFOqnvr3/965r2cytaUT2p7IU/zHyI34StzduahtEUpVm0orxWuJ47d25kPS2F\nsGXLltjjqydJlbmrOIHwSNWavvP51f3j0nlZ/0ZRPj+9XzW1Vtb3J3Cvsmp/EwAAABXFQxQAAECA\nukznpaXN2NqEPWZM9GMnVTOvJ2WM0mpubo4s9/X1pXqfpiKOHz/uYj/VoCO2Dhw44OKsJ2L1R/ul\nrVwfN2rMHzmlo02mTJniYj+dp59XR5f616yO3NOUYhVHsekIRP+3puddz5mfmtbvezSPQtPz5f/2\n9u3bN+x7ykrfJV3bqE1HR0fZh5CrgYGBsg8hNVqiAAAAAvAQBQAAEICHKAAAgAAN3Scqri9A1n2g\n/OGmWffXKYr2IfMrRmt/K+2fk7YPlE/7QSU5dOhQ0PZHKutZw/3zp+dW+7UkVeZOGvL/9ttvu3j8\n+PHDvr8q9Pehx20W/Y3qa/6w/Hrtt6i/lSw+g16nJ06cqHl7eaIPVH788haNZseOHWUfQmq0RAEA\nAATgIQoAACBAXabzpk6d6uIzZ86UeCTvqNf0nU9TK0nlEkLSErNmzYosDw4ODrueXwG8rPTUvHnz\nXKznxW9Gj/vu29vbI8uahtHP6KcRT5486WI9R37JBP0Oql69WMsx+JOCa1V2LWtw+vTpyHr1Osly\n1uUF9Pqr+iS0yE9/f3/ZhxDELyUT142i6qlqVe27LwAAQEXxEAUAABCgLtN5VUjhVYFWgjbLfoRZ\nluLSd74i03dJaeG45vKjR49Glv2qzEO0QnnSev5+u7u7XawT1vppRE19xU2CXEV+6lFH4Wlqr1FS\n5DricPXq1ZHXdFTl2rVrU21PJ6LeuXNnjUdXX5hI+YaXX3657EMIUuR3ppO55znLAS1RAAAAAXiI\nAgAACMBDFAAAQIC67BMVQvtilNXfYsqUKZHlJUuWuLi1tdXFflXnV1991cU69Fv7R5jdPKt7I8iz\nnEUW/a/iqjL7fcDihvn7Q/kvXbrkYh0O7Pd7SiobUDVJ5Qm0j0RPT08Rh1OadevWRZb9avVpVLEK\nuPb/0+866ZoN4d8/8+znUnXbtm0r+xCCFNknSvuh0icKAACgYniIAgAACND0tp87ynuHMow5iTbd\n6lBvs+hwb20e1GrPo51+rWnPeT3xUyG1pgqyoOf8i1/8YuS1ffv2ufi1115zsd+8fezYMRdrOs+v\n+K5pHU1zNkppgLQa/TrPg5YK0FScn1qOS734fzK0mn4W6UadLUCPyd92wX+6SuV/Vq71W9N0cmjX\njaHz3tTUFHu90RIFAAAQgIcoAACAAJUdneen8NTx48cLPJL6oc3gRfErUGvaSVNTSTo7O12cVIW5\npaXFxf7IxK1bt6baV1H8z7Fnzx4XDwwMpNqGVqD3R5foa6MthYfaaJoui4lesx4xWK+T6/rGjBkz\nbOyPWtQ00YwZM1xcxv28KubMmRNZTvu3RBU1+wUtUQAAAAF4iAIAAAjAQxQAAECAypY4qCc6G7tZ\ntOp0kXTWai330IjnvIr0p7Rs2bLIa9onCtmpYokDvR/oMYWW4Zg8ebKLk6q/F4Xh9uloPyi9Jvy/\nF3r+9LvWvo6HDh2KfU9ZtF/q0aNHM932qlWrIssbN27MdPtpUeIAAAAgJzxEAQAABKhsiYN6Ulb6\nzteIE3JqZfIqVCVPS6s4m0Wr7GuJDk3BmkUnXNYyBn5Tsg6T1mb/pOHhOmzYn8w55BrWY9Wh2f7x\n6VBjTVf479MK7T6d0DltOQstt+FPCJ0nPZerV692sT8BcZwHHnggsrxjx45sDqwOaXV1v5RH1SqW\n+yUJ9Hi1ZI+fktW0n6bpkn4PWVTjrlWe57+szxSCligAAIAAPEQBAAAEIJ2HSvEroIek8DQFEDeJ\n6q3UOiJq4cKFkWVN72nl9fnz50fWmzhxoouTUmx6njQF4E8iq/S8+KlfrVyt+/Wb7HXC2sWLF7v4\n6tWrkfW0OV7366c5NZ3pTyqtNDWXNJuBKjKFFyfp+1D6fba1tUVee/311zM9pqL4o9A0vZWUttLJ\n5/Xa8VM8R44cyeQ4s6Lpd7P015/+3vR+5/9WlN6fdCR23vT7uOOOO1ycVFFc7xn+OYq7v/tV3bOm\n15x/TCNFSxQAAEAAHqIAAAAC8BAFAAAQoJSK5VUbmgoAADAcKpYDAABkjIcoAACAAKWUOChj8kSt\njHz//fdHXnvppZdcrMPPBwYGgvalwyd1X+vXr091fP6QVa00nTSUtLOz08Va5XjmzJkuXrp0aeQ9\n2kS5e/duF6cdmu3TIftawbe7uzuyXtrhv4888oiLt23b5mJ/eLOe846Ojtj10g4H1uHF58+fj11P\nvzctE1DWBKE6KahZdLj8li1bYt+npRUuXLgw4v22t7dHlv0JU9O48847Xbxr167Y9R5++GEX62+3\nCpOyjgZ+WkPvL3od+eVF9BrRcgV+JfK0FenT0nuSlsrYv39/ZL2QUiZ6b/ar9uusAKF/S4YkTfoc\nN4GxWbQkgfLLRWhJAS0hkrZEjM5eYBb9O6rH5N+P9e9MyPn3S+L411Kt0nQ9oiUKAAAgAA9RAAAA\nAUZNxfIVK1a4eM+ePbHr1drsamb25JNPulgnl0xK5yWlmdJOLPzQQw8N+++a6vKbP7V5NTSFpw4f\nPjxsHOqXv/xlqvW06mxXV1fN+01K4akiqwWnoc3oZmaLFi1ycVI6LySFp9XGe3t7R/x+X1IKTyVN\nsoziNTc3u1jTPzrRtln03lrk72bBggUu1qr4aa+3JEmfo6iJ6TXllLaaf9b8Cc395bxknb4LQUsU\nAABAAB6iAAAAAtRlOi9kclhtWg1JXZjFj2DS0WBm0dESP/rRj4L2pfyRFHG+/e1vu/hb3/qWi7UJ\n2x/dV7VJPDE8fzLSuHSjn0JZtmxZbseUNs2ctSzSMLg1TddqtwSfjgDV67Snpyeyno7IzTPtpOlF\ns2h3Bh2tlkUqSCfe1ol2zaIpraLSWygeLVEAAAABeIgCAAAIwEMUAABAgLrsExVS2VT7Ufn9RuJo\nLt0s2g9Kq9N+6Utfiqz3hS98IdX2tSTByy+/nOo9IbSPlt8nKu1QfoyMVgA2S9+vTWk/lLSVg/2+\nK1q5HtWlfXXSftd5076U/r1QabXq8ePHu1j7VJkVV9bALy2gsyWEVONOy69YrudMS7D4ZWZ0pgPU\nH1qiAAAAAvAQBQAAEKBS6bysm7R1qKsOw01Lm2B9H/vYx1ysk6CORNYpPL+ZeIgOw61KqqAK8kyh\nhKTvfPq9aezTFJ5OsGpm9sYbb9R8HGrlypUuDpkoNnTCUB0+rhNb50kn1jUzmz17touTZj0IUcXf\npVYY18r3Ph2+r9+n3+0ipBtGCL/0RlGlOPxuIvqd6mwQaSa1Rf2gJQoAACAAD1EAAAABmt4uuG1R\nR4rlTavnJo1C00lbs5iAuAr0a9VRNv7oraKa2EcDPedFXuc6OspPQdc6CerDDz8cWV67dm1N20tL\nU61m0fOpqT1NoeR9zvW3k5RebRQ6Q4Om8/wRn4sXL3axfjf+tRdX9fzQoUOR5SwmQi+DpnvNotej\njsALSd36f6bjrnU/Ba2pzCpM1ltvhs57U1NTbBqWligAAIAAPEQBAAAE4CEKAAAgQKVKHGQtbsi/\nr1H6QcXRvgl+VV2tKqzVzJPKO+CGIvs+xcl6hviOjg4X9/T0pHqPfx5q7Wqp/RnNop9xcHCwpm2H\nGg39oJT2tdO+Tj6tCK4Vy/336KwR2n/IL8tRr32ipkyZElnu7+93cVElLPzfHf2g8kdLFAAAQAAe\nogAAAALURTpP0wtm0eZj5Tcfv/e973WxDvVct25dqv0uWbIksrx3795U78uzZMLSpUtdHFI12T8e\nHRJ7xx13uHjnzp0BR9eYQiqb+8O5V61a5eK0119Z5s+f7+Lf/OY3qd4zd+7cyPKRI0dqOoasU5RZ\n0HSNTmRbbzT1mpR21e8wadJ2/X1o+igp1a2prno+l3PmzHFxa2tr5DX9XDqDQZ4ptqImeW5U+n2m\nRUsUAABAAB6iAAAAApSSzhtKdaQd7ZK2ad8f1fHzn//cxX4F5DiPP/64i19//fVU7/FlncLTUYZZ\nT3yqlXQ1xg1JKby4dIifgk4a3VQ1aVN4mrLUFKBZ7em8KqrntJMKGTkZV23cLPr7iJt01yx6v9cR\nljqjQr3Rvyv+aHBN/+po5yqmqvEOHaGeFi1RAAAAAXiIAgAACMBDFAAAQIDEPlGf+9zn7Kc//ak1\nNzfbW2+9ZWZmzz77rH372992Q5r/7u/+zp588kkzM/v6179u//qv/2q33367/fM//7M98cQTw253\n3LhxZpa+T5Q/vFZzz2mHi6Ydmr5169bY/ap7773XxW+++WaqbYdK+xm1WjDKpeUizPK/Rsqgn7Gv\nr6/EI0He2tvbY1/T6vJ6T584cWJkPb1vh5QNqSI99osXL0Ze88t+DKFPVGNJbIn67Gc/a2vWrIn8\nW1NTkz3zzDO2efNm27x5s3uA2r59u/3gBz+w7du325o1a+wLX/gCJecBAEDDSnyI+sAHPhApxjhk\nuNEdzz//vD399NM2duxY6+josKVLl9r69euzO1IAAIAKCSpx8I1vfMP+7d/+zVatWmX/8A//YDNm\nzLDDhw/b6tWr3ToLFiyw3t7eYd9//vz5sKP9P1m3cD300EMufvnll2PXW7BggYuT0jM6ZNdv4s2T\nTjSMcvnXuFZobhQLFy508YsvvljikSBv+/bti31Ny3cklS4YO3asi7VkQj2XVtHSBX5JB51k2U9t\nonGMuGP5n//5n9v+/ftty5Yt1traan/5l38Zu24VZrgHAADIw4hbopqbm138+c9/3j72sY+ZmVlb\nW5sdOnTIvdbT02NtbW0ZHCIAAECxnn322VuuM+KHqL6+PjfR4n/913/ZPffcY2ZmH//4x+1Tn/qU\nPfPMM9bb22u7d++2d7/73SPdfCH8ptW4FN7UqVMjyz09PcOuN2PGjMgyk0Bi9+7dZR9CLrQS++TJ\nk13sjwwltdxYkrpQaDpP01vTp0+PXU/T3foesxujt82iVeJDKq3nQUcWaorSH22ukw4zcro++Nmz\noYeor33ta7HvSXyIevrpp23t2rV27Ngxa29vt6997Wv20ksv2ZYtW6ypqckWL15s/+///T8zM1ux\nYoU99dRTtmLFChszZox985vfJJ0HAAAaVuJD1Pe///2b/u1zn/tc7Ppf+cpX7Ctf+UrtRwUAAFBx\nVCwHAAAIEFTioEpCyglcuHAh1Xrve9/7Iss///nPh12PPlDVon11zp07V8oxDAwMlLLfvM2bN8/F\n+/fvd7HOWG9Gn6jRRPuYat8f7TtkFu0/tGzZMhf7fUq1L9HBgwddXJWq+Pq5tFp7UuV1/Ux+X1sq\nmFdHSL87WqIAAAAC8BAFAAAQoO7TeTqpZRbuv/9+F7/00kuZbhvFKCuFV0/8atJpU+E7duxw8alT\np2K3h9FD01M6S4VfpV/TVlruwE/naSq4iqUBNG2npR/8Ug36Wtzky6h/tEQBAAAE4CEKAAAgQN2n\n82qdzNi3ZcuWTLcHVIWmFNKOnktbjV8nlMXooteIFlg+fvx4ZD1d1lTXihUrIuvpteRff1WgqWut\nyu5XLNdj11SmP2MGo7vrGy1RAAAAAXiIAgAACMBDFAAAQIBSOjIM9c1ImhkcQLYWLlzo4u7u7lTv\n0T4fZvH9Nzo6OiLLW7duHdGx+dra2iLLOnQe1aKVyLW/z7hx4yLraT+8np4eF8+ZMyeynvYl0m34\n5Q6qUBVf+zr5pQu0+rXOojBr1qzIeidOnHBx2lIjqA5aogAAAALwEAUAABCglHTe0KSNZ8+eLWP3\nwKh0+vTpVOstWrTIxUklRDRFoROxZoH0Xf3QCd21erk/m0RcCtlPEc+dO9fFmk5ub2+PrLdnz54R\nH2sWtJTB4OBg7Hr6+bXKuf93jxRefaMlCgAAIAAPUQAAAAFKSedVLY2nFXL9qrNAo0ibztNRT0eP\nHo1dT0fQ+WkNHaWl6R40nilTprhYv3e/ir2OwtNrzL/n6og3VZW/G3rsSSME9+3bV8ThoGS0RAEA\nAATgIQoAACAAD1EAAAABRuXU6zrTuBn9oDA6JM0QoEPJ+/v7R7y9soabo3xaokCrdPuVuU+dOuVi\nvXaSymholXK/ZAJQBVyVAAAAAXiIAgAACDBq0nk6keXly5dLPBKMBjqJqln8xL1FSkrnnTt3LtU2\ndLLZrIecUxahuvwuEEqvA/3e/OH/cRXLJ0yYEFnW34qWT9BUIVAVtEQBAAAE4CEKAAAgwKhJ5xWZ\nwtNJOOOq72ZBUytmZr/zO7+T274wMpqaMqtGOi9J2hGqV65ccXHSKL477rjDxZoqHBgYiH1P2hTe\nypUrU62Hd+jIS7PoKLkkt99+u4vf9a53xa6n6TytXJ82/eZPwHvw4EEXz58/f8TbA4pESxQAAEAA\nHqIAAAAC8BAFAAAQoO77ROnM4MeOHSvxSG6odXj2woULI8s6NP306dOx79u+fXtN+x0NFixY4OKe\nnp7c9pO230nV+X274q7ttra2yPIf//Efu/ib3/xmpse0devWTLfXiPR7W7RoUeS1nTt3ulj7ivpl\nOfQ+5G9DaX863Z5/7WgfTu1b59OyBtqfLm0lfaBItEQBAAAE4CEKAAAgQF2k8x566KHI8oEDB1ys\nzcy/+c1vIuslVWjOU60TGusQ35GIS+dpM33Vh9pnbebMmZHlJUuWuPjEiRMuTluxO62kSVXrSdrU\ntH+ed+zY4eKy0uxp00e18lNden8qi/7m/S4AmmbTSuT+/VJTc0ndCA4fPpxqvbSOHDlS8zaAotAS\nBQAAEICHKAAAgABNbxdcBlabj5cvXx55TZtxdWRIc3NzZL2XX37ZxdoEXdbEwu973/siy35asQz6\ntWrl4fHjx0fWY6LX7Og5T5qwtSx6TEk/e/29pU2t+CNKQ1PSI5V0zmfNmuViTdf66fZr166NeL9p\nz2Wj6OzsdLGmas2qea03Gv8aG83nfNKkSS72/57pb1t/136q+rbbbrQfadcLfzLsob+PTU1Nsb9z\nWqIAAAAC8BAFAAAQgIcoAACAAKWUOHj66afNzGz//v2Rf+/q6nJx1Yfi6yz1/vDuqtF8sN8HasyY\nG5dAraUZcENHR0dkubu7u5Tj0KHu9957r4t/9atfxb7n7NmzLvb7HFy6dGnY95w5cyb0EHMzODiY\n27az7gel/TyqWB6jCmUbALPo7yPr34o/80IatEQBAAAE4CEKAAAgQCnpvIsXL5qZ2bp168rY/U1C\nUlo6ZPqnP/1p5sdUFP28kydPdnHWFbxHm/vvvz+yXFY6T1O5Wlk6iZYG8CtQx6XztPp7VeRZsTzr\nNHgVU3iKUig3aMV3HRJfxd8ARmbv3r0jfg8tUQAAAAF4iAIAAAhQSjqvr6+vjN067e3tkWVt9t+3\nb1/s+x5++GEXr127NtW+dBRf0rbV1KlTI8tFjXwihZedtJPurl69OrIcl+LWUXZm6Uev6ui6PXv2\nxK7X0tLi4oGBARfnOXGvWb4pZJ1seufOnbHrjRs3zsWa/tT7glk0zdna2uriKVOmRNbTKu9aGVln\nDjCLfnZND/rr/frXv3axpg79idkfeOABF+vsDf4oxbiJynt7eyPLmp6aP3/+sO8xi15jceneRpL0\nXWH0oSUKAAAgAA9RAAAAAUp7iDp16lRZuwaAuuJPoAqgGpreLngK8qHZkJ999ll79tlni9w1cEtc\nl6girktU0Wi5LoeeW4ZDOg/A/2/v/l3S+eM4gD8P9A9oyENUuEDDBAtB2qKgNFokF0kapGwpmmpp\nrJZs7gdENDiVLWWLh9NFtLick0EOBmYmtEUNkvAZgoP66vfz4fh87qB7Pia9O7jX8OR4Hm/ujoiI\ndGCJIiIiItLB8OW8iYmJP349ABEREZGZxsfHoShK132GlygiIiKin4DLeUREREQ6sEQRERER6cAS\nRURERKSDKSVKlmX4/X74fD7s7u6aMQIRAECSJAwPDyMUCmF0dBTA57fGIpEIBgcHEY1G//g7dUR6\nLS4uQhRFBINBbdv/5XBnZwc+nw9+vx/FYtGMkckCuuVyc3MTbrcboVAIoVAIhUJB22fFXBpeojqd\nDlZXVyHLMiqVCk5PT3F3d2f0GEQAPl+ipigKVFVFqVQCAGQyGUQiEdzf32NychKZTMbkKemnW1hY\ngCzLX7b1ymGlUkEul0OlUoEsy1hZWeEbzemf6JZLQRCwtrYGVVWhqipmZmYAWDeXhpeoUqkEr9cL\nSZJgt9sxNzeHfD5v9BhEmu8PqF5dXSGVSgEAUqkULi8vzRiLLGRsbAx9fX1ftvXKYT6fRzKZhN1u\nhyRJ8Hq92g0A0d/ULZfAf6+ZgHVzaXiJajQa8Hg82n+3241Go2H0GEQAPu+qpqamEA6HcXx8MuSB\naQAAAd9JREFUDABotVoQRREAIIoiWq2WmSOSRfXK4dPTE9xut3Ycr6FktL29PYyMjCCdTmvLzFbN\npeElShAEo09J1NPt7S1UVUWhUMDBwQFubm6+7BcEgZkl0/0uh8woGWV5eRm1Wg3lchlOpxPr6+s9\nj7VCLg0vUS6XC/V6Xftfr9e/tFciIzmdTgBAf38/4vE4SqUSRFHE8/MzAKDZbMLhcJg5IllUrxx+\nv4Y+Pj7C5XKZMiNZj8Ph0Er90tKStmRn1VwaXqLC4TCq1SoeHh7QbreRy+UQi8WMHoMI7+/veH19\nBQC8vb2hWCwiGAwiFoshm80CALLZLGZnZ80ckyyqVw5jsRjOzs7QbrdRq9VQrVa1J0uJ/rVms6n9\nvri40J7cs2oubYaf0GbD/v4+pqen0el0kE6nMTQ0ZPQYRGi1WojH4wCAj48PzM/PIxqNIhwOI5FI\n4OTkBJIk4fz83ORJ6adLJpO4vr7Gy8sLPB4Ptre3sbGx0TWHgUAAiUQCgUAANpsNh4eHllg2IeN9\nz+XW1hYURUG5XIYgCBgYGMDR0REA6+aS384jIiIi0oFvLCciIiLSgSWKiIiISAeWKCIiIiIdWKKI\niIiIdGCJIiIiItKBJYqIiIhIB5YoIiIiIh1+Ad4CxRoGhD90AAAAAElFTkSuQmCC\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0x7fd621d73990>"
-       ]
-      }
-     ],
-     "prompt_number": 11
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The third layer output, `conv3` (rectified, all 384 channels)"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "feat = net.blobs['conv3'].data[4]\n",
-      "vis_square(feat, padval=0.5)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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Okec+WKymeN9jDGirKHE81xm3v7CvTg1LbQRziVSkkiRJkiRJOrJDKFLOM57x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\nbZbwMQj4m+t2jYZrq6B4Xi+i19yv0P0ugbY/LCiZbdsu9UM7od5ro+1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tmZrYJHWYfAy0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C/MCPAMUmVb+sXzmRIFU/rJTol8H1sc5YD/w/vo4ptQh6HecjifX9cv6R+FZQLQDlAn83LFp8\nR1C5S/NW1daOA8aQ6wJqjtEelLRa9ZI52jZzey30C3OjNmKbvYU5xByNUWMoUygVMdqtdI+Kfof0\ndz/n8CBhT4v+tdCrEllbJ5e3KcxDxm3Dhg2SmnXMXkME8gUXXCBJuu222yQ191J+lvpHW5Eyxhhj\njKlkZIpUiRrF+86YR2blypWSmvejpbXcIlhjOUWH99dEQ/E9ck9Ev4NS3xqsHzKK0ydYPbHOEfSa\n9whyNc36Df2IVUYG92iVx7pTpdFzvCeP2XnxXyj1pSHzO/XIsErinOV6aD+/ozbQv219raIqEOcn\nVjj92K+oxxy9+ozlog2j9VejbqDSoQoCvhdk948Q3RbXGmOKL0zcixhrFBHGhjnLHGRNo1SheDDH\n4tziOuLYcxx8p/B9Yq/ct2+fpGYNcd34huATgoLG37HQ20YADxr2Oq6PcaDd+JbRD1GZYA9lrUR1\nvzTjfFs/1BRxbefU1n5B/7Hncg+jbmjuXsC85fuMy7D2nhQohazLuD63bt0qqVG3efvDXvOFL3xB\nUrPe+Mn12UfKGGOMMWbAjHWGXb5ZL1tjExMTwz6tMcYYY0xrJiYmkqq8FSljjDHGmEpG5iN1pCJV\n6qtUe45hqV+l54u+NRGisvDB4j126nzXX3+9pOZ9Me9/yUsUI0I4Ln4F+AnQ//gh8PSNP8ev//qv\nz3hdxhhjzCsNK1LGGGOMMZWMNLM5nvFLly6V1L5OEhAhQyRLbRTfsMjljkGxQplKKVJAZEGMMEjl\nJiHiJZVFtzQnijHGGPNKx4qUMcYYY0wlI1WkyPWRyqYb8ynxO3mBHn74YUlNfqF+5eQ444wzus77\nd3/3dz0dD18lcpHkAiWpqUal6lpQ/GoryBtjjDFmZqxIGWOMMcZUMlJFCr7+9a93/Y6SErOnolyV\nZqOthVpt1J+qheg4ssJyPUTVkWWVCuZkOeZ6e61bZCXKGGOMGSxWpIwxxhhjKpkVihRRd9RBQkmh\nPlQpvdaJIkoOUJRi/Z4c+DbhG0VepqgwoVShQFH5uFAhQgAACFxJREFUmuhF6l/RDuoAOarOGGOM\nmR1YkTLGGGOMqWRkitRxxx03qSChzJBf6b/+67+m/Q6fW7hwoSTpiSee6PoeGdJzVeZTcF5+1ipc\nsXI7v8dK2fh6oVwBitw3vvENSU30YKxcPihOPvlkSU0UZCqq0hhjjHmlY0XKGGOMMaaSkSlSxx9/\n/KRCE6P0UqCUUFMO3yoUqV5r9RFN1ysvvPCCpEZZQpGKpDKSA/mmyHSe+ly/ibX66GdjjDHGdGNF\nyhhjjDGmkpEpUkf6C+HbhG8S0WtAJvMY5UZGcxiWYpMj5m/qNcouZmw/7rjjejpeJGZAj8rc2NhY\nX89njDHGHCtYkTLGGGOMqWRkitSrXvWqKbX2UJ4iKCb/8R//IanxOcI3Csi39G//9m99aSNKTK42\nHj5bgE9RVMx6heNynREUO/o1Rvmh/KHorVq1quvz5K8yxhhjTBlWpIwxxhhjKhmZIvVjP/Zjk1F2\nKDooT7GWHn9H4Yk+VEDepRSvec1rJJXXsMspURAzokcfKRQ0ou9q80Fx/TFP1qte9SpJTZQd58Nn\nDAULBQ9li+v793//d0mNYgXx88YYY4zpxoqUMcYYY0wlI1OkDh48OOnLhOKUUoDwVULR4WckpyCh\nHBEdiDKEgpPKqJ5j/vz5Xb+j5KCw/eiP/qikRvnhek466SRJTVRfadRhVLR++qd/WlKjhKHooTCd\neOKJXec5fPiwpEaZo10pavvFGGOMOdaxImWMMcYYU8nIFKkjI+u++93vzvhZas3hU5VTUCIoXkS1\nzZs3r+t3lBp8kI4//nhJjfJDtCDgk7RgwQJJ0imnnDLteVGIyHQewdeLaMXaPFhPP/20pKmZ3fn9\nm9/8ZtVxjTHGGDMzVqSMMcYYYyoZmSJ1JETTpRSggwcPFh0nlb8JH6YlS5ZIanypfviHf1hSo9ig\nNNGOZ555pqs9P/ETPyFJGh8f7/rckVna29BWWUvRa43BCD5d+HL1Ky+XKePQoUNauHDhqJth/h+P\nx+zBYzG78Hi8jBUpY2YZTz311KibYI7A4zF78FjMLjweLzMyRWrjxo0655xzhnKuyy67bMa/E/UW\nOeuss6rONzExUfW9Wno939atW4c2FsYYY8yxhBUpY4wxxphKxjql6bv7yM/+7M/qS1/60rBPa4wx\nxhjTmo0bN+r++++f9m8jeZAyxhhjjDkW8Ks9Y4wxxphK/CBljDHGGFPJ0B+ktmzZomXLlmnJkiX6\n6Ec/OuzTG72ckX3NmjVat26dTj/9dEnSv/zLv+i8887T0qVLdf755zt31ID4lV/5Fc2bN0+rV6+e\n/L+Z+v6GG27QkiVLtGzZMt17772jaPIxzXTjMTExofnz52vdunVat26d7rnnnsm/eTwGyzPPPKNz\nzjlHK1eu1KpVq/SJT3xCktfIKEiNhdfHNHSGyH//9393Fi1a1Dl06FDnpZde6oyPj3f2798/zCaY\nTqezYMGCzgsvvND1f+9///s7H/3oRzudTqfz+7//+51rr712FE075nnggQc6u3fv7qxatWry/1J9\nv2/fvs74+HjnpZde6hw6dKizaNGizv/8z/+MpN3HKtONx8TEROcP//APp3zW4zF4Dh8+3NmzZ0+n\n0+l0vvOd73SWLl3a2b9/v9fICEiNhdfHVIaqSO3cuVOLFy/WggULNGfOHF166aW64447htkE8/90\nQozBnXfeqSuuuEKSdMUVV+j2228fRbOOed74xjdO1nKEVN/fcccdevvb3645c+ZowYIFWrx4sXbu\n3Dn0Nh/LTDce0tT1IXk8hsGJJ56otWvXSpLmzp2r5cuX67nnnvMaGQGpsZC8PiJDfZB67rnn9JM/\n+ZOTv8+fP39yYMzwGBsb07nnnqv169fr05/+tCTpW9/61mQx53nz5ulb3/rWKJv4iiLV988///xk\neSPJ62WY3HzzzRofH9fmzZsnXyN5PIbLU089pT179uiMM87wGhkxjMWZZ54pyesjMtQHKWq3mdGy\nY8cO7dmzR/fcc4/++I//WNu2bev6+9jYmMdqROT63uMyeN797nfr0KFDeuSRR3TSSSfp6quvTn7W\n4zEYXnzxRW3atEk33XSTjjvuuK6/eY0MlxdffFGXXHKJbrrpJs2dO9frYxqG+iD12te+drIQsPSy\nM9uRT7BmOJx00kmSXi4WffHFF2vnzp2aN2/eZPHmw4cP64QTThhlE19RpPo+rpdnn31Wr33ta0fS\nxlcSJ5xwwuTN+l3vetfk6wmPx3D4/ve/r02bNukd73iHLrroIkleI6OCsbj88ssnx8LrYypDfZBa\nv369Dhw4oKeeekovvfSSPv/5z+vCCy8cZhNe8Xz3u9/Vd77zHUnSf/7nf+ree+/V6tWrdeGFF+rW\nW2+VJN16662Ti8YMnlTfX3jhhfrc5z6nl156SYcOHdKBAwcmoyzN4Dh8+PDkv7/4xS9ORvR5PAZP\np9PR5s2btWLFCv3mb/7m5P97jQyf1Fh4fUzDsL3b77777s7SpUs7ixYt6lx//fXDPv0rnoMHD3bG\nx8c74+PjnZUrV06OwQsvvNB585vf3FmyZEnnvPPO6/zrv/7riFt6bHLppZd2TjrppM6cOXM68+fP\n73z2s5+dse8/8pGPdBYtWtQ59dRTO1u2bBlhy49N4njccsstnXe84x2d1atXd9asWdN561vf2vnm\nN785+XmPx2DZtm1bZ2xsrDM+Pt5Zu3ZtZ+3atZ177rnHa2QETDcWd999t9fHNLhEjDHGGGNMJc5s\nbowxxhhTiR+kjDHGGGMq8YOUMcYYY0wlfpAyxhhjjKnED1LGGGOMMZX4QcoYY4wxphI/SBljjDHG\nVOIHKWOMMcaYSv4PwNFhvU7XHUcAAAAASUVORK5CYII=\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0x7fd63064ffd0>"
-       ]
-      }
-     ],
-     "prompt_number": 12
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The fourth layer output, `conv4` (rectified, all 384 channels)"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "feat = net.blobs['conv4'].data[4]\n",
-      "vis_square(feat, padval=0.5)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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3h8fOeeE1eY0XPP+LLrpop+0BXo57YShweMucH/93qLvk/U8WFooX3rJnfXn2\nX8prIVMpBe0xDoyT1yZhvFCWyCRBAXFvorQiewqum5ovqAqoCF7XCMUt1d9Qu2ckakqq4nvb6tdc\nD+OFXaJcMr7YGwoe71PnCeXSs2hddaA/UW49y5D2UuqJqwWoLSjCO36Psa8N/cypeg6qOh6uZxcx\n5tgItkv2FWObqh3Wtr4VY/nc5z5XkvTNb35z5P22NphSV6kw7nzrW9+S1IyNr1XOJZdcIqlRpHLn\nRX9wHdgCWWXY5K233rrT40DbuZM7r2kjp0Rhp/R7n7sHjIPSOl6uerMGMl9zilSOUKSCIAiCIAgq\nmeo6UrW4Z4u3wN02XqffxebubvF+iElBAcrdzXbdK9A9eG/PY4RQGlL7Zt18880L/h/vGqWH/nKv\n1L2WtjVe6GePP0kdj+fb9AN1l/AqUteTgu9hF75juntxeC2p66TfcwpcrR3kKp2XxmQB3pnvyehK\nI/1z8cUXL3gcPo/deByNfw7lkfHnlf7zemt+PpwnSirf31ENwMMure6fmvNcE33l+zsCY+4KGNeQ\nqhFGuyhVXmMOUipkCmyXWCyUIdYCYohyqnstXGeth496icKXep/+IlaL8bvrrrskldcYRBXn8/50\ngfhW3vfzQglDvR7XvrB9wT6v9ANPU7zCvj+VmBba9jcV9sHjq2sJRSoIgiAIgqCSXVKRclxBScVm\n5Z6z4kW69+Leb99eicc+OZ5t5d5uKSheeF201/au3fdp8kyk3I7w7pUTe4QSiHJRu0chXmYqzsMV\nTVdSFjvErXgV7LZ4plLKTnyHAuzKd45HfeG4qUr8HGeh+Yp6yZzns6lYkdScxwZ8Lrt6mVJeaA8l\niJgorjGVeekZtrUe83XXXTfyN8pJTjWdNF652pU9+hWFhLUYRZD+SsVt+l6CfN6VRuyC/Vf5HO36\nHnipNd9tfNpwdd/n6JYtWyZzYoV03dvSM8FrYytDkQqCIAiCIKjkEaFItQXvBOUERQUFCq+J98ly\ny9E2Y8b3bAPPNsRL5nxQatruUYYXjzeeymrMwV1+KgMkl+HiKgFZhnjTpftVpSDuJeWFuMrA+dRm\n+JTWYml7nNRelDl8n65aL4z4G84jVVcMVcaVJO9P/i5VGhcaD86F19o+Z+7gsfv+isDcdIWJ76P+\nEYviMTXMEVdE+oZaX33Tdt/THB6DxFpEbBe2m4qhovaZ1wkCn9v87ftgYqvYEWs8Sg1xqbn4xGmP\nmWJ+YNfkKbm1AAAgAElEQVTMVTJzp3mfyz5JxWWWEopUEARBEARBJY8IRQrlAe8J74+7bTxbj4FJ\nedh4LcTauMJCBgTeCHEbZOzgHVLNOAXnxfc5rsc5cN5cZ20tEM7Ha9zUKjG1d/cOsV5cF16i1+Uq\nhRg5V3aGgnHHm63NaHJ1pTY+AAUzV5k+B9dBbaSUqpJTKGtZKGaQsexrC1GPGfE+T2XVodCgSGFz\nrgxxXNYSr1LfNs6RtQD12+cgWW6p+MC29KVE5Y6H7aAc5L5fOqdz2Z0bN26U1FSYHxeMH/ZVW6E+\nB2uKZ+4yf0rrNC12uv5WhSIVBEEQBEFQyUQVqb5iR3J4HSDusvk/Sg+veJ2p6rg8fyejw+9mPRbK\nMwPw3Hke7ftiuVeG4oW36pW/eR8vzL029yZ8rzkgboPrIj6krVfcN64U8jf9xnWXem1eu2fo+BTi\nD8g+LK26PBTYJfZfW3GdCu/Mq5SddI0TYXxc0dqZl9yXB+1zxOdmqarH94itcVCdmdsoUm2VCK47\n1efUYUJtbluDbFKUKk2shb62ptTQnO2ztqPgjSvmifMmBo/z4LW0TloOsg9ZC333AX4TsZNdXZmq\nJRSpIAiCIAiCSiaqSKWq+XYlVdHbn7OjEHGXz9146fkQ/+DeAX/7XmZ4U3juXk8nFW+B15val4v4\nC9rJxeCkYou8fZQUr+0yNF7rhfHkuvDa8Y5Q/Eq9dz6Pl+uKYteaIg5KH2rApPF+6uplY38pb5Xx\nbJtlSCwXGVOoOb6f3Y4qkZ9D32Pp9KUMUFG6bSXzFLlYrlys0bTB+OXi7HwNz30+Z/veT+NSpLBp\nzn+odvktIj4WtR97Yc4y10KRWphQpIIgCIIgCCqZqCI1VI2KlJfoVWbxVrnL5q681HtFyfA4CdrH\ni6Adj80Cfy7t4F3yOd/JmvMv9WZTmTHUBaI9YnrGjfeP7/Tu2Ze8X5oNRz/xPVcwUzVFUrFlOTyT\natKUZqiUqjnEcdCvqCtAf7oilYtPQZHyGj+AUsX+atL8udFViUJFTGX29oXvTtAVHzsyR4l1QU1f\nbLgt+VzkevtSCofKlitlXAoY/UrGNvaDCtw1w3dXJxSpIAiCIAiCSh4RdaSoistzczxiVwpKvQ8U\nERQbj0dAKfHn3K5MOanYLNrj1etWuULj7XmcADFGeDt4w16BOrfn2VC4N4n36c/nUUAY31LIkGLc\n/fsoLGROEStGLRnG1ZWXFE960pMkNf2Zqso8LrAXV4SxZ+wTO0idL59HGSIWytUOjuP27RXqXT3K\n1d0a0lunj6iM7Zm3Q7Xn11oby+R9+eCDD0pavDEu9H9tDbZg5zB3eSpBP3sl/2BhQpEKgiAIgiCo\n5BGhSOHV4YGjPNXebeNB+w7ggKfO5/CcUUDaxm2QFZWqicLO3XgRrrg5nB/ngbeK8kD1ZfZbGrrO\nl+PjguJBDBtxMFxnW0XKFRau//jjj5ckrV+/XtL8/sZb4+9SRYr4FFf+JgVVtF2RxC68vhvni0KH\n3Xj9Lezd7SVlP57VynGZr3jJ2L+zUJxPW/UUJYi2+X4qjnCo7D/mqleSfvKTn9xrO/Rp7T6afXP0\n0Ue3+nypComNsybSn7U14zgeleG3bNkiaf6cWKywOwFrHL+VPA14pFQ4Z354TckcoUgFQRAEQRBU\nMlFFiliUvr0j7q4h5dF2jf1JedqueOCtdM2USe3gjjLmMS8p743MHa/X5FWOPQOqFGKQar/v2YJk\nZXl1X0iNby0oca44uaJZyr//+79Lmp74jlQmTtvzQ8njNUWqenaqxg+KGPW32tBWGei6V1zb2lgp\nvP4RqlxbtdXPCwUGJSHVP+6JA0oMqiFzAzjP3JwgA9M/7zFgK1eulNTMPXZZoN4R/ZSyHZQTjoua\nyv99jeQpAe8Tx4jtoY4fddRRkprxYC3n++NW7YG1lvZR71Pvc/6MJ39z/fwWs+bS39gH1+sq9q4C\na2DbtTAUqSAIgiAIgkompkitXLlyzvsh9gWvDi+Avz3GCK+Cu2qvfE1MSt9wXqm99WDt2rWSpE2b\nNklq7t6pSYPHjTfs3pBnzXksi++1l6vHhVdKf3u2GnEieJtds6G88rSfd06BTGUcjYvUXni1tVSG\nVqLa7pU3bu+Z+Yi9e6V8+sdVDbdr5g3zheP0oUiWKivO0572NEmNgkLfMha8skbxvseHoc77Guhr\ni481fcHnUWqYQ16Dy+eeK18eQ+S7BzipTFrGnMzL1D6JxBodc8wxkhpb4Dz4G1vIVSrnuOyN53jc\nqI+TXw/nsXnzZkmNDfN0YNxzyW3fayF6LUGeCrjyh3LF9aHAsX8mT1U4rh8fpWrcGd3TSihSQRAE\nQRAElcw8PIF0g5mZGc3Ozo672SAIgiAIgtbMzs4mYwxDkQqCIAiCIKhkYjFSX/ziF+eeS5NhwvNe\nnoeT6UFsEc/HeU7Nc3ue2/JKvMCxxx4rSVn1y6sV14p0tJNrj+fVnGdpdhvnSX/99V//tSTpk5/8\npKTmebXHY6SyBT0egufvxEswPsQ2vfKVr5SUv76+oJ2zzjpr5HxSYCe1lcNLxy8FNViIpcrFD3Rt\nry3T3h72TR0v4mO2bt068jnPCsVu3/Oe9+gTn/iEpGYMvDYVx2QtYS4ddthhI+97xiZtkkV20kkn\nSZI++tGPjrRHOx7LxJrmsUasAR67w99kIL/oRS+SNLmxo5/oz9o1kn6kH+iXv/mbvxlpb2ho57LL\nLpPUZJaSHbhq1SpJjR0QW3bIIYdIauY6GdPYIn/zW0SNv1NPPXWk3aGhnb/927+V1GRfspce53vt\ntddKatYqxnm//faT1MQv8/9DDz1UUnP92PtznvMcSc1vkWd3em06WLNmjaQm5oz+d/g+53H66aeP\nXOfQ5NoJRSoIgiAIgqCSiSlS999//1y2DXeZKcWBarIoJXfccYekJiMEL4dMBpSJHKkaMHgjtEeG\nAnfNvhccd+/gtU/wVmkHxQhvBe8AhWjFihWSGi/J9/vy2jJktdF/eNW5fcE8A4a/PVtq0jt/l2aj\n9VV1l/FDLcDLxAv3/kE5ZdxQxB544AFJzXhRc6W2vlaKVMX7WjhfMr7GlZlE/+Ilp6pQM29RSnfc\nexIP2klltjKnbr75ZklpW2PMqMHl/8f2fAx8n0zmPmsUawTHoX2uiWy+trC2YINd6VonC7raPnOI\nNa6rbXrNOGyeNdfXvlK1m+O4vYwb7Au7or/4v6vmjLPPI58n2HnpLg2pceIeIJctO6k6XaWEIhUE\nQRAEQVDJxBSpPffcs7gqLDVBUvv9eK2SlBeHV4jSwN00d+V4gQceeODI+7RPdVeUHs7Hq7tSkyPl\nBePteE0X7vo5j5T3416SKxL0a1/1izweoq9qzn2TUs4WUi52hscF5GKdaPe73/3ugu/TLq9dq2lj\nb27vXSvng++z1RX6n+smToPzTc3/lLpD3BDn2aY/U7Eapaqnfy6l+jInWWu8kjR1lnz/TZ/zbWu6\nsUZRJ8gVKdR2jztbLDB+Rx55pCTpqquu6nQ81mBXRGrXTuYi9lW65gwFMUw+l7yuVCn85qHCu2rc\ndi/DtjUCfTeOUlgzUzFYXQlFKgiCIAiCoJKJKVJ77733nLeFF1ZatRbw9rjbxKtIPbfFc+U43F2j\nOPD/733ve5Kau3Y8X68mDK5YcF5tY0z4XCrOI4Wff+p5M9lQq1evltR4pd///veLzgvGrUThzddm\nCrX1CvuKNXJyFehz0A+uwHrMXl/4jgG1uP14de9SmFeoOsSulRyHvkspUqV4X6P8ED+JjfqedX7N\nrHlcE2qbq4BtbZ6xuvHGGxd8f7EqUUDcKGsYWWTEIrUd19R+jrVrXF/7xvouF/vvv7+kJqaotJo/\ndshvn2e4twXFDWXHlS2vxN8XnG9tHCzzbChCkQqCIAiCIKhkYorUQw89NOd9+T5PpR6w12rhbjUV\n24GH7TuD+/5YHIfzSe1/xff8bpcsQr6/ffv2kfe5u2ZHcf4mFsu9277wWJ2hlIzFRu1z93GDvXZV\ntkrB7rvGxHkMU+35M984H1ScHRXHVAYjcymXyZrC9zgDr2nl+4LSZ3jyKFisGfQN32ct5PyH3qdx\nseKK3rp16yRJ11133cTOqU9cRUdxazsHsVfsF7usnQccz/e9BK8flQN7z60JtSo2EMdKtiy/fV3j\nVSEUqSAIgiAIgkompkj99Kc/nXvOy10yd7ulXphn7aSq7vpzYc/e8895BXX+5hWvl3bIYABillIZ\nCdxV8zmq5XJeeB2lNWBKnxvj1aTqYU0rXWOkUHJS17tYvP6U19Y17ifFUPbhXmwpeOnEoTAPd4xH\nysW31caG8D2PAfG1wbPvvCIzyhQ2SU08vu+KQ20NNypPs7Zs2bJFUvssqWmDNZv+ZC2j33jakKtL\ntNioVYO9kj9zxn8LS5WZu+66S1KTeesZ620VnnHF23LdKGCsGcxTYu9qCUUqCIIgCIKgkokpUlLj\nveE91N6d8j28Rc+i4y4U5QiPmLtUPO9Sb81rwvhdOUpbrgbMTTfdJKl5rszdse9513dMzGJRoqCr\n10Icy2K77lI8Vi9X92rcUDOJ+edxRW2zJFFgvZ5WCfRNqiZdCjxtjy3xa/A1iM+ThcXnyRYjQzcV\ns1Fb74e1jDW2r2yyFCgbjC39WrvvZQr6g7Wb66K9obLGxo3PkVpQpLAvflMYL5RL1F6UUn7DfM3E\nnjhu1zje2littnhcMLXoWEO6KlKdbqSWL1+uxz/+8dptt920xx57aOPGjfrxj3+sl7zkJdq2bZuW\nL1+uL3zhC/NuNIIgCIIgCHYFOt1IzczM6Nvf/vacxylJGzZs0PHHH693vetdOvfcc7VhwwZt2LBh\nwe96hk3Xu9tUrIhn93EXzF112+fpfD51N13qIXtVY+A6hs7Sosox7d15552Spk/R6Mpijwsppa9K\n5H2DGkNcAn/X7hGIfWK3bRTLrvsx+tzwvz1rD+WLVxQaPsdawue9ErXHX7YlVSepb1DSiAFjdwgU\nlb7i9zx7y20eVXZcma1D0bZCeAp/2uP273HFvJ+LJeS3s6ti1tf+qDn4rcZuUgpzLZ1XXr8J+OpX\nv6pXvOIVkqRXvOIV+vKXv9y1iSAIgiAIgqmksyL1B3/wB9ptt930+te/Xq997Wv1wAMPzO08vu++\n+yYzzx5++OG5u0GPA6it7cDdt3/fvRi8Pz7Pc+NU3ESqjg7eqN+Vl9bd4XktXqfXqRrKm8RbpDow\nz4eHqug9adruV7ZYcKW170ylrtmSwHliX54x1DZr0uM0xuXVSo2aBqkMROY+fchcZq1Axef7rE0+\nhpPeq60tvtsEa1tpJe4cKE30F7FEfe8POWn6iudkPHyOMBdZO7BLlLBcrFnKXtuCffAbnMt4r8UV\nUeZVX09fOt1IXX755dp///31gx/8QMcff7zWrFkz8v7MzExSOvvpT386Nwi77757dVpyEARBEATB\nUFx88cU7fb/T3Qv7/+yzzz466aSTtHHjRu277766//77td9++2n79u1zWQHOnnvuOXLXvWOl866V\nlN0r8YwOPz7VhlHGSj1cvNOuz1mpFYOnPfRNJedLxsu9994rafx76AXdwIvDXr1CP9R66X1V1kd1\ncUUKNaGt8krGDSoEtZjafLd0P8v99ttv5DW1b6BD37k6zrXzPn3j9ZBgsSlSnC/XPVSNNlfz2Z2g\na8xO39TOPa7PVWFXoXOk9oX1pzPYHYpU6W9aX2ow7Y2r/ldbxe+4447TJZdckny/Wgf9+c9/Pjdp\nfvazn+kb3/iGjjjiCJ144om64IILJEkXXHCBXvjCF9Y2EQRBEARBMNVUSx8PPPCATjrpJEm/ubt9\n+ctfrhe84AU66qijdPLJJ+uTn/zkXPmDhXjUox6VzHDhrrjUuyC7je+xnw54HSlUMn+eT9aa414i\n32fn8eXLly94PqVVibl+zofvs8N236CkoRCUVlAPhsErr7tqAe6F8j5eJhlT/M3xiImbFB6HUVP/\naUdQHzjujsc5/PDDJTVzmc8wZ1euXCmpWSv4nGcQok4zN1FWXC1uW3mcsfF9L1Nq8NC71vcNigLX\nNVTcJWokNs84DV0vqy21Kn9KDW4b7+mKae43JWfP/Eb7Prm1sEZh5+OqK9U31TdSK1as0LXXXjvv\n/3vttZcuuuiiTicVBEEQBEGwGJhYhDdFPKVGIeEu1+s95WI18HDxKvFYAS+UmAzufjmuZyw4nlHA\nXf62bdskNd4qdZnaPn91L+Oee+5p9f1S8NpcuRhn1lMNKH6oB4yHZ+4sXbpUUuMF3nDDDZIaLwp7\nIn6D/nB7GTfE7WzdulVSY4eMC6+cP/bu3ncqM6qvjKlaiP9hnuAV19rdpk2bJDXXv+P6gGLksTnM\nVe8zbMnnLMoK8YOlsIYxVtgex/N9Nvk8qiS2zPk/6UlPatU+eDbU9u3bR9rrOx6S62UODq0seOwP\nytdiVTRSoKRi12SzldblQo2mfhlKK8oTa6HbI5/jt4k1ilhB/zywluZi41h7OS5/Dx3j5nGl2A1r\nKufRtiL/rpErGgRBEARBMAEmpkj95Cc/mbsbxEPl7rbtc/XUvj/HHHPMyPt45nhj3I1TR8k9ZJQB\nr5KLN8BdvXsHpd4e1+v7HQ0Fd914E4slNgo1AXw88NJQFRhHvKlUXAH/97gAvC7ssu9K76gO4PaC\n14QXipfE91AUb731VknN9VOXCXWD4+6488CO4L1j56gmuR3hsSO8SdpnHqFKgPdf12xAxn+hcU3F\nOQJj7Xvg0ac59c5jpOgLr7LONeLZ8zfn7J93W2VNueuuuxY8Dzx/xgybZ03hvOgr2mOtKVU2OB42\n5Gst/XbwwQdLauJT6UdsgX6nv7GJXEyOP62gv1A5eXVKlZFpx+tm+XjldgfAPug/j8ddKM5Qaua+\n2yn/57w8yy63VrI2cR6enVg7Xswz30/XY/ZSFe+XLVs28n3WstKYu1CkgiAIgiAIKpl5uK9iMW0a\nnZnR7OzsuJsNgiAIgiBozezsbFJND0UqCIIgCIKgkonFSJ199tlzz/F5/k6kPM8nSyFWw+vxvO51\nr5OksalftPPpT39aUhPf0HdWHM+TzzjjjJF2gf4kTsDjLF7wghdIauITiLXheTjH98wO2jn//PMl\nNTFEPF8n/oJxIF6C59I8P+eunnHyveI4j7e97W0LXt9Q0M6//Mu/SJpfO4g4AyppE79BZgyfp1I3\ncQBcP8/f6a83vvGNI+0ODe184AMfGDkv4HzZsYD4i9tvv33kc2SSET9w9913j7yfs8+hmJ2dndcW\n5+IVnH3XeyeX3UY755xzjqTyisycB31buq8o7fn1sSsDtkgMDTbI2kPcHXF2niFNDAn99Za3vGXB\n9oaCdt73vvdJKo+TXbJkycjnS6vk097nPve5Bd8nU5gK+Lyypq1YsUJSM37MEeYCawZr8aTmeq49\nfiOwc9ZkrpMYODLJPQYKu3vHO94hSdqwYcPIcfl811g1Yrqw3z//8z+XVN6fuViyHLl2QpEKgiAI\ngiCoZGKK1C9/+cs578Gr/bbFvZBaBaivTA8yVoaqz5QLa6PdVC2Mb3/72yPH8fPEG0l5zVyfZ5D4\njt38jULFK//vOxuuL7huzs+VNK9K7fuL0S8oUHh71Ejpa2f3WlKZK6gTuTpm119//U7fn0DYZRLO\npbTuDpRm3rbdG4w+bqtIpWDtyykx2N607UXntFUM2tb5cuh/sr5QYHi9+uqrJTVPSXi95ZZbFjwe\n2WKo8yiQ0wq/dVwvaxNrnteZcvw329fMvuB4tZX+h6qwD6FIBUEQBEEQVDIxRWpHUIK422y7Gzzf\nb7vvlUPNk5tvvnnk/8QhlJ7XuHds5/pRktauXTtyHt/5zndGPp+6O0cxOuKIIyQ13ojXcaI/qJbs\n4G37zuzEEtEOx+3qlffN5s2bJZXvdZgab6+IP237gE0bbXe2X8z0rQwR88T+n8TwoP763oN9Kwep\nGBR2lfA4u2nB1zbWUPqp7W8Ra9tQFeT7Zt9995UkPe95z5PUzEFiwm677bZWx+sai5Sj9jeecaVG\nIEocMYG5+nM5QpEKgiAIgiCoZCoUqZSyUUqugnUpHhvCXSzPz0tpG4/RFWJeOE/ab6v08Jycytb0\nR+m+Q57151WH2UtuaOiHtvErUPs9oB9Q7rZs2dLpeNNK3wqS74k47nm0mHHlA8/78MMPl9SorFdc\ncYWk/pQoniKQnYZNoD6n4vGmFda6WiUJ20UJbPvbMW44v6OPPlqS9PSnP12StHHjRknSWWed1ep4\nQ6vJteOCUsZuJ4wTMXahSAVBEARBEEyIqVCkahUEPFjPtqu9a/VsPerpQC6baVxwdw30H/3gO3eX\ngiKFd8nzfhQqSO3Rl9oPKsVQz9OpbULGVtvjo0TiVbbNtOL6+Tz9yfFqsznx8om9In6h7fF8v7W2\nYGfEwBFP0RXmc9vxYrwfyeBhU8fI91kktiSnRBHbUwoxWdgm0P60ZubmqI3bZBxQpYfOFusKNQa/\n8Y1vSGoUxNrM4mmNbyTelXhW9tbrS/UORSoIgiAIgqCSiSlSMzMzc8oJygcefGnWG554ql5PLXjG\nPP9HAegbPHvu4ku9IL/r5/zwzDn/tufNXTvHoWqwK3VdM1E4r6HqbLWtjO9QPReFlP7EuycDKee1\n8T5eO96+VwIvBftYtWqVpMabaht7llIdUOIYb66fecC442VjL8y/rlmzrAPYRamaMe21eoYEW0V9\nxUaovo9tMOa5vmob00N8K2sFsVm0h01dc801I+fZlaGz4ujXtooac55+XCyKHLs58DouUO6Gjodk\nXpDBjoLKb11XewpFKgiCIAiCoJKJKVKPfexj55QJ7kp5bVuHiee6PJ9GQehyblITE4Vy1Dd4+ryW\nxmC5IoUH79l6bb0h2keRQGlAKegLYs+mJebMoT9RXhgf+gOvm9igVD/z/kEHHSSpu7Lpe9uhOtQe\nx2HeuXeIiuHX2Xf8B0qixwDmKK33NQ30nenox0upvMQ/sr9pqj5Q2zGlztLll18uSVqzZo2k+QoV\nCoDv+9kWfiOYS0NVakehoJ3S7ENU3K6Zv4uVUmWHNZV4TX5z2DVjKKgbxuvBBx8sqbFPrytWSihS\nQRAEQRAElUxMkXrc4x43p/TwXJnn53gd3LXiDZA9xv+5e+3rLtbvpr0OUt/gge+9996SGkWtbRwB\n3ijnzSvHa4vvX9V170GHfp3W+AG8SfqVv4kBwuvPqQrYLbVpul4v7XWtx5XyllNqxLhqAfm8n7aK\n910YKhYERSZniyhXObW+VilDzWQtR/Wk3b7Gkt+A2rg4xiFH7EJQR+kuI6yFKJpDxSHnYC33zPS2\nhCIVBEEQBEFQycQUqcc85jFzmQ14MSgo3B2izBBZTyaFZ0vh5eFR12aGoOR0zT5qi8fitPXevG4U\n508/tH3O795z31lRfStcfYO3RP/xN/2KnZbW3EGRikrdO6dU6VuMDDX2pX3F52ozRnOwxhBHCChm\nXWOZWIP4bShdo/ntQDErjfdkTW4bO0M/EIu2WG2ZOEXstjSbre1vBf087n5ye2q7p6ITilQQBEEQ\nBEElE1Okdt999znlxWN8UERQArjLR4nyvd8We4ZEVwWMzBi8CO6yydRBQbnqqqt2ehxitdavXy9p\n/rg8UuB6PQOK/kA5zClSeOeMB9mK017tGLtJxYlgT3yOGEXib2rrgy32atjTTOl+mbV4prDv+1lq\n8zx14DfA42ZRlEprxbnS4Wstc5nzxrapGUfGLbs55Gxz+fLlkholbLHaMrFObZXEthn3vmvEuOCe\ngjWra5x1KFJBEARBEASVTEyReuihh+ayw1CU8GK4S8TLmdRz1No9AMcNigf9Q3+hmJTG8qAIcBy+\nN1S/44WghFF3Ca9uUqxcuVJSU6cJO6A2Ds/X8YpTMW2+5x/9ecABBwxx2sXgbbsXxvWhnJEd6HWa\nGC+nr0r1067YBfNh7SAOExtru2cbihNziuPxf16Zk7l40lz7rDUoVa7A+L6ZKYWJ80K5YveDxQKK\nn9eMa7v218YCjjtDl99MlLeu+76GIhUEQRAEQVDJxBSphe7YU1VhJ5X5MK1KlO92j2LA832y4lBC\nSis/o1BcccUVI8fzCuReqXvz5s07PU+8DfdW8GI93qGv/bhqITbowQcfHPk/14E3k4sf4PvuFXet\nWdIWt5dUPAAKsdcRcxifrnsa9kXflfcXgrlANtauimfdlYKKy76SxM+1rRTN3n1OX9l/zq233ipp\nfhwoShhzJRcnyhxvq8D1Ta52YKpWIWvzPvvsM/J3raIIrD0oXqjWtJ9SwJYtWzbyfdZSlM5aUKDW\nrl0rqZnPHLe2dmQoUkEQBEEQBJVMTJHaZ5995rwWanVwd4qiwt0hni93rewovnr1aknNfj0pTxov\nCWWGGA8yDPByeD7qz0+JHeEuGi9r6dKlI+cJZJ649+SVm1P1lDz7js9xt+7eRGrfrFq+//3vS0rH\nvFBzI+dt5jI4Us/Ta/c76otU+20VmJQX6/EA++67r6Qm7gNFzLNWsVvsg5gr/x79TqYW8wzcK83t\nj4VXiLfq2aClNVjwAplPnC/zlvaZh1wn85B2ieFK7Q04BPSVZzKytnCuxMiwBjA2d955p6R0LTff\nQ444UVfAvJYefcFawRqXq12GzaEud83MZc5gE1x37X6Q4yJ33UNlLJNpTX/x24cdsEZgT8D7jBvj\nzxxmXIE5yxzFTlJKk+/bmoPfMmCusmZwfZw3axh2zm8bv5XYN/ONtaF2H06HfuUpCr+pXdX1UKSC\nIAiCIAgqmXl4AgEpMzMzmp2dHXezQRAEQRAErZmdnU3G74YiFQRBEARBUMnEYqTGoUjRxmc+8xlJ\nzXPQVBYbsVc8h+V5Mc+veV7O9z2GiPbOO+88ScPv2Ud741L3Flt7xAcQ18Jz/1TmyqSuD3sh1o/M\nJTMqscQAACAASURBVOIIeI7PK58jlojPE5dAfAr2ynW+4Q1vGGl3aGhnw4YNkpr4CDJwOH/OFzxb\nlrgJj+Ui3oFMobe+9a3Ja0vVzoLSmnHEmJx22mmSpHPOOUdS0/cew0EcF6+8T7wX7REjwlqD7RJP\n9id/8ieSxj925557rqTh6vwwF8844wxJ0vvf/35Jw2e/cX1nnnmmpOF3b6C9888/X9L8jGAHO6F/\niJnjNbXrAHPlbW97myTprLPOktSsHbl9V4mxSsUO0T72y+f+8i//cuQ6+8Yr0NPO1772NUnNdXH+\n2OuNN94oqVk7nv3sZ0tqYrPI2uR9YrmI26a6wItf/OKdnl8oUkEQBEEQBJVMTJGSmgj/VPZaKdy1\n8+reBXexuXpKvh+Ve8DcreYyGvCQXUnwukq11815BDsnldU16TpVDpXO8aZcFbn77rtH/l6zZo2k\nxg54RVXxWjLjyGrbGXixzC+vHkxmGtePl8j6wP/JPHJvfsfj0RZjzNx11csprRnnNblQqGgHm2Pt\nILuKtYdzR/1GeeF9xgqFatK2OrRSk6pnNC7GvY8oNo0doDzxm+G7VHB+2AtPQVKKlEN2IPaNPfI3\ncwp7Q/lhDfFdLlCi+Ny4MqxTFeVR55/ylKdIkg4++GBJ8zPdeTpE/2NnrC2sqaxFXGepEhuKVBAE\nQRAEQSUTlTa6KlGAV5Py3lLPg3O4t8JdbG6Xe+6CUQrwOnjeXOtlpvYDwpuZdFXdSYMX5TFE7s3k\n6iaNG8YVu8CuUlW0r7zyygX/v2rVKkmN+uJxN23By6MfsS9Ul9L6Ubl6Ylw314vXiNfse0jujNSc\nTFXMzkHMSYqcyo1n67XqUrsB4OnT16X7ZDrPeMYzJDW2j9LB+Vx44YWS8msRtjmuXR5QXmrHq2+Y\nO6jGKBvYdNs1xNdo7IF2iOXzeEnWtpwS5XOdmEDGkVgir5XIeWzatEnS/H1WuX7mKL9ltb+tfeFr\nOfOMewv2b73lllskSTfffLOkpl+8lh6KlsdkZc+jwzUEQRAEQRA8onlEBNv0tSs9d/G5u9RST70t\nKY/8ka5EAcrc+vXrJTXe4zXXXCOp8ao9C3PSEDuEF4rXx2up4ujZbLzm9t9KwffWrVsnqbH/m266\nSVJ/dk68CNfp8Rgou7U7s3cBDxcVmmw6wMPn1eO38Hxzqhxj3VeMEH3JHnh42jfccIOkclU8d94O\ne/Uxx0pjeaBUifKK7LTT11MOwAZpDxu86qqrqo6X2jeWuYodsBawhtFurj99rnNcfgP57eK42Ddz\nO6U8plTtofcNzT39Yfy5LtZIlDL6m/dTv92sZSh3tFtqT6FIBUEQBEEQVDIxRepxj3vcXEbB1q1b\nOx2Lu/dURkNfNVCGqqUS9APxFex/xj5V7PfF374T+aTxmCPiD1BmsG8yZFJqAt4W9o+91mZ5opTh\n1RFPMVSmDsdFBeD6iUOapPKKp+9xa2T9cK47yyjcEZQbamtxjbSDwlWrUF199dWSmrEn9oaYkaHA\nkye7sa0iVQq/HbRDvaC+FSnmIrFDXa8nZ8PsLcdcxp5cpUZx8TXMFRfsDPvEnnhljfA6Sm53vs8l\ndjsUKF0oq8QsuULK9aP8eqzUPffcU9Qe/Uy71G8r/Y0IRSoIgiAIgqCSiSlSj3rUo5LP33ke6zUs\nvDIy4DmnntemnksHuxZe6RvvzjNLPKuybVYbChHZcV7nqS14f8wHvGCvrI/37c//ge/jxeGdts38\nQg055JBDJDXz6v777x9pp29Q2lAVUlXIxwkKANl79C0wBql4MRQm1i6ORywGY8m18vlczEoOYmA4\nHmOGDQ2l7rEWD632oi577M9Q9KWs5WLTuC5eATUYO+K6vZ/9qQlzmfHHLphjXm8JRZE1x9thHrB2\nkgUHKGC0g73nslsdj1Xiun3N43z4HPbua2oO1jbOFwWs9LchFKkgCIIgCIJKJqZI7czb5K7dM2Tw\nprjr5K6du9S+n48Hiwu8issvv1xS4827IuVxK23jULAzV3pyFfZT+B6AgHdZmq2G97V69WpJjVfX\nVn3A+6R9FDf3PodiWrIppcaTRvFwD5WYo5QC44oUni7fc3Wdta8vxYjjsUaSxefn0RXfC47+Kt0N\noi0ogJz/JDI6ayh9OuJ7P2InxCql7MPnDn/zyprF2lCq3FCHiZhAlCD/zfU6aChUbRUpr+ye2h+V\n9rzuVerzObgvYZ5jzzlCkQqCIAiCIKhkKutIcReKksDdLV4I2VlBsBDuveFFoVh1BXtEISWeBQWn\nrRfE54lnALzStt42x/OaPqXQHgqU7wDPdXO+ffUruHrhascksvdQWNgNHnKxQF4NnmsrjX2qrUrv\nkG1F9Xuupy9FirhBj3Mb6inBpCtq11I67kuXLpXUxBqhIPHbWLrGsIZgp6ndMXJgL65MeWwaf7eN\nUXJ8VwNi78jGg1TtPP7P2lxqL8w3lNRQpIIgCIIgCAZmKhUpSHmeQ2doBNOFZ3Hm8OfqZGK4cuLx\nAm3B2+H8auuMcZ5e2bt2T0a8ObzR2srmrkShQOGNjiurzqsXTxOlMUCopKh5qbHFBrCtrrE/jP2B\nBx44cr59K0VkyFKviv6Ypni3LjDHeR06JsvridXWvsPeWDtZ61gb2q6tfM/3kQVX3EoVKV+rUeKw\nH+4FfA3w7ELaZx7x9KpUkSIum++RLZkjFKkgCIIgCIJKplqRgrVr10pqdkxfrM/HgzpSikpODfB4\nDcCLaVtfyeG5fVfvlPPwPQDxsjyDJwdeFd5UV+WI9qkmjRfaNhOnlmmqA8eea+A25LvRA2OJh56y\nPfqWsetas8uVL2JM7rrrrk7HTcF1l8aWLBZYa1B4XK0tpbSuEp8jzpPfPMYPpcpjhLzOGXbEWgVc\nB58vtbMnPelJkhrl0Wvo+RpVmq2JnXpsFOdHP/h1sGZ6lihr4IoVKySV2zvtMQ9LfyNCkQqCIAiC\nIKhkUShSKFG1cPfel5KVUwiIIeHu2e/Kucvl7jfqX+2clPKEF5N6H2/MvUe8HV5rqzD3VeH7yCOP\nlCStW7dOUuN1up1t2bJl5G+HfqDGC8/5a/faA28PL5jj5hS52pou04hXuHa1LDWniXmiz1IqG0pF\nX2ofnjWeO+c1VOzSrhYbBYwbqiwKUNvfFN9b0eG3g5g22sPuLrvssp2263OV47HfKL89KEqsEaVr\nGe0yl/t+OuRrMe2wlpOJDF4/irpSbRUlYFyI+Yu99oIgCIIgCAZmUShSgIcOnp2UunvES+zr7jkX\nq+L7JDl4a6FElZHK1srFzqDsDBVTl4qHcfCiDj30UEnNTvWAF3TnnXdKarxdV5Jy+6+hBnzrW9+S\n1KgQxFscffTRI8ehX1NKEbVs8GqpRUTMle8FyPkTfwHEN6BckYEzhGpROia1kE0ErD2prCJAJeT8\nUnu3EVvU1151eObbtm2TVL93Xym76prGGkL2ZW2Gbi5Wh9+O733ve5Kk5cuXS2rsCqUkha+JW7du\nldScN2sNv4ltlc9/+7d/2+n7XSvZu91zPBQzf/+mm24a+T+KFGsUtSdL40ypncd8Zr4/73nP2+n3\nQpEKgiAIgiCoZGKKFBV2pcZTxVvj7pu7TO5uuYtOVWnFs3YPO7L8HlnglfCKkoJygneCt+EVxQ8/\n/HBJ0pIlSyQ1cQp4Obxu375dUuMtoYAtW7ZMknTYYYdJaqo+e/VnwAtK7WVHjBHxDBwHVSOlXnhV\nYEAhI04CxcqVKm8fxYlMHb6HcpWqf+XVjr1KM2oNShbH8axIr13DuO1Yb44+YiwYc49NYcy8Oj2e\nO8oQahy4rXAuXCPH4ZwYg1z19wMOOGDkeJxH2yrujBV9iHKSUor4PP3EnAH6h37wvc0YG88qoz2v\nh4TNEfPidYiYOynlxmu3gcfKoDz4PpZ8vxbiLWtrs2HrKCXgSuQ999wz8lqLt8Oca6tEcb2cZyou\nknnG+PM3a5VXJgfmttfU4/uctytdKHcet8nftRm/bbNaQ5EKgiAIgiCoZObhCaTSzMzMaHZ2dtzN\nBkEQBEEQtGZ2djYZTxqKVBAEQRAEQSUTi5EahyJFG7Vttd2HiHbOPPNMSU0VWM+U4Hkxz3HJaiJe\ngOfaxOL4fj/EpJx00kmSpE9/+tOSmswc4hqIFyEWiOvh/zzHJh7D4wuIJyDOguv77Gc/K6mJPaJ6\nLMe96KKLJDX1v17+8pdLkp785CdLkr7xjW9IauINiL+gf4g3Wb9+vSTpM5/5jKQmXiCXGZV6Du/Q\nTzxXf8tb3jJynUNDOxs2bJCU30uOWK1cvE2uPb8+4ksYB2K/vF3iFahJQ1wMdkt8x+rVqyVJp5xy\nyoLtDcXs7Kw+9rGPSWpsm3PzKvdts/qI7eH7p59++lyb44B2PvShD0lq5tp9990nqYlZ8dphnkFM\nDBJzPmVzXdfOFF5TjDn7tre9TZJ09tlnS2oqU7MGcR0e68W4EGPGWostegYpa8Ob3vQmSc3cI26S\nWC76pzb7zLPEhurPFG4vqTpRnCd2Qz+X/uYxnmeccYYk6YMf/KCkxh6H2pNwUv2ZIhSpIAiCIAiC\nSqa6jhTZRV67ZVx03eMLpQYPHy+Lu34UJM9AQanBq0IJIGPCvSyUIbwtFCtePfODCtp4KWQ+oDDg\nZVD3iIra4BkwZPJwfe6NkH3Fca688kpJjQKFl0wWmO8Nh/eaU2xKlSjg+j1zaNzkrgtqlagcqDOu\nRHm7qfbd60xlH44D1DXPzmIuM3dStpKq48T3ahWKvsBmU9lTnqEJKC2eGZ2DuZ36PBW4WdM4HxQi\nXrEdX4u8VhoKCWsDa2NK2UDd990KXGHCHnzcuS5e264hKYau11VKrmI554my2RaPGeJ4u1pl+xyh\nSAVBEARBEFQy1YpUX0pU21inHMSS4A25t8Td+A033CCpUVjwylB+iIXKVULPVbOl5gVKFq+33Xab\npPleCbFLOe+UWBivZcPx+D+Kjtf5AWKmUBg5P5Qo/7y3h9LF+FHPCLru5bbYvaddZS+75z73uZKk\nSy+9dOT/K1eulCTdfvvt2WMwt3hFicDWc5W3UzW5mOOTVqTagirt1fNT+O4RHrfoUNOM76HooC7T\nbqoekitoHIe1gvZZM4jXJG4UxYzPsaay5qPWszbVqrqpit2027be165KXxX5FxuhSAVBEARBEFQy\n1YpUX/SlRAFeUS4jAe+Kz6N8tFVA3BvyuAK8Iif1fLw0kwJv3pVBvA68fvqX/+NNepaZe214k/QP\n7bhqwPP7lOJCf9R6Q213CJ82ul5/33jMXynYB0qlx9eUgM0yx8hG4pyYK2RlleLf74vavck8jtBh\nbtEPpbs7eGxPak85r3Ttu0qgRLXtZxQq5iSxaa5cpdYSlDPfo42/a1VbzsOzAYeK32XnD+zulltu\nGaSdoB8W9y9IEARBEATBBHlEKFJ9U+qF4B3h1fHaVgFxb9XjFXIxVrWk9mPCu8M7x4sljsH3SkyB\nkoV3Tf+44sb7qfiWUoWPDB73zoeqdTIupkWJAnasbwtqAXE9Hh9TAnWFmDMoVBwLlRTVdOvWrUXH\n9f1A+2KomCtsonSPtBQpRYqxIn6T/Re7Qjyk19bzfVUdXxNRclibfC+3thAP6+0NNX6ldtkXZGgz\nX4iPHUpxY3zIIOe3YNOmTYO0NzShSAVBEARBEFQSitSApGKz8I5zGUQp3Atyb6kvUrVQ8MZQgmgf\nBQsFKVXTBsjkwVsmE8e9/r4UI+IoSuNFphX6Czviemq9Y69sTvxJ25o6eP3UFmoLGWCHHHKIpEZV\nQZG69tprs8fA5rBJroUK2tRkQ+Hw6v0piPXBhrqCIkYcWN+1t1h7chm/XcEWDz/8cEmNzfCKbaXU\nbcfHifEhTq5UnWRtYvxRKj0rMQd2gpLJcVBUrr766lbHm1boL+bc0LUbsU+UR16JBVts2bGhSAVB\nEARBEFQy1YrUtNfHyZ2fe694VygvKD5tY1xqs6L6wiuXo6zRD3gTuRorXD/xFSgRHiOVi48ozcpM\n7SG42EDFQEGijlhpdhvjQj/gpfN3rhpyCo/HaQs1gDx2rk3leY+VYczxsD3TtLTCd+r4paBooNzw\nN32G4pXLxps2sKUlS5ZIauYU/d42Y9orjWNLuTpNrInYCudB/Ch1rdqeD8dDZSVbD1v1ell9k6q0\n3zfjjsnyjHaukzjWvirMj4tQpIIgCIIgCCqZakVqWpUoyJ0fNWyA59B4n7UVtV2xGTfEPfhzdbw9\nvP/Sar9eh8q9fhS8VExZaS0evl+rmJSSU9C6wnWyd2HbGDm8PuJRiNdhHOintjF8jF+tonXTTTdJ\nml8Lij0rS/CxRanARlCkateW2qwvbAIFgzHARvqKvWqLz+W2sD8j30dRo7/b7jnH2sH3ec2dH+Ps\nSofXqGurQjMujDvKDbFaKHK021cGNfaBvdDetOzh1xWvUchv4lBrJkpi7dqUIxSpIAiCIAiCSqZa\nkZo0XZ9Pu/eJ99fVqyhVVLruMYiX5efrsTW8z3mRgVEa70G8CAqePx/PKVtt1YWhMkLGpRR2zcTy\nPRF9PzXsdqj6ZClQi4j9QilrM19cMcL2OUZbW2HOEiNTm/FJHBu2zvmg+g0VY5Oj6z6TZEkyd3mt\n3dPOx4v+R5VOxagx7j63UV1Zg9v2M0oQ14OCwnkwnthVX3OGcSF7kbU1t1fiYoFx8Bg4/mYc+4oN\nG0qJglCkgiAIgiAIKglFagHwqnheW3tXjCLkO9Bzt932eX3bfblQHPBu2iphKSWL6/G6THh9VKbG\neyPDJcVhhx0mSVq5cqWkxnv39ugvHw/6FWUl5X3gtbZVzEoZd+0T3z+M15zKgB24V+jZe7V0rf7N\nOOONt/HyuQbfA84zCkvnNH1JX3e9NhQc5hZ/l8YTTivEoGA7uRpyKRgn+oc1DFvwOlJ8ns+5Yuj2\nUAuxUdSRwi5QpvpWipgDXE9ttmhbavd+rIVxwW5qM9knTShSQRAEQRAElYQitQDcFddmEKDMoJDg\nveBl1Xq1eAlkyuToWicp5ZUQF0DcAgqQZ8jklChA2eJ4xMYAMTNe5dj39Mt5MXitQ1WCHxfU7KHf\n8CJvv/12SXlFirgcFDkyZlAVumbOUO35BS94QdX3sR/mS5uaMlw7toQigWdfaisOtc5qs/YA5YLz\n61ovp+86Q6jJbWGtaGs7XmmctZe1hMzUlMpMPF9KjcbWWTO67mrgKnbpWlxLbWxfLeNW1ffff39J\nzbjdeuutY22/L0KRCoIgCIIgqGRRKFJ4XXibvA51l167Bx7g9RDrs9iqFecghgzv1RWk++67T1Lj\nLTJevn8TXjkKF+Ps8QB4m+7tovyVKk27Sg0Wz9pbvny5pHJVwvsRL7Qvpa5rP6NGYEdt5jlqL7aG\nbXk1d6Av/HspW227V5uDysY11da3Ya7k4hjpS+Yo7aUyP/3/XhNtn332GTl/1FHiDlGhU3GMZNFh\na8Q+AdeD8sfnUjFCvrefwxpE+22q5JcwtIKDErqrwvgutkrmTihSQRAEQRAElUylIkXsB94f3o3H\nHKH84J3hdeB1TQrOk/NBwfG6S7nMFr5PZWe8NDJHJsW1114rqYkPwCtz5Yn/p7x4Pk/cCN4sfx9x\nxBGSmuwyXjkuXmrXuAcH++G4xCC1jf9AFeirJopnKKECYF+oCd5OVxUlB+eBd4lSmMIrl7uCxf/J\n1kNN8axCxn9HxcpjVugT5hIKFXWh6CtUY9rwavrYXteMRjJ5saVUTbYcOVtizSAGBVhzUoqUq39P\nf/rTJTXKlis8jBWKAtfBnoJcH2s0Kj0KnKugKFQch/7yNYC5kIoHRC0nvpK57HOYfSuf+MQnSmps\nGdvzbEpirbiugw46SNL8yvXY7ObNmxc8P0DhIxuxVH3lvBkH7L62ZqAzVPae1xrEbvg/CmfXWnk5\nuD7mR1flLxSpIAiCIAiCSmYensCGdjMzM5qdnR13s0EQBEEQBK2ZnZ1NKoahSAVBEARBEFSSjZF6\n9atfrX/913/VE5/4RN1www2SfhNP8JKXvETbtm3T8uXL9YUvfGHu2fA555yjf/zHf9Ruu+2mj3zk\nI8laMueff/7cc1Kej5LRQQwOz7UPPfRQSU1MBM/ZeS5NRWziGnje/MpXvlKS9KUvfWmkbZ7Dc7w7\n7rjjN53x/5+bUmmbWJ/bbrtNUvNcnmsllovzOPHEEyWpWG1rGxfh0A6vxMoQd8D5ES9AXALP0+l3\n4kZ4n7tuxoPjvOxlL5Mk/d3f/d3I/z2GjRgjsq7oH86DfqdaMNfP94gbOPXUUyVJZ599tqSm/+k3\nrzzP+PG5tv1KP2Iv2BPjTY0T6jURq/anf/qnkpr4je985zuSmrgS+hc7pV9e9KIXSZLe+973SpLW\nrVu34HUSR0DcBv+n/z0ugvfpd+I4nvGMZ0iSLrvsMknz+5/r5PqI81izZo2kJl6CWDbOhzgixpe/\n3/rWt0qSPv7xj0tq7NKrhfv8Zzy9DhnXlaqPNjs7Ozal2+fertreP/zDP0iaH7NCjND69eslNbbB\nWortEePE91lbmDvE073mNa8ZaRd4vzYOkjmMjTO3Tj/99AXbK8WzIh3WYmLSaOeLX/yipGbusuYu\nW7ZMUjMniROlHc6bNY+5wJrrMVovfvGLi66Pdvne9ddfv9PPO6zZ73jHO3baHufvig5rPefPnPds\nWtYMrvuv/uqvdtpe3+TaySpSr3rVq3ThhReO/G/Dhg06/vjjdeutt+r5z3++NmzYIOk3gXWf//zn\ntXnzZl144YV64xvf2HlTzCAIgiAIgmklq0gdc8wx8/YR+upXv6pLLrlEkvSKV7xCxx57rDZs2KCv\nfOUreulLX6o99thDy5cv16pVq7Rx40Y985nPnHfcBx98cK6iMni2FKSqnXLXyl0sGRW+wzd3+aX7\nFeUyBny/J+7m29J3XSPPAiTjJNV/eP6pjIVUbY9cvaFUxkduzzT6A8UG8FZQLryGjiswPj4pUFy8\nZhD95sqlZ4SR6fWpT31qp+2QYePHB/rpuuuuKzrvUlAD8OpQpBh35hn96NWp+RyZLd///vclNQot\nXjcKFPbnXjr9m6pfRf/maimVVOpPeb5Oau+2YBTWVIexQMHwtRV1caHMSqmZO7kae10zcnM1AVmr\nXF3Prc05WyRb0Ndkzgdbp3/5LfTfrlQ7zB3vH1/LcjAuT3nKUyTNV6SoUZfaS7C0/llqPrIWorz5\nGuFCzLTWAqyKkXrggQfmDGXfffed+4G87777RrYYWLp06eBpjEEQBEEQBJOicx2pmZmZne4/1WZv\nqrbVfTl2rh4Tz5Hda2pbKwPlwyusd90RvvZ8ppXa80/Vacp5pV4jKAdKTQpihFAruJ7avQvxMvG+\n77nnnqrjlIJih9179ehLL710we+594ciSVyH4+Oc6h/Op+vej15leyFQQtyzd1CReUW17htUT1RJ\n1iBUwGmvXI1SkFJ2UHK4LmyIv1NPAbBN4mT7hrWU+lCucoOrothqV1L1nFBUsE9smf7oSttadYwr\nayxrI+PK69BMq9JUStUdwL777jsnSW7fvn3OWJcsWTKyMNxzzz1zBbaCIAiCIAgWGxdffPFO369S\npE488URdcMEFOu2003TBBRfohS984dz/X/ayl+ntb3+77r33Xm3ZsmWuMm4JJR7njqS8T1eIuNtH\nESCTw2NDcnDXzF16rrpuW2qVHM6H76eOUxo/4ky6UnwO+j/ldTqMey6eoG28gUN/exxECtQLlBu3\nKxRRMl3wHokzoB3PWvRK46gwOdUmBfbA4/3cPlmlsYk5StaF0msi3s33iewbFA5UQdasm2++edB2\ngUxQQizajrlXeAdsirUVW0PZyKmPKCddq/2nYI0rVTpYM3NqdSk5dZY5xG8Vyl/tnKyF6/7Wt74l\naf5vYi6GEOWvFP+Nx1763p3CYc2j/7kXKK0Ef9xxx83FhS9Ethde+tKX6pJLLtEPf/hDHXjggTrz\nzDP17ne/WyeffLI++clPzpU/kKS1a9fq5JNP1tq1a7X77rvr/PPPb/VoLwiCIAiCYDGRvZH67Gc/\nu+D/L7roogX/f/rpp8/V6MjhNULa7mmWunt3BQXvhLt/vCDujtl3qzReAQ+b5/tdY6Tw7HkMirfq\n2V0puF5uWlPKR20R+677i00rqTpeZKn5nm9tob/x2lEjUl4yj8hRwmgfr4n5gb0yfzg/FCmvNeNe\nNrFkPn9Kd2Dn+DmFDfra/6tPxuUJE795yy23SGo8/XHtdo/NlMaOOanP02/YWNt+ZA3OKR4oCNh6\nKvbKlQ5srm3sTV/xqalYK86T8eA3py9ljuMCcz81B1lj/DeXfvPjOW2FEs6n9ulIV7w2XV9EZfMg\nCIIgCIJKOmftdYG7f+7S8TpKlRgndfftFa9p17PvvApuykvwjJPa8wUUhVrlY6i7bOgaKzSt+HiD\nKzlkk9aOM95eqj0/H+yRecH58D5eIvWcqPSfws+7a8wS51/q7TNf2mbl7kqgvLgH7llvfUNdoNrY\nn9x55epA5fA4QNR9bB+1njXcbYjYF6/5RmYsc4ZXr4vllbP7Uk85L8/GYy7SDsqkV/nnfIjhS9Xu\nc2XHf7Ny10M/8zSEtZ7flNya11ZJ65q5W0vpU65aQpEKgiAIgiCoZKKKVF8ZEp4J4XfhxPigeNEu\nd/mpoqHc7fPqx8HbaJu5sNjo6i3nntN3pfZ5e67qMeeL11arSNEOSlIqDoP/0w79TvucD8ejYn0q\na5R+8fPuq+5ZKW3HnXlV099da7G5QtEXKdtkzRpKkaLdaa1N5/GXvosB44n6iS1h295vKEEeZ4iy\n4xmsjHdun862axj97nWYmLt+fB8fvpdb0zgvvt/26QHf47eQuMehsiknRdenRjlCkQqCIAiCIKhk\nolIKXgAZH3gjbSHLClxp4O7evZFU1hbgDfDqe7v5XmbBKNQ7YnyH8grwatseP+Vd+vP0rt4ZO26j\npQAAIABJREFUShRecaoSP/EDxIG4902dLM4n562m6rL1VUV5KLqoM22VF5QPMiHp46EUImArLcYC\nJWGxV3hui9tiqp6T74mH7aM4eSa2j59niwHjn1Oc2qqqtJ+KCUpdn1daz9kh55+rIZiCtYbv7WpK\n1LiIO4AgCIIgCIJKJqpIec2P2rgEFCHu8lGMwLMC+TztcleeyyjwGCm8iHHVhJkUtXWkhs4mhFql\ny9UAwC7wzrrWGqL/nvCEJ0iar5jiVXrtG7zE2rgdj7GCvhRUjrPXXntJauaBe7Vt22Me+/UOEWtH\njBLnPi4l6uCDD5bUxGcuViWqa0yZ11vieB675Hv+kQVJv6FQkUnNODK+/Na4YuO1BbuOA+ebitdl\nLvqcqI1h6ysLbhprvS0mQpEKgiAIgiCoZKKKFF4G3gN36bVZWHgD/tyddjg+XhCfy93V++d9r71J\n1cYYF7UKRpfsqx0hxqhv5S9VlZf2fEf72n2w6D+8Ts/kwRv2eAVXWFP9mJovrvhCbn+5XOwgHH74\n4SPHQ9mjdhHQn7ksSWC+ubI1xHZTXesgtYUxJEuqdH/IaYX6TKy9bfvTbRbb4//8zVqCTbst8H/W\nYmyIuYbtuXLmGbEcl++3rT/E04qDDjpIUpNZC8ztobZOq3164FmSQ2e57WqEIhUEQRAEQVDJRBUp\nVzrwgL02Rg5qX3gFaUDJYF8jj4vIed54xtyl40Xi5fR9946XlKppMm5qY4S8SnFtddnabM4cKYWL\n8URhwR5rq1BTZZk9FFPX49l8eMk5xTM3X2j3hBNOkCStW7dOUlMZHfCmiXnauHHjgscjG3PFihUj\nx09V5m8b/5GaTyXHaTtGVM5mDRl67y/fdX6xe/7YHvtEtlWkfEy9cjnj4qoma6M/hfBxzK1d/nQB\nPG4xtVuGnz/tsXb4+DL3U2tA15iz2linVCZw33XVap82oUx6hv60EIpUEARBEARBJRNVpNwDr401\n8rt+j2VBieL43GW70sPdMoqWe0d4L3greD8e89IW7rZRBKjOy3XkFCm+l/K+8H5WrVolqfEaUWSG\nivEqjYlx/Dn/uLL/AAWpL7wfUsqce2set+DjhB0To+QxUby6Qrtp0yZJ0vbt2xc8j1xMHOdxySWX\nSKqPHSslFTO1EFxrTpFizpBJ6Woac5o+JBsstedZDhQVroXzdLV72sipydjesmXLRv6P2pkbs1RN\nNWBvPN8jr+1xUqC0sHay1vp+lyhP7OnHOPo+l3yOOEFfuz3jO3U+taQUKZRD3/eS81m/fr2kpp9Z\nG44++mhJzVMY1o4U2HVKEWyrRHE81sb99tuv1ffHRShSQRAEQRAElezam8T9f9xb8cwPr2Ce2qU+\n5cGnvN+2z4O9rlVpvEEuDoDrpR/wEmuVKLx1vFG8KF7xrvmb9vGKiEvBO8KLQ+FzBcX33/KsNtrx\nrE/P+HHlkP5wb3vovQFTuJ1wHh6vwfly3SiLqfN1Re+KK67Y6XnkvOK2SiMxiag5qXgMV4Sxa7z3\nEkXKz53YHWwDm0eJAt83E9vxvdq87lEpHo9HHaRpJxfXuG3btpHXvqD/qTjPXGBtREHk/yiGvg+q\nr31+PakahqwVxANim/yfNcgzijnv1G8ICpbbH3A8r0PFXGEOpZ5S5Cqz+3kxx773ve9Jmj/HiH8s\ntdfly5dLavqJWDHGh+P4bwLjhR3x/SVLlix4HG+PNY74TuYpaxW/pak4S8aD62e+l+6jG4pUEARB\nEARBJTMPD52mslCjMzOanZ0dd7NBEARBEAStmZ2dTT5dCkUqCIIgCIKgkonFSA2pSB1wwAGSpNe9\n7nWDt7UjtEPWF8/ViRki1ufLX/6ypOZ591FHHSWpeR6/ZcsWSU2GyOrVqyU1GQvEaq1cuVKS9LWv\nfU1S85yX9nhuzH5ePC/m+Tv95PtVkTXnlbVPPPHEkescGto555xzJM2PT6nNCuS6yI6kv0477bSR\ndqlZQj8SR8H3UzE7XhncaxsRx/HOd75TkvThD3945PuMD+NP3ARxDsQRcDyqKHuFdOJCuM5XvepV\nkqTzzjtP0vxqz9gncSnY1+233y6psWv2iaPfsEeP26Afx2kvH/rQhySlY1ToC9/lwCtp830yEt32\n3vOe98y1OQ5o5+///u8lNdlVZMsRA8KYEivisTVArAmxIH/8x38sSfr4xz8+0t5nPvMZSU1sCzZG\nrImPuccz0n5qrzufC+Puz0984hOS8hXmWRM985s13rM7mRvM5b/4i7+QJH3gAx+Q1PQT/c+awlzj\n+KzRzFHWIsaBtQW75vWUU06RJL3vfe+T1PQ/awlxql55/aUvfenIdX7nO9+R1MSAcZ6cPzFEb37z\nmyUNP3709xlnnCFJOv/88yU18bWeyexxum1rAHp7KUKRCoIgCIIgqGSXzNrL1Rzpi1QV5a9//euS\n5tcowVtw7/Cqq65a8PhkeODF3XLLLZKkO++8U5L0hje8QVKjUHDdtIdX4V4j3jbHg1QVW8+iGzec\nf18V3vEec7VnyPjy5+J4PynoP7xRFB7GBy8T8HqwD5QoMllonwwWvErsADvES0UN4NUzXVAByIQB\nvLY77rhDUqPOPPWpT5XUKHSoNPRfblxQXG+44QZJjT15/3P9a9asGTk/1BDfG5P+3bp169wxch6n\n17wqrbpfkjE4Dnxt8zpGkMv4ZQ3yV4d6UKWhtPRj6S4Gtbsm9EXpXoee4ZzKbPV+TNWRYs6ztru6\nzvFzNe0YFxQwbw8FCXxvQYfzYVw8W8+VxnHPC7dDP7+u+6Lm2ksRilQQBEEQBEElu6QiVVsdlue9\neAu5u+2U14XX6N9vW5cI74G7brwNjzPwCth49sRJoFzk+oUYLc6Tdmpr54wbYnqI2elasZ2K+Kgc\nXi8rBeOe2k/NK4cTf4BSSBwEn8M7TLWb21/Oxw9FjHY5P9/PjNg6+hV78urIHo/k14dimlMWiUNB\nQfNK7SiBnO9Ce/u19ZBr93+sZenSpZIatc1VwUmR2neS8/WYsV2FvvYBLf2+11JLPQUoraTPWs9a\n5+PjayBrS2q3CBTOlFo/bRX4+c0b9zx2QpEKgiAIgiCoZJdUpPy5cClkveEB56q5khngXgCxNXjM\nqQyXHMSSeFaUV8XlfF2x4rVUoeP7tIsyUfscvO+dw3PQT56xURtbRb/i1bUtuUb7qA54l5wfoPSg\nKNHfeLu5/svFBaWun+PjZRITRfuoFN/85jdHvvesZz1L0vw9EcHPt9SL9dguYsWI9/G4H7zRVLXi\nHanddb5vuu7j2FVByR3XoW8n7fEPBWpu2+vL7UeZwtfS0lirFAupsjXwm4l6XDKnpoFpsctQpIIg\nCIIgCCrZJRUpv8sv9eKIwSj1WlN37Xj2eMGQa5+sLN9TzRUFPy6KBB468QwpJcn3bvNMB88W4++2\noMwddthhkhrFiP2b+gbliP4gq6utIkV8AvW7UBE8likHMWdku5GxQ1wDMA6MC+df6hX6Dul+fm53\nV1999YLHSWUj0n/Yl9fTylHqveOFX3fddZLm762Iksc4MK9L+mnSSlRfYDuMFWpm6b6cKVizHNTE\noWJjapUd8JptbandT9PrE00Lvp8oaw/KFXOGV9Ry+iGV8c7cZ076fp+PdEKRCoIgCIIgqGSXVKRq\nvc+230tV1ub/OW8Hb4rK1HjcfD+l3LjShIJR6qHjtaBgeOYQXgbvp2JhcuDd0K+pzKC+ob3ajCi8\nW6/e29Y+UAlQDVJxOnh5XjGddnMxUChv4IpUX1mX2BWZPaX1xbxaeIpUFiBQjRlvGKV02jKJhsRj\nmdpWak6Rqufk6nffpGKzSulaW27cdaxYe5k7PAXoGpOE+u1rAePHmo69lK5lHI/fMs6fDN9c/zG+\n/Ia0/Q1g7aB/ahXEoQlFKgiCIAiCoJJdUpFyxl19FQ8ZLwBFgGw7FCc8bJ5jEy+QU1Lcm+AuvdTb\nyCkD9BfeXtf+6yuzZFwwfniLjFdbRYpxwQvDe3MvHCWK42MHeK05L47vpeIVusahOCht7POWy87s\nKz6JGCraq83OnSTE3/GKbeWq5aeoVeN8zFI2Vqpmog4yJqVxhKiuk2JS2Zz0e5vM051BfKjHuqUy\nz3NK3vLly0fOj90DsDf2fc0pRF4pvS2MS2o3hmkhFKkgCIIgCIJKFoUihZdT+nzUPfBxexupOkx4\nAXgPKDXc5XPevE+GDteN9+RenO/b1BXOm/MtrWni8Dyd4yzWzCn6FyWprXeF/aa8e9732DTsAW8M\nxdHnQW2dslq84niuunBf4+7HT2WaTSOod3j6jDF1e0rp65pdPUypzqXxkZ5pXEoqznRcoKSNK1aK\nucCc57eidI1Mqb+pfmwb44Z9HnnkkZKaCug77mcpNap0brxLd4NIwfGnZa/LFKFIBUEQBEEQVLIo\nFKm2kfrTWtuCmBuP8cBr4H3uvlEivBL20NeHwuD1g9rSV0bRuGF8eEVJqs1g4jioCV6Xi/FEWfK9\nHl2Z8jgBvLZUvEztOKS8X+yW+IucV+oKcVuFOcWk1Yw2oDLTV/RB25iPobLoUnO87a4Iiw3m1LgU\nKeY+7aJA+W4CgCLI+DCX/HP8RriCSMYwNfzA5yTfX7FihSTpwAMPlNT8JtG+K6LjqqM17U8zQpEK\ngiAIgiCoZFEoUpOiNqMj5d1wHM+yI2uP/xM7xd2+xyi5V5qLjSq9Dj7Hc3u8ELILHymgFngdqVol\nEG+d8XRvkAwnFBbGwWOl8Ab5nMdUpdSKWm8bbxbvF68Zr7pUHXG1o69aMNOeybMjxJpgW4xl24rk\nQ+2B5vWHACWtlJzC47W/sLFJ0bWOVQ7PLGX3CuYEc9mr9gNrMWtESl0+4IADJM23D3aXcEWKNR0F\ni34gG8/jMlHlUao4/xtvvHHB83mkEYpUEARBEARBJaFILQDZclRvbZtZUxovwOeoFF2KK0vUpMFL\n8FgrrgevJuXVeqwW5zeuiuTTAv2G14xX7nWeShUqrxHk3qF7mR43QfYj3vzee+89cn54l6gbqdox\n4DF6qYwYrg/1hM9RD412safbbrtt5P/A+fo+Xrl+zO2jNu1xEwuR2suslL4UKRQGxjSnzLC2oCBh\ns55xzFrEnPGxdTVy0pmX2DL7afaNXy82e++990pq1NTUuHr8ZIrULhip3QcYP5QwFCjsgt+Au+++\nW1Iznthv7W4JbdfOxUIoUkEQBEEQBJWEIrUA3G27cuDwvBvvAtp6yl33CsO7SMUzlNaBwktYrBk4\npeB9ofSkauUM9fzfK72nvE3iTHj90Y9+tODnbr311lbtk5njXqZ7iagU9JPvmUh8h3vzrrqkvP2c\nV5qzw9Lq2YsJbBE13Ofu4YcfLqnpU2KbGCPfHYHjMVZLly6V1KjPqIjYhEP8JsoVCofvC4qCwVrm\nMTaeecz1HXzwwQu2Oy7aPm1oi8dIbdq0qdX3U3O+lNT1YVduXyhozE3/bWOca2mrRGGnqPGx114Q\nBEEQBMEuxsQUqf/H3rkGaVZV5/+ZRCpJaSr5q0GUAWYYGIbhjggogxQBvEQhRFMUpCRGJV4RBRTl\norSAMCiIBoXSaOGtolKViLdQISBgQLkqIAx3BpCLVvxoVapMqvh/sH5zpp+e1Xuffc7bb4+s35eu\n7n7fc/bZe+19znrOWmu/8IUvnKP88DtP8WQ58RTLU6nHrHgsBe+94YADDpDUve/FK8Nrw4vC88eL\n4vx4V3vttdes85IRce+9984630JXYe2bWTMpfM849z6Is2Ac8ZYZd8ahpNC1ZlPiFdNOlKDWGDDs\ngp3XUQOeeuopSXGGzaQzhUqgQpSI4nn6ZgGilgB2QOwev2PHntHkyhQqDLFYQxXdIeywww6S5l4L\nbUctjOrtYDOsJStXrpTU2YireV5hGlWOMaFPOB5zhb5FgXAV9Jprrtlk+1A0aKdX+Ueh8jHmej2+\nju+zNkxaESrh/cA4sjbRfmystCciawz3FsZhsRFlV95xxx3zfo9+YXxrawxiP3zed+OIlCa/l9RW\n2l9oUpFKkiRJkiRpZMkzU0h9WbJkiWZmZhb6tEmSJEmSJL2ZmZkJ34KkIpUkSZIkSdLI1GKkFkKR\n4hylcxFXsGrVKknS9ddfP+h8F198saQu9sbfw1M9NopBIe7A9xLj/TJxGUcfffSs806a2v4c+3zn\nn3++pP6xYMRteKxd6Xxr166d9XmPbyHjiDiIvqIucRennnqqJOnCCy+U1MVuUXeJuAIyZYhncPug\nX4jPoNaPV6v+m7/5m1nXORbEuBHnQLvPOOOMiZwvYmZmZsFt8yc/+Ykk6a677pLUzVFiiMjQZM4z\ndxm7devWSepszXcTICbk9a9//azzLlu2TFJXg25oXR4ykIlHO/744yVJX/3qVyXNrXBNrFWpqrxX\nxCZO1eNIo7XFY2ta93YjJoc14UMf+pAk6eyzz5YUx+gQb8t5PVOUOccc9Oti/DjfBRdcIGludiPH\nZe4zDowr9sR1kE2JfXF++uvkk0+WVJ57HrfaukvAWPeGUvwr8ajvec97JHX3BvqN7FPmldfUY+1m\nXB955BFJ3ZrMcZin9C/32ohUpJIkSZIkSRrJOlLqnjqjWip4f48++mjV8Uq1P/CKosyJqK4Qf/e6\nQaWneLwUvCevBYJysVjr8uy8886SuqrJKH1kx/meeGQ20V9423hdeB983pXBKNsOJYj20I9UFcbL\nwQuPFEevqeP97uNDO7meKGuO7/n3GX8UqbFxL3yaVYsjNXdSXHfddZLKNa+wSc+6i3BPGkUKGGNs\nuTVzF1ulv3zt8b3xUFhKNfYA24iUqKg9fA5b6ls/yNvN2uj9xFoczanSWl5ScHxNRnXmunzuc53+\nd28f18Haz3XW7n8JjPe2224raW7tPLIO2TVhUns9Qknd9/72vQLp36gfuHd4pjbzl7pZZPbXzqtU\npJIkSZIkSRpZFIoUT5HTrlqKgoBSRE0Rr07bCt4j1Xx5X4u3wfvaSBEB3uP6cf14eJn+nt0p1Ttq\njUsYC7wh2sN14bXxu+//hPeEN0z/oMDx/9r9z7z/8OIYD4572223zXuc2torgKKG94WdMK4oTtE4\nuUK00KpNLYwf44RagJdYE4u20DW6iP2pVZrGYmiFaSCejrnkNbk4Dx68VzYv7euIily7u4IrVigL\npXsEc4Q6XLxlIFYGIvtgDpeupy+ujHB90T6SteMaKSWtSfjMtV122UVSp/zQX6X6WUAsIPbkdcyG\n3uOxJ6A/Wfuw36F7OLJG19aETEUqSZIkSZKkkUWhSI1dyqqvxw8eA9W3gnMJvA+8Jbwfnt5rY0v8\nKRlFqaRkRQoE3qXHA0xbiQKqO//3f/+3pM5L8jgCz/DBrvg73hDH4e+1oIwRE4UXh5KCd8/xI/ra\nlVfyRknj+n3vvhLEPSw2RQq7du+1z/rAGJWI4hP7Ql/6XB5LXe9ro/Ca17xGUpdNSDyh47bj6jvf\nY23i99q1oTULDFCKojm10047SZIOP/xwSdINN9wgaa4SBW5Lvp/l2LhyxHhi66ivKH60D/W5NkbH\nM4n7sn79eknd+HMv6Xs8xoufzEfufWO/dfJ7AO13ZZV7BrFzJaWNeVG7a0IqUkmSJEmSJI0sCkUK\nz9730Ov7fZ6ivX7OYoOYD7yQvllOrXvD+fvjHXfccdbfh+40PinwHkre43bbbSep80rIbsT7G2sP\nxCiOgVisEh7LFeF7S6J6UDuoVTFE0fKaR9Pcs25jJq0SSN010xelrLsIxmKsmCUnWhuiGBv4u7/7\nO0ldTbuDDjpIUqxMoYB4RqmfH0WhNnurVYFgDqMcRooUsVf8v7SP5ELvg+q4Chyt5X2zMFvvCa5A\nsaa0ZuexRhFLx1rn2ZitRG9dsFPWDFemua5ahdf3Ay2RilSSJEmSJEkjU1OklixZMifbjKdYnsZ5\niix5i3gtPPUu1h2igaddno77KlJD6/TQn0O9j4UCr6CkmLCTPJkjk7ouquuihNIuVA6PCwD+j72W\n4LjEKbjX2IrXDFrs409GFkruGNRmkZUgfm+hKa2Jl112maSu3lMpdgylwJU1r1FX8uhZy1njsF1X\nIlBbI4UCJatUu48Yl3/7t3+TNN0aZpsj9PNY6q/HCKKUjRVvG8U/16rYtfHErIm1GfupSCVJkiRJ\nkjQyNUXqj//4j+dUbMY7ccWkBE+/KFx9q7suNFzftJQAvEGUu9b36wtF7XjiZdRWoG8FJQplCjvl\n/L5PFl4NilTfWkd4WVHcRClexlkssVC1oEixPtTWtHk2c+211876WSJSJ4mZYo3AdlH/PauLz/nb\nBlRQ5ggK11hrNUoIc61v1lsyDszRsdRzp5SRP1btQ2oLesxgRCpSSZIkSZIkjUxNkdrYU8Djbn2/\n7e/18YIWK0Nrq9RmEkTgrdHvfesQ9YX6R2QF9lXi8DJbY8ocvBrspG+2Ip/Hm8b74ScKqWfERFml\nJS/K9wZk7vB3KqxT38rx9/yl+IDFAnbjtV/GgDg6xnLa2VyLFcbAY11QZT1jGlBHGTsHG26tkxXB\n3FssShS15Z4tYB+TilWr3atxrDjS2goCqUglSZIkSZI0sijqSI399LrYY36GMjQrEa8NJQ+lg2q0\ntXvP1TLUO/AaIEPthdgmrztVCzFGZH6RbYr3QvuimCWviUMWX9RPeFnsSI5KgBrA99mDz48z7T0s\n+4J9s6+W76c1BoxRVNX/2QZ97n3saw22iM2VlB/fDzL6/+8rY2WHbi5wD+Gegj3V7jhQomRvYynL\nXsesRCpSSZIkSZIkjSwKRQr6Zh89WxlaRdkVjRe84AWSupifsRWpF7/4xZLm7itV663RTrwd2oky\n1FeB5Lxcd6tigwKF18Lv2G/0ft3jRkqVzmkv/YgC5vvFLZa9EYdCvA3jy+9jZusxh1AnFyrLC093\noVXC0t6C2GRJzewb37jQbweYG/Tz2Ofve48aex/ZhQbFtjauF+WRNQ17IVasrzLl/e32R7wpawX9\nzdreqjSz60dtXGYqUkmSJEmSJI1MTZF6znOes+Hpte8+OI4/tU6qjhTH5f0v72NLMUtje6FDvWZi\nT3iKp9/GrBy9MShK9Jvv2F2Cz/uO6a3jjMJx9913N30fL4X4joceeqjqe3j9xGaB77/lcN2cZ8WK\nFZI6u+vbn16/bSh4rdjR0Ewlz7yZRN0ojk1fsIb0nVuuspYorQHYFmvGWPuGlio+R567q8asGdgk\n/Uc7+9ri2DC37rnnnlGPi3KJms8uCozn2LXZUBBdadlpp50kSffff/+o58P+uR7sevny5ZKk2267\nreo4ZJR7JjNrRF9FypU/V0w9vpR7G2ttqyJFBjT3mDe84Q3zfj4VqSRJkiRJkkampkj9yZ/8yZyd\nxFvrBW255ZaSuve4vB8F6uywLxZPzSgTeFeliH+edvFa+TznB/f4F1vWFDuk423S75OqpYPSxbjg\n/dTiXsXQOlwlUHpQ0vBKXBHDW3JvFPsiXoPjcN1eBwzvCXv0jBdYtmyZJGmXXXaZdVxXB0pK0557\n7imp86q9/bX7bmE3BxxwwKy/j5WlOXas3qbAllrjDrHpsWJxvO9aVUPi6VDzWtegSC2tXTP7MrQy\ntWfEOn0zdIF7Ee1iLpeUqNbaZ9jVvvvuK2ny1fxf85rXSJJWrlwpSbrxxhubzkv/u5I5qTWb87AW\n164ZL3nJSyR14/nrX/96k5/LOlJJkiRJkiQTZmqK1J/+6Z/OqViMh413SF2ciG222UaStPvuu0vq\nlANXPPDq8Co8G6gEXkz0fhfPHrweEwoEShbtob3uddJ+YmF+8YtfSOriD1ort/OenxgW+p/23Xnn\nnU3HLUHldPpxofd6K2WeuL3ssccekuYqPNgL4xh5v147h9/9+8B56B/3gnxvRNqDPfB5vCzaFcWk\nEc+Cwor3y/zj+6gRnJd56fuloTj6vDrwwANnXRfVxImBQrFjXOgXjzvxebrDDjvM6oeNFT7P1vH4\nSa+ZxneZi9Ec5zj0EUwqrhBc8aGdJU8ZRYrPP/XUU03nn1TWHf3pe5mVlCiuy/dnxTZLCopnzNb2\nJ2BHtD9SMqC0N1zE448/LqmbC8yBSdU7u+qqqyR1byvWr18vqV6dBvoFZYvvM04+t1kbPN6VWDDW\nBj7vcdT0L+PH8TxukzWY8fdnD5Qt2se6UJ2tWPWpJEmSJEmSZA5LnplCoYslS5ZoZmZmoU+bJEmS\nJEnSm5mZmVC5TEUqSZIkSZKkkanFSNUoUvvss4+kLkaIWJu+5yida9WqVZK62hF94X3sSSedVHU+\n3r8OrVDu10fsE8ctZT0SJ1J6zx+db2y8ujTnOffccyV1NUM8I4SYGa/n5O/diW/xGCVidT7wgQ/M\nOq/HSHm8De/ryawhVu7pp5+W1L1/x4shRoiYpfe///2SpHPOOWfWeWhPqb4UNW2iecH/aS/n+8Qn\nPiGpiwegn7Afj6ch9or4lNtvv32T52P8iOF75zvfKUn69Kc/PetzjKNXdH/00Uc3eVyH7EXfx+vD\nH/7whmsjHpCx4LO0jbkR1T2K6iLRV2eeeaakzla4djJ4OT5rlx+XNYCxIRYJW+H/9P2RRx4pSfr4\nxz8+6zzYTN+1ETw+lZgQruuzn/2spPo1IqJUEdzXligDlbi+oXvY9V3Ltt56a0ld+/v2R+l8pcr6\nzJUoS5L/s6a/4x3vkCRdfPHFkuLYKtYuxj26BxKzROa7x05xXZdeeqmkbr75OHlMk2eTsjYwf6L+\n4Hyf+tSnJHVrZW2mf9+dDEp2kopUkiRJkiRJI1Pda4/IeTxwz7hAWWn1tmrZf//9JXVP0Z4tSN2e\nqFpu3wrbeA0HH3ywJOnb3/72rP+TjUh/uBfndbIAhaS2Dlerl1nyKrw9pXagdHCd7iXghUVeKIoE\nXrVnlQGKi2daufIT7Uvm3iDXddNNN22yXZ6BhDqBWgJeobykREGpZk40b3xPwJJ3hkLB0qclAAAg\nAElEQVRV2l+M73sWpCuv9CP9UKtEAZ/n+xsrW2T30Dc+BvRxySYjpSqKkeA8nsEbHZc5xBzxvqXP\nvC89S612d/oIr0DtWVFj7d9YW2+K87sSxRijoPzwhz/sddxWfPcNMlGvueYaSePVRyopI6XrxC7d\nPkvtQ2EqZXWSxVeyB9oZZcSX6qKx5kb1t3wXEeYHn4/uaa7ojb2n5lQfpFj0onT40tYbdKIPbt/4\n+bvuuktS/Epj++23lxQ/SNWWUQCu67jjjpMkLV26VJL01a9+VdLc1wFOZGRD5fdaeD1RktdrZVY+\nFx2vNJ612w7Uvg7gwd4fpEitrb2uSI7347ZO6tp2OP7gz/n977w+4jpq7avkWJRSwjnvy1/+cknS\nrbfeKmnu/PRNozcGJ41r81dmfbeqKNG34KVvz+RwA49sAxvydH4gLIJXm7VlGtym+q5tEVGR2dL5\ngTnEjT96sPBXpox763XQHh7Mv/vd70pqL7Q5KXhA8YKUtD96VV1bFqP2nsqD29Dtp3iFyzxlvH1t\nYZ6UnEq+z+d5duDBbOh6kK/2kiRJkiRJGpmaIrXllltu8PwJhOtb/M29OV7tcNxaKH7mXgseeEke\nLb3yiPj6178uqXs6rr3+sWXJvoxdMcODsh28EBShyNtBQYy8rNpCoJF3gpeEXZQUIdQCxhVvyBWU\nsTYPrqV0Pl6BosQyPrQ/UkxRHQhyj3Av39lxxx0lSa961askdeN/xRVXzPoc9rCp+RepZyhHYwUt\nt1JaM2oVFO9DxuCwww6T1BVG/PKXvyxJuv766/s0c/CrPX/dPhTuFdHraG/vWOcF7Geh1mDeVqDE\n9WWs7clq1/yx1jJeXbP2oCh5O7D/vm8/GL+xxjEVqSRJkiRJkkampkj93//935yg5aHgffT1Qkht\n5WkVRQLv5r/+67/m/b6/t8Wj53hRmQPeZ/d9Pxs9fdcGd6OA8ZTfGmvjZRw4rm/yG3lFpKJTPoL3\n+B7/gH14IKyDEoWSsttuu0mSvvOd78z7PY/ZieIvuC6C46OAZnCF0Tc+7UsU5xCBYuObJEfzDfWA\nmCgPjC4penijpesjmN7LHwAbpv785z+ft73e7hpYG1A0KEmBbbVupTIp+q5lXBdjtXz5ckndRtWR\nIjW2cgRjH492YmOuKHDdHqRfW1Kklb7p9A5zges79NBDJUlnnXWWJOmnP/2pJOmiiy6SJN1xxx2b\nPA5Kq1O7ZkSMrZqzprz2ta+V1CXssKai2Lpy6/3beu8am1SkkiRJkiRJGpmaIvU///M/G1KFfSPI\nhd7cFuWAGBveS5PNV3rqdY85ep/rEMdRKlbnRFlPtU/nrTFd4BkPZBCRbYUXTGyZe098jzIPHtvm\n3iTUKjmUryhteg213hbjSXtdIcJu8Zp4v+/t7rsRKPT1KvvGc6CMEttFu2lvbbxFqdAsSiSxYrSP\neCXsozY2kTigGvgOcVwUGiTua7EpUn3BRn7wgx9I6sbQs7kc1NaS6jtpSsoRikU0hzwdHpsdK1Yo\ngnb7Zsq18Hl+UhKH60BpisaHucQaBH03ZY6Oix2NVW6CfqKswuZOKlJJkiRJkiSNTFWRAjx8vAkU\nl2233VZS99Q6VEmJKBUEJOYGb7X03r+vctD3uiZdhK72/CgyHqOCF4PCB17Gn/EeWutloSBjCG+q\nNrbNFceFztJzZbdU54lxaFWEa71/Pw9xIvQXf48KpKJ8tagojB0Zu5u7EuVQq47ri+pNAUrGtOsj\nlVRn5lBUo414QK6HtXjScw6lbOz++9a3vlX1OfrB5wjjXlLB+Rz3XIpBsx0T9jT2vefuu+8e9XjT\nIhWpJEmSJEmSRqZa2dzxLCee7smqw7taaI+ep3BiO6atnEw7jsGz/fD+iPki1gXvhiy3e++9V1I3\nfl4xm+/13XJnoegbc8R1LpbMEih5lUPbW4ptQnGif5hP2JHXdmIrmMir9q2l5oNYFuIgUSyGZjUt\nNLUZuig8ZMaSUexqKp+b1Nwrbbrr7YhABafW2AMPPCCpu3cwvpxvqNLYN1N2rC11+sK90mvUecxU\nhNfo4y0BqvC07jm1djNtUpFKkiRJkiRpZFEoUpHCg7dVm301Np49FNW9WWiirLaFAu8MLwEvjPEi\nFopNc/EKydLE20HZ8RixsSunj01przhYLEqUe6WluAk+77FO2D/XHylzpfHDfomtw+v1Pf/IAi3t\nVFA7Hhufm2NOq7J5LShnjmfMliB2KKqQPWklZSxFgfFiTWYcsSFsdixlzeP2JhWnOxTWXL831PYD\n18XaTv+i9E1rLWON8LcYi42iIvXWt75VL3rRizYUN5SkmZkZLV26VHvttZf22msvXXnllRv+d955\n52nHHXfUqlWrdNVVV02m1UmSJEmSJIuAoiv3lre8Re9973v193//9xv+tmTJEp100kk66aSTZn12\n3bp1+ta3vqV169bpySef1KGHHqoHHnig+f0qisa0IK6Ap+Fo/65SRszYLNQ+T8SERe/ZIyWRmjyM\nu+/EDmTBTZu+41cbdwAoJtgR/bpQvPCFL5z1e0mRirLuvNZNK08//bSkTiVxu+B37KaU2dNnZwA8\nd8YQZWexQWxOtHb2zagk62paEOcW7a/Yl/vuu2+Tf0dRGUu5wAZRR6N9JmsZWgG9L7X7t6JITvue\n67AWsSZMKwatRPEJ58ADD9xk2flNyfff+c53dMwxx2iLLbbQsmXLtMMOO+iWW24Zp6VJkiRJkiSL\njOYYqYsvvlhf/epXtc8+++jCCy/Un//5n+upp57S/vvvv+EzS5cu1ZNPPjlKQ2ug3lNrTJXHhuBF\noSRECkrJQx97n6dSBslY7/NrvZnoe+yV5nu9DYUszrFsy717FJxIgaxVWD0+AWXKFbCh1YdLeBwQ\nSitKzthVn1FTIpgvJXWiVCEdonHaFMwJFAG3pVa1bey96pjjUTzkYo8jBJQcbGIsRSqCtWeszG7m\nbN+4VCrnu8Iz9lxD3ea6h8bPcr1kAQ7dXSTa3aGWhdrdJKIUnwlN79ze9a53af369brjjjv04he/\nWCeffHL42cWayp4kSZIkSTKUJkUKL0OSjjvuOB1++OGSfufdbfwO+Yknntjg8c3HH/zBH+gP/uAP\nNngRPMXyNIgyRCYBMRbOUIVixYoVkroMBbzT0sNgKWZmaA2Ovt7uYskswWsuZUahWEUKhMcVTNqr\nxWuMau5EMTlRHIh7xz6Ori7U1giqxb1Bfh/LO3Yl9wUveMEox50k7JZAjTNUyGhtiSAWh5+1c7R2\nTk+7vhXqZak2mMPazff7qIZDGLvGIGtp33pU3Duive/Gwt9y9B0nB4WrVUHyXQhajxOtvUNh3jEu\npaxX1rRrr7123s813eE3Xmy+/e1vb8joO+KII/TNb35Tv/3tb7V+/Xo9+OCD2nfffYvHm3aBySRJ\nkiRJko157nOfq+c+97k6+OCD5/1cUZE65phjdP311+vXv/61ttlmG33sYx/TddddpzvuuENLlizR\n8uXL9fnPf16StHr1ah111FFavXq1nvOc5+iSSy6pegJ3L4I92njfy1N3yVssKTEoXbwH5v0rtVp4\n6OMp9eGHH571uQj3KvHIUeNad7jGm6Mf8HJalQR/n75YIH6CfnSb8QyXSWdulBQ07AUVg3HBG0Nh\nirxwP74rbiR3YM/E/mG//MROOS+KLPW6PFsQsKfttttOUufFYq94bb/85S83+T2Uw6222mrW+bne\njRXr+eBzeJ8PPvigpHr7jvZ6nA+PT6MydiuMfdQGxoC24gnzs6RITbtmHGNbq3Rgy14rLBpTr8Q9\nFiWVG1hrmFNRfGmtys/3IyWF4zD+rBWt6vPYMVfEAaOw9n0bUtseYsi4ft+doFaJiuKPuXci0tB+\nzlcbY1irpBYfpL7xjW/M+dtb3/rW8POnnXaaTjvttKqTJ0mSJEmSbM4seWYK6R9LlizRzMzMQp82\nSZIkSZKkNzMzM6GSlcFJSZIkSZIkjUxtr70aRSqK8YBSRgnnmLT6xfvk008/fdb5SvWIxj5f32yv\n2vpWfr5zzz1XUherxDgR20PcSG0dIOIKiGvgffaJJ54oSfrCF74w6zyPP/74rO/zvp3+vueee2b9\nn2w6Yo1uvvnmTbaDfiTmj/f9vgfcNttss8nzEO9BjBzfJy4CO91hhx0kSUcddZQk6bLLLpPUZfsR\nrxH1H+NGzJtnqxJ7RH+SSfuhD31o1nVyHq6HmKja/dsA++B8XPcJJ5ww63wloj3+apmZmdHHP/5x\nSfV76dGHeJq+lnAcbI9rfc973iNJ+tSnPiVpbowNx/GsO47DNXLNjBnt4HPErJxyyimStOH6onpX\nURxoXxizT37yk5Jim8B2oorfHjNFf3D9vrZ85jOfkRSP29577y2p2zPQY2u8Xdg0/UX/EH7yiU98\nYtb/x9oT0KE/yf569NFHJc3dO5C1gTntGbasdcuXL5fUXR/1qogBfO973zvrvBH0J+fxXQRWr14t\nqYu7jOyA83z5y1+edTzufYwzayH3dOyUtYPvcS/Bfv2e5tfHPCKmq+8aBtgHcE95+9vfPu/3UpFK\nkiRJkiRpZGqKlNQ9paKk4A3w9FmqCTJWpfChRF7MtttuK6lTKO6//35JXXbUAQccIEm6/PLL5z1+\nSZkDnsbdC911110lzfU2avvPFQK8TLwH2uXKSy18D3vwasDYR7QnHt4ZWWiuFL3iFa+Q1HnFt912\nm6RY+SBjJMrUWbdu3Sb/jhdU8ob8+/Qf41YqB8K4ReNH/5f29WKeoYJ4NmAttJfj9N1HDKUQFeH6\n66/v9f2NoS9d0fD6NlDKYEUdpa+8ajtzjvP5/pIR9BXtpU4RY4BS5h4ya0G05qxcuVJSNzc9K7Fv\nfZ6SLZT2nsMWIpvw6/DrdVAIo9p91AWj/7x9nvHLOExKiXJQGKO1kjkd9ReqdbQnXvS9KBuRTHhK\nGHH8NWvWSOrWpGjN83FAKfR7lfe7v03ytzYol9gD7Y7sg7W8VYmK2llrF6lIJUmSJEmSNDJVRcoV\ngb61NMaq/DwU32mcp+af/vSnm/z8kUceKUn67Gc/K6mLj+B3h6d7Yn2ip273DlBw8MYjr7yE93Pk\nxUeKmStq9Jc/7ePNOLQ3Umrw5qP+Jt4Abw+7i/a4K3khePXEM6A01saE+Tj5PBi6v1Sp5gvn5zqw\nPz9vbcyS1+Lx/ivtJcjemH2VrE0RVYkn1qHW9qmRhefPcakhBihS/L+kRNHXeP7eV3jEzF33kEt9\nhNJELT6nVCmduQqluMtSvSaUB9YerwWIigwlNZv4xqifUV5qK9TX2hyxRNEaUwv9H9UdQ3GL4ldb\n73lR7T36iTnKGo0qXIrv9fZgP9yriOHyecnaE423K7al64Do3lJag6J6WbU1F1ORSpIkSZIkaWSq\nihRPs5NWlqKn1LFwj52n2+jpmQrOxEahaJQo7THnigJP8zxVt74/dsUtglgwbwdewPve9z5Jnbd8\nySWXSOoyQiLwJqLr9yw+h5goJ/JOuI7ofHj9eLOHHHKIJOlf//Vf520HuL14O0rzoaQsomYQIxWd\nn/nAePF34oDIEMJLw64feuihWcfj/5EiWVuqru8+d/PhMUb0Bb+XYmNQNrB9FC2yiYCx8jHlPMuW\nLZPUxSqV9mxDiYjiHUtw/Og8pTXQx7A0dsR7lhSp6P/+95LiUFL8yIYbG8Z9//33l9SN749+9KNe\nx2HtiMahFP/YSmkc++4lGB2XtYC57HGa9FuUbekwr/rGCXv/0g6UMu4prKXRPbi0jy6kIpUkSZIk\nSdLIVBWphWLSGRn+VF7KPLnqqqskST/84Q8ljbdjOUoE7+Hx7vrGnnAc30OuBN6i7+mHV3HNNddI\nkv76r/961vEd32sPVaCkqLXuVO+UYoIY77vuumvWz1ZQgNw7j7xv+jfqD+JWiMfxvR7pT+zU42Dw\n3vg+3mDktaLekD1ZGys2STy2wT1iFAZsxT9PvJ7Hqtx3332SpEMPPVRSHOvCHOo791C+gCyvaVFS\nMkpzEluM4h+937G9SJFhLWIcmTv0c0m1r1XXHTKBV6xYISnOYnM805jzR7FqHqPWF9ZAKMUGTRpf\nw4iNYl65uh3BPCuNX9Sv9APzCTsrKZiZtZckSZIkSTJhnhWK1KTxp31XVGq/1wrvfT22xr1v3s+X\nMnd46kfRqn0q53x4D5GXT+ZL9P7Z/06cSCk+Ai+D62vt36G1SCKiDChUCPqL//v1Ylclr5V+oKaO\nqyYch89xPH7Sb3j7qApA/5LRRjv5nqsqiwEUC9TGUgyKxz5Fn/daXIAN0feoe6XYENRpr18FUT2g\nWrCJseLRovhElBiyHL2GXUQpJoy1ibpdpcr1Tqv6T/YZ2ZylbDbmmCtSKEZRHS/W0NoMa19T3F6m\npUSxlmD/zB/6EUWKeNSS8sr3o7jPEt6PrTUUI1KRSpIkSZIkaWSzUKRa6x9Ni9J7esD7QIHhKZ2n\n9lrwSlAQeLp3b6j2/TteW9+qv6V4ELL1UNCIN/Dqw97ukoIGxOZ4Ndy+1H6P8cLLLGW+RNl4jD/j\nGNXLYjxKWYr0J+qDe1+cj1grFCdX8hgHPof9EFfk1ZVRW1BfpkGk+pU8S5Qe+saz8yIPNlJQmDM+\nl1FUyHbzStBetyo6biu18Y61eD8TA/O6171OUqfMlOpNRcdzSjXSSni/lqr/A/1W2/+cx9vr6q6D\nPdXe67y/at+GTBrsOcrCbM1OHKPWXB/IXC6RilSSJEmSJEkji1qRYudpvBzqDeE9ogAMrQQ9LfDw\nUQJaa4fgxbJnGV4W3jneFIpQKa4gqlZbAm+T8XHI0GDvO/ZWc9yLqVUkWxWoVujPWsUswvfdKsVx\nlOqi0X9k67kag9qCgonihP15tifXxzigKDq0v7b2yiRACeibrcQYsNYQ51Wak309ZMaMOetjzf+j\nPhxac2/MWl0bwxqx3377SeraT7/WKlIlhipSjtf8i5RL1p6+bwvcPkoZ3ajbfK9UWZvj0f6hWX99\nKSlgrfbKGkcMVd9+7wvKk6vstQpuKlJJkiRJkiSNLGpFCqWJp27iCviJN1GbEbLY4Gm7pGhE+y45\nXnEbLwVvivOVMn/ci6r1KnwPO4f2uALiuNe5WN77O8TCRQpcLSiHXGdJcUJJQlmMVBfa5f1H7BNq\nCPMMb5bfqbHCdVKlOwI7cfuZRoyjx8nVVkbm86iNjAFquH+/NqvHKSlZKGNj15Eaq2adg43xlgA1\nFNWZPfUmVXm8FeYO9sFcjOZe36w6t49S5XbmdO2a58db6DjikuLbam/e/668RXi/RRn1/J01EpUd\nxZR1IOtIJUmSJEmSTJhFrUjxNI8HTdwCtUn8febmRm1sV23slHsj9B9P2XhdfWOJ+n6+5IVQg6U2\n3qE1dgzvEq9j7IwPvJqhcSv0A/ZQUriiGjROVHUbr47sPvoFJYx2EE+Dt4ZKEmWl0g8eg0fM3qTr\nc0ldLA6xJihJrCGlqvcoKMT63HvvvZLmKhEwqTo9JXW3Fdo/djwhnjtV/hmTVatWSZJ22GEHSdL1\n118/6nkjahVIYrlof2kusxYxR/vaNHOH87pihX1Sr6pvTFmtYjOt+lJ9ITYKFT4aH9aYaO8/YH7T\nr9tuu62k+N6YdaSSJEmSJEkmzKJWpIjp2WuvvSR1T6M777yzpPrYqKHVgEu0ZimNnW0Yvc/lqbxU\njTeir/dS2iMOvH5ULcTIPfnkk5v8P3aD98F5x/aG8R6HKlJeN2osO0W58vHDDrA/6j8Ri+eKEl49\nas+DDz64yfNFak9fr53sU9pP5hLXMV81Z1Q1V45qx+jlL3+5pE5JufPOOyXFHmttDEXffSCjelhD\nqVUzW2GMUC+JjVrobLLa2Bw+V9vPY2XoRvuRonxSA46acbVrcFSDjrnNvZDYO+yS629V/6E2lqkv\nHC+61/rbBt+Tj3Zhl/xkvrPbhlP71iQVqSRJkiRJkkYWtSLF0yDeJb8Tt3DNNddUHQeviHgJjocX\nUKrV4fA+lve2pf1/ovfhC1X3CC94aFXkvtA/K1eulCTttttukrrK13j7fYmUKIf367Wf78tYakGr\nMleitho3dhHVF8NO6ce+ClNtbBpKI/FJjB+xkOvWrZM0f7/7Hm++tx1tdw//gAMOkNTZKLEsZJ/5\nHniA0sLc5icKALaPSoqKzudoH/+n/SgLk1aQJgW2hAr8yCOPVH1vUopGRN+1f6zzRWsxihcV78eK\nZYoqidcqpLWMPW7cW5l/tfcw/xzzFEWO+QnR261ahS4VqSRJkiRJkkYWtSJ13333zfrZCpkKZP0R\n6+ExGLXgZUaKlu/fFEX+89RMfR48cJ6aeRpurVUDXo9rKKUdu1EA8d5RXFADaIerBVTc5r19K0Nr\n76CIUPOGmKLacUBVoBYRCgrjHdU24brpB8YfbyyyU4+rAOIEUFWGUtpDErtvtTPm47XXXiupLa6G\nvr3lllskzc168rWAPqfN9BXqF1lokQrGWPN9zsdYczxsiTWBSsqsRYwV30eZ8L4cq0L4pMFmmfuR\n8uFjXFI0+saatYKd8LNVffaYnigO0eEewFrrGdjEf7JGcDzfp3ShGTsrkOuOlKgoltCzXbFH5iMx\nUvRnpLLT/yVSkUqSJEmSJGlkyTNTKCixZMkSzczMLPRpkyRJkiRJejMzMxMqbalIJUmSJEmSNDK1\nGKmPf/zjE88iQ/Xqq361Zo5wns9//vOSpN13311SFw9xzz33SJJ+/vOfS+re6+69996zficmjPfr\nZBIRe3T//fdLkl7/+tfPOu+k4TznnXeepK5ydimWq7Y/iX8gjuDtb3/7rPNOGrcX4laIO/AYpZ12\n2kmS9NBDD0nqrp8YJ7wXst74yThznosvvnjW8YmD4Xdin4ibieIrqE1D3AkxS8QLvO1tb5t13knj\n/UlcArFjVE5nHcDeuQ7iF7zGDvOJ8aFfjz76aF100UWSujGgL8j8ow85V1SdntgIzkHMCm0+7bTT\nZl0btss1MnbRWGHjxC9yfjKKOS/tP/744yVJn/vc52b1AbZCVhx73QHHIZbEM4WpOE57iGs8+uij\nZ12fQ+atxzkydtT64/9R1h5z65RTTpEkffGLX5TUxY3S7h/96Eeb/D7jQj945XBqkrFW8X/Wlksv\nvVTS3LnHeGBHDz/8sKQu1oY5RfsYN85DPzN36c+1a9dKimN/1qxZI6mzU89spj2MU5QRy7idc845\ns9rtRBnlEXye/sG+fD5MGs5zySWXSOrmmfcn9k173Q6JNeN7xClH54tIRSpJkiRJkqSRqSlS8+0h\nteeee0qS7rjjjk3+/9BDD5XUeYcoPTA0Ow0vyRUUvCQycHga96dgvNk99thDUudFkBngNT3IMnMv\nBe+WekyrV6+WNNfrXGjw4vFi8coi77tW2RurUvhYMA5RthzKoBP1Q2TzeLHYAf2FV//Sl75UknTj\njTdu8vivfe1rJXVKJzVoUKTYK68vkX33BfugH5mfnl2J4kpGHZ+nP/DS+R7H3XhPQZQBV5o8Oyyq\nVM5c9UxXbD2yTdqCohHtIsDxSvVpUHw8C8vr6eBBR550aexQU10BKuFKFLALBf140003zXsc3w+S\nvc8OPPBASd043nrrrZLmKjCMn++7yjhh+zvuuKOkrgYh+Pe8H6PrpF2s2axdnk3oa0epUjZzN9pH\nlrmPnZVqtJUqvPfNCPfPj70XZES0dyKZvtF1Mk6RIsq9upSRXCIVqSRJkiRJkkampkjNp1JEShTg\nzUVP97X7LEUQm8H7dzxkrwkSeXt4CexJhneJJ+0xNRF4H3gneEeTqtRdC2PH9fi+ZniZtfsUOUP3\ne1qsRBkfPp54tSifeEv8nf49+OCDJXX7wl155ZWzjoMd9q2OjSqAIkrMXut40l7sPfKi2XkAL9f3\nZnT1CNVh4+PRx7SVz7iK58dirnNujslPYqCiMURtZk64DaMmEzNDzA3KFZ/nfFEldRQtFDPWoNa9\n+VBsmMNDK31fd911vT7vtkn76U9UyNaK2cwdFLJS/SYHVZfxd6UIJQ/FhLUd+tbEY85HMNexi2nX\nE1uo3Tmie3rpHso8i2A+D31mSEUqSZIkSZKkkUVd2TyC9+7R0/5YMTZ4j7yPrvXIecr1GBDPQqo9\nDvD0P7TS+VjQH94v//iP/yhJ2mqrrSRJX/rSlyTV77f1bMMr4bsqElVxJkZw6I70DjF5ZIahfLFD\nOvun1YLqgTLFddFun6945fQLqgvxQr6v3cbKlceZ1eKZlU5pP0TaGH0OpYI+JZsITxhFir4gVgal\nzM+DkoVC1epR8z2UmoVSGCJoD0oSNoLi1hrTg831VXBcmXTIQOU8jD/9OHZmOnNo2krUYoG1JYpl\nc8jiRP1mDY2+X9pHF1KRSpIkSZIkaWSzVKQgetrHox6Kv4fGKyrVRcKrJMYEr9JjrErgpXI+FDjf\nU60WPHcyWOgn9jarhXgA+oN+4PpQVF73utdJklasWCGpq6USsfXWW0tq967JwCBz6Oqrr246zkJD\nDB5eEeNM1lyUpVmKJaQ/iA+phSxYvHBipogPYa/JBx54oOp4nB9l17NVo32uXKHzelNk0m18ffwP\n2x5brWuFOcJeX4xxFL/G5z0rCqWDNcFVvr748cZaO1thbUGNxzZas8PoR2yPzNCxIAuQGDNX9Fr2\ni5S69lKr7vHHH5dUnvMOazT3oFY76YtnmxLTB2Qik6HP24t//ud/ljRXIYoyiGuVZxTgt771rZK6\nNY74z0iR4rwlUpFKkiRJkiRpZGqK1BZbbBEqSlR3/cu//EtJXVzBZZddJkm67bbbJHVP1zxt4w2M\n9dRN+/AOePpFQYiUArxivEWeamvftzoeW9XqNZKRg+LT6q27YoS3SPv+/d//XVIX20N/8X46ipVC\nAURp6AvKxEEHHSSp6//vf//7TcdbKFAw3ZuttePIW2NcWhU+xgn7Y3zIQKuF757PlOwAACAASURB\nVBE/QjyOe6mAWoPdEA+EPXGdXN/GSq/XOGPODo39Ke1qX1shmjnINUV9AF7XyePFUJJ8DayF8zN3\nPFNyoaEfWSu5flekandLwNZQZ8eue0T/1SoXtTDnbr755kHH2VRm60KAEhfFd3IPO/zwwyV1andU\nAzJ6VqiNFePtEHGeKHul+Re130lFKkmSJEmSpJGpKVLzZTP4flh4qGRI+Pto98KG1oQAvFrOT72f\nyCsFvCm+z3W0ZnCQWUA8xdCsvb7v2Ut4f5ChxM/aGi7EzLR6jR67RUYHdjPtivCRquHKIF5Wrb34\nPmOAt1VbrToCu2Pe9d05gJgo4l1QETy+Z99995XUxfBRAwZFC++QfkSF2Xg+8FnmzFgwpyMPtnZO\n9q1j5EoCihFzirFFEYkqbJfwfRmnTRQHR+Xx2tgYr0PVN5szqqgNzAn2bItie55tlOwPtZsMb1cO\nx4ZnBOI6vd7XUFKRSpIkSZIkaWRRZu2xh9mPf/xjSV2EPe+L8dJaMyJKcFze4+JtepZaBJ62KwF4\ne143qARxFSgOrRWmFwqPF+nrhZcUvwjeg6MgkqmBPU1bkUJBcS/VVQ7iGkpxORDt68ZxvRZRLXiH\nKIWoA56RUwIVoeTVoyBiN6gJrjjTH2PHRM4H11yKqZjUecFVQfqCMWbN4v+om/Q96qVnIrfGJY4N\n14cay1zAFqM9BUtwHLfB0n6SrNWoqV7zjP6P1vbFkjW62CBe0tc2xr815i8CJQr7H5tUpJIkSZIk\nSRpZlIoUist//Md/SJrrmaMY4aWMHQ/h1YZRoPAOS4oQT9N4eyg0PA23xqxsLu/b8YbxcvvurN0a\nA4ZagLfNeJH9NW24Lvf+3Uvue/0lxaq1P+k3FFayZ/sqUqWdBvD2iUFcunSppLLixN8XQqHtq6qO\nhauJrCFeZ4k+QJ2MatahmDAm/F7aa2+hFDkUNcaedrYqCbSbOEl/m1CKCSspSvyfe5Lb4tiVzX9f\nKO1ZOXaF/VKF+qGkIpUkSZIkSdLIolSkHH96dcVoKNHO6ez5hVezfPlySZ2HHsHTNEoMCoTXlaqF\n73Ec9glarPDUj4LRN9uw1etFiULZQLEcquQRc0X7o0rcJfBOI69/UrTuPck4ohL0zXjqe37UFyrc\nM7+pMROxEHtPTmt/S1dEfLd6bIo5U/LkXb1323aPPVLhh8awMCe9Jh5rHYoaqqjHqdbaFJ+LsvZa\nbRr8OlrjO5PfUZrrY7HllltKqt+jr0QqUkmSJEmSJI1sFoqUw/v8sTIiSu/J8fbwimr3zCNegads\n4iz67tyNN0jW2bp16yRJRxxxRK/jAE/jXDc1Wth3aCx42u/rzbd6idQuQUmk36P9zPoed6wYtb5x\nE0P3jWtVbrF7VAsU0do99qC26jfqBnE77PHHPmPR9W9OmVHE+tSq6h4/56omfdY6xk888cSsn762\noUjxd2yC/T7pezI7a0FZ8nhRrsPjS2lfX3WVtfOKK66Q1PXnbrvt1us4EcwJ9lBM+rFs2TJJ3bi2\njnMt3Ov63oNLpCKVJEmSJEnSyGapSLXGqETg/ZQ8ZryZ0tMstU54qsarG/qUTcwPilwrKFJ33323\npPHeE0fnIWZp0hksKE+33HKLpM4bHVrZG6aVgYMS1jc+BGoVVId4Feye+I++yiXtd0XX5xsV9zkv\ndaVKcTjTil9qgba2xhYxlyYVZ+d9ydix5vJ/VN9WuH6vBcheaFwfc3hoLBNV8seGNWHsuNVaFXdz\nZ6gd9WVS2bepSCVJkiRJkjSyKBSpVk8b8GB539rXW6tVGmr358HbIsaDGBe8O+os8V498k6p1P3K\nV75SkrTHHntIiuMRSlV6yaIjvgEvjf5vVfp8/BgPMntcmZo0Q2OiFhqvIA4oMuyvRv8Ss1WaL/T/\nzjvv3NQu4hX8eH3xzLDSfOO8jONC2c1CMDSeixgPFAt+sqbQZ7X1clijItV87Bp9DvFwgGKArQ1V\ngbE9GDv2hrcTffefLEHsFeM4VJGrhf7qu1fjtCjthbhQpCKVJEmSJEnSyJJnplD4YsmSJZqZmVno\n0yZJkiRJkvRmZmYmrBOWilSSJEmSJEkjU4uROvvss8OMBN4PU9U2iuwvvR9F9frkJz8pqYshirLu\nDjroIEnS6tWrJUk333yzJOmuu+6SJO2yyy6SulgPYlqoF3X66afPOu+k4Tx9z0eMFO0vvX8nDuMj\nH/mIJGnt2rWzvkcWIZ+L+pf9rqgVw/t/vs94M/5HHnmkpN/ZilTOYNlqq60kzd0hnhgu4kD4nfMT\np3HSSSdJ6vqTdvl1ErfhMWXEOhHn4fEYxI5xvve9732SpLPOOktSFxdSim/hONEec/QDsYJc7wc/\n+MFZ1zcpmJdnnHGGpC7Lj6w8vDquY/fdd5fU1acic4uaS8QYEoeCfTGfjznmGEm/G1+/tu22205S\nt0Zg8953xB7xd7cNxoZ4tbe85S2S5vZlKdvq4IMPltTVY2KNcZgD2Nqpp54qSfriF78oqYsjo130\nEWuRH4f4SPoBG6UPaTf9QJ9Oey1jTcCWPWaK8WWOekwXNsbcJYbszDPPlNTdG5jL9Ce2xpoRVdzm\nuL4WEN9IzNGxxx67yevDnjhf7b6kvv+s4/0ZxSHvuuuukro12e+1tN/jaH0covFjLaCdPi+wN+6t\nN9xwwybP7xm8xx13nKRu7eS6OF5rdp6PP+095ZRT5v1eKlJJkiRJkiSNTE2Ret7znhdm1/F0jDcY\nPWXWhnfV1hG6/vrrJXXKCU+leHF4M7SHv+PtbS5EikmEV/R2b55+KCkp1KtyBRGFAQXCx6u2loor\nUcDx8OJoP144mUuOe5ml+mGl6s6c1/cXYxxqM60iJQq8H0rZdn1r1pQ+7+NLvbKo/tSdd965yb/j\nnZe89EsvvVSSdPLJJ8/5Hxl/payeyINF6WBsUHiANYLPRX2ydOlSSdLRRx8tSbr88svnbQ/n8+NF\nmbWuRAG29Ytf/GJWOyM1v2RbkwYFwtcmV0AYBxSVH/zgB5s8HmsXCqDvk+r9ieLBvYWMY1ekOK7X\nwQJstlTDjXZFx3FoP0oW7eIeFGXbRWs9ClS09vD24v77769qH6DUcV3Yldsz8y6qzI8iGO33SX/Q\nj6x12Effiv/c26G2AnoqUkmSJEmSJI1MTZGqqfvAUz31k3hP6jFKtU+NpT314Bvf+IYkac2aNbP+\nzlPv8uXLJXV1oGqPu1jo63X65/39PN5MaS+60pjjVY9VMwW1gHZGNXxKdadcdaiFfsI7Qhkba8++\nWsauDN63Fs+kqgnDfF7n0Poy3jeuOnocXQRq7JVXXilJ+uEPf9jrvICHTp9Sa452EaeHYoGSQ804\nvu/txUbdIx8KigTXU3qLQLuYO9Gc5bgPPvjgvMdjbeprB7TX1WPwPQ8jSmsLx+m7V6GvIVEMV4mS\nYtNXiaJd22yzjaROIXrkkUckxWsR8ZMO6jpvifxe62sR9tM67/ke41K75qcilSRJkiRJ0sjUFKk+\nVbSJoSCGhqykWiUKRaGvZ0wGAd4aXgpeBt5Sa7VispXuueeeWcdf7OBluFI1dE87vKPW6sN423hD\nKFy00+MsiF8oeWVcF95pabzxlmkPqsW0qu/2VUyJU/nc5z4nSbrxxhsldd7/NddcM+/3fS/ISe3k\nDltvvfVEj78xHktSe214tldcccW8n4uywMAVEtY0jo9tRhm5tN+VIeZAbUxJ7W4U2HxtHJ6vKfSH\nKxms/aV7QGuZRGJvuOeUsuQivHJ7dJ7atwR8blK7N7Sq74A9YEf019B7A7Ffvi+s2xP90roLg8+7\n2vmdilSSJEmSJEkji2KvvVrwPnjKrX16xptp9Yx5uqa+FApVlElQC/WpyAzxp+3Fimei0A+uuJDx\n4bV5St6XZ0bVwp5yb3jDGyR1tXZuv/12SdItt9wy6/N4M1GmC94w7S21G6+LWD7UAffeFzozqq8X\nTX8Qt7By5UpJXW2hm266SVKsmlDbB0pe+VCGbM6wYsWKWT+JCXnssceqvl+r4DBHiGny46N8uG34\nXCDWBPzzpfaMtZEFx4n2iXSYA31V99b9P6FV5UeBYm1nfPq+1fBsPF8LSvekkmLVN+O2BG97Hn/8\n8abve/wy9zT60dvrylEUQ8Va6v3AvEKJ4vio6rV1uYDxYt65uh6RilSSJEmSJEkjm5UiBcS67LXX\nXpLmVgd2L2CsLDBqbuy9996Sxsu+2lyUKPCnfrwLjx8hdgWlCEXxe9/73rzHr62p4qxbt05Sp6Bw\nHGLQ+kIGE5k5XCfZm4D3QnYp3jr1k2gHCuqkY4bG4sILL5z1+6pVqyTNjTVzGAfw2j1j4+PRB5So\nV7/61ZI6G61VpLwyeLTWlOIpa+M9o+O75zxUySmBYkP9okiR2nPPPSV19bz6KgRObWzWUFD2uK7W\nzFMfB7IpPbYtYqeddpr1+7333itpmM1vCtbMl73sZZI6+6edfddQ1kC/PtRpKtbzuaeeemre46E8\n+XWj/jMPsYvWGCnWapTWUh0wSEUqSZIkSZKkkc1akYqykTxWx5WjVoWKp2EUJI5b8tB/36AfSrVh\n8Grw7mpjg1q9LRQx6oANBa8GRSWyG1QNqizTft7bc93YS5TBMjRjZtJElclLRFW3x6KljhvV7PHE\niQl56KGHeh0HD7hkI7VKimeG1sZgYXN4+rQjWpuiDFQ8/BJcB2sAe6WhPBELhqrLeYYqUtFaM6m1\nuFYpjPDx7ru2kYFM7BKK1Ngwh7zSOfbUF5QnX/N5i0Glf+Joqcl42223bfJ4rI0oRcC8o3+492Nv\npRgzYt+IC0WB4zz8v0QqUkmSJEmSJI1MTZH6oz/6o2LF5Qi8jlrvhqdZGOrxkw3Gcf34v+/U1kOq\n3SvNGfv9f1+8ZkypFgreDu/X8baoJk39pShTBfvB+yvFC/RlaA2XoUx6PFviV1BeUFDw9PvW52Et\nKa0pjDmxTJFK17fWmGdB4YmXlJlI6eob94ltEyeIZ4+Sg7LRGrPiRIpUlLXWt06Tw3iU6ntF1Gal\nRbBH4tVXXy1prp211rcC1iyUQ+YDcaE77rhj03G5TlfZqcCOYkXFcuZjpJT6HpEQjTtrHtfl84Pj\nkWGMHbNWcc+iFmCJVKSSJEmSJEkamZoiNYaXXPv+Gq8AL80j8fvW4KDt0Q7tyTCmHSOEF+XeT5TB\nQe0hMmzIVsQrw14ibxT7nJQdTTtLsFV5rqVlLSEbC8WB2JBJgWdL5iO7Jjie+epgK8wRYk5QEGqV\nF2wUBQ5PvK+6h21Ftdjw7MlWmxSRjQ+1fY8H7RuL1Xp+1h7icX1cUb1Rr1Gk+tZsw/6ffvppSZ0C\nNrTfUNe5DuwKRc/3dsSut99+e0ndbibA/PG1hDWZ47r6jWLHPKF/UKb4vn8Pxbb2bUoqUkmSJEmS\nJI1MTZEqeV411O6YjcLAU6tX5cUrq33PTCVynqpra85EDH3P/fsGmRxjw/twvK9I+cKLwvshnoVM\npAiqTuM94f0sW7ZM0txqvzB0z8bFzqSzWvE2+4ACFXm6Ea1V2hl7drNvhZgf1gpXSkrXwedY+4j3\nhLH3g6R/ae9YlbhRMErXy1xuVblRgojdQamptenWel6MA0qjgyLl49da29DvYdyT+o4TyiPZbvQ7\ndsDxuHeiJqNgUZfMY9u4R/tbAvqJ9jMu3PP5P+OG3bPWEsfq0E7uFSVSkUqSJEmSJGlkqjFSpfew\nY3kvPF17zQieVvFuauMLUNN4+h36PjmVqNm0xlN43Se8uTVr1kiqVxDxIj0bs+RdEiuFt8j7dewY\ne6vNBEnqqInr8awrxhabqVXI/XOMJWtHaS2I6lSxNqF4RZ4wa47vXYZnT90hPG6PI8Wjj5SOvuph\nKWaIv6PosBZHbxNqY5BqFUTWgtbMUdZmzldb6boWYuaiGm3ebsaPWCYnilWD0j2VtwEcp6+ixnFR\n8b2WHlApHXsgvjTajxTlzfeexI7cXiIFks9z3rHeAqQilSRJkiRJ0sjUFKktttii6FXgxeA91So3\n7jVECkTklZWofW/6+wpeQW3V5b60KjbYE14osWy082c/+1nVcbA7aqjgPeGt4WV5zaH99ttPUlfl\nmfageqAOTLquknudXg14odlnn30kxVWLS6BQRvO1JkbKPWs8WBSQVlWYtrWOKbbGHmeomhHU3eF8\neNS0H9UzymhGUYuyFPvWxCvFVHE+fpYUjtYK5Sg1vttC37pgEZPKPHUFE0WS7DXqm/G5M844Q5L0\nn//5n5K6uk+1UAE8+h7nZxx8D8cSrLWlewPj45XDS/bkx22NOfQagUNJRSpJkiRJkqSRqSlSNR4H\nniZPjbUK0qTrOpH9xfvW1vfm1NDwGA8UhNqsxFaIq8ALwRsoeV9kr7k3hbfD9fjxfT8yvCIyNfAq\n3Svuu+M7ihleO3Epte/78fLJwsObRomMjkNcwK233ippbq0ir/kyKciA4TpqM3lcyaLa8ND90Xbb\nbTdJ0s9//nNJc+0LleXVr361pM6+GDf2xVu3bp0k6fbbb5fUKX0bV1/2a/XsHVctUT+xPWzVs5b4\nv3vMqJSMdRSbwXG9QjNrHBWfS568x/8xxqwlzJG+yg7t4Di11Noy7S2tzbXKHvtaomgQQ9O3HhgZ\ntcQEsdbQz9TbYk1h7rPWsUcjdY/8+kpz74EHHpj1O3OQe8Ahhxwiqetn7K01G7CkYHFcriOKxRoK\n/ed1oLgXbG772KYilSRJkiRJ0siSZ6aQMrZkyRLNzMws9GmTJEmSJEl6MzMzEyqwqUglSZIkSZI0\nMrUYqXPOOWdDvEHfGBgnqnaK6vWNb3xD0twIf+ILyJwhQ4FYlug8fB7IkDnzzDNnnRd8B2yyybzC\nteMVz71mzOmnny5JOuussyR1sSK8dyfegc8Tw7PDDjtI6uIDiOmh36hFw3trMl8++MEPSpIuvPBC\nSV2Miu9kv+eee0qS7rrrLklzx7V2vOlHfo5VV6x0vi984QuSulgj+sV3RscOiKPAvrAT4jeI+SH+\ngziYf/iHf5DU9Sf9TTxNFAfB+aJ4B85HfAHjdPzxx8+6zknDeS699FJJXW2YKNaROBRipsi2Ja6J\n+UnsFv9nfp166qm64IILZv2NvvbYJfrE48BoAz+Zo8TJMSeZ65/4xCckdX2NzXhm77777iupi8Uh\nloc+wWaIgSFminbTl//yL/8iqRt7j/0itobrYu76WkOtM9rDT7IQTzzxREnS+eefP+v6+8J1MecZ\nQ/qL8eH61q5dK6lbu1ij+RxrE2sB101/+Zrie7kxfu94xztmnXfS+Fq22M9H7GApZo1Ysg9/+MOS\npEsuuURSN15RHSxi8vpmVRI75fc+7Ar7pV1R3TbmN/aAPdIu5g8Z26V+TEUqSZIkSZKkkakpUhtn\nv+BdtCpSHAulxb0vfscT5qmW8/H3UlYg5+m7Q7pnq5Wy/Ki7Q9YYT+0oMZ7Vhgf/ile8QlLnpXm9\nJxQOvGCUNK6b6rIHHHCApE5puvvuu2edr7Q33B133DHv9UXjTHYWmTDOpLMxgf7CS2G83D64fq9K\njNfFuKFUYXe+XxvePv1SysgpZdG5l9eyF53UKZZ9M6Ec1IIS9LtnMgH94vu3bZwFSF/iCUd1kfgO\nSgy/R6osx/UYCfe4oxpzHIe+8MrNnLeUJcXciHZhIHuwlPHre7SB2/jQPVE9y7G05x3XFfUjay+Z\n07SPccbWXWlbqLVjsdGa/VabPelri8+jCOpk+VrF2hjZnV8HayZrMWtvZN8QZcdif6W3RU4qUkmS\nJEmSJI1MTZHamLFqRbiXCSgzvjeen5caFrUedC3uxeJt4fUS07Jy5cpZ/4/eH/vTvtcp4mmcuABX\nOPi/P7Vz3fQfMSh9n86d2piovlV6JwVxFXg3KFL0S8lb8/HxWDf3yvEaS14c9N3JnvO3svfee0vq\nlCDOf8sttww6bl+89gwQcyjNVR6i2KhoreD7qLXY7lCItRhKaQ5F9a5aQSGgXzj/WMdHra8FJcH3\nagN+r903tRZicOgPjwudNJyf65vUrhJ98XsDylBkp9iNv+UgJmnnnXeWJP3oRz9qak/tWkf8arQD\nQF/mXSV+8Ytf6OCDD9Yuu+yiXXfdVf/0T/8k6Xc3+MMOO0wrV67Uq171qlmNOe+887Tjjjtq1apV\nuuqqq0ZpZJIkSZIkyWJkXkVqiy220EUXXaQ999xTv/nNb/TSl75Uhx12mC677DIddthhOuWUU3T+\n+edr7dq1Wrt2rdatW6dvfetbWrdunZ588kkdeuiheuCBBwZ7dXz/r/7qryRJ3//+9zf5uSi+wP/u\nlbnxXsZWoiCKJaJdHqcR7QQOrqTxlI+SwtM9cQMoKKWSYRyH46Pk9a127NTGwOH1Ev8wLeg/f+/u\newCiJJVi6zgOXpCPw9A4lBKtlfeJjeInewhGMUwRKEmt+2KRCUc1ayqcw8ZxUK5+YrvEDDGWrAEo\nVB4HhuKATQ7NLB4L+oK1yhU4FBPGHJUbBaDvnoD0F/3E8aMYJldfI7CJoWqpn2dSa/hRRx0lSTr2\n2GMlSV/72tckSZdddtlEzue0VjJfrJXBXTFkvt18881Nx2Mtju61zAfuaSUlivWhdjeVeZ9wttpq\nqw2p7M973vO0884768knn9R3v/tdvfnNb5YkvfnNb9YVV1whSfrOd76jY445RltssYWWLVumHXbY\nYcHl/yRJkiRJkoWiOkbq0Ucf1c9+9jPtt99++tWvfrUhnuhFL3rRBi/gqaee0v7777/hO0uXLh0l\n7uXd7363JOkNb3iDJOnqq6+W1P4enKfRkjfkNVA83sLrQ0WUvNnarEHwdvM0/+CDD0rqvDK88dri\n9Xgvnt3o8RBcNwpLKYaqr1cUKRdjx39EYFcoRYwf5+3r1QNxDrV7343FWOcrxfl4rSDoGwfjoF5Q\nE8bHfz6ly+cmnj01wFC5UINRWqIYlCgLcKHg/FEWmnvkQx1Z5gJKlCte2JbXh/LYM4c1JnpbwXk4\n/1gbcFBnqC+sdcQLPvzww5I6ZWrSa9KkYT5gV61rHPbZ997M+LLGl/Z7dbA7xpe1m/nPddXeY/vW\nt6p65/ab3/xGb3zjG/WZz3xmTjHKJUuWzPtAMlS6TZIkSZIkmRbXXnvtvP8vKlL/+7//qze+8Y06\n9thjdeSRR0r6nQr1y1/+UltttZWefvrpDVkzW2+99ax4pCeeeGJDFeb5IPYEL8e9Kqr1Uj+HGJYo\nJsornbtyVBvngJdBfADVgIm34H1rqWbL2PEV0cMp/cN5Su1yPJMJJcEVJfqTduCN+nt8Hrqj99YR\nkcJX6/XVVkBH6XDvA6/Fr7uvl+TgvUexVlzf2NtftsZIlfCaL7Tb7XyoIkVMFMf1/tvYLvzcke2h\nYt12222zfi/RN2NybFiDhtb2qoW1j7nEHOd3fjIGtXOUtSOyTWxm0vGDtbA7Brs1/OQnP5E0nhJV\n+3bDawOOxZo1ayRJK1askCRdeeWVkjrlrdQewF5a3xbttttukrpYqb5rIf1X6kdiDVn7S/fmgw8+\nWNdff334/3kVqWeeeUZve9vbtHr1ar3//e/f8PcjjjhCX/nKVyRJX/nKVzY8YB1xxBH65je/qd/+\n9rdav369HnzwwQ1bIyRJkiRJkvy+Ma8ideONN+rrX/+6dt99d+21116Sflfe4MMf/rCOOuoofelL\nX9KyZct0+eWXS5JWr16to446SqtXr9ZznvMcXXLJJVWv9ngf6xWf4Zvf/KakLp4BhcoVKb7vXkJt\nLE8EygReg+95x1N4RF+vBUWNp2R/Wo6enlGAhipgVA+maq17jXhDJa+I71F5PdrD0GlVZNinDG+o\nVIHeK4oD7Wa8vbp1hNcocrguj7PBPlCmxlY9avu9FuzM+43rc2VyaE0fP09r/MamKClRk97fsS+u\nAEwabJqx9fg36LvGMUci2+i7e0Tf8/YFO2nNPC1RmvPcR1FSxlKk/O3N+vXrJcVZmcC9we2hNgYp\ngrWq9R5Qmqdk46Fqj1UPbN4HqTVr1oQ3YwK+ndNOO02nnXba8JYlSZIkSZIschZFZXOIPP77779f\nUld5PMqcib7vMRV94WESbwSPGG9q7IwNjlcb24LXiFLH763xBZ5J05owQHYkP1v3faoFbwRlirpH\nxI5hRxB5T16dF/sp1cgpebuMh3txUcXzsSgpOLXxGYA3WpuR26oulPbd2hS111JSDwE12yueL3bw\nvPtmHznMBWyf/TCHMi2Fb9oxbq2wRpSUolawExSa0j2NONrWfTwjon1Wa/HxRXEj5o7fx1S1pdxr\nL0mSJEmSpJlFpUiV4KmyVGnbFZWS91PrnfpT7KRrh9R6bfQHXihKFjU1+r63JguTDKGhe+1BSYnC\n60V5dKLYL64XBYN2o0gRS+eKVIRXLKcf6V9Uib5eNd7SpOI/IjwOwsHbrVUM+9aG85jC2ti9SIki\naxelcWNqd1GoVUXpm0llPvalNsaHGBpXUbFhbLAUi4LNEpuF2osi8cgjj1S3fWOwtcUSe7a50Brj\nFWVWcw9jTeNz22+/vaROoYpiiYbGRE0azybF7oYqtU4qUkmSJEmSJI1sVooUT8WlzBX3eEvvxfHY\n8VKnvZ9WX/CWUezIqsIb5Wm8dr8mvM5ly5ZJkm644YbR2jofeMdR/SuvUcL1esVwxpGYtqjeWATH\nw2vxDCPOgx3WemW+g/tC4UV0HfprUqoL/Td0XmGXL33pSyV1Vbs3Vkw9CyxSwfg7cz9aI7CBxVJY\nuLY2HHOJ+D7G2Gt+1YLCRYzOUDWe+oJ9a8wNZWhNs80N1PRIkQKUxZ122mnW3yO7L709WChqYyKZ\n/9jx2PXJUpFKkiRJkiRpZLNSpKhwXKrb5JSUmNadtWvpmxXVFxQSYqU4D3EMfbPkUCZqa4qMTdRe\nshJRIGgn7/dRLNmTrRWOS80WMrdQA7A/lB76ueTl4w0tX758UPv6Uqs05L+doAAAIABJREFUTSpe\nxeM6Wuuc7bPPPpI6FalGnaGvvUIzx6hVZmo/h+IxtHZWRGm/Q28HtsrvfXc7oAYcqisKEmPY93pR\nSJhbC60QDd2dYHODe4JnIHvNOhSau+++W1IXb1q6124qTnEhqb2n1lY8d2rXzlSkkiRJkiRJGtms\nFCm8nr7e3tj7EvWFp3+8WjIiarPIHI95wUukKmxfr9NhHy8UHvfG8UZ5WncFyWvOoGh5bBGZQ3y+\n5C1ynlbvwqEGiu9UjzJD9iJeHN4d3hrXQX+UaqDg1S+0F16rpqA+ML+ID0K9ieJi+B4qg/cD/UM2\n5RNPPFHddqnrZ5RdVJkaBS3ak442Da0n41lxk1KioFY1RGFgzrWC4sC+lGRMogL3vV4+T+YnCtVC\nsVizBFtVWvqPtcl3MSCG6b777pNUtnfWdNbG6F6Cvbdm7a1atUpSOSuwL6zNrNlR+0tvifg+b0FK\npCKVJEmSJEnSyGalSDmeUUAsS986PUP3pitBDBZKEj/xFn71q1/1Op57cTx1R8fhKZ2ncLxVvA9+\np12PPfaYpM5L8EryeCM8rePl8TvfQ5nwyt0oT65w0B9RjBTjjTfD+3u825JXg5eFUoZ3TVwAEBsW\n2QNKFUoNx0VxYRyi63DFhkwZvkd/o6RyHN9LEbUBxQ+vlB3US+0AjhN5aaUMLcYTb9gV05UrV0rq\nskA5fqk+GeNM7SOyL+erY+XqWaRAcEzUNI7ZN8OQbKiS6s35YOw6Ng7XH52ndv9Lz/aDFStWSOpi\n0H76059K6sY0UkFZuzyb7NmC17xjzUPNZc5GSiqw1vga5HOd8WUeeMyPzw/WAuyZ/3N85jKKpCtS\nzP1SNiYK2VBYs1jTsSuUVFekPL6VtQg7ZN6zlta+zUpFKkmSJEmSpJElz7RuszzkpEuWaGZmZqFP\nmyRJkiRJ0puZmZlQaU1FKkmSJEmSpJGpxUhdcMEF4ftH3t/zHtlrwNSC6sVPsn94D0oGA+/7ySaK\nsseITeE9LO9neR/70Y9+dNb5Jo1fXwT9OLSaa+35xoLznHPOOZK6ceI9PrFNjANxIXvvvbekblzI\nyuM9OPEIjDdxBSeeeOKs8+F9eBVmj10iZgo89od4BmL4yFx605veJEm66KKLJEnbbLONpC5OgvZ7\nZlgEcThcL9mBxOideeaZkqTPfOYzkubGElI5nPlBfI3HXZDRwvwh7oJxYZ6ccsopkqRLLrlE0tx+\n4bqIVaM9feuecd7TTz99w7UxRvRZKc6LayhldRErctJJJ0maOxeGzjX6jtgZruPkk0+edT7iEek7\nYkGiuj5R7IrbJrsBHH/88bPOB8SYEFtS+0KDfsH2PVbnjDPOkCRdfPHFkrrr53vMVY8DJTYG27/j\njjtmHf+Vr3ylpM5GiBn627/9201eH/cI5jyxc0MzvznPueeeO+u6/F7EGsDcJS4Qu+D/XC/xrNg3\nMUs+92g//Y/d0J9cL+PpsXPYB/bC2ks7TjjhhFnX2ZdS5XWgH7jXnnXWWZK6+cBxaL/He65evVpS\nF0/rNQk5DvcI4k/f9773zd+uef+bJEmSJEmShExNkZqv/ghP55F3iBdSqtvj8HTqWWg8vZbqGOE9\n0C68vFL20bRx75gK0Xhbk6pc7nvftYK3hYLhey0yrngrVDYnswRQPFBasDMyX4DxxTvjc1E2aGn8\n8abxglwdwYvES3VVAQWo1ltDNcBeXTXw60BhpR9KexPSH4wH/cVPz8SK+od2tVbgh42/h+LSdy+4\n2vpCpTo8Q22dsWDsouwnbKGUqcz/fS4AtukZvRGtlcH9uNH4cN3cHzif13oD7gF+L2A8mUvYtvcT\nawmKDf2AIsb5x6pFyJzh+nwOlbL12N2DrLJShno091zZ414QVfLGfrwumdtDrbLk1H7es2lRiN2+\novpQrG0+r+h/+ovfa+vMpSKVJEmSJEnSyNQUqfk8Rp5+oxojxHK0KlL+tNo3ngHviKfV1sTH3Xff\nXZJ07733ShruzdbCeWqVKK8LVAvj595wqVK2g9KCchPV+MG7K1XbxevCjtw78fEca38uqkNHtXuI\nT3G4fpRErypNP1Mp3/dBczzGiXGIVIsI7yfUFJS16HzO0GrgG9eT66tE9cXVS2fo+Wtr4LGG0Xd+\nXjz1/fbbT1Kn0kbxpqxpkTKAUsOYD12roj36/O/MPVTWvms1n0dh8jmBYofixNrBXOxbhb8E10M7\nmDP8vfZeUmsnzD1imVjzWSN8HCNlFiWK44H3J/38yCOPVLVvKKzd2Adre2RfpfpW4GtjiVSkkiRJ\nkiRJGlnUlc15f+07mT/wwAPzfq92x+ZayMry981E+PetTA7Eeu2xxx6SpK997WutTezFnXfe2evz\nrft1ucJx0EEHSerGr1YRYzyJd4i80pe97GWSOtXghhtu2OTn3Isvebm1XkwEihFeYOTllLwo5sOe\ne+4pqeu/H/zgB7M+75lArhBxHP7fN54hgvO6ssZ5mL8lhQ8vvXYfLzKQWui7qwEKRl/w1BnL2tiL\nSFWkvVEMDOpnpDw42GS0dqKA+W4SQKY1NllSViJbJ34xshG+RwYvCkpUKRsFzpUL4DxcF3OV3znf\nWDFS7GLguwlwHsazr/JGxXG/N3J9HvtTUlYhirsE75colg08G3IoHss0dO9MxqFv/6cilSRJkiRJ\n0siiVKTwoNnHiadh3leX9odqjemJiLy+obEdKDY77rjjoONMmrH2BeM9fd84EuI98DaiWJ7ttttO\nkrTLLrtI6t7Tu73gneOV+Xt/BwUnirErgZ2UYrxK/XLddddJkm6++WZJZa+J6/MsVbdblNVop/Ra\nSsod/VhSpPoqZEOyZvvur9mqdpf2oIsotS9Smh588EFJXRZYyWaxCd8T0Inaj23XXl80h5mLvueb\nt/Oee+6pOg/H4a2CZ/x6hixqKMojCkurIuXKD7bNcVFUSjbPGhgpPlF8JfdOn/MoTaUsO+wvyhz2\nuRzZGd8/5JBDJHX1vu66665Nfj7C1fWx8X6KFFgnFakkSZIkSZJGpqZI/dmf/VkYA8FTMB463sD6\n9esllbOA3NuJdrUfSm0MRwQxPGNnhiw28Iqo4dHXq2CcSwrM97//fUmdd1ZSLqPjeVYhKkStd+Lg\ndeIFRtQqdbXv7zmeK0XuVdbGgHnNnb7UqhWTzrwbQm29Kac1s7fvmuUxX7XfxxNvjQEbK86O9qKU\nDI1NQrGjX1yddfg/cx6Vu7XWnitSrWt9KfYo+j9zyZVNX0NoJ3Pc76EoqiX7iN7SoAxSmZ17eF9F\nqnUN7ovHsJVIRSpJkiRJkqSRRRkjxXtXnm49vgBFozZCf6iH2zezpy+larYlJt2+VvAqPWOJOAy8\nz+j9PtQqMHjFP/7xj/s3diPwRrAbVIjaGJ8I7NczhyaN24XvK1erWgxVB4YquIuBVmVpoRi6BrRm\n6I4Fc5i55rsjRLFCpdp0rLHLli2b9/zMbXYhoNZcqxrbGkfLmkm8L+2Pri9SUGrvfbW15FCS+JzH\nDEZrCXOf2L3WNXRoVl4tUQ3LiFSkkiRJkiRJGlmUe+0BMS48zeKN9FWYeEpv9Q54Oo1iSSadSVBi\n0kpUKbYngvfZtI/9ofDy+lbSrmVo1WW/XuwGr88VnVpQoqZtL5OuoB/ZS9/4hqExWZOgti1RnaRJ\ngzKD6ts3o3Gh1dIIlAfmGjYbrXUlRQoFy7P0HDKUWeupID6WDdbu6sAaSUwR1x1VDHfFDsa+N3jN\nRM+CLEGdK+4Fk6bvLhqAslabpZuKVJIkSZIkSSNTU6Rq3pF6/SKefnm69P2YInivW/IOo4rKeGmu\nSJXqD9XSt5LzQsHTfKuXihe3evVqSdIOO+wgqXvaH1q3aFKgGBGbRVyM/94XFK3WzK++4J36/Jh0\nnE+kSPX1CheTEgW16uxQJYrz9I1Zoo+ZY2RZ1aq/Y1WcHoqv8RDFyNTG3JTUWFRT5s7QXQ0cj7+M\nYPyYq0uXLp319yj7L1pTPdasFV+7+lYA57pRbFHcyOgem9b4aOysNlYqFakkSZIkSZJGFmXWnsPT\nq/+s9bJqK3NHihDeAJkcgBc0VGEgM2OxKVI8zQ/NeiQTh37Cyxvb22vFY3f4HSUOLw5vsG/cAfEA\nKKqRWkEsH95jVJunNkaL/nUFyuuqoawOVdyAjKPfRyJVeuxsImwsUoNLmbrYGGp3LX0Vhlaiitoe\nh8jvXKfbfF+lpWTbKFG0j/4fK56zbwYyc5SYN/qlth4V6jo/W3epYE1kT0WUy751zlCZGc+x6kK5\ngtuaFQi19aM2nH/Q2ZIkSZIkSZ7FbBaKFN4VGRTE7gx96nRWrVolqVMOUL72228/Sd3+QDBWrAve\nFPtBoQDVeg881eNtoYDQX63wlB/tQF8LNVBoZ2sW4KRwLxXvzWuzkAFVqz6whx1eLdftmS9Qq3jV\n2l3kffvxuR6ul3GKvE2PvfJ5OGlllfHwHRCmwaTr2kQVq0s28sIXvlCStNVWW0nqFJWhaya27Mfx\nNaIUIxYpSCh82DhjG9ly35gfjyHienxPPpSTsStpu5KI0uRrPVl7xJfS36wpN910k6SywsW9gH7k\nenkLgsIa2QXXT4V31GYUKc8SLIGShuL3+OOP9/q+w7MA9oIC69mqzAdfO6Iahn33VV1cd7QkSZIk\nSZLNiM1CkQK8qpI39hd/8RdNx1+xYoWkzishNqr1eLWgULTWJ+J7tdVpa6Gfh3rdvO+/++67B7dp\nIUCRouYM3gxKS9QfeG/RvlV4vaXYsEkrLNHxaXfk5eLF4oVGcRqebYd3TYwhKgLerSt0keoBfI//\n962V1AIe9NZbb930fWJLqI2HZ7z99ttLku67776hTZwFfUpfYcNDs/KitwFk5HJdKD+PPfbYJo9T\nqoC9UDAXuJ4777xz1v/HyswGr2kYvXWgAjj3JOYU/Vkba+VvK1xtRrHi/9gNyhl2z5yP5motfd+2\nOF5/yteyaC1A2WU+jLU3JKQilSRJkiRJ0sjUFKn/9//+3wYPd9ttt5U0t04UT5G8x+Qp2d/z8jTN\n03NtNVKHGChqr9x7772SFq4SdWvMFe+vUTrw/KO4AvqN6yKWyuMNSsqAHw/lDiWnpLzgndEOvu+K\nj9fZ8qyzWvCmURzxStwLxmvz+AjiQIhT4Lo9m5T20W+egVOL17PqC+1zokyn0nlK8RTgWXtPP/30\nJj/nWbDePvrf42361JxhzWCN4ZprlRnWEtamVnWa8zMmKAx9Y0xqbR81mb4fS+V0T5727LLLLpI6\nG1q5cqWkrv+iitwRrD2ePdU305e1kTXE+w3FiXZia/QfSiL3lvvvv3/W5/pS+z0UvRtvvFFSN+f6\nxr1yHM8Y9nbw9oXrZBxZG7Efv7e6+kxMHmuhZ+lh77WZz9gBn++7Bx4w7qW1w3dTqI2RS0UqSZIk\nSZKkkSXPTGE78yVLlmhmZmahT5skSZIkSdKbmZmZ8C1PKlJJkiRJkiSNTC1GapKKFO9VTz31VEnS\n+eefL6l7H966xxvvZ3m/TCwHv3NN/OR9Me/1/T18Kc7Bd5Dn+8Rwvfe97511PvAMoVbIxCGu5C1v\neYsk6bzzzpvVfuIYoh3aPXPE/06/8l6a99Qf+MAHJPW3FcbZs9A8Jojx4+cpp5wy63yl6tFQu58Z\nGSd87swzz5QkrV27VtLcuAXsmLgCYsfoR/qf+AXa65lB/P2jH/2oJOmCCy6Q1NkXsUq+D1hUEZ2/\nl2r40I+f/vSnJXWZY4wz/RDFjUQ7t9Nuxpn6b6961av08Y9/XFKX7cQcvPXWWyXNjfEh3s2r+Hs2\nEn3INZ944omSpLPPPnvW5zzOsVT5vDZT19eWPffcc1Y7yTbzviRejTlFVhPnoy+xMWzq7W9/uyTp\n8ssvl9TFqTKXauP2aB/9QNwr52d83vnOd0qSLr30Uklzs8PYk43x8pp+HJ//kxVGbJHHop1wwgmS\n4rWldu6X4kiZM6eddpqkzl44bumFUBQnyVz1NY74z3e/+92SpHPPPVdSt/bQXsadmCviWoHjRFlw\n9DPfZ2357Gc/K6nrf+Y814t90t7I7n1e0A/cM1irP/axj81qb1Sjz9vd9xmgdA9KRSpJkiRJkqSR\nzaqOVC3uHfAU2/cpdO+995bUZb7wlM1TMseNsgTxaqKaFSg+Dz300Cb/jxfh9Xe8BgdP616/aCh4\n557V5v1byuKKvC7+7t6WZ4L0Be+/5FXSn66seIZL9H284Nr2RspcpMjQr3iLeF0onK7UuNcH7o3T\nXv9cSWHqm3Xo5+tb3yzKNPPMo40zwrxuEpm3PgeXLVsmqZu7eNa+xxu2Sb2nqC4SfexZVRwv8vD7\nZuqifu6+++6SujHzGm2cN6og7bspLF++fJPtI7uxtb5TVP0efG5FNki7ovpDzPlI+etbt6ikRKF4\nMjej66N/AUUEpaq0Z16k/EVrl48fChT3CPoHu4vuXaXsQL7HPcf/Tv9hN30r6vu8iPqBtbQ2m7Pv\nM0BtHbFUpJIkSZIkSRqZmiL1h3/4h811k6KKyJHXF3kppT3p7rrrLkmxZ1yqzRLVz4FIiQK8KPc6\nXNHgaZ+n9rESMfFGPV4EL5fzlvqhtEM7la8Zz7Fq3tTWKnFqFaa+Fd8jr7j03p44kl133VVSp2yx\n31bJG0NNcZh/tfEgtfheinjDpXbyPa6TPRqj/eZgY7tCefrJT34iKVZ8iB/Eg3YbxxapP0RMiStb\nzAVs3GGtYgzp46iGVgna6/tW+nXye6l+E33stfugb92mvnitMxQA5grKDf3L57n+vuq1Kyh9wQ5o\nx8MPPzzv531c6OdSO2rnpMfROnzf32bQf9GaXFKnWftdpcbOgXvS2PviOpPa87L2uKlIJUmSJEmS\nNLJZKlKRgtR3z63SU/JQZWSoMlT7FE98Bt5qayyLg1eCVw5476Wndb6H9xPFyOCd8dMVjVqIWyh5\niU5rJXyH+BX6pXYciOeJFCkypqKdygGFFS+VcYoUHR8fn1etldW9GjBeccnL5u+MI+oEylvExvtv\neUxIlB3kY8PvXrkc20DB8u/Rt1EfM6YoK0Pj/4C1BWVrqJoYqdn049h7k4ErKR6LRX/zk9i2PfbY\nQ1IXG1aKNcImPeYFpQv78H50m6W9njXosJZF94BSRnXteJbuEVyXr7GtldkBFd3Hi3swawnxpqXs\n1UmDUoYiR3vG2pc2FakkSZIkSZJGpqZI9VFN8Brw+HmqLsUglZjW03HEYYcdJqnzdry2RwTeLv2D\nlzA0viHau5Cx46ker83PV5vpg9eEl9S6nxL2gBdEu0vtcLUCRQe7i/qxVnFzPB6Hnd5bYdxRDTg+\nakikrEbtpQ7ZmjVrJEm33367pHqlzxVFxhOFszT3v/3tb1edBzaO50Dde/7zny+pU2xKajU24Kof\nSpPXHAOuLVJs8MxRJoYqAZ7JSRzjUBU6ah9q96QUKd/zsLRmsVcatl2rJnN9bpusndH1uTLEXGOO\nrFu3bt7zeSyc92dr7BBKodeDcjzzuBT7VEt0Po+d4nrpL+KCh86DvnD9ZOJz709FKkmSJEmSZMps\nFnWkeIrGC2uNoamFp3281LFijkpwHpSOSJFyLwyvmTiHsZQ2vAuPefH+L2VVlfCaJq3tH1thLHlv\nKF1eK6aEe6Gt8S2oLmS5oWy5HWDPteBN4631VQi932or+beycdwRfYk6WaqHA1GFcdQ8+tKvbeut\nt5bUKSWlejcRKAteYd2hfV7lvpQZWwKbdLW0No61VOF7LJhzrXWtXFHsa5PMBdZA1G+PfaPf/Hz0\nk6+pfcHOqP/F75HSg5147cPWOOWSQsl18vYIu/TdQPrSmnWJIt03jrqWVKSSJEmSJEka2SwUKWes\nejcRfbOUxgKvtlRN1Wuv4B3xtB/VvOkLx+V4Tqs34+Cd4C1NenxL9PWWhnqXHmdTqy74/mh4gR47\n2Neeiet44IEHJA2PI5j0eG6s9pSUKGJiyDryvcfuu+++TX4v6kPqNJUyfFFqokxIz66KjoeSQHYh\n32NN6FvBG7A9rwNUm2U4aSVqKPRbaU4xl7EHHydiumqVTj8f9ob9eYxYXxgfr+3nsFZ4RXSP4XO4\nF/n3WHN87eP/tAN77LtHY8TQtXZSpCKVJEmSJEnSyGapSI1F9H57WpAxxB58Ee6tuhc7llLEcT0m\ni/4aS2nAi1ms3kYEMUpLly6VVM6Si4gUxpL3jL3g5UWxan3tgfNS9TqilLEGk87Q2ThepqQU4Enz\nHcYQm45ifZgDPra1Y13KAMXzp8p/pIwRP0m7XUlqVaRon8/BxZbZDNG+lRHMAY+t8jWM43FvwA6i\n+lLRvQPlMaqTNdbuDeyhyBqycU01qVOisC/sn+thDlOHy/uT+RCtRf55lDzsnP4ZKz5yrF07xiYV\nqSRJkiRJkkaelYoUcRJDFSm8w9YMEoenfrwK2ulP895eapuwP9hYtUK4Ps/koV1jKQ14w3h5C5Ul\nORTPmKrdU85B+eP7qB+Mc+SF0f+lcWC8xgZvvRTXM2mlsaUyPUqLxy7huY+VWQme+RjVXCt53KwN\nzEnmDO1uzcYiZmdSttJKpDxxnfQHv6PIRWuIH4e1M9pVIBr30j3D7zHAuI0dhxutOVwv58POUKpY\nu0p2V3tP8aw6xmFolmB0/MVCKlJJkiRJkiSNLM7HuwnDU/LQ2Cie7sfaq42nfo5b6wXglaII4P3y\ne6v3Q2aQe3ce0zT0vXVrXMe0IR4HdaPVDvi+7wM1VjxAlHU5lNo4j0ln7W28P16r5+t1dqL/t1Ib\nV1iqyeYxKFwnf0dZ67u21dpuSX3si5/Xx4/YH+8X1rhtttlGUjduKEHEDjk+vtHbhNpdJSJc+QGu\np7TX3lgKDnZCPCXKG2sMbx2ifTD7xshxPM8cRpnjXtJ6TxqrhiTHwR6G9nMqUkmSJEmSJI08KxUp\n3lPzlLxy5UpJXf2cyJuJYH+vsSjtLO5Q5wdvgmrLeFWtT/9UgXVvEK9vWvW2Fht4562qhfcvXmAU\nI1ci8uodVIxJZ9WV4lCGsrHK4J4l1x7FkPB5st8ipWWoAjNWZjBzDjWU47J2tZ6Hekae9eWMpUTB\nihUrZv1OrFa09qAkUNEbhYz2lzJIfY6OfT2O1wSkfaU5jV1GdcegtqK8xxYxH1gDUM68Zly0prGb\nwt133z3r75GyyXmG3jta1W2PP6W/uO6ha1MqUkmSJEmSJI08qxQpvFO8Njx/fm+N1RkrS64Vnu69\neu9QxYj36s6LXvQiSZ1y5zFaffHsx9b34NF+ZdTm8ff2Q0FpcXuirhS1WUpEdcGw11pvac2aNZI6\nL5Wd1qP9pfDG+ipSJZXHFbAo0yaKtynFh6Akc57ddtttw/+op4Tn2vfamDujx1AEMSh9P3fPPfdI\n6vrssccekzSeqtj3OltVU1i/fv2s30trFv3CvpLY0FiZ02Pjdc1od20/l/qjtOaytrImuWLJ2hKd\nx8eVLD/ewniWp18v40MsGwpjawzatttu2/Q9FCnWRpTBsVTyVKSSJEmSJEkaWdSKFE/T2223nSTp\n3nvvldQ9laKI1CpC7kHzdMx5ogyFksJQu++S05qZ4e+h/f04+3/5+22u02Nj+npznJ+fPO1HsVSc\n16sL0w73klrfgxOPgGJB3ETf6yvFJfB/FCn2SOT6+u4wvmrVKkmdQkS/YdfYJe3hOvEO99prL0nS\nfvvtJ6mbJ8S7REpr1E4UR9QYvNLaGLBIqUIZxB74nfZRzbsU7+F7DG6sYDJX8dRd8YhA3SIbjLnB\n74ytq7R45owFnrYrBbV9x5yIqv1zHjIVaSfHJ1aK31EESrXNiFXydh9yyCGSpIcfflhStwbyuUiJ\nYg4yl/mcH9/ra7HWoxTwf2w+2v/T6yTx/2XLlknqbMrbiy15pXjGkfMRR0v7qdlHv7IWeIyR2zKK\nCNfD2teqKDLHGXeH6+Enaxd2Rb/V7pXotfL8XkS/AP2LPaBMYResQYxL1A9Ds0Vpb+mtU61yPOd7\nTa1KkiRJkiRJtOSZKWxes2TJEs3MzCz0aZMkSZIkSXozMzMTKsupSCVJkiRJkjQytRip+RQp3m/z\nHpn3psQpbLnllpK6miNUifX3n2eccYYk6VOf+pSkOJvNK3hHMTV8Lso44Jr4yftwMomIr/D30Vwn\nsV/Rzu/EDxCTddJJJ0mSzj33XEnd++OhtTY87gK4rrVr1876nEPMT3QdtXh/Rhx00EGz2nPzzTfP\n+j/xL6WqwqXzEUcRvWfnPT79H40D/fyhD31o3vOV7K0WYp7e9a53zTpfabwd4nNqYxJrx8/pGw9B\nu04//fQNc93nHDESYwnwXNN5550nqRxjgu1tv/32krqMSq97RKwNawFZeh/5yEckSWeffbYk6SUv\necms45EN5rZNDBdrEf3BWsr5DjzwQEnd2nLooYdKki688EJJ3VgQK0Rc3jXXXCOpixPcaaedJHWZ\nlI8++qgk6bbbbpPUxccR28NYv+lNb5IU2woxXMQC+RpNXSNidHzOcD5iro477jhJ0qWXXiqpi1Xi\n3uNxiYcffrgk6YYbbpj1eWBtJkaKmCDiRk844YR5r28ozAE4/fTTJ3o+h/Mw/0oV+oF7XxRDB9g7\nnzv++OMlSV/60pckzbVr74/aGDDg+8zDY489dt7PpyKVJEmSJEnSyKLM2ivtao/3gzLgdYMioqfS\nPfbYQ5J09dVXz/t9vJmSQuBVXPk9Oj/eT6TgcJyoFgj/x7vjJ14BXiftKNWHImMmyvQpZZgMVaL6\n7vCNFxqdl+su7W9VopTxUaug1NbcqfXqSkTj2LfO2Fj10krZqqV+xNtHzdlY+aPPmCsoU2P1pVPr\n6TIGd95557yfQ9FAOfM+8sxQVEXPFgNsluO5Ike/oER59pfbDkoQawRrMTAXUcJQovz7tKNUSR24\n7mhtIBsMpc9hzvned6xlnt2GcsYc+d73vjdv+/y89JvXWYogi45cf65JAAAgAElEQVS1Kpqb2DX9\nQW085qb3J+dnjngdJUBJxB64RzDXuOe4/WB/gIITVTyn3by14HyltZnz+9rJ9UdrRl8lCuiv6v1u\nm86SJEmSJEmSTE+Rev7zn7/hKZWn9761NEr7KkHJG8X7QHGqPW6EK2p9FRbHY7e8GiuKFu+b/Xx4\nq1FVWLwcjoPXxlN53/4gtoe4EI7L032pmmzfyub0c1QDZGiM0djU1g0reVNc74477ihJuv/++zf5\nub47uLcSzR9XaIeqQ15/bOP+ZE1Zt27dJr9L9XdigBZb9jDKBPGfHgvE9aEs4MlHY1yKl+T/KEv/\nv71zjbGqOt/4c2JJajK1WFsGZGwGuch9oBBKPxhrBJMmDdpAGm2wNGJMjEljNbb9YjO9qLVJS5G0\nsRdNSBqt8UNLmwoxTfBSEjpYII1OU6QMBhCxgk2gjcE2+/+B/+8cZs2sWXvvc9kzw/P7MnDOPvuy\nbnu9z3rfd6XU0lChCKEv/vWvfx32eZj3Cd+uvKT2Ety7d++w64fQJsNyChUHyjflS5d6V9B38yoi\nXC+mROFj9ZnPfEaS9OCDD456HO0ivE8Ux7A9cDx7F4Z9KlQQAeUrVNxifZvrUP4cl3csiB3H9WO7\nQ6TKP9aeef68Y7UVKWOMMcaYklSmSF155ZV1z3giHlhXxapYvXq1pMascd++fZLiUXVYc0Ujc/Cj\nIBKFyIyQouutKDL4C/BcRRUClIeYNYaVkdr5HSUo3DGc2TzKFdZjrBxT1hgKUFklqOi+XUQGlYX2\nVZZ7771XUiOzOBElKeusWW655RZJjWjCmCLVKWLtAT8KFNNmI+ioL6zsi63G1O7y+IKsX79eUmNM\n+cMf/lDqXsruThADS5ooJXYpAK4TqvnNQt2FvlZhpCZjSEw1jvm38v8wo3fe/S9TYxttIZZhHMI2\nFyoZlG+s7zL2kTGdqMqQsu+iGChA+LGGGcQh9H8Nn493RajY7Nq1a8zrU67UO+UQlhO7jYSE91HW\nXzVcbYmt9uR9h6RWwVCGU1iRMsYYY4wpSWWK1D//+c/6LJVZbjg7HBgYkNTIE4Ri9OKLL456zrKz\nf2bx5GKJUXSnaKwILOiyvlLh/kyhgpKyflCYYopBaMWkZvNld3ofr6Ss3RRYsatWrZKUXvcvG0kS\nQr6svJFPVUH5oBKh1Jb1lWLcoF8UiSakzFCgmlXxQpWtWcII3BixKKqyhPtmAmMLz4k/aZinJy/U\nGWN92fOEoBxw/pgiFUI5M6alcpjNnDlTUtpvNNzrsFnI3ffyyy9LKr+/LIR7OMagvilfokXDaM0U\n4f6sKQU3tuoRqvnh2E15U/6MNbFySEUIxyLcQ6xIGWOMMcaUpDJFKo81ynru7t27xzwu5bOTmpWy\no3kqwgWrJe+sGh8hFIiyFji/5/5CK471a74PfchS68BE6eG7xSw9pnSlZvGp+iA78qFDh8Y8z3gH\n6weF9Iknnujo9fEzGO8KIe2Hdtjs/YZ+NRdbqai1sWzt+GE+/PDDktJKEgpN+AzQ6rKnD6b8C1Hx\nUz5BeWHMwJ8TeL7QJyZvzrSQ0D+O85aFul+xYoWkhoKQ1wcnbB/smkF5hGNYUb/PMG9VWXjn7Nmz\np9DvWI2g3TKm51W0wnKIRcdBbDeGlO9iCOUWln+opIXvbO6HfsHfsrkN8/ZvK1LGGGOMMSUZl5nN\ni5Jar07NKvl93vXsvBE6zL7zrkfHYNaNNReeD6s5jOTJu+ceVhhWHFbHjBkzJI30DUv5ZYTZc7nf\ntWvXSmr4GWAlxSI9xjuUd7OZ3Ft1HzGabX/NgnrCfcSiVmmHWNGxdhH2v4tVItp8qo3m9WlKWdCt\n8ncD7psotNR1UdubhTKMKVuholFWkeJ31GGr7h8fnli9x1YRQmWGsS5Wr6l6CWlVNCcKDe+oEydO\nSEq38/CdVtanLm9904dRpIgoxscuFnEfgt8n5+H6qfunnXKdMFN/UfLmNLQiZYwxxhhTksoUqQ9/\n+MOFM5nnpejOz1gheXOa5PWR4rhWWa34J4QWfahYxZQo9jcibxb5l7jP2HlTikcIVi3KFJm3yawe\nZiKfqLRajShLSjVpNnIIaxLrEp/CvIyWgXw0sLq5HhFCqXHi4vYettVm8/ngw8M9tEphSNGsJV2W\nWAQrYyp/i+7TGBJGV5WFPkgmdRQu8nChstO2Ur5kYRRfs/fXqqg9FBnGbvJIpXydwvbabN4zovhi\nfTK2NyTlQD2k/IUpf5RGlKm8exc22z4hb87Hif0mM8YYY4ypkEp9pMKIglZRdJf6opnGUxEIWLE8\nX9H8UxD6tnC+0L8DayXl94EvD0pQat+sspnJUQiwXrBG3nzzTUmN8s67Xm7GJmXdNWsVY8WW7aeo\nBCk14B//+Iekxr5fqB+p6168j1uoEnJtxoSiliq7LtBnwl3t20XeaKFmo/XyghKCwsB1UQ/LjqGt\ngrZDxvHY+cMcfGG+pzCPV7NjFFFjzYIyw33hF5t614XtnectqtRSLjwPvmKxDPZh+XE99nVlNSTW\nbg4fPjzq50VXR/KCTxbPxXWc2dwYY4wxps1Upki9//779dkts9dQGWEWy2yR4/CdaBXMmvHhSUWR\npaxarLRUNGEKrGksbqLoml3/xZrB4o8pWaG/QFE4PxEmKFyxfb1Me2i2vdA+yraDMBtxzBqO5WpK\ncbFyG94jClVZf7bBwcFSv2sW+nrKb7Ns9FyMUEFhLAvVQcaksooUbYrnbBbqHTU8VJ5QGlBEIOYr\nhALXrCLVKl+3MDI7b0b4sK/ljeQOoXwZy2l3odpNuwjLlevzLi+bQ7BdftXhGMX95/WbtiJljDHG\nGFOSMRWpY8eO6ctf/rLeeecd1Wo13X333frqV7+q/v5+/fKXv6xnMX3kkUf0uc99TpL06KOP6qmn\nntJll12mxx9/XDfffPOo5+7q6hqxdxyzXRQLsuwyy2VWHMvmi09OWSsCK6TZvEZYAc0qAfi+MDvm\nuULlLu8sPVxfx58A6wIfJmg2Ko1ZPvVq2kOz7T4vZdvDmTNnhv0f6xS/o9BKRt3I238utn7D34T3\nTFnRt1q1V12rwCeL+8xrEbeKcExF+cAXB18Zxuyy0V/U8dDQUKnfh9CGwgjmkFQbjvn+lKVVfZL7\nIU9TXh8z3mm0ozBqL6zfFOE7pLu7e9j3vKvDfsX58X0qq6Q2uy9qjNg4kFfBG3MiNWXKFG3ZskXL\nli3TuXPntGLFCq1du1a1Wk3333+/7r///mHHDw4O6tlnn9Xg4KBOnDihNWvW6NChQxM+zN0YY4wx\nZjTGnEhNnz69PqPt6urSggULxsyoumPHDt1+++2aMmWKent7NWfOHA0MDGj16tUjjr3iiiuS+yEx\nGyR/DbP72Hp82dk/s2sUthixfYRCsBZSlnfR+0OJC/enyjtLxwpg3T6mRKXg+VI+M62yNltF3vor\nCn0k707hrQa/j6qiILFSY1ZmzNqN9YeivnN5fLfog6jc/L9opuqQOXPmSGo8e+j/l7L0Ua+XLl0q\nqaH0kG+H/EhVESpjPF/ZsSykWdWe8kMZoS/Eyi1v329VHqJWQcQ19583Epx3Bf62+KShHIXRqChV\n4fsdpZTf8w5hVQliCmreXT64X1YzQjU7dnyz/six6/DuTZFbKjp69KgOHDhQnxRt27ZNfX192rx5\nc33QeOutt4Y58/X09HhZxxhjjDGTllxRe+fOndOGDRu0detWdXV16Z577tG3vvUtSdJDDz2kBx54\nQE8++eSov43NQPPszs06d2gdxCzfMBdEXrAa//KXvwz7nNk6s+O8/hRYyFgBYebwotFPYYQGCkhZ\nmt0bjujGWK4PqNr/BPUB66nVShQ5RrgO7a7TGc9Tyl+zuWyuvfZaSdKRI0ckNaxA2nFKdQnzlWHF\nMjY0a01eHKFFXwkjZxkzyDfUKkK/Rcokr+8QfSTsSyhSeeG5GbPCNphSxVFrw9+F0Vn8nv+HCgZq\nNWNfTDnh9/j8lIW2GNuHtCiM1bE9BVEoaHP4LjG2Ux6M0WHfK5pZnNUYFJOiqjP3F0YxMhbSzhBA\n6DeUJ/VJOeNbyO9jeaRCwqhBxk6ei/bJ81LOKUWK9srxofJFu2Zsjo1Vsc9RZFMkFakPPvhA69ev\n18aNG3XrrbdKutD4a7WaarWa7rrrLg0MDEi6sBntsWPH6r89fvx4fYNaY4wxxpiJwtmzZ3X27Fnt\n3r17zONq2RiyQZZl2rRpk6666ipt2bKl/vnJkyfra6VbtmzRvn379PTTT2twcFBf+tKXNDAwUHc2\nP3z48AgroVarqb+/v4nHM8YYY4zpDP39/dFVljGX9vbs2aNf/epXWrp0qZYvXy7pQqqDZ555RgcP\nHlStVtOsWbP0s5/9TJK0cOFCffGLX9TChQv1oQ99SD/96U+bllqNMcYYY8YrYypSbbtoraapU6fW\nndTxbWJ9k3VW1lFZZ2ddHX+AMPKAdWqWEzdu3ChJdfUr5icQrkOH4COV8n3hOj/4wQ+G3U9IGOWU\nun4Iz/3Nb35z2HXbDdfhb97oPSi6t2J4vRR56ynv9a655hpJjQgs/AbCzPCxve7wS6Ad0O7wk3jo\noYeGXa/dFC3PVl1v27Ztkhr9GDU7lrMH/xr8Gyg/yp9yp9/w+b333qvvfOc7kloXVRajbFnybKl9\nMWPX+/a3vy2p4bNCGRAxmuqLlB3DfszflOt973vfG/O4VsH1fvzjH0saGbkZGzt4HvokfZ+xie/x\nScKwv++++4ZdN9WnP//5z0tq+Pbu379/2Pf4QoX7SlLPsefjXcDz8Q7kfnk38i4Moyd5Hv7S7rne\nE088IanhC8WuILQT+hb3vWjRIkmNcj548KCk+NiKT9XXv/71Ydct+m4oms8qb/+j/Civsrsz9Pf3\nj6lIOcGTMcYYY0xJKttr7+KZHREv4WwUq2D+/PmSGhYsyk2oYEEsmixmpaaUIHKUkPGc2Tb3G85S\nU/sghfeRV4mCmKKD0kEkTGhFtjpqrejsvl37JEGro+UuDpy4mNBqjBHL71U2G/REJVzeT2WPxopH\nQY5FKhEZhTUvtV+JapaiSlQIYw1tq2imZxSQvHmS2q1EhcTaROxzxuDw3VF0LOD8sXLhnRKLeEZh\nSuVAC88f1mcIfSUWic7vY0oJfSeV4452uXfv3lG/j5VnbNWFd1HeHIp5lajYdWJjat4I/jDqryhW\npIwxxhhjSlKZInWxhRHORvFNYV33pptuktRQVPbt2ycpnhOGWWqrCPfeS1kBYS6V0Aptl9XMrBwl\nj3Xh6667TpLq+yH+6U9/ktTwVeH5YgrMpRowgF8A7TOst5gi12wm+5BO7aXXLshRg/VK/4ipDJR7\n6nlp32NtQRXz7VixYoWkkbnjJjtXX321pPz5tFJ+h4xx+AhRF/SN1F6B4Z5x7VatQ1Ai6OMxBS6V\new9fIdo45RauTuA3SXmG+ZnKkvIh63Sm9k5dr1l1n7kC70h8wopiRcoYY4wxpiSVKVLTpk2LKiDM\nElk3JtoHC3Tu3LmS4r5QRWep+FpgTaWyrqfWc7Fq2pXhOuWDFWaKxkoiM/X1118vqWEF7dmzZ8zz\nVZ2hvFnK7sdEO8Jqzfv7mBIV25MuZfWj0LZqf7hOg+pABnj+H/MnydtvKMexlL/YuVauXCmpoRC8\n+uqrktJ1XFadDVWzVqmVsbYTRnOFY2qrIaqMXQ/wyaGOYv6ZVfsLhsoQSkrKNyyMKA99hTgvUWNA\nfUFKiULx42+eHUEuBqWKd1ZZpYg+S31VVW/h3n4pUmo+UYphPRXFipQxxhhjTEkqU6TG8mtgXZ1Z\n/ZtvvimpMbv+1Kc+1dJ7YX0fK6PorD+E2W+7fKGKRjgQFYgC+Prrr0uaeMpGWbDKi+YqwSrNuwN4\nWVIKTCpyZ7xD+8N6p58VrY8Qyq1MbpiXXnpJ0sg901IUVWex5L/2ta9JauQjIh/UH//4x0Lnw8eJ\nthBTGPB/RIFC+Th16lSh66XaJv6f+JaEPiYXR1SORqhslM2zVZYw/xLKXUqRiu0hGBK2F/b9TIFC\ngoJFvrDYuynWfskb1Sxl8y+1mlDRS5GqR9pbsz5dVqSMMcYYY0pSmSI11hprzApi1p3a7b4oqdwf\nRRlvuWxCv49Q2SAT/IkTJzp2T52krF8ImbWxBv/+9783dR9YR0X9bDqdy6fVhMoRz4PPWFlFinaM\nolyEVBRWq+CZd+zYIamhhMXy9aTA9yg1xjSrqldFp5QooC8W9Z/MS9i28yo74RidygN1qVA0cjml\nNKHQkiGgLFakjDHGGGNKMi4ymxeFCJBW5ethvbtdES3jnaVLl0pqWEGxbLUTnbztJYzSI/KjVf4b\nRdv+rFmzJElz5syRlI6yHG9glaNAhVmk8Uvhb1F/jKKRPFXQKgUs71g3XnKP4dPS6fxQeaHtNJvH\nKUarcxpOFJrd9zRGq1d7UG6J2iyLFSljjDHGmJJUpkg14/eBf1XMsi/qg0L0UFElhj3tWhUZ0WmI\nAMJaHCuScjJABFGqnomYIdoM/wQUlbKwDl/U+uW65JOaaIpUqj+WzU1DfV6qmffHolUKEGWbV0Xl\neBQJlDHU/rCOUznx2k27x7xOR9qOl77QrhyKRUmN+XzO6kNZJW1yvzmNMcYYY9pIZYrU1KlTkzlN\nsMRZF+UvuV9ivipFfVDK+gThMzPRFCny+PDcg4ODkia/j1jRjNn46rTKui+bYR1FjP2gyE00XnK7\npOA+6ZcouZRr0f5HOaImTHYltQytUgQo25RaSOQkdUMfSv2esXyy0umI23DvwkudvCo3ylVZnzaP\nQMYYY4wxJalMkcqzNs56bxht1a6dpZnN59nDS8q/gzoU9TdoF3mz8k40UkoNvk+p7MJEOtHuqK9m\n6436L+oLRH4vrKbFixdLkvbv39/U/XQK1JFQWS7rHxMqeqns2aY8qO7k2kNBYgwOM4KHe/qlxuqq\nfXrGu6pb1GdnvPgmjRcoDzL9s3rEGEx7Zh/Q1157rdR1rEgZY4wxxpSkMkUqj18R+WfCPD4oRkRE\n5J2Fx/IIsa5PJmv29stLXquqrKKBr9hEz3Ddbsj/RH3QLsi/tGDBAknS73//+zHP0y6rrqwvHtYT\nGdbxlZooihT9lj002fOR50LFKKrU0Z8nUq6eqvM7Fc2FRp0wBhHpS1sOVVbGOMaqVJtPqf60eSJo\nmyX0IWLM6FTuvJ6eHknS8ePHcx2PPyvvS78DyhHLNUe7btY/2IqUMcYYY0xJKlOkilj9oQ9E2X2R\nQusHBYooIjKmF1WOsNLaRausEKyvdvmYVU1MaaT9/PnPf+74PUkN5aRsRA3WFOv5fX19kqS//e1v\nkqSDBw82e4tthczsoWpB1F5KHaEe+R3td9WqVZI6n6unDLTBZiMMUSjK+jkWzcofHn/o0KFh/8f3\nJFS6WE1IjaXhPqf4zeX9fVFCn6h27bEXo2hm7om6Z2K7KNt/Yv7MtL9m1X0rUsYYY4wxJalMkZo+\nfXrSTwDrBGsHSxSrDIu27M7YWHX8LbuPD1FVUNbno9VgBYd7m0008vp1oNzRPmhfhw8fllQ8HxRR\nftRnKtovBu2qbHtg/f65556T1PCzafW+U+0CJY7M7ihslAeRSXxOPZIBnt9TDnyPf8PF7QLVjmO7\nu7uH/Qalg+OoW/y2UnCvecGCnj59uqSG6l0Wxqowc3i7ctnFosYoe3yLwszljN2UO+VM+fP/3t7e\nYeflewjVRlYPOD8qO32b+zp69Giu50uNCYwBPH9RX6pQhc77+zBH2kTxjaIPoyTSHsKI6aJjIe2c\nMaTVNBs9akXKGGOMMaYktayCpEa1Wk39/f2dvqwxxhhjTGH6+/ujPntWpIwxxhhjSlKZj9Rzzz1X\n92FhHR7/AXwjrr32WkkNX5DQjyH0ucAfgnXYO++8U5L03e9+V1Ijeyl/WRcNs5mSd4h18fC67CvF\n+jX38ZWvfEWS9POf/1xSPOLiRz/6kSRp9+7dkkbmNVqzZo2kht/B3r17h31PlODdd98tSdqyZYuk\nhk8QvlGszxNZw/OEfhr4ILFuzfesS7O+zfMZY4wx5gJWpIwxxhhjSlKZIvXuu+/q1KlTYx5z5MiR\nMb8PIzrIFhtGA6K0cD3WOWP7LBHlFSPMfYKCBqncH08//bQk6dVXXx31+xUrVkiSnnnmmVG/D3Of\nhM9BJAp/iRSJRX6QPTiMfqQcO5X11xhjjJloWJEyxhhjjClJZYrU+++/X/cxIidI2T3OUpmN8aHi\n/Py/VTuPh7llUjt2x5QofK3wjYrlhgn3BQpzr4SkAjPxQcOnKlS4Uuc3xhhjLlWsSBljjDHGlKQy\nRercuXN1JaSsEpWX0Men1b4/KGpQNoM1vlbbt2+XFM+6G+52n8p6m8q0zucch1JXQYoxY4wxZkJh\nRcoYY4wxpiSVKVKjwS7vze7IHe5vBKFyxHEoMEX3YuP3+FxBs/si4QMVU5Ji0YYhPG9ehYzyn6h7\n8hljjDGdxoqUMcYYY0xJKlOkrrjiihHRdq2KDgsVG5SZMEqPKDl8pYoqUmQEZ8fxGEQn4hOWgkzi\nKENlfa74Xeq65I+66qqrSl3HGGOMuVSxImWMMcYYU5LKFKmPfexjIxSpUEnCR+gTn/iEpJGZt6Gr\nq2vY/8MoQJQuFCn2okNJCq+DQoUixPnDDOb4MoU+UiFcJ69vE+dtVqFDicqriNk3yhhjjCmGFSlj\njDHGmJJUpkhdfvnldWUo5puEgpJSSj7+8Y9LavgEnTlzZtTjyIt0+eWXD/uLIoUShe9Ub2/vsPOG\nihTKVyofVV4lClJKVJhJPUVMUQvxnnrGGGNMMaxIGWOMMcaUpDJF6tSpU/VoupgihYJEJvIYZ8+e\nldSIokspQCgzXB8fInyZiF5DsXr33XfHPB+/7xRhPqwU3B95oigf76FnjDHGNIcVKWOMMcaYklSm\nSL333nsjMoCn9oSLcfr0aUkNX6qU0oIvEL5D+Grhk8X1jx49Ouz8MVL3i88VSlBMgZs6deqw72PH\nEdWXFzKw40t2+PDhUY/zHnvGGGNMMaxIGWOMMcaUpDJFarT96Mpm8Ia8UWcch+8TihF5rdjrL+Wb\nBbH8TB/96EclNRQkng8fJ/7iu4Ry9K9//SvXdfPyzjvvSEr7jlmJMsYYY4phRcoYY4wxpiSVKVLN\ngC8VPkUoKShDeRUdlKJwT7uiGb5RngCFC2WL85IBfdq0aZIaiheKVCrPU1m4H6IQYz5YlGerFTFj\njDFmsmJFyhhjjDGmJONKkULZQUHBtyfcOw+Fh73xyJNEdF2oEMXOT74o/LVee+21XPd59dVXS2oo\nTDNnzhz2fUxZQjkjGhDCPQdjcN+xaL4Yn/zkJyWN3GMQTp06pcsuu6yujFmRqpahoSHNmjWr6tsw\n/4/rY/zguhhfuD4uYEXKJDczNp0lnGibanF9jB9cF+ML18cFKlOkbrjhBt14440duVZ/f3+u4zZs\n2NDR67WKZq+3e/fujtWFMcYYM5mwImWMMcYYU5JaVkHyoM9+9rN66aWXOn1ZY4wxxpjC3HDDDXrx\nxRdH/a6SiZQxxhhjzGTAS3vGGGOMMSXxRMoYY4wxpiQdn0jt2rVL8+fP19y5c/XYY491+vJGUm9v\nr5YuXarly5dr1apVkqQzZ85o7dq1mjdvnm6++WbnkmoTd955p7q7u7VkyZL6Z2OV/aOPPqq5c+dq\n/vz5euGFF6q45UnNaPXR39+vnp4eLV++XMuXL9fOnTvr37k+2suxY8d04403atGiRVq8eLEef/xx\nSe4jVRCrC/ePUcg6yH//+99s9uzZ2dDQUHb+/Pmsr68vGxwc7OQtmCzLent7s9OnTw/77MEHH8we\ne+yxLMuy7Pvf/372jW98o4pbm/S8/PLL2f79+7PFixfXP4uV/euvv5719fVl58+fz4aGhrLZs2dn\n//vf/yq578nKaPXR39+f/fCHPxxxrOuj/Zw8eTI7cOBAlmVZdvbs2WzevHnZ4OCg+0gFxOrC/WMk\nHVWkBgYGNGfOHPX29mrKlCm67bbbtGPHjk7egvl/siDG4He/+502bdokSdq0aZN++9vfVnFbk57r\nr79eV1555bDPYmW/Y8cO3X777ZoyZYp6e3s1Z84cDQwMdPyeJzOj1Yc0sn9Iro9OMH36dC1btkyS\n1NXVpQULFujEiRPuIxUQqwvJ/SOkoxOpEydO6Jprrqn/v6enp14xpnPUajWtWbNGK1eu1C9+8QtJ\nF7aJ6e7uliR1d3fr1KlTVd7iJUWs7N966y319PTUj3N/6Rzbtm1TX1+fNm/eXF9Gcn10lqNHj+rA\ngQP69Kc/7T5SMdTF6tWrJbl/hHR0IlWr1Tp5ORNhz549OnDggHbu3Kmf/OQneuWVV4Z9X6vVXFcV\nkSp710v7ueeeezQ0NKSDBw9qxowZeuCBB6LHuj7aw7lz57R+/Xpt3bpVH/nIR4Z95z7SWc6dO6cN\nGzZo69at6urqcv8YhY5OpGbOnKljx47V/3/s2LFhM1jTGWbMmCHpwqbPX/jCFzQwMKDu7m69/fbb\nkqSTJ09q2rRpVd7iJUWs7MP+cvz48REbZJvWM23atPrL+q677qovT7g+OsMHH3yg9evX64477tCt\nt94qyX2kKqiLjRs31uvC/WMkHZ1IrVy5Um+88YaOHj2q8+fP69lnn9W6des6eQuXPP/5z3909uxZ\nSdK///1vvfDCC1qyZInWrVun7du3S5K2b99e7zSm/cTKft26dfr1r3+t8+fPa2hoSG+88UY9ytK0\nj5MnT9b//Zvf/KYe0ef6aD9Zlmnz5s1auHCh7rvvvvrn7iOdJ1YX7h+j0Gnv9ueffz6bN29eNnv2\n7OyRRx7p9OUveY4cOZL19fVlfX192aJFi+p1cPr06eymm27K5s6dm61duzZ77733Kr7Tycltt92W\nzZgxI5syZUrW09OTPfXUU2OW/cMPP5zNnj07u+6667Jdu+tes7EAAACgSURBVHZVeOeTk7A+nnzy\nyeyOO+7IlixZki1dujS75ZZbsrfffrt+vOujvbzyyitZrVbL+vr6smXLlmXLli3Ldu7c6T5SAaPV\nxfPPP+/+MQreIsYYY4wxpiTObG6MMcYYUxJPpIwxxhhjSuKJlDHGGGNMSTyRMsYYY4wpiSdSxhhj\njDEl8UTKGGOMMaYknkgZY4wxxpTEEyljjDHGmJL8HyFOTlbukMv2AAAAAElFTkSuQmCC\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0x7fd621d25190>"
-       ]
-      }
-     ],
-     "prompt_number": 13
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The fifth layer output, `conv5` (rectified, all 256 channels)"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "feat = net.blobs['conv5'].data[4]\n",
-      "vis_square(feat, padval=0.5)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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QkQIAAIjEhRQAAEAkUntoq8MtKb3izJYeyDJE3m1/c+sivW7dujC2d+/eoqaT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\nxEaKiIiISKf/AMt+wa3UEUvkAAAAAElFTkSuQmCC\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0x7fd621d1b3d0>"
-       ]
-      }
-     ],
-     "prompt_number": 14
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The fifth layer after pooling, `pool5`"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "feat = net.blobs['pool5'].data[4]\n",
-      "vis_square(feat, padval=1)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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GTMu3v7/fiGn7PS8vz2q72udN3M9bTUFBgRGzPW9tc9Gm9Gu0c1nT3t5uxOrq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\nIqDoAAAAAElFTkSuQmCC\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0x7fd621d7cd90>"
-       ]
-      }
-     ],
-     "prompt_number": 15
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The first fully connected layer, `fc6` (rectified)\n",
-      "\n",
-      "We show the output values and the histogram of the positive values"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "feat = net.blobs['fc6'].data[4]\n",
-      "plt.subplot(2, 1, 1)\n",
-      "plt.plot(feat.flat)\n",
-      "plt.subplot(2, 1, 2)\n",
-      "_ = plt.hist(feat.flat[feat.flat > 0], bins=100)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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SpqSP+cc+lmx+eVKtbu+Xeh+HP9Nzm7kk7cAZ5jhz+tx/v7RsWdqlSMfWrft+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\ni4iIiEgxDlhEREREinHAIiIiIlLsO5hP7FaK+KtUAAAAAElFTkSuQmCC\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0x7fd63064b890>"
-       ]
-      }
-     ],
-     "prompt_number": 16
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The second fully connected layer, `fc7` (rectified)"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "feat = net.blobs['fc7'].data[4]\n",
-      "plt.subplot(2, 1, 1)\n",
-      "plt.plot(feat.flat)\n",
-      "plt.subplot(2, 1, 2)\n",
-      "_ = plt.hist(feat.flat[feat.flat > 0], bins=100)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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oo7xIJvWCXOztt6XLL+/92bBhuT4cTjIZ6c//XFq/PtzYomC6nm64IdzOy6tX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\nGERyBQAAYND/B6hFJTD2mK/NAAAAAElFTkSuQmCC\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0x7fd6306fb450>"
-       ]
-      }
-     ],
-     "prompt_number": 17
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The final probability output, `prob`"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "feat = net.blobs['prob'].data[4]\n",
-      "plt.plot(feat.flat)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 18,
-       "text": [
-        "[<matplotlib.lines.Line2D at 0x7fd63060d990>]"
-       ]
-      },
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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+acS6dRH/+q8RU6akPhoAJpqx7Ja6I2ENDQ2xcuXKWLRoUVSr1Vi2bFl0dHTEqlWrIiJi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\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0x7fd630627290>"
-       ]
-      }
-     ],
-     "prompt_number": 18
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Let's see the top 5 predicted labels."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# load labels\n",
-      "imagenet_labels_filename = caffe_root + 'data/ilsvrc12/synset_words.txt'\n",
-      "try:\n",
-      "    labels = np.loadtxt(imagenet_labels_filename, str, delimiter='\\t')\n",
-      "except:\n",
-      "    !../data/ilsvrc12/get_ilsvrc_aux.sh\n",
-      "    labels = np.loadtxt(imagenet_labels_filename, str, delimiter='\\t')\n",
-      "\n",
-      "# sort top k predictions from softmax output\n",
-      "top_k = net.blobs['prob'].data[4].flatten().argsort()[-1:-6:-1]\n",
-      "print labels[top_k]"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "['n02123045 tabby, tabby cat' 'n02123159 tiger cat'\n",
-        " 'n02124075 Egyptian cat' 'n02119022 red fox, Vulpes vulpes'\n",
-        " 'n02127052 lynx, catamount']\n"
-       ]
-      }
-     ],
-     "prompt_number": 19
-    }
-   ],
-   "metadata": {}
-  }
- ]
-}
diff --git a/examples/finetune_flickr_style/assemble_data.py b/examples/finetune_flickr_style/assemble_data.py
index b4c995e8eae..09bfa2618a4 100755
--- a/examples/finetune_flickr_style/assemble_data.py
+++ b/examples/finetune_flickr_style/assemble_data.py
@@ -9,6 +9,7 @@
 import argparse
 import numpy as np
 import pandas as pd
+from skimage import io
 import multiprocessing
 
 # Flickr returns a special image if the request is unavailable.
@@ -27,6 +28,7 @@ def download_image(args_tuple):
             urllib.urlretrieve(url, filename)
         with open(filename) as f:
             assert hashlib.sha1(f.read()).hexdigest() != MISSING_IMAGE_SHA1
+        test_read_image = io.imread(filename)
         return True
     except KeyboardInterrupt:
         raise Exception()  # multiprocessing doesn't catch keyboard exceptions
@@ -48,6 +50,10 @@ def download_image(args_tuple):
         '-w', '--workers', type=int, default=-1,
         help="num workers used to download images. -x uses (all - x) cores [-1 default]."
     )
+    parser.add_argument(
+        '-l', '--labels', type=int, default=0,
+        help="if set to a positive value, only sample images from the first number of labels."
+    )
 
     args = parser.parse_args()
     np.random.seed(args.seed)
@@ -56,6 +62,8 @@ def download_image(args_tuple):
     csv_filename = os.path.join(example_dirname, 'flickr_style.csv.gz')
     df = pd.read_csv(csv_filename, index_col=0, compression='gzip')
     df = df.iloc[np.random.permutation(df.shape[0])]
+    if args.labels > 0:
+        df = df.loc[df['label'] < args.labels]
     if args.images > 0 and args.images < df.shape[0]:
         df = df.iloc[:args.images]
 
diff --git a/examples/finetune_flickr_style/style_names.txt b/examples/finetune_flickr_style/style_names.txt
new file mode 100644
index 00000000000..73090c97821
--- /dev/null
+++ b/examples/finetune_flickr_style/style_names.txt
@@ -0,0 +1,20 @@
+Detailed
+Pastel
+Melancholy
+Noir
+HDR
+Vintage
+Long Exposure
+Horror
+Sunny
+Bright
+Hazy
+Bokeh
+Serene
+Texture
+Ethereal
+Macro
+Depth of Field
+Geometric Composition
+Minimal
+Romantic
diff --git a/examples/finetune_pascal_detection/pascal_finetune_trainval_test.prototxt b/examples/finetune_pascal_detection/pascal_finetune_trainval_test.prototxt
index 5cd605bbf11..9dd2120acad 100644
--- a/examples/finetune_pascal_detection/pascal_finetune_trainval_test.prototxt
+++ b/examples/finetune_pascal_detection/pascal_finetune_trainval_test.prototxt
@@ -1,9 +1,17 @@
 name: "CaffeNet"
-layers {
+layer {
   name: "data"
-  type: WINDOW_DATA
+  type: "WindowData"
   top: "data"
   top: "label"
+  include {
+    phase: TRAIN
+  }
+  transform_param {
+    mirror: true
+    crop_size: 227
+    mean_file: "data/ilsvrc12/imagenet_mean.binaryproto"
+  }
   window_data_param {
     source: "examples/finetune_pascal_detection/window_file_2007_trainval.txt"
     batch_size: 128
@@ -13,18 +21,20 @@ layers {
     context_pad: 16
     crop_mode: "warp"
   }
+}
+layer {
+  name: "data"
+  type: "WindowData"
+  top: "data"
+  top: "label"
+  include {
+    phase: TEST
+  }
   transform_param {
     mirror: true
     crop_size: 227
     mean_file: "data/ilsvrc12/imagenet_mean.binaryproto"
   }
-  include: { phase: TRAIN }
-}
-layers {
-  name: "data"
-  type: WINDOW_DATA
-  top: "data"
-  top: "label"
   window_data_param {
     source: "examples/finetune_pascal_detection/window_file_2007_test.txt"
     batch_size: 128
@@ -34,22 +44,20 @@ layers {
     context_pad: 16
     crop_mode: "warp"
   }
-  transform_param {
-    mirror: true
-    crop_size: 227
-    mean_file: "data/ilsvrc12/imagenet_mean.binaryproto"
-  }
-  include: { phase: TEST }
 }
-layers {
+layer {
   name: "conv1"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "data"
   top: "conv1"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 96
     kernel_size: 11
@@ -64,15 +72,15 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu1"
-  type: RELU
+  type: "ReLU"
   bottom: "conv1"
   top: "conv1"
 }
-layers {
+layer {
   name: "pool1"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv1"
   top: "pool1"
   pooling_param {
@@ -81,9 +89,9 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "norm1"
-  type: LRN
+  type: "LRN"
   bottom: "pool1"
   top: "norm1"
   lrn_param {
@@ -92,15 +100,19 @@ layers {
     beta: 0.75
   }
 }
-layers {
+layer {
   name: "conv2"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "norm1"
   top: "conv2"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 256
     pad: 2
@@ -116,15 +128,15 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu2"
-  type: RELU
+  type: "ReLU"
   bottom: "conv2"
   top: "conv2"
 }
-layers {
+layer {
   name: "pool2"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv2"
   top: "pool2"
   pooling_param {
@@ -133,9 +145,9 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "norm2"
-  type: LRN
+  type: "LRN"
   bottom: "pool2"
   top: "norm2"
   lrn_param {
@@ -144,15 +156,19 @@ layers {
     beta: 0.75
   }
 }
-layers {
+layer {
   name: "conv3"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "norm2"
   top: "conv3"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 384
     pad: 1
@@ -167,21 +183,25 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu3"
-  type: RELU
+  type: "ReLU"
   bottom: "conv3"
   top: "conv3"
 }
-layers {
+layer {
   name: "conv4"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "conv3"
   top: "conv4"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 384
     pad: 1
@@ -197,21 +217,25 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu4"
-  type: RELU
+  type: "ReLU"
   bottom: "conv4"
   top: "conv4"
 }
-layers {
+layer {
   name: "conv5"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "conv4"
   top: "conv5"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 256
     pad: 1
@@ -227,15 +251,15 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu5"
-  type: RELU
+  type: "ReLU"
   bottom: "conv5"
   top: "conv5"
 }
-layers {
+layer {
   name: "pool5"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv5"
   top: "pool5"
   pooling_param {
@@ -244,15 +268,19 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "fc6"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "pool5"
   top: "fc6"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   inner_product_param {
     num_output: 4096
     weight_filler {
@@ -265,30 +293,34 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu6"
-  type: RELU
+  type: "ReLU"
   bottom: "fc6"
   top: "fc6"
 }
-layers {
+layer {
   name: "drop6"
-  type: DROPOUT
+  type: "Dropout"
   bottom: "fc6"
   top: "fc6"
   dropout_param {
     dropout_ratio: 0.5
   }
 }
-layers {
+layer {
   name: "fc7"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "fc6"
   top: "fc7"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   inner_product_param {
     num_output: 4096
     weight_filler {
@@ -301,30 +333,34 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu7"
-  type: RELU
+  type: "ReLU"
   bottom: "fc7"
   top: "fc7"
 }
-layers {
+layer {
   name: "drop7"
-  type: DROPOUT
+  type: "Dropout"
   bottom: "fc7"
   top: "fc7"
   dropout_param {
     dropout_ratio: 0.5
   }
 }
-layers {
+layer {
   name: "fc8_pascal"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "fc7"
   top: "fc8_pascal"
-  blobs_lr: 10
-  blobs_lr: 20
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 10
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 20
+    decay_mult: 0
+  }
   inner_product_param {
     num_output: 21
     weight_filler {
@@ -337,17 +373,19 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "loss"
-  type: SOFTMAX_LOSS
+  type: "SoftmaxWithLoss"
   bottom: "fc8_pascal"
   bottom: "label"
 }
-layers {
+layer {
   name: "accuracy"
-  type: ACCURACY
+  type: "Accuracy"
   bottom: "fc8_pascal"
   bottom: "label"
   top: "accuracy"
-  include { phase: TEST }
+  include {
+    phase: TEST
+  }
 }
diff --git a/examples/hdf5_classification.ipynb b/examples/hdf5_classification.ipynb
deleted file mode 100644
index 51d854fa142..00000000000
--- a/examples/hdf5_classification.ipynb
+++ /dev/null
@@ -1,947 +0,0 @@
-{
- "metadata": {
-  "description": "Use Caffe as a generic SGD optimizer to train logistic regression on non-image HDF5 data.",
-  "example_name": "Off-the-shelf SGD for classification",
-  "include_in_docs": true,
-  "priority": 4,
-  "signature": "sha256:c3b84add3bb83e91137f396a48f46d46bf7921b242fc42c58390b30806e5a028"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
-  {
-   "cells": [
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "# Classification with HDF5 data\n",
-      "\n",
-      "In this example we'll use Caffe to do simple logistic regression on a simple binary dataset, showcasing HDF5DataLayer functionality."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import os\n",
-      "import h5py\n",
-      "import shutil\n",
-      "import sklearn\n",
-      "import tempfile\n",
-      "import numpy as np\n",
-      "import pandas as pd\n",
-      "import sklearn.datasets\n",
-      "import sklearn.linear_model\n",
-      "import matplotlib.pyplot as plt\n",
-      "%matplotlib inline"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 1
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "X, y = sklearn.datasets.make_classification(\n",
-      "    n_samples=10000, n_features=4, n_redundant=0, n_informative=2, \n",
-      "    n_clusters_per_class=2, hypercube=False, random_state=0\n",
-      ")\n",
-      "\n",
-      "# Split into train and test\n",
-      "X, Xt, y, yt = sklearn.cross_validation.train_test_split(X, y)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 2
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# Visualize sample of the data\n",
-      "ind = np.random.permutation(X.shape[0])[:1000]\n",
-      "df = pd.DataFrame(X[ind])\n",
-      "_ = pd.scatter_matrix(df, figsize=(9, 9), diagonal='kde', marker='o', s=40, alpha=.4, c=y[ind])"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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sdhObNgUaQYyyAZsGC8ePo9br2INBVFluVOV1OlE7zKhqY2rN4TBht5v44Q+v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h3spVisI7TqfOfUBEqVAml9gTd/dBOdRqSSzbORL+B269H1jjG8I0Cj0SC0EiG8nqBeM6E0ZxH1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6hUakweD5Ik01Xr6RTyOA+Noq7lUKlUiE4n9XQa6Z6Gqtto3AuMfPDHy782b4gfpDSGw1Zy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\nk+zV24cEuflBVv7uPl3Hj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s0mtVyOrhMnMHV1Eb5+nfT6OqVIBFGlwjhdRukcpmdugsTG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fIuVFOp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\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ofF0Zryw3GB2N4/FsnPDL5RqlkvyuOiOHDy+27MvlKhSLVR55pPstJdiOjSWQpBo6nYpM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\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8+Z//+YOexn3DR//u8R7wflv9ep1In0+mnkthsJrQyjKRy1Os/fRV/OMjJBaWsXidNCU9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\nALgAAAAASUVORK5CYII=\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0x1155cf1d0>"
-       ]
-      }
-     ],
-     "prompt_number": 3
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# Train and test the scikit-learn SGD logistic regression.\n",
-      "clf = sklearn.linear_model.SGDClassifier(\n",
-      "    loss='log', n_iter=1000, penalty='l2', alpha=1e-3, class_weight='auto')\n",
-      "\n",
-      "clf.fit(X, y)\n",
-      "yt_pred = clf.predict(Xt)\n",
-      "print('Accuracy: {:.3f}'.format(sklearn.metrics.accuracy_score(yt, yt_pred)))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Accuracy: 0.763\n"
-       ]
-      }
-     ],
-     "prompt_number": 4
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# Write out the data to HDF5 files in a temp directory.\n",
-      "# This file is assumed to be caffe_root/examples/hdf5_classification.ipynb\n",
-      "dirname = os.path.abspath('./hdf5_classification/data')\n",
-      "if not os.path.exists(dirname):\n",
-      "    os.makedirs(dirname)\n",
-      "\n",
-      "train_filename = os.path.join(dirname, 'train.h5')\n",
-      "test_filename = os.path.join(dirname, 'test.h5')\n",
-      "\n",
-      "# HDF5DataLayer source should be a file containing a list of HDF5 filenames.\n",
-      "# To show this off, we'll list the same data file twice.\n",
-      "with h5py.File(train_filename, 'w') as f:\n",
-      "    f['data'] = X\n",
-      "    f['label'] = y.astype(np.float32)\n",
-      "with open(os.path.join(dirname, 'train.txt'), 'w') as f:\n",
-      "    f.write(train_filename + '\\n')\n",
-      "    f.write(train_filename + '\\n')\n",
-      "    \n",
-      "# HDF5 is pretty efficient, but can be further compressed.\n",
-      "comp_kwargs = {'compression': 'gzip', 'compression_opts': 1}\n",
-      "with h5py.File(test_filename, 'w') as f:\n",
-      "    f.create_dataset('data', data=Xt, **comp_kwargs)\n",
-      "    f.create_dataset('label', data=yt.astype(np.float32), **comp_kwargs)\n",
-      "with open(os.path.join(dirname, 'test.txt'), 'w') as f:\n",
-      "    f.write(test_filename + '\\n')"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 5
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# Run caffe. Scroll down in the output to see the final\n",
-      "# test accuracy, which should be about the same as above.\n",
-      "!cd .. && ./build/tools/caffe train -solver examples/hdf5_classification/solver.prototxt"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "I0905 01:07:27.099238 2129298192 caffe.cpp:90] Starting Optimization\r\n",
-        "I0905 01:07:27.100469 2129298192 solver.cpp:32] Initializing solver from parameters: \r\n",
-        "test_iter: 1000\r\n",
-        "test_interval: 1000\r\n",
-        "base_lr: 0.01\r\n",
-        "display: 1000\r\n",
-        "max_iter: 10000\r\n",
-        "lr_policy: \"step\"\r\n",
-        "gamma: 0.1\r\n",
-        "momentum: 0.9\r\n",
-        "weight_decay: 0.0005\r\n",
-        "stepsize: 5000\r\n",
-        "snapshot: 10000\r\n",
-        "snapshot_prefix: \"examples/hdf5_classification/data/train\"\r\n",
-        "solver_mode: CPU\r\n",
-        "net: \"examples/hdf5_classification/train_val.prototxt\"\r\n",
-        "I0905 01:07:27.100630 2129298192 solver.cpp:72] Creating training net from net file: examples/hdf5_classification/train_val.prototxt\r\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "I0905 01:07:27.100988 2129298192 net.cpp:275] The NetState phase (0) differed from the phase (1) specified by a rule in layer data\r\n",
-        "I0905 01:07:27.101011 2129298192 net.cpp:275] The NetState phase (0) differed from the phase (1) specified by a rule in layer accuracy\r\n",
-        "I0905 01:07:27.101022 2129298192 net.cpp:39] Initializing net from parameters: \r\n",
-        "name: \"LogisticRegressionNet\"\r\n",
-        "layers {\r\n",
-        "  top: \"data\"\r\n",
-        "  top: \"label\"\r\n",
-        "  name: \"data\"\r\n",
-        "  type: HDF5_DATA\r\n",
-        "  hdf5_data_param {\r\n",
-        "    source: \"examples/hdf5_classification/data/train.txt\"\r\n",
-        "    batch_size: 10\r\n",
-        "  }\r\n",
-        "  include {\r\n",
-        "    phase: TRAIN\r\n",
-        "  }\r\n",
-        "}\r\n",
-        "layers {\r\n",
-        "  bottom: \"data\"\r\n",
-        "  top: \"fc1\"\r\n",
-        "  name: \"fc1\"\r\n",
-        "  type: INNER_PRODUCT\r\n",
-        "  blobs_lr: 1\r\n",
-        "  blobs_lr: 2\r\n",
-        "  weight_decay: 1\r\n",
-        "  weight_decay: 0\r\n",
-        "  inner_product_param {\r\n",
-        "    num_output: 2\r\n",
-        "    weight_filler {\r\n",
-        "      type: \"gaussian\"\r\n",
-        "      std: 0.01\r\n",
-        "    }\r\n",
-        "    bias_filler {\r\n",
-        "      type: \"constant\"\r\n",
-        "      value: 0\r\n",
-        "    }\r\n",
-        "  }\r\n",
-        "}\r\n",
-        "layers {\r\n",
-        "  bottom: \"fc1\"\r\n",
-        "  bottom: \"label\"\r\n",
-        "  top: \"loss\"\r\n",
-        "  name: \"loss\"\r\n",
-        "  type: SOFTMAX_LOSS\r\n",
-        "}\r\n",
-        "state {\r\n",
-        "  phase: TRAIN\r\n",
-        "}\r\n",
-        "I0905 01:07:27.105614 2129298192 net.cpp:67] Creating Layer data\r\n",
-        "I0905 01:07:27.105664 2129298192 net.cpp:356] data -> data\r\n",
-        "I0905 01:07:27.105698 2129298192 net.cpp:356] data -> label\r\n",
-        "I0905 01:07:27.105710 2129298192 net.cpp:96] Setting up data\r\n",
-        "I0905 01:07:27.105717 2129298192 hdf5_data_layer.cpp:57] Loading filename from examples/hdf5_classification/data/train.txt\r\n",
-        "I0905 01:07:27.105813 2129298192 hdf5_data_layer.cpp:69] Number of files: 2\r\n",
-        "I0905 01:07:27.105828 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/train.h5\r\n",
-        "I0905 01:07:27.109418 2129298192 hdf5_data_layer.cpp:49] Successully loaded 7500 rows\r\n",
-        "I0905 01:07:27.109501 2129298192 hdf5_data_layer.cpp:81] output data size: 10,4,1,1\r\n",
-        "I0905 01:07:27.109522 2129298192 net.cpp:103] Top shape: 10 4 1 1 (40)\r\n",
-        "I0905 01:07:27.109531 2129298192 net.cpp:103] Top shape: 10 1 1 1 (10)\r\n",
-        "I0905 01:07:27.109560 2129298192 net.cpp:67] Creating Layer fc1\r\n",
-        "I0905 01:07:27.109570 2129298192 net.cpp:394] fc1 <- data\r\n",
-        "I0905 01:07:27.109590 2129298192 net.cpp:356] fc1 -> fc1\r\n",
-        "I0905 01:07:27.109618 2129298192 net.cpp:96] Setting up fc1\r\n",
-        "I0905 01:07:27.115136 2129298192 net.cpp:103] Top shape: 10 2 1 1 (20)\r\n",
-        "I0905 01:07:27.115190 2129298192 net.cpp:67] Creating Layer loss\r\n",
-        "I0905 01:07:27.115198 2129298192 net.cpp:394] loss <- fc1\r\n",
-        "I0905 01:07:27.115206 2129298192 net.cpp:394] loss <- label\r\n",
-        "I0905 01:07:27.115214 2129298192 net.cpp:356] loss -> loss\r\n",
-        "I0905 01:07:27.115224 2129298192 net.cpp:96] Setting up loss\r\n",
-        "I0905 01:07:27.115237 2129298192 net.cpp:103] Top shape: 1 1 1 1 (1)\r\n",
-        "I0905 01:07:27.115244 2129298192 net.cpp:109]     with loss weight 1\r\n",
-        "I0905 01:07:27.115260 2129298192 net.cpp:170] loss needs backward computation.\r\n",
-        "I0905 01:07:27.115267 2129298192 net.cpp:170] fc1 needs backward computation.\r\n",
-        "I0905 01:07:27.115273 2129298192 net.cpp:172] data does not need backward computation.\r\n",
-        "I0905 01:07:27.115278 2129298192 net.cpp:208] This network produces output loss\r\n",
-        "I0905 01:07:27.115288 2129298192 net.cpp:467] Collecting Learning Rate and Weight Decay.\r\n",
-        "I0905 01:07:27.115295 2129298192 net.cpp:219] Network initialization done.\r\n",
-        "I0905 01:07:27.115301 2129298192 net.cpp:220] Memory required for data: 284\r\n",
-        "I0905 01:07:27.115622 2129298192 solver.cpp:156] Creating test net (#0) specified by net file: examples/hdf5_classification/train_val.prototxt\r\n",
-        "I0905 01:07:27.115644 2129298192 net.cpp:275] The NetState phase (1) differed from the phase (0) specified by a rule in layer data\r\n",
-        "I0905 01:07:27.115656 2129298192 net.cpp:39] Initializing net from parameters: \r\n",
-        "name: \"LogisticRegressionNet\"\r\n",
-        "layers {\r\n",
-        "  top: \"data\"\r\n",
-        "  top: \"label\"\r\n",
-        "  name: \"data\"\r\n",
-        "  type: HDF5_DATA\r\n",
-        "  hdf5_data_param {\r\n",
-        "    source: \"examples/hdf5_classification/data/test.txt\"\r\n",
-        "    batch_size: 10\r\n",
-        "  }\r\n",
-        "  include {\r\n",
-        "    phase: TEST\r\n",
-        "  }\r\n",
-        "}\r\n",
-        "layers {\r\n",
-        "  bottom: \"data\"\r\n",
-        "  top: \"fc1\"\r\n",
-        "  name: \"fc1\"\r\n",
-        "  type: INNER_PRODUCT\r\n",
-        "  blobs_lr: 1\r\n",
-        "  blobs_lr: 2\r\n",
-        "  weight_decay: 1\r\n",
-        "  weight_decay: 0\r\n",
-        "  inner_product_param {\r\n",
-        "    num_output: 2\r\n",
-        "    weight_filler {\r\n",
-        "      type: \"gaussian\"\r\n",
-        "      std: 0.01\r\n",
-        "    }\r\n",
-        "    bias_filler {\r\n",
-        "      type: \"constant\"\r\n",
-        "      value: 0\r\n",
-        "    }\r\n",
-        "  }\r\n",
-        "}\r\n",
-        "layers {\r\n",
-        "  bottom: \"fc1\"\r\n",
-        "  bottom: \"label\"\r\n",
-        "  top: \"loss\"\r\n",
-        "  name: \"loss\"\r\n",
-        "  type: SOFTMAX_LOSS\r\n",
-        "}\r\n",
-        "layers {\r\n",
-        "  bottom: \"fc1\"\r\n",
-        "  bottom: \"label\"\r\n",
-        "  top: \"accuracy\"\r\n",
-        "  name: \"accuracy\"\r\n",
-        "  type: ACCURACY\r\n",
-        "  include {\r\n",
-        "    phase: TEST\r\n",
-        "  }\r\n",
-        "}\r\n",
-        "state {\r\n",
-        "  phase: TEST\r\n",
-        "}\r\n",
-        "I0905 01:07:27.115854 2129298192 net.cpp:67] Creating Layer data\r\n",
-        "I0905 01:07:27.115864 2129298192 net.cpp:356] data -> data\r\n",
-        "I0905 01:07:27.116004 2129298192 net.cpp:356] data -> label\r\n",
-        "I0905 01:07:27.116024 2129298192 net.cpp:96] Setting up data\r\n",
-        "I0905 01:07:27.116030 2129298192 hdf5_data_layer.cpp:57] Loading filename from examples/hdf5_classification/data/test.txt\r\n",
-        "I0905 01:07:27.116080 2129298192 hdf5_data_layer.cpp:69] Number of files: 1\r\n",
-        "I0905 01:07:27.116089 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/test.h5\r\n",
-        "I0905 01:07:27.117313 2129298192 hdf5_data_layer.cpp:49] Successully loaded 2500 rows\r\n",
-        "I0905 01:07:27.117348 2129298192 hdf5_data_layer.cpp:81] output data size: 10,4,1,1\r\n",
-        "I0905 01:07:27.117357 2129298192 net.cpp:103] Top shape: 10 4 1 1 (40)\r\n",
-        "I0905 01:07:27.117364 2129298192 net.cpp:103] Top shape: 10 1 1 1 (10)\r\n",
-        "I0905 01:07:27.117377 2129298192 net.cpp:67] Creating Layer label_data_1_split\r\n",
-        "I0905 01:07:27.117384 2129298192 net.cpp:394] label_data_1_split <- label\r\n",
-        "I0905 01:07:27.117393 2129298192 net.cpp:356] label_data_1_split -> label_data_1_split_0\r\n",
-        "I0905 01:07:27.117409 2129298192 net.cpp:356] label_data_1_split -> label_data_1_split_1\r\n",
-        "I0905 01:07:27.117419 2129298192 net.cpp:96] Setting up label_data_1_split\r\n",
-        "I0905 01:07:27.117427 2129298192 net.cpp:103] Top shape: 10 1 1 1 (10)\r\n",
-        "I0905 01:07:27.117434 2129298192 net.cpp:103] Top shape: 10 1 1 1 (10)\r\n",
-        "I0905 01:07:27.117444 2129298192 net.cpp:67] Creating Layer fc1\r\n",
-        "I0905 01:07:27.117449 2129298192 net.cpp:394] fc1 <- data\r\n",
-        "I0905 01:07:27.117470 2129298192 net.cpp:356] fc1 -> fc1\r\n",
-        "I0905 01:07:27.117478 2129298192 net.cpp:96] Setting up fc1\r\n",
-        "I0905 01:07:27.117506 2129298192 net.cpp:103] Top shape: 10 2 1 1 (20)\r\n",
-        "I0905 01:07:27.117519 2129298192 net.cpp:67] Creating Layer fc1_fc1_0_split\r\n",
-        "I0905 01:07:27.117527 2129298192 net.cpp:394] fc1_fc1_0_split <- fc1\r\n",
-        "I0905 01:07:27.117534 2129298192 net.cpp:356] fc1_fc1_0_split -> fc1_fc1_0_split_0\r\n",
-        "I0905 01:07:27.117543 2129298192 net.cpp:356] fc1_fc1_0_split -> fc1_fc1_0_split_1\r\n",
-        "I0905 01:07:27.117640 2129298192 net.cpp:96] Setting up fc1_fc1_0_split\r\n",
-        "I0905 01:07:27.117655 2129298192 net.cpp:103] Top shape: 10 2 1 1 (20)\r\n",
-        "I0905 01:07:27.117662 2129298192 net.cpp:103] Top shape: 10 2 1 1 (20)\r\n",
-        "I0905 01:07:27.117673 2129298192 net.cpp:67] Creating Layer loss\r\n",
-        "I0905 01:07:27.117679 2129298192 net.cpp:394] loss <- fc1_fc1_0_split_0\r\n",
-        "I0905 01:07:27.117687 2129298192 net.cpp:394] loss <- label_data_1_split_0\r\n",
-        "I0905 01:07:27.117696 2129298192 net.cpp:356] loss -> loss\r\n",
-        "I0905 01:07:27.117704 2129298192 net.cpp:96] Setting up loss\r\n",
-        "I0905 01:07:27.117717 2129298192 net.cpp:103] Top shape: 1 1 1 1 (1)\r\n",
-        "I0905 01:07:27.117723 2129298192 net.cpp:109]     with loss weight 1\r\n",
-        "I0905 01:07:27.117743 2129298192 net.cpp:67] Creating Layer accuracy\r\n",
-        "I0905 01:07:27.117749 2129298192 net.cpp:394] accuracy <- fc1_fc1_0_split_1\r\n",
-        "I0905 01:07:27.117756 2129298192 net.cpp:394] accuracy <- label_data_1_split_1\r\n",
-        "I0905 01:07:27.117764 2129298192 net.cpp:356] accuracy -> accuracy\r\n",
-        "I0905 01:07:27.117774 2129298192 net.cpp:96] Setting up accuracy\r\n",
-        "I0905 01:07:27.117781 2129298192 net.cpp:103] Top shape: 1 1 1 1 (1)\r\n",
-        "I0905 01:07:27.117789 2129298192 net.cpp:172] accuracy does not need backward computation.\r\n",
-        "I0905 01:07:27.117794 2129298192 net.cpp:170] loss needs backward computation.\r\n",
-        "I0905 01:07:27.117835 2129298192 net.cpp:170] fc1_fc1_0_split needs backward computation.\r\n",
-        "I0905 01:07:27.117842 2129298192 net.cpp:170] fc1 needs backward computation.\r\n",
-        "I0905 01:07:27.117848 2129298192 net.cpp:172] label_data_1_split does not need backward computation.\r\n",
-        "I0905 01:07:27.117854 2129298192 net.cpp:172] data does not need backward computation.\r\n",
-        "I0905 01:07:27.117861 2129298192 net.cpp:208] This network produces output accuracy\r\n",
-        "I0905 01:07:27.117866 2129298192 net.cpp:208] This network produces output loss\r\n",
-        "I0905 01:07:27.117877 2129298192 net.cpp:467] Collecting Learning Rate and Weight Decay.\r\n",
-        "I0905 01:07:27.117926 2129298192 net.cpp:219] Network initialization done.\r\n",
-        "I0905 01:07:27.117938 2129298192 net.cpp:220] Memory required for data: 528\r\n",
-        "I0905 01:07:27.117985 2129298192 solver.cpp:46] Solver scaffolding done.\r\n",
-        "I0905 01:07:27.117992 2129298192 solver.cpp:165] Solving LogisticRegressionNet\r\n",
-        "I0905 01:07:27.118026 2129298192 solver.cpp:251] Iteration 0, Testing net (#0)\r\n",
-        "I0905 01:07:27.123764 2129298192 solver.cpp:302]     Test net output #0: accuracy = 0.646801\r\n",
-        "I0905 01:07:27.123847 2129298192 solver.cpp:302]     Test net output #1: loss = 0.690777 (* 1 = 0.690777 loss)\r\n",
-        "I0905 01:07:27.123888 2129298192 solver.cpp:195] Iteration 0, loss = 0.689469\r\n",
-        "I0905 01:07:27.123898 2129298192 solver.cpp:210]     Train net output #0: loss = 0.689469 (* 1 = 0.689469 loss)\r\n",
-        "I0905 01:07:27.123915 2129298192 solver.cpp:405] Iteration 0, lr = 0.01\r\n",
-        "I0905 01:07:27.127096 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/train.h5\r\n",
-        "I0905 01:07:27.128094 2129298192 hdf5_data_layer.cpp:49] Successully loaded 7500 rows\r\n",
-        "I0905 01:07:27.129258 2129298192 solver.cpp:251] Iteration 1000, Testing net (#0)\r\n",
-        "I0905 01:07:27.135226 2129298192 solver.cpp:302]     Test net output #0: accuracy = 0.745599\r\n",
-        "I0905 01:07:27.135296 2129298192 solver.cpp:302]     Test net output #1: loss = 0.573658 (* 1 = 0.573658 loss)\r\n",
-        "I0905 01:07:27.135315 2129298192 solver.cpp:195] Iteration 1000, loss = 0.49682\r\n",
-        "I0905 01:07:27.135325 2129298192 solver.cpp:210]     Train net output #0: loss = 0.49682 (* 1 = 0.49682 loss)\r\n",
-        "I0905 01:07:27.135334 2129298192 solver.cpp:405] Iteration 1000, lr = 0.01\r\n",
-        "I0905 01:07:27.137315 2129298192 hdf5_data_layer.cpp:99] looping around to first file\r\n",
-        "I0905 01:07:27.137358 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/train.h5\r\n",
-        "I0905 01:07:27.138335 2129298192 hdf5_data_layer.cpp:49] Successully loaded 7500 rows\r\n",
-        "I0905 01:07:27.140410 2129298192 solver.cpp:251] Iteration 2000, Testing net (#0)\r\n",
-        "I0905 01:07:27.147435 2129298192 solver.cpp:302]     Test net output #0: accuracy = 0.746399\r\n",
-        "I0905 01:07:27.147514 2129298192 solver.cpp:302]     Test net output #1: loss = 0.582127 (* 1 = 0.582127 loss)\r\n",
-        "I0905 01:07:27.147541 2129298192 solver.cpp:195] Iteration 2000, loss = 0.555272\r\n",
-        "I0905 01:07:27.147553 2129298192 solver.cpp:210]     Train net output #0: loss = 0.555272 (* 1 = 0.555272 loss)\r\n",
-        "I0905 01:07:27.147565 2129298192 solver.cpp:405] Iteration 2000, lr = 0.01\r\n",
-        "I0905 01:07:27.148572 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/train.h5\r\n",
-        "I0905 01:07:27.149441 2129298192 hdf5_data_layer.cpp:49] Successully loaded 7500 rows\r\n",
-        "I0905 01:07:27.152377 2129298192 solver.cpp:251] Iteration 3000, Testing net (#0)\r\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "I0905 01:07:27.158655 2129298192 solver.cpp:302]     Test net output #0: accuracy = 0.696\r\n",
-        "I0905 01:07:27.158746 2129298192 solver.cpp:302]     Test net output #1: loss = 0.580239 (* 1 = 0.580239 loss)\r\n",
-        "I0905 01:07:27.158761 2129298192 hdf5_data_layer.cpp:99] looping around to first file\r\n",
-        "I0905 01:07:27.158768 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/train.h5\r\n",
-        "I0905 01:07:27.159765 2129298192 hdf5_data_layer.cpp:49] Successully loaded 7500 rows\r\n",
-        "I0905 01:07:27.159843 2129298192 solver.cpp:195] Iteration 3000, loss = 0.476517\r\n",
-        "I0905 01:07:27.159873 2129298192 solver.cpp:210]     Train net output #0: loss = 0.476517 (* 1 = 0.476517 loss)\r\n",
-        "I0905 01:07:27.159983 2129298192 solver.cpp:405] Iteration 3000, lr = 0.01\r\n",
-        "I0905 01:07:27.163079 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/train.h5\r\n",
-        "I0905 01:07:27.163602 2129298192 hdf5_data_layer.cpp:49] Successully loaded 7500 rows\r\n",
-        "I0905 01:07:27.164567 2129298192 solver.cpp:251] Iteration 4000, Testing net (#0)\r\n",
-        "I0905 01:07:27.170277 2129298192 solver.cpp:302]     Test net output #0: accuracy = 0.745599\r\n",
-        "I0905 01:07:27.170344 2129298192 solver.cpp:302]     Test net output #1: loss = 0.573658 (* 1 = 0.573658 loss)\r\n",
-        "I0905 01:07:27.170364 2129298192 solver.cpp:195] Iteration 4000, loss = 0.49682\r\n",
-        "I0905 01:07:27.170375 2129298192 solver.cpp:210]     Train net output #0: loss = 0.49682 (* 1 = 0.49682 loss)\r\n",
-        "I0905 01:07:27.170385 2129298192 solver.cpp:405] Iteration 4000, lr = 0.01\r\n",
-        "I0905 01:07:27.172350 2129298192 hdf5_data_layer.cpp:99] looping around to first file\r\n",
-        "I0905 01:07:27.172374 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/train.h5\r\n",
-        "I0905 01:07:27.173084 2129298192 hdf5_data_layer.cpp:49] Successully loaded 7500 rows\r\n",
-        "I0905 01:07:27.175192 2129298192 solver.cpp:251] Iteration 5000, Testing net (#0)\r\n",
-        "I0905 01:07:27.181659 2129298192 solver.cpp:302]     Test net output #0: accuracy = 0.746399\r\n",
-        "I0905 01:07:27.181710 2129298192 solver.cpp:302]     Test net output #1: loss = 0.582127 (* 1 = 0.582127 loss)\r\n",
-        "I0905 01:07:27.181730 2129298192 solver.cpp:195] Iteration 5000, loss = 0.555272\r\n",
-        "I0905 01:07:27.181740 2129298192 solver.cpp:210]     Train net output #0: loss = 0.555272 (* 1 = 0.555272 loss)\r\n",
-        "I0905 01:07:27.181748 2129298192 solver.cpp:405] Iteration 5000, lr = 0.001\r\n",
-        "I0905 01:07:27.182734 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/train.h5\r\n",
-        "I0905 01:07:27.183248 2129298192 hdf5_data_layer.cpp:49] Successully loaded 7500 rows\r\n",
-        "I0905 01:07:27.186180 2129298192 solver.cpp:251] Iteration 6000, Testing net (#0)\r\n",
-        "I0905 01:07:27.192646 2129298192 solver.cpp:302]     Test net output #0: accuracy = 0.7684\r\n",
-        "I0905 01:07:27.192751 2129298192 solver.cpp:302]     Test net output #1: loss = 0.574538 (* 1 = 0.574538 loss)\r\n",
-        "I0905 01:07:27.192766 2129298192 hdf5_data_layer.cpp:99] looping around to first file\r\n",
-        "I0905 01:07:27.192773 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/train.h5\r\n",
-        "I0905 01:07:27.193936 2129298192 hdf5_data_layer.cpp:49] Successully loaded 7500 rows\r\n",
-        "I0905 01:07:27.194007 2129298192 solver.cpp:195] Iteration 6000, loss = 0.464052\r\n",
-        "I0905 01:07:27.194036 2129298192 solver.cpp:210]     Train net output #0: loss = 0.464052 (* 1 = 0.464052 loss)\r\n",
-        "I0905 01:07:27.194051 2129298192 solver.cpp:405] Iteration 6000, lr = 0.001\r\n",
-        "I0905 01:07:27.197053 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/train.h5\r\n",
-        "I0905 01:07:27.198092 2129298192 hdf5_data_layer.cpp:49] Successully loaded 7500 rows\r\n",
-        "I0905 01:07:27.199162 2129298192 solver.cpp:251] Iteration 7000, Testing net (#0)\r\n",
-        "I0905 01:07:27.205195 2129298192 solver.cpp:302]     Test net output #0: accuracy = 0.7684\r\n",
-        "I0905 01:07:27.205298 2129298192 solver.cpp:302]     Test net output #1: loss = 0.574549 (* 1 = 0.574549 loss)\r\n",
-        "I0905 01:07:27.205327 2129298192 solver.cpp:195] Iteration 7000, loss = 0.495483\r\n",
-        "I0905 01:07:27.205338 2129298192 solver.cpp:210]     Train net output #0: loss = 0.495483 (* 1 = 0.495483 loss)\r\n",
-        "I0905 01:07:27.205353 2129298192 solver.cpp:405] Iteration 7000, lr = 0.001\r\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "I0905 01:07:27.207471 2129298192 hdf5_data_layer.cpp:99] looping around to first file\r\n",
-        "I0905 01:07:27.207489 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/train.h5\r\n",
-        "I0905 01:07:27.208534 2129298192 hdf5_data_layer.cpp:49] Successully loaded 7500 rows\r\n",
-        "I0905 01:07:27.210860 2129298192 solver.cpp:251] Iteration 8000, Testing net (#0)\r\n",
-        "I0905 01:07:27.216624 2129298192 solver.cpp:302]     Test net output #0: accuracy = 0.762\r\n",
-        "I0905 01:07:27.216704 2129298192 solver.cpp:302]     Test net output #1: loss = 0.574515 (* 1 = 0.574515 loss)\r\n",
-        "I0905 01:07:27.216723 2129298192 solver.cpp:195] Iteration 8000, loss = 0.524565\r\n",
-        "I0905 01:07:27.216733 2129298192 solver.cpp:210]     Train net output #0: loss = 0.524565 (* 1 = 0.524565 loss)\r\n",
-        "I0905 01:07:27.216743 2129298192 solver.cpp:405] Iteration 8000, lr = 0.001\r\n",
-        "I0905 01:07:27.217738 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/train.h5\r\n",
-        "I0905 01:07:27.218291 2129298192 hdf5_data_layer.cpp:49] Successully loaded 7500 rows\r\n",
-        "I0905 01:07:27.221294 2129298192 solver.cpp:251] Iteration 9000, Testing net (#0)\r\n",
-        "I0905 01:07:27.227104 2129298192 solver.cpp:302]     Test net output #0: accuracy = 0.7688\r\n",
-        "I0905 01:07:27.227171 2129298192 solver.cpp:302]     Test net output #1: loss = 0.574278 (* 1 = 0.574278 loss)\r\n",
-        "I0905 01:07:27.227183 2129298192 hdf5_data_layer.cpp:99] looping around to first file\r\n",
-        "I0905 01:07:27.227190 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/train.h5\r\n",
-        "I0905 01:07:27.228143 2129298192 hdf5_data_layer.cpp:49] Successully loaded 7500 rows\r\n",
-        "I0905 01:07:27.228210 2129298192 solver.cpp:195] Iteration 9000, loss = 0.461831\r\n",
-        "I0905 01:07:27.228240 2129298192 solver.cpp:210]     Train net output #0: loss = 0.461831 (* 1 = 0.461831 loss)\r\n",
-        "I0905 01:07:27.228252 2129298192 solver.cpp:405] Iteration 9000, lr = 0.001\r\n",
-        "I0905 01:07:27.231314 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/train.h5\r\n",
-        "I0905 01:07:27.232293 2129298192 hdf5_data_layer.cpp:49] Successully loaded 7500 rows\r\n",
-        "I0905 01:07:27.233417 2129298192 solver.cpp:319] Snapshotting to examples/hdf5_classification/data/train_iter_10000\r\n",
-        "I0905 01:07:27.233680 2129298192 solver.cpp:326] Snapshotting solver state to examples/hdf5_classification/data/train_iter_10000.solverstate\r\n",
-        "I0905 01:07:27.233795 2129298192 solver.cpp:232] Iteration 10000, loss = 0.49554\r\n",
-        "I0905 01:07:27.233814 2129298192 solver.cpp:251] Iteration 10000, Testing net (#0)\r\n",
-        "I0905 01:07:27.240015 2129298192 solver.cpp:302]     Test net output #0: accuracy = 0.768\r\n",
-        "I0905 01:07:27.240099 2129298192 solver.cpp:302]     Test net output #1: loss = 0.574488 (* 1 = 0.574488 loss)\r\n",
-        "I0905 01:07:27.240110 2129298192 solver.cpp:237] Optimization Done.\r\n",
-        "I0905 01:07:27.240118 2129298192 caffe.cpp:114] Optimization Done.\r\n"
-       ]
-      }
-     ],
-     "prompt_number": 6
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "If you look at the `train_val.prototxt`, you'll see that it's simple logistic regression.\n",
-      "We can make it a little more advanced by introducing a non-linearity between weights that take the input and weights that give the output -- now we have a two-layer neural network.\n",
-      "That network is given in `train_val2.prototxt`, and that's the only change made in `solver2.prototxt` which we will now use.\n",
-      "\n",
-      "The final accuracy of the network we'll train below should be higher than for the network above!"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "!cd .. && ./build/tools/caffe train -solver examples/hdf5_classification/solver2.prototxt"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "I0905 01:07:27.466722 2129298192 caffe.cpp:90] Starting Optimization\r\n",
-        "I0905 01:07:27.468166 2129298192 solver.cpp:32] Initializing solver from parameters: \r\n",
-        "test_iter: 1000\r\n",
-        "test_interval: 1000\r\n",
-        "base_lr: 0.01\r\n",
-        "display: 1000\r\n",
-        "max_iter: 10000\r\n",
-        "lr_policy: \"step\"\r\n",
-        "gamma: 0.1\r\n",
-        "momentum: 0.9\r\n",
-        "weight_decay: 0.0005\r\n",
-        "stepsize: 5000\r\n",
-        "snapshot: 10000\r\n",
-        "snapshot_prefix: \"examples/hdf5_classification/data/train\"\r\n",
-        "solver_mode: CPU\r\n",
-        "net: \"examples/hdf5_classification/train_val2.prototxt\"\r\n",
-        "I0905 01:07:27.468351 2129298192 solver.cpp:72] Creating training net from net file: examples/hdf5_classification/train_val2.prototxt\r\n",
-        "I0905 01:07:27.469081 2129298192 net.cpp:275] The NetState phase (0) differed from the phase (1) specified by a rule in layer data\r\n",
-        "I0905 01:07:27.469100 2129298192 net.cpp:275] The NetState phase (0) differed from the phase (1) specified by a rule in layer accuracy\r\n",
-        "I0905 01:07:27.469110 2129298192 net.cpp:39] Initializing net from parameters: \r\n",
-        "name: \"LogisticRegressionNet\"\r\n",
-        "layers {\r\n",
-        "  top: \"data\"\r\n",
-        "  top: \"label\"\r\n",
-        "  name: \"data\"\r\n",
-        "  type: HDF5_DATA\r\n",
-        "  hdf5_data_param {\r\n",
-        "    source: \"examples/hdf5_classification/data/train.txt\"\r\n",
-        "    batch_size: 10\r\n",
-        "  }\r\n",
-        "  include {\r\n",
-        "    phase: TRAIN\r\n",
-        "  }\r\n",
-        "}\r\n",
-        "layers {\r\n",
-        "  bottom: \"data\"\r\n",
-        "  top: \"fc1\"\r\n",
-        "  name: \"fc1\"\r\n",
-        "  type: INNER_PRODUCT\r\n",
-        "  blobs_lr: 1\r\n",
-        "  blobs_lr: 2\r\n",
-        "  weight_decay: 1\r\n",
-        "  weight_decay: 0\r\n",
-        "  inner_product_param {\r\n",
-        "    num_output: 40\r\n",
-        "    weight_filler {\r\n",
-        "      type: \"gaussian\"\r\n",
-        "      std: 0.01\r\n",
-        "    }\r\n",
-        "    bias_filler {\r\n",
-        "      type: \"constant\"\r\n",
-        "      value: 0\r\n",
-        "    }\r\n",
-        "  }\r\n",
-        "}\r\n",
-        "layers {\r\n",
-        "  bottom: \"fc1\"\r\n",
-        "  top: \"fc1\"\r\n",
-        "  name: \"relu1\"\r\n",
-        "  type: RELU\r\n",
-        "}\r\n",
-        "layers {\r\n",
-        "  bottom: \"fc1\"\r\n",
-        "  top: \"fc2\"\r\n",
-        "  name: \"fc2\"\r\n",
-        "  type: INNER_PRODUCT\r\n",
-        "  blobs_lr: 1\r\n",
-        "  blobs_lr: 2\r\n",
-        "  weight_decay: 1\r\n",
-        "  weight_decay: 0\r\n",
-        "  inner_product_param {\r\n",
-        "    num_output: 2\r\n",
-        "    weight_filler {\r\n",
-        "      type: \"gaussian\"\r\n",
-        "      std: 0.01\r\n",
-        "    }\r\n",
-        "    bias_filler {\r\n",
-        "      type: \"constant\"\r\n",
-        "      value: 0\r\n",
-        "    }\r\n",
-        "  }\r\n",
-        "}\r\n",
-        "layers {\r\n",
-        "  bottom: \"fc2\"\r\n",
-        "  bottom: \"label\"\r\n",
-        "  top: \"loss\"\r\n",
-        "  name: \"loss\"\r\n",
-        "  type: SOFTMAX_LOSS\r\n",
-        "}\r\n",
-        "state {\r\n",
-        "  phase: TRAIN\r\n",
-        "}\r\n",
-        "I0905 01:07:27.469447 2129298192 net.cpp:67] Creating Layer data\r\n",
-        "I0905 01:07:27.469467 2129298192 net.cpp:356] data -> data\r\n",
-        "I0905 01:07:27.469493 2129298192 net.cpp:356] data -> label\r\n",
-        "I0905 01:07:27.469503 2129298192 net.cpp:96] Setting up data\r\n",
-        "I0905 01:07:27.469511 2129298192 hdf5_data_layer.cpp:57] Loading filename from examples/hdf5_classification/data/train.txt\r\n",
-        "I0905 01:07:27.469558 2129298192 hdf5_data_layer.cpp:69] Number of files: 2\r\n",
-        "I0905 01:07:27.469569 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/train.h5\r\n",
-        "I0905 01:07:27.471978 2129298192 hdf5_data_layer.cpp:49] Successully loaded 7500 rows\r\n",
-        "I0905 01:07:27.471997 2129298192 hdf5_data_layer.cpp:81] output data size: 10,4,1,1\r\n",
-        "I0905 01:07:27.472008 2129298192 net.cpp:103] Top shape: 10 4 1 1 (40)\r\n",
-        "I0905 01:07:27.472015 2129298192 net.cpp:103] Top shape: 10 1 1 1 (10)\r\n",
-        "I0905 01:07:27.472026 2129298192 net.cpp:67] Creating Layer fc1\r\n",
-        "I0905 01:07:27.472033 2129298192 net.cpp:394] fc1 <- data\r\n",
-        "I0905 01:07:27.472045 2129298192 net.cpp:356] fc1 -> fc1\r\n",
-        "I0905 01:07:27.472060 2129298192 net.cpp:96] Setting up fc1\r\n",
-        "I0905 01:07:27.476827 2129298192 net.cpp:103] Top shape: 10 40 1 1 (400)\r\n",
-        "I0905 01:07:27.476857 2129298192 net.cpp:67] Creating Layer relu1\r\n",
-        "I0905 01:07:27.476865 2129298192 net.cpp:394] relu1 <- fc1\r\n",
-        "I0905 01:07:27.476872 2129298192 net.cpp:345] relu1 -> fc1 (in-place)\r\n",
-        "I0905 01:07:27.476881 2129298192 net.cpp:96] Setting up relu1\r\n",
-        "I0905 01:07:27.476888 2129298192 net.cpp:103] Top shape: 10 40 1 1 (400)\r\n",
-        "I0905 01:07:27.476896 2129298192 net.cpp:67] Creating Layer fc2\r\n",
-        "I0905 01:07:27.476902 2129298192 net.cpp:394] fc2 <- fc1\r\n",
-        "I0905 01:07:27.476909 2129298192 net.cpp:356] fc2 -> fc2\r\n",
-        "I0905 01:07:27.476918 2129298192 net.cpp:96] Setting up fc2\r\n",
-        "I0905 01:07:27.476932 2129298192 net.cpp:103] Top shape: 10 2 1 1 (20)\r\n",
-        "I0905 01:07:27.476955 2129298192 net.cpp:67] Creating Layer loss\r\n",
-        "I0905 01:07:27.476963 2129298192 net.cpp:394] loss <- fc2\r\n",
-        "I0905 01:07:27.476969 2129298192 net.cpp:394] loss <- label\r\n",
-        "I0905 01:07:27.476975 2129298192 net.cpp:356] loss -> loss\r\n",
-        "I0905 01:07:27.476984 2129298192 net.cpp:96] Setting up loss\r\n",
-        "I0905 01:07:27.477005 2129298192 net.cpp:103] Top shape: 1 1 1 1 (1)\r\n",
-        "I0905 01:07:27.477040 2129298192 net.cpp:109]     with loss weight 1\r\n",
-        "I0905 01:07:27.477051 2129298192 net.cpp:170] loss needs backward computation.\r\n",
-        "I0905 01:07:27.477058 2129298192 net.cpp:170] fc2 needs backward computation.\r\n",
-        "I0905 01:07:27.477063 2129298192 net.cpp:170] relu1 needs backward computation.\r\n",
-        "I0905 01:07:27.477069 2129298192 net.cpp:170] fc1 needs backward computation.\r\n",
-        "I0905 01:07:27.477076 2129298192 net.cpp:172] data does not need backward computation.\r\n",
-        "I0905 01:07:27.477080 2129298192 net.cpp:208] This network produces output loss\r\n",
-        "I0905 01:07:27.477099 2129298192 net.cpp:467] Collecting Learning Rate and Weight Decay.\r\n",
-        "I0905 01:07:27.477105 2129298192 net.cpp:219] Network initialization done.\r\n",
-        "I0905 01:07:27.477112 2129298192 net.cpp:220] Memory required for data: 3484\r\n",
-        "I0905 01:07:27.477455 2129298192 solver.cpp:156] Creating test net (#0) specified by net file: examples/hdf5_classification/train_val2.prototxt\r\n",
-        "I0905 01:07:27.477480 2129298192 net.cpp:275] The NetState phase (1) differed from the phase (0) specified by a rule in layer data\r\n",
-        "I0905 01:07:27.477494 2129298192 net.cpp:39] Initializing net from parameters: \r\n",
-        "name: \"LogisticRegressionNet\"\r\n",
-        "layers {\r\n",
-        "  top: \"data\"\r\n",
-        "  top: \"label\"\r\n",
-        "  name: \"data\"\r\n",
-        "  type: HDF5_DATA\r\n",
-        "  hdf5_data_param {\r\n",
-        "    source: \"examples/hdf5_classification/data/test.txt\"\r\n",
-        "    batch_size: 10\r\n",
-        "  }\r\n",
-        "  include {\r\n",
-        "    phase: TEST\r\n",
-        "  }\r\n",
-        "}\r\n",
-        "layers {\r\n",
-        "  bottom: \"data\"\r\n",
-        "  top: \"fc1\"\r\n",
-        "  name: \"fc1\"\r\n",
-        "  type: INNER_PRODUCT\r\n",
-        "  blobs_lr: 1\r\n",
-        "  blobs_lr: 2\r\n",
-        "  weight_decay: 1\r\n",
-        "  weight_decay: 0\r\n",
-        "  inner_product_param {\r\n",
-        "    num_output: 40\r\n",
-        "    weight_filler {\r\n",
-        "      type: \"gaussian\"\r\n",
-        "      std: 0.01\r\n",
-        "    }\r\n",
-        "    bias_filler {\r\n",
-        "      type: \"constant\"\r\n",
-        "      value: 0\r\n",
-        "    }\r\n",
-        "  }\r\n",
-        "}\r\n",
-        "layers {\r\n",
-        "  bottom: \"fc1\"\r\n",
-        "  top: \"fc1\"\r\n",
-        "  name: \"relu1\"\r\n",
-        "  type: RELU\r\n",
-        "}\r\n",
-        "layers {\r\n",
-        "  bottom: \"fc1\"\r\n",
-        "  top: \"fc2\"\r\n",
-        "  name: \"fc2\"\r\n",
-        "  type: INNER_PRODUCT\r\n",
-        "  blobs_lr: 1\r\n",
-        "  blobs_lr: 2\r\n",
-        "  weight_decay: 1\r\n",
-        "  weight_decay: 0\r\n",
-        "  inner_product_param {\r\n",
-        "    num_output: 2\r\n",
-        "    weight_filler {\r\n",
-        "      type: \"gaussian\"\r\n",
-        "      std: 0.01\r\n",
-        "    }\r\n",
-        "    bias_filler {\r\n",
-        "      type: \"constant\"\r\n",
-        "      value: 0\r\n",
-        "    }\r\n",
-        "  }\r\n",
-        "}\r\n",
-        "layers {\r\n",
-        "  bottom: \"fc2\"\r\n",
-        "  bottom: \"label\"\r\n",
-        "  top: \"loss\"\r\n",
-        "  name: \"loss\"\r\n",
-        "  type: SOFTMAX_LOSS\r\n",
-        "}\r\n",
-        "layers {\r\n",
-        "  bottom: \"fc2\"\r\n",
-        "  bottom: \"label\"\r\n",
-        "  top: \"accuracy\"\r\n",
-        "  name: \"accuracy\"\r\n",
-        "  type: ACCURACY\r\n",
-        "  include {\r\n",
-        "    phase: TEST\r\n",
-        "  }\r\n",
-        "}\r\n",
-        "state {\r\n",
-        "  phase: TEST\r\n",
-        "}\r\n",
-        "I0905 01:07:27.477839 2129298192 net.cpp:67] Creating Layer data\r\n",
-        "I0905 01:07:27.477850 2129298192 net.cpp:356] data -> data\r\n",
-        "I0905 01:07:27.477861 2129298192 net.cpp:356] data -> label\r\n",
-        "I0905 01:07:27.477870 2129298192 net.cpp:96] Setting up data\r\n",
-        "I0905 01:07:27.477876 2129298192 hdf5_data_layer.cpp:57] Loading filename from examples/hdf5_classification/data/test.txt\r\n",
-        "I0905 01:07:27.477902 2129298192 hdf5_data_layer.cpp:69] Number of files: 1\r\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "I0905 01:07:27.477910 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/test.h5\r\n",
-        "I0905 01:07:27.478999 2129298192 hdf5_data_layer.cpp:49] Successully loaded 2500 rows\r\n",
-        "I0905 01:07:27.479014 2129298192 hdf5_data_layer.cpp:81] output data size: 10,4,1,1\r\n",
-        "I0905 01:07:27.479022 2129298192 net.cpp:103] Top shape: 10 4 1 1 (40)\r\n",
-        "I0905 01:07:27.479028 2129298192 net.cpp:103] Top shape: 10 1 1 1 (10)\r\n",
-        "I0905 01:07:27.479038 2129298192 net.cpp:67] Creating Layer label_data_1_split\r\n",
-        "I0905 01:07:27.479044 2129298192 net.cpp:394] label_data_1_split <- label\r\n",
-        "I0905 01:07:27.479058 2129298192 net.cpp:356] label_data_1_split -> label_data_1_split_0\r\n",
-        "I0905 01:07:27.479069 2129298192 net.cpp:356] label_data_1_split -> label_data_1_split_1\r\n",
-        "I0905 01:07:27.479079 2129298192 net.cpp:96] Setting up label_data_1_split\r\n",
-        "I0905 01:07:27.479086 2129298192 net.cpp:103] Top shape: 10 1 1 1 (10)\r\n",
-        "I0905 01:07:27.479092 2129298192 net.cpp:103] Top shape: 10 1 1 1 (10)\r\n",
-        "I0905 01:07:27.479100 2129298192 net.cpp:67] Creating Layer fc1\r\n",
-        "I0905 01:07:27.480850 2129298192 net.cpp:394] fc1 <- data\r\n",
-        "I0905 01:07:27.480871 2129298192 net.cpp:356] fc1 -> fc1\r\n",
-        "I0905 01:07:27.480887 2129298192 net.cpp:96] Setting up fc1\r\n",
-        "I0905 01:07:27.480908 2129298192 net.cpp:103] Top shape: 10 40 1 1 (400)\r\n",
-        "I0905 01:07:27.480978 2129298192 net.cpp:67] Creating Layer relu1\r\n",
-        "I0905 01:07:27.480986 2129298192 net.cpp:394] relu1 <- fc1\r\n",
-        "I0905 01:07:27.480994 2129298192 net.cpp:345] relu1 -> fc1 (in-place)\r\n",
-        "I0905 01:07:27.481003 2129298192 net.cpp:96] Setting up relu1\r\n",
-        "I0905 01:07:27.481009 2129298192 net.cpp:103] Top shape: 10 40 1 1 (400)\r\n",
-        "I0905 01:07:27.481017 2129298192 net.cpp:67] Creating Layer fc2\r\n",
-        "I0905 01:07:27.481024 2129298192 net.cpp:394] fc2 <- fc1\r\n",
-        "I0905 01:07:27.481031 2129298192 net.cpp:356] fc2 -> fc2\r\n",
-        "I0905 01:07:27.481041 2129298192 net.cpp:96] Setting up fc2\r\n",
-        "I0905 01:07:27.481055 2129298192 net.cpp:103] Top shape: 10 2 1 1 (20)\r\n",
-        "I0905 01:07:27.481065 2129298192 net.cpp:67] Creating Layer fc2_fc2_0_split\r\n",
-        "I0905 01:07:27.481343 2129298192 net.cpp:394] fc2_fc2_0_split <- fc2\r\n",
-        "I0905 01:07:27.481360 2129298192 net.cpp:356] fc2_fc2_0_split -> fc2_fc2_0_split_0\r\n",
-        "I0905 01:07:27.481371 2129298192 net.cpp:356] fc2_fc2_0_split -> fc2_fc2_0_split_1\r\n",
-        "I0905 01:07:27.481379 2129298192 net.cpp:96] Setting up fc2_fc2_0_split\r\n",
-        "I0905 01:07:27.481387 2129298192 net.cpp:103] Top shape: 10 2 1 1 (20)\r\n",
-        "I0905 01:07:27.481392 2129298192 net.cpp:103] Top shape: 10 2 1 1 (20)\r\n",
-        "I0905 01:07:27.481401 2129298192 net.cpp:67] Creating Layer loss\r\n",
-        "I0905 01:07:27.481407 2129298192 net.cpp:394] loss <- fc2_fc2_0_split_0\r\n",
-        "I0905 01:07:27.481413 2129298192 net.cpp:394] loss <- label_data_1_split_0\r\n",
-        "I0905 01:07:27.481421 2129298192 net.cpp:356] loss -> loss\r\n",
-        "I0905 01:07:27.481434 2129298192 net.cpp:96] Setting up loss\r\n",
-        "I0905 01:07:27.481446 2129298192 net.cpp:103] Top shape: 1 1 1 1 (1)\r\n",
-        "I0905 01:07:27.481452 2129298192 net.cpp:109]     with loss weight 1\r\n",
-        "I0905 01:07:27.481466 2129298192 net.cpp:67] Creating Layer accuracy\r\n",
-        "I0905 01:07:27.481472 2129298192 net.cpp:394] accuracy <- fc2_fc2_0_split_1\r\n",
-        "I0905 01:07:27.481504 2129298192 net.cpp:394] accuracy <- label_data_1_split_1\r\n",
-        "I0905 01:07:27.481513 2129298192 net.cpp:356] accuracy -> accuracy\r\n",
-        "I0905 01:07:27.481521 2129298192 net.cpp:96] Setting up accuracy\r\n",
-        "I0905 01:07:27.481528 2129298192 net.cpp:103] Top shape: 1 1 1 1 (1)\r\n",
-        "I0905 01:07:27.481534 2129298192 net.cpp:172] accuracy does not need backward computation.\r\n",
-        "I0905 01:07:27.481540 2129298192 net.cpp:170] loss needs backward computation.\r\n",
-        "I0905 01:07:27.481545 2129298192 net.cpp:170] fc2_fc2_0_split needs backward computation.\r\n",
-        "I0905 01:07:27.481551 2129298192 net.cpp:170] fc2 needs backward computation.\r\n",
-        "I0905 01:07:27.481557 2129298192 net.cpp:170] relu1 needs backward computation.\r\n",
-        "I0905 01:07:27.481562 2129298192 net.cpp:170] fc1 needs backward computation.\r\n",
-        "I0905 01:07:27.481569 2129298192 net.cpp:172] label_data_1_split does not need backward computation.\r\n",
-        "I0905 01:07:27.481575 2129298192 net.cpp:172] data does not need backward computation.\r\n",
-        "I0905 01:07:27.481730 2129298192 net.cpp:208] This network produces output accuracy\r\n",
-        "I0905 01:07:27.481742 2129298192 net.cpp:208] This network produces output loss\r\n",
-        "I0905 01:07:27.481758 2129298192 net.cpp:467] Collecting Learning Rate and Weight Decay.\r\n",
-        "I0905 01:07:27.481766 2129298192 net.cpp:219] Network initialization done.\r\n",
-        "I0905 01:07:27.481771 2129298192 net.cpp:220] Memory required for data: 3728\r\n",
-        "I0905 01:07:27.481814 2129298192 solver.cpp:46] Solver scaffolding done.\r\n",
-        "I0905 01:07:27.481822 2129298192 solver.cpp:165] Solving LogisticRegressionNet\r\n",
-        "I0905 01:07:27.481844 2129298192 solver.cpp:251] Iteration 0, Testing net (#0)\r\n",
-        "I0905 01:07:27.488900 2129298192 solver.cpp:302]     Test net output #0: accuracy = 0.4924\r\n",
-        "I0905 01:07:27.488932 2129298192 solver.cpp:302]     Test net output #1: loss = 0.693168 (* 1 = 0.693168 loss)\r\n",
-        "I0905 01:07:27.488962 2129298192 solver.cpp:195] Iteration 0, loss = 0.692972\r\n",
-        "I0905 01:07:27.488973 2129298192 solver.cpp:210]     Train net output #0: loss = 0.692972 (* 1 = 0.692972 loss)\r\n",
-        "I0905 01:07:27.488984 2129298192 solver.cpp:405] Iteration 0, lr = 0.01\r\n",
-        "I0905 01:07:27.495033 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/train.h5\r\n",
-        "I0905 01:07:27.495604 2129298192 hdf5_data_layer.cpp:49] Successully loaded 7500 rows\r\n",
-        "I0905 01:07:27.497684 2129298192 solver.cpp:251] Iteration 1000, Testing net (#0)\r\n",
-        "I0905 01:07:27.504875 2129298192 solver.cpp:302]     Test net output #0: accuracy = 0.7744\r\n",
-        "I0905 01:07:27.504930 2129298192 solver.cpp:302]     Test net output #1: loss = 0.486552 (* 1 = 0.486552 loss)\r\n",
-        "I0905 01:07:27.504955 2129298192 solver.cpp:195] Iteration 1000, loss = 0.660151\r\n",
-        "I0905 01:07:27.504966 2129298192 solver.cpp:210]     Train net output #0: loss = 0.660151 (* 1 = 0.660151 loss)\r\n",
-        "I0905 01:07:27.504976 2129298192 solver.cpp:405] Iteration 1000, lr = 0.01\r\n",
-        "I0905 01:07:27.509419 2129298192 hdf5_data_layer.cpp:99] looping around to first file\r\n",
-        "I0905 01:07:27.509467 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/train.h5\r\n",
-        "I0905 01:07:27.510288 2129298192 hdf5_data_layer.cpp:49] Successully loaded 7500 rows\r\n",
-        "I0905 01:07:27.514822 2129298192 solver.cpp:251] Iteration 2000, Testing net (#0)\r\n",
-        "I0905 01:07:27.522342 2129298192 solver.cpp:302]     Test net output #0: accuracy = 0.8004\r\n",
-        "I0905 01:07:27.522444 2129298192 solver.cpp:302]     Test net output #1: loss = 0.447153 (* 1 = 0.447153 loss)\r\n",
-        "I0905 01:07:27.522483 2129298192 solver.cpp:195] Iteration 2000, loss = 0.505697\r\n",
-        "I0905 01:07:27.522495 2129298192 solver.cpp:210]     Train net output #0: loss = 0.505697 (* 1 = 0.505697 loss)\r\n",
-        "I0905 01:07:27.522507 2129298192 solver.cpp:405] Iteration 2000, lr = 0.01\r\n",
-        "I0905 01:07:27.524762 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/train.h5\r\n",
-        "I0905 01:07:27.525921 2129298192 hdf5_data_layer.cpp:49] Successully loaded 7500 rows\r\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "I0905 01:07:27.533335 2129298192 solver.cpp:251] Iteration 3000, Testing net (#0)\r\n",
-        "I0905 01:07:27.541055 2129298192 solver.cpp:302]     Test net output #0: accuracy = 0.8144\r\n",
-        "I0905 01:07:27.541146 2129298192 solver.cpp:302]     Test net output #1: loss = 0.421441 (* 1 = 0.421441 loss)\r\n",
-        "I0905 01:07:27.541160 2129298192 hdf5_data_layer.cpp:99] looping around to first file\r\n",
-        "I0905 01:07:27.541167 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/train.h5\r\n",
-        "I0905 01:07:27.542178 2129298192 hdf5_data_layer.cpp:49] Successully loaded 7500 rows\r\n",
-        "I0905 01:07:27.542261 2129298192 solver.cpp:195] Iteration 3000, loss = 0.242177\r\n",
-        "I0905 01:07:27.542284 2129298192 solver.cpp:210]     Train net output #0: loss = 0.242177 (* 1 = 0.242177 loss)\r\n",
-        "I0905 01:07:27.542310 2129298192 solver.cpp:405] Iteration 3000, lr = 0.01\r\n",
-        "I0905 01:07:27.549348 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/train.h5\r\n",
-        "I0905 01:07:27.550144 2129298192 hdf5_data_layer.cpp:49] Successully loaded 7500 rows\r\n",
-        "I0905 01:07:27.552340 2129298192 solver.cpp:251] Iteration 4000, Testing net (#0)\r\n",
-        "I0905 01:07:27.560089 2129298192 solver.cpp:302]     Test net output #0: accuracy = 0.784001\r\n",
-        "I0905 01:07:27.560227 2129298192 solver.cpp:302]     Test net output #1: loss = 0.4395 (* 1 = 0.4395 loss)\r\n",
-        "I0905 01:07:27.560286 2129298192 solver.cpp:195] Iteration 4000, loss = 1.01631\r\n",
-        "I0905 01:07:27.560302 2129298192 solver.cpp:210]     Train net output #0: loss = 1.01631 (* 1 = 1.01631 loss)\r\n",
-        "I0905 01:07:27.560315 2129298192 solver.cpp:405] Iteration 4000, lr = 0.01\r\n",
-        "I0905 01:07:27.565016 2129298192 hdf5_data_layer.cpp:99] looping around to first file\r\n",
-        "I0905 01:07:27.565101 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/train.h5\r\n",
-        "I0905 01:07:27.566145 2129298192 hdf5_data_layer.cpp:49] Successully loaded 7500 rows\r\n",
-        "I0905 01:07:27.570286 2129298192 solver.cpp:251] Iteration 5000, Testing net (#0)\r\n",
-        "I0905 01:07:27.577373 2129298192 solver.cpp:302]     Test net output #0: accuracy = 0.802\r\n",
-        "I0905 01:07:27.577426 2129298192 solver.cpp:302]     Test net output #1: loss = 0.463582 (* 1 = 0.463582 loss)\r\n",
-        "I0905 01:07:27.577452 2129298192 solver.cpp:195] Iteration 5000, loss = 0.632809\r\n",
-        "I0905 01:07:27.577463 2129298192 solver.cpp:210]     Train net output #0: loss = 0.632809 (* 1 = 0.632809 loss)\r\n",
-        "I0905 01:07:27.577564 2129298192 solver.cpp:405] Iteration 5000, lr = 0.001\r\n",
-        "I0905 01:07:27.579649 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/train.h5\r\n",
-        "I0905 01:07:27.580368 2129298192 hdf5_data_layer.cpp:49] Successully loaded 7500 rows\r\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "I0905 01:07:27.586956 2129298192 solver.cpp:251] Iteration 6000, Testing net (#0)\r\n",
-        "I0905 01:07:27.594288 2129298192 solver.cpp:302]     Test net output #0: accuracy = 0.822\r\n",
-        "I0905 01:07:27.594327 2129298192 solver.cpp:302]     Test net output #1: loss = 0.407026 (* 1 = 0.407026 loss)\r\n",
-        "I0905 01:07:27.594338 2129298192 hdf5_data_layer.cpp:99] looping around to first file\r\n",
-        "I0905 01:07:27.594344 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/train.h5\r\n",
-        "I0905 01:07:27.594861 2129298192 hdf5_data_layer.cpp:49] Successully loaded 7500 rows\r\n",
-        "I0905 01:07:27.594897 2129298192 solver.cpp:195] Iteration 6000, loss = 0.214342\r\n",
-        "I0905 01:07:27.594910 2129298192 solver.cpp:210]     Train net output #0: loss = 0.214342 (* 1 = 0.214342 loss)\r\n",
-        "I0905 01:07:27.594919 2129298192 solver.cpp:405] Iteration 6000, lr = 0.001\r\n",
-        "I0905 01:07:27.601003 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/train.h5\r\n",
-        "I0905 01:07:27.601380 2129298192 hdf5_data_layer.cpp:49] Successully loaded 7500 rows\r\n",
-        "I0905 01:07:27.603358 2129298192 solver.cpp:251] Iteration 7000, Testing net (#0)\r\n",
-        "I0905 01:07:27.610307 2129298192 solver.cpp:302]     Test net output #0: accuracy = 0.8264\r\n",
-        "I0905 01:07:27.610323 2129298192 solver.cpp:302]     Test net output #1: loss = 0.403283 (* 1 = 0.403283 loss)\r\n",
-        "I0905 01:07:27.610342 2129298192 solver.cpp:195] Iteration 7000, loss = 0.894732\r\n",
-        "I0905 01:07:27.610352 2129298192 solver.cpp:210]     Train net output #0: loss = 0.894732 (* 1 = 0.894732 loss)\r\n",
-        "I0905 01:07:27.610359 2129298192 solver.cpp:405] Iteration 7000, lr = 0.001\r\n",
-        "I0905 01:07:27.614289 2129298192 hdf5_data_layer.cpp:99] looping around to first file\r\n",
-        "I0905 01:07:27.614297 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/train.h5\r\n",
-        "I0905 01:07:27.614701 2129298192 hdf5_data_layer.cpp:49] Successully loaded 7500 rows\r\n",
-        "I0905 01:07:27.618602 2129298192 solver.cpp:251] Iteration 8000, Testing net (#0)\r\n",
-        "I0905 01:07:27.625637 2129298192 solver.cpp:302]     Test net output #0: accuracy = 0.8216\r\n",
-        "I0905 01:07:27.625661 2129298192 solver.cpp:302]     Test net output #1: loss = 0.402446 (* 1 = 0.402446 loss)\r\n",
-        "I0905 01:07:27.625680 2129298192 solver.cpp:195] Iteration 8000, loss = 0.500503\r\n",
-        "I0905 01:07:27.625690 2129298192 solver.cpp:210]     Train net output #0: loss = 0.500503 (* 1 = 0.500503 loss)\r\n",
-        "I0905 01:07:27.625707 2129298192 solver.cpp:405] Iteration 8000, lr = 0.001\r\n",
-        "I0905 01:07:27.627665 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/train.h5\r\n",
-        "I0905 01:07:27.628075 2129298192 hdf5_data_layer.cpp:49] Successully loaded 7500 rows\r\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "I0905 01:07:27.634202 2129298192 solver.cpp:251] Iteration 9000, Testing net (#0)\r\n",
-        "I0905 01:07:27.641368 2129298192 solver.cpp:302]     Test net output #0: accuracy = 0.8252\r\n",
-        "I0905 01:07:27.641412 2129298192 solver.cpp:302]     Test net output #1: loss = 0.404175 (* 1 = 0.404175 loss)\r\n",
-        "I0905 01:07:27.641422 2129298192 hdf5_data_layer.cpp:99] looping around to first file\r\n",
-        "I0905 01:07:27.641428 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/train.h5\r\n",
-        "I0905 01:07:27.641960 2129298192 hdf5_data_layer.cpp:49] Successully loaded 7500 rows\r\n",
-        "I0905 01:07:27.642004 2129298192 solver.cpp:195] Iteration 9000, loss = 0.201587\r\n",
-        "I0905 01:07:27.642016 2129298192 solver.cpp:210]     Train net output #0: loss = 0.201587 (* 1 = 0.201587 loss)\r\n",
-        "I0905 01:07:27.642026 2129298192 solver.cpp:405] Iteration 9000, lr = 0.001\r\n",
-        "I0905 01:07:27.648680 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/train.h5\r\n",
-        "I0905 01:07:27.649211 2129298192 hdf5_data_layer.cpp:49] Successully loaded 7500 rows\r\n",
-        "I0905 01:07:27.651327 2129298192 solver.cpp:319] Snapshotting to examples/hdf5_classification/data/train_iter_10000\r\n",
-        "I0905 01:07:27.651476 2129298192 solver.cpp:326] Snapshotting solver state to examples/hdf5_classification/data/train_iter_10000.solverstate\r\n",
-        "I0905 01:07:27.651564 2129298192 solver.cpp:232] Iteration 10000, loss = 0.935422\r\n",
-        "I0905 01:07:27.651582 2129298192 solver.cpp:251] Iteration 10000, Testing net (#0)\r\n",
-        "I0905 01:07:27.658738 2129298192 solver.cpp:302]     Test net output #0: accuracy = 0.826\r\n",
-        "I0905 01:07:27.658782 2129298192 solver.cpp:302]     Test net output #1: loss = 0.400826 (* 1 = 0.400826 loss)\r\n",
-        "I0905 01:07:27.658790 2129298192 solver.cpp:237] Optimization Done.\r\n",
-        "I0905 01:07:27.658797 2129298192 caffe.cpp:114] Optimization Done.\r\n"
-       ]
-      }
-     ],
-     "prompt_number": 7
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# Clean up (comment this out if you want to examine the hdf5_classification/data directory).\n",
-      "shutil.rmtree(dirname)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 8
-    }
-   ],
-   "metadata": {}
-  }
- ]
-}
diff --git a/examples/hdf5_classification/nonlinear_auto_test.prototxt b/examples/hdf5_classification/nonlinear_auto_test.prototxt
new file mode 100644
index 00000000000..53eda6ee8a0
--- /dev/null
+++ b/examples/hdf5_classification/nonlinear_auto_test.prototxt
@@ -0,0 +1,54 @@
+layer {
+  name: "data"
+  type: "HDF5Data"
+  top: "data"
+  top: "label"
+  hdf5_data_param {
+    source: "examples/hdf5_classification/data/test.txt"
+    batch_size: 10
+  }
+}
+layer {
+  name: "ip1"
+  type: "InnerProduct"
+  bottom: "data"
+  top: "ip1"
+  inner_product_param {
+    num_output: 40
+    weight_filler {
+      type: "xavier"
+    }
+  }
+}
+layer {
+  name: "relu1"
+  type: "ReLU"
+  bottom: "ip1"
+  top: "ip1"
+}
+layer {
+  name: "ip2"
+  type: "InnerProduct"
+  bottom: "ip1"
+  top: "ip2"
+  inner_product_param {
+    num_output: 2
+    weight_filler {
+      type: "xavier"
+    }
+  }
+}
+layer {
+  name: "accuracy"
+  type: "Accuracy"
+  bottom: "ip2"
+  bottom: "label"
+  top: "accuracy"
+}
+layer {
+  name: "loss"
+  type: "SoftmaxWithLoss"
+  bottom: "ip2"
+  bottom: "label"
+  top: "loss"
+}
diff --git a/examples/hdf5_classification/nonlinear_auto_train.prototxt b/examples/hdf5_classification/nonlinear_auto_train.prototxt
new file mode 100644
index 00000000000..fc0688fa652
--- /dev/null
+++ b/examples/hdf5_classification/nonlinear_auto_train.prototxt
@@ -0,0 +1,54 @@
+layer {
+  name: "data"
+  type: "HDF5Data"
+  top: "data"
+  top: "label"
+  hdf5_data_param {
+    source: "examples/hdf5_classification/data/train.txt"
+    batch_size: 10
+  }
+}
+layer {
+  name: "ip1"
+  type: "InnerProduct"
+  bottom: "data"
+  top: "ip1"
+  inner_product_param {
+    num_output: 40
+    weight_filler {
+      type: "xavier"
+    }
+  }
+}
+layer {
+  name: "relu1"
+  type: "ReLU"
+  bottom: "ip1"
+  top: "ip1"
+}
+layer {
+  name: "ip2"
+  type: "InnerProduct"
+  bottom: "ip1"
+  top: "ip2"
+  inner_product_param {
+    num_output: 2
+    weight_filler {
+      type: "xavier"
+    }
+  }
+}
+layer {
+  name: "accuracy"
+  type: "Accuracy"
+  bottom: "ip2"
+  bottom: "label"
+  top: "accuracy"
+}
+layer {
+  name: "loss"
+  type: "SoftmaxWithLoss"
+  bottom: "ip2"
+  bottom: "label"
+  top: "loss"
+}
diff --git a/examples/hdf5_classification/solver2.prototxt b/examples/hdf5_classification/nonlinear_solver.prototxt
similarity index 59%
rename from examples/hdf5_classification/solver2.prototxt
rename to examples/hdf5_classification/nonlinear_solver.prototxt
index 32a3693b4a1..b4aacf6e423 100644
--- a/examples/hdf5_classification/solver2.prototxt
+++ b/examples/hdf5_classification/nonlinear_solver.prototxt
@@ -1,5 +1,6 @@
-net: "examples/hdf5_classification/train_val2.prototxt"
-test_iter: 1000
+train_net: "examples/hdf5_classification/nonlinear_auto_train.prototxt"
+test_net: "examples/hdf5_classification/nonlinear_auto_test.prototxt"
+test_iter: 250
 test_interval: 1000
 base_lr: 0.01
 lr_policy: "step"
diff --git a/examples/hdf5_classification/train_val2.prototxt b/examples/hdf5_classification/nonlinear_train_val.prototxt
similarity index 62%
rename from examples/hdf5_classification/train_val2.prototxt
rename to examples/hdf5_classification/nonlinear_train_val.prototxt
index b6a75650ad3..8f7ef04f58a 100644
--- a/examples/hdf5_classification/train_val2.prototxt
+++ b/examples/hdf5_classification/nonlinear_train_val.prototxt
@@ -1,40 +1,47 @@
 name: "LogisticRegressionNet"
-layers {
+layer {
   name: "data"
-  type: HDF5_DATA
+  type: "HDF5Data"
   top: "data"
   top: "label"
+  include {
+    phase: TRAIN
+  }
   hdf5_data_param {
     source: "examples/hdf5_classification/data/train.txt"
     batch_size: 10
   }
-  include: { phase: TRAIN }
 }
-layers {
+layer {
   name: "data"
-  type: HDF5_DATA
+  type: "HDF5Data"
   top: "data"
   top: "label"
+  include {
+    phase: TEST
+  }
   hdf5_data_param {
     source: "examples/hdf5_classification/data/test.txt"
     batch_size: 10
   }
-  include: { phase: TEST }
 }
-layers {
+layer {
   name: "fc1"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "data"
   top: "fc1"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   inner_product_param {
     num_output: 40
     weight_filler {
-      type: "gaussian"
-      std: 0.01
+      type: "xavier"
     }
     bias_filler {
       type: "constant"
@@ -42,26 +49,29 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu1"
-  type: RELU
+  type: "ReLU"
   bottom: "fc1"
   top: "fc1"
 }
-layers {
+layer {
   name: "fc2"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "fc1"
   top: "fc2"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   inner_product_param {
     num_output: 2
     weight_filler {
-      type: "gaussian"
-      std: 0.01
+      type: "xavier"
     }
     bias_filler {
       type: "constant"
@@ -69,18 +79,20 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "loss"
-  type: SOFTMAX_LOSS
+  type: "SoftmaxWithLoss"
   bottom: "fc2"
   bottom: "label"
   top: "loss"
 }
-layers {
+layer {
   name: "accuracy"
-  type: ACCURACY
+  type: "Accuracy"
   bottom: "fc2"
   bottom: "label"
   top: "accuracy"
-  include: { phase: TEST }
+  include {
+    phase: TEST
+  }
 }
diff --git a/examples/hdf5_classification/solver.prototxt b/examples/hdf5_classification/solver.prototxt
index 040162076b8..8587b5a1e5a 100644
--- a/examples/hdf5_classification/solver.prototxt
+++ b/examples/hdf5_classification/solver.prototxt
@@ -1,5 +1,6 @@
-net: "examples/hdf5_classification/train_val.prototxt"
-test_iter: 1000
+train_net: "examples/hdf5_classification/logreg_auto_train.prototxt"
+test_net: "examples/hdf5_classification/logreg_auto_test.prototxt"
+test_iter: 250
 test_interval: 1000
 base_lr: 0.01
 lr_policy: "step"
diff --git a/examples/hdf5_classification/train_val.prototxt b/examples/hdf5_classification/train_val.prototxt
index b55b6644b17..13ddf47524a 100644
--- a/examples/hdf5_classification/train_val.prototxt
+++ b/examples/hdf5_classification/train_val.prototxt
@@ -1,40 +1,47 @@
 name: "LogisticRegressionNet"
-layers {
+layer {
   name: "data"
-  type: HDF5_DATA
+  type: "HDF5Data"
   top: "data"
   top: "label"
+  include {
+    phase: TRAIN
+  }
   hdf5_data_param {
     source: "examples/hdf5_classification/data/train.txt"
     batch_size: 10
   }
-  include: { phase: TRAIN }
 }
-layers {
+layer {
   name: "data"
-  type: HDF5_DATA
+  type: "HDF5Data"
   top: "data"
   top: "label"
+  include {
+    phase: TEST
+  }
   hdf5_data_param {
     source: "examples/hdf5_classification/data/test.txt"
     batch_size: 10
   }
-  include: { phase: TEST }
 }
-layers {
+layer {
   name: "fc1"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "data"
   top: "fc1"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   inner_product_param {
     num_output: 2
     weight_filler {
-      type: "gaussian"
-      std: 0.01
+      type: "xavier"
     }
     bias_filler {
       type: "constant"
@@ -42,18 +49,20 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "loss"
-  type: SOFTMAX_LOSS
+  type: "SoftmaxWithLoss"
   bottom: "fc1"
   bottom: "label"
   top: "loss"
 }
-layers {
+layer {
   name: "accuracy"
-  type: ACCURACY
+  type: "Accuracy"
   bottom: "fc1"
   bottom: "label"
   top: "accuracy"
-  include: { phase: TEST }
+  include {
+    phase: TEST
+  }
 }
diff --git a/examples/imagenet/make_imagenet_mean.sh b/examples/imagenet/make_imagenet_mean.sh
index d3d0c9af5d2..57f43766c4b 100755
--- a/examples/imagenet/make_imagenet_mean.sh
+++ b/examples/imagenet/make_imagenet_mean.sh
@@ -1,8 +1,12 @@
 #!/usr/bin/env sh
-# Compute the mean image from the imagenet training leveldb
+# Compute the mean image from the imagenet training lmdb
 # N.B. this is available in data/ilsvrc12
 
-./build/tools/compute_image_mean examples/imagenet/ilsvrc12_train_leveldb \
-  data/ilsvrc12/imagenet_mean.binaryproto
+EXAMPLE=examples/imagenet
+DATA=data/ilsvrc12
+TOOLS=build/tools
+
+$TOOLS/compute_image_mean $EXAMPLE/ilsvrc12_train_lmdb \
+  $DATA/imagenet_mean.binaryproto
 
 echo "Done."
diff --git a/examples/imagenet/readme.md b/examples/imagenet/readme.md
index 41384f9475b..b1ebfafbf46 100644
--- a/examples/imagenet/readme.md
+++ b/examples/imagenet/readme.md
@@ -26,7 +26,7 @@ We assume that you already have downloaded the ImageNet training data and valida
 
 You will first need to prepare some auxiliary data for training. This data can be downloaded by:
 
-    ./data/get_ilsvrc_aux.sh
+    ./data/ilsvrc12/get_ilsvrc_aux.sh
 
 The training and validation input are described in `train.txt` and `val.txt` as text listing all the files and their labels. Note that we use a different indexing for labels than the ILSVRC devkit: we sort the synset names in their ASCII order, and then label them from 0 to 999. See `synset_words.txt` for the synset/name mapping.
 
@@ -67,7 +67,7 @@ We will also lay out a protocol buffer for running the solver. Let's make a few
 * We will run in batches of 256, and run a total of 450,000 iterations (about 90 epochs).
 * For every 1,000 iterations, we test the learned net on the validation data.
 * We set the initial learning rate to 0.01, and decrease it every 100,000 iterations (about 20 epochs).
-* Information will be displayed every 20 epochs.
+* Information will be displayed every 20 iterations.
 * The network will be trained with momentum 0.9 and a weight decay of 0.0005.
 * For every 10,000 iterations, we will take a snapshot of the current status.
 
@@ -91,9 +91,9 @@ Resume Training?
 
 We all experience times when the power goes out, or we feel like rewarding ourself a little by playing Battlefield (does anyone still remember Quake?). Since we are snapshotting intermediate results during training, we will be able to resume from snapshots. This can be done as easy as:
 
-    ./build/tools/caffe train --solver=models/bvlc_reference_caffenet/solver.prototxt --snapshot=models/bvlc_reference_caffenet/caffenet_train_10000.solverstate
+    ./build/tools/caffe train --solver=models/bvlc_reference_caffenet/solver.prototxt --snapshot=models/bvlc_reference_caffenet/caffenet_train_iter_10000.solverstate
 
-where in the script `caffenet_train_10000.solverstate` is the solver state snapshot that stores all necessary information to recover the exact solver state (including the parameters, momentum history, etc).
+where in the script `caffenet_train_iter_10000.solverstate` is the solver state snapshot that stores all necessary information to recover the exact solver state (including the parameters, momentum history, etc).
 
 Parting Words
 -------------
diff --git a/examples/images/cat_gray.jpg b/examples/images/cat_gray.jpg
new file mode 100644
index 00000000000..43c5ce37716
Binary files /dev/null and b/examples/images/cat_gray.jpg differ
diff --git a/examples/mnist/lenet.prototxt b/examples/mnist/lenet.prototxt
index 491fad1b1c0..cb42610fe1e 100644
--- a/examples/mnist/lenet.prototxt
+++ b/examples/mnist/lenet.prototxt
@@ -4,13 +4,17 @@ input_dim: 64
 input_dim: 1
 input_dim: 28
 input_dim: 28
-layers {
+layer {
   name: "conv1"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "data"
   top: "conv1"
-  blobs_lr: 1
-  blobs_lr: 2
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
   convolution_param {
     num_output: 20
     kernel_size: 5
@@ -23,9 +27,9 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "pool1"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv1"
   top: "pool1"
   pooling_param {
@@ -34,13 +38,17 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "conv2"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "pool1"
   top: "conv2"
-  blobs_lr: 1
-  blobs_lr: 2
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
   convolution_param {
     num_output: 50
     kernel_size: 5
@@ -53,9 +61,9 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "pool2"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv2"
   top: "pool2"
   pooling_param {
@@ -64,13 +72,17 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "ip1"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "pool2"
   top: "ip1"
-  blobs_lr: 1
-  blobs_lr: 2
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
   inner_product_param {
     num_output: 500
     weight_filler {
@@ -81,19 +93,23 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu1"
-  type: RELU
+  type: "ReLU"
   bottom: "ip1"
   top: "ip1"
 }
-layers {
+layer {
   name: "ip2"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "ip1"
   top: "ip2"
-  blobs_lr: 1
-  blobs_lr: 2
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
   inner_product_param {
     num_output: 10
     weight_filler {
@@ -104,9 +120,9 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "prob"
-  type: SOFTMAX
+  type: "Softmax"
   bottom: "ip2"
   top: "prob"
 }
diff --git a/examples/mnist/lenet_auto_solver.prototxt b/examples/mnist/lenet_auto_solver.prototxt
new file mode 100644
index 00000000000..fa4bbf02710
--- /dev/null
+++ b/examples/mnist/lenet_auto_solver.prototxt
@@ -0,0 +1,24 @@
+# The train/test net protocol buffer definition
+train_net: "examples/mnist/lenet_auto_train.prototxt"
+test_net: "examples/mnist/lenet_auto_test.prototxt"
+# test_iter specifies how many forward passes the test should carry out.
+# In the case of MNIST, we have test batch size 100 and 100 test iterations,
+# covering the full 10,000 testing images.
+test_iter: 100
+# Carry out testing every 500 training iterations.
+test_interval: 500
+# The base learning rate, momentum and the weight decay of the network.
+base_lr: 0.01
+momentum: 0.9
+weight_decay: 0.0005
+# The learning rate policy
+lr_policy: "inv"
+gamma: 0.0001
+power: 0.75
+# Display every 100 iterations
+display: 100
+# The maximum number of iterations
+max_iter: 10000
+# snapshot intermediate results
+snapshot: 5000
+snapshot_prefix: "examples/mnist/lenet"
diff --git a/examples/mnist/lenet_multistep_solver.prototxt b/examples/mnist/lenet_multistep_solver.prototxt
new file mode 100644
index 00000000000..9b22b45ba6b
--- /dev/null
+++ b/examples/mnist/lenet_multistep_solver.prototxt
@@ -0,0 +1,29 @@
+# The train/test net protocol buffer definition
+net: "examples/mnist/lenet_train_test.prototxt"
+# test_iter specifies how many forward passes the test should carry out.
+# In the case of MNIST, we have test batch size 100 and 100 test iterations,
+# covering the full 10,000 testing images.
+test_iter: 100
+# Carry out testing every 500 training iterations.
+test_interval: 500
+# The base learning rate, momentum and the weight decay of the network.
+base_lr: 0.01
+momentum: 0.9
+weight_decay: 0.0005
+# The learning rate policy
+lr_policy: "multistep"
+gamma: 0.9
+stepvalue: 5000
+stepvalue: 7000
+stepvalue: 8000
+stepvalue: 9000
+stepvalue: 9500
+# Display every 100 iterations
+display: 100
+# The maximum number of iterations
+max_iter: 10000
+# snapshot intermediate results
+snapshot: 5000
+snapshot_prefix: "examples/mnist/lenet_multistep"
+# solver mode: CPU or GPU
+solver_mode: GPU
diff --git a/examples/mnist/lenet_parallel_solver.prototxt b/examples/mnist/lenet_parallel_solver.prototxt
new file mode 100644
index 00000000000..c38d9ae7c25
--- /dev/null
+++ b/examples/mnist/lenet_parallel_solver.prototxt
@@ -0,0 +1,28 @@
+# The train/test net protocol buffer definition
+net: "examples/mnist/lenet_train_test.prototxt"
+# test_iter specifies how many forward passes the test should carry out.
+# In the case of MNIST, we have test batch size 100 and 100 test iterations,
+# covering the full 10,000 testing images.
+test_iter: 100
+# Carry out testing every 500 training iterations.
+test_interval: 500
+# The base learning rate, momentum and the weight decay of the network.
+base_lr: 0.01
+momentum: 0.9
+weight_decay: 0.0005
+# The learning rate policy
+lr_policy: "inv"
+gamma: 0.0001
+power: 0.75
+# Display every 100 iterations
+display: 100
+# The maximum number of iterations
+max_iter: 10000
+# snapshot intermediate results
+snapshot: 5000
+snapshot_prefix: "examples/mnist/lenet"
+# solver mode: CPU or GPU
+solver_mode: GPU
+device_id: [0, 1]
+
+random_seed: 1231131204
diff --git a/examples/mnist/lenet_stepearly_solver.prototxt b/examples/mnist/lenet_stepearly_solver.prototxt
new file mode 100644
index 00000000000..efc6a335d8f
--- /dev/null
+++ b/examples/mnist/lenet_stepearly_solver.prototxt
@@ -0,0 +1,28 @@
+# The training protocol buffer definition
+train_net: "lenet_train.prototxt"
+# The testing protocol buffer definition
+test_net: "lenet_test.prototxt"
+# test_iter specifies how many forward passes the test should carry out.
+# In the case of MNIST, we have test batch size 100 and 100 test iterations,
+# covering the full 10,000 testing images.
+test_iter: 100
+# Carry out testing every 500 training iterations.
+test_interval: 500
+# The base learning rate, momentum and the weight decay of the network.
+base_lr: 0.01
+momentum: 0.9
+weight_decay: 0.0005
+# The learning rate policy
+lr_policy: "stepearly"
+gamma: 0.9
+stepearly: 1
+# Display every 100 iterations
+display: 100
+# The maximum number of iterations
+max_iter: 10000
+# snapshot intermediate results
+snapshot: 5000
+snapshot_prefix: "lenet"
+# solver mode: 0 for CPU and 1 for GPU
+solver_mode: 1
+device_id: 1
diff --git a/examples/mnist/lenet_train_test.prototxt b/examples/mnist/lenet_train_test.prototxt
index 2bd960b56aa..3c3ffd73c20 100644
--- a/examples/mnist/lenet_train_test.prototxt
+++ b/examples/mnist/lenet_train_test.prototxt
@@ -1,42 +1,50 @@
 name: "LeNet"
-layers {
+layer {
   name: "mnist"
-  type: DATA
+  type: "Data"
   top: "data"
   top: "label"
-  data_param {
-    source: "examples/mnist/mnist_train_lmdb"
-    backend: LMDB
-    batch_size: 64
+  include {
+    phase: TRAIN
   }
   transform_param {
     scale: 0.00390625
   }
-  include: { phase: TRAIN }
+  data_param {
+    source: "examples/mnist/mnist_train_lmdb"
+    batch_size: 32
+    backend: LMDB
+    shuffle: true
+  }
 }
-layers {
+layer {
   name: "mnist"
-  type: DATA
+  type: "Data"
   top: "data"
   top: "label"
-  data_param {
-    source: "examples/mnist/mnist_test_lmdb"
-    backend: LMDB
-    batch_size: 100
+  include {
+    phase: TEST
   }
   transform_param {
     scale: 0.00390625
   }
-  include: { phase: TEST }
+  data_param {
+    source: "examples/mnist/mnist_test_lmdb"
+    batch_size: 100
+    backend: LMDB
+  }
 }
-
-layers {
+layer {
   name: "conv1"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "data"
   top: "conv1"
-  blobs_lr: 1
-  blobs_lr: 2
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
   convolution_param {
     num_output: 20
     kernel_size: 5
@@ -49,9 +57,9 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "pool1"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv1"
   top: "pool1"
   pooling_param {
@@ -60,13 +68,17 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "conv2"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "pool1"
   top: "conv2"
-  blobs_lr: 1
-  blobs_lr: 2
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
   convolution_param {
     num_output: 50
     kernel_size: 5
@@ -79,9 +91,9 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "pool2"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv2"
   top: "pool2"
   pooling_param {
@@ -90,13 +102,17 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "ip1"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "pool2"
   top: "ip1"
-  blobs_lr: 1
-  blobs_lr: 2
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
   inner_product_param {
     num_output: 500
     weight_filler {
@@ -107,19 +123,23 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu1"
-  type: RELU
+  type: "ReLU"
   bottom: "ip1"
   top: "ip1"
 }
-layers {
+layer {
   name: "ip2"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "ip1"
   top: "ip2"
-  blobs_lr: 1
-  blobs_lr: 2
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
   inner_product_param {
     num_output: 10
     weight_filler {
@@ -130,17 +150,19 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "accuracy"
-  type: ACCURACY
+  type: "Accuracy"
   bottom: "ip2"
   bottom: "label"
   top: "accuracy"
-  include: { phase: TEST }
+  include {
+    phase: TEST
+  }
 }
-layers {
+layer {
   name: "loss"
-  type: SOFTMAX_LOSS
+  type: "SoftmaxWithLoss"
   bottom: "ip2"
   bottom: "label"
   top: "loss"
diff --git a/examples/mnist/mnist_autoencoder.prototxt b/examples/mnist/mnist_autoencoder.prototxt
index 0b33781a16f..563c7c91e52 100644
--- a/examples/mnist/mnist_autoencoder.prototxt
+++ b/examples/mnist/mnist_autoencoder.prototxt
@@ -1,67 +1,73 @@
 name: "MNISTAutoencoder"
-layers {
-  top: "data"
+layer {
   name: "data"
-  type: DATA
-  data_param {
-    source: "examples/mnist/mnist_train_lmdb"
-    backend: LMDB
-    batch_size: 100
+  type: "Data"
+  top: "data"
+  include {
+    phase: TRAIN
   }
   transform_param {
     scale: 0.0039215684
   }
-  include: { phase: TRAIN }
-}
-layers {
-  top: "data"
-  name: "data"
-  type: DATA
   data_param {
     source: "examples/mnist/mnist_train_lmdb"
-    backend: LMDB
     batch_size: 100
+    backend: LMDB
+  }
+}
+layer {
+  name: "data"
+  type: "Data"
+  top: "data"
+  include {
+    phase: TEST
+    stage: "test-on-train"
   }
   transform_param {
     scale: 0.0039215684
   }
-  include: {
-    phase: TEST
-    stage: 'test-on-train'
+  data_param {
+    source: "examples/mnist/mnist_train_lmdb"
+    batch_size: 100
+    backend: LMDB
   }
 }
-layers {
-  top: "data"
+layer {
   name: "data"
-  type: DATA
-  data_param {
-    source: "examples/mnist/mnist_test_lmdb"
-    backend: LMDB
-    batch_size: 100
+  type: "Data"
+  top: "data"
+  include {
+    phase: TEST
+    stage: "test-on-test"
   }
   transform_param {
     scale: 0.0039215684
   }
-  include: {
-    phase: TEST
-    stage: 'test-on-test'
+  data_param {
+    source: "examples/mnist/mnist_test_lmdb"
+    batch_size: 100
+    backend: LMDB
   }
 }
-layers {
+layer {
+  name: "flatdata"
+  type: "Flatten"
   bottom: "data"
   top: "flatdata"
-  name: "flatdata"
-  type: FLATTEN
 }
-layers {
+layer {
+  name: "encode1"
+  type: "InnerProduct"
   bottom: "data"
   top: "encode1"
-  name: "encode1"
-  type: INNER_PRODUCT
-  blobs_lr: 1
-  blobs_lr: 1
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 1
+    decay_mult: 0
+  }
   inner_product_param {
     num_output: 1000
     weight_filler {
@@ -75,21 +81,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "encode1neuron"
+  type: "Sigmoid"
   bottom: "encode1"
   top: "encode1neuron"
-  name: "encode1neuron"
-  type: SIGMOID
 }
-layers {
+layer {
+  name: "encode2"
+  type: "InnerProduct"
   bottom: "encode1neuron"
   top: "encode2"
-  name: "encode2"
-  type: INNER_PRODUCT
-  blobs_lr: 1
-  blobs_lr: 1
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 1
+    decay_mult: 0
+  }
   inner_product_param {
     num_output: 500
     weight_filler {
@@ -103,21 +113,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "encode2neuron"
+  type: "Sigmoid"
   bottom: "encode2"
   top: "encode2neuron"
-  name: "encode2neuron"
-  type: SIGMOID
 }
-layers {
+layer {
+  name: "encode3"
+  type: "InnerProduct"
   bottom: "encode2neuron"
   top: "encode3"
-  name: "encode3"
-  type: INNER_PRODUCT
-  blobs_lr: 1
-  blobs_lr: 1
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 1
+    decay_mult: 0
+  }
   inner_product_param {
     num_output: 250
     weight_filler {
@@ -131,21 +145,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "encode3neuron"
+  type: "Sigmoid"
   bottom: "encode3"
   top: "encode3neuron"
-  name: "encode3neuron"
-  type: SIGMOID
 }
-layers {
+layer {
+  name: "encode4"
+  type: "InnerProduct"
   bottom: "encode3neuron"
   top: "encode4"
-  name: "encode4"
-  type: INNER_PRODUCT
-  blobs_lr: 1
-  blobs_lr: 1
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 1
+    decay_mult: 0
+  }
   inner_product_param {
     num_output: 30
     weight_filler {
@@ -159,15 +177,19 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "decode4"
+  type: "InnerProduct"
   bottom: "encode4"
   top: "decode4"
-  name: "decode4"
-  type: INNER_PRODUCT
-  blobs_lr: 1
-  blobs_lr: 1
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 1
+    decay_mult: 0
+  }
   inner_product_param {
     num_output: 250
     weight_filler {
@@ -181,21 +203,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "decode4neuron"
+  type: "Sigmoid"
   bottom: "decode4"
   top: "decode4neuron"
-  name: "decode4neuron"
-  type: SIGMOID
 }
-layers {
+layer {
+  name: "decode3"
+  type: "InnerProduct"
   bottom: "decode4neuron"
   top: "decode3"
-  name: "decode3"
-  type: INNER_PRODUCT
-  blobs_lr: 1
-  blobs_lr: 1
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 1
+    decay_mult: 0
+  }
   inner_product_param {
     num_output: 500
     weight_filler {
@@ -209,21 +235,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "decode3neuron"
+  type: "Sigmoid"
   bottom: "decode3"
   top: "decode3neuron"
-  name: "decode3neuron"
-  type: SIGMOID
 }
-layers {
+layer {
+  name: "decode2"
+  type: "InnerProduct"
   bottom: "decode3neuron"
   top: "decode2"
-  name: "decode2"
-  type: INNER_PRODUCT
-  blobs_lr: 1
-  blobs_lr: 1
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 1
+    decay_mult: 0
+  }
   inner_product_param {
     num_output: 1000
     weight_filler {
@@ -237,21 +267,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "decode2neuron"
+  type: "Sigmoid"
   bottom: "decode2"
   top: "decode2neuron"
-  name: "decode2neuron"
-  type: SIGMOID
 }
-layers {
+layer {
+  name: "decode1"
+  type: "InnerProduct"
   bottom: "decode2neuron"
   top: "decode1"
-  name: "decode1"
-  type: INNER_PRODUCT
-  blobs_lr: 1
-  blobs_lr: 1
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 1
+    decay_mult: 0
+  }
   inner_product_param {
     num_output: 784
     weight_filler {
@@ -265,25 +299,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "loss"
+  type: "SigmoidCrossEntropyLoss"
   bottom: "decode1"
   bottom: "flatdata"
   top: "cross_entropy_loss"
-  name: "loss"
-  type: SIGMOID_CROSS_ENTROPY_LOSS
   loss_weight: 1
 }
-layers {
+layer {
+  name: "decode1neuron"
+  type: "Sigmoid"
   bottom: "decode1"
   top: "decode1neuron"
-  name: "decode1neuron"
-  type: SIGMOID
 }
-layers {
+layer {
+  name: "loss"
+  type: "EuclideanLoss"
   bottom: "decode1neuron"
   bottom: "flatdata"
   top: "l2_error"
-  name: "loss"
-  type: EUCLIDEAN_LOSS
   loss_weight: 0
 }
diff --git a/examples/mnist/readme.md b/examples/mnist/readme.md
index 33ed371b4a1..413d4a1f40b 100644
--- a/examples/mnist/readme.md
+++ b/examples/mnist/readme.md
@@ -38,9 +38,9 @@ Specifically, we will write a `caffe::NetParameter` (or in python, `caffe.proto.
 
 Currently, we will read the MNIST data from the lmdb we created earlier in the demo. This is defined by a data layer:
 
-    layers {
+    layer {
       name: "mnist"
-      type: DATA
+      type: "Data"
       data_param {
         source: "mnist_train_lmdb"
         backend: LMDB
@@ -57,14 +57,14 @@ Specifically, this layer has name `mnist`, type `data`, and it reads the data fr
 
 Let's define the first convolution layer:
 
-    layers {
+    layer {
       name: "conv1"
-      type: CONVOLUTION
-      blobs_lr: 1.
-      blobs_lr: 2.
+      type: "Convolution"
+      param { lr_mult: 1 }
+      param { lr_mult: 2 }
       convolution_param {
         num_output: 20
-        kernelsize: 5
+        kernel_size: 5
         stride: 1
         weight_filler {
           type: "xavier"
@@ -81,15 +81,15 @@ This layer takes the `data` blob (it is provided by the data layer), and produce
 
 The fillers allow us to randomly initialize the value of the weights and bias. For the weight filler, we will use the `xavier` algorithm that automatically determines the scale of initialization based on the number of input and output neurons. For the bias filler, we will simply initialize it as constant, with the default filling value 0.
 
-`blobs_lr` are the learning rate adjustments for the layer's learnable parameters. In this case, we will set the weight learning rate to be the same as the learning rate given by the solver during runtime, and the bias learning rate to be twice as large as that - this usually leads to better convergence rates.
+`lr_mult`s are the learning rate adjustments for the layer's learnable parameters. In this case, we will set the weight learning rate to be the same as the learning rate given by the solver during runtime, and the bias learning rate to be twice as large as that - this usually leads to better convergence rates.
 
 ### Writing the Pooling Layer
 
 Phew. Pooling layers are actually much easier to define:
 
-    layers {
+    layer {
       name: "pool1"
-      type: POOLING
+      type: "Pooling"
       pooling_param {
         kernel_size: 2
         stride: 2
@@ -107,11 +107,11 @@ Similarly, you can write up the second convolution and pooling layers. Check `$C
 
 Writing a fully connected layer is also simple:
 
-    layers {
+    layer {
       name: "ip1"
-      type: INNER_PRODUCT
-      blobs_lr: 1.
-      blobs_lr: 2.
+      type: "InnerProduct"
+      param { lr_mult: 1 }
+      param { lr_mult: 2 }
       inner_product_param {
         num_output: 500
         weight_filler {
@@ -125,15 +125,15 @@ Writing a fully connected layer is also simple:
       top: "ip1"
     }
 
-This defines a fully connected layer (for some legacy reason, Caffe calls it an `innerproduct` layer) with 500 outputs. All other lines look familiar, right?
+This defines a fully connected layer (known in Caffe as an `InnerProduct` layer) with 500 outputs. All other lines look familiar, right?
 
 ### Writing the ReLU Layer
 
 A ReLU Layer is also simple:
 
-    layers {
+    layer {
       name: "relu1"
-      type: RELU
+      type: "ReLU"
       bottom: "ip1"
       top: "ip1"
     }
@@ -142,11 +142,11 @@ Since ReLU is an element-wise operation, we can do *in-place* operations to save
 
 After the ReLU layer, we will write another innerproduct layer:
 
-    layers {
+    layer {
       name: "ip2"
-      type: INNER_PRODUCT
-      blobs_lr: 1.
-      blobs_lr: 2.
+      type: "InnerProduct"
+      param { lr_mult: 1 }
+      param { lr_mult: 2 }
       inner_product_param {
         num_output: 10
         weight_filler {
@@ -164,9 +164,9 @@ After the ReLU layer, we will write another innerproduct layer:
 
 Finally, we will write the loss!
 
-    layers {
+    layer {
       name: "loss"
-      type: SOFTMAX_LOSS
+      type: "SoftmaxWithLoss"
       bottom: "ip2"
       bottom: "label"
     }
@@ -178,7 +178,7 @@ The `softmax_loss` layer implements both the softmax and the multinomial logisti
 
 Layer definitions can include rules for whether and when they are included in the network definition, like the one below:
 
-    layers {
+    layer {
       // ...layer definition...
       include: { phase: TRAIN }
     }
@@ -190,7 +190,7 @@ In the above example, this layer will be included only in `TRAIN` phase.
 If we change `TRAIN` with `TEST`, then this layer will be used only in test phase.
 By default, that is without layer rules, a layer is always included in the network.
 Thus, `lenet_train_test.prototxt` has two `DATA` layers defined (with different `batch_size`), one for the training phase and one for the testing phase.
-Also, there is an `ACCURACY` layer which is included only in `TEST` phase for reporting the model accuracy every 100 iteration, as defined in `lenet_solver.prototxt`.
+Also, there is an `Accuracy` layer which is included only in `TEST` phase for reporting the model accuracy every 100 iteration, as defined in `lenet_solver.prototxt`.
 
 ## Define the MNIST Solver
 
@@ -282,3 +282,6 @@ You just did! All the training was carried out on the GPU. In fact, if you would
 and you will be using CPU for training. Isn't that easy?
 
 MNIST is a small dataset, so training with GPU does not really introduce too much benefit due to communication overheads. On larger datasets with more complex models, such as ImageNet, the computation speed difference will be more significant.
+
+### How to reduce the learning rate at fixed steps?
+Look at lenet_multistep_solver.prototxt
diff --git a/examples/mnist/train_lenet_parallel.sh b/examples/mnist/train_lenet_parallel.sh
new file mode 100755
index 00000000000..9e50bea5a45
--- /dev/null
+++ b/examples/mnist/train_lenet_parallel.sh
@@ -0,0 +1,3 @@
+#!/usr/bin/env sh
+export LD_LIBRARY_PATH=/usr/local/openmpi/lib/:$LD_LIBRARY_PATH
+/usr/local/openmpi/bin/mpirun -np 2 cmake_build/install/bin/caffe train --solver=examples/mnist/lenet_parallel_solver.prototxt
diff --git a/examples/net_surgery.ipynb b/examples/net_surgery.ipynb
index 64310016101..ff780fbb9f7 100644
--- a/examples/net_surgery.ipynb
+++ b/examples/net_surgery.ipynb
@@ -1,355 +1,6911 @@
 {
- "metadata": {
-  "description": "How to do net surgery and manually change model parameters, making a fully-convolutional classifier for dense feature extraction.",
-  "example_name": "Editing model parameters",
-  "include_in_docs": true,
-  "priority": 5,
-  "signature": "sha256:179fb20339497f5e64f6fbeb57987f27a962b7ae6d940c8fede2631aba9bffaf"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Net Surgery\n",
+    "\n",
+    "Caffe networks can be transformed to your particular needs by editing the model parameters. The data, diffs, and parameters of a net are all exposed in pycaffe.\n",
+    "\n",
+    "Roll up your sleeves for net surgery with pycaffe!"
+   ]
+  },
   {
-   "cells": [
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "import Image\n",
+    "\n",
+    "# Make sure that caffe is on the python path:\n",
+    "caffe_root = '../'  # this file is expected to be in {caffe_root}/examples\n",
+    "import sys\n",
+    "sys.path.insert(0, caffe_root + 'python')\n",
+    "\n",
+    "import caffe\n",
+    "\n",
+    "# configure plotting\n",
+    "plt.rcParams['figure.figsize'] = (10, 10)\n",
+    "plt.rcParams['image.interpolation'] = 'nearest'\n",
+    "plt.rcParams['image.cmap'] = 'gray'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Designer Filters\n",
+    "\n",
+    "To show how to load, manipulate, and save parameters we'll design our own filters into a simple network that's only a single convolution layer. This net has two blobs, `data` for the input and `conv` for the convolution output and one parameter `conv` for the convolution filter weights and biases."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "## Net Surgery for a Fully-Convolutional Model\n",
-      "\n",
-      "Caffe models can be transformed to your particular needs by editing the network parameters. In this example, we take the standard Caffe Reference ImageNet model \"CaffeNet\" and transform it into a fully-convolutional model for efficient, dense inference on large inputs. This model generates a classification map that covers a given input size instead of a single classification. In particular a 8 $\\times$ 8 classification map on a 451 $\\times$ 451 input gives 64x the output in only 3x the time. The computation exploits a natural efficiency of convolutional neural network (CNN) structure by dynamic programming in the forward pass from shallow to deep layers.\n",
-      "\n",
-      "To do so we translate the inner product classifier layers of CaffeNet into convolutional layers. This is the only change: the other layer types are agnostic to spatial size. Convolution is translation-invariant, activations are elementwise operations, and so on. The `fc6` inner product when carried out as convolution by `fc6-conv` turns into a 6 \\times 6 filter with stride 1 on `pool5`. Back in image space this gives a classification for each 227 $\\times$ 227 box with stride 32 in pixels. Remember the equation for output map / receptive field size, output = (input - kernel_size) / stride + 1, and work out the indexing details for a clear understanding.\n",
-      "\n",
-      "Roll up your sleeves for net surgery with pycaffe!"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "blobs ['data', 'conv']\n",
+      "params ['conv']\n"
      ]
     },
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "!diff imagenet/imagenet_full_conv.prototxt ../models/bvlc_reference_caffenet/deploy.prototxt"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "1,2c1\r\n",
-        "< # This file is for the net_surgery.ipynb example notebook.\r\n",
-        "< name: \"CaffeNetConv\"\r\n",
-        "---\r\n",
-        "> name: \"CaffeNet\"\r\n",
-        "4c3\r\n",
-        "< input_dim: 1\r\n",
-        "---\r\n",
-        "> input_dim: 10\r\n",
-        "6,7c5,6\r\n",
-        "< input_dim: 451\r\n",
-        "< input_dim: 451\r\n",
-        "---\r\n",
-        "> input_dim: 227\r\n",
-        "> input_dim: 227\r\n",
-        "152,153c151,152\r\n",
-        "<   name: \"fc6-conv\"\r\n",
-        "<   type: CONVOLUTION\r\n",
-        "---\r\n",
-        ">   name: \"fc6\"\r\n",
-        ">   type: INNER_PRODUCT\r\n",
-        "155,156c154,155\r\n",
-        "<   top: \"fc6-conv\"\r\n",
-        "<   convolution_param {\r\n",
-        "---\r\n",
-        ">   top: \"fc6\"\r\n",
-        ">   inner_product_param {\r\n",
-        "158d156\r\n",
-        "<     kernel_size: 6\r\n",
-        "164,165c162,163\r\n",
-        "<   bottom: \"fc6-conv\"\r\n",
-        "<   top: \"fc6-conv\"\r\n",
-        "---\r\n",
-        ">   bottom: \"fc6\"\r\n",
-        ">   top: \"fc6\"\r\n",
-        "170,171c168,169\r\n",
-        "<   bottom: \"fc6-conv\"\r\n",
-        "<   top: \"fc6-conv\"\r\n",
-        "---\r\n",
-        ">   bottom: \"fc6\"\r\n",
-        ">   top: \"fc6\"\r\n",
-        "177,181c175,179\r\n",
-        "<   name: \"fc7-conv\"\r\n",
-        "<   type: CONVOLUTION\r\n",
-        "<   bottom: \"fc6-conv\"\r\n",
-        "<   top: \"fc7-conv\"\r\n",
-        "<   convolution_param {\r\n",
-        "---\r\n",
-        ">   name: \"fc7\"\r\n",
-        ">   type: INNER_PRODUCT\r\n",
-        ">   bottom: \"fc6\"\r\n",
-        ">   top: \"fc7\"\r\n",
-        ">   inner_product_param {\r\n",
-        "183d180\r\n",
-        "<     kernel_size: 1\r\n",
-        "189,190c186,187\r\n",
-        "<   bottom: \"fc7-conv\"\r\n",
-        "<   top: \"fc7-conv\"\r\n",
-        "---\r\n",
-        ">   bottom: \"fc7\"\r\n",
-        ">   top: \"fc7\"\r\n",
-        "195,196c192,193\r\n",
-        "<   bottom: \"fc7-conv\"\r\n",
-        "<   top: \"fc7-conv\"\r\n",
-        "---\r\n",
-        ">   bottom: \"fc7\"\r\n",
-        ">   top: \"fc7\"\r\n",
-        "202,206c199,203\r\n",
-        "<   name: \"fc8-conv\"\r\n",
-        "<   type: CONVOLUTION\r\n",
-        "<   bottom: \"fc7-conv\"\r\n",
-        "<   top: \"fc8-conv\"\r\n",
-        "<   convolution_param {\r\n",
-        "---\r\n",
-        ">   name: \"fc8\"\r\n",
-        ">   type: INNER_PRODUCT\r\n",
-        ">   bottom: \"fc7\"\r\n",
-        ">   top: \"fc8\"\r\n",
-        ">   inner_product_param {\r\n",
-        "208d204\r\n",
-        "<     kernel_size: 1\r\n",
-        "214c210\r\n",
-        "<   bottom: \"fc8-conv\"\r\n",
-        "---\r\n",
-        ">   bottom: \"fc8\"\r\n"
-       ]
-      }
-     ],
-     "prompt_number": 1
-    },
-    {
-     "cell_type": "markdown",
+     "data": {
+      "image/png": [
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+       "n59+FodZjgAAAABJRU5ErkJggg==\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x112797f50>"
+      ]
+     },
      "metadata": {},
-     "source": [
-      "The only differences needed in the architecture are to change the fully-connected classifier inner product layers into convolutional layers with the right filter size -- 6 x 6, since the reference model classifiers take the 36 elements of `pool5` as input -- and stride 1 for dense classification. Note that the layers are renamed so that Caffe does not try to blindly load the old parameters when it maps layer names to the pretrained model."
-     ]
-    },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Load the net, list its data and params, and filter an example image.\n",
+    "caffe.set_mode_cpu()\n",
+    "net = caffe.Net('net_surgery/conv.prototxt', caffe.TEST)\n",
+    "print(\"blobs {}\\nparams {}\".format(net.blobs.keys(), net.params.keys()))\n",
+    "\n",
+    "# load image and prepare as a single input batch for Caffe\n",
+    "im = np.array(Image.open('images/cat_gray.jpg'))\n",
+    "plt.title(\"original image\")\n",
+    "plt.imshow(im)\n",
+    "plt.axis('off')\n",
+    "\n",
+    "im_input = im[np.newaxis, np.newaxis, :, :]\n",
+    "net.blobs['data'].reshape(*im_input.shape)\n",
+    "net.blobs['data'].data[...] = im_input"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The convolution weights are initialized from Gaussian noise while the biases are initialized to zero. These random filters give output somewhat like edge detections."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# Make sure that caffe is on the python path:\n",
-      "caffe_root = '../'  # this file is expected to be in {caffe_root}/examples\n",
-      "import sys\n",
-      "sys.path.insert(0, caffe_root + 'python')\n",
-      "\n",
-      "import caffe\n",
-      "\n",
-      "# Load the original network and extract the fully-connected layers' parameters.\n",
-      "net = caffe.Net('../models/bvlc_reference_caffenet/deploy.prototxt', '../models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel')\n",
-      "params = ['fc6', 'fc7', 'fc8']\n",
-      "# fc_params = {name: (weights, biases)}\n",
-      "fc_params = {pr: (net.params[pr][0].data, net.params[pr][1].data) for pr in params}\n",
-      "\n",
-      "for fc in params:\n",
-      "    print '{} weights are {} dimensional and biases are {} dimensional'.format(fc, fc_params[fc][0].shape, fc_params[fc][1].shape)"
-     ],
-     "language": "python",
+     "data": {
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+       "2bd927d927d927dvqu3Byb7t277t277t2759U20PTvZt3/Zt3/Zt3/btm2p7cLJv+7Zv+7Zv+7Zv\n",
+       "31Tbg5N927d927d927d9+6baHpzs277t277t277t2zfV/i+IAQDEy/wsagAAAABJRU5ErkJggg==\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x113e04090>"
+      ]
+     },
      "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "fc6 weights are (1, 1, 4096, 9216) dimensional and biases are (1, 1, 1, 4096) dimensional\n",
-        "fc7 weights are (1, 1, 4096, 4096) dimensional and biases are (1, 1, 1, 4096) dimensional\n",
-        "fc8 weights are (1, 1, 1000, 4096) dimensional and biases are (1, 1, 1, 1000) dimensional\n"
-       ]
-      }
-     ],
-     "prompt_number": 2
-    },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# helper show filter outputs\n",
+    "def show_filters(net):\n",
+    "    net.forward()\n",
+    "    plt.figure()\n",
+    "    filt_min, filt_max = net.blobs['conv'].data.min(), net.blobs['conv'].data.max()\n",
+    "    for i in range(3):\n",
+    "        plt.subplot(1,4,i+2)\n",
+    "        plt.title(\"filter #{} output\".format(i))\n",
+    "        plt.imshow(net.blobs['conv'].data[0, i], vmin=filt_min, vmax=filt_max)\n",
+    "        plt.tight_layout()\n",
+    "        plt.axis('off')\n",
+    "\n",
+    "# filter the image with initial \n",
+    "show_filters(net)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Raising the bias of a filter will correspondingly raise its output:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Consider the shapes of the inner product parameters. For weights and biases the zeroth and first dimensions are both 1. The second and third weight dimensions are the output and input sizes while the last bias dimension is the output size."
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "pre-surgery output mean -12.93\n",
+      "post-surgery output mean -11.93\n"
      ]
-    },
+    }
+   ],
+   "source": [
+    "# pick first filter output\n",
+    "conv0 = net.blobs['conv'].data[0, 0]\n",
+    "print(\"pre-surgery output mean {:.2f}\".format(conv0.mean()))\n",
+    "# set first filter bias to 10\n",
+    "net.params['conv'][1].data[0] = 1.\n",
+    "net.forward()\n",
+    "print(\"post-surgery output mean {:.2f}\".format(conv0.mean()))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Altering the filter weights is more exciting since we can assign any kernel like Gaussian blur, the Sobel operator for edges, and so on. The following surgery turns the 0th filter into a Gaussian blur and the 1st and 2nd filters into the horizontal and vertical gradient parts of the Sobel operator.\n",
+    "\n",
+    "See how the 0th output is blurred, the 1st picks up horizontal edges, and the 2nd picks up vertical edges."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# Load the fully-convolutional network to transplant the parameters.\n",
-      "net_full_conv = caffe.Net('imagenet/bvlc_caffenet_full_conv.prototxt', '../models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel')\n",
-      "params_full_conv = ['fc6-conv', 'fc7-conv', 'fc8-conv']\n",
-      "# conv_params = {name: (weights, biases)}\n",
-      "conv_params = {pr: (net_full_conv.params[pr][0].data, net_full_conv.params[pr][1].data) for pr in params_full_conv}\n",
-      "\n",
-      "for conv in params_full_conv:\n",
-      "    print '{} weights are {} dimensional and biases are {} dimensional'.format(conv, conv_params[conv][0].shape, conv_params[conv][1].shape)"
-     ],
-     "language": "python",
+     "data": {
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+       "IiIi4pXfPRZNtgyLF3IAAAAASUVORK5CYII=\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x112bc2190>"
+      ]
+     },
      "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "fc6-conv weights are (4096, 256, 6, 6) dimensional and biases are (1, 1, 1, 4096) dimensional\n",
-        "fc7-conv weights are (4096, 4096, 1, 1) dimensional and biases are (1, 1, 1, 4096) dimensional\n",
-        "fc8-conv weights are (1000, 4096, 1, 1) dimensional and biases are (1, 1, 1, 1000) dimensional\n"
-       ]
-      }
-     ],
-     "prompt_number": 3
-    },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ksize = net.params['conv'][0].data.shape[2:]\n",
+    "# make Gaussian blur\n",
+    "sigma = 1.\n",
+    "y, x = np.mgrid[-ksize[0]//2 + 1:ksize[0]//2 + 1, -ksize[1]//2 + 1:ksize[1]//2 + 1]\n",
+    "g = np.exp(-((x**2 + y**2)/(2.0*sigma**2)))\n",
+    "gaussian = (g / g.sum()).astype(np.float32)\n",
+    "net.params['conv'][0].data[0] = gaussian\n",
+    "# make Sobel operator for edge detection\n",
+    "net.params['conv'][0].data[1:] = 0.\n",
+    "sobel = np.array((-1, -2, -1, 0, 0, 0, 1, 2, 1), dtype=np.float32).reshape((3,3))\n",
+    "net.params['conv'][0].data[1, 0, 1:-1, 1:-1] = sobel  # horizontal\n",
+    "net.params['conv'][0].data[2, 0, 1:-1, 1:-1] = sobel.T  # vertical\n",
+    "show_filters(net)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "With net surgery, parameters can be transplanted across nets, regularized by custom per-parameter operations, and transformed according to your schemes."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Casting a Classifier into a Fully Convolutional Network\n",
+    "\n",
+    "Let's take the standard Caffe Reference ImageNet model \"CaffeNet\" and transform it into a fully convolutional net for efficient, dense inference on large inputs. This model generates a classification map that covers a given input size instead of a single classification. In particular a 8 $\\times$ 8 classification map on a 451 $\\times$ 451 input gives 64x the output in only 3x the time. The computation exploits a natural efficiency of convolutional network (convnet) structure by amortizing the computation of overlapping receptive fields.\n",
+    "\n",
+    "To do so we translate the `InnerProduct` matrix multiplication layers of CaffeNet into `Convolutional` layers. This is the only change: the other layer types are agnostic to spatial size. Convolution is translation-invariant, activations are elementwise operations, and so on. The `fc6` inner product when carried out as convolution by `fc6-conv` turns into a 6 \\times 6 filter with stride 1 on `pool5`. Back in image space this gives a classification for each 227 $\\times$ 227 box with stride 32 in pixels. Remember the equation for output map / receptive field size, output = (input - kernel_size) / stride + 1, and work out the indexing details for a clear understanding."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The convolution weights are arranged in output $\\times$ input $\\times$ height $\\times$ width dimensions. To map the inner product weights to convolution filters, we need to roll the flat inner product vectors into channel $\\times$ height $\\times$ width filter matrices.\n",
-      "\n",
-      "The biases are identical to those of the inner product -- let's transplant these first since no reshaping is needed."
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1,2c1\r\n",
+      "< # Fully convolutional network version of CaffeNet.\r\n",
+      "< name: \"CaffeNetConv\"\r\n",
+      "---\r\n",
+      "> name: \"CaffeNet\"\r\n",
+      "4c3\r\n",
+      "< input_dim: 1\r\n",
+      "---\r\n",
+      "> input_dim: 10\r\n",
+      "6,7c5,6\r\n",
+      "< input_dim: 451\r\n",
+      "< input_dim: 451\r\n",
+      "---\r\n",
+      "> input_dim: 227\r\n",
+      "> input_dim: 227\r\n",
+      "152,153c151,152\r\n",
+      "<   name: \"fc6-conv\"\r\n",
+      "<   type: \"Convolution\"\r\n",
+      "---\r\n",
+      ">   name: \"fc6\"\r\n",
+      ">   type: \"InnerProduct\"\r\n",
+      "155,156c154,155\r\n",
+      "<   top: \"fc6-conv\"\r\n",
+      "<   convolution_param {\r\n",
+      "---\r\n",
+      ">   top: \"fc6\"\r\n",
+      ">   inner_product_param {\r\n",
+      "158d156\r\n",
+      "<     kernel_size: 6\r\n",
+      "164,165c162,163\r\n",
+      "<   bottom: \"fc6-conv\"\r\n",
+      "<   top: \"fc6-conv\"\r\n",
+      "---\r\n",
+      ">   bottom: \"fc6\"\r\n",
+      ">   top: \"fc6\"\r\n",
+      "170,171c168,169\r\n",
+      "<   bottom: \"fc6-conv\"\r\n",
+      "<   top: \"fc6-conv\"\r\n",
+      "---\r\n",
+      ">   bottom: \"fc6\"\r\n",
+      ">   top: \"fc6\"\r\n",
+      "177,181c175,179\r\n",
+      "<   name: \"fc7-conv\"\r\n",
+      "<   type: \"Convolution\"\r\n",
+      "<   bottom: \"fc6-conv\"\r\n",
+      "<   top: \"fc7-conv\"\r\n",
+      "<   convolution_param {\r\n",
+      "---\r\n",
+      ">   name: \"fc7\"\r\n",
+      ">   type: \"InnerProduct\"\r\n",
+      ">   bottom: \"fc6\"\r\n",
+      ">   top: \"fc7\"\r\n",
+      ">   inner_product_param {\r\n",
+      "183d180\r\n",
+      "<     kernel_size: 1\r\n",
+      "189,190c186,187\r\n",
+      "<   bottom: \"fc7-conv\"\r\n",
+      "<   top: \"fc7-conv\"\r\n",
+      "---\r\n",
+      ">   bottom: \"fc7\"\r\n",
+      ">   top: \"fc7\"\r\n",
+      "195,196c192,193\r\n",
+      "<   bottom: \"fc7-conv\"\r\n",
+      "<   top: \"fc7-conv\"\r\n",
+      "---\r\n",
+      ">   bottom: \"fc7\"\r\n",
+      ">   top: \"fc7\"\r\n",
+      "202,206c199,203\r\n",
+      "<   name: \"fc8-conv\"\r\n",
+      "<   type: \"Convolution\"\r\n",
+      "<   bottom: \"fc7-conv\"\r\n",
+      "<   top: \"fc8-conv\"\r\n",
+      "<   convolution_param {\r\n",
+      "---\r\n",
+      ">   name: \"fc8\"\r\n",
+      ">   type: \"InnerProduct\"\r\n",
+      ">   bottom: \"fc7\"\r\n",
+      ">   top: \"fc8\"\r\n",
+      ">   inner_product_param {\r\n",
+      "208d204\r\n",
+      "<     kernel_size: 1\r\n",
+      "214c210\r\n",
+      "<   bottom: \"fc8-conv\"\r\n",
+      "---\r\n",
+      ">   bottom: \"fc8\"\r\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "for pr, pr_conv in zip(params, params_full_conv):\n",
-      "    conv_params[pr_conv][1][...] = fc_params[pr][1]"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 4
-    },
+    }
+   ],
+   "source": [
+    "!diff net_surgery/bvlc_caffenet_full_conv.prototxt ../models/bvlc_reference_caffenet/deploy.prototxt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The only differences needed in the architecture are to change the fully connected classifier inner product layers into convolutional layers with the right filter size -- 6 x 6, since the reference model classifiers take the 36 elements of `pool5` as input -- and stride 1 for dense classification. Note that the layers are renamed so that Caffe does not try to blindly load the old parameters when it maps layer names to the pretrained model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The output channels have the leading dimension of both the inner product and convolution weights, so the parameters are translated by reshaping the flat input dimensional parameter vector from the inner product into the channel $\\times$ height $\\times$ width filter shape."
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "fc6 weights are (4096, 9216) dimensional and biases are (4096,) dimensional\n",
+      "fc7 weights are (4096, 4096) dimensional and biases are (4096,) dimensional\n",
+      "fc8 weights are (1000, 4096) dimensional and biases are (1000,) dimensional\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "for pr, pr_conv in zip(params, params_full_conv):\n",
-      "    out, in_, h, w = conv_params[pr_conv][0].shape\n",
-      "    W = fc_params[pr][0].reshape((out, in_, h, w))\n",
-      "    conv_params[pr_conv][0][...] = W"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 5
-    },
+    }
+   ],
+   "source": [
+    "# Make sure that caffe is on the python path:\n",
+    "caffe_root = '../'  # this file is expected to be in {caffe_root}/examples\n",
+    "import sys\n",
+    "sys.path.insert(0, caffe_root + 'python')\n",
+    "\n",
+    "import caffe\n",
+    "\n",
+    "# Load the original network and extract the fully connected layers' parameters.\n",
+    "net = caffe.Net('../models/bvlc_reference_caffenet/deploy.prototxt', \n",
+    "                '../models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel', \n",
+    "                caffe.TEST)\n",
+    "params = ['fc6', 'fc7', 'fc8']\n",
+    "# fc_params = {name: (weights, biases)}\n",
+    "fc_params = {pr: (net.params[pr][0].data, net.params[pr][1].data) for pr in params}\n",
+    "\n",
+    "for fc in params:\n",
+    "    print '{} weights are {} dimensional and biases are {} dimensional'.format(fc, fc_params[fc][0].shape, fc_params[fc][1].shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Consider the shapes of the inner product parameters. The weight dimensions are the output and input sizes while the bias dimension is the output size."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Next, save the new model weights."
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "fc6-conv weights are (4096, 256, 6, 6) dimensional and biases are (4096,) dimensional\n",
+      "fc7-conv weights are (4096, 4096, 1, 1) dimensional and biases are (4096,) dimensional\n",
+      "fc8-conv weights are (1000, 4096, 1, 1) dimensional and biases are (1000,) dimensional\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "net_full_conv.save('imagenet/bvlc_caffenet_full_conv.caffemodel')"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 6
-    },
+    }
+   ],
+   "source": [
+    "# Load the fully convolutional network to transplant the parameters.\n",
+    "net_full_conv = caffe.Net('net_surgery/bvlc_caffenet_full_conv.prototxt', \n",
+    "                          '../models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel',\n",
+    "                          caffe.TEST)\n",
+    "params_full_conv = ['fc6-conv', 'fc7-conv', 'fc8-conv']\n",
+    "# conv_params = {name: (weights, biases)}\n",
+    "conv_params = {pr: (net_full_conv.params[pr][0].data, net_full_conv.params[pr][1].data) for pr in params_full_conv}\n",
+    "\n",
+    "for conv in params_full_conv:\n",
+    "    print '{} weights are {} dimensional and biases are {} dimensional'.format(conv, conv_params[conv][0].shape, conv_params[conv][1].shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The convolution weights are arranged in output $\\times$ input $\\times$ height $\\times$ width dimensions. To map the inner product weights to convolution filters, we could roll the flat inner product vectors into channel $\\times$ height $\\times$ width filter matrices, but actually these are identical in memory (as row major arrays) so we can assign them directly.\n",
+    "\n",
+    "The biases are identical to those of the inner product.\n",
+    "\n",
+    "Let's transplant!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "for pr, pr_conv in zip(params, params_full_conv):\n",
+    "    conv_params[pr_conv][0].flat = fc_params[pr][0].flat  # flat unrolls the arrays\n",
+    "    conv_params[pr_conv][1][...] = fc_params[pr][1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next, save the new model weights."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "net_full_conv.save('net_surgery/bvlc_caffenet_full_conv.caffemodel')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To conclude, let's make a classification map from the example cat image and visualize the confidence of \"tiger cat\" as a probability heatmap. This gives an 8-by-8 prediction on overlapping regions of the 451 $\\times$ 451 input."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "To conclude, let's make a classification map from the example cat image and visualize the confidence as a probability heatmap. This gives an 8-by-8 prediction on overlapping regions of the 451 $\\times$ 451 input."
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[282 282 281 281 281 281 277 282]\n",
+      " [281 283 283 281 281 281 281 282]\n",
+      " [283 283 283 283 283 283 287 282]\n",
+      " [283 283 283 281 283 283 283 259]\n",
+      " [283 283 283 283 283 283 283 259]\n",
+      " [283 283 283 283 283 283 259 259]\n",
+      " [283 283 283 283 259 259 259 277]\n",
+      " [335 335 283 259 263 263 263 277]]\n"
      ]
     },
     {
-     "cell_type": "code",
-     "collapsed": true,
-     "input": [
-      "import numpy as np\n",
-      "import matplotlib.pyplot as plt\n",
-      "%matplotlib inline\n",
-      "\n",
-      "# load input and configure preprocessing\n",
-      "im = caffe.io.load_image('images/cat.jpg')\n",
-      "net_full_conv.set_phase_test()\n",
-      "net_full_conv.set_mean('data', np.load('../python/caffe/imagenet/ilsvrc_2012_mean.npy'))\n",
-      "net_full_conv.set_channel_swap('data', (2,1,0))\n",
-      "net_full_conv.set_raw_scale('data', 255.0)\n",
-      "# make classification map by forward and print prediction indices at each location\n",
-      "out = net_full_conv.forward_all(data=np.asarray([net_full_conv.preprocess('data', im)]))\n",
-      "print out['prob'][0].argmax(axis=0)\n",
-      "# show net input and confidence map (probability of the top prediction at each location)\n",
-      "plt.subplot(1, 2, 1)\n",
-      "plt.imshow(net_full_conv.deprocess('data', net_full_conv.blobs['data'].data[0]))\n",
-      "plt.subplot(1, 2, 2)\n",
-      "plt.imshow(out['prob'][0].max(axis=0))"
-     ],
-     "language": "python",
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x12379a690>"
+      ]
+     },
+     "execution_count": 11,
      "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "[[282 282 281 281 281 281 277 282]\n",
-        " [281 283 281 281 281 281 281 282]\n",
-        " [283 283 283 283 283 283 281 282]\n",
-        " [283 283 283 281 283 283 283 259]\n",
-        " [283 283 283 283 283 283 283 259]\n",
-        " [283 283 283 283 283 283 259 259]\n",
-        " [283 283 283 283 259 259 259 277]\n",
-        " [335 335 283 283 263 263 263 277]]\n"
-       ]
-      },
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 7,
-       "text": [
-        "<matplotlib.image.AxesImage at 0x7f283815ce50>"
-       ]
-      },
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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P/fif5nv/qe/hg5//WQaLoKBJUC/M5oqGjBNHPybMpDJyq/KCK9VpXB1/gAkinmQjZXKY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0SuRppFZSBX+fZeq7gDcq1kdNEVCCp37RkBXEYwj0ZjdY7Gk475QiF5ewd/yux031FlX/md2FGy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\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0x7f285ddfca50>"
-       ]
-      }
-     ],
-     "prompt_number": 7
+     "output_type": "execute_result"
     },
     {
-     "cell_type": "markdown",
+     "data": {
+      "image/png": [
+       "iVBORw0KGgoAAAANSUhEUgAAAXMAAAC5CAYAAADavt/0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n",
+       "AAALEgAACxIB0t1+/AAAIABJREFUeJzsvWnMbWl23/Vbz7T3PtM73aHGrqrurrbdbbttuulYVhqT\n",
+       "gcgycQwIGVlYFiCQhSCRAUu2IyHExxAhWSIKcsRoRSJBfLAi5AgMIU4CiWxIYqc73XZPRVXXcOu+\n",
+       "953OtPczLT48b7dN1O6qxHVz7e7z+/See/Z99nnPfe561l7Df4mqcuDAgQMHfn9jnvQHOHDgwIED\n",
+       "v3sOxvzAgQMHvgE4GPMDBw4c+AbgYMwPHDhw4BuAgzE/cODAgW8ADsb8wIEDB74BeCzGXES+X0Q+\n",
+       "KyKfE5Gfehz3OHDgwIEDv4W813XmImKB3wD+KPA68KvAj6jqZ97TGx04cODAga/yODzzTwCfV9VX\n",
+       "VDUBfwn4ocdwnwMHDhw4cMvjMObPAq/9ttdfvv2zAwcOHDjwmHgcxvygD3DgwIED/5Rxj2HN14Hn\n",
+       "f9vr52ne+VcRkYPBP/BYUVV5Evc97O0Dj5vfaW8/DmP+fwMvi8iLwBvAvwb8yD960dPf889gMO3Z\n",
+       "wCjGG7QX1IM4gxGhqiJiEFMINdMZIe935H3ECRgpqCkoiqpiUgUUjGCtRUSotVJrJidlt1NSugEs\n",
+       "xhi2byaWzw7YXlgOM+YLz2J2zKyb4Z1jHEfOLy94eH4BCMMM7GBx3qA2Y5zFONCijGOmZAUpCFBq\n",
+       "pVah1szqeGC2MtRJmR5Zbq4i41bJSTHGMF2NPPOtc3zvEWvJ2aLVUKtBFFQV1YK9/X1s8IgD64Qa\n",
+       "KkpGfaWkSiqFGoWyVXStCLC6f8S954/50Hc/z73nnmV5Z4Hzls1mw9tvvcErX3yDh2894pW//oDh\n",
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+       "9LP99Jddvv7oRNMF4OW9xh7gYeB7wAvAv7GH1SxACdgGKnvRlG/nz8nJybkPyHeA5uTk5NwH5ME8\n",
+       "Jycn5z4gD+Y5OTk59wF5MM/Jycm5D8iDeU5OTs59QB7Mc3Jycu4D8mCek5OTcx+QB/OcnJyc+4D/\n",
+       "A43ph1xlbAoPAAAAAElFTkSuQmCC\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x12666be50>"
+      ]
+     },
      "metadata": {},
-     "source": [
-      "The classifications include various cats -- 282 = tiger cat, 281 = tabby, 283 = persian -- and foxes and other mammals.\n",
-      "\n",
-      "In this way the fully-connected layers can be extracted as dense features across an image (see `net_full_conv.blobs['fc6'].data` for instance), which is perhaps more useful than the classification map itself.\n",
-      "\n",
-      "Note that this model isn't totally appropriate for sliding-window detection since it was trained for whole-image classification. Nevertheless it can work just fine. Sliding-window training and finetuning can be done by defining a sliding-window ground truth and loss such that a loss map is made for every location and solving as usual. (This is an exercise for the reader.)"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "*A thank you to Rowland Depp for first suggesting this trick.*"
-     ]
+     "output_type": "display_data"
     }
    ],
-   "metadata": {}
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "# load input and configure preprocessing\n",
+    "im = caffe.io.load_image('images/cat.jpg')\n",
+    "transformer = caffe.io.Transformer({'data': net_full_conv.blobs['data'].data.shape})\n",
+    "transformer.set_mean('data', np.load('../python/caffe/imagenet/ilsvrc_2012_mean.npy').mean(1).mean(1))\n",
+    "transformer.set_transpose('data', (2,0,1))\n",
+    "transformer.set_channel_swap('data', (2,1,0))\n",
+    "transformer.set_raw_scale('data', 255.0)\n",
+    "# make classification map by forward and print prediction indices at each location\n",
+    "out = net_full_conv.forward_all(data=np.asarray([transformer.preprocess('data', im)]))\n",
+    "print out['prob'][0].argmax(axis=0)\n",
+    "# show net input and confidence map (probability of the top prediction at each location)\n",
+    "plt.subplot(1, 2, 1)\n",
+    "plt.imshow(transformer.deprocess('data', net_full_conv.blobs['data'].data[0]))\n",
+    "plt.subplot(1, 2, 2)\n",
+    "plt.imshow(out['prob'][0,281])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The classifications include various cats -- 282 = tiger cat, 281 = tabby, 283 = persian -- and foxes and other mammals.\n",
+    "\n",
+    "In this way the fully connected layers can be extracted as dense features across an image (see `net_full_conv.blobs['fc6'].data` for instance), which is perhaps more useful than the classification map itself.\n",
+    "\n",
+    "Note that this model isn't totally appropriate for sliding-window detection since it was trained for whole-image classification. Nevertheless it can work just fine. Sliding-window training and finetuning can be done by defining a sliding-window ground truth and loss such that a loss map is made for every location and solving as usual. (This is an exercise for the reader.)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*A thank you to Rowland Depp for first suggesting this trick.*"
+   ]
   }
- ]
+ ],
+ "metadata": {
+  "description": "How to do net surgery and manually change model parameters for custom use.",
+  "example_name": "Editing model parameters",
+  "include_in_docs": true,
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.9"
+  },
+  "priority": 5
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
 }
diff --git a/examples/imagenet/bvlc_caffenet_full_conv.prototxt b/examples/net_surgery/bvlc_caffenet_full_conv.prototxt
similarity index 78%
rename from examples/imagenet/bvlc_caffenet_full_conv.prototxt
rename to examples/net_surgery/bvlc_caffenet_full_conv.prototxt
index 395b0f0162f..3c951970fc1 100644
--- a/examples/imagenet/bvlc_caffenet_full_conv.prototxt
+++ b/examples/net_surgery/bvlc_caffenet_full_conv.prototxt
@@ -1,13 +1,13 @@
-# This file is for the net_surgery.ipynb example notebook.
+# Fully convolutional network version of CaffeNet.
 name: "CaffeNetConv"
 input: "data"
 input_dim: 1
 input_dim: 3
 input_dim: 451
 input_dim: 451
-layers {
+layer {
   name: "conv1"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "data"
   top: "conv1"
   convolution_param {
@@ -16,15 +16,15 @@ layers {
     stride: 4
   }
 }
-layers {
+layer {
   name: "relu1"
-  type: RELU
+  type: "ReLU"
   bottom: "conv1"
   top: "conv1"
 }
-layers {
+layer {
   name: "pool1"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv1"
   top: "pool1"
   pooling_param {
@@ -33,9 +33,9 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "norm1"
-  type: LRN
+  type: "LRN"
   bottom: "pool1"
   top: "norm1"
   lrn_param {
@@ -44,9 +44,9 @@ layers {
     beta: 0.75
   }
 }
-layers {
+layer {
   name: "conv2"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "norm1"
   top: "conv2"
   convolution_param {
@@ -56,15 +56,15 @@ layers {
     group: 2
   }
 }
-layers {
+layer {
   name: "relu2"
-  type: RELU
+  type: "ReLU"
   bottom: "conv2"
   top: "conv2"
 }
-layers {
+layer {
   name: "pool2"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv2"
   top: "pool2"
   pooling_param {
@@ -73,9 +73,9 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "norm2"
-  type: LRN
+  type: "LRN"
   bottom: "pool2"
   top: "norm2"
   lrn_param {
@@ -84,9 +84,9 @@ layers {
     beta: 0.75
   }
 }
-layers {
+layer {
   name: "conv3"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "norm2"
   top: "conv3"
   convolution_param {
@@ -95,15 +95,15 @@ layers {
     kernel_size: 3
   }
 }
-layers {
+layer {
   name: "relu3"
-  type: RELU
+  type: "ReLU"
   bottom: "conv3"
   top: "conv3"
 }
-layers {
+layer {
   name: "conv4"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "conv3"
   top: "conv4"
   convolution_param {
@@ -113,15 +113,15 @@ layers {
     group: 2
   }
 }
-layers {
+layer {
   name: "relu4"
-  type: RELU
+  type: "ReLU"
   bottom: "conv4"
   top: "conv4"
 }
-layers {
+layer {
   name: "conv5"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "conv4"
   top: "conv5"
   convolution_param {
@@ -131,15 +131,15 @@ layers {
     group: 2
   }
 }
-layers {
+layer {
   name: "relu5"
-  type: RELU
+  type: "ReLU"
   bottom: "conv5"
   top: "conv5"
 }
-layers {
+layer {
   name: "pool5"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv5"
   top: "pool5"
   pooling_param {
@@ -148,9 +148,9 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "fc6-conv"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "pool5"
   top: "fc6-conv"
   convolution_param {
@@ -158,24 +158,24 @@ layers {
     kernel_size: 6
   }
 }
-layers {
+layer {
   name: "relu6"
-  type: RELU
+  type: "ReLU"
   bottom: "fc6-conv"
   top: "fc6-conv"
 }
-layers {
+layer {
   name: "drop6"
-  type: DROPOUT
+  type: "Dropout"
   bottom: "fc6-conv"
   top: "fc6-conv"
   dropout_param {
     dropout_ratio: 0.5
   }
 }
-layers {
+layer {
   name: "fc7-conv"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "fc6-conv"
   top: "fc7-conv"
   convolution_param {
@@ -183,24 +183,24 @@ layers {
     kernel_size: 1
   }
 }
-layers {
+layer {
   name: "relu7"
-  type: RELU
+  type: "ReLU"
   bottom: "fc7-conv"
   top: "fc7-conv"
 }
-layers {
+layer {
   name: "drop7"
-  type: DROPOUT
+  type: "Dropout"
   bottom: "fc7-conv"
   top: "fc7-conv"
   dropout_param {
     dropout_ratio: 0.5
   }
 }
-layers {
+layer {
   name: "fc8-conv"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "fc7-conv"
   top: "fc8-conv"
   convolution_param {
@@ -208,9 +208,9 @@ layers {
     kernel_size: 1
   }
 }
-layers {
+layer {
   name: "prob"
-  type: SOFTMAX
+  type: "Softmax"
   bottom: "fc8-conv"
   top: "prob"
 }
diff --git a/examples/net_surgery/conv.prototxt b/examples/net_surgery/conv.prototxt
new file mode 100644
index 00000000000..9444c63ab74
--- /dev/null
+++ b/examples/net_surgery/conv.prototxt
@@ -0,0 +1,26 @@
+# Simple single-layer network to showcase editing model parameters.
+name: "convolution"
+input: "data"
+input_dim: 1
+input_dim: 1
+input_dim: 100
+input_dim: 100
+layer {
+  name: "conv"
+  type: "Convolution"
+  bottom: "data"
+  top: "conv"
+  convolution_param {
+    num_output: 3
+    kernel_size: 5
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
diff --git a/examples/pycaffe/caffenet.py b/examples/pycaffe/caffenet.py
new file mode 100644
index 00000000000..06c5a02d4ee
--- /dev/null
+++ b/examples/pycaffe/caffenet.py
@@ -0,0 +1,55 @@
+from caffe import layers as L, params as P, to_proto
+from caffe.proto import caffe_pb2
+from __future__ import print_function
+
+# helper function for common structures
+
+def conv_relu(bottom, ks, nout, stride=1, pad=0, group=1):
+    conv = L.Convolution(bottom, kernel_size=ks, stride=stride,
+                                num_output=nout, pad=pad, group=group)
+    return conv, L.ReLU(conv, in_place=True)
+
+def fc_relu(bottom, nout):
+    fc = L.InnerProduct(bottom, num_output=nout)
+    return fc, L.ReLU(fc, in_place=True)
+
+def max_pool(bottom, ks, stride=1):
+    return L.Pooling(bottom, pool=P.Pooling.MAX, kernel_size=ks, stride=stride)
+
+def caffenet(lmdb, batch_size=256, include_acc=False):
+    data, label = L.Data(source=lmdb, backend=P.Data.LMDB, batch_size=batch_size, ntop=2,
+        transform_param=dict(crop_size=227, mean_value=[104, 117, 123], mirror=True))
+
+    # the net itself
+    conv1, relu1 = conv_relu(data, 11, 96, stride=4)
+    pool1 = max_pool(relu1, 3, stride=2)
+    norm1 = L.LRN(pool1, local_size=5, alpha=1e-4, beta=0.75)
+    conv2, relu2 = conv_relu(norm1, 5, 256, pad=2, group=2)
+    pool2 = max_pool(relu2, 3, stride=2)
+    norm2 = L.LRN(pool2, local_size=5, alpha=1e-4, beta=0.75)
+    conv3, relu3 = conv_relu(norm2, 3, 384, pad=1)
+    conv4, relu4 = conv_relu(relu3, 3, 384, pad=1, group=2)
+    conv5, relu5 = conv_relu(relu4, 3, 256, pad=1, group=2)
+    pool5 = max_pool(relu5, 3, stride=2)
+    fc6, relu6 = fc_relu(pool5, 4096)
+    drop6 = L.Dropout(relu6, in_place=True)
+    fc7, relu7 = fc_relu(drop6, 4096)
+    drop7 = L.Dropout(relu7, in_place=True)
+    fc8 = L.InnerProduct(drop7, num_output=1000)
+    loss = L.SoftmaxWithLoss(fc8, label)
+
+    if include_acc:
+        acc = L.Accuracy(fc8, label)
+        return to_proto(loss, acc)
+    else:
+        return to_proto(loss)
+
+def make_net():
+    with open('train.prototxt', 'w') as f:
+        print(caffenet('/path/to/caffe-train-lmdb'), file=f)
+
+    with open('test.prototxt', 'w') as f:
+        print(caffenet('/path/to/caffe-val-lmdb', batch_size=50, include_acc=True), file=f)
+
+if __name__ == '__main__':
+    make_net()
diff --git a/examples/pycaffe/layers/pyloss.py b/examples/pycaffe/layers/pyloss.py
new file mode 100644
index 00000000000..6200e6bbc55
--- /dev/null
+++ b/examples/pycaffe/layers/pyloss.py
@@ -0,0 +1,37 @@
+import caffe
+import numpy as np
+
+
+class EuclideanLossLayer(caffe.Layer):
+    """
+    Compute the Euclidean Loss in the same manner as the C++ EuclideanLossLayer
+    to demonstrate the class interface for developing layers in Python.
+    """
+
+    def setup(self, bottom, top):
+        # check input pair
+        if len(bottom) != 2:
+            raise Exception("Need two inputs to compute distance.")
+
+    def reshape(self, bottom, top):
+        # check input dimensions match
+        if bottom[0].count != bottom[1].count:
+            raise Exception("Inputs must have the same dimension.")
+        # difference is shape of inputs
+        self.diff = np.zeros_like(bottom[0].data, dtype=np.float32)
+        # loss output is scalar
+        top[0].reshape(1)
+
+    def forward(self, bottom, top):
+        self.diff[...] = bottom[0].data - bottom[1].data
+        top[0].data[...] = np.sum(self.diff**2) / bottom[0].num / 2.
+
+    def backward(self, top, propagate_down, bottom):
+        for i in range(2):
+            if not propagate_down[i]:
+                continue
+            if i == 0:
+                sign = 1
+            else:
+                sign = -1
+            bottom[i].diff[...] = sign * self.diff / bottom[i].num
diff --git a/examples/pycaffe/linreg.prototxt b/examples/pycaffe/linreg.prototxt
new file mode 100644
index 00000000000..c0fb0776d0a
--- /dev/null
+++ b/examples/pycaffe/linreg.prototxt
@@ -0,0 +1,60 @@
+name: 'LinearRegressionExample'
+# define a simple network for linear regression on dummy data
+# that computes the loss by a PythonLayer.
+layer {
+  type: 'DummyData'
+  name: 'x'
+  top: 'x'
+  dummy_data_param {
+    shape: { dim: 10 dim: 3 dim: 2 }
+    data_filler: { type: 'gaussian' }
+  }
+}
+layer {
+  type: 'DummyData'
+  name: 'y'
+  top: 'y'
+  dummy_data_param {
+    shape: { dim: 10 dim: 3 dim: 2 }
+    data_filler: { type: 'gaussian' }
+  }
+}
+# include InnerProduct layers for parameters
+# so the net will need backward
+layer {
+  type: 'InnerProduct'
+  name: 'ipx'
+  top: 'ipx'
+  bottom: 'x'
+  inner_product_param {
+    num_output: 10
+    weight_filler { type: 'xavier' }
+  }
+}
+layer {
+  type: 'InnerProduct'
+  name: 'ipy'
+  top: 'ipy'
+  bottom: 'y'
+  inner_product_param {
+    num_output: 10
+    weight_filler { type: 'xavier' }
+  }
+}
+layer {
+  type: 'Python'
+  name: 'loss'
+  top: 'loss'
+  bottom: 'ipx'
+  bottom: 'ipy'
+  python_param {
+    # the module name -- usually the filename -- that needs to be in $PYTHONPATH
+    module: 'pyloss'
+    # the layer name -- the class name in the module
+    layer: 'EuclideanLossLayer'
+  }
+  # set loss weight so Caffe knows this is a loss layer.
+  # since PythonLayer inherits directly from Layer, this isn't automatically
+  # known to Caffe
+  loss_weight: 1
+}
diff --git a/examples/siamese/convert_mnist_siamese_data.cpp b/examples/siamese/convert_mnist_siamese_data.cpp
index 400d15a2705..71c56a0ae61 100644
--- a/examples/siamese/convert_mnist_siamese_data.cpp
+++ b/examples/siamese/convert_mnist_siamese_data.cpp
@@ -36,7 +36,7 @@ void convert_dataset(const char* image_filename, const char* label_filename,
   std::ifstream image_file(image_filename, std::ios::in | std::ios::binary);
   std::ifstream label_file(label_filename, std::ios::in | std::ios::binary);
   CHECK(image_file) << "Unable to open file " << image_filename;
-  CHECK(label_file) << "Unable to open file " << label_file;
+  CHECK(label_file) << "Unable to open file " << label_filename;
   // Read the magic and the meta data
   uint32_t magic;
   uint32_t num_items;
diff --git a/examples/siamese/mnist_siamese.ipynb b/examples/siamese/mnist_siamese.ipynb
index 5abd0469ba6..1a4e30eda43 100644
--- a/examples/siamese/mnist_siamese.ipynb
+++ b/examples/siamese/mnist_siamese.ipynb
@@ -1,158 +1,1909 @@
 {
- "metadata": {
-  "description": "Extracting features and plotting the Siamese network embedding.",
-  "example_name": "Siamese network embedding",
-  "include_in_docs": true,
-  "priority": 6
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
+ "cells": [
   {
-   "cells": [
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "### Setup\n",
-      "\n",
-      "Import Caffe and the usual modules."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import numpy as np\n",
-      "import matplotlib.pyplot as plt\n",
-      "%matplotlib inline\n",
-      "\n",
-      "# Make sure that caffe is on the python path:\n",
-      "caffe_root = '../../'  # this file is expected to be in {caffe_root}/examples/siamese\n",
-      "import sys\n",
-      "sys.path.insert(0, caffe_root + 'python')\n",
-      "\n",
-      "import caffe"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 1
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "### Load the trained net\n",
-      "\n",
-      "Load the model definition and weights and set to CPU mode TEST phase computation with input scaling."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "MODEL_FILE = 'mnist_siamese.prototxt'\n",
-      "# decrease if you want to preview during training\n",
-      "PRETRAINED_FILE = 'mnist_siamese_iter_50000.caffemodel' \n",
-      "net = caffe.Net(MODEL_FILE, PRETRAINED_FILE)\n",
-      "net.set_phase_test()\n",
-      "net.set_mode_cpu()\n",
-      "net.set_input_scale('data', 0.00390625)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 2
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "### Load some MNIST test data"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "TEST_DATA_FILE = '../../data/mnist/t10k-images-idx3-ubyte'\n",
-      "TEST_LABEL_FILE = '../../data/mnist/t10k-labels-idx1-ubyte'\n",
-      "n = 10000\n",
-      "\n",
-      "with open(TEST_DATA_FILE, 'rb') as f:\n",
-      "    f.read(16) # skip the header\n",
-      "    raw_data = np.fromstring(f.read(n * 28*28), dtype=np.uint8)\n",
-      "\n",
-      "with open(TEST_LABEL_FILE, 'rb') as f:\n",
-      "    f.read(8) # skip the header\n",
-      "    labels = np.fromstring(f.read(n), dtype=np.uint8)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 3
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "### Generate the Siamese features"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# reshape and preprocess\n",
-      "caffe_in = raw_data.reshape(n, 28, 28).transpose((1,2,0))\n",
-      "caffe_in = net.preprocess('data', caffe_in) \n",
-      "caffe_in = caffe_in.reshape((n,1,28,28))\n",
-      "# pass data through network\n",
-      "out = net.forward_all(data=caffe_in)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 4
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "### Visualize the learned Siamese embedding"
-     ]
-    },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Setup\n",
+    "\n",
+    "Import Caffe and the usual modules."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "# Make sure that caffe is on the python path:\n",
+    "caffe_root = '../../'  # this file is expected to be in {caffe_root}/examples/siamese\n",
+    "import sys\n",
+    "sys.path.insert(0, caffe_root + 'python')\n",
+    "\n",
+    "import caffe"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Load the trained net\n",
+    "\n",
+    "Load the model definition and weights and set to CPU mode TEST phase computation with input scaling."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "MODEL_FILE = 'mnist_siamese.prototxt'\n",
+    "# decrease if you want to preview during training\n",
+    "PRETRAINED_FILE = 'mnist_siamese_iter_50000.caffemodel' \n",
+    "caffe.set_mode_cpu()\n",
+    "net = caffe.Net(MODEL_FILE, PRETRAINED_FILE, caffe.TEST)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Load some MNIST test data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "TEST_DATA_FILE = '../../data/mnist/t10k-images-idx3-ubyte'\n",
+    "TEST_LABEL_FILE = '../../data/mnist/t10k-labels-idx1-ubyte'\n",
+    "n = 10000\n",
+    "\n",
+    "with open(TEST_DATA_FILE, 'rb') as f:\n",
+    "    f.read(16) # skip the header\n",
+    "    raw_data = np.fromstring(f.read(n * 28*28), dtype=np.uint8)\n",
+    "\n",
+    "with open(TEST_LABEL_FILE, 'rb') as f:\n",
+    "    f.read(8) # skip the header\n",
+    "    labels = np.fromstring(f.read(n), dtype=np.uint8)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Generate the Siamese features"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# reshape and preprocess\n",
+    "caffe_in = raw_data.reshape(n, 1, 28, 28) * 0.00390625 # manually scale data instead of using `caffe.io.Transformer`\n",
+    "out = net.forward_all(data=caffe_in)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Visualize the learned Siamese embedding"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "feat = out['feat']\n",
-      "f = plt.figure(figsize=(16,9))\n",
-      "c = ['#ff0000', '#ffff00', '#00ff00', '#00ffff', '#0000ff', \n",
-      "     '#ff00ff', '#990000', '#999900', '#009900', '#009999']\n",
-      "for i in range(10):\n",
-      "    plt.plot(feat[labels==i,0].flatten(), feat[labels==i,1].flatten(), '.', c=c[i])\n",
-      "plt.legend(['0', '1', '2', '3', '4', '5', '6', '7', '8', '9'])\n",
-      "plt.grid()\n",
-      "plt.show()"
-     ],
-     "language": "python",
+     "data": {
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+       "AABAVv1/lzHCzGUnjVoAAAAASUVORK5CYII=\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fadf4552a90>"
+      ]
+     },
      "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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vvyz37G3axPI/+f2yMpZP6nCI5+9yMRH34YfMI+rxsP9NTSzf1OlkOZbReDG5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ipWuXwuRdj48c8KYHSM3FeaVGpvihiJtGPGGSeNCPWS5jMap9zmuiJU6VJAmirJo9byPotErYfM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\nfjV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AM0Y4baXXS9+3v83yHTsYuGwtqVDj93POILJLy8u5fOUK8StXkvJWARaXlXFhfJxL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Ljd3mXj2ioIglActLjQW6PvL6GSG6VqZywDczr6rx1oJRYz2gW8ibJeahlw64CWhD6dwF8D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\nAABJRU5ErkJggg==\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0x7f31d5144c50>"
-       ]
-      }
-     ],
-     "prompt_number": 5
+     "output_type": "display_data"
     }
    ],
-   "metadata": {}
+   "source": [
+    "feat = out['feat']\n",
+    "f = plt.figure(figsize=(16,9))\n",
+    "c = ['#ff0000', '#ffff00', '#00ff00', '#00ffff', '#0000ff', \n",
+    "     '#ff00ff', '#990000', '#999900', '#009900', '#009999']\n",
+    "for i in range(10):\n",
+    "    plt.plot(feat[labels==i,0].flatten(), feat[labels==i,1].flatten(), '.', c=c[i])\n",
+    "plt.legend(['0', '1', '2', '3', '4', '5', '6', '7', '8', '9'])\n",
+    "plt.grid()\n",
+    "plt.show()"
+   ]
   }
- ]
+ ],
+ "metadata": {
+  "description": "Extracting features and plotting the Siamese network embedding.",
+  "example_name": "Siamese network embedding",
+  "include_in_docs": true,
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.9"
+  },
+  "priority": 7
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
 }
diff --git a/examples/siamese/mnist_siamese.prototxt b/examples/siamese/mnist_siamese.prototxt
index 8dd42e9c1b5..0e903f85909 100644
--- a/examples/siamese/mnist_siamese.prototxt
+++ b/examples/siamese/mnist_siamese.prototxt
@@ -4,23 +4,26 @@ input_dim: 10000
 input_dim: 1
 input_dim: 28
 input_dim: 28
-
-layers {
+layer {
   name: "conv1"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "data"
   top: "conv1"
-  blobs_lr: 1
-  blobs_lr: 2
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
   convolution_param {
     num_output: 20
     kernel_size: 5
     stride: 1
   }
 }
-layers {
+layer {
   name: "pool1"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv1"
   top: "pool1"
   pooling_param {
@@ -29,22 +32,26 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "conv2"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "pool1"
   top: "conv2"
-  blobs_lr: 1
-  blobs_lr: 2
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
   convolution_param {
     num_output: 50
     kernel_size: 5
     stride: 1
   }
 }
-layers {
+layer {
   name: "pool2"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv2"
   top: "pool2"
   pooling_param {
@@ -53,42 +60,53 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "ip1"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "pool2"
   top: "ip1"
-  blobs_lr: 1
-  blobs_lr: 2
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
   inner_product_param {
     num_output: 500
   }
 }
-layers {
+layer {
   name: "relu1"
-  type: RELU
+  type: "ReLU"
   bottom: "ip1"
   top: "ip1"
 }
-layers {
+layer {
   name: "ip2"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "ip1"
   top: "ip2"
-  blobs_lr: 1
-  blobs_lr: 2
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
   inner_product_param {
     num_output: 10
   }
 }
-
-layers {
+layer {
   name: "feat"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "ip2"
   top: "feat"
-  blobs_lr: 1
-  blobs_lr: 2
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
   inner_product_param {
     num_output: 2
   }
diff --git a/examples/siamese/mnist_siamese_train_test.prototxt b/examples/siamese/mnist_siamese_train_test.prototxt
index 92361c31dc7..8ff864f556f 100644
--- a/examples/siamese/mnist_siamese_train_test.prototxt
+++ b/examples/siamese/mnist_siamese_train_test.prototxt
@@ -1,50 +1,60 @@
 name: "mnist_siamese_train_test"
-layers {
+layer {
   name: "pair_data"
-  type: DATA
+  type: "Data"
   top: "pair_data"
   top: "sim"
+  include {
+    phase: TRAIN
+  }
+  transform_param {
+    scale: 0.00390625
+  }
   data_param {
     source: "examples/siamese/mnist_siamese_train_leveldb"
-    scale: 0.00390625
     batch_size: 64
   }
-  include: { phase: TRAIN }
 }
-layers {
+layer {
   name: "pair_data"
-  type: DATA
+  type: "Data"
   top: "pair_data"
   top: "sim"
+  include {
+    phase: TEST
+  }
+  transform_param {
+    scale: 0.00390625
+  }
   data_param {
     source: "examples/siamese/mnist_siamese_test_leveldb"
-    scale: 0.00390625
     batch_size: 100
   }
-  include: { phase: TEST }
 }
-layers {
-    name: "slice_pair"
-    type: SLICE
-    bottom: "pair_data"
-    top: "data"
-    top: "data_p"
-    slice_param {
-        slice_dim: 1
-        slice_point: 1
-    }
+layer {
+  name: "slice_pair"
+  type: "Slice"
+  bottom: "pair_data"
+  top: "data"
+  top: "data_p"
+  slice_param {
+    slice_dim: 1
+    slice_point: 1
+  }
 }
-
-
-
-
-layers {
+layer {
   name: "conv1"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "data"
   top: "conv1"
-  blobs_lr: 1
-  blobs_lr: 2
+  param {
+    name: "conv1_w"
+    lr_mult: 1
+  }
+  param {
+    name: "conv1_b"
+    lr_mult: 2
+  }
   convolution_param {
     num_output: 20
     kernel_size: 5
@@ -56,12 +66,10 @@ layers {
       type: "constant"
     }
   }
-  param: "conv1_w"
-  param: "conv1_b"
 }
-layers {
+layer {
   name: "pool1"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv1"
   top: "pool1"
   pooling_param {
@@ -70,13 +78,19 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "conv2"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "pool1"
   top: "conv2"
-  blobs_lr: 1
-  blobs_lr: 2
+  param {
+    name: "conv2_w"
+    lr_mult: 1
+  }
+  param {
+    name: "conv2_b"
+    lr_mult: 2
+  }
   convolution_param {
     num_output: 50
     kernel_size: 5
@@ -88,12 +102,10 @@ layers {
       type: "constant"
     }
   }
-  param: "conv2_w"
-  param: "conv2_b"
 }
-layers {
+layer {
   name: "pool2"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv2"
   top: "pool2"
   pooling_param {
@@ -102,13 +114,19 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "ip1"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "pool2"
   top: "ip1"
-  blobs_lr: 1
-  blobs_lr: 2
+  param {
+    name: "ip1_w"
+    lr_mult: 1
+  }
+  param {
+    name: "ip1_b"
+    lr_mult: 2
+  }
   inner_product_param {
     num_output: 500
     weight_filler {
@@ -118,22 +136,26 @@ layers {
       type: "constant"
     }
   }
-  param: "ip1_w"
-  param: "ip1_b"
 }
-layers {
+layer {
   name: "relu1"
-  type: RELU
+  type: "ReLU"
   bottom: "ip1"
   top: "ip1"
 }
-layers {
+layer {
   name: "ip2"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "ip1"
   top: "ip2"
-  blobs_lr: 1
-  blobs_lr: 2
+  param {
+    name: "ip2_w"
+    lr_mult: 1
+  }
+  param {
+    name: "ip2_b"
+    lr_mult: 2
+  }
   inner_product_param {
     num_output: 10
     weight_filler {
@@ -143,17 +165,20 @@ layers {
       type: "constant"
     }
   }
-  param: "ip2_w"
-  param: "ip2_b"
 }
-
-layers {
+layer {
   name: "feat"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "ip2"
   top: "feat"
-  blobs_lr: 1
-  blobs_lr: 2
+  param {
+    name: "feat_w"
+    lr_mult: 1
+  }
+  param {
+    name: "feat_b"
+    lr_mult: 2
+  }
   inner_product_param {
     num_output: 2
     weight_filler {
@@ -163,19 +188,20 @@ layers {
       type: "constant"
     }
   }
-  param: "feat_w"
-  param: "feat_b"
 }
-
-
-
-layers {
+layer {
   name: "conv1_p"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "data_p"
   top: "conv1_p"
-  blobs_lr: 1
-  blobs_lr: 2
+  param {
+    name: "conv1_w"
+    lr_mult: 1
+  }
+  param {
+    name: "conv1_b"
+    lr_mult: 2
+  }
   convolution_param {
     num_output: 20
     kernel_size: 5
@@ -187,12 +213,10 @@ layers {
       type: "constant"
     }
   }
-  param: "conv1_w"
-  param: "conv1_b"
 }
-layers {
+layer {
   name: "pool1_p"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv1_p"
   top: "pool1_p"
   pooling_param {
@@ -201,13 +225,19 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "conv2_p"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "pool1_p"
   top: "conv2_p"
-  blobs_lr: 1
-  blobs_lr: 2
+  param {
+    name: "conv2_w"
+    lr_mult: 1
+  }
+  param {
+    name: "conv2_b"
+    lr_mult: 2
+  }
   convolution_param {
     num_output: 50
     kernel_size: 5
@@ -219,12 +249,10 @@ layers {
       type: "constant"
     }
   }
-  param: "conv2_w"
-  param: "conv2_b"
 }
-layers {
+layer {
   name: "pool2_p"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv2_p"
   top: "pool2_p"
   pooling_param {
@@ -233,13 +261,19 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "ip1_p"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "pool2_p"
   top: "ip1_p"
-  blobs_lr: 1
-  blobs_lr: 2
+  param {
+    name: "ip1_w"
+    lr_mult: 1
+  }
+  param {
+    name: "ip1_b"
+    lr_mult: 2
+  }
   inner_product_param {
     num_output: 500
     weight_filler {
@@ -249,22 +283,26 @@ layers {
       type: "constant"
     }
   }
-  param: "ip1_w"
-  param: "ip1_b"
 }
-layers {
+layer {
   name: "relu1_p"
-  type: RELU
+  type: "ReLU"
   bottom: "ip1_p"
   top: "ip1_p"
 }
-layers {
+layer {
   name: "ip2_p"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "ip1_p"
   top: "ip2_p"
-  blobs_lr: 1
-  blobs_lr: 2
+  param {
+    name: "ip2_w"
+    lr_mult: 1
+  }
+  param {
+    name: "ip2_b"
+    lr_mult: 2
+  }
   inner_product_param {
     num_output: 10
     weight_filler {
@@ -274,17 +312,20 @@ layers {
       type: "constant"
     }
   }
-  param: "ip2_w"
-  param: "ip2_b"
 }
-
-layers {
+layer {
   name: "feat_p"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "ip2_p"
   top: "feat_p"
-  blobs_lr: 1
-  blobs_lr: 2
+  param {
+    name: "feat_w"
+    lr_mult: 1
+  }
+  param {
+    name: "feat_b"
+    lr_mult: 2
+  }
   inner_product_param {
     num_output: 2
     weight_filler {
@@ -294,20 +335,15 @@ layers {
       type: "constant"
     }
   }
-  param: "feat_w"
-  param: "feat_b"
 }
-
-
-
-layers {
-    name: "loss"
-    type: CONTRASTIVE_LOSS
-    contrastive_loss_param {
-        margin: 1.0
-    }
-    bottom: "feat"
-    bottom: "feat_p"
-    bottom: "sim"
-    top: "loss"
+layer {
+  name: "loss"
+  type: "ContrastiveLoss"
+  bottom: "feat"
+  bottom: "feat_p"
+  bottom: "sim"
+  top: "loss"
+  contrastive_loss_param {
+    margin: 1
+  }
 }
diff --git a/examples/siamese/readme.md b/examples/siamese/readme.md
index ce98ec10819..83db8c94395 100644
--- a/examples/siamese/readme.md
+++ b/examples/siamese/readme.md
@@ -39,13 +39,19 @@ exactly the same as the [LeNet model](mnist.html), the only difference is that
 we have replaced the top layers that produced probabilities over the 10 digit
 classes with a linear "feature" layer that produces a 2 dimensional vector.
 
-    layers {
+    layer {
       name: "feat"
-      type: INNER_PRODUCT
+      type: "InnerProduct"
       bottom: "ip2"
       top: "feat"
-      blobs_lr: 1
-      blobs_lr: 2
+      param {
+        name: "feat_w"
+        lr_mult: 1
+      }
+      param {
+        name: "feat_b"
+        lr_mult: 2
+      }
       inner_product_param {
         num_output: 2
       }
@@ -64,17 +70,19 @@ earlier. Each entry in this database contains the image data for a pair of
 images (`pair_data`) and a binary label saying if they belong to the same class
 or different classes (`sim`).
 
-    layers {
+    layer {
       name: "pair_data"
-      type: DATA
+      type: "Data"
       top: "pair_data"
       top: "sim"
-      data_param {
-        source: "examples/siamese/mnist-siamese-train-leveldb"
+      include { phase: TRAIN }
+      transform_param {
         scale: 0.00390625
+      }
+      data_param {
+        source: "examples/siamese/mnist_siamese_train_leveldb"
         batch_size: 64
       }
-      include: { phase: TRAIN }
     }
 
 In order to pack a pair of images into the same blob in the database we pack one
@@ -83,16 +91,16 @@ so we add a slice layer after the data layer. This takes the `pair_data` and
 slices it along the channel dimension so that we have a single image in `data`
 and its paired image in `data_p.`
 
-    layers {
-        name: "slice_pair"
-        type: SLICE
-        bottom: "pair_data"
-        top: "data"
-        top: "data_p"
-        slice_param {
-            slice_dim: 1
-            slice_point: 1
-        }
+    layer {
+      name: "slice_pair"
+      type: "Slice"
+      bottom: "pair_data"
+      top: "data"
+      top: "data_p"
+      slice_param {
+        slice_dim: 1
+        slice_point: 1
+      }
     }
 
 ### Building the First Side of the Siamese Net
@@ -105,17 +113,17 @@ parameters allows Caffe to share the parameters between layers on both sides of
 the siamese net. In the definition this looks like:
 
     ...
-    param: "conv1_w"
-    param: "conv1_b"
+    param { name: "conv1_w" ...  }
+    param { name: "conv1_b" ...  }
     ...
-    param: "conv2_w"
-    param: "conv2_b"
+    param { name: "conv2_w" ...  }
+    param { name: "conv2_b" ...  }
     ...
-    param: "ip1_w"
-    param: "ip1_b"
+    param { name: "ip1_w" ...  }
+    param { name: "ip1_b" ...  }
     ...
-    param: "ip2_w"
-    param: "ip2_b"
+    param { name: "ip2_w" ...  }
+    param { name: "ip2_b" ...  }
     ...
 
 ### Building the Second Side of the Siamese Net
@@ -133,9 +141,9 @@ an Invariant Mapping". This loss function encourages matching pairs to be close
 together in feature space while pushing non-matching pairs apart. This cost
 function is implemented with the `CONTRASTIVE_LOSS` layer:
 
-    layers {
+    layer {
         name: "loss"
-        type: CONTRASTIVE_LOSS
+        type: "ContrastiveLoss"
         contrastive_loss_param {
             margin: 1.0
         }
diff --git a/examples/web_demo/app.py b/examples/web_demo/app.py
index d33fc92f078..09411f33f10 100644
--- a/examples/web_demo/app.py
+++ b/examples/web_demo/app.py
@@ -10,13 +10,14 @@
 import tornado.httpserver
 import numpy as np
 import pandas as pd
-from PIL import Image as PILImage
+from PIL import Image
 import cStringIO as StringIO
 import urllib
-import caffe
 import exifutil
 
-REPO_DIRNAME = os.path.abspath(os.path.dirname(__file__) + '/../..')
+import caffe
+
+REPO_DIRNAME = os.path.abspath(os.path.dirname(os.path.abspath(__file__)) + '/../..')
 UPLOAD_FOLDER = '/tmp/caffe_demos_uploads'
 ALLOWED_IMAGE_EXTENSIONS = set(['png', 'bmp', 'jpg', 'jpe', 'jpeg', 'gif'])
 
@@ -80,7 +81,7 @@ def classify_upload():
 
 def embed_image_html(image):
     """Creates an image embedded in HTML base64 format."""
-    image_pil = PILImage.fromarray((255 * image).astype('uint8'))
+    image_pil = Image.fromarray((255 * image).astype('uint8'))
     image_pil = image_pil.resize((256, 256))
     string_buf = StringIO.StringIO()
     image_pil.save(string_buf, format='png')
@@ -112,17 +113,20 @@ class ImagenetClassifier(object):
         if not os.path.exists(val):
             raise Exception(
                 "File for {} is missing. Should be at: {}".format(key, val))
-    default_args['image_dim'] = 227
+    default_args['image_dim'] = 256
     default_args['raw_scale'] = 255.
-    default_args['gpu_mode'] = False
 
     def __init__(self, model_def_file, pretrained_model_file, mean_file,
                  raw_scale, class_labels_file, bet_file, image_dim, gpu_mode):
         logging.info('Loading net and associated files...')
+        if gpu_mode:
+            caffe.set_mode_gpu()
+        else:
+            caffe.set_mode_cpu()
         self.net = caffe.Classifier(
             model_def_file, pretrained_model_file,
             image_dims=(image_dim, image_dim), raw_scale=raw_scale,
-            mean=np.load(mean_file), channel_swap=(2, 1, 0), gpu=gpu_mode
+            mean=np.load(mean_file).mean(1).mean(1), channel_swap=(2, 1, 0)
         )
 
         with open(class_labels_file) as f:
@@ -206,8 +210,9 @@ def start_from_terminal(app):
     opts, args = parser.parse_args()
     ImagenetClassifier.default_args.update({'gpu_mode': opts.gpu})
 
-    # Initialize classifier
+    # Initialize classifier + warm start by forward for allocation
     app.clf = ImagenetClassifier(**ImagenetClassifier.default_args)
+    app.clf.net.forward()
 
     if opts.debug:
         app.run(debug=True, host='0.0.0.0', port=opts.port)
diff --git a/include/caffe/blob.hpp b/include/caffe/blob.hpp
index ef10aea53f0..94f251b271a 100644
--- a/include/caffe/blob.hpp
+++ b/include/caffe/blob.hpp
@@ -1,11 +1,17 @@
 #ifndef CAFFE_BLOB_HPP_
 #define CAFFE_BLOB_HPP_
 
+#include <algorithm>
+#include <string>
+#include <vector>
+
 #include "caffe/common.hpp"
 #include "caffe/proto/caffe.pb.h"
 #include "caffe/syncedmem.hpp"
 #include "caffe/util/math_functions.hpp"
 
+const int kMaxBlobAxes = INT_MAX;
+
 namespace caffe {
 
 /**
@@ -19,10 +25,16 @@ template <typename Dtype>
 class Blob {
  public:
   Blob()
-       : data_(), diff_(), num_(0), channels_(0), height_(0), width_(0),
-       count_(0), capacity_(0) {}
+       : data_(), diff_(), count_(0), capacity_(0) {}
+
+  /// @brief Deprecated; use <code>Blob(const vector<int>& shape)</code>.
   explicit Blob(const int num, const int channels, const int height,
-    const int width);
+      const int width);
+  explicit Blob(const vector<int>& shape);
+
+  /// @brief Deprecated; use <code>Reshape(const vector<int>& shape)</code>.
+  void Reshape(const int num, const int channels, const int height,
+      const int width);
   /**
    * @brief Change the dimensions of the blob, allocating new memory if
    *        necessary.
@@ -37,25 +49,133 @@ class Blob {
    * an error; either Net::Forward or Net::Reshape need to be called to
    * propagate the new input shape to higher layers.
    */
-  void Reshape(const int num, const int channels, const int height,
-    const int width);
+  void Reshape(const vector<int>& shape);
+  void Reshape(const BlobShape& shape);
   void ReshapeLike(const Blob& other);
-  inline int num() const { return num_; }
-  inline int channels() const { return channels_; }
-  inline int height() const { return height_; }
-  inline int width() const { return width_; }
+  inline string shape_string() const {
+    ostringstream stream;
+    for (int i = 0; i < shape_.size(); ++i) {
+      stream << shape_[i] << " ";
+    }
+    stream << "(" << count_ << ")";
+    return stream.str();
+  }
+  inline const vector<int>& shape() const { return shape_; }
+  /**
+   * @brief Returns the dimension of the index-th axis (or the negative index-th
+   *        axis from the end, if index is negative).
+   *
+   * @param index the axis index, which may be negative as it will be
+   *        "canonicalized" using CanonicalAxisIndex.
+   *        Dies on out of range index.
+   */
+  inline int shape(int index) const {
+    return shape_[CanonicalAxisIndex(index)];
+  }
+  inline int num_axes() const { return shape_.size(); }
   inline int count() const { return count_; }
+
+  /**
+   * @brief Compute the volume of a slice; i.e., the product of dimensions
+   *        among a range of axes.
+   *
+   * @param start_axis The first axis to include in the slice.
+   *
+   * @param end_axis The first axis to exclude from the slice.
+   */
+  inline int count(int start_axis, int end_axis) const {
+    CHECK_LE(start_axis, end_axis);
+    CHECK_GE(start_axis, 0);
+    CHECK_GE(end_axis, 0);
+    CHECK_LE(start_axis, num_axes());
+    CHECK_LE(end_axis, num_axes());
+    int count = 1;
+    for (int i = start_axis; i < end_axis; ++i) {
+      count *= shape(i);
+    }
+    return count;
+  }
+  /**
+   * @brief Compute the volume of a slice spanning from a particular first
+   *        axis to the final axis.
+   *
+   * @param start_axis The first axis to include in the slice.
+   */
+  inline int count(int start_axis) const {
+    return count(start_axis, num_axes());
+  }
+
+  /**
+   * @brief Returns the 'canonical' version of a (usually) user-specified axis,
+   *        allowing for negative indexing (e.g., -1 for the last axis).
+   *
+   * @param index the axis index.
+   *        If 0 <= index < num_axes(), return index.
+   *        If -num_axes <= index <= -1, return (num_axes() - (-index)),
+   *        e.g., the last axis index (num_axes() - 1) if index == -1,
+   *        the second to last if index == -2, etc.
+   *        Dies on out of range index.
+   */
+  inline int CanonicalAxisIndex(int axis_index) const {
+    CHECK_GE(axis_index, -num_axes())
+        << "axis " << axis_index << " out of range for " << num_axes()
+        << "-D Blob with shape " << shape_string();
+    CHECK_LT(axis_index, num_axes())
+        << "axis " << axis_index << " out of range for " << num_axes()
+        << "-D Blob with shape " << shape_string();
+    if (axis_index < 0) {
+      return axis_index + num_axes();
+    }
+    return axis_index;
+  }
+
+  /// @brief Deprecated legacy shape accessor num: use shape(0) instead.
+  inline int num() const { return LegacyShape(0); }
+  /// @brief Deprecated legacy shape accessor channels: use shape(1) instead.
+  inline int channels() const { return LegacyShape(1); }
+  /// @brief Deprecated legacy shape accessor height: use shape(2) instead.
+  inline int height() const { return LegacyShape(2); }
+  /// @brief Deprecated legacy shape accessor width: use shape(3) instead.
+  inline int width() const { return LegacyShape(3); }
+  inline int LegacyShape(int index) const {
+    CHECK_LE(num_axes(), 4)
+        << "Cannot use legacy accessors on Blobs with > 4 axes.";
+    CHECK_LT(index, 4);
+    CHECK_GE(index, -4);
+    if (index >= num_axes() || index < -num_axes()) {
+      // Axis is out of range, but still in [0, 3] (or [-4, -1] for reverse
+      // indexing) -- this special case simulates the one-padding used to fill
+      // extraneous axes of legacy blobs.
+      return 1;
+    }
+    return shape(index);
+  }
+
   inline int offset(const int n, const int c = 0, const int h = 0,
       const int w = 0) const {
     CHECK_GE(n, 0);
-    CHECK_LE(n, num_);
-    CHECK_GE(channels_, 0);
-    CHECK_LE(c, channels_);
-    CHECK_GE(height_, 0);
-    CHECK_LE(h, height_);
-    CHECK_GE(width_, 0);
-    CHECK_LE(w, width_);
-    return ((n * channels_ + c) * height_ + h) * width_ + w;
+    CHECK_LE(n, num());
+    CHECK_GE(channels(), 0);
+    CHECK_LE(c, channels());
+    CHECK_GE(height(), 0);
+    CHECK_LE(h, height());
+    CHECK_GE(width(), 0);
+    CHECK_LE(w, width());
+    return ((n * channels() + c) * height() + h) * width() + w;
+  }
+
+  inline int offset(const vector<int>& indices) const {
+    CHECK_LE(indices.size(), num_axes());
+    int offset = 0;
+    for (int i = 0; i < num_axes(); ++i) {
+      offset *= shape(i);
+      if (indices.size() > i) {
+        CHECK_GE(indices[i], 0);
+        CHECK_LT(indices[i], shape(i));
+        offset += indices[i];
+      }
+    }
+    return offset;
   }
   /**
    * @brief Copy from a source Blob.
@@ -71,12 +191,20 @@ class Blob {
 
   inline Dtype data_at(const int n, const int c, const int h,
       const int w) const {
-    return *(cpu_data() + offset(n, c, h, w));
+    return cpu_data()[offset(n, c, h, w)];
   }
 
   inline Dtype diff_at(const int n, const int c, const int h,
       const int w) const {
-    return *(cpu_diff() + offset(n, c, h, w));
+    return cpu_diff()[offset(n, c, h, w)];
+  }
+
+  inline Dtype data_at(const vector<int>& index) const {
+    return cpu_data()[offset(index)];
+  }
+
+  inline Dtype diff_at(const vector<int>& index) const {
+    return cpu_diff()[offset(index)];
   }
 
   inline const shared_ptr<SyncedMemory>& data() const {
@@ -99,17 +227,26 @@ class Blob {
   Dtype* mutable_cpu_diff();
   Dtype* mutable_gpu_diff();
   void Update();
-  void FromProto(const BlobProto& proto);
+  void FromProto(const BlobProto& proto, bool reshape = true);
   void ToProto(BlobProto* proto, bool write_diff = false) const;
 
   /// @brief Compute the sum of absolute values (L1 norm) of the data.
   Dtype asum_data() const;
   /// @brief Compute the sum of absolute values (L1 norm) of the diff.
   Dtype asum_diff() const;
+  /// @brief Compute the sum of squares (L2 norm squared) of the data.
+  Dtype sumsq_data() const;
+  /// @brief Compute the sum of squares (L2 norm squared) of the diff.
+  Dtype sumsq_diff() const;
+
+  /// @brief Scale the blob data by a constant factor.
+  void scale_data(Dtype scale_factor);
+  /// @brief Scale the blob diff by a constant factor.
+  void scale_diff(Dtype scale_factor);
 
   /**
    * @brief Set the data_ shared_ptr to point to the SyncedMemory holding the
-   *        data_ of Blob other -- useful in Layer&s which simply perform a copy
+   *        data_ of Blob other -- useful in Layer%s which simply perform a copy
    *        in their Forward pass.
    *
    * This deallocates the SyncedMemory holding this Blob's data_, as
@@ -118,21 +255,24 @@ class Blob {
   void ShareData(const Blob& other);
   /**
    * @brief Set the diff_ shared_ptr to point to the SyncedMemory holding the
-   *        diff_ of Blob other -- useful in Layer&s which simply perform a copy
+   *        diff_ of Blob other -- useful in Layer%s which simply perform a copy
    *        in their Forward pass.
    *
    * This deallocates the SyncedMemory holding this Blob's diff_, as
    * shared_ptr calls its destructor when reset with the "=" operator.
    */
   void ShareDiff(const Blob& other);
+  bool IsSharingDiff(const Blob* other);
+
+  void SetDiffStorage(shared_ptr<SyncedMemory>& storage);
+  void SetDataStorage(shared_ptr<SyncedMemory>& storage);
+
+  bool ShapeEquals(const BlobProto& other);
 
  protected:
   shared_ptr<SyncedMemory> data_;
   shared_ptr<SyncedMemory> diff_;
-  int num_;
-  int channels_;
-  int height_;
-  int width_;
+  vector<int> shape_;
   int count_;
   int capacity_;
 
diff --git a/include/caffe/caffe.hpp b/include/caffe/caffe.hpp
index 0af9ef04c43..3c829f2f9b0 100644
--- a/include/caffe/caffe.hpp
+++ b/include/caffe/caffe.hpp
@@ -8,6 +8,7 @@
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
 #include "caffe/layer.hpp"
+#include "caffe/layer_factory.hpp"
 #include "caffe/net.hpp"
 #include "caffe/proto/caffe.pb.h"
 #include "caffe/solver.hpp"
diff --git a/include/caffe/common.hpp b/include/caffe/common.hpp
index 9c6eb4d6834..4f93eff7369 100644
--- a/include/caffe/common.hpp
+++ b/include/caffe/common.hpp
@@ -5,6 +5,7 @@
 #include <gflags/gflags.h>
 #include <glog/logging.h>
 
+#include <climits>
 #include <cmath>
 #include <fstream>  // NOLINT(readability/streams)
 #include <iostream>  // NOLINT(readability/streams)
@@ -15,11 +16,26 @@
 #include <utility>  // pair
 #include <vector>
 
+#ifdef USE_MPI
+  #include "mpi.h"
+
+#define MPI_CHECK(cond) \
+do { \
+    int status = cond; \
+    CHECK_EQ(status, MPI_SUCCESS) << " " \
+      << "MPI Error Code: " << status; \
+  } while (0)
+#endif
+
+#ifdef WITH_PYTHON_LAYER
+#include <boost/python.hpp>
+#endif
+
 #include "caffe/util/device_alternate.hpp"
 
 // gflags 2.1 issue: namespace google was changed to gflags without warning.
-// Luckily we will be able to use GFLAGS_GFAGS_H_ to detect if it is version
-// 2.1. If yes , we will add a temporary solution to redirect the namespace.
+// Luckily we will be able to use GFLAGS_GFLAGS_H_ to detect if it is version
+// 2.1. If yes, we will add a temporary solution to redirect the namespace.
 // TODO(Yangqing): Once gflags solves the problem in a more elegant way, let's
 // remove the following hack.
 #ifndef GFLAGS_GFLAGS_H_
@@ -34,13 +50,39 @@ private:\
 
 // Instantiate a class with float and double specifications.
 #define INSTANTIATE_CLASS(classname) \
+  char gInstantiationGuard##classname; \
   template class classname<float>; \
   template class classname<double>
 
+#define INSTANTIATE_LAYER_GPU_FORWARD(classname) \
+  template void classname<float>::Forward_gpu( \
+      const std::vector<Blob<float>*>& bottom, \
+      const std::vector<Blob<float>*>& top); \
+  template void classname<double>::Forward_gpu( \
+      const std::vector<Blob<double>*>& bottom, \
+      const std::vector<Blob<double>*>& top);
+
+#define INSTANTIATE_LAYER_GPU_BACKWARD(classname) \
+  template void classname<float>::Backward_gpu( \
+      const std::vector<Blob<float>*>& top, \
+      const std::vector<bool>& propagate_down, \
+      const std::vector<Blob<float>*>& bottom); \
+  template void classname<double>::Backward_gpu( \
+      const std::vector<Blob<double>*>& top, \
+      const std::vector<bool>& propagate_down, \
+      const std::vector<Blob<double>*>& bottom)
+
+#define INSTANTIATE_LAYER_GPU_FUNCS(classname) \
+  INSTANTIATE_LAYER_GPU_FORWARD(classname); \
+  INSTANTIATE_LAYER_GPU_BACKWARD(classname)
+
 // A simple macro to mark codes that are not implemented, so that when the code
 // is executed we will see a fatal log.
 #define NOT_IMPLEMENTED LOG(FATAL) << "Not Implemented Yet"
 
+// See PR #1236
+namespace cv { class Mat; }
+
 namespace caffe {
 
 // We will use the boost shared_ptr instead of the new C++11 one mainly
@@ -51,6 +93,7 @@ using boost::shared_ptr;
 using std::fstream;
 using std::ios;
 using std::isnan;
+using std::isinf;
 using std::iterator;
 using std::make_pair;
 using std::map;
@@ -65,7 +108,13 @@ using std::vector;
 // Currently it initializes google flags and google logging.
 void GlobalInit(int* pargc, char*** pargv);
 
-// A singleton class to hold common caffe stuff, such as the handler that
+// A global function to clear up remaining stuffs
+void GlobalFinalize();
+
+// Header for system entropy source
+int64_t cluster_seedgen(bool sync=true);
+
+  // A singleton class to hold common caffe stuff, such as the handler that
 // caffe is going to use for cublas, curand, etc.
 class Caffe {
  public:
@@ -77,8 +126,6 @@ class Caffe {
     return *singleton_;
   }
   enum Brew { CPU, GPU };
-  enum Phase { TRAIN, TEST };
-
 
   // This random number generator facade hides boost and CUDA rng
   // implementation from one another (for cross-platform compatibility).
@@ -110,16 +157,12 @@ class Caffe {
 
   // Returns the mode: running on CPU or GPU.
   inline static Brew mode() { return Get().mode_; }
-  // Returns the phase: TRAIN or TEST.
-  inline static Phase phase() { return Get().phase_; }
   // The setters for the variables
   // Sets the mode. It is recommended that you don't change the mode halfway
   // into the program since that may cause allocation of pinned memory being
   // freed in a non-pinned way, which may cause problems - I haven't verified
   // it personally but better to note it here in the header file.
   inline static void set_mode(Brew mode) { Get().mode_ = mode; }
-  // Sets the phase.
-  inline static void set_phase(Phase phase) { Get().phase_ = phase; }
   // Sets the random seed of both boost and curand
   static void set_random_seed(const unsigned int seed);
   // Sets the device. Since we have cublas and curand stuff, set device also
@@ -128,6 +171,42 @@ class Caffe {
   // Prints the current GPU status.
   static void DeviceQuery();
 
+#ifdef USE_MPI
+  enum PARALLEL_MODE { NO, MPI };
+
+  //Returns current parallel mode, No or MPI
+  inline static PARALLEL_MODE parallel_mode() {return Get().parallel_mode_;}
+  // Setter of parallel mode
+  inline static void set_parallel_mode(PARALLEL_MODE mode) {Get().parallel_mode_ = mode;}
+
+  //Returns MPI_MY_RANK
+  inline static int MPI_my_rank(){return Get().mpi_my_rank_;}
+  inline static int MPI_all_rank(){return Get().mpi_all_rank_;}
+  inline static void MPI_build_rank(){
+    MPI_Comm_rank(MPI_COMM_WORLD, &(Get().mpi_my_rank_));
+    MPI_Comm_size(MPI_COMM_WORLD, &(Get().mpi_all_rank_));
+  }
+  inline static int device_id(){return Get().device_id_;}
+  inline static int remaining_sub_iter(){return Get().remaining_sub_iter_;}
+  inline static void set_remaining_sub_iter(int n){Get().remaining_sub_iter_ = n;}
+
+  // Functions for splitting MPI_Comm to fast distributed training.
+  inline static void MPI_split_comm(const int color, const int key) {
+    MPI_Comm intra_comm;
+    MPI_Comm_split(MPI_COMM_WORLD, color, key, &intra_comm);
+  }
+#endif
+
+#ifdef WITH_PYTHON_LAYER
+  inline static PyThreadState* py_tstate(){return Get().py_tstate_;}
+  inline static void set_py_tstate(PyThreadState* new_state){Get().py_tstate_ = new_state;}
+#endif
+
+#ifdef USE_CUDNN
+  inline static int cudnn_mem_richness(){return Get().cudnn_mem_richness_;}
+  inline static void set_cudnn_mem_richness(int richness){Get().cudnn_mem_richness_ = richness;}
+#endif
+
  protected:
 #ifndef CPU_ONLY
   cublasHandle_t cublas_handle_;
@@ -135,8 +214,24 @@ class Caffe {
 #endif
   shared_ptr<RNG> random_generator_;
 
+#ifdef USE_CUDNN
+  int cudnn_mem_richness_;
+#endif
+
+#ifdef USE_MPI
+
+  PARALLEL_MODE parallel_mode_;
+  int mpi_my_rank_;
+  int mpi_all_rank_;
+  int device_id_;
+  int remaining_sub_iter_;
+#endif
+
+#ifdef WITH_PYTHON_LAYER
+  PyThreadState* py_tstate_;
+#endif
+
   Brew mode_;
-  Phase phase_;
   static shared_ptr<Caffe> singleton_;
 
  private:
diff --git a/include/caffe/common_layers.hpp b/include/caffe/common_layers.hpp
index 1f945ca34e9..29858a1e6f3 100644
--- a/include/caffe/common_layers.hpp
+++ b/include/caffe/common_layers.hpp
@@ -13,6 +13,10 @@
 #include "caffe/neuron_layers.hpp"
 #include "caffe/proto/caffe.pb.h"
 
+#ifdef USE_CUDNN
+#include "caffe/util/cudnn.hpp"
+#endif
+
 namespace caffe {
 
 /**
@@ -39,13 +43,11 @@ class ArgMaxLayer : public Layer<Dtype> {
   explicit ArgMaxLayer(const LayerParameter& param)
       : Layer<Dtype>(param) {}
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
 
-  virtual inline LayerParameter_LayerType type() const {
-    return LayerParameter_LayerType_ARGMAX;
-  }
+  virtual inline const char* type() const { return "ArgMax"; }
   virtual inline int ExactNumBottomBlobs() const { return 1; }
   virtual inline int ExactNumTopBlobs() const { return 1; }
 
@@ -62,10 +64,10 @@ class ArgMaxLayer : public Layer<Dtype> {
    *      @f$ (for @f$ K = 1 @f$).
    */
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   /// @brief Not implemented (non-differentiable function)
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
     NOT_IMPLEMENTED;
   }
   bool out_max_val_;
@@ -82,13 +84,11 @@ class ConcatLayer : public Layer<Dtype> {
   explicit ConcatLayer(const LayerParameter& param)
       : Layer<Dtype>(param) {}
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
 
-  virtual inline LayerParameter_LayerType type() const {
-    return LayerParameter_LayerType_CONCAT;
-  }
+  virtual inline const char* type() const { return "Concat"; }
   virtual inline int MinBottomBlobs() const { return 2; }
   virtual inline int ExactNumTopBlobs() const { return 1; }
 
@@ -103,24 +103,24 @@ class ConcatLayer : public Layer<Dtype> {
    *   - K @f$ (N \times C \times H \times W) @f$
    *      the inputs @f$ x_K @f$
    * @param top output Blob vector (length 1)
-   *   -# @f$ (KN \times C \times H \times W) @f$ if concat_dim == 0, or
-   *      @f$ (N \times KC \times H \times W) @f$ if concat_dim == 1:
+   *   -# @f$ (KN \times C \times H \times W) @f$ if axis == 0, or
+   *      @f$ (N \times KC \times H \times W) @f$ if axis == 1:
    *      the concatenated output @f$
    *        y = [\begin{array}{cccc} x_1 & x_2 & ... & x_K \end{array}]
    *      @f$
    */
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
 
   /**
    * @brief Computes the error gradient w.r.t. the concatenate inputs.
    *
    * @param top output Blob vector (length 1), providing the error gradient with
    *        respect to the outputs
-   *   -# @f$ (KN \times C \times H \times W) @f$ if concat_dim == 0, or
-   *      @f$ (N \times KC \times H \times W) @f$ if concat_dim == 1:
+   *   -# @f$ (KN \times C \times H \times W) @f$ if axis == 0, or
+   *      @f$ (N \times KC \times H \times W) @f$ if axis == 1:
    *      containing error gradients @f$ \frac{\partial E}{\partial y} @f$
    *      with respect to concatenated outputs @f$ y @f$
    * @param propagate_down see Layer::Backward.
@@ -137,17 +137,14 @@ class ConcatLayer : public Layer<Dtype> {
    *        @f$
    */
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
 
-  Blob<Dtype> col_bob_;
   int count_;
-  int num_;
-  int channels_;
-  int height_;
-  int width_;
-  int concat_dim_;
+  int num_concats_;
+  int concat_input_size_;
+  int concat_axis_;
 };
 
 /**
@@ -162,25 +159,23 @@ class EltwiseLayer : public Layer<Dtype> {
   explicit EltwiseLayer(const LayerParameter& param)
       : Layer<Dtype>(param) {}
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
 
-  virtual inline LayerParameter_LayerType type() const {
-    return LayerParameter_LayerType_ELTWISE;
-  }
+  virtual inline const char* type() const { return "Eltwise"; }
   virtual inline int MinBottomBlobs() const { return 2; }
   virtual inline int ExactNumTopBlobs() const { return 1; }
 
  protected:
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
 
   EltwiseParameter_EltwiseOp op_;
   vector<Dtype> coeffs_;
@@ -189,6 +184,69 @@ class EltwiseLayer : public Layer<Dtype> {
   bool stable_prod_grad_;
 };
 
+/**
+ * @brief Takes two+ Blobs, interprets last Blob as a selector and
+ *  filter remaining Blobs accordingly with selector data (0 means that
+ * the corresponding item has to be filtered, non-zero means that corresponding
+ * item needs to stay).
+ */
+template <typename Dtype>
+class FilterLayer : public Layer<Dtype> {
+ public:
+  explicit FilterLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual inline const char* type() const { return "Filter"; }
+  virtual inline int MinBottomBlobs() const { return 2; }
+  virtual inline int MinTopBlobs() const { return 1; }
+
+ protected:
+  /**
+   * @param bottom input Blob vector (length 2+)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the inputs to be filtered @f$ x_1 @f$
+   *   -# ...
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the inputs to be filtered @f$ x_K @f$
+   *   -# @f$ (N \times 1 \times 1 \times 1) @f$
+   *      the selector blob
+   * @param top output Blob vector (length 1+)
+   *   -# @f$ (S \times C \times H \times W) @f$ ()
+   *        the filtered output @f$ x_1 @f$
+   *        where S is the number of items
+   *        that haven't been filtered
+   *      @f$ (S \times C \times H \times W) @f$
+   *        the filtered output @f$ x_K @f$
+   *        where S is the number of items
+   *        that haven't been filtered
+   */
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+    const vector<Blob<Dtype>*>& top);
+
+  /**
+   * @brief Computes the error gradient w.r.t. the forwarded inputs.
+   *
+   * @param top output Blob vector (length 1+), providing the error gradient with
+   *        respect to the outputs
+   * @param propagate_down see Layer::Backward.
+   * @param bottom input Blob vector (length 2+), into which the top error
+   *        gradient is copied
+   */
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+    const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+  bool first_reshape_;
+  vector<int> indices_to_forward_;
+};
+
 /**
  * @brief Reshapes the input Blob into flat vectors.
  *
@@ -205,14 +263,19 @@ class FlattenLayer : public Layer<Dtype> {
   explicit FlattenLayer(const LayerParameter& param)
       : Layer<Dtype>(param) {}
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
 
-  virtual inline LayerParameter_LayerType type() const {
-    return LayerParameter_LayerType_FLATTEN;
-  }
+  virtual inline const char* type() const { return "Flatten"; }
   virtual inline int ExactNumBottomBlobs() const { return 1; }
   virtual inline int ExactNumTopBlobs() const { return 1; }
 
+  virtual inline bool is_sharing_data(int top_id, int bottom_id){
+    return top_id == bottom_id;
+  }
+  virtual inline bool is_sharing_diff(int top_id, int bottom_id){
+    return top_id == bottom_id;
+  }
+
  protected:
   /**
    * @param bottom input Blob vector (length 2+)
@@ -223,9 +286,7 @@ class FlattenLayer : public Layer<Dtype> {
    *      the outputs -- i.e., the (virtually) copied, flattened inputs
    */
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
-  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
 
   /**
    * @brief Computes the error gradient w.r.t. the concatenate inputs.
@@ -237,11 +298,8 @@ class FlattenLayer : public Layer<Dtype> {
    *        gradient is (virtually) copied
    */
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
 
-  int count_;
 };
 
 /**
@@ -256,25 +314,23 @@ class InnerProductLayer : public Layer<Dtype> {
   explicit InnerProductLayer(const LayerParameter& param)
       : Layer<Dtype>(param) {}
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
 
-  virtual inline LayerParameter_LayerType type() const {
-    return LayerParameter_LayerType_INNER_PRODUCT;
-  }
+  virtual inline const char* type() const { return "InnerProduct"; }
   virtual inline int ExactNumBottomBlobs() const { return 1; }
   virtual inline int ExactNumTopBlobs() const { return 1; }
 
  protected:
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
 
   int M_;
   int K_;
@@ -294,28 +350,117 @@ class MVNLayer : public Layer<Dtype> {
   explicit MVNLayer(const LayerParameter& param)
       : Layer<Dtype>(param) {}
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
 
-  virtual inline LayerParameter_LayerType type() const {
-    return LayerParameter_LayerType_MVN;
-  }
+  virtual inline const char* type() const { return "MVN"; }
   virtual inline int ExactNumBottomBlobs() const { return 1; }
   virtual inline int ExactNumTopBlobs() const { return 1; }
 
  protected:
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-     const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+     const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
 
   Blob<Dtype> mean_, variance_, temp_;
 
   /// sum_multiplier is used to carry out sum using BLAS
   Blob<Dtype> sum_multiplier_;
+  Dtype eps_;
+};
+
+/*
+ * @brief Reshapes the input Blob into an arbitrary-sized output Blob.
+ *
+ * Note: similarly to FlattenLayer, this layer does not change the input values
+ * (see FlattenLayer, Blob::ShareData and Blob::ShareDiff).
+ */
+template <typename Dtype>
+class ReshapeLayer : public Layer<Dtype> {
+ public:
+  explicit ReshapeLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual inline const char* type() const { return "Reshape"; }
+  virtual inline int ExactNumBottomBlobs() const { return 1; }
+  virtual inline int ExactNumTopBlobs() const { return 1; }
+
+  virtual inline bool is_sharing_data(int top_id, int bottom_id) {
+    return top_id == bottom_id;
+  }
+  virtual inline bool is_sharing_diff(int top_id, int bottom_id) {
+    return top_id == bottom_id;
+  }
+
+ protected:
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {}
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {}
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
+
+  /// @brief vector of axes indices whose dimensions we'll copy from the bottom
+  vector<int> copy_axes_;
+  /// @brief the index of the axis whose dimension we infer, or -1 if none
+  int inferred_axis_;
+  /// @brief the product of the "constant" output dimensions
+  int constant_count_;
+};
+
+/**
+ * @brief Compute "reductions" -- operations that return a scalar output Blob
+ *        for an input Blob of arbitrary size, such as the sum, absolute sum,
+ *        and sum of squares.
+ *
+ * TODO(dox): thorough documentation for Forward, Backward, and proto params.
+ */
+template <typename Dtype>
+class ReductionLayer : public Layer<Dtype> {
+ public:
+  explicit ReductionLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual inline const char* type() const { return "Reduction"; }
+  virtual inline int ExactNumBottomBlobs() const { return 1; }
+  virtual inline int ExactNumTopBlobs() const { return 1; }
+
+ protected:
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+  /// @brief the reduction operation performed by the layer
+  ReductionParameter_ReductionOp op_;
+  /// @brief a scalar coefficient applied to all outputs
+  Dtype coeff_;
+  /// @brief the index of the first input axis to reduce
+  int axis_;
+  /// @brief the number of reductions performed
+  int num_;
+  /// @brief the input size of each reduction
+  int dim_;
+  /// @brief a helper Blob used for summation (op_ == SUM)
+  Blob<Dtype> sum_multiplier_;
 };
 
 /**
@@ -328,25 +473,23 @@ class SilenceLayer : public Layer<Dtype> {
   explicit SilenceLayer(const LayerParameter& param)
       : Layer<Dtype>(param) {}
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {}
+      const vector<Blob<Dtype>*>& top) {}
 
-  virtual inline LayerParameter_LayerType type() const {
-    return LayerParameter_LayerType_SILENCE;
-  }
+  virtual inline const char* type() const { return "Silence"; }
   virtual inline int MinBottomBlobs() const { return 1; }
   virtual inline int ExactNumTopBlobs() const { return 0; }
 
  protected:
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {}
+      const vector<Blob<Dtype>*>& top) {}
   // We can't define Forward_gpu here, since STUB_GPU will provide
   // its own definition for CPU_ONLY mode.
   virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
 };
 
 /**
@@ -360,24 +503,25 @@ class SoftmaxLayer : public Layer<Dtype> {
   explicit SoftmaxLayer(const LayerParameter& param)
       : Layer<Dtype>(param) {}
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
 
-  virtual inline LayerParameter_LayerType type() const {
-    return LayerParameter_LayerType_SOFTMAX;
-  }
+  virtual inline const char* type() const { return "Softmax"; }
   virtual inline int ExactNumBottomBlobs() const { return 1; }
   virtual inline int ExactNumTopBlobs() const { return 1; }
 
  protected:
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-     const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+     const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
 
+  int outer_num_;
+  int inner_num_;
+  int softmax_axis_;
   /// sum_multiplier is used to carry out sum using BLAS
   Blob<Dtype> sum_multiplier_;
   /// scale is an intermediate Blob to hold temporary results.
@@ -393,22 +537,23 @@ template <typename Dtype>
 class CuDNNSoftmaxLayer : public SoftmaxLayer<Dtype> {
  public:
   explicit CuDNNSoftmaxLayer(const LayerParameter& param)
-      : SoftmaxLayer<Dtype>(param) {}
+      : SoftmaxLayer<Dtype>(param), handles_setup_(false) {}
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual ~CuDNNSoftmaxLayer();
 
  protected:
   virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-     const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+     const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
 
+  bool handles_setup_;
   cudnnHandle_t             handle_;
-  cudnnTensor4dDescriptor_t bottom_desc_;
-  cudnnTensor4dDescriptor_t top_desc_;
+  cudnnTensorDescriptor_t bottom_desc_;
+  cudnnTensorDescriptor_t top_desc_;
 };
 #endif
 
@@ -424,23 +569,25 @@ class SplitLayer : public Layer<Dtype> {
   explicit SplitLayer(const LayerParameter& param)
       : Layer<Dtype>(param) {}
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
 
-  virtual inline LayerParameter_LayerType type() const {
-    return LayerParameter_LayerType_SPLIT;
-  }
+  virtual inline const char* type() const { return "Split"; }
   virtual inline int ExactNumBottomBlobs() const { return 1; }
   virtual inline int MinTopBlobs() const { return 1; }
 
+  virtual inline bool is_sharing_data(int top_id, int bottom_id) {
+    return true;
+  }
+
  protected:
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
 
   int count_;
 };
@@ -457,34 +604,394 @@ class SliceLayer : public Layer<Dtype> {
   explicit SliceLayer(const LayerParameter& param)
       : Layer<Dtype>(param) {}
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
 
-  virtual inline LayerParameter_LayerType type() const {
-    return LayerParameter_LayerType_SLICE;
-  }
+  virtual inline const char* type() const { return "Slice"; }
   virtual inline int ExactNumBottomBlobs() const { return 1; }
   virtual inline int MinTopBlobs() const { return 2; }
 
  protected:
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
 
-  Blob<Dtype> col_bob_;
   int count_;
+  int num_slices_;
+  int slice_size_;
+  int slice_axis_;
+  vector<int> slice_point_;
+};
+
+
+/**
+ * @brief Gather data in parallel mode
+ *
+ * TODO(dox): thorough documentation for Forward, Backward, and proto params.
+ */
+  template <typename Dtype>
+  class GatherLayer : public Layer<Dtype> {
+  public:
+    explicit GatherLayer(const LayerParameter& param)
+        : Layer<Dtype>(param) {}
+    virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+                            const vector<Blob<Dtype>*>& top);
+    virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+                         const vector<Blob<Dtype>*>& top);
+
+    virtual inline const char* type() const { return "Gather"; }
+    virtual inline int MinBottomBlobs() const { return 1; }
+    virtual inline int MinTopBlobs() const { return 1; }
+
+    virtual inline bool EqualNumBottomTopBlobs() const { return true; }
+    virtual inline bool is_gathering() {return true;}
+
+    virtual inline bool is_sharing_data(int top_id, int bottom_id){
+#ifndef USE_MPI
+      return top_id == bottom_id;
+#else
+      return (top_id == bottom_id) && (Caffe::parallel_mode()!=Caffe::MPI);
+#endif
+    }
+    virtual inline bool is_sharing_diff(int top_id, int bottom_id){
+#ifndef USE_MPI
+      return top_id == bottom_id;
+#else
+      return (top_id == bottom_id) && (Caffe::parallel_mode()!=Caffe::MPI);
+#endif
+    }
+
+  protected:
+    virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+                             const vector<Blob<Dtype>*>& top);
+    virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+                             const vector<Blob<Dtype>*>& top);
+    virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+                              const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+    virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+                              const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+  };
+
+/**
+ * @brief Scatter data in parallel mode
+ *
+ * TODO(dox): thorough documentation for Forward, Backward, and proto params.
+ */
+  template <typename Dtype>
+  class ScatterLayer : public Layer<Dtype> {
+  public:
+      explicit ScatterLayer(const LayerParameter& param)
+          : Layer<Dtype>(param) {}
+      virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+                              const vector<Blob<Dtype>*>& top);
+      virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+                           const vector<Blob<Dtype>*>& top);
+
+      virtual inline const char* type() const { return "Scatter"; }
+      virtual inline int MinBottomBlobs() const { return 1; }
+      virtual inline int MinTopBlobs() const { return 1; }
+      inline virtual bool is_scattering() {return true;}
+
+      virtual inline bool EqualNumBottomTopBlobs() const { return true; }
+
+      virtual inline bool is_sharing_data(int top_id, int bottom_id){
+#ifndef USE_MPI
+        return top_id == bottom_id;
+#else
+        return (top_id == bottom_id) && (Caffe::parallel_mode()!=Caffe::MPI);
+#endif
+      }
+      virtual inline bool is_sharing_diff(int top_id, int bottom_id){
+#ifndef USE_MPI
+        return top_id == bottom_id;
+#else
+        return (top_id == bottom_id) && (Caffe::parallel_mode()!=Caffe::MPI);
+#endif
+      }
+
+  protected:
+      virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+                               const vector<Blob<Dtype>*>& top);
+      virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+                               const vector<Blob<Dtype>*>& top);
+      virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+                                const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+      virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+                                const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+  };
+
+
+/**
+ * @brief Batch normalization the input blob along the channel axis while
+ *        averaging over the spatial axes.
+ *
+ * TODO(dox): thorough documentation for Forward, Backward, and proto params.
+ */
+template <typename Dtype>
+class BNLayer : public Layer<Dtype> {
+ public:
+  explicit BNLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual inline const char* type() const { return "BN"; }
+  virtual inline int ExactNumBottomBlobs() const { return 1; }
+  virtual inline int ExactNumTopBlobs() const { return 1; }
+
+ protected:
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+  void AverageAllExceptChannel(const Dtype* input, Dtype* output);
+  void BroadcastChannel(const Dtype* input, Dtype* output);
+
+  bool frozen_;
+  Dtype bn_momentum_;
+  Dtype bn_eps_;
+
   int num_;
   int channels_;
   int height_;
   int width_;
-  int slice_dim_;
-  vector<int> slice_point_;
+
+  Blob<Dtype> broadcast_buffer_;
+  Blob<Dtype> spatial_statistic_;
+  Blob<Dtype> batch_statistic_;
+
+  Blob<Dtype> x_norm_;
+  Blob<Dtype> x_inv_std_;
+
+  Blob<Dtype> spatial_sum_multiplier_;
+  Blob<Dtype> batch_sum_multiplier_;
+};
+
+
+#if defined(USE_CUDNN) 
+#if CUDNN_VERSION_MIN(5, 0, 0)
+/**
+ * @brief cuDNN implementation of BNLayer.
+ *        Fallback to BNLayer for CPU mode.
+ */
+template <typename Dtype>
+class CuDNNBNLayer : public BNLayer<Dtype> {
+ public:
+  explicit CuDNNBNLayer(const LayerParameter& param)
+      : BNLayer<Dtype>(param), handles_setup_(false) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual ~CuDNNBNLayer();
+
+  virtual inline const char* type() const { return "BN"; }
+  virtual inline int ExactNumBottomBlobs() const { return 1; }
+  virtual inline int ExactNumTopBlobs() const { return 1; }
+
+ protected:
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+  bool handles_setup_;
+  cudnnHandle_t handle_;
+  cudnnTensorDescriptor_t bottom_desc_;
+  cudnnTensorDescriptor_t top_desc_;
+  cudnnTensorDescriptor_t bn_param_desc_;
+
+  Blob<Dtype> save_mean_;
+  Blob<Dtype> save_inv_variance_;
+};
+#endif
+#endif
+
+/**
+* @brief Normalizes input to unit-length vector
+*/
+template <typename Dtype>
+class NormalizeLayer : public Layer<Dtype> {
+public:
+    explicit NormalizeLayer(const LayerParameter& param)
+        : Layer<Dtype>(param) {}
+    virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+                         const vector<Blob<Dtype>*>& top);
+
+    virtual inline const char* type() const { return "Normalize"; }
+    virtual inline int ExactNumBottomBlobs() const { return 1; }
+    virtual inline int ExactNumTopBlobs() const { return 1; }
+
+protected:
+    virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+                             const vector<Blob<Dtype>*>& top);
+    virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+                             const vector<Blob<Dtype>*>& top);
+    virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+                              const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+    virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+                              const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+    Blob<Dtype> sum_multiplier_, norm_, squared_;
+};
+
+
+template <typename Dtype>
+class ScaleLayer: public Layer<Dtype> {
+public:
+    explicit ScaleLayer(const LayerParameter& param)
+            : Layer<Dtype>(param) {}
+    virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+                            const vector<Blob<Dtype>*>& top);
+    virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+                         const vector<Blob<Dtype>*>& top);
+
+    virtual inline const char* type() const { return "Scale"; }
+    // Scale
+    virtual inline int MinBottomBlobs() const { return 1; }
+    virtual inline int MaxBottomBlobs() const { return 2; }
+    virtual inline int ExactNumTopBlobs() const { return 1; }
+
+protected:
+    /**
+     * In the below shape specifications, @f$ i @f$ denotes the value of the
+     * `axis` field given by `this->layer_param_.scale_param().axis()`, after
+     * canonicalization (i.e., conversion from negative to positive index,
+     * if applicable).
+     *
+     * @param bottom input Blob vector (length 2)
+     *   -# @f$ (d_0 \times ... \times
+     *           d_i \times ... \times d_j \times ... \times d_n) @f$
+     *      the first factor @f$ x @f$
+     *   -# @f$ (d_i \times ... \times d_j) @f$
+     *      the second factor @f$ y @f$
+     * @param top output Blob vector (length 1)
+     *   -# @f$ (d_0 \times ... \times
+     *           d_i \times ... \times d_j \times ... \times d_n) @f$
+     *      the product @f$ z = x y @f$ computed after "broadcasting" y.
+     *      Equivalent to tiling @f$ y @f$ to have the same shape as @f$ x @f$,
+     *      then computing the elementwise product.
+     */
+    virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+                             const vector<Blob<Dtype>*>& top);
+    virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+                             const vector<Blob<Dtype>*>& top);
+    virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+                              const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+    virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+                              const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+    shared_ptr<Layer<Dtype> > bias_layer_;
+    vector<Blob<Dtype>*> bias_bottom_vec_;
+    vector<bool> bias_propagate_down_;
+    int bias_param_id_;
+
+    Blob<Dtype> sum_multiplier_;
+    Blob<Dtype> sum_result_;
+    Blob<Dtype> temp_;
+    int axis_;
+    int outer_dim_, scale_dim_, inner_dim_;
+};
+
+/**
+ * @brief Computes a sum of two input Blobs, with the shape of the
+ *        latter Blob "broadcast" to match the shape of the former.
+ *        Equivalent to tiling the latter Blob, then computing the elementwise
+ *        sum.
+ *
+ * The second input may be omitted, in which case it's learned as a parameter
+ * of the layer.
+ */
+template <typename Dtype>
+class BiasLayer : public Layer<Dtype> {
+public:
+    explicit BiasLayer(const LayerParameter& param)
+            : Layer<Dtype>(param) {}
+    virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+                            const vector<Blob<Dtype>*>& top);
+    virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+                         const vector<Blob<Dtype>*>& top);
+
+    virtual inline const char* type() const { return "Bias"; }
+    virtual inline int MinBottomBlobs() const { return 1; }
+    virtual inline int MaxBottomBlobs() const { return 2; }
+    virtual inline int ExactNumTopBlobs() const { return 1; }
+
+    virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+                             const vector<Blob<Dtype>*>& top);
+    virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+                             const vector<Blob<Dtype>*>& top);
+    virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+                              const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+    virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+                              const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+private:
+    Blob<Dtype> bias_multiplier_;
+    int outer_dim_, bias_dim_, inner_dim_, dim_;
+};
+
+
+/**
+ * @brief Compute "reductions" -- operations that return a scalar output Blob
+ *        for an input Blob of arbitrary size, such as the sum, absolute sum,
+ *        and sum of squares.
+ *
+ * TODO(dox): thorough documentation for Forward, Backward, and proto params.
+ */
+template <typename Dtype>
+class BatchReductionLayer : public Layer<Dtype> {
+public:
+    explicit BatchReductionLayer(const LayerParameter& param)
+            : Layer<Dtype>(param) {}
+    virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+                            const vector<Blob<Dtype>*>& top);
+    virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+                         const vector<Blob<Dtype>*>& top);
+
+    virtual inline const char* type() const { return "BatchReduction"; }
+    virtual inline int ExactNumBottomBlobs() const { return 1; }
+    virtual inline int ExactNumTopBlobs() const { return 1; }
+
+protected:
+    virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+                             const vector<Blob<Dtype>*>& top);
+    virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+                             const vector<Blob<Dtype>*>& top);
+    virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+                              const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+    virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+                              const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+    /// @brief the reduction operation performed by the layer
+    ReductionParameter_ReductionOp op_;
+    /// @brief the index of the first input axis to reduce
+    int axis_;
+    /// @brief the number of reductions performed
+    int num_;
+    /// @brief the step of reduction
+    int step_;
+    /// @brief a helper Blob used for transferring ticks to GPU
+    Blob<Dtype> ticks_blob_;
+    vector<int> levels_;
+    vector<int> ticks_;
+
+    Blob<Dtype> argsort_idx_;
 };
 
 }  // namespace caffe
diff --git a/include/caffe/data_layers.hpp b/include/caffe/data_layers.hpp
index 8e2637b0658..688974a1b20 100644
--- a/include/caffe/data_layers.hpp
+++ b/include/caffe/data_layers.hpp
@@ -7,8 +7,6 @@
 
 #include "boost/scoped_ptr.hpp"
 #include "hdf5.h"
-#include "leveldb/db.h"
-#include "lmdb.h"
 
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
@@ -17,12 +15,11 @@
 #include "caffe/internal_thread.hpp"
 #include "caffe/layer.hpp"
 #include "caffe/proto/caffe.pb.h"
+#include "caffe/util/db.hpp"
+#include "caffe/util/rng.hpp"
 
 namespace caffe {
 
-#define HDF5_DATA_DATASET_NAME "data"
-#define HDF5_DATA_LABEL_NAME "label"
-
 /**
  * @brief Provides base for data layers that feed blobs to the Net.
  *
@@ -30,99 +27,129 @@ namespace caffe {
  */
 template <typename Dtype>
 class BaseDataLayer : public Layer<Dtype> {
- public:
-  explicit BaseDataLayer(const LayerParameter& param);
-  virtual ~BaseDataLayer() {}
-  // LayerSetUp: implements common data layer setup functionality, and calls
-  // DataLayerSetUp to do special data layer setup for individual layer types.
-  // This method may not be overridden except by the BasePrefetchingDataLayer.
-  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
-  virtual void DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {}
-  // Data layers have no bottoms, so reshaping is trivial.
-  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {}
-
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {}
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {}
-
-  int datum_channels() const { return datum_channels_; }
-  int datum_height() const { return datum_height_; }
-  int datum_width() const { return datum_width_; }
-  int datum_size() const { return datum_size_; }
-
- protected:
-  TransformationParameter transform_param_;
-  DataTransformer<Dtype> data_transformer_;
-  int datum_channels_;
-  int datum_height_;
-  int datum_width_;
-  int datum_size_;
-  Blob<Dtype> data_mean_;
-  const Dtype* mean_;
-  Caffe::Phase phase_;
-  bool output_labels_;
+public:
+	explicit BaseDataLayer(const LayerParameter& param);
+	// LayerSetUp: implements common data layer setup functionality, and calls
+	// DataLayerSetUp to do special data layer setup for individual layer types.
+	// This method may not be overridden except by the BasePrefetchingDataLayer.
+	virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+			const vector<Blob<Dtype>*>& top);
+	virtual void DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
+			const vector<Blob<Dtype>*>& top) {}
+	// Data layers have no bottoms, so reshaping is trivial.
+	virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+			const vector<Blob<Dtype>*>& top) {}
+
+	virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+			const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
+	virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+			const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
+
+#ifdef USE_MPI
+	/**
+	 * @brief call advance_cursor() for `step` times to offset the data access for parallel training
+	 */
+	inline virtual void OffsetCursor(int step){
+		if (Caffe::parallel_mode() == Caffe::MPI){
+			for (int i = 0; i < step; ++i) this->advance_cursor();
+		}
+	}
+#endif
+
+protected:
+
+#ifdef USE_MPI
+	/**
+	 * @brief The core utility for parallel based data access
+	 *
+	 * This move the "cursor" defined in each data layer one step forward
+	 */
+	inline virtual void advance_cursor(){
+		LOG(FATAL)<<"Data must implement advance_cursor() method to be involved in the parallel training";
+	}
+#endif
+
+	TransformationParameter transform_param_;
+	shared_ptr<DataTransformer<Dtype> > data_transformer_;
+	bool output_labels_;
 };
 
 template <typename Dtype>
 class BasePrefetchingDataLayer :
-    public BaseDataLayer<Dtype>, public InternalThread {
- public:
-  explicit BasePrefetchingDataLayer(const LayerParameter& param)
-      : BaseDataLayer<Dtype>(param) {}
-  virtual ~BasePrefetchingDataLayer() {}
-  // LayerSetUp: implements common data layer setup functionality, and calls
-  // DataLayerSetUp to do special data layer setup for individual layer types.
-  // This method may not be overridden.
-  void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
-
-  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
-  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
-
-  virtual void CreatePrefetchThread();
-  virtual void JoinPrefetchThread();
-  // The thread's function
-  virtual void InternalThreadEntry() {}
-
- protected:
-  Blob<Dtype> prefetch_data_;
-  Blob<Dtype> prefetch_label_;
+		public BaseDataLayer<Dtype>, public InternalThread {
+		public:
+	explicit BasePrefetchingDataLayer(const LayerParameter& param)
+	: BaseDataLayer<Dtype>(param) {}
+	// LayerSetUp: implements common data layer setup functionality, and calls
+	// DataLayerSetUp to do special data layer setup for individual layer types.
+	// This method may not be overridden.
+	void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+			const vector<Blob<Dtype>*>& top);
+
+	virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+			const vector<Blob<Dtype>*>& top);
+	virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+			const vector<Blob<Dtype>*>& top);
+
+	virtual void CreatePrefetchThread();
+	virtual void JoinPrefetchThread();
+	// The thread's function
+	virtual void InternalThreadEntry() {}
+
+		protected:
+	Blob<Dtype> prefetch_data_;
+	Blob<Dtype> prefetch_label_;
+	Blob<Dtype> transformed_data_;
 };
 
 template <typename Dtype>
 class DataLayer : public BasePrefetchingDataLayer<Dtype> {
- public:
-  explicit DataLayer(const LayerParameter& param)
-      : BasePrefetchingDataLayer<Dtype>(param) {}
-  virtual ~DataLayer();
-  virtual void DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
-
-  virtual inline LayerParameter_LayerType type() const {
-    return LayerParameter_LayerType_DATA;
-  }
-  virtual inline int ExactNumBottomBlobs() const { return 0; }
-  virtual inline int MinTopBlobs() const { return 1; }
-  virtual inline int MaxTopBlobs() const { return 2; }
-
- protected:
-  virtual void InternalThreadEntry();
-
-  // LEVELDB
-  shared_ptr<leveldb::DB> db_;
-  shared_ptr<leveldb::Iterator> iter_;
-  // LMDB
-  MDB_env* mdb_env_;
-  MDB_dbi mdb_dbi_;
-  MDB_txn* mdb_txn_;
-  MDB_cursor* mdb_cursor_;
-  MDB_val mdb_key_, mdb_value_;
+public:
+	explicit DataLayer(const LayerParameter& param)
+	: BasePrefetchingDataLayer<Dtype>(param) {}
+	virtual ~DataLayer();
+	virtual void DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
+			const vector<Blob<Dtype>*>& top);
+
+	virtual inline const char* type() const { return "Data"; }
+	virtual inline int ExactNumBottomBlobs() const { return 0; }
+	virtual inline int MinTopBlobs() const { return 1; }
+	virtual inline int MaxTopBlobs() const { return 2; }
+
+
+protected:
+	virtual void InternalThreadEntry();
+
+#ifdef USE_MPI
+	inline virtual void advance_cursor() {
+		if (cur_input_mode_ == SEQUENCE) {
+			cursor_->Next();
+			if (!cursor_->valid()) {
+				DLOG(INFO) << "Restarting data prefetching from start.";
+				cursor_->SeekToFirst();
+
+				if (this->layer_param_.data_param().shuffle() == true){
+					LOG(INFO)<<"Entering shuffling mode after first epoch";
+					cur_input_mode_ = SHUFFLE;
+					shuffle(shuffle_key_pool_.begin(), shuffle_key_pool_.end());
+					shuffle_cursor_ = shuffle_key_pool_.begin();
+				}
+			}
+		}else if (cur_input_mode_ == SHUFFLE){
+			//NO OP
+		}
+	}
+#endif
+
+	shared_ptr<db::DB> db_;
+	shared_ptr<db::Cursor> cursor_;
+
+	enum InputMode{
+			SEQUENCE, SHUFFLE
+	};
+	InputMode cur_input_mode_;
+	vector<string> shuffle_key_pool_;
+	vector<string>::iterator shuffle_cursor_;
 };
 
 /**
@@ -132,31 +159,29 @@ class DataLayer : public BasePrefetchingDataLayer<Dtype> {
  */
 template <typename Dtype>
 class DummyDataLayer : public Layer<Dtype> {
- public:
-  explicit DummyDataLayer(const LayerParameter& param)
-      : Layer<Dtype>(param) {}
-  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
-  // Data layers have no bottoms, so reshaping is trivial.
-  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {}
-
-  virtual inline LayerParameter_LayerType type() const {
-    return LayerParameter_LayerType_DUMMY_DATA;
-  }
-  virtual inline int ExactNumBottomBlobs() const { return 0; }
-  virtual inline int MinTopBlobs() const { return 1; }
-
- protected:
-  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {}
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {}
-
-  vector<shared_ptr<Filler<Dtype> > > fillers_;
-  vector<bool> refill_;
+public:
+	explicit DummyDataLayer(const LayerParameter& param)
+	: Layer<Dtype>(param) {}
+	virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+			const vector<Blob<Dtype>*>& top);
+	// Data layers have no bottoms, so reshaping is trivial.
+	virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+			const vector<Blob<Dtype>*>& top) {}
+
+	virtual inline const char* type() const { return "DummyData"; }
+	virtual inline int ExactNumBottomBlobs() const { return 0; }
+	virtual inline int MinTopBlobs() const { return 1; }
+
+protected:
+	virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+			const vector<Blob<Dtype>*>& top);
+	virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+			const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
+	virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+			const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
+
+	vector<shared_ptr<Filler<Dtype> > > fillers_;
+	vector<bool> refill_;
 };
 
 /**
@@ -166,39 +191,38 @@ class DummyDataLayer : public Layer<Dtype> {
  */
 template <typename Dtype>
 class HDF5DataLayer : public Layer<Dtype> {
- public:
-  explicit HDF5DataLayer(const LayerParameter& param)
-      : Layer<Dtype>(param) {}
-  virtual ~HDF5DataLayer();
-  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
-  // Data layers have no bottoms, so reshaping is trivial.
-  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {}
-
-  virtual inline LayerParameter_LayerType type() const {
-    return LayerParameter_LayerType_HDF5_DATA;
-  }
-  virtual inline int ExactNumBottomBlobs() const { return 0; }
-  virtual inline int ExactNumTopBlobs() const { return 2; }
-
- protected:
-  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
-  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {}
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {}
-  virtual void LoadHDF5FileData(const char* filename);
-
-  std::vector<std::string> hdf_filenames_;
-  unsigned int num_files_;
-  unsigned int current_file_;
-  hsize_t current_row_;
-  Blob<Dtype> data_blob_;
-  Blob<Dtype> label_blob_;
+public:
+	explicit HDF5DataLayer(const LayerParameter& param)
+	: Layer<Dtype>(param) {}
+	virtual ~HDF5DataLayer();
+	virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+			const vector<Blob<Dtype>*>& top);
+	// Data layers have no bottoms, so reshaping is trivial.
+	virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+			const vector<Blob<Dtype>*>& top) {}
+
+	virtual inline const char* type() const { return "HDF5Data"; }
+	virtual inline int ExactNumBottomBlobs() const { return 0; }
+	virtual inline int MinTopBlobs() const { return 1; }
+
+protected:
+	virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+			const vector<Blob<Dtype>*>& top);
+	virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+			const vector<Blob<Dtype>*>& top);
+	virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+			const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
+	virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+			const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
+	virtual void LoadHDF5FileData(const char* filename);
+
+	std::vector<std::string> hdf_filenames_;
+	unsigned int num_files_;
+	unsigned int current_file_;
+	hsize_t current_row_;
+	std::vector<shared_ptr<Blob<Dtype> > > hdf_blobs_;
+	std::vector<unsigned int> data_permutation_;
+	std::vector<unsigned int> file_permutation_;
 };
 
 /**
@@ -208,39 +232,39 @@ class HDF5DataLayer : public Layer<Dtype> {
  */
 template <typename Dtype>
 class HDF5OutputLayer : public Layer<Dtype> {
- public:
-  explicit HDF5OutputLayer(const LayerParameter& param);
-  virtual ~HDF5OutputLayer();
-  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {}
-  // Data layers have no bottoms, so reshaping is trivial.
-  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {}
-
-  virtual inline LayerParameter_LayerType type() const {
-    return LayerParameter_LayerType_HDF5_OUTPUT;
-  }
-  // TODO: no limit on the number of blobs
-  virtual inline int ExactNumBottomBlobs() const { return 2; }
-  virtual inline int ExactNumTopBlobs() const { return 0; }
-
-  inline std::string file_name() const { return file_name_; }
-
- protected:
-  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
-  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
-  virtual void SaveBlobs();
-
-  std::string file_name_;
-  hid_t file_id_;
-  Blob<Dtype> data_blob_;
-  Blob<Dtype> label_blob_;
+public:
+	explicit HDF5OutputLayer(const LayerParameter& param)
+	: Layer<Dtype>(param), file_opened_(false) {}
+	virtual ~HDF5OutputLayer();
+	virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+			const vector<Blob<Dtype>*>& top);
+	// Data layers have no bottoms, so reshaping is trivial.
+	virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+			const vector<Blob<Dtype>*>& top) {}
+
+	virtual inline const char* type() const { return "HDF5Output"; }
+	// TODO: no limit on the number of blobs
+	virtual inline int ExactNumBottomBlobs() const { return 2; }
+	virtual inline int ExactNumTopBlobs() const { return 0; }
+
+	inline std::string file_name() const { return file_name_; }
+
+protected:
+	virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+			const vector<Blob<Dtype>*>& top);
+	virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+			const vector<Blob<Dtype>*>& top);
+	virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+			const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+	virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+			const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+	virtual void SaveBlobs();
+
+	bool file_opened_;
+	std::string file_name_;
+	hid_t file_id_;
+	Blob<Dtype> data_blob_;
+	Blob<Dtype> label_blob_;
 };
 
 /**
@@ -250,28 +274,139 @@ class HDF5OutputLayer : public Layer<Dtype> {
  */
 template <typename Dtype>
 class ImageDataLayer : public BasePrefetchingDataLayer<Dtype> {
- public:
-  explicit ImageDataLayer(const LayerParameter& param)
-      : BasePrefetchingDataLayer<Dtype>(param) {}
-  virtual ~ImageDataLayer();
-  virtual void DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
-
-  virtual inline LayerParameter_LayerType type() const {
-    return LayerParameter_LayerType_IMAGE_DATA;
-  }
-  virtual inline int ExactNumBottomBlobs() const { return 0; }
-  virtual inline int ExactNumTopBlobs() const { return 2; }
-
- protected:
-  shared_ptr<Caffe::RNG> prefetch_rng_;
-  virtual void ShuffleImages();
-  virtual void InternalThreadEntry();
-
-  vector<std::pair<std::string, int> > lines_;
-  int lines_id_;
+public:
+	explicit ImageDataLayer(const LayerParameter& param)
+	: BasePrefetchingDataLayer<Dtype>(param) {}
+	virtual ~ImageDataLayer();
+	virtual void DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
+			const vector<Blob<Dtype>*>& top);
+
+	virtual inline const char* type() const { return "ImageData"; }
+	virtual inline int ExactNumBottomBlobs() const { return 0; }
+	virtual inline int ExactNumTopBlobs() const { return 2; }
+
+protected:
+	shared_ptr<Caffe::RNG> prefetch_rng_;
+	virtual void ShuffleImages();
+	virtual void InternalThreadEntry();
+
+#ifdef USE_MPI
+	inline virtual void advance_cursor(){
+		lines_id_++;
+		if (lines_id_ >= lines_.size()) {
+			// We have reached the end. Restart from the first.
+			DLOG(INFO) << "Restarting data prefetching from start.";
+			lines_id_ = 0;
+			if (this->layer_param_.image_data_param().shuffle()) {
+				ShuffleImages();
+			}
+		}
+	}
+#endif
+
+	vector<std::pair<std::string, int> > lines_;
+	int lines_id_;
 };
 
+/**
+ * @brief Provides data to the Net from video files.
+ *
+ * TODO(dox): thorough documentation for Forward and proto params.
+ */
+template <typename Dtype>
+class VideoDataLayer : public BasePrefetchingDataLayer<Dtype> {
+public:
+	explicit VideoDataLayer(const LayerParameter& param)
+	: BasePrefetchingDataLayer<Dtype>(param) {}
+	virtual ~VideoDataLayer();
+	virtual void DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
+			const vector<Blob<Dtype>*>& top);
+
+	virtual inline const char* type() const { return "VideoData"; }
+	virtual inline int ExactNumBottomBlobs() const { return 0; }
+	virtual inline int ExactNumTopBlobs() const { return 2; }
+
+protected:
+	shared_ptr<Caffe::RNG> prefetch_rng_;
+	shared_ptr<Caffe::RNG> prefetch_rng_2_;
+	shared_ptr<Caffe::RNG> prefetch_rng_1_;
+	shared_ptr<Caffe::RNG> frame_prefetch_rng_;
+	virtual void ShuffleVideos();
+	virtual void InternalThreadEntry();
+
+#ifdef USE_MPI
+	inline virtual void advance_cursor(){
+		lines_id_++;
+		if (lines_id_ >= lines_.size()) {
+			// We have reached the end. Restart from the first.
+			DLOG(INFO) << "Restarting data prefetching from start.";
+			lines_id_ = 0;
+			if (this->layer_param_.video_data_param().shuffle()) {
+				ShuffleVideos();
+			}
+		}
+	}
+#endif
+
+	vector<std::pair<std::string, int> > lines_;
+	vector<int> lines_duration_;
+	int lines_id_;
+	string name_pattern_;
+};
+
+
+/**
+ * @brief Provides data to the Net from untrimmed video files.
+ *
+ * TODO(dox): thorough documentation for Forward and proto params.
+ */
+template <typename Dtype>
+class SequenceDataLayer : public BasePrefetchingDataLayer<Dtype> {
+public:
+	explicit SequenceDataLayer(const LayerParameter& param)
+	: BasePrefetchingDataLayer<Dtype>(param) {}
+	virtual ~SequenceDataLayer();
+	virtual void DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
+			const vector<Blob<Dtype>*>& top);
+
+	virtual inline const char* type() const { return "SequenceData"; }
+	virtual inline int ExactNumBottomBlobs() const { return 0; }
+	virtual inline int ExactNumTopBlobs() const { return 2; }
+
+protected:
+	// for shuffling lines_, lines_duration_, lines_shot_
+	shared_ptr<Caffe::RNG> prefetch_rng_;
+	shared_ptr<Caffe::RNG> prefetch_rng_3_;
+	shared_ptr<Caffe::RNG> prefetch_rng_2_;
+        shared_ptr<Caffe::RNG> prefetch_rng_1_;
+        // for random picking shot and frame
+	shared_ptr<Caffe::RNG> frame_prefetch_rng_;
+
+	virtual void ShuffleSequences();
+	virtual void InternalThreadEntry();
+
+#ifdef USE_MPI
+	inline virtual void advance_cursor(){
+		lines_id_++;
+		if (lines_id_ >= lines_.size()) {
+			// We have reached the end. Restart from the first.
+			DLOG(INFO) << "Restarting data prefetching from start.";
+			lines_id_ = 0;
+			if (this->layer_param_.sequence_data_param().shuffle()) {
+				ShuffleSequences();
+			}
+		}
+	}
+#endif
+
+	vector<std::pair<std::string, int> > lines_;
+	vector<int> lines_duration_;
+        vector<vector<std::pair<int, int> > > lines_shot_;
+	int lines_id_;
+	string name_pattern_;
+};
+
+
 /**
  * @brief Provides data to the Net from memory.
  *
@@ -279,38 +414,42 @@ class ImageDataLayer : public BasePrefetchingDataLayer<Dtype> {
  */
 template <typename Dtype>
 class MemoryDataLayer : public BaseDataLayer<Dtype> {
- public:
-  explicit MemoryDataLayer(const LayerParameter& param)
-      : BaseDataLayer<Dtype>(param), has_new_data_(false) {}
-  virtual void DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
-
-  virtual inline LayerParameter_LayerType type() const {
-    return LayerParameter_LayerType_MEMORY_DATA;
-  }
-  virtual inline int ExactNumBottomBlobs() const { return 0; }
-  virtual inline int ExactNumTopBlobs() const { return 2; }
-
-  virtual void AddDatumVector(const vector<Datum>& datum_vector);
-
-  // Reset should accept const pointers, but can't, because the memory
-  //  will be given to Blob, which is mutable
-  void Reset(Dtype* data, Dtype* label, int n);
-
-  int batch_size() { return batch_size_; }
-
- protected:
-  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
-
-  int batch_size_;
-  Dtype* data_;
-  Dtype* labels_;
-  int n_;
-  int pos_;
-  Blob<Dtype> added_data_;
-  Blob<Dtype> added_label_;
-  bool has_new_data_;
+public:
+	explicit MemoryDataLayer(const LayerParameter& param)
+	: BaseDataLayer<Dtype>(param), has_new_data_(false) {}
+	virtual void DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
+			const vector<Blob<Dtype>*>& top);
+
+	virtual inline const char* type() const { return "MemoryData"; }
+	virtual inline int ExactNumBottomBlobs() const { return 0; }
+	virtual inline int ExactNumTopBlobs() const { return 2; }
+
+	virtual void AddDatumVector(const vector<Datum>& datum_vector);
+	virtual void AddMatVector(const vector<cv::Mat>& mat_vector,
+			const vector<int>& labels);
+
+	// Reset should accept const pointers, but can't, because the memory
+	//  will be given to Blob, which is mutable
+	void Reset(Dtype* data, Dtype* label, int n);
+	void set_batch_size(int new_size);
+
+	int batch_size() { return batch_size_; }
+	int channels() { return channels_; }
+	int height() { return height_; }
+	int width() { return width_; }
+
+protected:
+	virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+			const vector<Blob<Dtype>*>& top);
+
+	int batch_size_, channels_, height_, width_, size_;
+	Dtype* data_;
+	Dtype* labels_;
+	int n_;
+	size_t pos_;
+	Blob<Dtype> added_data_;
+	Blob<Dtype> added_label_;
+	bool has_new_data_;
 };
 
 /**
@@ -321,28 +460,40 @@ class MemoryDataLayer : public BaseDataLayer<Dtype> {
  */
 template <typename Dtype>
 class WindowDataLayer : public BasePrefetchingDataLayer<Dtype> {
- public:
-  explicit WindowDataLayer(const LayerParameter& param)
-      : BasePrefetchingDataLayer<Dtype>(param) {}
-  virtual ~WindowDataLayer();
-  virtual void DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
-
-  virtual inline LayerParameter_LayerType type() const {
-    return LayerParameter_LayerType_WINDOW_DATA;
-  }
-  virtual inline int ExactNumBottomBlobs() const { return 0; }
-  virtual inline int ExactNumTopBlobs() const { return 2; }
-
- protected:
-  virtual unsigned int PrefetchRand();
-  virtual void InternalThreadEntry();
-
-  shared_ptr<Caffe::RNG> prefetch_rng_;
-  vector<std::pair<std::string, vector<int> > > image_database_;
-  enum WindowField { IMAGE_INDEX, LABEL, OVERLAP, X1, Y1, X2, Y2, NUM };
-  vector<vector<float> > fg_windows_;
-  vector<vector<float> > bg_windows_;
+public:
+	explicit WindowDataLayer(const LayerParameter& param)
+	: BasePrefetchingDataLayer<Dtype>(param) {}
+	virtual ~WindowDataLayer();
+	virtual void DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
+			const vector<Blob<Dtype>*>& top);
+
+	virtual inline const char* type() const { return "WindowData"; }
+	virtual inline int ExactNumBottomBlobs() const { return 0; }
+	virtual inline int ExactNumTopBlobs() const { return 2; }
+
+protected:
+	virtual unsigned int PrefetchRand();
+	virtual void InternalThreadEntry();
+
+#ifdef USE_MPI
+	inline virtual void advance_cursor(){
+		//TODO: remove this
+		PrefetchRand();
+		this->transform_param_.mirror() && PrefetchRand();
+	}
+#endif
+
+	shared_ptr<Caffe::RNG> prefetch_rng_;
+	vector<std::pair<std::string, vector<int> > > image_database_;
+	enum WindowField { IMAGE_INDEX, LABEL, OVERLAP, X1, Y1, X2, Y2, NUM };
+	vector<vector<float> > fg_windows_;
+	vector<vector<float> > bg_windows_;
+	Blob<Dtype> data_mean_;
+	vector<Dtype> mean_values_;
+	bool has_mean_file_;
+	bool has_mean_values_;
+	bool cache_images_;
+	vector<std::pair<std::string, Datum > > image_database_cache_;
 };
 
 }  // namespace caffe
diff --git a/include/caffe/data_transformer.hpp b/include/caffe/data_transformer.hpp
index 5d5134f5db5..f10f24985dc 100644
--- a/include/caffe/data_transformer.hpp
+++ b/include/caffe/data_transformer.hpp
@@ -1,6 +1,9 @@
 #ifndef CAFFE_DATA_TRANSFORMER_HPP
 #define CAFFE_DATA_TRANSFORMER_HPP
 
+#include <vector>
+
+#include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/proto/caffe.pb.h"
 
@@ -13,40 +16,138 @@ namespace caffe {
 template <typename Dtype>
 class DataTransformer {
  public:
-  explicit DataTransformer(const TransformationParameter& param)
-    : param_(param) {
-    phase_ = Caffe::phase();
-  }
+  explicit DataTransformer(const TransformationParameter& param, Phase phase);
   virtual ~DataTransformer() {}
 
+  /**
+   * @brief Initialize the Random number generations if needed by the
+   *    transformation.
+   */
   void InitRand();
 
   /**
    * @brief Applies the transformation defined in the data layer's
    * transform_param block to the data.
    *
-   * @param batch_item_id
-   *    Datum position within the batch. This is used to compute the
-   *    writing position in the top blob's data
    * @param datum
    *    Datum containing the data to be transformed.
-   * @param mean
-   * @param transformed_data
-   *    This is meant to be the top blob's data. The transformed data will be
-   *    written at the appropriate place within the blob's data.
+   * @param transformed_blob
+   *    This is destination blob. It can be part of top blob's data if
+   *    set_cpu_data() is used. See data_layer.cpp for an example.
    */
-  void Transform(const int batch_item_id, const Datum& datum,
-                 const Dtype* mean, Dtype* transformed_data);
+  void Transform(const Datum& datum, Blob<Dtype>* transformed_blob);
+
+  /**
+   * @brief Applies the transformation defined in the data layer's
+   * transform_param block to a vector of Datum.
+   *
+   * @param datum_vector
+   *    A vector of Datum containing the data to be transformed.
+   * @param transformed_blob
+   *    This is destination blob. It can be part of top blob's data if
+   *    set_cpu_data() is used. See memory_layer.cpp for an example.
+   */
+  void Transform(const vector<Datum> & datum_vector,
+                Blob<Dtype>* transformed_blob);
+
+  /**
+   * @brief Applies the transformation defined in the data layer's
+   * transform_param block to a vector of Mat.
+   *
+   * @param mat_vector
+   *    A vector of Mat containing the data to be transformed.
+   * @param transformed_blob
+   *    This is destination blob. It can be part of top blob's data if
+   *    set_cpu_data() is used. See memory_layer.cpp for an example.
+   */
+  void Transform(const vector<cv::Mat> & mat_vector,
+                Blob<Dtype>* transformed_blob);
+
+  /**
+   * @brief Applies the transformation defined in the data layer's
+   * transform_param block to a cv::Mat
+   *
+   * @param cv_img
+   *    cv::Mat containing the data to be transformed.
+   * @param transformed_blob
+   *    This is destination blob. It can be part of top blob's data if
+   *    set_cpu_data() is used. See image_data_layer.cpp for an example.
+   */
+  void Transform(const cv::Mat& cv_img, Blob<Dtype>* transformed_blob);
+
+  /**
+   * @brief Applies the same transformation defined in the data layer's
+   * transform_param block to all the num images in a input_blob.
+   *
+   * @param input_blob
+   *    A Blob containing the data to be transformed. It applies the same
+   *    transformation to all the num images in the blob.
+   * @param transformed_blob
+   *    This is destination blob, it will contain as many images as the
+   *    input blob. It can be part of top blob's data.
+   */
+  void Transform(Blob<Dtype>* input_blob, Blob<Dtype>* transformed_blob);
+
+  /**
+   * @brief Infers the shape of transformed_blob will have when
+   *    the transformation is applied to the data.
+   *
+   * @param datum
+   *    Datum containing the data to be transformed.
+   */
+  vector<int> InferBlobShape(const Datum& datum);
+  /**
+   * @brief Infers the shape of transformed_blob will have when
+   *    the transformation is applied to the data.
+   *    It uses the first element to infer the shape of the blob.
+   *
+   * @param datum_vector
+   *    A vector of Datum containing the data to be transformed.
+   */
+  vector<int> InferBlobShape(const vector<Datum> & datum_vector);
+  /**
+   * @brief Infers the shape of transformed_blob will have when
+   *    the transformation is applied to the data.
+   *    It uses the first element to infer the shape of the blob.
+   *
+   * @param mat_vector
+   *    A vector of Mat containing the data to be transformed.
+   */
+  vector<int> InferBlobShape(const vector<cv::Mat> & mat_vector);
+  /**
+   * @brief Infers the shape of transformed_blob will have when
+   *    the transformation is applied to the data.
+   *
+   * @param cv_img
+   *    cv::Mat containing the data to be transformed.
+   */
+  vector<int> InferBlobShape(const cv::Mat& cv_img);
 
  protected:
-  virtual unsigned int Rand();
+   /**
+   * @brief Generates a random integer from Uniform({0, 1, ..., n-1}).
+   *
+   * @param n
+   *    The upperbound (exclusive) value of the random number.
+   * @return
+   *    A uniformly random integer value from ({0, 1, ..., n-1}).
+   */
+  virtual int Rand(int n);
 
+  void Transform(const Datum& datum, Dtype* transformed_data);
   // Tranformation parameters
   TransformationParameter param_;
 
 
   shared_ptr<Caffe::RNG> rng_;
-  Caffe::Phase phase_;
+  Phase phase_;
+  Blob<Dtype> data_mean_;
+  vector<Dtype> mean_values_;
+
+  vector<float> custom_scale_ratios_;
+  int max_distort_;
+
+  bool org_size_proc_;
 };
 
 }  // namespace caffe
diff --git a/include/caffe/filler.hpp b/include/caffe/filler.hpp
index 136ce958aed..888f4a4ba3b 100644
--- a/include/caffe/filler.hpp
+++ b/include/caffe/filler.hpp
@@ -76,13 +76,12 @@ class GaussianFiller : public Filler<Dtype> {
     CHECK_GE(sparse, -1);
     if (sparse >= 0) {
       // Sparse initialization is implemented for "weight" blobs; i.e. matrices.
-      // These have num == channels == 1; height is number of inputs; width is
+      // These have num == channels == 1; width is number of inputs; height is
       // number of outputs.  The 'sparse' variable specifies the mean number
       // of non-zero input weights for a given output.
-      CHECK_EQ(blob->num(), 1);
-      CHECK_EQ(blob->channels(), 1);
-      int num_inputs = blob->height();
-      Dtype non_zero_probability = Dtype(sparse) / Dtype(num_inputs);
+      CHECK_GE(blob->num_axes(), 1);
+      const int num_outputs = blob->shape(0);
+      Dtype non_zero_probability = Dtype(sparse) / Dtype(num_outputs);
       rand_vec_.reset(new SyncedMemory(blob->count() * sizeof(int)));
       int* mask = reinterpret_cast<int*>(rand_vec_->mutable_cpu_data());
       caffe_rng_bernoulli(blob->count(), non_zero_probability, mask);
@@ -127,17 +126,18 @@ class PositiveUnitballFiller : public Filler<Dtype> {
 };
 
 /**
- * @brief Fills a Blob with values @f$ x \sim U(-a, +a) @f$ where @f$ a @f$
- *        is set inversely proportional to the number of incoming nodes.
+ * @brief Fills a Blob with values @f$ x \sim U(-a, +a) @f$ where @f$ a @f$ is
+ *        set inversely proportional to number of incoming nodes, outgoing
+ *        nodes, or their average.
  *
  * A Filler based on the paper [Bengio and Glorot 2010]: Understanding
- * the difficulty of training deep feedforward neuralnetworks, but does not
- * use the fan_out value.
+ * the difficulty of training deep feedforward neuralnetworks.
  *
- * It fills the incoming matrix by randomly sampling uniform data from
- * [-scale, scale] where scale = sqrt(3 / fan_in) where fan_in is the number
- * of input nodes. You should make sure the input blob has shape (num, a, b, c)
- * where a * b * c = fan_in.
+ * It fills the incoming matrix by randomly sampling uniform data from [-scale,
+ * scale] where scale = sqrt(3 / n) where n is the fan_in, fan_out, or their
+ * average, depending on the variance_norm option. You should make sure the
+ * input blob has shape (num, a, b, c) where a * b * c = fan_in and num * b * c
+ * = fan_out. Note that this is currently not the case for inner product layers.
  *
  * TODO(dox): make notation in above comment consistent with rest & use LaTeX.
  */
@@ -149,7 +149,16 @@ class XavierFiller : public Filler<Dtype> {
   virtual void Fill(Blob<Dtype>* blob) {
     CHECK(blob->count());
     int fan_in = blob->count() / blob->num();
-    Dtype scale = sqrt(Dtype(3) / fan_in);
+    int fan_out = blob->count() / blob->channels();
+    Dtype n = fan_in;  // default to fan_in
+    if (this->filler_param_.variance_norm() ==
+        FillerParameter_VarianceNorm_AVERAGE) {
+      n = (fan_in + fan_out) / Dtype(2);
+    } else if (this->filler_param_.variance_norm() ==
+        FillerParameter_VarianceNorm_FAN_OUT) {
+      n = fan_out;
+    }
+    Dtype scale = sqrt(Dtype(3) / n);
     caffe_rng_uniform<Dtype>(blob->count(), -scale, scale,
         blob->mutable_cpu_data());
     CHECK_EQ(this->filler_param_.sparse(), -1)
@@ -157,6 +166,101 @@ class XavierFiller : public Filler<Dtype> {
   }
 };
 
+/**
+ * @brief Fills a Blob with values @f$ x \sim N(0, \sigma^2) @f$ where
+ *        @f$ \sigma^2 @f$ is set inversely proportional to number of incoming
+ *        nodes, outgoing nodes, or their average.
+ *
+ * A Filler based on the paper [He, Zhang, Ren and Sun 2015]: Specifically
+ * accounts for ReLU nonlinearities.
+ *
+ * Aside: for another perspective on the scaling factor, see the derivation of
+ * [Saxe, McClelland, and Ganguli 2013 (v3)].
+ *
+ * It fills the incoming matrix by randomly sampling Gaussian data with std =
+ * sqrt(2 / n) where n is the fan_in, fan_out, or their average, depending on
+ * the variance_norm option. You should make sure the input blob has shape (num,
+ * a, b, c) where a * b * c = fan_in and num * b * c = fan_out. Note that this
+ * is currently not the case for inner product layers.
+ */
+template <typename Dtype>
+class MSRAFiller : public Filler<Dtype> {
+ public:
+  explicit MSRAFiller(const FillerParameter& param)
+      : Filler<Dtype>(param) {}
+  virtual void Fill(Blob<Dtype>* blob) {
+    CHECK(blob->count());
+    int fan_in = blob->count() / blob->num();
+    int fan_out = blob->count() / blob->channels();
+    Dtype n = fan_in;  // default to fan_in
+    if (this->filler_param_.variance_norm() ==
+        FillerParameter_VarianceNorm_AVERAGE) {
+      n = (fan_in + fan_out) / Dtype(2);
+    } else if (this->filler_param_.variance_norm() ==
+        FillerParameter_VarianceNorm_FAN_OUT) {
+      n = fan_out;
+    }
+    Dtype std = sqrt(Dtype(2) / n);
+    caffe_rng_gaussian<Dtype>(blob->count(), Dtype(0), std,
+        blob->mutable_cpu_data());
+    CHECK_EQ(this->filler_param_.sparse(), -1)
+         << "Sparsity not supported by this Filler.";
+  }
+};
+
+/*!
+@brief Fills a Blob with coefficients for bilinear interpolation.
+
+A common use case is with the DeconvolutionLayer acting as upsampling.
+You can upsample a feature map with shape of (B, C, H, W) by any integer factor
+using the following proto.
+\code
+layer {
+  name: "upsample", type: "Deconvolution"
+  bottom: "{{bottom_name}}" top: "{{top_name}}"
+  convolution_param {
+    kernel_size: {{2 * factor - factor % 2}} stride: {{factor}}
+    num_output: {{C}} group: {{C}}
+    pad: {{ceil((factor - 1) / 2.)}}
+    weight_filler: { type: "bilinear" } bias_term: false
+  }
+  param { lr_mult: 0 decay_mult: 0 }
+}
+\endcode
+Please use this by replacing `{{}}` with your values. By specifying
+`num_output: {{C}} group: {{C}}`, it behaves as
+channel-wise convolution. The filter shape of this deconvolution layer will be
+(C, 1, K, K) where K is `kernel_size`, and this filler will set a (K, K)
+interpolation kernel for every channel of the filter identically. The resulting
+shape of the top feature map will be (B, C, factor * H, factor * W).
+Note that the learning rate and the
+weight decay are set to 0 in order to keep coefficient values of bilinear
+interpolation unchanged during training. If you apply this to an image, this
+operation is equivalent to the following call in Python with Scikit.Image.
+\code{.py}
+out = skimage.transform.rescale(img, factor, mode='constant', cval=0)
+\endcode
+ */
+template <typename Dtype>
+class BilinearFiller : public Filler<Dtype> {
+ public:
+  explicit BilinearFiller(const FillerParameter& param)
+      : Filler<Dtype>(param) {}
+  virtual void Fill(Blob<Dtype>* blob) {
+    CHECK_EQ(blob->num_axes(), 4) << "Blob must be 4 dim.";
+    CHECK_EQ(blob->width(), blob->height()) << "Filter must be square";
+    Dtype* data = blob->mutable_cpu_data();
+    int f = ceil(blob->width() / 2.);
+    float c = (2 * f - 1 - f % 2) / (2. * f);
+    for (int i = 0; i < blob->count(); ++i) {
+      float x = i % blob->width();
+      float y = (i / blob->width()) % blob->height();
+      data[i] = (1 - fabs(x / f - c)) * (1 - fabs(y / f - c));
+    }
+    CHECK_EQ(this->filler_param_.sparse(), -1)
+         << "Sparsity not supported by this Filler.";
+  }
+};
 
 /**
  * @brief Get a specific filler from the specification given in FillerParameter.
@@ -177,6 +281,10 @@ Filler<Dtype>* GetFiller(const FillerParameter& param) {
     return new UniformFiller<Dtype>(param);
   } else if (type == "xavier") {
     return new XavierFiller<Dtype>(param);
+  } else if (type == "msra") {
+    return new MSRAFiller<Dtype>(param);
+  } else if (type == "bilinear") {
+    return new BilinearFiller<Dtype>(param);
   } else {
     CHECK(false) << "Unknown filler name: " << param.type();
   }
diff --git a/include/caffe/internal_thread.hpp b/include/caffe/internal_thread.hpp
index 6a106e6eefa..815ca54605e 100644
--- a/include/caffe/internal_thread.hpp
+++ b/include/caffe/internal_thread.hpp
@@ -3,21 +3,13 @@
 
 #include "caffe/common.hpp"
 
-namespace caffe {
-
 /**
- * A minimal wrapper for boost::thread to force host compilation for boost
- * Defined in caffe/util/thread.hpp
+ Forward declare boost::thread instead of including boost/thread.hpp
+ to avoid a boost/NVCC issues (#1009, #1010) on OSX.
  */
-class Thread {
- public:
-  template<typename Callable, class A1>
-  Thread(Callable func, A1 a1);
-  void join();
-  bool joinable();
- private:
-  void* thread_;
-};
+namespace boost { class thread; }
+
+namespace caffe {
 
 /**
  * Virtual class encapsulate boost::thread for use in base class
@@ -26,7 +18,7 @@ class Thread {
  */
 class InternalThread {
  public:
-  InternalThread() : thread_(NULL) {}
+  InternalThread() : thread_() {}
   virtual ~InternalThread();
 
   /** Returns true if the thread was successfully started. **/
@@ -35,16 +27,16 @@ class InternalThread {
   /** Will not return until the internal thread has exited. */
   bool WaitForInternalThreadToExit();
 
-  bool is_started() const { return thread_ != NULL && thread_->joinable(); }
+  bool is_started() const;
 
  protected:
   /* Implement this method in your subclass
       with the code you want your thread to run. */
   virtual void InternalThreadEntry() {}
 
-  caffe::Thread* thread_;
+  shared_ptr<boost::thread> thread_;
 };
 
 }  // namespace caffe
 
-#endif
+#endif  // CAFFE_INTERNAL_THREAD_HPP_
diff --git a/include/caffe/layer.hpp b/include/caffe/layer.hpp
index e160075b939..2319fe26c7c 100644
--- a/include/caffe/layer.hpp
+++ b/include/caffe/layer.hpp
@@ -7,6 +7,7 @@
 
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
+#include "caffe/layer_factory.hpp"
 #include "caffe/proto/caffe.pb.h"
 #include "caffe/util/device_alternate.hpp"
 
@@ -16,11 +17,11 @@ namespace caffe {
  * @brief An interface for the units of computation which can be composed into a
  *        Net.
  *
- * Layer&s must implement a Forward function, in which they take their input
- * (bottom) Blob&s (if any) and compute their output Blob&s (if any).
+ * Layer%s must implement a Forward function, in which they take their input
+ * (bottom) Blob%s (if any) and compute their output Blob%s (if any).
  * They may also implement a Backward function, in which they compute the error
- * gradients with respect to their input Blob&s, given the error gradients with
- * their output Blob&s.
+ * gradients with respect to their input Blob%s, given the error gradients with
+ * their output Blob%s.
  */
 template <typename Dtype>
 class Layer {
@@ -32,7 +33,8 @@ class Layer {
    */
   explicit Layer(const LayerParameter& param)
     : layer_param_(param) {
-      // The only thing we do is to copy blobs if there are any.
+      // Set phase and copy blobs (if there are any).
+      phase_ = param.phase();
       if (layer_param_.blobs_size() > 0) {
         blobs_.resize(layer_param_.blobs_size());
         for (int i = 0; i < layer_param_.blobs_size(); ++i) {
@@ -40,6 +42,17 @@ class Layer {
           blobs_[i]->FromProto(layer_param_.blobs(i));
         }
       }
+
+      #ifdef USE_MPI
+      //If this is a gather layer, all it subsequent layer doesn't need gradient sync.
+      //We will only change itself's property here,
+      //subsequent layers will be inferred in the Net
+    if (is_gathering()){
+        set_need_sync(false);
+      }else{
+        set_need_sync(true);
+      }
+      #endif
     }
   virtual ~Layer() {}
 
@@ -56,8 +69,9 @@ class Layer {
    * Sets up the loss weight multiplier blobs for any non-zero loss weights.
    * This method may not be overridden.
    */
-  void SetUp(const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
-    CheckBlobCounts(bottom, *top);
+  void SetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+    CheckBlobCounts(bottom, top);
     LayerSetUp(bottom, top);
     Reshape(bottom, top);
     SetLossWeights(top);
@@ -80,7 +94,7 @@ class Layer {
    * adjust the top blob sizes.
    */
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {}
+      const vector<Blob<Dtype>*>& top) {}
 
   /**
    * @brief Adjust the shapes of top blobs and internal buffers to accomodate
@@ -95,7 +109,7 @@ class Layer {
    * accomodate the bottom blobs.
    */
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) = 0;
+      const vector<Blob<Dtype>*>& top) = 0;
 
   /**
    * @brief Given the bottom blobs, compute the top blobs and the loss.
@@ -115,7 +129,7 @@ class Layer {
    * Your layer should implement Forward_cpu and (optionally) Forward_gpu.
    */
   inline Dtype Forward(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
 
   /**
    * @brief Given the top blob error gradients, compute the bottom blob error
@@ -140,7 +154,7 @@ class Layer {
    */
   inline void Backward(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down,
-      vector<Blob<Dtype>*>* bottom);
+      const vector<Blob<Dtype>*>& bottom);
 
   /**
    * @brief Returns the vector of learnable parameter blobs.
@@ -177,18 +191,9 @@ class Layer {
   }
 
   /**
-   * @brief Returns the layer type as an enum value.
-   */
-  virtual inline LayerParameter_LayerType type() const {
-    return LayerParameter_LayerType_NONE;
-  }
-
-  /**
-   * @brief Returns the layer type name.
+   * @brief Returns the layer type.
    */
-  virtual inline const string& type_name() const {
-    return LayerParameter_LayerType_Name(type());
-  }
+  virtual inline const char* type() const { return ""; }
 
   /**
    * @brief Returns the exact number of bottom blobs required by the layer,
@@ -291,10 +296,30 @@ class Layer {
     param_propagate_down_[param_id] = value;
   }
 
+  #ifdef USE_MPI
+  /**
+   * @brief Checks whether the layer accepts specifed parallel type
+   *
+   * If not supported, will halt the program with hints
+   */
+  inline virtual bool is_gathering() {return false;}
+  inline virtual bool is_scattering() {return false;}
+  inline bool need_sync(){return need_sync_;}
+  inline void set_need_sync(bool val){need_sync_ = val;}
+  #endif
+
+  /**
+   * @brief express whether this layer shares the data/diff between bottom and top
+   */
+  virtual inline bool is_sharing_data(int top_id, int bottom_id){return false;}
+  virtual inline bool is_sharing_diff(int top_id, int bottom_id){return false;}
+
 
  protected:
   /** The protobuf that stores the layer parameters */
   LayerParameter layer_param_;
+  /** The phase: TRAIN or TEST */
+  Phase phase_;
   /** The vector that stores the learnable parameters as a set of blobs. */
   vector<shared_ptr<Blob<Dtype> > > blobs_;
   /** Vector indicating whether to compute the diff of each param blob. */
@@ -304,15 +329,22 @@ class Layer {
    *  the objective function. */
   vector<Dtype> loss_;
 
+  #ifdef USE_MPI
+  /**
+   * For parallel use
+   */
+  bool need_sync_;
+  #endif
+
   /** @brief Using the CPU device, compute the layer output. */
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) = 0;
+      const vector<Blob<Dtype>*>& top) = 0;
   /**
    * @brief Using the GPU device, compute the layer output.
    *        Fall back to Forward_cpu() if unavailable.
    */
   virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
+      const vector<Blob<Dtype>*>& top) {
     // LOG(WARNING) << "Using CPU code as backup.";
     return Forward_cpu(bottom, top);
   }
@@ -323,7 +355,7 @@ class Layer {
    */
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down,
-      vector<Blob<Dtype>*>* bottom) = 0;
+      const vector<Blob<Dtype>*>& bottom) = 0;
   /**
    * @brief Using the GPU device, compute the gradients for any parameters and
    *        for the bottom blobs if propagate_down is true.
@@ -331,7 +363,7 @@ class Layer {
    */
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down,
-      vector<Blob<Dtype>*>* bottom) {
+      const vector<Blob<Dtype>*>& bottom) {
     // LOG(WARNING) << "Using CPU code as backup.";
     Backward_cpu(top, propagate_down, bottom);
   }
@@ -345,37 +377,37 @@ class Layer {
                                const vector<Blob<Dtype>*>& top) {
     if (ExactNumBottomBlobs() >= 0) {
       CHECK_EQ(ExactNumBottomBlobs(), bottom.size())
-          << type_name() << " Layer takes " << ExactNumBottomBlobs()
+          << type() << " Layer takes " << ExactNumBottomBlobs()
           << " bottom blob(s) as input.";
     }
     if (MinBottomBlobs() >= 0) {
       CHECK_LE(MinBottomBlobs(), bottom.size())
-          << type_name() << " Layer takes at least " << MinBottomBlobs()
+          << type() << " Layer takes at least " << MinBottomBlobs()
           << " bottom blob(s) as input.";
     }
     if (MaxBottomBlobs() >= 0) {
       CHECK_GE(MaxBottomBlobs(), bottom.size())
-          << type_name() << " Layer takes at most " << MaxBottomBlobs()
+          << type() << " Layer takes at most " << MaxBottomBlobs()
           << " bottom blob(s) as input.";
     }
     if (ExactNumTopBlobs() >= 0) {
       CHECK_EQ(ExactNumTopBlobs(), top.size())
-          << type_name() << " Layer produces " << ExactNumTopBlobs()
+          << type() << " Layer produces " << ExactNumTopBlobs()
           << " top blob(s) as output.";
     }
     if (MinTopBlobs() >= 0) {
       CHECK_LE(MinTopBlobs(), top.size())
-          << type_name() << " Layer produces at least " << MinTopBlobs()
+          << type() << " Layer produces at least " << MinTopBlobs()
           << " top blob(s) as output.";
     }
     if (MaxTopBlobs() >= 0) {
       CHECK_GE(MaxTopBlobs(), top.size())
-          << type_name() << " Layer produces at most " << MaxTopBlobs()
+          << type() << " Layer produces at most " << MaxTopBlobs()
           << " top blob(s) as output.";
     }
     if (EqualNumBottomTopBlobs()) {
       CHECK_EQ(bottom.size(), top.size())
-          << type_name() << " Layer produces one top blob as output for each "
+          << type() << " Layer produces one top blob as output for each "
           << "bottom blob input.";
     }
   }
@@ -384,17 +416,17 @@ class Layer {
    * Called by SetUp to initialize the weights associated with any top blobs in
    * the loss function. Store non-zero loss weights in the diff blob.
    */
-  inline void SetLossWeights(vector<Blob<Dtype>*>* top) {
+  inline void SetLossWeights(const vector<Blob<Dtype>*>& top) {
     const int num_loss_weights = layer_param_.loss_weight_size();
     if (num_loss_weights) {
-      CHECK_EQ(top->size(), num_loss_weights) << "loss_weight must be "
+      CHECK_EQ(top.size(), num_loss_weights) << "loss_weight must be "
           "unspecified or specified once per top blob.";
-      for (int top_id = 0; top_id < top->size(); ++top_id) {
+      for (int top_id = 0; top_id < top.size(); ++top_id) {
         const Dtype loss_weight = layer_param_.loss_weight(top_id);
         if (loss_weight == Dtype(0)) { continue; }
         this->set_loss(top_id, loss_weight);
-        const int count = (*top)[top_id]->count();
-        Dtype* loss_multiplier = (*top)[top_id]->mutable_cpu_diff();
+        const int count = top[top_id]->count();
+        Dtype* loss_multiplier = top[top_id]->mutable_cpu_diff();
         caffe_set(count, loss_weight, loss_multiplier);
       }
     }
@@ -408,27 +440,28 @@ class Layer {
 // functions.
 template <typename Dtype>
 inline Dtype Layer<Dtype>::Forward(const vector<Blob<Dtype>*>& bottom,
-    vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& top) {
   Dtype loss = 0;
+  Reshape(bottom, top);
   switch (Caffe::mode()) {
   case Caffe::CPU:
     Forward_cpu(bottom, top);
-    for (int top_id = 0; top_id < top->size(); ++top_id) {
+    for (int top_id = 0; top_id < top.size(); ++top_id) {
       if (!this->loss(top_id)) { continue; }
-      const int count = (*top)[top_id]->count();
-      const Dtype* data = (*top)[top_id]->cpu_data();
-      const Dtype* loss_weights = (*top)[top_id]->cpu_diff();
+      const int count = top[top_id]->count();
+      const Dtype* data = top[top_id]->cpu_data();
+      const Dtype* loss_weights = top[top_id]->cpu_diff();
       loss += caffe_cpu_dot(count, data, loss_weights);
     }
     break;
   case Caffe::GPU:
     Forward_gpu(bottom, top);
 #ifndef CPU_ONLY
-    for (int top_id = 0; top_id < top->size(); ++top_id) {
+    for (int top_id = 0; top_id < top.size(); ++top_id) {
       if (!this->loss(top_id)) { continue; }
-      const int count = (*top)[top_id]->count();
-      const Dtype* data = (*top)[top_id]->gpu_data();
-      const Dtype* loss_weights = (*top)[top_id]->gpu_diff();
+      const int count = top[top_id]->count();
+      const Dtype* data = top[top_id]->gpu_data();
+      const Dtype* loss_weights = top[top_id]->gpu_diff();
       Dtype blob_loss = 0;
       caffe_gpu_dot(count, data, loss_weights, &blob_loss);
       loss += blob_loss;
@@ -444,7 +477,7 @@ inline Dtype Layer<Dtype>::Forward(const vector<Blob<Dtype>*>& bottom,
 template <typename Dtype>
 inline void Layer<Dtype>::Backward(const vector<Blob<Dtype>*>& top,
     const vector<bool>& propagate_down,
-    vector<Blob<Dtype>*>* bottom) {
+    const vector<Blob<Dtype>*>& bottom) {
   switch (Caffe::mode()) {
   case Caffe::CPU:
     Backward_cpu(top, propagate_down, bottom);
@@ -468,10 +501,6 @@ void Layer<Dtype>::ToProto(LayerParameter* param, bool write_diff) {
   }
 }
 
-// The layer factory function
-template <typename Dtype>
-Layer<Dtype>* GetLayer(const LayerParameter& param);
-
 }  // namespace caffe
 
 #endif  // CAFFE_LAYER_H_
diff --git a/include/caffe/layer_factory.hpp b/include/caffe/layer_factory.hpp
new file mode 100644
index 00000000000..2fcd93869a0
--- /dev/null
+++ b/include/caffe/layer_factory.hpp
@@ -0,0 +1,127 @@
+/**
+ * @brief A layer factory that allows one to register layers.
+ * During runtime, registered layers could be called by passing a LayerParameter
+ * protobuffer to the CreateLayer function:
+ *
+ *     LayerRegistry<Dtype>::CreateLayer(param);
+ *
+ * There are two ways to register a layer. Assuming that we have a layer like:
+ *
+ *   template <typename Dtype>
+ *   class MyAwesomeLayer : public Layer<Dtype> {
+ *     // your implementations
+ *   };
+ *
+ * and its type is its C++ class name, but without the "Layer" at the end
+ * ("MyAwesomeLayer" -> "MyAwesome").
+ *
+ * If the layer is going to be created simply by its constructor, in your c++
+ * file, add the following line:
+ *
+ *    REGISTER_LAYER_CLASS(MyAwesome);
+ *
+ * Or, if the layer is going to be created by another creator function, in the
+ * format of:
+ *
+ *    template <typename Dtype>
+ *    Layer<Dtype*> GetMyAwesomeLayer(const LayerParameter& param) {
+ *      // your implementation
+ *    }
+ *
+ * (for example, when your layer has multiple backends, see GetConvolutionLayer
+ * for a use case), then you can register the creator function instead, like
+ *
+ * REGISTER_LAYER_CREATOR(MyAwesome, GetMyAwesomeLayer)
+ *
+ * Note that each layer type should only be registered once.
+ */
+
+#ifndef CAFFE_LAYER_FACTORY_H_
+#define CAFFE_LAYER_FACTORY_H_
+
+#include <map>
+#include <string>
+
+#include "caffe/common.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+namespace caffe {
+
+template <typename Dtype>
+class Layer;
+
+template <typename Dtype>
+class LayerRegistry {
+ public:
+  typedef shared_ptr<Layer<Dtype> > (*Creator)(const LayerParameter&);
+  typedef std::map<string, Creator> CreatorRegistry;
+
+  static CreatorRegistry& Registry() {
+    static CreatorRegistry* g_registry_ = new CreatorRegistry();
+    return *g_registry_;
+  }
+
+  // Adds a creator.
+  static void AddCreator(const string& type, Creator creator) {
+    CreatorRegistry& registry = Registry();
+    CHECK_EQ(registry.count(type), 0)
+        << "Layer type " << type << " already registered.";
+    registry[type] = creator;
+  }
+
+  // Get a layer using a LayerParameter.
+  static shared_ptr<Layer<Dtype> > CreateLayer(const LayerParameter& param) {
+    LOG(INFO) << "Creating layer " << param.name();
+    const string& type = param.type();
+    CreatorRegistry& registry = Registry();
+    CHECK_EQ(registry.count(type), 1) << "Unknown layer type: " << type
+        << " (known types: " << LayerTypeList() << ")";
+    return registry[type](param);
+  }
+
+ private:
+  // Layer registry should never be instantiated - everything is done with its
+  // static variables.
+  LayerRegistry() {}
+
+  static string LayerTypeList() {
+    CreatorRegistry& registry = Registry();
+    string layer_types;
+    for (typename CreatorRegistry::iterator iter = registry.begin();
+         iter != registry.end(); ++iter) {
+      if (iter != registry.begin()) {
+        layer_types += ", ";
+      }
+      layer_types += iter->first;
+    }
+    return layer_types;
+  }
+};
+
+
+template <typename Dtype>
+class LayerRegisterer {
+ public:
+  LayerRegisterer(const string& type,
+                  shared_ptr<Layer<Dtype> > (*creator)(const LayerParameter&)) {
+    // LOG(INFO) << "Registering layer type: " << type;
+    LayerRegistry<Dtype>::AddCreator(type, creator);
+  }
+};
+
+
+#define REGISTER_LAYER_CREATOR(type, creator)                                  \
+  static LayerRegisterer<float> g_creator_f_##type(#type, creator<float>);     \
+  static LayerRegisterer<double> g_creator_d_##type(#type, creator<double>)    \
+
+#define REGISTER_LAYER_CLASS(type)                                             \
+  template <typename Dtype>                                                    \
+  shared_ptr<Layer<Dtype> > Creator_##type##Layer(const LayerParameter& param) \
+  {                                                                            \
+    return shared_ptr<Layer<Dtype> >(new type##Layer<Dtype>(param));           \
+  }                                                                            \
+  REGISTER_LAYER_CREATOR(type, Creator_##type##Layer)
+
+}  // namespace caffe
+
+#endif  // CAFFE_LAYER_FACTORY_H_
diff --git a/include/caffe/loss_layers.hpp b/include/caffe/loss_layers.hpp
index 08aa7752d4a..c13b5d7c2fc 100644
--- a/include/caffe/loss_layers.hpp
+++ b/include/caffe/loss_layers.hpp
@@ -33,14 +33,11 @@ class AccuracyLayer : public Layer<Dtype> {
   explicit AccuracyLayer(const LayerParameter& param)
       : Layer<Dtype>(param) {}
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
-
-  virtual inline LayerParameter_LayerType type() const {
-    return LayerParameter_LayerType_ACCURACY;
-  }
+      const vector<Blob<Dtype>*>& top);
 
+  virtual inline const char* type() const { return "Accuracy"; }
   virtual inline int ExactNumBottomBlobs() const { return 2; }
   virtual inline int ExactNumTopBlobs() const { return 1; }
 
@@ -70,18 +67,25 @@ class AccuracyLayer : public Layer<Dtype> {
    *      @f$
    */
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
 
 
   /// @brief Not implemented -- AccuracyLayer cannot be used as a loss.
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
     for (int i = 0; i < propagate_down.size(); ++i) {
       if (propagate_down[i]) { NOT_IMPLEMENTED; }
     }
   }
 
+  int label_axis_, outer_num_, inner_num_;
+
   int top_k_;
+
+  /// Whether to ignore instances with a certain label.
+  bool has_ignore_label_;
+  /// The label indicating that an instance should be ignored.
+  int ignore_label_;
 };
 
 /**
@@ -98,9 +102,9 @@ class LossLayer : public Layer<Dtype> {
   explicit LossLayer(const LayerParameter& param)
      : Layer<Dtype>(param) {}
   virtual void LayerSetUp(
-      const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top);
   virtual void Reshape(
-      const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top);
 
   virtual inline int ExactNumBottomBlobs() const { return 2; }
 
@@ -151,12 +155,10 @@ class ContrastiveLossLayer : public LossLayer<Dtype> {
   explicit ContrastiveLossLayer(const LayerParameter& param)
       : LossLayer<Dtype>(param), diff_() {}
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
 
   virtual inline int ExactNumBottomBlobs() const { return 3; }
-  virtual inline LayerParameter_LayerType type() const {
-    return LayerParameter_LayerType_CONTRASTIVE_LOSS;
-  }
+  virtual inline const char* type() const { return "ContrastiveLoss"; }
   /**
    * Unlike most loss layers, in the ContrastiveLossLayer we can backpropagate
    * to the first two inputs.
@@ -168,14 +170,14 @@ class ContrastiveLossLayer : public LossLayer<Dtype> {
  protected:
   /// @copydoc ContrastiveLossLayer
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
 
   /**
    * @brief Computes the Contrastive error gradient w.r.t. the inputs.
-   * 
-   * Computes the gradients with respect to the two input vectors (bottom[0] and 
+   *
+   * Computes the gradients with respect to the two input vectors (bottom[0] and
    * bottom[1]), but not the similarity label (bottom[2]).
    *
    * @param top output Blob vector (length 1), providing the error gradient with
@@ -194,13 +196,13 @@ class ContrastiveLossLayer : public LossLayer<Dtype> {
    *      the features @f$a@f$; Backward fills their diff with
    *      gradients if propagate_down[0]
    *   -# @f$ (N \times C \times 1 \times 1) @f$
-   *      the features @f$b@f$; Backward fills their diff with gradients if 
+   *      the features @f$b@f$; Backward fills their diff with gradients if
    *      propagate_down[1]
    */
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
 
   Blob<Dtype> diff_;  // cached for backward pass
   Blob<Dtype> dist_sq_;  // cached for backward pass
@@ -240,12 +242,9 @@ class EuclideanLossLayer : public LossLayer<Dtype> {
   explicit EuclideanLossLayer(const LayerParameter& param)
       : LossLayer<Dtype>(param), diff_() {}
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
-
-  virtual inline LayerParameter_LayerType type() const {
-    return LayerParameter_LayerType_EUCLIDEAN_LOSS;
-  }
+      const vector<Blob<Dtype>*>& top);
 
+  virtual inline const char* type() const { return "EuclideanLoss"; }
   /**
    * Unlike most loss layers, in the EuclideanLossLayer we can backpropagate
    * to both inputs -- override to return true and always allow force_backward.
@@ -257,9 +256,9 @@ class EuclideanLossLayer : public LossLayer<Dtype> {
  protected:
   /// @copydoc EuclideanLossLayer
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
 
   /**
    * @brief Computes the Euclidean error gradient w.r.t. the inputs.
@@ -295,9 +294,9 @@ class EuclideanLossLayer : public LossLayer<Dtype> {
    *      @f$ if propagate_down[1]
    */
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
 
   Blob<Dtype> diff_;
 };
@@ -351,14 +350,12 @@ class HingeLossLayer : public LossLayer<Dtype> {
   explicit HingeLossLayer(const LayerParameter& param)
       : LossLayer<Dtype>(param) {}
 
-  virtual inline LayerParameter_LayerType type() const {
-    return LayerParameter_LayerType_HINGE_LOSS;
-  }
+  virtual inline const char* type() const { return "HingeLoss"; }
 
  protected:
   /// @copydoc HingeLossLayer
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
 
   /**
    * @brief Computes the hinge loss error gradient w.r.t. the predictions.
@@ -388,7 +385,7 @@ class HingeLossLayer : public LossLayer<Dtype> {
    *      the labels -- ignored as we can't compute their error gradients
    */
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
 };
 
 /**
@@ -429,9 +426,9 @@ class InfogainLossLayer : public LossLayer<Dtype> {
   explicit InfogainLossLayer(const LayerParameter& param)
       : LossLayer<Dtype>(param), infogain_() {}
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
 
   // InfogainLossLayer takes 2-3 bottom Blobs; if there are 3 the third should
   // be the infogain matrix.  (Otherwise the infogain matrix is loaded from a
@@ -440,14 +437,12 @@ class InfogainLossLayer : public LossLayer<Dtype> {
   virtual inline int MinBottomBlobs() const { return 2; }
   virtual inline int MaxBottomBlobs() const { return 3; }
 
-  virtual inline LayerParameter_LayerType type() const {
-    return LayerParameter_LayerType_INFOGAIN_LOSS;
-  }
+  virtual inline const char* type() const { return "InfogainLoss"; }
 
  protected:
   /// @copydoc InfogainLossLayer
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
 
   /**
    * @brief Computes the infogain loss error gradient w.r.t. the predictions.
@@ -482,7 +477,7 @@ class InfogainLossLayer : public LossLayer<Dtype> {
    *      gradient computation is not implemented.
    */
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
 
   Blob<Dtype> infogain_;
 };
@@ -522,16 +517,89 @@ class MultinomialLogisticLossLayer : public LossLayer<Dtype> {
   explicit MultinomialLogisticLossLayer(const LayerParameter& param)
       : LossLayer<Dtype>(param) {}
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
 
-  virtual inline LayerParameter_LayerType type() const {
-    return LayerParameter_LayerType_MULTINOMIAL_LOGISTIC_LOSS;
-  }
+  virtual inline const char* type() const { return "MultinomialLogisticLoss"; }
+
+ protected:
+  /// @copydoc MultinomialLogisticLossLayer
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  /**
+   * @brief Computes the multinomial logistic loss error gradient w.r.t. the
+   *        predictions.
+   *
+   * Gradients cannot be computed with respect to the label inputs (bottom[1]),
+   * so this method ignores bottom[1] and requires !propagate_down[1], crashing
+   * if propagate_down[1] is set.
+   *
+   * @param top output Blob vector (length 1), providing the error gradient with
+   *      respect to the outputs
+   *   -# @f$ (1 \times 1 \times 1 \times 1) @f$
+   *      This Blob's diff will simply contain the loss_weight* @f$ \lambda @f$,
+   *      as @f$ \lambda @f$ is the coefficient of this layer's output
+   *      @f$\ell_i@f$ in the overall Net loss
+   *      @f$ E = \lambda_i \ell_i + \mbox{other loss terms}@f$; hence
+   *      @f$ \frac{\partial E}{\partial \ell_i} = \lambda_i @f$.
+   *      (*Assuming that this top Blob is not used as a bottom (input) by any
+   *      other layer of the Net.)
+   * @param propagate_down see Layer::Backward.
+   *      propagate_down[1] must be false as we can't compute gradients with
+   *      respect to the labels.
+   * @param bottom input Blob vector (length 2)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the predictions @f$ \hat{p} @f$; Backward computes diff
+   *      @f$ \frac{\partial E}{\partial \hat{p}} @f$
+   *   -# @f$ (N \times 1 \times 1 \times 1) @f$
+   *      the labels -- ignored as we can't compute their error gradients
+   */
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+};
+/**
+ * @brief Computes the multinomial logistic loss for a one-of-many
+ *        classification task, directly taking a predicted probability
+ *        distribution as input.
+ *
+ * When predictions are not already a probability distribution, you should
+ * instead use the SoftmaxWithLossLayer, which maps predictions to a
+ * distribution using the SoftmaxLayer, before computing the multinomial
+ * logistic loss. The SoftmaxWithLossLayer should be preferred over separate
+ * SoftmaxLayer + MultinomialLogisticLossLayer
+ * as its gradient computation is more numerically stable.
+ *
+ * @param bottom input Blob vector (length 2)
+ *   -# @f$ (N \times C \times H \times W) @f$
+ *      the predictions @f$ \hat{p} @f$, a Blob with values in
+ *      @f$ [0, 1] @f$ indicating the predicted probability of each of the
+ *      @f$ K = CHW @f$ classes.  Each prediction vector @f$ \hat{p}_n @f$
+ *      should sum to 1 as in a probability distribution: @f$
+ *      \forall n \sum\limits_{k=1}^K \hat{p}_{nk} = 1 @f$.
+ *   -# @f$ (N \times 1 \times 1 \times 1) @f$
+ *      the labels @f$ l @f$, an integer-valued Blob with values
+ *      @f$ l_n \in [0, 1, 2, ..., K - 1] @f$
+ *      indicating the correct class label among the @f$ K @f$ classes
+ * @param top output Blob vector (length 1)
+ *   -# @f$ (1 \times 1 \times 1 \times 1) @f$
+ *      the computed multinomial logistic loss: @f$ E =
+ *        \frac{-1}{N} \sum\limits_{n=1}^N \log(\hat{p}_{n,l_n})
+ *      @f$
+ */
+template <typename Dtype>
+class MultilabelLogisticLossLayer : public LossLayer<Dtype> {
+ public:
+  explicit MultilabelLogisticLossLayer(const LayerParameter& param)
+      : LossLayer<Dtype>(param) {}
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual inline const char* type() const { return "MultilabelLogisticLoss"; }
 
  protected:
   /// @copydoc MultinomialLogisticLossLayer
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
 
   /**
    * @brief Computes the multinomial logistic loss error gradient w.r.t. the
@@ -562,9 +630,10 @@ class MultinomialLogisticLossLayer : public LossLayer<Dtype> {
    *      the labels -- ignored as we can't compute their error gradients
    */
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
 };
 
+
 /**
  * @brief Computes the cross-entropy (logistic) loss @f$
  *          E = \frac{-1}{n} \sum\limits_{n=1}^N \left[
@@ -602,20 +671,16 @@ class SigmoidCrossEntropyLossLayer : public LossLayer<Dtype> {
           sigmoid_layer_(new SigmoidLayer<Dtype>(param)),
           sigmoid_output_(new Blob<Dtype>()) {}
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
 
-  virtual inline LayerParameter_LayerType type() const {
-    return LayerParameter_LayerType_SIGMOID_CROSS_ENTROPY_LOSS;
-  }
+  virtual inline const char* type() const { return "SigmoidCrossEntropyLoss"; }
 
  protected:
   /// @copydoc SigmoidCrossEntropyLossLayer
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
-  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
 
   /**
    * @brief Computes the sigmoid cross-entropy loss error gradient w.r.t. the
@@ -648,9 +713,9 @@ class SigmoidCrossEntropyLossLayer : public LossLayer<Dtype> {
    *      the labels -- ignored as we can't compute their error gradients
    */
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
 
   /// The internal SigmoidLayer used to map predictions to probabilities.
   shared_ptr<SigmoidLayer<Dtype> > sigmoid_layer_;
@@ -696,20 +761,22 @@ template <typename Dtype> class SoftmaxLayer;
 template <typename Dtype>
 class SoftmaxWithLossLayer : public LossLayer<Dtype> {
  public:
+   /**
+    * @param param provides LossParameter loss_param, with options:
+    *  - ignore_label (optional)
+    *    Specify a label value that should be ignored when computing the loss.
+    *  - normalize (optional, default true)
+    *    If true, the loss is normalized by the number of (nonignored) labels
+    *    present; otherwise the loss is simply summed over spatial locations.
+    */
   explicit SoftmaxWithLossLayer(const LayerParameter& param)
-      : LossLayer<Dtype>(param),
-        softmax_layer_(new SoftmaxLayer<Dtype>(param)) {}
+      : LossLayer<Dtype>(param) {}
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
 
-  virtual inline LayerParameter_LayerType type() const {
-    return LayerParameter_LayerType_SOFTMAX_LOSS;
-  }
-  virtual inline int ExactNumBottomBlobs() const { return -1; }
-  virtual inline int MinBottomBlobs() const { return 2; }
-  virtual inline int MaxBottomBlobs() const { return 3; }
+  virtual inline const char* type() const { return "SoftmaxWithLoss"; }
   virtual inline int ExactNumTopBlobs() const { return -1; }
   virtual inline int MinTopBlobs() const { return 1; }
   virtual inline int MaxTopBlobs() const { return 2; }
@@ -717,10 +784,9 @@ class SoftmaxWithLossLayer : public LossLayer<Dtype> {
  protected:
   /// @copydoc SoftmaxWithLossLayer
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
-
+      const vector<Blob<Dtype>*>& top);
   /**
    * @brief Computes the softmax loss error gradient w.r.t. the predictions.
    *
@@ -749,18 +815,68 @@ class SoftmaxWithLossLayer : public LossLayer<Dtype> {
    *      the labels -- ignored as we can't compute their error gradients
    */
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
 
   /// The internal SoftmaxLayer used to map predictions to a distribution.
-  shared_ptr<SoftmaxLayer<Dtype> > softmax_layer_;
+  shared_ptr<Layer<Dtype> > softmax_layer_;
   /// prob stores the output probability predictions from the SoftmaxLayer.
   Blob<Dtype> prob_;
   /// bottom vector holder used in call to the underlying SoftmaxLayer::Forward
   vector<Blob<Dtype>*> softmax_bottom_vec_;
   /// top vector holder used in call to the underlying SoftmaxLayer::Forward
   vector<Blob<Dtype>*> softmax_top_vec_;
+  /// Whether to ignore instances with a certain label.
+  bool has_ignore_label_;
+  /// The label indicating that an instance should be ignored.
+  int ignore_label_;
+  /// Whether to normalize the loss by the total number of values present
+  /// (otherwise just by the batch size).
+  bool normalize_;
+
+  int softmax_axis_, outer_num_, inner_num_;
+};
+
+template <typename Dtype>
+class SmoothL1LossLayer : public LossLayer<Dtype> {
+public:
+    explicit SmoothL1LossLayer(const LayerParameter& param)
+        : LossLayer<Dtype>(param), diff_() {}
+    virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+                            const vector<Blob<Dtype>*>& top);
+    virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+                         const vector<Blob<Dtype>*>& top);
+
+    virtual inline const char* type() const { return "SmoothL1Loss"; }
+
+    virtual inline int ExactNumBottomBlobs() const { return -1; }
+    virtual inline int MinBottomBlobs() const { return 2; }
+    virtual inline int MaxBottomBlobs() const { return 3; }
+
+    /**
+    * Unlike most loss layers, in the SmoothL1LossLayer we can backpropagate
+    * to both inputs -- override to return true and always allow force_backward.
+    */
+    virtual inline bool AllowForceBackward(const int bottom_index) const {
+      return true;
+    }
+
+protected:
+    virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+                             const vector<Blob<Dtype>*>& top);
+    virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+                             const vector<Blob<Dtype>*>& top);
+
+    virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+                              const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+    virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+                              const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+    Blob<Dtype> diff_;
+    Blob<Dtype> errors_;
+    bool has_weights_;
 };
 
 }  // namespace caffe
diff --git a/include/caffe/net.hpp b/include/caffe/net.hpp
index 1d06dc45533..75d915713da 100644
--- a/include/caffe/net.hpp
+++ b/include/caffe/net.hpp
@@ -24,7 +24,7 @@ template <typename Dtype>
 class Net {
  public:
   explicit Net(const NetParameter& param);
-  explicit Net(const string& param_file);
+  explicit Net(const string& param_file, Phase phase);
   virtual ~Net() {}
 
   /// @brief Initialize a network with a NetParameter.
@@ -89,7 +89,7 @@ class Net {
    * @brief For an already initialized net, implicitly copies (i.e., using no
    *        additional memory) the pre-trained layers from another Net.
    */
-  void ShareTrainedLayersWith(Net* other);
+  void ShareTrainedLayersWith(const Net* other);
   // For an already initialized net, CopyTrainedLayersFrom() copies the already
   // trained layers from another net parameter instance.
   /**
@@ -99,51 +99,80 @@ class Net {
   void CopyTrainedLayersFrom(const NetParameter& param);
   void CopyTrainedLayersFrom(const string trained_filename);
   /// @brief Writes the net to a proto.
-  void ToProto(NetParameter* param, bool write_diff = false);
+  void ToProto(NetParameter* param, bool write_diff = false) const;
 
   /// @brief returns the network name.
-  inline const string& name() { return name_; }
+  inline const string& name() const { return name_; }
   /// @brief returns the layer names
-  inline const vector<string>& layer_names() { return layer_names_; }
+  inline const vector<string>& layer_names() const { return layer_names_; }
   /// @brief returns the blob names
-  inline const vector<string>& blob_names() { return blob_names_; }
+  inline const vector<string>& blob_names() const { return blob_names_; }
   /// @brief returns the blobs
-  inline const vector<shared_ptr<Blob<Dtype> > >& blobs() { return blobs_; }
+  inline const vector<shared_ptr<Blob<Dtype> > >& blobs() const {
+    return blobs_;
+  }
   /// @brief returns the layers
-  inline const vector<shared_ptr<Layer<Dtype> > >& layers() { return layers_; }
+  inline const vector<shared_ptr<Layer<Dtype> > >& layers() const {
+    return layers_;
+  }
+  /// @brief returns the phase: TRAIN or TEST
+  inline Phase phase() const { return phase_; }
   /**
    * @brief returns the bottom vecs for each layer -- usually you won't
    *        need this unless you do per-layer checks such as gradients.
    */
-  inline vector<vector<Blob<Dtype>*> >& bottom_vecs() { return bottom_vecs_; }
+  inline const vector<vector<Blob<Dtype>*> >& bottom_vecs() const {
+    return bottom_vecs_;
+  }
   /**
    * @brief returns the top vecs for each layer -- usually you won't
    *        need this unless you do per-layer checks such as gradients.
    */
-  inline vector<vector<Blob<Dtype>*> >& top_vecs() { return top_vecs_; }
-  inline vector<vector<bool> >& bottom_need_backward() {
+  inline const vector<vector<Blob<Dtype>*> >& top_vecs() const {
+    return top_vecs_;
+  }
+  inline const vector<vector<bool> >& bottom_need_backward() const {
     return bottom_need_backward_;
   }
-  inline vector<Dtype>& blob_loss_weights() {
+  inline const vector<Dtype>& blob_loss_weights() const {
     return blob_loss_weights_;
   }
+  inline const vector<bool>& layer_need_backward() const {
+    return layer_need_backward_;
+  }
   /// @brief returns the parameters
-  inline vector<shared_ptr<Blob<Dtype> > >& params() { return params_; }
+  inline const vector<shared_ptr<Blob<Dtype> > >& params() const {
+    return params_;
+  }
   /// @brief returns the parameter learning rate multipliers
-  inline vector<float>& params_lr() { return params_lr_; }
-  inline vector<float>& params_weight_decay() { return params_weight_decay_; }
-  const map<string, int>& param_names_index() { return param_names_index_; }
+  inline const vector<float>& params_lr() const { return params_lr_; }
+  inline const vector<float>& params_weight_decay() const {
+    return params_weight_decay_;
+  }
+  const map<string, int>& param_names_index() const {
+    return param_names_index_;
+  }
+  inline const vector<int>& param_owners() const { return param_owners_; }
+  inline const vector<pair<int ,int> >& param_layer_indices() const {return param_layer_indices_;}
   /// @brief Input and output blob numbers
-  inline int num_inputs() { return net_input_blobs_.size(); }
-  inline int num_outputs() { return net_output_blobs_.size(); }
-  inline vector<Blob<Dtype>*>& input_blobs() { return net_input_blobs_; }
-  inline vector<Blob<Dtype>*>& output_blobs() { return net_output_blobs_; }
-  inline vector<int>& input_blob_indices() { return net_input_blob_indices_; }
-  inline vector<int>& output_blob_indices() { return net_output_blob_indices_; }
-  bool has_blob(const string& blob_name);
-  const shared_ptr<Blob<Dtype> > blob_by_name(const string& blob_name);
-  bool has_layer(const string& layer_name);
-  const shared_ptr<Layer<Dtype> > layer_by_name(const string& layer_name);
+  inline int num_inputs() const { return net_input_blobs_.size(); }
+  inline int num_outputs() const { return net_output_blobs_.size(); }
+  inline const vector<Blob<Dtype>*>& input_blobs() const {
+    return net_input_blobs_;
+  }
+  inline const vector<Blob<Dtype>*>& output_blobs() const {
+    return net_output_blobs_;
+  }
+  inline const vector<int>& input_blob_indices() const {
+    return net_input_blob_indices_;
+  }
+  inline const vector<int>& output_blob_indices() const {
+    return net_output_blob_indices_;
+  }
+  bool has_blob(const string& blob_name) const;
+  const shared_ptr<Blob<Dtype> > blob_by_name(const string& blob_name) const;
+  bool has_layer(const string& layer_name) const;
+  const shared_ptr<Layer<Dtype> > layer_by_name(const string& layer_name) const;
 
   void set_debug_info(const bool value) { debug_info_ = value; }
 
@@ -158,6 +187,13 @@ class Net {
   static bool StateMeetsRule(const NetState& state, const NetStateRule& rule,
       const string& layer_name);
 
+ /**
+  * @brief helper for retriving the layer related to a blob
+  */
+ inline shared_ptr<Layer<Dtype> > layer_by_param(int param_id){
+  return layers_[param_layer_indices_[param_id].first];
+ }
+
  protected:
   // Helpers for Init.
   /// @brief Append a new input or top blob to the net.
@@ -172,6 +208,8 @@ class Net {
   void AppendParam(const NetParameter& param, const int layer_id,
                    const int param_id);
 
+  /// @brief Helper for displaying debug info in Forward about input Blobs.
+  void InputDebugInfo(const int layer_id);
   /// @brief Helper for displaying debug info in Forward.
   void ForwardDebugInfo(const int layer_id);
   /// @brief Helper for displaying debug info in Backward.
@@ -182,6 +220,13 @@ class Net {
   /// @brief Get misc parameters, e.g. the LR multiplier and weight decay.
   void GetLearningRateAndWeightDecay();
 
+  /// @brief do a dry run to decide blob dependency
+  void MemoryOptimize();
+
+  /// @brief The network name
+  string name_;
+  /// @brief The phase: TRAIN or TEST
+  Phase phase_;
   /// @brief Individual layers in the net
   vector<shared_ptr<Layer<Dtype> > > layers_;
   vector<string> layer_names_;
@@ -204,16 +249,17 @@ class Net {
   /// Vector of weight in the loss (or objective) function of each net blob,
   /// indexed by blob_id.
   vector<Dtype> blob_loss_weights_;
+  vector<vector<int> > param_id_vecs_;
   vector<int> param_owners_;
   vector<string> param_display_names_;
   vector<pair<int, int> > param_layer_indices_;
+  vector<pair<int, int> > top_layer_indices_;
   map<string, int> param_names_index_;
   /// blob indices for the input and the output of the net
   vector<int> net_input_blob_indices_;
   vector<int> net_output_blob_indices_;
   vector<Blob<Dtype>*> net_input_blobs_;
   vector<Blob<Dtype>*> net_output_blobs_;
-  string name_;
   /// The parameters in the network.
   vector<shared_ptr<Blob<Dtype> > > params_;
   /// the learning rate multipliers
@@ -225,6 +271,10 @@ class Net {
   /// Whether to compute and display debug info for the net.
   bool debug_info_;
 
+  /// Memory optimization related stuff.
+  vector< shared_ptr<SyncedMemory> > shared_storage_;
+  std::set<string> excluded_blob_names_;
+
   DISABLE_COPY_AND_ASSIGN(Net);
 };
 
diff --git a/include/caffe/neuron_layers.hpp b/include/caffe/neuron_layers.hpp
index 0968a2007dc..75092b409b9 100644
--- a/include/caffe/neuron_layers.hpp
+++ b/include/caffe/neuron_layers.hpp
@@ -27,11 +27,8 @@ class NeuronLayer : public Layer<Dtype> {
   explicit NeuronLayer(const LayerParameter& param)
      : Layer<Dtype>(param) {}
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
 
-  virtual inline LayerParameter_LayerType type() const {
-    return LayerParameter_LayerType_NONE;
-  }
   virtual inline int ExactNumBottomBlobs() const { return 1; }
   virtual inline int ExactNumTopBlobs() const { return 1; }
 };
@@ -52,20 +49,18 @@ class AbsValLayer : public NeuronLayer<Dtype> {
   explicit AbsValLayer(const LayerParameter& param)
       : NeuronLayer<Dtype>(param) {}
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
 
-  virtual inline LayerParameter_LayerType type() const {
-    return LayerParameter_LayerType_ABSVAL;
-  }
+  virtual inline const char* type() const { return "AbsVal"; }
   virtual inline int ExactNumBottomBlobs() const { return 1; }
   virtual inline int ExactNumTopBlobs() const { return 1; }
 
  protected:
   /// @copydoc AbsValLayer
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
 
   /**
    * @brief Computes the error gradient w.r.t. the absolute value inputs.
@@ -85,9 +80,9 @@ class AbsValLayer : public NeuronLayer<Dtype> {
    *      @f$ if propagate_down[0]
    */
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
 };
 
 /**
@@ -113,16 +108,14 @@ class BNLLLayer : public NeuronLayer<Dtype> {
   explicit BNLLLayer(const LayerParameter& param)
       : NeuronLayer<Dtype>(param) {}
 
-  virtual inline LayerParameter_LayerType type() const {
-    return LayerParameter_LayerType_BNLL;
-  }
+  virtual inline const char* type() const { return "BNLL"; }
 
  protected:
   /// @copydoc BNLLLayer
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
 
   /**
    * @brief Computes the error gradient w.r.t. the BNLL inputs.
@@ -141,9 +134,9 @@ class BNLLLayer : public NeuronLayer<Dtype> {
    *      @f$ if propagate_down[0]
    */
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
 };
 
 /**
@@ -169,13 +162,11 @@ class DropoutLayer : public NeuronLayer<Dtype> {
   explicit DropoutLayer(const LayerParameter& param)
       : NeuronLayer<Dtype>(param) {}
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
 
-  virtual inline LayerParameter_LayerType type() const {
-    return LayerParameter_LayerType_DROPOUT;
-  }
+  virtual inline const char* type() const { return "Dropout"; }
 
  protected:
   /**
@@ -195,13 +186,13 @@ class DropoutLayer : public NeuronLayer<Dtype> {
    *      @f$ y_{\mbox{test}} = \mathbb{E}[y_{\mbox{train}}] = x @f$.
    */
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
 
   /// when divided by UINT_MAX, the randomly generated values @f$u\sim U(0,1)@f$
   Blob<unsigned int> rand_vec_;
@@ -212,6 +203,136 @@ class DropoutLayer : public NeuronLayer<Dtype> {
   unsigned int uint_thres_;
 };
 
+/**
+ * @brief Computes @f$ y = \gamma ^ {\alpha x + \beta} @f$,
+ *        as specified by the scale @f$ \alpha @f$, shift @f$ \beta @f$,
+ *        and base @f$ \gamma @f$.
+ */
+template <typename Dtype>
+class ExpLayer : public NeuronLayer<Dtype> {
+ public:
+  /**
+   * @param param provides ExpParameter exp_param,
+   *     with ExpLayer options:
+   *   - scale (\b optional, default 1) the scale @f$ \alpha @f$
+   *   - shift (\b optional, default 0) the shift @f$ \beta @f$
+   *   - base (\b optional, default -1 for a value of @f$ e \approx 2.718 @f$)
+   *         the base @f$ \gamma @f$
+   */
+  explicit ExpLayer(const LayerParameter& param)
+      : NeuronLayer<Dtype>(param) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual inline const char* type() const { return "Exp"; }
+
+ protected:
+  /**
+   * @param bottom input Blob vector (length 1)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the inputs @f$ x @f$
+   * @param top output Blob vector (length 1)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the computed outputs @f$
+   *        y = \gamma ^ {\alpha x + \beta}
+   *      @f$
+   */
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  /**
+   * @brief Computes the error gradient w.r.t. the exp inputs.
+   *
+   * @param top output Blob vector (length 1), providing the error gradient with
+   *      respect to the outputs
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      containing error gradients @f$ \frac{\partial E}{\partial y} @f$
+   *      with respect to computed outputs @f$ y @f$
+   * @param propagate_down see Layer::Backward.
+   * @param bottom input Blob vector (length 1)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the inputs @f$ x @f$; Backward fills their diff with
+   *      gradients @f$
+   *        \frac{\partial E}{\partial x} =
+   *            \frac{\partial E}{\partial y} y \alpha \log_e(gamma)
+   *      @f$ if propagate_down[0]
+   */
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+  Dtype inner_scale_, outer_scale_;
+};
+
+/**
+ * @brief Computes @f$ y = log_{\gamma}(\alpha x + \beta) @f$,
+ *        as specified by the scale @f$ \alpha @f$, shift @f$ \beta @f$,
+ *        and base @f$ \gamma @f$.
+ */
+template <typename Dtype>
+class LogLayer : public NeuronLayer<Dtype> {
+ public:
+  /**
+   * @param param provides LogParameter log_param,
+   *     with LogLayer options:
+   *   - scale (\b optional, default 1) the scale @f$ \alpha @f$
+   *   - shift (\b optional, default 0) the shift @f$ \beta @f$
+   *   - base (\b optional, default -1 for a value of @f$ e \approx 2.718 @f$)
+   *         the base @f$ \gamma @f$
+   */
+  explicit LogLayer(const LayerParameter& param)
+      : NeuronLayer<Dtype>(param) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual inline const char* type() const { return "Log"; }
+
+ protected:
+  /**
+   * @param bottom input Blob vector (length 1)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the inputs @f$ x @f$
+   * @param top output Blob vector (length 1)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the computed outputs @f$
+   *        y = log_{\gamma}(\alpha x + \beta)
+   *      @f$
+   */
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  /**
+   * @brief Computes the error gradient w.r.t. the exp inputs.
+   *
+   * @param top output Blob vector (length 1), providing the error gradient with
+   *      respect to the outputs
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      containing error gradients @f$ \frac{\partial E}{\partial y} @f$
+   *      with respect to computed outputs @f$ y @f$
+   * @param propagate_down see Layer::Backward.
+   * @param bottom input Blob vector (length 1)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the inputs @f$ x @f$; Backward fills their diff with
+   *      gradients @f$
+   *        \frac{\partial E}{\partial x} =
+   *            \frac{\partial E}{\partial y} y \alpha \log_e(gamma)
+   *      @f$ if propagate_down[0]
+   */
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+  Dtype base_scale_;
+  Dtype input_scale_, input_shift_;
+  Dtype backward_num_scale_;
+};
+
 /**
  * @brief Computes @f$ y = (\alpha x + \beta) ^ \gamma @f$,
  *        as specified by the scale @f$ \alpha @f$, shift @f$ \beta @f$,
@@ -230,11 +351,9 @@ class PowerLayer : public NeuronLayer<Dtype> {
   explicit PowerLayer(const LayerParameter& param)
       : NeuronLayer<Dtype>(param) {}
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
 
-  virtual inline LayerParameter_LayerType type() const {
-    return LayerParameter_LayerType_POWER;
-  }
+  virtual inline const char* type() const { return "Power"; }
 
  protected:
   /**
@@ -248,9 +367,9 @@ class PowerLayer : public NeuronLayer<Dtype> {
    *      @f$
    */
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
 
   /**
    * @brief Computes the error gradient w.r.t. the power inputs.
@@ -273,9 +392,9 @@ class PowerLayer : public NeuronLayer<Dtype> {
    *      @f$ if propagate_down[0]
    */
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
 
   /// @brief @f$ \gamma @f$ from layer_param_.power_param()
   Dtype power_;
@@ -303,9 +422,7 @@ class ReLULayer : public NeuronLayer<Dtype> {
   explicit ReLULayer(const LayerParameter& param)
       : NeuronLayer<Dtype>(param) {}
 
-  virtual inline LayerParameter_LayerType type() const {
-    return LayerParameter_LayerType_RELU;
-  }
+  virtual inline const char* type() const { return "ReLU"; }
 
  protected:
   /**
@@ -320,9 +437,9 @@ class ReLULayer : public NeuronLayer<Dtype> {
    *      the computed outputs are @f$ y = \max(0, x) + \nu \min(0, x) @f$.
    */
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
 
   /**
    * @brief Computes the error gradient w.r.t. the ReLU inputs.
@@ -353,9 +470,9 @@ class ReLULayer : public NeuronLayer<Dtype> {
    *      @f$.
    */
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
 };
 
 #ifdef USE_CUDNN
@@ -366,22 +483,24 @@ template <typename Dtype>
 class CuDNNReLULayer : public ReLULayer<Dtype> {
  public:
   explicit CuDNNReLULayer(const LayerParameter& param)
-      : ReLULayer<Dtype>(param) {}
+      : ReLULayer<Dtype>(param), handles_setup_(false) {}
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual ~CuDNNReLULayer();
 
  protected:
   virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
 
+  bool handles_setup_;
   cudnnHandle_t             handle_;
-  cudnnTensor4dDescriptor_t bottom_desc_;
-  cudnnTensor4dDescriptor_t top_desc_;
+  cudnnTensorDescriptor_t bottom_desc_;
+  cudnnTensorDescriptor_t top_desc_;
+  cudnnActivationDescriptor_t activation_desc_;
 };
 #endif
 
@@ -399,9 +518,7 @@ class SigmoidLayer : public NeuronLayer<Dtype> {
   explicit SigmoidLayer(const LayerParameter& param)
       : NeuronLayer<Dtype>(param) {}
 
-  virtual inline LayerParameter_LayerType type() const {
-    return LayerParameter_LayerType_SIGMOID;
-  }
+  virtual inline const char* type() const { return "Sigmoid"; }
 
  protected:
   /**
@@ -415,9 +532,9 @@ class SigmoidLayer : public NeuronLayer<Dtype> {
    *      @f$
    */
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
 
   /**
    * @brief Computes the error gradient w.r.t. the sigmoid inputs.
@@ -437,9 +554,9 @@ class SigmoidLayer : public NeuronLayer<Dtype> {
    *      @f$ if propagate_down[0]
    */
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
 };
 
 #ifdef USE_CUDNN
@@ -450,22 +567,24 @@ template <typename Dtype>
 class CuDNNSigmoidLayer : public SigmoidLayer<Dtype> {
  public:
   explicit CuDNNSigmoidLayer(const LayerParameter& param)
-      : SigmoidLayer<Dtype>(param) {}
+      : SigmoidLayer<Dtype>(param), handles_setup_(false) {}
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual ~CuDNNSigmoidLayer();
 
  protected:
   virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
 
+  bool handles_setup_;
   cudnnHandle_t             handle_;
-  cudnnTensor4dDescriptor_t bottom_desc_;
-  cudnnTensor4dDescriptor_t top_desc_;
+  cudnnTensorDescriptor_t bottom_desc_;
+  cudnnTensorDescriptor_t top_desc_;
+  cudnnActivationDescriptor_t activation_desc_;
 };
 #endif
 
@@ -483,9 +602,7 @@ class TanHLayer : public NeuronLayer<Dtype> {
   explicit TanHLayer(const LayerParameter& param)
       : NeuronLayer<Dtype>(param) {}
 
-  virtual inline LayerParameter_LayerType type() const {
-    return LayerParameter_LayerType_TANH;
-  }
+  virtual inline const char* type() const { return "TanH"; }
 
  protected:
   /**
@@ -499,9 +616,9 @@ class TanHLayer : public NeuronLayer<Dtype> {
    *      @f$
    */
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
 
   /**
    * @brief Computes the error gradient w.r.t. the sigmoid inputs.
@@ -523,9 +640,9 @@ class TanHLayer : public NeuronLayer<Dtype> {
    *      @f$ if propagate_down[0]
    */
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
 };
 
 #ifdef USE_CUDNN
@@ -536,22 +653,24 @@ template <typename Dtype>
 class CuDNNTanHLayer : public TanHLayer<Dtype> {
  public:
   explicit CuDNNTanHLayer(const LayerParameter& param)
-      : TanHLayer<Dtype>(param) {}
+      : TanHLayer<Dtype>(param), handles_setup_(false) {}
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual ~CuDNNTanHLayer();
 
  protected:
   virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
 
+  bool handles_setup_;
   cudnnHandle_t             handle_;
-  cudnnTensor4dDescriptor_t bottom_desc_;
-  cudnnTensor4dDescriptor_t top_desc_;
+  cudnnTensorDescriptor_t bottom_desc_;
+  cudnnTensorDescriptor_t top_desc_;
+  cudnnActivationDescriptor_t activation_desc_;
 };
 #endif
 
@@ -571,11 +690,9 @@ class ThresholdLayer : public NeuronLayer<Dtype> {
   explicit ThresholdLayer(const LayerParameter& param)
       : NeuronLayer<Dtype>(param) {}
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
 
-  virtual inline LayerParameter_LayerType type() const {
-    return LayerParameter_LayerType_THRESHOLD;
-  }
+  virtual inline const char* type() const { return "Threshold"; }
 
  protected:
   /**
@@ -593,18 +710,103 @@ class ThresholdLayer : public NeuronLayer<Dtype> {
    *      @f$
    */
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   /// @brief Not implemented (non-differentiable function)
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
     NOT_IMPLEMENTED;
   }
 
   Dtype threshold_;
 };
 
+/**
+ * @brief Parameterized Rectified Linear Unit non-linearity @f$
+ *        y_i = \max(0, x_i) + a_i \min(0, x_i)
+ *        @f$. The differences from ReLULayer are 1) negative slopes are
+ *        learnable though backprop and 2) negative slopes can vary across
+ *        channels. The number of axes of input blob should be greater than or
+ *        equal to 2. The 1st axis (0-based) is seen as channels.
+ */
+template <typename Dtype>
+class PReLULayer : public NeuronLayer<Dtype> {
+ public:
+  /**
+   * @param param provides PReLUParameter prelu_param,
+   *     with PReLULayer options:
+   *   - filler (\b optional, FillerParameter,
+   *     default {'type': constant 'value':0.25}).
+   *   - channel_shared (\b optional, default false).
+   *     negative slopes are shared across channels.
+   */
+  explicit PReLULayer(const LayerParameter& param)
+      : NeuronLayer<Dtype>(param) {}
+
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual inline const char* type() const { return "PReLU"; }
+
+ protected:
+  /**
+   * @param bottom input Blob vector (length 1)
+   *   -# @f$ (N \times C \times ...) @f$
+   *      the inputs @f$ x @f$
+   * @param top output Blob vector (length 1)
+   *   -# @f$ (N \times C \times ...) @f$
+   *      the computed outputs for each channel @f$i@f$ @f$
+   *        y_i = \max(0, x_i) + a_i \min(0, x_i)
+   *      @f$.
+   */
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  /**
+   * @brief Computes the error gradient w.r.t. the PReLU inputs.
+   *
+   * @param top output Blob vector (length 1), providing the error gradient with
+   *      respect to the outputs
+   *   -# @f$ (N \times C \times ...) @f$
+   *      containing error gradients @f$ \frac{\partial E}{\partial y} @f$
+   *      with respect to computed outputs @f$ y @f$
+   * @param propagate_down see Layer::Backward.
+   * @param bottom input Blob vector (length 1)
+   *   -# @f$ (N \times C \times ...) @f$
+   *      the inputs @f$ x @f$; For each channel @f$i@f$, backward fills their
+   *      diff with gradients @f$
+   *        \frac{\partial E}{\partial x_i} = \left\{
+   *        \begin{array}{lr}
+   *            a_i \frac{\partial E}{\partial y_i} & \mathrm{if} \; x_i \le 0 \\
+   *            \frac{\partial E}{\partial y_i} & \mathrm{if} \; x_i > 0
+   *        \end{array} \right.
+   *      @f$.
+   *      If param_propagate_down_[0] is true, it fills the diff with gradients
+   *      @f$
+   *        \frac{\partial E}{\partial a_i} = \left\{
+   *        \begin{array}{lr}
+   *            \sum_{x_i} x_i \frac{\partial E}{\partial y_i} & \mathrm{if} \; x_i \le 0 \\
+   *            0 & \mathrm{if} \; x_i > 0
+   *        \end{array} \right.
+   *      @f$.
+   */
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+  bool channel_shared_;
+  Blob<Dtype> multiplier_;  // dot multiplier for backward computation of params
+  Blob<Dtype> backward_buff_;  // temporary buffer for backward computation
+  Blob<Dtype> bottom_memory_;  // memory for in-place computation
+};
+
 }  // namespace caffe
 
 #endif  // CAFFE_NEURON_LAYERS_HPP_
diff --git a/include/caffe/python_layer.hpp b/include/caffe/python_layer.hpp
new file mode 100644
index 00000000000..0866b2a8ddd
--- /dev/null
+++ b/include/caffe/python_layer.hpp
@@ -0,0 +1,137 @@
+#ifndef CAFFE_PYTHON_LAYER_HPP_
+#define CAFFE_PYTHON_LAYER_HPP_
+
+#include <boost/python.hpp>
+#include <vector>
+
+#include "caffe/layer.hpp"
+
+namespace bp = boost::python;
+#include <boost/thread.hpp>
+
+namespace caffe {
+
+extern boost::mutex mtx_;
+
+template <typename Dtype>
+class PythonLayer : public Layer<Dtype> {
+ public:
+  PythonLayer(PyObject* self, const LayerParameter& param)
+      : Layer<Dtype>(param), self_(bp::handle<>(bp::borrowed(self))) { }
+
+  virtual ~PythonLayer(){
+
+  }
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+      boost::lock_guard<boost::mutex> lock(mtx_);
+    //ensure the GIL
+    PyGILState_STATE state;
+    state = PyGILState_Ensure();
+    self_.attr("param_str") = bp::str(
+            this->layer_param_.python_param().param_str());
+    self_.attr("phase") = bp::str(
+        (this->phase_ == TRAIN)?"train":"test"
+    );
+    self_.attr("_prefetch") = false;
+    try {
+      self_.attr("setup")(bottom, top);
+      prefetch_ = self_.attr("_prefetch");
+    } catch (bp::error_already_set) {
+      PyErr_Print();
+      throw;
+    }
+    PyGILState_Release(state);
+    MaybeStartPrefetchThread();
+  }
+
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+    boost::lock_guard<boost::mutex> lock(mtx_);
+    PyGILState_STATE state;
+    state = PyGILState_Ensure();
+    try {
+      self_.attr("reshape")(bottom, top);
+    } catch (bp::error_already_set) {
+      PyErr_Print();
+      throw;
+    }
+    PyGILState_Release(state);
+  }
+
+  virtual inline const char* type() const { return "Python"; }
+
+ protected:
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+    boost::lock_guard<boost::mutex> lock(mtx_);
+    WaitForPrefetchThread();
+    PyGILState_STATE state;
+    state = PyGILState_Ensure();
+    try {
+      self_.attr("forward")(bottom, top);
+    } catch (bp::error_already_set) {
+      PyErr_Print();
+      throw;
+    }
+    PyGILState_Release(state);
+
+    MaybeStartPrefetchThread();
+  }
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+    boost::lock_guard<boost::mutex> lock(mtx_);
+    PyGILState_STATE state;
+    state = PyGILState_Ensure();
+    try {
+      self_.attr("backward")(top, propagate_down, bottom);
+    } catch (bp::error_already_set) {
+      PyErr_Print();
+      throw;
+    }
+    PyGILState_Release(state);
+  }
+
+  void PrefetchThread(){
+    boost::lock_guard<boost::mutex> lock(mtx_);
+    PyThreadState* tstate = PyEval_SaveThread();
+    PyGILState_STATE state;
+    state = PyGILState_Ensure();
+    try {
+      self_.attr("prefetch")();
+    } catch (bp::error_already_set) {
+      PyErr_Print();
+      throw;
+    }
+    PyGILState_Release(state);
+    PyEval_RestoreThread(tstate);
+  }
+
+  void MaybeStartPrefetchThread(){
+    if (prefetch_){
+      thread_.reset(
+        new boost::thread(&PythonLayer::PrefetchThread, this));
+    }
+
+  }
+
+  void WaitForPrefetchThread(){
+    if ((thread_.get() != NULL) && thread_->joinable()){
+      try {
+        thread_->join();
+      } catch (...) {
+        throw;
+      }
+    }
+  }
+
+ private:
+  bp::object self_;
+  bool prefetch_;
+  shared_ptr<boost::thread> thread_;
+
+};
+
+}  // namespace caffe
+
+#endif
diff --git a/include/caffe/solver.hpp b/include/caffe/solver.hpp
index 6fd159d0b98..dcb68f4d262 100644
--- a/include/caffe/solver.hpp
+++ b/include/caffe/solver.hpp
@@ -11,7 +11,7 @@ namespace caffe {
 /**
  * @brief An interface for classes that perform optimization on Net%s.
  *
- * Requires implementation of ComputeUpdateValue to compute a parameter update
+ * Requires implementation of ApplyUpdate to compute a parameter update
  * given the current state of the Net parameters.
  */
 template <typename Dtype>
@@ -26,18 +26,21 @@ class Solver {
   // in a non-zero iter number to resume training for a pre-trained net.
   virtual void Solve(const char* resume_file = NULL);
   inline void Solve(const string resume_file) { Solve(resume_file.c_str()); }
+  void Step(int iters);
+  // The Restore function implements how one should restore the solver to a
+  // previously snapshotted state. You should implement the RestoreSolverState()
+  // function that restores the state from a SolverState protocol buffer.
+  void Restore(const char* resume_file);
   virtual ~Solver() {}
   inline shared_ptr<Net<Dtype> > net() { return net_; }
   inline const vector<shared_ptr<Net<Dtype> > >& test_nets() {
     return test_nets_;
   }
+  int iter() { return iter_; }
 
  protected:
-  // PreSolve is run before any solving iteration starts, allowing one to
-  // put up some scaffold.
-  virtual void PreSolve() {}
-  // Get the update value for the current iteration.
-  virtual void ComputeUpdateValue() = 0;
+  // Make and apply the update value for the current iteration.
+  virtual void ApplyUpdate() = 0;
   // The Solver::Snapshot function implements the basic snapshotting utility
   // that stores the learned net. You should implement the SnapshotSolverState()
   // function that produces a SolverState protocol buffer that needs to be
@@ -47,15 +50,19 @@ class Solver {
   void TestAll();
   void Test(const int test_net_id = 0);
   virtual void SnapshotSolverState(SolverState* state) = 0;
-  // The Restore function implements how one should restore the solver to a
-  // previously snapshotted state. You should implement the RestoreSolverState()
-  // function that restores the state from a SolverState protocol buffer.
-  void Restore(const char* resume_file);
   virtual void RestoreSolverState(const SolverState& state) = 0;
   void DisplayOutputBlobs(const int net_id);
 
+#ifdef USE_MPI
+    void SyncGradient();
+    void SyncData();
+    void SyncOutput(shared_ptr<Net<Dtype> > net);
+    Dtype SyncLoss(Dtype loss);
+#endif
+
   SolverParameter param_;
   int iter_;
+  int current_step_;
   shared_ptr<Net<Dtype> > net_;
   vector<shared_ptr<Net<Dtype> > > test_nets_;
 
@@ -71,16 +78,20 @@ template <typename Dtype>
 class SGDSolver : public Solver<Dtype> {
  public:
   explicit SGDSolver(const SolverParameter& param)
-      : Solver<Dtype>(param) {}
+      : Solver<Dtype>(param) { PreSolve(); }
   explicit SGDSolver(const string& param_file)
-      : Solver<Dtype>(param_file) {}
+      : Solver<Dtype>(param_file) { PreSolve(); }
 
   const vector<shared_ptr<Blob<Dtype> > >& history() { return history_; }
 
  protected:
-  virtual void PreSolve();
+  void PreSolve();
   Dtype GetLearningRate();
-  virtual void ComputeUpdateValue();
+  virtual void ApplyUpdate();
+  virtual void Normalize(int param_id);
+  virtual void Regularize(int param_id);
+  virtual void ComputeUpdateValue(int param_id, Dtype rate);
+  virtual void ClipGradients();
   virtual void SnapshotSolverState(SolverState * state);
   virtual void RestoreSolverState(const SolverState& state);
   // history maintains the historical momentum data.
@@ -101,7 +112,7 @@ class NesterovSolver : public SGDSolver<Dtype> {
       : SGDSolver<Dtype>(param_file) {}
 
  protected:
-  virtual void ComputeUpdateValue();
+  virtual void ComputeUpdateValue(int param_id, Dtype rate);
 
   DISABLE_COPY_AND_ASSIGN(NesterovSolver);
 };
@@ -115,7 +126,7 @@ class AdaGradSolver : public SGDSolver<Dtype> {
       : SGDSolver<Dtype>(param_file) { constructor_sanity_check(); }
 
  protected:
-  virtual void ComputeUpdateValue();
+  virtual void ComputeUpdateValue(int param_id, Dtype rate);
   void constructor_sanity_check() {
     CHECK_EQ(0, this->param_.momentum())
         << "Momentum cannot be used with AdaGrad.";
diff --git a/include/caffe/syncedmem.hpp b/include/caffe/syncedmem.hpp
index db8d0e80e12..9f412076bb9 100644
--- a/include/caffe/syncedmem.hpp
+++ b/include/caffe/syncedmem.hpp
@@ -12,7 +12,7 @@ namespace caffe {
 // cudaMallocHost and cudaFree functions in order to create pinned memory.
 // However, those codes rely on the existence of a cuda GPU (I don't know
 // why that is a must since allocating memory should not be accessing the
-// GPU resorce, but it just creates an error as of Cuda 5.0) and will cause
+// GPU resource, but it just creates an error as of Cuda 5.0) and will cause
 // problem when running on a machine without GPU. Thus, we simply define
 // these two functions for safety and possible future change if the problem
 // of calling cuda functions disappears in a future version.
@@ -24,6 +24,7 @@ namespace caffe {
 
 inline void CaffeMallocHost(void** ptr, size_t size) {
   *ptr = malloc(size);
+  CHECK(*ptr) << "host allocation of size " << size << " failed";
 }
 
 inline void CaffeFreeHost(void* ptr) {
@@ -55,6 +56,7 @@ class SyncedMemory {
   SyncedHead head() { return head_; }
   size_t size() { return size_; }
 
+  void Resize(size_t new_size);
  private:
   void to_cpu();
   void to_gpu();
diff --git a/include/caffe/test/test_caffe_main.hpp b/include/caffe/test/test_caffe_main.hpp
index 438acf2bf17..fc156091476 100644
--- a/include/caffe/test/test_caffe_main.hpp
+++ b/include/caffe/test/test_caffe_main.hpp
@@ -15,7 +15,7 @@ using std::cout;
 using std::endl;
 
 #ifdef CMAKE_BUILD
-  #include <cmake_test_defines.hpp.gen.cmake>
+  #include "caffe_config.h"
 #else
   #define CUDA_TEST_DEVICE -1
   #define CMAKE_SOURCE_DIR "src/"
@@ -40,34 +40,36 @@ class MultiDeviceTest : public ::testing::Test {
 
 typedef ::testing::Types<float, double> TestDtypes;
 
-struct FloatCPU {
-  typedef float Dtype;
+template <typename TypeParam>
+struct CPUDevice {
+  typedef TypeParam Dtype;
   static const Caffe::Brew device = Caffe::CPU;
 };
 
-struct DoubleCPU {
-  typedef double Dtype;
-  static const Caffe::Brew device = Caffe::CPU;
+template <typename Dtype>
+class CPUDeviceTest : public MultiDeviceTest<CPUDevice<Dtype> > {
 };
 
 #ifdef CPU_ONLY
 
-typedef ::testing::Types<FloatCPU, DoubleCPU> TestDtypesAndDevices;
+typedef ::testing::Types<CPUDevice<float>,
+                         CPUDevice<double> > TestDtypesAndDevices;
 
 #else
 
-struct FloatGPU {
-  typedef float Dtype;
+template <typename TypeParam>
+struct GPUDevice {
+  typedef TypeParam Dtype;
   static const Caffe::Brew device = Caffe::GPU;
 };
 
-struct DoubleGPU {
-  typedef double Dtype;
-  static const Caffe::Brew device = Caffe::GPU;
+template <typename Dtype>
+class GPUDeviceTest : public MultiDeviceTest<GPUDevice<Dtype> > {
 };
 
-typedef ::testing::Types<FloatCPU, DoubleCPU, FloatGPU, DoubleGPU>
-    TestDtypesAndDevices;
+typedef ::testing::Types<CPUDevice<float>, CPUDevice<double>,
+                         GPUDevice<float>, GPUDevice<double> >
+                         TestDtypesAndDevices;
 
 #endif
 
diff --git a/include/caffe/test/test_gradient_check_util.hpp b/include/caffe/test/test_gradient_check_util.hpp
index 5a8d382ff26..cc5dcbad0ee 100644
--- a/include/caffe/test/test_gradient_check_util.hpp
+++ b/include/caffe/test/test_gradient_check_util.hpp
@@ -30,24 +30,24 @@ class GradientChecker {
   // layers.
   // Note that after the gradient check, we do not guarantee that the data
   // stored in the layer parameters and the blobs are unchanged.
-  void CheckGradient(Layer<Dtype>* layer, vector<Blob<Dtype>*>* bottom,
-      vector<Blob<Dtype>*>* top, int check_bottom = -1) {
-      layer->SetUp(*bottom, top);
+  void CheckGradient(Layer<Dtype>* layer, const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top, int check_bottom = -1) {
+      layer->SetUp(bottom, top);
       CheckGradientSingle(layer, bottom, top, check_bottom, -1, -1);
   }
   void CheckGradientExhaustive(Layer<Dtype>* layer,
-      vector<Blob<Dtype>*>* bottom, vector<Blob<Dtype>*>* top,
+      const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top,
       int check_bottom = -1);
 
   // CheckGradientEltwise can be used to test layers that perform element-wise
   // computation only (e.g., neuron layers) -- where (d y_i) / (d x_j) = 0 when
   // i != j.
   void CheckGradientEltwise(Layer<Dtype>* layer,
-      vector<Blob<Dtype>*>* bottom, vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top);
 
-  void CheckGradientSingle(Layer<Dtype>* layer, vector<Blob<Dtype>*>* bottom,
-      vector<Blob<Dtype>*>* top, int check_bottom, int top_id,
-      int top_data_id, bool element_wise = false);
+  void CheckGradientSingle(Layer<Dtype>* layer,
+      const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top,
+      int check_bottom, int top_id, int top_data_id, bool element_wise = false);
 
   // Checks the gradient of a network. This network should not have any data
   // layers or loss layers, since the function does not explicitly deal with
@@ -57,8 +57,8 @@ class GradientChecker {
       const vector<Blob<Dtype>*>& input);
 
  protected:
-  Dtype GetObjAndGradient(const Layer<Dtype>& layer, vector<Blob<Dtype>*>* top,
-      int top_id = -1, int top_data_id = -1);
+  Dtype GetObjAndGradient(const Layer<Dtype>& layer,
+      const vector<Blob<Dtype>*>& top, int top_id = -1, int top_data_id = -1);
   Dtype stepsize_;
   Dtype threshold_;
   unsigned int seed_;
@@ -69,40 +69,43 @@ class GradientChecker {
 
 template <typename Dtype>
 void GradientChecker<Dtype>::CheckGradientSingle(Layer<Dtype>* layer,
-    vector<Blob<Dtype>*>* bottom, vector<Blob<Dtype>*>* top,
+    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top,
     int check_bottom, int top_id, int top_data_id, bool element_wise) {
   if (element_wise) {
     CHECK_EQ(0, layer->blobs().size());
     CHECK_LE(0, top_id);
     CHECK_LE(0, top_data_id);
-    const int top_count = (*top)[top_id]->count();
-    for (int blob_id = 0; blob_id < bottom->size(); ++blob_id) {
-      CHECK_EQ(top_count, (*bottom)[blob_id]->count());
+    const int top_count = top[top_id]->count();
+    for (int blob_id = 0; blob_id < bottom.size(); ++blob_id) {
+      CHECK_EQ(top_count, bottom[blob_id]->count());
     }
   }
-  // First, figure out what blobs we need to check against.
+  // First, figure out what blobs we need to check against, and zero init
+  // parameter blobs.
   vector<Blob<Dtype>*> blobs_to_check;
-  vector<bool> propagate_down(bottom->size(), check_bottom < 0);
+  vector<bool> propagate_down(bottom.size(), check_bottom < 0);
   for (int i = 0; i < layer->blobs().size(); ++i) {
-    blobs_to_check.push_back(layer->blobs()[i].get());
+    Blob<Dtype>* blob = layer->blobs()[i].get();
+    caffe_set(blob->count(), static_cast<Dtype>(0), blob->mutable_cpu_diff());
+    blobs_to_check.push_back(blob);
   }
   if (check_bottom < 0) {
-    for (int i = 0; i < bottom->size(); ++i) {
-      blobs_to_check.push_back((*bottom)[i]);
+    for (int i = 0; i < bottom.size(); ++i) {
+      blobs_to_check.push_back(bottom[i]);
     }
   } else {
-    CHECK_LT(check_bottom, bottom->size());
-    blobs_to_check.push_back((*bottom)[check_bottom]);
+    CHECK_LT(check_bottom, bottom.size());
+    blobs_to_check.push_back(bottom[check_bottom]);
     propagate_down[check_bottom] = true;
   }
   // Compute the gradient analytically using Backward
   Caffe::set_random_seed(seed_);
   // Ignore the loss from the layer (it's just the weighted sum of the losses
   // from the top blobs, whose gradients we may want to test individually).
-  layer->Forward(*bottom, top);
+  layer->Forward(bottom, top);
   // Get additional loss from the objective
   GetObjAndGradient(*layer, top, top_id, top_data_id);
-  layer->Backward(*top, propagate_down, bottom);
+  layer->Backward(top, propagate_down, bottom);
   // Store computed gradients for all checked blobs
   vector<shared_ptr<Blob<Dtype> > >
       computed_gradient_blobs(blobs_to_check.size());
@@ -127,8 +130,8 @@ void GradientChecker<Dtype>::CheckGradientSingle(Layer<Dtype>* layer,
     //     << current_blob->count() << " parameters.";
     for (int feat_id = 0; feat_id < current_blob->count(); ++feat_id) {
       // For an element-wise layer, we only need to do finite differencing to
-      // compute the derivative of (*top)[top_id][top_data_id] w.r.t.
-      // (*bottom)[blob_id][i] only for i == top_data_id.  For any other
+      // compute the derivative of top[top_id][top_data_id] w.r.t.
+      // bottom[blob_id][i] only for i == top_data_id.  For any other
       // i != top_data_id, we know the derivative is 0 by definition, and simply
       // check that that's true.
       Dtype estimated_gradient = 0;
@@ -139,13 +142,13 @@ void GradientChecker<Dtype>::CheckGradientSingle(Layer<Dtype>* layer,
         // Compute loss with stepsize_ added to input.
         current_blob->mutable_cpu_data()[feat_id] += stepsize_;
         Caffe::set_random_seed(seed_);
-        layer->Forward(*bottom, top);
+        layer->Forward(bottom, top);
         positive_objective =
             GetObjAndGradient(*layer, top, top_id, top_data_id);
         // Compute loss with stepsize_ subtracted from input.
         current_blob->mutable_cpu_data()[feat_id] -= stepsize_ * 2;
         Caffe::set_random_seed(seed_);
-        layer->Forward(*bottom, top);
+        layer->Forward(bottom, top);
         negative_objective =
             GetObjAndGradient(*layer, top, top_id, top_data_id);
         // Recover original input value.
@@ -179,13 +182,14 @@ void GradientChecker<Dtype>::CheckGradientSingle(Layer<Dtype>* layer,
 
 template <typename Dtype>
 void GradientChecker<Dtype>::CheckGradientExhaustive(Layer<Dtype>* layer,
-    vector<Blob<Dtype>*>* bottom, vector<Blob<Dtype>*>* top, int check_bottom) {
-  layer->SetUp(*bottom, top);
-  CHECK_GT(top->size(), 0) << "Exhaustive mode requires at least one top blob.";
+    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top,
+    int check_bottom) {
+  layer->SetUp(bottom, top);
+  CHECK_GT(top.size(), 0) << "Exhaustive mode requires at least one top blob.";
   // LOG(ERROR) << "Exhaustive Mode.";
-  for (int i = 0; i < top->size(); ++i) {
+  for (int i = 0; i < top.size(); ++i) {
     // LOG(ERROR) << "Exhaustive: blob " << i << " size " << top[i]->count();
-    for (int j = 0; j < (*top)[i]->count(); ++j) {
+    for (int j = 0; j < top[i]->count(); ++j) {
       // LOG(ERROR) << "Exhaustive: blob " << i << " data " << j;
       CheckGradientSingle(layer, bottom, top, check_bottom, i, j);
     }
@@ -194,13 +198,13 @@ void GradientChecker<Dtype>::CheckGradientExhaustive(Layer<Dtype>* layer,
 
 template <typename Dtype>
 void GradientChecker<Dtype>::CheckGradientEltwise(Layer<Dtype>* layer,
-    vector<Blob<Dtype>*>* bottom, vector<Blob<Dtype>*>* top) {
-  layer->SetUp(*bottom, top);
-  CHECK_GT(top->size(), 0) << "Eltwise mode requires at least one top blob.";
+    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
+  layer->SetUp(bottom, top);
+  CHECK_GT(top.size(), 0) << "Eltwise mode requires at least one top blob.";
   const int check_bottom = -1;
   const bool element_wise = true;
-  for (int i = 0; i < top->size(); ++i) {
-    for (int j = 0; j < (*top)[i]->count(); ++j) {
+  for (int i = 0; i < top.size(); ++i) {
+    for (int j = 0; j < top[i]->count(); ++j) {
       CheckGradientSingle(layer, bottom, top, check_bottom, i, j, element_wise);
     }
   }
@@ -221,12 +225,12 @@ void GradientChecker<Dtype>::CheckGradientNet(
 
 template <typename Dtype>
 Dtype GradientChecker<Dtype>::GetObjAndGradient(const Layer<Dtype>& layer,
-    vector<Blob<Dtype>*>* top, int top_id, int top_data_id) {
+    const vector<Blob<Dtype>*>& top, int top_id, int top_data_id) {
   Dtype loss = 0;
   if (top_id < 0) {
     // the loss will be half of the sum of squares of all outputs
-    for (int i = 0; i < top->size(); ++i) {
-      Blob<Dtype>* top_blob = (*top)[i];
+    for (int i = 0; i < top.size(); ++i) {
+      Blob<Dtype>* top_blob = top[i];
       const Dtype* top_blob_data = top_blob->cpu_data();
       Dtype* top_blob_diff = top_blob->mutable_cpu_diff();
       int count = top_blob->count();
@@ -239,14 +243,14 @@ Dtype GradientChecker<Dtype>::GetObjAndGradient(const Layer<Dtype>& layer,
     loss /= 2.;
   } else {
     // the loss will be the top_data_id-th element in the top_id-th blob.
-    for (int i = 0; i < top->size(); ++i) {
-      Blob<Dtype>* top_blob = (*top)[i];
+    for (int i = 0; i < top.size(); ++i) {
+      Blob<Dtype>* top_blob = top[i];
       Dtype* top_blob_diff = top_blob->mutable_cpu_diff();
       caffe_set(top_blob->count(), Dtype(0), top_blob_diff);
     }
     const Dtype loss_weight = 2;
-    loss = (*top)[top_id]->cpu_data()[top_data_id] * loss_weight;
-    (*top)[top_id]->mutable_cpu_diff()[top_data_id] = loss_weight;
+    loss = top[top_id]->cpu_data()[top_data_id] * loss_weight;
+    top[top_id]->mutable_cpu_diff()[top_data_id] = loss_weight;
   }
   return loss;
 }
diff --git a/include/caffe/util/benchmark.hpp b/include/caffe/util/benchmark.hpp
index f7ef8eaf3ee..d63582776ee 100644
--- a/include/caffe/util/benchmark.hpp
+++ b/include/caffe/util/benchmark.hpp
@@ -11,10 +11,11 @@ class Timer {
  public:
   Timer();
   virtual ~Timer();
-  void Start();
-  void Stop();
-  float MilliSeconds();
-  float Seconds();
+  virtual void Start();
+  virtual void Stop();
+  virtual float MilliSeconds();
+  virtual float MicroSeconds();
+  virtual float Seconds();
 
   inline bool initted() { return initted_; }
   inline bool running() { return running_; }
@@ -33,6 +34,17 @@ class Timer {
   boost::posix_time::ptime start_cpu_;
   boost::posix_time::ptime stop_cpu_;
   float elapsed_milliseconds_;
+  float elapsed_microseconds_;
+};
+
+class CPUTimer : public Timer {
+ public:
+  explicit CPUTimer();
+  virtual ~CPUTimer() {}
+  virtual void Start();
+  virtual void Stop();
+  virtual float MilliSeconds();
+  virtual float MicroSeconds();
 };
 
 }  // namespace caffe
diff --git a/include/caffe/util/channel.hpp b/include/caffe/util/channel.hpp
new file mode 100644
index 00000000000..f96bf2e3544
--- /dev/null
+++ b/include/caffe/util/channel.hpp
@@ -0,0 +1,76 @@
+//
+// Created by alex on 8/25/15.
+//
+
+#ifndef CAFFE_CHANNEL_HPP
+#define CAFFE_CHANNEL_HPP
+
+#ifdef USE_MPI
+
+#include <boost/shared_ptr.hpp>
+#include <boost/atomic.hpp>
+#include <boost/thread.hpp>
+#include <queue>
+
+using std::queue;
+using boost::mutex;
+using boost::condition_variable;
+using boost::shared_ptr;
+using boost::atomic;
+
+namespace caffe {
+
+enum OperationType {
+    OP_SUM_ALL, OP_GATHER, OP_SCATTER, OP_BROADCAST
+};
+
+class MPIJob {
+public:
+  void* src_ptr_; // str_ptr_==NULL indicates IN_PLACE operation
+  void* dst_ptr_;
+  int count_;
+  int dtype_size_;
+  OperationType op_;
+};
+
+class MPIComm{
+  public:
+    ~MPIComm();
+    inline static MPIComm& Get() {
+      if (!singleton_.get()) {
+        singleton_.reset(new MPIComm());
+        singleton_->StartProcessing();
+      }
+      return *singleton_;
+    }
+
+    inline static void AddMPIJob(MPIJob job){ Get().AddJob(job);};
+    inline static void Syncrhonize(){Get().WaitAll();}
+
+  private:
+    MPIComm();
+
+    void ThreadFunc();
+    void DispatchJob(MPIJob& job);
+    bool IsRunning();
+    bool IsIdle();
+    void StartProcessing();
+    void EndProcessing();
+    void AddJob(MPIJob new_job);
+    void WaitAll();
+
+    queue<MPIJob> task_queue_;
+    mutable mutex queue_mutex_;
+    atomic<bool> running_, started_;
+    shared_ptr<boost::thread> thread_;
+    condition_variable cond_work_;
+    condition_variable cond_finish_;
+
+    static shared_ptr<MPIComm> singleton_;
+
+};
+};
+
+#endif //USE_MPI
+
+#endif //CAFFE_CHANNEL_HPP
diff --git a/include/caffe/util/cudnn.hpp b/include/caffe/util/cudnn.hpp
index aca5bd713fc..129df52d328 100644
--- a/include/caffe/util/cudnn.hpp
+++ b/include/caffe/util/cudnn.hpp
@@ -2,10 +2,14 @@
 #define CAFFE_UTIL_CUDNN_H_
 #ifdef USE_CUDNN
 
-#include <cudnn.h>
+#include "cudnn.h"
 
+#include "caffe/common.hpp"
 #include "caffe/proto/caffe.pb.h"
 
+#define CUDNN_VERSION_MIN(major, minor, patch) \
+    (CUDNN_VERSION >= (major * 1000 + minor * 100 + patch))
+
 #define CUDNN_CHECK(condition) \
   do { \
     cudnnStatus_t status = condition; \
@@ -49,42 +53,46 @@ template <typename Dtype> class dataType;
 template<> class dataType<float>  {
  public:
   static const cudnnDataType_t type = CUDNN_DATA_FLOAT;
+  static float oneval, zeroval;
+  static const void *one, *zero;
 };
 template<> class dataType<double> {
  public:
   static const cudnnDataType_t type = CUDNN_DATA_DOUBLE;
+  static double oneval, zeroval;
+  static const void *one, *zero;
 };
 
 template <typename Dtype>
-inline void createTensor4dDesc(cudnnTensor4dDescriptor_t* desc) {
-  CUDNN_CHECK(cudnnCreateTensor4dDescriptor(desc));
+inline void createTensor4dDesc(cudnnTensorDescriptor_t* desc) {
+  CUDNN_CHECK(cudnnCreateTensorDescriptor(desc));
 }
 
 template <typename Dtype>
-inline void setTensor4dDesc(cudnnTensor4dDescriptor_t* desc,
+inline void setTensor4dDesc(cudnnTensorDescriptor_t* desc,
     int n, int c, int h, int w,
     int stride_n, int stride_c, int stride_h, int stride_w) {
   CUDNN_CHECK(cudnnSetTensor4dDescriptorEx(*desc, dataType<Dtype>::type,
-      n, c, h, w, stride_n, stride_c, stride_h, stride_w));
+        n, c, h, w, stride_n, stride_c, stride_h, stride_w));
 }
 
 template <typename Dtype>
-inline void setTensor4dDesc(cudnnTensor4dDescriptor_t* desc,
+inline void setTensor4dDesc(cudnnTensorDescriptor_t* desc,
     int n, int c, int h, int w) {
   const int stride_w = 1;
   const int stride_h = w * stride_w;
   const int stride_c = h * stride_h;
   const int stride_n = c * stride_c;
   setTensor4dDesc<Dtype>(desc, n, c, h, w,
-      stride_n, stride_c, stride_h, stride_w);
+                         stride_n, stride_c, stride_h, stride_w);
 }
 
 template <typename Dtype>
 inline void createFilterDesc(cudnnFilterDescriptor_t* desc,
     int n, int c, int h, int w) {
   CUDNN_CHECK(cudnnCreateFilterDescriptor(desc));
-  CUDNN_CHECK(cudnnSetFilterDescriptor(*desc, dataType<Dtype>::type,
-      n, c, h, w));
+  CUDNN_CHECK(cudnnSetFilter4dDescriptor(*desc, dataType<Dtype>::type,
+                                         CUDNN_TENSOR_NCHW, n, c, h, w));
 }
 
 template <typename Dtype>
@@ -94,29 +102,36 @@ inline void createConvolutionDesc(cudnnConvolutionDescriptor_t* conv) {
 
 template <typename Dtype>
 inline void setConvolutionDesc(cudnnConvolutionDescriptor_t* conv,
-    cudnnTensor4dDescriptor_t bottom, cudnnFilterDescriptor_t filter,
+    cudnnTensorDescriptor_t bottom, cudnnFilterDescriptor_t filter,
     int pad_h, int pad_w, int stride_h, int stride_w) {
-  CUDNN_CHECK(cudnnSetConvolutionDescriptor(*conv, bottom, filter,
+  CUDNN_CHECK(cudnnSetConvolution2dDescriptor(*conv,
       pad_h, pad_w, stride_h, stride_w, 1, 1, CUDNN_CROSS_CORRELATION));
 }
 
 template <typename Dtype>
-inline void createPoolingDesc(cudnnPoolingDescriptor_t* conv,
+inline void createPoolingDesc(cudnnPoolingDescriptor_t* pool_desc,
     PoolingParameter_PoolMethod poolmethod, cudnnPoolingMode_t* mode,
-    int h, int w, int stride_h, int stride_w) {
+    int h, int w, int pad_h, int pad_w, int stride_h, int stride_w) {
   switch (poolmethod) {
   case PoolingParameter_PoolMethod_MAX:
     *mode = CUDNN_POOLING_MAX;
     break;
   case PoolingParameter_PoolMethod_AVE:
-    *mode = CUDNN_POOLING_AVERAGE;
+    *mode = CUDNN_POOLING_AVERAGE_COUNT_INCLUDE_PADDING;
     break;
   default:
     LOG(FATAL) << "Unknown pooling method.";
   }
-  CUDNN_CHECK(cudnnCreatePoolingDescriptor(conv));
-  CUDNN_CHECK(cudnnSetPoolingDescriptor(*conv, *mode, h, w,
-        stride_h, stride_w));
+  CUDNN_CHECK(cudnnCreatePoolingDescriptor(pool_desc));
+#if CUDNN_VERSION_MIN(5, 0, 0)
+  CUDNN_CHECK(cudnnSetPooling2dDescriptor(*pool_desc, *mode,
+                                          CUDNN_PROPAGATE_NAN, h, w,
+                                          pad_h, pad_w, stride_h, stride_w));
+#else
+  CUDNN_CHECK(cudnnSetPooling2dDescriptor_v4(*pool_desc, *mode,
+                                          CUDNN_PROPAGATE_NAN, h, w,
+                                          pad_h, pad_w, stride_h, stride_w));
+#endif
 }
 
 }  // namespace cudnn
diff --git a/include/caffe/util/db.hpp b/include/caffe/util/db.hpp
new file mode 100644
index 00000000000..2b16d0b3076
--- /dev/null
+++ b/include/caffe/util/db.hpp
@@ -0,0 +1,55 @@
+#ifndef CAFFE_UTIL_DB_HPP
+#define CAFFE_UTIL_DB_HPP
+
+#include <string>
+
+#include "caffe/common.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+namespace caffe { namespace db {
+
+enum Mode { READ, WRITE, NEW };
+
+class Cursor {
+ public:
+  Cursor() { }
+  virtual ~Cursor() { }
+  virtual void SeekToFirst() = 0;
+  virtual void Next() = 0;
+  virtual string  Lookup(string key) = 0;
+  virtual string key() = 0;
+  virtual string value() = 0;
+  virtual bool valid() = 0;
+
+  DISABLE_COPY_AND_ASSIGN(Cursor);
+};
+
+class Transaction {
+ public:
+  Transaction() { }
+  virtual ~Transaction() { }
+  virtual void Put(const string& key, const string& value) = 0;
+  virtual void Commit() = 0;
+
+  DISABLE_COPY_AND_ASSIGN(Transaction);
+};
+
+class DB {
+ public:
+  DB() { }
+  virtual ~DB() { }
+  virtual void Open(const string& source, Mode mode) = 0;
+  virtual void Close() = 0;
+  virtual Cursor* NewCursor() = 0;
+  virtual Transaction* NewTransaction() = 0;
+
+  DISABLE_COPY_AND_ASSIGN(DB);
+};
+
+DB* GetDB(DataParameter::DB backend);
+DB* GetDB(const string& backend);
+
+}  // namespace db
+}  // namespace caffe
+
+#endif  // CAFFE_UTIL_DB_HPP
diff --git a/include/caffe/util/db_leveldb.hpp b/include/caffe/util/db_leveldb.hpp
new file mode 100644
index 00000000000..77eb03c5b4e
--- /dev/null
+++ b/include/caffe/util/db_leveldb.hpp
@@ -0,0 +1,78 @@
+#ifndef CAFFE_UTIL_DB_LEVELDB_HPP
+#define CAFFE_UTIL_DB_LEVELDB_HPP
+
+#include <string>
+
+#include "leveldb/db.h"
+#include "leveldb/write_batch.h"
+
+#include "caffe/util/db.hpp"
+
+namespace caffe { namespace db {
+
+class LevelDBCursor : public Cursor {
+ public:
+  explicit LevelDBCursor(leveldb::Iterator* iter)
+    : iter_(iter) { SeekToFirst(); }
+  ~LevelDBCursor() { delete iter_; }
+  virtual void SeekToFirst() { iter_->SeekToFirst(); }
+  virtual void Next() { iter_->Next(); }
+  virtual string Lookup(string key){Seek(key); return value();};
+  virtual string key() { return iter_->key().ToString(); }
+  virtual string value() { return iter_->value().ToString(); }
+  virtual bool valid() { return iter_->Valid(); }
+
+ private:
+  leveldb::Iterator* iter_;
+
+  void Seek(string key){
+    iter_->Seek(leveldb::Slice(key.c_str(), key.size()));
+  };
+};
+
+class LevelDBTransaction : public Transaction {
+ public:
+  explicit LevelDBTransaction(leveldb::DB* db) : db_(db) { CHECK_NOTNULL(db_); }
+  virtual void Put(const string& key, const string& value) {
+    batch_.Put(key, value);
+  }
+  virtual void Commit() {
+    leveldb::Status status = db_->Write(leveldb::WriteOptions(), &batch_);
+    CHECK(status.ok()) << "Failed to write batch to leveldb "
+                       << std::endl << status.ToString();
+  }
+
+ private:
+  leveldb::DB* db_;
+  leveldb::WriteBatch batch_;
+
+  DISABLE_COPY_AND_ASSIGN(LevelDBTransaction);
+};
+
+class LevelDB : public DB {
+ public:
+  LevelDB() : db_(NULL) { }
+  virtual ~LevelDB() { Close(); }
+  virtual void Open(const string& source, Mode mode);
+  virtual void Close() {
+    if (db_ != NULL) {
+      delete db_;
+      db_ = NULL;
+    }
+  }
+  virtual LevelDBCursor* NewCursor() {
+    return new LevelDBCursor(db_->NewIterator(leveldb::ReadOptions()));
+  }
+  virtual LevelDBTransaction* NewTransaction() {
+    return new LevelDBTransaction(db_);
+  }
+
+ private:
+  leveldb::DB* db_;
+};
+
+
+}  // namespace db
+}  // namespace caffe
+
+#endif  // CAFFE_UTIL_DB_LEVELDB_HPP
diff --git a/include/caffe/util/db_lmdb.hpp b/include/caffe/util/db_lmdb.hpp
new file mode 100644
index 00000000000..6773ee8c3d5
--- /dev/null
+++ b/include/caffe/util/db_lmdb.hpp
@@ -0,0 +1,111 @@
+#ifndef CAFFE_UTIL_DB_LMDB_HPP
+#define CAFFE_UTIL_DB_LMDB_HPP
+
+#include <string>
+
+#include "lmdb.h"
+
+#include "caffe/util/db.hpp"
+
+namespace caffe { namespace db {
+
+inline void MDB_CHECK(int mdb_status) {
+  CHECK_EQ(mdb_status, MDB_SUCCESS) << mdb_strerror(mdb_status);
+}
+
+class LMDBCursor : public Cursor {
+ public:
+  explicit LMDBCursor(MDB_txn* mdb_txn, MDB_cursor* mdb_cursor)
+    : mdb_txn_(mdb_txn), mdb_cursor_(mdb_cursor), valid_(false) {
+    SeekToFirst();
+    mdb_search_key_.mv_data = (char*)malloc(1024);
+    CHECK(mdb_search_key_.mv_data)<<"failed to allocation buffer for LMDB search key";
+  }
+  virtual ~LMDBCursor() {
+    mdb_cursor_close(mdb_cursor_);
+    mdb_txn_abort(mdb_txn_);
+  }
+  virtual void SeekToFirst() { Seek(MDB_FIRST); }
+  virtual void Next() { Seek(MDB_NEXT); }
+  virtual string Lookup(string key){Seek(key); return value();};
+  virtual string key() {
+    return string(static_cast<const char*>(mdb_key_.mv_data), mdb_key_.mv_size);
+  }
+  virtual string value() {
+    return string(static_cast<const char*>(mdb_value_.mv_data),
+        mdb_value_.mv_size);
+  }
+  virtual bool valid() { return valid_; }
+
+ private:
+  void Seek(MDB_cursor_op op) {
+    int mdb_status = mdb_cursor_get(mdb_cursor_, &mdb_key_, &mdb_value_, op);
+    if (mdb_status == MDB_NOTFOUND) {
+      valid_ = false;
+    } else {
+      MDB_CHECK(mdb_status);
+      valid_ = true;
+    }
+  }
+
+  void Seek(string key){
+    strcpy((char*)mdb_search_key_.mv_data, key.c_str());
+    mdb_search_key_.mv_size = key.length();
+    int mdb_status = mdb_cursor_get(mdb_cursor_,
+                                    &mdb_search_key_, NULL,
+                                    MDB_SET);
+    if (mdb_status == MDB_NOTFOUND) {
+      valid_ = false;
+    } else {
+      MDB_CHECK(mdb_status);
+      valid_ = true;
+    }
+    Seek(MDB_GET_CURRENT);
+  }
+
+  MDB_txn* mdb_txn_;
+  MDB_cursor* mdb_cursor_;
+  MDB_val mdb_key_, mdb_value_;
+
+  MDB_val mdb_search_key_;
+  bool valid_;
+};
+
+class LMDBTransaction : public Transaction {
+ public:
+  explicit LMDBTransaction(MDB_dbi* mdb_dbi, MDB_txn* mdb_txn)
+    : mdb_dbi_(mdb_dbi), mdb_txn_(mdb_txn) { }
+  virtual void Put(const string& key, const string& value);
+  virtual void Commit() { MDB_CHECK(mdb_txn_commit(mdb_txn_)); }
+
+ private:
+  MDB_dbi* mdb_dbi_;
+  MDB_txn* mdb_txn_;
+
+  DISABLE_COPY_AND_ASSIGN(LMDBTransaction);
+};
+
+class LMDB : public DB {
+ public:
+  LMDB() : mdb_env_(NULL) { }
+  virtual ~LMDB() { Close(); }
+  virtual void Open(const string& source, Mode mode);
+  virtual void Close() {
+    if (mdb_env_ != NULL) {
+      mdb_dbi_close(mdb_env_, mdb_dbi_);
+      mdb_env_close(mdb_env_);
+      mdb_env_ = NULL;
+    }
+  }
+  virtual LMDBCursor* NewCursor();
+  virtual LMDBTransaction* NewTransaction();
+
+ private:
+  MDB_env* mdb_env_;
+  MDB_dbi mdb_dbi_;
+};
+
+}  // namespace db
+}  // namespace caffe
+
+#endif  // CAFFE_UTIL_DB_LMDB_HPP
diff --git a/include/caffe/util/device_alternate.hpp b/include/caffe/util/device_alternate.hpp
index 3df28a49ac3..6ea595dba2d 100644
--- a/include/caffe/util/device_alternate.hpp
+++ b/include/caffe/util/device_alternate.hpp
@@ -7,27 +7,27 @@
 
 // Stub out GPU calls as unavailable.
 
-#define NO_GPU LOG(FATAL) << "CPU-only Mode: cannot make GPU call."
+#define NO_GPU LOG(FATAL) << "Cannot use GPU in CPU-only Caffe: check mode."
 
 #define STUB_GPU(classname) \
 template <typename Dtype> \
 void classname<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom, \
-    vector<Blob<Dtype>*>* top) { NO_GPU; } \
+    const vector<Blob<Dtype>*>& top) { NO_GPU; } \
 template <typename Dtype> \
 void classname<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top, \
     const vector<bool>& propagate_down, \
-    vector<Blob<Dtype>*>* bottom) { NO_GPU; } \
+    const vector<Blob<Dtype>*>& bottom) { NO_GPU; } \
 
 #define STUB_GPU_FORWARD(classname, funcname) \
 template <typename Dtype> \
 void classname<Dtype>::funcname##_##gpu(const vector<Blob<Dtype>*>& bottom, \
-    vector<Blob<Dtype>*>* top) { NO_GPU; } \
+    const vector<Blob<Dtype>*>& top) { NO_GPU; } \
 
 #define STUB_GPU_BACKWARD(classname, funcname) \
 template <typename Dtype> \
 void classname<Dtype>::funcname##_##gpu(const vector<Blob<Dtype>*>& top, \
     const vector<bool>& propagate_down, \
-    vector<Blob<Dtype>*>* bottom) { NO_GPU; } \
+    const vector<Blob<Dtype>*>& bottom) { NO_GPU; } \
 
 #else  // Normal GPU + CPU Caffe.
 
diff --git a/include/caffe/util/io.hpp b/include/caffe/util/io.hpp
index 8dd338d2603..642c1507799 100644
--- a/include/caffe/util/io.hpp
+++ b/include/caffe/util/io.hpp
@@ -9,15 +9,11 @@
 #include "hdf5_hl.h"
 
 #include "caffe/blob.hpp"
+#include "caffe/common.hpp"
 #include "caffe/proto/caffe.pb.h"
 
 #define HDF5_NUM_DIMS 4
 
-namespace leveldb {
-// Forward declaration for leveldb::Options to be used in GetlevelDBOptions().
-struct Options;
-}
-
 namespace caffe {
 
 using ::google::protobuf::Message;
@@ -25,27 +21,27 @@ using ::google::protobuf::Message;
 inline void MakeTempFilename(string* temp_filename) {
   temp_filename->clear();
   *temp_filename = "/tmp/caffe_test.XXXXXX";
-  char* temp_filename_cstr = new char[temp_filename->size()];
+  char* temp_filename_cstr = new char[temp_filename->size() + 1];
   // NOLINT_NEXT_LINE(runtime/printf)
   strcpy(temp_filename_cstr, temp_filename->c_str());
   int fd = mkstemp(temp_filename_cstr);
   CHECK_GE(fd, 0) << "Failed to open a temporary file at: " << *temp_filename;
   close(fd);
   *temp_filename = temp_filename_cstr;
-  delete temp_filename_cstr;
+  delete[] temp_filename_cstr;
 }
 
 inline void MakeTempDir(string* temp_dirname) {
   temp_dirname->clear();
   *temp_dirname = "/tmp/caffe_test.XXXXXX";
-  char* temp_dirname_cstr = new char[temp_dirname->size()];
+  char* temp_dirname_cstr = new char[temp_dirname->size() + 1];
   // NOLINT_NEXT_LINE(runtime/printf)
   strcpy(temp_dirname_cstr, temp_dirname->c_str());
   char* mkdtemp_result = mkdtemp(temp_dirname_cstr);
   CHECK(mkdtemp_result != NULL)
       << "Failed to create a temporary directory at: " << *temp_dirname;
   *temp_dirname = temp_dirname_cstr;
-  delete temp_dirname_cstr;
+  delete[] temp_dirname_cstr;
 }
 
 bool ReadProtoFromTextFile(const char* filename, Message* proto);
@@ -89,34 +85,81 @@ inline void WriteProtoToBinaryFile(
   WriteProtoToBinaryFile(proto, filename.c_str());
 }
 
+bool ReadFileToDatum(const string& filename, const int label, Datum* datum);
+
+inline bool ReadFileToDatum(const string& filename, Datum* datum) {
+  return ReadFileToDatum(filename, -1, datum);
+}
+
 bool ReadImageToDatum(const string& filename, const int label,
-    const int height, const int width, const bool is_color, Datum* datum);
+    const int height, const int width, const bool is_color,
+    const std::string & encoding, Datum* datum);
+
+bool ReadSegmentFlowToDatum(const string& filename, const int label,
+    const vector<int> offsets, const int height, const int width, const int length, Datum* datum, const char* name_pattern);
+
+bool ReadSegmentRGBToDatum(const string& filename, const int label,
+    const vector<int> offsets, const int height, const int width, const int length, Datum* datum, bool is_color,
+                           const char* name_pattern);
+
+inline bool ReadImageToDatum(const string& filename, const int label,
+    const int height, const int width, const bool is_color, Datum* datum) {
+  return ReadImageToDatum(filename, label, height, width, is_color,
+                          "", datum);
+}
 
 inline bool ReadImageToDatum(const string& filename, const int label,
     const int height, const int width, Datum* datum) {
   return ReadImageToDatum(filename, label, height, width, true, datum);
 }
 
+inline bool ReadImageToDatum(const string& filename, const int label,
+    const bool is_color, Datum* datum) {
+  return ReadImageToDatum(filename, label, 0, 0, is_color, datum);
+}
+
 inline bool ReadImageToDatum(const string& filename, const int label,
     Datum* datum) {
-  return ReadImageToDatum(filename, label, 0, 0, datum);
+  return ReadImageToDatum(filename, label, 0, 0, true, datum);
+}
+
+inline bool ReadImageToDatum(const string& filename, const int label,
+    const std::string & encoding, Datum* datum) {
+  return ReadImageToDatum(filename, label, 0, 0, true, encoding, datum);
 }
 
-leveldb::Options GetLevelDBOptions();
+bool DecodeDatumNative(Datum* datum);
+bool DecodeDatum(Datum* datum, bool is_color);
+
+cv::Mat ReadImageToCVMat(const string& filename,
+    const int height, const int width, const bool is_color);
+
+cv::Mat ReadImageToCVMat(const string& filename,
+    const int height, const int width);
+
+cv::Mat ReadImageToCVMat(const string& filename,
+    const bool is_color);
+
+cv::Mat ReadImageToCVMat(const string& filename);
+
+cv::Mat DecodeDatumToCVMatNative(const Datum& datum);
+cv::Mat DecodeDatumToCVMat(const Datum& datum, bool is_color);
+
+void CVMatToDatum(const cv::Mat& cv_img, Datum* datum);
 
 template <typename Dtype>
 void hdf5_load_nd_dataset_helper(
-  hid_t file_id, const char* dataset_name_, int min_dim, int max_dim,
-  Blob<Dtype>* blob);
+    hid_t file_id, const char* dataset_name_, int min_dim, int max_dim,
+    Blob<Dtype>* blob);
 
 template <typename Dtype>
 void hdf5_load_nd_dataset(
-  hid_t file_id, const char* dataset_name_, int min_dim, int max_dim,
-  Blob<Dtype>* blob);
+    hid_t file_id, const char* dataset_name_, int min_dim, int max_dim,
+    Blob<Dtype>* blob);
 
 template <typename Dtype>
 void hdf5_save_nd_dataset(
-  const hid_t file_id, const string dataset_name, const Blob<Dtype>& blob);
+    const hid_t file_id, const string& dataset_name, const Blob<Dtype>& blob);
 
 }  // namespace caffe
 
diff --git a/include/caffe/util/math_functions.hpp b/include/caffe/util/math_functions.hpp
index 62467fb5c52..2cacd8e72cd 100644
--- a/include/caffe/util/math_functions.hpp
+++ b/include/caffe/util/math_functions.hpp
@@ -12,7 +12,7 @@
 
 namespace caffe {
 
-// Decaf gemm provides a simpler interface to the gemm functions, with the
+// Caffe gemm provides a simpler interface to the gemm functions, with the
 // limitation that the data has to be contiguous in memory.
 template <typename Dtype>
 void caffe_cpu_gemm(const CBLAS_TRANSPOSE TransA,
@@ -88,6 +88,9 @@ void caffe_rng_bernoulli(const int n, const Dtype p, unsigned int* r);
 template <typename Dtype>
 void caffe_exp(const int n, const Dtype* a, Dtype* y);
 
+template <typename Dtype>
+void caffe_log(const int n, const Dtype* a, Dtype* y);
+
 template <typename Dtype>
 void caffe_abs(const int n, const Dtype* a, Dtype* y);
 
@@ -108,7 +111,7 @@ Dtype caffe_cpu_asum(const int n, const Dtype* x);
 // the branchless, type-safe version from
 // http://stackoverflow.com/questions/1903954/is-there-a-standard-sign-function-signum-sgn-in-c-c
 template<typename Dtype>
-inline char caffe_sign(Dtype val) {
+inline int8_t caffe_sign(Dtype val) {
   return (Dtype(0) < val) - (val < Dtype(0));
 }
 
@@ -127,12 +130,6 @@ inline char caffe_sign(Dtype val) {
     } \
   }
 
-#define INSTANTIATE_CAFFE_CPU_UNARY_FUNC(name) \
-  template <> \
-  void caffe_cpu_##name<float>(const int n, const float* x, float* y); \
-  template <> \
-  void caffe_cpu_##name<double>(const int n, const double* x, double* y)
-
 // output is 1 for the positives, 0 for zero, and -1 for the negatives
 DEFINE_CAFFE_CPU_UNARY_FUNC(sign, y[i] = caffe_sign<Dtype>(x[i]));
 
@@ -140,7 +137,8 @@ DEFINE_CAFFE_CPU_UNARY_FUNC(sign, y[i] = caffe_sign<Dtype>(x[i]));
 // The name sngbit is meant to avoid conflicts with std::signbit in the macro.
 // The extra parens are needed because CUDA < 6.5 defines signbit as a macro,
 // and we don't want that to expand here when CUDA headers are also included.
-DEFINE_CAFFE_CPU_UNARY_FUNC(sgnbit, y[i] = (std::signbit)(x[i]));
+DEFINE_CAFFE_CPU_UNARY_FUNC(sgnbit, \
+    y[i] = static_cast<bool>((std::signbit)(x[i])));
 
 DEFINE_CAFFE_CPU_UNARY_FUNC(fabs, y[i] = std::fabs(x[i]));
 
@@ -205,6 +203,12 @@ void caffe_gpu_div(const int N, const Dtype* a, const Dtype* b, Dtype* y);
 template <typename Dtype>
 void caffe_gpu_abs(const int n, const Dtype* a, Dtype* y);
 
+template <typename Dtype>
+void caffe_gpu_exp(const int n, const Dtype* a, Dtype* y);
+
+template <typename Dtype>
+void caffe_gpu_log(const int n, const Dtype* a, Dtype* y);
+
 template <typename Dtype>
 void caffe_gpu_powx(const int n, const Dtype* a, const Dtype b, Dtype* y);
 
@@ -269,7 +273,7 @@ void caffe_gpu_##name<double>(const int n, const double* x, double* y) { \
       n, x, y); \
 }
 
-#endif  // CPU_ONLY
+#endif  // !CPU_ONLY
 
 }  // namespace caffe
 
diff --git a/include/caffe/util/mkl_alternate.hpp b/include/caffe/util/mkl_alternate.hpp
index 32fdbf79932..3355b6658a3 100644
--- a/include/caffe/util/mkl_alternate.hpp
+++ b/include/caffe/util/mkl_alternate.hpp
@@ -33,6 +33,7 @@ extern "C" {
 
 DEFINE_VSL_UNARY_FUNC(Sqr, y[i] = a[i] * a[i]);
 DEFINE_VSL_UNARY_FUNC(Exp, y[i] = exp(a[i]));
+DEFINE_VSL_UNARY_FUNC(Ln, y[i] = log(a[i]));
 DEFINE_VSL_UNARY_FUNC(Abs, y[i] = fabs(a[i]));
 
 // A simple way to define the vsl unary functions with singular parameter b.
diff --git a/include/caffe/util/mpi_functions.hpp b/include/caffe/util/mpi_functions.hpp
new file mode 100644
index 00000000000..b6f593f62fd
--- /dev/null
+++ b/include/caffe/util/mpi_functions.hpp
@@ -0,0 +1,29 @@
+//
+// Created by alex on 8/25/15.
+//
+
+#ifndef CAFFE_MPI_FUNCTIONS_HPP
+#define CAFFE_MPI_FUNCTIONS_HPP
+
+namespace caffe {
+  template <typename Dtype>
+  void caffe_iallreduce(Dtype* data, int count);
+
+  template <typename Dtype>
+  void caffe_iallreduce(Dtype* src_data, Dtype* dst_data, int count);
+
+  template <typename Dtype>
+  void caffe_iallgather(Dtype* src_data, Dtype* dst_data, int count);
+
+  template <typename Dtype>
+  void caffe_iscatter(Dtype* src_data, Dtype* dst_data, int count);
+
+  template <typename Dtype>
+  void caffe_ibcast(Dtype* data, int count);
+
+  void mpi_force_synchronize();
+
+
+}
+
+#endif //CAFFE_MPI_FUNCTIONS_HPP_HPP
diff --git a/include/caffe/util/thread.hpp b/include/caffe/util/thread.hpp
deleted file mode 100644
index 7251402cf1a..00000000000
--- a/include/caffe/util/thread.hpp
+++ /dev/null
@@ -1,25 +0,0 @@
-#ifndef CAFFE_THREAD_CPP_HPP_
-#define CAFFE_THREAD_CPP_HPP_
-
-#include <boost/thread.hpp>
-#include "caffe/common.hpp"
-#include "caffe/internal_thread.hpp"
-
-namespace caffe {
-
-template<typename Callable, class A1>
-Thread::Thread(Callable func, A1 a1) {
-  this->thread_ = new boost::thread(func, a1);
-}
-
-void Thread::join() {
-  static_cast<boost::thread*>(this->thread_)->join();
-}
-
-bool Thread::joinable() {
-  return static_cast<boost::thread*>(this->thread_)->joinable();
-}
-
-}  // namespace caffe
-
-#endif
diff --git a/include/caffe/util/upgrade_proto.hpp b/include/caffe/util/upgrade_proto.hpp
index 45483685133..c1f21a0d4d8 100644
--- a/include/caffe/util/upgrade_proto.hpp
+++ b/include/caffe/util/upgrade_proto.hpp
@@ -4,13 +4,15 @@
 #include <string>
 
 #include "caffe/proto/caffe.pb.h"
-#include "caffe/proto/caffe_pretty_print.pb.h"
 
 namespace caffe {
 
+// Return true iff the net is not the current version.
+bool NetNeedsUpgrade(const NetParameter& net_param);
+
 // Return true iff any layer contains parameters specified using
 // deprecated V0LayerParameter.
-bool NetNeedsUpgrade(const NetParameter& net_param);
+bool NetNeedsV0ToV1Upgrade(const NetParameter& net_param);
 
 // Perform all necessary transformations to upgrade a V0NetParameter into a
 // NetParameter (including upgrading padding layers and LayerParameters).
@@ -23,11 +25,11 @@ bool UpgradeV0Net(const NetParameter& v0_net_param, NetParameter* net_param);
 void UpgradeV0PaddingLayers(const NetParameter& param,
                             NetParameter* param_upgraded_pad);
 
-// Upgrade a single V0LayerConnection to the new LayerParameter format.
-bool UpgradeLayerParameter(const LayerParameter& v0_layer_connection,
-                           LayerParameter* layer_param);
+// Upgrade a single V0LayerConnection to the V1LayerParameter format.
+bool UpgradeV0LayerParameter(const V1LayerParameter& v0_layer_connection,
+                             V1LayerParameter* layer_param);
 
-LayerParameter_LayerType UpgradeV0LayerType(const string& type);
+V1LayerParameter_LayerType UpgradeV0LayerType(const string& type);
 
 // Return true iff any layer contains deprecated data transformation parameters.
 bool NetNeedsDataUpgrade(const NetParameter& net_param);
@@ -36,13 +38,20 @@ bool NetNeedsDataUpgrade(const NetParameter& net_param);
 // into a TransformationParameter.
 void UpgradeNetDataTransformation(NetParameter* net_param);
 
-// Convert a NetParameter to NetParameterPrettyPrint used for dumping to
-// proto text files.
-void NetParameterToPrettyPrint(const NetParameter& param,
-                               NetParameterPrettyPrint* pretty_param);
+// Return true iff the Net contains any layers specified as V1LayerParameters.
+bool NetNeedsV1ToV2Upgrade(const NetParameter& net_param);
+
+// Perform all necessary transformations to upgrade a NetParameter with
+// deprecated V1LayerParameters.
+bool UpgradeV1Net(const NetParameter& v1_net_param, NetParameter* net_param);
+
+bool UpgradeV1LayerParameter(const V1LayerParameter& v1_layer_param,
+                             LayerParameter* layer_param);
+
+const char* UpgradeV1LayerType(const V1LayerParameter_LayerType type);
 
 // Check for deprecations and upgrade the NetParameter as needed.
-void UpgradeNetAsNeeded(NetParameter* param);
+bool UpgradeNetAsNeeded(const string& param_file, NetParameter* param);
 
 // Read parameters from a file into a NetParameter proto message.
 void ReadNetParamsFromTextFileOrDie(const string& param_file,
diff --git a/include/caffe/vision_layers.hpp b/include/caffe/vision_layers.hpp
index 1e7f3fcb297..ea125d10947 100644
--- a/include/caffe/vision_layers.hpp
+++ b/include/caffe/vision_layers.hpp
@@ -5,6 +5,8 @@
 #include <utility>
 #include <vector>
 
+#include <boost/unordered_map.hpp>
+
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/common_layers.hpp"
@@ -16,6 +18,101 @@
 
 namespace caffe {
 
+/**
+ * @brief Abstract base class that factors out the BLAS code common to
+ *        ConvolutionLayer and DeconvolutionLayer.
+ */
+template <typename Dtype>
+class BaseConvolutionLayer : public Layer<Dtype> {
+ public:
+  explicit BaseConvolutionLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual inline int MinBottomBlobs() const { return 1; }
+  virtual inline int MinTopBlobs() const { return 1; }
+  virtual inline bool EqualNumBottomTopBlobs() const { return true; }
+
+ protected:
+  // Helper functions that abstract away the column buffer and gemm arguments.
+  // The last argument in forward_cpu_gemm is so that we can skip the im2col if
+  // we just called weight_cpu_gemm with the same input.
+  void forward_cpu_gemm(const Dtype* input, const Dtype* weights,
+      Dtype* output, bool skip_im2col = false);
+  void forward_cpu_bias(Dtype* output, const Dtype* bias);
+  void backward_cpu_gemm(const Dtype* input, const Dtype* weights,
+      Dtype* output);
+  void weight_cpu_gemm(const Dtype* input, const Dtype* output, Dtype*
+      weights);
+  void backward_cpu_bias(Dtype* bias, const Dtype* input);
+
+#ifndef CPU_ONLY
+  void forward_gpu_gemm(const Dtype* col_input, const Dtype* weights,
+      Dtype* output, bool skip_im2col = false);
+  void forward_gpu_bias(Dtype* output, const Dtype* bias);
+  void backward_gpu_gemm(const Dtype* input, const Dtype* weights,
+      Dtype* col_output);
+  void weight_gpu_gemm(const Dtype* col_input, const Dtype* output, Dtype*
+      weights);
+  void backward_gpu_bias(Dtype* bias, const Dtype* input);
+#endif
+
+  // reverse_dimensions should return true iff we are implementing deconv, so
+  // that conv helpers know which dimensions are which.
+  virtual bool reverse_dimensions() = 0;
+  // Compute height_out_ and width_out_ from other parameters.
+  virtual void compute_output_shape() = 0;
+
+  int kernel_h_, kernel_w_;
+  int stride_h_, stride_w_;
+  int num_;
+  int channels_;
+  int pad_h_, pad_w_;
+  int height_, width_;
+  int group_;
+  int num_output_;
+  int height_out_, width_out_;
+  bool bias_term_;
+  bool is_1x1_;
+
+ private:
+  // wrap im2col/col2im so we don't have to remember the (long) argument lists
+  inline void conv_im2col_cpu(const Dtype* data, Dtype* col_buff) {
+    im2col_cpu(data, conv_in_channels_, conv_in_height_, conv_in_width_,
+        kernel_h_, kernel_w_, pad_h_, pad_w_, stride_h_, stride_w_, col_buff);
+  }
+  inline void conv_col2im_cpu(const Dtype* col_buff, Dtype* data) {
+    col2im_cpu(col_buff, conv_in_channels_, conv_in_height_, conv_in_width_,
+        kernel_h_, kernel_w_, pad_h_, pad_w_, stride_h_, stride_w_, data);
+  }
+#ifndef CPU_ONLY
+  inline void conv_im2col_gpu(const Dtype* data, Dtype* col_buff) {
+    im2col_gpu(data, conv_in_channels_, conv_in_height_, conv_in_width_,
+        kernel_h_, kernel_w_, pad_h_, pad_w_, stride_h_, stride_w_, col_buff);
+  }
+  inline void conv_col2im_gpu(const Dtype* col_buff, Dtype* data) {
+    col2im_gpu(col_buff, conv_in_channels_, conv_in_height_, conv_in_width_,
+        kernel_h_, kernel_w_, pad_h_, pad_w_, stride_h_, stride_w_, data);
+  }
+#endif
+
+  int conv_out_channels_;
+  int conv_in_channels_;
+  int conv_out_spatial_dim_;
+  int conv_in_height_;
+  int conv_in_width_;
+  int kernel_dim_;
+  int weight_offset_;
+  int col_offset_;
+  int output_offset_;
+
+  Blob<Dtype> col_buffer_;
+  Blob<Dtype> bias_multiplier_;
+};
+
 /**
  * @brief Convolves the input image with a bank of learned filters,
  *        and (optionally) adds biases.
@@ -33,7 +130,7 @@ namespace caffe {
  *   the output channel N' columns of the output matrix.
  */
 template <typename Dtype>
-class ConvolutionLayer : public Layer<Dtype> {
+class ConvolutionLayer : public BaseConvolutionLayer<Dtype> {
  public:
   /**
    * @param param provides ConvolutionParameter convolution_param,
@@ -64,51 +161,56 @@ class ConvolutionLayer : public Layer<Dtype> {
    *    kernels + stream parallelism) engines.
    */
   explicit ConvolutionLayer(const LayerParameter& param)
-      : Layer<Dtype>(param) {}
-  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
-  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      : BaseConvolutionLayer<Dtype>(param) {}
 
-  virtual inline LayerParameter_LayerType type() const {
-    return LayerParameter_LayerType_CONVOLUTION;
-  }
-  virtual inline int MinBottomBlobs() const { return 1; }
-  virtual inline int MinTopBlobs() const { return 1; }
-  virtual inline bool EqualNumBottomTopBlobs() const { return true; }
+  virtual inline const char* type() const { return "Convolution"; }
 
  protected:
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  virtual inline bool reverse_dimensions() { return false; }
+  virtual void compute_output_shape();
+};
 
-  int kernel_h_, kernel_w_;
-  int stride_h_, stride_w_;
-  int num_;
-  int channels_;
-  int pad_h_, pad_w_;
-  int height_, width_;
-  int group_;
-  int num_output_;
-  int height_out_, width_out_;
-  bool bias_term_;
+/**
+ * @brief Convolve the input with a bank of learned filters, and (optionally)
+ *        add biases, treating filters and convolution parameters in the
+ *        opposite sense as ConvolutionLayer.
+ *
+ *   ConvolutionLayer computes each output value by dotting an input window with
+ *   a filter; DeconvolutionLayer multiplies each input value by a filter
+ *   elementwise, and sums over the resulting output windows. In other words,
+ *   DeconvolutionLayer is ConvolutionLayer with the forward and backward passes
+ *   reversed. DeconvolutionLayer reuses ConvolutionParameter for its
+ *   parameters, but they take the opposite sense as in ConvolutionLayer (so
+ *   padding is removed from the output rather than added to the input, and
+ *   stride results in upsampling rather than downsampling).
+ */
+template <typename Dtype>
+class DeconvolutionLayer : public BaseConvolutionLayer<Dtype> {
+ public:
+  explicit DeconvolutionLayer(const LayerParameter& param)
+      : BaseConvolutionLayer<Dtype>(param) {}
 
-  /// M_ is the channel dimension of the output for a single group, which is the
-  /// leading dimension of the filter matrix.
-  int M_;
-  /// K_ is the dimension of an unrolled input for a single group, which is the
-  /// leading dimension of the data matrix.
-  int K_;
-  /// N_ is the spatial dimension of the output, the H x W, which are the last
-  /// dimensions of the data and filter matrices.
-  int N_;
-  Blob<Dtype> col_buffer_;
-  Blob<Dtype> bias_multiplier_;
+  virtual inline const char* type() const { return "Deconvolution"; }
+
+ protected:
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  virtual inline bool reverse_dimensions() { return true; }
+  virtual void compute_output_shape();
 };
 
 #ifdef USE_CUDNN
@@ -130,26 +232,66 @@ template <typename Dtype>
 class CuDNNConvolutionLayer : public ConvolutionLayer<Dtype> {
  public:
   explicit CuDNNConvolutionLayer(const LayerParameter& param)
-      : ConvolutionLayer<Dtype>(param) {}
+      : ConvolutionLayer<Dtype>(param), handles_setup_(false) {}
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual ~CuDNNConvolutionLayer();
 
+  static void RuntimeOptimize(size_t mem_limit);
+
  protected:
   virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+  void AdjustWorkSpaces();
 
+  bool handles_setup_;
   cudnnHandle_t* handle_;
   cudaStream_t*  stream_;
-  vector<cudnnTensor4dDescriptor_t> bottom_descs_, top_descs_;
-  cudnnTensor4dDescriptor_t    bias_desc_;
+  vector<cudnnTensorDescriptor_t> bottom_descs_, top_descs_;
+  cudnnTensorDescriptor_t    bias_desc_;
   cudnnFilterDescriptor_t      filter_desc_;
   vector<cudnnConvolutionDescriptor_t> conv_descs_;
   int bottom_offset_, top_offset_, weight_offset_, bias_offset_;
+
+  // algorithms for forward and backwards convolutions
+  cudnnConvolutionFwdAlgo_t *fwd_algo_;
+  cudnnConvolutionBwdFilterAlgo_t *bwd_filter_algo_;
+  cudnnConvolutionBwdDataAlgo_t *bwd_data_algo_;
+
+  size_t *workspace_fwd_sizes_;
+  size_t *workspace_bwd_data_sizes_;
+  size_t *workspace_bwd_filter_sizes_;
+
+  /** We prefer using a series of managed memory blocks to a single memory pool
+   *  The latter approach is prone to problem and has issues in memory alignment
+   **/
+  size_t workspaceSizeInBytes_fwd;  // size of underlying storage
+  static vector<shared_ptr<SyncedMemory> > workspaceData_fwd;  // underlying storage
+
+  size_t workspaceSizeInBytes_bwd;  // size of underlying storage
+  static vector<shared_ptr<SyncedMemory> > workspaceData_bwd_filter;  // underlying storage
+  static vector<shared_ptr<SyncedMemory> > workspaceData_bwd_data;  // underlying storage
+
+  vector<vector<int> > prev_bottom_shapes_;
+
+  struct PerfReg {
+      vector<vector<cudnnConvolutionFwdAlgoPerf_t> > fwd_perf;
+      vector<vector<cudnnConvolutionBwdFilterAlgoPerf_t> > bwd_filter_perf;
+      vector<vector<cudnnConvolutionBwdDataAlgoPerf_t> > bwd_data_perf;
+      vector<int> fwd_algo;
+      vector<int> bwd_filter_algo;
+      vector<int> bwd_data_algo;
+  };
+
+  PerfReg layer_perf_;
+
+  static boost::unordered_map<CuDNNConvolutionLayer*, PerfReg*>  perf_reg;
+  static bool need_optimize_;
 };
 #endif
 
@@ -166,25 +308,23 @@ class Im2colLayer : public Layer<Dtype> {
   explicit Im2colLayer(const LayerParameter& param)
       : Layer<Dtype>(param) {}
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
 
-  virtual inline LayerParameter_LayerType type() const {
-    return LayerParameter_LayerType_IM2COL;
-  }
+  virtual inline const char* type() const { return "Im2col"; }
   virtual inline int ExactNumBottomBlobs() const { return 1; }
   virtual inline int ExactNumTopBlobs() const { return 1; }
 
  protected:
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
 
   int kernel_h_, kernel_w_;
   int stride_h_, stride_w_;
@@ -208,43 +348,42 @@ class LRNLayer : public Layer<Dtype> {
   explicit LRNLayer(const LayerParameter& param)
       : Layer<Dtype>(param) {}
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
 
-  virtual inline LayerParameter_LayerType type() const {
-    return LayerParameter_LayerType_LRN;
-  }
+  virtual inline const char* type() const { return "LRN"; }
   virtual inline int ExactNumBottomBlobs() const { return 1; }
   virtual inline int ExactNumTopBlobs() const { return 1; }
 
  protected:
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
 
   virtual void CrossChannelForward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void CrossChannelForward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void WithinChannelForward(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void CrossChannelBackward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
   virtual void CrossChannelBackward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
   virtual void WithinChannelBackward(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
 
   int size_;
   int pre_pad_;
   Dtype alpha_;
   Dtype beta_;
+  Dtype k_;
   int num_;
   int channels_;
   int height_;
@@ -285,13 +424,11 @@ class PoolingLayer : public Layer<Dtype> {
   explicit PoolingLayer(const LayerParameter& param)
       : Layer<Dtype>(param) {}
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
 
-  virtual inline LayerParameter_LayerType type() const {
-    return LayerParameter_LayerType_POOLING;
-  }
+  virtual inline const char* type() const { return "Pooling"; }
   virtual inline int ExactNumBottomBlobs() const { return 1; }
   virtual inline int MinTopBlobs() const { return 1; }
   // MAX POOL layers can output an extra top blob for the mask;
@@ -303,13 +440,13 @@ class PoolingLayer : public Layer<Dtype> {
 
  protected:
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
 
   int kernel_h_, kernel_w_;
   int stride_h_, stride_w_;
@@ -317,6 +454,7 @@ class PoolingLayer : public Layer<Dtype> {
   int channels_;
   int height_, width_;
   int pooled_height_, pooled_width_;
+  bool global_pooling_;
   Blob<Dtype> rand_idx_;
   Blob<int> max_idx_;
 };
@@ -330,26 +468,136 @@ template <typename Dtype>
 class CuDNNPoolingLayer : public PoolingLayer<Dtype> {
  public:
   explicit CuDNNPoolingLayer(const LayerParameter& param)
-      : PoolingLayer<Dtype>(param) {}
+      : PoolingLayer<Dtype>(param), handles_setup_(false) {}
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual ~CuDNNPoolingLayer();
+  // Currently, cuDNN does not support the extra top blob.
+  virtual inline int MinTopBlobs() const { return -1; }
+  virtual inline int ExactNumTopBlobs() const { return 1; }
 
  protected:
   virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
 
+  bool handles_setup_;
   cudnnHandle_t             handle_;
-  cudnnTensor4dDescriptor_t bottom_desc_, top_desc_;
+  cudnnTensorDescriptor_t bottom_desc_, top_desc_;
   cudnnPoolingDescriptor_t  pooling_desc_;
   cudnnPoolingMode_t        mode_;
 };
 #endif
 
+/**
+ * @brief Does spatial pyramid pooling on the input image
+ *        by taking the max, average, etc. within regions
+ *        so that the result vector of different sized
+ *        images are of the same size.
+ */
+template <typename Dtype>
+class SPPLayer : public Layer<Dtype> {
+ public:
+  explicit SPPLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual inline const char* type() const { return "SPP"; }
+  virtual inline int ExactNumBottomBlobs() const { return 1; }
+  virtual inline int MinTopBlobs() const { return 1; }
+  // MAX POOL layers can output an extra top blob for the mask;
+  // others can only output the pooled inputs.
+  virtual inline int MaxTopBlobs() const {
+    return (this->layer_param_.pooling_param().pool() ==
+            PoolingParameter_PoolMethod_MAX) ? 2 : 1;
+  }
+
+ protected:
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  // calculates the kernel and stride dimensions for the pooling layer,
+  // returns a correctly configured LayerParameter for a PoolingLayer
+  virtual LayerParameter GetPoolingParam(const int pyramid_level,
+      const int bottom_h, const int bottom_w, const SPPParameter spp_param);
+
+  int pyramid_height_;
+  int bottom_h_, bottom_w_;
+  int channels_;
+  int kernel_h_, kernel_w_;
+  int pad_h_, pad_w_;
+
+  /// the internal Split layer that feeds the pooling layers
+  shared_ptr<SplitLayer<Dtype> > split_layer_;
+  /// top vector holder used in call to the underlying SplitLayer::Forward
+  vector<Blob<Dtype>*> split_top_vec_;
+  /// bottom vector holder used in call to the underlying PoolingLayer::Forward
+  vector<vector<Blob<Dtype>*>*> pooling_bottom_vecs_;
+  /// the internal Pooling layers of different kernel sizes
+  vector<shared_ptr<PoolingLayer<Dtype> > > pooling_layers_;
+  /// top vector holders used in call to the underlying PoolingLayer::Forward
+  vector<vector<Blob<Dtype>*>*> pooling_top_vecs_;
+  /// pooling_outputs stores the outputs of the PoolingLayers
+  vector<Blob<Dtype>*> pooling_outputs_;
+  /// the internal Flatten layers that the Pooling layers feed into
+  vector<FlattenLayer<Dtype>*> flatten_layers_;
+  /// top vector holders used in call to the underlying FlattenLayer::Forward
+  vector<vector<Blob<Dtype>*>*> flatten_top_vecs_;
+  /// flatten_outputs stores the outputs of the FlattenLayers
+  vector<Blob<Dtype>*> flatten_outputs_;
+  /// bottom vector holder used in call to the underlying ConcatLayer::Forward
+  vector<Blob<Dtype>*> concat_bottom_vec_;
+  /// the internal Concat layers that the Flatten layers feed into
+  shared_ptr<ConcatLayer<Dtype> > concat_layer_;
+};
+
+/**
+ * @brief ROIPoolingLayer - Region of Interest Pooling Layer
+ */
+    template <typename Dtype>
+    class ROIPoolingLayer : public Layer<Dtype> {
+    public:
+        explicit ROIPoolingLayer(const LayerParameter& param)
+                : Layer<Dtype>(param) {}
+        virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+                                const vector<Blob<Dtype>*>& top);
+        virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+                             const vector<Blob<Dtype>*>& top);
+
+        virtual inline const char* type() const { return "ROIPooling"; }
+
+        virtual inline int MinBottomBlobs() const { return 2; }
+        virtual inline int MaxBottomBlobs() const { return 2; }
+        virtual inline int MinTopBlobs() const { return 1; }
+        virtual inline int MaxTopBlobs() const { return 1; }
+
+    protected:
+        virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+                                 const vector<Blob<Dtype>*>& top);
+        virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+                                 const vector<Blob<Dtype>*>& top);
+        virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+                                  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+        virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+                                  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+        int channels_;
+        int height_;
+        int width_;
+        int pooled_height_;
+        int pooled_width_;
+        Dtype spatial_scale_;
+        Blob<int> max_idx_;
+    };
+
+
 }  // namespace caffe
 
 #endif  // CAFFE_VISION_LAYERS_HPP_
diff --git a/matlab/+caffe/+test/test_net.m b/matlab/+caffe/+test/test_net.m
new file mode 100644
index 00000000000..3dabe84d111
--- /dev/null
+++ b/matlab/+caffe/+test/test_net.m
@@ -0,0 +1,96 @@
+classdef test_net < matlab.unittest.TestCase
+  
+  properties
+    num_output
+    model_file
+    net
+  end
+  
+  methods (Static)
+    function model_file = simple_net_file(num_output)
+      model_file = tempname();
+      fid = fopen(model_file, 'w');
+      fprintf(fid, [ ...
+        'name: "testnet" force_backward: true\n' ...
+        'layer { type: "DummyData" name: "data" top: "data" top: "label"\n' ...
+        'dummy_data_param { num: 5 channels: 2 height: 3 width: 4\n' ...
+        '    num: 5 channels: 1 height: 1 width: 1\n' ...
+        '    data_filler { type: "gaussian" std: 1 }\n' ...
+        '    data_filler { type: "constant" } } }\n' ...
+        'layer { type: "Convolution" name: "conv" bottom: "data" top: "conv"\n' ...
+        '  convolution_param { num_output: 11 kernel_size: 2 pad: 3\n' ...
+        '    weight_filler { type: "gaussian" std: 1 }\n' ...
+        '    bias_filler { type: "constant" value: 2 } }\n' ...
+        '    param { decay_mult: 1 } param { decay_mult: 0 }\n' ...
+        '    }\n' ...
+        'layer { type: "InnerProduct" name: "ip" bottom: "conv" top: "ip"\n' ...
+        '  inner_product_param { num_output: ' num2str(num_output) ...
+        '    weight_filler { type: "gaussian" std: 2.5 }\n' ...
+        '    bias_filler { type: "constant" value: -3 } } }\n' ...
+        'layer { type: "SoftmaxWithLoss" name: "loss" bottom: "ip" bottom: "label"\n' ...
+        '  top: "loss" }' ]);
+      fclose(fid);
+    end
+  end
+  methods
+    function self = test_net()
+      self.num_output = 13;
+      self.model_file = caffe.test.test_net.simple_net_file(self.num_output);
+      self.net = caffe.Net(self.model_file, 'train');
+      % also make sure get_solver runs
+      caffe.get_net(self.model_file, 'train');
+      
+      % fill in valid labels
+      self.net.blobs('label').set_data(randi( ...
+        self.num_output - 1, self.net.blobs('label').shape));
+      
+      delete(self.model_file);
+    end
+  end
+  methods (Test)
+    function self = test_blob(self)
+      self.net.blobs('data').set_data(10 * ones(self.net.blobs('data').shape));
+      self.verifyEqual(self.net.blobs('data').get_data(), ...
+        10 * ones(self.net.blobs('data').shape, 'single'));
+      self.net.blobs('data').set_diff(-2 * ones(self.net.blobs('data').shape));
+      self.verifyEqual(self.net.blobs('data').get_diff(), ...
+        -2 * ones(self.net.blobs('data').shape, 'single'));
+      original_shape = self.net.blobs('data').shape;
+      self.net.blobs('data').reshape([6 5 4 3 2 1]);
+      self.verifyEqual(self.net.blobs('data').shape, [6 5 4 3 2 1]);
+      self.net.blobs('data').reshape(original_shape);
+      self.net.reshape();
+    end
+    function self = test_layer(self)
+      self.verifyEqual(self.net.params('conv', 1).shape, [2 2 2 11]);
+      self.verifyEqual(self.net.layers('conv').params(2).shape, 11);
+      self.verifyEqual(self.net.layers('conv').type(), 'Convolution');
+    end
+    function test_forward_backward(self)
+      self.net.forward_prefilled();
+      self.net.backward_prefilled();
+    end
+    function test_inputs_outputs(self)
+      self.verifyEqual(self.net.inputs, cell(0, 1))
+      self.verifyEqual(self.net.outputs, {'loss'});
+    end
+    function test_save_and_read(self)
+      weights_file = tempname();
+      self.net.save(weights_file);
+      model_file2 = caffe.test.test_net.simple_net_file(self.num_output);
+      net2 = caffe.Net(model_file2, 'train');
+      net2.copy_from(weights_file);
+      net3 = caffe.Net(model_file2, weights_file, 'train');
+      delete(model_file2);
+      delete(weights_file);
+      for l = 1:length(self.net.layer_vec)
+        for i = 1:length(self.net.layer_vec(l).params)
+          self.verifyEqual(self.net.layer_vec(l).params(i).get_data(), ...
+            net2.layer_vec(l).params(i).get_data());
+          self.verifyEqual(self.net.layer_vec(l).params(i).get_data(), ...
+            net3.layer_vec(l).params(i).get_data());
+        end
+      end
+    end
+  end
+end
diff --git a/matlab/+caffe/+test/test_solver.m b/matlab/+caffe/+test/test_solver.m
new file mode 100644
index 00000000000..739258b0e85
--- /dev/null
+++ b/matlab/+caffe/+test/test_solver.m
@@ -0,0 +1,45 @@
+classdef test_solver < matlab.unittest.TestCase
+  
+  properties
+    num_output
+    solver
+  end
+  
+  methods
+    function self = test_solver()
+      self.num_output = 13;
+      model_file = caffe.test.test_net.simple_net_file(self.num_output);
+      solver_file = tempname();
+      
+      fid = fopen(solver_file, 'w');
+      fprintf(fid, [ ...
+        'net: "'  model_file  '"\n' ...
+        'test_iter: 10 test_interval: 10 base_lr: 0.01 momentum: 0.9\n' ...
+        'weight_decay: 0.0005 lr_policy: "inv" gamma: 0.0001 power: 0.75\n' ...
+        'display: 100 max_iter: 100 snapshot_after_train: false\n' ]);
+      fclose(fid);
+      
+      self.solver = caffe.Solver(solver_file);
+      % also make sure get_solver runs
+      caffe.get_solver(solver_file);
+      caffe.set_mode_cpu();
+      % fill in valid labels
+      self.solver.net.blobs('label').set_data(randi( ...
+        self.num_output - 1, self.solver.net.blobs('label').shape));
+      self.solver.test_nets(1).blobs('label').set_data(randi( ...
+        self.num_output - 1, self.solver.test_nets(1).blobs('label').shape));
+      
+      delete(solver_file);
+      delete(model_file);
+    end
+  end
+  methods (Test)
+    function test_solve(self)
+      self.verifyEqual(self.solver.iter(), 0)
+      self.solver.step(30);
+      self.verifyEqual(self.solver.iter(), 30)
+      self.solver.solve()
+      self.verifyEqual(self.solver.iter(), 100)
+    end
+  end
+end
diff --git a/matlab/+caffe/Blob.m b/matlab/+caffe/Blob.m
new file mode 100644
index 00000000000..e39f7ee3f20
--- /dev/null
+++ b/matlab/+caffe/Blob.m
@@ -0,0 +1,78 @@
+classdef Blob < handle
+  % Wrapper class of caffe::Blob in matlab
+  
+  properties (Access = private)
+    hBlob_self
+  end
+  
+  methods
+    function self = Blob(hBlob_blob)
+      CHECK(is_valid_handle(hBlob_blob), 'invalid Blob handle');
+      
+      % setup self handle
+      self.hBlob_self = hBlob_blob;
+    end
+    function shape = shape(self)
+      shape = caffe_('blob_get_shape', self.hBlob_self);
+    end
+    function reshape(self, shape)
+      shape = self.check_and_preprocess_shape(shape);
+      caffe_('blob_reshape', self.hBlob_self, shape);
+    end
+    function data = get_data(self)
+      data = caffe_('blob_get_data', self.hBlob_self);
+    end
+    function set_data(self, data)
+      data = self.check_and_preprocess_data(data);
+      caffe_('blob_set_data', self.hBlob_self, data);
+    end
+    function diff = get_diff(self)
+      diff = caffe_('blob_get_diff', self.hBlob_self);
+    end
+    function set_diff(self, diff)
+      diff = self.check_and_preprocess_data(diff);
+      caffe_('blob_set_diff', self.hBlob_self, diff);
+    end
+  end
+  
+  methods (Access = private)
+    function shape = check_and_preprocess_shape(~, shape)
+      CHECK(isempty(shape) || (isnumeric(shape) && isrow(shape)), ...
+        'shape must be a integer row vector');
+      shape = double(shape);
+    end
+    function data = check_and_preprocess_data(self, data)
+      CHECK(isnumeric(data), 'data or diff must be numeric types');
+      self.check_data_size_matches(data);
+      if ~isa(data, 'single')
+        data = single(data);
+      end
+    end
+    function check_data_size_matches(self, data)
+      % check whether size of data matches shape of this blob
+      % note: matlab arrays always have at least 2 dimensions. To compare
+      % shape between size of data and shape of this blob, extend shape of
+      % this blob to have at least 2 dimensions
+      self_shape_extended = self.shape;
+      if isempty(self_shape_extended)
+        % target blob is a scalar (0 dim)
+        self_shape_extended = [1, 1];
+      elseif isscalar(self_shape_extended)
+        % target blob is a vector (1 dim)
+        self_shape_extended = [self_shape_extended, 1];
+      end
+      % Also, matlab cannot have tailing dimension 1 for ndim > 2, so you
+      % cannot create 20 x 10 x 1 x 1 array in matlab as it becomes 20 x 10
+      % Extend matlab arrays to have tailing dimension 1 during shape match
+      data_size_extended = ...
+        [size(data), ones(1, length(self_shape_extended) - ndims(data))];
+      is_matched = ...
+        (length(self_shape_extended) == length(data_size_extended)) ...
+        && all(self_shape_extended == data_size_extended);
+      CHECK(is_matched, ...
+        sprintf('%s, input data/diff size: [ %s] vs target blob shape: [ %s]', ...
+        'input data/diff size does not match target blob shape', ...
+        sprintf('%d ', data_size_extended), sprintf('%d ', self_shape_extended)));
+    end
+  end
+end
diff --git a/matlab/+caffe/Layer.m b/matlab/+caffe/Layer.m
new file mode 100644
index 00000000000..4c2023101a5
--- /dev/null
+++ b/matlab/+caffe/Layer.m
@@ -0,0 +1,32 @@
+classdef Layer < handle
+  % Wrapper class of caffe::Layer in matlab
+  
+  properties (Access = private)
+    hLayer_self
+    attributes
+    % attributes fields:
+    %     hBlob_blobs
+  end
+  properties (SetAccess = private)
+    params
+  end
+  
+  methods
+    function self = Layer(hLayer_layer)
+      CHECK(is_valid_handle(hLayer_layer), 'invalid Layer handle');
+      
+      % setup self handle and attributes
+      self.hLayer_self = hLayer_layer;
+      self.attributes = caffe_('layer_get_attr', self.hLayer_self);
+      
+      % setup weights
+      self.params = caffe.Blob.empty();
+      for n = 1:length(self.attributes.hBlob_blobs)
+        self.params(n) = caffe.Blob(self.attributes.hBlob_blobs(n));
+      end
+    end
+    function layer_type = type(self)
+      layer_type = caffe_('layer_get_type', self.hLayer_self);
+    end
+  end
+end
diff --git a/matlab/+caffe/Net.m b/matlab/+caffe/Net.m
new file mode 100644
index 00000000000..e6295bba1a4
--- /dev/null
+++ b/matlab/+caffe/Net.m
@@ -0,0 +1,133 @@
+classdef Net < handle
+  % Wrapper class of caffe::Net in matlab
+  
+  properties (Access = private)
+    hNet_self
+    attributes
+    % attribute fields
+    %     hLayer_layers
+    %     hBlob_blobs
+    %     input_blob_indices
+    %     output_blob_indices
+    %     layer_names
+    %     blob_names
+  end
+  properties (SetAccess = private)
+    layer_vec
+    blob_vec
+    inputs
+    outputs
+    name2layer_index
+    name2blob_index
+    layer_names
+    blob_names
+  end
+  
+  methods
+    function self = Net(varargin)
+      % decide whether to construct a net from model_file or handle
+      if ~(nargin == 1 && isstruct(varargin{1}))
+        % construct a net from model_file
+        self = caffe.get_net(varargin{:});
+        return
+      end
+      % construct a net from handle
+      hNet_net = varargin{1};
+      CHECK(is_valid_handle(hNet_net), 'invalid Net handle');
+      
+      % setup self handle and attributes
+      self.hNet_self = hNet_net;
+      self.attributes = caffe_('net_get_attr', self.hNet_self);
+      
+      % setup layer_vec
+      self.layer_vec = caffe.Layer.empty();
+      for n = 1:length(self.attributes.hLayer_layers)
+        self.layer_vec(n) = caffe.Layer(self.attributes.hLayer_layers(n));
+      end
+      
+      % setup blob_vec
+      self.blob_vec = caffe.Blob.empty();
+      for n = 1:length(self.attributes.hBlob_blobs);
+        self.blob_vec(n) = caffe.Blob(self.attributes.hBlob_blobs(n));
+      end
+      
+      % setup input and output blob and their names
+      % note: add 1 to indices as matlab is 1-indexed while C++ is 0-indexed
+      self.inputs = ...
+        self.attributes.blob_names(self.attributes.input_blob_indices + 1);
+      self.outputs = ...
+        self.attributes.blob_names(self.attributes.output_blob_indices + 1);
+      
+      % create map objects to map from name to layers and blobs
+      self.name2layer_index = containers.Map(self.attributes.layer_names, ...
+        1:length(self.attributes.layer_names));
+      self.name2blob_index = containers.Map(self.attributes.blob_names, ...
+        1:length(self.attributes.blob_names));
+      
+      % expose layer_names and blob_names for public read access
+      self.layer_names = self.attributes.layer_names;
+      self.blob_names = self.attributes.blob_names;
+    end
+    function layer = layers(self, layer_name)
+      CHECK(ischar(layer_name), 'layer_name must be a string');
+      layer = self.layer_vec(self.name2layer_index(layer_name));
+    end
+    function blob = blobs(self, blob_name)
+      CHECK(ischar(blob_name), 'blob_name must be a string');
+      blob = self.blob_vec(self.name2blob_index(blob_name));
+    end
+    function blob = params(self, layer_name, blob_index)
+      CHECK(ischar(layer_name), 'layer_name must be a string');
+      CHECK(isscalar(blob_index), 'blob_index must be a scalar');
+      blob = self.layer_vec(self.name2layer_index(layer_name)).params(blob_index);
+    end
+    function forward_prefilled(self)
+      caffe_('net_forward', self.hNet_self);
+    end
+    function backward_prefilled(self)
+      caffe_('net_backward', self.hNet_self);
+    end
+    function res = forward(self, input_data)
+      CHECK(iscell(input_data), 'input_data must be a cell array');
+      CHECK(length(input_data) == length(self.inputs), ...
+        'input data cell length must match input blob number');
+      % copy data to input blobs
+      for n = 1:length(self.inputs)
+        self.blobs(self.inputs{n}).set_data(input_data{n});
+      end
+      self.forward_prefilled();
+      % retrieve data from output blobs
+      res = cell(length(self.outputs), 1);
+      for n = 1:length(self.outputs)
+        res{n} = self.blobs(self.outputs{n}).get_data();
+      end
+    end
+    function res = backward(self, output_diff)
+      CHECK(iscell(output_diff), 'output_diff must be a cell array');
+      CHECK(length(output_diff) == length(self.outputs), ...
+        'output diff cell length must match output blob number');
+      % copy diff to output blobs
+      for n = 1:length(self.outputs)
+        self.blobs(self.outputs{n}).set_diff(output_diff{n});
+      end
+      self.backward_prefilled();
+      % retrieve diff from input blobs
+      res = cell(length(self.inputs), 1);
+      for n = 1:length(self.inputs)
+        res{n} = self.blobs(self.inputs{n}).get_diff();
+      end
+    end
+    function copy_from(self, weights_file)
+      CHECK(ischar(weights_file), 'weights_file must be a string');
+      CHECK_FILE_EXIST(weights_file);
+      caffe_('net_copy_from', self.hNet_self, weights_file);
+    end
+    function reshape(self)
+      caffe_('net_reshape', self.hNet_self);
+    end
+    function save(self, weights_file)
+      CHECK(ischar(weights_file), 'weights_file must be a string');
+      caffe_('net_save', self.hNet_self, weights_file);
+    end
+  end
+end
diff --git a/matlab/+caffe/Solver.m b/matlab/+caffe/Solver.m
new file mode 100644
index 00000000000..f8bdc4e22b2
--- /dev/null
+++ b/matlab/+caffe/Solver.m
@@ -0,0 +1,56 @@
+classdef Solver < handle
+  % Wrapper class of caffe::SGDSolver in matlab
+  
+  properties (Access = private)
+    hSolver_self
+    attributes
+    % attribute fields
+    %     hNet_net
+    %     hNet_test_nets
+  end
+  properties (SetAccess = private)
+    net
+    test_nets
+  end
+  
+  methods
+    function self = Solver(varargin)
+      % decide whether to construct a solver from solver_file or handle
+      if ~(nargin == 1 && isstruct(varargin{1}))
+        % construct a solver from solver_file
+        self = caffe.get_solver(varargin{:});
+        return
+      end
+      % construct a solver from handle
+      hSolver_solver = varargin{1};
+      CHECK(is_valid_handle(hSolver_solver), 'invalid Solver handle');
+      
+      % setup self handle and attributes
+      self.hSolver_self = hSolver_solver;
+      self.attributes = caffe_('solver_get_attr', self.hSolver_self);
+      
+      % setup net and test_nets
+      self.net = caffe.Net(self.attributes.hNet_net);
+      self.test_nets = caffe.Net.empty();
+      for n = 1:length(self.attributes.hNet_test_nets)
+        self.test_nets(n) = caffe.Net(self.attributes.hNet_test_nets(n));
+      end
+    end
+    function iter = iter(self)
+      iter = caffe_('solver_get_iter', self.hSolver_self);
+    end
+    function restore(self, snapshot_filename)
+      CHECK(ischar(snapshot_filename), 'snapshot_filename must be a string');
+      CHECK_FILE_EXIST(snapshot_filename);
+      caffe_('solver_restore', self.hSolver_self, snapshot_filename);
+    end
+    function solve(self)
+      caffe_('solver_solve', self.hSolver_self);
+    end
+    function step(self, iters)
+      CHECK(isscalar(iters) && iters > 0, 'iters must be positive integer');
+      iters = double(iters);
+      caffe_('solver_step', self.hSolver_self, iters);
+    end
+  end
+end
diff --git a/matlab/+caffe/get_net.m b/matlab/+caffe/get_net.m
new file mode 100644
index 00000000000..4b5683eb82e
--- /dev/null
+++ b/matlab/+caffe/get_net.m
@@ -0,0 +1,37 @@
+function net = get_net(varargin)
+% net = get_net(model_file, phase_name) or
+% net = get_net(model_file, weights_file, phase_name)
+%   Construct a net from model_file, and load weights from weights_file
+%   phase_name can only be 'train' or 'test'
+
+CHECK(nargin == 2 || nargin == 3, ['usage: ' ...
+  'net = get_net(model_file, phase_name) or ' ...
+  'net = get_net(model_file, weights_file, phase_name)']);
+if nargin == 3
+  model_file = varargin{1};
+  weights_file = varargin{2};
+  phase_name = varargin{3};
+elseif nargin == 2
+  model_file = varargin{1};
+  phase_name = varargin{2};
+end
+
+CHECK(ischar(model_file), 'model_file must be a string');
+CHECK(ischar(phase_name), 'phase_name must be a string');
+CHECK_FILE_EXIST(model_file);
+CHECK(strcmp(phase_name, 'train') || strcmp(phase_name, 'test'), ...
+  sprintf('phase_name can only be %strain%s or %stest%s', ...
+  char(39), char(39), char(39), char(39)));
+
+% construct caffe net from model_file
+hNet = caffe_('get_net', model_file, phase_name);
+net = caffe.Net(hNet);
+
+% load weights from weights_file
+if nargin == 3
+  CHECK(ischar(weights_file), 'weights_file must be a string');
+  CHECK_FILE_EXIST(weights_file);
+  net.copy_from(weights_file);
+end
+
+end
diff --git a/matlab/+caffe/get_solver.m b/matlab/+caffe/get_solver.m
new file mode 100644
index 00000000000..74d576eb31b
--- /dev/null
+++ b/matlab/+caffe/get_solver.m
@@ -0,0 +1,10 @@
+function solver = get_solver(solver_file)
+% solver = get_solver(solver_file)
+%   Construct a Solver object from solver_file
+
+CHECK(ischar(solver_file), 'solver_file must be a string');
+CHECK_FILE_EXIST(solver_file);
+pSolver = caffe_('get_solver', solver_file);
+solver = caffe.Solver(pSolver);
+
+end
diff --git a/matlab/+caffe/imagenet/ilsvrc_2012_mean.mat b/matlab/+caffe/imagenet/ilsvrc_2012_mean.mat
new file mode 100644
index 00000000000..21df3d39aaa
Binary files /dev/null and b/matlab/+caffe/imagenet/ilsvrc_2012_mean.mat differ
diff --git a/matlab/+caffe/io.m b/matlab/+caffe/io.m
new file mode 100644
index 00000000000..af8369ddfab
--- /dev/null
+++ b/matlab/+caffe/io.m
@@ -0,0 +1,33 @@
+classdef io
+  % a class for input and output functions
+  
+  methods (Static)
+    function im_data = load_image(im_file)
+      % im_data = load_image(im_file)
+      %   load an image from disk into Caffe-supported data format
+      %   switch channels from RGB to BGR, make width the fastest dimension
+      %   and convert to single
+      %   returns im_data in W x H x C. For colored images, C = 3 in BGR
+      %   channels, and for grayscale images, C = 1
+      CHECK(ischar(im_file), 'im_file must be a string');
+      CHECK_FILE_EXIST(im_file);
+      im_data = imread(im_file);
+      % permute channels from RGB to BGR for colored images
+      if size(im_data, 3) == 3
+        im_data = im_data(:, :, [3, 2, 1]);
+      end
+      % flip width and height to make width the fastest dimension
+      im_data = permute(im_data, [2, 1, 3]);
+      % convert from uint8 to single
+      im_data = single(im_data);
+    end
+    function mean_data = read_mean(mean_proto_file)
+      % mean_data = read_mean(mean_proto_file)
+      %   read image mean data from binaryproto file
+      %   returns mean_data in W x H x C with BGR channels
+      CHECK(ischar(mean_proto_file), 'mean_proto_file must be a string');
+      CHECK_FILE_EXIST(mean_proto_file);
+      mean_data = caffe_('read_mean', mean_proto_file);
+    end
+  end
+end
diff --git a/matlab/+caffe/private/CHECK.m b/matlab/+caffe/private/CHECK.m
new file mode 100644
index 00000000000..21706549cfa
--- /dev/null
+++ b/matlab/+caffe/private/CHECK.m
@@ -0,0 +1,7 @@
+function CHECK(expr, error_msg)
+
+if ~expr
+  error(error_msg);
+end
+
+end
diff --git a/matlab/+caffe/private/CHECK_FILE_EXIST.m b/matlab/+caffe/private/CHECK_FILE_EXIST.m
new file mode 100644
index 00000000000..8c80fb8094f
--- /dev/null
+++ b/matlab/+caffe/private/CHECK_FILE_EXIST.m
@@ -0,0 +1,7 @@
+function CHECK_FILE_EXIST(filename)
+
+if exist(filename, 'file') == 0
+  error('%s does not exist', filename);
+end
+
+end
diff --git a/matlab/+caffe/private/caffe_.cpp b/matlab/+caffe/private/caffe_.cpp
new file mode 100644
index 00000000000..4e0ebc1c00a
--- /dev/null
+++ b/matlab/+caffe/private/caffe_.cpp
@@ -0,0 +1,546 @@
+//
+// caffe_.cpp provides wrappers of the caffe::Solver class, caffe::Net class,
+// caffe::Layer class and caffe::Blob class and some caffe::Caffe functions,
+// so that one could easily use Caffe from matlab.
+// Note that for matlab, we will simply use float as the data type.
+
+// Internally, data is stored with dimensions reversed from Caffe's:
+// e.g., if the Caffe blob axes are (num, channels, height, width),
+// the matcaffe data is stored as (width, height, channels, num)
+// where width is the fastest dimension.
+
+#include <sstream>
+#include <string>
+#include <vector>
+
+#include "mex.h"
+
+#include "caffe/caffe.hpp"
+
+#define MEX_ARGS int nlhs, mxArray **plhs, int nrhs, const mxArray **prhs
+
+using namespace caffe;  // NOLINT(build/namespaces)
+
+// Do CHECK and throw a Mex error if check fails
+inline void mxCHECK(bool expr, const char* msg) {
+  if (!expr) {
+    mexErrMsgTxt(msg);
+  }
+}
+inline void mxERROR(const char* msg) { mexErrMsgTxt(msg); }
+
+// Check if a file exists and can be opened
+void mxCHECK_FILE_EXIST(const char* file) {
+  std::ifstream f(file);
+  if (!f.good()) {
+    f.close();
+    std::string msg("Could not open file ");
+    msg += file;
+    mxERROR(msg.c_str());
+  }
+  f.close();
+}
+
+// The pointers to caffe::Solver and caffe::Net instances
+static vector<shared_ptr<Solver<float> > > solvers_;
+static vector<shared_ptr<Net<float> > > nets_;
+// init_key is generated at the beginning and everytime you call reset
+static double init_key = static_cast<double>(caffe_rng_rand());
+
+/** -----------------------------------------------------------------
+ ** data conversion functions
+ **/
+// Enum indicates which blob memory to use
+enum WhichMemory { DATA, DIFF };
+
+// Copy matlab array to Blob data or diff
+static void mx_mat_to_blob(const mxArray* mx_mat, Blob<float>* blob,
+    WhichMemory data_or_diff) {
+  mxCHECK(blob->count() == mxGetNumberOfElements(mx_mat),
+      "number of elements in target blob doesn't match that in input mxArray");
+  const float* mat_mem_ptr = reinterpret_cast<const float*>(mxGetData(mx_mat));
+  float* blob_mem_ptr = NULL;
+  switch (Caffe::mode()) {
+  case Caffe::CPU:
+    blob_mem_ptr = (data_or_diff == DATA ?
+        blob->mutable_cpu_data() : blob->mutable_cpu_diff());
+    break;
+  case Caffe::GPU:
+    blob_mem_ptr = (data_or_diff == DATA ?
+        blob->mutable_gpu_data() : blob->mutable_gpu_diff());
+    break;
+  default:
+    mxERROR("Unknown Caffe mode");
+  }
+  caffe_copy(blob->count(), mat_mem_ptr, blob_mem_ptr);
+}
+
+// Copy Blob data or diff to matlab array
+static mxArray* blob_to_mx_mat(const Blob<float>* blob,
+    WhichMemory data_or_diff) {
+  const int num_axes = blob->num_axes();
+  vector<mwSize> dims(num_axes);
+  for (int blob_axis = 0, mat_axis = num_axes - 1; blob_axis < num_axes;
+       ++blob_axis, --mat_axis) {
+    dims[mat_axis] = static_cast<mwSize>(blob->shape(blob_axis));
+  }
+  // matlab array needs to have at least one dimension, convert scalar to 1-dim
+  if (num_axes == 0) {
+    dims.push_back(1);
+  }
+  mxArray* mx_mat =
+      mxCreateNumericArray(dims.size(), dims.data(), mxSINGLE_CLASS, mxREAL);
+  float* mat_mem_ptr = reinterpret_cast<float*>(mxGetData(mx_mat));
+  const float* blob_mem_ptr = NULL;
+  switch (Caffe::mode()) {
+  case Caffe::CPU:
+    blob_mem_ptr = (data_or_diff == DATA ? blob->cpu_data() : blob->cpu_diff());
+    break;
+  case Caffe::GPU:
+    blob_mem_ptr = (data_or_diff == DATA ? blob->gpu_data() : blob->gpu_diff());
+    break;
+  default:
+    mxERROR("Unknown Caffe mode");
+  }
+  caffe_copy(blob->count(), blob_mem_ptr, mat_mem_ptr);
+  return mx_mat;
+}
+
+// Convert vector<int> to matlab row vector
+static mxArray* int_vec_to_mx_vec(const vector<int>& int_vec) {
+  mxArray* mx_vec = mxCreateDoubleMatrix(int_vec.size(), 1, mxREAL);
+  double* vec_mem_ptr = mxGetPr(mx_vec);
+  for (int i = 0; i < int_vec.size(); i++) {
+    vec_mem_ptr[i] = static_cast<double>(int_vec[i]);
+  }
+  return mx_vec;
+}
+
+// Convert vector<string> to matlab cell vector of strings
+static mxArray* str_vec_to_mx_strcell(const vector<std::string>& str_vec) {
+  mxArray* mx_strcell = mxCreateCellMatrix(str_vec.size(), 1);
+  for (int i = 0; i < str_vec.size(); i++) {
+    mxSetCell(mx_strcell, i, mxCreateString(str_vec[i].c_str()));
+  }
+  return mx_strcell;
+}
+
+/** -----------------------------------------------------------------
+ ** handle and pointer conversion functions
+ ** a handle is a struct array with the following fields
+ **   (uint64) ptr      : the pointer to the C++ object
+ **   (double) init_key : caffe initialization key
+ **/
+// Convert a handle in matlab to a pointer in C++. Check if init_key matches
+template <typename T>
+static T* handle_to_ptr(const mxArray* mx_handle) {
+  mxArray* mx_ptr = mxGetField(mx_handle, 0, "ptr");
+  mxArray* mx_init_key = mxGetField(mx_handle, 0, "init_key");
+  mxCHECK(mxIsUint64(mx_ptr), "pointer type must be uint64");
+  mxCHECK(mxGetScalar(mx_init_key) == init_key,
+      "Could not convert handle to pointer due to invalid init_key. "
+      "The object might have been cleared.");
+  return reinterpret_cast<T*>(*reinterpret_cast<uint64_t*>(mxGetData(mx_ptr)));
+}
+
+// Create a handle struct vector, without setting up each handle in it
+template <typename T>
+static mxArray* create_handle_vec(int ptr_num) {
+  const int handle_field_num = 2;
+  const char* handle_fields[handle_field_num] = { "ptr", "init_key" };
+  return mxCreateStructMatrix(ptr_num, 1, handle_field_num, handle_fields);
+}
+
+// Set up a handle in a handle struct vector by its index
+template <typename T>
+static void setup_handle(const T* ptr, int index, mxArray* mx_handle_vec) {
+  mxArray* mx_ptr = mxCreateNumericMatrix(1, 1, mxUINT64_CLASS, mxREAL);
+  *reinterpret_cast<uint64_t*>(mxGetData(mx_ptr)) =
+      reinterpret_cast<uint64_t>(ptr);
+  mxSetField(mx_handle_vec, index, "ptr", mx_ptr);
+  mxSetField(mx_handle_vec, index, "init_key", mxCreateDoubleScalar(init_key));
+}
+
+// Convert a pointer in C++ to a handle in matlab
+template <typename T>
+static mxArray* ptr_to_handle(const T* ptr) {
+  mxArray* mx_handle = create_handle_vec<T>(1);
+  setup_handle(ptr, 0, mx_handle);
+  return mx_handle;
+}
+
+// Convert a vector of shared_ptr in C++ to handle struct vector
+template <typename T>
+static mxArray* ptr_vec_to_handle_vec(const vector<shared_ptr<T> >& ptr_vec) {
+  mxArray* mx_handle_vec = create_handle_vec<T>(ptr_vec.size());
+  for (int i = 0; i < ptr_vec.size(); i++) {
+    setup_handle(ptr_vec[i].get(), i, mx_handle_vec);
+  }
+  return mx_handle_vec;
+}
+
+/** -----------------------------------------------------------------
+ ** matlab command functions: caffe_(api_command, arg1, arg2, ...)
+ **/
+// Usage: caffe_('get_solver', solver_file);
+static void get_solver(MEX_ARGS) {
+  mxCHECK(nrhs == 1 && mxIsChar(prhs[0]),
+      "Usage: caffe_('get_solver', solver_file)");
+  char* solver_file = mxArrayToString(prhs[0]);
+  mxCHECK_FILE_EXIST(solver_file);
+  shared_ptr<Solver<float> > solver(new caffe::SGDSolver<float>(solver_file));
+  solvers_.push_back(solver);
+  plhs[0] = ptr_to_handle<Solver<float> >(solver.get());
+  mxFree(solver_file);
+}
+
+// Usage: caffe_('solver_get_attr', hSolver)
+static void solver_get_attr(MEX_ARGS) {
+  mxCHECK(nrhs == 1 && mxIsStruct(prhs[0]),
+      "Usage: caffe_('solver_get_attr', hSolver)");
+  Solver<float>* solver = handle_to_ptr<Solver<float> >(prhs[0]);
+  const int solver_attr_num = 2;
+  const char* solver_attrs[solver_attr_num] = { "hNet_net", "hNet_test_nets" };
+  mxArray* mx_solver_attr = mxCreateStructMatrix(1, 1, solver_attr_num,
+      solver_attrs);
+  mxSetField(mx_solver_attr, 0, "hNet_net",
+      ptr_to_handle<Net<float> >(solver->net().get()));
+  mxSetField(mx_solver_attr, 0, "hNet_test_nets",
+      ptr_vec_to_handle_vec<Net<float> >(solver->test_nets()));
+  plhs[0] = mx_solver_attr;
+}
+
+// Usage: caffe_('solver_get_iter', hSolver)
+static void solver_get_iter(MEX_ARGS) {
+  mxCHECK(nrhs == 1 && mxIsStruct(prhs[0]),
+      "Usage: caffe_('solver_get_iter', hSolver)");
+  Solver<float>* solver = handle_to_ptr<Solver<float> >(prhs[0]);
+  plhs[0] = mxCreateDoubleScalar(solver->iter());
+}
+
+// Usage: caffe_('solver_restore', hSolver, snapshot_file)
+static void solver_restore(MEX_ARGS) {
+  mxCHECK(nrhs == 2 && mxIsStruct(prhs[0]) && mxIsChar(prhs[1]),
+      "Usage: caffe_('solver_restore', hSolver, snapshot_file)");
+  Solver<float>* solver = handle_to_ptr<Solver<float> >(prhs[0]);
+  char* snapshot_file = mxArrayToString(prhs[1]);
+  mxCHECK_FILE_EXIST(snapshot_file);
+  solver->Restore(snapshot_file);
+  mxFree(snapshot_file);
+}
+
+// Usage: caffe_('solver_solve', hSolver)
+static void solver_solve(MEX_ARGS) {
+  mxCHECK(nrhs == 1 && mxIsStruct(prhs[0]),
+      "Usage: caffe_('solver_solve', hSolver)");
+  Solver<float>* solver = handle_to_ptr<Solver<float> >(prhs[0]);
+  solver->Solve();
+}
+
+// Usage: caffe_('solver_step', hSolver, iters)
+static void solver_step(MEX_ARGS) {
+  mxCHECK(nrhs == 2 && mxIsStruct(prhs[0]) && mxIsDouble(prhs[1]),
+      "Usage: caffe_('solver_step', hSolver, iters)");
+  Solver<float>* solver = handle_to_ptr<Solver<float> >(prhs[0]);
+  int iters = mxGetScalar(prhs[1]);
+  solver->Step(iters);
+}
+
+// Usage: caffe_('get_net', model_file, phase_name)
+static void get_net(MEX_ARGS) {
+  mxCHECK(nrhs == 2 && mxIsChar(prhs[0]) && mxIsChar(prhs[1]),
+      "Usage: caffe_('get_net', model_file, phase_name)");
+  char* model_file = mxArrayToString(prhs[0]);
+  char* phase_name = mxArrayToString(prhs[1]);
+  mxCHECK_FILE_EXIST(model_file);
+  Phase phase;
+  if (strcmp(phase_name, "train") == 0) {
+      phase = TRAIN;
+  } else if (strcmp(phase_name, "test") == 0) {
+      phase = TEST;
+  } else {
+    mxERROR("Unknown phase");
+  }
+  shared_ptr<Net<float> > net(new caffe::Net<float>(model_file, phase));
+  nets_.push_back(net);
+  plhs[0] = ptr_to_handle<Net<float> >(net.get());
+  mxFree(model_file);
+  mxFree(phase_name);
+}
+
+// Usage: caffe_('net_get_attr', hNet)
+static void net_get_attr(MEX_ARGS) {
+  mxCHECK(nrhs == 1 && mxIsStruct(prhs[0]),
+      "Usage: caffe_('net_get_attr', hNet)");
+  Net<float>* net = handle_to_ptr<Net<float> >(prhs[0]);
+  const int net_attr_num = 6;
+  const char* net_attrs[net_attr_num] = { "hLayer_layers", "hBlob_blobs",
+      "input_blob_indices", "output_blob_indices", "layer_names", "blob_names"};
+  mxArray* mx_net_attr = mxCreateStructMatrix(1, 1, net_attr_num,
+      net_attrs);
+  mxSetField(mx_net_attr, 0, "hLayer_layers",
+      ptr_vec_to_handle_vec<Layer<float> >(net->layers()));
+  mxSetField(mx_net_attr, 0, "hBlob_blobs",
+      ptr_vec_to_handle_vec<Blob<float> >(net->blobs()));
+  mxSetField(mx_net_attr, 0, "input_blob_indices",
+      int_vec_to_mx_vec(net->input_blob_indices()));
+  mxSetField(mx_net_attr, 0, "output_blob_indices",
+      int_vec_to_mx_vec(net->output_blob_indices()));
+  mxSetField(mx_net_attr, 0, "layer_names",
+      str_vec_to_mx_strcell(net->layer_names()));
+  mxSetField(mx_net_attr, 0, "blob_names",
+      str_vec_to_mx_strcell(net->blob_names()));
+  plhs[0] = mx_net_attr;
+}
+
+// Usage: caffe_('net_forward', hNet)
+static void net_forward(MEX_ARGS) {
+  mxCHECK(nrhs == 1 && mxIsStruct(prhs[0]),
+      "Usage: caffe_('net_forward', hNet)");
+  Net<float>* net = handle_to_ptr<Net<float> >(prhs[0]);
+  net->ForwardPrefilled();
+}
+
+// Usage: caffe_('net_backward', hNet)
+static void net_backward(MEX_ARGS) {
+  mxCHECK(nrhs == 1 && mxIsStruct(prhs[0]),
+      "Usage: caffe_('net_backward', hNet)");
+  Net<float>* net = handle_to_ptr<Net<float> >(prhs[0]);
+  net->Backward();
+}
+
+// Usage: caffe_('net_copy_from', hNet, weights_file)
+static void net_copy_from(MEX_ARGS) {
+  mxCHECK(nrhs == 2 && mxIsStruct(prhs[0]) && mxIsChar(prhs[1]),
+      "Usage: caffe_('net_copy_from', hNet, weights_file)");
+  Net<float>* net = handle_to_ptr<Net<float> >(prhs[0]);
+  char* weights_file = mxArrayToString(prhs[1]);
+  mxCHECK_FILE_EXIST(weights_file);
+  net->CopyTrainedLayersFrom(weights_file);
+  mxFree(weights_file);
+}
+
+// Usage: caffe_('net_reshape', hNet)
+static void net_reshape(MEX_ARGS) {
+  mxCHECK(nrhs == 1 && mxIsStruct(prhs[0]),
+      "Usage: caffe_('net_reshape', hNet)");
+  Net<float>* net = handle_to_ptr<Net<float> >(prhs[0]);
+  net->Reshape();
+}
+
+// Usage: caffe_('net_save', hNet, save_file)
+static void net_save(MEX_ARGS) {
+  mxCHECK(nrhs == 2 && mxIsStruct(prhs[0]) && mxIsChar(prhs[1]),
+      "Usage: caffe_('net_save', hNet, save_file)");
+  Net<float>* net = handle_to_ptr<Net<float> >(prhs[0]);
+  char* weights_file = mxArrayToString(prhs[1]);
+  NetParameter net_param;
+  net->ToProto(&net_param, false);
+  WriteProtoToBinaryFile(net_param, weights_file);
+  mxFree(weights_file);
+}
+
+// Usage: caffe_('layer_get_attr', hLayer)
+static void layer_get_attr(MEX_ARGS) {
+  mxCHECK(nrhs == 1 && mxIsStruct(prhs[0]),
+      "Usage: caffe_('layer_get_attr', hLayer)");
+  Layer<float>* layer = handle_to_ptr<Layer<float> >(prhs[0]);
+  const int layer_attr_num = 1;
+  const char* layer_attrs[layer_attr_num] = { "hBlob_blobs" };
+  mxArray* mx_layer_attr = mxCreateStructMatrix(1, 1, layer_attr_num,
+      layer_attrs);
+  mxSetField(mx_layer_attr, 0, "hBlob_blobs",
+      ptr_vec_to_handle_vec<Blob<float> >(layer->blobs()));
+  plhs[0] = mx_layer_attr;
+}
+
+// Usage: caffe_('layer_get_type', hLayer)
+static void layer_get_type(MEX_ARGS) {
+  mxCHECK(nrhs == 1 && mxIsStruct(prhs[0]),
+      "Usage: caffe_('layer_get_type', hLayer)");
+  Layer<float>* layer = handle_to_ptr<Layer<float> >(prhs[0]);
+  plhs[0] = mxCreateString(layer->type());
+}
+
+// Usage: caffe_('blob_get_shape', hBlob)
+static void blob_get_shape(MEX_ARGS) {
+  mxCHECK(nrhs == 1 && mxIsStruct(prhs[0]),
+      "Usage: caffe_('blob_get_shape', hBlob)");
+  Blob<float>* blob = handle_to_ptr<Blob<float> >(prhs[0]);
+  const int num_axes = blob->num_axes();
+  mxArray* mx_shape = mxCreateDoubleMatrix(1, num_axes, mxREAL);
+  double* shape_mem_mtr = mxGetPr(mx_shape);
+  for (int blob_axis = 0, mat_axis = num_axes - 1; blob_axis < num_axes;
+       ++blob_axis, --mat_axis) {
+    shape_mem_mtr[mat_axis] = static_cast<double>(blob->shape(blob_axis));
+  }
+  plhs[0] = mx_shape;
+}
+
+// Usage: caffe_('blob_reshape', hBlob, new_shape)
+static void blob_reshape(MEX_ARGS) {
+  mxCHECK(nrhs == 2 && mxIsStruct(prhs[0]) && mxIsDouble(prhs[1]),
+      "Usage: caffe_('blob_reshape', hBlob, new_shape)");
+  Blob<float>* blob = handle_to_ptr<Blob<float> >(prhs[0]);
+  const mxArray* mx_shape = prhs[1];
+  double* shape_mem_mtr = mxGetPr(mx_shape);
+  const int num_axes = mxGetNumberOfElements(mx_shape);
+  vector<int> blob_shape(num_axes);
+  for (int blob_axis = 0, mat_axis = num_axes - 1; blob_axis < num_axes;
+       ++blob_axis, --mat_axis) {
+    blob_shape[blob_axis] = static_cast<int>(shape_mem_mtr[mat_axis]);
+  }
+  blob->Reshape(blob_shape);
+}
+
+// Usage: caffe_('blob_get_data', hBlob)
+static void blob_get_data(MEX_ARGS) {
+  mxCHECK(nrhs == 1 && mxIsStruct(prhs[0]),
+      "Usage: caffe_('blob_get_data', hBlob)");
+  Blob<float>* blob = handle_to_ptr<Blob<float> >(prhs[0]);
+  plhs[0] = blob_to_mx_mat(blob, DATA);
+}
+
+// Usage: caffe_('blob_set_data', hBlob, new_data)
+static void blob_set_data(MEX_ARGS) {
+  mxCHECK(nrhs == 2 && mxIsStruct(prhs[0]) && mxIsSingle(prhs[1]),
+      "Usage: caffe_('blob_set_data', hBlob, new_data)");
+  Blob<float>* blob = handle_to_ptr<Blob<float> >(prhs[0]);
+  mx_mat_to_blob(prhs[1], blob, DATA);
+}
+
+// Usage: caffe_('blob_get_diff', hBlob)
+static void blob_get_diff(MEX_ARGS) {
+  mxCHECK(nrhs == 1 && mxIsStruct(prhs[0]),
+      "Usage: caffe_('blob_get_diff', hBlob)");
+  Blob<float>* blob = handle_to_ptr<Blob<float> >(prhs[0]);
+  plhs[0] = blob_to_mx_mat(blob, DIFF);
+}
+
+// Usage: caffe_('blob_set_diff', hBlob, new_diff)
+static void blob_set_diff(MEX_ARGS) {
+  mxCHECK(nrhs == 2 && mxIsStruct(prhs[0]) && mxIsSingle(prhs[1]),
+      "Usage: caffe_('blob_set_diff', hBlob, new_diff)");
+  Blob<float>* blob = handle_to_ptr<Blob<float> >(prhs[0]);
+  mx_mat_to_blob(prhs[1], blob, DIFF);
+}
+
+// Usage: caffe_('set_mode_cpu')
+static void set_mode_cpu(MEX_ARGS) {
+  mxCHECK(nrhs == 0, "Usage: caffe_('set_mode_cpu')");
+  Caffe::set_mode(Caffe::CPU);
+}
+
+// Usage: caffe_('set_mode_gpu')
+static void set_mode_gpu(MEX_ARGS) {
+  mxCHECK(nrhs == 0, "Usage: caffe_('set_mode_gpu')");
+  Caffe::set_mode(Caffe::GPU);
+}
+
+// Usage: caffe_('set_device', device_id)
+static void set_device(MEX_ARGS) {
+  mxCHECK(nrhs == 1 && mxIsDouble(prhs[0]),
+      "Usage: caffe_('set_device', device_id)");
+  int device_id = static_cast<int>(mxGetScalar(prhs[0]));
+  Caffe::SetDevice(device_id);
+}
+
+// Usage: caffe_('get_init_key')
+static void get_init_key(MEX_ARGS) {
+  mxCHECK(nrhs == 0, "Usage: caffe_('get_init_key')");
+  plhs[0] = mxCreateDoubleScalar(init_key);
+}
+
+// Usage: caffe_('reset')
+static void reset(MEX_ARGS) {
+  mxCHECK(nrhs == 0, "Usage: caffe_('reset')");
+  // Clear solvers and stand-alone nets
+  mexPrintf("Cleared %d solvers and %d stand-alone nets\n",
+      solvers_.size(), nets_.size());
+  solvers_.clear();
+  nets_.clear();
+  // Generate new init_key, so that handles created before becomes invalid
+  init_key = static_cast<double>(caffe_rng_rand());
+}
+
+// Usage: caffe_('read_mean', mean_proto_file)
+static void read_mean(MEX_ARGS) {
+  mxCHECK(nrhs == 1 && mxIsChar(prhs[0]),
+      "Usage: caffe_('read_mean', mean_proto_file)");
+  char* mean_proto_file = mxArrayToString(prhs[0]);
+  mxCHECK_FILE_EXIST(mean_proto_file);
+  Blob<float> data_mean;
+  BlobProto blob_proto;
+  bool result = ReadProtoFromBinaryFile(mean_proto_file, &blob_proto);
+  mxCHECK(result, "Could not read your mean file");
+  data_mean.FromProto(blob_proto);
+  plhs[0] = blob_to_mx_mat(&data_mean, DATA);
+  mxFree(mean_proto_file);
+}
+
+/** -----------------------------------------------------------------
+ ** Available commands.
+ **/
+struct handler_registry {
+  string cmd;
+  void (*func)(MEX_ARGS);
+};
+
+static handler_registry handlers[] = {
+  // Public API functions
+  { "get_solver",         get_solver      },
+  { "solver_get_attr",    solver_get_attr },
+  { "solver_get_iter",    solver_get_iter },
+  { "solver_restore",     solver_restore  },
+  { "solver_solve",       solver_solve    },
+  { "solver_step",        solver_step     },
+  { "get_net",            get_net         },
+  { "net_get_attr",       net_get_attr    },
+  { "net_forward",        net_forward     },
+  { "net_backward",       net_backward    },
+  { "net_copy_from",      net_copy_from   },
+  { "net_reshape",        net_reshape     },
+  { "net_save",           net_save        },
+  { "layer_get_attr",     layer_get_attr  },
+  { "layer_get_type",     layer_get_type  },
+  { "blob_get_shape",     blob_get_shape  },
+  { "blob_reshape",       blob_reshape    },
+  { "blob_get_data",      blob_get_data   },
+  { "blob_set_data",      blob_set_data   },
+  { "blob_get_diff",      blob_get_diff   },
+  { "blob_set_diff",      blob_set_diff   },
+  { "set_mode_cpu",       set_mode_cpu    },
+  { "set_mode_gpu",       set_mode_gpu    },
+  { "set_device",         set_device      },
+  { "get_init_key",       get_init_key    },
+  { "reset",              reset           },
+  { "read_mean",          read_mean       },
+  // The end.
+  { "END",                NULL            },
+};
+
+/** -----------------------------------------------------------------
+ ** matlab entry point.
+ **/
+// Usage: caffe_(api_command, arg1, arg2, ...)
+void mexFunction(MEX_ARGS) {
+  mexLock();  // Avoid clearing the mex file.
+  mxCHECK(nrhs > 0, "Usage: caffe_(api_command, arg1, arg2, ...)");
+  // Handle input command
+  char* cmd = mxArrayToString(prhs[0]);
+  bool dispatched = false;
+  // Dispatch to cmd handler
+  for (int i = 0; handlers[i].func != NULL; i++) {
+    if (handlers[i].cmd.compare(cmd) == 0) {
+      handlers[i].func(nlhs, plhs, nrhs-1, prhs+1);
+      dispatched = true;
+      break;
+    }
+  }
+  if (!dispatched) {
+    ostringstream error_msg;
+    error_msg << "Unknown command '" << cmd << "'";
+    mxERROR(error_msg.str().c_str());
+  }
+  mxFree(cmd);
+}
diff --git a/matlab/+caffe/private/is_valid_handle.m b/matlab/+caffe/private/is_valid_handle.m
new file mode 100644
index 00000000000..a0648ecdf61
--- /dev/null
+++ b/matlab/+caffe/private/is_valid_handle.m
@@ -0,0 +1,27 @@
+function valid = is_valid_handle(hObj)
+% valid = is_valid_handle(hObj) or is_valid_handle('get_new_init_key')
+%   Check if a handle is valid (has the right data type and init_key matches)
+%   Use is_valid_handle('get_new_init_key') to get new init_key from C++;
+
+% a handle is a struct array with the following fields
+%   (uint64) ptr      : the pointer to the C++ object
+%   (double) init_key : caffe initialization key
+
+persistent init_key;
+if isempty(init_key)
+  init_key = caffe_('get_init_key');
+end
+
+% is_valid_handle('get_new_init_key') to get new init_key from C++;
+if ischar(hObj) && strcmp(hObj, 'get_new_init_key')
+  init_key = caffe_('get_init_key');
+  return
+else
+  % check whether data types are correct and init_key matches
+  valid = isstruct(hObj) ...
+    && isscalar(hObj.ptr) && isa(hObj.ptr, 'uint64') ...
+    && isscalar(hObj.init_key) && isa(hObj.init_key, 'double') ...
+    && hObj.init_key == init_key;
+end
+
+end
diff --git a/matlab/+caffe/reset_all.m b/matlab/+caffe/reset_all.m
new file mode 100644
index 00000000000..a8b33dee8d5
--- /dev/null
+++ b/matlab/+caffe/reset_all.m
@@ -0,0 +1,8 @@
+function reset_all()
+% reset_all()
+%   clear all solvers and stand-alone nets and reset Caffe to initial status
+
+caffe_('reset');
+is_valid_handle('get_new_init_key');
+
+end
diff --git a/matlab/+caffe/run_tests.m b/matlab/+caffe/run_tests.m
new file mode 100644
index 00000000000..93896855ac2
--- /dev/null
+++ b/matlab/+caffe/run_tests.m
@@ -0,0 +1,19 @@
+function results = run_tests()
+% results = run_tests()
+%   run all tests in this caffe matlab wrapper package
+
+% use CPU for testing
+caffe.set_mode_cpu();
+
+% reset caffe before testing
+caffe.reset_all();
+
+% put all test cases here
+results = [...
+  run(caffe.test.test_net) ...
+  run(caffe.test.test_solver) ];
+
+% reset caffe after testing
+caffe.reset_all();
+
+end
diff --git a/matlab/+caffe/set_device.m b/matlab/+caffe/set_device.m
new file mode 100644
index 00000000000..f94068cbe98
--- /dev/null
+++ b/matlab/+caffe/set_device.m
@@ -0,0 +1,11 @@
+function set_device(device_id)
+% set_device(device_id)
+%   set Caffe's GPU device ID
+
+CHECK(isscalar(device_id) && device_id >= 0, ...
+  'device_id must be non-negative integer');
+device_id = double(device_id);
+
+caffe_('set_device', device_id);
+
+end
diff --git a/matlab/+caffe/set_mode_cpu.m b/matlab/+caffe/set_mode_cpu.m
new file mode 100644
index 00000000000..a87e0e2852b
--- /dev/null
+++ b/matlab/+caffe/set_mode_cpu.m
@@ -0,0 +1,7 @@
+function set_mode_cpu()
+% set_mode_cpu()
+%   set Caffe to CPU mode
+
+caffe_('set_mode_cpu');
+
+end
diff --git a/matlab/+caffe/set_mode_gpu.m b/matlab/+caffe/set_mode_gpu.m
new file mode 100644
index 00000000000..78e5f6773a1
--- /dev/null
+++ b/matlab/+caffe/set_mode_gpu.m
@@ -0,0 +1,7 @@
+function set_mode_gpu()
+% set_mode_gpu()
+%   set Caffe to GPU mode
+
+caffe_('set_mode_gpu');
+
+end
diff --git a/matlab/CMakeLists.txt b/matlab/CMakeLists.txt
index f6a03ee4625..4b0d549f07f 100644
--- a/matlab/CMakeLists.txt
+++ b/matlab/CMakeLists.txt
@@ -1 +1,72 @@
-project( Matlab )
\ No newline at end of file
+# Builds Matlab (or Octave) interface. In case of Matlab caffe must be
+# compield as shared library. Octave can link static or shared caffe library
+# To install octave run: sudo apt-get install liboctave-dev
+
+if(NOT BUILD_matlab)
+  return()
+endif()
+
+if(HAVE_MATLAB AND Octave_compiler)
+  set(build_using ${Matlab_build_mex_using})
+elseif(HAVE_MATLAB AND NOT Octave_compiler)
+  set(build_using "Matlab")
+elseif(NOT HAVE_MATLAB AND Octave_compiler)
+  set(build_using "Octave")
+else()
+  return()
+endif()
+
+if(NOT BUILD_SHARED_LIBS AND build_using MATCHES Matlab)
+  message(FATAL_ERROR "Matlab MEX interface (with default mex options file) can only be built if caffe is compiled as shared library. Please enable 'BUILD_SHARED_LIBS' in CMake. Aternativelly you can switch to Octave compiler.")
+endif()
+
+# helper function to set proper mex file extention
+function(caffe_fetch_and_set_proper_mexext mexfile_variable)
+  execute_process(COMMAND ${Matlab_mexext} OUTPUT_STRIP_TRAILING_WHITESPACE RESULT_VARIABLE res OUTPUT_VARIABLE ext)
+  if(res MATCHES 0)
+    get_filename_component(folder  ${${mexfile_variable}} PATH)
+    get_filename_component(name_we ${${mexfile_variable}} NAME_WE)
+    set(${mexfile_variable} ${folder}/${name_we}.${ext} PARENT_SCOPE)
+  endif()
+endfunction()
+
+# global settings
+file(GLOB Matlab_srcs +caffe/private/caffe_.cpp)
+set(Matlab_caffe_mex ${PROJECT_SOURCE_DIR}/matlab/+caffe/private/caffe_.mex)
+
+caffe_get_current_cflags(cflags)
+caffe_parse_linker_libs(Caffe_LINKER_LIBS folders libflags macos_frameworks)
+set(folders $<TARGET_LINKER_FILE_DIR:caffe> ${folders})
+
+# prepare linker flag lists
+string(REPLACE ";" ";-L" link_folders "-L${folders}")
+string(REPLACE ";" ":"  rpath_folders   "${folders}")
+
+if(build_using MATCHES "Matlab")
+  set(libflags -lcaffe${CAffe_POSTFIX} ${libflags}) # Matlab R2014a complans for -Wl,--whole-archive
+
+  caffe_fetch_and_set_proper_mexext(Matlab_caffe_mex)
+  add_custom_command(OUTPUT ${Matlab_caffe_mex} COMMAND ${Matlab_mex}
+      ARGS -output ${Matlab_caffe_mex} ${Matlab_srcs} ${cflags} ${link_folders} ${libflags}
+      DEPENDS caffe COMMENT "Building Matlab interface: ${Matlab_caffe_mex}" VERBATIM)
+  add_custom_target(matlab ALL DEPENDS ${Matlab_caffe_mex} SOURCES ${Matlab_srcs})
+
+elseif(build_using MATCHES "Octave")
+
+  if("${CMAKE_CXX_COMPILER_ID}" STREQUAL "Clang")
+    set(libflags -Wl,-force_load,$<TARGET_LINKER_FILE:caffe> ${libflags})
+  elseif("${CMAKE_CXX_COMPILER_ID}" STREQUAL "GNU")
+    set(libflags -Wl,--whole-archive -lcaffe${CAffe_POSTFIX} -Wl,--no-whole-archive ${libflags})
+  endif()
+
+  add_custom_command(OUTPUT ${Matlab_caffe_mex} COMMAND ${Octave_compiler}
+      ARGS --mex -o ${Matlab_caffe_mex} ${Matlab_srcs} ${cflags} ${link_folders} ${libflags} -Wl,-rpath,${rpath_folders}
+      DEPENDS caffe COMMENT "Building Octave interface: ${Matlab_caffe_mex}" VERBATIM)
+
+  add_custom_target(octave ALL DEPENDS ${Matlab_caffe_mex} SOURCES ${Matlab_srcs})
+endif()
+
+# ---[ Install
+file(GLOB mfiles caffe/*.m)
+install(FILES ${mfiles} ${Matlab_caffe_mex} DESTINATION matlab)
+
diff --git a/matlab/caffe/ilsvrc_2012_mean.mat b/matlab/caffe/ilsvrc_2012_mean.mat
deleted file mode 100644
index f1da25c84a1..00000000000
Binary files a/matlab/caffe/ilsvrc_2012_mean.mat and /dev/null differ
diff --git a/matlab/caffe/matcaffe.cpp b/matlab/caffe/matcaffe.cpp
deleted file mode 100644
index 83786caed0d..00000000000
--- a/matlab/caffe/matcaffe.cpp
+++ /dev/null
@@ -1,398 +0,0 @@
-//
-// matcaffe.cpp provides a wrapper of the caffe::Net class as well as some
-// caffe::Caffe functions so that one could easily call it from matlab.
-// Note that for matlab, we will simply use float as the data type.
-
-#include <string>
-#include <vector>
-
-#include "mex.h"
-
-#include "caffe/caffe.hpp"
-
-#define MEX_ARGS int nlhs, mxArray **plhs, int nrhs, const mxArray **prhs
-
-using namespace caffe;  // NOLINT(build/namespaces)
-
-// The pointer to the internal caffe::Net instance
-static shared_ptr<Net<float> > net_;
-static int init_key = -2;
-
-// Five things to be aware of:
-//   caffe uses row-major order
-//   matlab uses column-major order
-//   caffe uses BGR color channel order
-//   matlab uses RGB color channel order
-//   images need to have the data mean subtracted
-//
-// Data coming in from matlab needs to be in the order
-//   [width, height, channels, images]
-// where width is the fastest dimension.
-// Here is the rough matlab for putting image data into the correct
-// format:
-//   % convert from uint8 to single
-//   im = single(im);
-//   % reshape to a fixed size (e.g., 227x227)
-//   im = imresize(im, [IMAGE_DIM IMAGE_DIM], 'bilinear');
-//   % permute from RGB to BGR and subtract the data mean (already in BGR)
-//   im = im(:,:,[3 2 1]) - data_mean;
-//   % flip width and height to make width the fastest dimension
-//   im = permute(im, [2 1 3]);
-//
-// If you have multiple images, cat them with cat(4, ...)
-//
-// The actual forward function. It takes in a cell array of 4-D arrays as
-// input and outputs a cell array.
-
-static mxArray* do_forward(const mxArray* const bottom) {
-  vector<Blob<float>*>& input_blobs = net_->input_blobs();
-  CHECK_EQ(static_cast<unsigned int>(mxGetDimensions(bottom)[0]),
-      input_blobs.size());
-  for (unsigned int i = 0; i < input_blobs.size(); ++i) {
-    const mxArray* const elem = mxGetCell(bottom, i);
-    CHECK(mxIsSingle(elem))
-        << "MatCaffe require single-precision float point data";
-    CHECK_EQ(mxGetNumberOfElements(elem), input_blobs[i]->count())
-        << "MatCaffe input size does not match the input size of the network";
-    const float* const data_ptr =
-        reinterpret_cast<const float* const>(mxGetPr(elem));
-    switch (Caffe::mode()) {
-    case Caffe::CPU:
-      caffe_copy(input_blobs[i]->count(), data_ptr,
-          input_blobs[i]->mutable_cpu_data());
-      break;
-    case Caffe::GPU:
-      caffe_copy(input_blobs[i]->count(), data_ptr,
-          input_blobs[i]->mutable_gpu_data());
-      break;
-    default:
-      LOG(FATAL) << "Unknown Caffe mode.";
-    }  // switch (Caffe::mode())
-  }
-  const vector<Blob<float>*>& output_blobs = net_->ForwardPrefilled();
-  mxArray* mx_out = mxCreateCellMatrix(output_blobs.size(), 1);
-  for (unsigned int i = 0; i < output_blobs.size(); ++i) {
-    // internally data is stored as (width, height, channels, num)
-    // where width is the fastest dimension
-    mwSize dims[4] = {output_blobs[i]->width(), output_blobs[i]->height(),
-      output_blobs[i]->channels(), output_blobs[i]->num()};
-    mxArray* mx_blob =  mxCreateNumericArray(4, dims, mxSINGLE_CLASS, mxREAL);
-    mxSetCell(mx_out, i, mx_blob);
-    float* data_ptr = reinterpret_cast<float*>(mxGetPr(mx_blob));
-    switch (Caffe::mode()) {
-    case Caffe::CPU:
-      caffe_copy(output_blobs[i]->count(), output_blobs[i]->cpu_data(),
-          data_ptr);
-      break;
-    case Caffe::GPU:
-      caffe_copy(output_blobs[i]->count(), output_blobs[i]->gpu_data(),
-          data_ptr);
-      break;
-    default:
-      LOG(FATAL) << "Unknown Caffe mode.";
-    }  // switch (Caffe::mode())
-  }
-
-  return mx_out;
-}
-
-static mxArray* do_backward(const mxArray* const top_diff) {
-  vector<Blob<float>*>& output_blobs = net_->output_blobs();
-  vector<Blob<float>*>& input_blobs = net_->input_blobs();
-  CHECK_EQ(static_cast<unsigned int>(mxGetDimensions(top_diff)[0]),
-      output_blobs.size());
-  // First, copy the output diff
-  for (unsigned int i = 0; i < output_blobs.size(); ++i) {
-    const mxArray* const elem = mxGetCell(top_diff, i);
-    const float* const data_ptr =
-        reinterpret_cast<const float* const>(mxGetPr(elem));
-    switch (Caffe::mode()) {
-    case Caffe::CPU:
-      caffe_copy(output_blobs[i]->count(), data_ptr,
-          output_blobs[i]->mutable_cpu_diff());
-      break;
-    case Caffe::GPU:
-      caffe_copy(output_blobs[i]->count(), data_ptr,
-          output_blobs[i]->mutable_gpu_diff());
-      break;
-    default:
-      LOG(FATAL) << "Unknown Caffe mode.";
-    }  // switch (Caffe::mode())
-  }
-  // LOG(INFO) << "Start";
-  net_->Backward();
-  // LOG(INFO) << "End";
-  mxArray* mx_out = mxCreateCellMatrix(input_blobs.size(), 1);
-  for (unsigned int i = 0; i < input_blobs.size(); ++i) {
-    // internally data is stored as (width, height, channels, num)
-    // where width is the fastest dimension
-    mwSize dims[4] = {input_blobs[i]->width(), input_blobs[i]->height(),
-      input_blobs[i]->channels(), input_blobs[i]->num()};
-    mxArray* mx_blob =  mxCreateNumericArray(4, dims, mxSINGLE_CLASS, mxREAL);
-    mxSetCell(mx_out, i, mx_blob);
-    float* data_ptr = reinterpret_cast<float*>(mxGetPr(mx_blob));
-    switch (Caffe::mode()) {
-    case Caffe::CPU:
-      caffe_copy(input_blobs[i]->count(), input_blobs[i]->cpu_diff(), data_ptr);
-      break;
-    case Caffe::GPU:
-      caffe_copy(input_blobs[i]->count(), input_blobs[i]->gpu_diff(), data_ptr);
-      break;
-    default:
-      LOG(FATAL) << "Unknown Caffe mode.";
-    }  // switch (Caffe::mode())
-  }
-
-  return mx_out;
-}
-
-static mxArray* do_get_weights() {
-  const vector<shared_ptr<Layer<float> > >& layers = net_->layers();
-  const vector<string>& layer_names = net_->layer_names();
-
-  // Step 1: count the number of layers with weights
-  int num_layers = 0;
-  {
-    string prev_layer_name = "";
-    for (unsigned int i = 0; i < layers.size(); ++i) {
-      vector<shared_ptr<Blob<float> > >& layer_blobs = layers[i]->blobs();
-      if (layer_blobs.size() == 0) {
-        continue;
-      }
-      if (layer_names[i] != prev_layer_name) {
-        prev_layer_name = layer_names[i];
-        num_layers++;
-      }
-    }
-  }
-
-  // Step 2: prepare output array of structures
-  mxArray* mx_layers;
-  {
-    const mwSize dims[2] = {num_layers, 1};
-    const char* fnames[2] = {"weights", "layer_names"};
-    mx_layers = mxCreateStructArray(2, dims, 2, fnames);
-  }
-
-  // Step 3: copy weights into output
-  {
-    string prev_layer_name = "";
-    int mx_layer_index = 0;
-    for (unsigned int i = 0; i < layers.size(); ++i) {
-      vector<shared_ptr<Blob<float> > >& layer_blobs = layers[i]->blobs();
-      if (layer_blobs.size() == 0) {
-        continue;
-      }
-
-      mxArray* mx_layer_cells = NULL;
-      if (layer_names[i] != prev_layer_name) {
-        prev_layer_name = layer_names[i];
-        const mwSize dims[2] = {static_cast<mwSize>(layer_blobs.size()), 1};
-        mx_layer_cells = mxCreateCellArray(2, dims);
-        mxSetField(mx_layers, mx_layer_index, "weights", mx_layer_cells);
-        mxSetField(mx_layers, mx_layer_index, "layer_names",
-            mxCreateString(layer_names[i].c_str()));
-        mx_layer_index++;
-      }
-
-      for (unsigned int j = 0; j < layer_blobs.size(); ++j) {
-        // internally data is stored as (width, height, channels, num)
-        // where width is the fastest dimension
-        mwSize dims[4] = {layer_blobs[j]->width(), layer_blobs[j]->height(),
-            layer_blobs[j]->channels(), layer_blobs[j]->num()};
-
-        mxArray* mx_weights =
-          mxCreateNumericArray(4, dims, mxSINGLE_CLASS, mxREAL);
-        mxSetCell(mx_layer_cells, j, mx_weights);
-        float* weights_ptr = reinterpret_cast<float*>(mxGetPr(mx_weights));
-
-        switch (Caffe::mode()) {
-        case Caffe::CPU:
-          caffe_copy(layer_blobs[j]->count(), layer_blobs[j]->cpu_data(),
-              weights_ptr);
-          break;
-        case Caffe::GPU:
-          caffe_copy(layer_blobs[j]->count(), layer_blobs[j]->gpu_data(),
-              weights_ptr);
-          break;
-        default:
-          LOG(FATAL) << "Unknown caffe mode: " << Caffe::mode();
-        }
-      }
-    }
-  }
-
-  return mx_layers;
-}
-
-static void get_weights(MEX_ARGS) {
-  plhs[0] = do_get_weights();
-}
-
-static void set_mode_cpu(MEX_ARGS) {
-  Caffe::set_mode(Caffe::CPU);
-}
-
-static void set_mode_gpu(MEX_ARGS) {
-  Caffe::set_mode(Caffe::GPU);
-}
-
-static void set_phase_train(MEX_ARGS) {
-  Caffe::set_phase(Caffe::TRAIN);
-}
-
-static void set_phase_test(MEX_ARGS) {
-  Caffe::set_phase(Caffe::TEST);
-}
-
-static void set_device(MEX_ARGS) {
-  if (nrhs != 1) {
-    LOG(ERROR) << "Only given " << nrhs << " arguments";
-    mexErrMsgTxt("Wrong number of arguments");
-  }
-
-  int device_id = static_cast<int>(mxGetScalar(prhs[0]));
-  Caffe::SetDevice(device_id);
-}
-
-static void get_init_key(MEX_ARGS) {
-  plhs[0] = mxCreateDoubleScalar(init_key);
-}
-
-static void init(MEX_ARGS) {
-  if (nrhs != 2) {
-    LOG(ERROR) << "Only given " << nrhs << " arguments";
-    mexErrMsgTxt("Wrong number of arguments");
-  }
-
-  char* param_file = mxArrayToString(prhs[0]);
-  char* model_file = mxArrayToString(prhs[1]);
-
-  net_.reset(new Net<float>(string(param_file)));
-  net_->CopyTrainedLayersFrom(string(model_file));
-
-  mxFree(param_file);
-  mxFree(model_file);
-
-  init_key = random();  // NOLINT(caffe/random_fn)
-
-  if (nlhs == 1) {
-    plhs[0] = mxCreateDoubleScalar(init_key);
-  }
-}
-
-static void reset(MEX_ARGS) {
-  if (net_) {
-    net_.reset();
-    init_key = -2;
-    LOG(INFO) << "Network reset, call init before use it again";
-  }
-}
-
-static void forward(MEX_ARGS) {
-  if (nrhs != 1) {
-    LOG(ERROR) << "Only given " << nrhs << " arguments";
-    mexErrMsgTxt("Wrong number of arguments");
-  }
-
-  plhs[0] = do_forward(prhs[0]);
-}
-
-static void backward(MEX_ARGS) {
-  if (nrhs != 1) {
-    LOG(ERROR) << "Only given " << nrhs << " arguments";
-    mexErrMsgTxt("Wrong number of arguments");
-  }
-
-  plhs[0] = do_backward(prhs[0]);
-}
-
-static void is_initialized(MEX_ARGS) {
-  if (!net_) {
-    plhs[0] = mxCreateDoubleScalar(0);
-  } else {
-    plhs[0] = mxCreateDoubleScalar(1);
-  }
-}
-
-static void read_mean(MEX_ARGS) {
-    if (nrhs != 1) {
-        mexErrMsgTxt("Usage: caffe('read_mean', 'path_to_binary_mean_file'");
-        return;
-    }
-    const string& mean_file = mxArrayToString(prhs[0]);
-    Blob<float> data_mean;
-    LOG(INFO) << "Loading mean file from" << mean_file;
-    BlobProto blob_proto;
-    bool result = ReadProtoFromBinaryFile(mean_file.c_str(), &blob_proto);
-    if (!result) {
-        mexErrMsgTxt("Couldn't read the file");
-        return;
-    }
-    data_mean.FromProto(blob_proto);
-    mwSize dims[4] = {data_mean.width(), data_mean.height(),
-                      data_mean.channels(), data_mean.num() };
-    mxArray* mx_blob =  mxCreateNumericArray(4, dims, mxSINGLE_CLASS, mxREAL);
-    float* data_ptr = reinterpret_cast<float*>(mxGetPr(mx_blob));
-    caffe_copy(data_mean.count(), data_mean.cpu_data(), data_ptr);
-    mexWarnMsgTxt("Remember that Caffe saves in [width, height, channels]"
-                  " format and channels are also BGR!");
-    plhs[0] = mx_blob;
-}
-
-/** -----------------------------------------------------------------
- ** Available commands.
- **/
-struct handler_registry {
-  string cmd;
-  void (*func)(MEX_ARGS);
-};
-
-static handler_registry handlers[] = {
-  // Public API functions
-  { "forward",            forward         },
-  { "backward",           backward        },
-  { "init",               init            },
-  { "is_initialized",     is_initialized  },
-  { "set_mode_cpu",       set_mode_cpu    },
-  { "set_mode_gpu",       set_mode_gpu    },
-  { "set_phase_train",    set_phase_train },
-  { "set_phase_test",     set_phase_test  },
-  { "set_device",         set_device      },
-  { "get_weights",        get_weights     },
-  { "get_init_key",       get_init_key    },
-  { "reset",              reset           },
-  { "read_mean",          read_mean       },
-  // The end.
-  { "END",                NULL            },
-};
-
-
-/** -----------------------------------------------------------------
- ** matlab entry point: caffe(api_command, arg1, arg2, ...)
- **/
-void mexFunction(MEX_ARGS) {
-  if (nrhs == 0) {
-    LOG(ERROR) << "No API command given";
-    mexErrMsgTxt("An API command is requires");
-    return;
-  }
-
-  { // Handle input command
-    char *cmd = mxArrayToString(prhs[0]);
-    bool dispatched = false;
-    // Dispatch to cmd handler
-    for (int i = 0; handlers[i].func != NULL; i++) {
-      if (handlers[i].cmd.compare(cmd) == 0) {
-        handlers[i].func(nlhs, plhs, nrhs-1, prhs+1);
-        dispatched = true;
-        break;
-      }
-    }
-    if (!dispatched) {
-      LOG(ERROR) << "Unknown command `" << cmd << "'";
-      mexErrMsgTxt("API command not recognized");
-    }
-    mxFree(cmd);
-  }
-}
diff --git a/matlab/caffe/matcaffe_batch.m b/matlab/caffe/matcaffe_batch.m
deleted file mode 100644
index f6d1aa83b84..00000000000
--- a/matlab/caffe/matcaffe_batch.m
+++ /dev/null
@@ -1,75 +0,0 @@
-function [scores,list_im] = matcaffe_batch(list_im, use_gpu)
-% scores = matcaffe_batch(list_im, use_gpu)
-%
-% Demo of the matlab wrapper using the ILSVRC network.
-%
-% input
-%   list_im  list of images files
-%   use_gpu  1 to use the GPU, 0 to use the CPU
-%
-% output
-%   scores   1000 x num_images ILSVRC output vector
-%
-% You may need to do the following before you start matlab:
-%  $ export LD_LIBRARY_PATH=/opt/intel/mkl/lib/intel64:/usr/local/cuda/lib64
-%  $ export LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libstdc++.so.6
-% Or the equivalent based on where things are installed on your system
-%
-% Usage:
-%  scores = matcaffe_batch({'peppers.png','onion.png'});
-%  scores = matcaffe_batch('list_images.txt', 1);
-if nargin < 1
-  % For test purposes
-  list_im = {'peppers.png','onions.png'};
-end
-if ischar(list_im)
-    %Assume it is a file contaning the list of images
-    filename = list_im;
-    list_im = read_cell(filename);
-end
-% Adjust the batch size and dim to match with models/bvlc_reference_caffenet/deploy.prototxt
-batch_size = 10;
-dim = 1000;
-disp(list_im)
-if mod(length(list_im),batch_size)
-    warning(['Assuming batches of ' num2str(batch_size) ' images rest will be filled with zeros'])
-end
-
-% init caffe network (spews logging info)
-if exist('use_gpu', 'var')
-  matcaffe_init(use_gpu);
-else
-  matcaffe_init();
-end
-
-d = load('ilsvrc_2012_mean');
-IMAGE_MEAN = d.image_mean;
-
-% prepare input
-
-num_images = length(list_im);
-scores = zeros(dim,num_images,'single');
-num_batches = ceil(length(list_im)/batch_size)
-initic=tic;
-for bb = 1 : num_batches
-    batchtic = tic;
-    range = 1+batch_size*(bb-1):min(num_images,batch_size * bb);
-    tic
-    input_data = prepare_batch(list_im(range),IMAGE_MEAN,batch_size);
-    toc, tic
-    fprintf('Batch %d out of %d %.2f%% Complete ETA %.2f seconds\n',...
-        bb,num_batches,bb/num_batches*100,toc(initic)/bb*(num_batches-bb));
-    output_data = caffe('forward', {input_data});
-    toc
-    output_data = squeeze(output_data{1});
-    scores(:,range) = output_data(:,mod(range-1,batch_size)+1);
-    toc(batchtic)
-end
-toc(initic);
-
-if exist('filename', 'var')
-    save([filename '.probs.mat'],'list_im','scores','-v7.3');
-end
-
-
-
diff --git a/matlab/caffe/matcaffe_demo.m b/matlab/caffe/matcaffe_demo.m
deleted file mode 100644
index a931f910cbf..00000000000
--- a/matlab/caffe/matcaffe_demo.m
+++ /dev/null
@@ -1,110 +0,0 @@
-function [scores, maxlabel] = matcaffe_demo(im, use_gpu)
-% scores = matcaffe_demo(im, use_gpu)
-%
-% Demo of the matlab wrapper using the ILSVRC network.
-%
-% input
-%   im       color image as uint8 HxWx3
-%   use_gpu  1 to use the GPU, 0 to use the CPU
-%
-% output
-%   scores   1000-dimensional ILSVRC score vector
-%
-% You may need to do the following before you start matlab:
-%  $ export LD_LIBRARY_PATH=/opt/intel/mkl/lib/intel64:/usr/local/cuda-5.5/lib64
-%  $ export LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libstdc++.so.6
-% Or the equivalent based on where things are installed on your system
-%
-% Usage:
-%  im = imread('../../examples/images/cat.jpg');
-%  scores = matcaffe_demo(im, 1);
-%  [score, class] = max(scores);
-% Five things to be aware of:
-%   caffe uses row-major order
-%   matlab uses column-major order
-%   caffe uses BGR color channel order
-%   matlab uses RGB color channel order
-%   images need to have the data mean subtracted
-
-% Data coming in from matlab needs to be in the order 
-%   [width, height, channels, images]
-% where width is the fastest dimension.
-% Here is the rough matlab for putting image data into the correct
-% format:
-%   % convert from uint8 to single
-%   im = single(im);
-%   % reshape to a fixed size (e.g., 227x227)
-%   im = imresize(im, [IMAGE_DIM IMAGE_DIM], 'bilinear');
-%   % permute from RGB to BGR and subtract the data mean (already in BGR)
-%   im = im(:,:,[3 2 1]) - data_mean;
-%   % flip width and height to make width the fastest dimension
-%   im = permute(im, [2 1 3]);
-
-% If you have multiple images, cat them with cat(4, ...)
-
-% The actual forward function. It takes in a cell array of 4-D arrays as
-% input and outputs a cell array. 
-
-
-% init caffe network (spews logging info)
-if exist('use_gpu', 'var')
-  matcaffe_init(use_gpu);
-else
-  matcaffe_init();
-end
-
-if nargin < 1
-  % For demo purposes we will use the peppers image
-  im = imread('peppers.png');
-end
-
-% prepare oversampled input
-% input_data is Height x Width x Channel x Num
-tic;
-input_data = {prepare_image(im)};
-toc;
-
-% do forward pass to get scores
-% scores are now Width x Height x Channels x Num
-tic;
-scores = caffe('forward', input_data);
-toc;
-
-scores = scores{1};
-size(scores)
-scores = squeeze(scores);
-scores = mean(scores,2);
-
-[~,maxlabel] = max(scores);
-
-% ------------------------------------------------------------------------
-function images = prepare_image(im)
-% ------------------------------------------------------------------------
-d = load('ilsvrc_2012_mean');
-IMAGE_MEAN = d.image_mean;
-IMAGE_DIM = 256;
-CROPPED_DIM = 227;
-
-% resize to fixed input size
-im = single(im);
-im = imresize(im, [IMAGE_DIM IMAGE_DIM], 'bilinear');
-% permute from RGB to BGR (IMAGE_MEAN is already BGR)
-im = im(:,:,[3 2 1]) - IMAGE_MEAN;
-
-% oversample (4 corners, center, and their x-axis flips)
-images = zeros(CROPPED_DIM, CROPPED_DIM, 3, 10, 'single');
-indices = [0 IMAGE_DIM-CROPPED_DIM] + 1;
-curr = 1;
-for i = indices
-  for j = indices
-    images(:, :, :, curr) = ...
-        permute(im(i:i+CROPPED_DIM-1, j:j+CROPPED_DIM-1, :), [2 1 3]);
-    images(:, :, :, curr+5) = images(end:-1:1, :, :, curr);
-    curr = curr + 1;
-  end
-end
-center = floor(indices(2) / 2)+1;
-images(:,:,:,5) = ...
-    permute(im(center:center+CROPPED_DIM-1,center:center+CROPPED_DIM-1,:), ...
-        [2 1 3]);
-images(:,:,:,10) = images(end:-1:1, :, :, curr);
diff --git a/matlab/caffe/matcaffe_init.m b/matlab/caffe/matcaffe_init.m
deleted file mode 100644
index 7cc6935758e..00000000000
--- a/matlab/caffe/matcaffe_init.m
+++ /dev/null
@@ -1,44 +0,0 @@
-function  matcaffe_init(use_gpu, model_def_file, model_file)
-% matcaffe_init(model_def_file, model_file, use_gpu)
-% Initilize matcaffe wrapper
-
-if nargin < 1
-  % By default use CPU
-  use_gpu = 0;
-end
-if nargin < 2 || isempty(model_def_file)
-  % By default use imagenet_deploy
-  model_def_file = '../../models/bvlc_reference_caffenet/deploy.prototxt';
-end
-if nargin < 3 || isempty(model_file)
-  % By default use caffe reference model
-  model_file = '../../models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel';
-end
-
-
-if caffe('is_initialized') == 0
-  if exist(model_file, 'file') == 0
-    % NOTE: you'll have to get the pre-trained ILSVRC network
-    error('You need a network model file');
-  end
-  if ~exist(model_def_file,'file')
-    % NOTE: you'll have to get network definition
-    error('You need the network prototxt definition');
-  end
-  caffe('init', model_def_file, model_file)
-end
-fprintf('Done with init\n');
-
-% set to use GPU or CPU
-if use_gpu
-  fprintf('Using GPU Mode\n');
-  caffe('set_mode_gpu');
-else
-  fprintf('Using CPU Mode\n');
-  caffe('set_mode_cpu');
-end
-fprintf('Done with set_mode\n');
-
-% put into test mode
-caffe('set_phase_test');
-fprintf('Done with set_phase_test\n');
diff --git a/matlab/caffe/prepare_batch.m b/matlab/caffe/prepare_batch.m
deleted file mode 100644
index 345c8eb5f0b..00000000000
--- a/matlab/caffe/prepare_batch.m
+++ /dev/null
@@ -1,41 +0,0 @@
-% ------------------------------------------------------------------------
-function images = prepare_batch(image_files,IMAGE_MEAN,batch_size)
-% ------------------------------------------------------------------------
-if nargin < 2
-    d = load('ilsvrc_2012_mean');
-    IMAGE_MEAN = d.image_mean; 
-end
-num_images = length(image_files);
-if nargin < 3
-    batch_size = num_images;
-end
-
-IMAGE_DIM = 256;
-CROPPED_DIM = 227;
-indices = [0 IMAGE_DIM-CROPPED_DIM] + 1;
-center = floor(indices(2) / 2)+1;
-
-num_images = length(image_files);
-images = zeros(CROPPED_DIM,CROPPED_DIM,3,batch_size,'single');
-
-parfor i=1:num_images
-    % read file
-    fprintf('%c Preparing %s\n',13,image_files{i});
-    try
-        im = imread(image_files{i});
-        % resize to fixed input size
-        im = single(im);
-        im = imresize(im, [IMAGE_DIM IMAGE_DIM], 'bilinear');
-        % Transform GRAY to RGB
-        if size(im,3) == 1
-            im = cat(3,im,im,im);
-        end
-        % permute from RGB to BGR (IMAGE_MEAN is already BGR)
-        im = im(:,:,[3 2 1]) - IMAGE_MEAN;
-        % Crop the center of the image
-        images(:,:,:,i) = permute(im(center:center+CROPPED_DIM-1,...
-            center:center+CROPPED_DIM-1,:),[2 1 3]);
-    catch
-        warning('Problems with file',image_files{i});
-    end
-end
\ No newline at end of file
diff --git a/matlab/caffe/print_cell.m b/matlab/caffe/print_cell.m
deleted file mode 100644
index 864340d4be9..00000000000
--- a/matlab/caffe/print_cell.m
+++ /dev/null
@@ -1,42 +0,0 @@
-function res=print_cell(input,file,linesep,cellsep)
-assert(iscell(input),'The input should be a cell')
-if nargin < 4
-    cellsep = '\t';
-end
-if nargin < 3
-    linesep = '\n';
-end
-if exist('file','var') && ~isempty(file)
-    %%
-    fid = fopen(file,'w');
-    for l=1:length(input)
-        if iscell(input{l})
-            for i=1:length(input{l})
-                fprintf(fid,['%s' cellsep],input{l}{i});
-            end
-            fprintf(fid,linesep);
-        else
-            if size(input,2) > 1
-                for i=1:size(input,2)
-                    fprintf(fid,'%s ',input{l,i});
-                end
-                fprintf(fid,linesep);
-            else
-                fprintf(fid,['%s' linesep],input{l});
-            end
-        end
-    end
-    fclose(fid);
-else
-    res = '';
-    for l=1:length(input)
-        if iscell(input{l})
-            for i=1:length(input{l})
-                res = [res sprintf([cellsep{1} '%s' cellsep{2}],input{l}{i})];
-            end
-            res = [res sprintf(linesep)];
-        else
-            res = [res sprintf(['%s' linesep],input{l}(:))];
-        end
-    end
-end
\ No newline at end of file
diff --git a/matlab/caffe/read_cell.m b/matlab/caffe/read_cell.m
deleted file mode 100644
index 19831167106..00000000000
--- a/matlab/caffe/read_cell.m
+++ /dev/null
@@ -1,21 +0,0 @@
-function res=read_cell(filename,linesep,cellsep)
-if nargin < 2, linesep='\n'; end
-if nargin < 3, cellsep = '\t'; end
-if exist(filename,'file')
-    fid = fopen(filename);
-else
-    % Assume that filename is either a file ide or a string
-    fid = filename;
-end
-
-fileLines = textscan(fid,'%s','delimiter',linesep,'BufSize',100000);
-
-fileLines = fileLines{1};
-
-if regexp(fileLines{1},cellsep,'once')
-    fileLines = regexprep(fileLines,['^' cellsep '|' cellsep '$'],'');
-    res = regexp(fileLines,cellsep,'split');
-    res = cell2matcell(res);
-else
-    res = fileLines;
-end
diff --git a/matlab/demo/classification_demo.m b/matlab/demo/classification_demo.m
new file mode 100644
index 00000000000..2b60332970b
--- /dev/null
+++ b/matlab/demo/classification_demo.m
@@ -0,0 +1,147 @@
+function [scores, maxlabel] = classification_demo(im, use_gpu)
+% [scores, maxlabel] = classification_demo(im, use_gpu)
+%
+% Image classification demo using BVLC CaffeNet.
+%
+% IMPORTANT: before you run this demo, you should download BVLC CaffeNet
+% from Model Zoo (http://caffe.berkeleyvision.org/model_zoo.html)
+%
+% ****************************************************************************
+% For detailed documentation and usage on Caffe's Matlab interface, please
+% refer to Caffe Interface Tutorial at
+% http://caffe.berkeleyvision.org/tutorial/interfaces.html#matlab
+% ****************************************************************************
+%
+% input
+%   im       color image as uint8 HxWx3
+%   use_gpu  1 to use the GPU, 0 to use the CPU
+%
+% output
+%   scores   1000-dimensional ILSVRC score vector
+%   maxlabel the label of the highest score
+%
+% You may need to do the following before you start matlab:
+%  $ export LD_LIBRARY_PATH=/opt/intel/mkl/lib/intel64:/usr/local/cuda-5.5/lib64
+%  $ export LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libstdc++.so.6
+% Or the equivalent based on where things are installed on your system
+%
+% Usage:
+%  im = imread('../../examples/images/cat.jpg');
+%  scores = classification_demo(im, 1);
+%  [score, class] = max(scores);
+% Five things to be aware of:
+%   caffe uses row-major order
+%   matlab uses column-major order
+%   caffe uses BGR color channel order
+%   matlab uses RGB color channel order
+%   images need to have the data mean subtracted
+
+% Data coming in from matlab needs to be in the order
+%   [width, height, channels, images]
+% where width is the fastest dimension.
+% Here is the rough matlab for putting image data into the correct
+% format in W x H x C with BGR channels:
+%   % permute channels from RGB to BGR
+%   im_data = im(:, :, [3, 2, 1]);
+%   % flip width and height to make width the fastest dimension
+%   im_data = permute(im_data, [2, 1, 3]);
+%   % convert from uint8 to single
+%   im_data = single(im_data);
+%   % reshape to a fixed size (e.g., 227x227).
+%   im_data = imresize(im_data, [IMAGE_DIM IMAGE_DIM], 'bilinear');
+%   % subtract mean_data (already in W x H x C with BGR channels)
+%   im_data = im_data - mean_data;
+
+% If you have multiple images, cat them with cat(4, ...)
+
+% Add caffe/matlab to you Matlab search PATH to use matcaffe
+if exist('../+caffe', 'dir')
+  addpath('..');
+else
+  error('Please run this demo from caffe/matlab/demo');
+end
+
+% Set caffe mode
+if exist('use_gpu', 'var') && use_gpu
+  caffe.set_mode_gpu();
+  gpu_id = 0;  % we will use the first gpu in this demo
+  caffe.set_device(gpu_id);
+else
+  caffe.set_mode_cpu();
+end
+
+% Initialize the network using BVLC CaffeNet for image classification
+% Weights (parameter) file needs to be downloaded from Model Zoo.
+model_dir = '../../models/bvlc_reference_caffenet/';
+net_model = [model_dir 'deploy.prototxt'];
+net_weights = [model_dir 'bvlc_reference_caffenet.caffemodel'];
+phase = 'test'; % run with phase test (so that dropout isn't applied)
+if ~exist(net_weights, 'file')
+  error('Please download CaffeNet from Model Zoo before you run this demo');
+end
+
+% Initialize a network
+net = caffe.Net(net_model, net_weights, phase);
+
+if nargin < 1
+  % For demo purposes we will use the cat image
+  fprintf('using caffe/examples/images/cat.jpg as input image\n');
+  im = imread('../../examples/images/cat.jpg');
+end
+
+% prepare oversampled input
+% input_data is Height x Width x Channel x Num
+tic;
+input_data = {prepare_image(im)};
+toc;
+
+% do forward pass to get scores
+% scores are now Channels x Num, where Channels == 1000
+tic;
+% The net forward function. It takes in a cell array of N-D arrays
+% (where N == 4 here) containing data of input blob(s) and outputs a cell
+% array containing data from output blob(s)
+scores = net.forward(input_data);
+toc;
+
+scores = scores{1};
+scores = mean(scores, 2);  % take average scores over 10 crops
+
+[~, maxlabel] = max(scores);
+
+% call caffe.reset_all() to reset caffe
+caffe.reset_all();
+
+% ------------------------------------------------------------------------
+function crops_data = prepare_image(im)
+% ------------------------------------------------------------------------
+% caffe/matlab/+caffe/imagenet/ilsvrc_2012_mean.mat contains mean_data that
+% is already in W x H x C with BGR channels
+d = load('../+caffe/imagenet/ilsvrc_2012_mean.mat');
+mean_data = d.mean_data;
+IMAGE_DIM = 256;
+CROPPED_DIM = 227;
+
+% Convert an image returned by Matlab's imread to im_data in caffe's data
+% format: W x H x C with BGR channels
+im_data = im(:, :, [3, 2, 1]);  % permute channels from RGB to BGR
+im_data = permute(im_data, [2, 1, 3]);  % flip width and height
+im_data = single(im_data);  % convert from uint8 to single
+im_data = imresize(im_data, [IMAGE_DIM IMAGE_DIM], 'bilinear');  % resize im_data
+im_data = im_data - mean_data;  % subtract mean_data (already in W x H x C, BGR)
+
+% oversample (4 corners, center, and their x-axis flips)
+crops_data = zeros(CROPPED_DIM, CROPPED_DIM, 3, 10, 'single');
+indices = [0 IMAGE_DIM-CROPPED_DIM] + 1;
+n = 1;
+for i = indices
+  for j = indices
+    crops_data(:, :, :, n) = im_data(i:i+CROPPED_DIM-1, j:j+CROPPED_DIM-1, :);
+    crops_data(:, :, :, n+5) = crops_data(end:-1:1, :, :, n);
+    n = n + 1;
+  end
+end
+center = floor(indices(2) / 2) + 1;
+crops_data(:,:,:,5) = ...
+  im_data(center:center+CROPPED_DIM-1,center:center+CROPPED_DIM-1,:);
+crops_data(:,:,:,10) = crops_data(end:-1:1, :, :, 5);
diff --git a/matlab/hdf5creation/.gitignore b/matlab/hdf5creation/.gitignore
new file mode 100644
index 00000000000..e2333dd1776
--- /dev/null
+++ b/matlab/hdf5creation/.gitignore
@@ -0,0 +1,2 @@
+*.h5
+list.txt
diff --git a/matlab/hdf5creation/demo.m b/matlab/hdf5creation/demo.m
new file mode 100644
index 00000000000..4f9f7b5a454
--- /dev/null
+++ b/matlab/hdf5creation/demo.m
@@ -0,0 +1,64 @@
+%% WRITING TO HDF5
+filename='trial.h5';
+
+num_total_samples=10000;
+% to simulate data being read from disk / generated etc.
+data_disk=rand(5,5,1,num_total_samples); 
+label_disk=rand(10,num_total_samples); 
+
+chunksz=100;
+created_flag=false;
+totalct=0;
+for batchno=1:num_total_samples/chunksz
+  fprintf('batch no. %d\n', batchno);
+  last_read=(batchno-1)*chunksz;
+
+  % to simulate maximum data to be held in memory before dumping to hdf5 file 
+  batchdata=data_disk(:,:,1,last_read+1:last_read+chunksz); 
+  batchlabs=label_disk(:,last_read+1:last_read+chunksz);
+
+  % store to hdf5
+  startloc=struct('dat',[1,1,1,totalct+1], 'lab', [1,totalct+1]);
+  curr_dat_sz=store2hdf5(filename, batchdata, batchlabs, ~created_flag, startloc, chunksz); 
+  created_flag=true;% flag set so that file is created only once
+  totalct=curr_dat_sz(end);% updated dataset size (#samples)
+end
+
+% display structure of the stored HDF5 file
+h5disp(filename);
+
+%% READING FROM HDF5
+
+% Read data and labels for samples #1000 to 1999
+data_rd=h5read(filename, '/data', [1 1 1 1000], [5, 5, 1, 1000]);
+label_rd=h5read(filename, '/label', [1 1000], [10, 1000]);
+fprintf('Testing ...\n');
+try 
+  assert(isequal(data_rd, single(data_disk(:,:,:,1000:1999))), 'Data do not match');
+  assert(isequal(label_rd, single(label_disk(:,1000:1999))), 'Labels do not match');
+
+  fprintf('Success!\n');
+catch err
+  fprintf('Test failed ...\n');
+  getReport(err)
+end
+
+%delete(filename);
+
+% CREATE list.txt containing filename, to be used as source for HDF5_DATA_LAYER
+FILE=fopen('list.txt', 'w');
+fprintf(FILE, '%s', filename);
+fclose(FILE);
+fprintf('HDF5 filename listed in %s \n', 'list.txt');
+
+% NOTE: In net definition prototxt, use list.txt as input to HDF5_DATA as: 
+% layer {
+%   name: "data"
+%   type: "HDF5Data"
+%   top: "data"
+%   top: "labelvec"
+%   hdf5_data_param {
+%     source: "/path/to/list.txt"
+%     batch_size: 64
+%   }
+% }
diff --git a/matlab/hdf5creation/store2hdf5.m b/matlab/hdf5creation/store2hdf5.m
new file mode 100644
index 00000000000..0a0016dca40
--- /dev/null
+++ b/matlab/hdf5creation/store2hdf5.m
@@ -0,0 +1,59 @@
+function [curr_dat_sz, curr_lab_sz] = store2hdf5(filename, data, labels, create, startloc, chunksz)  
+  % *data* is W*H*C*N matrix of images should be normalized (e.g. to lie between 0 and 1) beforehand
+  % *label* is D*N matrix of labels (D labels per sample) 
+  % *create* [0/1] specifies whether to create file newly or to append to previously created file, useful to store information in batches when a dataset is too big to be held in memory  (default: 1)
+  % *startloc* (point at which to start writing data). By default, 
+  % if create=1 (create mode), startloc.data=[1 1 1 1], and startloc.lab=[1 1]; 
+  % if create=0 (append mode), startloc.data=[1 1 1 K+1], and startloc.lab = [1 K+1]; where K is the current number of samples stored in the HDF
+  % chunksz (used only in create mode), specifies number of samples to be stored per chunk (see HDF5 documentation on chunking) for creating HDF5 files with unbounded maximum size - TLDR; higher chunk sizes allow faster read-write operations 
+
+  % verify that format is right
+  dat_dims=size(data);
+  lab_dims=size(labels);
+  num_samples=dat_dims(end);
+
+  assert(lab_dims(end)==num_samples, 'Number of samples should be matched between data and labels');
+
+  if ~exist('create','var')
+    create=true;
+  end
+
+  
+  if create
+    %fprintf('Creating dataset with %d samples\n', num_samples);
+    if ~exist('chunksz', 'var')
+      chunksz=1000;
+    end
+    if exist(filename, 'file')
+      fprintf('Warning: replacing existing file %s \n', filename);
+      delete(filename);
+    end      
+    h5create(filename, '/data', [dat_dims(1:end-1) Inf], 'Datatype', 'single', 'ChunkSize', [dat_dims(1:end-1) chunksz]); % width, height, channels, number 
+    h5create(filename, '/label', [lab_dims(1:end-1) Inf], 'Datatype', 'single', 'ChunkSize', [lab_dims(1:end-1) chunksz]); % width, height, channels, number 
+    if ~exist('startloc','var') 
+      startloc.dat=[ones(1,length(dat_dims)-1), 1];
+      startloc.lab=[ones(1,length(lab_dims)-1), 1];
+    end 
+  else  % append mode
+    if ~exist('startloc','var')
+      info=h5info(filename);
+      prev_dat_sz=info.Datasets(1).Dataspace.Size;
+      prev_lab_sz=info.Datasets(2).Dataspace.Size;
+      assert(prev_dat_sz(1:end-1)==dat_dims(1:end-1), 'Data dimensions must match existing dimensions in dataset');
+      assert(prev_lab_sz(1:end-1)==lab_dims(1:end-1), 'Label dimensions must match existing dimensions in dataset');
+      startloc.dat=[ones(1,length(dat_dims)-1), prev_dat_sz(end)+1];
+      startloc.lab=[ones(1,length(lab_dims)-1), prev_lab_sz(end)+1];
+    end
+  end
+
+  if ~isempty(data)
+    h5write(filename, '/data', single(data), startloc.dat, size(data));
+    h5write(filename, '/label', single(labels), startloc.lab, size(labels));  
+  end
+
+  if nargout
+    info=h5info(filename);
+    curr_dat_sz=info.Datasets(1).Dataspace.Size;
+    curr_lab_sz=info.Datasets(2).Dataspace.Size;
+  end
+end
diff --git a/models/action_recognition/cuhk_action_spatial_vgg_16_deploy.prototxt b/models/action_recognition/cuhk_action_spatial_vgg_16_deploy.prototxt
new file mode 100644
index 00000000000..c67819bc7d2
--- /dev/null
+++ b/models/action_recognition/cuhk_action_spatial_vgg_16_deploy.prototxt
@@ -0,0 +1,339 @@
+name: "CUHK_Action_SpatialNet"
+input: "data"
+input_dim: 50
+input_dim: 3
+input_dim: 224
+input_dim: 224
+layer {
+  name: "conv1_1"
+  type: "Convolution"
+  bottom: "data"
+  top: "conv1_1"
+  convolution_param {
+    num_output: 64
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "relu1_1"
+  type: "ReLU"
+  bottom: "conv1_1"
+  top: "conv1_1"
+}
+layer {
+  name: "conv1_2"
+  type: "Convolution"
+  bottom: "conv1_1"
+  top: "conv1_2"
+  convolution_param {
+    num_output: 64
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "relu1_2"
+  type: "ReLU"
+  bottom: "conv1_2"
+  top: "conv1_2"
+}
+layer {
+  name: "pool1"
+  type: "Pooling"
+  bottom: "conv1_2"
+  top: "pool1"
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+  }
+}
+layer {
+  name: "conv2_1"
+  type: "Convolution"
+  bottom: "pool1"
+  top: "conv2_1"
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "relu2_1"
+  type: "ReLU"
+  bottom: "conv2_1"
+  top: "conv2_1"
+}
+layer {
+  name: "conv2_2"
+  type: "Convolution"
+  bottom: "conv2_1"
+  top: "conv2_2"
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "relu2_2"
+  type: "ReLU"
+  bottom: "conv2_2"
+  top: "conv2_2"
+}
+layer {
+  name: "pool2"
+  type: "Pooling"
+  bottom: "conv2_2"
+  top: "pool2"
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+  }
+}
+layer {
+  name: "conv3_1"
+  type: "Convolution"
+  bottom: "pool2"
+  top: "conv3_1"
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "relu3_1"
+  type: "ReLU"
+  bottom: "conv3_1"
+  top: "conv3_1"
+}
+layer {
+  name: "conv3_2"
+  type: "Convolution"
+  bottom: "conv3_1"
+  top: "conv3_2"
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "relu3_2"
+  type: "ReLU"
+  bottom: "conv3_2"
+  top: "conv3_2"
+}
+layer {
+  name: "conv3_3"
+  type: "Convolution"
+  bottom: "conv3_2"
+  top: "conv3_3"
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "relu3_3"
+  type: "ReLU"
+  bottom: "conv3_3"
+  top: "conv3_3"
+}
+layer {
+  name: "pool3"
+  type: "Pooling"
+  bottom: "conv3_3"
+  top: "pool3"
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+  }
+}
+layer {
+  name: "conv4_1"
+  type: "Convolution"
+  bottom: "pool3"
+  top: "conv4_1"
+  convolution_param {
+    num_output: 512
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "relu4_1"
+  type: "ReLU"
+  bottom: "conv4_1"
+  top: "conv4_1"
+}
+layer {
+  name: "conv4_2"
+  type: "Convolution"
+  bottom: "conv4_1"
+  top: "conv4_2"
+  convolution_param {
+    num_output: 512
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "relu4_2"
+  type: "ReLU"
+  bottom: "conv4_2"
+  top: "conv4_2"
+}
+layer {
+  name: "conv4_3"
+  type: "Convolution"
+  bottom: "conv4_2"
+  top: "conv4_3"
+  convolution_param {
+    num_output: 512
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "relu4_3"
+  type: "ReLU"
+  bottom: "conv4_3"
+  top: "conv4_3"
+}
+layer {
+  name: "pool4"
+  type: "Pooling"
+  bottom: "conv4_3"
+  top: "pool4"
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+  }
+}
+layer {
+  name: "conv5_1"
+  type: "Convolution"
+  bottom: "pool4"
+  top: "conv5_1"
+  convolution_param {
+    num_output: 512
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "relu5_1"
+  type: "ReLU"
+  bottom: "conv5_1"
+  top: "conv5_1"
+}
+layer {
+  name: "conv5_2"
+  type: "Convolution"
+  bottom: "conv5_1"
+  top: "conv5_2"
+  convolution_param {
+    num_output: 512
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "relu5_2"
+  type: "ReLU"
+  bottom: "conv5_2"
+  top: "conv5_2"
+}
+layer {
+  name: "conv5_3"
+  type: "Convolution"
+  bottom: "conv5_2"
+  top: "conv5_3"
+  convolution_param {
+    num_output: 512
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "relu5_3"
+  type: "ReLU"
+  bottom: "conv5_3"
+  top: "conv5_3"
+}
+layer {
+  name: "pool5"
+  type: "Pooling"
+  bottom: "conv5_3"
+  top: "pool5"
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+  }
+}
+layer {
+  name: "fc6"
+  type: "InnerProduct"
+  bottom: "pool5"
+  top: "fc6"
+  inner_product_param {
+    num_output: 4096
+  }
+}
+layer {
+  name: "relu6"
+  type: "ReLU"
+  bottom: "fc6"
+  top: "fc6"
+}
+layer {
+  name: "drop6"
+  type: "Dropout"
+  bottom: "fc6"
+  top: "fc6"
+  dropout_param {
+    dropout_ratio: 0.95
+  }
+}
+layer {
+  name: "fc7"
+  type: "InnerProduct"
+  bottom: "fc6"
+  top: "fc7"
+  inner_product_param {
+    num_output: 4096
+  }
+}
+layer {
+  name: "relu7"
+  type: "ReLU"
+  bottom: "fc7"
+  top: "fc7"
+}
+layer {
+  name: "drop7"
+  type: "Dropout"
+  bottom: "fc7"
+  top: "fc7"
+  dropout_param {
+    dropout_ratio: 0.9
+  }
+}
+layer {
+  name: "fc8-1"
+  type: "InnerProduct"
+  bottom: "fc7"
+  top: "fc8-1"
+  inner_product_param {
+    num_output: 101
+  }
+}
diff --git a/models/action_recognition/cuhk_action_temporal_vgg_16_flow_deploy.prototxt b/models/action_recognition/cuhk_action_temporal_vgg_16_flow_deploy.prototxt
new file mode 100644
index 00000000000..e5c2e1a039e
--- /dev/null
+++ b/models/action_recognition/cuhk_action_temporal_vgg_16_flow_deploy.prototxt
@@ -0,0 +1,339 @@
+name: "CUHK_Action_TemporalNet"
+input: "data"
+input_dim: 50
+input_dim: 20
+input_dim: 224
+input_dim: 224
+layer {
+  name: "conv1_1"
+  type: "Convolution"
+  bottom: "data"
+  top: "conv1_1"
+  convolution_param {
+    num_output: 64
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "relu1_1"
+  type: "ReLU"
+  bottom: "conv1_1"
+  top: "conv1_1"
+}
+layer {
+  name: "conv1_2"
+  type: "Convolution"
+  bottom: "conv1_1"
+  top: "conv1_2"
+  convolution_param {
+    num_output: 64
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "relu1_2"
+  type: "ReLU"
+  bottom: "conv1_2"
+  top: "conv1_2"
+}
+layer {
+  name: "pool1"
+  type: "Pooling"
+  bottom: "conv1_2"
+  top: "pool1"
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+  }
+}
+layer {
+  name: "conv2_1"
+  type: "Convolution"
+  bottom: "pool1"
+  top: "conv2_1"
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "relu2_1"
+  type: "ReLU"
+  bottom: "conv2_1"
+  top: "conv2_1"
+}
+layer {
+  name: "conv2_2"
+  type: "Convolution"
+  bottom: "conv2_1"
+  top: "conv2_2"
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "relu2_2"
+  type: "ReLU"
+  bottom: "conv2_2"
+  top: "conv2_2"
+}
+layer {
+  name: "pool2"
+  type: "Pooling"
+  bottom: "conv2_2"
+  top: "pool2"
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+  }
+}
+layer {
+  name: "conv3_1"
+  type: "Convolution"
+  bottom: "pool2"
+  top: "conv3_1"
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "relu3_1"
+  type: "ReLU"
+  bottom: "conv3_1"
+  top: "conv3_1"
+}
+layer {
+  name: "conv3_2"
+  type: "Convolution"
+  bottom: "conv3_1"
+  top: "conv3_2"
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "relu3_2"
+  type: "ReLU"
+  bottom: "conv3_2"
+  top: "conv3_2"
+}
+layer {
+  name: "conv3_3"
+  type: "Convolution"
+  bottom: "conv3_2"
+  top: "conv3_3"
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "relu3_3"
+  type: "ReLU"
+  bottom: "conv3_3"
+  top: "conv3_3"
+}
+layer {
+  name: "pool3"
+  type: "Pooling"
+  bottom: "conv3_3"
+  top: "pool3"
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+  }
+}
+layer {
+  name: "conv4_1"
+  type: "Convolution"
+  bottom: "pool3"
+  top: "conv4_1"
+  convolution_param {
+    num_output: 512
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "relu4_1"
+  type: "ReLU"
+  bottom: "conv4_1"
+  top: "conv4_1"
+}
+layer {
+  name: "conv4_2"
+  type: "Convolution"
+  bottom: "conv4_1"
+  top: "conv4_2"
+  convolution_param {
+    num_output: 512
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "relu4_2"
+  type: "ReLU"
+  bottom: "conv4_2"
+  top: "conv4_2"
+}
+layer {
+  name: "conv4_3"
+  type: "Convolution"
+  bottom: "conv4_2"
+  top: "conv4_3"
+  convolution_param {
+    num_output: 512
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "relu4_3"
+  type: "ReLU"
+  bottom: "conv4_3"
+  top: "conv4_3"
+}
+layer {
+  name: "pool4"
+  type: "Pooling"
+  bottom: "conv4_3"
+  top: "pool4"
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+  }
+}
+layer {
+  name: "conv5_1"
+  type: "Convolution"
+  bottom: "pool4"
+  top: "conv5_1"
+  convolution_param {
+    num_output: 512
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "relu5_1"
+  type: "ReLU"
+  bottom: "conv5_1"
+  top: "conv5_1"
+}
+layer {
+  name: "conv5_2"
+  type: "Convolution"
+  bottom: "conv5_1"
+  top: "conv5_2"
+  convolution_param {
+    num_output: 512
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "relu5_2"
+  type: "ReLU"
+  bottom: "conv5_2"
+  top: "conv5_2"
+}
+layer {
+  name: "conv5_3"
+  type: "Convolution"
+  bottom: "conv5_2"
+  top: "conv5_3"
+  convolution_param {
+    num_output: 512
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "relu5_3"
+  type: "ReLU"
+  bottom: "conv5_3"
+  top: "conv5_3"
+}
+layer {
+  name: "pool5"
+  type: "Pooling"
+  bottom: "conv5_3"
+  top: "pool5"
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+  }
+}
+layer {
+  name: "fc6"
+  type: "InnerProduct"
+  bottom: "pool5"
+  top: "fc6"
+  inner_product_param {
+    num_output: 4096
+  }
+}
+layer {
+  name: "relu6"
+  type: "ReLU"
+  bottom: "fc6"
+  top: "fc6"
+}
+layer {
+  name: "drop6"
+  type: "Dropout"
+  bottom: "fc6"
+  top: "fc6"
+  dropout_param {
+    dropout_ratio: 0.9
+  }
+}
+layer {
+  name: "fc7"
+  type: "InnerProduct"
+  bottom: "fc6"
+  top: "fc7"
+  inner_product_param {
+    num_output: 4096
+  }
+}
+layer {
+  name: "relu7"
+  type: "ReLU"
+  bottom: "fc7"
+  top: "fc7"
+}
+layer {
+  name: "drop7"
+  type: "Dropout"
+  bottom: "fc7"
+  top: "fc7"
+  dropout_param {
+    dropout_ratio: 0.8
+  }
+}
+layer {
+  name: "fc8"
+  type: "InnerProduct"
+  bottom: "fc7"
+  top: "fc8"
+  inner_product_param {
+    num_output: 101
+  }
+}
diff --git a/models/action_recognition/readme.md b/models/action_recognition/readme.md
new file mode 100644
index 00000000000..78844e744c2
--- /dev/null
+++ b/models/action_recognition/readme.md
@@ -0,0 +1,36 @@
+##Spatial net (RGB input) models:
+| name | caffemodel | caffemodel_url | license | caffe_commit |
+| --- | --- | --- | --- | --- |
+| Spatial UCF101 Split1 | cuhk_action_spatial_vgg_16_split1.caffemodel | http://mmlab.siat.ac.cn/very_deep_two_stream_model/cuhk_action_spatial_vgg_16_split1.caffemodel | license: non-commercial | d26b3b8b8eec182a27ce9871752fedd374b63650 
+| Spatial UCF101 Split2 | cuhk_action_spatial_vgg_16_split2.caffemodel | http://mmlab.siat.ac.cn/very_deep_two_stream_model/cuhk_action_spatial_vgg_16_split2.caffemodel | license: non-commercial | d26b3b8b8eec182a27ce9871752fedd374b63650
+| Spatial UCF101 Split3 | cuhk_action_spatial_vgg_16_split3.caffemodel | http://mmlab.siat.ac.cn/very_deep_two_stream_model/cuhk_action_spatial_vgg_16_split3.caffemodel | license: non-commercial | d26b3b8b8eec182a27ce9871752fedd374b63650
+
+##Temporal net (optical flow input) models:
+| name | caffemodel | caffemodel_url | license | caffe_commit |
+| --- | --- | --- | --- | --- |
+| Temporal UCF101 Split1 | cuhk_action_temporal_vgg_16_split1.caffemodel | http://mmlab.siat.ac.cn/very_deep_two_stream_model/cuhk_action_temporal_vgg_16_split1.caffemodel | license: non-commercial | d26b3b8b8eec182a27ce9871752fedd374b63650 
+| Temporal UCF101 Split2 | cuhk_action_temporal_vgg_16_split2.caffemodel | http://mmlab.siat.ac.cn/very_deep_two_stream_model/cuhk_action_temporal_vgg_16_split2.caffemodel | license: non-commercial | d26b3b8b8eec182a27ce9871752fedd374b63650
+| Temporal UCF101 Split3 | cuhk_action_temporal_vgg_16_split3.caffemodel | http://mmlab.siat.ac.cn/very_deep_two_stream_model/cuhk_action_temporal_vgg_16_split3.caffemodel | license: non-commercial | d26b3b8b8eec182a27ce9871752fedd374b63650
+
+These models are trained using the strategy described in 
+the [Arxvi report](http://arxiv.org/abs/1507.02159). Model and training configurations are set according to the original report. 
+
+The model parameters are initialized with the public available VGG-16 model and trained on the UCF-101 dataset. 
+The modified initialization models are provided
+
+[Spatial](http://mmlab.siat.ac.cn/pretrain/vgg_16_action_rgb_pretrain.caffemodel), [Temporal](http://mmlab.siat.ac.cn/pretrain/vgg_16_action_flow_pretrain.caffemodel).
+
+The bundled models are the iteration 15,000 snapshots using corresponding solvers.
+
+[Project page](http://personal.ie.cuhk.edu.hk/~xy012/others/action_recog/).
+
+These models were trained by Limin Wang @wanglimin and Yuanjun Xiong @yjxiong.
+
+----
+
+**Note**:
+The training model `prototxt` file contains `"Gather"` layers which only work properly with this fork when "USE_MPI" is on. It is also possible to train the model with official Caffe codebase. You may need to incorporate the `VideoDataLayer`, remove `Gather` layers and restore all blob names suffixed by "_local" to their original names with out the suffix.
+
+## License
+
+The models are released for non-commercial use.
diff --git a/models/action_recognition/vgg_16_flow_solver.prototxt b/models/action_recognition/vgg_16_flow_solver.prototxt
new file mode 100644
index 00000000000..2e389e69896
--- /dev/null
+++ b/models/action_recognition/vgg_16_flow_solver.prototxt
@@ -0,0 +1,16 @@
+net: "models/action_recognition/vgg_16_flow_train_val_fast.prototxt"
+test_iter: 170
+test_interval: 1000
+base_lr: 0.005
+lr_policy: "step"
+gamma: 0.1
+stepsize: 10000
+display: 20
+max_iter: 30000
+momentum: 0.9
+weight_decay: 0.0005
+snapshot: 1000
+snapshot_prefix: "cuhk_action_recognition_16_split1_flow"
+iter_size: 3
+device_id: [0,1,2,3]
+test_initialization: false
diff --git a/models/action_recognition/vgg_16_flow_train_val_fast.prototxt b/models/action_recognition/vgg_16_flow_train_val_fast.prototxt
new file mode 100644
index 00000000000..66c17071f7f
--- /dev/null
+++ b/models/action_recognition/vgg_16_flow_train_val_fast.prototxt
@@ -0,0 +1,551 @@
+name: "CUHK_Action_TemporalNet"
+layer {
+  name: "data"
+  type: "VideoData"
+  top: "data"
+  top: "label_local"
+  video_data_param {
+    source: "models/action_recognition/dataset_file_examples/train_flow_split1.txt"
+    batch_size: 22
+    new_length: 10
+    num_segments: 1
+    modality: FLOW
+  }
+  transform_param{
+    crop_size: 224
+    mirror: true
+    fix_crop: true
+	multi_scale: true
+	max_distort: 1
+    scale_ratios: [1,.875,.75]
+    is_flow: true
+	mean_value: [128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128]
+  }
+  include: { phase: TRAIN }
+}
+layer {
+  name: "data"
+  type: "VideoData"
+  top: "data"
+  top: "label_local"
+  video_data_param {
+    source: "models/action_recognition/dataset_file_examples/val_flow_split1.txt"
+    batch_size: 5
+    new_length: 10
+    num_segments: 1
+    modality: FLOW
+  }
+  transform_param{
+    crop_size: 224
+    mirror: false
+    is_flow: true
+    mean_value: [128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128]
+  }
+  include: { phase: TEST }
+}
+layer {
+  name: "conv1_1"
+  type: "Convolution"
+  bottom: "data"
+  top: "conv1_1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 64
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "relu1_1"
+  type: "ReLU"
+  bottom: "conv1_1"
+  top: "conv1_1"
+}
+layer {
+  name: "conv1_2"
+  type: "Convolution"
+  bottom: "conv1_1"
+  top: "conv1_2"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 64
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "relu1_2"
+  type: "ReLU"
+  bottom: "conv1_2"
+  top: "conv1_2"
+}
+layer {
+  name: "pool1"
+  type: "Pooling"
+  bottom: "conv1_2"
+  top: "pool1"
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+  }
+}
+layer {
+  name: "conv2_1"
+  type: "Convolution"
+  bottom: "pool1"
+  top: "conv2_1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "relu2_1"
+  type: "ReLU"
+  bottom: "conv2_1"
+  top: "conv2_1"
+}
+layer {
+  name: "conv2_2"
+  type: "Convolution"
+  bottom: "conv2_1"
+  top: "conv2_2"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "relu2_2"
+  type: "ReLU"
+  bottom: "conv2_2"
+  top: "conv2_2"
+}
+layer {
+  name: "pool2"
+  type: "Pooling"
+  bottom: "conv2_2"
+  top: "pool2"
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+  }
+}
+layer {
+  name: "conv3_1"
+  type: "Convolution"
+  bottom: "pool2"
+  top: "conv3_1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "relu3_1"
+  type: "ReLU"
+  bottom: "conv3_1"
+  top: "conv3_1"
+}
+layer {
+  name: "conv3_2"
+  type: "Convolution"
+  bottom: "conv3_1"
+  top: "conv3_2"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "relu3_2"
+  type: "ReLU"
+  bottom: "conv3_2"
+  top: "conv3_2"
+}
+layer {
+  name: "conv3_3"
+  type: "Convolution"
+  bottom: "conv3_2"
+  top: "conv3_3"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "relu3_3"
+  type: "ReLU"
+  bottom: "conv3_3"
+  top: "conv3_3"
+}
+layer {
+  name: "pool3"
+  type: "Pooling"
+  bottom: "conv3_3"
+  top: "pool3"
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+  }
+}
+layer {
+  name: "conv4_1"
+  type: "Convolution"
+  bottom: "pool3"
+  top: "conv4_1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 512
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "relu4_1"
+  type: "ReLU"
+  bottom: "conv4_1"
+  top: "conv4_1"
+}
+layer {
+  name: "conv4_2"
+  type: "Convolution"
+  bottom: "conv4_1"
+  top: "conv4_2"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 512
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "relu4_2"
+  type: "ReLU"
+  bottom: "conv4_2"
+  top: "conv4_2"
+}
+layer {
+  name: "conv4_3"
+  type: "Convolution"
+  bottom: "conv4_2"
+  top: "conv4_3"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 512
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "relu4_3"
+  type: "ReLU"
+  bottom: "conv4_3"
+  top: "conv4_3"
+}
+layer {
+  name: "pool4"
+  type: "Pooling"
+  bottom: "conv4_3"
+  top: "pool4"
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+  }
+}
+layer {
+  name: "conv5_1"
+  type: "Convolution"
+  bottom: "pool4"
+  top: "conv5_1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 512
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "relu5_1"
+  type: "ReLU"
+  bottom: "conv5_1"
+  top: "conv5_1"
+}
+layer {
+  name: "conv5_2"
+  type: "Convolution"
+  bottom: "conv5_1"
+  top: "conv5_2"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 512
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "relu5_2"
+  type: "ReLU"
+  bottom: "conv5_2"
+  top: "conv5_2"
+}
+layer {
+  name: "conv5_3"
+  type: "Convolution"
+  bottom: "conv5_2"
+  top: "conv5_3"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 512
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "relu5_3"
+  type: "ReLU"
+  bottom: "conv5_3"
+  top: "conv5_3"
+}
+layer {
+  name: "pool5"
+  type: "Pooling"
+  bottom: "conv5_3"
+  top: "pool5_local"
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+  }
+}
+layer {
+  name: "gather"
+  type: "Gather"
+  bottom: "pool5_local"
+  bottom: "label_local"
+  top: "pool5"
+  top: "label"
+}
+layer {
+  name: "fc6"
+  type: "InnerProduct"
+  bottom: "pool5"
+  top: "fc6"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  inner_product_param {
+    num_output: 4096
+  }
+}
+layer {
+  name: "relu6"
+  type: "ReLU"
+  bottom: "fc6"
+  top: "fc6"
+}
+layer {
+  name: "drop6"
+  type: "Dropout"
+  bottom: "fc6"
+  top: "fc6"
+  dropout_param {
+    dropout_ratio: 0.9
+  }
+}
+layer {
+  name: "fc7"
+  type: "InnerProduct"
+  bottom: "fc6"
+  top: "fc7"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  inner_product_param {
+    num_output: 4096
+  }
+}
+layer {
+  name: "relu7"
+  type: "ReLU"
+  bottom: "fc7"
+  top: "fc7"
+}
+layer {
+  name: "drop7"
+  type: "Dropout"
+  bottom: "fc7"
+  top: "fc7"
+  dropout_param {
+    dropout_ratio: 0.8
+  }
+}
+layer {
+  name: "fc8"
+  type: "InnerProduct"
+  bottom: "fc7"
+  top: "fc8"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  inner_product_param {
+    num_output: 101
+    weight_filler {
+      type: "gaussian"
+	  std: 0.001
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "loss"
+  type: "SoftmaxWithLoss"
+  bottom: "fc8"
+  bottom: "label"
+  include {
+    phase: TRAIN
+  }
+}
+layer {
+  name: "loss"
+  type: "SoftmaxWithLoss"
+  bottom: "fc8"
+  bottom: "label"
+  top: "loss"
+  include {
+    phase: TEST
+  }
+}
+layer {
+  name: "accuracy"
+  type: "Accuracy"
+  bottom: "fc8"
+  bottom: "label"
+  top: "accuracy"
+  include {
+    phase: TEST
+  }
+}
diff --git a/models/action_recognition/vgg_16_rgb_solver.prototxt b/models/action_recognition/vgg_16_rgb_solver.prototxt
new file mode 100644
index 00000000000..46403d6a944
--- /dev/null
+++ b/models/action_recognition/vgg_16_rgb_solver.prototxt
@@ -0,0 +1,15 @@
+net: "models/action_recognition/vgg_16_rgb_train_val_fast.prototxt"
+test_iter: 170
+test_interval: 1000
+base_lr: 0.001
+lr_policy: "step"
+gamma: 0.1
+stepsize: 4000
+display: 20
+max_iter: 10000
+momentum: 0.9
+weight_decay: 0.0005
+snapshot: 1000
+snapshot_prefix: "cuhk_action_recognition_vgg_16_split1_rgb"
+iter_size: 4
+device_id: [0,1,2,3]
diff --git a/models/action_recognition/vgg_16_rgb_train_val_fast.prototxt b/models/action_recognition/vgg_16_rgb_train_val_fast.prototxt
new file mode 100644
index 00000000000..87eaf085a42
--- /dev/null
+++ b/models/action_recognition/vgg_16_rgb_train_val_fast.prototxt
@@ -0,0 +1,553 @@
+name: "CUHK_Action_SpatialNet"
+layer {
+  name: "data"
+  type: "VideoData"
+  top: "data"
+  top: "label_local"
+  video_data_param {
+    source: "examples/action_recognition/dataset_file_examples/train_rgb_split1.txt"
+    batch_size: 16
+    new_width: 340
+    new_height: 256
+    new_length: 1
+    num_segments: 1
+    modality: RGB
+  }
+  transform_param{
+    crop_size: 224
+    mirror: true
+    fix_crop: true
+    multi_scale: true
+    max_distort: 1
+    scale_ratios: [1,.875,.75,.66]
+    mean_value: [104, 117, 123]
+  }
+  include: { phase: TRAIN }
+}
+layer {
+  name: "data"
+  type: "VideoData"
+  top: "data"
+  top: "label_local"
+  video_data_param {
+    source: "examples/action_recognition/dataset_file_examples/val_rgb_split1.txt"
+    batch_size: 5
+    new_length: 1
+    new_height: 256
+    new_width: 340
+    num_segments: 1
+    modality: RGB
+  }
+  transform_param{
+    crop_size: 224
+    mirror: false
+    mean_value: [104, 117, 123]
+  }
+  include: { phase: TEST }
+}
+layer {
+  name: "conv1_1"
+  type: "Convolution"
+  bottom: "data"
+  top: "conv1_1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 64
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "relu1_1"
+  type: "ReLU"
+  bottom: "conv1_1"
+  top: "conv1_1"
+}
+layer {
+  name: "conv1_2"
+  type: "Convolution"
+  bottom: "conv1_1"
+  top: "conv1_2"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 64
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "relu1_2"
+  type: "ReLU"
+  bottom: "conv1_2"
+  top: "conv1_2"
+}
+layer {
+  name: "pool1"
+  type: "Pooling"
+  bottom: "conv1_2"
+  top: "pool1"
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+  }
+}
+layer {
+  name: "conv2_1"
+  type: "Convolution"
+  bottom: "pool1"
+  top: "conv2_1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "relu2_1"
+  type: "ReLU"
+  bottom: "conv2_1"
+  top: "conv2_1"
+}
+layer {
+  name: "conv2_2"
+  type: "Convolution"
+  bottom: "conv2_1"
+  top: "conv2_2"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "relu2_2"
+  type: "ReLU"
+  bottom: "conv2_2"
+  top: "conv2_2"
+}
+layer {
+  name: "pool2"
+  type: "Pooling"
+  bottom: "conv2_2"
+  top: "pool2"
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+  }
+}
+layer {
+  name: "conv3_1"
+  type: "Convolution"
+  bottom: "pool2"
+  top: "conv3_1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "relu3_1"
+  type: "ReLU"
+  bottom: "conv3_1"
+  top: "conv3_1"
+}
+layer {
+  name: "conv3_2"
+  type: "Convolution"
+  bottom: "conv3_1"
+  top: "conv3_2"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "relu3_2"
+  type: "ReLU"
+  bottom: "conv3_2"
+  top: "conv3_2"
+}
+layer {
+  name: "conv3_3"
+  type: "Convolution"
+  bottom: "conv3_2"
+  top: "conv3_3"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "relu3_3"
+  type: "ReLU"
+  bottom: "conv3_3"
+  top: "conv3_3"
+}
+layer {
+  name: "pool3"
+  type: "Pooling"
+  bottom: "conv3_3"
+  top: "pool3"
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+  }
+}
+layer {
+  name: "conv4_1"
+  type: "Convolution"
+  bottom: "pool3"
+  top: "conv4_1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 512
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "relu4_1"
+  type: "ReLU"
+  bottom: "conv4_1"
+  top: "conv4_1"
+}
+layer {
+  name: "conv4_2"
+  type: "Convolution"
+  bottom: "conv4_1"
+  top: "conv4_2"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 512
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "relu4_2"
+  type: "ReLU"
+  bottom: "conv4_2"
+  top: "conv4_2"
+}
+layer {
+  name: "conv4_3"
+  type: "Convolution"
+  bottom: "conv4_2"
+  top: "conv4_3"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 512
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "relu4_3"
+  type: "ReLU"
+  bottom: "conv4_3"
+  top: "conv4_3"
+}
+layer {
+  name: "pool4"
+  type: "Pooling"
+  bottom: "conv4_3"
+  top: "pool4"
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+  }
+}
+layer {
+  name: "conv5_1"
+  type: "Convolution"
+  bottom: "pool4"
+  top: "conv5_1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 512
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "relu5_1"
+  type: "ReLU"
+  bottom: "conv5_1"
+  top: "conv5_1"
+}
+layer {
+  name: "conv5_2"
+  type: "Convolution"
+  bottom: "conv5_1"
+  top: "conv5_2"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 512
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "relu5_2"
+  type: "ReLU"
+  bottom: "conv5_2"
+  top: "conv5_2"
+}
+layer {
+  name: "conv5_3"
+  type: "Convolution"
+  bottom: "conv5_2"
+  top: "conv5_3"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 512
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "relu5_3"
+  type: "ReLU"
+  bottom: "conv5_3"
+  top: "conv5_3"
+}
+layer {
+  name: "pool5"
+  type: "Pooling"
+  bottom: "conv5_3"
+  top: "pool5_local"
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+  }
+}
+layer {
+  name: "gather"
+  type: "Gather"
+  bottom: "pool5_local"
+  bottom: "label_local"
+  top: "pool5"
+  top: "label"
+}
+layer {
+  name: "fc6"
+  type: "InnerProduct"
+  bottom: "pool5"
+  top: "fc6"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  inner_product_param {
+    num_output: 4096
+  }
+}
+layer {
+  name: "relu6"
+  type: "ReLU"
+  bottom: "fc6"
+  top: "fc6"
+}
+layer {
+  name: "drop6"
+  type: "Dropout"
+  bottom: "fc6"
+  top: "fc6"
+  dropout_param {
+    dropout_ratio: 0.9
+  }
+}
+layer {
+  name: "fc7"
+  type: "InnerProduct"
+  bottom: "fc6"
+  top: "fc7"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  inner_product_param {
+    num_output: 4096
+  }
+}
+layer {
+  name: "relu7"
+  type: "ReLU"
+  bottom: "fc7"
+  top: "fc7"
+}
+layer {
+  name: "drop7"
+  type: "Dropout"
+  bottom: "fc7"
+  top: "fc7"
+  dropout_param {
+    dropout_ratio: 0.9
+  }
+}
+layer {
+  name: "fc8-1"
+  type: "InnerProduct"
+  bottom: "fc7"
+  top: "fc8-1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  inner_product_param {
+    num_output: 101
+    weight_filler {
+      type: "gaussian"
+      std: 0.001
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "loss"
+  type: "SoftmaxWithLoss"
+  bottom: "fc8-1"
+  bottom: "label"
+  include {
+    phase: TRAIN
+  }
+}
+layer {
+  name: "loss"
+  type: "SoftmaxWithLoss"
+  bottom: "fc8-1"
+  bottom: "label"
+  top: "loss"
+  include {
+    phase: TEST
+  }
+}
+layer {
+  name: "accuracy"
+  type: "Accuracy"
+  bottom: "fc8-1"
+  bottom: "label"
+  top: "accuracy"
+  include {
+    phase: TEST
+  }
+}
diff --git a/models/bvlc_alexnet/deploy.prototxt b/models/bvlc_alexnet/deploy.prototxt
index d010753f3fc..ced055b85d0 100644
--- a/models/bvlc_alexnet/deploy.prototxt
+++ b/models/bvlc_alexnet/deploy.prototxt
@@ -4,241 +4,273 @@ input_dim: 10
 input_dim: 3
 input_dim: 227
 input_dim: 227
-layers {
+layer {
   name: "conv1"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  type: "Convolution"
+  bottom: "data"
+  top: "conv1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 96
     kernel_size: 11
     stride: 4
   }
-  bottom: "data"
-  top: "conv1"
 }
-layers {
+layer {
   name: "relu1"
-  type: RELU
+  type: "ReLU"
   bottom: "conv1"
   top: "conv1"
 }
-layers {
+layer {
   name: "norm1"
-  type: LRN
+  type: "LRN"
+  bottom: "conv1"
+  top: "norm1"
   lrn_param {
     local_size: 5
     alpha: 0.0001
     beta: 0.75
   }
-  bottom: "conv1"
-  top: "norm1"
 }
-layers {
+layer {
   name: "pool1"
-  type: POOLING
+  type: "Pooling"
+  bottom: "norm1"
+  top: "pool1"
   pooling_param {
     pool: MAX
     kernel_size: 3
     stride: 2
   }
-  bottom: "norm1"
-  top: "pool1"
 }
-layers {
+layer {
   name: "conv2"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  type: "Convolution"
+  bottom: "pool1"
+  top: "conv2"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 256
     pad: 2
     kernel_size: 5
     group: 2
   }
-  bottom: "pool1"
-  top: "conv2"
 }
-layers {
+layer {
   name: "relu2"
-  type: RELU
+  type: "ReLU"
   bottom: "conv2"
   top: "conv2"
 }
-layers {
+layer {
   name: "norm2"
-  type: LRN
+  type: "LRN"
+  bottom: "conv2"
+  top: "norm2"
   lrn_param {
     local_size: 5
     alpha: 0.0001
     beta: 0.75
   }
-  bottom: "conv2"
-  top: "norm2"
 }
-layers {
+layer {
   name: "pool2"
-  type: POOLING
+  type: "Pooling"
+  bottom: "norm2"
+  top: "pool2"
   pooling_param {
     pool: MAX
     kernel_size: 3
     stride: 2
   }
-  bottom: "norm2"
-  top: "pool2"
 }
-layers {
+layer {
   name: "conv3"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  type: "Convolution"
+  bottom: "pool2"
+  top: "conv3"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 384
     pad: 1
     kernel_size: 3
   }
-  bottom: "pool2"
-  top: "conv3"
 }
-layers {
+layer {
   name: "relu3"
-  type: RELU
+  type: "ReLU"
   bottom: "conv3"
   top: "conv3"
 }
-layers {
+layer {
   name: "conv4"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  type: "Convolution"
+  bottom: "conv3"
+  top: "conv4"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 384
     pad: 1
     kernel_size: 3
     group: 2
   }
-  bottom: "conv3"
-  top: "conv4"
 }
-layers {
+layer {
   name: "relu4"
-  type: RELU
+  type: "ReLU"
   bottom: "conv4"
   top: "conv4"
 }
-layers {
+layer {
   name: "conv5"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  type: "Convolution"
+  bottom: "conv4"
+  top: "conv5"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 256
     pad: 1
     kernel_size: 3
     group: 2
   }
-  bottom: "conv4"
-  top: "conv5"
 }
-layers {
+layer {
   name: "relu5"
-  type: RELU
+  type: "ReLU"
   bottom: "conv5"
   top: "conv5"
 }
-layers {
+layer {
   name: "pool5"
-  type: POOLING
+  type: "Pooling"
+  bottom: "conv5"
+  top: "pool5"
   pooling_param {
     pool: MAX
     kernel_size: 3
     stride: 2
   }
-  bottom: "conv5"
-  top: "pool5"
 }
-layers {
+layer {
   name: "fc6"
-  type: INNER_PRODUCT
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  type: "InnerProduct"
+  bottom: "pool5"
+  top: "fc6"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   inner_product_param {
     num_output: 4096
   }
-  bottom: "pool5"
-  top: "fc6"
 }
-layers {
+layer {
   name: "relu6"
-  type: RELU
+  type: "ReLU"
   bottom: "fc6"
   top: "fc6"
 }
-layers {
+layer {
   name: "drop6"
-  type: DROPOUT
+  type: "Dropout"
+  bottom: "fc6"
+  top: "fc6"
   dropout_param {
     dropout_ratio: 0.5
   }
-  bottom: "fc6"
-  top: "fc6"
 }
-layers {
+layer {
   name: "fc7"
-  type: INNER_PRODUCT
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  type: "InnerProduct"
+  bottom: "fc6"
+  top: "fc7"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   inner_product_param {
     num_output: 4096
   }
-  bottom: "fc6"
-  top: "fc7"
 }
-layers {
+layer {
   name: "relu7"
-  type: RELU
+  type: "ReLU"
   bottom: "fc7"
   top: "fc7"
 }
-layers {
+layer {
   name: "drop7"
-  type: DROPOUT
+  type: "Dropout"
+  bottom: "fc7"
+  top: "fc7"
   dropout_param {
     dropout_ratio: 0.5
   }
-  bottom: "fc7"
-  top: "fc7"
 }
-layers {
+layer {
   name: "fc8"
-  type: INNER_PRODUCT
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  type: "InnerProduct"
+  bottom: "fc7"
+  top: "fc8"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   inner_product_param {
     num_output: 1000
   }
-  bottom: "fc7"
-  top: "fc8"
 }
-layers {
+layer {
   name: "prob"
-  type: SOFTMAX
+  type: "Softmax"
   bottom: "fc8"
   top: "prob"
 }
diff --git a/models/bvlc_alexnet/readme.md b/models/bvlc_alexnet/readme.md
index 20c393ff26b..008d690f7f4 100644
--- a/models/bvlc_alexnet/readme.md
+++ b/models/bvlc_alexnet/readme.md
@@ -2,7 +2,7 @@
 name: BVLC AlexNet Model
 caffemodel: bvlc_alexnet.caffemodel
 caffemodel_url: http://dl.caffe.berkeleyvision.org/bvlc_alexnet.caffemodel
-license: non-commercial
+license: unrestricted
 sha1: 9116a64c0fbe4459d18f4bb6b56d647b63920377
 caffe_commit: 709dc15af4a06bebda027c1eb2b3f3e3375d5077
 ---
@@ -18,8 +18,8 @@ The best validation performance during training was iteration 358,000 with valid
 This model obtains a top-1 accuracy 57.1% and a top-5 accuracy 80.2% on the validation set, using just the center crop.
 (Using the average of 10 crops, (4 + 1 center) * 2 mirror, should obtain a bit higher accuracy.)
 
+This model was trained by Evan Shelhamer @shelhamer
+
 ## License
 
-The data used to train this model comes from the ImageNet project, which distributes its database to researchers who agree to a following term of access:
-"Researcher shall use the Database only for non-commercial research and educational purposes."
-Accordingly, this model is distributed under a non-commercial license.
+This model is released for unrestricted use.
diff --git a/models/bvlc_alexnet/train_val.prototxt b/models/bvlc_alexnet/train_val.prototxt
index 717b6fa447c..588b4ea7cb5 100644
--- a/models/bvlc_alexnet/train_val.prototxt
+++ b/models/bvlc_alexnet/train_val.prototxt
@@ -1,47 +1,55 @@
 name: "AlexNet"
-layers {
+layer {
   name: "data"
-  type: DATA
+  type: "Data"
   top: "data"
   top: "label"
-  data_param {
-    source: "examples/imagenet/ilsvrc12_train_lmdb"
-    backend: LMDB
-    batch_size: 256
+  include {
+    phase: TRAIN
   }
   transform_param {
+    mirror: true
     crop_size: 227
     mean_file: "data/ilsvrc12/imagenet_mean.binaryproto"
-    mirror: true
   }
-  include: { phase: TRAIN }
+  data_param {
+    source: "examples/imagenet/ilsvrc12_train_lmdb"
+    batch_size: 256
+    backend: LMDB
+  }
 }
-layers {
+layer {
   name: "data"
-  type: DATA
+  type: "Data"
   top: "data"
   top: "label"
-  data_param {
-    source: "examples/imagenet/ilsvrc12_val_lmdb"
-    backend: LMDB
-    batch_size: 50
+  include {
+    phase: TEST
   }
   transform_param {
+    mirror: false
     crop_size: 227
     mean_file: "data/ilsvrc12/imagenet_mean.binaryproto"
-    mirror: false
   }
-  include: { phase: TEST }
+  data_param {
+    source: "examples/imagenet/ilsvrc12_val_lmdb"
+    batch_size: 50
+    backend: LMDB
+  }
 }
-layers {
+layer {
   name: "conv1"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "data"
   top: "conv1"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 96
     kernel_size: 11
@@ -56,15 +64,15 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu1"
-  type: RELU
+  type: "ReLU"
   bottom: "conv1"
   top: "conv1"
 }
-layers {
+layer {
   name: "norm1"
-  type: LRN
+  type: "LRN"
   bottom: "conv1"
   top: "norm1"
   lrn_param {
@@ -73,9 +81,9 @@ layers {
     beta: 0.75
   }
 }
-layers {
+layer {
   name: "pool1"
-  type: POOLING
+  type: "Pooling"
   bottom: "norm1"
   top: "pool1"
   pooling_param {
@@ -84,15 +92,19 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "conv2"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "pool1"
   top: "conv2"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 256
     pad: 2
@@ -108,15 +120,15 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu2"
-  type: RELU
+  type: "ReLU"
   bottom: "conv2"
   top: "conv2"
 }
-layers {
+layer {
   name: "norm2"
-  type: LRN
+  type: "LRN"
   bottom: "conv2"
   top: "norm2"
   lrn_param {
@@ -125,9 +137,9 @@ layers {
     beta: 0.75
   }
 }
-layers {
+layer {
   name: "pool2"
-  type: POOLING
+  type: "Pooling"
   bottom: "norm2"
   top: "pool2"
   pooling_param {
@@ -136,15 +148,19 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "conv3"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "pool2"
   top: "conv3"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 384
     pad: 1
@@ -159,21 +175,25 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu3"
-  type: RELU
+  type: "ReLU"
   bottom: "conv3"
   top: "conv3"
 }
-layers {
+layer {
   name: "conv4"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "conv3"
   top: "conv4"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 384
     pad: 1
@@ -189,21 +209,25 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu4"
-  type: RELU
+  type: "ReLU"
   bottom: "conv4"
   top: "conv4"
 }
-layers {
+layer {
   name: "conv5"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "conv4"
   top: "conv5"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 256
     pad: 1
@@ -219,15 +243,15 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu5"
-  type: RELU
+  type: "ReLU"
   bottom: "conv5"
   top: "conv5"
 }
-layers {
+layer {
   name: "pool5"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv5"
   top: "pool5"
   pooling_param {
@@ -236,15 +260,19 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "fc6"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "pool5"
   top: "fc6"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   inner_product_param {
     num_output: 4096
     weight_filler {
@@ -257,30 +285,34 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu6"
-  type: RELU
+  type: "ReLU"
   bottom: "fc6"
   top: "fc6"
 }
-layers {
+layer {
   name: "drop6"
-  type: DROPOUT
+  type: "Dropout"
   bottom: "fc6"
   top: "fc6"
   dropout_param {
     dropout_ratio: 0.5
   }
 }
-layers {
+layer {
   name: "fc7"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "fc6"
   top: "fc7"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   inner_product_param {
     num_output: 4096
     weight_filler {
@@ -293,30 +325,34 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu7"
-  type: RELU
+  type: "ReLU"
   bottom: "fc7"
   top: "fc7"
 }
-layers {
+layer {
   name: "drop7"
-  type: DROPOUT
+  type: "Dropout"
   bottom: "fc7"
   top: "fc7"
   dropout_param {
     dropout_ratio: 0.5
   }
 }
-layers {
+layer {
   name: "fc8"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "fc7"
   top: "fc8"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   inner_product_param {
     num_output: 1000
     weight_filler {
@@ -329,17 +365,19 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "accuracy"
-  type: ACCURACY
+  type: "Accuracy"
   bottom: "fc8"
   bottom: "label"
   top: "accuracy"
-  include: { phase: TEST }
+  include {
+    phase: TEST
+  }
 }
-layers {
+layer {
   name: "loss"
-  type: SOFTMAX_LOSS
+  type: "SoftmaxWithLoss"
   bottom: "fc8"
   bottom: "label"
   top: "loss"
diff --git a/models/bvlc_googlenet/deploy.prototxt b/models/bvlc_googlenet/deploy.prototxt
new file mode 100644
index 00000000000..4648bf26efc
--- /dev/null
+++ b/models/bvlc_googlenet/deploy.prototxt
@@ -0,0 +1,2156 @@
+name: "GoogleNet"
+input: "data"
+input_dim: 10
+input_dim: 3
+input_dim: 224
+input_dim: 224
+layer {
+  name: "conv1/7x7_s2"
+  type: "Convolution"
+  bottom: "data"
+  top: "conv1/7x7_s2"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 64
+    pad: 3
+    kernel_size: 7
+    stride: 2
+    weight_filler {
+      type: "xavier"
+      std: 0.1
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "conv1/relu_7x7"
+  type: "ReLU"
+  bottom: "conv1/7x7_s2"
+  top: "conv1/7x7_s2"
+}
+layer {
+  name: "pool1/3x3_s2"
+  type: "Pooling"
+  bottom: "conv1/7x7_s2"
+  top: "pool1/3x3_s2"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "pool1/norm1"
+  type: "LRN"
+  bottom: "pool1/3x3_s2"
+  top: "pool1/norm1"
+  lrn_param {
+    local_size: 5
+    alpha: 0.0001
+    beta: 0.75
+  }
+}
+layer {
+  name: "conv2/3x3_reduce"
+  type: "Convolution"
+  bottom: "pool1/norm1"
+  top: "conv2/3x3_reduce"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 64
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.1
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "conv2/relu_3x3_reduce"
+  type: "ReLU"
+  bottom: "conv2/3x3_reduce"
+  top: "conv2/3x3_reduce"
+}
+layer {
+  name: "conv2/3x3"
+  type: "Convolution"
+  bottom: "conv2/3x3_reduce"
+  top: "conv2/3x3"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 192
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "conv2/relu_3x3"
+  type: "ReLU"
+  bottom: "conv2/3x3"
+  top: "conv2/3x3"
+}
+layer {
+  name: "conv2/norm2"
+  type: "LRN"
+  bottom: "conv2/3x3"
+  top: "conv2/norm2"
+  lrn_param {
+    local_size: 5
+    alpha: 0.0001
+    beta: 0.75
+  }
+}
+layer {
+  name: "pool2/3x3_s2"
+  type: "Pooling"
+  bottom: "conv2/norm2"
+  top: "pool2/3x3_s2"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "inception_3a/1x1"
+  type: "Convolution"
+  bottom: "pool2/3x3_s2"
+  top: "inception_3a/1x1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 64
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "inception_3a/relu_1x1"
+  type: "ReLU"
+  bottom: "inception_3a/1x1"
+  top: "inception_3a/1x1"
+}
+layer {
+  name: "inception_3a/3x3_reduce"
+  type: "Convolution"
+  bottom: "pool2/3x3_s2"
+  top: "inception_3a/3x3_reduce"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 96
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.09
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "inception_3a/relu_3x3_reduce"
+  type: "ReLU"
+  bottom: "inception_3a/3x3_reduce"
+  top: "inception_3a/3x3_reduce"
+}
+layer {
+  name: "inception_3a/3x3"
+  type: "Convolution"
+  bottom: "inception_3a/3x3_reduce"
+  top: "inception_3a/3x3"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "inception_3a/relu_3x3"
+  type: "ReLU"
+  bottom: "inception_3a/3x3"
+  top: "inception_3a/3x3"
+}
+layer {
+  name: "inception_3a/5x5_reduce"
+  type: "Convolution"
+  bottom: "pool2/3x3_s2"
+  top: "inception_3a/5x5_reduce"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 16
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.2
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "inception_3a/relu_5x5_reduce"
+  type: "ReLU"
+  bottom: "inception_3a/5x5_reduce"
+  top: "inception_3a/5x5_reduce"
+}
+layer {
+  name: "inception_3a/5x5"
+  type: "Convolution"
+  bottom: "inception_3a/5x5_reduce"
+  top: "inception_3a/5x5"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 32
+    pad: 2
+    kernel_size: 5
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "inception_3a/relu_5x5"
+  type: "ReLU"
+  bottom: "inception_3a/5x5"
+  top: "inception_3a/5x5"
+}
+layer {
+  name: "inception_3a/pool"
+  type: "Pooling"
+  bottom: "pool2/3x3_s2"
+  top: "inception_3a/pool"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layer {
+  name: "inception_3a/pool_proj"
+  type: "Convolution"
+  bottom: "inception_3a/pool"
+  top: "inception_3a/pool_proj"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 32
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.1
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "inception_3a/relu_pool_proj"
+  type: "ReLU"
+  bottom: "inception_3a/pool_proj"
+  top: "inception_3a/pool_proj"
+}
+layer {
+  name: "inception_3a/output"
+  type: "Concat"
+  bottom: "inception_3a/1x1"
+  bottom: "inception_3a/3x3"
+  bottom: "inception_3a/5x5"
+  bottom: "inception_3a/pool_proj"
+  top: "inception_3a/output"
+}
+layer {
+  name: "inception_3b/1x1"
+  type: "Convolution"
+  bottom: "inception_3a/output"
+  top: "inception_3b/1x1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "inception_3b/relu_1x1"
+  type: "ReLU"
+  bottom: "inception_3b/1x1"
+  top: "inception_3b/1x1"
+}
+layer {
+  name: "inception_3b/3x3_reduce"
+  type: "Convolution"
+  bottom: "inception_3a/output"
+  top: "inception_3b/3x3_reduce"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.09
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "inception_3b/relu_3x3_reduce"
+  type: "ReLU"
+  bottom: "inception_3b/3x3_reduce"
+  top: "inception_3b/3x3_reduce"
+}
+layer {
+  name: "inception_3b/3x3"
+  type: "Convolution"
+  bottom: "inception_3b/3x3_reduce"
+  top: "inception_3b/3x3"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 192
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "inception_3b/relu_3x3"
+  type: "ReLU"
+  bottom: "inception_3b/3x3"
+  top: "inception_3b/3x3"
+}
+layer {
+  name: "inception_3b/5x5_reduce"
+  type: "Convolution"
+  bottom: "inception_3a/output"
+  top: "inception_3b/5x5_reduce"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 32
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.2
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "inception_3b/relu_5x5_reduce"
+  type: "ReLU"
+  bottom: "inception_3b/5x5_reduce"
+  top: "inception_3b/5x5_reduce"
+}
+layer {
+  name: "inception_3b/5x5"
+  type: "Convolution"
+  bottom: "inception_3b/5x5_reduce"
+  top: "inception_3b/5x5"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 96
+    pad: 2
+    kernel_size: 5
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "inception_3b/relu_5x5"
+  type: "ReLU"
+  bottom: "inception_3b/5x5"
+  top: "inception_3b/5x5"
+}
+layer {
+  name: "inception_3b/pool"
+  type: "Pooling"
+  bottom: "inception_3a/output"
+  top: "inception_3b/pool"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layer {
+  name: "inception_3b/pool_proj"
+  type: "Convolution"
+  bottom: "inception_3b/pool"
+  top: "inception_3b/pool_proj"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 64
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.1
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "inception_3b/relu_pool_proj"
+  type: "ReLU"
+  bottom: "inception_3b/pool_proj"
+  top: "inception_3b/pool_proj"
+}
+layer {
+  name: "inception_3b/output"
+  type: "Concat"
+  bottom: "inception_3b/1x1"
+  bottom: "inception_3b/3x3"
+  bottom: "inception_3b/5x5"
+  bottom: "inception_3b/pool_proj"
+  top: "inception_3b/output"
+}
+layer {
+  name: "pool3/3x3_s2"
+  type: "Pooling"
+  bottom: "inception_3b/output"
+  top: "pool3/3x3_s2"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "inception_4a/1x1"
+  type: "Convolution"
+  bottom: "pool3/3x3_s2"
+  top: "inception_4a/1x1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 192
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "inception_4a/relu_1x1"
+  type: "ReLU"
+  bottom: "inception_4a/1x1"
+  top: "inception_4a/1x1"
+}
+layer {
+  name: "inception_4a/3x3_reduce"
+  type: "Convolution"
+  bottom: "pool3/3x3_s2"
+  top: "inception_4a/3x3_reduce"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 96
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.09
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "inception_4a/relu_3x3_reduce"
+  type: "ReLU"
+  bottom: "inception_4a/3x3_reduce"
+  top: "inception_4a/3x3_reduce"
+}
+layer {
+  name: "inception_4a/3x3"
+  type: "Convolution"
+  bottom: "inception_4a/3x3_reduce"
+  top: "inception_4a/3x3"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 208
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "inception_4a/relu_3x3"
+  type: "ReLU"
+  bottom: "inception_4a/3x3"
+  top: "inception_4a/3x3"
+}
+layer {
+  name: "inception_4a/5x5_reduce"
+  type: "Convolution"
+  bottom: "pool3/3x3_s2"
+  top: "inception_4a/5x5_reduce"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 16
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.2
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "inception_4a/relu_5x5_reduce"
+  type: "ReLU"
+  bottom: "inception_4a/5x5_reduce"
+  top: "inception_4a/5x5_reduce"
+}
+layer {
+  name: "inception_4a/5x5"
+  type: "Convolution"
+  bottom: "inception_4a/5x5_reduce"
+  top: "inception_4a/5x5"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 48
+    pad: 2
+    kernel_size: 5
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "inception_4a/relu_5x5"
+  type: "ReLU"
+  bottom: "inception_4a/5x5"
+  top: "inception_4a/5x5"
+}
+layer {
+  name: "inception_4a/pool"
+  type: "Pooling"
+  bottom: "pool3/3x3_s2"
+  top: "inception_4a/pool"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layer {
+  name: "inception_4a/pool_proj"
+  type: "Convolution"
+  bottom: "inception_4a/pool"
+  top: "inception_4a/pool_proj"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 64
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.1
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "inception_4a/relu_pool_proj"
+  type: "ReLU"
+  bottom: "inception_4a/pool_proj"
+  top: "inception_4a/pool_proj"
+}
+layer {
+  name: "inception_4a/output"
+  type: "Concat"
+  bottom: "inception_4a/1x1"
+  bottom: "inception_4a/3x3"
+  bottom: "inception_4a/5x5"
+  bottom: "inception_4a/pool_proj"
+  top: "inception_4a/output"
+}
+layer {
+  name: "inception_4b/1x1"
+  type: "Convolution"
+  bottom: "inception_4a/output"
+  top: "inception_4b/1x1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 160
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "inception_4b/relu_1x1"
+  type: "ReLU"
+  bottom: "inception_4b/1x1"
+  top: "inception_4b/1x1"
+}
+layer {
+  name: "inception_4b/3x3_reduce"
+  type: "Convolution"
+  bottom: "inception_4a/output"
+  top: "inception_4b/3x3_reduce"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 112
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.09
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "inception_4b/relu_3x3_reduce"
+  type: "ReLU"
+  bottom: "inception_4b/3x3_reduce"
+  top: "inception_4b/3x3_reduce"
+}
+layer {
+  name: "inception_4b/3x3"
+  type: "Convolution"
+  bottom: "inception_4b/3x3_reduce"
+  top: "inception_4b/3x3"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 224
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "inception_4b/relu_3x3"
+  type: "ReLU"
+  bottom: "inception_4b/3x3"
+  top: "inception_4b/3x3"
+}
+layer {
+  name: "inception_4b/5x5_reduce"
+  type: "Convolution"
+  bottom: "inception_4a/output"
+  top: "inception_4b/5x5_reduce"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 24
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.2
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "inception_4b/relu_5x5_reduce"
+  type: "ReLU"
+  bottom: "inception_4b/5x5_reduce"
+  top: "inception_4b/5x5_reduce"
+}
+layer {
+  name: "inception_4b/5x5"
+  type: "Convolution"
+  bottom: "inception_4b/5x5_reduce"
+  top: "inception_4b/5x5"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 64
+    pad: 2
+    kernel_size: 5
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "inception_4b/relu_5x5"
+  type: "ReLU"
+  bottom: "inception_4b/5x5"
+  top: "inception_4b/5x5"
+}
+layer {
+  name: "inception_4b/pool"
+  type: "Pooling"
+  bottom: "inception_4a/output"
+  top: "inception_4b/pool"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layer {
+  name: "inception_4b/pool_proj"
+  type: "Convolution"
+  bottom: "inception_4b/pool"
+  top: "inception_4b/pool_proj"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 64
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.1
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "inception_4b/relu_pool_proj"
+  type: "ReLU"
+  bottom: "inception_4b/pool_proj"
+  top: "inception_4b/pool_proj"
+}
+layer {
+  name: "inception_4b/output"
+  type: "Concat"
+  bottom: "inception_4b/1x1"
+  bottom: "inception_4b/3x3"
+  bottom: "inception_4b/5x5"
+  bottom: "inception_4b/pool_proj"
+  top: "inception_4b/output"
+}
+layer {
+  name: "inception_4c/1x1"
+  type: "Convolution"
+  bottom: "inception_4b/output"
+  top: "inception_4c/1x1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "inception_4c/relu_1x1"
+  type: "ReLU"
+  bottom: "inception_4c/1x1"
+  top: "inception_4c/1x1"
+}
+layer {
+  name: "inception_4c/3x3_reduce"
+  type: "Convolution"
+  bottom: "inception_4b/output"
+  top: "inception_4c/3x3_reduce"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.09
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "inception_4c/relu_3x3_reduce"
+  type: "ReLU"
+  bottom: "inception_4c/3x3_reduce"
+  top: "inception_4c/3x3_reduce"
+}
+layer {
+  name: "inception_4c/3x3"
+  type: "Convolution"
+  bottom: "inception_4c/3x3_reduce"
+  top: "inception_4c/3x3"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "inception_4c/relu_3x3"
+  type: "ReLU"
+  bottom: "inception_4c/3x3"
+  top: "inception_4c/3x3"
+}
+layer {
+  name: "inception_4c/5x5_reduce"
+  type: "Convolution"
+  bottom: "inception_4b/output"
+  top: "inception_4c/5x5_reduce"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 24
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.2
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "inception_4c/relu_5x5_reduce"
+  type: "ReLU"
+  bottom: "inception_4c/5x5_reduce"
+  top: "inception_4c/5x5_reduce"
+}
+layer {
+  name: "inception_4c/5x5"
+  type: "Convolution"
+  bottom: "inception_4c/5x5_reduce"
+  top: "inception_4c/5x5"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 64
+    pad: 2
+    kernel_size: 5
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "inception_4c/relu_5x5"
+  type: "ReLU"
+  bottom: "inception_4c/5x5"
+  top: "inception_4c/5x5"
+}
+layer {
+  name: "inception_4c/pool"
+  type: "Pooling"
+  bottom: "inception_4b/output"
+  top: "inception_4c/pool"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layer {
+  name: "inception_4c/pool_proj"
+  type: "Convolution"
+  bottom: "inception_4c/pool"
+  top: "inception_4c/pool_proj"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 64
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.1
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "inception_4c/relu_pool_proj"
+  type: "ReLU"
+  bottom: "inception_4c/pool_proj"
+  top: "inception_4c/pool_proj"
+}
+layer {
+  name: "inception_4c/output"
+  type: "Concat"
+  bottom: "inception_4c/1x1"
+  bottom: "inception_4c/3x3"
+  bottom: "inception_4c/5x5"
+  bottom: "inception_4c/pool_proj"
+  top: "inception_4c/output"
+}
+layer {
+  name: "inception_4d/1x1"
+  type: "Convolution"
+  bottom: "inception_4c/output"
+  top: "inception_4d/1x1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 112
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "inception_4d/relu_1x1"
+  type: "ReLU"
+  bottom: "inception_4d/1x1"
+  top: "inception_4d/1x1"
+}
+layer {
+  name: "inception_4d/3x3_reduce"
+  type: "Convolution"
+  bottom: "inception_4c/output"
+  top: "inception_4d/3x3_reduce"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 144
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.09
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "inception_4d/relu_3x3_reduce"
+  type: "ReLU"
+  bottom: "inception_4d/3x3_reduce"
+  top: "inception_4d/3x3_reduce"
+}
+layer {
+  name: "inception_4d/3x3"
+  type: "Convolution"
+  bottom: "inception_4d/3x3_reduce"
+  top: "inception_4d/3x3"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 288
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "inception_4d/relu_3x3"
+  type: "ReLU"
+  bottom: "inception_4d/3x3"
+  top: "inception_4d/3x3"
+}
+layer {
+  name: "inception_4d/5x5_reduce"
+  type: "Convolution"
+  bottom: "inception_4c/output"
+  top: "inception_4d/5x5_reduce"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 32
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.2
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "inception_4d/relu_5x5_reduce"
+  type: "ReLU"
+  bottom: "inception_4d/5x5_reduce"
+  top: "inception_4d/5x5_reduce"
+}
+layer {
+  name: "inception_4d/5x5"
+  type: "Convolution"
+  bottom: "inception_4d/5x5_reduce"
+  top: "inception_4d/5x5"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 64
+    pad: 2
+    kernel_size: 5
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "inception_4d/relu_5x5"
+  type: "ReLU"
+  bottom: "inception_4d/5x5"
+  top: "inception_4d/5x5"
+}
+layer {
+  name: "inception_4d/pool"
+  type: "Pooling"
+  bottom: "inception_4c/output"
+  top: "inception_4d/pool"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layer {
+  name: "inception_4d/pool_proj"
+  type: "Convolution"
+  bottom: "inception_4d/pool"
+  top: "inception_4d/pool_proj"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 64
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.1
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "inception_4d/relu_pool_proj"
+  type: "ReLU"
+  bottom: "inception_4d/pool_proj"
+  top: "inception_4d/pool_proj"
+}
+layer {
+  name: "inception_4d/output"
+  type: "Concat"
+  bottom: "inception_4d/1x1"
+  bottom: "inception_4d/3x3"
+  bottom: "inception_4d/5x5"
+  bottom: "inception_4d/pool_proj"
+  top: "inception_4d/output"
+}
+layer {
+  name: "inception_4e/1x1"
+  type: "Convolution"
+  bottom: "inception_4d/output"
+  top: "inception_4e/1x1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 256
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "inception_4e/relu_1x1"
+  type: "ReLU"
+  bottom: "inception_4e/1x1"
+  top: "inception_4e/1x1"
+}
+layer {
+  name: "inception_4e/3x3_reduce"
+  type: "Convolution"
+  bottom: "inception_4d/output"
+  top: "inception_4e/3x3_reduce"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 160
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.09
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "inception_4e/relu_3x3_reduce"
+  type: "ReLU"
+  bottom: "inception_4e/3x3_reduce"
+  top: "inception_4e/3x3_reduce"
+}
+layer {
+  name: "inception_4e/3x3"
+  type: "Convolution"
+  bottom: "inception_4e/3x3_reduce"
+  top: "inception_4e/3x3"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 320
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "inception_4e/relu_3x3"
+  type: "ReLU"
+  bottom: "inception_4e/3x3"
+  top: "inception_4e/3x3"
+}
+layer {
+  name: "inception_4e/5x5_reduce"
+  type: "Convolution"
+  bottom: "inception_4d/output"
+  top: "inception_4e/5x5_reduce"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 32
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.2
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "inception_4e/relu_5x5_reduce"
+  type: "ReLU"
+  bottom: "inception_4e/5x5_reduce"
+  top: "inception_4e/5x5_reduce"
+}
+layer {
+  name: "inception_4e/5x5"
+  type: "Convolution"
+  bottom: "inception_4e/5x5_reduce"
+  top: "inception_4e/5x5"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 2
+    kernel_size: 5
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "inception_4e/relu_5x5"
+  type: "ReLU"
+  bottom: "inception_4e/5x5"
+  top: "inception_4e/5x5"
+}
+layer {
+  name: "inception_4e/pool"
+  type: "Pooling"
+  bottom: "inception_4d/output"
+  top: "inception_4e/pool"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layer {
+  name: "inception_4e/pool_proj"
+  type: "Convolution"
+  bottom: "inception_4e/pool"
+  top: "inception_4e/pool_proj"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.1
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "inception_4e/relu_pool_proj"
+  type: "ReLU"
+  bottom: "inception_4e/pool_proj"
+  top: "inception_4e/pool_proj"
+}
+layer {
+  name: "inception_4e/output"
+  type: "Concat"
+  bottom: "inception_4e/1x1"
+  bottom: "inception_4e/3x3"
+  bottom: "inception_4e/5x5"
+  bottom: "inception_4e/pool_proj"
+  top: "inception_4e/output"
+}
+layer {
+  name: "pool4/3x3_s2"
+  type: "Pooling"
+  bottom: "inception_4e/output"
+  top: "pool4/3x3_s2"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "inception_5a/1x1"
+  type: "Convolution"
+  bottom: "pool4/3x3_s2"
+  top: "inception_5a/1x1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 256
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "inception_5a/relu_1x1"
+  type: "ReLU"
+  bottom: "inception_5a/1x1"
+  top: "inception_5a/1x1"
+}
+layer {
+  name: "inception_5a/3x3_reduce"
+  type: "Convolution"
+  bottom: "pool4/3x3_s2"
+  top: "inception_5a/3x3_reduce"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 160
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.09
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "inception_5a/relu_3x3_reduce"
+  type: "ReLU"
+  bottom: "inception_5a/3x3_reduce"
+  top: "inception_5a/3x3_reduce"
+}
+layer {
+  name: "inception_5a/3x3"
+  type: "Convolution"
+  bottom: "inception_5a/3x3_reduce"
+  top: "inception_5a/3x3"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 320
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "inception_5a/relu_3x3"
+  type: "ReLU"
+  bottom: "inception_5a/3x3"
+  top: "inception_5a/3x3"
+}
+layer {
+  name: "inception_5a/5x5_reduce"
+  type: "Convolution"
+  bottom: "pool4/3x3_s2"
+  top: "inception_5a/5x5_reduce"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 32
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.2
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "inception_5a/relu_5x5_reduce"
+  type: "ReLU"
+  bottom: "inception_5a/5x5_reduce"
+  top: "inception_5a/5x5_reduce"
+}
+layer {
+  name: "inception_5a/5x5"
+  type: "Convolution"
+  bottom: "inception_5a/5x5_reduce"
+  top: "inception_5a/5x5"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 2
+    kernel_size: 5
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "inception_5a/relu_5x5"
+  type: "ReLU"
+  bottom: "inception_5a/5x5"
+  top: "inception_5a/5x5"
+}
+layer {
+  name: "inception_5a/pool"
+  type: "Pooling"
+  bottom: "pool4/3x3_s2"
+  top: "inception_5a/pool"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layer {
+  name: "inception_5a/pool_proj"
+  type: "Convolution"
+  bottom: "inception_5a/pool"
+  top: "inception_5a/pool_proj"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.1
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "inception_5a/relu_pool_proj"
+  type: "ReLU"
+  bottom: "inception_5a/pool_proj"
+  top: "inception_5a/pool_proj"
+}
+layer {
+  name: "inception_5a/output"
+  type: "Concat"
+  bottom: "inception_5a/1x1"
+  bottom: "inception_5a/3x3"
+  bottom: "inception_5a/5x5"
+  bottom: "inception_5a/pool_proj"
+  top: "inception_5a/output"
+}
+layer {
+  name: "inception_5b/1x1"
+  type: "Convolution"
+  bottom: "inception_5a/output"
+  top: "inception_5b/1x1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 384
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "inception_5b/relu_1x1"
+  type: "ReLU"
+  bottom: "inception_5b/1x1"
+  top: "inception_5b/1x1"
+}
+layer {
+  name: "inception_5b/3x3_reduce"
+  type: "Convolution"
+  bottom: "inception_5a/output"
+  top: "inception_5b/3x3_reduce"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 192
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.09
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "inception_5b/relu_3x3_reduce"
+  type: "ReLU"
+  bottom: "inception_5b/3x3_reduce"
+  top: "inception_5b/3x3_reduce"
+}
+layer {
+  name: "inception_5b/3x3"
+  type: "Convolution"
+  bottom: "inception_5b/3x3_reduce"
+  top: "inception_5b/3x3"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 384
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "inception_5b/relu_3x3"
+  type: "ReLU"
+  bottom: "inception_5b/3x3"
+  top: "inception_5b/3x3"
+}
+layer {
+  name: "inception_5b/5x5_reduce"
+  type: "Convolution"
+  bottom: "inception_5a/output"
+  top: "inception_5b/5x5_reduce"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 48
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.2
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "inception_5b/relu_5x5_reduce"
+  type: "ReLU"
+  bottom: "inception_5b/5x5_reduce"
+  top: "inception_5b/5x5_reduce"
+}
+layer {
+  name: "inception_5b/5x5"
+  type: "Convolution"
+  bottom: "inception_5b/5x5_reduce"
+  top: "inception_5b/5x5"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 2
+    kernel_size: 5
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "inception_5b/relu_5x5"
+  type: "ReLU"
+  bottom: "inception_5b/5x5"
+  top: "inception_5b/5x5"
+}
+layer {
+  name: "inception_5b/pool"
+  type: "Pooling"
+  bottom: "inception_5a/output"
+  top: "inception_5b/pool"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layer {
+  name: "inception_5b/pool_proj"
+  type: "Convolution"
+  bottom: "inception_5b/pool"
+  top: "inception_5b/pool_proj"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.1
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "inception_5b/relu_pool_proj"
+  type: "ReLU"
+  bottom: "inception_5b/pool_proj"
+  top: "inception_5b/pool_proj"
+}
+layer {
+  name: "inception_5b/output"
+  type: "Concat"
+  bottom: "inception_5b/1x1"
+  bottom: "inception_5b/3x3"
+  bottom: "inception_5b/5x5"
+  bottom: "inception_5b/pool_proj"
+  top: "inception_5b/output"
+}
+layer {
+  name: "pool5/7x7_s1"
+  type: "Pooling"
+  bottom: "inception_5b/output"
+  top: "pool5/7x7_s1"
+  pooling_param {
+    pool: AVE
+    kernel_size: 7
+    stride: 1
+  }
+}
+layer {
+  name: "pool5/drop_7x7_s1"
+  type: "Dropout"
+  bottom: "pool5/7x7_s1"
+  top: "pool5/7x7_s1"
+  dropout_param {
+    dropout_ratio: 0.4
+  }
+}
+layer {
+  name: "loss3/classifier"
+  type: "InnerProduct"
+  bottom: "pool5/7x7_s1"
+  top: "loss3/classifier"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  inner_product_param {
+    num_output: 1000
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "prob"
+  type: "Softmax"
+  bottom: "loss3/classifier"
+  top: "prob"
+}
diff --git a/models/bvlc_googlenet/quick_solver.prototxt b/models/bvlc_googlenet/quick_solver.prototxt
new file mode 100644
index 00000000000..5d2f7ee70b9
--- /dev/null
+++ b/models/bvlc_googlenet/quick_solver.prototxt
@@ -0,0 +1,15 @@
+net: "models/bvlc_googlenet/train_val.prototxt"
+test_iter: 1000
+test_interval: 4000
+test_initialization: false
+display: 40
+average_loss: 40
+base_lr: 0.01
+lr_policy: "poly"
+power: 0.5
+max_iter: 2400000
+momentum: 0.9
+weight_decay: 0.0002
+snapshot: 40000
+snapshot_prefix: "models/bvlc_googlenet/bvlc_googlenet_quick"
+solver_mode: GPU
diff --git a/models/bvlc_googlenet/readme.md b/models/bvlc_googlenet/readme.md
new file mode 100644
index 00000000000..061b6d74530
--- /dev/null
+++ b/models/bvlc_googlenet/readme.md
@@ -0,0 +1,32 @@
+---
+name: BVLC GoogleNet Model
+caffemodel: bvlc_googlenet.caffemodel
+caffemodel_url: http://dl.caffe.berkeleyvision.org/bvlc_googlenet.caffemodel
+license: unrestricted
+sha1: 405fc5acd08a3bb12de8ee5e23a96bec22f08204
+caffe_commit: bc614d1bd91896e3faceaf40b23b72dab47d44f5
+---
+
+This model is a replication of the model described in the [GoogleNet](http://arxiv.org/abs/1409.4842) publication. We would like to thank Christian Szegedy for all his help in the replication of GoogleNet model.
+
+Differences:
+- not training with the relighting data-augmentation;
+- not training with the scale or aspect-ratio data-augmentation;
+- uses "xavier" to initialize the weights instead of "gaussian";
+- quick_solver.prototxt uses a different learning rate decay policy than the original solver.prototxt, that allows a much faster training (60 epochs vs 250 epochs);
+
+The bundled model is the iteration 2,400,000 snapshot (60 epochs) using quick_solver.prototxt
+
+This bundled model obtains a top-1 accuracy 68.7% (31.3% error) and a top-5 accuracy 88.9% (11.1% error) on the validation set, using just the center crop.
+(Using the average of 10 crops, (4 + 1 center) * 2 mirror, should obtain a bit higher accuracy.)
+
+Timings for bvlc_googlenet with cuDNN using batch_size:128 on a K40c:
+ - Average Forward pass: 562.841 ms.
+ - Average Backward pass: 1123.84 ms.
+ - Average Forward-Backward: 1688.8 ms.
+
+This model was trained by Sergio Guadarrama @sguada
+
+## License
+
+This model is released for unrestricted use.
diff --git a/models/bvlc_googlenet/solver.prototxt b/models/bvlc_googlenet/solver.prototxt
new file mode 100644
index 00000000000..d7d1788173c
--- /dev/null
+++ b/models/bvlc_googlenet/solver.prototxt
@@ -0,0 +1,16 @@
+net: "models/bvlc_googlenet/train_val.prototxt"
+test_iter: 1000
+test_interval: 4000
+test_initialization: false
+display: 40
+average_loss: 40
+base_lr: 0.01
+lr_policy: "step"
+stepsize: 320000
+gamma: 0.96
+max_iter: 10000000
+momentum: 0.9
+weight_decay: 0.0002
+snapshot: 40000
+snapshot_prefix: "models/bvlc_googlenet/bvlc_googlenet"
+solver_mode: GPU
diff --git a/models/bvlc_googlenet/train_val.prototxt b/models/bvlc_googlenet/train_val.prototxt
new file mode 100644
index 00000000000..5dee3abe28f
--- /dev/null
+++ b/models/bvlc_googlenet/train_val.prototxt
@@ -0,0 +1,2433 @@
+name: "GoogleNet"
+layer {
+  name: "data"
+  type: "Data"
+  top: "data"
+  top: "label"
+  include {
+    phase: TRAIN
+  }
+  transform_param {
+    mirror: true
+    crop_size: 224
+    mean_value: 104
+    mean_value: 117
+    mean_value: 123
+  }
+  data_param {
+    source: "examples/imagenet/ilsvrc12_train_lmdb"
+    batch_size: 32
+    backend: LMDB
+  }
+}
+layer {
+  name: "data"
+  type: "Data"
+  top: "data"
+  top: "label"
+  include {
+    phase: TEST
+  }
+  transform_param {
+    mirror: false
+    crop_size: 224
+    mean_value: 104
+    mean_value: 117
+    mean_value: 123
+  }
+  data_param {
+    source: "examples/imagenet/ilsvrc12_val_lmdb"
+    batch_size: 50
+    backend: LMDB
+  }
+}
+layer {
+  name: "conv1/7x7_s2"
+  type: "Convolution"
+  bottom: "data"
+  top: "conv1/7x7_s2"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 64
+    pad: 3
+    kernel_size: 7
+    stride: 2
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "conv1/relu_7x7"
+  type: "ReLU"
+  bottom: "conv1/7x7_s2"
+  top: "conv1/7x7_s2"
+}
+layer {
+  name: "pool1/3x3_s2"
+  type: "Pooling"
+  bottom: "conv1/7x7_s2"
+  top: "pool1/3x3_s2"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "pool1/norm1"
+  type: "LRN"
+  bottom: "pool1/3x3_s2"
+  top: "pool1/norm1"
+  lrn_param {
+    local_size: 5
+    alpha: 0.0001
+    beta: 0.75
+  }
+}
+layer {
+  name: "conv2/3x3_reduce"
+  type: "Convolution"
+  bottom: "pool1/norm1"
+  top: "conv2/3x3_reduce"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 64
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "conv2/relu_3x3_reduce"
+  type: "ReLU"
+  bottom: "conv2/3x3_reduce"
+  top: "conv2/3x3_reduce"
+}
+layer {
+  name: "conv2/3x3"
+  type: "Convolution"
+  bottom: "conv2/3x3_reduce"
+  top: "conv2/3x3"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 192
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "conv2/relu_3x3"
+  type: "ReLU"
+  bottom: "conv2/3x3"
+  top: "conv2/3x3"
+}
+layer {
+  name: "conv2/norm2"
+  type: "LRN"
+  bottom: "conv2/3x3"
+  top: "conv2/norm2"
+  lrn_param {
+    local_size: 5
+    alpha: 0.0001
+    beta: 0.75
+  }
+}
+layer {
+  name: "pool2/3x3_s2"
+  type: "Pooling"
+  bottom: "conv2/norm2"
+  top: "pool2/3x3_s2"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "inception_3a/1x1"
+  type: "Convolution"
+  bottom: "pool2/3x3_s2"
+  top: "inception_3a/1x1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 64
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "inception_3a/relu_1x1"
+  type: "ReLU"
+  bottom: "inception_3a/1x1"
+  top: "inception_3a/1x1"
+}
+layer {
+  name: "inception_3a/3x3_reduce"
+  type: "Convolution"
+  bottom: "pool2/3x3_s2"
+  top: "inception_3a/3x3_reduce"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 96
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "inception_3a/relu_3x3_reduce"
+  type: "ReLU"
+  bottom: "inception_3a/3x3_reduce"
+  top: "inception_3a/3x3_reduce"
+}
+layer {
+  name: "inception_3a/3x3"
+  type: "Convolution"
+  bottom: "inception_3a/3x3_reduce"
+  top: "inception_3a/3x3"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "inception_3a/relu_3x3"
+  type: "ReLU"
+  bottom: "inception_3a/3x3"
+  top: "inception_3a/3x3"
+}
+layer {
+  name: "inception_3a/5x5_reduce"
+  type: "Convolution"
+  bottom: "pool2/3x3_s2"
+  top: "inception_3a/5x5_reduce"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 16
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "inception_3a/relu_5x5_reduce"
+  type: "ReLU"
+  bottom: "inception_3a/5x5_reduce"
+  top: "inception_3a/5x5_reduce"
+}
+layer {
+  name: "inception_3a/5x5"
+  type: "Convolution"
+  bottom: "inception_3a/5x5_reduce"
+  top: "inception_3a/5x5"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 32
+    pad: 2
+    kernel_size: 5
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "inception_3a/relu_5x5"
+  type: "ReLU"
+  bottom: "inception_3a/5x5"
+  top: "inception_3a/5x5"
+}
+layer {
+  name: "inception_3a/pool"
+  type: "Pooling"
+  bottom: "pool2/3x3_s2"
+  top: "inception_3a/pool"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layer {
+  name: "inception_3a/pool_proj"
+  type: "Convolution"
+  bottom: "inception_3a/pool"
+  top: "inception_3a/pool_proj"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 32
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "inception_3a/relu_pool_proj"
+  type: "ReLU"
+  bottom: "inception_3a/pool_proj"
+  top: "inception_3a/pool_proj"
+}
+layer {
+  name: "inception_3a/output"
+  type: "Concat"
+  bottom: "inception_3a/1x1"
+  bottom: "inception_3a/3x3"
+  bottom: "inception_3a/5x5"
+  bottom: "inception_3a/pool_proj"
+  top: "inception_3a/output"
+}
+layer {
+  name: "inception_3b/1x1"
+  type: "Convolution"
+  bottom: "inception_3a/output"
+  top: "inception_3b/1x1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "inception_3b/relu_1x1"
+  type: "ReLU"
+  bottom: "inception_3b/1x1"
+  top: "inception_3b/1x1"
+}
+layer {
+  name: "inception_3b/3x3_reduce"
+  type: "Convolution"
+  bottom: "inception_3a/output"
+  top: "inception_3b/3x3_reduce"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "inception_3b/relu_3x3_reduce"
+  type: "ReLU"
+  bottom: "inception_3b/3x3_reduce"
+  top: "inception_3b/3x3_reduce"
+}
+layer {
+  name: "inception_3b/3x3"
+  type: "Convolution"
+  bottom: "inception_3b/3x3_reduce"
+  top: "inception_3b/3x3"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 192
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "inception_3b/relu_3x3"
+  type: "ReLU"
+  bottom: "inception_3b/3x3"
+  top: "inception_3b/3x3"
+}
+layer {
+  name: "inception_3b/5x5_reduce"
+  type: "Convolution"
+  bottom: "inception_3a/output"
+  top: "inception_3b/5x5_reduce"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 32
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "inception_3b/relu_5x5_reduce"
+  type: "ReLU"
+  bottom: "inception_3b/5x5_reduce"
+  top: "inception_3b/5x5_reduce"
+}
+layer {
+  name: "inception_3b/5x5"
+  type: "Convolution"
+  bottom: "inception_3b/5x5_reduce"
+  top: "inception_3b/5x5"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 96
+    pad: 2
+    kernel_size: 5
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "inception_3b/relu_5x5"
+  type: "ReLU"
+  bottom: "inception_3b/5x5"
+  top: "inception_3b/5x5"
+}
+layer {
+  name: "inception_3b/pool"
+  type: "Pooling"
+  bottom: "inception_3a/output"
+  top: "inception_3b/pool"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layer {
+  name: "inception_3b/pool_proj"
+  type: "Convolution"
+  bottom: "inception_3b/pool"
+  top: "inception_3b/pool_proj"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 64
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "inception_3b/relu_pool_proj"
+  type: "ReLU"
+  bottom: "inception_3b/pool_proj"
+  top: "inception_3b/pool_proj"
+}
+layer {
+  name: "inception_3b/output"
+  type: "Concat"
+  bottom: "inception_3b/1x1"
+  bottom: "inception_3b/3x3"
+  bottom: "inception_3b/5x5"
+  bottom: "inception_3b/pool_proj"
+  top: "inception_3b/output"
+}
+layer {
+  name: "pool3/3x3_s2"
+  type: "Pooling"
+  bottom: "inception_3b/output"
+  top: "pool3/3x3_s2"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "inception_4a/1x1"
+  type: "Convolution"
+  bottom: "pool3/3x3_s2"
+  top: "inception_4a/1x1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 192
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "inception_4a/relu_1x1"
+  type: "ReLU"
+  bottom: "inception_4a/1x1"
+  top: "inception_4a/1x1"
+}
+layer {
+  name: "inception_4a/3x3_reduce"
+  type: "Convolution"
+  bottom: "pool3/3x3_s2"
+  top: "inception_4a/3x3_reduce"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 96
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "inception_4a/relu_3x3_reduce"
+  type: "ReLU"
+  bottom: "inception_4a/3x3_reduce"
+  top: "inception_4a/3x3_reduce"
+}
+layer {
+  name: "inception_4a/3x3"
+  type: "Convolution"
+  bottom: "inception_4a/3x3_reduce"
+  top: "inception_4a/3x3"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 208
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "inception_4a/relu_3x3"
+  type: "ReLU"
+  bottom: "inception_4a/3x3"
+  top: "inception_4a/3x3"
+}
+layer {
+  name: "inception_4a/5x5_reduce"
+  type: "Convolution"
+  bottom: "pool3/3x3_s2"
+  top: "inception_4a/5x5_reduce"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 16
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "inception_4a/relu_5x5_reduce"
+  type: "ReLU"
+  bottom: "inception_4a/5x5_reduce"
+  top: "inception_4a/5x5_reduce"
+}
+layer {
+  name: "inception_4a/5x5"
+  type: "Convolution"
+  bottom: "inception_4a/5x5_reduce"
+  top: "inception_4a/5x5"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 48
+    pad: 2
+    kernel_size: 5
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "inception_4a/relu_5x5"
+  type: "ReLU"
+  bottom: "inception_4a/5x5"
+  top: "inception_4a/5x5"
+}
+layer {
+  name: "inception_4a/pool"
+  type: "Pooling"
+  bottom: "pool3/3x3_s2"
+  top: "inception_4a/pool"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layer {
+  name: "inception_4a/pool_proj"
+  type: "Convolution"
+  bottom: "inception_4a/pool"
+  top: "inception_4a/pool_proj"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 64
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "inception_4a/relu_pool_proj"
+  type: "ReLU"
+  bottom: "inception_4a/pool_proj"
+  top: "inception_4a/pool_proj"
+}
+layer {
+  name: "inception_4a/output"
+  type: "Concat"
+  bottom: "inception_4a/1x1"
+  bottom: "inception_4a/3x3"
+  bottom: "inception_4a/5x5"
+  bottom: "inception_4a/pool_proj"
+  top: "inception_4a/output"
+}
+layer {
+  name: "loss1/ave_pool"
+  type: "Pooling"
+  bottom: "inception_4a/output"
+  top: "loss1/ave_pool"
+  pooling_param {
+    pool: AVE
+    kernel_size: 5
+    stride: 3
+  }
+}
+layer {
+  name: "loss1/conv"
+  type: "Convolution"
+  bottom: "loss1/ave_pool"
+  top: "loss1/conv"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "loss1/relu_conv"
+  type: "ReLU"
+  bottom: "loss1/conv"
+  top: "loss1/conv"
+}
+layer {
+  name: "loss1/fc"
+  type: "InnerProduct"
+  bottom: "loss1/conv"
+  top: "loss1/fc"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  inner_product_param {
+    num_output: 1024
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "loss1/relu_fc"
+  type: "ReLU"
+  bottom: "loss1/fc"
+  top: "loss1/fc"
+}
+layer {
+  name: "loss1/drop_fc"
+  type: "Dropout"
+  bottom: "loss1/fc"
+  top: "loss1/fc"
+  dropout_param {
+    dropout_ratio: 0.7
+  }
+}
+layer {
+  name: "loss1/classifier"
+  type: "InnerProduct"
+  bottom: "loss1/fc"
+  top: "loss1/classifier"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  inner_product_param {
+    num_output: 1000
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "loss1/loss"
+  type: "SoftmaxWithLoss"
+  bottom: "loss1/classifier"
+  bottom: "label"
+  top: "loss1/loss1"
+  loss_weight: 0.3
+}
+layer {
+  name: "loss1/top-1"
+  type: "Accuracy"
+  bottom: "loss1/classifier"
+  bottom: "label"
+  top: "loss1/top-1"
+  include {
+    phase: TEST
+  }
+}
+layer {
+  name: "loss1/top-5"
+  type: "Accuracy"
+  bottom: "loss1/classifier"
+  bottom: "label"
+  top: "loss1/top-5"
+  include {
+    phase: TEST
+  }
+  accuracy_param {
+    top_k: 5
+  }
+}
+layer {
+  name: "inception_4b/1x1"
+  type: "Convolution"
+  bottom: "inception_4a/output"
+  top: "inception_4b/1x1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 160
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "inception_4b/relu_1x1"
+  type: "ReLU"
+  bottom: "inception_4b/1x1"
+  top: "inception_4b/1x1"
+}
+layer {
+  name: "inception_4b/3x3_reduce"
+  type: "Convolution"
+  bottom: "inception_4a/output"
+  top: "inception_4b/3x3_reduce"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 112
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "inception_4b/relu_3x3_reduce"
+  type: "ReLU"
+  bottom: "inception_4b/3x3_reduce"
+  top: "inception_4b/3x3_reduce"
+}
+layer {
+  name: "inception_4b/3x3"
+  type: "Convolution"
+  bottom: "inception_4b/3x3_reduce"
+  top: "inception_4b/3x3"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 224
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "inception_4b/relu_3x3"
+  type: "ReLU"
+  bottom: "inception_4b/3x3"
+  top: "inception_4b/3x3"
+}
+layer {
+  name: "inception_4b/5x5_reduce"
+  type: "Convolution"
+  bottom: "inception_4a/output"
+  top: "inception_4b/5x5_reduce"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 24
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "inception_4b/relu_5x5_reduce"
+  type: "ReLU"
+  bottom: "inception_4b/5x5_reduce"
+  top: "inception_4b/5x5_reduce"
+}
+layer {
+  name: "inception_4b/5x5"
+  type: "Convolution"
+  bottom: "inception_4b/5x5_reduce"
+  top: "inception_4b/5x5"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 64
+    pad: 2
+    kernel_size: 5
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "inception_4b/relu_5x5"
+  type: "ReLU"
+  bottom: "inception_4b/5x5"
+  top: "inception_4b/5x5"
+}
+layer {
+  name: "inception_4b/pool"
+  type: "Pooling"
+  bottom: "inception_4a/output"
+  top: "inception_4b/pool"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layer {
+  name: "inception_4b/pool_proj"
+  type: "Convolution"
+  bottom: "inception_4b/pool"
+  top: "inception_4b/pool_proj"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 64
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "inception_4b/relu_pool_proj"
+  type: "ReLU"
+  bottom: "inception_4b/pool_proj"
+  top: "inception_4b/pool_proj"
+}
+layer {
+  name: "inception_4b/output"
+  type: "Concat"
+  bottom: "inception_4b/1x1"
+  bottom: "inception_4b/3x3"
+  bottom: "inception_4b/5x5"
+  bottom: "inception_4b/pool_proj"
+  top: "inception_4b/output"
+}
+layer {
+  name: "inception_4c/1x1"
+  type: "Convolution"
+  bottom: "inception_4b/output"
+  top: "inception_4c/1x1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "inception_4c/relu_1x1"
+  type: "ReLU"
+  bottom: "inception_4c/1x1"
+  top: "inception_4c/1x1"
+}
+layer {
+  name: "inception_4c/3x3_reduce"
+  type: "Convolution"
+  bottom: "inception_4b/output"
+  top: "inception_4c/3x3_reduce"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "inception_4c/relu_3x3_reduce"
+  type: "ReLU"
+  bottom: "inception_4c/3x3_reduce"
+  top: "inception_4c/3x3_reduce"
+}
+layer {
+  name: "inception_4c/3x3"
+  type: "Convolution"
+  bottom: "inception_4c/3x3_reduce"
+  top: "inception_4c/3x3"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "inception_4c/relu_3x3"
+  type: "ReLU"
+  bottom: "inception_4c/3x3"
+  top: "inception_4c/3x3"
+}
+layer {
+  name: "inception_4c/5x5_reduce"
+  type: "Convolution"
+  bottom: "inception_4b/output"
+  top: "inception_4c/5x5_reduce"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 24
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "inception_4c/relu_5x5_reduce"
+  type: "ReLU"
+  bottom: "inception_4c/5x5_reduce"
+  top: "inception_4c/5x5_reduce"
+}
+layer {
+  name: "inception_4c/5x5"
+  type: "Convolution"
+  bottom: "inception_4c/5x5_reduce"
+  top: "inception_4c/5x5"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 64
+    pad: 2
+    kernel_size: 5
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "inception_4c/relu_5x5"
+  type: "ReLU"
+  bottom: "inception_4c/5x5"
+  top: "inception_4c/5x5"
+}
+layer {
+  name: "inception_4c/pool"
+  type: "Pooling"
+  bottom: "inception_4b/output"
+  top: "inception_4c/pool"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layer {
+  name: "inception_4c/pool_proj"
+  type: "Convolution"
+  bottom: "inception_4c/pool"
+  top: "inception_4c/pool_proj"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 64
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "inception_4c/relu_pool_proj"
+  type: "ReLU"
+  bottom: "inception_4c/pool_proj"
+  top: "inception_4c/pool_proj"
+}
+layer {
+  name: "inception_4c/output"
+  type: "Concat"
+  bottom: "inception_4c/1x1"
+  bottom: "inception_4c/3x3"
+  bottom: "inception_4c/5x5"
+  bottom: "inception_4c/pool_proj"
+  top: "inception_4c/output"
+}
+layer {
+  name: "inception_4d/1x1"
+  type: "Convolution"
+  bottom: "inception_4c/output"
+  top: "inception_4d/1x1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 112
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "inception_4d/relu_1x1"
+  type: "ReLU"
+  bottom: "inception_4d/1x1"
+  top: "inception_4d/1x1"
+}
+layer {
+  name: "inception_4d/3x3_reduce"
+  type: "Convolution"
+  bottom: "inception_4c/output"
+  top: "inception_4d/3x3_reduce"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 144
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "inception_4d/relu_3x3_reduce"
+  type: "ReLU"
+  bottom: "inception_4d/3x3_reduce"
+  top: "inception_4d/3x3_reduce"
+}
+layer {
+  name: "inception_4d/3x3"
+  type: "Convolution"
+  bottom: "inception_4d/3x3_reduce"
+  top: "inception_4d/3x3"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 288
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "inception_4d/relu_3x3"
+  type: "ReLU"
+  bottom: "inception_4d/3x3"
+  top: "inception_4d/3x3"
+}
+layer {
+  name: "inception_4d/5x5_reduce"
+  type: "Convolution"
+  bottom: "inception_4c/output"
+  top: "inception_4d/5x5_reduce"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 32
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "inception_4d/relu_5x5_reduce"
+  type: "ReLU"
+  bottom: "inception_4d/5x5_reduce"
+  top: "inception_4d/5x5_reduce"
+}
+layer {
+  name: "inception_4d/5x5"
+  type: "Convolution"
+  bottom: "inception_4d/5x5_reduce"
+  top: "inception_4d/5x5"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 64
+    pad: 2
+    kernel_size: 5
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "inception_4d/relu_5x5"
+  type: "ReLU"
+  bottom: "inception_4d/5x5"
+  top: "inception_4d/5x5"
+}
+layer {
+  name: "inception_4d/pool"
+  type: "Pooling"
+  bottom: "inception_4c/output"
+  top: "inception_4d/pool"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layer {
+  name: "inception_4d/pool_proj"
+  type: "Convolution"
+  bottom: "inception_4d/pool"
+  top: "inception_4d/pool_proj"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 64
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "inception_4d/relu_pool_proj"
+  type: "ReLU"
+  bottom: "inception_4d/pool_proj"
+  top: "inception_4d/pool_proj"
+}
+layer {
+  name: "inception_4d/output"
+  type: "Concat"
+  bottom: "inception_4d/1x1"
+  bottom: "inception_4d/3x3"
+  bottom: "inception_4d/5x5"
+  bottom: "inception_4d/pool_proj"
+  top: "inception_4d/output"
+}
+layer {
+  name: "loss2/ave_pool"
+  type: "Pooling"
+  bottom: "inception_4d/output"
+  top: "loss2/ave_pool"
+  pooling_param {
+    pool: AVE
+    kernel_size: 5
+    stride: 3
+  }
+}
+layer {
+  name: "loss2/conv"
+  type: "Convolution"
+  bottom: "loss2/ave_pool"
+  top: "loss2/conv"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "loss2/relu_conv"
+  type: "ReLU"
+  bottom: "loss2/conv"
+  top: "loss2/conv"
+}
+layer {
+  name: "loss2/fc"
+  type: "InnerProduct"
+  bottom: "loss2/conv"
+  top: "loss2/fc"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  inner_product_param {
+    num_output: 1024
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "loss2/relu_fc"
+  type: "ReLU"
+  bottom: "loss2/fc"
+  top: "loss2/fc"
+}
+layer {
+  name: "loss2/drop_fc"
+  type: "Dropout"
+  bottom: "loss2/fc"
+  top: "loss2/fc"
+  dropout_param {
+    dropout_ratio: 0.7
+  }
+}
+layer {
+  name: "loss2/classifier"
+  type: "InnerProduct"
+  bottom: "loss2/fc"
+  top: "loss2/classifier"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  inner_product_param {
+    num_output: 1000
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "loss2/loss"
+  type: "SoftmaxWithLoss"
+  bottom: "loss2/classifier"
+  bottom: "label"
+  top: "loss2/loss1"
+  loss_weight: 0.3
+}
+layer {
+  name: "loss2/top-1"
+  type: "Accuracy"
+  bottom: "loss2/classifier"
+  bottom: "label"
+  top: "loss2/top-1"
+  include {
+    phase: TEST
+  }
+}
+layer {
+  name: "loss2/top-5"
+  type: "Accuracy"
+  bottom: "loss2/classifier"
+  bottom: "label"
+  top: "loss2/top-5"
+  include {
+    phase: TEST
+  }
+  accuracy_param {
+    top_k: 5
+  }
+}
+layer {
+  name: "inception_4e/1x1"
+  type: "Convolution"
+  bottom: "inception_4d/output"
+  top: "inception_4e/1x1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 256
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "inception_4e/relu_1x1"
+  type: "ReLU"
+  bottom: "inception_4e/1x1"
+  top: "inception_4e/1x1"
+}
+layer {
+  name: "inception_4e/3x3_reduce"
+  type: "Convolution"
+  bottom: "inception_4d/output"
+  top: "inception_4e/3x3_reduce"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 160
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "inception_4e/relu_3x3_reduce"
+  type: "ReLU"
+  bottom: "inception_4e/3x3_reduce"
+  top: "inception_4e/3x3_reduce"
+}
+layer {
+  name: "inception_4e/3x3"
+  type: "Convolution"
+  bottom: "inception_4e/3x3_reduce"
+  top: "inception_4e/3x3"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 320
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "inception_4e/relu_3x3"
+  type: "ReLU"
+  bottom: "inception_4e/3x3"
+  top: "inception_4e/3x3"
+}
+layer {
+  name: "inception_4e/5x5_reduce"
+  type: "Convolution"
+  bottom: "inception_4d/output"
+  top: "inception_4e/5x5_reduce"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 32
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "inception_4e/relu_5x5_reduce"
+  type: "ReLU"
+  bottom: "inception_4e/5x5_reduce"
+  top: "inception_4e/5x5_reduce"
+}
+layer {
+  name: "inception_4e/5x5"
+  type: "Convolution"
+  bottom: "inception_4e/5x5_reduce"
+  top: "inception_4e/5x5"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 2
+    kernel_size: 5
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "inception_4e/relu_5x5"
+  type: "ReLU"
+  bottom: "inception_4e/5x5"
+  top: "inception_4e/5x5"
+}
+layer {
+  name: "inception_4e/pool"
+  type: "Pooling"
+  bottom: "inception_4d/output"
+  top: "inception_4e/pool"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layer {
+  name: "inception_4e/pool_proj"
+  type: "Convolution"
+  bottom: "inception_4e/pool"
+  top: "inception_4e/pool_proj"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "inception_4e/relu_pool_proj"
+  type: "ReLU"
+  bottom: "inception_4e/pool_proj"
+  top: "inception_4e/pool_proj"
+}
+layer {
+  name: "inception_4e/output"
+  type: "Concat"
+  bottom: "inception_4e/1x1"
+  bottom: "inception_4e/3x3"
+  bottom: "inception_4e/5x5"
+  bottom: "inception_4e/pool_proj"
+  top: "inception_4e/output"
+}
+layer {
+  name: "pool4/3x3_s2"
+  type: "Pooling"
+  bottom: "inception_4e/output"
+  top: "pool4/3x3_s2"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "inception_5a/1x1"
+  type: "Convolution"
+  bottom: "pool4/3x3_s2"
+  top: "inception_5a/1x1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 256
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "inception_5a/relu_1x1"
+  type: "ReLU"
+  bottom: "inception_5a/1x1"
+  top: "inception_5a/1x1"
+}
+layer {
+  name: "inception_5a/3x3_reduce"
+  type: "Convolution"
+  bottom: "pool4/3x3_s2"
+  top: "inception_5a/3x3_reduce"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 160
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "inception_5a/relu_3x3_reduce"
+  type: "ReLU"
+  bottom: "inception_5a/3x3_reduce"
+  top: "inception_5a/3x3_reduce"
+}
+layer {
+  name: "inception_5a/3x3"
+  type: "Convolution"
+  bottom: "inception_5a/3x3_reduce"
+  top: "inception_5a/3x3"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 320
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "inception_5a/relu_3x3"
+  type: "ReLU"
+  bottom: "inception_5a/3x3"
+  top: "inception_5a/3x3"
+}
+layer {
+  name: "inception_5a/5x5_reduce"
+  type: "Convolution"
+  bottom: "pool4/3x3_s2"
+  top: "inception_5a/5x5_reduce"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 32
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "inception_5a/relu_5x5_reduce"
+  type: "ReLU"
+  bottom: "inception_5a/5x5_reduce"
+  top: "inception_5a/5x5_reduce"
+}
+layer {
+  name: "inception_5a/5x5"
+  type: "Convolution"
+  bottom: "inception_5a/5x5_reduce"
+  top: "inception_5a/5x5"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 2
+    kernel_size: 5
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "inception_5a/relu_5x5"
+  type: "ReLU"
+  bottom: "inception_5a/5x5"
+  top: "inception_5a/5x5"
+}
+layer {
+  name: "inception_5a/pool"
+  type: "Pooling"
+  bottom: "pool4/3x3_s2"
+  top: "inception_5a/pool"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layer {
+  name: "inception_5a/pool_proj"
+  type: "Convolution"
+  bottom: "inception_5a/pool"
+  top: "inception_5a/pool_proj"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "inception_5a/relu_pool_proj"
+  type: "ReLU"
+  bottom: "inception_5a/pool_proj"
+  top: "inception_5a/pool_proj"
+}
+layer {
+  name: "inception_5a/output"
+  type: "Concat"
+  bottom: "inception_5a/1x1"
+  bottom: "inception_5a/3x3"
+  bottom: "inception_5a/5x5"
+  bottom: "inception_5a/pool_proj"
+  top: "inception_5a/output"
+}
+layer {
+  name: "inception_5b/1x1"
+  type: "Convolution"
+  bottom: "inception_5a/output"
+  top: "inception_5b/1x1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 384
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "inception_5b/relu_1x1"
+  type: "ReLU"
+  bottom: "inception_5b/1x1"
+  top: "inception_5b/1x1"
+}
+layer {
+  name: "inception_5b/3x3_reduce"
+  type: "Convolution"
+  bottom: "inception_5a/output"
+  top: "inception_5b/3x3_reduce"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 192
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "inception_5b/relu_3x3_reduce"
+  type: "ReLU"
+  bottom: "inception_5b/3x3_reduce"
+  top: "inception_5b/3x3_reduce"
+}
+layer {
+  name: "inception_5b/3x3"
+  type: "Convolution"
+  bottom: "inception_5b/3x3_reduce"
+  top: "inception_5b/3x3"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 384
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "inception_5b/relu_3x3"
+  type: "ReLU"
+  bottom: "inception_5b/3x3"
+  top: "inception_5b/3x3"
+}
+layer {
+  name: "inception_5b/5x5_reduce"
+  type: "Convolution"
+  bottom: "inception_5a/output"
+  top: "inception_5b/5x5_reduce"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 48
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "inception_5b/relu_5x5_reduce"
+  type: "ReLU"
+  bottom: "inception_5b/5x5_reduce"
+  top: "inception_5b/5x5_reduce"
+}
+layer {
+  name: "inception_5b/5x5"
+  type: "Convolution"
+  bottom: "inception_5b/5x5_reduce"
+  top: "inception_5b/5x5"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 2
+    kernel_size: 5
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "inception_5b/relu_5x5"
+  type: "ReLU"
+  bottom: "inception_5b/5x5"
+  top: "inception_5b/5x5"
+}
+layer {
+  name: "inception_5b/pool"
+  type: "Pooling"
+  bottom: "inception_5a/output"
+  top: "inception_5b/pool"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layer {
+  name: "inception_5b/pool_proj"
+  type: "Convolution"
+  bottom: "inception_5b/pool"
+  top: "inception_5b/pool_proj"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layer {
+  name: "inception_5b/relu_pool_proj"
+  type: "ReLU"
+  bottom: "inception_5b/pool_proj"
+  top: "inception_5b/pool_proj"
+}
+layer {
+  name: "inception_5b/output"
+  type: "Concat"
+  bottom: "inception_5b/1x1"
+  bottom: "inception_5b/3x3"
+  bottom: "inception_5b/5x5"
+  bottom: "inception_5b/pool_proj"
+  top: "inception_5b/output"
+}
+layer {
+  name: "pool5/7x7_s1"
+  type: "Pooling"
+  bottom: "inception_5b/output"
+  top: "pool5/7x7_s1"
+  pooling_param {
+    pool: AVE
+    kernel_size: 7
+    stride: 1
+  }
+}
+layer {
+  name: "pool5/drop_7x7_s1"
+  type: "Dropout"
+  bottom: "pool5/7x7_s1"
+  top: "pool5/7x7_s1"
+  dropout_param {
+    dropout_ratio: 0.4
+  }
+}
+layer {
+  name: "loss3/classifier"
+  type: "InnerProduct"
+  bottom: "pool5/7x7_s1"
+  top: "loss3/classifier"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  inner_product_param {
+    num_output: 1000
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "loss3/loss3"
+  type: "SoftmaxWithLoss"
+  bottom: "loss3/classifier"
+  bottom: "label"
+  top: "loss3/loss3"
+  loss_weight: 1
+}
+layer {
+  name: "loss3/top-1"
+  type: "Accuracy"
+  bottom: "loss3/classifier"
+  bottom: "label"
+  top: "loss3/top-1"
+  include {
+    phase: TEST
+  }
+}
+layer {
+  name: "loss3/top-5"
+  type: "Accuracy"
+  bottom: "loss3/classifier"
+  bottom: "label"
+  top: "loss3/top-5"
+  include {
+    phase: TEST
+  }
+  accuracy_param {
+    top_k: 5
+  }
+}
diff --git a/models/bvlc_reference_caffenet/deploy.prototxt b/models/bvlc_reference_caffenet/deploy.prototxt
index 4e494f420b5..29ccf1469f7 100644
--- a/models/bvlc_reference_caffenet/deploy.prototxt
+++ b/models/bvlc_reference_caffenet/deploy.prototxt
@@ -4,9 +4,9 @@ input_dim: 10
 input_dim: 3
 input_dim: 227
 input_dim: 227
-layers {
+layer {
   name: "conv1"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "data"
   top: "conv1"
   convolution_param {
@@ -15,15 +15,15 @@ layers {
     stride: 4
   }
 }
-layers {
+layer {
   name: "relu1"
-  type: RELU
+  type: "ReLU"
   bottom: "conv1"
   top: "conv1"
 }
-layers {
+layer {
   name: "pool1"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv1"
   top: "pool1"
   pooling_param {
@@ -32,9 +32,9 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "norm1"
-  type: LRN
+  type: "LRN"
   bottom: "pool1"
   top: "norm1"
   lrn_param {
@@ -43,9 +43,9 @@ layers {
     beta: 0.75
   }
 }
-layers {
+layer {
   name: "conv2"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "norm1"
   top: "conv2"
   convolution_param {
@@ -55,15 +55,15 @@ layers {
     group: 2
   }
 }
-layers {
+layer {
   name: "relu2"
-  type: RELU
+  type: "ReLU"
   bottom: "conv2"
   top: "conv2"
 }
-layers {
+layer {
   name: "pool2"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv2"
   top: "pool2"
   pooling_param {
@@ -72,9 +72,9 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "norm2"
-  type: LRN
+  type: "LRN"
   bottom: "pool2"
   top: "norm2"
   lrn_param {
@@ -83,9 +83,9 @@ layers {
     beta: 0.75
   }
 }
-layers {
+layer {
   name: "conv3"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "norm2"
   top: "conv3"
   convolution_param {
@@ -94,15 +94,15 @@ layers {
     kernel_size: 3
   }
 }
-layers {
+layer {
   name: "relu3"
-  type: RELU
+  type: "ReLU"
   bottom: "conv3"
   top: "conv3"
 }
-layers {
+layer {
   name: "conv4"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "conv3"
   top: "conv4"
   convolution_param {
@@ -112,15 +112,15 @@ layers {
     group: 2
   }
 }
-layers {
+layer {
   name: "relu4"
-  type: RELU
+  type: "ReLU"
   bottom: "conv4"
   top: "conv4"
 }
-layers {
+layer {
   name: "conv5"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "conv4"
   top: "conv5"
   convolution_param {
@@ -130,15 +130,15 @@ layers {
     group: 2
   }
 }
-layers {
+layer {
   name: "relu5"
-  type: RELU
+  type: "ReLU"
   bottom: "conv5"
   top: "conv5"
 }
-layers {
+layer {
   name: "pool5"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv5"
   top: "pool5"
   pooling_param {
@@ -147,66 +147,66 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "fc6"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "pool5"
   top: "fc6"
   inner_product_param {
     num_output: 4096
   }
 }
-layers {
+layer {
   name: "relu6"
-  type: RELU
+  type: "ReLU"
   bottom: "fc6"
   top: "fc6"
 }
-layers {
+layer {
   name: "drop6"
-  type: DROPOUT
+  type: "Dropout"
   bottom: "fc6"
   top: "fc6"
   dropout_param {
     dropout_ratio: 0.5
   }
 }
-layers {
+layer {
   name: "fc7"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "fc6"
   top: "fc7"
   inner_product_param {
     num_output: 4096
   }
 }
-layers {
+layer {
   name: "relu7"
-  type: RELU
+  type: "ReLU"
   bottom: "fc7"
   top: "fc7"
 }
-layers {
+layer {
   name: "drop7"
-  type: DROPOUT
+  type: "Dropout"
   bottom: "fc7"
   top: "fc7"
   dropout_param {
     dropout_ratio: 0.5
   }
 }
-layers {
+layer {
   name: "fc8"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "fc7"
   top: "fc8"
   inner_product_param {
     num_output: 1000
   }
 }
-layers {
+layer {
   name: "prob"
-  type: SOFTMAX
+  type: "Softmax"
   bottom: "fc8"
   top: "prob"
 }
diff --git a/models/bvlc_reference_caffenet/readme.md b/models/bvlc_reference_caffenet/readme.md
index d1c6269ae73..671e47a5056 100644
--- a/models/bvlc_reference_caffenet/readme.md
+++ b/models/bvlc_reference_caffenet/readme.md
@@ -2,7 +2,7 @@
 name: BVLC CaffeNet Model
 caffemodel: bvlc_reference_caffenet.caffemodel
 caffemodel_url: http://dl.caffe.berkeleyvision.org/bvlc_reference_caffenet.caffemodel
-license: non-commercial
+license: unrestricted
 sha1: 4c8d77deb20ea792f84eb5e6d0a11ca0a8660a46
 caffe_commit: 709dc15af4a06bebda027c1eb2b3f3e3375d5077
 ---
@@ -18,8 +18,8 @@ The best validation performance during training was iteration 313,000 with valid
 This model obtains a top-1 accuracy 57.4% and a top-5 accuracy 80.4% on the validation set, using just the center crop.
 (Using the average of 10 crops, (4 + 1 center) * 2 mirror, should obtain a bit higher accuracy still.)
 
+This model was trained by Jeff Donahue @jeffdonahue
+
 ## License
 
-The data used to train this model comes from the ImageNet project, which distributes its database to researchers who agree to a following term of access:
-"Researcher shall use the Database only for non-commercial research and educational purposes."
-Accordingly, this model is distributed under a non-commercial license.
+This model is released for unrestricted use.
diff --git a/models/bvlc_reference_caffenet/train_val.prototxt b/models/bvlc_reference_caffenet/train_val.prototxt
index 073d8aeff4a..c79472e09ab 100644
--- a/models/bvlc_reference_caffenet/train_val.prototxt
+++ b/models/bvlc_reference_caffenet/train_val.prototxt
@@ -1,47 +1,71 @@
 name: "CaffeNet"
-layers {
+layer {
   name: "data"
-  type: DATA
+  type: "Data"
   top: "data"
   top: "label"
-  data_param {
-    source: "examples/imagenet/ilsvrc12_train_lmdb"
-    backend: LMDB
-    batch_size: 256
+  include {
+    phase: TRAIN
   }
   transform_param {
+    mirror: true
     crop_size: 227
     mean_file: "data/ilsvrc12/imagenet_mean.binaryproto"
-    mirror: true
   }
-  include: { phase: TRAIN }
+# mean pixel / channel-wise mean instead of mean image
+#  transform_param {
+#    crop_size: 227
+#    mean_value: 104
+#    mean_value: 117
+#    mean_value: 123
+#    mirror: true
+#  }
+  data_param {
+    source: "examples/imagenet/ilsvrc12_train_lmdb"
+    batch_size: 256
+    backend: LMDB
+  }
 }
-layers {
+layer {
   name: "data"
-  type: DATA
+  type: "Data"
   top: "data"
   top: "label"
-  data_param {
-    source: "examples/imagenet/ilsvrc12_val_lmdb"
-    backend: LMDB
-    batch_size: 50
+  include {
+    phase: TEST
   }
   transform_param {
+    mirror: false
     crop_size: 227
     mean_file: "data/ilsvrc12/imagenet_mean.binaryproto"
-    mirror: false
   }
-  include: { phase: TEST }
+# mean pixel / channel-wise mean instead of mean image
+#  transform_param {
+#    crop_size: 227
+#    mean_value: 104
+#    mean_value: 117
+#    mean_value: 123
+#    mirror: true
+#  }
+  data_param {
+    source: "examples/imagenet/ilsvrc12_val_lmdb"
+    batch_size: 50
+    backend: LMDB
+  }
 }
-layers {
+layer {
   name: "conv1"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "data"
   top: "conv1"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 96
     kernel_size: 11
@@ -56,15 +80,15 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu1"
-  type: RELU
+  type: "ReLU"
   bottom: "conv1"
   top: "conv1"
 }
-layers {
+layer {
   name: "pool1"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv1"
   top: "pool1"
   pooling_param {
@@ -73,9 +97,9 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "norm1"
-  type: LRN
+  type: "LRN"
   bottom: "pool1"
   top: "norm1"
   lrn_param {
@@ -84,15 +108,19 @@ layers {
     beta: 0.75
   }
 }
-layers {
+layer {
   name: "conv2"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "norm1"
   top: "conv2"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 256
     pad: 2
@@ -108,15 +136,15 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu2"
-  type: RELU
+  type: "ReLU"
   bottom: "conv2"
   top: "conv2"
 }
-layers {
+layer {
   name: "pool2"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv2"
   top: "pool2"
   pooling_param {
@@ -125,9 +153,9 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "norm2"
-  type: LRN
+  type: "LRN"
   bottom: "pool2"
   top: "norm2"
   lrn_param {
@@ -136,15 +164,19 @@ layers {
     beta: 0.75
   }
 }
-layers {
+layer {
   name: "conv3"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "norm2"
   top: "conv3"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 384
     pad: 1
@@ -159,21 +191,25 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu3"
-  type: RELU
+  type: "ReLU"
   bottom: "conv3"
   top: "conv3"
 }
-layers {
+layer {
   name: "conv4"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "conv3"
   top: "conv4"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 384
     pad: 1
@@ -189,21 +225,25 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu4"
-  type: RELU
+  type: "ReLU"
   bottom: "conv4"
   top: "conv4"
 }
-layers {
+layer {
   name: "conv5"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "conv4"
   top: "conv5"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 256
     pad: 1
@@ -219,15 +259,15 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu5"
-  type: RELU
+  type: "ReLU"
   bottom: "conv5"
   top: "conv5"
 }
-layers {
+layer {
   name: "pool5"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv5"
   top: "pool5"
   pooling_param {
@@ -236,15 +276,19 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "fc6"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "pool5"
   top: "fc6"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   inner_product_param {
     num_output: 4096
     weight_filler {
@@ -257,30 +301,34 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu6"
-  type: RELU
+  type: "ReLU"
   bottom: "fc6"
   top: "fc6"
 }
-layers {
+layer {
   name: "drop6"
-  type: DROPOUT
+  type: "Dropout"
   bottom: "fc6"
   top: "fc6"
   dropout_param {
     dropout_ratio: 0.5
   }
 }
-layers {
+layer {
   name: "fc7"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "fc6"
   top: "fc7"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   inner_product_param {
     num_output: 4096
     weight_filler {
@@ -293,30 +341,34 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu7"
-  type: RELU
+  type: "ReLU"
   bottom: "fc7"
   top: "fc7"
 }
-layers {
+layer {
   name: "drop7"
-  type: DROPOUT
+  type: "Dropout"
   bottom: "fc7"
   top: "fc7"
   dropout_param {
     dropout_ratio: 0.5
   }
 }
-layers {
+layer {
   name: "fc8"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "fc7"
   top: "fc8"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   inner_product_param {
     num_output: 1000
     weight_filler {
@@ -329,17 +381,19 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "accuracy"
-  type: ACCURACY
+  type: "Accuracy"
   bottom: "fc8"
   bottom: "label"
   top: "accuracy"
-  include: { phase: TEST }
+  include {
+    phase: TEST
+  }
 }
-layers {
+layer {
   name: "loss"
-  type: SOFTMAX_LOSS
+  type: "SoftmaxWithLoss"
   bottom: "fc8"
   bottom: "label"
   top: "loss"
diff --git a/models/bvlc_reference_rcnn_ilsvrc13/deploy.prototxt b/models/bvlc_reference_rcnn_ilsvrc13/deploy.prototxt
index ef75a0a5e95..ea9cf98a926 100644
--- a/models/bvlc_reference_rcnn_ilsvrc13/deploy.prototxt
+++ b/models/bvlc_reference_rcnn_ilsvrc13/deploy.prototxt
@@ -4,9 +4,9 @@ input_dim: 10
 input_dim: 3
 input_dim: 227
 input_dim: 227
-layers {
+layer {
   name: "conv1"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "data"
   top: "conv1"
   convolution_param {
@@ -15,15 +15,15 @@ layers {
     stride: 4
   }
 }
-layers {
+layer {
   name: "relu1"
-  type: RELU
+  type: "ReLU"
   bottom: "conv1"
   top: "conv1"
 }
-layers {
+layer {
   name: "pool1"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv1"
   top: "pool1"
   pooling_param {
@@ -32,9 +32,9 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "norm1"
-  type: LRN
+  type: "LRN"
   bottom: "pool1"
   top: "norm1"
   lrn_param {
@@ -43,9 +43,9 @@ layers {
     beta: 0.75
   }
 }
-layers {
+layer {
   name: "conv2"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "norm1"
   top: "conv2"
   convolution_param {
@@ -55,15 +55,15 @@ layers {
     group: 2
   }
 }
-layers {
+layer {
   name: "relu2"
-  type: RELU
+  type: "ReLU"
   bottom: "conv2"
   top: "conv2"
 }
-layers {
+layer {
   name: "pool2"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv2"
   top: "pool2"
   pooling_param {
@@ -72,9 +72,9 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "norm2"
-  type: LRN
+  type: "LRN"
   bottom: "pool2"
   top: "norm2"
   lrn_param {
@@ -83,9 +83,9 @@ layers {
     beta: 0.75
   }
 }
-layers {
+layer {
   name: "conv3"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "norm2"
   top: "conv3"
   convolution_param {
@@ -94,15 +94,15 @@ layers {
     kernel_size: 3
   }
 }
-layers {
+layer {
   name: "relu3"
-  type: RELU
+  type: "ReLU"
   bottom: "conv3"
   top: "conv3"
 }
-layers {
+layer {
   name: "conv4"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "conv3"
   top: "conv4"
   convolution_param {
@@ -112,15 +112,15 @@ layers {
     group: 2
   }
 }
-layers {
+layer {
   name: "relu4"
-  type: RELU
+  type: "ReLU"
   bottom: "conv4"
   top: "conv4"
 }
-layers {
+layer {
   name: "conv5"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "conv4"
   top: "conv5"
   convolution_param {
@@ -130,15 +130,15 @@ layers {
     group: 2
   }
 }
-layers {
+layer {
   name: "relu5"
-  type: RELU
+  type: "ReLU"
   bottom: "conv5"
   top: "conv5"
 }
-layers {
+layer {
   name: "pool5"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv5"
   top: "pool5"
   pooling_param {
@@ -147,48 +147,48 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "fc6"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "pool5"
   top: "fc6"
   inner_product_param {
     num_output: 4096
   }
 }
-layers {
+layer {
   name: "relu6"
-  type: RELU
+  type: "ReLU"
   bottom: "fc6"
   top: "fc6"
 }
-layers {
+layer {
   name: "drop6"
-  type: DROPOUT
+  type: "Dropout"
   bottom: "fc6"
   top: "fc6"
   dropout_param {
     dropout_ratio: 0.5
   }
 }
-layers {
+layer {
   name: "fc7"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "fc6"
   top: "fc7"
   inner_product_param {
     num_output: 4096
   }
 }
-layers {
+layer {
   name: "relu7"
-  type: RELU
+  type: "ReLU"
   bottom: "fc7"
   top: "fc7"
 }
-layers {
+layer {
   name: "drop7"
-  type: DROPOUT
+  type: "Dropout"
   bottom: "fc7"
   top: "fc7"
   dropout_param {
@@ -196,9 +196,9 @@ layers {
   }
 }
 # R-CNN classification layer made from R-CNN ILSVRC13 SVMs.
-layers {
+layer {
   name: "fc-rcnn"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "fc7"
   top: "fc-rcnn"
   inner_product_param {
diff --git a/models/bvlc_reference_rcnn_ilsvrc13/readme.md b/models/bvlc_reference_rcnn_ilsvrc13/readme.md
index fb8f26d15df..9a11a24d8f8 100644
--- a/models/bvlc_reference_rcnn_ilsvrc13/readme.md
+++ b/models/bvlc_reference_rcnn_ilsvrc13/readme.md
@@ -2,7 +2,7 @@
 name: BVLC Reference RCNN ILSVRC13 Model
 caffemodel: bvlc_reference_rcnn_ilsvrc13.caffemodel
 caffemodel_url: http://dl.caffe.berkeleyvision.org/bvlc_reference_rcnn_ilsvrc13.caffemodel
-license: non-commercial
+license: unrestricted
 sha1: bdd8abb885819cba5e2fe1eb36235f2319477e64
 caffe_commit: a7e397abbda52c0b90323c23ab95bdeabee90a98
 ---
@@ -13,8 +13,8 @@ Try the [detection example](http://nbviewer.ipython.org/github/BVLC/caffe/blob/m
 
 *N.B. For research purposes, make use of the official R-CNN package and not this example.*
 
+This model was trained by Ross Girshick @rbgirshick
+
 ## License
 
-The data used to train this model comes from the ImageNet project, which distributes its database to researchers who agree to a following term of access:
-"Researcher shall use the Database only for non-commercial research and educational purposes."
-Accordingly, this model is distributed under a non-commercial license.
+This model is released for unrestricted use.
diff --git a/models/finetune_flickr_style/deploy.prototxt b/models/finetune_flickr_style/deploy.prototxt
new file mode 100644
index 00000000000..4a924f74927
--- /dev/null
+++ b/models/finetune_flickr_style/deploy.prototxt
@@ -0,0 +1,342 @@
+name: "FlickrStyleCaffeNet"
+input: "data"
+input_dim: 10
+input_dim: 3
+input_dim: 227
+input_dim: 227
+layer {
+  name: "conv1"
+  type: "Convolution"
+  bottom: "data"
+  top: "conv1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 96
+    kernel_size: 11
+    stride: 4
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "relu1"
+  type: "ReLU"
+  bottom: "conv1"
+  top: "conv1"
+}
+layer {
+  name: "pool1"
+  type: "Pooling"
+  bottom: "conv1"
+  top: "pool1"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "norm1"
+  type: "LRN"
+  bottom: "pool1"
+  top: "norm1"
+  lrn_param {
+    local_size: 5
+    alpha: 0.0001
+    beta: 0.75
+  }
+}
+layer {
+  name: "conv2"
+  type: "Convolution"
+  bottom: "norm1"
+  top: "conv2"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 2
+    kernel_size: 5
+    group: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+      value: 1
+    }
+  }
+}
+layer {
+  name: "relu2"
+  type: "ReLU"
+  bottom: "conv2"
+  top: "conv2"
+}
+layer {
+  name: "pool2"
+  type: "Pooling"
+  bottom: "conv2"
+  top: "pool2"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "norm2"
+  type: "LRN"
+  bottom: "pool2"
+  top: "norm2"
+  lrn_param {
+    local_size: 5
+    alpha: 0.0001
+    beta: 0.75
+  }
+}
+layer {
+  name: "conv3"
+  type: "Convolution"
+  bottom: "norm2"
+  top: "conv3"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 384
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "relu3"
+  type: "ReLU"
+  bottom: "conv3"
+  top: "conv3"
+}
+layer {
+  name: "conv4"
+  type: "Convolution"
+  bottom: "conv3"
+  top: "conv4"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 384
+    pad: 1
+    kernel_size: 3
+    group: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+      value: 1
+    }
+  }
+}
+layer {
+  name: "relu4"
+  type: "ReLU"
+  bottom: "conv4"
+  top: "conv4"
+}
+layer {
+  name: "conv5"
+  type: "Convolution"
+  bottom: "conv4"
+  top: "conv5"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+    group: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+      value: 1
+    }
+  }
+}
+layer {
+  name: "relu5"
+  type: "ReLU"
+  bottom: "conv5"
+  top: "conv5"
+}
+layer {
+  name: "pool5"
+  type: "Pooling"
+  bottom: "conv5"
+  top: "pool5"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "fc6"
+  type: "InnerProduct"
+  bottom: "pool5"
+  top: "fc6"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  inner_product_param {
+    num_output: 4096
+    weight_filler {
+      type: "gaussian"
+      std: 0.005
+    }
+    bias_filler {
+      type: "constant"
+      value: 1
+    }
+  }
+}
+layer {
+  name: "relu6"
+  type: "ReLU"
+  bottom: "fc6"
+  top: "fc6"
+}
+layer {
+  name: "drop6"
+  type: "Dropout"
+  bottom: "fc6"
+  top: "fc6"
+  dropout_param {
+    dropout_ratio: 0.5
+  }
+}
+layer {
+  name: "fc7"
+  type: "InnerProduct"
+  bottom: "fc6"
+  top: "fc7"
+  # Note that lr_mult can be set to 0 to disable any fine-tuning of this, and any other, layer
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  inner_product_param {
+    num_output: 4096
+    weight_filler {
+      type: "gaussian"
+      std: 0.005
+    }
+    bias_filler {
+      type: "constant"
+      value: 1
+    }
+  }
+}
+layer {
+  name: "relu7"
+  type: "ReLU"
+  bottom: "fc7"
+  top: "fc7"
+}
+layer {
+  name: "drop7"
+  type: "Dropout"
+  bottom: "fc7"
+  top: "fc7"
+  dropout_param {
+    dropout_ratio: 0.5
+  }
+}
+layer {
+  name: "fc8_flickr"
+  type: "InnerProduct"
+  bottom: "fc7"
+  top: "fc8_flickr"
+  # lr_mult is set to higher than for other layers, because this layer is starting from random while the others are already trained
+  param {
+    lr_mult: 10
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 20
+    decay_mult: 0
+  }
+  inner_product_param {
+    num_output: 20
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "prob"
+  type: "Softmax"
+  bottom: "fc8_flickr"
+  top: "prob"
+}
diff --git a/models/finetune_flickr_style/readme.md b/models/finetune_flickr_style/readme.md
index d2a8a95f669..aac7f7c96e8 100644
--- a/models/finetune_flickr_style/readme.md
+++ b/models/finetune_flickr_style/readme.md
@@ -15,6 +15,8 @@ The final performance:
     I1017 07:36:17.370730 31333 solver.cpp:247] Iteration 100000, Testing net (#0)
     I1017 07:36:34.248730 31333 solver.cpp:298]     Test net output #0: accuracy = 0.3916
 
+This model was trained by Sergey Karayev @sergeyk
+
 ## License
 
 The Flickr Style dataset contains only URLs to images.
diff --git a/models/finetune_flickr_style/train_val.prototxt b/models/finetune_flickr_style/train_val.prototxt
index 7155c492360..848a426c914 100644
--- a/models/finetune_flickr_style/train_val.prototxt
+++ b/models/finetune_flickr_style/train_val.prototxt
@@ -1,49 +1,57 @@
 name: "FlickrStyleCaffeNet"
-layers {
+layer {
   name: "data"
-  type: IMAGE_DATA
+  type: "ImageData"
   top: "data"
   top: "label"
+  include {
+    phase: TRAIN
+  }
+  transform_param {
+    mirror: true
+    crop_size: 227
+    mean_file: "data/ilsvrc12/imagenet_mean.binaryproto"
+  }
   image_data_param {
     source: "data/flickr_style/train.txt"
     batch_size: 50
     new_height: 256
     new_width: 256
   }
-  transform_param {
-    crop_size: 227
-    mean_file: "data/ilsvrc12/imagenet_mean.binaryproto"
-    mirror: true
-  }
-  include: { phase: TRAIN }
 }
-layers {
+layer {
   name: "data"
-  type: IMAGE_DATA
+  type: "ImageData"
   top: "data"
   top: "label"
+  include {
+    phase: TEST
+  }
+  transform_param {
+    mirror: false
+    crop_size: 227
+    mean_file: "data/ilsvrc12/imagenet_mean.binaryproto"
+  }
   image_data_param {
     source: "data/flickr_style/test.txt"
     batch_size: 50
     new_height: 256
     new_width: 256
   }
-  transform_param {
-    crop_size: 227
-    mean_file: "data/ilsvrc12/imagenet_mean.binaryproto"
-    mirror: false
-  }
-  include: { phase: TEST }
 }
-layers {
+layer {
   name: "conv1"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "data"
   top: "conv1"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 96
     kernel_size: 11
@@ -58,15 +66,15 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu1"
-  type: RELU
+  type: "ReLU"
   bottom: "conv1"
   top: "conv1"
 }
-layers {
+layer {
   name: "pool1"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv1"
   top: "pool1"
   pooling_param {
@@ -75,9 +83,9 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "norm1"
-  type: LRN
+  type: "LRN"
   bottom: "pool1"
   top: "norm1"
   lrn_param {
@@ -86,15 +94,19 @@ layers {
     beta: 0.75
   }
 }
-layers {
+layer {
   name: "conv2"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "norm1"
   top: "conv2"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 256
     pad: 2
@@ -110,15 +122,15 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu2"
-  type: RELU
+  type: "ReLU"
   bottom: "conv2"
   top: "conv2"
 }
-layers {
+layer {
   name: "pool2"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv2"
   top: "pool2"
   pooling_param {
@@ -127,9 +139,9 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "norm2"
-  type: LRN
+  type: "LRN"
   bottom: "pool2"
   top: "norm2"
   lrn_param {
@@ -138,15 +150,19 @@ layers {
     beta: 0.75
   }
 }
-layers {
+layer {
   name: "conv3"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "norm2"
   top: "conv3"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 384
     pad: 1
@@ -161,21 +177,25 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu3"
-  type: RELU
+  type: "ReLU"
   bottom: "conv3"
   top: "conv3"
 }
-layers {
+layer {
   name: "conv4"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "conv3"
   top: "conv4"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 384
     pad: 1
@@ -191,21 +211,25 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu4"
-  type: RELU
+  type: "ReLU"
   bottom: "conv4"
   top: "conv4"
 }
-layers {
+layer {
   name: "conv5"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "conv4"
   top: "conv5"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 256
     pad: 1
@@ -221,15 +245,15 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu5"
-  type: RELU
+  type: "ReLU"
   bottom: "conv5"
   top: "conv5"
 }
-layers {
+layer {
   name: "pool5"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv5"
   top: "pool5"
   pooling_param {
@@ -238,15 +262,19 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "fc6"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "pool5"
   top: "fc6"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   inner_product_param {
     num_output: 4096
     weight_filler {
@@ -259,31 +287,35 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu6"
-  type: RELU
+  type: "ReLU"
   bottom: "fc6"
   top: "fc6"
 }
-layers {
+layer {
   name: "drop6"
-  type: DROPOUT
+  type: "Dropout"
   bottom: "fc6"
   top: "fc6"
   dropout_param {
     dropout_ratio: 0.5
   }
 }
-layers {
+layer {
   name: "fc7"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "fc6"
   top: "fc7"
-  # Note that blobs_lr can be set to 0 to disable any fine-tuning of this, and any other, layer
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  # Note that lr_mult can be set to 0 to disable any fine-tuning of this, and any other, layer
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   inner_product_param {
     num_output: 4096
     weight_filler {
@@ -296,31 +328,35 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu7"
-  type: RELU
+  type: "ReLU"
   bottom: "fc7"
   top: "fc7"
 }
-layers {
+layer {
   name: "drop7"
-  type: DROPOUT
+  type: "Dropout"
   bottom: "fc7"
   top: "fc7"
   dropout_param {
     dropout_ratio: 0.5
   }
 }
-layers {
+layer {
   name: "fc8_flickr"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "fc7"
   top: "fc8_flickr"
-  # blobs_lr is set to higher than for other layers, because this layer is starting from random while the others are already trained
-  blobs_lr: 10
-  blobs_lr: 20
-  weight_decay: 1
-  weight_decay: 0
+  # lr_mult is set to higher than for other layers, because this layer is starting from random while the others are already trained
+  param {
+    lr_mult: 10
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 20
+    decay_mult: 0
+  }
   inner_product_param {
     num_output: 20
     weight_filler {
@@ -333,17 +369,20 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "loss"
-  type: SOFTMAX_LOSS
+  type: "SoftmaxWithLoss"
   bottom: "fc8_flickr"
   bottom: "label"
+  top: "loss"
 }
-layers {
+layer {
   name: "accuracy"
-  type: ACCURACY
+  type: "Accuracy"
   bottom: "fc8_flickr"
   bottom: "label"
   top: "accuracy"
-  include: { phase: TEST }
+  include {
+    phase: TEST
+  }
 }
diff --git a/python/CMakeLists.txt b/python/CMakeLists.txt
index 8642c39367a..3722e8225c6 100644
--- a/python/CMakeLists.txt
+++ b/python/CMakeLists.txt
@@ -1,20 +1,37 @@
-project( Python )
+if(NOT HAVE_PYTHON)
+  message(STATUS "Python interface is disabled or not all required dependecies found. Building without it...")
+  return()
+endif()
 
-#    Python
-find_package(PythonLibs REQUIRED)
-include_directories(${PYTHON_INCLUDE_DIRS})
+include_directories(${PYTHON_INCLUDE_DIRS} ${NUMPY_INCLUDE_DIR} ${Boost_INCLUDE_DIRS})
+file(GLOB_RECURSE python_srcs ${PROJECT_SOURCE_DIR}/python/*.cpp)
 
-#    Boost.Python
-find_package(Boost 1.46 COMPONENTS python REQUIRED)
-include_directories(${Boost_INCLUDE_DIRS})
+add_library(pycaffe SHARED ${python_srcs})
+target_link_libraries(pycaffe ${Caffe_LINK} ${PYTHON_LIBRARIES} ${Boost_LIBRARIES})
+if(USE_MPI)
+target_link_libraries(pycaffe ${MPI_CXX_LIBRARIES})
+endif()
+set_target_properties(pycaffe PROPERTIES PREFIX "" OUTPUT_NAME "_caffe")
+caffe_default_properties(pycaffe)
 
-file(GLOB_RECURSE Python_SOURCES ${CMAKE_CURRENT_SOURCE_DIR}/*.cpp)
+if(UNIX OR APPLE)
+    set(__linkname "${PROJECT_SOURCE_DIR}/python/caffe/_caffe.so")
+    add_custom_command(TARGET pycaffe POST_BUILD
+                       COMMAND ln -sf $<TARGET_LINKER_FILE:pycaffe> "${__linkname}"
+                       COMMAND ${CMAKE_COMMAND} -E make_directory ${PROJECT_SOURCE_DIR}/python/caffe/proto
+                       COMMAND touch ${PROJECT_SOURCE_DIR}/python/caffe/proto/__init__.py
+                       COMMAND cp ${proto_gen_folder}/*.py ${PROJECT_SOURCE_DIR}/python/caffe/proto/
+                       COMMENT "Creating symlink ${__linkname} -> ${PROJECT_BINARY_DIR}/lib/_caffe${CAffe_POSTFIX}.so")
+endif()
+
+# ---[ Install
+file(GLOB files1 *.py requirements.txt)
+install(FILES ${files1} DESTINATION python)
+
+file(GLOB files2 caffe/*.py)
+install(FILES  ${files2} DESTINATION python/caffe)
+install(TARGETS pycaffe  DESTINATION python/caffe)
+install(DIRECTORY caffe/imagenet caffe/proto caffe/test DESTINATION python/caffe)
 
-add_library(pycaffe SHARED ${Python_SOURCES})
-target_link_libraries(pycaffe caffe ${PYTHON_LIBRARIES} ${Boost_LIBRARIES})
 
-###    Install    #################################################################################
 
-install(DIRECTORY caffe DESTINATION python)
-install(FILES requirements.txt DESTINATION python)
-install(TARGETS pycaffe DESTINATION python/caffe)
\ No newline at end of file
diff --git a/python/bn_convert_style.py b/python/bn_convert_style.py
new file mode 100644
index 00000000000..e7217f721ed
--- /dev/null
+++ b/python/bn_convert_style.py
@@ -0,0 +1,51 @@
+import numpy as np
+import sys
+import os
+import os.path as osp
+from argparse import ArgumentParser
+
+pycaffe_dir = osp.dirname(__file__)
+if osp.join(pycaffe_dir) not in sys.path:
+    sys.path.insert(0, pycaffe_dir)
+import caffe
+
+
+def main(args):
+    net = caffe.Net(args.model, args.weights, caffe.TEST)
+    conversion = args.conversion
+    eps = args.epsilon
+    for name, param in net.params.iteritems():
+        if name.endswith('_bn'):
+            if conversion == 'var_to_inv_std':
+                var = param[3].data
+                inv_std = np.power(var + eps, -0.5)
+                param[3].data[...] = inv_std
+            elif conversion == 'inv_std_to_var':
+                inv_std = param[3].data
+                var = np.power(inv_std, -2) - eps
+                param[3].data[...] = var
+            else:
+                raise ValueError("Unknown conversion {}".format(conversion))
+    if args.output is None:
+        name, ext = osp.splitext(args.weights)
+        suffix = conversion.split('_to_')[-1]
+        args.output = name + '_' + suffix + ext
+    net.save(args.output)
+
+
+if __name__ == '__main__':
+    parser = ArgumentParser(
+        description="This script converts between two styles of BN models. "
+                    "Specifically, in history we have two versions of BN "
+                    "implementation---one storing running variance, and"
+                    "the other storing running inverse std.")
+    parser.add_argument('model', help="The deploy prototxt")
+    parser.add_argument('weights', help="The caffemodel")
+    parser.add_argument('--output', '-o', help="Output caffemodel")
+    parser.add_argument('--conversion', type=str, default="inv_std_to_var",
+                        choices=['inv_std_to_var', 'var_to_inv_std'],
+                        help='can be "var_to_inv_std" or "inv_std_to_var"')
+    parser.add_argument('--epsilon', type=float, default=1e-5,
+                        help='the epsilon in the inverse, default to 1e-5')
+    args = parser.parse_args()
+    main(args)
diff --git a/python/caffe/__init__.py b/python/caffe/__init__.py
index 430bfce2ba5..1b2da510a90 100644
--- a/python/caffe/__init__.py
+++ b/python/caffe/__init__.py
@@ -1,4 +1,7 @@
 from .pycaffe import Net, SGDSolver
+from ._caffe import set_mode_cpu, set_mode_gpu, set_device, Layer, get_solver
+from .proto.caffe_pb2 import TRAIN, TEST
 from .classifier import Classifier
 from .detector import Detector
-import io
+from . import io
+from .net_spec import layers, params, NetSpec, to_proto
diff --git a/python/caffe/_caffe.cpp b/python/caffe/_caffe.cpp
index 5a81a42329b..cc87a459a47 100644
--- a/python/caffe/_caffe.cpp
+++ b/python/caffe/_caffe.cpp
@@ -1,16 +1,21 @@
-// pycaffe provides a wrapper of the caffe::Net class as well as some
-// caffe::Caffe functions so that one could easily call it from Python.
-// Note that for Python, we will simply use float as the data type.
+#include <Python.h>  // NOLINT(build/include_alpha)
 
+// Produce deprecation warnings (needs to come before arrayobject.h inclusion).
+#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION
+
+#include <boost/make_shared.hpp>
+#include <boost/python.hpp>
+#include <boost/python/raw_function.hpp>
 #include <boost/python/suite/indexing/vector_indexing_suite.hpp>
+#include <numpy/arrayobject.h>
 
 // these need to be included after boost on OS X
 #include <string>  // NOLINT(build/include_order)
 #include <vector>  // NOLINT(build/include_order)
 #include <fstream>  // NOLINT
 
-#include "_caffe.hpp"
 #include "caffe/caffe.hpp"
+#include "caffe/python_layer.hpp"
 
 // Temporary solution for numpy < 1.7 versions: old macro, no promises.
 // You're strongly advised to upgrade to >= 1.7.
@@ -19,12 +24,35 @@
 #define PyArray_SetBaseObject(arr, x) (PyArray_BASE(arr) = (x))
 #endif
 
+/* Fix to avoid registration warnings in pycaffe (#3960) */
+#define BP_REGISTER_SHARED_PTR_TO_PYTHON(PTR) do { \
+  const boost::python::type_info info = \
+    boost::python::type_id<shared_ptr<PTR > >(); \
+  const boost::python::converter::registration* reg = \
+    boost::python::converter::registry::query(info); \
+  if (reg == NULL) { \
+    bp::register_ptr_to_python<shared_ptr<PTR > >(); \
+  } else if ((*reg).m_to_python == NULL) { \
+    bp::register_ptr_to_python<shared_ptr<PTR > >(); \
+  } \
+} while (0)
+
+namespace bp = boost::python;
+
 namespace caffe {
 
-// for convenience, check that input files can be opened, and raise an
+// For Python, for now, we'll just always use float as the type.
+typedef float Dtype;
+const int NPY_DTYPE = NPY_FLOAT32;
+
+// Selecting mode.
+void set_mode_cpu() { Caffe::set_mode(Caffe::CPU); }
+void set_mode_gpu() { Caffe::set_mode(Caffe::GPU); }
+
+// For convenience, check that input files can be opened, and raise an
 // exception that boost will send to Python if not (caffe could still crash
 // later if the input files are disturbed before they are actually used, but
-// this saves frustration in most cases)
+// this saves frustration in most cases).
 static void CheckFile(const string& filename) {
     std::ifstream f(filename.c_str());
     if (!f.good()) {
@@ -34,42 +62,7 @@ static void CheckFile(const string& filename) {
     f.close();
 }
 
-bp::object PyBlobWrap::get_data() {
-  npy_intp dims[] = {num(), channels(), height(), width()};
-
-  PyObject *obj = PyArray_SimpleNewFromData(4, dims, NPY_FLOAT32,
-                                            blob_->mutable_cpu_data());
-  PyArray_SetBaseObject(reinterpret_cast<PyArrayObject *>(obj), self_);
-  Py_INCREF(self_);
-  bp::handle<> h(obj);
-
-  return bp::object(h);
-}
-
-bp::object PyBlobWrap::get_diff() {
-  npy_intp dims[] = {num(), channels(), height(), width()};
-
-  PyObject *obj = PyArray_SimpleNewFromData(4, dims, NPY_FLOAT32,
-                                            blob_->mutable_cpu_diff());
-  PyArray_SetBaseObject(reinterpret_cast<PyArrayObject *>(obj), self_);
-  Py_INCREF(self_);
-  bp::handle<> h(obj);
-
-  return bp::object(h);
-}
-
-PyNet::PyNet(string param_file, string pretrained_param_file) {
-  Init(param_file);
-  CheckFile(pretrained_param_file);
-  net_->CopyTrainedLayersFrom(pretrained_param_file);
-}
-
-void PyNet::Init(string param_file) {
-  CheckFile(param_file);
-  net_.reset(new Net<float>(param_file));
-}
-
-void PyNet::check_contiguous_array(PyArrayObject* arr, string name,
+void CheckContiguousArray(PyArrayObject* arr, string name,
     int channels, int height, int width) {
   if (!(PyArray_FLAGS(arr) & NPY_ARRAY_C_CONTIGUOUS)) {
     throw std::runtime_error(name + " must be C contiguous");
@@ -91,10 +84,39 @@ void PyNet::check_contiguous_array(PyArrayObject* arr, string name,
   }
 }
 
-void PyNet::set_input_arrays(bp::object data_obj, bp::object labels_obj) {
+// Net constructor for passing phase as int
+shared_ptr<Net<Dtype> > Net_Init(
+    string param_file, int phase) {
+  CheckFile(param_file);
+
+  shared_ptr<Net<Dtype> > net(new Net<Dtype>(param_file,
+      static_cast<Phase>(phase)));
+  return net;
+}
+
+// Net construct-and-load convenience constructor
+shared_ptr<Net<Dtype> > Net_Init_Load(
+    string param_file, string pretrained_param_file, int phase) {
+  CheckFile(param_file);
+  CheckFile(pretrained_param_file);
+
+  shared_ptr<Net<Dtype> > net(new Net<Dtype>(param_file,
+      static_cast<Phase>(phase)));
+  net->CopyTrainedLayersFrom(pretrained_param_file);
+  return net;
+}
+
+void Net_Save(const Net<Dtype>& net, string filename) {
+  NetParameter net_param;
+  net.ToProto(&net_param, false);
+  WriteProtoToBinaryFile(net_param, filename.c_str());
+}
+
+void Net_SetInputArrays(Net<Dtype>* net, bp::object data_obj,
+    bp::object labels_obj) {
   // check that this network has an input MemoryDataLayer
-  shared_ptr<MemoryDataLayer<float> > md_layer =
-    boost::dynamic_pointer_cast<MemoryDataLayer<float> >(net_->layers()[0]);
+  shared_ptr<MemoryDataLayer<Dtype> > md_layer =
+    boost::dynamic_pointer_cast<MemoryDataLayer<Dtype> >(net->layers()[0]);
   if (!md_layer) {
     throw std::runtime_error("set_input_arrays may only be called if the"
         " first layer is a MemoryDataLayer");
@@ -105,9 +127,9 @@ void PyNet::set_input_arrays(bp::object data_obj, bp::object labels_obj) {
       reinterpret_cast<PyArrayObject*>(data_obj.ptr());
   PyArrayObject* labels_arr =
       reinterpret_cast<PyArrayObject*>(labels_obj.ptr());
-  check_contiguous_array(data_arr, "data array", md_layer->datum_channels(),
-      md_layer->datum_height(), md_layer->datum_width());
-  check_contiguous_array(labels_arr, "labels array", 1, 1, 1);
+  CheckContiguousArray(data_arr, "data array", md_layer->channels(),
+      md_layer->height(), md_layer->width());
+  CheckContiguousArray(labels_arr, "labels array", 1, 1, 1);
   if (PyArray_DIMS(data_arr)[0] != PyArray_DIMS(labels_arr)[0]) {
     throw std::runtime_error("data and labels must have the same first"
         " dimension");
@@ -117,88 +139,179 @@ void PyNet::set_input_arrays(bp::object data_obj, bp::object labels_obj) {
         " multiple of batch size");
   }
 
-  // hold references
-  input_data_ = data_obj;
-  input_labels_ = labels_obj;
-
-  md_layer->Reset(static_cast<float*>(PyArray_DATA(data_arr)),
-      static_cast<float*>(PyArray_DATA(labels_arr)),
+  md_layer->Reset(static_cast<Dtype*>(PyArray_DATA(data_arr)),
+      static_cast<Dtype*>(PyArray_DATA(labels_arr)),
       PyArray_DIMS(data_arr)[0]);
 }
 
-PySGDSolver::PySGDSolver(const string& param_file) {
-  // as in PyNet, (as a convenience, not a guarantee), create a Python
-  // exception if param_file can't be opened
-  CheckFile(param_file);
-  solver_.reset(new SGDSolver<float>(param_file));
-  // we need to explicitly store the net wrapper, rather than constructing
-  // it on the fly, so that it can hold references to Python objects
-  net_.reset(new PyNet(solver_->net()));
+Solver<Dtype>* GetSolverFromFile(const string& filename) {
+  SolverParameter param;
+  ReadProtoFromTextFileOrDie(filename, &param);
+  return GetSolver<Dtype>(param);
 }
 
-void PySGDSolver::SolveResume(const string& resume_file) {
-  CheckFile(resume_file);
-  return solver_->Solve(resume_file);
+struct NdarrayConverterGenerator {
+  template <typename T> struct apply;
+};
+
+template <>
+struct NdarrayConverterGenerator::apply<Dtype*> {
+  struct type {
+    PyObject* operator() (Dtype* data) const {
+      // Just store the data pointer, and add the shape information in postcall.
+      return PyArray_SimpleNewFromData(0, NULL, NPY_DTYPE, data);
+    }
+    const PyTypeObject* get_pytype() {
+      return &PyArray_Type;
+    }
+  };
+};
+
+struct NdarrayCallPolicies : public bp::default_call_policies {
+  typedef NdarrayConverterGenerator result_converter;
+  PyObject* postcall(PyObject* pyargs, PyObject* result) {
+    bp::object pyblob = bp::extract<bp::tuple>(pyargs)()[0];
+    shared_ptr<Blob<Dtype> > blob =
+      bp::extract<shared_ptr<Blob<Dtype> > >(pyblob);
+    // Free the temporary pointer-holding array, and construct a new one with
+    // the shape information from the blob.
+    void* data = PyArray_DATA(reinterpret_cast<PyArrayObject*>(result));
+    Py_DECREF(result);
+    const int num_axes = blob->num_axes();
+    vector<npy_intp> dims(blob->shape().begin(), blob->shape().end());
+    PyObject *arr_obj = PyArray_SimpleNewFromData(num_axes, dims.data(),
+                                                  NPY_FLOAT32, data);
+    // SetBaseObject steals a ref, so we need to INCREF.
+    Py_INCREF(pyblob.ptr());
+    PyArray_SetBaseObject(reinterpret_cast<PyArrayObject*>(arr_obj),
+        pyblob.ptr());
+    return arr_obj;
+  }
+};
+
+bp::object Blob_Reshape(bp::tuple args, bp::dict kwargs) {
+  if (bp::len(kwargs) > 0) {
+    throw std::runtime_error("Blob.reshape takes no kwargs");
+  }
+  Blob<Dtype>* self = bp::extract<Blob<Dtype>*>(args[0]);
+  vector<int> shape(bp::len(args) - 1);
+  for (int i = 1; i < bp::len(args); ++i) {
+    shape[i - 1] = bp::extract<int>(args[i]);
+  }
+  self->Reshape(shape);
+  // We need to explicitly return None to use bp::raw_function.
+  return bp::object();
 }
 
+BOOST_PYTHON_MEMBER_FUNCTION_OVERLOADS(SolveOverloads, Solve, 0, 1);
+
 BOOST_PYTHON_MODULE(_caffe) {
   // below, we prepend an underscore to methods that will be replaced
   // in Python
-  bp::class_<PyNet, shared_ptr<PyNet> >(
-      "Net", bp::init<string, string>())
-      .def(bp::init<string>())
-      .def("_forward",              &PyNet::Forward)
-      .def("_backward",             &PyNet::Backward)
-      .def("reshape",               &PyNet::Reshape)
-      .def("set_mode_cpu",          &PyNet::set_mode_cpu)
-      .def("set_mode_gpu",          &PyNet::set_mode_gpu)
-      .def("set_phase_train",       &PyNet::set_phase_train)
-      .def("set_phase_test",        &PyNet::set_phase_test)
-      .def("set_device",            &PyNet::set_device)
-      .add_property("_blobs",       &PyNet::blobs)
-      .add_property("layers",       &PyNet::layers)
-      .add_property("_blob_names",  &PyNet::blob_names)
-      .add_property("_layer_names", &PyNet::layer_names)
-      .add_property("inputs",       &PyNet::inputs)
-      .add_property("outputs",      &PyNet::outputs)
-      .add_property("mean",         &PyNet::mean_)
-      .add_property("input_scale",  &PyNet::input_scale_)
-      .add_property("raw_scale",    &PyNet::raw_scale_)
-      .add_property("channel_swap", &PyNet::channel_swap_)
-      .def("_set_input_arrays",     &PyNet::set_input_arrays)
-      .def("save",                  &PyNet::save);
-
-  bp::class_<PyBlob<float>, PyBlobWrap>(
-      "Blob", bp::no_init)
-      .add_property("num",      &PyBlob<float>::num)
-      .add_property("channels", &PyBlob<float>::channels)
-      .add_property("height",   &PyBlob<float>::height)
-      .add_property("width",    &PyBlob<float>::width)
-      .add_property("count",    &PyBlob<float>::count)
-      .def("reshape",           &PyBlob<float>::Reshape)
-      .add_property("data",     &PyBlobWrap::get_data)
-      .add_property("diff",     &PyBlobWrap::get_diff);
-
-  bp::class_<PyLayer>(
-      "Layer", bp::no_init)
-      .add_property("blobs", &PyLayer::blobs);
-
-  bp::class_<PySGDSolver, boost::noncopyable>(
-      "SGDSolver", bp::init<string>())
-      .add_property("net", &PySGDSolver::net)
-      .def("solve",        &PySGDSolver::Solve)
-      .def("solve",        &PySGDSolver::SolveResume);
-
-  bp::class_<vector<PyBlob<float> > >("BlobVec")
-      .def(bp::vector_indexing_suite<vector<PyBlob<float> >, true>());
-
-  bp::class_<vector<PyLayer> >("LayerVec")
-      .def(bp::vector_indexing_suite<vector<PyLayer>, true>());
+  // Caffe utility functions
+  bp::def("set_mode_cpu", &set_mode_cpu);
+  bp::def("set_mode_gpu", &set_mode_gpu);
+  bp::def("set_device", &Caffe::SetDevice);
+
+  bp::class_<Net<Dtype>, shared_ptr<Net<Dtype> >, boost::noncopyable >("Net",
+    bp::no_init)
+    .def("__init__", bp::make_constructor(&Net_Init))
+    .def("__init__", bp::make_constructor(&Net_Init_Load))
+    .def("_forward", &Net<Dtype>::ForwardFromTo)
+    .def("_backward", &Net<Dtype>::BackwardFromTo)
+    .def("reshape", &Net<Dtype>::Reshape)
+    // The cast is to select a particular overload.
+    .def("copy_from", static_cast<void (Net<Dtype>::*)(const string)>(
+        &Net<Dtype>::CopyTrainedLayersFrom))
+    .def("share_with", &Net<Dtype>::ShareTrainedLayersWith)
+    .add_property("_blobs", bp::make_function(&Net<Dtype>::blobs,
+        bp::return_internal_reference<>()))
+    .add_property("layers", bp::make_function(&Net<Dtype>::layers,
+        bp::return_internal_reference<>()))
+    .add_property("_blob_names", bp::make_function(&Net<Dtype>::blob_names,
+        bp::return_value_policy<bp::copy_const_reference>()))
+    .add_property("_layer_names", bp::make_function(&Net<Dtype>::layer_names,
+        bp::return_value_policy<bp::copy_const_reference>()))
+    .add_property("_inputs", bp::make_function(&Net<Dtype>::input_blob_indices,
+        bp::return_value_policy<bp::copy_const_reference>()))
+    .add_property("_outputs",
+        bp::make_function(&Net<Dtype>::output_blob_indices,
+        bp::return_value_policy<bp::copy_const_reference>()))
+    .def("_set_input_arrays", &Net_SetInputArrays,
+        bp::with_custodian_and_ward<1, 2, bp::with_custodian_and_ward<1, 3> >())
+    .def("save", &Net_Save);
+  BP_REGISTER_SHARED_PTR_TO_PYTHON(Net<Dtype>);
+
+  bp::class_<Blob<Dtype>, shared_ptr<Blob<Dtype> >, boost::noncopyable>(
+    "Blob", bp::no_init)
+    .add_property("num",      &Blob<Dtype>::num)
+    .add_property("channels", &Blob<Dtype>::channels)
+    .add_property("height",   &Blob<Dtype>::height)
+    .add_property("width",    &Blob<Dtype>::width)
+    .add_property("count",    static_cast<int (Blob<Dtype>::*)() const>(
+        &Blob<Dtype>::count))
+    .def("reshape",           bp::raw_function(&Blob_Reshape))
+    .add_property("data",     bp::make_function(&Blob<Dtype>::mutable_cpu_data,
+          NdarrayCallPolicies()))
+    .add_property("diff",     bp::make_function(&Blob<Dtype>::mutable_cpu_diff,
+          NdarrayCallPolicies()));
+  BP_REGISTER_SHARED_PTR_TO_PYTHON(Blob<Dtype>);
+
+  bp::class_<Layer<Dtype>, shared_ptr<PythonLayer<Dtype> >,
+    boost::noncopyable>("Layer", bp::init<const LayerParameter&>())
+    .add_property("blobs", bp::make_function(&Layer<Dtype>::blobs,
+          bp::return_internal_reference<>()))
+    .def("setup", &Layer<Dtype>::LayerSetUp)
+    .def("reshape", &Layer<Dtype>::Reshape)
+    .add_property("type", bp::make_function(&Layer<Dtype>::type));
+  BP_REGISTER_SHARED_PTR_TO_PYTHON(Layer<Dtype>);
+
+  bp::class_<LayerParameter>("LayerParameter", bp::no_init);
+
+  bp::class_<Solver<Dtype>, shared_ptr<Solver<Dtype> >, boost::noncopyable>(
+    "Solver", bp::no_init)
+    .add_property("net", &Solver<Dtype>::net)
+    .add_property("test_nets", bp::make_function(&Solver<Dtype>::test_nets,
+          bp::return_internal_reference<>()))
+    .add_property("iter", &Solver<Dtype>::iter)
+    .def("solve", static_cast<void (Solver<Dtype>::*)(const char*)>(
+          &Solver<Dtype>::Solve), SolveOverloads())
+    .def("step", &Solver<Dtype>::Step)
+    .def("restore", &Solver<Dtype>::Restore);
+  BP_REGISTER_SHARED_PTR_TO_PYTHON(Solver<Dtype>);
+
+  bp::class_<SGDSolver<Dtype>, bp::bases<Solver<Dtype> >,
+    shared_ptr<SGDSolver<Dtype> >, boost::noncopyable>(
+        "SGDSolver", bp::init<string>());
+  bp::class_<NesterovSolver<Dtype>, bp::bases<Solver<Dtype> >,
+    shared_ptr<NesterovSolver<Dtype> >, boost::noncopyable>(
+        "NesterovSolver", bp::init<string>());
+  bp::class_<AdaGradSolver<Dtype>, bp::bases<Solver<Dtype> >,
+    shared_ptr<AdaGradSolver<Dtype> >, boost::noncopyable>(
+        "AdaGradSolver", bp::init<string>());
+
+  bp::def("get_solver", &GetSolverFromFile,
+      bp::return_value_policy<bp::manage_new_object>());
 
+  // vector wrappers for all the vector types we use
+  bp::class_<vector<shared_ptr<Blob<Dtype> > > >("BlobVec")
+    .def(bp::vector_indexing_suite<vector<shared_ptr<Blob<Dtype> > >, true>());
+  bp::class_<vector<Blob<Dtype>*> >("RawBlobVec")
+    .def(bp::vector_indexing_suite<vector<Blob<Dtype>*>, true>());
+  bp::class_<vector<shared_ptr<Layer<Dtype> > > >("LayerVec")
+    .def(bp::vector_indexing_suite<vector<shared_ptr<Layer<Dtype> > >, true>());
   bp::class_<vector<string> >("StringVec")
-      .def(bp::vector_indexing_suite<vector<string> >());
+    .def(bp::vector_indexing_suite<vector<string> >());
+  bp::class_<vector<int> >("IntVec")
+    .def(bp::vector_indexing_suite<vector<int> >());
+  bp::class_<vector<shared_ptr<Net<Dtype> > > >("NetVec")
+    .def(bp::vector_indexing_suite<vector<shared_ptr<Net<Dtype> > >, true>());
+  bp::class_<vector<bool> >("BoolVec")
+    .def(bp::vector_indexing_suite<vector<bool> >());
 
-  import_array();
+  // boost python expects a void (missing) return value, while import_array
+  // returns NULL for python3. import_array1() forces a void return value.
+  import_array1();
 }
 
 }  // namespace caffe
diff --git a/python/caffe/_caffe.hpp b/python/caffe/_caffe.hpp
deleted file mode 100644
index ba04d276351..00000000000
--- a/python/caffe/_caffe.hpp
+++ /dev/null
@@ -1,182 +0,0 @@
-#ifndef PYTHON_CAFFE__CAFFE_HPP_
-#define PYTHON_CAFFE__CAFFE_HPP_
-
-#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION
-
-#include <boost/python.hpp>
-#include <boost/shared_ptr.hpp>
-#include <numpy/arrayobject.h>
-
-// these need to be included after boost on OS X
-#include <string>  // NOLINT(build/include_order)
-#include <vector>  // NOLINT(build/include_order)
-
-#include "caffe/caffe.hpp"
-
-namespace bp = boost::python;
-using boost::shared_ptr;
-
-namespace caffe {
-
-// wrap shared_ptr<Blob> in a class that we construct in C++ and pass
-// to Python
-template <typename Dtype>
-class PyBlob {
- public:
-  explicit PyBlob(const shared_ptr<Blob<Dtype> > &blob)
-      : blob_(blob) {}
-
-  int num() const { return blob_->num(); }
-  int channels() const { return blob_->channels(); }
-  int height() const { return blob_->height(); }
-  int width() const { return blob_->width(); }
-  int count() const { return blob_->count(); }
-  void Reshape(const int n, const int c, const int h, const int w) {
-    return blob_->Reshape(n, c, h, w);
-  }
-
-  // this is here only to satisfy boost's vector_indexing_suite
-  bool operator == (const PyBlob &other) {
-      return this->blob_ == other.blob_;
-  }
-
- protected:
-  shared_ptr<Blob<Dtype> > blob_;
-};
-
-// We need another wrapper (used as boost::python's HeldType) that receives a
-// self PyObject * which we can use as ndarray.base, so that data/diff memory
-// is not freed while still being used in Python.
-class PyBlobWrap : public PyBlob<float> {
- public:
-  PyBlobWrap(PyObject *p, const PyBlob<float> &blob)
-      : PyBlob<float>(blob), self_(p) {}
-
-  bp::object get_data();
-  bp::object get_diff();
-
- private:
-  PyObject *self_;
-};
-
-class PyLayer {
- public:
-  explicit PyLayer(const shared_ptr<Layer<float> > &layer)
-    : layer_(layer) {}
-
-  vector<PyBlob<float> > blobs() {
-    return vector<PyBlob<float> >(layer_->blobs().begin(),
-        layer_->blobs().end());
-  }
-
-  // this is here only to satisfy boost's vector_indexing_suite
-  bool operator == (const PyLayer &other) {
-      return this->layer_ == other.layer_;
-  }
-
- protected:
-  shared_ptr<Layer<float> > layer_;
-};
-
-class PyNet {
- public:
-  // For cases where parameters will be determined later by the Python user,
-  // create a Net with unallocated parameters (which will not be zero-filled
-  // when accessed).
-  explicit PyNet(string param_file) { Init(param_file); }
-  PyNet(string param_file, string pretrained_param_file);
-  explicit PyNet(shared_ptr<Net<float> > net)
-      : net_(net) {}
-  virtual ~PyNet() {}
-
-  void Init(string param_file);
-
-
-  // Generate Python exceptions for badly shaped or discontiguous arrays.
-  inline void check_contiguous_array(PyArrayObject* arr, string name,
-      int channels, int height, int width);
-
-  void Forward(int start, int end) { net_->ForwardFromTo(start, end); }
-  void Backward(int start, int end) { net_->BackwardFromTo(start, end); }
-  void Reshape() { net_->Reshape(); }
-
-  void set_input_arrays(bp::object data_obj, bp::object labels_obj);
-
-  // Save the network weights to binary proto for net surgeries.
-  void save(string filename) {
-    NetParameter net_param;
-    net_->ToProto(&net_param, false);
-    WriteProtoToBinaryFile(net_param, filename.c_str());
-  }
-
-  // The caffe::Caffe utility functions.
-  void set_mode_cpu() { Caffe::set_mode(Caffe::CPU); }
-  void set_mode_gpu() { Caffe::set_mode(Caffe::GPU); }
-  void set_phase_train() { Caffe::set_phase(Caffe::TRAIN); }
-  void set_phase_test() { Caffe::set_phase(Caffe::TEST); }
-  void set_device(int device_id) { Caffe::SetDevice(device_id); }
-
-  vector<PyBlob<float> > blobs() {
-    return vector<PyBlob<float> >(net_->blobs().begin(), net_->blobs().end());
-  }
-
-  vector<PyLayer> layers() {
-    return vector<PyLayer>(net_->layers().begin(), net_->layers().end());
-  }
-
-  vector<string> blob_names() { return net_->blob_names(); }
-  vector<string> layer_names() { return net_->layer_names(); }
-
-  bp::list inputs() {
-    bp::list input_blob_names;
-    for (int i = 0; i < net_->input_blob_indices().size(); ++i) {
-      input_blob_names.append(
-          net_->blob_names()[net_->input_blob_indices()[i]]);
-    }
-    return input_blob_names;
-  }
-
-  bp::list outputs() {
-    bp::list output_blob_names;
-    for (int i = 0; i < net_->output_blob_indices().size(); ++i) {
-      output_blob_names.append(
-          net_->blob_names()[net_->output_blob_indices()[i]]);
-    }
-    return output_blob_names;
-  }
-
-  // Input preprocessing configuration attributes. These are public for
-  // direct access from Python.
-  bp::dict mean_;
-  bp::dict input_scale_;
-  bp::dict raw_scale_;
-  bp::dict channel_swap_;
-
- protected:
-  // The pointer to the internal caffe::Net instant.
-  shared_ptr<Net<float> > net_;
-  // if taking input from an ndarray, we need to hold references
-  bp::object input_data_;
-  bp::object input_labels_;
-};
-
-class PySGDSolver {
- public:
-  explicit PySGDSolver(const string& param_file);
-
-  shared_ptr<PyNet> net() { return net_; }
-  void Solve() { return solver_->Solve(); }
-  void SolveResume(const string& resume_file);
-
- protected:
-  shared_ptr<PyNet> net_;
-  shared_ptr<SGDSolver<float> > solver_;
-};
-
-// Declare the module init function created by boost::python, so that we can
-// use this module from C++ when embedding Python.
-PyMODINIT_FUNC init_caffe(void);
-
-}  // namespace caffe
-
-#endif
diff --git a/python/caffe/classifier.py b/python/caffe/classifier.py
index fe471ca13b1..537193db8f8 100644
--- a/python/caffe/classifier.py
+++ b/python/caffe/classifier.py
@@ -12,57 +12,60 @@ class Classifier(caffe.Net):
     """
     Classifier extends Net for image class prediction
     by scaling, center cropping, or oversampling.
+
+    Parameters
+    ----------
+    image_dims : dimensions to scale input for cropping/sampling.
+        Default is to scale to net input size for whole-image crop.
+    mean, input_scale, raw_scale, channel_swap: params for
+        preprocessing options.
     """
     def __init__(self, model_file, pretrained_file, image_dims=None,
-                 gpu=False, mean=None, input_scale=None, raw_scale=None,
+                 mean=None, input_scale=None, raw_scale=None,
                  channel_swap=None):
-        """
-        Take
-        image_dims: dimensions to scale input for cropping/sampling.
-            Default is to scale to net input size for whole-image crop.
-        gpu, mean, input_scale, raw_scale, channel_swap: params for
-            preprocessing options.
-        """
-        caffe.Net.__init__(self, model_file, pretrained_file)
-        self.set_phase_test()
-
-        if gpu:
-            self.set_mode_gpu()
-        else:
-            self.set_mode_cpu()
+        caffe.Net.__init__(self, model_file, pretrained_file, caffe.TEST)
 
+        # configure pre-processing
+        in_ = self.inputs[0]
+        self.transformer = caffe.io.Transformer(
+            {in_: self.blobs[in_].data.shape})
+        self.transformer.set_transpose(in_, (2, 0, 1))
         if mean is not None:
-            self.set_mean(self.inputs[0], mean)
+            self.transformer.set_mean(in_, mean)
         if input_scale is not None:
-            self.set_input_scale(self.inputs[0], input_scale)
+            self.transformer.set_input_scale(in_, input_scale)
         if raw_scale is not None:
-            self.set_raw_scale(self.inputs[0], raw_scale)
+            self.transformer.set_raw_scale(in_, raw_scale)
         if channel_swap is not None:
-            self.set_channel_swap(self.inputs[0], channel_swap)
+            self.transformer.set_channel_swap(in_, channel_swap)
 
-        self.crop_dims = np.array(self.blobs[self.inputs[0]].data.shape[2:])
+        self.crop_dims = np.array(self.blobs[in_].data.shape[2:])
         if not image_dims:
             image_dims = self.crop_dims
         self.image_dims = image_dims
 
-
     def predict(self, inputs, oversample=True):
         """
         Predict classification probabilities of inputs.
 
-        Take
-        inputs: iterable of (H x W x K) input ndarrays.
-        oversample: average predictions across center, corners, and mirrors
-                    when True (default). Center-only prediction when False.
+        Parameters
+        ----------
+        inputs : iterable of (H x W x K) input ndarrays.
+        oversample : boolean
+            average predictions across center, corners, and mirrors
+            when True (default). Center-only prediction when False.
 
-        Give
-        predictions: (N x C) ndarray of class probabilities
-                     for N images and C classes.
+        Returns
+        -------
+        predictions: (N x C) ndarray of class probabilities for N images and C
+            classes.
         """
         # Scale to standardize input dimensions.
         input_ = np.zeros((len(inputs),
-            self.image_dims[0], self.image_dims[1], inputs[0].shape[2]),
-            dtype=np.float32)
+                           self.image_dims[0],
+                           self.image_dims[1],
+                           inputs[0].shape[2]),
+                          dtype=np.float32)
         for ix, in_ in enumerate(inputs):
             input_[ix] = caffe.io.resize_image(in_, self.image_dims)
 
@@ -79,12 +82,12 @@ def predict(self, inputs, oversample=True):
             input_ = input_[:, crop[0]:crop[2], crop[1]:crop[3], :]
 
         # Classify
-        caffe_in = np.zeros(np.array(input_.shape)[[0,3,1,2]],
+        caffe_in = np.zeros(np.array(input_.shape)[[0, 3, 1, 2]],
                             dtype=np.float32)
         for ix, in_ in enumerate(input_):
-            caffe_in[ix] = self.preprocess(self.inputs[0], in_)
+            caffe_in[ix] = self.transformer.preprocess(self.inputs[0], in_)
         out = self.forward_all(**{self.inputs[0]: caffe_in})
-        predictions = out[self.outputs[0]].squeeze(axis=(2,3))
+        predictions = out[self.outputs[0]]
 
         # For oversampling, average predictions across crops.
         if oversample:
diff --git a/python/caffe/detector.py b/python/caffe/detector.py
index f219b6105e1..75cd3b1202f 100644
--- a/python/caffe/detector.py
+++ b/python/caffe/detector.py
@@ -23,48 +23,48 @@ class Detector(caffe.Net):
     """
     Detector extends Net for windowed detection by a list of crops or
     selective search proposals.
+
+    Parameters
+    ----------
+    mean, input_scale, raw_scale, channel_swap : params for preprocessing
+        options.
+    context_pad : amount of surrounding context to take s.t. a `context_pad`
+        sized border of pixels in the network input image is context, as in
+        R-CNN feature extraction.
     """
-    def __init__(self, model_file, pretrained_file, gpu=False, mean=None,
+    def __init__(self, model_file, pretrained_file, mean=None,
                  input_scale=None, raw_scale=None, channel_swap=None,
                  context_pad=None):
-        """
-        Take
-        gpu, mean, input_scale, raw_scale, channel_swap: params for
-            preprocessing options.
-        context_pad: amount of surrounding context to take s.t. a `context_pad`
-            sized border of pixels in the network input image is context, as in
-            R-CNN feature extraction.
-        """
-        caffe.Net.__init__(self, model_file, pretrained_file)
-        self.set_phase_test()
-
-        if gpu:
-            self.set_mode_gpu()
-        else:
-            self.set_mode_cpu()
+        caffe.Net.__init__(self, model_file, pretrained_file, caffe.TEST)
 
+        # configure pre-processing
+        in_ = self.inputs[0]
+        self.transformer = caffe.io.Transformer(
+            {in_: self.blobs[in_].data.shape})
+        self.transformer.set_transpose(in_, (2, 0, 1))
         if mean is not None:
-            self.set_mean(self.inputs[0], mean)
+            self.transformer.set_mean(in_, mean)
         if input_scale is not None:
-            self.set_input_scale(self.inputs[0], input_scale)
+            self.transformer.set_input_scale(in_, input_scale)
         if raw_scale is not None:
-            self.set_raw_scale(self.inputs[0], raw_scale)
+            self.transformer.set_raw_scale(in_, raw_scale)
         if channel_swap is not None:
-            self.set_channel_swap(self.inputs[0], channel_swap)
+            self.transformer.set_channel_swap(in_, channel_swap)
 
         self.configure_crop(context_pad)
 
-
     def detect_windows(self, images_windows):
         """
         Do windowed detection over given images and windows. Windows are
         extracted then warped to the input dimensions of the net.
 
-        Take
+        Parameters
+        ----------
         images_windows: (image filename, window list) iterable.
         context_crop: size of context border to crop in pixels.
 
-        Give
+        Returns
+        -------
         detections: list of {filename: image filename, window: crop coordinates,
             predictions: prediction vector} dicts.
         """
@@ -76,13 +76,14 @@ def detect_windows(self, images_windows):
                 window_inputs.append(self.crop(image, window))
 
         # Run through the net (warping windows to input dimensions).
+        in_ = self.inputs[0]
         caffe_in = np.zeros((len(window_inputs), window_inputs[0].shape[2])
-                            + self.blobs[self.inputs[0]].data.shape[2:],
+                            + self.blobs[in_].data.shape[2:],
                             dtype=np.float32)
         for ix, window_in in enumerate(window_inputs):
-            caffe_in[ix] = self.preprocess(self.inputs[0], window_in)
-        out = self.forward_all(**{self.inputs[0]: caffe_in})
-        predictions = out[self.outputs[0]].squeeze(axis=(2,3))
+            caffe_in[ix] = self.transformer.preprocess(in_, window_in)
+        out = self.forward_all(**{in_: caffe_in})
+        predictions = out[self.outputs[0]].squeeze(axis=(2, 3))
 
         # Package predictions with images and windows.
         detections = []
@@ -97,16 +98,17 @@ def detect_windows(self, images_windows):
                 ix += 1
         return detections
 
-
     def detect_selective_search(self, image_fnames):
         """
         Do windowed detection over Selective Search proposals by extracting
         the crop and warping to the input dimensions of the net.
 
-        Take
+        Parameters
+        ----------
         image_fnames: list
 
-        Give
+        Returns
+        -------
         detections: list of {filename: image filename, window: crop coordinates,
             predictions: prediction vector} dicts.
         """
@@ -120,17 +122,18 @@ def detect_selective_search(self, image_fnames):
         # Run windowed detection on the selective search list.
         return self.detect_windows(zip(image_fnames, windows_list))
 
-
     def crop(self, im, window):
         """
         Crop a window from the image for detection. Include surrounding context
         according to the `context_pad` configuration.
 
-        Take
+        Parameters
+        ----------
         im: H x W x K image ndarray to crop.
         window: bounding box coordinates as ymin, xmin, ymax, xmax.
 
-        Give
+        Returns
+        -------
         crop: cropped window.
         """
         # Crop window from the image.
@@ -170,35 +173,44 @@ def crop(self, im, window):
             # with mean padding
             context_crop = im[box[0]:box[2], box[1]:box[3]]
             context_crop = caffe.io.resize_image(context_crop, (crop_h, crop_w))
-            crop = self.crop_mean.copy()
+            crop = np.ones(self.crop_dims, dtype=np.float32) * self.crop_mean
             crop[pad_y:(pad_y + crop_h), pad_x:(pad_x + crop_w)] = context_crop
 
         return crop
 
-
     def configure_crop(self, context_pad):
         """
-        Configure amount of context for cropping.
+        Configure crop dimensions and amount of context for cropping.
         If context is included, make the special input mean for context padding.
 
-        Take
-        context_pad: amount of context for cropping.
+        Parameters
+        ----------
+        context_pad : amount of context for cropping.
         """
+        # crop dimensions
+        in_ = self.inputs[0]
+        tpose = self.transformer.transpose[in_]
+        inv_tpose = [tpose[t] for t in tpose]
+        self.crop_dims = np.array(self.blobs[in_].data.shape[1:])[inv_tpose]
+        #.transpose(inv_tpose)
+        # context padding
         self.context_pad = context_pad
         if self.context_pad:
-            raw_scale = self.raw_scale.get(self.inputs[0])
-            channel_order = self.channel_swap.get(self.inputs[0])
+            in_ = self.inputs[0]
+            transpose = self.transformer.transpose.get(in_)
+            channel_order = self.transformer.channel_swap.get(in_)
+            raw_scale = self.transformer.raw_scale.get(in_)
             # Padding context crops needs the mean in unprocessed input space.
-            mean = self.mean.get(self.inputs[0])
+            mean = self.transformer.mean.get(in_)
             if mean is not None:
-                crop_mean = mean.copy().transpose((1,2,0))
+                inv_transpose = [transpose[t] for t in transpose]
+                crop_mean = mean.copy().transpose(inv_transpose)
                 if channel_order is not None:
                     channel_order_inverse = [channel_order.index(i)
-                                            for i in range(crop_mean.shape[2])]
-                    crop_mean = crop_mean[:,:, channel_order_inverse]
+                                             for i in range(crop_mean.shape[2])]
+                    crop_mean = crop_mean[:, :, channel_order_inverse]
                 if raw_scale is not None:
                     crop_mean /= raw_scale
                 self.crop_mean = crop_mean
             else:
-                self.crop_mean = np.zeros(self.blobs[self.inputs[0]].data.shape,
-                                          dtype=np.float32)
+                self.crop_mean = np.zeros(self.crop_dims, dtype=np.float32)
diff --git a/python/caffe/draw.py b/python/caffe/draw.py
index f8631cfa09e..324929deca4 100644
--- a/python/caffe/draw.py
+++ b/python/caffe/draw.py
@@ -1,76 +1,213 @@
 """
 Caffe network visualization: draw the NetParameter protobuffer.
 
-NOTE: this requires pydot>=1.0.2, which is not included in requirements.txt
-since it requires graphviz and other prerequisites outside the scope of the
-Caffe.
+
+.. note::
+
+    This requires pydot>=1.0.2, which is not included in requirements.txt since
+    it requires graphviz and other prerequisites outside the scope of the
+    Caffe.
 """
 
 from caffe.proto import caffe_pb2
-from google.protobuf import text_format
 import pydot
 
 # Internal layer and blob styles.
-LAYER_STYLE = {'shape': 'record', 'fillcolor': '#6495ED',
-         'style': 'filled'}
-NEURON_LAYER_STYLE = {'shape': 'record', 'fillcolor': '#90EE90',
-         'style': 'filled'}
-BLOB_STYLE = {'shape': 'octagon', 'fillcolor': '#F0E68C',
-        'style': 'filled'}
-def get_enum_name_by_value():
-  desc = caffe_pb2.LayerParameter.LayerType.DESCRIPTOR
-  d = {}
-  for k,v in desc.values_by_name.items():
-    d[v.number] = k
-  return d
-
-def get_pydot_graph(caffe_net):
-  pydot_graph = pydot.Dot(caffe_net.name, graph_type='digraph', rankdir="BT")
-  pydot_nodes = {}
-  pydot_edges = []
-  d = get_enum_name_by_value()
-  for layer in caffe_net.layers:
-    name = layer.name
-    layertype = d[layer.type]
-    if (len(layer.bottom) == 1 and len(layer.top) == 1 and
-        layer.bottom[0] == layer.top[0]):
-      # We have an in-place neuron layer.
-      pydot_nodes[name + '_' + layertype] = pydot.Node(
-          '%s (%s)' % (name, layertype), **NEURON_LAYER_STYLE)
+LAYER_STYLE_DEFAULT = {'shape': 'record',
+                       'fillcolor': '#6495ED',
+                       'style': 'filled'}
+NEURON_LAYER_STYLE = {'shape': 'record',
+                      'fillcolor': '#90EE90',
+                      'style': 'filled'}
+BLOB_STYLE = {'shape': 'octagon',
+              'fillcolor': '#E0E0E0',
+              'style': 'filled'}
+
+
+def get_pooling_types_dict():
+    """Get dictionary mapping pooling type number to type name
+    """
+    desc = caffe_pb2.PoolingParameter.PoolMethod.DESCRIPTOR
+    d = {}
+    for k, v in desc.values_by_name.items():
+        d[v.number] = k
+    return d
+
+
+def get_edge_label(layer):
+    """Define edge label based on layer type.
+    """
+
+    if layer.type == 'Data':
+        edge_label = 'Batch ' + str(layer.data_param.batch_size)
+    elif layer.type == 'Convolution':
+        edge_label = str(layer.convolution_param.num_output)
+    elif layer.type == 'InnerProduct':
+        edge_label = str(layer.inner_product_param.num_output)
     else:
-      pydot_nodes[name + '_' + layertype] = pydot.Node(
-          '%s (%s)' % (name, layertype), **LAYER_STYLE)
-    for bottom_blob in layer.bottom:
-      pydot_nodes[bottom_blob + '_blob'] = pydot.Node(
-        '%s' % (bottom_blob), **BLOB_STYLE)
-      pydot_edges.append((bottom_blob + '_blob', name + '_' + layertype))
-    for top_blob in layer.top:
-      pydot_nodes[top_blob + '_blob'] = pydot.Node(
-        '%s' % (top_blob))
-      pydot_edges.append((name + '_' + layertype, top_blob + '_blob'))
-  # Now, add the nodes and edges to the graph.
-  for node in pydot_nodes.values():
-    pydot_graph.add_node(node)
-  for edge in pydot_edges:
-    pydot_graph.add_edge(
-        pydot.Edge(pydot_nodes[edge[0]], pydot_nodes[edge[1]]))
-  return pydot_graph
-
-def draw_net(caffe_net, ext='png'):
-  """Draws a caffe net and returns the image string encoded using the given
-  extension.
-
-  Input:
-    caffe_net: a caffe.proto.caffe_pb2.NetParameter protocol buffer.
-    ext: the image extension. Default 'png'.
-  """
-  return get_pydot_graph(caffe_net).create(format=ext)
-
-def draw_net_to_file(caffe_net, filename):
-  """Draws a caffe net, and saves it to file using the format given as the
-  file extension. Use '.raw' to output raw text that you can manually feed
-  to graphviz to draw graphs.
-  """
-  ext = filename[filename.rfind('.')+1:]
-  with open(filename, 'wb') as fid:
-    fid.write(draw_net(caffe_net, ext))
+        edge_label = '""'
+
+    return edge_label
+
+
+def get_layer_label(layer, rankdir):
+    """Define node label based on layer type.
+
+    Parameters
+    ----------
+    layer : ?
+    rankdir : {'LR', 'TB', 'BT'}
+        Direction of graph layout.
+
+    Returns
+    -------
+    string :
+        A label for the current layer
+    """
+
+    if rankdir in ('TB', 'BT'):
+        # If graph orientation is vertical, horizontal space is free and
+        # vertical space is not; separate words with spaces
+        separator = ' '
+    else:
+        # If graph orientation is horizontal, vertical space is free and
+        # horizontal space is not; separate words with newlines
+        separator = '\\n'
+
+    if layer.type == 'Convolution':
+        # Outer double quotes needed or else colon characters don't parse
+        # properly
+        node_label = '"%s%s(%s)%skernel size: %d%sstride: %d%spad: %d"' %\
+                     (layer.name,
+                      separator,
+                      layer.type,
+                      separator,
+                      layer.convolution_param.kernel_size,
+                      separator,
+                      layer.convolution_param.stride,
+                      separator,
+                      layer.convolution_param.pad)
+    elif layer.type == 'Pooling':
+        pooling_types_dict = get_pooling_types_dict()
+        node_label = '"%s%s(%s %s)%skernel size: %d%sstride: %d%spad: %d"' %\
+                     (layer.name,
+                      separator,
+                      pooling_types_dict[layer.pooling_param.pool],
+                      layer.type,
+                      separator,
+                      layer.pooling_param.kernel_size,
+                      separator,
+                      layer.pooling_param.stride,
+                      separator,
+                      layer.pooling_param.pad)
+    else:
+        node_label = '"%s%s(%s)"' % (layer.name, separator, layer.type)
+    return node_label
+
+
+def choose_color_by_layertype(layertype):
+    """Define colors for nodes based on the layer type.
+    """
+    color = '#6495ED'  # Default
+    if layertype == 'Convolution':
+        color = '#FF5050'
+    elif layertype == 'Pooling':
+        color = '#FF9900'
+    elif layertype == 'InnerProduct':
+        color = '#CC33FF'
+    return color
+
+
+def get_pydot_graph(caffe_net, rankdir, label_edges=True):
+    """Create a data structure which represents the `caffe_net`.
+
+    Parameters
+    ----------
+    caffe_net : object
+    rankdir : {'LR', 'TB', 'BT'}
+        Direction of graph layout.
+    label_edges : boolean, optional
+        Label the edges (default is True).
+
+    Returns
+    -------
+    pydot graph object
+    """
+    pydot_graph = pydot.Dot(caffe_net.name,
+                            graph_type='digraph',
+                            rankdir=rankdir)
+    pydot_nodes = {}
+    pydot_edges = []
+    for layer in caffe_net.layer:
+        node_label = get_layer_label(layer, rankdir)
+        node_name = "%s_%s" % (layer.name, layer.type)
+        if (len(layer.bottom) == 1 and len(layer.top) == 1 and
+           layer.bottom[0] == layer.top[0]):
+            # We have an in-place neuron layer.
+            pydot_nodes[node_name] = pydot.Node(node_label,
+                                                **NEURON_LAYER_STYLE)
+        else:
+            layer_style = LAYER_STYLE_DEFAULT
+            layer_style['fillcolor'] = choose_color_by_layertype(layer.type)
+            pydot_nodes[node_name] = pydot.Node(node_label, **layer_style)
+        for bottom_blob in layer.bottom:
+            pydot_nodes[bottom_blob + '_blob'] = pydot.Node('%s' % bottom_blob,
+                                                            **BLOB_STYLE)
+            edge_label = '""'
+            pydot_edges.append({'src': bottom_blob + '_blob',
+                                'dst': node_name,
+                                'label': edge_label})
+        for top_blob in layer.top:
+            pydot_nodes[top_blob + '_blob'] = pydot.Node('%s' % (top_blob))
+            if label_edges:
+                edge_label = get_edge_label(layer)
+            else:
+                edge_label = '""'
+            pydot_edges.append({'src': node_name,
+                                'dst': top_blob + '_blob',
+                                'label': edge_label})
+    # Now, add the nodes and edges to the graph.
+    for node in pydot_nodes.values():
+        pydot_graph.add_node(node)
+    for edge in pydot_edges:
+        pydot_graph.add_edge(
+            pydot.Edge(pydot_nodes[edge['src']],
+                       pydot_nodes[edge['dst']],
+                       label=edge['label']))
+    return pydot_graph
+
+
+def draw_net(caffe_net, rankdir, ext='png'):
+    """Draws a caffe net and returns the image string encoded using the given
+    extension.
+
+    Parameters
+    ----------
+    caffe_net : a caffe.proto.caffe_pb2.NetParameter protocol buffer.
+    ext : string, optional
+        The image extension (the default is 'png').
+
+    Returns
+    -------
+    string :
+        Postscript representation of the graph.
+    """
+    return get_pydot_graph(caffe_net, rankdir).create(format=ext)
+
+
+def draw_net_to_file(caffe_net, filename, rankdir='LR'):
+    """Draws a caffe net, and saves it to file using the format given as the
+    file extension. Use '.raw' to output raw text that you can manually feed
+    to graphviz to draw graphs.
+
+    Parameters
+    ----------
+    caffe_net : a caffe.proto.caffe_pb2.NetParameter protocol buffer.
+    filename : string
+        The path to a file where the networks visualization will be stored.
+    rankdir : {'LR', 'TB', 'BT'}
+        Direction of graph layout.
+    """
+    ext = filename[filename.rfind('.')+1:]
+    with open(filename, 'wb') as fid:
+        fid.write(draw_net(caffe_net, rankdir, ext))
diff --git a/python/caffe/io.py b/python/caffe/io.py
index aabcfddbbeb..fc96266085f 100644
--- a/python/caffe/io.py
+++ b/python/caffe/io.py
@@ -3,20 +3,289 @@
 from scipy.ndimage import zoom
 from skimage.transform import resize
 
-from caffe.proto import caffe_pb2
+try:
+    # Python3 will most likely not be able to load protobuf
+    from caffe.proto import caffe_pb2
+except:
+    import sys
+    if sys.version_info >= (3, 0):
+        print("Failed to include caffe_pb2, things might go wrong!")
+    else:
+        raise
+
+
+## proto / datum / ndarray conversion
+def blobproto_to_array(blob, return_diff=False):
+    """
+    Convert a blob proto to an array. In default, we will just return the data,
+    unless return_diff is True, in which case we will return the diff.
+    """
+    if return_diff:
+        return np.array(blob.diff).reshape(
+            blob.num, blob.channels, blob.height, blob.width)
+    else:
+        return np.array(blob.data).reshape(
+            blob.num, blob.channels, blob.height, blob.width)
+
+
+def array_to_blobproto(arr, diff=None):
+    """Converts a 4-dimensional array to blob proto. If diff is given, also
+    convert the diff. You need to make sure that arr and diff have the same
+    shape, and this function does not do sanity check.
+    """
+    if arr.ndim != 4:
+        raise ValueError('Incorrect array shape.')
+    blob = caffe_pb2.BlobProto()
+    blob.num, blob.channels, blob.height, blob.width = arr.shape
+    blob.data.extend(arr.astype(float).flat)
+    if diff is not None:
+        blob.diff.extend(diff.astype(float).flat)
+    return blob
+
+
+def arraylist_to_blobprotovecor_str(arraylist):
+    """Converts a list of arrays to a serialized blobprotovec, which could be
+    then passed to a network for processing.
+    """
+    vec = caffe_pb2.BlobProtoVector()
+    vec.blobs.extend([array_to_blobproto(arr) for arr in arraylist])
+    return vec.SerializeToString()
+
+
+def blobprotovector_str_to_arraylist(str):
+    """Converts a serialized blobprotovec to a list of arrays.
+    """
+    vec = caffe_pb2.BlobProtoVector()
+    vec.ParseFromString(str)
+    return [blobproto_to_array(blob) for blob in vec.blobs]
+
+
+def array_to_datum(arr, label=0):
+    """Converts a 3-dimensional array to datum. If the array has dtype uint8,
+    the output data will be encoded as a string. Otherwise, the output data
+    will be stored in float format.
+    """
+    if arr.ndim != 3:
+        raise ValueError('Incorrect array shape.')
+    datum = caffe_pb2.Datum()
+    datum.channels, datum.height, datum.width = arr.shape
+    if arr.dtype == np.uint8:
+        datum.data = arr.tostring()
+    else:
+        datum.float_data.extend(arr.flat)
+    datum.label = label
+    return datum
+
+
+def datum_to_array(datum):
+    """Converts a datum to an array. Note that the label is not returned,
+    as one can easily get it by calling datum.label.
+    """
+    if len(datum.data):
+        return np.fromstring(datum.data, dtype=np.uint8).reshape(
+            datum.channels, datum.height, datum.width)
+    else:
+        return np.array(datum.float_data).astype(float).reshape(
+            datum.channels, datum.height, datum.width)
+
+
+## Pre-processing
+
+class Transformer:
+    """
+    Transform input for feeding into a Net.
+
+    Note: this is mostly for illustrative purposes and it is likely better
+    to define your own input preprocessing routine for your needs.
+
+    Parameters
+    ----------
+    net : a Net for which the input should be prepared
+    """
+    def __init__(self, inputs):
+        self.inputs = inputs
+        self.transpose = {}
+        self.channel_swap = {}
+        self.raw_scale = {}
+        self.mean = {}
+        self.input_scale = {}
+
+    def __check_input(self, in_):
+        if in_ not in self.inputs:
+            raise Exception('{} is not one of the net inputs: {}'.format(
+                in_, self.inputs))
+
+    def preprocess(self, in_, data):
+        """
+        Format input for Caffe:
+        - convert to single
+        - resize to input dimensions (preserving number of channels)
+        - transpose dimensions to K x H x W
+        - reorder channels (for instance color to BGR)
+        - scale raw input (e.g. from [0, 1] to [0, 255] for ImageNet models)
+        - subtract mean
+        - scale feature
+
+        Parameters
+        ----------
+        in_ : name of input blob to preprocess for
+        data : (H' x W' x K) ndarray
+
+        Returns
+        -------
+        caffe_in : (K x H x W) ndarray for input to a Net
+        """
+        self.__check_input(in_)
+        caffe_in = data.astype(np.float32, copy=False)
+        transpose = self.transpose.get(in_)
+        channel_swap = self.channel_swap.get(in_)
+        raw_scale = self.raw_scale.get(in_)
+        mean = self.mean.get(in_)
+        input_scale = self.input_scale.get(in_)
+        in_dims = self.inputs[in_][2:]
+        if caffe_in.shape[:2] != in_dims:
+            caffe_in = resize_image(caffe_in, in_dims)
+        if transpose is not None:
+            caffe_in = caffe_in.transpose(transpose)
+        if channel_swap is not None:
+            caffe_in = caffe_in[channel_swap, :, :]
+        if raw_scale is not None:
+            caffe_in *= raw_scale
+        if mean is not None:
+            caffe_in -= mean
+        if input_scale is not None:
+            caffe_in *= input_scale
+        return caffe_in
+
+    def deprocess(self, in_, data):
+        """
+        Invert Caffe formatting; see preprocess().
+        """
+        self.__check_input(in_)
+        decaf_in = data.copy().squeeze()
+        transpose = self.transpose.get(in_)
+        channel_swap = self.channel_swap.get(in_)
+        raw_scale = self.raw_scale.get(in_)
+        mean = self.mean.get(in_)
+        input_scale = self.input_scale.get(in_)
+        if input_scale is not None:
+            decaf_in /= input_scale
+        if mean is not None:
+            decaf_in += mean
+        if raw_scale is not None:
+            decaf_in /= raw_scale
+        if channel_swap is not None:
+            decaf_in = decaf_in[channel_swap, :, :]
+        if transpose is not None:
+            decaf_in = decaf_in.transpose([transpose[t] for t in transpose])
+        return decaf_in
+
+    def set_transpose(self, in_, order):
+        """
+        Set the input channel order for e.g. RGB to BGR conversion
+        as needed for the reference ImageNet model.
+
+        Parameters
+        ----------
+        in_ : which input to assign this channel order
+        order : the order to transpose the dimensions
+        """
+        self.__check_input(in_)
+        if len(order) != len(self.inputs[in_]) - 1:
+            raise Exception('Transpose order needs to have the same number of '
+                            'dimensions as the input.')
+        self.transpose[in_] = order
+
+    def set_channel_swap(self, in_, order):
+        """
+        Set the input channel order for e.g. RGB to BGR conversion
+        as needed for the reference ImageNet model.
+        N.B. this assumes the channels are the first dimension AFTER transpose.
 
+        Parameters
+        ----------
+        in_ : which input to assign this channel order
+        order : the order to take the channels.
+            (2,1,0) maps RGB to BGR for example.
+        """
+        self.__check_input(in_)
+        if len(order) != self.inputs[in_][1]:
+            raise Exception('Channel swap needs to have the same number of '
+                            'dimensions as the input channels.')
+        self.channel_swap[in_] = order
+
+    def set_raw_scale(self, in_, scale):
+        """
+        Set the scale of raw features s.t. the input blob = input * scale.
+        While Python represents images in [0, 1], certain Caffe models
+        like CaffeNet and AlexNet represent images in [0, 255] so the raw_scale
+        of these models must be 255.
+
+        Parameters
+        ----------
+        in_ : which input to assign this scale factor
+        scale : scale coefficient
+        """
+        self.__check_input(in_)
+        self.raw_scale[in_] = scale
+
+    def set_mean(self, in_, mean):
+        """
+        Set the mean to subtract for centering the data.
+
+        Parameters
+        ----------
+        in_ : which input to assign this mean.
+        mean : mean ndarray (input dimensional or broadcastable)
+        """
+        self.__check_input(in_)
+        ms = mean.shape
+        if mean.ndim == 1:
+            # broadcast channels
+            if ms[0] != self.inputs[in_][1]:
+                raise ValueError('Mean channels incompatible with input.')
+            mean = mean[:, np.newaxis, np.newaxis]
+        else:
+            # elementwise mean
+            if len(ms) == 2:
+                ms = (1,) + ms
+            if len(ms) != 3:
+                raise ValueError('Mean shape invalid')
+            if ms != self.inputs[in_][1:]:
+                raise ValueError('Mean shape incompatible with input shape.')
+        self.mean[in_] = mean
+
+    def set_input_scale(self, in_, scale):
+        """
+        Set the scale of preprocessed inputs s.t. the blob = blob * scale.
+        N.B. input_scale is done AFTER mean subtraction and other preprocessing
+        while raw_scale is done BEFORE.
+
+        Parameters
+        ----------
+        in_ : which input to assign this scale factor
+        scale : scale coefficient
+        """
+        self.__check_input(in_)
+        self.input_scale[in_] = scale
+
+
+## Image IO
 
 def load_image(filename, color=True):
     """
     Load an image converting from grayscale or alpha as needed.
 
-    Take
-    filename: string
-    color: flag for color format. True (default) loads as RGB while False
+    Parameters
+    ----------
+    filename : string
+    color : boolean
+        flag for color format. True (default) loads as RGB while False
         loads as intensity (if image is already grayscale).
 
-    Give
-    image: an image with type np.float32 in range [0, 1]
+    Returns
+    -------
+    image : an image with type np.float32 in range [0, 1]
         of size (H x W x 3) in RGB or
         of size (H x W x 1) in grayscale.
     """
@@ -34,20 +303,30 @@ def resize_image(im, new_dims, interp_order=1):
     """
     Resize an image array with interpolation.
 
-    Take
-    im: (H x W x K) ndarray
-    new_dims: (height, width) tuple of new dimensions.
-    interp_order: interpolation order, default is linear.
+    Parameters
+    ----------
+    im : (H x W x K) ndarray
+    new_dims : (height, width) tuple of new dimensions.
+    interp_order : interpolation order, default is linear.
 
-    Give
-    im: resized ndarray with shape (new_dims[0], new_dims[1], K)
+    Returns
+    -------
+    im : resized ndarray with shape (new_dims[0], new_dims[1], K)
     """
     if im.shape[-1] == 1 or im.shape[-1] == 3:
-        # skimage is fast but only understands {1,3} channel images in [0, 1].
         im_min, im_max = im.min(), im.max()
-        im_std = (im - im_min) / (im_max - im_min)
-        resized_std = resize(im_std, new_dims, order=interp_order)
-        resized_im = resized_std * (im_max - im_min) + im_min
+        if im_max > im_min:
+            # skimage is fast but only understands {1,3} channel images
+            # in [0, 1].
+            im_std = (im - im_min) / (im_max - im_min)
+            resized_std = resize(im_std, new_dims, order=interp_order)
+            resized_im = resized_std * (im_max - im_min) + im_min
+        else:
+            # the image is a constant -- avoid divide by 0
+            ret = np.empty((new_dims[0], new_dims[1], im.shape[-1]),
+                           dtype=np.float32)
+            ret.fill(im_min)
+            return ret
     else:
         # ndimage interpolates anything but more slowly.
         scale = tuple(np.array(new_dims) / np.array(im.shape[:2]))
@@ -59,12 +338,14 @@ def oversample(images, crop_dims):
     """
     Crop images into the four corners, center, and their mirrored versions.
 
-    Take
-    image: iterable of (H x W x K) ndarrays
-    crop_dims: (height, width) tuple for the crops.
+    Parameters
+    ----------
+    image : iterable of (H x W x K) ndarrays
+    crop_dims : (height, width) tuple for the crops.
 
-    Give
-    crops: (10*N x H x W x K) ndarray of crops for number of inputs N.
+    Returns
+    -------
+    crops : (10*N x H x W x K) ndarray of crops for number of inputs N.
     """
     # Dimensions and center.
     im_shape = np.array(images[0].shape)
@@ -88,7 +369,7 @@ def oversample(images, crop_dims):
 
     # Extract crops
     crops = np.empty((10 * len(images), crop_dims[0], crop_dims[1],
-                            im_shape[-1]), dtype=np.float32)
+                      im_shape[-1]), dtype=np.float32)
     ix = 0
     for im in images:
         for crop in crops_ix:
@@ -96,76 +377,3 @@ def oversample(images, crop_dims):
             ix += 1
         crops[ix-5:ix] = crops[ix-5:ix, :, ::-1, :]  # flip for mirrors
     return crops
-
-
-def blobproto_to_array(blob, return_diff=False):
-  """Convert a blob proto to an array. In default, we will just return the data,
-  unless return_diff is True, in which case we will return the diff.
-  """
-  if return_diff:
-    return np.array(blob.diff).reshape(
-        blob.num, blob.channels, blob.height, blob.width)
-  else:
-    return np.array(blob.data).reshape(
-        blob.num, blob.channels, blob.height, blob.width)
-
-
-def array_to_blobproto(arr, diff=None):
-  """Converts a 4-dimensional array to blob proto. If diff is given, also
-  convert the diff. You need to make sure that arr and diff have the same
-  shape, and this function does not do sanity check.
-  """
-  if arr.ndim != 4:
-    raise ValueError('Incorrect array shape.')
-  blob = caffe_pb2.BlobProto()
-  blob.num, blob.channels, blob.height, blob.width = arr.shape;
-  blob.data.extend(arr.astype(float).flat)
-  if diff is not None:
-    blob.diff.extend(diff.astype(float).flat)
-  return blob
-
-
-def arraylist_to_blobprotovecor_str(arraylist):
-  """Converts a list of arrays to a serialized blobprotovec, which could be
-  then passed to a network for processing.
-  """
-  vec = caffe_pb2.BlobProtoVector()
-  vec.blobs.extend([array_to_blobproto(arr) for arr in arraylist])
-  return vec.SerializeToString()
-
-
-def blobprotovector_str_to_arraylist(str):
-  """Converts a serialized blobprotovec to a list of arrays.
-  """
-  vec = caffe_pb2.BlobProtoVector()
-  vec.ParseFromString(str)
-  return [blobproto_to_array(blob) for blob in vec.blobs]
-
-
-def array_to_datum(arr, label=0):
-  """Converts a 3-dimensional array to datum. If the array has dtype uint8,
-  the output data will be encoded as a string. Otherwise, the output data
-  will be stored in float format.
-  """
-  if arr.ndim != 3:
-    raise ValueError('Incorrect array shape.')
-  datum = caffe_pb2.Datum()
-  datum.channels, datum.height, datum.width = arr.shape
-  if arr.dtype == np.uint8:
-    datum.data = arr.tostring()
-  else:
-    datum.float_data.extend(arr.flat)
-  datum.label = label
-  return datum
-
-
-def datum_to_array(datum):
-  """Converts a datum to an array. Note that the label is not returned,
-  as one can easily get it by calling datum.label.
-  """
-  if len(datum.data):
-    return np.fromstring(datum.data, dtype = np.uint8).reshape(
-        datum.channels, datum.height, datum.width)
-  else:
-    return np.array(datum.float_data).astype(float).reshape(
-        datum.channels, datum.height, datum.width)
diff --git a/python/caffe/net_spec.py b/python/caffe/net_spec.py
new file mode 100644
index 00000000000..1b4814a45c6
--- /dev/null
+++ b/python/caffe/net_spec.py
@@ -0,0 +1,204 @@
+"""Python net specification.
+
+This module provides a way to write nets directly in Python, using a natural,
+functional style. See examples/python_nets/caffenet.py for an example.
+
+Currently this works as a thin wrapper around the Python protobuf interface,
+with layers and parameters automatically generated for the "layers" and
+"params" pseudo-modules, which are actually objects using __getattr__ magic
+to generate protobuf messages.
+
+Note that when using to_proto or Top.to_proto, names of intermediate blobs will
+be automatically generated. To explicitly specify blob names, use the NetSpec
+class -- assign to its attributes directly to name layers, and call
+NetSpec.to_proto to serialize all assigned layers.
+
+This interface is expected to continue to evolve as Caffe gains new capabilities
+for specifying nets. In particular, the automatically generated layer names
+are not guaranteed to be forward-compatible.
+"""
+
+from collections import OrderedDict
+
+from .proto import caffe_pb2
+from google import protobuf
+import six
+
+
+def param_name_dict():
+    """Find out the correspondence between layer names and parameter names."""
+
+    layer = caffe_pb2.LayerParameter()
+    # get all parameter names (typically underscore case) and corresponding
+    # type names (typically camel case), which contain the layer names
+    # (note that not all parameters correspond to layers, but we'll ignore that)
+    param_names = [s for s in dir(layer) if s.endswith('_param')]
+    param_type_names = [type(getattr(layer, s)).__name__ for s in param_names]
+    # strip the final '_param' or 'Parameter'
+    param_names = [s[:-len('_param')] for s in param_names]
+    param_type_names = [s[:-len('Parameter')] for s in param_type_names]
+    return dict(zip(param_type_names, param_names))
+
+
+def to_proto(*tops):
+    """Generate a NetParameter that contains all layers needed to compute
+    all arguments."""
+
+    if not isinstance(tops, tuple):
+        tops = (tops,)
+    layers = OrderedDict()
+    autonames = {}
+    for top in tops:
+        top.fn._to_proto(layers, {}, autonames)
+    net = caffe_pb2.NetParameter()
+    net.layer.extend(layers.values())
+    return net
+
+
+def assign_proto(proto, name, val):
+    """Assign a Python object to a protobuf message, based on the Python
+    type (in recursive fashion). Lists become repeated fields/messages, dicts
+    become messages, and other types are assigned directly."""
+
+    if isinstance(val, list):
+        if isinstance(val[0], dict):
+            for item in val:
+                proto_item = getattr(proto, name).add()
+                for k, v in six.iteritems(item):
+                    assign_proto(proto_item, k, v)
+        else:
+            getattr(proto, name).extend(val)
+    elif isinstance(val, dict):
+        for k, v in six.iteritems(val):
+            assign_proto(getattr(proto, name), k, v)
+    else:
+        setattr(proto, name, val)
+
+
+class Top(object):
+    """A Top specifies a single output blob (which could be one of several
+    produced by a layer.)"""
+
+    def __init__(self, fn, n):
+        self.fn = fn
+        self.n = n
+
+    def to_proto(self):
+        """Generate a NetParameter that contains all layers needed to compute
+        this top."""
+
+        return to_proto(self)
+
+
+class Function(object):
+    """A Function specifies a layer, its parameters, and its inputs (which
+    are Tops from other layers)."""
+
+    def __init__(self, type_name, inputs, params):
+        self.type_name = type_name
+        self.inputs = inputs
+        self.params = params
+        self.ntop = self.params.get('ntop', 1)
+        # use del to make sure kwargs are not double-processed as layer params
+        if 'ntop' in self.params:
+            del self.params['ntop']
+        self.in_place = self.params.get('in_place', False)
+        if 'in_place' in self.params:
+            del self.params['in_place']
+        self.tops = tuple(Top(self, n) for n in range(self.ntop))
+
+    def _get_name(self, top, names, autonames):
+        if top not in names:
+            n = autonames.setdefault(top.fn.type_name, 1)
+            autonames[top.fn.type_name] += 1
+            names[top] = top.fn.type_name + str(n)
+        return names[top]
+
+    def _to_proto(self, layers, names, autonames):
+        if self in layers:
+            return
+        bottom_names = []
+        for inp in self.inputs:
+            inp.fn._to_proto(layers, names, autonames)
+            bottom_names.append(layers[inp.fn].top[inp.n])
+        layer = caffe_pb2.LayerParameter()
+        layer.type = self.type_name
+        layer.bottom.extend(bottom_names)
+
+        if self.in_place:
+            layer.top.extend(layer.bottom)
+        else:
+            for top in self.tops:
+                layer.top.append(self._get_name(top, names, autonames))
+        layer.name = self._get_name(self.tops[0], names, autonames)
+
+        for k, v in six.iteritems(self.params):
+            # special case to handle generic *params
+            if k.endswith('param'):
+                assign_proto(layer, k, v)
+            else:
+                try:
+                    assign_proto(getattr(layer,
+                        _param_names[self.type_name] + '_param'), k, v)
+                except (AttributeError, KeyError):
+                    assign_proto(layer, k, v)
+
+        layers[self] = layer
+
+
+class NetSpec(object):
+    """A NetSpec contains a set of Tops (assigned directly as attributes).
+    Calling NetSpec.to_proto generates a NetParameter containing all of the
+    layers needed to produce all of the assigned Tops, using the assigned
+    names."""
+
+    def __init__(self):
+        super(NetSpec, self).__setattr__('tops', OrderedDict())
+
+    def __setattr__(self, name, value):
+        self.tops[name] = value
+
+    def __getattr__(self, name):
+        return self.tops[name]
+
+    def to_proto(self):
+        names = {v: k for k, v in six.iteritems(self.tops)}
+        autonames = {}
+        layers = OrderedDict()
+        for name, top in six.iteritems(self.tops):
+            top.fn._to_proto(layers, names, autonames)
+        net = caffe_pb2.NetParameter()
+        net.layer.extend(layers.values())
+        return net
+
+
+class Layers(object):
+    """A Layers object is a pseudo-module which generates functions that specify
+    layers; e.g., Layers().Convolution(bottom, kernel_size=3) will produce a Top
+    specifying a 3x3 convolution applied to bottom."""
+
+    def __getattr__(self, name):
+        def layer_fn(*args, **kwargs):
+            fn = Function(name, args, kwargs)
+            if fn.ntop == 1:
+                return fn.tops[0]
+            else:
+                return fn.tops
+        return layer_fn
+
+
+class Parameters(object):
+    """A Parameters object is a pseudo-module which generates constants used
+    in layer parameters; e.g., Parameters().Pooling.MAX is the value used
+    to specify max pooling."""
+
+    def __getattr__(self, name):
+       class Param:
+            def __getattr__(self, param_name):
+                return getattr(getattr(caffe_pb2, name + 'Parameter'), param_name)
+       return Param()
+
+
+_param_names = param_name_dict()
+layers = Layers()
+params = Parameters()
diff --git a/python/caffe/pycaffe.py b/python/caffe/pycaffe.py
index 31dc1f9b001..e8a676a26d2 100644
--- a/python/caffe/pycaffe.py
+++ b/python/caffe/pycaffe.py
@@ -4,7 +4,10 @@
 """
 
 from collections import OrderedDict
-from itertools import izip_longest
+try:
+    from itertools import izip_longest
+except:
+    from itertools import zip_longest as izip_longest
 import numpy as np
 
 from ._caffe import Net, SGDSolver
@@ -35,20 +38,34 @@ def _Net_params(self):
                         for name, lr in zip(self._layer_names, self.layers)
                         if len(lr.blobs) > 0])
 
+
+@property
+def _Net_inputs(self):
+    return [list(self.blobs.keys())[i] for i in self._inputs]
+
+
+@property
+def _Net_outputs(self):
+    return [list(self.blobs.keys())[i] for i in self._outputs]
+
+
 def _Net_forward(self, blobs=None, start=None, end=None, **kwargs):
     """
     Forward pass: prepare inputs and run the net forward.
 
-    Take
-    blobs: list of blobs to return in addition to output blobs.
-    kwargs: Keys are input blob names and values are blob ndarrays.
-            For formatting inputs for Caffe, see Net.preprocess().
-            If None, input is taken from data layers.
-    start: optional name of layer at which to begin the forward pass
-    end: optional name of layer at which to finish the forward pass (inclusive)
-
-    Give
-    outs: {blob name: blob ndarray} dict.
+    Parameters
+    ----------
+    blobs : list of blobs to return in addition to output blobs.
+    kwargs : Keys are input blob names and values are blob ndarrays.
+             For formatting inputs for Caffe, see Net.preprocess().
+             If None, input is taken from data layers.
+    start : optional name of layer at which to begin the forward pass
+    end : optional name of layer at which to finish the forward pass
+          (inclusive)
+
+    Returns
+    -------
+    outs : {blob name: blob ndarray} dict.
     """
     if blobs is None:
         blobs = []
@@ -71,8 +88,6 @@ def _Net_forward(self, blobs=None, start=None, end=None, **kwargs):
         # Set input according to defined shapes and make arrays single and
         # C-contiguous as Caffe expects.
         for in_, blob in kwargs.iteritems():
-            if blob.ndim != 4:
-                raise Exception('{} blob is not 4-d'.format(in_))
             if blob.shape[0] != self.blobs[in_].num:
                 raise Exception('Input is not batch sized')
             self.blobs[in_].data[...] = blob
@@ -87,14 +102,17 @@ def _Net_backward(self, diffs=None, start=None, end=None, **kwargs):
     """
     Backward pass: prepare diffs and run the net backward.
 
-    Take
-    diffs: list of diffs to return in addition to bottom diffs.
-    kwargs: Keys are output blob names and values are diff ndarrays.
+    Parameters
+    ----------
+    diffs : list of diffs to return in addition to bottom diffs.
+    kwargs : Keys are output blob names and values are diff ndarrays.
             If None, top diffs are taken from forward loss.
-    start: optional name of layer at which to begin the backward pass
-    end: optional name of layer at which to finish the backward pass (inclusive)
+    start : optional name of layer at which to begin the backward pass
+    end : optional name of layer at which to finish the backward pass
+        (inclusive)
 
-    Give
+    Returns
+    -------
     outs: {blob name: diff ndarray} dict.
     """
     if diffs is None:
@@ -134,13 +152,15 @@ def _Net_forward_all(self, blobs=None, **kwargs):
     """
     Run net forward in batches.
 
-    Take
-    blobs: list of blobs to extract as in forward()
-    kwargs: Keys are input blob names and values are blob ndarrays.
-            Refer to forward().
+    Parameters
+    ----------
+    blobs : list of blobs to extract as in forward()
+    kwargs : Keys are input blob names and values are blob ndarrays.
+             Refer to forward().
 
-    Give
-    all_outs: {blob name: list of blobs} dict.
+    Returns
+    -------
+    all_outs : {blob name: list of blobs} dict.
     """
     # Collect outputs from batches
     all_outs = {out: [] for out in set(self.outputs + (blobs or []))}
@@ -163,14 +183,16 @@ def _Net_forward_backward_all(self, blobs=None, diffs=None, **kwargs):
     """
     Run net forward + backward in batches.
 
-    Take
+    Parameters
+    ----------
     blobs: list of blobs to extract as in forward()
     diffs: list of diffs to extract as in backward()
     kwargs: Keys are input (for forward) and output (for backward) blob names
             and values are ndarrays. Refer to forward() and backward().
             Prefilled variants are called for lack of input or output blobs.
 
-    Give
+    Returns
+    -------
     all_blobs: {blob name: blob ndarray} dict.
     all_diffs: {blob name: diff ndarray} dict.
     """
@@ -202,138 +224,6 @@ def _Net_forward_backward_all(self, blobs=None, diffs=None, **kwargs):
     return all_outs, all_diffs
 
 
-def _Net_set_mean(self, input_, mean, mode='elementwise'):
-    """
-    Set the mean to subtract for data centering.
-
-    Take
-    input_: which input to assign this mean.
-    mean: mean K x H x W ndarray (input dimensional or broadcastable)
-    mode: elementwise = use the whole mean (and check dimensions)
-          channel = channel constant (e.g. mean pixel instead of mean image)
-    """
-    if input_ not in self.inputs:
-        raise Exception('Input not in {}'.format(self.inputs))
-    in_shape = self.blobs[input_].data.shape
-    if mode == 'elementwise':
-        if mean.shape[1:] != in_shape[2:]:
-            # Resize mean (which requires H x W x K input).
-            mean = caffe.io.resize_image(mean.transpose((1,2,0)),
-                                         in_shape[2:]).transpose((2,0,1))
-        self.mean[input_] = mean
-    elif mode == 'channel':
-        self.mean[input_] = mean.mean(1).mean(1).reshape((in_shape[1], 1, 1))
-    else:
-        raise Exception('Mode not in {}'.format(['elementwise', 'channel']))
-
-
-def _Net_set_input_scale(self, input_, scale):
-    """
-    Set the scale of preprocessed inputs s.t. the blob = blob * scale.
-    N.B. input_scale is done AFTER mean subtraction and other preprocessing
-    while raw_scale is done BEFORE.
-
-    Take
-    input_: which input to assign this scale factor
-    scale: scale coefficient
-    """
-    if input_ not in self.inputs:
-        raise Exception('Input not in {}'.format(self.inputs))
-    self.input_scale[input_] = scale
-
-
-def _Net_set_raw_scale(self, input_, scale):
-    """
-    Set the scale of raw features s.t. the input blob = input * scale.
-    While Python represents images in [0, 1], certain Caffe models
-    like CaffeNet and AlexNet represent images in [0, 255] so the raw_scale
-    of these models must be 255.
-
-    Take
-    input_: which input to assign this scale factor
-    scale: scale coefficient
-    """
-    if input_ not in self.inputs:
-        raise Exception('Input not in {}'.format(self.inputs))
-    self.raw_scale[input_] = scale
-
-
-def _Net_set_channel_swap(self, input_, order):
-    """
-    Set the input channel order for e.g. RGB to BGR conversion
-    as needed for the reference ImageNet model.
-
-    Take
-    input_: which input to assign this channel order
-    order: the order to take the channels.
-           (2,1,0) maps RGB to BGR for example.
-    """
-    if input_ not in self.inputs:
-        raise Exception('Input not in {}'.format(self.inputs))
-    self.channel_swap[input_] = order
-
-
-def _Net_preprocess(self, input_name, input_):
-    """
-    Format input for Caffe:
-    - convert to single
-    - resize to input dimensions (preserving number of channels)
-    - reorder channels (for instance color to BGR)
-    - scale raw input (e.g. from [0, 1] to [0, 255] for ImageNet models)
-    - transpose dimensions to K x H x W
-    - subtract mean
-    - scale feature
-
-    Take
-    input_name: name of input blob to preprocess for
-    input_: (H' x W' x K) ndarray
-
-    Give
-    caffe_inputs: (K x H x W) ndarray
-    """
-    caffe_in = input_.astype(np.float32, copy=False)
-    mean = self.mean.get(input_name)
-    input_scale = self.input_scale.get(input_name)
-    raw_scale = self.raw_scale.get(input_name)
-    channel_order = self.channel_swap.get(input_name)
-    in_size = self.blobs[input_name].data.shape[2:]
-    if caffe_in.shape[:2] != in_size:
-        caffe_in = caffe.io.resize_image(caffe_in, in_size)
-    if channel_order is not None:
-        caffe_in = caffe_in[:, :, channel_order]
-    caffe_in = caffe_in.transpose((2, 0, 1))
-    if raw_scale is not None:
-        caffe_in *= raw_scale
-    if mean is not None:
-        caffe_in -= mean
-    if input_scale is not None:
-        caffe_in *= input_scale
-    return caffe_in
-
-
-def _Net_deprocess(self, input_name, input_):
-    """
-    Invert Caffe formatting; see Net.preprocess().
-    """
-    decaf_in = input_.copy().squeeze()
-    mean = self.mean.get(input_name)
-    input_scale = self.input_scale.get(input_name)
-    raw_scale = self.raw_scale.get(input_name)
-    channel_order = self.channel_swap.get(input_name)
-    if input_scale is not None:
-        decaf_in /= input_scale
-    if mean is not None:
-        decaf_in += mean
-    if raw_scale is not None:
-        decaf_in /= raw_scale
-    decaf_in = decaf_in.transpose((1,2,0))
-    if channel_order is not None:
-        channel_order_inverse = [channel_order.index(i)
-                                 for i in range(decaf_in.shape[2])]
-        decaf_in = decaf_in[:, :, channel_order_inverse]
-    return decaf_in
-
-
 def _Net_set_input_arrays(self, data, labels):
     """
     Set input arrays of the in-memory MemoryDataLayer.
@@ -349,11 +239,13 @@ def _Net_batch(self, blobs):
     """
     Batch blob lists according to net's batch size.
 
-    Take
+    Parameters
+    ----------
     blobs: Keys blob names and values are lists of blobs (of any length).
            Naturally, all the lists should have the same length.
 
-    Give (yield)
+    Yields
+    ------
     batch: {blob name: list of blobs} dict for a single batch.
     """
     num = len(blobs.itervalues().next())
@@ -376,7 +268,6 @@ def _Net_batch(self, blobs):
                                                  padding])
         yield padded_batch
 
-
 # Attach methods to Net.
 Net.blobs = _Net_blobs
 Net.params = _Net_params
@@ -384,11 +275,7 @@ def _Net_batch(self, blobs):
 Net.backward = _Net_backward
 Net.forward_all = _Net_forward_all
 Net.forward_backward_all = _Net_forward_backward_all
-Net.set_mean = _Net_set_mean
-Net.set_input_scale = _Net_set_input_scale
-Net.set_raw_scale = _Net_set_raw_scale
-Net.set_channel_swap = _Net_set_channel_swap
-Net.preprocess = _Net_preprocess
-Net.deprocess = _Net_deprocess
 Net.set_input_arrays = _Net_set_input_arrays
 Net._batch = _Net_batch
+Net.inputs = _Net_inputs
+Net.outputs = _Net_outputs
diff --git a/python/caffe/test/test_net.py b/python/caffe/test/test_net.py
new file mode 100644
index 00000000000..cc367477752
--- /dev/null
+++ b/python/caffe/test/test_net.py
@@ -0,0 +1,80 @@
+import unittest
+import tempfile
+import os
+import numpy as np
+
+import caffe
+
+
+def simple_net_file(num_output):
+    """Make a simple net prototxt, based on test_net.cpp, returning the name
+    of the (temporary) file."""
+
+    f = tempfile.NamedTemporaryFile(delete=False)
+    f.write("""name: 'testnet' force_backward: true
+    layer { type: 'DummyData' name: 'data' top: 'data' top: 'label'
+      dummy_data_param { num: 5 channels: 2 height: 3 width: 4
+        num: 5 channels: 1 height: 1 width: 1
+        data_filler { type: 'gaussian' std: 1 }
+        data_filler { type: 'constant' } } }
+    layer { type: 'Convolution' name: 'conv' bottom: 'data' top: 'conv'
+      convolution_param { num_output: 11 kernel_size: 2 pad: 3
+        weight_filler { type: 'gaussian' std: 1 }
+        bias_filler { type: 'constant' value: 2 } }
+        param { decay_mult: 1 } param { decay_mult: 0 }
+        }
+    layer { type: 'InnerProduct' name: 'ip' bottom: 'conv' top: 'ip'
+      inner_product_param { num_output: """ + str(num_output) + """
+        weight_filler { type: 'gaussian' std: 2.5 }
+        bias_filler { type: 'constant' value: -3 } } }
+    layer { type: 'SoftmaxWithLoss' name: 'loss' bottom: 'ip' bottom: 'label'
+      top: 'loss' }""")
+    f.close()
+    return f.name
+
+
+class TestNet(unittest.TestCase):
+    def setUp(self):
+        self.num_output = 13
+        net_file = simple_net_file(self.num_output)
+        self.net = caffe.Net(net_file, caffe.TRAIN)
+        # fill in valid labels
+        self.net.blobs['label'].data[...] = \
+                np.random.randint(self.num_output,
+                    size=self.net.blobs['label'].data.shape)
+        os.remove(net_file)
+
+    def test_memory(self):
+        """Check that holding onto blob data beyond the life of a Net is OK"""
+
+        params = sum(map(list, self.net.params.itervalues()), [])
+        blobs = self.net.blobs.values()
+        del self.net
+
+        # now sum everything (forcing all memory to be read)
+        total = 0
+        for p in params:
+            total += p.data.sum() + p.diff.sum()
+        for bl in blobs:
+            total += bl.data.sum() + bl.diff.sum()
+
+    def test_forward_backward(self):
+        self.net.forward()
+        self.net.backward()
+
+    def test_inputs_outputs(self):
+        self.assertEqual(self.net.inputs, [])
+        self.assertEqual(self.net.outputs, ['loss'])
+
+    def test_save_and_read(self):
+        f = tempfile.NamedTemporaryFile(delete=False)
+        f.close()
+        self.net.save(f.name)
+        net_file = simple_net_file(self.num_output)
+        net2 = caffe.Net(net_file, f.name, caffe.TRAIN)
+        os.remove(net_file)
+        os.remove(f.name)
+        for name in self.net.params:
+            for i in range(len(self.net.params[name])):
+                self.assertEqual(abs(self.net.params[name][i].data
+                    - net2.params[name][i].data).sum(), 0)
diff --git a/python/caffe/test/test_net_spec.py b/python/caffe/test/test_net_spec.py
new file mode 100644
index 00000000000..65b73b96f73
--- /dev/null
+++ b/python/caffe/test/test_net_spec.py
@@ -0,0 +1,67 @@
+import unittest
+import tempfile
+import caffe
+from caffe import layers as L
+from caffe import params as P
+
+def lenet(batch_size):
+    n = caffe.NetSpec()
+    n.data, n.label = L.DummyData(shape=[dict(dim=[batch_size, 1, 28, 28]),
+                                         dict(dim=[batch_size, 1, 1, 1])],
+                                  transform_param=dict(scale=1./255), ntop=2)
+    n.conv1 = L.Convolution(n.data, kernel_size=5, num_output=20,
+        weight_filler=dict(type='xavier'))
+    n.pool1 = L.Pooling(n.conv1, kernel_size=2, stride=2, pool=P.Pooling.MAX)
+    n.conv2 = L.Convolution(n.pool1, kernel_size=5, num_output=50,
+        weight_filler=dict(type='xavier'))
+    n.pool2 = L.Pooling(n.conv2, kernel_size=2, stride=2, pool=P.Pooling.MAX)
+    n.ip1 = L.InnerProduct(n.pool2, num_output=500,
+        weight_filler=dict(type='xavier'))
+    n.relu1 = L.ReLU(n.ip1, in_place=True)
+    n.ip2 = L.InnerProduct(n.relu1, num_output=10,
+        weight_filler=dict(type='xavier'))
+    n.loss = L.SoftmaxWithLoss(n.ip2, n.label)
+    return n.to_proto()
+
+def anon_lenet(batch_size):
+    data, label = L.DummyData(shape=[dict(dim=[batch_size, 1, 28, 28]),
+                                     dict(dim=[batch_size, 1, 1, 1])],
+                              transform_param=dict(scale=1./255), ntop=2)
+    conv1 = L.Convolution(data, kernel_size=5, num_output=20,
+        weight_filler=dict(type='xavier'))
+    pool1 = L.Pooling(conv1, kernel_size=2, stride=2, pool=P.Pooling.MAX)
+    conv2 = L.Convolution(pool1, kernel_size=5, num_output=50,
+        weight_filler=dict(type='xavier'))
+    pool2 = L.Pooling(conv2, kernel_size=2, stride=2, pool=P.Pooling.MAX)
+    ip1 = L.InnerProduct(pool2, num_output=500,
+        weight_filler=dict(type='xavier'))
+    relu1 = L.ReLU(ip1, in_place=True)
+    ip2 = L.InnerProduct(relu1, num_output=10,
+        weight_filler=dict(type='xavier'))
+    loss = L.SoftmaxWithLoss(ip2, label)
+    return loss.to_proto()
+
+class TestNetSpec(unittest.TestCase):
+    def load_net(self, net_proto):
+        f = tempfile.NamedTemporaryFile(delete=False)
+        f.write(str(net_proto))
+        f.close()
+        return caffe.Net(f.name, caffe.TEST)
+
+    def test_lenet(self):
+        """Construct and build the Caffe version of LeNet."""
+
+        net_proto = lenet(50)
+        # check that relu is in-place
+        self.assertEqual(net_proto.layer[6].bottom,
+                net_proto.layer[6].top)
+        net = self.load_net(net_proto)
+        # check that all layers are present
+        self.assertEqual(len(net.layers), 9)
+
+        # now the check the version with automatically-generated layer names
+        net_proto = anon_lenet(50)
+        self.assertEqual(net_proto.layer[6].bottom,
+                net_proto.layer[6].top)
+        net = self.load_net(net_proto)
+        self.assertEqual(len(net.layers), 9)
diff --git a/python/caffe/test/test_python_layer.py b/python/caffe/test/test_python_layer.py
new file mode 100644
index 00000000000..6fba49143bb
--- /dev/null
+++ b/python/caffe/test/test_python_layer.py
@@ -0,0 +1,63 @@
+import unittest
+import tempfile
+import os
+
+import caffe
+
+
+class SimpleLayer(caffe.Layer):
+    """A layer that just multiplies by ten"""
+
+    def setup(self, bottom, top):
+        pass
+
+    def reshape(self, bottom, top):
+        top[0].reshape(*bottom[0].data.shape)
+
+    def forward(self, bottom, top):
+        top[0].data[...] = 10 * bottom[0].data
+
+    def backward(self, top, propagate_down, bottom):
+        bottom[0].diff[...] = 10 * top[0].diff
+
+
+def python_net_file():
+    with tempfile.NamedTemporaryFile(delete=False) as f:
+        f.write("""name: 'pythonnet' force_backward: true
+        input: 'data' input_shape { dim: 10 dim: 9 dim: 8 }
+        layer { type: 'Python' name: 'one' bottom: 'data' top: 'one'
+          python_param { module: 'test_python_layer' layer: 'SimpleLayer' } }
+        layer { type: 'Python' name: 'two' bottom: 'one' top: 'two'
+          python_param { module: 'test_python_layer' layer: 'SimpleLayer' } }
+        layer { type: 'Python' name: 'three' bottom: 'two' top: 'three'
+          python_param { module: 'test_python_layer' layer: 'SimpleLayer' } }""")
+        return f.name
+
+
+class TestPythonLayer(unittest.TestCase):
+    def setUp(self):
+        net_file = python_net_file()
+        self.net = caffe.Net(net_file, caffe.TRAIN)
+        os.remove(net_file)
+
+    def test_forward(self):
+        x = 8
+        self.net.blobs['data'].data[...] = x
+        self.net.forward()
+        for y in self.net.blobs['three'].data.flat:
+            self.assertEqual(y, 10**3 * x)
+
+    def test_backward(self):
+        x = 7
+        self.net.blobs['three'].diff[...] = x
+        self.net.backward()
+        for y in self.net.blobs['data'].diff.flat:
+            self.assertEqual(y, 10**3 * x)
+
+    def test_reshape(self):
+        s = 4
+        self.net.blobs['data'].reshape(s, s, s, s)
+        self.net.forward()
+        for blob in self.net.blobs.itervalues():
+            for d in blob.data.shape:
+                self.assertEqual(s, d)
diff --git a/python/caffe/test/test_solver.py b/python/caffe/test/test_solver.py
new file mode 100644
index 00000000000..09b974dad66
--- /dev/null
+++ b/python/caffe/test/test_solver.py
@@ -0,0 +1,52 @@
+import unittest
+import tempfile
+import os
+import numpy as np
+
+import caffe
+from test_net import simple_net_file
+
+
+class TestSolver(unittest.TestCase):
+    def setUp(self):
+        self.num_output = 13
+        net_f = simple_net_file(self.num_output)
+        f = tempfile.NamedTemporaryFile(delete=False)
+        f.write("""net: '""" + net_f + """'
+        test_iter: 10 test_interval: 10 base_lr: 0.01 momentum: 0.9
+        weight_decay: 0.0005 lr_policy: 'inv' gamma: 0.0001 power: 0.75
+        display: 100 max_iter: 100 snapshot_after_train: false""")
+        f.close()
+        self.solver = caffe.SGDSolver(f.name)
+        # also make sure get_solver runs
+        caffe.get_solver(f.name)
+        caffe.set_mode_cpu()
+        # fill in valid labels
+        self.solver.net.blobs['label'].data[...] = \
+                np.random.randint(self.num_output,
+                    size=self.solver.net.blobs['label'].data.shape)
+        self.solver.test_nets[0].blobs['label'].data[...] = \
+                np.random.randint(self.num_output,
+                    size=self.solver.test_nets[0].blobs['label'].data.shape)
+        os.remove(f.name)
+        os.remove(net_f)
+
+    def test_solve(self):
+        self.assertEqual(self.solver.iter, 0)
+        self.solver.solve()
+        self.assertEqual(self.solver.iter, 100)
+
+    def test_net_memory(self):
+        """Check that nets survive after the solver is destroyed."""
+
+        nets = [self.solver.net] + list(self.solver.test_nets)
+        self.assertEqual(len(nets), 2)
+        del self.solver
+
+        total = 0
+        for net in nets:
+            for ps in net.params.itervalues():
+                for p in ps:
+                    total += p.data.sum() + p.diff.sum()
+            for bl in net.blobs.itervalues():
+                total += bl.data.sum() + bl.diff.sum()
diff --git a/python/classify.py b/python/classify.py
index 873b5e38f19..4544c51b4c2 100755
--- a/python/classify.py
+++ b/python/classify.py
@@ -60,8 +60,8 @@ def main(argv):
         "--mean_file",
         default=os.path.join(pycaffe_dir,
                              'caffe/imagenet/ilsvrc_2012_mean.npy'),
-        help="Data set image mean of H x W x K dimensions (numpy array). " +
-             "Set to '' for no mean subtraction."
+        help="Data set image mean of [Channels x Height x Width] dimensions " +
+             "(numpy array). Set to '' for no mean subtraction."
     )
     parser.add_argument(
         "--input_scale",
@@ -96,33 +96,41 @@ def main(argv):
     if args.channel_swap:
         channel_swap = [int(s) for s in args.channel_swap.split(',')]
 
+    if args.gpu:
+        caffe.set_mode_gpu()
+        print("GPU mode")
+    else:
+        caffe.set_mode_cpu()
+        print("CPU mode")
+
     # Make classifier.
     classifier = caffe.Classifier(args.model_def, args.pretrained_model,
-            image_dims=image_dims, gpu=args.gpu, mean=mean,
+            image_dims=image_dims, mean=mean,
             input_scale=args.input_scale, raw_scale=args.raw_scale,
             channel_swap=channel_swap)
 
-    if args.gpu:
-        print 'GPU mode'
-
     # Load numpy array (.npy), directory glob (*.jpg), or image file.
     args.input_file = os.path.expanduser(args.input_file)
     if args.input_file.endswith('npy'):
+        print("Loading file: %s" % args.input_file)
         inputs = np.load(args.input_file)
     elif os.path.isdir(args.input_file):
+        print("Loading folder: %s" % args.input_file)
         inputs =[caffe.io.load_image(im_f)
                  for im_f in glob.glob(args.input_file + '/*.' + args.ext)]
     else:
+        print("Loading file: %s" % args.input_file)
         inputs = [caffe.io.load_image(args.input_file)]
 
-    print "Classifying %d inputs." % len(inputs)
+    print("Classifying %d inputs." % len(inputs))
 
     # Classify.
     start = time.time()
     predictions = classifier.predict(inputs, not args.center_only)
-    print "Done in %.2f s." % (time.time() - start)
+    print("Done in %.2f s." % (time.time() - start))
 
     # Save
+    print("Saving results into %s" % args.output_file)
     np.save(args.output_file, predictions)
 
 
diff --git a/python/convert_to_fully_conv.py b/python/convert_to_fully_conv.py
new file mode 100644
index 00000000000..5aae78cc1ce
--- /dev/null
+++ b/python/convert_to_fully_conv.py
@@ -0,0 +1,84 @@
+import numpy as np
+import os
+import os.path as osp
+import sys
+import google.protobuf as pb
+from argparse import ArgumentParser
+
+pycaffe_dir = osp.dirname(__file__)
+if osp.join(pycaffe_dir) not in sys.path:
+    sys.path.insert(0, pycaffe_dir)
+import caffe
+from caffe.proto import caffe_pb2
+
+
+def main(args):
+    caffe.set_mode_cpu()
+    fc_net = caffe.Net(args.model, args.weights, caffe.TEST)
+    # make fully conv prototxt
+    fc_proto = caffe_pb2.NetParameter()
+    with open(args.model, 'r') as f:
+        pb.text_format.Parse(f.read(), fc_proto)
+    layers = []
+    fc_to_conv_dic = {}
+    for layer in fc_proto.layer:
+        if layer.type != 'InnerProduct':
+            layers.append(layer)
+            continue
+        new_ = caffe_pb2.LayerParameter()
+        new_.name = layer.name + '_conv'
+        fc_to_conv_dic[layer.name] = new_.name
+        new_.type = 'Convolution'
+        new_.bottom.extend(layer.bottom)
+        new_.top.extend(layer.top)
+        new_.convolution_param.num_output = layer.inner_product_param.num_output
+        bottom_shape = fc_net.blobs[layer.bottom[0]].data.shape
+        if len(bottom_shape) == 4:
+            new_.convolution_param.kernel_h = bottom_shape[2]
+            new_.convolution_param.kernel_w = bottom_shape[3]
+        else:
+            new_.convolution_param.kernel_size = 1
+        layers.append(new_)
+    conv_proto = caffe_pb2.NetParameter()
+    conv_proto.CopyFrom(fc_proto)
+    del(conv_proto.layer[:])
+    conv_proto.layer.extend(layers)
+    if args.save_model is None:
+        name, ext = osp.splitext(args.model)
+        args.save_model = name + '_fully_conv' + ext
+    with open(args.save_model, 'w') as f:
+        f.write(pb.text_format.MessageToString(conv_proto))
+    # make fully conv parameters
+    conv_net = caffe.Net(args.save_model, args.weights, caffe.TEST)
+    for fc, conv in fc_to_conv_dic.iteritems():
+        conv_net.params[conv][0].data.flat = fc_net.params[fc][0].data.flat
+        conv_net.params[conv][1].data[...] = fc_net.params[fc][1].data
+    if args.save_weights is None:
+        name, ext = osp.splitext(args.weights)
+        args.save_weights = name + '_fully_conv' + ext
+    conv_net.save(args.save_weights)
+    print args.model, args.weights
+
+
+if __name__ == '__main__':
+    parser = ArgumentParser(
+        description="Convert fully connected layers to convolution layers"
+    )
+    parser.add_argument(
+        'model',
+        help="Path to input deploy prototxt"
+    )
+    parser.add_argument(
+        'weights',
+        help="Path to input caffemodel"
+    )
+    parser.add_argument(
+        '--save_model',
+        help="Path to output deploy prototxt"
+    )
+    parser.add_argument(
+        '--save_weights',
+        help="Path to output caffemodel"
+    )
+    args = parser.parse_args()
+    main(args)
\ No newline at end of file
diff --git a/python/detect.py b/python/detect.py
index b67b500aafd..691098f5c53 100755
--- a/python/detect.py
+++ b/python/detect.py
@@ -102,22 +102,27 @@ def main(argv):
     mean, channel_swap = None, None
     if args.mean_file:
         mean = np.load(args.mean_file)
+        if mean.shape[1:] != (1, 1):
+            mean = mean.mean(1).mean(1)
     if args.channel_swap:
         channel_swap = [int(s) for s in args.channel_swap.split(',')]
 
+    if args.gpu:
+        caffe.set_mode_gpu()
+        print("GPU mode")
+    else:
+        caffe.set_mode_cpu()
+        print("CPU mode")
+
     # Make detector.
-    detector = caffe.Detector(args.model_def, args.pretrained_model,
-            gpu=args.gpu, mean=mean,
+    detector = caffe.Detector(args.model_def, args.pretrained_model, mean=mean,
             input_scale=args.input_scale, raw_scale=args.raw_scale,
             channel_swap=channel_swap,
             context_pad=args.context_pad)
 
-    if args.gpu:
-        print 'GPU mode'
-
     # Load input.
     t = time.time()
-    print('Loading input...')
+    print("Loading input...")
     if args.input_file.lower().endswith('txt'):
         with open(args.input_file) as f:
             inputs = [_.strip() for _ in f.readlines()]
diff --git a/python/draw_net.py b/python/draw_net.py
index ba488294275..ec76a744da3 100755
--- a/python/draw_net.py
+++ b/python/draw_net.py
@@ -2,24 +2,44 @@
 """
 Draw a graph of the net architecture.
 """
-import os
+from argparse import ArgumentParser, ArgumentDefaultsHelpFormatter
 from google.protobuf import text_format
 
-import caffe, caffe.draw
+import caffe
+import caffe.draw
 from caffe.proto import caffe_pb2
 
 
-def main(argv):
-    if len(argv) != 3:
-        print 'Usage: %s input_net_proto_file output_image_file' % \
-                os.path.basename(sys.argv[0])
-    else:
-        net = caffe_pb2.NetParameter()
-        text_format.Merge(open(sys.argv[1]).read(), net)
-        print 'Drawing net to %s' % sys.argv[2]
-        caffe.draw.draw_net_to_file(net, sys.argv[2])
+def parse_args():
+    """Parse input arguments
+    """
+
+    parser = ArgumentParser(description=__doc__,
+                            formatter_class=ArgumentDefaultsHelpFormatter)
+
+    parser.add_argument('input_net_proto_file',
+                        help='Input network prototxt file')
+    parser.add_argument('output_image_file',
+                        help='Output image file')
+    parser.add_argument('--rankdir',
+                        help=('One of TB (top-bottom, i.e., vertical), '
+                              'RL (right-left, i.e., horizontal), or another '
+                              'valid dot option; see '
+                              'http://www.graphviz.org/doc/info/'
+                              'attrs.html#k:rankdir'),
+                        default='LR')
+
+    args = parser.parse_args()
+    return args
+
+
+def main():
+    args = parse_args()
+    net = caffe_pb2.NetParameter()
+    text_format.Merge(open(args.input_net_proto_file).read(), net)
+    print('Drawing net to %s' % args.output_image_file)
+    caffe.draw.draw_net_to_file(net, args.output_image_file, args.rankdir)
 
 
 if __name__ == '__main__':
-    import sys
-    main(sys.argv)
+    main()
diff --git a/python/gen_bn_inference.py b/python/gen_bn_inference.py
new file mode 100644
index 00000000000..63eba24a24a
--- /dev/null
+++ b/python/gen_bn_inference.py
@@ -0,0 +1,171 @@
+import numpy as np
+import sys
+import os
+import os.path as osp
+import google.protobuf as pb
+from argparse import ArgumentParser
+
+pycaffe_dir = osp.dirname(__file__)
+if osp.join(pycaffe_dir) not in sys.path:
+    sys.path.insert(0, pycaffe_dir)
+import caffe
+from caffe.proto import caffe_pb2
+
+
+def update_blob_name(blobs, old, new):
+    if old not in blobs: return
+    names = list(blobs)
+    names[names.index(old)] = new
+    del(blobs[:])
+    blobs.extend(names)
+
+
+def check(old_net, new_net, input_name='data'):
+    caffe.set_device(0)
+    caffe.set_mode_gpu()
+    inputs = np.random.rand(*old_net.blobs[input_name].data.shape)
+    inputs = inputs.astype(np.float32)
+    old_net.blobs[input_name].data[...] = inputs
+    new_net.blobs[input_name].data[...] = inputs
+    ans = old_net.forward()
+    out = new_net.forward()
+    for k in ans:
+        assert np.allclose(ans[k], out[k]), "Conversion failed"
+
+
+def main(args):
+    # Set default output file names
+    if args.output_model is None:
+        file_name = osp.splitext(args.model)[0]
+        args.output_model = file_name + '_inference.prototxt'
+    if args.output_weights is None:
+        file_name = osp.splitext(args.weights)[0]
+        args.output_weights = file_name + '_inference.caffemodel'
+    with open(args.model) as f:
+        model = caffe_pb2.NetParameter()
+        pb.text_format.Parse(f.read(), model)
+
+    # Determince the BN layers to be absorbed or replaced
+    # Create the new layers
+    new_layers = []
+    absorbed, replaced = {}, {}
+    for i, layer in enumerate(model.layer):
+        if layer.type != 'BN':
+            new_layers.append(layer)
+            continue
+        assert len(layer.bottom) == 1
+        assert len(layer.top) == 1
+        bottom_blob = layer.bottom[0]
+        top_blob = layer.top[0]
+        # Check if can be absorbed. As there could be some inplace layers,
+        # for example, conv -> relu -> bn. In such case, the BN cannot be
+        # absorbed.
+        can_be_absorbed = False
+        for j in xrange(i - 1, -1, -1):
+            if bottom_blob in model.layer[j].top:
+                if model.layer[j].type not in ['Convolution', 'InnerProduct']:
+                    can_be_absorbed = False
+                    break
+                else:
+                    can_be_absorbed = True
+                    bottom_layer = model.layer[j]
+        if can_be_absorbed:
+            # Rename the blob in the top layers
+            for j in xrange(i + 1, len(model.layer)):
+                update_blob_name(model.layer[j].bottom, top_blob, bottom_blob)
+                update_blob_name(model.layer[j].top, top_blob, bottom_blob)
+            if bottom_layer.type == 'Convolution':
+                bottom_layer.convolution_param.bias_term = True
+            elif bottom_layer.type == 'InnerProduct':
+                bottom_layer.inner_product_param.bias_term = True
+            absorbed[layer.name] = bottom_layer.name
+        elif args.replace_by == 'affine':
+            # Replace by an scale bias layer
+            new_layer = caffe_pb2.LayerParameter()
+            new_layer.name = layer.name + '_affine'
+            new_layer.type = 'Scale'
+            new_layer.bottom.extend([bottom_blob])
+            new_layer.top.extend([top_blob])
+            new_layer.scale_param.bias_term = True
+            replaced[layer.name] = new_layer.name
+            new_layers.append(new_layer)
+        elif args.replace_by == 'frozen':
+            # Freeze the BN layer
+            layer.bn_param.frozen = True
+            del(layer.param[:])
+            param = caffe_pb2.ParamSpec()
+            param.lr_mult = 0
+            param.decay_mult = 0
+            layer.param.extend([param] * 2)
+            new_layers.append(layer)
+
+    # Save the prototxt
+    output_model = caffe_pb2.NetParameter()
+    output_model.CopyFrom(model)
+    del(output_model.layer[:])
+    output_model.layer.extend(new_layers)
+    with open(args.output_model, 'w') as f:
+        f.write(pb.text_format.MessageToString(output_model))
+
+    # Copy the parameters
+    weights = caffe.Net(args.model, args.weights, caffe.TEST)
+    output_weights = caffe.Net(args.output_model, caffe.TEST)
+    for name in np.intersect1d(weights.params.keys(),
+                               output_weights.params.keys()):
+        # Some original conv / inner product layers do not have bias_term
+        for i in xrange(min(len(weights.params[name]),
+                            len(output_weights.params[name]))):
+            output_weights.params[name][i].data[...] = \
+                weights.params[name][i].data.copy()
+
+    # Absorb the BN parameters
+    for old, new in absorbed.iteritems():
+        scale, bias, mean, tmp = [p.data.ravel() for p in weights.params[old]]
+        invstd = tmp if args.bn_style == 'invstd' else \
+                 np.power(tmp + args.epsilon, -0.5)
+        W, b = output_weights.params[new]
+        assert W.data.ndim == 4 or W.data.ndim == 2
+        assert b.data.ndim == 1
+        if W.data.ndim == 4:
+            W.data[...] = (W.data * scale[:, None, None, None]
+                                  * invstd[:, None, None, None])
+        elif W.data.ndim == 2:
+            W.data[...] = W.data * scale[:, None] * invstd[:, None]
+        b.data[...] = (b.data[...] - mean) * scale * invstd + bias
+
+    # Fill up the affine layers
+    for old, new in replaced.iteritems():
+        scale, bias, mean, tmp = [p.data.ravel() for p in weights.params[old]]
+        invstd = tmp if args.bn_style == 'invstd' else \
+                 np.power(tmp + args.epsilon, -0.5)
+        W, b = output_weights.params[new]
+        assert W.data.ndim == 1
+        assert b.data.ndim == 1
+        W.data[...] = scale * invstd
+        b.data[...] = bias - scale * mean * invstd
+
+    # Check if the conversion is correct
+    check(weights, output_weights)
+
+    # Save the caffemodel
+    output_weights.save(args.output_weights)
+
+
+if __name__ == '__main__':
+    parser = ArgumentParser(
+            description="Generate Batch Normalized model for inference")
+    parser.add_argument('model', help="The net definition prototxt")
+    parser.add_argument('weights', help="The weights caffemodel")
+    parser.add_argument('--output_model')
+    parser.add_argument('--output_weights')
+    parser.add_argument('--bn_style', type=str, default='var',
+                        choices=['var', 'invstd'])
+    parser.add_argument('--epsilon', type=float, default=1e-5,
+                        help="The epsilon only used when bn_style is 'var'")
+    parser.add_argument('--replace_by', type=str, default='affine',
+                        choices=['affine', 'frozen'],
+                        help="When a BN layer cannot be absorbed, replace it "
+                             "by either affine (scale + bias) layers or a "
+                             "frozen BN layer")
+    args = parser.parse_args()
+    main(args)
diff --git a/python/polyak_average.py b/python/polyak_average.py
new file mode 100755
index 00000000000..80233cd0c97
--- /dev/null
+++ b/python/polyak_average.py
@@ -0,0 +1,60 @@
+#!/usr/bin/env python
+"""
+Example usage: To average caffenet_iter_10000.caffemodel,
+caffenet_iter_20000.caffemodel, and caffenet_iter_30000.caffemodel. Use command
+
+python2 polyak_average.py caffenet_deploy.prototxt caffenet_polyak.caffemodel \
+  --weight_prefix caffenet --iter_range "(10000,30001,10000)"
+"""
+import numpy as np
+import os.path as osp
+import sys
+from argparse import ArgumentParser
+
+pycaffe_dir = osp.dirname(__file__)
+if osp.join(pycaffe_dir) not in sys.path:
+    sys.path.insert(0, pycaffe_dir)
+import caffe
+
+
+def main(args):
+    if args.weight_files is not None:
+        weight_files = args.weight_files
+    else:
+        weight_files = [args.weight_prefix + '_iter_{}.caffemodel'.format(it)
+                        for it in args.iter_range]
+    assert len(weight_files) > 0, "Must have at least one caffemodel"
+    net = caffe.Net(args.model, weight_files[0], caffe.TEST)
+    count = {param_name: 1 for param_name in net.params.keys()}
+    for weight_file in weight_files[1:]:
+        tmp = caffe.Net(args.model, weight_file, caffe.TEST)
+        for param_name in np.intersect1d(net.params.keys(), tmp.params.keys()):
+            count[param_name] += 1
+            for w, v in zip(net.params[param_name], tmp.params[param_name]):
+                w.data[...] += v.data
+    for param_name in net.params:
+        if count[param_name] <= 1: continue
+        for w in net.params[param_name]:
+            w.data[...] /= count[param_name]
+    net.save(args.output)
+
+
+if __name__ == '__main__':
+    parser = ArgumentParser()
+    parser.add_argument('model', help="Net definition prototxt")
+    parser.add_argument('output', help="Path for saving the output")
+    parser.add_argument('--weight_files', type=str, nargs='+',
+        help="A list of caffemodels")
+    parser.add_argument('--weight_prefix', help="Prefix of caffemodels")
+    parser.add_argument('--iter_range',
+        help="Iteration range complementary with the prefix. In the form of "
+             "(begin, end, step), where begin is inclusive while end is "
+             "exclusive.")
+    args = parser.parse_args()
+    if args.weight_files is None and \
+            (args.weight_prefix is None or args.iter_range is None):
+        raise ValueError("Must provider either weight files or weight prefix "
+                         "and iter range.")
+    if args.iter_range is not None:
+        args.iter_range = eval('xrange' + args.iter_range)
+    main(args)
diff --git a/python/requirements.txt b/python/requirements.txt
index 4c35dcb0b6f..e7d89e67f48 100644
--- a/python/requirements.txt
+++ b/python/requirements.txt
@@ -2,9 +2,8 @@ Cython>=0.19.2
 numpy>=1.7.1
 scipy>=0.13.2
 scikit-image>=0.9.3
-scikit-learn>=0.14.1
 matplotlib>=1.3.1
-ipython>=1.1.0
+ipython>=3.0.0
 h5py>=2.2.0
 leveldb>=0.191
 networkx>=1.8.1
@@ -13,3 +12,6 @@ pandas>=0.12.0
 python-dateutil>=1.4,<2
 protobuf>=2.5.0
 python-gflags>=2.0
+pyyaml>=3.10
+Pillow>=2.3.0
+six>=1.1.0
\ No newline at end of file
diff --git a/scripts/build_docs.sh b/scripts/build_docs.sh
index f8ace0ead43..0e28bd71631 100755
--- a/scripts/build_docs.sh
+++ b/scripts/build_docs.sh
@@ -3,7 +3,7 @@
 
 PORT=${1:-4000}
 
-echo "usage: build.sh [port]"
+echo "usage: build_docs.sh [port]"
 
 # Find the docs dir, no matter where the script is called
 ROOT_DIR="$( cd "$(dirname "$0")"/.. ; pwd -P )"
diff --git a/scripts/cpp_lint.py b/scripts/cpp_lint.py
index 1b7c6c0536c..f750489f4f9 100755
--- a/scripts/cpp_lint.py
+++ b/scripts/cpp_lint.py
@@ -1,4 +1,4 @@
-#!/usr/bin/python
+#!/usr/bin/python2
 #
 # Copyright (c) 2009 Google Inc. All rights reserved.
 #
diff --git a/scripts/travis/travis_build_and_test.sh b/scripts/travis/travis_build_and_test.sh
index dec4d097c17..bcaa00d96f9 100755
--- a/scripts/travis/travis_build_and_test.sh
+++ b/scripts/travis/travis_build_and_test.sh
@@ -7,11 +7,14 @@ MAKE="make --jobs=$NUM_THREADS --keep-going"
 if $WITH_CMAKE; then
   mkdir build
   cd build
-  cmake -DBUILD_PYTHON=ON -DBUILD_EXAMPLES=ON -DCMAKE_BUILD_TYPE=Release -DCPU_ONLY=ON ..
-  $MAKE
   if ! $WITH_CUDA; then
+    cmake -DBUILD_python=ON -DCMAKE_BUILD_TYPE=Release -DCPU_ONLY=ON ..
+    $MAKE
     $MAKE runtest
-    $MAKE lint
+    #$MAKE lint
+  else
+    cmake -DBUILD_python=ON -DCMAKE_BUILD_TYPE=Release -DCPU_ONLY=OFF ..
+    $MAKE
   fi
   $MAKE clean
   cd -
@@ -26,6 +29,7 @@ else
   $MAKE all
   $MAKE test
   $MAKE pycaffe
+  $MAKE pytest
   $MAKE warn
   if ! $WITH_CUDA; then
     $MAKE lint
diff --git a/scripts/travis/travis_install.sh b/scripts/travis/travis_install.sh
index e17f253ecdc..e1af02a2165 100755
--- a/scripts/travis/travis_install.sh
+++ b/scripts/travis/travis_install.sh
@@ -47,13 +47,31 @@ if $WITH_CUDA; then
 fi
 
 # Install LMDB
-LMDB_URL=ftp://ftp.openldap.org/pub/OpenLDAP/openldap-release/openldap-2.4.39.tgz
-LMDB_FILE=/tmp/openldap.tgz
+LMDB_URL=https://github.com/LMDB/lmdb/archive/LMDB_0.9.14.tar.gz
+LMDB_FILE=/tmp/lmdb.tar.gz
 pushd .
-curl $LMDB_URL -o $LMDB_FILE
+wget $LMDB_URL -O $LMDB_FILE
 tar -C /tmp -xzvf $LMDB_FILE
-cd /tmp/openldap*/libraries/liblmdb/
+cd /tmp/lmdb*/libraries/liblmdb/
 $MAKE
 $MAKE install
 popd
 rm -f $LMDB_FILE
+
+# Install the Python runtime dependencies via miniconda (this is much faster
+# than using pip for everything).
+if [ -d "$HOME/miniconda" ]; then
+  # Control will enter here if $DIRECTORY exists.
+  echo "Removing previous miniconda"
+  rm -rf $HOME/miniconda
+fi
+
+wget http://repo.continuum.io/miniconda/Miniconda-latest-Linux-x86_64.sh -O miniconda.sh
+chmod +x miniconda.sh
+./miniconda.sh -b -p $HOME/miniconda
+export PATH="$HOME/miniconda/bin:$PATH"
+conda update --yes conda
+conda install --yes numpy scipy matplotlib scikit-image pip
+pip install protobuf
+
+# Install OpenMPI 1.
diff --git a/scripts/travis/travis_setup_makefile_config.sh b/scripts/travis/travis_setup_makefile_config.sh
index a309bb3d6fe..ba326262bf8 100755
--- a/scripts/travis/travis_setup_makefile_config.sh
+++ b/scripts/travis/travis_setup_makefile_config.sh
@@ -5,10 +5,19 @@ set -e
 mv Makefile.config.example Makefile.config
 
 if $WITH_CUDA; then
-  # Remove default gencode set; only generate compute_50.
-  sed -i 's/-gencode arch=.*\\//' Makefile.config
-  sed -i 's/CUDA_ARCH :=//' Makefile.config
+  # Only generate compute_50.
   GENCODE="-gencode arch=compute_50,code=sm_50"
   GENCODE="$GENCODE -gencode arch=compute_50,code=compute_50"
   echo "CUDA_ARCH := $GENCODE" >> Makefile.config
 fi
+
+cat << 'EOF' >> Makefile.config
+ANACONDA_HOME := $(HOME)/miniconda
+PYTHON_INCLUDE := $(ANACONDA_HOME)/include \
+		$(ANACONDA_HOME)/include/python2.7 \
+		$(ANACONDA_HOME)/lib/python2.7/site-packages/numpy/core/include
+PYTHON_LIB := $(ANACONDA_HOME)/lib
+INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include
+LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib
+WITH_PYTHON_LAYER := 1
+EOF
diff --git a/scripts/upload_model_to_gist.sh b/scripts/upload_model_to_gist.sh
index 2dfbabd72a3..3c4fd64e3fc 100755
--- a/scripts/upload_model_to_gist.sh
+++ b/scripts/upload_model_to_gist.sh
@@ -7,7 +7,7 @@ if [ ! -f $DIRNAME/readme.md ]; then
     echo "  <dirname>/readme.md must exist"
 fi
 cd $DIRNAME
-FILES=`find . -type f -maxdepth 1 ! -name "*.caffemodel*" | xargs echo`
+FILES=`find . -maxdepth 1 -type f ! -name "*.caffemodel*" | xargs echo`
 
 # Check for gist tool.
 gist -v >/dev/null 2>&1 || { echo >&2 "I require 'gist' but it's not installed. Do 'gem install gist'."; exit 1; }
diff --git a/src/caffe/CMakeLists.txt b/src/caffe/CMakeLists.txt
index 86c7c7eb4a3..40e6c11f5b0 100644
--- a/src/caffe/CMakeLists.txt
+++ b/src/caffe/CMakeLists.txt
@@ -1,133 +1,36 @@
-project( CaffeSrc )
+# generate protobuf sources
+file(GLOB proto_files proto/*.proto)
+caffe_protobuf_generate_cpp_py(${proto_gen_folder} proto_srcs proto_hdrs proto_python ${proto_files})
 
-#    Threads
-find_package(Threads REQUIRED)
+# include python files either to force generation
+add_library(proto STATIC ${proto_hdrs} ${proto_srcs} ${proto_python})
+set(Caffe_LINKER_LIBS proto ${Caffe_LINKER_LIBS}) # note, crucial to prepend!
+caffe_default_properties(proto)
 
-#	Google-glog
-find_package(Glog REQUIRED)
-include_directories(${GLOG_INCLUDE_DIRS})
+# --[ Caffe library
 
-#   Google-gflags
-find_package(GFlags REQUIRED)
-include_directories(${GFLAGS_INCLUDE_DIRS})
+# creates 'test_srcs', 'srcs', 'test_cuda', 'cuda' lists
+caffe_pickup_caffe_sources(${PROJECT_SOURCE_DIR})
 
-#   BLAS
-if(BLAS STREQUAL "atlas")
-
-    find_package(Atlas REQUIRED)
-    include_directories(${Atlas_INCLUDE_DIR})
-    set(BLAS_LIBRARIES ${Atlas_LIBRARIES})
-
-elseif(BLAS STREQUAL "open")
-
-    find_package(OpenBLAS REQUIRED)
-    include_directories(${OpenBLAS_INCLUDE_DIR})
-    set(BLAS_LIBRARIES ${OpenBLAS_LIB})
-
-elseif(BLAS STREQUAL "mkl")
-
-    find_package(MKL REQUIRED)
-    include_directories(${MKL_INCLUDE_DIR})
-    set(BLAS_LIBRARIES ${MKL_LIBRARIES})
-
-endif()
-
-#    HDF5
-find_package(HDF5 COMPONENTS HL REQUIRED)
-include_directories(${HDF5_INCLUDE_DIRS})
-
-#    OpenCV
-find_package(OpenCV REQUIRED core highgui imgproc)
-include_directories(${OpenCV_INCLUDE_DIRS})
-
-#    LevelDB
-find_package(LevelDB REQUIRED)
-include_directories(${LEVELDB_INCLUDE})
-if(LEVELDB_FOUND)
-    find_package(Snappy REQUIRED)
-    include_directories(${SNAPPY_INCLUDE_DIR})
-    set(LEVELDB_LIBS
-        ${LEVELDB_LIBS}
-        ${SNAPPY_LIBS}
-    )
+if(HAVE_CUDA)
+  caffe_cuda_compile(cuda_objs ${cuda})
+  list(APPEND srcs ${cuda_objs} ${cuda})
 endif()
 
-#    LMDB
-find_package(LMDB REQUIRED)
-include_directories(${LMDB_INCLUDE_DIR})
-
-#    Boost
-find_package(Boost 1.46 COMPONENTS system thread REQUIRED)
-include_directories( ${Boost_INCLUDE_DIR} )
-link_directories( ${Boost_LIBRARY_DIRS} )
-
-add_subdirectory(proto)
-
-#    Recursively find source files
-##    test sources
-file(GLOB_RECURSE TEST_CPP_SOURCES ${CMAKE_CURRENT_SOURCE_DIR}/test_*.cpp)
-
-##    all cpp sources
-file(GLOB_RECURSE CPP_SOURCES ${CMAKE_CURRENT_SOURCE_DIR}/*.cpp)
-
-##    remove test sources from cpp sources
-list(REMOVE_ITEM CPP_SOURCES ${TEST_CPP_SOURCES})
-
-add_library(caffe ${CPP_SOURCES})
-# both depend on proto
-add_dependencies(caffe proto)
-
-#    CUDA
-if(NOT CPU_ONLY)
-    set(CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS}
-            -gencode arch=compute_20,code=sm_20
-            -gencode arch=compute_20,code=sm_21
-            -gencode arch=compute_30,code=sm_30
-            -gencode arch=compute_35,code=sm_35
-    )
-    
-# https://github.com/ComputationalRadiationPhysics/picongpu/blob/master/src/picongpu/CMakeLists.txt
-    # work-arounds
-if(Boost_VERSION EQUAL 105500)
-    # see https://svn.boost.org/trac/boost/ticket/9392
-    message(STATUS "Boost: Applying noinline work around")
-    # avoid warning for CMake >= 2.8.12
-    set(CUDA_NVCC_FLAGS
-      "${CUDA_NVCC_FLAGS} \"-DBOOST_NOINLINE=__attribute__((noinline))\" ")
-endif(Boost_VERSION EQUAL 105500)
-
-    #    cuda sources
-    file(GLOB_RECURSE CU_SOURCES ${CMAKE_CURRENT_SOURCE_DIR}/*.cu)
-    file(GLOB_RECURSE TEST_CU_SOURCES ${CMAKE_CURRENT_SOURCE_DIR}/test_*.cu)
-    list(REMOVE_ITEM CU_SOURCES ${TEST_CU_SOURCES})
-    cuda_add_library(caffe_cu ${CU_SOURCES})
-    add_dependencies(caffe_cu proto)
-    target_link_libraries(caffe caffe_cu
-        ${CUDA_CUBLAS_LIBRARIES}
-        ${CUDA_curand_LIBRARY}
-    )
-endif()
+add_library(caffe ${srcs})
+target_link_libraries(caffe proto ${Caffe_LINKER_LIBS})
+caffe_default_properties(caffe)
 
-target_link_libraries(caffe proto
-        ${BLAS_LIBRARIES}
-        ${Boost_LIBRARIES}
-        ${CMAKE_THREAD_LIBS_INIT}
-        ${GFLAGS_LIBRARIES}
-        ${GLOG_LIBRARIES}
-        ${HDF5_LIBRARIES}
-        ${LEVELDB_LIBS}
-        ${LMDB_LIBRARIES}
-        ${OpenCV_LIBS}
-)
+# ---[ Tests
+ add_subdirectory(test)
 
-#set output directory
-set_target_properties(caffe PROPERTIES
-        ARCHIVE_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/lib
-        LIBRARY_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/lib
-)
+# ---[ Install
+install(DIRECTORY ${Caffe_INCLUDE_DIR}/caffe DESTINATION include)
+install(FILES ${proto_hdrs} DESTINATION include/caffe/proto)
+install(TARGETS caffe proto EXPORT CaffeTargets DESTINATION lib)
 
-add_subdirectory(test)
+file(WRITE ${PROJECT_BINARY_DIR}/__init__.py)
+list(APPEND proto_python ${PROJECT_BINARY_DIR}/__init__.py)
+install(PROGRAMS ${proto_python} DESTINATION python/caffe/proto)
 
-###    Install    #################################################################################
 
-install(TARGETS caffe DESTINATION lib)
diff --git a/src/caffe/blob.cpp b/src/caffe/blob.cpp
index cfffc379eb1..a892c2e65d2 100644
--- a/src/caffe/blob.cpp
+++ b/src/caffe/blob.cpp
@@ -1,3 +1,6 @@
+#include <climits>
+#include <vector>
+
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/syncedmem.hpp"
@@ -8,25 +11,53 @@ namespace caffe {
 template <typename Dtype>
 void Blob<Dtype>::Reshape(const int num, const int channels, const int height,
     const int width) {
-  CHECK_GE(num, 0);
-  CHECK_GE(channels, 0);
-  CHECK_GE(height, 0);
-  CHECK_GE(width, 0);
-  num_ = num;
-  channels_ = channels;
-  height_ = height;
-  width_ = width;
-  count_ = num_ * channels_ * height_ * width_;
+  vector<int> shape(4);
+  shape[0] = num;
+  shape[1] = channels;
+  shape[2] = height;
+  shape[3] = width;
+  Reshape(shape);
+}
+
+template <typename Dtype>
+void Blob<Dtype>::Reshape(const vector<int>& shape) {
+  CHECK_LE(shape.size(), kMaxBlobAxes);
+  count_ = 1;
+  shape_.resize(shape.size());
+  for (int i = 0; i < shape.size(); ++i) {
+    CHECK_GE(shape[i], 0);
+    CHECK_LE(shape[i], INT_MAX / count_) << "blob size exceeds INT_MAX";
+    count_ *= shape[i];
+    shape_[i] = shape[i];
+  }
   if (count_ > capacity_) {
     capacity_ = count_;
-    data_.reset(new SyncedMemory(capacity_ * sizeof(Dtype)));
-    diff_.reset(new SyncedMemory(capacity_ * sizeof(Dtype)));
+    if (data_){
+      data_->Resize(capacity_ * sizeof(Dtype));
+    }else {
+      data_.reset(new SyncedMemory(capacity_ * sizeof(Dtype)));
+    }
+    if (diff_){
+      diff_->Resize(capacity_ * sizeof(Dtype));
+    }else {
+      diff_.reset(new SyncedMemory(capacity_ * sizeof(Dtype)));
+    }
   }
 }
 
+template <typename Dtype>
+void Blob<Dtype>::Reshape(const BlobShape& shape) {
+  CHECK_LE(shape.dim_size(), kMaxBlobAxes);
+  vector<int> shape_vec(shape.dim_size());
+  for (int i = 0; i < shape.dim_size(); ++i) {
+    shape_vec[i] = shape.dim(i);
+  }
+  Reshape(shape_vec);
+}
+
 template <typename Dtype>
 void Blob<Dtype>::ReshapeLike(const Blob<Dtype>& other) {
-  Reshape(other.num(), other.channels(), other.height(), other.width());
+  Reshape(other.shape());
 }
 
 template <typename Dtype>
@@ -37,6 +68,13 @@ Blob<Dtype>::Blob(const int num, const int channels, const int height,
   Reshape(num, channels, height, width);
 }
 
+template <typename Dtype>
+Blob<Dtype>::Blob(const vector<int>& shape)
+  // capacity_ must be initialized before calling Reshape
+  : capacity_(0) {
+  Reshape(shape);
+}
+
 template <typename Dtype>
 const Dtype* Blob<Dtype>::cpu_data() const {
   CHECK(data_);
@@ -64,6 +102,7 @@ const Dtype* Blob<Dtype>::cpu_diff() const {
 template <typename Dtype>
 const Dtype* Blob<Dtype>::gpu_diff() const {
   CHECK(diff_);
+  CHECK_GE(diff_->size(), count_ * sizeof(Dtype));
   return (const Dtype*)diff_->gpu_data();
 }
 
@@ -88,6 +127,7 @@ Dtype* Blob<Dtype>::mutable_cpu_diff() {
 template <typename Dtype>
 Dtype* Blob<Dtype>::mutable_gpu_diff() {
   CHECK(diff_);
+  CHECK_GE(diff_->size(), count_ * sizeof(Dtype));
   return static_cast<Dtype*>(diff_->mutable_gpu_data());
 }
 
@@ -103,6 +143,22 @@ void Blob<Dtype>::ShareDiff(const Blob& other) {
   diff_ = other.diff();
 }
 
+template <typename Dtype>
+bool Blob<Dtype>::IsSharingDiff(const Blob* other) {
+  CHECK_EQ(count_, other->count());
+  return diff_ == other->diff();
+}
+
+template <typename Dtype>
+void Blob<Dtype>::SetDiffStorage(shared_ptr<SyncedMemory>& storage){
+  diff_ = storage;
+}
+
+template <typename Dtype>
+void Blob<Dtype>::SetDataStorage(shared_ptr<SyncedMemory>& storage) {
+  data_ = storage;
+}
+
 // The "update" method is used for parameter blobs in a Net, which are stored
 // as Blob<float> or Blob<double> -- hence we do not define it for
 // Blob<int> or Blob<unsigned int>.
@@ -205,12 +261,174 @@ Dtype Blob<Dtype>::asum_diff() const {
   return 0;
 }
 
+template <> unsigned int Blob<unsigned int>::sumsq_data() const {
+  NOT_IMPLEMENTED;
+  return 0;
+}
+
+template <> int Blob<int>::sumsq_data() const {
+  NOT_IMPLEMENTED;
+  return 0;
+}
+
+template <typename Dtype>
+Dtype Blob<Dtype>::sumsq_data() const {
+  Dtype sumsq;
+  const Dtype* data;
+  if (!data_) { return 0; }
+  switch (data_->head()) {
+  case SyncedMemory::HEAD_AT_CPU:
+    data = cpu_data();
+    sumsq = caffe_cpu_dot(count_, data, data);
+    break;
+  case SyncedMemory::HEAD_AT_GPU:
+  case SyncedMemory::SYNCED:
+#ifndef CPU_ONLY
+    data = gpu_data();
+    caffe_gpu_dot(count_, data, data, &sumsq);
+#else
+    NO_GPU;
+#endif
+    break;
+  case SyncedMemory::UNINITIALIZED:
+    return 0;
+  default:
+    LOG(FATAL) << "Unknown SyncedMemory head state: " << data_->head();
+  }
+  return sumsq;
+}
+
+template <> unsigned int Blob<unsigned int>::sumsq_diff() const {
+  NOT_IMPLEMENTED;
+  return 0;
+}
+
+template <> int Blob<int>::sumsq_diff() const {
+  NOT_IMPLEMENTED;
+  return 0;
+}
+
+template <typename Dtype>
+Dtype Blob<Dtype>::sumsq_diff() const {
+  Dtype sumsq;
+  const Dtype* diff;
+  if (!diff_) { return 0; }
+  switch (diff_->head()) {
+  case SyncedMemory::HEAD_AT_CPU:
+    diff = cpu_diff();
+    sumsq = caffe_cpu_dot(count_, diff, diff);
+    break;
+  case SyncedMemory::HEAD_AT_GPU:
+  case SyncedMemory::SYNCED:
+#ifndef CPU_ONLY
+    diff = gpu_diff();
+    caffe_gpu_dot(count_, diff, diff, &sumsq);
+    break;
+#else
+    NO_GPU;
+#endif
+  case SyncedMemory::UNINITIALIZED:
+    return 0;
+  default:
+    LOG(FATAL) << "Unknown SyncedMemory head state: " << data_->head();
+  }
+  return sumsq;
+}
+
+template <> void Blob<unsigned int>::scale_data(unsigned int scale_factor) {
+  NOT_IMPLEMENTED;
+}
+
+template <> void Blob<int>::scale_data(int scale_factor) {
+  NOT_IMPLEMENTED;
+}
+
+template <typename Dtype>
+void Blob<Dtype>::scale_data(Dtype scale_factor) {
+  Dtype* data;
+  if (!data_) { return; }
+  switch (data_->head()) {
+  case SyncedMemory::HEAD_AT_CPU:
+    data = mutable_cpu_data();
+    caffe_scal(count_, scale_factor, data);
+    return;
+  case SyncedMemory::HEAD_AT_GPU:
+  case SyncedMemory::SYNCED:
+#ifndef CPU_ONLY
+    data = mutable_gpu_data();
+    caffe_gpu_scal(count_, scale_factor, data);
+    return;
+#else
+    NO_GPU;
+#endif
+  case SyncedMemory::UNINITIALIZED:
+    return;
+  default:
+    LOG(FATAL) << "Unknown SyncedMemory head state: " << data_->head();
+  }
+}
+
+template <> void Blob<unsigned int>::scale_diff(unsigned int scale_factor) {
+  NOT_IMPLEMENTED;
+}
+
+template <> void Blob<int>::scale_diff(int scale_factor) {
+  NOT_IMPLEMENTED;
+}
+
+template <typename Dtype>
+void Blob<Dtype>::scale_diff(Dtype scale_factor) {
+  Dtype* diff;
+  if (!diff_) { return; }
+  switch (diff_->head()) {
+  case SyncedMemory::HEAD_AT_CPU:
+    diff = mutable_cpu_diff();
+    caffe_scal(count_, scale_factor, diff);
+    return;
+  case SyncedMemory::HEAD_AT_GPU:
+  case SyncedMemory::SYNCED:
+#ifndef CPU_ONLY
+    diff = mutable_gpu_diff();
+    caffe_gpu_scal(count_, scale_factor, diff);
+    return;
+#else
+    NO_GPU;
+#endif
+  case SyncedMemory::UNINITIALIZED:
+    return;
+  default:
+    LOG(FATAL) << "Unknown SyncedMemory head state: " << diff_->head();
+  }
+}
+
+template <typename Dtype>
+bool Blob<Dtype>::ShapeEquals(const BlobProto& other) {
+  if (other.has_num() || other.has_channels() ||
+      other.has_height() || other.has_width()) {
+    // Using deprecated 4D Blob dimensions --
+    // shape is (num, channels, height, width).
+    // Note: we do not use the normal Blob::num(), Blob::channels(), etc.
+    // methods as these index from the beginning of the blob shape, where legacy
+    // parameter blobs were indexed from the end of the blob shape (e.g., bias
+    // Blob shape (1 x 1 x 1 x N), IP layer weight Blob shape (1 x 1 x M x N)).
+    return shape_.size() <= 4 &&
+           LegacyShape(-4) == other.num() &&
+           LegacyShape(-3) == other.channels() &&
+           LegacyShape(-2) == other.height() &&
+           LegacyShape(-1) == other.width();
+  }
+  vector<int> other_shape(other.shape().dim_size());
+  for (int i = 0; i < other.shape().dim_size(); ++i) {
+    other_shape[i] = other.shape().dim(i);
+  }
+  return shape_ == other_shape;
+}
+
 template <typename Dtype>
 void Blob<Dtype>::CopyFrom(const Blob& source, bool copy_diff, bool reshape) {
-  if (num_ != source.num() || channels_ != source.channels() ||
-      height_ != source.height() || width_ != source.width()) {
+  if (source.count() != count_ || source.shape() != shape_) {
     if (reshape) {
-      Reshape(source.num(), source.channels(), source.height(), source.width());
+      ReshapeLike(source);
     } else {
       LOG(FATAL) << "Trying to copy blobs of different sizes.";
     }
@@ -240,8 +458,28 @@ void Blob<Dtype>::CopyFrom(const Blob& source, bool copy_diff, bool reshape) {
 }
 
 template <typename Dtype>
-void Blob<Dtype>::FromProto(const BlobProto& proto) {
-  Reshape(proto.num(), proto.channels(), proto.height(), proto.width());
+void Blob<Dtype>::FromProto(const BlobProto& proto, bool reshape) {
+  if (reshape) {
+    vector<int> shape;
+    if (proto.has_num() || proto.has_channels() ||
+        proto.has_height() || proto.has_width()) {
+      // Using deprecated 4D Blob dimensions --
+      // shape is (num, channels, height, width).
+      shape.resize(4);
+      shape[0] = proto.num();
+      shape[1] = proto.channels();
+      shape[2] = proto.height();
+      shape[3] = proto.width();
+    } else {
+      shape.resize(proto.shape().dim_size());
+      for (int i = 0; i < proto.shape().dim_size(); ++i) {
+        shape[i] = proto.shape().dim(i);
+      }
+    }
+    Reshape(shape);
+  } else {
+    CHECK(ShapeEquals(proto)) << "shape mismatch (reshape not set)";
+  }
   // copy data
   Dtype* data_vec = mutable_cpu_data();
   for (int i = 0; i < count_; ++i) {
@@ -257,10 +495,10 @@ void Blob<Dtype>::FromProto(const BlobProto& proto) {
 
 template <typename Dtype>
 void Blob<Dtype>::ToProto(BlobProto* proto, bool write_diff) const {
-  proto->set_num(num_);
-  proto->set_channels(channels_);
-  proto->set_height(height_);
-  proto->set_width(width_);
+  proto->clear_shape();
+  for (int i = 0; i < shape_.size(); ++i) {
+    proto->mutable_shape()->add_dim(shape_[i]);
+  }
   proto->clear_data();
   proto->clear_diff();
   const Dtype* data_vec = cpu_data();
diff --git a/src/caffe/common.cpp b/src/caffe/common.cpp
index 94fdf924f44..62d1ef5f4d0 100644
--- a/src/caffe/common.cpp
+++ b/src/caffe/common.cpp
@@ -10,7 +10,7 @@ namespace caffe {
 shared_ptr<Caffe> Caffe::singleton_;
 
 // random seeding
-int64_t cluster_seedgen(void) {
+int64_t cluster_seedgen(bool sync) {
   int64_t s, seed, pid;
   FILE* f = fopen("/dev/urandom", "rb");
   if (f && fread(&seed, 1, sizeof(seed), f) == sizeof(seed)) {
@@ -31,16 +31,53 @@ int64_t cluster_seedgen(void) {
 
 
 void GlobalInit(int* pargc, char*** pargv) {
+
   // Google flags.
   ::gflags::ParseCommandLineFlags(pargc, pargv, true);
   // Google logging.
   ::google::InitGoogleLogging(*(pargv)[0]);
+  // Provide a backtrace on segfault.
+  ::google::InstallFailureSignalHandler();
+
+#ifdef USE_MPI
+  //try start MPI communication system
+  int provided_thread_support;
+  MPI_Init_thread(pargc, pargv, MPI_THREAD_MULTIPLE, &provided_thread_support);
+
+  CHECK_GE(provided_thread_support, MPI_THREAD_SERIALIZED)<<" Cannot activate MPI thread support";
+
+  Caffe::MPI_build_rank();
+
+  if (Caffe::MPI_all_rank() > 1) {
+    Caffe::set_parallel_mode(Caffe::MPI);
+    LOG(INFO)<<"Running parallel training with MPI support!";
+  }else{
+    Caffe::set_parallel_mode(Caffe::NO);
+    LOG(INFO)<<"You are running caffe compiled with MPI support. Now it's running in non-parallel model";
+  }
+
+  //disable slave processes from logging to stderr
+  //also enable logging only events above ERROR level to logfile.
+  if (Caffe::MPI_my_rank() != 0){
+    FLAGS_logtostderr = false;
+    FLAGS_minloglevel = 2;
+  }
+#endif
+
+}
+
+void GlobalFinalize(){
+  //Add something here
+
+  #ifdef USE_MPI
+  MPI_Finalize();
+  #endif
 }
 
 #ifdef CPU_ONLY  // CPU-only Caffe.
 
 Caffe::Caffe()
-    : random_generator_(), mode_(Caffe::CPU), phase_(Caffe::TRAIN) { }
+    : random_generator_(), mode_(Caffe::CPU) { }
 
 Caffe::~Caffe() { }
 
@@ -84,7 +121,8 @@ void* Caffe::RNG::generator() {
 
 Caffe::Caffe()
     : cublas_handle_(NULL), curand_generator_(NULL), random_generator_(),
-    mode_(Caffe::CPU), phase_(Caffe::TRAIN) {
+    mode_(Caffe::CPU) {
+  #ifndef USE_MPI
   // Try to create a cublas handler, and report an error if failed (but we will
   // keep the program running as one might just want to run CPU code).
   if (cublasCreate(&cublas_handle_) != CUBLAS_STATUS_SUCCESS) {
@@ -97,6 +135,19 @@ Caffe::Caffe()
       != CURAND_STATUS_SUCCESS) {
     LOG(ERROR) << "Cannot create Curand generator. Curand won't be available.";
   }
+  #else
+  // we are not trying to create the any cuda stuff here
+  // because on exclusive mode GPUs it will cause program fail
+  // Reason: no device id assigned at this time, all processes will try to access gpu 0.
+  #endif
+
+  #ifdef USE_CUDNN
+    cudnn_mem_richness_ = 1;
+  #endif
+
+  #ifdef WITH_PYTHON_LAYER
+  py_tstate_ = NULL;
+  #endif
 }
 
 Caffe::~Caffe() {
@@ -126,8 +177,9 @@ void Caffe::set_random_seed(const unsigned int seed) {
 
 void Caffe::SetDevice(const int device_id) {
   int current_device;
+  std::cout<<"Setting device "<<device_id<<"\n";
   CUDA_CHECK(cudaGetDevice(&current_device));
-  if (current_device == device_id) {
+  if (current_device == device_id && Get().cublas_handle_ && Get().curand_generator_) {
     return;
   }
   // The call to cudaSetDevice must come before any calls to Get, which
@@ -142,6 +194,9 @@ void Caffe::SetDevice(const int device_id) {
       CURAND_RNG_PSEUDO_DEFAULT));
   CURAND_CHECK(curandSetPseudoRandomGeneratorSeed(Get().curand_generator_,
       cluster_seedgen()));
+#ifdef USE_MPI
+  Get().device_id_ = device_id;
+#endif
 }
 
 void Caffe::DeviceQuery() {
diff --git a/src/caffe/data_transformer.cpp b/src/caffe/data_transformer.cpp
index 7150fd99c18..d6609ec2637 100644
--- a/src/caffe/data_transformer.cpp
+++ b/src/caffe/data_transformer.cpp
@@ -1,97 +1,799 @@
+#include <opencv2/core/core.hpp>
+
 #include <string>
+#include <vector>
+#include <opencv2/imgproc/imgproc.hpp>
 
 #include "caffe/data_transformer.hpp"
+#include "caffe/util/io.hpp"
 #include "caffe/util/math_functions.hpp"
 #include "caffe/util/rng.hpp"
 
 namespace caffe {
 
 template<typename Dtype>
-void DataTransformer<Dtype>::Transform(const int batch_item_id,
-                                       const Datum& datum,
-                                       const Dtype* mean,
+DataTransformer<Dtype>::DataTransformer(const TransformationParameter& param,
+    Phase phase)
+    : param_(param), phase_(phase) {
+  // check if we want to use mean_file
+  if (param_.has_mean_file()) {
+    CHECK_EQ(param_.mean_value_size(), 0) <<
+      "Cannot specify mean_file and mean_value at the same time";
+    const string& mean_file = param.mean_file();
+    LOG(INFO) << "Loading mean file from: " << mean_file;
+    BlobProto blob_proto;
+    ReadProtoFromBinaryFileOrDie(mean_file.c_str(), &blob_proto);
+    data_mean_.FromProto(blob_proto);
+  }
+  // check if we want to use mean_value
+  if (param_.mean_value_size() > 0) {
+    CHECK(param_.has_mean_file() == false) <<
+      "Cannot specify mean_file and mean_value at the same time";
+    for (int c = 0; c < param_.mean_value_size(); ++c) {
+      mean_values_.push_back(param_.mean_value(c));
+    }
+  }
+
+  //load multiscale info
+  max_distort_ = param_.max_distort();
+  custom_scale_ratios_.clear();
+  for (int i = 0; i < param_.scale_ratios_size(); ++i){
+    custom_scale_ratios_.push_back(param_.scale_ratios(i));
+  }
+  org_size_proc_ = param.original_image();
+}
+
+
+
+/** @build fixed crop offsets for random selection
+ */
+void fillFixOffset(int datum_height, int datum_width, int crop_height, int crop_width,
+                   bool more_crop,
+                   vector<pair<int , int> >& offsets){
+  int height_off = (datum_height - crop_height)/4;
+  int width_off = (datum_width - crop_width)/4;
+
+  offsets.clear();
+  offsets.push_back(pair<int, int>(0, 0)); //upper left
+  offsets.push_back(pair<int, int>(0, 4 * width_off)); //upper right
+  offsets.push_back(pair<int, int>(4 * height_off, 0)); //lower left
+  offsets.push_back(pair<int, int>(4 * height_off, 4 *width_off)); //lower right
+  offsets.push_back(pair<int, int>(2 * height_off, 2 * width_off)); //center
+
+  //will be used when more_fix_crop is set to true
+  if (more_crop) {
+    offsets.push_back(pair<int, int>(0, 2 * width_off)); //top center
+    offsets.push_back(pair<int, int>(4 * height_off, 2 * width_off)); //bottom center
+    offsets.push_back(pair<int, int>(2 * height_off, 0)); //left center
+    offsets.push_back(pair<int, int>(2 * height_off, 4 * width_off)); //right center
+
+    offsets.push_back(pair<int, int>(1 * height_off, 1 * width_off)); //upper left quarter
+    offsets.push_back(pair<int, int>(1 * height_off, 3 * width_off)); //upper right quarter
+    offsets.push_back(pair<int, int>(3 * height_off, 1 * width_off)); //lower left quarter
+    offsets.push_back(pair<int, int>(3 * height_off, 3 * width_off)); //lower right quarter
+  }
+}
+
+float _scale_rates[] = {1.0, .875, .75, .66};
+vector<float> default_scale_rates(_scale_rates, _scale_rates + sizeof(_scale_rates)/ sizeof(_scale_rates[0]) );
+
+/**
+ * @generate crop size when multi-scale cropping is requested
+ */
+void fillCropSize(int input_height, int input_width,
+                 int net_input_height, int net_input_width,
+                 vector<pair<int, int> >& crop_sizes,
+                 int max_distort, vector<float>& custom_scale_ratios){
+    crop_sizes.clear();
+
+    vector<float>& scale_rates = (custom_scale_ratios.size() > 0)?custom_scale_ratios:default_scale_rates;
+    int base_size = std::min(input_height, input_width);
+    for (int h = 0; h < scale_rates.size(); ++h){
+      int crop_h = int(base_size * scale_rates[h]);
+      crop_h = (abs(crop_h - net_input_height) < 3)?net_input_height:crop_h;
+      for (int w = 0; w < scale_rates.size(); ++w){
+        int crop_w = int(base_size * scale_rates[w]);
+        crop_w = (abs(crop_w - net_input_width) < 3)?net_input_width:crop_w;
+
+        //append this cropping size into the list
+        if (abs(h-w)<=max_distort) {
+          crop_sizes.push_back(pair<int, int>(crop_h, crop_w));
+        }
+      }
+    }
+}
+
+/**
+ * @generate crop size and offset when process original images
+ */
+void sampleRandomCropSize(int img_height, int img_width,
+                          int& crop_height, int& crop_width,
+                          float min_scale=0.08, float max_scale=1.0, float min_as=0.75, float max_as=1.33){
+  float total_area = img_height * img_width;
+  float area_ratio = 0;
+  float target_area = 0;
+  float aspect_ratio = 0;
+  float flip_coin = 0;
+
+  int attempt = 0;
+
+  while (attempt < 10) {
+    // sample scale and area
+    caffe_rng_uniform(1, min_scale, max_scale, &area_ratio);
+    target_area = total_area * area_ratio;
+
+    caffe_rng_uniform(1, float(0), float(1), &flip_coin);
+    if (flip_coin > 0.5){
+        std::swap(crop_height, crop_width);
+    }
+
+    // sample aspect ratio
+    caffe_rng_uniform(1, min_as, max_as, &aspect_ratio);
+    crop_height = int(sqrt(target_area / aspect_ratio));
+    crop_width = int(sqrt(target_area * aspect_ratio));
+
+    if (crop_height <= img_height && crop_width <= img_width){
+      return;
+    }
+    attempt ++;
+  }
+
+  // fallback to normal 256-224 style size crop
+  crop_height = img_height / 8 * 7;
+  crop_width = img_width / 8 * 7;
+}
+
+
+template<typename Dtype>
+void DataTransformer<Dtype>::Transform(const Datum& datum,
                                        Dtype* transformed_data) {
+
+
   const string& data = datum.data();
-  const int channels = datum.channels();
-  const int height = datum.height();
-  const int width = datum.width();
-  const int size = datum.channels() * datum.height() * datum.width();
+  const int datum_channels = datum.channels();
+  const int datum_height = datum.height();
+  const int datum_width = datum.width();
 
   const int crop_size = param_.crop_size();
-  const bool mirror = param_.mirror();
   const Dtype scale = param_.scale();
+  const bool do_mirror = param_.mirror() && Rand(2);
+  const bool has_mean_file = param_.has_mean_file();
+  const bool has_uint8 = data.size() > 0;
+  const bool has_mean_values = mean_values_.size() > 0;
+  const bool do_multi_scale = param_.multi_scale();
+  vector<pair<int, int> > offset_pairs;
+  vector<pair<int, int> > crop_size_pairs;
+  cv::Mat multi_scale_bufferM;
+
+  CHECK_GT(datum_channels, 0);
+  CHECK_GE(datum_height, crop_size);
+  CHECK_GE(datum_width, crop_size);
+
+  Dtype* mean = NULL;
+  if (has_mean_file) {
+    CHECK_EQ(datum_channels, data_mean_.channels());
+    CHECK_EQ(datum_height, data_mean_.height());
+    CHECK_EQ(datum_width, data_mean_.width());
+    mean = data_mean_.mutable_cpu_data();
+  }
+  if (has_mean_values) {
+    CHECK(mean_values_.size() == 1 || mean_values_.size() == datum_channels) <<
+     "Specify either 1 mean_value or as many as channels: " << datum_channels;
+    if (datum_channels > 1 && mean_values_.size() == 1) {
+      // Replicate the mean_value for simplicity
+      for (int c = 1; c < datum_channels; ++c) {
+        mean_values_.push_back(mean_values_[0]);
+      }
+    }
+  }
 
-  if (mirror && crop_size == 0) {
-    LOG(FATAL) << "Current implementation requires mirror and crop_size to be "
-               << "set at the same time.";
+  if (!crop_size && do_multi_scale){
+    LOG(ERROR)<< "Multi scale augmentation is only activated with crop_size set.";
   }
 
+  int height = datum_height;
+  int width = datum_width;
+
+  int h_off = 0;
+  int w_off = 0;
+  int crop_height = 0;
+  int crop_width = 0;
+  bool need_imgproc = false;
   if (crop_size) {
-    CHECK(data.size()) << "Image cropping only support uint8 data";
-    int h_off, w_off;
+    height = crop_size;
+    width = crop_size;
     // We only do random crop when we do training.
-    if (phase_ == Caffe::TRAIN) {
-      h_off = Rand() % (height - crop_size);
-      w_off = Rand() % (width - crop_size);
+    if (phase_ == TRAIN) {
+      // If in training and we need multi-scale cropping, reset the crop size params
+      if (do_multi_scale){
+        fillCropSize(datum_height, datum_width, crop_size, crop_size, crop_size_pairs,
+                     max_distort_, custom_scale_ratios_);
+        int sel = Rand(crop_size_pairs.size());
+        crop_height = crop_size_pairs[sel].first;
+        crop_width = crop_size_pairs[sel].second;
+      }else{
+        crop_height = crop_size;
+        crop_width = crop_size;
+      }
+      if (param_.fix_crop()){
+        fillFixOffset(datum_height, datum_width, crop_height, crop_width,
+                      param_.more_fix_crop(), offset_pairs);
+        int sel = Rand(offset_pairs.size());
+        h_off = offset_pairs[sel].first;
+        w_off = offset_pairs[sel].second;
+      }else{
+        h_off = Rand(datum_height - crop_height + 1);
+        w_off = Rand(datum_width - crop_width + 1);
+      }
+
     } else {
-      h_off = (height - crop_size) / 2;
-      w_off = (width - crop_size) / 2;
-    }
-    if (mirror && Rand() % 2) {
-      // Copy mirrored version
-      for (int c = 0; c < channels; ++c) {
-        for (int h = 0; h < crop_size; ++h) {
-          for (int w = 0; w < crop_size; ++w) {
-            int data_index = (c * height + h + h_off) * width + w + w_off;
-            int top_index = ((batch_item_id * channels + c) * crop_size + h)
-                * crop_size + (crop_size - 1 - w);
-            Dtype datum_element =
-                static_cast<Dtype>(static_cast<uint8_t>(data[data_index]));
+      crop_height = crop_size;
+      crop_width = crop_size;
+      h_off = (datum_height - crop_size) / 2;
+      w_off = (datum_width - crop_size) / 2;
+    }
+  }
+
+  need_imgproc = do_multi_scale && crop_size && ((crop_height != crop_size) || (crop_width != crop_size));
+
+  Dtype datum_element;
+  int top_index, data_index;
+  for (int c = 0; c < datum_channels; ++c) {
+
+    // image resize etc needed
+    if (need_imgproc){
+      cv::Mat M(datum_height, datum_width, has_uint8?CV_8UC1:CV_32FC1);
+
+      //put the datum content to a cvMat
+      for (int h = 0; h < datum_height; ++h) {
+        for (int w = 0; w < datum_width; ++w) {
+          int data_index = (c * datum_height + h) * datum_width + w;
+          if (has_uint8) {
+            M.at<uchar>(h, w) = static_cast<uint8_t>(data[data_index]);
+          }else{
+            M.at<float>(h, w) = datum.float_data(data_index);
+          }
+        }
+      }
+
+      //resize the cropped patch to network input size
+      cv::Mat cropM(M, cv::Rect(w_off, h_off, crop_width, crop_height));
+      cv::resize(cropM, multi_scale_bufferM, cv::Size(crop_size, crop_size));
+      cropM.release();
+    }
+    for (int h = 0; h < height; ++h) {
+      for (int w = 0; w < width; ++w) {
+        data_index = (c * datum_height + h_off + h) * datum_width + w_off + w;
+        if (do_mirror) {
+          top_index = (c * height + h) * width + (width - 1 - w);
+        } else {
+          top_index = (c * height + h) * width + w;
+        }
+        if (need_imgproc){
+          if (has_uint8){
+        	  if (param_.is_flow() && do_mirror && c%2 == 0)
+        		  datum_element = 255 - static_cast<Dtype>(multi_scale_bufferM.at<uint8_t>(h, w));
+        	  else
+        		  datum_element = static_cast<Dtype>(multi_scale_bufferM.at<uint8_t>(h, w));
+          }else {
+        	  if (param_.is_flow() && do_mirror && c%2 == 0)
+        		  datum_element = 255 - static_cast<Dtype>(multi_scale_bufferM.at<float>(h, w));
+        	  else
+        		  datum_element = static_cast<Dtype>(multi_scale_bufferM.at<float>(h, w));
+          }
+        }else {
+          if (has_uint8) {
+        	  if (param_.is_flow() && do_mirror && c%2 == 0)
+        		  datum_element = 255 - static_cast<Dtype>(static_cast<uint8_t>(data[data_index]));
+        	  else
+        		  datum_element = static_cast<Dtype>(static_cast<uint8_t>(data[data_index]));
+          } else {
+        	  if (param_.is_flow() && do_mirror && c%2 == 0)
+        		  datum_element = 255 - datum.float_data(data_index);
+        	  else
+        		  datum_element = datum.float_data(data_index);
+          }
+        }
+        if (has_mean_file) {
+          if (do_multi_scale) {
+            int fixed_data_index = (c * datum_height +  h) * datum_width + w;
+            transformed_data[top_index] =
+                (datum_element - mean[fixed_data_index]) * scale;
+          }else{
             transformed_data[top_index] =
                 (datum_element - mean[data_index]) * scale;
           }
+        } else {
+          if (has_mean_values) {
+            transformed_data[top_index] =
+                (datum_element - mean_values_[c]) * scale;
+          } else {
+            transformed_data[top_index] = datum_element * scale;
+          }
         }
       }
+    }
+  }
+  multi_scale_bufferM.release();
+}
+
+template<typename Dtype>
+void DataTransformer<Dtype>::Transform(const Datum& datum,
+                                       Blob<Dtype>* transformed_blob) {
+  // If datum is encoded, decoded and transform the cv::image.
+  if (datum.encoded()) {
+    CHECK(!(param_.force_color() && param_.force_gray()))
+        << "cannot set both force_color and force_gray";
+    cv::Mat cv_img;
+    if (param_.force_color() || param_.force_gray()) {
+    // If force_color then decode in color otherwise decode in gray.
+      cv_img = DecodeDatumToCVMat(datum, param_.force_color());
     } else {
-      // Normal copy
-      for (int c = 0; c < channels; ++c) {
-        for (int h = 0; h < crop_size; ++h) {
-          for (int w = 0; w < crop_size; ++w) {
-            int top_index = ((batch_item_id * channels + c) * crop_size + h)
-                * crop_size + w;
-            int data_index = (c * height + h + h_off) * width + w + w_off;
-            Dtype datum_element =
-                static_cast<Dtype>(static_cast<uint8_t>(data[data_index]));
+      cv_img = DecodeDatumToCVMatNative(datum);
+    }
+    // Transform the cv::image into blob.
+    return Transform(cv_img, transformed_blob);
+  } else {
+    if (param_.force_color() || param_.force_gray()) {
+      LOG(ERROR) << "force_color and force_gray only for encoded datum";
+    }
+  }
+
+  const int crop_size = param_.crop_size();
+  const int datum_channels = datum.channels();
+  const int datum_height = datum.height();
+  const int datum_width = datum.width();
+
+  // Check dimensions.
+  const int channels = transformed_blob->channels();
+  const int height = transformed_blob->height();
+  const int width = transformed_blob->width();
+  const int num = transformed_blob->num();
+
+  CHECK_EQ(channels, datum_channels);
+  CHECK_LE(height, datum_height);
+  CHECK_LE(width, datum_width);
+  CHECK_GE(num, 1);
+
+  if (crop_size) {
+    CHECK_EQ(crop_size, height);
+    CHECK_EQ(crop_size, width);
+  } else {
+    CHECK_EQ(datum_height, height);
+    CHECK_EQ(datum_width, width);
+  }
+
+  Dtype* transformed_data = transformed_blob->mutable_cpu_data();
+  Transform(datum, transformed_data);
+}
+
+template<typename Dtype>
+void DataTransformer<Dtype>::Transform(const vector<Datum> & datum_vector,
+                                       Blob<Dtype>* transformed_blob) {
+  const int datum_num = datum_vector.size();
+  const int num = transformed_blob->num();
+  const int channels = transformed_blob->channels();
+  const int height = transformed_blob->height();
+  const int width = transformed_blob->width();
+
+  CHECK_GT(datum_num, 0) << "There is no datum to add";
+  CHECK_LE(datum_num, num) <<
+    "The size of datum_vector must be no greater than transformed_blob->num()";
+  Blob<Dtype> uni_blob(1, channels, height, width);
+  for (int item_id = 0; item_id < datum_num; ++item_id) {
+    int offset = transformed_blob->offset(item_id);
+    uni_blob.set_cpu_data(transformed_blob->mutable_cpu_data() + offset);
+    Transform(datum_vector[item_id], &uni_blob);
+  }
+}
+
+template<typename Dtype>
+void DataTransformer<Dtype>::Transform(const vector<cv::Mat> & mat_vector,
+                                       Blob<Dtype>* transformed_blob) {
+  const int mat_num = mat_vector.size();
+  const int num = transformed_blob->num();
+  const int channels = transformed_blob->channels();
+  const int height = transformed_blob->height();
+  const int width = transformed_blob->width();
+
+  CHECK_GT(mat_num, 0) << "There is no MAT to add";
+  CHECK_EQ(mat_num, num) <<
+    "The size of mat_vector must be equals to transformed_blob->num()";
+  Blob<Dtype> uni_blob(1, channels, height, width);
+  for (int item_id = 0; item_id < mat_num; ++item_id) {
+    int offset = transformed_blob->offset(item_id);
+    uni_blob.set_cpu_data(transformed_blob->mutable_cpu_data() + offset);
+    Transform(mat_vector[item_id], &uni_blob);
+  }
+}
+
+template<typename Dtype>
+void DataTransformer<Dtype>::Transform(const cv::Mat& cv_img,
+                                       Blob<Dtype>* transformed_blob) {
+  const int crop_size = param_.crop_size();
+  const int img_channels = cv_img.channels();
+  const int img_height = cv_img.rows;
+  const int img_width = cv_img.cols;
+
+  // Check dimensions.
+  const int channels = transformed_blob->channels();
+  const int height = transformed_blob->height();
+  const int width = transformed_blob->width();
+  const int num = transformed_blob->num();
+
+  CHECK_EQ(channels, img_channels);
+
+  if (!org_size_proc_) {
+    CHECK_LE(height, img_height);
+    CHECK_LE(width, img_width);
+  }
+  CHECK_GE(num, 1);
+
+  CHECK(cv_img.depth() == CV_8U) << "Image data type must be unsigned byte";
+
+  const Dtype scale = param_.scale();
+  const bool do_mirror = param_.mirror() && Rand(2);
+  const bool has_mean_file = param_.has_mean_file();
+  const bool has_mean_values = mean_values_.size() > 0;
+  const bool do_multi_scale = param_.multi_scale();
+
+  vector<pair<int, int> > offset_pairs;
+  vector<pair<int, int> > crop_size_pairs;
+
+  cv::Mat cv_cropped_img;
+
+  CHECK_GT(img_channels, 0);
+  if (!org_size_proc_) {
+    CHECK_GE(img_height, crop_size);
+    CHECK_GE(img_width, crop_size);
+  }
+
+  Dtype *mean = NULL;
+  if (has_mean_file) {
+    CHECK_EQ(img_channels, data_mean_.channels());
+    CHECK_EQ(img_height, data_mean_.height());
+    CHECK_EQ(img_width, data_mean_.width());
+    mean = data_mean_.mutable_cpu_data();
+  }
+  if (has_mean_values) {
+    CHECK(mean_values_.size() == 1 || mean_values_.size() == img_channels) <<
+                                                                           "Specify either 1 mean_value or as many as channels: " <<
+                                                                           img_channels;
+    if (img_channels > 1 && mean_values_.size() == 1) {
+      // Replicate the mean_value for simplicity
+      for (int c = 1; c < img_channels; ++c) {
+        mean_values_.push_back(mean_values_[0]);
+      }
+    }
+  }
+
+  int h_off = 0;
+  int w_off = 0;
+  int crop_height = 0;
+  int crop_width = 0;
+
+  if (!org_size_proc_) {
+    if (crop_size) {
+      CHECK_EQ(crop_size, height);
+      CHECK_EQ(crop_size, width);
+      // We only do random crop when we do training.
+      if (phase_ == TRAIN) {
+        if (do_multi_scale) {
+          fillCropSize(img_height, img_width, crop_size, crop_size, crop_size_pairs,
+                       max_distort_, custom_scale_ratios_);
+          int sel = Rand(crop_size_pairs.size());
+          crop_height = crop_size_pairs[sel].first;
+          crop_width = crop_size_pairs[sel].second;
+        } else {
+          crop_height = crop_size;
+          crop_width = crop_size;
+        }
+        if (param_.fix_crop()) {
+          fillFixOffset(img_height, img_width, crop_height, crop_width,
+                        param_.more_fix_crop(), offset_pairs);
+          int sel = Rand(offset_pairs.size());
+          h_off = offset_pairs[sel].first;
+          w_off = offset_pairs[sel].second;
+        } else {
+          h_off = Rand(img_height - crop_height + 1);
+          w_off = Rand(img_width - crop_width + 1);
+        }
+      } else {
+        h_off = (img_height - crop_size) / 2;
+        w_off = (img_width - crop_size) / 2;
+        crop_width = crop_size;
+        crop_height = crop_size;
+      }
+      cv::Rect roi(w_off, h_off, crop_width, crop_height);
+      // if resize needed, first put the resized image into a buffer, then copy back.
+      if (do_multi_scale && ((crop_height != crop_size) || (crop_width != crop_size))) {
+        cv::Mat crop_bufferM(cv_img, roi);
+        cv::resize(crop_bufferM, cv_cropped_img, cv::Size(crop_size, crop_size));
+        crop_bufferM.release();
+      } else {
+        cv_cropped_img = cv_img(roi);
+      }
+    } else {
+      CHECK_EQ(img_height, height);
+      CHECK_EQ(img_width, width);
+      cv_cropped_img = cv_img;
+    }
+  }else{
+    CHECK(crop_size>0)<<"in original image processing mode, crop size must be specified";
+    CHECK_EQ(crop_size, height);
+    CHECK_EQ(crop_size, width);
+    if (phase_ == TRAIN) {
+      // in training, we randomly crop different sized crops
+      sampleRandomCropSize(img_height, img_width, crop_height, crop_width);
+
+
+
+      h_off = (crop_height < img_height)?Rand(img_height - crop_height):0;
+      w_off = (crop_width < img_width)?Rand(img_width - crop_width):0;
+    }else{
+      // in testing, we first resize the image to sizeof (8/7*crop_size) then crop the central patch
+      h_off = img_height / 14;
+      w_off = img_width / 14;
+      crop_height = img_height / 8 * 7;
+      crop_width = img_width / 8 * 7;
+    }
+
+    cv::Rect roi(w_off, h_off, crop_width, crop_height);
+
+    // resize is always needed in original image mode
+    cv::Mat crop_bufferM(cv_img, roi);
+    cv::resize(crop_bufferM, cv_cropped_img, cv::Size(crop_size, crop_size), 0, 0, CV_INTER_CUBIC);
+    crop_bufferM.release();
+  }
+
+  CHECK(cv_cropped_img.data);
+
+  Dtype *transformed_data = transformed_blob->mutable_cpu_data();
+  int top_index;
+  for (int h = 0; h < height; ++h) {
+    const uchar *ptr = cv_cropped_img.ptr<uchar>(h);
+    int img_index = 0;
+    for (int w = 0; w < width; ++w) {
+      for (int c = 0; c < img_channels; ++c) {
+        if (do_mirror) {
+          top_index = (c * height + h) * width + (width - 1 - w);
+        } else {
+          top_index = (c * height + h) * width + w;
+        }
+        // int top_index = (c * height + h) * width + w;
+        Dtype pixel = static_cast<Dtype>(ptr[img_index++]);
+        if (has_mean_file) {
+          //we will use a fixed position of mean map for multi-scale.
+          int mean_index = (do_multi_scale) ?
+                           (c * img_height + h) * img_width + w
+                                            : (c * img_height + h_off + h) * img_width + w_off + w;
+          if (param_.is_flow() && do_mirror && c % 2 == 0)
             transformed_data[top_index] =
-                (datum_element - mean[data_index]) * scale;
+                (255 - pixel - mean[mean_index]) * scale;
+          else
+            transformed_data[top_index] =
+                (pixel - mean[mean_index]) * scale;
+        } else {
+          if (has_mean_values) {
+            if (param_.is_flow() && do_mirror && c % 2 == 0)
+              transformed_data[top_index] =
+                  (255 - pixel - mean_values_[c]) * scale;
+            else
+              transformed_data[top_index] =
+                  (pixel - mean_values_[c]) * scale;
+          } else {
+            if (param_.is_flow() && do_mirror && c % 2 == 0)
+              transformed_data[top_index] = (255 - pixel) * scale;
+            else
+              transformed_data[top_index] = pixel * scale;
           }
         }
       }
     }
+  }
+  cv_cropped_img.release();
+}
+
+template<typename Dtype>
+void DataTransformer<Dtype>::Transform(Blob<Dtype>* input_blob,
+                                       Blob<Dtype>* transformed_blob) {
+  const int crop_size = param_.crop_size();
+  const int input_num = input_blob->num();
+  const int input_channels = input_blob->channels();
+  const int input_height = input_blob->height();
+  const int input_width = input_blob->width();
+
+  if (transformed_blob->count() == 0) {
+    // Initialize transformed_blob with the right shape.
+    if (crop_size) {
+      transformed_blob->Reshape(input_num, input_channels,
+                                crop_size, crop_size);
+    } else {
+      transformed_blob->Reshape(input_num, input_channels,
+                                input_height, input_width);
+    }
+  }
+
+  const int num = transformed_blob->num();
+  const int channels = transformed_blob->channels();
+  const int height = transformed_blob->height();
+  const int width = transformed_blob->width();
+  const int size = transformed_blob->count();
+
+  CHECK_LE(input_num, num);
+  CHECK_EQ(input_channels, channels);
+  CHECK_GE(input_height, height);
+  CHECK_GE(input_width, width);
+
+
+  const Dtype scale = param_.scale();
+  const bool do_mirror = param_.mirror() && Rand(2);
+  const bool has_mean_file = param_.has_mean_file();
+  const bool has_mean_values = mean_values_.size() > 0;
+
+  int h_off = 0;
+  int w_off = 0;
+  if (crop_size) {
+    CHECK_EQ(crop_size, height);
+    CHECK_EQ(crop_size, width);
+    // We only do random crop when we do training.
+    if (phase_ == TRAIN) {
+      h_off = Rand(input_height - crop_size + 1);
+      w_off = Rand(input_width - crop_size + 1);
+    } else {
+      h_off = (input_height - crop_size) / 2;
+      w_off = (input_width - crop_size) / 2;
+    }
   } else {
-    // we will prefer to use data() first, and then try float_data()
-    if (data.size()) {
-      for (int j = 0; j < size; ++j) {
-        Dtype datum_element =
-            static_cast<Dtype>(static_cast<uint8_t>(data[j]));
-        transformed_data[j + batch_item_id * size] =
-            (datum_element - mean[j]) * scale;
-      }
+    CHECK_EQ(input_height, height);
+    CHECK_EQ(input_width, width);
+  }
+
+  Dtype* input_data = input_blob->mutable_cpu_data();
+  if (has_mean_file) {
+    CHECK_EQ(input_channels, data_mean_.channels());
+    CHECK_EQ(input_height, data_mean_.height());
+    CHECK_EQ(input_width, data_mean_.width());
+    for (int n = 0; n < input_num; ++n) {
+      int offset = input_blob->offset(n);
+      caffe_sub(data_mean_.count(), input_data + offset,
+            data_mean_.cpu_data(), input_data + offset);
+    }
+  }
+
+  if (has_mean_values) {
+    CHECK(mean_values_.size() == 1 || mean_values_.size() == input_channels) <<
+     "Specify either 1 mean_value or as many as channels: " << input_channels;
+    if (mean_values_.size() == 1) {
+      caffe_add_scalar(input_blob->count(), -(mean_values_[0]), input_data);
     } else {
-      for (int j = 0; j < size; ++j) {
-        transformed_data[j + batch_item_id * size] =
-            (datum.float_data(j) - mean[j]) * scale;
+      for (int n = 0; n < input_num; ++n) {
+        for (int c = 0; c < input_channels; ++c) {
+          int offset = input_blob->offset(n, c);
+          caffe_add_scalar(input_height * input_width, -(mean_values_[c]),
+            input_data + offset);
+        }
       }
     }
   }
+
+  Dtype* transformed_data = transformed_blob->mutable_cpu_data();
+
+  for (int n = 0; n < input_num; ++n) {
+    int top_index_n = n * channels;
+    int data_index_n = n * channels;
+    for (int c = 0; c < channels; ++c) {
+      int top_index_c = (top_index_n + c) * height;
+      int data_index_c = (data_index_n + c) * input_height + h_off;
+      for (int h = 0; h < height; ++h) {
+        int top_index_h = (top_index_c + h) * width;
+        int data_index_h = (data_index_c + h) * input_width + w_off;
+        if (do_mirror) {
+          int top_index_w = top_index_h + width - 1;
+          for (int w = 0; w < width; ++w) {
+        	  if (param_.is_flow() && c%2 == 0)
+        		  transformed_data[top_index_w-w] = 255 - input_data[data_index_h + w];
+        	  else
+        		  transformed_data[top_index_w-w] = input_data[data_index_h + w];
+          }
+        } else {
+          for (int w = 0; w < width; ++w) {
+            transformed_data[top_index_h + w] = input_data[data_index_h + w];
+          }
+        }
+      }
+    }
+  }
+  if (scale != Dtype(1)) {
+    DLOG(INFO) << "Scale: " << scale;
+    caffe_scal(size, scale, transformed_data);
+  }
+}
+
+template<typename Dtype>
+vector<int> DataTransformer<Dtype>::InferBlobShape(const Datum& datum) {
+  if (datum.encoded()) {
+    CHECK(!(param_.force_color() && param_.force_gray()))
+        << "cannot set both force_color and force_gray";
+    cv::Mat cv_img;
+    if (param_.force_color() || param_.force_gray()) {
+    // If force_color then decode in color otherwise decode in gray.
+      cv_img = DecodeDatumToCVMat(datum, param_.force_color());
+    } else {
+      cv_img = DecodeDatumToCVMatNative(datum);
+    }
+    // InferBlobShape using the cv::image.
+    return InferBlobShape(cv_img);
+  }
+
+  const int crop_size = param_.crop_size();
+  const int datum_channels = datum.channels();
+  const int datum_height = datum.height();
+  const int datum_width = datum.width();
+  // Check dimensions.
+  CHECK_GT(datum_channels, 0);
+  CHECK_GE(datum_height, crop_size);
+  CHECK_GE(datum_width, crop_size);
+  // Build BlobShape.
+  vector<int> shape(4);
+  shape[0] = 1;
+  shape[1] = datum_channels;
+  shape[2] = (crop_size)? crop_size: datum_height;
+  shape[3] = (crop_size)? crop_size: datum_width;
+  return shape;
+}
+
+template<typename Dtype>
+vector<int> DataTransformer<Dtype>::InferBlobShape(
+    const vector<Datum> & datum_vector) {
+  const int num = datum_vector.size();
+  CHECK_GT(num, 0) << "There is no datum to in the vector";
+  // Use first datum in the vector to InferBlobShape.
+  vector<int> shape = InferBlobShape(datum_vector[0]);
+  // Adjust num to the size of the vector.
+  shape[0] = num;
+  return shape;
+}
+
+template<typename Dtype>
+vector<int> DataTransformer<Dtype>::InferBlobShape(const cv::Mat& cv_img) {
+  const int crop_size = param_.crop_size();
+  const int img_channels = cv_img.channels();
+  const int img_height = cv_img.rows;
+  const int img_width = cv_img.cols;
+  // Check dimensions.
+  CHECK_GT(img_channels, 0);
+  if (!org_size_proc_) {
+    CHECK_GE(img_height, crop_size);
+    CHECK_GE(img_width, crop_size);
+  }
+  // Build BlobShape.
+  vector<int> shape(4);
+  shape[0] = 1;
+  shape[1] = img_channels;
+  shape[2] = (crop_size)? crop_size: img_height;
+  shape[3] = (crop_size)? crop_size: img_width;
+  return shape;
+}
+
+template<typename Dtype>
+vector<int> DataTransformer<Dtype>::InferBlobShape(
+    const vector<cv::Mat> & mat_vector) {
+  const int num = mat_vector.size();
+  CHECK_GT(num, 0) << "There is no cv_img to in the vector";
+  // Use first cv_img in the vector to InferBlobShape.
+  vector<int> shape = InferBlobShape(mat_vector[0]);
+  // Adjust num to the size of the vector.
+  shape[0] = num;
+  return shape;
 }
 
 template <typename Dtype>
 void DataTransformer<Dtype>::InitRand() {
-  const bool needs_rand = (phase_ == Caffe::TRAIN) &&
-      (param_.mirror() || param_.crop_size());
+  const bool needs_rand = param_.mirror() ||
+      (phase_ == TRAIN && param_.crop_size());
   if (needs_rand) {
-    const unsigned int rng_seed = caffe_rng_rand();
+    const unsigned int rng_seed =caffe_rng_rand();
     rng_.reset(new Caffe::RNG(rng_seed));
   } else {
     rng_.reset();
@@ -99,11 +801,12 @@ void DataTransformer<Dtype>::InitRand() {
 }
 
 template <typename Dtype>
-unsigned int DataTransformer<Dtype>::Rand() {
+int DataTransformer<Dtype>::Rand(int n) {
   CHECK(rng_);
+  CHECK_GT(n, 0);
   caffe::rng_t* rng =
       static_cast<caffe::rng_t*>(rng_->generator());
-  return (*rng)();
+  return ((*rng)() % n);
 }
 
 INSTANTIATE_CLASS(DataTransformer);
diff --git a/src/caffe/internal_thread.cpp b/src/caffe/internal_thread.cpp
index d7b6ae206cf..c2d19d433b4 100644
--- a/src/caffe/internal_thread.cpp
+++ b/src/caffe/internal_thread.cpp
@@ -1,23 +1,24 @@
+#include <boost/thread.hpp>
 #include "caffe/internal_thread.hpp"
 
-#include "caffe/util/thread.hpp"
-
 namespace caffe {
 
 InternalThread::~InternalThread() {
   WaitForInternalThreadToExit();
-  if (thread_ != NULL) {
-    delete thread_;
-  }
 }
 
+bool InternalThread::is_started() const {
+  return thread_.get() != NULL && thread_->joinable();
+}
+
+
 bool InternalThread::StartInternalThread() {
   if (!WaitForInternalThreadToExit()) {
     return false;
   }
   try {
-    thread_ = new caffe::Thread
-        (&InternalThread::InternalThreadEntry, this);
+    thread_.reset(
+        new boost::thread(&InternalThread::InternalThreadEntry, this));
   } catch (...) {
     return false;
   }
diff --git a/src/caffe/layer_factory.cpp b/src/caffe/layer_factory.cpp
index b78167f21eb..fd3659bfa64 100644
--- a/src/caffe/layer_factory.cpp
+++ b/src/caffe/layer_factory.cpp
@@ -1,17 +1,23 @@
 #include <string>
 
 #include "caffe/layer.hpp"
+#include "caffe/layer_factory.hpp"
 #include "caffe/proto/caffe.pb.h"
 #include "caffe/vision_layers.hpp"
 
-namespace caffe {
+#ifdef WITH_PYTHON_LAYER
+#include "caffe/python_layer.hpp"
+#endif
+
+#ifdef USE_CUDNN
+#include "caffe/util/cudnn.hpp"
+#endif
 
-// GetLayer() defines the overall layer factory. The Get*Layer() functions
-// define factories for layers with multiple computational engines.
+namespace caffe {
 
 // Get convolution layer according to engine.
 template <typename Dtype>
-ConvolutionLayer<Dtype>* GetConvolutionLayer(const string& name,
+shared_ptr<Layer<Dtype> > GetConvolutionLayer(
     const LayerParameter& param) {
   ConvolutionParameter_Engine engine = param.convolution_param().engine();
   if (engine == ConvolutionParameter_Engine_DEFAULT) {
@@ -21,25 +27,21 @@ ConvolutionLayer<Dtype>* GetConvolutionLayer(const string& name,
 #endif
   }
   if (engine == ConvolutionParameter_Engine_CAFFE) {
-    return new ConvolutionLayer<Dtype>(param);
+    return shared_ptr<Layer<Dtype> >(new ConvolutionLayer<Dtype>(param));
 #ifdef USE_CUDNN
   } else if (engine == ConvolutionParameter_Engine_CUDNN) {
-    return new CuDNNConvolutionLayer<Dtype>(param);
+    return shared_ptr<Layer<Dtype> >(new CuDNNConvolutionLayer<Dtype>(param));
 #endif
   } else {
-    LOG(FATAL) << "Layer " << name << " has unknown engine.";
+    LOG(FATAL) << "Layer " << param.name() << " has unknown engine.";
   }
 }
 
-template ConvolutionLayer<float>* GetConvolutionLayer(const string& name,
-    const LayerParameter& param);
-template ConvolutionLayer<double>* GetConvolutionLayer(const string& name,
-    const LayerParameter& param);
+REGISTER_LAYER_CREATOR(Convolution, GetConvolutionLayer);
 
 // Get pooling layer according to engine.
 template <typename Dtype>
-PoolingLayer<Dtype>* GetPoolingLayer(const string& name,
-    const LayerParameter& param) {
+shared_ptr<Layer<Dtype> > GetPoolingLayer(const LayerParameter& param) {
   PoolingParameter_Engine engine = param.pooling_param().engine();
   if (engine == PoolingParameter_Engine_DEFAULT) {
     engine = PoolingParameter_Engine_CAFFE;
@@ -48,25 +50,28 @@ PoolingLayer<Dtype>* GetPoolingLayer(const string& name,
 #endif
   }
   if (engine == PoolingParameter_Engine_CAFFE) {
-    return new PoolingLayer<Dtype>(param);
+    return shared_ptr<Layer<Dtype> >(new PoolingLayer<Dtype>(param));
 #ifdef USE_CUDNN
   } else if (engine == PoolingParameter_Engine_CUDNN) {
-    return new CuDNNPoolingLayer<Dtype>(param);
+    PoolingParameter p_param = param.pooling_param();
+    if (p_param.pad() || p_param.pad_h() || p_param.pad_w() ||
+        param.top_size() > 1) {
+      LOG(INFO) << "CUDNN does not support padding or multiple tops. "
+                << "Using Caffe's own pooling layer.";
+      return shared_ptr<Layer<Dtype> >(new PoolingLayer<Dtype>(param));
+    }
+    return shared_ptr<Layer<Dtype> >(new CuDNNPoolingLayer<Dtype>(param));
 #endif
   } else {
-    LOG(FATAL) << "Layer " << name << " has unknown engine.";
+    LOG(FATAL) << "Layer " << param.name() << " has unknown engine.";
   }
 }
 
-template PoolingLayer<float>* GetPoolingLayer(const string& name,
-    const LayerParameter& param);
-template PoolingLayer<double>* GetPoolingLayer(const string& name,
-    const LayerParameter& param);
+REGISTER_LAYER_CREATOR(Pooling, GetPoolingLayer);
 
 // Get relu layer according to engine.
 template <typename Dtype>
-ReLULayer<Dtype>* GetReLULayer(const string& name,
-    const LayerParameter& param) {
+shared_ptr<Layer<Dtype> > GetReLULayer(const LayerParameter& param) {
   ReLUParameter_Engine engine = param.relu_param().engine();
   if (engine == ReLUParameter_Engine_DEFAULT) {
     engine = ReLUParameter_Engine_CAFFE;
@@ -75,25 +80,21 @@ ReLULayer<Dtype>* GetReLULayer(const string& name,
 #endif
   }
   if (engine == ReLUParameter_Engine_CAFFE) {
-    return new ReLULayer<Dtype>(param);
+    return shared_ptr<Layer<Dtype> >(new ReLULayer<Dtype>(param));
 #ifdef USE_CUDNN
   } else if (engine == ReLUParameter_Engine_CUDNN) {
-    return new CuDNNReLULayer<Dtype>(param);
+    return shared_ptr<Layer<Dtype> >(new CuDNNReLULayer<Dtype>(param));
 #endif
   } else {
-    LOG(FATAL) << "Layer " << name << " has unknown engine.";
+    LOG(FATAL) << "Layer " << param.name() << " has unknown engine.";
   }
 }
 
-template ReLULayer<float>* GetReLULayer(const string& name,
-    const LayerParameter& param);
-template ReLULayer<double>* GetReLULayer(const string& name,
-    const LayerParameter& param);
+REGISTER_LAYER_CREATOR(ReLU, GetReLULayer);
 
 // Get sigmoid layer according to engine.
 template <typename Dtype>
-SigmoidLayer<Dtype>* GetSigmoidLayer(const string& name,
-    const LayerParameter& param) {
+shared_ptr<Layer<Dtype> > GetSigmoidLayer(const LayerParameter& param) {
   SigmoidParameter_Engine engine = param.sigmoid_param().engine();
   if (engine == SigmoidParameter_Engine_DEFAULT) {
     engine = SigmoidParameter_Engine_CAFFE;
@@ -102,25 +103,44 @@ SigmoidLayer<Dtype>* GetSigmoidLayer(const string& name,
 #endif
   }
   if (engine == SigmoidParameter_Engine_CAFFE) {
-    return new SigmoidLayer<Dtype>(param);
+    return shared_ptr<Layer<Dtype> >(new SigmoidLayer<Dtype>(param));
 #ifdef USE_CUDNN
   } else if (engine == SigmoidParameter_Engine_CUDNN) {
-    return new CuDNNSigmoidLayer<Dtype>(param);
+    return shared_ptr<Layer<Dtype> >(new CuDNNSigmoidLayer<Dtype>(param));
 #endif
   } else {
-    LOG(FATAL) << "Layer " << name << " has unknown engine.";
+    LOG(FATAL) << "Layer " << param.name() << " has unknown engine.";
   }
 }
 
-template SigmoidLayer<float>* GetSigmoidLayer(const string& name,
-    const LayerParameter& param);
-template SigmoidLayer<double>* GetSigmoidLayer(const string& name,
-    const LayerParameter& param);
+REGISTER_LAYER_CREATOR(Sigmoid, GetSigmoidLayer);
+
+// Get softmax layer according to engine.
+template <typename Dtype>
+shared_ptr<Layer<Dtype> > GetSoftmaxLayer(const LayerParameter& param) {
+  SoftmaxParameter_Engine engine = param.softmax_param().engine();
+  if (engine == SoftmaxParameter_Engine_DEFAULT) {
+    engine = SoftmaxParameter_Engine_CAFFE;
+#ifdef USE_CUDNN
+    engine = SoftmaxParameter_Engine_CUDNN;
+#endif
+  }
+  if (engine == SoftmaxParameter_Engine_CAFFE) {
+    return shared_ptr<Layer<Dtype> >(new SoftmaxLayer<Dtype>(param));
+#ifdef USE_CUDNN
+  } else if (engine == SoftmaxParameter_Engine_CUDNN) {
+    return shared_ptr<Layer<Dtype> >(new CuDNNSoftmaxLayer<Dtype>(param));
+#endif
+  } else {
+    LOG(FATAL) << "Layer " << param.name() << " has unknown engine.";
+  }
+}
+
+REGISTER_LAYER_CREATOR(Softmax, GetSoftmaxLayer);
 
 // Get tanh layer according to engine.
 template <typename Dtype>
-TanHLayer<Dtype>* GetTanHLayer(const string& name,
-    const LayerParameter& param) {
+shared_ptr<Layer<Dtype> > GetTanHLayer(const LayerParameter& param) {
   TanHParameter_Engine engine = param.tanh_param().engine();
   if (engine == TanHParameter_Engine_DEFAULT) {
     engine = TanHParameter_Engine_CAFFE;
@@ -129,138 +149,77 @@ TanHLayer<Dtype>* GetTanHLayer(const string& name,
 #endif
   }
   if (engine == TanHParameter_Engine_CAFFE) {
-    return new TanHLayer<Dtype>(param);
+    return shared_ptr<Layer<Dtype> >(new TanHLayer<Dtype>(param));
 #ifdef USE_CUDNN
   } else if (engine == TanHParameter_Engine_CUDNN) {
-    return new CuDNNTanHLayer<Dtype>(param);
+    return shared_ptr<Layer<Dtype> >(new CuDNNTanHLayer<Dtype>(param));
 #endif
   } else {
-    LOG(FATAL) << "Layer " << name << " has unknown engine.";
+    LOG(FATAL) << "Layer " << param.name() << " has unknown engine.";
   }
 }
 
-template TanHLayer<float>* GetTanHLayer(const string& name,
-    const LayerParameter& param);
-template TanHLayer<double>* GetTanHLayer(const string& name,
-    const LayerParameter& param);
+REGISTER_LAYER_CREATOR(TanH, GetTanHLayer);
 
-// Get softmax layer according to engine.
+// Get bn layer according to engine.
 template <typename Dtype>
-SoftmaxLayer<Dtype>* GetSoftmaxLayer(const string& name,
-    const LayerParameter& param) {
-  SoftmaxParameter_Engine engine = param.softmax_param().engine();
-  if (engine == SoftmaxParameter_Engine_DEFAULT) {
-    engine = SoftmaxParameter_Engine_CAFFE;
-#ifdef USE_CUDNN
-    engine = SoftmaxParameter_Engine_CUDNN;
+shared_ptr<Layer<Dtype> > GetBNLayer(const LayerParameter& param) {
+  BNParameter_Engine engine = param.bn_param().engine();
+  if (engine == BNParameter_Engine_DEFAULT) {
+    engine = BNParameter_Engine_CAFFE;
+#if defined(USE_CUDNN)
+#if CUDNN_VERSION_MIN(5, 0, 0)
+     engine = BNParameter_Engine_CUDNN;
+#endif
 #endif
   }
-  if (engine == SoftmaxParameter_Engine_CAFFE) {
-    return new SoftmaxLayer<Dtype>(param);
-#ifdef USE_CUDNN
-  } else if (engine == SoftmaxParameter_Engine_CUDNN) {
-    return new CuDNNSoftmaxLayer<Dtype>(param);
+  if (engine == BNParameter_Engine_CAFFE) {
+    LOG(INFO) << "Layer " << param.name() << " is using CAFFE engine.";
+    return shared_ptr<Layer<Dtype> >(new BNLayer<Dtype>(param));
+#if defined(USE_CUDNN)
+#if CUDNN_VERSION_MIN(5, 0, 0)
+  } else if (engine == BNParameter_Engine_CUDNN) {
+    LOG(INFO) << "Layer " << param.name() << " is using CUDNN engine.";
+    return shared_ptr<Layer<Dtype> >(new CuDNNBNLayer<Dtype>(param));
+#endif
 #endif
   } else {
-    LOG(FATAL) << "Layer " << name << " has unknown engine.";
+    LOG(FATAL) << "Layer " << param.name() << " calls cuDNN engine, but cuDNN version higher than 5.0 is not found";
   }
 }
 
-template SoftmaxLayer<float>* GetSoftmaxLayer(const string& name,
-    const LayerParameter& param);
-template SoftmaxLayer<double>* GetSoftmaxLayer(const string& name,
-    const LayerParameter& param);
+REGISTER_LAYER_CREATOR(BN, GetBNLayer);
 
-// A function to get a specific layer from the specification given in
-// LayerParameter. Ideally this would be replaced by a factory pattern,
-// but we will leave it this way for now.
+#ifdef WITH_PYTHON_LAYER
+PyThreadState* tstate = NULL;
 template <typename Dtype>
-Layer<Dtype>* GetLayer(const LayerParameter& param) {
-  const string& name = param.name();
-  const LayerParameter_LayerType& type = param.type();
-  switch (type) {
-  case LayerParameter_LayerType_ACCURACY:
-    return new AccuracyLayer<Dtype>(param);
-  case LayerParameter_LayerType_ABSVAL:
-    return new AbsValLayer<Dtype>(param);
-  case LayerParameter_LayerType_ARGMAX:
-    return new ArgMaxLayer<Dtype>(param);
-  case LayerParameter_LayerType_BNLL:
-    return new BNLLLayer<Dtype>(param);
-  case LayerParameter_LayerType_CONCAT:
-    return new ConcatLayer<Dtype>(param);
-  case LayerParameter_LayerType_CONTRASTIVE_LOSS:
-    return new ContrastiveLossLayer<Dtype>(param);
-  case LayerParameter_LayerType_CONVOLUTION:
-    return GetConvolutionLayer<Dtype>(name, param);
-  case LayerParameter_LayerType_DATA:
-    return new DataLayer<Dtype>(param);
-  case LayerParameter_LayerType_DROPOUT:
-    return new DropoutLayer<Dtype>(param);
-  case LayerParameter_LayerType_DUMMY_DATA:
-    return new DummyDataLayer<Dtype>(param);
-  case LayerParameter_LayerType_EUCLIDEAN_LOSS:
-    return new EuclideanLossLayer<Dtype>(param);
-  case LayerParameter_LayerType_ELTWISE:
-    return new EltwiseLayer<Dtype>(param);
-  case LayerParameter_LayerType_FLATTEN:
-    return new FlattenLayer<Dtype>(param);
-  case LayerParameter_LayerType_HDF5_DATA:
-    return new HDF5DataLayer<Dtype>(param);
-  case LayerParameter_LayerType_HDF5_OUTPUT:
-    return new HDF5OutputLayer<Dtype>(param);
-  case LayerParameter_LayerType_HINGE_LOSS:
-    return new HingeLossLayer<Dtype>(param);
-  case LayerParameter_LayerType_IMAGE_DATA:
-    return new ImageDataLayer<Dtype>(param);
-  case LayerParameter_LayerType_IM2COL:
-    return new Im2colLayer<Dtype>(param);
-  case LayerParameter_LayerType_INFOGAIN_LOSS:
-    return new InfogainLossLayer<Dtype>(param);
-  case LayerParameter_LayerType_INNER_PRODUCT:
-    return new InnerProductLayer<Dtype>(param);
-  case LayerParameter_LayerType_LRN:
-    return new LRNLayer<Dtype>(param);
-  case LayerParameter_LayerType_MEMORY_DATA:
-    return new MemoryDataLayer<Dtype>(param);
-  case LayerParameter_LayerType_MVN:
-    return new MVNLayer<Dtype>(param);
-  case LayerParameter_LayerType_MULTINOMIAL_LOGISTIC_LOSS:
-    return new MultinomialLogisticLossLayer<Dtype>(param);
-  case LayerParameter_LayerType_POOLING:
-    return GetPoolingLayer<Dtype>(name, param);
-  case LayerParameter_LayerType_POWER:
-    return new PowerLayer<Dtype>(param);
-  case LayerParameter_LayerType_RELU:
-    return GetReLULayer<Dtype>(name, param);
-  case LayerParameter_LayerType_SILENCE:
-    return new SilenceLayer<Dtype>(param);
-  case LayerParameter_LayerType_SIGMOID:
-    return GetSigmoidLayer<Dtype>(name, param);
-  case LayerParameter_LayerType_SIGMOID_CROSS_ENTROPY_LOSS:
-    return new SigmoidCrossEntropyLossLayer<Dtype>(param);
-  case LayerParameter_LayerType_SLICE:
-    return new SliceLayer<Dtype>(param);
-  case LayerParameter_LayerType_SOFTMAX:
-    return GetSoftmaxLayer<Dtype>(name, param);
-  case LayerParameter_LayerType_SOFTMAX_LOSS:
-    return new SoftmaxWithLossLayer<Dtype>(param);
-  case LayerParameter_LayerType_SPLIT:
-    return new SplitLayer<Dtype>(param);
-  case LayerParameter_LayerType_TANH:
-    return GetTanHLayer<Dtype>(name, param);
-  case LayerParameter_LayerType_WINDOW_DATA:
-    return new WindowDataLayer<Dtype>(param);
-  case LayerParameter_LayerType_NONE:
-    LOG(FATAL) << "Layer " << name << " has unspecified type.";
-  default:
-    LOG(FATAL) << "Layer " << name << " has unknown type " << type;
+shared_ptr<Layer<Dtype> > GetPythonLayer(const LayerParameter& param) {
+  boost::lock_guard<boost::mutex> lock(mtx_);
+  if (Caffe::py_tstate() == NULL){
+    Py_Initialize();
+    PyEval_InitThreads();
+    PyThreadState* tstate = PyEval_SaveThread();
+    Caffe::set_py_tstate(tstate);
+  }
+  PyGILState_STATE state;
+  state = PyGILState_Ensure();
+  try {
+
+    bp::object module = bp::import(param.python_param().module().c_str());
+    bp::object layer = module.attr(param.python_param().layer().c_str())(param);
+    return bp::extract<shared_ptr<PythonLayer<Dtype> > >(layer)();
+  } catch (bp::error_already_set) {
+    PyErr_Print();
+    throw;
   }
-  // just to suppress old compiler warnings.
-  return (Layer<Dtype>*)(NULL);
+  PyGILState_Release(state);
 }
 
-template Layer<float>* GetLayer(const LayerParameter& param);
-template Layer<double>* GetLayer(const LayerParameter& param);
+REGISTER_LAYER_CREATOR(Python, GetPythonLayer);
+
+boost::mutex mtx_;
+#endif
 
+// Layers that use their constructor as their default creator should be
+// registered in their corresponding cpp files. Do not register them here.
 }  // namespace caffe
diff --git a/src/caffe/layers/absval_layer.cpp b/src/caffe/layers/absval_layer.cpp
index ce9d05cc764..5ce28c9e2b4 100644
--- a/src/caffe/layers/absval_layer.cpp
+++ b/src/caffe/layers/absval_layer.cpp
@@ -8,30 +8,29 @@ namespace caffe {
 
 template <typename Dtype>
 void AbsValLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
+      const vector<Blob<Dtype>*>& top) {
   NeuronLayer<Dtype>::LayerSetUp(bottom, top);
-  CHECK_NE((*top)[0], bottom[0]) << this->type_name() << " Layer does not "
+  CHECK_NE(top[0], bottom[0]) << this->type() << " Layer does not "
     "allow in-place computation.";
 }
 
 template <typename Dtype>
 void AbsValLayer<Dtype>::Forward_cpu(
-    const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
-  const int count = (*top)[0]->count();
-  Dtype* top_data = (*top)[0]->mutable_cpu_data();
+    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
+  const int count = top[0]->count();
+  Dtype* top_data = top[0]->mutable_cpu_data();
   caffe_abs(count, bottom[0]->cpu_data(), top_data);
 }
 
 template <typename Dtype>
 void AbsValLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
-    const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
+    const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
   const int count = top[0]->count();
-  const Dtype* top_data = top[0]->cpu_data();
   const Dtype* top_diff = top[0]->cpu_diff();
   if (propagate_down[0]) {
-    const Dtype* bottom_data = (*bottom)[0]->cpu_data();
-    Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
-    caffe_div(count, top_data, bottom_data, bottom_diff);
+    const Dtype* bottom_data = bottom[0]->cpu_data();
+    Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
+    caffe_cpu_sign(count, bottom_data, bottom_diff);
     caffe_mul(count, bottom_diff, top_diff, bottom_diff);
   }
 }
@@ -41,6 +40,6 @@ STUB_GPU(AbsValLayer);
 #endif
 
 INSTANTIATE_CLASS(AbsValLayer);
-
+REGISTER_LAYER_CLASS(AbsVal);
 
 }  // namespace caffe
diff --git a/src/caffe/layers/absval_layer.cu b/src/caffe/layers/absval_layer.cu
index 46778aa79b1..91f3c77fe9a 100644
--- a/src/caffe/layers/absval_layer.cu
+++ b/src/caffe/layers/absval_layer.cu
@@ -8,27 +8,27 @@ namespace caffe {
 
 template <typename Dtype>
 void AbsValLayer<Dtype>::Forward_gpu(
-    const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
-  const int count = (*top)[0]->count();
-  Dtype* top_data = (*top)[0]->mutable_gpu_data();
+    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
+  const int count = top[0]->count();
+  Dtype* top_data = top[0]->mutable_gpu_data();
   caffe_gpu_abs(count, bottom[0]->gpu_data(), top_data);
 }
 
 template <typename Dtype>
 void AbsValLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
-    const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
+    const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
   const int count = top[0]->count();
   const Dtype* top_data = top[0]->gpu_data();
   const Dtype* top_diff = top[0]->gpu_diff();
   if (propagate_down[0]) {
-    const Dtype* bottom_data = (*bottom)[0]->gpu_data();
-    Dtype* bottom_diff = (*bottom)[0]->mutable_gpu_diff();
-    caffe_gpu_div(count, top_data, bottom_data, bottom_diff);
+    const Dtype* bottom_data = bottom[0]->gpu_data();
+    Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
+    caffe_gpu_sign(count, bottom_data, bottom_diff);
     caffe_gpu_mul(count, bottom_diff, top_diff, bottom_diff);
   }
 }
 
-INSTANTIATE_CLASS(AbsValLayer);
+INSTANTIATE_LAYER_GPU_FUNCS(AbsValLayer);
 
 
 }  // namespace caffe
diff --git a/src/caffe/layers/accuracy_layer.cpp b/src/caffe/layers/accuracy_layer.cpp
index 3e69bc84faa..90aad675ed3 100644
--- a/src/caffe/layers/accuracy_layer.cpp
+++ b/src/caffe/layers/accuracy_layer.cpp
@@ -12,57 +12,80 @@ namespace caffe {
 
 template <typename Dtype>
 void AccuracyLayer<Dtype>::LayerSetUp(
-  const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
+  const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
   top_k_ = this->layer_param_.accuracy_param().top_k();
+
+  has_ignore_label_ =
+    this->layer_param_.accuracy_param().has_ignore_label();
+  if (has_ignore_label_) {
+    ignore_label_ = this->layer_param_.accuracy_param().ignore_label();
+  }
 }
 
 template <typename Dtype>
 void AccuracyLayer<Dtype>::Reshape(
-  const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
-  CHECK_EQ(bottom[0]->num(), bottom[1]->num())
-      << "The data and label should have the same number.";
-  CHECK_LE(top_k_, bottom[0]->count() / bottom[0]->num())
+  const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
+  CHECK_LE(top_k_, bottom[0]->count() / bottom[1]->count())
       << "top_k must be less than or equal to the number of classes.";
-  CHECK_EQ(bottom[1]->channels(), 1);
-  CHECK_EQ(bottom[1]->height(), 1);
-  CHECK_EQ(bottom[1]->width(), 1);
-  (*top)[0]->Reshape(1, 1, 1, 1);
+  label_axis_ =
+      bottom[0]->CanonicalAxisIndex(this->layer_param_.accuracy_param().axis());
+  outer_num_ = bottom[0]->count(0, label_axis_);
+  inner_num_ = bottom[0]->count(label_axis_ + 1);
+  CHECK_EQ(outer_num_ * inner_num_, bottom[1]->count())
+      << "Number of labels must match number of predictions; "
+      << "e.g., if label axis == 1 and prediction shape is (N, C, H, W), "
+      << "label count (number of labels) must be N*H*W, "
+      << "with integer values in {0, 1, ..., C-1}.";
+  vector<int> top_shape(0);  // Accuracy is a scalar; 0 axes.
+  top[0]->Reshape(top_shape);
 }
 
 template <typename Dtype>
 void AccuracyLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-    vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& top) {
   Dtype accuracy = 0;
   const Dtype* bottom_data = bottom[0]->cpu_data();
   const Dtype* bottom_label = bottom[1]->cpu_data();
-  int num = bottom[0]->num();
-  int dim = bottom[0]->count() / bottom[0]->num();
+  const int dim = bottom[0]->count() / outer_num_;
+  const int num_labels = bottom[0]->shape(label_axis_);
   vector<Dtype> maxval(top_k_+1);
   vector<int> max_id(top_k_+1);
-  for (int i = 0; i < num; ++i) {
-    // Top-k accuracy
-    std::vector<std::pair<Dtype, int> > bottom_data_vector;
-    for (int j = 0; j < dim; ++j) {
-      bottom_data_vector.push_back(
-          std::make_pair(bottom_data[i * dim + j], j));
-    }
-    std::partial_sort(
-        bottom_data_vector.begin(), bottom_data_vector.begin() + top_k_,
-        bottom_data_vector.end(), std::greater<std::pair<Dtype, int> >());
-    // check if true label is in top k predictions
-    for (int k = 0; k < top_k_; k++) {
-      if (bottom_data_vector[k].second == static_cast<int>(bottom_label[i])) {
-        ++accuracy;
-        break;
+  int count = 0;
+  for (int i = 0; i < outer_num_; ++i) {
+    for (int j = 0; j < inner_num_; ++j) {
+      const int label_value =
+          static_cast<int>(bottom_label[i * inner_num_ + j]);
+      if (has_ignore_label_ && label_value == ignore_label_) {
+        continue;
+      }
+      DCHECK_GE(label_value, 0);
+      DCHECK_LT(label_value, num_labels);
+      // Top-k accuracy
+      std::vector<std::pair<Dtype, int> > bottom_data_vector;
+      for (int k = 0; k < num_labels; ++k) {
+        bottom_data_vector.push_back(std::make_pair(
+            bottom_data[i * dim + k * inner_num_ + j], k));
+      }
+      std::partial_sort(
+          bottom_data_vector.begin(), bottom_data_vector.begin() + top_k_,
+          bottom_data_vector.end(), std::greater<std::pair<Dtype, int> >());
+      // check if true label is in top k predictions
+      for (int k = 0; k < top_k_; k++) {
+        if (bottom_data_vector[k].second == label_value) {
+          ++accuracy;
+          break;
+        }
       }
+      ++count;
     }
   }
 
   // LOG(INFO) << "Accuracy: " << accuracy;
-  (*top)[0]->mutable_cpu_data()[0] = accuracy / num;
+  top[0]->mutable_cpu_data()[0] = accuracy / count;
   // Accuracy layer should not be used as a loss function.
 }
 
 INSTANTIATE_CLASS(AccuracyLayer);
+REGISTER_LAYER_CLASS(Accuracy);
 
 }  // namespace caffe
diff --git a/src/caffe/layers/argmax_layer.cpp b/src/caffe/layers/argmax_layer.cpp
index 0d1a107257b..c4040cdcaaa 100644
--- a/src/caffe/layers/argmax_layer.cpp
+++ b/src/caffe/layers/argmax_layer.cpp
@@ -10,7 +10,7 @@ namespace caffe {
 
 template <typename Dtype>
 void ArgMaxLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
+      const vector<Blob<Dtype>*>& top) {
   out_max_val_ = this->layer_param_.argmax_param().out_max_val();
   top_k_ = this->layer_param_.argmax_param().top_k();
   CHECK_GE(top_k_, 1) << " top k must not be less than 1.";
@@ -20,21 +20,21 @@ void ArgMaxLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 void ArgMaxLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
+      const vector<Blob<Dtype>*>& top) {
   if (out_max_val_) {
     // Produces max_ind and max_val
-    (*top)[0]->Reshape(bottom[0]->num(), 2, top_k_, 1);
+    top[0]->Reshape(bottom[0]->num(), 2, top_k_, 1);
   } else {
     // Produces only max_ind
-    (*top)[0]->Reshape(bottom[0]->num(), 1, top_k_, 1);
+    top[0]->Reshape(bottom[0]->num(), 1, top_k_, 1);
   }
 }
 
 template <typename Dtype>
 void ArgMaxLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-    vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& top) {
   const Dtype* bottom_data = bottom[0]->cpu_data();
-  Dtype* top_data = (*top)[0]->mutable_cpu_data();
+  Dtype* top_data = top[0]->mutable_cpu_data();
   int num = bottom[0]->num();
   int dim = bottom[0]->count() / bottom[0]->num();
   for (int i = 0; i < num; ++i) {
@@ -47,16 +47,17 @@ void ArgMaxLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
         bottom_data_vector.begin(), bottom_data_vector.begin() + top_k_,
         bottom_data_vector.end(), std::greater<std::pair<Dtype, int> >());
     for (int j = 0; j < top_k_; ++j) {
-      top_data[(*top)[0]->offset(i, 0, j)] = bottom_data_vector[j].second;
+      top_data[top[0]->offset(i, 0, j)] = bottom_data_vector[j].second;
     }
     if (out_max_val_) {
       for (int j = 0; j < top_k_; ++j) {
-        top_data[(*top)[0]->offset(i, 1, j)] = bottom_data_vector[j].first;
+        top_data[top[0]->offset(i, 1, j)] = bottom_data_vector[j].first;
       }
     }
   }
 }
 
 INSTANTIATE_CLASS(ArgMaxLayer);
+REGISTER_LAYER_CLASS(ArgMax);
 
 }  // namespace caffe
diff --git a/src/caffe/layers/base_conv_layer.cpp b/src/caffe/layers/base_conv_layer.cpp
new file mode 100644
index 00000000000..ccb3adc7e89
--- /dev/null
+++ b/src/caffe/layers/base_conv_layer.cpp
@@ -0,0 +1,298 @@
+#include <vector>
+
+#include "caffe/filler.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/util/im2col.hpp"
+#include "caffe/util/math_functions.hpp"
+#include "caffe/vision_layers.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void BaseConvolutionLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+  CHECK_EQ(4, bottom[0]->num_axes()) << "Input must have 4 axes, "
+      << "corresponding to (num, channels, height, width)";
+  // Configure the kernel size, padding, stride, and inputs.
+  ConvolutionParameter conv_param = this->layer_param_.convolution_param();
+  CHECK(!conv_param.has_kernel_size() !=
+      !(conv_param.has_kernel_h() && conv_param.has_kernel_w()))
+      << "Filter size is kernel_size OR kernel_h and kernel_w; not both";
+  CHECK(conv_param.has_kernel_size() ||
+      (conv_param.has_kernel_h() && conv_param.has_kernel_w()))
+      << "For non-square filters both kernel_h and kernel_w are required.";
+  CHECK((!conv_param.has_pad() && conv_param.has_pad_h()
+      && conv_param.has_pad_w())
+      || (!conv_param.has_pad_h() && !conv_param.has_pad_w()))
+      << "pad is pad OR pad_h and pad_w are required.";
+  CHECK((!conv_param.has_stride() && conv_param.has_stride_h()
+      && conv_param.has_stride_w())
+      || (!conv_param.has_stride_h() && !conv_param.has_stride_w()))
+      << "Stride is stride OR stride_h and stride_w are required.";
+  if (conv_param.has_kernel_size()) {
+    kernel_h_ = kernel_w_ = conv_param.kernel_size();
+  } else {
+    kernel_h_ = conv_param.kernel_h();
+    kernel_w_ = conv_param.kernel_w();
+  }
+  CHECK_GT(kernel_h_, 0) << "Filter dimensions cannot be zero.";
+  CHECK_GT(kernel_w_, 0) << "Filter dimensions cannot be zero.";
+  if (!conv_param.has_pad_h()) {
+    pad_h_ = pad_w_ = conv_param.pad();
+  } else {
+    pad_h_ = conv_param.pad_h();
+    pad_w_ = conv_param.pad_w();
+  }
+  if (!conv_param.has_stride_h()) {
+    stride_h_ = stride_w_ = conv_param.stride();
+  } else {
+    stride_h_ = conv_param.stride_h();
+    stride_w_ = conv_param.stride_w();
+  }
+  // Special case: im2col is the identity for 1x1 convolution with stride 1
+  // and no padding, so flag for skipping the buffer and transformation.
+  is_1x1_ = kernel_w_ == 1 && kernel_h_ == 1
+      && stride_h_ == 1 && stride_w_ == 1 && pad_h_ == 0 && pad_w_ == 0;
+  // Configure output channels and groups.
+  channels_ = bottom[0]->channels();
+  num_output_ = this->layer_param_.convolution_param().num_output();
+  CHECK_GT(num_output_, 0);
+  group_ = this->layer_param_.convolution_param().group();
+  CHECK_EQ(channels_ % group_, 0);
+  CHECK_EQ(num_output_ % group_, 0)
+      << "Number of output should be multiples of group.";
+  if (reverse_dimensions()) {
+    conv_out_channels_ = channels_;
+    conv_in_channels_ = num_output_;
+  } else {
+    conv_out_channels_ = num_output_;
+    conv_in_channels_ = channels_;
+  }
+  // Handle the parameters: weights and biases.
+  // - blobs_[0] holds the filter weights
+  // - blobs_[1] holds the biases (optional)
+  bias_term_ = this->layer_param_.convolution_param().bias_term();
+  if (this->blobs_.size() > 0) {
+    LOG(INFO) << "Skipping parameter initialization";
+  } else {
+    if (bias_term_) {
+      this->blobs_.resize(2);
+    } else {
+      this->blobs_.resize(1);
+    }
+    // Initialize and fill the weights:
+    // output channels x input channels per-group x kernel height x kernel width
+    this->blobs_[0].reset(new Blob<Dtype>(
+        conv_out_channels_, conv_in_channels_ / group_, kernel_h_, kernel_w_));
+    shared_ptr<Filler<Dtype> > weight_filler(GetFiller<Dtype>(
+        this->layer_param_.convolution_param().weight_filler()));
+    weight_filler->Fill(this->blobs_[0].get());
+    // If necessary, initialize and fill the biases.
+    if (bias_term_) {
+      vector<int> bias_shape(1, num_output_);
+      this->blobs_[1].reset(new Blob<Dtype>(bias_shape));
+      shared_ptr<Filler<Dtype> > bias_filler(GetFiller<Dtype>(
+          this->layer_param_.convolution_param().bias_filler()));
+      bias_filler->Fill(this->blobs_[1].get());
+    }
+  }
+  // Propagate gradients to the parameters (as directed by backward pass).
+  this->param_propagate_down_.resize(this->blobs_.size(), true);
+}
+
+template <typename Dtype>
+void BaseConvolutionLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+  CHECK_EQ(4, bottom[0]->num_axes()) << "Input must have 4 axes, "
+      << "corresponding to (num, channels, height, width)";
+  num_ = bottom[0]->num();
+  height_ = bottom[0]->height();
+  width_ = bottom[0]->width();
+  CHECK_EQ(bottom[0]->channels(), channels_) << "Input size incompatible with"
+    " convolution kernel.";
+  // TODO: generalize to handle inputs of different shapes.
+  for (int bottom_id = 1; bottom_id < bottom.size(); ++bottom_id) {
+    CHECK_EQ(num_, bottom[bottom_id]->num()) << "Inputs must have same num.";
+    CHECK_EQ(channels_, bottom[bottom_id]->channels())
+        << "Inputs must have same channels.";
+    CHECK_EQ(height_, bottom[bottom_id]->height())
+        << "Inputs must have same height.";
+    CHECK_EQ(width_, bottom[bottom_id]->width())
+        << "Inputs must have same width.";
+  }
+  // Shape the tops.
+  compute_output_shape();
+  for (int top_id = 0; top_id < top.size(); ++top_id) {
+    top[top_id]->Reshape(num_, num_output_, height_out_, width_out_);
+  }
+  if (reverse_dimensions()) {
+    conv_in_height_ = height_out_;
+    conv_in_width_ = width_out_;
+    conv_out_spatial_dim_ = height_ * width_;
+  } else {
+    conv_in_height_ = height_;
+    conv_in_width_ = width_;
+    conv_out_spatial_dim_ = height_out_ * width_out_;
+  }
+  kernel_dim_ = conv_in_channels_ * kernel_h_ * kernel_w_;
+  weight_offset_ = conv_out_channels_ * kernel_dim_ / group_ / group_;
+  col_offset_ = kernel_dim_ * conv_out_spatial_dim_ / group_;
+  output_offset_ = conv_out_channels_ * conv_out_spatial_dim_ / group_;
+  // The im2col result buffer will only hold one image at a time to avoid
+  // overly large memory usage. In the special case of 1x1 convolution
+  // it goes lazily unused to save memory.
+  if (reverse_dimensions()) {
+    col_buffer_.Reshape(1, kernel_dim_, height_, width_);
+  } else {
+    col_buffer_.Reshape(1, kernel_dim_, height_out_, width_out_);
+  }
+  // Set up the all ones "bias multiplier" for adding biases by BLAS
+  if (bias_term_) {
+    vector<int> bias_multiplier_shape(1, height_out_ * width_out_);
+    bias_multiplier_.Reshape(bias_multiplier_shape);
+    caffe_set(bias_multiplier_.count(), Dtype(1),
+        bias_multiplier_.mutable_cpu_data());
+  }
+}
+
+template <typename Dtype>
+void BaseConvolutionLayer<Dtype>::forward_cpu_gemm(const Dtype* input,
+    const Dtype* weights, Dtype* output, bool skip_im2col) {
+  const Dtype* col_buff = input;
+  if (!is_1x1_) {
+    if (!skip_im2col) {
+      conv_im2col_cpu(input, col_buffer_.mutable_cpu_data());
+    }
+    col_buff = col_buffer_.cpu_data();
+  }
+  for (int g = 0; g < group_; ++g) {
+    caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, conv_out_channels_ /
+        group_, conv_out_spatial_dim_, kernel_dim_ / group_,
+        (Dtype)1., weights + weight_offset_ * g, col_buff + col_offset_ * g,
+        (Dtype)0., output + output_offset_ * g);
+  }
+}
+
+template <typename Dtype>
+void BaseConvolutionLayer<Dtype>::forward_cpu_bias(Dtype* output,
+    const Dtype* bias) {
+  caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num_output_,
+      height_out_ * width_out_, 1, (Dtype)1., bias, bias_multiplier_.cpu_data(),
+      (Dtype)1., output);
+}
+
+template <typename Dtype>
+void BaseConvolutionLayer<Dtype>::backward_cpu_gemm(const Dtype* output,
+    const Dtype* weights, Dtype* input) {
+  Dtype* col_buff = col_buffer_.mutable_cpu_data();
+  if (is_1x1_) {
+    col_buff = input;
+  }
+  for (int g = 0; g < group_; ++g) {
+    caffe_cpu_gemm<Dtype>(CblasTrans, CblasNoTrans, kernel_dim_ / group_,
+        conv_out_spatial_dim_, conv_out_channels_ / group_,
+        (Dtype)1., weights + weight_offset_ * g, output + output_offset_ * g,
+        (Dtype)0., col_buff + col_offset_ * g);
+  }
+  if (!is_1x1_) {
+    conv_col2im_cpu(col_buff, input);
+  }
+}
+
+template <typename Dtype>
+void BaseConvolutionLayer<Dtype>::weight_cpu_gemm(const Dtype* input,
+    const Dtype* output, Dtype* weights) {
+  const Dtype* col_buff = input;
+  if (!is_1x1_) {
+    conv_im2col_cpu(input, col_buffer_.mutable_cpu_data());
+    col_buff = col_buffer_.cpu_data();
+  }
+  for (int g = 0; g < group_; ++g) {
+    caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasTrans, conv_out_channels_ / group_,
+        kernel_dim_ / group_, conv_out_spatial_dim_,
+        (Dtype)1., output + output_offset_ * g, col_buff + col_offset_ * g,
+        (Dtype)1., weights + weight_offset_ * g);
+  }
+}
+
+template <typename Dtype>
+void BaseConvolutionLayer<Dtype>::backward_cpu_bias(Dtype* bias,
+    const Dtype* input) {
+  caffe_cpu_gemv<Dtype>(CblasNoTrans, num_output_, height_out_ * width_out_, 1.,
+      input, bias_multiplier_.cpu_data(), 1., bias);
+}
+
+#ifndef CPU_ONLY
+
+template <typename Dtype>
+void BaseConvolutionLayer<Dtype>::forward_gpu_gemm(const Dtype* input,
+    const Dtype* weights, Dtype* output, bool skip_im2col) {
+  const Dtype* col_buff = input;
+  if (!is_1x1_) {
+    if (!skip_im2col) {
+      conv_im2col_gpu(input, col_buffer_.mutable_gpu_data());
+    }
+    col_buff = col_buffer_.gpu_data();
+  }
+  for (int g = 0; g < group_; ++g) {
+    caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, conv_out_channels_ /
+        group_, conv_out_spatial_dim_, kernel_dim_ / group_,
+        (Dtype)1., weights + weight_offset_ * g, col_buff + col_offset_ * g,
+        (Dtype)0., output + output_offset_ * g);
+  }
+}
+
+template <typename Dtype>
+void BaseConvolutionLayer<Dtype>::forward_gpu_bias(Dtype* output,
+    const Dtype* bias) {
+  caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num_output_,
+      height_out_ * width_out_, 1, (Dtype)1., bias, bias_multiplier_.gpu_data(),
+      (Dtype)1., output);
+}
+
+template <typename Dtype>
+void BaseConvolutionLayer<Dtype>::backward_gpu_gemm(const Dtype* output,
+    const Dtype* weights, Dtype* input) {
+  Dtype* col_buff = col_buffer_.mutable_gpu_data();
+  if (is_1x1_) {
+    col_buff = input;
+  }
+  for (int g = 0; g < group_; ++g) {
+    caffe_gpu_gemm<Dtype>(CblasTrans, CblasNoTrans, kernel_dim_ / group_,
+        conv_out_spatial_dim_, conv_out_channels_ / group_,
+        (Dtype)1., weights + weight_offset_ * g, output + output_offset_ * g,
+        (Dtype)0., col_buff + col_offset_ * g);
+  }
+  if (!is_1x1_) {
+    conv_col2im_gpu(col_buff, input);
+  }
+}
+
+template <typename Dtype>
+void BaseConvolutionLayer<Dtype>::weight_gpu_gemm(const Dtype* input,
+    const Dtype* output, Dtype* weights) {
+  const Dtype* col_buff = input;
+  if (!is_1x1_) {
+    conv_im2col_gpu(input, col_buffer_.mutable_gpu_data());
+    col_buff = col_buffer_.gpu_data();
+  }
+  for (int g = 0; g < group_; ++g) {
+    caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasTrans, conv_out_channels_ / group_,
+        kernel_dim_ / group_, conv_out_spatial_dim_,
+        (Dtype)1., output + output_offset_ * g, col_buff + col_offset_ * g,
+        (Dtype)1., weights + weight_offset_ * g);
+  }
+}
+
+template <typename Dtype>
+void BaseConvolutionLayer<Dtype>::backward_gpu_bias(Dtype* bias,
+    const Dtype* input) {
+  caffe_gpu_gemv<Dtype>(CblasNoTrans, num_output_, height_out_ * width_out_, 1.,
+      input, bias_multiplier_.gpu_data(), 1., bias);
+}
+
+#endif  // !CPU_ONLY
+
+INSTANTIATE_CLASS(BaseConvolutionLayer);
+
+}  // namespace caffe
diff --git a/src/caffe/layers/base_data_layer.cpp b/src/caffe/layers/base_data_layer.cpp
index 9b1d55831a7..b0cb6cc599f 100644
--- a/src/caffe/layers/base_data_layer.cpp
+++ b/src/caffe/layers/base_data_layer.cpp
@@ -9,49 +9,27 @@ namespace caffe {
 template <typename Dtype>
 BaseDataLayer<Dtype>::BaseDataLayer(const LayerParameter& param)
     : Layer<Dtype>(param),
-      transform_param_(param.transform_param()),
-      data_transformer_(transform_param_) {
+      transform_param_(param.transform_param()) {
 }
 
 template <typename Dtype>
 void BaseDataLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
-  if (top->size() == 1) {
+      const vector<Blob<Dtype>*>& top) {
+  if (top.size() == 1) {
     output_labels_ = false;
   } else {
     output_labels_ = true;
   }
+  data_transformer_.reset(
+      new DataTransformer<Dtype>(transform_param_, this->phase_));
+  data_transformer_->InitRand();
+  // The subclasses should setup the size of bottom and top
   DataLayerSetUp(bottom, top);
-  // The subclasses should setup the datum channels, height and width
-  CHECK_GT(datum_channels_, 0);
-  CHECK_GT(datum_height_, 0);
-  CHECK_GT(datum_width_, 0);
-  if (transform_param_.crop_size() > 0) {
-    CHECK_GE(datum_height_, transform_param_.crop_size());
-    CHECK_GE(datum_width_, transform_param_.crop_size());
-  }
-  // check if we want to have mean
-  if (transform_param_.has_mean_file()) {
-    const string& mean_file = transform_param_.mean_file();
-    LOG(INFO) << "Loading mean file from" << mean_file;
-    BlobProto blob_proto;
-    ReadProtoFromBinaryFileOrDie(mean_file.c_str(), &blob_proto);
-    data_mean_.FromProto(blob_proto);
-    CHECK_GE(data_mean_.num(), 1);
-    CHECK_GE(data_mean_.channels(), datum_channels_);
-    CHECK_GE(data_mean_.height(), datum_height_);
-    CHECK_GE(data_mean_.width(), datum_width_);
-  } else {
-    // Simply initialize an all-empty mean.
-    data_mean_.Reshape(1, datum_channels_, datum_height_, datum_width_);
-  }
-  mean_ = data_mean_.cpu_data();
-  data_transformer_.InitRand();
 }
 
 template <typename Dtype>
 void BasePrefetchingDataLayer<Dtype>::LayerSetUp(
-    const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
   BaseDataLayer<Dtype>::LayerSetUp(bottom, top);
   // Now, start the prefetch thread. Before calling prefetch, we make two
   // cpu_data calls so that the prefetch thread does not accidentally make
@@ -61,6 +39,10 @@ void BasePrefetchingDataLayer<Dtype>::LayerSetUp(
   if (this->output_labels_) {
     this->prefetch_label_.mutable_cpu_data();
   }
+#ifdef USE_MPI
+  //advance (my_rank) mini-batches to be ready for first run
+  BaseDataLayer<Dtype>::OffsetCursor(top[0]->num() * Caffe::MPI_my_rank());
+#endif
   DLOG(INFO) << "Initializing prefetch";
   this->CreatePrefetchThread();
   DLOG(INFO) << "Prefetch initialized.";
@@ -68,8 +50,7 @@ void BasePrefetchingDataLayer<Dtype>::LayerSetUp(
 
 template <typename Dtype>
 void BasePrefetchingDataLayer<Dtype>::CreatePrefetchThread() {
-  this->phase_ = Caffe::phase();
-  this->data_transformer_.InitRand();
+  this->data_transformer_->InitRand();
   CHECK(StartInternalThread()) << "Thread execution failed";
 }
 
@@ -80,17 +61,31 @@ void BasePrefetchingDataLayer<Dtype>::JoinPrefetchThread() {
 
 template <typename Dtype>
 void BasePrefetchingDataLayer<Dtype>::Forward_cpu(
-    const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
   // First, join the thread
   JoinPrefetchThread();
+  DLOG(INFO) << "Thread joined";
+  // Reshape to loaded data.
+  top[0]->ReshapeLike(prefetch_data_);
   // Copy the data
   caffe_copy(prefetch_data_.count(), prefetch_data_.cpu_data(),
-             (*top)[0]->mutable_cpu_data());
+             top[0]->mutable_cpu_data());
+  DLOG(INFO) << "Prefetch copied";
   if (this->output_labels_) {
+    // Reshape to loaded labels.
+    top[1]->ReshapeLike(prefetch_label_);
+    // Copy the labels.
     caffe_copy(prefetch_label_.count(), prefetch_label_.cpu_data(),
-               (*top)[1]->mutable_cpu_data());
+               top[1]->mutable_cpu_data());
   }
+
+#ifdef USE_MPI
+  //advance (all_rank - (my_rank+1)) mini-batches to be ready for next run
+  BaseDataLayer<Dtype>::OffsetCursor(top[0]->num() * (Caffe::MPI_all_rank() - 1));
+#endif
+
   // Start a new prefetch thread
+  DLOG(INFO) << "CreatePrefetchThread";
   CreatePrefetchThread();
 }
 
diff --git a/src/caffe/layers/base_data_layer.cu b/src/caffe/layers/base_data_layer.cu
index 8189c79c9d4..f044cbd7e1f 100644
--- a/src/caffe/layers/base_data_layer.cu
+++ b/src/caffe/layers/base_data_layer.cu
@@ -6,20 +6,29 @@ namespace caffe {
 
 template <typename Dtype>
 void BasePrefetchingDataLayer<Dtype>::Forward_gpu(
-    const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
   // First, join the thread
   JoinPrefetchThread();
+  // Reshape to loaded data.
+  top[0]->ReshapeLike(this->prefetch_data_);
   // Copy the data
   caffe_copy(prefetch_data_.count(), prefetch_data_.cpu_data(),
-      (*top)[0]->mutable_gpu_data());
+      top[0]->mutable_gpu_data());
   if (this->output_labels_) {
+    // Reshape to loaded labels.
+    top[1]->ReshapeLike(prefetch_label_);
+    // Copy the labels.
     caffe_copy(prefetch_label_.count(), prefetch_label_.cpu_data(),
-        (*top)[1]->mutable_gpu_data());
+        top[1]->mutable_gpu_data());
   }
+#ifdef USE_MPI
+  //advance (all_rank - (my_rank+1)) mini-batches to be ready for next run
+  BaseDataLayer<Dtype>::OffsetCursor(top[0]->num() * (Caffe::MPI_all_rank() - 1));
+#endif
   // Start a new prefetch thread
   CreatePrefetchThread();
 }
 
-INSTANTIATE_CLASS(BasePrefetchingDataLayer);
+INSTANTIATE_LAYER_GPU_FORWARD(BasePrefetchingDataLayer);
 
 }  // namespace caffe
diff --git a/src/caffe/layers/batch_reduction_layer.cpp b/src/caffe/layers/batch_reduction_layer.cpp
new file mode 100644
index 00000000000..8d2954f42e6
--- /dev/null
+++ b/src/caffe/layers/batch_reduction_layer.cpp
@@ -0,0 +1,210 @@
+#include <algorithm>
+#include <cfloat>
+#include <vector>
+#include <caffe/caffe.hpp>
+
+#include "caffe/layer.hpp"
+#include "caffe/util/math_functions.hpp"
+#include "caffe/vision_layers.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void BatchReductionLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+  op_ = this->layer_param_.batch_reduction_param().reduction_param().operation();
+  axis_ = bottom[0]->CanonicalAxisIndex(
+      this->layer_param_.batch_reduction_param().reduction_param().axis());
+
+  // load levels
+  int n_level = this->layer_param_.batch_reduction_param().level_size();
+  if (n_level == 0) {
+    this->layer_param_.mutable_batch_reduction_param()->add_level(1);
+    n_level = 1;
+  }
+  levels_.reserve(this->layer_param_.batch_reduction_param().level_size());
+
+  for (int i = 0; i < n_level; ++i){
+    levels_.push_back(this->layer_param_.batch_reduction_param().level(i));
+    ticks_.push_back(levels_.back() * levels_.back());
+  }
+
+  // top-k reduction currently only works with single level
+  if (op_ == ReductionParameter_ReductionOp_TOPK){
+    CHECK(n_level <= 1)<<"For now top-k reduction only works with 1 level";
+  }
+
+}
+
+template <typename Dtype>
+void BatchReductionLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+
+  vector<int> top_shape(bottom[0]->shape().begin(),
+                        bottom[0]->shape().begin() + axis_);
+
+  // if levels = [1], we do global reduction instead
+  if ((levels_.size() != 1) || (levels_[0] != 1)){
+    top_shape.push_back(levels_.size());
+    int red_dim = 0;
+    for (int i = 0; i < ticks_.size(); ++i) red_dim += ticks_[i];
+    CHECK_EQ(red_dim, bottom[0]->shape(axis_));
+  }else{
+    ticks_[0] = bottom[0]->shape(axis_); // levels=[1] means we reduce along the whole axis
+  }
+
+  for (int i = axis_ + 1; i < bottom[0]->shape().size(); ++i){
+    top_shape.push_back(bottom[0]->shape()[i]);
+  }
+  top[0]->Reshape(top_shape);
+
+  step_ = bottom[0]->count(axis_+1);
+  num_ = bottom[0]->count(0, axis_);
+
+  //LOG_INFO<<num_<<" "<<step_;
+  CHECK_EQ(step_ * num_ * levels_.size(), top[0]->count());
+
+  // will add these later
+  if (op_ == ReductionParameter_ReductionOp_SUMSQ || op_ == ReductionParameter_ReductionOp_ASUM){
+    NOT_IMPLEMENTED;
+  }
+
+  ticks_blob_.Reshape(ticks_.size(), 1, 1, 1);
+  Dtype* tick_data = ticks_blob_.mutable_cpu_data();
+  for (int i = 0; i < levels_.size(); ++i){
+    tick_data[i] = ticks_[i];
+  }
+
+  // reshape idx blob in top-k case
+  if (op_ == ReductionParameter_ReductionOp_TOPK){
+    argsort_idx_.Reshape(bottom[0]->shape());
+  }else{
+    argsort_idx_.Reshape(1,1,1,1);
+  }
+}
+
+template <typename Dtype>
+bool comparator( const std::pair<Dtype, int>& left, const std::pair<Dtype, int>& right){
+  return left.first >= right.first; // use descending order here
+}
+
+template <typename Dtype>
+void BatchReductionLayer<Dtype>::Forward_cpu(
+    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
+
+  const Dtype* bottom_data = bottom[0]->cpu_data();
+  Dtype* top_data = top[0]->mutable_cpu_data();
+  Dtype* idx_data = argsort_idx_.mutable_cpu_data();
+
+
+  caffe_set(top[0]->count(), Dtype(0), top_data);
+
+  if (op_ != ReductionParameter_ReductionOp_TOPK) {
+    for (int n = 0; n < num_; ++n) {
+      //printf(" levels: %d\n", levels_.size());
+      for (int l = 0; l < levels_.size(); ++l) {
+        int tick = ticks_[l];
+        Dtype coeff = (op_ == ReductionParameter_ReductionOp_MEAN) ? Dtype(1) / Dtype(tick) : Dtype(1);
+        for (int t = 0; t < tick; ++t) {
+          caffe_cpu_axpby(step_, coeff, bottom_data, Dtype(1), top_data);
+          bottom_data += step_;
+        }
+        top_data += step_;
+      }
+    }
+  }else {
+    int k = this->layer_param_.batch_reduction_param().reduction_param().k();
+    int tick = ticks_[0];
+
+    vector<std::pair<Dtype, int> > buffer;
+    buffer.resize(tick);
+
+
+    // num_ outer loops
+    caffe_set(top[0]->count(), Dtype(0), top_data);
+    caffe_set(bottom[0]->count(), Dtype(0), idx_data);
+    for (int n = 0; n < num_; ++n) {
+      // step_ inner loops
+      for (int i = 0; i < step_; ++i) {
+        //fill data
+        for (int t = 0; t < tick; ++t){
+          buffer[t] = std::make_pair(bottom_data[t * step_ + i], t);
+        }
+        // perform sort
+        std::sort(buffer.begin(), buffer.end(), comparator<Dtype>);
+
+        // obtain output and index
+        Dtype accum = 0;
+        for (int k_out = 0; k_out < k; ++k_out){
+          std::pair<Dtype, int>& p = buffer[k_out];
+          accum += p.first;
+          idx_data[p.second*step_ + i] = k_out+1;
+        }
+        // set top data
+        top_data[i] = accum / Dtype(k);
+      }
+      top_data += step_;
+      bottom_data += tick*step_;
+      idx_data += tick*step_;
+
+    }
+
+
+  }
+
+
+}
+
+template <typename Dtype>
+void BatchReductionLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
+    const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+  if (!propagate_down[0]) { return; }
+  // Get bottom_data, if needed.
+  const Dtype* top_diff = top[0]->cpu_diff();
+  Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
+  Dtype* idx_data = argsort_idx_.mutable_cpu_data();
+
+  if (op_ != ReductionParameter_ReductionOp_TOPK) {
+    for (int i = 0; i < num_; ++i) {
+      for (int l = 0; l < levels_.size(); ++l) {
+        int tick = ticks_[l];
+        Dtype coeff = (op_ == ReductionParameter_ReductionOp_MEAN) ? Dtype(1) / Dtype(tick) : Dtype(1);
+        for (int t = 0; t < tick; ++t) {
+          caffe_cpu_axpby(step_, coeff, top_diff, Dtype(0), bottom_diff);
+          //offset bottom_data each input step
+          bottom_diff += step_;
+        }
+        //offset bottom_data each output step
+        top_diff += step_;
+      }
+    }
+  }else {
+    int tick = ticks_[0];
+    int k = this->layer_param_.batch_reduction_param().reduction_param().k();
+
+    // num_ outer loops
+    for (int n = 0; n < num_; ++n) {
+      // step_ inner loops
+      for (int i = 0; i < step_; ++i) {
+        //fill data
+        Dtype diff = top_diff[i] / Dtype(k);
+        for (int t = 0; t < tick; ++t){
+          bottom_diff[t * step_ + i] = (idx_data[t * step_ + i] >= 1)?diff:0;
+        }
+
+      }
+      top_diff += step_;
+      bottom_diff += tick*step_;
+      idx_data += tick*step_;
+    }
+  }
+}
+
+#ifdef CPU_ONLY
+STUB_GPU(BatchReductionLayer);
+#endif
+
+INSTANTIATE_CLASS(BatchReductionLayer);
+REGISTER_LAYER_CLASS(BatchReduction);
+
+}  // namespace caffe
diff --git a/src/caffe/layers/batch_reduction_layer.cu b/src/caffe/layers/batch_reduction_layer.cu
new file mode 100644
index 00000000000..f107efd9855
--- /dev/null
+++ b/src/caffe/layers/batch_reduction_layer.cu
@@ -0,0 +1,87 @@
+#include <cfloat>
+#include <vector>
+
+#include "caffe/layer.hpp"
+#include "caffe/util/math_functions.hpp"
+#include "caffe/common_layers.hpp"
+
+namespace caffe {
+
+// The CUDA kernel actually runs the reduction
+template <typename Dtype>
+__global__ void BatchReductionForwardKer(const int step, const int num,
+                                         const int n_level, const Dtype* ticks,
+                                         const bool mean, const bool forward,
+                                         Dtype* bottom, Dtype* top) {
+    Dtype* bottom_ptr = bottom;
+    Dtype* top_ptr = top;
+    CUDA_KERNEL_LOOP(index, step){
+        for (int n = 0; n < num; ++n){
+            for (int l = 0; l < n_level; ++l){
+                int tick = ticks[l];
+                Dtype coeff = (mean)? Dtype(1)/Dtype(tick) : Dtype(1);
+                for (int t = 0; t < tick; ++t){
+                    if (forward){
+                        top_ptr[index] += bottom_ptr[index] * coeff;
+                    }else{
+                        bottom_ptr[index] = top_ptr[index] * coeff;
+                    }
+                    bottom_ptr += step;
+                }
+                top_ptr += step;
+            }
+        }
+    }
+}
+
+template <typename Dtype>
+void BatchReductionLayer<Dtype>::Forward_gpu(
+    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
+    if (op_ != ReductionParameter_ReductionOp_TOPK){
+        const Dtype* bottom_data = bottom[0]->gpu_data();
+        Dtype* top_data = top[0]->mutable_gpu_data();
+        const Dtype* tick_data = this->ticks_blob_.gpu_data();
+        const bool kMean = (this->op_ == ReductionParameter_ReductionOp_MEAN);
+        const int n_level = this->levels_.size();
+
+        const bool kForward = true; // forward
+
+        caffe_gpu_set(top[0]->count(), Dtype(0), top_data);
+        //invoke kernel
+        BatchReductionForwardKer<Dtype>  // NOLINT_NEXT_LINE(whitespace/operators)
+        <<<CAFFE_GET_BLOCKS(step_), CAFFE_CUDA_NUM_THREADS>>>(
+            step_, num_, n_level, tick_data,
+            kMean, kForward, (Dtype*)bottom_data, top_data);
+    }else{
+        // Top-K reduction only supports CPU implementation
+        Forward_cpu(bottom, top);
+    }
+
+}
+
+template <typename Dtype>
+void BatchReductionLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
+    const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+    if (op_ != ReductionParameter_ReductionOp_TOPK){
+        const Dtype *top_diff = top[0]->gpu_diff();
+        Dtype *bottom_diff = bottom[0]->mutable_gpu_diff();
+        const Dtype *tick_data = this->ticks_blob_.gpu_data();
+        const bool kMean = (this->op_ == ReductionParameter_ReductionOp_MEAN);
+        const int n_level = this->levels_.size();
+
+        const bool kForward = false; // backward
+
+        //invoke kernel
+        BatchReductionForwardKer<Dtype>  // NOLINT_NEXT_LINE(whitespace/operators)
+        <<<CAFFE_GET_BLOCKS(step_), CAFFE_CUDA_NUM_THREADS>>>(
+            step_, num_, n_level, tick_data,
+            kMean, kForward, bottom_diff, (Dtype*)top_diff);
+    }else{
+        // Top-K reduction only supports CPU implementation
+        Backward_cpu(top, propagate_down, bottom);
+    }
+}
+
+INSTANTIATE_LAYER_GPU_FUNCS(BatchReductionLayer);
+
+}  // namespace caffe
diff --git a/src/caffe/layers/bias_layer.cpp b/src/caffe/layers/bias_layer.cpp
new file mode 100644
index 00000000000..93348217286
--- /dev/null
+++ b/src/caffe/layers/bias_layer.cpp
@@ -0,0 +1,121 @@
+#include <vector>
+
+#include "caffe/filler.hpp"
+#include "caffe/common_layers.hpp"
+#include "caffe/util/math_functions.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void BiasLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+  if (bottom.size() == 1 && this->blobs_.size() > 0) {
+    LOG(INFO) << "Skipping parameter initialization";
+  } else if (bottom.size() == 1) {
+    // bias is a learned parameter; initialize it
+    const BiasParameter& param = this->layer_param_.bias_param();
+    const int axis = bottom[0]->CanonicalAxisIndex(param.axis());
+    const int num_axes = param.num_axes();
+    CHECK_GE(num_axes, -1) << "num_axes must be non-negative, "
+                           << "or -1 to extend to the end of bottom[0]";
+    if (num_axes >= 0) {
+      CHECK_GE(bottom[0]->num_axes(), axis + num_axes)
+          << "bias blob's shape extends past bottom[0]'s shape when applied "
+          << "starting with bottom[0] axis = " << axis;
+    }
+    this->blobs_.resize(1);
+    const vector<int>::const_iterator& shape_start =
+        bottom[0]->shape().begin() + axis;
+    const vector<int>::const_iterator& shape_end =
+        (num_axes == -1) ? bottom[0]->shape().end() : (shape_start + num_axes);
+    vector<int> bias_shape(shape_start, shape_end);
+    this->blobs_[0].reset(new Blob<Dtype>(bias_shape));
+    shared_ptr<Filler<Dtype> > filler(GetFiller<Dtype>(param.filler()));
+    filler->Fill(this->blobs_[0].get());
+  }
+  this->param_propagate_down_.resize(this->blobs_.size(), true);
+}
+
+template <typename Dtype>
+void BiasLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+  const BiasParameter& param = this->layer_param_.bias_param();
+  Blob<Dtype>* bias = (bottom.size() > 1) ? bottom[1] : this->blobs_[0].get();
+  // Always set axis == 0 in special case where bias is a scalar
+  // (num_axes == 0). Mathematically equivalent for any choice of axis, so the
+  // actual setting can be safely ignored; and computation is most efficient
+  // with axis == 0 and (therefore) outer_dim_ == 1.
+  const int axis = (bias->num_axes() == 0) ?
+      0 : bottom[0]->CanonicalAxisIndex(param.axis());
+  CHECK_GE(bottom[0]->num_axes(), axis + bias->num_axes())
+      << "bias blob's shape extends past bottom[0]'s shape when applied "
+      << "starting with bottom[0] axis = " << axis;
+  for (int i = 0; i < bias->num_axes(); ++i) {
+    CHECK_EQ(bottom[0]->shape(axis + i), bias->shape(i))
+        << "dimension mismatch between bottom[0]->shape(" << axis + i
+        << ") and bias->shape(" << i << ")";
+  }
+  outer_dim_ = bottom[0]->count(0, axis);
+  bias_dim_ = bias->count();
+  inner_dim_ = bottom[0]->count(axis + bias->num_axes());
+  dim_ = bias_dim_ * inner_dim_;
+  if (bottom[0] != top[0]) {
+    top[0]->ReshapeLike(*bottom[0]);
+  }
+  bias_multiplier_.Reshape(vector<int>(1, inner_dim_));
+  if (bias_multiplier_.cpu_data()[inner_dim_ - 1] != Dtype(1)) {
+    caffe_set(inner_dim_, Dtype(1), bias_multiplier_.mutable_cpu_data());
+  }
+}
+
+template <typename Dtype>
+void BiasLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+  const Dtype* bias_data =
+      ((bottom.size() > 1) ? bottom[1] : this->blobs_[0].get())->cpu_data();
+  Dtype* top_data = top[0]->mutable_cpu_data();
+  if (bottom[0] != top[0]) {
+    const Dtype* bottom_data = bottom[0]->cpu_data();
+    caffe_copy(bottom[0]->count(), bottom_data, top_data);
+  }
+  for (int n = 0; n < outer_dim_; ++n) {
+    caffe_cpu_gemm(CblasNoTrans, CblasNoTrans, bias_dim_,
+        inner_dim_, 1, Dtype(1), bias_data,
+        bias_multiplier_.cpu_data(), Dtype(1), top_data);
+    top_data += dim_;
+  }
+}
+
+template <typename Dtype>
+void BiasLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+  if (propagate_down[0] && bottom[0] != top[0]) {
+    const Dtype* top_diff = top[0]->cpu_diff();
+    Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
+    caffe_copy(bottom[0]->count(), top_diff, bottom_diff);
+  }
+  // in-place, we don't need to do anything with the data diff
+  const bool bias_param = (bottom.size() == 1);
+  if ((!bias_param && propagate_down[1]) ||
+      (bias_param && this->param_propagate_down_[0])) {
+    const Dtype* top_diff = top[0]->cpu_diff();
+    Dtype* bias_diff = (bias_param ? this->blobs_[0].get() : bottom[1])
+        ->mutable_cpu_diff();
+    bool accum = bias_param;
+    for (int n = 0; n < outer_dim_; ++n) {
+      caffe_cpu_gemv(CblasNoTrans, bias_dim_, inner_dim_, Dtype(1),
+          top_diff, bias_multiplier_.cpu_data(), Dtype(accum), bias_diff);
+      top_diff += dim_;
+      accum = true;
+    }
+  }
+}
+
+#ifdef CPU_ONLY
+STUB_GPU(BiasLayer);
+#endif
+
+INSTANTIATE_CLASS(BiasLayer);
+REGISTER_LAYER_CLASS(Bias);
+
+}  // namespace caffe
diff --git a/src/caffe/layers/bias_layer.cu b/src/caffe/layers/bias_layer.cu
new file mode 100644
index 00000000000..3d643a293bd
--- /dev/null
+++ b/src/caffe/layers/bias_layer.cu
@@ -0,0 +1,59 @@
+#include <vector>
+
+#include "caffe/filler.hpp"
+#include "caffe/common_layers.hpp"
+#include "caffe/util/math_functions.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+__global__ void BiasForward(const int n, const Dtype* in,
+    const Dtype* bias, const int bias_dim, const int inner_dim,
+    Dtype* out) {
+  CUDA_KERNEL_LOOP(index, n) {
+    const int bias_index = (index / inner_dim) % bias_dim;
+    out[index] = in[index] + bias[bias_index];
+  }
+}
+
+template <typename Dtype>
+void BiasLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+  const int count = top[0]->count();
+  const Dtype* bottom_data = bottom[0]->gpu_data();
+  const Dtype* bias_data =
+      ((bottom.size() > 1) ? bottom[1] : this->blobs_[0].get())->gpu_data();
+  Dtype* top_data = top[0]->mutable_gpu_data();
+  BiasForward<Dtype>  // NOLINT_NEXT_LINE(whitespace/operators)
+      <<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
+      count, bottom_data, bias_data, bias_dim_, inner_dim_, top_data);
+}
+
+template <typename Dtype>
+void BiasLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+  if (propagate_down[0] && bottom[0] != top[0]) {
+    const Dtype* top_diff = top[0]->gpu_diff();
+    Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
+    caffe_copy(bottom[0]->count(), top_diff, bottom_diff);
+  }
+  // in-place, we don't need to do anything with the data diff
+  const bool bias_param = (bottom.size() == 1);
+  if ((!bias_param && propagate_down[1]) ||
+      (bias_param && this->param_propagate_down_[0])) {
+    const Dtype* top_diff = top[0]->gpu_diff();
+    Dtype* bias_diff = (bias_param ? this->blobs_[0].get() : bottom[1])
+        ->mutable_gpu_diff();
+    bool accum = bias_param;
+    for (int n = 0; n < outer_dim_; ++n) {
+      caffe_gpu_gemv(CblasNoTrans, bias_dim_, inner_dim_, Dtype(1),
+          top_diff, bias_multiplier_.gpu_data(), Dtype(accum), bias_diff);
+      top_diff += dim_;
+      accum = true;
+    }
+  }
+}
+
+INSTANTIATE_LAYER_GPU_FUNCS(BiasLayer);
+
+}  // namespace caffe
diff --git a/src/caffe/layers/bn_layer.cpp b/src/caffe/layers/bn_layer.cpp
new file mode 100644
index 00000000000..e0b51a611ed
--- /dev/null
+++ b/src/caffe/layers/bn_layer.cpp
@@ -0,0 +1,345 @@
+#include <algorithm>
+#include <vector>
+
+#include "caffe/common_layers.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/util/math_functions.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void BNLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+  frozen_ = this->layer_param_.bn_param().frozen();
+  bn_momentum_ = this->layer_param_.bn_param().momentum();
+  bn_eps_ = this->layer_param_.bn_param().eps();
+  // Initialize parameters
+  if (this->blobs_.size() > 0) {
+    LOG(INFO) << "Skipping parameter initialization";
+  } else {
+    this->blobs_.resize(4);
+    vector<int> shape;
+    shape.push_back(1);
+    shape.push_back(bottom[0]->channels());
+    // slope
+    this->blobs_[0].reset(new Blob<Dtype>(shape));
+    shared_ptr<Filler<Dtype> > slope_filler(GetFiller<Dtype>(
+        this->layer_param_.bn_param().slope_filler()));
+    slope_filler->Fill(this->blobs_[0].get());
+    // bias
+    this->blobs_[1].reset(new Blob<Dtype>(shape));
+    shared_ptr<Filler<Dtype> > bias_filler(GetFiller<Dtype>(
+        this->layer_param_.bn_param().bias_filler()));
+    bias_filler->Fill(this->blobs_[1].get());
+    // moving average mean
+    this->blobs_[2].reset(new Blob<Dtype>(shape));
+    caffe_set(this->blobs_[2]->count(), Dtype(0),
+        this->blobs_[2]->mutable_cpu_data());
+    // moving average variance
+    this->blobs_[3].reset(new Blob<Dtype>(shape));
+    caffe_set(this->blobs_[3]->count(), frozen_ ? Dtype(1) : Dtype(0),
+        this->blobs_[3]->mutable_cpu_data());
+  }
+  this->param_propagate_down_.resize(this->blobs_.size(), true);
+
+  // runing average stats does not use weight decay and learning rate
+  while (this->layer_param_.param_size() < 4){
+    this->layer_param_.mutable_param()->Add();
+  }
+  this->layer_param_.mutable_param(2)->set_lr_mult(Dtype(0));
+  this->layer_param_.mutable_param(2)->set_decay_mult(Dtype(0));
+
+  this->layer_param_.mutable_param(3)->set_lr_mult(Dtype(0));
+  this->layer_param_.mutable_param(3)->set_decay_mult(Dtype(0));
+
+  // shutdown scale and bias update in frozen mode
+  if (this->frozen_){
+    // slope
+    this->layer_param_.mutable_param(0)->set_lr_mult(Dtype(0));
+    this->layer_param_.mutable_param(0)->set_decay_mult(Dtype(0));
+
+    // bias
+    this->layer_param_.mutable_param(1)->set_lr_mult(Dtype(0));
+    this->layer_param_.mutable_param(1)->set_decay_mult(Dtype(0));
+  }
+}
+
+template <typename Dtype>
+void BNLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+  num_ = bottom[0]->num();
+  channels_ = bottom[0]->channels();
+  height_ = bottom[0]->height();
+  width_ = bottom[0]->width();
+
+  top[0]->ReshapeLike(*(bottom[0]));
+
+  broadcast_buffer_.ReshapeLike(*(bottom[0]));
+  spatial_statistic_.Reshape(num_, channels_, 1, 1);
+  batch_statistic_.Reshape(1, channels_, 1, 1);
+
+  x_norm_.ReshapeLike(*(bottom[0]));
+  x_inv_std_.ReshapeLike(batch_statistic_);
+
+  spatial_sum_multiplier_.Reshape(1, 1, height_, width_);
+  caffe_set(spatial_sum_multiplier_.count(), Dtype(1),
+      spatial_sum_multiplier_.mutable_cpu_data());
+  batch_sum_multiplier_.Reshape(num_, 1, 1, 1);
+  caffe_set(batch_sum_multiplier_.count(), Dtype(1),
+      batch_sum_multiplier_.mutable_cpu_data());
+}
+
+template <typename Dtype>
+void BNLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+  const vector<Blob<Dtype>*>& top) {
+  const Dtype* const_bottom_data = bottom[0]->cpu_data();
+  const Dtype* const_top_data = top[0]->cpu_data();
+  Dtype* top_data = top[0]->mutable_cpu_data();
+
+  const Dtype* scale_data = this->blobs_[0]->cpu_data();
+  const Dtype* shift_data = this->blobs_[1]->cpu_data();
+
+  // Mean normalization
+  if (frozen_ || this->phase_ == TEST) {
+    // Use the moving average mean
+    caffe_copy(batch_statistic_.count(), this->blobs_[2]->cpu_data(),
+        batch_statistic_.mutable_cpu_data());
+  } else {
+    // Compute the mean by averaging over spatial and batch dimensions.
+    caffe_cpu_gemv<Dtype>(CblasNoTrans, num_ * channels_, height_ * width_,
+        Dtype(1) / (height_ * width_), const_bottom_data,
+        spatial_sum_multiplier_.cpu_data(), Dtype(0),
+        spatial_statistic_.mutable_cpu_data());
+    caffe_cpu_gemv<Dtype>(CblasTrans, num_, channels_,
+        Dtype(1) / num_, spatial_statistic_.cpu_data(),
+        batch_sum_multiplier_.cpu_data(), Dtype(0),
+        batch_statistic_.mutable_cpu_data());
+    // Add to the moving average
+    if (!frozen_) {
+      caffe_cpu_axpby(batch_statistic_.count(),
+          Dtype(1) - bn_momentum_, batch_statistic_.cpu_data(),
+          bn_momentum_, this->blobs_[2]->mutable_cpu_data());
+    }
+  }
+  // Broadcast the mean vector
+  caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num_, channels_, 1,
+      Dtype(1), batch_sum_multiplier_.cpu_data(), batch_statistic_.cpu_data(),
+      Dtype(0), spatial_statistic_.mutable_cpu_data());
+  caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num_ * channels_,
+      height_ * width_, 1, Dtype(-1),
+      spatial_statistic_.cpu_data(), spatial_sum_multiplier_.cpu_data(),
+      Dtype(0), broadcast_buffer_.mutable_cpu_data());
+  // Subtract
+  caffe_add(broadcast_buffer_.count(), const_bottom_data,
+      broadcast_buffer_.cpu_data(), top_data);
+
+  // Variance normalization
+  if (frozen_ || this->phase_ == TEST) {
+    // Use the moving average variance
+    caffe_copy(batch_statistic_.count(), this->blobs_[3]->cpu_data(),
+        batch_statistic_.mutable_cpu_data());
+  } else {
+    // calculate batch variance
+    caffe_powx(broadcast_buffer_.count(), const_top_data, Dtype(2),
+        broadcast_buffer_.mutable_cpu_data());
+    caffe_cpu_gemv<Dtype>(CblasNoTrans, num_ * channels_, height_ * width_,
+        Dtype(1) / (height_ * width_), broadcast_buffer_.cpu_data(),
+        spatial_sum_multiplier_.cpu_data(), Dtype(0),
+        spatial_statistic_.mutable_cpu_data());
+    caffe_cpu_gemv<Dtype>(CblasTrans, num_, channels_, Dtype(1) / num_,
+        spatial_statistic_.cpu_data(), batch_sum_multiplier_.cpu_data(),
+        Dtype(0), batch_statistic_.mutable_cpu_data());
+
+    // Add to the moving average
+    caffe_cpu_axpby(batch_statistic_.count(),
+        Dtype(1) - bn_momentum_, batch_statistic_.cpu_data(),
+        bn_momentum_, this->blobs_[3]->mutable_cpu_data());
+  }
+
+  // Add eps
+  caffe_add_scalar(batch_statistic_.count(), bn_eps_,
+                   batch_statistic_.mutable_cpu_data());
+  // Inverse standard deviation
+  caffe_powx(batch_statistic_.count(), batch_statistic_.cpu_data(),
+             Dtype(-0.5), batch_statistic_.mutable_cpu_data());
+
+  // Broadcast the inverse std
+  caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num_, channels_, 1,
+        Dtype(1), batch_sum_multiplier_.cpu_data(), batch_statistic_.cpu_data(),
+        Dtype(0), spatial_statistic_.mutable_cpu_data());
+  caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num_ * channels_,
+      height_ * width_, 1, Dtype(1),
+      spatial_statistic_.cpu_data(), spatial_sum_multiplier_.cpu_data(),
+      Dtype(0), broadcast_buffer_.mutable_cpu_data());
+  // Multiply with the inverse std
+  caffe_mul(broadcast_buffer_.count(), const_top_data,
+      broadcast_buffer_.cpu_data(), top_data);
+
+  // Save the normalized inputs and std for backprop
+  if (!frozen_) {
+    caffe_copy(broadcast_buffer_.count(), const_top_data,
+        x_norm_.mutable_cpu_data());
+    caffe_copy(batch_statistic_.count(), batch_statistic_.cpu_data(),
+        x_inv_std_.mutable_cpu_data());
+  }
+
+  // Scale
+  caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num_, channels_, 1,
+      Dtype(1), batch_sum_multiplier_.cpu_data(), scale_data,
+      Dtype(0), spatial_statistic_.mutable_cpu_data());
+  caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num_ * channels_,
+      height_ * width_, 1, Dtype(1),
+      spatial_statistic_.cpu_data(), spatial_sum_multiplier_.cpu_data(),
+      Dtype(0), broadcast_buffer_.mutable_cpu_data());
+  caffe_mul(broadcast_buffer_.count(), const_top_data,
+      broadcast_buffer_.cpu_data(), top_data);
+
+  // Shift
+  caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num_, channels_, 1,
+      Dtype(1), batch_sum_multiplier_.cpu_data(), shift_data,
+      Dtype(0), spatial_statistic_.mutable_cpu_data());
+  caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num_ * channels_,
+      height_ * width_, 1, Dtype(1),
+      spatial_statistic_.cpu_data(), spatial_sum_multiplier_.cpu_data(),
+      Dtype(0), broadcast_buffer_.mutable_cpu_data());
+  caffe_add(broadcast_buffer_.count(), const_top_data,
+      broadcast_buffer_.cpu_data(), top_data);
+}
+
+template <typename Dtype>
+void BNLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
+  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+  if (frozen_) {
+    if (propagate_down[0]) {
+      const Dtype* const_top_diff = top[0]->cpu_diff();
+      Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
+      // Use the moving average variance
+      caffe_copy(batch_statistic_.count(), this->blobs_[3]->cpu_data(),
+          batch_statistic_.mutable_cpu_data());
+      caffe_add_scalar(batch_statistic_.count(), bn_eps_,
+          batch_statistic_.mutable_cpu_data());
+      caffe_powx(batch_statistic_.count(), batch_statistic_.cpu_data(),
+          Dtype(-0.5), batch_statistic_.mutable_cpu_data());
+      // Divide slope with std
+      caffe_mul(batch_statistic_.count(), this->blobs_[0]->cpu_data(),
+          batch_statistic_.cpu_data(), batch_statistic_.mutable_cpu_data());
+      // Broadcast
+      caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num_, channels_, 1,
+          Dtype(1), batch_sum_multiplier_.cpu_data(), batch_statistic_.cpu_data(),
+          Dtype(0), spatial_statistic_.mutable_cpu_data());
+      caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num_ * channels_,
+          height_ * width_, 1, Dtype(1),
+          spatial_statistic_.cpu_data(), spatial_sum_multiplier_.cpu_data(),
+          Dtype(0), broadcast_buffer_.mutable_cpu_data());
+      // Elementwise multiply top grad with (slope / std)
+      caffe_mul(broadcast_buffer_.count(), const_top_diff,
+          broadcast_buffer_.cpu_data(), bottom_diff);
+    }
+    return;
+  }
+
+  // gradient w.r.t. slope
+  if (this->param_propagate_down_[0]) {
+    const Dtype* const_top_diff = top[0]->cpu_diff();
+    Dtype* scale_diff = this->blobs_[0]->mutable_cpu_diff();
+    caffe_mul(broadcast_buffer_.count(), x_norm_.cpu_data(), const_top_diff,
+        broadcast_buffer_.mutable_cpu_data());
+    caffe_cpu_gemv<Dtype>(CblasNoTrans, num_ * channels_, height_ * width_,
+        Dtype(1), broadcast_buffer_.cpu_data(),
+        spatial_sum_multiplier_.cpu_data(), Dtype(0),
+        spatial_statistic_.mutable_cpu_data());
+    caffe_cpu_gemv<Dtype>(CblasTrans, num_, channels_, Dtype(1),
+        spatial_statistic_.cpu_data(), batch_sum_multiplier_.cpu_data(),
+        Dtype(1), scale_diff);
+  }
+
+  // gradient w.r.t. bias
+  if (this->param_propagate_down_[1]) {
+    const Dtype* const_top_diff = top[0]->cpu_diff();
+    Dtype* shift_diff = this->blobs_[1]->mutable_cpu_diff();
+    caffe_cpu_gemv<Dtype>(CblasNoTrans, num_ * channels_, height_ * width_,
+        Dtype(1), const_top_diff, spatial_sum_multiplier_.cpu_data(),
+        Dtype(0), spatial_statistic_.mutable_cpu_data());
+    caffe_cpu_gemv<Dtype>(CblasTrans, num_, channels_, Dtype(1),
+        spatial_statistic_.cpu_data(), batch_sum_multiplier_.cpu_data(),
+        Dtype(1), shift_diff);
+  }
+
+  // gradient w.r.t. normalized inputs
+  if (propagate_down[0]) {
+    const Dtype* const_top_diff = top[0]->cpu_diff();
+    const Dtype* const_bottom_diff = bottom[0]->cpu_diff();
+    Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
+    const Dtype* scale_data = this->blobs_[0]->cpu_data();
+
+    caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num_, channels_, 1,
+        Dtype(1), batch_sum_multiplier_.cpu_data(), scale_data,
+        Dtype(0), spatial_statistic_.mutable_cpu_data());
+    caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num_ * channels_,
+        height_ * width_, 1, Dtype(1), spatial_statistic_.cpu_data(),
+        spatial_sum_multiplier_.cpu_data(), Dtype(0),
+        broadcast_buffer_.mutable_cpu_data());
+    caffe_mul(broadcast_buffer_.count(), const_top_diff,
+        broadcast_buffer_.cpu_data(), broadcast_buffer_.mutable_cpu_data());
+
+    // sum of x_hat * (dl / dx_hat)
+    caffe_mul(broadcast_buffer_.count(), x_norm_.cpu_data(),
+        broadcast_buffer_.cpu_data(), bottom_diff);
+    caffe_cpu_gemv<Dtype>(CblasNoTrans, num_ * channels_, height_ * width_,
+        Dtype(1), const_bottom_diff, spatial_sum_multiplier_.cpu_data(),
+        Dtype(0), spatial_statistic_.mutable_cpu_data());
+    caffe_cpu_gemv<Dtype>(CblasTrans, num_, channels_, Dtype(1),
+        spatial_statistic_.cpu_data(), batch_sum_multiplier_.cpu_data(),
+        Dtype(0), batch_statistic_.mutable_cpu_data());
+
+    // x_hat times the sum
+    caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num_, channels_, 1,
+        Dtype(1), batch_sum_multiplier_.cpu_data(), batch_statistic_.cpu_data(),
+        Dtype(0), spatial_statistic_.mutable_cpu_data());
+    caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num_ * channels_,
+        height_ * width_, 1, Dtype(1),
+        spatial_statistic_.cpu_data(), spatial_sum_multiplier_.cpu_data(),
+        Dtype(0), bottom_diff);
+    caffe_mul(broadcast_buffer_.count(), x_norm_.cpu_data(),
+        const_bottom_diff, bottom_diff);
+
+    // Subtract the average of x_hat times the sum
+    caffe_cpu_gemv<Dtype>(CblasNoTrans, num_ * channels_, height_ * width_,
+        Dtype(1), broadcast_buffer_.cpu_data(),
+        spatial_sum_multiplier_.cpu_data(), Dtype(0),
+        spatial_statistic_.mutable_cpu_data());
+    caffe_cpu_gemv<Dtype>(CblasTrans, num_, channels_, Dtype(1),
+        spatial_statistic_.cpu_data(), batch_sum_multiplier_.cpu_data(),
+        Dtype(0), batch_statistic_.mutable_cpu_data());
+    caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num_, channels_, 1,
+        Dtype(1), batch_sum_multiplier_.cpu_data(), batch_statistic_.cpu_data(),
+        Dtype(0), spatial_statistic_.mutable_cpu_data());
+    caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num_ * channels_,
+        height_ * width_, 1, Dtype(1),
+        spatial_statistic_.cpu_data(), spatial_sum_multiplier_.cpu_data(),
+        Dtype(1), bottom_diff);
+    caffe_cpu_axpby(broadcast_buffer_.count(), Dtype(1),
+        broadcast_buffer_.cpu_data(), Dtype(-1) / (num_ * height_ * width_),
+        bottom_diff);
+
+    // Multiply with the inverse std
+    caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num_, channels_, 1,
+        Dtype(1), batch_sum_multiplier_.cpu_data(), x_inv_std_.cpu_data(),
+        Dtype(0), spatial_statistic_.mutable_cpu_data());
+    caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num_ * channels_,
+        height_ * width_, 1, Dtype(1),
+        spatial_statistic_.cpu_data(), spatial_sum_multiplier_.cpu_data(),
+        Dtype(0), broadcast_buffer_.mutable_cpu_data());
+    caffe_mul(broadcast_buffer_.count(), const_bottom_diff,
+        broadcast_buffer_.cpu_data(), bottom_diff);
+  }
+}
+
+
+#ifdef CPU_ONLY
+STUB_GPU(BNLayer);
+#endif
+
+INSTANTIATE_CLASS(BNLayer);
+
+}  // namespace caffe
diff --git a/src/caffe/layers/bn_layer.cu b/src/caffe/layers/bn_layer.cu
new file mode 100644
index 00000000000..cbcce0874c9
--- /dev/null
+++ b/src/caffe/layers/bn_layer.cu
@@ -0,0 +1,256 @@
+#include <algorithm>
+#include <vector>
+
+#include "caffe/common_layers.hpp"
+#include "caffe/filler.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/util/math_functions.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void BNLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+  const vector<Blob<Dtype>*>& top) {
+  const Dtype* const_bottom_data = bottom[0]->gpu_data();
+  const Dtype* const_top_data = top[0]->gpu_data();
+  Dtype* top_data = top[0]->mutable_gpu_data();
+
+  const Dtype* scale_data = this->blobs_[0]->gpu_data();
+  const Dtype* shift_data = this->blobs_[1]->gpu_data();
+
+  // Mean normalization
+  if (frozen_ || this->phase_ == TEST) {
+    // Use the moving average mean
+    caffe_copy(batch_statistic_.count(), this->blobs_[2]->gpu_data(),
+        batch_statistic_.mutable_gpu_data());
+  } else {
+    // Compute the mean by averaging over spatial and batch dimensions.
+    caffe_gpu_gemv<Dtype>(CblasNoTrans, num_ * channels_, height_ * width_,
+        Dtype(1) / (height_ * width_), const_bottom_data,
+        spatial_sum_multiplier_.gpu_data(), Dtype(0),
+        spatial_statistic_.mutable_gpu_data());
+    caffe_gpu_gemv<Dtype>(CblasTrans, num_, channels_,
+        Dtype(1) / num_, spatial_statistic_.gpu_data(),
+        batch_sum_multiplier_.gpu_data(), Dtype(0),
+        batch_statistic_.mutable_gpu_data());
+    // Add to the moving average
+    if (!frozen_) {
+      caffe_gpu_axpby(batch_statistic_.count(),
+          Dtype(1) - bn_momentum_, batch_statistic_.gpu_data(),
+          bn_momentum_, this->blobs_[2]->mutable_gpu_data());
+    }
+  }
+  // Broadcast the mean vector
+  caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num_, channels_, 1,
+      Dtype(1), batch_sum_multiplier_.gpu_data(), batch_statistic_.gpu_data(),
+      Dtype(0), spatial_statistic_.mutable_gpu_data());
+  caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num_ * channels_,
+      height_ * width_, 1, Dtype(-1),
+      spatial_statistic_.gpu_data(), spatial_sum_multiplier_.gpu_data(),
+      Dtype(0), broadcast_buffer_.mutable_gpu_data());
+  // Subtract
+  caffe_gpu_add(broadcast_buffer_.count(), const_bottom_data,
+      broadcast_buffer_.gpu_data(), top_data);
+
+  // Variance normalization
+  if (frozen_ || this->phase_ == TEST) {
+    // Use the moving average variance
+    caffe_copy(batch_statistic_.count(), this->blobs_[3]->gpu_data(),
+        batch_statistic_.mutable_gpu_data());
+  } else {
+    caffe_gpu_powx(broadcast_buffer_.count(), const_top_data, Dtype(2),
+        broadcast_buffer_.mutable_gpu_data());
+    caffe_gpu_gemv<Dtype>(CblasNoTrans, num_ * channels_, height_ * width_,
+        Dtype(1) / (height_ * width_), broadcast_buffer_.gpu_data(),
+        spatial_sum_multiplier_.gpu_data(), Dtype(0),
+        spatial_statistic_.mutable_gpu_data());
+    caffe_gpu_gemv<Dtype>(CblasTrans, num_, channels_, Dtype(1) / num_,
+        spatial_statistic_.gpu_data(), batch_sum_multiplier_.gpu_data(),
+        Dtype(0), batch_statistic_.mutable_gpu_data());
+
+    // Add to the moving average
+    caffe_gpu_axpby(batch_statistic_.count(),
+        Dtype(1) - bn_momentum_, batch_statistic_.gpu_data(),
+        bn_momentum_, this->blobs_[3]->mutable_gpu_data());
+  }
+
+  // Add eps
+  caffe_gpu_add_scalar(batch_statistic_.count(), bn_eps_,
+        batch_statistic_.mutable_gpu_data());
+  // Inverse standard deviation
+  caffe_gpu_powx(batch_statistic_.count(), batch_statistic_.gpu_data(),
+        Dtype(-0.5), batch_statistic_.mutable_gpu_data());
+  // Broadcast the inverse std
+  caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num_, channels_, 1,
+      Dtype(1), batch_sum_multiplier_.gpu_data(), batch_statistic_.gpu_data(),
+      Dtype(0), spatial_statistic_.mutable_gpu_data());
+  caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num_ * channels_,
+      height_ * width_, 1, Dtype(1),
+      spatial_statistic_.gpu_data(), spatial_sum_multiplier_.gpu_data(),
+      Dtype(0), broadcast_buffer_.mutable_gpu_data());
+  // Multiply with the inverse std
+  caffe_gpu_mul(broadcast_buffer_.count(), const_top_data,
+      broadcast_buffer_.gpu_data(), top_data);
+
+  // Save the normalized inputs and std for backprop
+  if (!frozen_) {
+    caffe_copy(broadcast_buffer_.count(), const_top_data,
+        x_norm_.mutable_gpu_data());
+    caffe_copy(batch_statistic_.count(), batch_statistic_.gpu_data(),
+        x_inv_std_.mutable_gpu_data());
+  }
+
+  // Scale
+  caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num_, channels_, 1,
+      Dtype(1), batch_sum_multiplier_.gpu_data(), scale_data,
+      Dtype(0), spatial_statistic_.mutable_gpu_data());
+  caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num_ * channels_,
+      height_ * width_, 1, Dtype(1),
+      spatial_statistic_.gpu_data(), spatial_sum_multiplier_.gpu_data(),
+      Dtype(0), broadcast_buffer_.mutable_gpu_data());
+  caffe_gpu_mul(broadcast_buffer_.count(), const_top_data,
+      broadcast_buffer_.gpu_data(), top_data);
+
+  // Shift
+  caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num_, channels_, 1,
+      Dtype(1), batch_sum_multiplier_.gpu_data(), shift_data,
+      Dtype(0), spatial_statistic_.mutable_gpu_data());
+  caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num_ * channels_,
+      height_ * width_, 1, Dtype(1),
+      spatial_statistic_.gpu_data(), spatial_sum_multiplier_.gpu_data(),
+      Dtype(0), broadcast_buffer_.mutable_gpu_data());
+  caffe_gpu_add(broadcast_buffer_.count(), const_top_data,
+      broadcast_buffer_.gpu_data(), top_data);
+}
+
+template <typename Dtype>
+void BNLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
+  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+  if (frozen_) {
+    if (propagate_down[0]) {
+      const Dtype* const_top_diff = top[0]->gpu_diff();
+      Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
+      // Use the moving average variance
+      caffe_copy(batch_statistic_.count(), this->blobs_[3]->gpu_data(),
+          batch_statistic_.mutable_gpu_data());
+      caffe_gpu_add_scalar(batch_statistic_.count(), bn_eps_,
+          batch_statistic_.mutable_gpu_data());
+      caffe_gpu_powx(batch_statistic_.count(), batch_statistic_.gpu_data(),
+          Dtype(-0.5), batch_statistic_.mutable_gpu_data());
+      // Multiple slope with inverse std
+      caffe_gpu_mul(batch_statistic_.count(), this->blobs_[0]->gpu_data(),
+          batch_statistic_.gpu_data(), batch_statistic_.mutable_gpu_data());
+      // Broadcast
+      caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num_, channels_, 1,
+          Dtype(1), batch_sum_multiplier_.gpu_data(), batch_statistic_.gpu_data(),
+          Dtype(0), spatial_statistic_.mutable_gpu_data());
+      caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num_ * channels_,
+          height_ * width_, 1, Dtype(1),
+          spatial_statistic_.gpu_data(), spatial_sum_multiplier_.gpu_data(),
+          Dtype(0), broadcast_buffer_.mutable_gpu_data());
+      // Elementwise multiply top grad with (slope / std)
+      caffe_gpu_mul(broadcast_buffer_.count(), const_top_diff,
+          broadcast_buffer_.gpu_data(), bottom_diff);
+    }
+    return;
+  }
+
+  // gradient w.r.t. slope
+  if (this->param_propagate_down_[0]) {
+    const Dtype* const_top_diff = top[0]->gpu_diff();
+    Dtype* scale_diff = this->blobs_[0]->mutable_gpu_diff();
+    caffe_gpu_mul(broadcast_buffer_.count(), x_norm_.gpu_data(), const_top_diff,
+        broadcast_buffer_.mutable_gpu_data());
+    caffe_gpu_gemv<Dtype>(CblasNoTrans, num_ * channels_, height_ * width_,
+        Dtype(1), broadcast_buffer_.gpu_data(),
+        spatial_sum_multiplier_.gpu_data(), Dtype(0),
+        spatial_statistic_.mutable_gpu_data());
+    caffe_gpu_gemv<Dtype>(CblasTrans, num_, channels_, Dtype(1),
+        spatial_statistic_.gpu_data(), batch_sum_multiplier_.gpu_data(),
+        Dtype(1), scale_diff);
+  }
+
+  // gradient w.r.t. bias
+  if (this->param_propagate_down_[1]) {
+    const Dtype* const_top_diff = top[0]->gpu_diff();
+    Dtype* shift_diff = this->blobs_[1]->mutable_gpu_diff();
+    caffe_gpu_gemv<Dtype>(CblasNoTrans, num_ * channels_, height_ * width_,
+        Dtype(1), const_top_diff, spatial_sum_multiplier_.gpu_data(),
+        Dtype(0), spatial_statistic_.mutable_gpu_data());
+    caffe_gpu_gemv<Dtype>(CblasTrans, num_, channels_, Dtype(1),
+        spatial_statistic_.gpu_data(), batch_sum_multiplier_.gpu_data(),
+        Dtype(1), shift_diff);
+  }
+
+  // gradient w.r.t. normalized inputs
+  if (propagate_down[0]) {
+    const Dtype* const_top_diff = top[0]->gpu_diff();
+    const Dtype* const_bottom_diff = bottom[0]->gpu_diff();
+    Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
+    const Dtype* scale_data = this->blobs_[0]->gpu_data();
+    caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num_, channels_, 1,
+        Dtype(1), batch_sum_multiplier_.gpu_data(), scale_data,
+        Dtype(0), spatial_statistic_.mutable_gpu_data());
+    caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num_ * channels_,
+        height_ * width_, 1, Dtype(1), spatial_statistic_.gpu_data(),
+        spatial_sum_multiplier_.gpu_data(), Dtype(0),
+        broadcast_buffer_.mutable_gpu_data());
+    caffe_gpu_mul(broadcast_buffer_.count(), const_top_diff,
+        broadcast_buffer_.gpu_data(), broadcast_buffer_.mutable_gpu_data());
+
+    // sum of x_hat * (dl / dx_hat)
+    caffe_gpu_mul(broadcast_buffer_.count(), x_norm_.gpu_data(),
+        broadcast_buffer_.gpu_data(), bottom_diff);
+    caffe_gpu_gemv<Dtype>(CblasNoTrans, num_ * channels_, height_ * width_,
+        Dtype(1), const_bottom_diff, spatial_sum_multiplier_.gpu_data(),
+        Dtype(0), spatial_statistic_.mutable_gpu_data());
+    caffe_gpu_gemv<Dtype>(CblasTrans, num_, channels_, Dtype(1),
+        spatial_statistic_.gpu_data(), batch_sum_multiplier_.gpu_data(),
+        Dtype(0), batch_statistic_.mutable_gpu_data());
+
+    // x_hat times the sum
+    caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num_, channels_, 1,
+        Dtype(1), batch_sum_multiplier_.gpu_data(), batch_statistic_.gpu_data(),
+        Dtype(0), spatial_statistic_.mutable_gpu_data());
+    caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num_ * channels_,
+        height_ * width_, 1, Dtype(1),
+        spatial_statistic_.gpu_data(), spatial_sum_multiplier_.gpu_data(),
+        Dtype(0), bottom_diff);
+    caffe_gpu_mul(broadcast_buffer_.count(), x_norm_.gpu_data(),
+        const_bottom_diff, bottom_diff);
+
+    // Subtract the average of x_hat times the sum
+    caffe_gpu_gemv<Dtype>(CblasNoTrans, num_ * channels_, height_ * width_,
+        Dtype(1), broadcast_buffer_.gpu_data(),
+        spatial_sum_multiplier_.gpu_data(), Dtype(0),
+        spatial_statistic_.mutable_gpu_data());
+    caffe_gpu_gemv<Dtype>(CblasTrans, num_, channels_, Dtype(1),
+        spatial_statistic_.gpu_data(), batch_sum_multiplier_.gpu_data(),
+        Dtype(0), batch_statistic_.mutable_gpu_data());
+    caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num_, channels_, 1,
+        Dtype(1), batch_sum_multiplier_.gpu_data(), batch_statistic_.gpu_data(),
+        Dtype(0), spatial_statistic_.mutable_gpu_data());
+    caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num_ * channels_,
+        height_ * width_, 1, Dtype(1),
+        spatial_statistic_.gpu_data(), spatial_sum_multiplier_.gpu_data(),
+        Dtype(1), bottom_diff);
+    caffe_gpu_axpby(broadcast_buffer_.count(), Dtype(1),
+        broadcast_buffer_.gpu_data(), Dtype(-1) / (num_ * height_ * width_),
+        bottom_diff);
+
+    // Multiply with the inverse std
+    caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num_, channels_, 1,
+        Dtype(1), batch_sum_multiplier_.gpu_data(), x_inv_std_.gpu_data(),
+        Dtype(0), spatial_statistic_.mutable_gpu_data());
+    caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num_ * channels_,
+        height_ * width_, 1, Dtype(1),
+        spatial_statistic_.gpu_data(), spatial_sum_multiplier_.gpu_data(),
+        Dtype(0), broadcast_buffer_.mutable_gpu_data());
+    caffe_gpu_mul(broadcast_buffer_.count(), const_bottom_diff,
+        broadcast_buffer_.gpu_data(), bottom_diff);
+  }
+}
+
+INSTANTIATE_LAYER_GPU_FUNCS(BNLayer);
+
+}  // namespace caffe
diff --git a/src/caffe/layers/bnll_layer.cpp b/src/caffe/layers/bnll_layer.cpp
index ef98326a23e..9ba0ea9a715 100644
--- a/src/caffe/layers/bnll_layer.cpp
+++ b/src/caffe/layers/bnll_layer.cpp
@@ -10,9 +10,9 @@ const float kBNLL_THRESHOLD = 50.;
 
 template <typename Dtype>
 void BNLLLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-    vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& top) {
   const Dtype* bottom_data = bottom[0]->cpu_data();
-  Dtype* top_data = (*top)[0]->mutable_cpu_data();
+  Dtype* top_data = top[0]->mutable_cpu_data();
   const int count = bottom[0]->count();
   for (int i = 0; i < count; ++i) {
     top_data[i] = bottom_data[i] > 0 ?
@@ -24,12 +24,12 @@ void BNLLLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
 template <typename Dtype>
 void BNLLLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
     const vector<bool>& propagate_down,
-    vector<Blob<Dtype>*>* bottom) {
+    const vector<Blob<Dtype>*>& bottom) {
   if (propagate_down[0]) {
-    const Dtype* bottom_data = (*bottom)[0]->cpu_data();
+    const Dtype* bottom_data = bottom[0]->cpu_data();
     const Dtype* top_diff = top[0]->cpu_diff();
-    Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
-    const int count = (*bottom)[0]->count();
+    Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
+    const int count = bottom[0]->count();
     Dtype expval;
     for (int i = 0; i < count; ++i) {
       expval = exp(std::min(bottom_data[i], Dtype(kBNLL_THRESHOLD)));
@@ -43,6 +43,6 @@ STUB_GPU(BNLLLayer);
 #endif
 
 INSTANTIATE_CLASS(BNLLLayer);
-
+REGISTER_LAYER_CLASS(BNLL);
 
 }  // namespace caffe
diff --git a/src/caffe/layers/bnll_layer.cu b/src/caffe/layers/bnll_layer.cu
index b940133b4b3..d963d0687d2 100644
--- a/src/caffe/layers/bnll_layer.cu
+++ b/src/caffe/layers/bnll_layer.cu
@@ -19,9 +19,9 @@ __global__ void BNLLForward(const int n, const Dtype* in, Dtype* out) {
 
 template <typename Dtype>
 void BNLLLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-    vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& top) {
   const Dtype* bottom_data = bottom[0]->gpu_data();
-  Dtype* top_data = (*top)[0]->mutable_gpu_data();
+  Dtype* top_data = top[0]->mutable_gpu_data();
   const int count = bottom[0]->count();
   // NOLINT_NEXT_LINE(whitespace/operators)
   BNLLForward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
@@ -41,12 +41,12 @@ __global__ void BNLLBackward(const int n, const Dtype* in_diff,
 template <typename Dtype>
 void BNLLLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
     const vector<bool>& propagate_down,
-    vector<Blob<Dtype>*>* bottom) {
+    const vector<Blob<Dtype>*>& bottom) {
   if (propagate_down[0]) {
-    const Dtype* bottom_data = (*bottom)[0]->gpu_data();
+    const Dtype* bottom_data = bottom[0]->gpu_data();
     const Dtype* top_diff = top[0]->gpu_diff();
-    Dtype* bottom_diff = (*bottom)[0]->mutable_gpu_diff();
-    const int count = (*bottom)[0]->count();
+    Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
+    const int count = bottom[0]->count();
     // NOLINT_NEXT_LINE(whitespace/operators)
     BNLLBackward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
         count, top_diff, bottom_data, bottom_diff);
@@ -54,7 +54,7 @@ void BNLLLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
   }
 }
 
-INSTANTIATE_CLASS(BNLLLayer);
+INSTANTIATE_LAYER_GPU_FUNCS(BNLLLayer);
 
 
 }  // namespace caffe
diff --git a/src/caffe/layers/concat_layer.cpp b/src/caffe/layers/concat_layer.cpp
index 10a11f1bb7c..1cac8fc3387 100644
--- a/src/caffe/layers/concat_layer.cpp
+++ b/src/caffe/layers/concat_layer.cpp
@@ -8,96 +8,84 @@ namespace caffe {
 
 template <typename Dtype>
 void ConcatLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
-  concat_dim_ = this->layer_param_.concat_param().concat_dim();
-  CHECK_GE(concat_dim_, 0) <<
-    "concat_dim should be >= 0";
-  CHECK_LE(concat_dim_, 1) <<
-    "For now concat_dim <=1, it can only concat num and channels";
+      const vector<Blob<Dtype>*>& top) {
+  const ConcatParameter& concat_param = this->layer_param_.concat_param();
+  CHECK(!(concat_param.has_axis() && concat_param.has_concat_dim()))
+      << "Either axis or concat_dim should be specified; not both.";
 }
 
 template <typename Dtype>
 void ConcatLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
+      const vector<Blob<Dtype>*>& top) {
+  const int num_axes = bottom[0]->num_axes();
+  const ConcatParameter& concat_param = this->layer_param_.concat_param();
+  if (concat_param.has_concat_dim()) {
+    concat_axis_ = static_cast<int>(concat_param.concat_dim());
+    // Don't allow negative indexing for concat_dim, a uint32 -- almost
+    // certainly unintended.
+    CHECK_GE(concat_axis_, 0) << "casting concat_dim from uint32 to int32 "
+        << "produced negative result; concat_dim must satisfy "
+        << "0 <= concat_dim < " << kMaxBlobAxes;
+    CHECK_LT(concat_axis_, num_axes) << "concat_dim out of range.";
+  } else {
+    concat_axis_ = bottom[0]->CanonicalAxisIndex(concat_param.axis());
+  }
   // Initialize with the first blob.
-  count_ = bottom[0]->count();
-  num_ = bottom[0]->num();
-  channels_ = bottom[0]->channels();
-  height_ = bottom[0]->height();
-  width_ = bottom[0]->width();
+  vector<int> top_shape = bottom[0]->shape();
+  num_concats_ = bottom[0]->count(0, concat_axis_);
+  concat_input_size_ = bottom[0]->count(concat_axis_ + 1);
+  int bottom_count_sum = bottom[0]->count();
   for (int i = 1; i < bottom.size(); ++i) {
-    count_ += bottom[i]->count();
-    if (concat_dim_== 0) {
-      num_ += bottom[i]->num();
-    } else if (concat_dim_ == 1) {
-      channels_ += bottom[i]->channels();
-    } else if (concat_dim_ == 2) {
-      height_ += bottom[i]->height();
-    } else if (concat_dim_ == 3) {
-      width_ += bottom[i]->width();
+    CHECK_EQ(num_axes, bottom[i]->num_axes())
+        << "All inputs must have the same #axes.";
+    for (int j = 0; j < num_axes; ++j) {
+      if (j == concat_axis_) { continue; }
+      CHECK_EQ(top_shape[j], bottom[i]->shape(j))
+          << "All inputs must have the same shape, except at concat_axis.";
     }
+    bottom_count_sum += bottom[i]->count();
+    top_shape[concat_axis_] += bottom[i]->shape(concat_axis_);
   }
-  (*top)[0]->Reshape(num_, channels_, height_, width_);
-  CHECK_EQ(count_, (*top)[0]->count());
+  top[0]->Reshape(top_shape);
+  CHECK_EQ(bottom_count_sum, top[0]->count());
 }
 
 template <typename Dtype>
 void ConcatLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
-  Dtype* top_data = (*top)[0]->mutable_cpu_data();
-  if (concat_dim_== 0) {
-    int offset_num = 0;
-    for (int i = 0; i < bottom.size(); ++i) {
-      const Dtype* bottom_data = bottom[i]->cpu_data();
-      int num_elem = bottom[i]->count();
-      caffe_copy(num_elem, bottom_data, top_data+(*top)[0]->offset(offset_num));
-      offset_num += bottom[i]->num();
+      const vector<Blob<Dtype>*>& top) {
+  Dtype* top_data = top[0]->mutable_cpu_data();
+  int offset_concat_axis = 0;
+  const int top_concat_axis = top[0]->shape(concat_axis_);
+  for (int i = 0; i < bottom.size(); ++i) {
+    const Dtype* bottom_data = bottom[i]->cpu_data();
+    const int bottom_concat_axis = bottom[i]->shape(concat_axis_);
+    for (int n = 0; n < num_concats_; ++n) {
+      caffe_copy(bottom_concat_axis * concat_input_size_,
+          bottom_data + n * bottom_concat_axis * concat_input_size_,
+          top_data + (n * top_concat_axis + offset_concat_axis)
+              * concat_input_size_);
     }
-  } else if (concat_dim_ == 1) {
-    int offset_channel = 0;
-    for (int i = 0; i < bottom.size(); ++i) {
-      const Dtype* bottom_data = bottom[i]->cpu_data();
-      int num_elem =
-        bottom[i]->channels()*bottom[i]->height()*bottom[i]->width();
-      for (int n = 0; n < num_; ++n) {
-        caffe_copy(num_elem, bottom_data+bottom[i]->offset(n),
-          top_data+(*top)[0]->offset(n, offset_channel));
-      }
-      offset_channel += bottom[i]->channels();
-    }  // concat_dim_ is guaranteed to be 0 or 1 by LayerSetUp.
+    offset_concat_axis += bottom_concat_axis;
   }
 }
 
 template <typename Dtype>
 void ConcatLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
   const Dtype* top_diff = top[0]->cpu_diff();
-  if (concat_dim_ == 0) {
-    int offset_num = 0;
-    for (int i = 0; i < bottom->size(); ++i) {
-      Blob<Dtype>* blob = (*bottom)[i];
-      if (propagate_down[i]) {
-        Dtype* bottom_diff = blob->mutable_cpu_diff();
-        caffe_copy(blob->count(), top_diff + top[0]->offset(offset_num),
-                   bottom_diff);
-      }
-      offset_num += blob->num();
-    }
-  } else if (concat_dim_ == 1) {
-    int offset_channel = 0;
-    for (int i = 0; i < bottom->size(); ++i) {
-      Blob<Dtype>* blob = (*bottom)[i];
-      if (propagate_down[i]) {
-        Dtype* bottom_diff = blob->mutable_cpu_diff();
-        int num_elem = blob->channels()*blob->height()*blob->width();
-        for (int n = 0; n < num_; ++n) {
-          caffe_copy(num_elem, top_diff + top[0]->offset(n, offset_channel),
-                     bottom_diff + blob->offset(n));
-        }
-      }
-      offset_channel += blob->channels();
+  int offset_concat_axis = 0;
+  const int top_concat_axis = top[0]->shape(concat_axis_);
+  for (int i = 0; i < bottom.size(); ++i) {
+    if (!propagate_down[i]) { continue; }
+    Dtype* bottom_diff = bottom[i]->mutable_cpu_diff();
+    const int bottom_concat_axis = bottom[i]->shape(concat_axis_);
+    for (int n = 0; n < num_concats_; ++n) {
+      caffe_copy(bottom_concat_axis * concat_input_size_, top_diff +
+          (n * top_concat_axis + offset_concat_axis) * concat_input_size_,
+          bottom_diff + n * bottom_concat_axis * concat_input_size_);
     }
-  }  // concat_dim_ is guaranteed to be 0 or 1 by LayerSetUp.
+    offset_concat_axis += bottom_concat_axis;
+  }
 }
 
 #ifdef CPU_ONLY
@@ -105,5 +93,6 @@ STUB_GPU(ConcatLayer);
 #endif
 
 INSTANTIATE_CLASS(ConcatLayer);
+REGISTER_LAYER_CLASS(Concat);
 
 }  // namespace caffe
diff --git a/src/caffe/layers/concat_layer.cu b/src/caffe/layers/concat_layer.cu
index 99c55da25cb..8f2e85d8f52 100644
--- a/src/caffe/layers/concat_layer.cu
+++ b/src/caffe/layers/concat_layer.cu
@@ -7,70 +7,65 @@
 namespace caffe {
 
 template <typename Dtype>
-void ConcatLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
-  Dtype* top_data = (*top)[0]->mutable_gpu_data();
-  if (concat_dim_ == 0) {
-    int offset_num = 0;
-    for (int i = 0; i < bottom.size(); ++i) {
-      const Dtype* bottom_data = bottom[i]->gpu_data();
-      caffe_copy(bottom[i]->count(), bottom_data,
-        top_data + (*top)[0]->offset(offset_num));
-      offset_num += bottom[i]->num();
-    }
-  } else if (concat_dim_ == 1) {
-    int offset_channel = 0;
-    for (int i = 0; i < bottom.size(); ++i) {
-      const Dtype* bottom_data = bottom[i]->gpu_data();
-      int num_elem =
-        bottom[i]->channels() * bottom[i]->height() * bottom[i]->width();
-      for (int n = 0; n < num_; ++n) {
-        caffe_copy(num_elem, bottom_data+bottom[i]->offset(n),
-          top_data + (*top)[0]->offset(n, offset_channel));
-      }
-      offset_channel += bottom[i]->channels();
+__global__ void Concat(const int nthreads, const Dtype* in_data,
+    const bool forward, const int num_concats, const int concat_size,
+    const int top_concat_axis, const int bottom_concat_axis,
+    const int offset_concat_axis, Dtype* out_data) {
+  CUDA_KERNEL_LOOP(index, nthreads) {
+    const int total_concat_size = concat_size * bottom_concat_axis;
+    const int concat_num = index / total_concat_size;
+    const int concat_index = index % total_concat_size;
+    const int top_index = concat_index +
+        (concat_num * top_concat_axis + offset_concat_axis) * concat_size;
+    if (forward) {
+      out_data[top_index] = in_data[index];
+    } else {
+      out_data[index] = in_data[top_index];
     }
-  } else {
-    LOG(FATAL) << "concat_dim along dim" << concat_dim_ <<
-      " not implemented yet";
+  }
+}
+
+template <typename Dtype>
+void ConcatLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+  Dtype* top_data = top[0]->mutable_gpu_data();
+  int offset_concat_axis = 0;
+  const int top_concat_axis = top[0]->shape(concat_axis_);
+  const bool kForward = true;
+  for (int i = 0; i < bottom.size(); ++i) {
+    const Dtype* bottom_data = bottom[i]->gpu_data();
+    const int bottom_concat_axis = bottom[i]->shape(concat_axis_);
+    const int bottom_concat_size = bottom_concat_axis * concat_input_size_;
+    const int nthreads = bottom_concat_size * num_concats_;
+    Concat<Dtype>  // NOLINT_NEXT_LINE(whitespace/operators)
+        <<<CAFFE_GET_BLOCKS(nthreads), CAFFE_CUDA_NUM_THREADS>>>(
+        nthreads, bottom_data, kForward, num_concats_, concat_input_size_,
+        top_concat_axis, bottom_concat_axis, offset_concat_axis, top_data);
+    offset_concat_axis += bottom_concat_axis;
   }
 }
 
 template <typename Dtype>
 void ConcatLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
   const Dtype* top_diff = top[0]->gpu_diff();
-  if (concat_dim_ == 0) {
-    int offset_num = 0;
-    for (int i = 0; i < bottom->size(); ++i) {
-      Blob<Dtype>* blob = (*bottom)[i];
-      if (propagate_down[i]) {
-        Dtype* bottom_diff = blob->mutable_gpu_diff();
-        caffe_copy(blob->count(), top_diff + top[0]->offset(offset_num),
-                       bottom_diff);
-      }
-      offset_num += blob->num();
-    }
-  } else if (concat_dim_ == 1) {
-    int offset_channel = 0;
-    for (int i = 0; i < bottom->size(); ++i) {
-      Blob<Dtype>* blob = (*bottom)[i];
-      if (propagate_down[i]) {
-        Dtype* bottom_diff = blob->mutable_gpu_diff();
-        int num_elem = blob->channels()*blob->height()*blob->width();
-        for (int n = 0; n < num_; ++n) {
-          caffe_copy(num_elem, top_diff + top[0]->offset(n, offset_channel),
-                         bottom_diff + blob->offset(n));
-        }
-      }
-      offset_channel += blob->channels();
-    }
-  } else {
-    LOG(FATAL) << "concat_dim along dim" << concat_dim_ <<
-      " not implemented yet";
+  int offset_concat_axis = 0;
+  const int top_concat_axis = top[0]->shape(concat_axis_);
+  const bool kForward = false;
+  for (int i = 0; i < bottom.size(); ++i) {
+    if (!propagate_down[i]) { continue; }
+    Dtype* bottom_diff = bottom[i]->mutable_gpu_diff();
+    const int bottom_concat_axis = bottom[i]->shape(concat_axis_);
+    const int bottom_concat_size = bottom_concat_axis * concat_input_size_;
+    const int nthreads = bottom_concat_size * num_concats_;
+    Concat<Dtype>  // NOLINT_NEXT_LINE(whitespace/operators)
+        <<<CAFFE_GET_BLOCKS(nthreads), CAFFE_CUDA_NUM_THREADS>>>(
+        nthreads, top_diff, kForward, num_concats_, concat_input_size_,
+        top_concat_axis, bottom_concat_axis, offset_concat_axis, bottom_diff);
+    offset_concat_axis += bottom_concat_axis;
   }
 }
 
-INSTANTIATE_CLASS(ConcatLayer);
+INSTANTIATE_LAYER_GPU_FUNCS(ConcatLayer);
 
 }  // namespace caffe
diff --git a/src/caffe/layers/contrastive_loss_layer.cpp b/src/caffe/layers/contrastive_loss_layer.cpp
index 072a5a535be..25e167819d3 100644
--- a/src/caffe/layers/contrastive_loss_layer.cpp
+++ b/src/caffe/layers/contrastive_loss_layer.cpp
@@ -10,7 +10,7 @@ namespace caffe {
 
 template <typename Dtype>
 void ContrastiveLossLayer<Dtype>::LayerSetUp(
-  const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
+  const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
   LossLayer<Dtype>::LayerSetUp(bottom, top);
   CHECK_EQ(bottom[0]->channels(), bottom[1]->channels());
   CHECK_EQ(bottom[0]->height(), 1);
@@ -32,7 +32,7 @@ void ContrastiveLossLayer<Dtype>::LayerSetUp(
 template <typename Dtype>
 void ContrastiveLossLayer<Dtype>::Forward_cpu(
     const vector<Blob<Dtype>*>& bottom,
-    vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& top) {
   int count = bottom[0]->count();
   caffe_sub(
       count,
@@ -41,6 +41,8 @@ void ContrastiveLossLayer<Dtype>::Forward_cpu(
       diff_.mutable_cpu_data());  // a_i-b_i
   const int channels = bottom[0]->channels();
   Dtype margin = this->layer_param_.contrastive_loss_param().margin();
+  bool legacy_version =
+      this->layer_param_.contrastive_loss_param().legacy_version();
   Dtype loss(0.0);
   for (int i = 0; i < bottom[0]->num(); ++i) {
     dist_sq_.mutable_cpu_data()[i] = caffe_cpu_dot(channels,
@@ -48,27 +50,34 @@ void ContrastiveLossLayer<Dtype>::Forward_cpu(
     if (static_cast<int>(bottom[2]->cpu_data()[i])) {  // similar pairs
       loss += dist_sq_.cpu_data()[i];
     } else {  // dissimilar pairs
-      loss += std::max(margin-dist_sq_.cpu_data()[i], Dtype(0.0));
+      if (legacy_version) {
+        loss += std::max(margin - dist_sq_.cpu_data()[i], Dtype(0.0));
+      } else {
+        Dtype dist = std::max(margin - sqrt(dist_sq_.cpu_data()[i]), 0.0);
+        loss += dist*dist;
+      }
     }
   }
   loss = loss / static_cast<Dtype>(bottom[0]->num()) / Dtype(2);
-  (*top)[0]->mutable_cpu_data()[0] = loss;
+  top[0]->mutable_cpu_data()[0] = loss;
 }
 
 template <typename Dtype>
 void ContrastiveLossLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
-    const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
+    const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
   Dtype margin = this->layer_param_.contrastive_loss_param().margin();
+  bool legacy_version =
+      this->layer_param_.contrastive_loss_param().legacy_version();
   for (int i = 0; i < 2; ++i) {
     if (propagate_down[i]) {
       const Dtype sign = (i == 0) ? 1 : -1;
       const Dtype alpha = sign * top[0]->cpu_diff()[0] /
-          static_cast<Dtype>((*bottom)[i]->num());
-      int num = (*bottom)[i]->num();
-      int channels = (*bottom)[i]->channels();
+          static_cast<Dtype>(bottom[i]->num());
+      int num = bottom[i]->num();
+      int channels = bottom[i]->channels();
       for (int j = 0; j < num; ++j) {
-        Dtype* bout = (*bottom)[i]->mutable_cpu_diff();
-        if (static_cast<int>((*bottom)[2]->cpu_data()[j])) {  // similar pairs
+        Dtype* bout = bottom[i]->mutable_cpu_diff();
+        if (static_cast<int>(bottom[2]->cpu_data()[j])) {  // similar pairs
           caffe_cpu_axpby(
               channels,
               alpha,
@@ -76,10 +85,20 @@ void ContrastiveLossLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
               Dtype(0.0),
               bout + (j*channels));
         } else {  // dissimilar pairs
-          if ((margin-dist_sq_.cpu_data()[j]) > Dtype(0.0)) {
+          Dtype mdist(0.0);
+          Dtype beta(0.0);
+          if (legacy_version) {
+            mdist = margin - dist_sq_.cpu_data()[j];
+            beta = -alpha;
+          } else {
+            Dtype dist = sqrt(dist_sq_.cpu_data()[j]);
+            mdist = margin - dist;
+            beta = -alpha * mdist / (dist + Dtype(1e-4));
+          }
+          if (mdist > Dtype(0.0)) {
             caffe_cpu_axpby(
                 channels,
-                -alpha,
+                beta,
                 diff_.cpu_data() + (j*channels),
                 Dtype(0.0),
                 bout + (j*channels));
@@ -97,5 +116,6 @@ STUB_GPU(ContrastiveLossLayer);
 #endif
 
 INSTANTIATE_CLASS(ContrastiveLossLayer);
+REGISTER_LAYER_CLASS(ContrastiveLoss);
 
 }  // namespace caffe
diff --git a/src/caffe/layers/contrastive_loss_layer.cu b/src/caffe/layers/contrastive_loss_layer.cu
index 672ad5bc2f8..931239316ac 100644
--- a/src/caffe/layers/contrastive_loss_layer.cu
+++ b/src/caffe/layers/contrastive_loss_layer.cu
@@ -10,7 +10,7 @@ namespace caffe {
 
 template <typename Dtype>
 void ContrastiveLossLayer<Dtype>::Forward_gpu(
-    const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
   const int count = bottom[0]->count();
   caffe_gpu_sub(
       count,
@@ -32,21 +32,29 @@ void ContrastiveLossLayer<Dtype>::Forward_gpu(
       Dtype(0.0),
       dist_sq_.mutable_gpu_data());  // \Sum (a_i-b_i)^2
   Dtype margin = this->layer_param_.contrastive_loss_param().margin();
+  bool legacy_version =
+      this->layer_param_.contrastive_loss_param().legacy_version();
   Dtype loss(0.0);
   for (int i = 0; i < bottom[0]->num(); ++i) {
     if (static_cast<int>(bottom[2]->cpu_data()[i])) {  // similar pairs
       loss += dist_sq_.cpu_data()[i];
     } else {  // dissimilar pairs
-      loss += std::max(margin-dist_sq_.cpu_data()[i], Dtype(0.0));
+      if (legacy_version) {
+        loss += std::max(margin - dist_sq_.cpu_data()[i], Dtype(0.0));
+      } else {
+        Dtype dist = std::max(margin - sqrt(dist_sq_.cpu_data()[i]),
+                              Dtype(0.0));
+        loss += dist*dist;
+      }
     }
   }
   loss = loss / static_cast<Dtype>(bottom[0]->num()) / Dtype(2);
-  (*top)[0]->mutable_cpu_data()[0] = loss;
+  top[0]->mutable_cpu_data()[0] = loss;
 }
 
 template <typename Dtype>
-__global__ void CLLForward(const int count, const int channels,
-    const Dtype margin, const Dtype alpha,
+__global__ void CLLBackward(const int count, const int channels,
+    const Dtype margin, const bool legacy_version, const Dtype alpha,
     const Dtype* y, const Dtype* diff, const Dtype* dist_sq,
     Dtype *bottom_diff) {
   CUDA_KERNEL_LOOP(i, count) {
@@ -54,8 +62,18 @@ __global__ void CLLForward(const int count, const int channels,
     if (static_cast<int>(y[n])) {  // similar pairs
       bottom_diff[i] = alpha * diff[i];
     } else {  // dissimilar pairs
-      if ((margin-dist_sq[n]) > 0.0) {
-        bottom_diff[i] = -alpha * diff[i];
+      Dtype mdist(0.0);
+      Dtype beta(0.0);
+      if (legacy_version) {
+        mdist = (margin - dist_sq[n]);
+        beta = -alpha;
+      } else {
+        Dtype dist = sqrt(dist_sq[n]);
+        mdist = (margin - dist);
+        beta = -alpha * mdist / (dist + Dtype(1e-4)) * diff[i];
+      }
+      if (mdist > 0.0) {
+        bottom_diff[i] = beta;
       } else {
         bottom_diff[i] = 0;
       }
@@ -65,27 +83,29 @@ __global__ void CLLForward(const int count, const int channels,
 
 template <typename Dtype>
 void ContrastiveLossLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
-    const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
+    const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
   for (int i = 0; i < 2; ++i) {
     if (propagate_down[i]) {
-      const int count = (*bottom)[0]->count();
-      const int channels = (*bottom)[0]->channels();
+      const int count = bottom[0]->count();
+      const int channels = bottom[0]->channels();
       Dtype margin = this->layer_param_.contrastive_loss_param().margin();
+      const bool legacy_version =
+          this->layer_param_.contrastive_loss_param().legacy_version();
       const Dtype sign = (i == 0) ? 1 : -1;
       const Dtype alpha = sign * top[0]->cpu_diff()[0] /
-          static_cast<Dtype>((*bottom)[0]->num());
+          static_cast<Dtype>(bottom[0]->num());
       // NOLINT_NEXT_LINE(whitespace/operators)
-      CLLForward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
-          count, channels, margin, alpha,
-          (*bottom)[2]->gpu_data(),  // pair similarity 0 or 1
+      CLLBackward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
+          count, channels, margin, legacy_version, alpha,
+          bottom[2]->gpu_data(),  // pair similarity 0 or 1
           diff_.gpu_data(),  // the cached eltwise difference between a and b
           dist_sq_.gpu_data(),  // the cached square distance between a and b
-          (*bottom)[i]->mutable_gpu_diff());
+          bottom[i]->mutable_gpu_diff());
       CUDA_POST_KERNEL_CHECK;
     }
   }
 }
 
-INSTANTIATE_CLASS(ContrastiveLossLayer);
+INSTANTIATE_LAYER_GPU_FUNCS(ContrastiveLossLayer);
 
 }  // namespace caffe
diff --git a/src/caffe/layers/conv_layer.cpp b/src/caffe/layers/conv_layer.cpp
index 58918fd4baf..928ef5ee468 100644
--- a/src/caffe/layers/conv_layer.cpp
+++ b/src/caffe/layers/conv_layer.cpp
@@ -9,157 +9,26 @@
 namespace caffe {
 
 template <typename Dtype>
-void ConvolutionLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
-  // Configure the kernel size, padding, stride, and inputs.
-  ConvolutionParameter conv_param = this->layer_param_.convolution_param();
-  CHECK(!conv_param.has_kernel_size() !=
-      !(conv_param.has_kernel_h() && conv_param.has_kernel_w()))
-      << "Filter size is kernel_size OR kernel_h and kernel_w; not both";
-  CHECK(conv_param.has_kernel_size() ||
-      (conv_param.has_kernel_h() && conv_param.has_kernel_w()))
-      << "For non-square filters both kernel_h and kernel_w are required.";
-  CHECK((!conv_param.has_pad() && conv_param.has_pad_h()
-      && conv_param.has_pad_w())
-      || (!conv_param.has_pad_h() && !conv_param.has_pad_w()))
-      << "pad is pad OR pad_h and pad_w are required.";
-  CHECK((!conv_param.has_stride() && conv_param.has_stride_h()
-      && conv_param.has_stride_w())
-      || (!conv_param.has_stride_h() && !conv_param.has_stride_w()))
-      << "Stride is stride OR stride_h and stride_w are required.";
-  if (conv_param.has_kernel_size()) {
-    kernel_h_ = kernel_w_ = conv_param.kernel_size();
-  } else {
-    kernel_h_ = conv_param.kernel_h();
-    kernel_w_ = conv_param.kernel_w();
-  }
-  CHECK_GT(kernel_h_, 0) << "Filter dimensions cannot be zero.";
-  CHECK_GT(kernel_w_, 0) << "Filter dimensions cannot be zero.";
-  if (!conv_param.has_pad_h()) {
-    pad_h_ = pad_w_ = conv_param.pad();
-  } else {
-    pad_h_ = conv_param.pad_h();
-    pad_w_ = conv_param.pad_w();
-  }
-  if (!conv_param.has_stride_h()) {
-    stride_h_ = stride_w_ = conv_param.stride();
-  } else {
-    stride_h_ = conv_param.stride_h();
-    stride_w_ = conv_param.stride_w();
-  }
-  // Configure output channels and groups.
-  channels_ = bottom[0]->channels();
-  num_output_ = this->layer_param_.convolution_param().num_output();
-  CHECK_GT(num_output_, 0);
-  group_ = this->layer_param_.convolution_param().group();
-  CHECK_EQ(channels_ % group_, 0);
-  CHECK_EQ(num_output_ % group_, 0)
-      << "Number of output should be multiples of group.";
-  // Handle the parameters: weights and biases.
-  // - blobs_[0] holds the filter weights
-  // - blobs_[1] holds the biases (optional)
-  bias_term_ = this->layer_param_.convolution_param().bias_term();
-  if (this->blobs_.size() > 0) {
-    LOG(INFO) << "Skipping parameter initialization";
-  } else {
-    if (bias_term_) {
-      this->blobs_.resize(2);
-    } else {
-      this->blobs_.resize(1);
-    }
-    // Initialize and fill the weights:
-    // output channels x input channels per-group x kernel height x kernel width
-    this->blobs_[0].reset(new Blob<Dtype>(
-        num_output_, channels_ / group_, kernel_h_, kernel_w_));
-    shared_ptr<Filler<Dtype> > weight_filler(GetFiller<Dtype>(
-        this->layer_param_.convolution_param().weight_filler()));
-    weight_filler->Fill(this->blobs_[0].get());
-    // If necessary, initialize and fill the biases:
-    // 1 x 1 x 1 x output channels
-    if (bias_term_) {
-      this->blobs_[1].reset(new Blob<Dtype>(1, 1, 1, num_output_));
-      shared_ptr<Filler<Dtype> > bias_filler(GetFiller<Dtype>(
-          this->layer_param_.convolution_param().bias_filler()));
-      bias_filler->Fill(this->blobs_[1].get());
-    }
-  }
-  // Propagate gradients to the parameters (as directed by backward pass).
-  this->param_propagate_down_.resize(this->blobs_.size(), true);
-}
-
-template <typename Dtype>
-void ConvolutionLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
-  num_ = bottom[0]->num();
-  height_ = bottom[0]->height();
-  width_ = bottom[0]->width();
-  CHECK_EQ(bottom[0]->channels(), channels_) << "Input size incompatible with"
-    " convolution kernel.";
-  // TODO: generalize to handle inputs of different shapes.
-  for (int bottom_id = 1; bottom_id < bottom.size(); ++bottom_id) {
-    CHECK_EQ(num_, bottom[bottom_id]->num()) << "Inputs must have same num.";
-    CHECK_EQ(channels_, bottom[bottom_id]->channels())
-        << "Inputs must have same channels.";
-    CHECK_EQ(height_, bottom[bottom_id]->height())
-        << "Inputs must have same height.";
-    CHECK_EQ(width_, bottom[bottom_id]->width())
-        << "Inputs must have same width.";
-  }
-  // Shape the tops.
-  height_out_ =
-      (height_ + 2 * pad_h_ - kernel_h_) / stride_h_ + 1;
-  width_out_ = (width_ + 2 * pad_w_ - kernel_w_) / stride_w_ + 1;
-  for (int top_id = 0; top_id < top->size(); ++top_id) {
-    (*top)[top_id]->Reshape(num_, num_output_, height_out_, width_out_);
-  }
-  // Prepare the matrix multiplication computation.
-  // Each input will be convolved as a single GEMM.
-  M_ = num_output_ / group_;
-  K_ = channels_ * kernel_h_ * kernel_w_ / group_;
-  N_ = height_out_ * width_out_;
-  // The im2col result buffer will only hold one image at a time to avoid
-  // overly large memory usage.
-  col_buffer_.Reshape(
-      1, channels_ * kernel_h_ * kernel_w_, height_out_, width_out_);
-  for (int top_id = 0; top_id < top->size(); ++top_id) {
-    (*top)[top_id]->Reshape(num_, num_output_, height_out_, width_out_);
-  }
-  // Set up the all ones "bias multiplier" for adding biases by BLAS
-  if (bias_term_) {
-    bias_multiplier_.Reshape(1, 1, 1, N_);
-    caffe_set(N_, Dtype(1), bias_multiplier_.mutable_cpu_data());
-  }
+void ConvolutionLayer<Dtype>::compute_output_shape() {
+  this->height_out_ = (this->height_ + 2 * this->pad_h_ - this->kernel_h_)
+      / this->stride_h_ + 1;
+  this->width_out_ = (this->width_ + 2 * this->pad_w_ - this->kernel_w_)
+      / this->stride_w_ + 1;
 }
 
 template <typename Dtype>
 void ConvolutionLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
+      const vector<Blob<Dtype>*>& top) {
+  const Dtype* weight = this->blobs_[0]->cpu_data();
   for (int i = 0; i < bottom.size(); ++i) {
     const Dtype* bottom_data = bottom[i]->cpu_data();
-    Dtype* top_data = (*top)[i]->mutable_cpu_data();
-    Dtype* col_data = col_buffer_.mutable_cpu_data();
-    const Dtype* weight = this->blobs_[0]->cpu_data();
-    int weight_offset = M_ * K_;  // number of filter parameters in a group
-    int col_offset = K_ * N_;  // number of values in an input region / column
-    int top_offset = M_ * N_;  // number of values in an output region / column
-    for (int n = 0; n < num_; ++n) {
-      // im2col transformation: unroll input regions for filtering
-      // into column matrix for multplication.
-      im2col_cpu(bottom_data + bottom[i]->offset(n), channels_, height_,
-          width_, kernel_h_, kernel_w_, pad_h_, pad_w_, stride_h_, stride_w_,
-          col_data);
-      // Take inner products for groups.
-      for (int g = 0; g < group_; ++g) {
-        caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, M_, N_, K_,
-          (Dtype)1., weight + weight_offset * g, col_data + col_offset * g,
-          (Dtype)0., top_data + (*top)[i]->offset(n) + top_offset * g);
-      }
-      // Add bias.
-      if (bias_term_) {
-        caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num_output_,
-            N_, 1, (Dtype)1., this->blobs_[1]->cpu_data(),
-            bias_multiplier_.cpu_data(),
-            (Dtype)1., top_data + (*top)[i]->offset(n));
+    Dtype* top_data = top[i]->mutable_cpu_data();
+    for (int n = 0; n < this->num_; ++n) {
+      this->forward_cpu_gemm(bottom_data + bottom[i]->offset(n), weight,
+          top_data + top[i]->offset(n));
+      if (this->bias_term_) {
+        const Dtype* bias = this->blobs_[1]->cpu_data();
+        this->forward_cpu_bias(top_data + top[i]->offset(n), bias);
       }
     }
   }
@@ -167,72 +36,31 @@ void ConvolutionLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 void ConvolutionLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
-  const Dtype* weight = NULL;
-  Dtype* weight_diff = NULL;
-  if (this->param_propagate_down_[0]) {
-    weight = this->blobs_[0]->cpu_data();
-    weight_diff = this->blobs_[0]->mutable_cpu_diff();
-    caffe_set(this->blobs_[0]->count(), Dtype(0), weight_diff);
-  }
-  Dtype* bias_diff = NULL;
-  if (bias_term_ && this->param_propagate_down_[1]) {
-    bias_diff = this->blobs_[1]->mutable_cpu_diff();
-    caffe_set(this->blobs_[1]->count(), Dtype(0), bias_diff);
-  }
-  const int weight_offset = M_ * K_;
-  const int col_offset = K_ * N_;
-  const int top_offset = M_ * N_;
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+  const Dtype* weight = this->blobs_[0]->cpu_data();
+  Dtype* weight_diff = this->blobs_[0]->mutable_cpu_diff();
   for (int i = 0; i < top.size(); ++i) {
-    const Dtype* top_diff = NULL;
+    const Dtype* top_diff = top[i]->cpu_diff();
+    const Dtype* bottom_data = bottom[i]->cpu_data();
+    Dtype* bottom_diff = bottom[i]->mutable_cpu_diff();
     // Bias gradient, if necessary.
-    if (bias_term_ && this->param_propagate_down_[1]) {
-      top_diff = top[i]->cpu_diff();
-      for (int n = 0; n < num_; ++n) {
-        caffe_cpu_gemv<Dtype>(CblasNoTrans, num_output_, N_,
-            1., top_diff + top[0]->offset(n),
-            bias_multiplier_.cpu_data(), 1.,
-            bias_diff);
+    if (this->bias_term_ && this->param_propagate_down_[1]) {
+      Dtype* bias_diff = this->blobs_[1]->mutable_cpu_diff();
+      for (int n = 0; n < this->num_; ++n) {
+        this->backward_cpu_bias(bias_diff, top_diff + top[i]->offset(n));
       }
     }
     if (this->param_propagate_down_[0] || propagate_down[i]) {
-      if (!top_diff) {
-        top_diff = top[i]->cpu_diff();
-      }
-      Dtype* col_data = col_buffer_.mutable_cpu_data();
-      Dtype* col_diff = col_buffer_.mutable_cpu_diff();
-      const Dtype* bottom_data = (*bottom)[i]->cpu_data();
-      Dtype* bottom_diff = (*bottom)[i]->mutable_cpu_diff();
-      for (int n = 0; n < num_; ++n) {
-        // Since we saved memory in the forward pass by not storing all col
-        // data, we will need to recompute them.
-        im2col_cpu(bottom_data + (*bottom)[i]->offset(n), channels_, height_,
-                   width_, kernel_h_, kernel_w_, pad_h_, pad_w_,
-                   stride_h_, stride_w_, col_data);
+      for (int n = 0; n < this->num_; ++n) {
         // gradient w.r.t. weight. Note that we will accumulate diffs.
         if (this->param_propagate_down_[0]) {
-          for (int g = 0; g < group_; ++g) {
-            caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasTrans, M_, K_, N_,
-                (Dtype)1., top_diff + top[i]->offset(n) + top_offset * g,
-                col_data + col_offset * g, (Dtype)1.,
-                weight_diff + weight_offset * g);
-          }
+          this->weight_cpu_gemm(bottom_data + bottom[i]->offset(n),
+              top_diff + top[i]->offset(n), weight_diff);
         }
         // gradient w.r.t. bottom data, if necessary.
         if (propagate_down[i]) {
-          if (weight == NULL) {
-            weight = this->blobs_[0]->cpu_data();
-          }
-          for (int g = 0; g < group_; ++g) {
-            caffe_cpu_gemm<Dtype>(CblasTrans, CblasNoTrans, K_, N_, M_,
-                (Dtype)1., weight + weight_offset * g,
-                top_diff + top[i]->offset(n) + top_offset * g,
-                (Dtype)0., col_diff + col_offset * g);
-          }
-          // col2im back to the data
-          col2im_cpu(col_diff, channels_, height_, width_,
-              kernel_h_, kernel_w_, pad_h_, pad_w_,
-              stride_h_, stride_w_, bottom_diff + (*bottom)[i]->offset(n));
+          this->backward_cpu_gemm(top_diff + top[i]->offset(n), weight,
+              bottom_diff + bottom[i]->offset(n));
         }
       }
     }
diff --git a/src/caffe/layers/conv_layer.cu b/src/caffe/layers/conv_layer.cu
index 43f76a2368f..b8a98ff7cc9 100644
--- a/src/caffe/layers/conv_layer.cu
+++ b/src/caffe/layers/conv_layer.cu
@@ -8,117 +8,57 @@
 
 namespace caffe {
 
-/// @brief refer to CPU forward -- the BLAS implementation is the same.
 template <typename Dtype>
 void ConvolutionLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
+      const vector<Blob<Dtype>*>& top) {
+  const Dtype* weight = this->blobs_[0]->gpu_data();
   for (int i = 0; i < bottom.size(); ++i) {
     const Dtype* bottom_data = bottom[i]->gpu_data();
-    Dtype* top_data = (*top)[i]->mutable_gpu_data();
-    Dtype* col_data = col_buffer_.mutable_gpu_data();
-    const Dtype* weight = this->blobs_[0]->gpu_data();
-    int weight_offset = M_ * K_;
-    int col_offset = K_ * N_;
-    int top_offset = M_ * N_;
-    for (int n = 0; n < num_; ++n) {
-      // im2col transformation: unroll input regions for filtering
-      // into column matrix for multplication.
-      im2col_gpu(bottom_data + bottom[i]->offset(n), channels_, height_,
-          width_, kernel_h_, kernel_w_, pad_h_, pad_w_, stride_h_, stride_w_,
-          col_data);
-      // Take inner products for groups.
-      for (int g = 0; g < group_; ++g) {
-        caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, M_, N_, K_,
-          (Dtype)1., weight + weight_offset * g, col_data + col_offset * g,
-          (Dtype)0., top_data + (*top)[i]->offset(n) + top_offset * g);
-      }
-      // Add bias.
-      if (bias_term_) {
-        caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num_output_,
-            N_, 1, (Dtype)1., this->blobs_[1]->gpu_data(),
-            bias_multiplier_.gpu_data(),
-            (Dtype)1., top_data + (*top)[i]->offset(n));
+    Dtype* top_data = top[i]->mutable_gpu_data();
+    for (int n = 0; n < this->num_; ++n) {
+      this->forward_gpu_gemm(bottom_data + bottom[i]->offset(n), weight,
+          top_data + top[i]->offset(n));
+      if (this->bias_term_) {
+        const Dtype* bias = this->blobs_[1]->gpu_data();
+        this->forward_gpu_bias(top_data + top[i]->offset(n), bias);
       }
     }
   }
 }
 
-/// @brief refer to CPU backward -- the BLAS implementation is the same.
 template <typename Dtype>
 void ConvolutionLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
-  const Dtype* weight = NULL;
-  Dtype* weight_diff = NULL;
-  if (this->param_propagate_down_[0]) {
-    weight = this->blobs_[0]->gpu_data();
-    weight_diff = this->blobs_[0]->mutable_gpu_diff();
-    caffe_gpu_set(this->blobs_[0]->count(), Dtype(0), weight_diff);
-  }
-  Dtype* bias_diff = NULL;
-  if (bias_term_ && this->param_propagate_down_[1]) {
-    bias_diff = this->blobs_[1]->mutable_gpu_diff();
-    caffe_gpu_set(this->blobs_[1]->count(), Dtype(0), bias_diff);
-  }
-  const int weight_offset = M_ * K_;
-  const int col_offset = K_ * N_;
-  const int top_offset = M_ * N_;
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+  const Dtype* weight = this->blobs_[0]->gpu_data();
+  Dtype* weight_diff = this->blobs_[0]->mutable_gpu_diff();
   for (int i = 0; i < top.size(); ++i) {
-    const Dtype* top_diff = NULL;
+    const Dtype* top_diff = top[i]->gpu_diff();
     // Bias gradient, if necessary.
-    if (bias_term_ && this->param_propagate_down_[1]) {
-      top_diff = top[i]->gpu_diff();
-      for (int n = 0; n < num_; ++n) {
-        caffe_gpu_gemv<Dtype>(CblasNoTrans, num_output_, N_,
-            1., top_diff + top[0]->offset(n),
-            bias_multiplier_.gpu_data(), 1.,
-            bias_diff);
+    if (this->bias_term_ && this->param_propagate_down_[1]) {
+      Dtype* bias_diff = this->blobs_[1]->mutable_gpu_diff();
+      for (int n = 0; n < this->num_; ++n) {
+        this->backward_gpu_bias(bias_diff, top_diff + top[i]->offset(n));
       }
     }
     if (this->param_propagate_down_[0] || propagate_down[i]) {
-      if (!top_diff) {
-        top_diff = top[i]->gpu_diff();
-      }
-      Dtype* col_data = col_buffer_.mutable_gpu_data();
-      Dtype* col_diff = col_buffer_.mutable_gpu_diff();
-      const Dtype* bottom_data = (*bottom)[i]->gpu_data();
-      Dtype* bottom_diff = (*bottom)[i]->mutable_gpu_diff();
-      for (int n = 0; n < num_; ++n) {
-        // Since we saved memory in the forward pass by not storing all col
-        // data, we will need to recompute them.
-        im2col_gpu(bottom_data + (*bottom)[i]->offset(n), channels_, height_,
-                   width_, kernel_h_, kernel_w_, pad_h_, pad_w_,
-                   stride_h_, stride_w_, col_data);
+      const Dtype* bottom_data = bottom[i]->gpu_data();
+      Dtype* bottom_diff = bottom[i]->mutable_gpu_diff();
+      for (int n = 0; n < this->num_; ++n) {
         // gradient w.r.t. weight. Note that we will accumulate diffs.
         if (this->param_propagate_down_[0]) {
-          for (int g = 0; g < group_; ++g) {
-            caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasTrans, M_, K_, N_,
-                (Dtype)1., top_diff + top[i]->offset(n) + top_offset * g,
-                col_data + col_offset * g, (Dtype)1.,
-                weight_diff + weight_offset * g);
-          }
+          this->weight_gpu_gemm(bottom_data + bottom[i]->offset(n),
+              top_diff + top[i]->offset(n), weight_diff);
         }
-        // gradient w.r.t. bottom data, if necessary
+        // gradient w.r.t. bottom data, if necessary.
         if (propagate_down[i]) {
-          if (weight == NULL) {
-            weight = this->blobs_[0]->gpu_data();
-          }
-          for (int g = 0; g < group_; ++g) {
-            caffe_gpu_gemm<Dtype>(CblasTrans, CblasNoTrans, K_, N_, M_,
-                (Dtype)1., weight + weight_offset * g,
-                top_diff + top[i]->offset(n) + top_offset * g,
-                (Dtype)0., col_diff + col_offset * g);
-          }
-          // col2im back to the data
-          col2im_gpu(col_diff, channels_, height_, width_,
-              kernel_h_, kernel_w_, pad_h_, pad_w_, stride_h_, stride_w_,
-              bottom_diff + (*bottom)[i]->offset(n));
+          this->backward_gpu_gemm(top_diff + top[i]->offset(n), weight,
+              bottom_diff + bottom[i]->offset(n));
         }
       }
     }
   }
 }
 
-
-INSTANTIATE_CLASS(ConvolutionLayer);
+INSTANTIATE_LAYER_GPU_FUNCS(ConvolutionLayer);
 
 }  // namespace caffe
diff --git a/src/caffe/layers/cudnn_bn_layer.cpp b/src/caffe/layers/cudnn_bn_layer.cpp
new file mode 100644
index 00000000000..a73cf0dc8ce
--- /dev/null
+++ b/src/caffe/layers/cudnn_bn_layer.cpp
@@ -0,0 +1,83 @@
+#ifdef USE_CUDNN
+#include <algorithm>
+#include <cfloat>
+#include <vector>
+
+#include "thrust/device_vector.h"
+
+#include "caffe/layer.hpp"
+#include "caffe/util/math_functions.hpp"
+#include "caffe/vision_layers.hpp"
+
+#if CUDNN_VERSION_MIN(5, 0, 0)
+
+namespace caffe {
+
+template <typename Dtype>
+void CuDNNBNLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+  BNLayer<Dtype>::LayerSetUp(bottom, top);
+  save_mean_.ReshapeLike(*(this->blobs_[2]));
+  save_inv_variance_.ReshapeLike(*(this->blobs_[3]));
+
+  // Initialize CUDNN.
+  CUDNN_CHECK(cudnnCreate(&handle_));
+  cudnn::createTensor4dDesc<Dtype>(&bottom_desc_);
+  cudnn::createTensor4dDesc<Dtype>(&top_desc_);
+  cudnn::createTensor4dDesc<Dtype>(&bn_param_desc_);
+  handles_setup_ = true;
+  
+  LOG(INFO)<<"using cuDNN BN engine";
+}
+
+template <typename Dtype>
+void CuDNNBNLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+  // Do not call BNLayer::Reshape function as some members are unnecessary
+  this->num_ = bottom[0]->num();
+  this->channels_ = bottom[0]->channels();
+  this->height_ = bottom[0]->height();
+  this->width_ = bottom[0]->width();
+
+  top[0]->ReshapeLike(*(bottom[0]));
+
+  // CUDNN tensors
+  cudnn::setTensor4dDesc<Dtype>(&bottom_desc_, this->num_, this->channels_,
+                                this->height_, this->width_);
+  cudnn::setTensor4dDesc<Dtype>(&top_desc_, this->num_, this->channels_,
+                                this->height_, this->width_);
+  // Fix to the spatial mode
+  CUDNN_CHECK(cudnnDeriveBNTensorDescriptor(bn_param_desc_,
+      bottom_desc_, CUDNN_BATCHNORM_SPATIAL));
+
+  if (this->frozen_){
+    this->broadcast_buffer_.ReshapeLike(*(bottom[0]));
+    this->spatial_statistic_.Reshape(this->num_, this->channels_, 1, 1);
+    this->batch_statistic_.Reshape(1, this->channels_, 1, 1);
+
+    this->spatial_sum_multiplier_.Reshape(1, 1, this->height_, this->width_);
+    caffe_set(this->spatial_sum_multiplier_.count(), Dtype(1),
+      this->spatial_sum_multiplier_.mutable_cpu_data());
+    this->batch_sum_multiplier_.Reshape(this->num_, 1, 1, 1);
+    caffe_set(this->batch_sum_multiplier_.count(), Dtype(1),
+      this->batch_sum_multiplier_.mutable_cpu_data());
+
+  }
+}
+
+template <typename Dtype>
+CuDNNBNLayer<Dtype>::~CuDNNBNLayer() {
+  // Check that handles have been setup before destroying.
+  if (!handles_setup_) { return; }
+
+  cudnnDestroyTensorDescriptor(bottom_desc_);
+  cudnnDestroyTensorDescriptor(top_desc_);
+  cudnnDestroyTensorDescriptor(bn_param_desc_);
+  cudnnDestroy(handle_);
+}
+
+INSTANTIATE_CLASS(CuDNNBNLayer);
+
+}  // namespace caffe
+#endif
+#endif
diff --git a/src/caffe/layers/cudnn_bn_layer.cu b/src/caffe/layers/cudnn_bn_layer.cu
new file mode 100644
index 00000000000..66d1e5aa0c5
--- /dev/null
+++ b/src/caffe/layers/cudnn_bn_layer.cu
@@ -0,0 +1,98 @@
+#ifdef USE_CUDNN
+#include <algorithm>
+#include <cfloat>
+#include <vector>
+
+#include "thrust/device_vector.h"
+
+#include "caffe/layer.hpp"
+#include "caffe/util/math_functions.hpp"
+#include "caffe/vision_layers.hpp"
+
+#if CUDNN_VERSION_MIN(5, 0, 0)
+
+namespace caffe {
+
+template <typename Dtype>
+void CuDNNBNLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+    const vector<Blob<Dtype>*>& top) {
+  const Dtype* bottom_data = bottom[0]->gpu_data();
+  Dtype* top_data = top[0]->mutable_gpu_data();
+  const Dtype* scale_data = this->blobs_[0]->gpu_data();
+  const Dtype* bias_data = this->blobs_[1]->gpu_data();
+
+  const double epsilon = max(this->bn_eps_, CUDNN_BN_MIN_EPSILON);
+
+  if (this->phase_ == TEST || this->frozen_) {
+    const Dtype* running_mean_data = this->blobs_[2]->gpu_data();
+    const Dtype* running_variance_data = this->blobs_[3]->gpu_data();
+    CUDNN_CHECK(cudnnBatchNormalizationForwardInference(handle_,
+        CUDNN_BATCHNORM_SPATIAL,
+        cudnn::dataType<Dtype>::one,
+        cudnn::dataType<Dtype>::zero,
+        bottom_desc_, bottom_data,
+        top_desc_, top_data,
+        bn_param_desc_, scale_data, bias_data,
+        running_mean_data, running_variance_data,
+        epsilon));
+  } else {
+    Dtype* running_mean_data = this->blobs_[2]->mutable_gpu_data();
+    Dtype* running_variance_data = this->blobs_[3]->mutable_gpu_data();
+    Dtype* save_mean_data = save_mean_.mutable_gpu_data();
+    Dtype* save_inv_variance_data = save_inv_variance_.mutable_gpu_data();
+    CUDNN_CHECK(cudnnBatchNormalizationForwardTraining(handle_,
+        CUDNN_BATCHNORM_SPATIAL,
+        cudnn::dataType<Dtype>::one,
+        cudnn::dataType<Dtype>::zero,
+        bottom_desc_, bottom_data,
+        top_desc_, top_data,
+        bn_param_desc_, scale_data, bias_data,
+        1 - this->bn_momentum_,
+        running_mean_data, running_variance_data,
+        epsilon,
+        save_mean_data, save_inv_variance_data));
+  }
+}
+
+template <typename Dtype>
+void CuDNNBNLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
+    const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+   if (this->frozen_){
+     BNLayer<Dtype>::Backward_gpu(top, propagate_down, bottom);
+     return;
+   }
+  if (propagate_down[0] || this->param_propagate_down_[0] ||
+      this->param_propagate_down_[1]) {
+    const Dtype* top_diff = top[0]->gpu_diff();
+    const Dtype* bottom_data = bottom[0]->gpu_data();
+    Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
+    const Dtype* scale_data = this->blobs_[0]->gpu_data();
+    Dtype* scale_diff = this->blobs_[0]->mutable_gpu_diff();
+    Dtype* bias_diff = this->blobs_[1]->mutable_gpu_diff();
+    const Dtype* save_mean_data = save_mean_.gpu_data();
+    const Dtype* save_inv_variance_data = save_inv_variance_.gpu_data();
+
+    const double epsilon = max(this->bn_eps_, CUDNN_BN_MIN_EPSILON);
+
+    CUDNN_CHECK(cudnnBatchNormalizationBackward(handle_,
+        CUDNN_BATCHNORM_SPATIAL,
+        cudnn::dataType<Dtype>::one,
+        cudnn::dataType<Dtype>::zero,
+        cudnn::dataType<Dtype>::one,
+        cudnn::dataType<Dtype>::one,
+        bottom_desc_, bottom_data,
+        top_desc_, top_diff,
+        bottom_desc_, bottom_diff,
+        bn_param_desc_, scale_data,
+        scale_diff, bias_diff,
+        epsilon,
+        save_mean_data, save_inv_variance_data));
+
+  }
+}
+
+INSTANTIATE_LAYER_GPU_FUNCS(CuDNNBNLayer);
+
+}  // namespace caffe
+#endif
+#endif
diff --git a/src/caffe/layers/cudnn_conv_layer.cpp b/src/caffe/layers/cudnn_conv_layer.cpp
index 137bbab1976..6111dcf234f 100644
--- a/src/caffe/layers/cudnn_conv_layer.cpp
+++ b/src/caffe/layers/cudnn_conv_layer.cpp
@@ -1,4 +1,5 @@
 #ifdef USE_CUDNN
+#include <algorithm>
 #include <vector>
 
 #include "caffe/filler.hpp"
@@ -7,25 +8,242 @@
 #include "caffe/util/math_functions.hpp"
 #include "caffe/vision_layers.hpp"
 
+#include <boost/unordered_map.hpp>
+#include <cudnn.h>
+
+using boost::unordered_map;
+
 namespace caffe {
 
 // Set to three for the benefit of the backward pass, which
 // can use separate streams for calculating the gradient w.r.t.
 // bias, filter weights, and bottom data for each group independently
-#define CUDNN_STREAMS_PER_GROUP 3
+#define CUDNN_FWD_STREAMS_PER_GROUP 1
+#define CUDNN_BWD_STREAMS_PER_GROUP 2
+
+template <typename Dtype>
+unordered_map<CuDNNConvolutionLayer<Dtype>*, typename CuDNNConvolutionLayer<Dtype>::PerfReg*> CuDNNConvolutionLayer<Dtype>::perf_reg;
+
+template <typename Dtype>
+bool CuDNNConvolutionLayer<Dtype>::need_optimize_ = true;
+
+template <typename Dtype>
+vector<shared_ptr<SyncedMemory> > CuDNNConvolutionLayer<Dtype>::workspaceData_fwd;
+template <typename Dtype>
+vector<shared_ptr<SyncedMemory> > CuDNNConvolutionLayer<Dtype>::workspaceData_bwd_filter;
+template <typename Dtype>
+vector<shared_ptr<SyncedMemory> > CuDNNConvolutionLayer<Dtype>::workspaceData_bwd_data;
+
+typedef struct {
+    float total_time;
+    vector<int> choices;
+}MemRecord;
+
+void updateDict(unordered_map<size_t, MemRecord>& dict, const size_t key, const float time, const vector<int>& choices){
+  MemRecord rec;
+  rec.total_time = time;
+  rec.choices = choices;
+  dict[key] = rec;
+}
+
+template <typename Dtype, typename PerfType>
+void runTransitFunc(unordered_map<size_t, MemRecord>& new_dict, unordered_map<size_t, MemRecord>& prev_dict,
+              const vector<PerfType>& perf, const size_t mem_limit){
+  new_dict.clear();
+  int mem_tick = (Caffe::cudnn_mem_richness()>0)?Caffe::cudnn_mem_richness() * 1000 : 1000;
+  for (size_t i_algo = 0; i_algo < perf.size(); ++i_algo){
+    PerfType algo_perf = perf[i_algo];
+    size_t mem = (algo_perf.memory + mem_tick -1) / mem_tick ;
+    float time = algo_perf.time;
+    if (time < 0){
+      continue;
+    }
+    for (unordered_map<size_t, MemRecord>::iterator mc = prev_dict.begin(); mc != prev_dict.end(); ++mc){
+      size_t new_mem = mc->first + mem;
+
+      if (new_mem > mem_limit){
+        continue;
+      }
+      float new_time = mc->second.total_time + time;
+      bool update = false;
+      if (new_dict.find(new_mem) == new_dict.end()){
+        update = true;
+      }else{
+        MemRecord& ext_rec = new_dict[new_mem];
+        if (ext_rec.total_time > new_time){
+          update = true;
+        }
+      }
+
+      if (update) {
+        vector<int> ch = mc->second.choices;
+        ch.push_back(i_algo);
+        updateDict(new_dict, new_mem, new_time, ch);
+        //LOG(INFO)<<new_mem;
+      }
+    }
+  }
+  prev_dict = new_dict;
+};
+
+template <typename Dtype, typename PerfType>
+void initTransitFunc(unordered_map<size_t, MemRecord>& new_dict,
+                    const vector<PerfType>& perf, const size_t mem_limit){
+  new_dict.clear();
+  int mem_tick = (Caffe::cudnn_mem_richness()>0)?Caffe::cudnn_mem_richness() * 1000 : 1000;
+  for (size_t i_algo = 0; i_algo < perf.size(); ++i_algo){
+    PerfType algo_perf = perf[i_algo];
+    size_t mem = (algo_perf.memory + mem_tick -1) / mem_tick;
+    if (mem > mem_limit) continue;
+    float time = algo_perf.time;
+    if (time < 0){
+      continue;
+    }
+    if (new_dict.find(mem) == new_dict.end()){
+      vector<int> tmp;
+      tmp.push_back(i_algo);
+      updateDict(new_dict, mem, time, tmp);
+    }else{
+      //check and update
+      MemRecord& rec = new_dict[mem];
+      if (time < rec.total_time){
+        //update
+        vector<int> tmp;
+        tmp.push_back(i_algo);
+        updateDict(new_dict,mem, time, tmp);
+      }
+    }
+  }
+};
+
+template <typename Dtype>
+void CuDNNConvolutionLayer<Dtype>::RuntimeOptimize(size_t mem_limit) {
+
+  if (!need_optimize_){
+    return;
+  }
+  unordered_map<size_t, MemRecord> prev_dict;
+  unordered_map<size_t, MemRecord> new_dict;
+
+  mem_limit *= (Caffe::cudnn_mem_richness() > 0);
+  //iterate
+  for (typename unordered_map<CuDNNConvolutionLayer *, PerfReg *>::iterator layer_reg = perf_reg.begin();
+       layer_reg != perf_reg.end(); ++layer_reg) {
+    PerfReg &layer_perf = *(layer_reg->second);
+
+    //foward
+    for (int x = 0; x < layer_perf.fwd_perf.size(); ++x){
+      if (prev_dict.size() == 0) {
+        initTransitFunc<Dtype, cudnnConvolutionFwdAlgoPerf_t>(prev_dict, layer_perf.fwd_perf[x], mem_limit);
+      } else
+        runTransitFunc<Dtype, cudnnConvolutionFwdAlgoPerf_t>(new_dict, prev_dict, layer_perf.fwd_perf[x], mem_limit);
+    
+}
+    //bwd filter
+    for (int x = 0; x < layer_perf.bwd_filter_perf.size(); ++x) {
+      runTransitFunc<Dtype, cudnnConvolutionBwdFilterAlgoPerf_t>(new_dict,
+                                                                 prev_dict,
+                                                                 layer_perf.bwd_filter_perf[x],
+                                                                 mem_limit);
+    }
+    //bwd data
+    for (int x = 0; x < layer_perf.bwd_data_perf.size(); ++x)
+      runTransitFunc<Dtype, cudnnConvolutionBwdDataAlgoPerf_t>(new_dict,
+                                                               prev_dict,
+                                                               layer_perf.bwd_data_perf[x],
+                                                               mem_limit);
+  }
+
+  // find optimal
+  MemRecord *min_rec = &prev_dict.begin()->second;
+  for (unordered_map<size_t, MemRecord>::iterator mc = prev_dict.begin(); mc != prev_dict.end(); ++mc) {
+    if (mc->second.total_time < min_rec->total_time) {
+      min_rec = &mc->second;
+    }
+  }
+
+  //set optimal result
+  vector<int> &choices = min_rec->choices;
+  int cnt = 0;
+  for (typename unordered_map<CuDNNConvolutionLayer *, PerfReg *>::iterator layer_reg = perf_reg.begin();
+       layer_reg != perf_reg.end(); ++layer_reg){
+    PerfReg &layer_perf = *(layer_reg->second);
+    for (int x = 0; x < layer_perf.fwd_perf.size(); ++x) {
+      layer_perf.fwd_algo[x] = choices[cnt++];
+    }
+
+    for (int x = 0; x < layer_perf.bwd_filter_perf.size(); ++x) {
+      layer_perf.bwd_filter_algo[x] = choices[cnt++];
+    }
+
+    for (int x = 0; x < layer_perf.fwd_perf.size(); ++x) {
+      layer_perf.bwd_data_algo[x] = choices[cnt++];
+    }
+    layer_reg->first->AdjustWorkSpaces();
+  }
+
+  need_optimize_ = false;
+  LOG(INFO)<<"Optimized cudnn conv";
+}
 
 /**
  * TODO(dox) explain cuDNN interface
  */
 template <typename Dtype>
 void CuDNNConvolutionLayer<Dtype>::LayerSetUp(
-    const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
   ConvolutionLayer<Dtype>::LayerSetUp(bottom, top);
   // Initialize CUDA streams and cuDNN.
-  stream_         = new cudaStream_t[this->group_ * CUDNN_STREAMS_PER_GROUP];
-  handle_         = new cudnnHandle_t[this->group_ * CUDNN_STREAMS_PER_GROUP];
+  int total_streams_per_group = CUDNN_FWD_STREAMS_PER_GROUP + CUDNN_BWD_STREAMS_PER_GROUP;
+  stream_         = new cudaStream_t[this->group_ * total_streams_per_group];
+  handle_         = new cudnnHandle_t[this->group_ * total_streams_per_group];
+
+  // Initialize algorithm arrays
+  fwd_algo_       = new cudnnConvolutionFwdAlgo_t[bottom.size()];
+  bwd_filter_algo_= new cudnnConvolutionBwdFilterAlgo_t[bottom.size()];
+  bwd_data_algo_  = new cudnnConvolutionBwdDataAlgo_t[bottom.size()];
+
+  // initialize size arrays
+  workspace_fwd_sizes_ = new size_t[bottom.size()];
+  workspace_bwd_filter_sizes_ = new size_t[bottom.size()];
+  workspace_bwd_data_sizes_ = new size_t[bottom.size()];
 
-  for (int g = 0; g < this->group_ * CUDNN_STREAMS_PER_GROUP; g++) {
+  // initilized perf reg
+  layer_perf_.bwd_filter_perf.resize(bottom.size());
+  layer_perf_.bwd_data_perf.resize(bottom.size());
+  layer_perf_.fwd_perf.resize(bottom.size());
+
+  // register the layer to cudnn conv registry for global planning
+  perf_reg[this] = &layer_perf_;
+
+
+  layer_perf_.bwd_filter_algo.resize(bottom.size());
+  layer_perf_.bwd_data_algo.resize(bottom.size());
+  layer_perf_.fwd_algo.resize(bottom.size());
+
+
+  // workspace data sizes start with zero
+  workspaceSizeInBytes_fwd = workspaceSizeInBytes_bwd = 0;
+  for (int i = workspaceData_fwd.size(); i < this->group_; ++i)
+    workspaceData_fwd.push_back(shared_ptr<SyncedMemory>(new SyncedMemory()));
+  for (int i = workspaceData_bwd_filter.size(); i < this->group_; ++i)
+    workspaceData_bwd_filter.push_back(shared_ptr<SyncedMemory>(new SyncedMemory()));
+  for (int i = workspaceData_bwd_data.size(); i < this->group_; ++i)
+    workspaceData_bwd_data.push_back(shared_ptr<SyncedMemory>(new SyncedMemory()));
+
+
+  for (size_t i = 0; i < bottom.size(); ++i) {
+    // initialize all to default algorithms
+    fwd_algo_[i] = (cudnnConvolutionFwdAlgo_t)0;
+    bwd_filter_algo_[i] = (cudnnConvolutionBwdFilterAlgo_t)0;
+    bwd_data_algo_[i] = (cudnnConvolutionBwdDataAlgo_t)0;
+    // default algorithms don't require workspace
+    workspace_fwd_sizes_[i] = 0;
+    workspace_bwd_data_sizes_[i] = 0;
+    workspace_bwd_filter_sizes_[i] = 0;
+  }
+
+  for (int g = 0; g < this->group_ * total_streams_per_group; g++) {
     CUDA_CHECK(cudaStreamCreate(&stream_[g]));
     CUDNN_CHECK(cudnnCreate(&handle_[g]));
     CUDNN_CHECK(cudnnSetStream(handle_[g], stream_[g]));
@@ -43,10 +261,10 @@ void CuDNNConvolutionLayer<Dtype>::LayerSetUp(
 
   // Create tensor descriptor(s) for data and corresponding convolution(s).
   for (int i = 0; i < bottom.size(); i++) {
-    cudnnTensor4dDescriptor_t bottom_desc;
+    cudnnTensorDescriptor_t bottom_desc;
     cudnn::createTensor4dDesc<Dtype>(&bottom_desc);
     bottom_descs_.push_back(bottom_desc);
-    cudnnTensor4dDescriptor_t top_desc;
+    cudnnTensorDescriptor_t top_desc;
     cudnn::createTensor4dDesc<Dtype>(&top_desc);
     top_descs_.push_back(top_desc);
     cudnnConvolutionDescriptor_t conv_desc;
@@ -58,37 +276,104 @@ void CuDNNConvolutionLayer<Dtype>::LayerSetUp(
   if (this->bias_term_) {
     cudnn::createTensor4dDesc<Dtype>(&bias_desc_);
   }
+
+  handles_setup_ = true;
+
+  prev_bottom_shapes_.resize(bottom.size(), vector<int>());
 }
 
 template <typename Dtype>
 void CuDNNConvolutionLayer<Dtype>::Reshape(
-    const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
   ConvolutionLayer<Dtype>::Reshape(bottom, top);
   bottom_offset_ = (this->channels_ / this->group_)
       * this->height_ * this->width_;
   top_offset_ = (this->num_output_ / this->group_)
       * this->height_out_ * this->width_out_;
 
+  // Specify workspace limit for kernels directly until we have a
+  // planning strategy and a rewrite of Caffe's GPU memory mangagement.
+  //
+  // However this can be tuned by the "richness" parameter in the solver protobuf
+  // By setting richness, you can increase the memory available to cuDNN and thus
+  // let it choose fast but space consuming algorithms.
   for (int i = 0; i < bottom.size(); i++) {
+    if (prev_bottom_shapes_[i] == bottom[i]->shape()) continue;
+    prev_bottom_shapes_[i] = bottom[i]->shape();
+
     cudnn::setTensor4dDesc<Dtype>(&bottom_descs_[i],
-        this->num_,
-        this->channels_ / this->group_,
-        this->height_, this->width_,
-        this->channels_ * this->height_ * this->width_,
-        this->height_ * this->width_,
-        this->width_, 1);
+                                  this->num_,
+                                  this->channels_ / this->group_,
+                                  this->height_, this->width_,
+                                  this->channels_ * this->height_ * this->width_,
+                                  this->height_ * this->width_,
+                                  this->width_, 1);
     cudnn::setTensor4dDesc<Dtype>(&top_descs_[i],
-        this->num_,
-        this->num_output_ / this->group_,
-        this->height_out_, this->width_out_,
-        this->num_output_ * this->height_out_ * this->width_out_,
-        this->height_out_ * this->width_out_,
-        this->width_out_, 1);
+                                  this->num_,
+                                  this->num_output_ / this->group_,
+                                  this->height_out_, this->width_out_,
+                                  this->num_output_ * this->height_out_ * this->width_out_,
+                                  this->height_out_ * this->width_out_,
+                                  this->width_out_, 1);
     cudnn::setConvolutionDesc<Dtype>(&conv_descs_[i], bottom_descs_[i],
-        filter_desc_, this->pad_h_, this->pad_w_,
-        this->stride_h_, this->stride_w_);
+                                     filter_desc_, this->pad_h_, this->pad_w_,
+                                     this->stride_h_, this->stride_w_);
+
+    // choose forward and backward algorithms + workspace(s)
+    const int kRequestedForwardAlgoCount = 6;
+    vector<cudnnConvolutionFwdAlgoPerf_t> fwd_perf;
+    fwd_perf.resize(kRequestedForwardAlgoCount);
+    int returnedAlgoCount;
+    CUDNN_CHECK(cudnnFindConvolutionForwardAlgorithm(handle_[0],
+                                                     bottom_descs_[i],
+                                                     filter_desc_,
+                                                     conv_descs_[i],
+                                                     top_descs_[i],
+                                                     kRequestedForwardAlgoCount,
+                                                     &returnedAlgoCount,
+                                                     &fwd_perf[0]));
+    layer_perf_.fwd_perf[i] =
+        vector<cudnnConvolutionFwdAlgoPerf_t>(fwd_perf.begin(), fwd_perf.begin() + returnedAlgoCount);
+
+
+    // choose backward algorithm for filter
+    const int kRequestedBackwardFilterAlgoCount = 4;
+    vector<cudnnConvolutionBwdFilterAlgoPerf_t> bwd_filter_perf;
+    bwd_filter_perf.resize(kRequestedBackwardFilterAlgoCount);
+    CUDNN_CHECK(cudnnFindConvolutionBackwardFilterAlgorithm(handle_[0],
+                                                            bottom_descs_[i],
+                                                            top_descs_[i],
+                                                            conv_descs_[i],
+                                                            filter_desc_,
+                                                            kRequestedBackwardFilterAlgoCount,
+                                                            &returnedAlgoCount,
+                                                            &bwd_filter_perf[0]));
+    layer_perf_.bwd_filter_perf[i] = vector<cudnnConvolutionBwdFilterAlgoPerf_t>(bwd_filter_perf.begin(),
+                                                                                 bwd_filter_perf.begin()
+                                                                                     + returnedAlgoCount);
+    if (layer_perf_.bwd_filter_perf[i][0].algo == 2){
+      LOG(INFO)<<"fft context time "<<layer_perf_.bwd_filter_perf[i][0].time<<" mem "<<layer_perf_.bwd_filter_perf[i][0].memory;
+    }
+
+    // choose backward algo for data
+    const int kRequestedBackwardDataAlgoCount = 4;
+    vector<cudnnConvolutionBwdDataAlgoPerf_t> bwd_data_perf;
+    bwd_data_perf.resize(kRequestedBackwardDataAlgoCount);
+    CUDNN_CHECK(cudnnFindConvolutionBackwardDataAlgorithm(handle_[0],
+                                                          filter_desc_,
+                                                          top_descs_[i],
+                                                          conv_descs_[i],
+                                                          bottom_descs_[i],
+                                                          kRequestedBackwardDataAlgoCount,
+                                                          &returnedAlgoCount,
+                                                          &bwd_data_perf[0]));
+    layer_perf_.bwd_data_perf[i] = vector<cudnnConvolutionBwdDataAlgoPerf_t>(bwd_data_perf.begin(),
+                                                                             bwd_data_perf.begin() + returnedAlgoCount);
+
+    need_optimize_ = true;
   }
 
+
   // Tensor descriptor for bias.
   if (this->bias_term_) {
     cudnn::setTensor4dDesc<Dtype>(&bias_desc_,
@@ -96,25 +381,97 @@ void CuDNNConvolutionLayer<Dtype>::Reshape(
   }
 }
 
+template<typename Dtype>
+void CuDNNConvolutionLayer<Dtype>::AdjustWorkSpaces() {
+
+  for (int x = 0; x < layer_perf_.fwd_algo.size(); ++x){
+    cudnnConvolutionFwdAlgo_t new_algo = layer_perf_.fwd_perf[x][0].algo;
+    size_t new_mem = layer_perf_.fwd_perf[x][0].memory;
+      fwd_algo_[x] = new_algo;
+      workspace_fwd_sizes_[x] = new_mem;
+      if(workspace_fwd_sizes_[x] > workspaceData_fwd[0]->size()){
+        for (int g = 0; g < this->group_; ++g){
+          workspaceData_fwd[g].reset(new SyncedMemory(workspace_fwd_sizes_[x]));
+        }
+      }
+  }
+
+  for (int x = 0; x < layer_perf_.bwd_filter_algo.size(); ++x){
+    cudnnConvolutionBwdFilterAlgo_t new_algo = layer_perf_.bwd_filter_perf[x][0].algo;
+    size_t new_mem = layer_perf_.bwd_filter_perf[x][0].memory;
+
+      bwd_filter_algo_[x] = new_algo;
+      workspace_bwd_filter_sizes_[x] = new_mem;
+      if(workspace_bwd_filter_sizes_[x] > workspaceData_bwd_filter[0]->size()){
+        for (int g = 0; g < this->group_; ++g){
+          workspaceData_bwd_filter[g].reset(new SyncedMemory(new_mem));
+        }
+      }
+  }
+
+  for (int x = 0; x < layer_perf_.bwd_data_algo.size(); ++x){
+    cudnnConvolutionBwdDataAlgo_t new_algo = layer_perf_.bwd_data_perf[x][0].algo;
+    size_t new_mem = layer_perf_.bwd_data_perf[x][0].memory;
+      bwd_data_algo_[x] = new_algo;
+      workspace_bwd_data_sizes_[x] = new_mem;
+      if(workspace_bwd_data_sizes_[x] > workspaceData_bwd_data[0]->size()){
+        for (int g = 0; g < this->group_; ++g){
+          workspaceData_bwd_data[g].reset(new SyncedMemory(new_mem));
+        }
+      }
+  }
+
+
+}
+
 template <typename Dtype>
 CuDNNConvolutionLayer<Dtype>::~CuDNNConvolutionLayer() {
+  // Check that handles have been setup before destroying.
+  if (!handles_setup_) { return; }
+
   for (int i = 0; i < bottom_descs_.size(); i++) {
-    cudnnDestroyTensor4dDescriptor(bottom_descs_[i]);
-    cudnnDestroyTensor4dDescriptor(top_descs_[i]);
+    cudnnDestroyTensorDescriptor(bottom_descs_[i]);
+    cudnnDestroyTensorDescriptor(top_descs_[i]);
     cudnnDestroyConvolutionDescriptor(conv_descs_[i]);
   }
   if (this->bias_term_) {
-    cudnnDestroyTensor4dDescriptor(bias_desc_);
+    cudnnDestroyTensorDescriptor(bias_desc_);
   }
   cudnnDestroyFilterDescriptor(filter_desc_);
 
-  for (int g = 0; g < this->group_ * CUDNN_STREAMS_PER_GROUP; g++) {
+  int total_stream_per_group = CUDNN_FWD_STREAMS_PER_GROUP + CUDNN_BWD_STREAMS_PER_GROUP;
+  for (int g = 0; g < this->group_ * total_stream_per_group; g++) {
     cudaStreamDestroy(stream_[g]);
     cudnnDestroy(handle_[g]);
   }
 
+  // release all allocated workspace memory blocks.
+  if (workspaceData_bwd_filter.size()){
+   workspaceData_bwd_filter.clear();
+   workspaceData_bwd_data.clear();
+   workspaceData_fwd.clear();
+  }
+
+  // unregister the layer perf
+  typename boost::unordered_map<CuDNNConvolutionLayer*, PerfReg*>::iterator
+          it = perf_reg.find(this);
+  if (it != perf_reg.end()){
+    perf_reg.erase(it);
+  }
+
   delete [] stream_;
   delete [] handle_;
+  delete [] fwd_algo_;
+  delete [] bwd_filter_algo_;
+  delete [] bwd_data_algo_;
+  delete [] workspace_fwd_sizes_;
+  delete [] workspace_bwd_data_sizes_;
+  delete [] workspace_bwd_filter_sizes_;
+
+  if (perf_reg.find(this) != perf_reg.end()){
+    // un-register when the layer gets destroyed
+    perf_reg.erase(this);
+  }
 }
 
 INSTANTIATE_CLASS(CuDNNConvolutionLayer);
diff --git a/src/caffe/layers/cudnn_conv_layer.cu b/src/caffe/layers/cudnn_conv_layer.cu
index 2af13309e18..c55482c4193 100644
--- a/src/caffe/layers/cudnn_conv_layer.cu
+++ b/src/caffe/layers/cudnn_conv_layer.cu
@@ -13,29 +13,41 @@ __global__ void sync_conv_groups() { }
 
 template <typename Dtype>
 void CuDNNConvolutionLayer<Dtype>::Forward_gpu(
-    const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
   for (int i = 0; i < bottom.size(); ++i) {
     const Dtype* bottom_data = bottom[i]->gpu_data();
-    Dtype* top_data = (*top)[i]->mutable_gpu_data();
+    Dtype* top_data = top[i]->mutable_gpu_data();
     const Dtype* weight = this->blobs_[0]->gpu_data();
 
     // Forward through cuDNN in parallel over groups.
     for (int g = 0; g < this->group_; g++) {
       // Filters.
       CUDNN_CHECK(cudnnConvolutionForward(handle_[g],
-          bottom_descs_[i], bottom_data + bottom_offset_ * g,
-          filter_desc_, weight + weight_offset_ * g,
-          conv_descs_[i],
-          top_descs_[i], top_data + top_offset_ * g,
-          CUDNN_RESULT_NO_ACCUMULATE));
+            cudnn::dataType<Dtype>::one,
+            bottom_descs_[i], bottom_data + bottom_offset_ * g,
+            filter_desc_, weight + weight_offset_ * g,
+            conv_descs_[i],
+            fwd_algo_[i], workspaceData_fwd[g]->mutable_gpu_data(),
+            workspace_fwd_sizes_[i],
+            cudnn::dataType<Dtype>::zero,
+            top_descs_[i], top_data + top_offset_ * g));
 
       // Bias.
       if (this->bias_term_) {
         const Dtype* bias_data = this->blobs_[1]->gpu_data();
-        Dtype alpha = 1.;
-        CUDNN_CHECK(cudnnAddTensor4d(handle_[g], CUDNN_ADD_SAME_C, &alpha,
-            bias_desc_, bias_data + bias_offset_ * g,
-            top_descs_[i], top_data + top_offset_ * g));
+#if CUDNN_VERSION_MIN(4, 0, 0)
+        CUDNN_CHECK(cudnnAddTensor(handle_[g],
+              cudnn::dataType<Dtype>::one,
+              bias_desc_, bias_data + bias_offset_ * g,
+              cudnn::dataType<Dtype>::one,
+              top_descs_[i], top_data + top_offset_ * g));
+#else
+        CUDNN_CHECK(cudnnAddTensor(handle_[g], CUDNN_ADD_SAME_C,
+              cudnn::dataType<Dtype>::one,
+              bias_desc_, bias_data + bias_offset_ * g,
+              cudnn::dataType<Dtype>::one,
+              top_descs_[i], top_data + top_offset_ * g));
+#endif
       }
     }
 
@@ -48,18 +60,16 @@ void CuDNNConvolutionLayer<Dtype>::Forward_gpu(
 
 template <typename Dtype>
 void CuDNNConvolutionLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
-    const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
+    const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
   const Dtype* weight = NULL;
   Dtype* weight_diff = NULL;
   if (this->param_propagate_down_[0]) {
     weight = this->blobs_[0]->gpu_data();
     weight_diff = this->blobs_[0]->mutable_gpu_diff();
-    caffe_gpu_set(this->blobs_[0]->count(), Dtype(0), weight_diff);
   }
   Dtype* bias_diff = NULL;
   if (this->bias_term_ && this->param_propagate_down_[1]) {
     bias_diff = this->blobs_[1]->mutable_gpu_diff();
-    caffe_gpu_set(this->blobs_[1]->count(), Dtype(0), bias_diff);
   }
   for (int i = 0; i < top.size(); ++i) {
     const Dtype* top_diff = top[i]->gpu_diff();
@@ -68,31 +78,43 @@ void CuDNNConvolutionLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
       // Gradient w.r.t. bias.
       if (this->bias_term_ && this->param_propagate_down_[1]) {
         CUDNN_CHECK(cudnnConvolutionBackwardBias(handle_[0*this->group_ + g],
-            top_descs_[i],  top_diff + top_offset_ * g,
-            bias_desc_, bias_diff + bias_offset_ * g,
-            CUDNN_RESULT_ACCUMULATE));
+              cudnn::dataType<Dtype>::one,
+              top_descs_[i],  top_diff + top_offset_ * g,
+              cudnn::dataType<Dtype>::one,
+              bias_desc_, bias_diff + bias_offset_ * g));
       }
 
       // Gradient w.r.t. weights.
       if (this->param_propagate_down_[0]) {
-        const Dtype* bottom_data = (*bottom)[i]->gpu_data();
-        CUDNN_CHECK(cudnnConvolutionBackwardFilter(handle_[1*this->group_ + g],
-            bottom_descs_[i], bottom_data + bottom_offset_ * g,
-            top_descs_[i],    top_diff + top_offset_ * g,
-            conv_descs_[i],
-            filter_desc_, weight_diff + weight_offset_ * g,
-            CUDNN_RESULT_ACCUMULATE));
+        const Dtype* bottom_data = bottom[i]->gpu_data();
+        CUDNN_CHECK(cudnnConvolutionBackwardFilter(
+              handle_[1*this->group_ + g],
+              cudnn::dataType<Dtype>::one,
+              bottom_descs_[i], bottom_data + bottom_offset_ * g,
+              top_descs_[i],    top_diff + top_offset_ * g,
+              conv_descs_[i],
+              bwd_filter_algo_[i], workspaceData_bwd_filter[g]->mutable_gpu_data(),
+              workspace_bwd_filter_sizes_[i],
+              cudnn::dataType<Dtype>::one,
+              filter_desc_, weight_diff + weight_offset_ * g));
       }
 
       // Gradient w.r.t. bottom data.
       if (propagate_down[i]) {
-        Dtype* bottom_diff = (*bottom)[i]->mutable_gpu_diff();
-        CUDNN_CHECK(cudnnConvolutionBackwardData(handle_[2*this->group_ + g],
-            filter_desc_, weight + weight_offset_ * g,
-            top_descs_[i],    top_diff + top_offset_ * g,
-            conv_descs_[i],
-            bottom_descs_[i], bottom_diff + bottom_offset_ * g,
-            CUDNN_RESULT_NO_ACCUMULATE));
+        if (weight == NULL) {
+          weight = this->blobs_[0]->gpu_data();
+        }
+        Dtype* bottom_diff = bottom[i]->mutable_gpu_diff();
+        CUDNN_CHECK(cudnnConvolutionBackwardData(
+              handle_[2*this->group_ + g],
+              cudnn::dataType<Dtype>::one,
+              filter_desc_, weight + weight_offset_ * g,
+              top_descs_[i], top_diff + top_offset_ * g,
+              conv_descs_[i],
+              bwd_data_algo_[i], workspaceData_bwd_data[g]->mutable_gpu_data(),
+              workspace_bwd_data_sizes_[i],
+              cudnn::dataType<Dtype>::zero,
+              bottom_descs_[i], bottom_diff + bottom_offset_ * g));
       }
     }
 
@@ -103,7 +125,7 @@ void CuDNNConvolutionLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
   }
 }
 
-INSTANTIATE_CLASS(CuDNNConvolutionLayer);
+INSTANTIATE_LAYER_GPU_FUNCS(CuDNNConvolutionLayer);
 
 }  // namespace caffe
 #endif
diff --git a/src/caffe/layers/cudnn_pooling_layer.cpp b/src/caffe/layers/cudnn_pooling_layer.cpp
index 5aea0dc886e..c92c4e477b5 100644
--- a/src/caffe/layers/cudnn_pooling_layer.cpp
+++ b/src/caffe/layers/cudnn_pooling_layer.cpp
@@ -11,20 +11,21 @@ namespace caffe {
 
 template <typename Dtype>
 void CuDNNPoolingLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-    vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& top) {
   PoolingLayer<Dtype>::LayerSetUp(bottom, top);
-
   CUDNN_CHECK(cudnnCreate(&handle_));
   cudnn::createTensor4dDesc<Dtype>(&bottom_desc_);
   cudnn::createTensor4dDesc<Dtype>(&top_desc_);
   cudnn::createPoolingDesc<Dtype>(&pooling_desc_,
       this->layer_param_.pooling_param().pool(), &mode_,
-      this->kernel_h_, this->kernel_w_, this->stride_h_, this->stride_w_);
+      this->kernel_h_, this->kernel_w_, this->pad_h_, this->pad_w_,
+      this->stride_h_, this->stride_w_);
+  handles_setup_ = true;
 }
 
 template <typename Dtype>
 void CuDNNPoolingLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
-    vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& top) {
   PoolingLayer<Dtype>::Reshape(bottom, top);
   cudnn::setTensor4dDesc<Dtype>(&bottom_desc_, bottom[0]->num(),
       this->channels_, this->height_, this->width_);
@@ -34,8 +35,11 @@ void CuDNNPoolingLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 CuDNNPoolingLayer<Dtype>::~CuDNNPoolingLayer() {
-  cudnnDestroyTensor4dDescriptor(bottom_desc_);
-  cudnnDestroyTensor4dDescriptor(top_desc_);
+  // Check that handles have been setup before destroying.
+  if (!handles_setup_) { return; }
+
+  cudnnDestroyTensorDescriptor(bottom_desc_);
+  cudnnDestroyTensorDescriptor(top_desc_);
   cudnnDestroyPoolingDescriptor(pooling_desc_);
   cudnnDestroy(handle_);
 }
diff --git a/src/caffe/layers/cudnn_pooling_layer.cu b/src/caffe/layers/cudnn_pooling_layer.cu
index 99c409dcc96..a952b855a48 100644
--- a/src/caffe/layers/cudnn_pooling_layer.cu
+++ b/src/caffe/layers/cudnn_pooling_layer.cu
@@ -11,42 +11,35 @@ namespace caffe {
 
 template <typename Dtype>
 void CuDNNPoolingLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-    vector<Blob<Dtype>*>* top) {
-  // Fallback to Caffe for padded pooling, max top mask.
-  if ((this->pad_h_ > 0 || this->pad_w_ > 0) || (*top).size() > 1) {
-    LOG(WARNING) << "Falling back to standard Caffe for padded pooling.";
-    return PoolingLayer<Dtype>::Forward_gpu(bottom, top);
-  }
-
+    const vector<Blob<Dtype>*>& top) {
   const Dtype* bottom_data = bottom[0]->gpu_data();
-  Dtype* top_data = (*top)[0]->mutable_gpu_data();
+  Dtype* top_data = top[0]->mutable_gpu_data();
   CUDNN_CHECK(cudnnPoolingForward(handle_, pooling_desc_,
-      bottom_desc_, bottom_data, top_desc_, top_data));
+        cudnn::dataType<Dtype>::one,
+        bottom_desc_, bottom_data,
+        cudnn::dataType<Dtype>::zero,
+        top_desc_, top_data));
 }
 
 template <typename Dtype>
 void CuDNNPoolingLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
-    const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
+    const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
   if (!propagate_down[0]) {
     return;
   }
-
-  // Fallback to Caffe for padded pooling, max top mask.
-  if ((this->pad_h_ > 0 || this->pad_w_ > 0) || top.size() > 1) {
-    LOG(WARNING) << "Falling back to standard Caffe for padded pooling.";
-    return PoolingLayer<Dtype>::Backward_gpu(top, propagate_down, bottom);
-  }
-
   const Dtype* top_diff = top[0]->gpu_diff();
   const Dtype* top_data = top[0]->gpu_data();
-  const Dtype* bottom_data = (*bottom)[0]->gpu_data();
-  Dtype* bottom_diff = (*bottom)[0]->mutable_gpu_diff();
+  const Dtype* bottom_data = bottom[0]->gpu_data();
+  Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
   CUDNN_CHECK(cudnnPoolingBackward(handle_, pooling_desc_,
-      top_desc_, top_data, top_desc_, top_diff,
-      bottom_desc_, bottom_data, bottom_desc_, bottom_diff));
+        cudnn::dataType<Dtype>::one,
+        top_desc_, top_data, top_desc_, top_diff,
+        bottom_desc_, bottom_data,
+        cudnn::dataType<Dtype>::zero,
+        bottom_desc_, bottom_diff));
 }
 
-INSTANTIATE_CLASS(CuDNNPoolingLayer);
+INSTANTIATE_LAYER_GPU_FUNCS(CuDNNPoolingLayer);
 
 }  // namespace caffe
 #endif
diff --git a/src/caffe/layers/cudnn_relu_layer.cpp b/src/caffe/layers/cudnn_relu_layer.cpp
index 083868f572f..9b79911a612 100644
--- a/src/caffe/layers/cudnn_relu_layer.cpp
+++ b/src/caffe/layers/cudnn_relu_layer.cpp
@@ -1,6 +1,7 @@
 #ifdef USE_CUDNN
 #include <algorithm>
 #include <vector>
+#include <cudnn.h>
 
 #include "caffe/layer.hpp"
 #include "caffe/vision_layers.hpp"
@@ -9,17 +10,20 @@ namespace caffe {
 
 template <typename Dtype>
 void CuDNNReLULayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
+      const vector<Blob<Dtype>*>& top) {
   ReLULayer<Dtype>::LayerSetUp(bottom, top);
   // initialize cuDNN
   CUDNN_CHECK(cudnnCreate(&handle_));
   cudnn::createTensor4dDesc<Dtype>(&bottom_desc_);
   cudnn::createTensor4dDesc<Dtype>(&top_desc_);
+  handles_setup_ = true;
+  cudnnCreateActivationDescriptor(&activation_desc_);
+  cudnnSetActivationDescriptor(activation_desc_, CUDNN_ACTIVATION_RELU, CUDNN_PROPAGATE_NAN, 0.0);
 }
 
 template <typename Dtype>
 void CuDNNReLULayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
+      const vector<Blob<Dtype>*>& top) {
   ReLULayer<Dtype>::Reshape(bottom, top);
   const int N = bottom[0]->num();
   const int K = bottom[0]->channels();
@@ -31,8 +35,12 @@ void CuDNNReLULayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 CuDNNReLULayer<Dtype>::~CuDNNReLULayer() {
-  cudnnDestroyTensor4dDescriptor(this->bottom_desc_);
-  cudnnDestroyTensor4dDescriptor(this->top_desc_);
+  // Check that handles have been setup before destroying.
+  if (!handles_setup_) { return; }
+
+  cudnnDestroyActivationDescriptor(this->activation_desc_);
+  cudnnDestroyTensorDescriptor(this->bottom_desc_);
+  cudnnDestroyTensorDescriptor(this->top_desc_);
   cudnnDestroy(this->handle_);
 }
 
diff --git a/src/caffe/layers/cudnn_relu_layer.cu b/src/caffe/layers/cudnn_relu_layer.cu
index a8519f340cc..5af2eb8fc08 100644
--- a/src/caffe/layers/cudnn_relu_layer.cu
+++ b/src/caffe/layers/cudnn_relu_layer.cu
@@ -9,23 +9,35 @@ namespace caffe {
 
 template <typename Dtype>
 void CuDNNReLULayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-    vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& top) {
   // Fallback to standard Caffe for leaky ReLU.
   if (ReLULayer<Dtype>::layer_param_.relu_param().negative_slope() != 0) {
     return ReLULayer<Dtype>::Forward_gpu(bottom, top);
   }
 
   const Dtype* bottom_data = bottom[0]->gpu_data();
-  Dtype* top_data = (*top)[0]->mutable_gpu_data();
+  Dtype* top_data = top[0]->mutable_gpu_data();
+#if CUDNN_VERSION_MIN(5,0,0)
   CUDNN_CHECK(cudnnActivationForward(this->handle_,
-      CUDNN_ACTIVATION_RELU,
-      this->bottom_desc_, bottom_data, this->top_desc_, top_data));
+        activation_desc_,
+        cudnn::dataType<Dtype>::one,
+        this->bottom_desc_, bottom_data,
+        cudnn::dataType<Dtype>::zero,
+        this->top_desc_, top_data));
+#else
+    CUDNN_CHECK(cudnnActivationForward_v4(this->handle_,
+        activation_desc_,
+        cudnn::dataType<Dtype>::one,
+        this->bottom_desc_, bottom_data,
+        cudnn::dataType<Dtype>::zero,
+        this->top_desc_, top_data));
+#endif
 }
 
 template <typename Dtype>
 void CuDNNReLULayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
     const vector<bool>& propagate_down,
-    vector<Blob<Dtype>*>* bottom) {
+    const vector<Blob<Dtype>*>& bottom) {
   if (!propagate_down[0]) {
     return;
   }
@@ -37,15 +49,28 @@ void CuDNNReLULayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
 
   const Dtype* top_data = top[0]->gpu_data();
   const Dtype* top_diff = top[0]->gpu_diff();
-  const Dtype* bottom_data = (*bottom)[0]->gpu_data();
-  Dtype* bottom_diff = (*bottom)[0]->mutable_gpu_diff();
+  const Dtype* bottom_data = bottom[0]->gpu_data();
+  Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
+#if CUDNN_VERSION_MIN(5,0,0)
   CUDNN_CHECK(cudnnActivationBackward(this->handle_,
-      CUDNN_ACTIVATION_RELU,
-      this->top_desc_, top_data, this->top_desc_, top_diff,
-      this->bottom_desc_, bottom_data, this->bottom_desc_, bottom_diff));
+        activation_desc_,
+        cudnn::dataType<Dtype>::one,
+        this->top_desc_, top_data, this->top_desc_, top_diff,
+        this->bottom_desc_, bottom_data,
+        cudnn::dataType<Dtype>::zero,
+        this->bottom_desc_, bottom_diff));
+#else
+  CUDNN_CHECK(cudnnActivationBackward_v4(this->handle_,
+        activation_desc_,
+        cudnn::dataType<Dtype>::one,
+        this->top_desc_, top_data, this->top_desc_, top_diff,
+        this->bottom_desc_, bottom_data,
+        cudnn::dataType<Dtype>::zero,
+        this->bottom_desc_, bottom_diff));
+#endif
 }
 
-INSTANTIATE_CLASS(CuDNNReLULayer);
+INSTANTIATE_LAYER_GPU_FUNCS(CuDNNReLULayer);
 
 }  // namespace caffe
 #endif
diff --git a/src/caffe/layers/cudnn_sigmoid_layer.cpp b/src/caffe/layers/cudnn_sigmoid_layer.cpp
index 3fe800db6f4..58e4f91ce99 100644
--- a/src/caffe/layers/cudnn_sigmoid_layer.cpp
+++ b/src/caffe/layers/cudnn_sigmoid_layer.cpp
@@ -1,6 +1,7 @@
 #ifdef USE_CUDNN
 #include <algorithm>
 #include <vector>
+#include <cudnn.h>
 
 #include "caffe/layer.hpp"
 #include "caffe/vision_layers.hpp"
@@ -9,17 +10,20 @@ namespace caffe {
 
 template <typename Dtype>
 void CuDNNSigmoidLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
+      const vector<Blob<Dtype>*>& top) {
   SigmoidLayer<Dtype>::LayerSetUp(bottom, top);
   // initialize cuDNN
   CUDNN_CHECK(cudnnCreate(&handle_));
   cudnn::createTensor4dDesc<Dtype>(&bottom_desc_);
   cudnn::createTensor4dDesc<Dtype>(&top_desc_);
+  handles_setup_ = true;
+  cudnnCreateActivationDescriptor(&activation_desc_);
+  cudnnSetActivationDescriptor(activation_desc_, CUDNN_ACTIVATION_SIGMOID, CUDNN_PROPAGATE_NAN, 0.0);
 }
 
 template <typename Dtype>
 void CuDNNSigmoidLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
+      const vector<Blob<Dtype>*>& top) {
   SigmoidLayer<Dtype>::Reshape(bottom, top);
   const int N = bottom[0]->num();
   const int K = bottom[0]->channels();
@@ -31,8 +35,12 @@ void CuDNNSigmoidLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 CuDNNSigmoidLayer<Dtype>::~CuDNNSigmoidLayer() {
-  cudnnDestroyTensor4dDescriptor(this->bottom_desc_);
-  cudnnDestroyTensor4dDescriptor(this->top_desc_);
+  // Check that handles have been setup before destroying.
+  if (!handles_setup_) { return; }
+
+  cudnnDestroyActivationDescriptor(this->activation_desc_);
+  cudnnDestroyTensorDescriptor(this->bottom_desc_);
+  cudnnDestroyTensorDescriptor(this->top_desc_);
   cudnnDestroy(this->handle_);
 }
 
diff --git a/src/caffe/layers/cudnn_sigmoid_layer.cu b/src/caffe/layers/cudnn_sigmoid_layer.cu
index 43019bd78ae..fdc6478a7f4 100644
--- a/src/caffe/layers/cudnn_sigmoid_layer.cu
+++ b/src/caffe/layers/cudnn_sigmoid_layer.cu
@@ -9,33 +9,58 @@ namespace caffe {
 
 template <typename Dtype>
 void CuDNNSigmoidLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-    vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& top) {
   const Dtype* bottom_data = bottom[0]->gpu_data();
-  Dtype* top_data = (*top)[0]->mutable_gpu_data();
+  Dtype* top_data = top[0]->mutable_gpu_data();
+#if CUDNN_VERSION_MIN(5,0,0)
   CUDNN_CHECK(cudnnActivationForward(this->handle_,
-      CUDNN_ACTIVATION_SIGMOID,
-      this->bottom_desc_, bottom_data, this->top_desc_, top_data));
+        activation_desc_,
+        cudnn::dataType<Dtype>::one,
+        this->bottom_desc_, bottom_data,
+        cudnn::dataType<Dtype>::zero,
+        this->top_desc_, top_data));
+#else
+    CUDNN_CHECK(cudnnActivationForward_v4(this->handle_,
+        activation_desc_,
+        cudnn::dataType<Dtype>::one,
+        this->bottom_desc_, bottom_data,
+        cudnn::dataType<Dtype>::zero,
+        this->top_desc_, top_data));
+#endif
 }
 
 template <typename Dtype>
 void CuDNNSigmoidLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
     const vector<bool>& propagate_down,
-    vector<Blob<Dtype>*>* bottom) {
+    const vector<Blob<Dtype>*>& bottom) {
   if (!propagate_down[0]) {
     return;
   }
 
   const Dtype* top_data = top[0]->gpu_data();
   const Dtype* top_diff = top[0]->gpu_diff();
-  const Dtype* bottom_data = (*bottom)[0]->gpu_data();
-  Dtype* bottom_diff = (*bottom)[0]->mutable_gpu_diff();
+  const Dtype* bottom_data = bottom[0]->gpu_data();
+  Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
+#if CUDNN_VERSION_MIN(5,0,0)
   CUDNN_CHECK(cudnnActivationBackward(this->handle_,
-      CUDNN_ACTIVATION_SIGMOID,
-      this->top_desc_, top_data, this->top_desc_, top_diff,
-      this->bottom_desc_, bottom_data, this->bottom_desc_, bottom_diff));
+        activation_desc_,
+        cudnn::dataType<Dtype>::one,
+        this->top_desc_, top_data, this->top_desc_, top_diff,
+        this->bottom_desc_, bottom_data,
+        cudnn::dataType<Dtype>::zero,
+        this->bottom_desc_, bottom_diff));
+#else
+  CUDNN_CHECK(cudnnActivationBackward_v4(this->handle_,
+        activation_desc_,
+        cudnn::dataType<Dtype>::one,
+        this->top_desc_, top_data, this->top_desc_, top_diff,
+        this->bottom_desc_, bottom_data,
+        cudnn::dataType<Dtype>::zero,
+        this->bottom_desc_, bottom_diff));
+#endif
 }
 
-INSTANTIATE_CLASS(CuDNNSigmoidLayer);
+INSTANTIATE_LAYER_GPU_FUNCS(CuDNNSigmoidLayer);
 
 }  // namespace caffe
 #endif
diff --git a/src/caffe/layers/cudnn_softmax_layer.cpp b/src/caffe/layers/cudnn_softmax_layer.cpp
index 79ba5237ae3..77a3225adcd 100644
--- a/src/caffe/layers/cudnn_softmax_layer.cpp
+++ b/src/caffe/layers/cudnn_softmax_layer.cpp
@@ -13,30 +13,34 @@ namespace caffe {
 
 template <typename Dtype>
 void CuDNNSoftmaxLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
+      const vector<Blob<Dtype>*>& top) {
   SoftmaxLayer<Dtype>::LayerSetUp(bottom, top);
   // Initialize CUDNN.
   CUDNN_CHECK(cudnnCreate(&handle_));
   cudnn::createTensor4dDesc<Dtype>(&bottom_desc_);
   cudnn::createTensor4dDesc<Dtype>(&top_desc_);
+  handles_setup_ = true;
 }
 
 template <typename Dtype>
 void CuDNNSoftmaxLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
+      const vector<Blob<Dtype>*>& top) {
   SoftmaxLayer<Dtype>::Reshape(bottom, top);
-  int N = bottom[0]->num();
-  int K = bottom[0]->channels();
-  int H = bottom[0]->height();
-  int W = bottom[0]->width();
+  int N = this->outer_num_;
+  int K = bottom[0]->shape(this->softmax_axis_);
+  int H = this->inner_num_;
+  int W = 1;
   cudnn::setTensor4dDesc<Dtype>(&bottom_desc_, N, K, H, W);
   cudnn::setTensor4dDesc<Dtype>(&top_desc_, N, K, H, W);
 }
 
 template <typename Dtype>
 CuDNNSoftmaxLayer<Dtype>::~CuDNNSoftmaxLayer() {
-  cudnnDestroyTensor4dDescriptor(bottom_desc_);
-  cudnnDestroyTensor4dDescriptor(top_desc_);
+  // Check that handles have been setup before destroying.
+  if (!handles_setup_) { return; }
+
+  cudnnDestroyTensorDescriptor(bottom_desc_);
+  cudnnDestroyTensorDescriptor(top_desc_);
   cudnnDestroy(handle_);
 }
 
diff --git a/src/caffe/layers/cudnn_softmax_layer.cu b/src/caffe/layers/cudnn_softmax_layer.cu
index 300bdc496c8..a9e2fcefaf7 100644
--- a/src/caffe/layers/cudnn_softmax_layer.cu
+++ b/src/caffe/layers/cudnn_softmax_layer.cu
@@ -13,29 +13,36 @@ namespace caffe {
 
 template <typename Dtype>
 void CuDNNSoftmaxLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-    vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& top) {
   const Dtype* bottom_data = bottom[0]->gpu_data();
-  Dtype* top_data = (*top)[0]->mutable_gpu_data();
+  Dtype* top_data = top[0]->mutable_gpu_data();
   CUDNN_CHECK(cudnnSoftmaxForward(handle_, CUDNN_SOFTMAX_ACCURATE,
-      CUDNN_SOFTMAX_MODE_CHANNEL,
-      bottom_desc_, bottom_data, top_desc_, top_data));
+        CUDNN_SOFTMAX_MODE_CHANNEL,
+        cudnn::dataType<Dtype>::one,
+        bottom_desc_, bottom_data,
+        cudnn::dataType<Dtype>::zero,
+        top_desc_, top_data));
 }
 
 template <typename Dtype>
 void CuDNNSoftmaxLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
-    const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
+    const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
   if (propagate_down[0]) {
     const Dtype* top_data = top[0]->gpu_data();
     const Dtype* top_diff = top[0]->gpu_diff();
-    const Dtype* bottom_data = (*bottom)[0]->gpu_data();
-    Dtype* bottom_diff = (*bottom)[0]->mutable_gpu_diff();
+    const Dtype* bottom_data = bottom[0]->gpu_data();
+    Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
+
     CUDNN_CHECK(cudnnSoftmaxBackward(handle_, CUDNN_SOFTMAX_ACCURATE,
-        CUDNN_SOFTMAX_MODE_CHANNEL,
-        top_desc_, top_data, top_desc_, top_diff, bottom_desc_, bottom_diff));
+          CUDNN_SOFTMAX_MODE_CHANNEL,
+          cudnn::dataType<Dtype>::one,
+          top_desc_, top_data, top_desc_, top_diff,
+          cudnn::dataType<Dtype>::zero,
+          bottom_desc_, bottom_diff));
   }
 }
 
-INSTANTIATE_CLASS(CuDNNSoftmaxLayer);
+INSTANTIATE_LAYER_GPU_FUNCS(CuDNNSoftmaxLayer);
 
 }  // namespace caffe
 #endif
diff --git a/src/caffe/layers/cudnn_tanh_layer.cpp b/src/caffe/layers/cudnn_tanh_layer.cpp
index 7a5c06f6596..395d7339c63 100644
--- a/src/caffe/layers/cudnn_tanh_layer.cpp
+++ b/src/caffe/layers/cudnn_tanh_layer.cpp
@@ -1,6 +1,7 @@
 #ifdef USE_CUDNN
 #include <algorithm>
 #include <vector>
+#include <cudnn.h>
 
 #include "caffe/layer.hpp"
 #include "caffe/vision_layers.hpp"
@@ -9,17 +10,20 @@ namespace caffe {
 
 template <typename Dtype>
 void CuDNNTanHLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
+      const vector<Blob<Dtype>*>& top) {
   TanHLayer<Dtype>::LayerSetUp(bottom, top);
   // initialize cuDNN
   CUDNN_CHECK(cudnnCreate(&handle_));
   cudnn::createTensor4dDesc<Dtype>(&bottom_desc_);
   cudnn::createTensor4dDesc<Dtype>(&top_desc_);
+  handles_setup_ = true;
+  cudnnCreateActivationDescriptor(&activation_desc_);
+  cudnnSetActivationDescriptor(activation_desc_, CUDNN_ACTIVATION_TANH, CUDNN_PROPAGATE_NAN, 0.0);
 }
 
 template <typename Dtype>
 void CuDNNTanHLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
+      const vector<Blob<Dtype>*>& top) {
   TanHLayer<Dtype>::Reshape(bottom, top);
   const int N = bottom[0]->num();
   const int K = bottom[0]->channels();
@@ -31,8 +35,12 @@ void CuDNNTanHLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 CuDNNTanHLayer<Dtype>::~CuDNNTanHLayer() {
-  cudnnDestroyTensor4dDescriptor(this->bottom_desc_);
-  cudnnDestroyTensor4dDescriptor(this->top_desc_);
+  // Check that handles have been setup before destroying.
+  if (!handles_setup_) { return; }
+
+  cudnnDestroyActivationDescriptor(this->activation_desc_);
+  cudnnDestroyTensorDescriptor(this->bottom_desc_);
+  cudnnDestroyTensorDescriptor(this->top_desc_);
   cudnnDestroy(this->handle_);
 }
 
diff --git a/src/caffe/layers/cudnn_tanh_layer.cu b/src/caffe/layers/cudnn_tanh_layer.cu
index c475b08c0ee..f11b787abc5 100644
--- a/src/caffe/layers/cudnn_tanh_layer.cu
+++ b/src/caffe/layers/cudnn_tanh_layer.cu
@@ -9,33 +9,59 @@ namespace caffe {
 
 template <typename Dtype>
 void CuDNNTanHLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-    vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& top) {
   const Dtype* bottom_data = bottom[0]->gpu_data();
-  Dtype* top_data = (*top)[0]->mutable_gpu_data();
+  Dtype* top_data = top[0]->mutable_gpu_data();
+#if CUDNN_VERSION_MIN(5,0,0)
   CUDNN_CHECK(cudnnActivationForward(this->handle_,
-      CUDNN_ACTIVATION_TANH,
-      this->bottom_desc_, bottom_data, this->top_desc_, top_data));
+        activation_desc_,
+        cudnn::dataType<Dtype>::one,
+        this->bottom_desc_, bottom_data,
+        cudnn::dataType<Dtype>::zero,
+        this->top_desc_, top_data));
+#else
+    CUDNN_CHECK(cudnnActivationForward_v4(this->handle_,
+        activation_desc_,
+        cudnn::dataType<Dtype>::one,
+        this->bottom_desc_, bottom_data,
+        cudnn::dataType<Dtype>::zero,
+        this->top_desc_, top_data));
+#endif
 }
 
 template <typename Dtype>
 void CuDNNTanHLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
     const vector<bool>& propagate_down,
-    vector<Blob<Dtype>*>* bottom) {
+    const vector<Blob<Dtype>*>& bottom) {
   if (!propagate_down[0]) {
     return;
   }
 
   const Dtype* top_data = top[0]->gpu_data();
   const Dtype* top_diff = top[0]->gpu_diff();
-  const Dtype* bottom_data = (*bottom)[0]->gpu_data();
-  Dtype* bottom_diff = (*bottom)[0]->mutable_gpu_diff();
+  const Dtype* bottom_data = bottom[0]->gpu_data();
+  Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
+
+#if CUDNN_VERSION_MIN(5,0,0)
   CUDNN_CHECK(cudnnActivationBackward(this->handle_,
-      CUDNN_ACTIVATION_TANH,
-      this->top_desc_, top_data, this->top_desc_, top_diff,
-      this->bottom_desc_, bottom_data, this->bottom_desc_, bottom_diff));
+        activation_desc_,
+        cudnn::dataType<Dtype>::one,
+        this->top_desc_, top_data, this->top_desc_, top_diff,
+        this->bottom_desc_, bottom_data,
+        cudnn::dataType<Dtype>::zero,
+        this->bottom_desc_, bottom_diff));
+#else
+  CUDNN_CHECK(cudnnActivationBackward_v4(this->handle_,
+        activation_desc_,
+        cudnn::dataType<Dtype>::one,
+        this->top_desc_, top_data, this->top_desc_, top_diff,
+        this->bottom_desc_, bottom_data,
+        cudnn::dataType<Dtype>::zero,
+        this->bottom_desc_, bottom_diff));
+#endif
 }
 
-INSTANTIATE_CLASS(CuDNNTanHLayer);
+INSTANTIATE_LAYER_GPU_FUNCS(CuDNNTanHLayer);
 
 }  // namespace caffe
 #endif
diff --git a/src/caffe/layers/data_layer.cpp b/src/caffe/layers/data_layer.cpp
index d2071e2fa4f..5662de07de9 100644
--- a/src/caffe/layers/data_layer.cpp
+++ b/src/caffe/layers/data_layer.cpp
@@ -1,4 +1,5 @@
-#include <leveldb/db.h>
+#include <opencv2/core/core.hpp>
+
 #include <stdint.h>
 
 #include <string>
@@ -8,6 +9,7 @@
 #include "caffe/data_layers.hpp"
 #include "caffe/layer.hpp"
 #include "caffe/proto/caffe.pb.h"
+#include "caffe/util/benchmark.hpp"
 #include "caffe/util/io.hpp"
 #include "caffe/util/math_functions.hpp"
 #include "caffe/util/rng.hpp"
@@ -17,192 +19,134 @@ namespace caffe {
 template <typename Dtype>
 DataLayer<Dtype>::~DataLayer<Dtype>() {
   this->JoinPrefetchThread();
-  // clean up the database resources
-  switch (this->layer_param_.data_param().backend()) {
-  case DataParameter_DB_LEVELDB:
-    break;  // do nothing
-  case DataParameter_DB_LMDB:
-    mdb_cursor_close(mdb_cursor_);
-    mdb_close(mdb_env_, mdb_dbi_);
-    mdb_txn_abort(mdb_txn_);
-    mdb_env_close(mdb_env_);
-    break;
-  default:
-    LOG(FATAL) << "Unknown database backend";
-  }
 }
 
 template <typename Dtype>
 void DataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
+      const vector<Blob<Dtype>*>& top) {
+  // Set initial input mode to sequence
+  cur_input_mode_ = SEQUENCE;
+
   // Initialize DB
-  switch (this->layer_param_.data_param().backend()) {
-  case DataParameter_DB_LEVELDB:
-    {
-    leveldb::DB* db_temp;
-    leveldb::Options options = GetLevelDBOptions();
-    options.create_if_missing = false;
-    LOG(INFO) << "Opening leveldb " << this->layer_param_.data_param().source();
-    leveldb::Status status = leveldb::DB::Open(
-        options, this->layer_param_.data_param().source(), &db_temp);
-    CHECK(status.ok()) << "Failed to open leveldb "
-                       << this->layer_param_.data_param().source() << std::endl
-                       << status.ToString();
-    db_.reset(db_temp);
-    iter_.reset(db_->NewIterator(leveldb::ReadOptions()));
-    iter_->SeekToFirst();
-    }
-    break;
-  case DataParameter_DB_LMDB:
-    CHECK_EQ(mdb_env_create(&mdb_env_), MDB_SUCCESS) << "mdb_env_create failed";
-    CHECK_EQ(mdb_env_set_mapsize(mdb_env_, 1099511627776), MDB_SUCCESS);  // 1TB
-    CHECK_EQ(mdb_env_open(mdb_env_,
-             this->layer_param_.data_param().source().c_str(),
-             MDB_RDONLY|MDB_NOTLS, 0664), MDB_SUCCESS) << "mdb_env_open failed";
-    CHECK_EQ(mdb_txn_begin(mdb_env_, NULL, MDB_RDONLY, &mdb_txn_), MDB_SUCCESS)
-        << "mdb_txn_begin failed";
-    CHECK_EQ(mdb_open(mdb_txn_, NULL, 0, &mdb_dbi_), MDB_SUCCESS)
-        << "mdb_open failed";
-    CHECK_EQ(mdb_cursor_open(mdb_txn_, mdb_dbi_, &mdb_cursor_), MDB_SUCCESS)
-        << "mdb_cursor_open failed";
-    LOG(INFO) << "Opening lmdb " << this->layer_param_.data_param().source();
-    CHECK_EQ(mdb_cursor_get(mdb_cursor_, &mdb_key_, &mdb_value_, MDB_FIRST),
-        MDB_SUCCESS) << "mdb_cursor_get failed";
-    break;
-  default:
-    LOG(FATAL) << "Unknown database backend";
-  }
+  db_.reset(db::GetDB(this->layer_param_.data_param().backend()));
+  db_->Open(this->layer_param_.data_param().source(), db::READ);
+  cursor_.reset(db_->NewCursor());
 
-  // Check if we would need to randomly skip a few data points
+  // Check if we should randomly skip a few data points
   if (this->layer_param_.data_param().rand_skip()) {
     unsigned int skip = caffe_rng_rand() %
                         this->layer_param_.data_param().rand_skip();
     LOG(INFO) << "Skipping first " << skip << " data points.";
     while (skip-- > 0) {
-      switch (this->layer_param_.data_param().backend()) {
-      case DataParameter_DB_LEVELDB:
-        iter_->Next();
-        if (!iter_->Valid()) {
-          iter_->SeekToFirst();
-        }
-        break;
-      case DataParameter_DB_LMDB:
-        if (mdb_cursor_get(mdb_cursor_, &mdb_key_, &mdb_value_, MDB_NEXT)
-            != MDB_SUCCESS) {
-          CHECK_EQ(mdb_cursor_get(mdb_cursor_, &mdb_key_, &mdb_value_,
-                   MDB_FIRST), MDB_SUCCESS);
-        }
-        break;
-      default:
-        LOG(FATAL) << "Unknown database backend";
-      }
+      cursor_->Next();
     }
   }
-  // Read a data point, and use it to initialize the top blob.
+  // Read a data point, to initialize the prefetch and top blobs.
   Datum datum;
-  switch (this->layer_param_.data_param().backend()) {
-  case DataParameter_DB_LEVELDB:
-    datum.ParseFromString(iter_->value().ToString());
-    break;
-  case DataParameter_DB_LMDB:
-    datum.ParseFromArray(mdb_value_.mv_data, mdb_value_.mv_size);
-    break;
-  default:
-    LOG(FATAL) << "Unknown database backend";
-  }
+  datum.ParseFromString(cursor_->value());
+  // Use data_transformer to infer the expected blob shape from datum.
+  vector<int> top_shape = this->data_transformer_->InferBlobShape(datum);
+  this->transformed_data_.Reshape(top_shape);
+  // Reshape top[0] and prefetch_data according to the batch_size.
+  top_shape[0] = this->layer_param_.data_param().batch_size();
+  this->prefetch_data_.Reshape(top_shape);
+  top[0]->ReshapeLike(this->prefetch_data_);
 
-  // image
-  int crop_size = this->layer_param_.transform_param().crop_size();
-  if (crop_size > 0) {
-    (*top)[0]->Reshape(this->layer_param_.data_param().batch_size(),
-                       datum.channels(), crop_size, crop_size);
-    this->prefetch_data_.Reshape(this->layer_param_.data_param().batch_size(),
-        datum.channels(), crop_size, crop_size);
-  } else {
-    (*top)[0]->Reshape(
-        this->layer_param_.data_param().batch_size(), datum.channels(),
-        datum.height(), datum.width());
-    this->prefetch_data_.Reshape(this->layer_param_.data_param().batch_size(),
-        datum.channels(), datum.height(), datum.width());
-  }
-  LOG(INFO) << "output data size: " << (*top)[0]->num() << ","
-      << (*top)[0]->channels() << "," << (*top)[0]->height() << ","
-      << (*top)[0]->width();
+  LOG(INFO) << "output data size: " << top[0]->num() << ","
+      << top[0]->channels() << "," << top[0]->height() << ","
+      << top[0]->width();
   // label
   if (this->output_labels_) {
-    (*top)[1]->Reshape(this->layer_param_.data_param().batch_size(), 1, 1, 1);
-    this->prefetch_label_.Reshape(this->layer_param_.data_param().batch_size(),
-        1, 1, 1);
+    vector<int> label_shape(1, this->layer_param_.data_param().batch_size());
+    top[1]->Reshape(label_shape);
+    this->prefetch_label_.Reshape(label_shape);
   }
-  // datum size
-  this->datum_channels_ = datum.channels();
-  this->datum_height_ = datum.height();
-  this->datum_width_ = datum.width();
-  this->datum_size_ = datum.channels() * datum.height() * datum.width();
+
 }
 
 // This function is used to create a thread that prefetches the data.
 template <typename Dtype>
 void DataLayer<Dtype>::InternalThreadEntry() {
-  Datum datum;
+  CPUTimer batch_timer;
+  batch_timer.Start();
+  double read_time = 0;
+  double trans_time = 0;
+  CPUTimer timer;
   CHECK(this->prefetch_data_.count());
+  CHECK(this->transformed_data_.count());
+
+  // Reshape according to the first datum of each batch
+  // on single input batches allows for inputs of varying dimension.
+  const int batch_size = this->layer_param_.data_param().batch_size();
+  Datum datum;
+  datum.ParseFromString(cursor_->value());
+  // Use data_transformer to infer the expected blob shape from datum.
+  vector<int> top_shape = this->data_transformer_->InferBlobShape(datum);
+  this->transformed_data_.Reshape(top_shape);
+  // Reshape prefetch_data according to the batch_size.
+  top_shape[0] = batch_size;
+  this->prefetch_data_.Reshape(top_shape);
+
   Dtype* top_data = this->prefetch_data_.mutable_cpu_data();
   Dtype* top_label = NULL;  // suppress warnings about uninitialized variables
+
   if (this->output_labels_) {
     top_label = this->prefetch_label_.mutable_cpu_data();
   }
-  const int batch_size = this->layer_param_.data_param().batch_size();
-
+  timer.Start();
   for (int item_id = 0; item_id < batch_size; ++item_id) {
-    // get a blob
-    switch (this->layer_param_.data_param().backend()) {
-    case DataParameter_DB_LEVELDB:
-      CHECK(iter_);
-      CHECK(iter_->Valid());
-      datum.ParseFromString(iter_->value().ToString());
-      break;
-    case DataParameter_DB_LMDB:
-      CHECK_EQ(mdb_cursor_get(mdb_cursor_, &mdb_key_,
-              &mdb_value_, MDB_GET_CURRENT), MDB_SUCCESS);
-      datum.ParseFromArray(mdb_value_.mv_data,
-          mdb_value_.mv_size);
-      break;
-    default:
-      LOG(FATAL) << "Unknown database backend";
+    // get a datum
+    Datum datum;
+    if (cur_input_mode_ == SEQUENCE) {
+      datum.ParseFromString(cursor_->value());
+      // put the key into shuffle pool
+      shuffle_key_pool_.push_back(cursor_->key());
+    }else if (cur_input_mode_ == SHUFFLE){
+      datum.ParseFromString(cursor_->Lookup(*shuffle_cursor_));
     }
-
+    read_time += timer.MicroSeconds();
+    timer.Start();
     // Apply data transformations (mirror, scale, crop...)
-    this->data_transformer_.Transform(item_id, datum, this->mean_, top_data);
-
+    int offset = this->prefetch_data_.offset(item_id);
+    this->transformed_data_.set_cpu_data(top_data + offset);
+    this->data_transformer_->Transform(datum, &(this->transformed_data_));
+    // Copy label.
     if (this->output_labels_) {
       top_label[item_id] = datum.label();
     }
-
-    // go to the next iter
-    switch (this->layer_param_.data_param().backend()) {
-    case DataParameter_DB_LEVELDB:
-      iter_->Next();
-      if (!iter_->Valid()) {
-        // We have reached the end. Restart from the first.
+    trans_time += timer.MicroSeconds();
+    timer.Start();
+    // go to the next item.
+    if (cur_input_mode_ == SEQUENCE) {
+      cursor_->Next();
+      if (!cursor_->valid()) {
         DLOG(INFO) << "Restarting data prefetching from start.";
-        iter_->SeekToFirst();
+        cursor_->SeekToFirst();
+
+        if (this->layer_param_.data_param().shuffle() == true){
+          LOG(INFO)<<"Entering shuffling mode after first epoch";
+          cur_input_mode_ = SHUFFLE;
+          shuffle(shuffle_key_pool_.begin(), shuffle_key_pool_.end());
+          shuffle_cursor_ = shuffle_key_pool_.begin();
+        }
       }
-      break;
-    case DataParameter_DB_LMDB:
-      if (mdb_cursor_get(mdb_cursor_, &mdb_key_,
-              &mdb_value_, MDB_NEXT) != MDB_SUCCESS) {
-        // We have reached the end. Restart from the first.
-        DLOG(INFO) << "Restarting data prefetching from start.";
-        CHECK_EQ(mdb_cursor_get(mdb_cursor_, &mdb_key_,
-                &mdb_value_, MDB_FIRST), MDB_SUCCESS);
+    } else if (cur_input_mode_ == SHUFFLE){
+      shuffle_cursor_++;
+      if (shuffle_cursor_ == shuffle_key_pool_.end()){
+        LOG(INFO)<<"Restarting stream and shuffle again";
+        shuffle(shuffle_key_pool_.begin(), shuffle_key_pool_.end());
+        shuffle_cursor_ = shuffle_key_pool_.begin();
       }
-      break;
-    default:
-      LOG(FATAL) << "Unknown database backend";
     }
   }
+  timer.Stop();
+  batch_timer.Stop();
+  DLOG(INFO) << "Prefetch batch: " << batch_timer.MilliSeconds() << " ms.";
+  DLOG(INFO) << "     Read time: " << read_time / 1000 << " ms.";
+  DLOG(INFO) << "Transform time: " << trans_time / 1000 << " ms.";
 }
 
 INSTANTIATE_CLASS(DataLayer);
+REGISTER_LAYER_CLASS(Data);
 
 }  // namespace caffe
diff --git a/src/caffe/layers/deconv_layer.cpp b/src/caffe/layers/deconv_layer.cpp
new file mode 100644
index 00000000000..a4612963b6b
--- /dev/null
+++ b/src/caffe/layers/deconv_layer.cpp
@@ -0,0 +1,79 @@
+#include <vector>
+
+#include "caffe/filler.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/util/im2col.hpp"
+#include "caffe/util/math_functions.hpp"
+#include "caffe/vision_layers.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void DeconvolutionLayer<Dtype>::compute_output_shape() {
+  this->height_out_ = this->stride_h_ * (this->height_ - 1) + this->kernel_h_
+      - 2 * this->pad_h_;
+  this->width_out_ = this->stride_w_ * (this->width_ - 1) + this->kernel_w_
+      - 2 * this->pad_w_;
+}
+
+template <typename Dtype>
+void DeconvolutionLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+  const Dtype* weight = this->blobs_[0]->cpu_data();
+  for (int i = 0; i < bottom.size(); ++i) {
+    const Dtype* bottom_data = bottom[i]->cpu_data();
+    Dtype* top_data = top[i]->mutable_cpu_data();
+    for (int n = 0; n < this->num_; ++n) {
+      this->backward_cpu_gemm(bottom_data + bottom[i]->offset(n), weight,
+          top_data + top[i]->offset(n));
+      if (this->bias_term_) {
+        const Dtype* bias = this->blobs_[1]->cpu_data();
+        this->forward_cpu_bias(top_data + top[i]->offset(n), bias);
+      }
+    }
+  }
+}
+
+template <typename Dtype>
+void DeconvolutionLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+  const Dtype* weight = this->blobs_[0]->cpu_data();
+  Dtype* weight_diff = this->blobs_[0]->mutable_cpu_diff();
+  for (int i = 0; i < top.size(); ++i) {
+    const Dtype* top_diff = top[i]->cpu_diff();
+    const Dtype* bottom_data = bottom[i]->cpu_data();
+    Dtype* bottom_diff = bottom[i]->mutable_cpu_diff();
+    // Bias gradient, if necessary.
+    if (this->bias_term_ && this->param_propagate_down_[1]) {
+      Dtype* bias_diff = this->blobs_[1]->mutable_cpu_diff();
+      for (int n = 0; n < this->num_; ++n) {
+        this->backward_cpu_bias(bias_diff, top_diff + top[i]->offset(n));
+      }
+    }
+    if (this->param_propagate_down_[0] || propagate_down[i]) {
+      for (int n = 0; n < this->num_; ++n) {
+        // Gradient w.r.t. weight. Note that we will accumulate diffs.
+        if (this->param_propagate_down_[0]) {
+          this->weight_cpu_gemm(top_diff + top[i]->offset(n),
+              bottom_data + bottom[i]->offset(n), weight_diff);
+        }
+        // Gradient w.r.t. bottom data, if necessary, reusing the column buffer
+        // we might have just computed above.
+        if (propagate_down[i]) {
+          this->forward_cpu_gemm(top_diff + top[i]->offset(n), weight,
+              bottom_diff + bottom[i]->offset(n),
+              this->param_propagate_down_[0]);
+        }
+      }
+    }
+  }
+}
+
+#ifdef CPU_ONLY
+STUB_GPU(DeconvolutionLayer);
+#endif
+
+INSTANTIATE_CLASS(DeconvolutionLayer);
+REGISTER_LAYER_CLASS(Deconvolution);
+
+}  // namespace caffe
diff --git a/src/caffe/layers/deconv_layer.cu b/src/caffe/layers/deconv_layer.cu
new file mode 100644
index 00000000000..39bc4de8c66
--- /dev/null
+++ b/src/caffe/layers/deconv_layer.cu
@@ -0,0 +1,64 @@
+#include <vector>
+
+#include "caffe/filler.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/util/im2col.hpp"
+#include "caffe/util/math_functions.hpp"
+#include "caffe/vision_layers.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void DeconvolutionLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+  const Dtype* weight = this->blobs_[0]->gpu_data();
+  for (int i = 0; i < bottom.size(); ++i) {
+    const Dtype* bottom_data = bottom[i]->gpu_data();
+    Dtype* top_data = top[i]->mutable_gpu_data();
+    for (int n = 0; n < this->num_; ++n) {
+      this->backward_gpu_gemm(bottom_data + bottom[i]->offset(n), weight,
+          top_data + top[i]->offset(n));
+      if (this->bias_term_) {
+        const Dtype* bias = this->blobs_[1]->gpu_data();
+        this->forward_gpu_bias(top_data + top[i]->offset(n), bias);
+      }
+    }
+  }
+}
+
+template <typename Dtype>
+void DeconvolutionLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+  const Dtype* weight = this->blobs_[0]->gpu_data();
+  Dtype* weight_diff = this->blobs_[0]->mutable_gpu_diff();
+  for (int i = 0; i < top.size(); ++i) {
+    const Dtype* top_diff = top[i]->gpu_diff();
+    const Dtype* bottom_data = bottom[i]->gpu_data();
+    Dtype* bottom_diff = bottom[i]->mutable_gpu_diff();
+    // Bias gradient, if necessary.
+    if (this->bias_term_ && this->param_propagate_down_[1]) {
+      Dtype* bias_diff = this->blobs_[1]->mutable_gpu_diff();
+      for (int n = 0; n < this->num_; ++n) {
+        this->backward_gpu_bias(bias_diff, top_diff + top[i]->offset(n));
+      }
+    }
+    if (this->param_propagate_down_[0] || propagate_down[i]) {
+      for (int n = 0; n < this->num_; ++n) {
+        // gradient w.r.t. weight. Note that we will accumulate diffs.
+        if (this->param_propagate_down_[0]) {
+          this->weight_gpu_gemm(top_diff + top[i]->offset(n),
+              bottom_data + bottom[i]->offset(n), weight_diff);
+        }
+        // gradient w.r.t. bottom data, if necessary.
+        if (propagate_down[i]) {
+          this->forward_gpu_gemm(top_diff + top[i]->offset(n), weight,
+              bottom_diff + bottom[i]->offset(n));
+        }
+      }
+    }
+  }
+}
+
+INSTANTIATE_LAYER_GPU_FUNCS(DeconvolutionLayer);
+
+}  // namespace caffe
diff --git a/src/caffe/layers/dropout_layer.cpp b/src/caffe/layers/dropout_layer.cpp
index 47feb1d2543..ec1256fd2fa 100644
--- a/src/caffe/layers/dropout_layer.cpp
+++ b/src/caffe/layers/dropout_layer.cpp
@@ -12,7 +12,7 @@ namespace caffe {
 
 template <typename Dtype>
 void DropoutLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
+      const vector<Blob<Dtype>*>& top) {
   NeuronLayer<Dtype>::LayerSetUp(bottom, top);
   threshold_ = this->layer_param_.dropout_param().dropout_ratio();
   DCHECK(threshold_ > 0.);
@@ -23,7 +23,7 @@ void DropoutLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 void DropoutLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
+      const vector<Blob<Dtype>*>& top) {
   NeuronLayer<Dtype>::Reshape(bottom, top);
   // Set up the cache for random number generation
   rand_vec_.Reshape(bottom[0]->num(), bottom[0]->channels(),
@@ -32,12 +32,12 @@ void DropoutLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 void DropoutLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-    vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& top) {
   const Dtype* bottom_data = bottom[0]->cpu_data();
-  Dtype* top_data = (*top)[0]->mutable_cpu_data();
+  Dtype* top_data = top[0]->mutable_cpu_data();
   unsigned int* mask = rand_vec_.mutable_cpu_data();
   const int count = bottom[0]->count();
-  if (Caffe::phase() == Caffe::TRAIN) {
+  if (this->phase_ == TRAIN) {
     // Create random numbers
     caffe_rng_bernoulli(count, 1. - threshold_, mask);
     for (int i = 0; i < count; ++i) {
@@ -51,13 +51,13 @@ void DropoutLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
 template <typename Dtype>
 void DropoutLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
     const vector<bool>& propagate_down,
-    vector<Blob<Dtype>*>* bottom) {
+    const vector<Blob<Dtype>*>& bottom) {
   if (propagate_down[0]) {
     const Dtype* top_diff = top[0]->cpu_diff();
-    Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
-    if (Caffe::phase() == Caffe::TRAIN) {
+    Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
+    if (this->phase_ == TRAIN) {
       const unsigned int* mask = rand_vec_.cpu_data();
-      const int count = (*bottom)[0]->count();
+      const int count = bottom[0]->count();
       for (int i = 0; i < count; ++i) {
         bottom_diff[i] = top_diff[i] * mask[i] * scale_;
       }
@@ -73,6 +73,6 @@ STUB_GPU(DropoutLayer);
 #endif
 
 INSTANTIATE_CLASS(DropoutLayer);
-
+REGISTER_LAYER_CLASS(Dropout);
 
 }  // namespace caffe
diff --git a/src/caffe/layers/dropout_layer.cu b/src/caffe/layers/dropout_layer.cu
index 9756c862183..f9ea04f4acf 100644
--- a/src/caffe/layers/dropout_layer.cu
+++ b/src/caffe/layers/dropout_layer.cu
@@ -22,11 +22,11 @@ __global__ void DropoutForward(const int n, const Dtype* in,
 
 template <typename Dtype>
 void DropoutLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-    vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& top) {
   const Dtype* bottom_data = bottom[0]->gpu_data();
-  Dtype* top_data = (*top)[0]->mutable_gpu_data();
+  Dtype* top_data = top[0]->mutable_gpu_data();
   const int count = bottom[0]->count();
-  if (Caffe::phase() == Caffe::TRAIN) {
+  if (this->phase_ == TRAIN) {
     unsigned int* mask =
         static_cast<unsigned int*>(rand_vec_.mutable_gpu_data());
     caffe_gpu_rng_uniform(count, mask);
@@ -52,14 +52,14 @@ __global__ void DropoutBackward(const int n, const Dtype* in_diff,
 template <typename Dtype>
 void DropoutLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
     const vector<bool>& propagate_down,
-    vector<Blob<Dtype>*>* bottom) {
+    const vector<Blob<Dtype>*>& bottom) {
   if (propagate_down[0]) {
     const Dtype* top_diff = top[0]->gpu_diff();
-    Dtype* bottom_diff = (*bottom)[0]->mutable_gpu_diff();
-    if (Caffe::phase() == Caffe::TRAIN) {
+    Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
+    if (this->phase_ == TRAIN) {
       const unsigned int* mask =
           static_cast<const unsigned int*>(rand_vec_.gpu_data());
-      const int count = (*bottom)[0]->count();
+      const int count = bottom[0]->count();
       // NOLINT_NEXT_LINE(whitespace/operators)
       DropoutBackward<Dtype><<<CAFFE_GET_BLOCKS(count),
         CAFFE_CUDA_NUM_THREADS>>>(
@@ -71,7 +71,7 @@ void DropoutLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
   }
 }
 
-INSTANTIATE_CLASS(DropoutLayer);
+INSTANTIATE_LAYER_GPU_FUNCS(DropoutLayer);
 
 
 }  // namespace caffe
diff --git a/src/caffe/layers/dummy_data_layer.cpp b/src/caffe/layers/dummy_data_layer.cpp
index 883f2528ef8..6b0d617464c 100644
--- a/src/caffe/layers/dummy_data_layer.cpp
+++ b/src/caffe/layers/dummy_data_layer.cpp
@@ -8,26 +8,38 @@ namespace caffe {
 
 template <typename Dtype>
 void DummyDataLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
-  const int num_top = top->size();
+      const vector<Blob<Dtype>*>& top) {
+  const int num_top = top.size();
   const DummyDataParameter& param = this->layer_param_.dummy_data_param();
   const int num_data_filler = param.data_filler_size();
   CHECK(num_data_filler == 0 || num_data_filler == 1 ||
         num_data_filler == num_top)
       << "Number of data fillers must be 0, 1 or equal to the number of tops: "
       << num_top << "; you specified " << num_data_filler << " data fillers.";
-  CHECK(param.num_size() == 1 || param.num_size() == num_top)
-      << "Must specify either a single (1) 'num' or one for each top blob "
-      << "(" << num_top << "); you specified " << param.num_size() << ".";
-  CHECK(param.channels_size() == 1 || param.channels_size() == num_top)
-      << "Must specify either a single (1) 'channels' or one for each top blob "
-      << "(" << num_top << "); you specified " << param.channels_size() << ".";
-  CHECK(param.height_size() == 1 || param.height_size() == num_top)
-      << "Must specify either a single (1) 'height' or one for each top blob "
-      << "(" << num_top << "); you specified " << param.height_size() << ".";
-  CHECK(param.width_size() == 1 || param.width_size() == num_top)
-      << "Must specify either a single (1) 'width' or one for each top blob "
-      << "(" << num_top << "); you specified " << param.width_size() << ".";
+
+  const bool legacy_dims = param.num_size() || param.channels_size() ||
+                           param.height_size() || param.width_size();
+  if (legacy_dims) {
+    CHECK_EQ(0, param.shape_size())
+        << "Both shape and legacy fields were specified";
+    // Using deprecated 4D output dim specifiers.
+    CHECK(param.num_size() == 1 || param.num_size() == num_top)
+        << "Must specify 'num' once, or once per top blob "
+        << "(" << num_top << "); specified " << param.num_size() << ".";
+    CHECK(param.channels_size() == 1 || param.channels_size() == num_top)
+        << "Must specify 'channels' once, or once per top blob "
+        << "(" << num_top << "); specified " << param.channels_size() << ".";
+    CHECK(param.height_size() == 1 || param.height_size() == num_top)
+        << "Must specify 'height' once, or once per top blob "
+        << "(" << num_top << "); specified " << param.height_size() << ".";
+    CHECK(param.width_size() == 1 || param.width_size() == num_top)
+        << "Must specify 'width' once, or once per top blob "
+        << "(" << num_top << "); specified " << param.width_size() << ".";
+  } else {
+    CHECK(param.shape_size() == 1 || param.shape_size() == num_top)
+        << "Must specify 'shape' once, or once per top blob "
+        << "(" << num_top << "); specified " << param.shape_size() << ".";
+  }
   // refill_[i] tells Forward i whether or not to actually refill top Blob i.
   // If refill_[i] is false, Forward does nothing for Blob i. We use this to
   // avoid wastefully refilling "constant" Blobs in every forward pass.
@@ -63,14 +75,19 @@ void DummyDataLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
     }
   }
   for (int i = 0; i < num_top; ++i) {
-    const int num = (param.num_size() == 1) ? param.num(0) : param.num(i);
-    const int channels =
-        (param.channels_size() == 1) ? param.channels(0) : param.channels(i);
-    const int height =
-        (param.height_size() == 1) ? param.height(0) : param.height(i);
-    const int width =
-        (param.width_size() == 1) ? param.width(0) : param.width(i);
-    (*top)[i]->Reshape(num, channels, height, width);
+    if (legacy_dims) {
+      const int num = (param.num_size() == 1) ? param.num(0) : param.num(i);
+      const int channels =
+          (param.channels_size() == 1) ? param.channels(0) : param.channels(i);
+      const int height =
+          (param.height_size() == 1) ? param.height(0) : param.height(i);
+      const int width =
+          (param.width_size() == 1) ? param.width(0) : param.width(i);
+      top[i]->Reshape(num, channels, height, width);
+    } else {
+      const int shape_index = (param.shape_size() == 1) ? 0 : i;
+      top[i]->Reshape(param.shape(shape_index));
+    }
   }
   // Run Forward once, with refill_ inverted, to fill the constant Blobs.
   this->Forward(bottom, top);
@@ -83,15 +100,16 @@ void DummyDataLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 void DummyDataLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
-  for (int i = 0; i < top->size(); ++i) {
+      const vector<Blob<Dtype>*>& top) {
+  for (int i = 0; i < top.size(); ++i) {
     const int filler_id = (fillers_.size() > 1) ? i : 0;
     if (refill_[filler_id]) {
-      fillers_[filler_id]->Fill((*top)[i]);
+      fillers_[filler_id]->Fill(top[i]);
     }
   }
 }
 
 INSTANTIATE_CLASS(DummyDataLayer);
+REGISTER_LAYER_CLASS(DummyData);
 
 }  // namespace caffe
diff --git a/src/caffe/layers/eltwise_layer.cpp b/src/caffe/layers/eltwise_layer.cpp
index 569560f97d3..a80700736bd 100644
--- a/src/caffe/layers/eltwise_layer.cpp
+++ b/src/caffe/layers/eltwise_layer.cpp
@@ -9,7 +9,7 @@ namespace caffe {
 
 template <typename Dtype>
 void EltwiseLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
+      const vector<Blob<Dtype>*>& top) {
   CHECK(this->layer_param().eltwise_param().coeff_size() == 0
       || this->layer_param().eltwise_param().coeff_size() == bottom.size()) <<
       "Eltwise Layer takes one coefficient per bottom blob.";
@@ -30,33 +30,26 @@ void EltwiseLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 void EltwiseLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
-  const int num = bottom[0]->num();
-  const int channels = bottom[0]->channels();
-  const int height = bottom[0]->height();
-  const int width = bottom[0]->width();
+      const vector<Blob<Dtype>*>& top) {
   for (int i = 1; i < bottom.size(); ++i) {
-    CHECK_EQ(num, bottom[i]->num());
-    CHECK_EQ(channels, bottom[i]->channels());
-    CHECK_EQ(height, bottom[i]->height());
-    CHECK_EQ(width, bottom[i]->width());
+    CHECK(bottom[i]->shape() == bottom[0]->shape());
   }
-  (*top)[0]->Reshape(num, channels, height, width);
+  top[0]->ReshapeLike(*bottom[0]);
   // If max operation, we will initialize the vector index part.
   if (this->layer_param_.eltwise_param().operation() ==
-      EltwiseParameter_EltwiseOp_MAX && top->size() == 1) {
-    max_idx_.Reshape(bottom[0]->num(), channels, height, width);
+      EltwiseParameter_EltwiseOp_MAX && top.size() == 1) {
+    max_idx_.Reshape(bottom[0]->shape());
   }
 }
 
 template <typename Dtype>
 void EltwiseLayer<Dtype>::Forward_cpu(
-    const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
   int* mask = NULL;
   const Dtype* bottom_data_a = NULL;
   const Dtype* bottom_data_b = NULL;
-  const int count = (*top)[0]->count();
-  Dtype* top_data = (*top)[0]->mutable_cpu_data();
+  const int count = top[0]->count();
+  Dtype* top_data = top[0]->mutable_cpu_data();
   switch (op_) {
   case EltwiseParameter_EltwiseOp_PROD:
     caffe_mul(count, bottom[0]->cpu_data(), bottom[1]->cpu_data(), top_data);
@@ -106,26 +99,26 @@ void EltwiseLayer<Dtype>::Forward_cpu(
 
 template <typename Dtype>
 void EltwiseLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
-    const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
+    const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
   const int* mask = NULL;
   const int count = top[0]->count();
   const Dtype* top_data = top[0]->cpu_data();
   const Dtype* top_diff = top[0]->cpu_diff();
-  for (int i = 0; i < bottom->size(); ++i) {
+  for (int i = 0; i < bottom.size(); ++i) {
     if (propagate_down[i]) {
-      const Dtype* bottom_data = (*bottom)[i]->cpu_data();
-      Dtype* bottom_diff = (*bottom)[i]->mutable_cpu_diff();
+      const Dtype* bottom_data = bottom[i]->cpu_data();
+      Dtype* bottom_diff = bottom[i]->mutable_cpu_diff();
       switch (op_) {
       case EltwiseParameter_EltwiseOp_PROD:
         if (stable_prod_grad_) {
           bool initialized = false;
-          for (int j = 0; j < bottom->size(); ++j) {
+          for (int j = 0; j < bottom.size(); ++j) {
             if (i == j) { continue; }
             if (!initialized) {
-              caffe_copy(count, (*bottom)[j]->cpu_data(), bottom_diff);
+              caffe_copy(count, bottom[j]->cpu_data(), bottom_diff);
               initialized = true;
             } else {
-              caffe_mul(count, (*bottom)[j]->cpu_data(), bottom_diff,
+              caffe_mul(count, bottom[j]->cpu_data(), bottom_diff,
                         bottom_diff);
             }
           }
@@ -163,6 +156,6 @@ STUB_GPU(EltwiseLayer);
 #endif
 
 INSTANTIATE_CLASS(EltwiseLayer);
-
+REGISTER_LAYER_CLASS(Eltwise);
 
 }  // namespace caffe
diff --git a/src/caffe/layers/eltwise_layer.cu b/src/caffe/layers/eltwise_layer.cu
index 16cb6cc77e3..2247870d97f 100644
--- a/src/caffe/layers/eltwise_layer.cu
+++ b/src/caffe/layers/eltwise_layer.cu
@@ -33,10 +33,10 @@ __global__ void MaxForward(const int nthreads, const Dtype* bottom_data_a,
 
 template <typename Dtype>
 void EltwiseLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-    vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& top) {
   int* mask = NULL;
-  const int count = (*top)[0]->count();
-  Dtype* top_data = (*top)[0]->mutable_gpu_data();
+  const int count = top[0]->count();
+  Dtype* top_data = top[0]->mutable_gpu_data();
   switch (op_) {
   case EltwiseParameter_EltwiseOp_PROD:
     caffe_gpu_mul(count, bottom[0]->gpu_data(), bottom[1]->gpu_data(),
@@ -82,26 +82,26 @@ __global__ void MaxBackward(const int nthreads, const Dtype* top_diff,
 
 template <typename Dtype>
 void EltwiseLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
-    const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
+    const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
   const int* mask = NULL;
   const int count = top[0]->count();
   const Dtype* top_data = top[0]->gpu_data();
   const Dtype* top_diff = top[0]->gpu_diff();
-  for (int i = 0; i < bottom->size(); ++i) {
+  for (int i = 0; i < bottom.size(); ++i) {
     if (propagate_down[i]) {
-      const Dtype* bottom_data = (*bottom)[i]->gpu_data();
-      Dtype* bottom_diff = (*bottom)[i]->mutable_gpu_diff();
+      const Dtype* bottom_data = bottom[i]->gpu_data();
+      Dtype* bottom_diff = bottom[i]->mutable_gpu_diff();
       switch (op_) {
       case EltwiseParameter_EltwiseOp_PROD:
         if (stable_prod_grad_) {
           bool initialized = false;
-          for (int j = 0; j < bottom->size(); ++j) {
+          for (int j = 0; j < bottom.size(); ++j) {
             if (i == j) { continue; }
             if (!initialized) {
-              caffe_copy(count, (*bottom)[j]->gpu_data(), bottom_diff);
+              caffe_copy(count, bottom[j]->gpu_data(), bottom_diff);
               initialized = true;
             } else {
-              caffe_gpu_mul(count, (*bottom)[j]->gpu_data(), bottom_diff,
+              caffe_gpu_mul(count, bottom[j]->gpu_data(), bottom_diff,
                             bottom_diff);
             }
           }
@@ -130,6 +130,6 @@ void EltwiseLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
   }
 }
 
-INSTANTIATE_CLASS(EltwiseLayer);
+INSTANTIATE_LAYER_GPU_FUNCS(EltwiseLayer);
 
 }  // namespace caffe
diff --git a/src/caffe/layers/euclidean_loss_layer.cpp b/src/caffe/layers/euclidean_loss_layer.cpp
index 1b4a13d2ddc..80efa31b22c 100644
--- a/src/caffe/layers/euclidean_loss_layer.cpp
+++ b/src/caffe/layers/euclidean_loss_layer.cpp
@@ -9,18 +9,16 @@ namespace caffe {
 
 template <typename Dtype>
 void EuclideanLossLayer<Dtype>::Reshape(
-  const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
+  const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
   LossLayer<Dtype>::Reshape(bottom, top);
-  CHECK_EQ(bottom[0]->channels(), bottom[1]->channels());
-  CHECK_EQ(bottom[0]->height(), bottom[1]->height());
-  CHECK_EQ(bottom[0]->width(), bottom[1]->width());
-  diff_.Reshape(bottom[0]->num(), bottom[0]->channels(),
-      bottom[0]->height(), bottom[0]->width());
+  CHECK_EQ(bottom[0]->count(1), bottom[1]->count(1))
+      << "Inputs must have the same dimension.";
+  diff_.ReshapeLike(*bottom[0]);
 }
 
 template <typename Dtype>
 void EuclideanLossLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-    vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& top) {
   int count = bottom[0]->count();
   caffe_sub(
       count,
@@ -29,22 +27,22 @@ void EuclideanLossLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       diff_.mutable_cpu_data());
   Dtype dot = caffe_cpu_dot(count, diff_.cpu_data(), diff_.cpu_data());
   Dtype loss = dot / bottom[0]->num() / Dtype(2);
-  (*top)[0]->mutable_cpu_data()[0] = loss;
+  top[0]->mutable_cpu_data()[0] = loss;
 }
 
 template <typename Dtype>
 void EuclideanLossLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
-    const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
+    const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
   for (int i = 0; i < 2; ++i) {
     if (propagate_down[i]) {
       const Dtype sign = (i == 0) ? 1 : -1;
-      const Dtype alpha = sign * top[0]->cpu_diff()[0] / (*bottom)[i]->num();
+      const Dtype alpha = sign * top[0]->cpu_diff()[0] / bottom[i]->num();
       caffe_cpu_axpby(
-          (*bottom)[i]->count(),              // count
+          bottom[i]->count(),              // count
           alpha,                              // alpha
           diff_.cpu_data(),                   // a
           Dtype(0),                           // beta
-          (*bottom)[i]->mutable_cpu_diff());  // b
+          bottom[i]->mutable_cpu_diff());  // b
     }
   }
 }
@@ -54,5 +52,6 @@ STUB_GPU(EuclideanLossLayer);
 #endif
 
 INSTANTIATE_CLASS(EuclideanLossLayer);
+REGISTER_LAYER_CLASS(EuclideanLoss);
 
 }  // namespace caffe
diff --git a/src/caffe/layers/euclidean_loss_layer.cu b/src/caffe/layers/euclidean_loss_layer.cu
index 70b1b9ee9ea..5b1de3ad2d9 100644
--- a/src/caffe/layers/euclidean_loss_layer.cu
+++ b/src/caffe/layers/euclidean_loss_layer.cu
@@ -9,7 +9,7 @@ namespace caffe {
 
 template <typename Dtype>
 void EuclideanLossLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-    vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& top) {
   int count = bottom[0]->count();
   caffe_gpu_sub(
       count,
@@ -19,26 +19,26 @@ void EuclideanLossLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
   Dtype dot;
   caffe_gpu_dot(count, diff_.gpu_data(), diff_.gpu_data(), &dot);
   Dtype loss = dot / bottom[0]->num() / Dtype(2);
-  (*top)[0]->mutable_cpu_data()[0] = loss;
+  top[0]->mutable_cpu_data()[0] = loss;
 }
 
 template <typename Dtype>
 void EuclideanLossLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
-    const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
+    const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
   for (int i = 0; i < 2; ++i) {
     if (propagate_down[i]) {
       const Dtype sign = (i == 0) ? 1 : -1;
-      const Dtype alpha = sign * top[0]->cpu_diff()[0] / (*bottom)[i]->num();
+      const Dtype alpha = sign * top[0]->cpu_diff()[0] / bottom[i]->num();
       caffe_gpu_axpby(
-          (*bottom)[i]->count(),              // count
+          bottom[i]->count(),              // count
           alpha,                              // alpha
           diff_.gpu_data(),                   // a
           Dtype(0),                           // beta
-          (*bottom)[i]->mutable_gpu_diff());  // b
+          bottom[i]->mutable_gpu_diff());  // b
     }
   }
 }
 
-INSTANTIATE_CLASS(EuclideanLossLayer);
+INSTANTIATE_LAYER_GPU_FUNCS(EuclideanLossLayer);
 
 }  // namespace caffe
diff --git a/src/caffe/layers/exp_layer.cpp b/src/caffe/layers/exp_layer.cpp
new file mode 100644
index 00000000000..c7e7c60cfad
--- /dev/null
+++ b/src/caffe/layers/exp_layer.cpp
@@ -0,0 +1,69 @@
+#include <algorithm>
+#include <vector>
+
+#include "caffe/layer.hpp"
+#include "caffe/util/math_functions.hpp"
+#include "caffe/vision_layers.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void ExpLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+  NeuronLayer<Dtype>::LayerSetUp(bottom, top);
+  const Dtype base = this->layer_param_.exp_param().base();
+  if (base != Dtype(-1)) {
+    CHECK_GT(base, 0) << "base must be strictly positive.";
+  }
+  // If base == -1, interpret the base as e and set log_base = 1 exactly.
+  // Otherwise, calculate its log explicitly.
+  const Dtype log_base = (base == Dtype(-1)) ? Dtype(1) : log(base);
+  CHECK(!isnan(log_base))
+      << "NaN result: log(base) = log(" << base << ") = " << log_base;
+  CHECK(!isinf(log_base))
+      << "Inf result: log(base) = log(" << base << ") = " << log_base;
+  const Dtype input_scale = this->layer_param_.exp_param().scale();
+  const Dtype input_shift = this->layer_param_.exp_param().shift();
+  inner_scale_ = log_base * input_scale;
+  outer_scale_ = (input_shift == Dtype(0)) ? Dtype(1) : pow(base, input_shift);
+}
+
+template <typename Dtype>
+void ExpLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+    const vector<Blob<Dtype>*>& top) {
+  const int count = bottom[0]->count();
+  const Dtype* bottom_data = bottom[0]->cpu_data();
+  Dtype* top_data = top[0]->mutable_cpu_data();
+  if (inner_scale_ == Dtype(1)) {
+    caffe_exp(count, bottom_data, top_data);
+  } else {
+    caffe_cpu_scale(count, inner_scale_, bottom_data, top_data);
+    caffe_exp(count, top_data, top_data);
+  }
+  if (outer_scale_ != Dtype(1)) {
+    caffe_scal(count, outer_scale_, top_data);
+  }
+}
+
+template <typename Dtype>
+void ExpLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
+    const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+  if (!propagate_down[0]) { return; }
+  const int count = bottom[0]->count();
+  const Dtype* top_data = top[0]->cpu_data();
+  const Dtype* top_diff = top[0]->cpu_diff();
+  Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
+  caffe_mul(count, top_data, top_diff, bottom_diff);
+  if (inner_scale_ != Dtype(1)) {
+    caffe_scal(count, inner_scale_, bottom_diff);
+  }
+}
+
+#ifdef CPU_ONLY
+STUB_GPU(ExpLayer);
+#endif
+
+INSTANTIATE_CLASS(ExpLayer);
+REGISTER_LAYER_CLASS(Exp);
+
+}  // namespace caffe
diff --git a/src/caffe/layers/exp_layer.cu b/src/caffe/layers/exp_layer.cu
new file mode 100644
index 00000000000..2d75d8dd6c7
--- /dev/null
+++ b/src/caffe/layers/exp_layer.cu
@@ -0,0 +1,44 @@
+#include <algorithm>
+#include <vector>
+
+#include "caffe/layer.hpp"
+#include "caffe/util/math_functions.hpp"
+#include "caffe/vision_layers.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void ExpLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+    const vector<Blob<Dtype>*>& top) {
+  const int count = bottom[0]->count();
+  const Dtype* bottom_data = bottom[0]->gpu_data();
+  Dtype* top_data = top[0]->mutable_gpu_data();
+  if (inner_scale_ == Dtype(1)) {
+    caffe_gpu_exp(count, bottom_data, top_data);
+  } else {
+    caffe_gpu_scale(count, inner_scale_, bottom_data, top_data);
+    caffe_gpu_exp(count, top_data, top_data);
+  }
+  if (outer_scale_ != Dtype(1)) {
+    caffe_gpu_scal(count, outer_scale_, top_data);
+  }
+}
+
+template <typename Dtype>
+void ExpLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
+    const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+  if (!propagate_down[0]) { return; }
+  const int count = bottom[0]->count();
+  const Dtype* top_data = top[0]->gpu_data();
+  const Dtype* top_diff = top[0]->gpu_diff();
+  Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
+  caffe_gpu_mul(count, top_data, top_diff, bottom_diff);
+  if (inner_scale_ != Dtype(1)) {
+    caffe_gpu_scal(count, inner_scale_, bottom_diff);
+  }
+}
+
+INSTANTIATE_LAYER_GPU_FUNCS(ExpLayer);
+
+
+}  // namespace caffe
diff --git a/src/caffe/layers/filter_layer.cpp b/src/caffe/layers/filter_layer.cpp
new file mode 100644
index 00000000000..be1db32dbaa
--- /dev/null
+++ b/src/caffe/layers/filter_layer.cpp
@@ -0,0 +1,127 @@
+#include <algorithm>
+#include <vector>
+
+#include "caffe/layer.hpp"
+#include "caffe/util/math_functions.hpp"
+#include "caffe/vision_layers.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void FilterLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+  CHECK_EQ(top.size(), bottom.size() - 1);
+  first_reshape_ = true;
+}
+
+template <typename Dtype>
+void FilterLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+  // bottom[0...k-1] are the blobs to filter
+  // bottom[last] is the "selector_blob"
+  int selector_index = bottom.size() - 1;
+  for (int i = 1; i < bottom[selector_index]->num_axes(); ++i) {
+    CHECK_EQ(bottom[selector_index]->shape(i), 1)
+        << "Selector blob dimensions must be singletons (1), except the first";
+  }
+  for (int i = 0; i < bottom.size() - 1; ++i) {
+    CHECK_EQ(bottom[selector_index]->shape(0), bottom[i]->shape(0)) <<
+        "Each bottom should have the same 0th dimension as the selector blob";
+  }
+
+  const Dtype* bottom_data_selector = bottom[selector_index]->cpu_data();
+  indices_to_forward_.clear();
+
+  // look for non-zero elements in bottom[0]. Items of each bottom that
+  // have the same index as the items in bottom[0] with value == non-zero
+  // will be forwarded
+  for (int item_id = 0; item_id < bottom[selector_index]->shape(0); ++item_id) {
+    // we don't need an offset because item size == 1
+    const Dtype* tmp_data_selector = bottom_data_selector + item_id;
+    if (*tmp_data_selector) {
+      indices_to_forward_.push_back(item_id);
+    }
+  }
+  // only filtered items will be forwarded
+  int new_tops_num = indices_to_forward_.size();
+  // init
+  if (first_reshape_) {
+    new_tops_num = bottom[0]->shape(0);
+    first_reshape_ = false;
+  }
+  for (int t = 0; t < top.size(); ++t) {
+    int num_axes = bottom[t]->num_axes();
+    vector<int> shape_top(num_axes);
+    shape_top[0] = new_tops_num;
+    for (int ts = 1; ts < num_axes; ++ts)
+      shape_top[ts] = bottom[t]->shape(ts);
+    top[t]->Reshape(shape_top);
+  }
+}
+
+template <typename Dtype>
+void FilterLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+  int new_tops_num = indices_to_forward_.size();
+  // forward all filtered items for all bottoms but the Selector (bottom[last])
+  for (int t = 0; t < top.size(); ++t) {
+    const Dtype* bottom_data = bottom[t]->cpu_data();
+    Dtype* top_data = top[t]->mutable_cpu_data();
+    int dim = bottom[t]->count() / bottom[t]->shape(0);
+    for (int n = 0; n < new_tops_num; ++n) {
+      int data_offset_top = n * dim;
+      int data_offset_bottom = indices_to_forward_[n] * bottom[t]->count(1);
+      caffe_copy(dim, bottom_data + data_offset_bottom,
+          top_data + data_offset_top);
+    }
+  }
+}
+
+template <typename Dtype>
+void FilterLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+  if (propagate_down[bottom.size() - 1]) {
+    LOG(FATAL) << this->type()
+               << "Layer cannot backpropagate to filter index inputs";
+  }
+  for (int i = 0; i < top.size(); i++) {
+    // bottom[last] is the selector and never needs backpropagation
+    // so we can iterate over top vector because top.size() == bottom.size() -1
+    if (propagate_down[i]) {
+      const int dim = top[i]->count() / top[i]->shape(0);
+      int next_to_backward_offset = 0;
+      int batch_offset = 0;
+      int data_offset_bottom = 0;
+      int data_offset_top = 0;
+      for (int n = 0; n < bottom[i]->shape(0); n++) {
+        data_offset_bottom = n * dim;
+        if (next_to_backward_offset >= indices_to_forward_.size()) {
+          // we already visited all items that were been forwarded, so
+          // just set to zero remaining ones
+          caffe_set(dim, Dtype(0),
+              bottom[i]->mutable_cpu_diff() + data_offset_bottom);
+        } else {
+          batch_offset = indices_to_forward_[next_to_backward_offset];
+          if (n != batch_offset) {  // this data was not been forwarded
+            caffe_set(dim, Dtype(0),
+                bottom[i]->mutable_cpu_diff() + data_offset_bottom);
+          } else {  // this data was been forwarded
+            data_offset_top = next_to_backward_offset * dim;
+            next_to_backward_offset++;  // point to next forwarded item index
+            caffe_copy(dim, top[i]->mutable_cpu_diff() + data_offset_top,
+                bottom[i]->mutable_cpu_diff() + data_offset_bottom);
+          }
+        }
+      }
+    }
+  }
+}
+
+#ifdef CPU_ONLY
+STUB_GPU(FilterLayer);
+#endif
+
+INSTANTIATE_CLASS(FilterLayer);
+REGISTER_LAYER_CLASS(Filter);
+
+}  // namespace caffe
diff --git a/src/caffe/layers/filter_layer.cu b/src/caffe/layers/filter_layer.cu
new file mode 100644
index 00000000000..cf929eeeadf
--- /dev/null
+++ b/src/caffe/layers/filter_layer.cu
@@ -0,0 +1,70 @@
+#include <vector>
+
+#include "caffe/layer.hpp"
+#include "caffe/util/math_functions.hpp"
+#include "caffe/vision_layers.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void FilterLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+  int new_tops_num = indices_to_forward_.size();
+  // forward all filtered items for all bottoms but the Selector (bottom[last])
+  for (int t = 0; t < top.size(); ++t) {
+    const Dtype* bottom_data = bottom[t]->gpu_data();
+    Dtype* top_data = top[t]->mutable_gpu_data();
+    int dim = bottom[t]->count() / bottom[t]->shape(0);
+    for (int n = 0; n < new_tops_num; ++n) {
+      int data_offset_top = n * dim;
+      int data_offset_bottom = indices_to_forward_[n] * dim;
+      caffe_copy(dim, bottom_data + data_offset_bottom,
+          top_data + data_offset_top);
+    }
+  }
+}
+
+template <typename Dtype>
+void FilterLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+  if (propagate_down[bottom.size() - 1]) {
+    LOG(FATAL) << this->type()
+               << "Layer cannot backpropagate to filter index inputs";
+  }
+  for (int i = 0; i < top.size(); ++i) {
+    // bottom[last] is the selector and never needs backpropagation
+    // so we can iterate over top vector because top.size() == bottom.size() -1
+    if (propagate_down[i]) {
+      const int dim = top[i]->count() / top[i]->shape(0);
+      int next_to_backward_offset = 0;
+      int batch_offset = 0;
+      int data_offset_bottom = 0;
+      int data_offset_top = 0;
+      for (int n = 0; n < bottom[i]->shape(0); ++n) {
+        if (next_to_backward_offset >= indices_to_forward_.size()) {
+          // we already visited all items that were been forwarded, so
+          // just set to zero remaining ones
+          data_offset_bottom = n * dim;
+          caffe_gpu_set(dim, Dtype(0),
+              bottom[i]->mutable_gpu_diff() + data_offset_bottom);
+        } else {
+          batch_offset = indices_to_forward_[next_to_backward_offset];
+          data_offset_bottom = n * dim;
+          if (n != batch_offset) {  // this data was not been forwarded
+            caffe_gpu_set(dim, Dtype(0),
+                bottom[i]->mutable_gpu_diff() + data_offset_bottom);
+          } else {  // this data was been forwarded
+            data_offset_top = next_to_backward_offset * dim;
+            ++next_to_backward_offset;  // point to next forwarded item index
+            caffe_copy(dim, top[i]->mutable_gpu_diff() + data_offset_top,
+                bottom[i]->mutable_gpu_diff() + data_offset_bottom);
+          }
+        }
+      }
+    }
+  }
+}
+
+INSTANTIATE_LAYER_GPU_FUNCS(FilterLayer);
+
+}  // namespace caffe
diff --git a/src/caffe/layers/flatten_layer.cpp b/src/caffe/layers/flatten_layer.cpp
index 65310cd1669..f7e5c9c2172 100644
--- a/src/caffe/layers/flatten_layer.cpp
+++ b/src/caffe/layers/flatten_layer.cpp
@@ -8,31 +8,37 @@ namespace caffe {
 
 template <typename Dtype>
 void FlattenLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
-  int channels_out = bottom[0]->channels() * bottom[0]->height()
-      * bottom[0]->width();
-  (*top)[0]->Reshape(bottom[0]->num(), channels_out, 1, 1);
-  count_ = bottom[0]->num() * channels_out;
-  CHECK_EQ(count_, bottom[0]->count());
-  CHECK_EQ(count_, (*top)[0]->count());
+      const vector<Blob<Dtype>*>& top) {
+  const int start_axis = bottom[0]->CanonicalAxisIndex(
+      this->layer_param_.flatten_param().axis());
+  const int end_axis = bottom[0]->CanonicalAxisIndex(
+      this->layer_param_.flatten_param().end_axis());
+  vector<int> top_shape;
+  for (int i = 0; i < start_axis; ++i) {
+    top_shape.push_back(bottom[0]->shape(i));
+  }
+  const int flattened_dim = bottom[0]->count(start_axis, end_axis + 1);
+  top_shape.push_back(flattened_dim);
+  for (int i = end_axis + 1; i < bottom[0]->num_axes(); ++i) {
+    top_shape.push_back(bottom[0]->shape(i));
+  }
+  top[0]->Reshape(top_shape);
+  CHECK_EQ(top[0]->count(), bottom[0]->count());
 }
 
 template <typename Dtype>
 void FlattenLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
-  (*top)[0]->ShareData(*bottom[0]);
+      const vector<Blob<Dtype>*>& top) {
+  top[0]->ShareData(*bottom[0]);
 }
 
 template <typename Dtype>
 void FlattenLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
-  (*bottom)[0]->ShareDiff(*top[0]);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+  bottom[0]->ShareDiff(*top[0]);
 }
 
-#ifdef CPU_ONLY
-STUB_GPU(FlattenLayer);
-#endif
-
 INSTANTIATE_CLASS(FlattenLayer);
+REGISTER_LAYER_CLASS(Flatten);
 
 }  // namespace caffe
diff --git a/src/caffe/layers/flatten_layer.cu b/src/caffe/layers/flatten_layer.cu
deleted file mode 100644
index ff23f523fee..00000000000
--- a/src/caffe/layers/flatten_layer.cu
+++ /dev/null
@@ -1,23 +0,0 @@
-#include <vector>
-
-#include "caffe/layer.hpp"
-#include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
-
-namespace caffe {
-
-template <typename Dtype>
-void FlattenLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
-  (*top)[0]->ShareData(*bottom[0]);
-}
-
-template <typename Dtype>
-void FlattenLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
-  (*bottom)[0]->ShareDiff(*top[0]);
-}
-
-INSTANTIATE_CLASS(FlattenLayer);
-
-}  // namespace caffe
diff --git a/src/caffe/layers/gather_layer.cpp b/src/caffe/layers/gather_layer.cpp
new file mode 100644
index 00000000000..0134e4e1cb7
--- /dev/null
+++ b/src/caffe/layers/gather_layer.cpp
@@ -0,0 +1,82 @@
+#include <vector>
+
+#include "caffe/common_layers.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/util/mpi_functions.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void GatherLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+                                    const vector<Blob<Dtype>*>& top) {
+  //Sanity check
+  CHECK_EQ(bottom.size(), bottom.size())<<"Must have equal number of top and bottom blobs";
+
+
+}
+
+  template <typename Dtype>
+void GatherLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
+                                  const vector<Blob<Dtype>*>& top) {
+#ifdef USE_MPI
+    for (int i = 0; i < bottom.size(); ++i){
+      vector<int> gathered_shape(bottom[i]->shape());
+      gathered_shape[0] *= (Caffe::parallel_mode()==Caffe::MPI)?Caffe::MPI_all_rank():1;
+      top[i]->Reshape(gathered_shape);
+
+      if (Caffe::parallel_mode()!=Caffe::MPI){
+        //if not in MPI mode, simply share data
+        top[i]->ShareData(*bottom[i]);
+        top[i]->ShareDiff(*bottom[i]);
+      }
+    }
+#else
+  for (int i = 0; i < bottom.size(); ++i){
+    top[i]->ReshapeLike(*bottom[i]);
+    top[i]->ShareData(*bottom[i]);
+    top[i]->ShareDiff(*bottom[i]);
+  }
+#endif
+}
+
+template <typename Dtype>
+void GatherLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+                                    const vector<Blob<Dtype>*>& top) {
+  #ifdef USE_MPI
+  if (Caffe::parallel_mode() == Caffe::MPI){
+    for (int i = 0; i < bottom.size(); ++i) {
+      //Gather the bottom to the top
+      caffe_iallgather((Dtype*)bottom[i]->cpu_data(),top[i]->mutable_cpu_data(), bottom[i]->count());
+      mpi_force_synchronize();
+    }
+  }
+  #endif
+  //Do nothing if not if MPI mode
+}
+
+template <typename Dtype>
+void GatherLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
+                                     const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+  #ifdef USE_MPI
+    if (Caffe::parallel_mode() == Caffe::MPI){
+      for (int i = 0; i < bottom.size(); ++i) {
+        //Gather the bottom to the top
+        if (propagate_down[i]) {
+          caffe_iscatter((Dtype*)top[i]->cpu_diff(),bottom[i]->mutable_cpu_diff(), bottom[i]->count());
+          mpi_force_synchronize();
+          //compensate the scale on diff IMPORTANT
+          caffe_scal(bottom[i]->count(), Dtype(Caffe::MPI_all_rank()),
+                         bottom[i]->mutable_cpu_diff());
+        }
+      }
+    }
+  #endif
+}
+
+#ifdef CPU_ONLY
+STUB_GPU(GatherLayer);
+#endif
+
+INSTANTIATE_CLASS(GatherLayer);
+REGISTER_LAYER_CLASS(Gather);
+}
diff --git a/src/caffe/layers/gather_layer.cu b/src/caffe/layers/gather_layer.cu
new file mode 100644
index 00000000000..841098414df
--- /dev/null
+++ b/src/caffe/layers/gather_layer.cu
@@ -0,0 +1,50 @@
+#include <vector>
+
+#include "caffe/common_layers.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/util/math_functions.hpp"
+#include "caffe/util/mpi_functions.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void GatherLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+const vector<Blob<Dtype>*>& top) {
+
+  #ifdef USE_MPI
+
+  if (Caffe::parallel_mode() == Caffe::MPI){
+    CUDA_CHECK(cudaDeviceSynchronize());
+    for (int i = 0; i < bottom.size(); ++i) {
+      //Gather the bottom to the top
+      caffe_iallgather((Dtype*)bottom[i]->gpu_data(),top[i]->mutable_gpu_data(), bottom[i]->count());
+      mpi_force_synchronize();
+    }
+  }
+  #endif
+  //Do nothing if not in MPI mode
+}
+
+template <typename Dtype>
+void GatherLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
+const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+  #ifdef USE_MPI
+    if (Caffe::parallel_mode() == Caffe::MPI){
+      CUDA_CHECK(cudaDeviceSynchronize());
+      for (int i = 0; i < bottom.size(); ++i) {
+          //Scatter the top diff to buttom
+          if (propagate_down[i]) {
+          caffe_iscatter((Dtype*)top[i]->gpu_diff(),bottom[i]->mutable_gpu_diff(), bottom[i]->count());
+          mpi_force_synchronize();
+          //compensate the scale on diff IMPORTANT
+          caffe_gpu_scal(bottom[i]->count(), Dtype(Caffe::MPI_all_rank()),
+                         bottom[i]->mutable_gpu_diff());
+        }
+      }
+    }
+  #endif
+}
+
+INSTANTIATE_LAYER_GPU_FUNCS(GatherLayer);
+
+}  // namespace caffe
diff --git a/src/caffe/layers/hdf5_data_layer.cpp b/src/caffe/layers/hdf5_data_layer.cpp
index 3f1396a9acc..8a782f7e524 100644
--- a/src/caffe/layers/hdf5_data_layer.cpp
+++ b/src/caffe/layers/hdf5_data_layer.cpp
@@ -14,9 +14,9 @@
 #include "hdf5_hl.h"
 #include "stdint.h"
 
+#include "caffe/data_layers.hpp"
 #include "caffe/layer.hpp"
 #include "caffe/util/io.hpp"
-#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
@@ -26,35 +26,58 @@ HDF5DataLayer<Dtype>::~HDF5DataLayer<Dtype>() { }
 // Load data and label from HDF5 filename into the class property blobs.
 template <typename Dtype>
 void HDF5DataLayer<Dtype>::LoadHDF5FileData(const char* filename) {
-  LOG(INFO) << "Loading HDF5 file" << filename;
+  DLOG(INFO) << "Loading HDF5 file: " << filename;
   hid_t file_id = H5Fopen(filename, H5F_ACC_RDONLY, H5P_DEFAULT);
   if (file_id < 0) {
-    LOG(ERROR) << "Failed opening HDF5 file" << filename;
-    return;
+    LOG(FATAL) << "Failed opening HDF5 file: " << filename;
   }
 
-  const int MIN_DATA_DIM = 2;
-  const int MAX_DATA_DIM = 4;
-  hdf5_load_nd_dataset(
-    file_id, "data",  MIN_DATA_DIM, MAX_DATA_DIM, &data_blob_);
+  int top_size = this->layer_param_.top_size();
+  hdf_blobs_.resize(top_size);
 
-  const int MIN_LABEL_DIM = 1;
-  const int MAX_LABEL_DIM = 2;
-  hdf5_load_nd_dataset(
-    file_id, "label", MIN_LABEL_DIM, MAX_LABEL_DIM, &label_blob_);
+  const int MIN_DATA_DIM = 1;
+  const int MAX_DATA_DIM = INT_MAX;
+
+  for (int i = 0; i < top_size; ++i) {
+    hdf_blobs_[i] = shared_ptr<Blob<Dtype> >(new Blob<Dtype>());
+    hdf5_load_nd_dataset(file_id, this->layer_param_.top(i).c_str(),
+        MIN_DATA_DIM, MAX_DATA_DIM, hdf_blobs_[i].get());
+  }
 
   herr_t status = H5Fclose(file_id);
-  CHECK_GE(status, 0) << "Failed to close HDF5 file " << filename;
-  CHECK_EQ(data_blob_.num(), label_blob_.num());
-  LOG(INFO) << "Successully loaded " << data_blob_.num() << " rows";
+  CHECK_GE(status, 0) << "Failed to close HDF5 file: " << filename;
+
+  // MinTopBlobs==1 guarantees at least one top blob
+  CHECK_GE(hdf_blobs_[0]->num_axes(), 1) << "Input must have at least 1 axis.";
+  const int num = hdf_blobs_[0]->shape(0);
+  for (int i = 1; i < top_size; ++i) {
+    CHECK_EQ(hdf_blobs_[i]->shape(0), num);
+  }
+  // Default to identity permutation.
+  data_permutation_.clear();
+  data_permutation_.resize(hdf_blobs_[0]->shape(0));
+  for (int i = 0; i < hdf_blobs_[0]->shape(0); i++)
+    data_permutation_[i] = i;
+
+  // Shuffle if needed.
+  if (this->layer_param_.hdf5_data_param().shuffle()) {
+    std::random_shuffle(data_permutation_.begin(), data_permutation_.end());
+    DLOG(INFO) << "Successully loaded " << hdf_blobs_[0]->shape(0)
+               << " rows (shuffled)";
+  } else {
+    DLOG(INFO) << "Successully loaded " << hdf_blobs_[0]->shape(0) << " rows";
+  }
 }
 
 template <typename Dtype>
 void HDF5DataLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
+      const vector<Blob<Dtype>*>& top) {
+  // Refuse transformation parameters since HDF5 is totally generic.
+  CHECK(!this->layer_param_.has_transform_param()) <<
+      this->type() << " does not transform data.";
   // Read the source to parse the filenames.
   const string& source = this->layer_param_.hdf5_data_param().source();
-  LOG(INFO) << "Loading filename from " << source;
+  LOG(INFO) << "Loading list of HDF5 filenames from: " << source;
   hdf_filenames_.clear();
   std::ifstream source_file(source.c_str());
   if (source_file.is_open()) {
@@ -62,51 +85,75 @@ void HDF5DataLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
     while (source_file >> line) {
       hdf_filenames_.push_back(line);
     }
+  } else {
+    LOG(FATAL) << "Failed to open source file: " << source;
   }
   source_file.close();
   num_files_ = hdf_filenames_.size();
   current_file_ = 0;
-  LOG(INFO) << "Number of files: " << num_files_;
+  LOG(INFO) << "Number of HDF5 files: " << num_files_;
+  CHECK_GE(num_files_, 1) << "Must have at least 1 HDF5 filename listed in "
+    << source;
+
+  file_permutation_.clear();
+  file_permutation_.resize(num_files_);
+  // Default to identity permutation.
+  for (int i = 0; i < num_files_; i++) {
+    file_permutation_[i] = i;
+  }
+
+  // Shuffle if needed.
+  if (this->layer_param_.hdf5_data_param().shuffle()) {
+    std::random_shuffle(file_permutation_.begin(), file_permutation_.end());
+  }
 
   // Load the first HDF5 file and initialize the line counter.
-  LoadHDF5FileData(hdf_filenames_[current_file_].c_str());
+  LoadHDF5FileData(hdf_filenames_[file_permutation_[current_file_]].c_str());
   current_row_ = 0;
 
   // Reshape blobs.
   const int batch_size = this->layer_param_.hdf5_data_param().batch_size();
-  (*top)[0]->Reshape(batch_size, data_blob_.channels(),
-                     data_blob_.height(), data_blob_.width());
-  (*top)[1]->Reshape(batch_size, label_blob_.channels(),
-                     label_blob_.height(), label_blob_.width());
-  LOG(INFO) << "output data size: " << (*top)[0]->num() << ","
-      << (*top)[0]->channels() << "," << (*top)[0]->height() << ","
-      << (*top)[0]->width();
+  const int top_size = this->layer_param_.top_size();
+  vector<int> top_shape;
+  for (int i = 0; i < top_size; ++i) {
+    top_shape.resize(hdf_blobs_[i]->num_axes());
+    top_shape[0] = batch_size;
+    for (int j = 1; j < top_shape.size(); ++j) {
+      top_shape[j] = hdf_blobs_[i]->shape(j);
+    }
+    top[i]->Reshape(top_shape);
+  }
 }
 
 template <typename Dtype>
 void HDF5DataLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
+      const vector<Blob<Dtype>*>& top) {
   const int batch_size = this->layer_param_.hdf5_data_param().batch_size();
-  const int data_count = (*top)[0]->count() / (*top)[0]->num();
-  const int label_data_count = (*top)[1]->count() / (*top)[1]->num();
-
   for (int i = 0; i < batch_size; ++i, ++current_row_) {
-    if (current_row_ == data_blob_.num()) {
+    if (current_row_ == hdf_blobs_[0]->shape(0)) {
       if (num_files_ > 1) {
-        current_file_ += 1;
+        ++current_file_;
         if (current_file_ == num_files_) {
           current_file_ = 0;
-          LOG(INFO) << "looping around to first file";
+          if (this->layer_param_.hdf5_data_param().shuffle()) {
+            std::random_shuffle(file_permutation_.begin(),
+                                file_permutation_.end());
+          }
+          DLOG(INFO) << "Looping around to first file.";
         }
-        LoadHDF5FileData(hdf_filenames_[current_file_].c_str());
+        LoadHDF5FileData(
+            hdf_filenames_[file_permutation_[current_file_]].c_str());
       }
       current_row_ = 0;
+      if (this->layer_param_.hdf5_data_param().shuffle())
+        std::random_shuffle(data_permutation_.begin(), data_permutation_.end());
+    }
+    for (int j = 0; j < this->layer_param_.top_size(); ++j) {
+      int data_dim = top[j]->count() / top[j]->shape(0);
+      caffe_copy(data_dim,
+          &hdf_blobs_[j]->cpu_data()[data_permutation_[current_row_]
+            * data_dim], &top[j]->mutable_cpu_data()[i * data_dim]);
     }
-    caffe_copy(data_count, &data_blob_.cpu_data()[current_row_ * data_count],
-               &(*top)[0]->mutable_cpu_data()[i * data_count]);
-    caffe_copy(label_data_count,
-               &label_blob_.cpu_data()[current_row_ * label_data_count],
-               &(*top)[1]->mutable_cpu_data()[i * label_data_count]);
   }
 }
 
@@ -115,5 +162,6 @@ STUB_GPU_FORWARD(HDF5DataLayer, Forward);
 #endif
 
 INSTANTIATE_CLASS(HDF5DataLayer);
+REGISTER_LAYER_CLASS(HDF5Data);
 
 }  // namespace caffe
diff --git a/src/caffe/layers/hdf5_data_layer.cu b/src/caffe/layers/hdf5_data_layer.cu
index 79cc536eb28..5e3e4ced141 100644
--- a/src/caffe/layers/hdf5_data_layer.cu
+++ b/src/caffe/layers/hdf5_data_layer.cu
@@ -10,42 +10,44 @@ TODO:
 #include "hdf5.h"
 #include "hdf5_hl.h"
 
+#include "caffe/data_layers.hpp"
 #include "caffe/layer.hpp"
 #include "caffe/util/io.hpp"
-#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
 template <typename Dtype>
 void HDF5DataLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
+      const vector<Blob<Dtype>*>& top) {
   const int batch_size = this->layer_param_.hdf5_data_param().batch_size();
-  const int data_count = (*top)[0]->count() / (*top)[0]->num();
-  const int label_data_count = (*top)[1]->count() / (*top)[1]->num();
-
   for (int i = 0; i < batch_size; ++i, ++current_row_) {
-    if (current_row_ == data_blob_.num()) {
+    if (current_row_ == hdf_blobs_[0]->shape(0)) {
       if (num_files_ > 1) {
         current_file_ += 1;
-
         if (current_file_ == num_files_) {
           current_file_ = 0;
-          LOG(INFO) << "looping around to first file";
+          if (this->layer_param_.hdf5_data_param().shuffle()) {
+            std::random_shuffle(file_permutation_.begin(),
+                                file_permutation_.end());
+          }
+          DLOG(INFO) << "Looping around to first file.";
         }
-
-        LoadHDF5FileData(hdf_filenames_[current_file_].c_str());
+        LoadHDF5FileData(
+            hdf_filenames_[file_permutation_[current_file_]].c_str());
       }
       current_row_ = 0;
+      if (this->layer_param_.hdf5_data_param().shuffle())
+        std::random_shuffle(data_permutation_.begin(), data_permutation_.end());
+    }
+    for (int j = 0; j < this->layer_param_.top_size(); ++j) {
+      int data_dim = top[j]->count() / top[j]->shape(0);
+      caffe_copy(data_dim,
+          &hdf_blobs_[j]->cpu_data()[data_permutation_[current_row_]
+            * data_dim], &top[j]->mutable_gpu_data()[i * data_dim]);
     }
-    caffe_copy(data_count,
-        &data_blob_.cpu_data()[current_row_ * data_count],
-        &(*top)[0]->mutable_gpu_data()[i * data_count]);
-    caffe_copy(label_data_count,
-        &label_blob_.cpu_data()[current_row_ * label_data_count],
-        &(*top)[1]->mutable_gpu_data()[i * label_data_count]);
   }
 }
 
-INSTANTIATE_CLASS(HDF5DataLayer);
+INSTANTIATE_LAYER_GPU_FUNCS(HDF5DataLayer);
 
 }  // namespace caffe
diff --git a/src/caffe/layers/hdf5_output_layer.cpp b/src/caffe/layers/hdf5_output_layer.cpp
index 3cdbbb31a6a..f63375c3dc6 100644
--- a/src/caffe/layers/hdf5_output_layer.cpp
+++ b/src/caffe/layers/hdf5_output_layer.cpp
@@ -12,25 +12,27 @@
 namespace caffe {
 
 template <typename Dtype>
-HDF5OutputLayer<Dtype>::HDF5OutputLayer(const LayerParameter& param)
-    : Layer<Dtype>(param),
-      file_name_(param.hdf5_output_param().file_name()) {
-  /* create a HDF5 file */
+void HDF5OutputLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+    const vector<Blob<Dtype>*>& top) {
+  file_name_ = this->layer_param_.hdf5_output_param().file_name();
   file_id_ = H5Fcreate(file_name_.c_str(), H5F_ACC_TRUNC, H5P_DEFAULT,
                        H5P_DEFAULT);
   CHECK_GE(file_id_, 0) << "Failed to open HDF5 file" << file_name_;
+  file_opened_ = true;
 }
 
 template <typename Dtype>
 HDF5OutputLayer<Dtype>::~HDF5OutputLayer<Dtype>() {
-  herr_t status = H5Fclose(file_id_);
-  CHECK_GE(status, 0) << "Failed to close HDF5 file " << file_name_;
+  if (file_opened_) {
+    herr_t status = H5Fclose(file_id_);
+    CHECK_GE(status, 0) << "Failed to close HDF5 file " << file_name_;
+  }
 }
 
 template <typename Dtype>
 void HDF5OutputLayer<Dtype>::SaveBlobs() {
   // TODO: no limit on the number of blobs
-  LOG(INFO) << "Saving HDF5 file" << file_name_;
+  LOG(INFO) << "Saving HDF5 file " << file_name_;
   CHECK_EQ(data_blob_.num(), label_blob_.num()) <<
       "data blob and label blob must have the same batch size";
   hdf5_save_nd_dataset(file_id_, HDF5_DATA_DATASET_NAME, data_blob_);
@@ -40,7 +42,7 @@ void HDF5OutputLayer<Dtype>::SaveBlobs() {
 
 template <typename Dtype>
 void HDF5OutputLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
+      const vector<Blob<Dtype>*>& top) {
   CHECK_GE(bottom.size(), 2);
   CHECK_EQ(bottom[0]->num(), bottom[1]->num());
   data_blob_.Reshape(bottom[0]->num(), bottom[0]->channels(),
@@ -61,7 +63,7 @@ void HDF5OutputLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 void HDF5OutputLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
   return;
 }
 
@@ -70,5 +72,6 @@ STUB_GPU(HDF5OutputLayer);
 #endif
 
 INSTANTIATE_CLASS(HDF5OutputLayer);
+REGISTER_LAYER_CLASS(HDF5Output);
 
 }  // namespace caffe
diff --git a/src/caffe/layers/hdf5_output_layer.cu b/src/caffe/layers/hdf5_output_layer.cu
index 0813c02a440..ae497c34fc2 100644
--- a/src/caffe/layers/hdf5_output_layer.cu
+++ b/src/caffe/layers/hdf5_output_layer.cu
@@ -13,7 +13,7 @@ namespace caffe {
 
 template <typename Dtype>
 void HDF5OutputLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
+      const vector<Blob<Dtype>*>& top) {
   CHECK_GE(bottom.size(), 2);
   CHECK_EQ(bottom[0]->num(), bottom[1]->num());
   data_blob_.Reshape(bottom[0]->num(), bottom[0]->channels(),
@@ -34,10 +34,10 @@ void HDF5OutputLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 void HDF5OutputLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
   return;
 }
 
-INSTANTIATE_CLASS(HDF5OutputLayer);
+INSTANTIATE_LAYER_GPU_FUNCS(HDF5OutputLayer);
 
 }  // namespace caffe
diff --git a/src/caffe/layers/hinge_loss_layer.cpp b/src/caffe/layers/hinge_loss_layer.cpp
index 8022aae279c..a2fb2a18309 100644
--- a/src/caffe/layers/hinge_loss_layer.cpp
+++ b/src/caffe/layers/hinge_loss_layer.cpp
@@ -12,7 +12,7 @@ namespace caffe {
 
 template <typename Dtype>
 void HingeLossLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-    vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& top) {
   const Dtype* bottom_data = bottom[0]->cpu_data();
   Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
   const Dtype* label = bottom[1]->cpu_data();
@@ -30,7 +30,7 @@ void HingeLossLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
         Dtype(0), 1 + bottom_diff[i * dim + j]);
     }
   }
-  Dtype* loss = (*top)[0]->mutable_cpu_data();
+  Dtype* loss = top[0]->mutable_cpu_data();
   switch (this->layer_param_.hinge_loss_param().norm()) {
   case HingeLossParameter_Norm_L1:
     loss[0] = caffe_cpu_asum(count, bottom_diff) / num;
@@ -45,16 +45,16 @@ void HingeLossLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 void HingeLossLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
-    const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
+    const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
   if (propagate_down[1]) {
-    LOG(FATAL) << this->type_name()
+    LOG(FATAL) << this->type()
                << " Layer cannot backpropagate to label inputs.";
   }
   if (propagate_down[0]) {
-    Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
-    const Dtype* label = (*bottom)[1]->cpu_data();
-    int num = (*bottom)[0]->num();
-    int count = (*bottom)[0]->count();
+    Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
+    const Dtype* label = bottom[1]->cpu_data();
+    int num = bottom[0]->num();
+    int count = bottom[0]->count();
     int dim = count / num;
 
     for (int i = 0; i < num; ++i) {
@@ -77,5 +77,6 @@ void HingeLossLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
 }
 
 INSTANTIATE_CLASS(HingeLossLayer);
+REGISTER_LAYER_CLASS(HingeLoss);
 
 }  // namespace caffe
diff --git a/src/caffe/layers/im2col_layer.cpp b/src/caffe/layers/im2col_layer.cpp
index 870d5a9bde3..1c802714e33 100644
--- a/src/caffe/layers/im2col_layer.cpp
+++ b/src/caffe/layers/im2col_layer.cpp
@@ -9,7 +9,7 @@ namespace caffe {
 
 template <typename Dtype>
 void Im2colLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
+      const vector<Blob<Dtype>*>& top) {
   ConvolutionParameter conv_param = this->layer_param_.convolution_param();
   CHECK(!conv_param.has_kernel_size() !=
       !(conv_param.has_kernel_h() && conv_param.has_kernel_w()))
@@ -49,11 +49,13 @@ void Im2colLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 void Im2colLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
+      const vector<Blob<Dtype>*>& top) {
+  CHECK_EQ(4, bottom[0]->num_axes()) << "Input must have 4 axes, "
+      << "corresponding to (num, channels, height, width)";
   channels_ = bottom[0]->channels();
   height_ = bottom[0]->height();
   width_ = bottom[0]->width();
-  (*top)[0]->Reshape(
+  top[0]->Reshape(
       bottom[0]->num(), channels_ * kernel_h_ * kernel_w_,
       (height_ + 2 * pad_h_ - kernel_h_) / stride_h_ + 1,
       (width_ + 2 * pad_w_ - kernel_w_) / stride_w_ + 1);
@@ -61,25 +63,25 @@ void Im2colLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 void Im2colLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
+      const vector<Blob<Dtype>*>& top) {
   const Dtype* bottom_data = bottom[0]->cpu_data();
-  Dtype* top_data = (*top)[0]->mutable_cpu_data();
+  Dtype* top_data = top[0]->mutable_cpu_data();
   for (int n = 0; n < bottom[0]->num(); ++n) {
     im2col_cpu(bottom_data + bottom[0]->offset(n), channels_, height_,
         width_, kernel_h_, kernel_w_, pad_h_, pad_w_,
-        stride_h_, stride_w_, top_data + (*top)[0]->offset(n));
+        stride_h_, stride_w_, top_data + top[0]->offset(n));
   }
 }
 
 template <typename Dtype>
 void Im2colLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
   const Dtype* top_diff = top[0]->cpu_diff();
-  Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
+  Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
   for (int n = 0; n < top[0]->num(); ++n) {
     col2im_cpu(top_diff + top[0]->offset(n), channels_, height_, width_,
         kernel_h_, kernel_w_, pad_h_, pad_w_,
-        stride_h_, stride_w_, bottom_diff + (*bottom)[0]->offset(n));
+        stride_h_, stride_w_, bottom_diff + bottom[0]->offset(n));
   }
 }
 
@@ -88,5 +90,6 @@ STUB_GPU(Im2colLayer);
 #endif
 
 INSTANTIATE_CLASS(Im2colLayer);
+REGISTER_LAYER_CLASS(Im2col);
 
 }  // namespace caffe
diff --git a/src/caffe/layers/im2col_layer.cu b/src/caffe/layers/im2col_layer.cu
index 8df061d88e1..9c338b14cb7 100644
--- a/src/caffe/layers/im2col_layer.cu
+++ b/src/caffe/layers/im2col_layer.cu
@@ -9,29 +9,29 @@ namespace caffe {
 
 template <typename Dtype>
 void Im2colLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
+      const vector<Blob<Dtype>*>& top) {
   const Dtype* bottom_data = bottom[0]->gpu_data();
-  Dtype* top_data = (*top)[0]->mutable_gpu_data();
+  Dtype* top_data = top[0]->mutable_gpu_data();
   for (int n = 0; n < bottom[0]->num(); ++n) {
     im2col_gpu(bottom_data + bottom[0]->offset(n), channels_, height_,
         width_, kernel_h_, kernel_w_, pad_h_, pad_w_,
-        stride_h_, stride_w_, top_data + (*top)[0]->offset(n));
+        stride_h_, stride_w_, top_data + top[0]->offset(n));
   }
 }
 
 template <typename Dtype>
 void Im2colLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
   const Dtype* top_diff = top[0]->gpu_diff();
-  Dtype* bottom_diff = (*bottom)[0]->mutable_gpu_diff();
+  Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
   for (int n = 0; n < top[0]->num(); ++n) {
     col2im_gpu(top_diff + top[0]->offset(n), channels_, height_, width_,
         kernel_h_, kernel_w_, pad_h_, pad_w_,
-        stride_h_, stride_w_, bottom_diff + (*bottom)[0]->offset(n));
+        stride_h_, stride_w_, bottom_diff + bottom[0]->offset(n));
   }
 }
 
 
-INSTANTIATE_CLASS(Im2colLayer);
+INSTANTIATE_LAYER_GPU_FUNCS(Im2colLayer);
 
 }  // namespace caffe
diff --git a/src/caffe/layers/image_data_layer.cpp b/src/caffe/layers/image_data_layer.cpp
index e4a575bec93..18c035cba9d 100644
--- a/src/caffe/layers/image_data_layer.cpp
+++ b/src/caffe/layers/image_data_layer.cpp
@@ -1,3 +1,5 @@
+#include <opencv2/core/core.hpp>
+
 #include <fstream>  // NOLINT(readability/streams)
 #include <iostream>  // NOLINT(readability/streams)
 #include <string>
@@ -6,6 +8,7 @@
 
 #include "caffe/data_layers.hpp"
 #include "caffe/layer.hpp"
+#include "caffe/util/benchmark.hpp"
 #include "caffe/util/io.hpp"
 #include "caffe/util/math_functions.hpp"
 #include "caffe/util/rng.hpp"
@@ -19,9 +22,12 @@ ImageDataLayer<Dtype>::~ImageDataLayer<Dtype>() {
 
 template <typename Dtype>
 void ImageDataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
+      const vector<Blob<Dtype>*>& top) {
   const int new_height = this->layer_param_.image_data_param().new_height();
   const int new_width  = this->layer_param_.image_data_param().new_width();
+  const bool is_color  = this->layer_param_.image_data_param().is_color();
+  string root_folder = this->layer_param_.image_data_param().root_folder();
+
   CHECK((new_height == 0 && new_width == 0) ||
       (new_height > 0 && new_width > 0)) << "Current implementation requires "
       "new_height and new_width to be set at the same time.";
@@ -53,34 +59,25 @@ void ImageDataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
     CHECK_GT(lines_.size(), skip) << "Not enough points to skip";
     lines_id_ = skip;
   }
-  // Read a data point, and use it to initialize the top blob.
-  Datum datum;
-  CHECK(ReadImageToDatum(lines_[lines_id_].first, lines_[lines_id_].second,
-                         new_height, new_width, &datum));
-  // image
-  const int crop_size = this->layer_param_.transform_param().crop_size();
+  // Read an image, and use it to initialize the top blob.
+  cv::Mat cv_img = ReadImageToCVMat(root_folder + lines_[lines_id_].first,
+                                    new_height, new_width, is_color);
+  // Use data_transformer to infer the expected blob shape from a cv_image.
+  vector<int> top_shape = this->data_transformer_->InferBlobShape(cv_img);
+  this->transformed_data_.Reshape(top_shape);
+  // Reshape prefetch_data and top[0] according to the batch_size.
   const int batch_size = this->layer_param_.image_data_param().batch_size();
-  if (crop_size > 0) {
-    (*top)[0]->Reshape(batch_size, datum.channels(), crop_size, crop_size);
-    this->prefetch_data_.Reshape(batch_size, datum.channels(), crop_size,
-                                 crop_size);
-  } else {
-    (*top)[0]->Reshape(batch_size, datum.channels(), datum.height(),
-                       datum.width());
-    this->prefetch_data_.Reshape(batch_size, datum.channels(), datum.height(),
-        datum.width());
-  }
-  LOG(INFO) << "output data size: " << (*top)[0]->num() << ","
-      << (*top)[0]->channels() << "," << (*top)[0]->height() << ","
-      << (*top)[0]->width();
+  top_shape[0] = batch_size;
+  this->prefetch_data_.Reshape(top_shape);
+  top[0]->ReshapeLike(this->prefetch_data_);
+
+  LOG(INFO) << "output data size: " << top[0]->num() << ","
+      << top[0]->channels() << "," << top[0]->height() << ","
+      << top[0]->width();
   // label
-  (*top)[1]->Reshape(batch_size, 1, 1, 1);
-  this->prefetch_label_.Reshape(batch_size, 1, 1, 1);
-  // datum size
-  this->datum_channels_ = datum.channels();
-  this->datum_height_ = datum.height();
-  this->datum_width_ = datum.width();
-  this->datum_size_ = datum.channels() * datum.height() * datum.width();
+  vector<int> label_shape(1, batch_size);
+  top[1]->Reshape(label_shape);
+  this->prefetch_label_.Reshape(label_shape);
 }
 
 template <typename Dtype>
@@ -93,30 +90,52 @@ void ImageDataLayer<Dtype>::ShuffleImages() {
 // This function is used to create a thread that prefetches the data.
 template <typename Dtype>
 void ImageDataLayer<Dtype>::InternalThreadEntry() {
-  Datum datum;
+  CPUTimer batch_timer;
+  batch_timer.Start();
+  double read_time = 0;
+  double trans_time = 0;
+  CPUTimer timer;
   CHECK(this->prefetch_data_.count());
-  Dtype* top_data = this->prefetch_data_.mutable_cpu_data();
-  Dtype* top_label = this->prefetch_label_.mutable_cpu_data();
+  CHECK(this->transformed_data_.count());
   ImageDataParameter image_data_param = this->layer_param_.image_data_param();
   const int batch_size = image_data_param.batch_size();
   const int new_height = image_data_param.new_height();
   const int new_width = image_data_param.new_width();
+  const bool is_color = image_data_param.is_color();
+  string root_folder = image_data_param.root_folder();
+
+  // Reshape according to the first image of each batch
+  // on single input batches allows for inputs of varying dimension.
+  cv::Mat cv_img = ReadImageToCVMat(root_folder + lines_[lines_id_].first,
+      new_height, new_width, is_color);
+  // Use data_transformer to infer the expected blob shape from a cv_img.
+  vector<int> top_shape = this->data_transformer_->InferBlobShape(cv_img);
+  this->transformed_data_.Reshape(top_shape);
+  // Reshape prefetch_data according to the batch_size.
+  top_shape[0] = batch_size;
+  this->prefetch_data_.Reshape(top_shape);
+
+  Dtype* prefetch_data = this->prefetch_data_.mutable_cpu_data();
+  Dtype* prefetch_label = this->prefetch_label_.mutable_cpu_data();
 
   // datum scales
   const int lines_size = lines_.size();
   for (int item_id = 0; item_id < batch_size; ++item_id) {
     // get a blob
+    timer.Start();
     CHECK_GT(lines_size, lines_id_);
-    if (!ReadImageToDatum(lines_[lines_id_].first,
-          lines_[lines_id_].second,
-          new_height, new_width, &datum)) {
-      continue;
-    }
-
-    // Apply transformations (mirror, crop...) to the data
-    this->data_transformer_.Transform(item_id, datum, this->mean_, top_data);
+    cv::Mat cv_img = ReadImageToCVMat(root_folder + lines_[lines_id_].first,
+        new_height, new_width, is_color);
+    CHECK(cv_img.data) << "Could not load " << lines_[lines_id_].first;
+    read_time += timer.MicroSeconds();
+    timer.Start();
+    // Apply transformations (mirror, crop...) to the image
+    int offset = this->prefetch_data_.offset(item_id);
+    this->transformed_data_.set_cpu_data(prefetch_data + offset);
+    this->data_transformer_->Transform(cv_img, &(this->transformed_data_));
+    trans_time += timer.MicroSeconds();
 
-    top_label[item_id] = datum.label();
+    prefetch_label[item_id] = lines_[lines_id_].second;
     // go to the next iter
     lines_id_++;
     if (lines_id_ >= lines_size) {
@@ -128,8 +147,13 @@ void ImageDataLayer<Dtype>::InternalThreadEntry() {
       }
     }
   }
+  batch_timer.Stop();
+  DLOG(INFO) << "Prefetch batch: " << batch_timer.MilliSeconds() << " ms.";
+  DLOG(INFO) << "     Read time: " << read_time / 1000 << " ms.";
+  DLOG(INFO) << "Transform time: " << trans_time / 1000 << " ms.";
 }
 
 INSTANTIATE_CLASS(ImageDataLayer);
+REGISTER_LAYER_CLASS(ImageData);
 
 }  // namespace caffe
diff --git a/src/caffe/layers/infogain_loss_layer.cpp b/src/caffe/layers/infogain_loss_layer.cpp
index 894cb69811a..a1e0b40de0e 100644
--- a/src/caffe/layers/infogain_loss_layer.cpp
+++ b/src/caffe/layers/infogain_loss_layer.cpp
@@ -12,7 +12,7 @@ namespace caffe {
 
 template <typename Dtype>
 void InfogainLossLayer<Dtype>::LayerSetUp(
-    const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
   LossLayer<Dtype>::LayerSetUp(bottom, top);
   if (bottom.size() < 3) {
     CHECK(this->layer_param_.infogain_loss_param().has_source())
@@ -26,7 +26,7 @@ void InfogainLossLayer<Dtype>::LayerSetUp(
 
 template <typename Dtype>
 void InfogainLossLayer<Dtype>::Reshape(
-    const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
   LossLayer<Dtype>::Reshape(bottom, top);
   Blob<Dtype>* infogain = NULL;
   if (bottom.size() < 3) {
@@ -48,7 +48,7 @@ void InfogainLossLayer<Dtype>::Reshape(
 
 template <typename Dtype>
 void InfogainLossLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-    vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& top) {
   const Dtype* bottom_data = bottom[0]->cpu_data();
   const Dtype* bottom_label = bottom[1]->cpu_data();
   const Dtype* infogain_mat = NULL;
@@ -67,33 +67,33 @@ void InfogainLossLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       loss -= infogain_mat[label * dim + j] * log(prob);
     }
   }
-  (*top)[0]->mutable_cpu_data()[0] = loss / num;
+  top[0]->mutable_cpu_data()[0] = loss / num;
 }
 
 template <typename Dtype>
 void InfogainLossLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
     const vector<bool>& propagate_down,
-    vector<Blob<Dtype>*>* bottom) {
+    const vector<Blob<Dtype>*>& bottom) {
   if (propagate_down[1]) {
-    LOG(FATAL) << this->type_name()
+    LOG(FATAL) << this->type()
                << " Layer cannot backpropagate to label inputs.";
   }
   if (propagate_down.size() > 2 && propagate_down[2]) {
-    LOG(FATAL) << this->type_name()
+    LOG(FATAL) << this->type()
                << " Layer cannot backpropagate to infogain inputs.";
   }
   if (propagate_down[0]) {
-    const Dtype* bottom_data = (*bottom)[0]->cpu_data();
-    const Dtype* bottom_label = (*bottom)[1]->cpu_data();
+    const Dtype* bottom_data = bottom[0]->cpu_data();
+    const Dtype* bottom_label = bottom[1]->cpu_data();
     const Dtype* infogain_mat = NULL;
-    if (bottom->size() < 3) {
+    if (bottom.size() < 3) {
       infogain_mat = infogain_.cpu_data();
     } else {
-      infogain_mat = (*bottom)[2]->cpu_data();
+      infogain_mat = bottom[2]->cpu_data();
     }
-    Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
-    int num = (*bottom)[0]->num();
-    int dim = (*bottom)[0]->count() / (*bottom)[0]->num();
+    Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
+    int num = bottom[0]->num();
+    int dim = bottom[0]->count() / bottom[0]->num();
     const Dtype scale = - top[0]->cpu_diff()[0] / num;
     for (int i = 0; i < num; ++i) {
       const int label = static_cast<int>(bottom_label[i]);
@@ -106,5 +106,5 @@ void InfogainLossLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
 }
 
 INSTANTIATE_CLASS(InfogainLossLayer);
-
+REGISTER_LAYER_CLASS(InfogainLoss);
 }  // namespace caffe
diff --git a/src/caffe/layers/inner_product_layer.cpp b/src/caffe/layers/inner_product_layer.cpp
index ecd05a030db..83c3235eb71 100644
--- a/src/caffe/layers/inner_product_layer.cpp
+++ b/src/caffe/layers/inner_product_layer.cpp
@@ -11,11 +11,16 @@ namespace caffe {
 
 template <typename Dtype>
 void InnerProductLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
+      const vector<Blob<Dtype>*>& top) {
   const int num_output = this->layer_param_.inner_product_param().num_output();
   bias_term_ = this->layer_param_.inner_product_param().bias_term();
   N_ = num_output;
-  K_ = bottom[0]->count() / bottom[0]->num();
+  const int axis = bottom[0]->CanonicalAxisIndex(
+      this->layer_param_.inner_product_param().axis());
+  // Dimensions starting from "axis" are "flattened" into a single
+  // length K_ vector. For example, if bottom[0]'s shape is (N, C, H, W),
+  // and axis == 1, N inner products with dimension CHW are performed.
+  K_ = bottom[0]->count(axis);
   // Check if we need to set up the weights
   if (this->blobs_.size() > 0) {
     LOG(INFO) << "Skipping parameter initialization";
@@ -26,14 +31,18 @@ void InnerProductLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       this->blobs_.resize(1);
     }
     // Intialize the weight
-    this->blobs_[0].reset(new Blob<Dtype>(1, 1, N_, K_));
+    vector<int> weight_shape(2);
+    weight_shape[0] = N_;
+    weight_shape[1] = K_;
+    this->blobs_[0].reset(new Blob<Dtype>(weight_shape));
     // fill the weights
     shared_ptr<Filler<Dtype> > weight_filler(GetFiller<Dtype>(
         this->layer_param_.inner_product_param().weight_filler()));
     weight_filler->Fill(this->blobs_[0].get());
     // If necessary, intiialize and fill the bias term
     if (bias_term_) {
-      this->blobs_[1].reset(new Blob<Dtype>(1, 1, 1, N_));
+      vector<int> bias_shape(1, N_);
+      this->blobs_[1].reset(new Blob<Dtype>(bias_shape));
       shared_ptr<Filler<Dtype> > bias_filler(GetFiller<Dtype>(
           this->layer_param_.inner_product_param().bias_filler()));
       bias_filler->Fill(this->blobs_[1].get());
@@ -44,24 +53,35 @@ void InnerProductLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 void InnerProductLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
+      const vector<Blob<Dtype>*>& top) {
   // Figure out the dimensions
-  M_ = bottom[0]->num();
-  CHECK_EQ(bottom[0]->count() / bottom[0]->num(), K_) << "Input size "
-    "incompatible with inner product parameters.";
-  (*top)[0]->Reshape(bottom[0]->num(), N_, 1, 1);
+  const int axis = bottom[0]->CanonicalAxisIndex(
+      this->layer_param_.inner_product_param().axis());
+  const int new_K = bottom[0]->count(axis);
+  CHECK_EQ(K_, new_K)
+      << "Input size incompatible with inner product parameters.";
+  // The first "axis" dimensions are independent inner products; the total
+  // number of these is M_, the product over these dimensions.
+  M_ = bottom[0]->count(0, axis);
+  // The top shape will be the bottom shape with the flattened axes dropped,
+  // and replaced by a single axis with dimension num_output (N_).
+  vector<int> top_shape = bottom[0]->shape();
+  top_shape.resize(axis + 1);
+  top_shape[axis] = N_;
+  top[0]->Reshape(top_shape);
   // Set up the bias multiplier
   if (bias_term_) {
-    bias_multiplier_.Reshape(1, 1, 1, M_);
+    vector<int> bias_shape(1, M_);
+    bias_multiplier_.Reshape(bias_shape);
     caffe_set(M_, Dtype(1), bias_multiplier_.mutable_cpu_data());
   }
 }
 
 template <typename Dtype>
 void InnerProductLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-    vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& top) {
   const Dtype* bottom_data = bottom[0]->cpu_data();
-  Dtype* top_data = (*top)[0]->mutable_cpu_data();
+  Dtype* top_data = top[0]->mutable_cpu_data();
   const Dtype* weight = this->blobs_[0]->cpu_data();
   caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasTrans, M_, N_, K_, (Dtype)1.,
       bottom_data, weight, (Dtype)0., top_data);
@@ -75,19 +95,19 @@ void InnerProductLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
 template <typename Dtype>
 void InnerProductLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
     const vector<bool>& propagate_down,
-    vector<Blob<Dtype>*>* bottom) {
+    const vector<Blob<Dtype>*>& bottom) {
   if (this->param_propagate_down_[0]) {
     const Dtype* top_diff = top[0]->cpu_diff();
-    const Dtype* bottom_data = (*bottom)[0]->cpu_data();
+    const Dtype* bottom_data = bottom[0]->cpu_data();
     // Gradient with respect to weight
     caffe_cpu_gemm<Dtype>(CblasTrans, CblasNoTrans, N_, K_, M_, (Dtype)1.,
-        top_diff, bottom_data, (Dtype)0., this->blobs_[0]->mutable_cpu_diff());
+        top_diff, bottom_data, (Dtype)1., this->blobs_[0]->mutable_cpu_diff());
   }
   if (bias_term_ && this->param_propagate_down_[1]) {
     const Dtype* top_diff = top[0]->cpu_diff();
     // Gradient with respect to bias
     caffe_cpu_gemv<Dtype>(CblasTrans, M_, N_, (Dtype)1., top_diff,
-        bias_multiplier_.cpu_data(), (Dtype)0.,
+        bias_multiplier_.cpu_data(), (Dtype)1.,
         this->blobs_[1]->mutable_cpu_diff());
   }
   if (propagate_down[0]) {
@@ -95,7 +115,7 @@ void InnerProductLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
     // Gradient with respect to bottom data
     caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, M_, K_, N_, (Dtype)1.,
         top_diff, this->blobs_[0]->cpu_data(), (Dtype)0.,
-        (*bottom)[0]->mutable_cpu_diff());
+        bottom[0]->mutable_cpu_diff());
   }
 }
 
@@ -104,5 +124,6 @@ STUB_GPU(InnerProductLayer);
 #endif
 
 INSTANTIATE_CLASS(InnerProductLayer);
+REGISTER_LAYER_CLASS(InnerProduct);
 
 }  // namespace caffe
diff --git a/src/caffe/layers/inner_product_layer.cu b/src/caffe/layers/inner_product_layer.cu
index 3a0d4388352..dd90cac12a8 100644
--- a/src/caffe/layers/inner_product_layer.cu
+++ b/src/caffe/layers/inner_product_layer.cu
@@ -11,9 +11,9 @@ namespace caffe {
 
 template <typename Dtype>
 void InnerProductLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-    vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& top) {
   const Dtype* bottom_data = bottom[0]->gpu_data();
-  Dtype* top_data = (*top)[0]->mutable_gpu_data();
+  Dtype* top_data = top[0]->mutable_gpu_data();
   const Dtype* weight = this->blobs_[0]->gpu_data();
   caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasTrans, M_, N_, K_, (Dtype)1.,
       bottom_data, weight, (Dtype)0., top_data);
@@ -27,19 +27,19 @@ void InnerProductLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
 template <typename Dtype>
 void InnerProductLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
     const vector<bool>& propagate_down,
-    vector<Blob<Dtype>*>* bottom) {
+    const vector<Blob<Dtype>*>& bottom) {
   if (this->param_propagate_down_[0]) {
     const Dtype* top_diff = top[0]->gpu_diff();
-    const Dtype* bottom_data = (*bottom)[0]->gpu_data();
+    const Dtype* bottom_data = bottom[0]->gpu_data();
     // Gradient with respect to weight
     caffe_gpu_gemm<Dtype>(CblasTrans, CblasNoTrans, N_, K_, M_, (Dtype)1.,
-        top_diff, bottom_data, (Dtype)0., this->blobs_[0]->mutable_gpu_diff());
+        top_diff, bottom_data, (Dtype)1., this->blobs_[0]->mutable_gpu_diff());
   }
   if (bias_term_ && this->param_propagate_down_[1]) {
     const Dtype* top_diff = top[0]->gpu_diff();
     // Gradient with respect to bias
     caffe_gpu_gemv<Dtype>(CblasTrans, M_, N_, (Dtype)1., top_diff,
-        bias_multiplier_.gpu_data(), (Dtype)0.,
+        bias_multiplier_.gpu_data(), (Dtype)1.,
         this->blobs_[1]->mutable_gpu_diff());
   }
   if (propagate_down[0]) {
@@ -47,10 +47,10 @@ void InnerProductLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
     // Gradient with respect to bottom data
     caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, M_, K_, N_, (Dtype)1.,
         top_diff, this->blobs_[0]->gpu_data(), (Dtype)0.,
-        (*bottom)[0]->mutable_gpu_diff());
+        bottom[0]->mutable_gpu_diff());
   }
 }
 
-INSTANTIATE_CLASS(InnerProductLayer);
+INSTANTIATE_LAYER_GPU_FUNCS(InnerProductLayer);
 
 }  // namespace caffe
diff --git a/src/caffe/layers/log_layer.cpp b/src/caffe/layers/log_layer.cpp
new file mode 100644
index 00000000000..55a227f6226
--- /dev/null
+++ b/src/caffe/layers/log_layer.cpp
@@ -0,0 +1,87 @@
+#include <algorithm>
+#include <vector>
+
+#include "caffe/layer.hpp"
+#include "caffe/neuron_layers.hpp"
+#include "caffe/util/math_functions.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void LogLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+  NeuronLayer<Dtype>::LayerSetUp(bottom, top);
+  const Dtype base = this->layer_param_.log_param().base();
+  if (base != Dtype(-1)) {
+    CHECK_GT(base, 0) << "base must be strictly positive.";
+  }
+  // If base == -1, interpret the base as e and set log_base = 1 exactly.
+  // Otherwise, calculate its log explicitly.
+  const Dtype log_base = (base == Dtype(-1)) ? Dtype(1) : log(base);
+  CHECK(!isnan(log_base))
+      << "NaN result: log(base) = log(" << base << ") = " << log_base;
+  CHECK(!isinf(log_base))
+      << "Inf result: log(base) = log(" << base << ") = " << log_base;
+  base_scale_ = Dtype(1) / log_base;
+  CHECK(!isnan(base_scale_))
+      << "NaN result: 1/log(base) = 1/log(" << base << ") = " << base_scale_;
+  CHECK(!isinf(base_scale_))
+      << "Inf result: 1/log(base) = 1/log(" << base << ") = " << base_scale_;
+  input_scale_ = this->layer_param_.log_param().scale();
+  input_shift_ = this->layer_param_.log_param().shift();
+  backward_num_scale_ = input_scale_ / log_base;
+}
+
+template <typename Dtype>
+void LogLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+    const vector<Blob<Dtype>*>& top) {
+  const int count = bottom[0]->count();
+  const Dtype* bottom_data = bottom[0]->cpu_data();
+  Dtype* top_data = top[0]->mutable_cpu_data();
+  if (input_scale_ == Dtype(1) && input_shift_ == Dtype(0)) {
+    caffe_log(count, bottom_data, top_data);
+  } else {
+    caffe_copy(count, bottom_data, top_data);
+    if (input_scale_ != Dtype(1)) {
+      caffe_scal(count, input_scale_, top_data);
+    }
+    if (input_shift_ != Dtype(0)) {
+      caffe_add_scalar(count, input_shift_, top_data);
+    }
+    caffe_log(count, top_data, top_data);
+  }
+  if (base_scale_ != Dtype(1)) {
+    caffe_scal(count, base_scale_, top_data);
+  }
+}
+
+template <typename Dtype>
+void LogLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
+    const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+  if (!propagate_down[0]) { return; }
+  const int count = bottom[0]->count();
+  const Dtype* bottom_data = bottom[0]->cpu_data();
+  const Dtype* top_diff = top[0]->cpu_diff();
+  Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
+  caffe_copy(count, bottom_data, bottom_diff);
+  if (input_scale_ != Dtype(1)) {
+    caffe_scal(count, input_scale_, bottom_diff);
+  }
+  if (input_shift_ != Dtype(0)) {
+    caffe_add_scalar(count, input_shift_, bottom_diff);
+  }
+  caffe_powx(count, bottom_diff, Dtype(-1), bottom_diff);
+  if (backward_num_scale_ != Dtype(1)) {
+    caffe_scal(count, backward_num_scale_, bottom_diff);
+  }
+  caffe_mul(count, top_diff, bottom_diff, bottom_diff);
+}
+
+#ifdef CPU_ONLY
+STUB_GPU(LogLayer);
+#endif
+
+INSTANTIATE_CLASS(LogLayer);
+REGISTER_LAYER_CLASS(Log);
+
+}  // namespace caffe
diff --git a/src/caffe/layers/log_layer.cu b/src/caffe/layers/log_layer.cu
new file mode 100644
index 00000000000..847c86cd10c
--- /dev/null
+++ b/src/caffe/layers/log_layer.cu
@@ -0,0 +1,57 @@
+#include <algorithm>
+#include <vector>
+
+#include "caffe/layer.hpp"
+#include "caffe/neuron_layers.hpp"
+#include "caffe/util/math_functions.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void LogLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+    const vector<Blob<Dtype>*>& top) {
+  const int count = bottom[0]->count();
+  const Dtype* bottom_data = bottom[0]->gpu_data();
+  Dtype* top_data = top[0]->mutable_gpu_data();
+  if (input_scale_ == Dtype(1) && input_shift_ == Dtype(0)) {
+    caffe_gpu_log(count, bottom_data, top_data);
+  } else {
+    caffe_copy(count, bottom_data, top_data);
+    if (input_scale_ != Dtype(1)) {
+      caffe_gpu_scal(count, input_scale_, top_data);
+    }
+    if (input_shift_ != Dtype(0)) {
+      caffe_gpu_add_scalar(count, input_shift_, top_data);
+    }
+    caffe_gpu_log(count, top_data, top_data);
+  }
+  if (base_scale_ != Dtype(1)) {
+    caffe_gpu_scal(count, base_scale_, top_data);
+  }
+}
+
+template <typename Dtype>
+void LogLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
+    const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+  if (!propagate_down[0]) { return; }
+    const int count = bottom[0]->count();
+    const Dtype* bottom_data = bottom[0]->gpu_data();
+    const Dtype* top_diff = top[0]->gpu_diff();
+    Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
+    caffe_copy(count, bottom_data, bottom_diff);
+    if (input_scale_ != Dtype(1)) {
+      caffe_gpu_scal(count, input_scale_, bottom_diff);
+    }
+    if (input_shift_ != Dtype(0)) {
+      caffe_gpu_add_scalar(count, input_shift_, bottom_diff);
+    }
+    caffe_gpu_powx(count, bottom_diff, Dtype(-1), bottom_diff);
+    if (backward_num_scale_ != Dtype(1)) {
+      caffe_gpu_scal(count, backward_num_scale_, bottom_diff);
+    }
+    caffe_gpu_mul(count, top_diff, bottom_diff, bottom_diff);
+}
+
+INSTANTIATE_LAYER_GPU_FUNCS(LogLayer);
+
+}  // namespace caffe
diff --git a/src/caffe/layers/loss_layer.cpp b/src/caffe/layers/loss_layer.cpp
index 9eb9dbd5c5b..3496a5c2a8a 100644
--- a/src/caffe/layers/loss_layer.cpp
+++ b/src/caffe/layers/loss_layer.cpp
@@ -12,7 +12,7 @@ namespace caffe {
 
 template <typename Dtype>
 void LossLayer<Dtype>::LayerSetUp(
-    const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
   // LossLayers have a non-zero (1) loss by default.
   if (this->layer_param_.loss_weight_size() == 0) {
     this->layer_param_.add_loss_weight(Dtype(1));
@@ -21,10 +21,11 @@ void LossLayer<Dtype>::LayerSetUp(
 
 template <typename Dtype>
 void LossLayer<Dtype>::Reshape(
-    const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
   CHECK_EQ(bottom[0]->num(), bottom[1]->num())
       << "The data and label should have the same number.";
-  (*top)[0]->Reshape(1, 1, 1, 1);
+  vector<int> loss_shape(0);  // Loss layers output a scalar; 0 axes.
+  top[0]->Reshape(loss_shape);
 }
 
 INSTANTIATE_CLASS(LossLayer);
diff --git a/src/caffe/layers/lrn_layer.cpp b/src/caffe/layers/lrn_layer.cpp
index d9e41e9c137..36c1ace4c99 100644
--- a/src/caffe/layers/lrn_layer.cpp
+++ b/src/caffe/layers/lrn_layer.cpp
@@ -8,12 +8,13 @@ namespace caffe {
 
 template <typename Dtype>
 void LRNLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
+      const vector<Blob<Dtype>*>& top) {
   size_ = this->layer_param_.lrn_param().local_size();
   CHECK_EQ(size_ % 2, 1) << "LRN only supports odd values for local_size";
   pre_pad_ = (size_ - 1) / 2;
   alpha_ = this->layer_param_.lrn_param().alpha();
   beta_ = this->layer_param_.lrn_param().beta();
+  k_ = this->layer_param_.lrn_param().k();
   if (this->layer_param_.lrn_param().norm_region() ==
       LRNParameter_NormRegion_WITHIN_CHANNEL) {
     // Set up split_layer_ to use inputs in the numerator and denominator.
@@ -22,7 +23,7 @@ void LRNLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
     split_top_vec_.push_back(&square_input_);
     LayerParameter split_param;
     split_layer_.reset(new SplitLayer<Dtype>(split_param));
-    split_layer_->SetUp(bottom, &split_top_vec_);
+    split_layer_->SetUp(bottom, split_top_vec_);
     // Set up square_layer_ to square the inputs.
     square_bottom_vec_.clear();
     square_top_vec_.clear();
@@ -31,7 +32,7 @@ void LRNLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
     LayerParameter square_param;
     square_param.mutable_power_param()->set_power(Dtype(2));
     square_layer_.reset(new PowerLayer<Dtype>(square_param));
-    square_layer_->SetUp(square_bottom_vec_, &square_top_vec_);
+    square_layer_->SetUp(square_bottom_vec_, square_top_vec_);
     // Set up pool_layer_ to sum over square neighborhoods of the input.
     pool_top_vec_.clear();
     pool_top_vec_.push_back(&pool_output_);
@@ -41,7 +42,7 @@ void LRNLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
     pool_param.mutable_pooling_param()->set_pad(pre_pad_);
     pool_param.mutable_pooling_param()->set_kernel_size(size_);
     pool_layer_.reset(new PoolingLayer<Dtype>(pool_param));
-    pool_layer_->SetUp(square_top_vec_, &pool_top_vec_);
+    pool_layer_->SetUp(square_top_vec_, pool_top_vec_);
     // Set up power_layer_ to compute (1 + alpha_/N^2 s)^-beta_, where s is
     // the sum of a squared neighborhood (the output of pool_layer_).
     power_top_vec_.clear();
@@ -51,7 +52,7 @@ void LRNLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
     power_param.mutable_power_param()->set_scale(alpha_);
     power_param.mutable_power_param()->set_shift(Dtype(1));
     power_layer_.reset(new PowerLayer<Dtype>(power_param));
-    power_layer_->SetUp(pool_top_vec_, &power_top_vec_);
+    power_layer_->SetUp(pool_top_vec_, power_top_vec_);
     // Set up a product_layer_ to compute outputs by multiplying inputs by the
     // inverse demoninator computed by the power layer.
     product_bottom_vec_.clear();
@@ -67,21 +68,23 @@ void LRNLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 void LRNLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
+      const vector<Blob<Dtype>*>& top) {
+  CHECK_EQ(4, bottom[0]->num_axes()) << "Input must have 4 axes, "
+      << "corresponding to (num, channels, height, width)";
   num_ = bottom[0]->num();
   channels_ = bottom[0]->channels();
   height_ = bottom[0]->height();
   width_ = bottom[0]->width();
   switch (this->layer_param_.lrn_param().norm_region()) {
   case LRNParameter_NormRegion_ACROSS_CHANNELS:
-    (*top)[0]->Reshape(num_, channels_, height_, width_);
+    top[0]->Reshape(num_, channels_, height_, width_);
     scale_.Reshape(num_, channels_, height_, width_);
     break;
   case LRNParameter_NormRegion_WITHIN_CHANNEL:
-    split_layer_->Reshape(bottom, &split_top_vec_);
-    square_layer_->Reshape(square_bottom_vec_, &square_top_vec_);
-    pool_layer_->Reshape(square_top_vec_, &pool_top_vec_);
-    power_layer_->Reshape(pool_top_vec_, &power_top_vec_);
+    split_layer_->Reshape(bottom, split_top_vec_);
+    square_layer_->Reshape(square_bottom_vec_, square_top_vec_);
+    pool_layer_->Reshape(square_top_vec_, pool_top_vec_);
+    power_layer_->Reshape(pool_top_vec_, power_top_vec_);
     product_layer_->Reshape(product_bottom_vec_, top);
     break;
   }
@@ -89,7 +92,7 @@ void LRNLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 void LRNLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-    vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& top) {
   switch (this->layer_param_.lrn_param().norm_region()) {
   case LRNParameter_NormRegion_ACROSS_CHANNELS:
     CrossChannelForward_cpu(bottom, top);
@@ -104,13 +107,13 @@ void LRNLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 void LRNLayer<Dtype>::CrossChannelForward_cpu(
-    const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
   const Dtype* bottom_data = bottom[0]->cpu_data();
-  Dtype* top_data = (*top)[0]->mutable_cpu_data();
+  Dtype* top_data = top[0]->mutable_cpu_data();
   Dtype* scale_data = scale_.mutable_cpu_data();
   // start with the constant value
   for (int i = 0; i < scale_.count(); ++i) {
-    scale_data[i] = 1.;
+    scale_data[i] = k_;
   }
   Blob<Dtype> padded_square(1, channels_ + size_ - 1, height_, width_);
   Dtype* padded_square_data = padded_square.mutable_cpu_data();
@@ -151,17 +154,17 @@ void LRNLayer<Dtype>::CrossChannelForward_cpu(
 
 template <typename Dtype>
 void LRNLayer<Dtype>::WithinChannelForward(
-    const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
-  split_layer_->Forward(bottom, &split_top_vec_);
-  square_layer_->Forward(square_bottom_vec_, &square_top_vec_);
-  pool_layer_->Forward(square_top_vec_, &pool_top_vec_);
-  power_layer_->Forward(pool_top_vec_, &power_top_vec_);
+    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
+  split_layer_->Forward(bottom, split_top_vec_);
+  square_layer_->Forward(square_bottom_vec_, square_top_vec_);
+  pool_layer_->Forward(square_top_vec_, pool_top_vec_);
+  power_layer_->Forward(pool_top_vec_, power_top_vec_);
   product_layer_->Forward(product_bottom_vec_, top);
 }
 
 template <typename Dtype>
 void LRNLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
-    const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
+    const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
   switch (this->layer_param_.lrn_param().norm_region()) {
   case LRNParameter_NormRegion_ACROSS_CHANNELS:
     CrossChannelBackward_cpu(top, propagate_down, bottom);
@@ -177,12 +180,12 @@ void LRNLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
 template <typename Dtype>
 void LRNLayer<Dtype>::CrossChannelBackward_cpu(
     const vector<Blob<Dtype>*>& top, const vector<bool>& propagate_down,
-    vector<Blob<Dtype>*>* bottom) {
+    const vector<Blob<Dtype>*>& bottom) {
   const Dtype* top_diff = top[0]->cpu_diff();
   const Dtype* top_data = top[0]->cpu_data();
-  const Dtype* bottom_data = (*bottom)[0]->cpu_data();
+  const Dtype* bottom_data = bottom[0]->cpu_data();
   const Dtype* scale_data = scale_.cpu_data();
-  Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
+  Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
   Blob<Dtype> padded_ratio(1, channels_ + size_ - 1, height_, width_);
   Blob<Dtype> accum_ratio(1, 1, height_, width_);
   Dtype* padded_ratio_data = padded_ratio.mutable_cpu_data();
@@ -232,14 +235,14 @@ void LRNLayer<Dtype>::CrossChannelBackward_cpu(
 template <typename Dtype>
 void LRNLayer<Dtype>::WithinChannelBackward(
     const vector<Blob<Dtype>*>& top, const vector<bool>& propagate_down,
-    vector<Blob<Dtype>*>* bottom) {
+    const vector<Blob<Dtype>*>& bottom) {
   if (propagate_down[0]) {
     vector<bool> product_propagate_down(2, true);
-    product_layer_->Backward(top, product_propagate_down, &product_bottom_vec_);
-    power_layer_->Backward(power_top_vec_, propagate_down, &pool_top_vec_);
-    pool_layer_->Backward(pool_top_vec_, propagate_down, &square_top_vec_);
+    product_layer_->Backward(top, product_propagate_down, product_bottom_vec_);
+    power_layer_->Backward(power_top_vec_, propagate_down, pool_top_vec_);
+    pool_layer_->Backward(pool_top_vec_, propagate_down, square_top_vec_);
     square_layer_->Backward(square_top_vec_, propagate_down,
-                            &square_bottom_vec_);
+                            square_bottom_vec_);
     split_layer_->Backward(split_top_vec_, propagate_down, bottom);
   }
 }
@@ -251,6 +254,6 @@ STUB_GPU_BACKWARD(LRNLayer, CrossChannelBackward);
 #endif
 
 INSTANTIATE_CLASS(LRNLayer);
-
+REGISTER_LAYER_CLASS(LRN);
 
 }  // namespace caffe
diff --git a/src/caffe/layers/lrn_layer.cu b/src/caffe/layers/lrn_layer.cu
index d6cb23bf245..001b3c34ac1 100644
--- a/src/caffe/layers/lrn_layer.cu
+++ b/src/caffe/layers/lrn_layer.cu
@@ -7,46 +7,46 @@
 namespace caffe {
 
 template <typename Dtype>
-__global__ void LRNFillScale(const int nthreads, const Dtype* in,
+__global__ void LRNFillScale(const int nthreads, const Dtype* const in,
     const int num, const int channels, const int height,
     const int width, const int size, const Dtype alpha_over_size,
-    Dtype* scale) {
+    const Dtype k, Dtype* const scale) {
   CUDA_KERNEL_LOOP(index, nthreads) {
     // find out the local offset
-    int w = index % width;
-    int h = (index / width) % height;
-    int n = index / width / height;
-    int offset = (n * channels * height + h) * width + w;
-    int step = height * width;
-    in += offset;
-    scale += offset;
+    const int w = index % width;
+    const int h = (index / width) % height;
+    const int n = index / width / height;
+    const int offset = (n * channels * height + h) * width + w;
+    const int step = height * width;
+    const Dtype* const in_off = in + offset;
+    Dtype* const scale_off = scale + offset;
     int head = 0;
-    int pre_pad = (size - 1) / 2;
-    int post_pad = size - pre_pad - 1;
+    const int pre_pad = (size - 1) / 2;
+    const int post_pad = size - pre_pad - 1;
     Dtype accum_scale = 0;
     // fill the scale at [n, :, h, w]
     // accumulate values
-    while (head < post_pad) {
-      accum_scale += in[head * step] * in[head * step];
-      ++head;
-    }
-    // until we reach size, nothing needs to be subtracted
-    while (head < size) {
-      accum_scale += in[head * step] * in[head * step];
-      scale[(head - post_pad) * step] = 1. + accum_scale * alpha_over_size;
+    while (head < post_pad && head < channels) {
+      accum_scale += in_off[head * step] * in_off[head * step];
       ++head;
     }
     // both add and subtract
     while (head < channels) {
-      accum_scale += in[head * step] * in[head * step];
-      accum_scale -= in[(head - size) * step] * in[(head - size) * step];
-      scale[(head - post_pad) * step] = 1. + accum_scale * alpha_over_size;
+      accum_scale += in_off[head * step] * in_off[head * step];
+      if (head - size >= 0) {
+        accum_scale -= in_off[(head - size) * step]
+                       * in_off[(head - size) * step];
+      }
+      scale_off[(head - post_pad) * step] = k + accum_scale * alpha_over_size;
       ++head;
     }
     // subtract only
     while (head < channels + post_pad) {
-      accum_scale -= in[(head - size) * step] * in[(head - size) * step];
-      scale[(head - post_pad) * step] = 1. + accum_scale * alpha_over_size;
+      if (head - size >= 0) {
+        accum_scale -= in_off[(head - size) * step]
+                       * in_off[(head - size) * step];
+      }
+      scale_off[(head - post_pad) * step] = k + accum_scale * alpha_over_size;
       ++head;
     }
   }
@@ -55,7 +55,7 @@ __global__ void LRNFillScale(const int nthreads, const Dtype* in,
 
 template <typename Dtype>
 void LRNLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-    vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& top) {
   switch (this->layer_param_.lrn_param().norm_region()) {
   case LRNParameter_NormRegion_ACROSS_CHANNELS:
     CrossChannelForward_gpu(bottom, top);
@@ -70,8 +70,8 @@ void LRNLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
 
 // TODO: check if it would be faster to just put it into the previous kernel.
 template <typename Dtype>
-__global__ void LRNComputeOutput(const int nthreads, const Dtype* in,
-    const Dtype* scale, const Dtype negative_beta, Dtype* out) {
+__global__ void LRNComputeOutput(const int nthreads, const Dtype* const in,
+    const Dtype* const scale, const Dtype negative_beta, Dtype* const out) {
   CUDA_KERNEL_LOOP(index, nthreads) {
     out[index] = in[index] * pow(scale[index], negative_beta);
   }
@@ -79,10 +79,10 @@ __global__ void LRNComputeOutput(const int nthreads, const Dtype* in,
 
 template <typename Dtype>
 void LRNLayer<Dtype>::CrossChannelForward_gpu(
-    const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
   // First, compute scale
   const Dtype* bottom_data = bottom[0]->gpu_data();
-  Dtype* top_data = (*top)[0]->mutable_gpu_data();
+  Dtype* top_data = top[0]->mutable_gpu_data();
   Dtype* scale_data = scale_.mutable_gpu_data();
   // We will launch one kernel for each pixel location, and have the kernel
   // go through all the channels.
@@ -90,7 +90,7 @@ void LRNLayer<Dtype>::CrossChannelForward_gpu(
   // NOLINT_NEXT_LINE(whitespace/operators)
   LRNFillScale<<<CAFFE_GET_BLOCKS(n_threads), CAFFE_CUDA_NUM_THREADS>>>(
       n_threads, bottom_data, num_, channels_, height_, width_, size_,
-      alpha_ / size_, scale_data);
+      alpha_ / size_, k_, scale_data);
   CUDA_POST_KERNEL_CHECK;
   n_threads = bottom[0]->count();
   // NOLINT_NEXT_LINE(whitespace/operators)
@@ -98,11 +98,15 @@ void LRNLayer<Dtype>::CrossChannelForward_gpu(
       n_threads, bottom_data, scale_data, -beta_, top_data);
   CUDA_POST_KERNEL_CHECK;
 }
+template void LRNLayer<float>::CrossChannelForward_gpu(
+    const vector<Blob<float>*>& bottom, const vector<Blob<float>*>& top);
+template void LRNLayer<double>::CrossChannelForward_gpu(
+    const vector<Blob<double>*>& bottom, const vector<Blob<double>*>& top);
 
 
 template <typename Dtype>
 void LRNLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
-    const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
+    const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
   switch (this->layer_param_.lrn_param().norm_region()) {
   case LRNParameter_NormRegion_ACROSS_CHANNELS:
     CrossChannelBackward_gpu(top, propagate_down, bottom);
@@ -116,61 +120,58 @@ void LRNLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
 }
 
 template <typename Dtype>
-__global__ void LRNComputeDiff(const int nthreads, const Dtype* bottom_data,
-    const Dtype* top_data, const Dtype* scale, const Dtype* top_diff,
+__global__ void LRNComputeDiff(const int nthreads,
+    const Dtype* const bottom_data, const Dtype* const top_data,
+    const Dtype* const scale, const Dtype* const top_diff,
     const int num, const int channels, const int height,
     const int width, const int size, const Dtype negative_beta,
-    const Dtype cache_ratio,
-    Dtype* bottom_diff) {
+    const Dtype cache_ratio, Dtype* const bottom_diff) {
   CUDA_KERNEL_LOOP(index, nthreads) {
     // find out the local offset
-    int w = index % width;
-    int h = (index / width) % height;
-    int n = index / width / height;
-    int offset = (n * channels * height + h) * width + w;
-    int step = height * width;
-    bottom_data += offset;
-    top_data += offset;
-    scale += offset;
-    top_diff += offset;
-    bottom_diff += offset;
+    const int w = index % width;
+    const int h = (index / width) % height;
+    const int n = index / width / height;
+    const int offset = (n * channels * height + h) * width + w;
+    const int step = height * width;
+    const Dtype* const bottom_off = bottom_data + offset;
+    const Dtype* const top_off = top_data + offset;
+    const Dtype* const scale_off = scale + offset;
+    const Dtype* const top_diff_off = top_diff + offset;
+    Dtype* const bottom_diff_off = bottom_diff + offset;
     int head = 0;
-    int pre_pad = size - (size + 1) / 2;
-    int post_pad = size - pre_pad - 1;
+    const int pre_pad = size - (size + 1) / 2;
+    const int post_pad = size - pre_pad - 1;
     Dtype accum_ratio = 0;
     // accumulate values
-    while (head < post_pad) {
-      accum_ratio += top_diff[head * step] * top_data[head * step] /
-          scale[head * step];
-      ++head;
-    }
-    // until we reach size, nothing needs to be subtracted
-    while (head < size) {
-      accum_ratio += top_diff[head * step] * top_data[head * step] /
-          scale[head * step];
-      bottom_diff[(head - post_pad) * step] = top_diff[(head - post_pad) * step]
-          * pow(scale[(head - post_pad) * step], negative_beta) - cache_ratio *
-          bottom_data[(head - post_pad) * step] * accum_ratio;
+    while (head < post_pad && head < channels) {
+      accum_ratio += top_diff_off[head * step] * top_off[head * step] /
+          scale_off[head * step];
       ++head;
     }
     // both add and subtract
     while (head < channels) {
-      accum_ratio += top_diff[head * step] * top_data[head * step] /
-          scale[head * step];
-      accum_ratio -= top_diff[(head - size) * step] *
-          top_data[(head - size) * step] / scale[(head - size) * step];
-      bottom_diff[(head - post_pad) * step] = top_diff[(head - post_pad) * step]
-          * pow(scale[(head - post_pad) * step], negative_beta) - cache_ratio *
-          bottom_data[(head - post_pad) * step] * accum_ratio;
+      accum_ratio += top_diff_off[head * step] * top_off[head * step] /
+          scale_off[head * step];
+      if (head - size >= 0) {
+        accum_ratio -= top_diff_off[(head - size) * step] *
+            top_off[(head - size) * step] / scale_off[(head - size) * step];
+      }
+      bottom_diff_off[(head - post_pad) * step] =
+          top_diff_off[(head - post_pad) * step]
+            * pow(scale_off[(head - post_pad) * step], negative_beta)
+          - cache_ratio * bottom_off[(head - post_pad) * step] * accum_ratio;
       ++head;
     }
     // subtract only
     while (head < channels + post_pad) {
-      accum_ratio -= top_diff[(head - size) * step] *
-          top_data[(head - size) * step] / scale[(head - size) * step];
-      bottom_diff[(head - post_pad) * step] = top_diff[(head - post_pad) * step]
-          * pow(scale[(head - post_pad) * step], negative_beta) - cache_ratio *
-          bottom_data[(head - post_pad) * step] * accum_ratio;
+      if (head - size >= 0) {
+        accum_ratio -= top_diff_off[(head - size) * step] *
+            top_off[(head - size) * step] / scale_off[(head - size) * step];
+      }
+      bottom_diff_off[(head - post_pad) * step] =
+          top_diff_off[(head - post_pad) * step]
+            * pow(scale_off[(head - post_pad) * step], negative_beta)
+          - cache_ratio * bottom_off[(head - post_pad) * step] * accum_ratio;
       ++head;
     }
   }
@@ -179,17 +180,24 @@ __global__ void LRNComputeDiff(const int nthreads, const Dtype* bottom_data,
 template <typename Dtype>
 void LRNLayer<Dtype>::CrossChannelBackward_gpu(
     const vector<Blob<Dtype>*>& top, const vector<bool>& propagate_down,
-    vector<Blob<Dtype>*>* bottom) {
+    const vector<Blob<Dtype>*>& bottom) {
   int n_threads = num_ * height_ * width_;
   // NOLINT_NEXT_LINE(whitespace/operators)
   LRNComputeDiff<<<CAFFE_GET_BLOCKS(n_threads), CAFFE_CUDA_NUM_THREADS>>>(
-      n_threads, (*bottom)[0]->gpu_data(), top[0]->gpu_data(),
+      n_threads, bottom[0]->gpu_data(), top[0]->gpu_data(),
       scale_.gpu_data(), top[0]->gpu_diff(), num_, channels_, height_, width_,
       size_, -beta_, Dtype(2. * alpha_ * beta_ / size_),
-      (*bottom)[0]->mutable_gpu_diff());
+      bottom[0]->mutable_gpu_diff());
 }
+template void LRNLayer<float>::CrossChannelBackward_gpu(
+    const vector<Blob<float>*>& top, const vector<bool>& propagate_down,
+    const vector<Blob<float>*>& bottom);
+template void LRNLayer<double>::CrossChannelBackward_gpu(
+    const vector<Blob<double>*>& top, const vector<bool>& propagate_down,
+    const vector<Blob<double>*>& bottom);
+
 
 
-INSTANTIATE_CLASS(LRNLayer);
+INSTANTIATE_LAYER_GPU_FUNCS(LRNLayer);
 
 }  // namespace caffe
diff --git a/src/caffe/layers/memory_data_layer.cpp b/src/caffe/layers/memory_data_layer.cpp
index ab631a884fb..42de4198bc4 100644
--- a/src/caffe/layers/memory_data_layer.cpp
+++ b/src/caffe/layers/memory_data_layer.cpp
@@ -1,3 +1,5 @@
+#include <opencv2/core/core.hpp>
+
 #include <vector>
 
 #include "caffe/data_layers.hpp"
@@ -8,22 +10,20 @@ namespace caffe {
 
 template <typename Dtype>
 void MemoryDataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
-     vector<Blob<Dtype>*>* top) {
+     const vector<Blob<Dtype>*>& top) {
   batch_size_ = this->layer_param_.memory_data_param().batch_size();
-  this->datum_channels_ = this->layer_param_.memory_data_param().channels();
-  this->datum_height_ = this->layer_param_.memory_data_param().height();
-  this->datum_width_ = this->layer_param_.memory_data_param().width();
-  this->datum_size_ = this->datum_channels_ * this->datum_height_ *
-      this->datum_width_;
-  CHECK_GT(batch_size_ * this->datum_size_, 0) <<
+  channels_ = this->layer_param_.memory_data_param().channels();
+  height_ = this->layer_param_.memory_data_param().height();
+  width_ = this->layer_param_.memory_data_param().width();
+  size_ = channels_ * height_ * width_;
+  CHECK_GT(batch_size_ * size_, 0) <<
       "batch_size, channels, height, and width must be specified and"
       " positive in memory_data_param";
-  (*top)[0]->Reshape(batch_size_, this->datum_channels_, this->datum_height_,
-                     this->datum_width_);
-  (*top)[1]->Reshape(batch_size_, 1, 1, 1);
-  added_data_.Reshape(batch_size_, this->datum_channels_, this->datum_height_,
-                      this->datum_width_);
-  added_label_.Reshape(batch_size_, 1, 1, 1);
+  vector<int> label_shape(1, batch_size_);
+  top[0]->Reshape(batch_size_, channels_, height_, width_);
+  top[1]->Reshape(label_shape);
+  added_data_.Reshape(batch_size_, channels_, height_, width_);
+  added_label_.Reshape(label_shape);
   data_ = NULL;
   labels_ = NULL;
   added_data_.cpu_data();
@@ -33,23 +33,47 @@ void MemoryDataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
 template <typename Dtype>
 void MemoryDataLayer<Dtype>::AddDatumVector(const vector<Datum>& datum_vector) {
   CHECK(!has_new_data_) <<
-      "Can't add Datum when earlier ones haven't been consumed"
-      << " by the upper layers";
+      "Can't add data until current data has been consumed.";
   size_t num = datum_vector.size();
-  CHECK_GT(num, 0) << "There is no datum to add";
-  CHECK_LE(num, batch_size_) <<
-      "The number of added datum must be no greater than the batch size";
-
+  CHECK_GT(num, 0) << "There is no datum to add.";
+  CHECK_EQ(num % batch_size_, 0) <<
+      "The added data must be a multiple of the batch size.";
+  added_data_.Reshape(num, channels_, height_, width_);
+  added_label_.Reshape(num, 1, 1, 1);
+  // Apply data transformations (mirror, scale, crop...)
+  this->data_transformer_->Transform(datum_vector, &added_data_);
+  // Copy Labels
+  Dtype* top_label = added_label_.mutable_cpu_data();
+  for (int item_id = 0; item_id < num; ++item_id) {
+    top_label[item_id] = datum_vector[item_id].label();
+  }
+  // num_images == batch_size_
   Dtype* top_data = added_data_.mutable_cpu_data();
+  Reset(top_data, top_label, num);
+  has_new_data_ = true;
+}
+
+template <typename Dtype>
+void MemoryDataLayer<Dtype>::AddMatVector(const vector<cv::Mat>& mat_vector,
+    const vector<int>& labels) {
+  size_t num = mat_vector.size();
+  CHECK(!has_new_data_) <<
+      "Can't add mat until current data has been consumed.";
+  CHECK_GT(num, 0) << "There is no mat to add";
+  CHECK_EQ(num % batch_size_, 0) <<
+      "The added data must be a multiple of the batch size.";
+  added_data_.Reshape(num, channels_, height_, width_);
+  added_label_.Reshape(num, 1, 1, 1);
+  // Apply data transformations (mirror, scale, crop...)
+  this->data_transformer_->Transform(mat_vector, &added_data_);
+  // Copy Labels
   Dtype* top_label = added_label_.mutable_cpu_data();
-  for (int batch_item_id = 0; batch_item_id < num; ++batch_item_id) {
-    // Apply data transformations (mirror, scale, crop...)
-    this->data_transformer_.Transform(
-        batch_item_id, datum_vector[batch_item_id], this->mean_, top_data);
-    top_label[batch_item_id] = datum_vector[batch_item_id].label();
+  for (int item_id = 0; item_id < num; ++item_id) {
+    top_label[item_id] = labels[item_id];
   }
   // num_images == batch_size_
-  Reset(top_data, top_label, batch_size_);
+  Dtype* top_data = added_data_.mutable_cpu_data();
+  Reset(top_data, top_label, num);
   has_new_data_ = true;
 }
 
@@ -58,22 +82,40 @@ void MemoryDataLayer<Dtype>::Reset(Dtype* data, Dtype* labels, int n) {
   CHECK(data);
   CHECK(labels);
   CHECK_EQ(n % batch_size_, 0) << "n must be a multiple of batch size";
+  // Warn with transformation parameters since a memory array is meant to
+  // be generic and no transformations are done with Reset().
+  if (this->layer_param_.has_transform_param()) {
+    LOG(WARNING) << this->type() << " does not transform array data on Reset()";
+  }
   data_ = data;
   labels_ = labels;
   n_ = n;
   pos_ = 0;
 }
 
+template <typename Dtype>
+void MemoryDataLayer<Dtype>::set_batch_size(int new_size) {
+  CHECK(!has_new_data_) <<
+      "Can't change batch_size until current data has been consumed.";
+  batch_size_ = new_size;
+  added_data_.Reshape(batch_size_, channels_, height_, width_);
+  added_label_.Reshape(batch_size_, 1, 1, 1);
+}
+
 template <typename Dtype>
 void MemoryDataLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
+      const vector<Blob<Dtype>*>& top) {
   CHECK(data_) << "MemoryDataLayer needs to be initalized by calling Reset";
-  (*top)[0]->set_cpu_data(data_ + pos_ * this->datum_size_);
-  (*top)[1]->set_cpu_data(labels_ + pos_);
+  top[0]->Reshape(batch_size_, channels_, height_, width_);
+  top[1]->Reshape(batch_size_, 1, 1, 1);
+  top[0]->set_cpu_data(data_ + pos_ * size_);
+  top[1]->set_cpu_data(labels_ + pos_);
   pos_ = (pos_ + batch_size_) % n_;
-  has_new_data_ = false;
+  if (pos_ == 0)
+    has_new_data_ = false;
 }
 
 INSTANTIATE_CLASS(MemoryDataLayer);
+REGISTER_LAYER_CLASS(MemoryData);
 
 }  // namespace caffe
diff --git a/src/caffe/layers/multilabel_logistic_loss_layer.cpp b/src/caffe/layers/multilabel_logistic_loss_layer.cpp
new file mode 100644
index 00000000000..a83342f25c2
--- /dev/null
+++ b/src/caffe/layers/multilabel_logistic_loss_layer.cpp
@@ -0,0 +1,79 @@
+#include <algorithm>
+#include <cfloat>
+#include <cmath>
+#include <vector>
+
+#include "caffe/layer.hpp"
+#include "caffe/util/io.hpp"
+#include "caffe/util/math_functions.hpp"
+#include "caffe/vision_layers.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void MultilabelLogisticLossLayer<Dtype>::Reshape(
+    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
+  LossLayer<Dtype>::Reshape(bottom, top);
+  CHECK_EQ(bottom[1]->channels(), 1);
+  CHECK_EQ(bottom[1]->height(), 1);
+  CHECK_EQ(bottom[1]->width(), 1);
+}
+
+template <typename Dtype>
+void MultilabelLogisticLossLayer<Dtype>::Forward_cpu(
+    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
+  const Dtype* bottom_data = bottom[0]->cpu_data();
+  const Dtype* bottom_label = bottom[1]->cpu_data();
+  int num = bottom[0]->num();
+  int dim = bottom[0]->count() / bottom[0]->num();
+  Dtype loss = 0;
+  for (int i = 0; i < num; ++i) {
+    int label = static_cast<int>(bottom_label[i]);
+    for (int j = 0; j< dim; ++j) {
+      Dtype prob;
+      if (j == label)
+        prob = std::max(bottom_data[i * dim + j], Dtype(kLOG_THRESHOLD));
+      else
+        prob = std::max(1 - bottom_data[i * dim + j], Dtype(kLOG_THRESHOLD));
+      
+      loss -= log(prob);
+    }
+  }
+  top[0]->mutable_cpu_data()[0] = loss / num;
+}
+
+template <typename Dtype>
+void MultilabelLogisticLossLayer<Dtype>::Backward_cpu(
+    const vector<Blob<Dtype>*>& top, const vector<bool>& propagate_down,
+    const vector<Blob<Dtype>*>& bottom) {
+  if (propagate_down[1]) {
+    LOG(FATAL) << this->type()
+               << " Layer cannot backpropagate to label inputs.";
+  }
+  if (propagate_down[0]) {
+    const Dtype* bottom_data = bottom[0]->cpu_data();
+    const Dtype* bottom_label = bottom[1]->cpu_data();
+    Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
+    int num = bottom[0]->num();
+    int dim = bottom[0]->count() / bottom[0]->num();
+    caffe_set(bottom[0]->count(), Dtype(0), bottom_diff);
+    const Dtype scale = - top[0]->cpu_diff()[0] / num;
+    for (int i = 0; i < num; ++i) {
+      int label = static_cast<int>(bottom_label[i]);
+      for (int j = 0; j < dim; ++j) {
+        Dtype prob;
+        if (j == label)
+          prob = std::max(bottom_data[i * dim + j], Dtype(kLOG_THRESHOLD));
+        else
+          prob = -1 * std::max(1 - bottom_data[i * dim + j], Dtype(kLOG_THRESHOLD));
+        
+        bottom_diff[i * dim + j] = scale / prob;
+      }  
+    }
+  }
+}
+
+INSTANTIATE_CLASS(MultilabelLogisticLossLayer);
+REGISTER_LAYER_CLASS(MultilabelLogisticLoss);
+
+}  // namespace caffe
diff --git a/src/caffe/layers/multinomial_logistic_loss_layer.cpp b/src/caffe/layers/multinomial_logistic_loss_layer.cpp
index c0fe1966a4d..4267a594a0f 100644
--- a/src/caffe/layers/multinomial_logistic_loss_layer.cpp
+++ b/src/caffe/layers/multinomial_logistic_loss_layer.cpp
@@ -12,7 +12,7 @@ namespace caffe {
 
 template <typename Dtype>
 void MultinomialLogisticLossLayer<Dtype>::Reshape(
-    const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
   LossLayer<Dtype>::Reshape(bottom, top);
   CHECK_EQ(bottom[1]->channels(), 1);
   CHECK_EQ(bottom[1]->height(), 1);
@@ -21,7 +21,7 @@ void MultinomialLogisticLossLayer<Dtype>::Reshape(
 
 template <typename Dtype>
 void MultinomialLogisticLossLayer<Dtype>::Forward_cpu(
-    const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
   const Dtype* bottom_data = bottom[0]->cpu_data();
   const Dtype* bottom_label = bottom[1]->cpu_data();
   int num = bottom[0]->num();
@@ -33,24 +33,24 @@ void MultinomialLogisticLossLayer<Dtype>::Forward_cpu(
         bottom_data[i * dim + label], Dtype(kLOG_THRESHOLD));
     loss -= log(prob);
   }
-  (*top)[0]->mutable_cpu_data()[0] = loss / num;
+  top[0]->mutable_cpu_data()[0] = loss / num;
 }
 
 template <typename Dtype>
 void MultinomialLogisticLossLayer<Dtype>::Backward_cpu(
     const vector<Blob<Dtype>*>& top, const vector<bool>& propagate_down,
-    vector<Blob<Dtype>*>* bottom) {
+    const vector<Blob<Dtype>*>& bottom) {
   if (propagate_down[1]) {
-    LOG(FATAL) << this->type_name()
+    LOG(FATAL) << this->type()
                << " Layer cannot backpropagate to label inputs.";
   }
   if (propagate_down[0]) {
-    const Dtype* bottom_data = (*bottom)[0]->cpu_data();
-    const Dtype* bottom_label = (*bottom)[1]->cpu_data();
-    Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
-    int num = (*bottom)[0]->num();
-    int dim = (*bottom)[0]->count() / (*bottom)[0]->num();
-    caffe_set((*bottom)[0]->count(), Dtype(0), bottom_diff);
+    const Dtype* bottom_data = bottom[0]->cpu_data();
+    const Dtype* bottom_label = bottom[1]->cpu_data();
+    Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
+    int num = bottom[0]->num();
+    int dim = bottom[0]->count() / bottom[0]->num();
+    caffe_set(bottom[0]->count(), Dtype(0), bottom_diff);
     const Dtype scale = - top[0]->cpu_diff()[0] / num;
     for (int i = 0; i < num; ++i) {
       int label = static_cast<int>(bottom_label[i]);
@@ -62,5 +62,6 @@ void MultinomialLogisticLossLayer<Dtype>::Backward_cpu(
 }
 
 INSTANTIATE_CLASS(MultinomialLogisticLossLayer);
+REGISTER_LAYER_CLASS(MultinomialLogisticLoss);
 
 }  // namespace caffe
diff --git a/src/caffe/layers/mvn_layer.cpp b/src/caffe/layers/mvn_layer.cpp
index 6a57b3ea7fc..3e79bddcdde 100644
--- a/src/caffe/layers/mvn_layer.cpp
+++ b/src/caffe/layers/mvn_layer.cpp
@@ -9,8 +9,8 @@ namespace caffe {
 
 template <typename Dtype>
 void MVNLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
-  (*top)[0]->Reshape(bottom[0]->num(), bottom[0]->channels(),
+      const vector<Blob<Dtype>*>& top) {
+  top[0]->Reshape(bottom[0]->num(), bottom[0]->channels(),
       bottom[0]->height(), bottom[0]->width());
   mean_.Reshape(bottom[0]->num(), bottom[0]->channels(),
       1, 1);
@@ -22,13 +22,14 @@ void MVNLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
       bottom[0]->height(), bottom[0]->width());
   Dtype* multiplier_data = sum_multiplier_.mutable_cpu_data();
   caffe_set(sum_multiplier_.count(), Dtype(1), multiplier_data);
+  eps_ = this->layer_param_.mvn_param().eps();
 }
 
 template <typename Dtype>
 void MVNLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-    vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& top) {
   const Dtype* bottom_data = bottom[0]->cpu_data();
-  Dtype* top_data = (*top)[0]->mutable_cpu_data();
+  Dtype* top_data = top[0]->mutable_cpu_data();
   int num;
   if (this->layer_param_.mvn_param().across_channels())
     num = bottom[0]->num();
@@ -36,7 +37,6 @@ void MVNLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
     num = bottom[0]->num() * bottom[0]->channels();
 
   int dim = bottom[0]->count() / num;
-  Dtype eps = 1e-10;
 
   if (this->layer_param_.mvn_param().normalize_variance()) {
     // put the squares of bottom into temp_
@@ -66,7 +66,7 @@ void MVNLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
     caffe_powx(variance_.count(), variance_.cpu_data(), Dtype(0.5),
           variance_.mutable_cpu_data());
 
-    caffe_add_scalar(variance_.count(), eps, variance_.mutable_cpu_data());
+    caffe_add_scalar(variance_.count(), eps_, variance_.mutable_cpu_data());
 
     caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, dim, 1, 1.,
           variance_.cpu_data(), sum_multiplier_.cpu_data(), 0.,
@@ -89,20 +89,19 @@ void MVNLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
 template <typename Dtype>
 void MVNLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
     const vector<bool>& propagate_down,
-    vector<Blob<Dtype>*>* bottom) {
+    const vector<Blob<Dtype>*>& bottom) {
   const Dtype* top_diff = top[0]->cpu_diff();
   const Dtype* top_data = top[0]->cpu_data();
-  const Dtype* bottom_data = (*bottom)[0]->cpu_data();
-  Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
+  const Dtype* bottom_data = bottom[0]->cpu_data();
+  Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
 
   int num;
   if (this->layer_param_.mvn_param().across_channels())
-    num = (*bottom)[0]->num();
+    num = bottom[0]->num();
   else
-    num = (*bottom)[0]->num() * (*bottom)[0]->channels();
+    num = bottom[0]->num() * bottom[0]->channels();
 
-  int dim = (*bottom)[0]->count() / num;
-  Dtype eps = 1e-10;
+  int dim = bottom[0]->count() / num;
 
   if (this->layer_param_.mvn_param().normalize_variance()) {
     caffe_mul(temp_.count(), top_data, top_diff, bottom_diff);
@@ -125,24 +124,6 @@ void MVNLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
     // put the squares of bottom into temp_
     caffe_powx(temp_.count(), bottom_data, Dtype(2),
         temp_.mutable_cpu_data());
-
-    // computes variance using var(X) = E(X^2) - (EX)^2
-    caffe_cpu_gemv<Dtype>(CblasNoTrans, num, dim, 1. / dim, bottom_data,
-        sum_multiplier_.cpu_data(), 0., mean_.mutable_cpu_data());  // EX
-    caffe_cpu_gemv<Dtype>(CblasNoTrans, num, dim, 1. / dim, temp_.cpu_data(),
-        sum_multiplier_.cpu_data(), 0.,
-        variance_.mutable_cpu_data());  // E(X^2)
-    caffe_powx(mean_.count(), mean_.cpu_data(), Dtype(2),
-        temp_.mutable_cpu_data());  // (EX)^2
-    caffe_sub(mean_.count(), variance_.cpu_data(), temp_.cpu_data(),
-        variance_.mutable_cpu_data());  // variance
-
-    // normalize variance
-    caffe_powx(variance_.count(), variance_.cpu_data(), Dtype(0.5),
-          variance_.mutable_cpu_data());
-
-    caffe_add_scalar(variance_.count(), eps, variance_.mutable_cpu_data());
-
     caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, dim, 1, 1.,
         variance_.cpu_data(), sum_multiplier_.cpu_data(), 0.,
         temp_.mutable_cpu_data());
@@ -159,6 +140,6 @@ STUB_GPU(MVNLayer);
 #endif
 
 INSTANTIATE_CLASS(MVNLayer);
-
+REGISTER_LAYER_CLASS(MVN);
 
 }  // namespace caffe
diff --git a/src/caffe/layers/mvn_layer.cu b/src/caffe/layers/mvn_layer.cu
index 2c02dfe1ddc..3888a0c7106 100644
--- a/src/caffe/layers/mvn_layer.cu
+++ b/src/caffe/layers/mvn_layer.cu
@@ -9,9 +9,9 @@ namespace caffe {
 
 template <typename Dtype>
 void MVNLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-    vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& top) {
   const Dtype* bottom_data = bottom[0]->gpu_data();
-  Dtype* top_data = (*top)[0]->mutable_gpu_data();
+  Dtype* top_data = top[0]->mutable_gpu_data();
   int num;
   if (this->layer_param_.mvn_param().across_channels())
     num = bottom[0]->num();
@@ -36,8 +36,6 @@ void MVNLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
     caffe_gpu_sub(mean_.count(), variance_.gpu_data(), temp_.gpu_data(),
         variance_.mutable_gpu_data());  // variance
 
-    Dtype eps = 1e-10;
-
     // do mean and variance normalization
     // subtract mean
     caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, dim, 1, -1.,
@@ -50,7 +48,7 @@ void MVNLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
     caffe_gpu_powx(variance_.count(), variance_.gpu_data(), Dtype(0.5),
           variance_.mutable_gpu_data());
 
-    caffe_gpu_add_scalar(variance_.count(), eps, variance_.mutable_gpu_data());
+    caffe_gpu_add_scalar(variance_.count(), eps_, variance_.mutable_gpu_data());
 
     caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, dim, 1, 1.,
           variance_.gpu_data(), sum_multiplier_.gpu_data(), 0.,
@@ -73,21 +71,19 @@ void MVNLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
 template <typename Dtype>
 void MVNLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
     const vector<bool>& propagate_down,
-    vector<Blob<Dtype>*>* bottom) {
+    const vector<Blob<Dtype>*>& bottom) {
   const Dtype* top_diff = top[0]->gpu_diff();
   const Dtype* top_data = top[0]->gpu_data();
-  const Dtype* bottom_data = (*bottom)[0]->gpu_data();
-  Dtype* bottom_diff = (*bottom)[0]->mutable_gpu_diff();
+  const Dtype* bottom_data = bottom[0]->gpu_data();
+  Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
 
   int num;
   if (this->layer_param_.mvn_param().across_channels())
-    num = (*bottom)[0]->num();
+    num = bottom[0]->num();
   else
-    num = (*bottom)[0]->num() * (*bottom)[0]->channels();
-
-  int dim = (*bottom)[0]->count() / num;
+    num = bottom[0]->num() * bottom[0]->channels();
 
-  Dtype eps = 1e-10;
+  int dim = bottom[0]->count() / num;
 
   if (this->layer_param_.mvn_param().normalize_variance()) {
     caffe_gpu_mul(temp_.count(), top_data, top_diff, bottom_diff);
@@ -111,23 +107,6 @@ void MVNLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
     caffe_gpu_powx(temp_.count(), bottom_data, Dtype(2),
         temp_.mutable_gpu_data());
 
-    // computes variance using var(X) = E(X^2) - (EX)^2
-    caffe_gpu_gemv<Dtype>(CblasNoTrans, num, dim, 1. / dim, bottom_data,
-        sum_multiplier_.gpu_data(), 0., mean_.mutable_gpu_data());  // EX
-    caffe_gpu_gemv<Dtype>(CblasNoTrans, num, dim, 1. / dim, temp_.gpu_data(),
-        sum_multiplier_.gpu_data(), 0.,
-        variance_.mutable_gpu_data());  // E(X^2)
-    caffe_gpu_powx(mean_.count(), mean_.gpu_data(), Dtype(2),
-        temp_.mutable_gpu_data());  // (EX)^2
-    caffe_gpu_sub(mean_.count(), variance_.gpu_data(), temp_.gpu_data(),
-        variance_.mutable_gpu_data());  // variance
-
-    // normalize variance
-    caffe_gpu_powx(variance_.count(), variance_.gpu_data(), Dtype(0.5),
-          variance_.mutable_gpu_data());
-
-    caffe_gpu_add_scalar(variance_.count(), eps, variance_.mutable_gpu_data());
-
     caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, dim, 1, 1.,
         variance_.gpu_data(), sum_multiplier_.gpu_data(), 0.,
         temp_.mutable_gpu_data());
@@ -139,7 +118,7 @@ void MVNLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
 }
 
 
-INSTANTIATE_CLASS(MVNLayer);
+INSTANTIATE_LAYER_GPU_FUNCS(MVNLayer);
 
 
 }  // namespace caffe
diff --git a/src/caffe/layers/neuron_layer.cpp b/src/caffe/layers/neuron_layer.cpp
index c28e36ea23b..ba67b43878e 100644
--- a/src/caffe/layers/neuron_layer.cpp
+++ b/src/caffe/layers/neuron_layer.cpp
@@ -7,8 +7,8 @@ namespace caffe {
 
 template <typename Dtype>
 void NeuronLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
-  (*top)[0]->ReshapeLike(*bottom[0]);
+      const vector<Blob<Dtype>*>& top) {
+  top[0]->ReshapeLike(*bottom[0]);
 }
 
 INSTANTIATE_CLASS(NeuronLayer);
diff --git a/src/caffe/layers/normalize_layer.cpp b/src/caffe/layers/normalize_layer.cpp
new file mode 100644
index 00000000000..8708cabe524
--- /dev/null
+++ b/src/caffe/layers/normalize_layer.cpp
@@ -0,0 +1,61 @@
+#include <algorithm>
+#include <vector>
+#include <cmath>
+
+#include "caffe/layer.hpp"
+#include "caffe/util/math_functions.hpp"
+#include "caffe/common_layers.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void NormalizeLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+  top[0]->Reshape(bottom[0]->num(), bottom[0]->channels(),
+      bottom[0]->height(), bottom[0]->width());
+  squared_.Reshape(bottom[0]->num(), bottom[0]->channels(), 
+    bottom[0]->height(), bottom[0]->width());
+}
+
+template <typename Dtype>
+void NormalizeLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+    const vector<Blob<Dtype>*>& top) {
+  const Dtype* bottom_data = bottom[0]->cpu_data();
+  Dtype* top_data = top[0]->mutable_cpu_data();
+  Dtype* squared_data = squared_.mutable_cpu_data();
+  int n = bottom[0]->num();
+  int d = bottom[0]->count() / n;
+  caffe_sqr<Dtype>(n*d, bottom_data, squared_data);
+  for (int i=0; i<n; ++i) {
+    Dtype normsqr = caffe_cpu_asum<Dtype>(d, squared_data+i*d);
+    caffe_cpu_scale<Dtype>(d, pow(normsqr, -0.5), bottom_data+i*d, top_data+i*d);
+  }
+}
+
+template <typename Dtype>
+void NormalizeLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
+    const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+  const Dtype* top_diff = top[0]->cpu_diff();
+  const Dtype* top_data = top[0]->cpu_data();
+  const Dtype* bottom_data = bottom[0]->cpu_data();
+  Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
+  int n = top[0]->num();
+  int d = top[0]->count() / n;
+  for (int i=0; i<n; ++i) {
+    Dtype a = caffe_cpu_dot(d, top_data+i*d, top_diff+i*d);
+    caffe_cpu_scale(d, a, top_data+i*d, bottom_diff+i*d);
+    caffe_sub(d, top_diff+i*d, bottom_diff+i*d, bottom_diff+i*d);
+    a = caffe_cpu_dot(d, bottom_data+i*d, bottom_data+i*d);
+    caffe_cpu_scale(d, Dtype(pow(a, -0.5)), bottom_diff+i*d, bottom_diff+i*d);
+  }
+}
+
+
+#ifdef CPU_ONLY
+STUB_GPU(NormalizeLayer);
+#endif
+
+INSTANTIATE_CLASS(NormalizeLayer);
+REGISTER_LAYER_CLASS(Normalize);
+
+}  // namespace caffe
\ No newline at end of file
diff --git a/src/caffe/layers/normalize_layer.cu b/src/caffe/layers/normalize_layer.cu
new file mode 100644
index 00000000000..50d209bcafe
--- /dev/null
+++ b/src/caffe/layers/normalize_layer.cu
@@ -0,0 +1,51 @@
+#include <algorithm>
+#include <cfloat>
+#include <vector>
+
+#include "thrust/device_vector.h"
+
+#include "caffe/layer.hpp"
+#include "caffe/util/math_functions.hpp"
+#include "caffe/common_layers.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void NormalizeLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+    const vector<Blob<Dtype>*>& top) {
+  const Dtype* bottom_data = bottom[0]->gpu_data();
+  Dtype* top_data = top[0]->mutable_gpu_data();
+  Dtype* squared_data = squared_.mutable_gpu_data();
+  Dtype normsqr;
+  int n = bottom[0]->num();
+  int d = bottom[0]->count() / n;
+  caffe_gpu_powx(n*d, bottom_data, Dtype(2), squared_data);
+  for (int i=0; i<n; ++i) {
+    caffe_gpu_asum<Dtype>(d, squared_data+i*d, &normsqr);
+    caffe_gpu_scale<Dtype>(d, pow(normsqr, -0.5), bottom_data+i*d, top_data+i*d);
+  }
+}
+
+template <typename Dtype>
+void NormalizeLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
+    const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+  const Dtype* top_diff = top[0]->gpu_diff();
+  const Dtype* top_data = top[0]->gpu_data();
+  const Dtype* bottom_data = bottom[0]->gpu_data();
+  Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
+  int n = top[0]->num();
+  int d = top[0]->count() / n;
+  Dtype a;
+  for (int i=0; i<n; ++i) {
+    caffe_gpu_dot(d, top_data+i*d, top_diff+i*d, &a);
+    caffe_gpu_scale(d, a, top_data+i*d, bottom_diff+i*d);
+    caffe_gpu_sub(d, top_diff+i*d, bottom_diff+i*d, bottom_diff+i*d);
+    caffe_gpu_dot(d, bottom_data+i*d, bottom_data+i*d, &a);
+    caffe_gpu_scale(d, Dtype(pow(a, -0.5)), bottom_diff+i*d, bottom_diff+i*d);
+  }
+}
+
+INSTANTIATE_LAYER_GPU_FUNCS(NormalizeLayer);
+
+
+}  // namespace caffe
\ No newline at end of file
diff --git a/src/caffe/layers/pooling_layer.cpp b/src/caffe/layers/pooling_layer.cpp
index 8e8ffad66a0..c8d41499455 100644
--- a/src/caffe/layers/pooling_layer.cpp
+++ b/src/caffe/layers/pooling_layer.cpp
@@ -15,14 +15,20 @@ using std::max;
 
 template <typename Dtype>
 void PoolingLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
+      const vector<Blob<Dtype>*>& top) {
   PoolingParameter pool_param = this->layer_param_.pooling_param();
-  CHECK(!pool_param.has_kernel_size() !=
+  if (pool_param.global_pooling()) {
+    CHECK(!(pool_param.has_kernel_size() ||
+      pool_param.has_kernel_h() || pool_param.has_kernel_w()))
+      << "With Global_pooling: true Filter size cannot specified";
+  } else {
+    CHECK(!pool_param.has_kernel_size() !=
       !(pool_param.has_kernel_h() && pool_param.has_kernel_w()))
       << "Filter size is kernel_size OR kernel_h and kernel_w; not both";
-  CHECK(pool_param.has_kernel_size() ||
+    CHECK(pool_param.has_kernel_size() ||
       (pool_param.has_kernel_h() && pool_param.has_kernel_w()))
       << "For non-square filters both kernel_h and kernel_w are required.";
+  }
   CHECK((!pool_param.has_pad() && pool_param.has_pad_h()
       && pool_param.has_pad_w())
       || (!pool_param.has_pad_h() && !pool_param.has_pad_w()))
@@ -31,11 +37,17 @@ void PoolingLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       && pool_param.has_stride_w())
       || (!pool_param.has_stride_h() && !pool_param.has_stride_w()))
       << "Stride is stride OR stride_h and stride_w are required.";
-  if (pool_param.has_kernel_size()) {
-    kernel_h_ = kernel_w_ = pool_param.kernel_size();
+  global_pooling_ = pool_param.global_pooling();
+  if (global_pooling_) {
+    kernel_h_ = bottom[0]->height();
+    kernel_w_ = bottom[0]->width();
   } else {
-    kernel_h_ = pool_param.kernel_h();
-    kernel_w_ = pool_param.kernel_w();
+    if (pool_param.has_kernel_size()) {
+      kernel_h_ = kernel_w_ = pool_param.kernel_size();
+    } else {
+      kernel_h_ = pool_param.kernel_h();
+      kernel_w_ = pool_param.kernel_w();
+    }
   }
   CHECK_GT(kernel_h_, 0) << "Filter dimensions cannot be zero.";
   CHECK_GT(kernel_w_, 0) << "Filter dimensions cannot be zero.";
@@ -51,6 +63,10 @@ void PoolingLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
     stride_h_ = pool_param.stride_h();
     stride_w_ = pool_param.stride_w();
   }
+  if (global_pooling_) {
+    CHECK(pad_h_ == 0 && pad_w_ == 0 && stride_h_ == 1 && stride_w_ == 1)
+      << "With Global_pooling: true; only pad = 0 and stride = 1";
+  }
   if (pad_h_ != 0 || pad_w_ != 0) {
     CHECK(this->layer_param_.pooling_param().pool()
         == PoolingParameter_PoolMethod_AVE
@@ -64,10 +80,16 @@ void PoolingLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 void PoolingLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
+      const vector<Blob<Dtype>*>& top) {
+  CHECK_EQ(4, bottom[0]->num_axes()) << "Input must have 4 axes, "
+      << "corresponding to (num, channels, height, width)";
   channels_ = bottom[0]->channels();
   height_ = bottom[0]->height();
   width_ = bottom[0]->width();
+  if (global_pooling_) {
+    kernel_h_ = bottom[0]->height();
+    kernel_w_ = bottom[0]->width();
+  }
   pooled_height_ = static_cast<int>(ceil(static_cast<float>(
       height_ + 2 * pad_h_ - kernel_h_) / stride_h_)) + 1;
   pooled_width_ = static_cast<int>(ceil(static_cast<float>(
@@ -84,14 +106,14 @@ void PoolingLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
     CHECK_LT((pooled_height_ - 1) * stride_h_, height_ + pad_h_);
     CHECK_LT((pooled_width_ - 1) * stride_w_, width_ + pad_w_);
   }
-  (*top)[0]->Reshape(bottom[0]->num(), channels_, pooled_height_,
+  top[0]->Reshape(bottom[0]->num(), channels_, pooled_height_,
       pooled_width_);
-  if (top->size() > 1) {
-    (*top)[1]->ReshapeLike(*(*top)[0]);
+  if (top.size() > 1) {
+    top[1]->ReshapeLike(*top[0]);
   }
   // If max pooling, we will initialize the vector index part.
   if (this->layer_param_.pooling_param().pool() ==
-      PoolingParameter_PoolMethod_MAX && top->size() == 1) {
+      PoolingParameter_PoolMethod_MAX && top.size() == 1) {
     max_idx_.Reshape(bottom[0]->num(), channels_, pooled_height_,
         pooled_width_);
   }
@@ -107,12 +129,12 @@ void PoolingLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
 // case?
 template <typename Dtype>
 void PoolingLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
+      const vector<Blob<Dtype>*>& top) {
   const Dtype* bottom_data = bottom[0]->cpu_data();
-  Dtype* top_data = (*top)[0]->mutable_cpu_data();
-  const int top_count = (*top)[0]->count();
+  Dtype* top_data = top[0]->mutable_cpu_data();
+  const int top_count = top[0]->count();
   // We'll output the mask to top[1] if it's of size >1.
-  const bool use_top_mask = top->size() > 1;
+  const bool use_top_mask = top.size() > 1;
   int* mask = NULL;  // suppress warnings about uninitalized variables
   Dtype* top_mask = NULL;
   // Different pooling methods. We explicitly do the switch outside the for
@@ -121,7 +143,7 @@ void PoolingLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
   case PoolingParameter_PoolMethod_MAX:
     // Initialize
     if (use_top_mask) {
-      top_mask = (*top)[1]->mutable_cpu_data();
+      top_mask = top[1]->mutable_cpu_data();
       caffe_set(top_count, Dtype(-1), top_mask);
     } else {
       mask = max_idx_.mutable_cpu_data();
@@ -157,11 +179,11 @@ void PoolingLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
         }
         // compute offset
         bottom_data += bottom[0]->offset(0, 1);
-        top_data += (*top)[0]->offset(0, 1);
+        top_data += top[0]->offset(0, 1);
         if (use_top_mask) {
-          top_mask += (*top)[0]->offset(0, 1);
+          top_mask += top[0]->offset(0, 1);
         } else {
-          mask += (*top)[0]->offset(0, 1);
+          mask += top[0]->offset(0, 1);
         }
       }
     }
@@ -195,7 +217,7 @@ void PoolingLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
         }
         // compute offset
         bottom_data += bottom[0]->offset(0, 1);
-        top_data += (*top)[0]->offset(0, 1);
+        top_data += top[0]->offset(0, 1);
       }
     }
     break;
@@ -209,15 +231,15 @@ void PoolingLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 void PoolingLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
   if (!propagate_down[0]) {
     return;
   }
   const Dtype* top_diff = top[0]->cpu_diff();
-  Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
+  Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
   // Different pooling methods. We explicitly do the switch outside the for
   // loop to save time, although this results in more codes.
-  caffe_set((*bottom)[0]->count(), Dtype(0), bottom_diff);
+  caffe_set(bottom[0]->count(), Dtype(0), bottom_diff);
   // We'll output the mask to top[1] if it's of size >1.
   const bool use_top_mask = top.size() > 1;
   const int* mask = NULL;  // suppress warnings about uninitialized variables
@@ -240,7 +262,7 @@ void PoolingLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
             bottom_diff[bottom_index] += top_diff[index];
           }
         }
-        bottom_diff += (*bottom)[0]->offset(0, 1);
+        bottom_diff += bottom[0]->offset(0, 1);
         top_diff += top[0]->offset(0, 1);
         if (use_top_mask) {
           top_mask += top[0]->offset(0, 1);
@@ -274,7 +296,7 @@ void PoolingLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
           }
         }
         // offset
-        bottom_diff += (*bottom)[0]->offset(0, 1);
+        bottom_diff += bottom[0]->offset(0, 1);
         top_diff += top[0]->offset(0, 1);
       }
     }
@@ -294,5 +316,4 @@ STUB_GPU(PoolingLayer);
 
 INSTANTIATE_CLASS(PoolingLayer);
 
-
 }  // namespace caffe
diff --git a/src/caffe/layers/pooling_layer.cu b/src/caffe/layers/pooling_layer.cu
index e64128b87f2..ca4b13f7c41 100644
--- a/src/caffe/layers/pooling_layer.cu
+++ b/src/caffe/layers/pooling_layer.cu
@@ -9,31 +9,32 @@
 namespace caffe {
 
 template <typename Dtype>
-__global__ void MaxPoolForward(const int nthreads, const Dtype* bottom_data,
-    const int num, const int channels, const int height,
-    const int width, const int pooled_height, const int pooled_width,
-    const int kernel_h, const int kernel_w, const int stride_h,
-    const int stride_w, const int pad_h, const int pad_w, Dtype* top_data,
-    int* mask, Dtype* top_mask) {
+__global__ void MaxPoolForward(const int nthreads,
+    const Dtype* const bottom_data, const int num, const int channels,
+    const int height, const int width, const int pooled_height,
+    const int pooled_width, const int kernel_h, const int kernel_w,
+    const int stride_h, const int stride_w, const int pad_h, const int pad_w,
+    Dtype* const top_data, int* mask, Dtype* top_mask) {
   CUDA_KERNEL_LOOP(index, nthreads) {
-    int pw = index % pooled_width;
-    int ph = (index / pooled_width) % pooled_height;
-    int c = (index / pooled_width / pooled_height) % channels;
-    int n = index / pooled_width / pooled_height / channels;
+    const int pw = index % pooled_width;
+    const int ph = (index / pooled_width) % pooled_height;
+    const int c = (index / pooled_width / pooled_height) % channels;
+    const int n = index / pooled_width / pooled_height / channels;
     int hstart = ph * stride_h - pad_h;
     int wstart = pw * stride_w - pad_w;
-    int hend = min(hstart + kernel_h, height);
-    int wend = min(wstart + kernel_w, width);
+    const int hend = min(hstart + kernel_h, height);
+    const int wend = min(wstart + kernel_w, width);
     hstart = max(hstart, 0);
     wstart = max(wstart, 0);
     Dtype maxval = -FLT_MAX;
     int maxidx = -1;
-    bottom_data += (n * channels + c) * height * width;
+    const Dtype* const bottom_slice =
+        bottom_data + (n * channels + c) * height * width;
     for (int h = hstart; h < hend; ++h) {
       for (int w = wstart; w < wend; ++w) {
-        if (bottom_data[h * width + w] > maxval) {
+        if (bottom_slice[h * width + w] > maxval) {
           maxidx = h * width + w;
-          maxval = bottom_data[maxidx];
+          maxval = bottom_slice[maxidx];
         }
       }
     }
@@ -47,30 +48,32 @@ __global__ void MaxPoolForward(const int nthreads, const Dtype* bottom_data,
 }
 
 template <typename Dtype>
-__global__ void AvePoolForward(const int nthreads, const Dtype* bottom_data,
-    const int num, const int channels, const int height,
-    const int width, const int pooled_height, const int pooled_width,
-    const int kernel_h, const int kernel_w, const int stride_h,
-    const int stride_w, const int pad_h, const int pad_w, Dtype* top_data) {
+__global__ void AvePoolForward(const int nthreads,
+    const Dtype* const bottom_data, const int num, const int channels,
+    const int height, const int width, const int pooled_height,
+    const int pooled_width, const int kernel_h, const int kernel_w,
+    const int stride_h, const int stride_w, const int pad_h, const int pad_w,
+    Dtype* const top_data) {
   CUDA_KERNEL_LOOP(index, nthreads) {
-    int pw = index % pooled_width;
-    int ph = (index / pooled_width) % pooled_height;
-    int c = (index / pooled_width / pooled_height) % channels;
-    int n = index / pooled_width / pooled_height / channels;
+    const int pw = index % pooled_width;
+    const int ph = (index / pooled_width) % pooled_height;
+    const int c = (index / pooled_width / pooled_height) % channels;
+    const int n = index / pooled_width / pooled_height / channels;
     int hstart = ph * stride_h - pad_h;
     int wstart = pw * stride_w - pad_w;
     int hend = min(hstart + kernel_h, height + pad_h);
     int wend = min(wstart + kernel_w, width + pad_w);
-    int pool_size = (hend - hstart) * (wend - wstart);
+    const int pool_size = (hend - hstart) * (wend - wstart);
     hstart = max(hstart, 0);
     wstart = max(wstart, 0);
     hend = min(hend, height);
     wend = min(wend, width);
     Dtype aveval = 0;
-    bottom_data += (n * channels + c) * height * width;
+    const Dtype* const bottom_slice =
+        bottom_data + (n * channels + c) * height * width;
     for (int h = hstart; h < hend; ++h) {
       for (int w = wstart; w < wend; ++w) {
-        aveval += bottom_data[h * width + w];
+        aveval += bottom_slice[h * width + w];
       }
     }
     top_data[index] = aveval / pool_size;
@@ -79,37 +82,38 @@ __global__ void AvePoolForward(const int nthreads, const Dtype* bottom_data,
 
 template <typename Dtype>
 __global__ void StoPoolForwardTrain(const int nthreads,
-    const Dtype* bottom_data,
+    const Dtype* const bottom_data,
     const int num, const int channels, const int height,
     const int width, const int pooled_height, const int pooled_width,
     const int kernel_h, const int kernel_w, const int stride_h,
-    const int stride_w, Dtype* rand_idx, Dtype* top_data) {
+    const int stride_w, Dtype* const rand_idx, Dtype* const top_data) {
   CUDA_KERNEL_LOOP(index, nthreads) {
-    int pw = index % pooled_width;
-    int ph = (index / pooled_width) % pooled_height;
-    int c = (index / pooled_width / pooled_height) % channels;
-    int n = index / pooled_width / pooled_height / channels;
-    int hstart = ph * stride_h;
-    int hend = min(hstart + kernel_h, height);
-    int wstart = pw * stride_w;
-    int wend = min(wstart + kernel_w, width);
+    const int pw = index % pooled_width;
+    const int ph = (index / pooled_width) % pooled_height;
+    const int c = (index / pooled_width / pooled_height) % channels;
+    const int n = index / pooled_width / pooled_height / channels;
+    const int hstart = ph * stride_h;
+    const int hend = min(hstart + kernel_h, height);
+    const int wstart = pw * stride_w;
+    const int wend = min(wstart + kernel_w, width);
     Dtype cumsum = 0.;
-    bottom_data += (n * channels + c) * height * width;
+    const Dtype* const bottom_slice =
+        bottom_data + (n * channels + c) * height * width;
     // First pass: get sum
     for (int h = hstart; h < hend; ++h) {
       for (int w = wstart; w < wend; ++w) {
-        cumsum += bottom_data[h * width + w];
+        cumsum += bottom_slice[h * width + w];
       }
     }
-    float thres = rand_idx[index] * cumsum;
+    const float thres = rand_idx[index] * cumsum;
     // Second pass: get value, and set index.
     cumsum = 0;
     for (int h = hstart; h < hend; ++h) {
       for (int w = wstart; w < wend; ++w) {
-        cumsum += bottom_data[h * width + w];
+        cumsum += bottom_slice[h * width + w];
         if (cumsum >= thres) {
           rand_idx[index] = ((n * channels + c) * height + h) * width + w;
-          top_data[index] = bottom_data[h * width + w];
+          top_data[index] = bottom_slice[h * width + w];
           return;
         }
       }
@@ -120,29 +124,30 @@ __global__ void StoPoolForwardTrain(const int nthreads,
 
 template <typename Dtype>
 __global__ void StoPoolForwardTest(const int nthreads,
-    const Dtype* bottom_data,
+    const Dtype* const bottom_data,
     const int num, const int channels, const int height,
     const int width, const int pooled_height, const int pooled_width,
     const int kernel_h, const int kernel_w, const int stride_h,
-    const int stride_w, Dtype* top_data) {
+    const int stride_w, Dtype* const top_data) {
   CUDA_KERNEL_LOOP(index, nthreads) {
-    int pw = index % pooled_width;
-    int ph = (index / pooled_width) % pooled_height;
-    int c = (index / pooled_width / pooled_height) % channels;
-    int n = index / pooled_width / pooled_height / channels;
-    int hstart = ph * stride_h;
-    int hend = min(hstart + kernel_h, height);
-    int wstart = pw * stride_w;
-    int wend = min(wstart + kernel_w, width);
+    const int pw = index % pooled_width;
+    const int ph = (index / pooled_width) % pooled_height;
+    const int c = (index / pooled_width / pooled_height) % channels;
+    const int n = index / pooled_width / pooled_height / channels;
+    const int hstart = ph * stride_h;
+    const int hend = min(hstart + kernel_h, height);
+    const int wstart = pw * stride_w;
+    const int wend = min(wstart + kernel_w, width);
     // We set cumsum to be 0 to avoid divide-by-zero problems
     Dtype cumsum = FLT_MIN;
     Dtype cumvalues = 0.;
-    bottom_data += (n * channels + c) * height * width;
+    const Dtype* const bottom_slice =
+        bottom_data + (n * channels + c) * height * width;
     // First pass: get sum
     for (int h = hstart; h < hend; ++h) {
       for (int w = wstart; w < wend; ++w) {
-        cumsum += bottom_data[h * width + w];
-        cumvalues += bottom_data[h * width + w] * bottom_data[h * width + w];
+        cumsum += bottom_slice[h * width + w];
+        cumvalues += bottom_slice[h * width + w] * bottom_slice[h * width + w];
       }
     }
     top_data[index] = cumvalues / cumsum;
@@ -152,18 +157,18 @@ __global__ void StoPoolForwardTest(const int nthreads,
 
 template <typename Dtype>
 void PoolingLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
+      const vector<Blob<Dtype>*>& top) {
   const Dtype* bottom_data = bottom[0]->gpu_data();
-  Dtype* top_data = (*top)[0]->mutable_gpu_data();
-  int count = (*top)[0]->count();
+  Dtype* top_data = top[0]->mutable_gpu_data();
+  int count = top[0]->count();
   // We'll output the mask to top[1] if it's of size >1.
-  const bool use_top_mask = top->size() > 1;
+  const bool use_top_mask = top.size() > 1;
   int* mask = NULL;
   Dtype* top_mask = NULL;
   switch (this->layer_param_.pooling_param().pool()) {
   case PoolingParameter_PoolMethod_MAX:
     if (use_top_mask) {
-      top_mask = (*top)[1]->mutable_gpu_data();
+      top_mask = top[1]->mutable_gpu_data();
     } else {
       mask = max_idx_.mutable_gpu_data();
     }
@@ -182,7 +187,7 @@ void PoolingLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
         kernel_w_, stride_h_, stride_w_, pad_h_, pad_w_, top_data);
     break;
   case PoolingParameter_PoolMethod_STOCHASTIC:
-    if (Caffe::phase() == Caffe::TRAIN) {
+    if (this->phase_ == TRAIN) {
       // We need to create the random index as well.
       caffe_gpu_rng_uniform(count, Dtype(0), Dtype(1),
                             rand_idx_.mutable_gpu_data());
@@ -210,43 +215,43 @@ void PoolingLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
 
 
 template <typename Dtype>
-__global__ void MaxPoolBackward(const int nthreads, const Dtype* top_diff,
-    const int* mask, const Dtype* top_mask, const int num, const int channels,
-    const int height, const int width, const int pooled_height,
-    const int pooled_width, const int kernel_h, const int kernel_w,
-    const int stride_h, const int stride_w, const int pad_h, const int pad_w,
-    Dtype* bottom_diff) {
+__global__ void MaxPoolBackward(const int nthreads, const Dtype* const top_diff,
+    const int* const mask, const Dtype* const top_mask, const int num,
+    const int channels, const int height, const int width,
+    const int pooled_height, const int pooled_width, const int kernel_h,
+    const int kernel_w, const int stride_h, const int stride_w, const int pad_h,
+    const int pad_w, Dtype* const bottom_diff) {
   CUDA_KERNEL_LOOP(index, nthreads) {
     // find out the local index
     // find out the local offset
-    int w = index % width;
-    int h = (index / width) % height;
-    int c = (index / width / height) % channels;
-    int n = index / width / height / channels;
-    int phstart =
-        (h + pad_h < kernel_h) ? 0 : (h + pad_h - kernel_h) / stride_h + 1;
-    int phend = min((h + pad_h) / stride_h + 1, pooled_height);
-    int pwstart =
-        (w + pad_w < kernel_w) ? 0 : (w + pad_w - kernel_w) / stride_w + 1;
-    int pwend = min((w + pad_w) / stride_w + 1, pooled_width);
+    const int w = index % width;
+    const int h = (index / width) % height;
+    const int c = (index / width / height) % channels;
+    const int n = index / width / height / channels;
+    const int phstart =
+         (h + pad_h < kernel_h) ? 0 : (h + pad_h - kernel_h) / stride_h + 1;
+    const int phend = min((h + pad_h) / stride_h + 1, pooled_height);
+    const int pwstart =
+         (w + pad_w < kernel_w) ? 0 : (w + pad_w - kernel_w) / stride_w + 1;
+    const int pwend = min((w + pad_w) / stride_w + 1, pooled_width);
     Dtype gradient = 0;
-    int offset = (n * channels + c) * pooled_height * pooled_width;
-    top_diff += offset;
+    const int offset = (n * channels + c) * pooled_height * pooled_width;
+    const Dtype* const top_diff_slice = top_diff + offset;
     if (mask) {
-      mask += offset;
+      const int* const mask_slice = mask + offset;
       for (int ph = phstart; ph < phend; ++ph) {
         for (int pw = pwstart; pw < pwend; ++pw) {
-          if (mask[ph * pooled_width + pw] == h * width + w) {
-            gradient += top_diff[ph * pooled_width + pw];
+          if (mask_slice[ph * pooled_width + pw] == h * width + w) {
+            gradient += top_diff_slice[ph * pooled_width + pw];
           }
         }
       }
     } else {
-      top_mask += offset;
+      const Dtype* const top_mask_slice = top_mask + offset;
       for (int ph = phstart; ph < phend; ++ph) {
         for (int pw = pwstart; pw < pwend; ++pw) {
-          if (top_mask[ph * pooled_width + pw] == h * width + w) {
-            gradient += top_diff[ph * pooled_width + pw];
+          if (top_mask_slice[ph * pooled_width + pw] == h * width + w) {
+            gradient += top_diff_slice[ph * pooled_width + pw];
           }
         }
       }
@@ -256,25 +261,26 @@ __global__ void MaxPoolBackward(const int nthreads, const Dtype* top_diff,
 }
 
 template <typename Dtype>
-__global__ void AvePoolBackward(const int nthreads, const Dtype* top_diff,
+__global__ void AvePoolBackward(const int nthreads, const Dtype* const top_diff,
     const int num, const int channels, const int height,
     const int width, const int pooled_height, const int pooled_width,
     const int kernel_h, const int kernel_w, const int stride_h,
     const int stride_w, const int pad_h, const int pad_w,
-    Dtype* bottom_diff) {
+    Dtype* const bottom_diff) {
   CUDA_KERNEL_LOOP(index, nthreads) {
     // find out the local index
     // find out the local offset
-    int w = index % width + pad_w;
-    int h = (index / width) % height + pad_h;
-    int c = (index / width / height) % channels;
-    int n = index / width / height / channels;
-    int phstart = (h < kernel_h) ? 0 : (h - kernel_h) / stride_h + 1;
-    int phend = min(h / stride_h + 1, pooled_height);
-    int pwstart = (w < kernel_w) ? 0 : (w - kernel_w) / stride_w + 1;
-    int pwend = min(w / stride_w + 1, pooled_width);
+    const int w = index % width + pad_w;
+    const int h = (index / width) % height + pad_h;
+    const int c = (index / width / height) % channels;
+    const int n = index / width / height / channels;
+    const int phstart = (h < kernel_h) ? 0 : (h - kernel_h) / stride_h + 1;
+    const int phend = min(h / stride_h + 1, pooled_height);
+    const int pwstart = (w < kernel_w) ? 0 : (w - kernel_w) / stride_w + 1;
+    const int pwend = min(w / stride_w + 1, pooled_width);
     Dtype gradient = 0;
-    top_diff += (n * channels + c) * pooled_height * pooled_width;
+    const Dtype* const top_diff_slice =
+        top_diff + (n * channels + c) * pooled_height * pooled_width;
     for (int ph = phstart; ph < phend; ++ph) {
       for (int pw = pwstart; pw < pwend; ++pw) {
         // figure out the pooling size
@@ -283,7 +289,7 @@ __global__ void AvePoolBackward(const int nthreads, const Dtype* top_diff,
         int hend = min(hstart + kernel_h, height + pad_h);
         int wend = min(wstart + kernel_w, width + pad_w);
         int pool_size = (hend - hstart) * (wend - wstart);
-        gradient += top_diff[ph * pooled_width + pw] / pool_size;
+        gradient += top_diff_slice[ph * pooled_width + pw] / pool_size;
       }
     }
     bottom_diff[index] = gradient;
@@ -293,29 +299,31 @@ __global__ void AvePoolBackward(const int nthreads, const Dtype* top_diff,
 
 template <typename Dtype>
 __global__ void StoPoolBackward(const int nthreads,
-    const Dtype* rand_idx, const Dtype* top_diff,
+    const Dtype* const rand_idx, const Dtype* const top_diff,
     const int num, const int channels, const int height,
     const int width, const int pooled_height, const int pooled_width,
     const int kernel_h, const int kernel_w, const int stride_h,
-    const int stride_w, Dtype* bottom_diff) {
+    const int stride_w, Dtype* const bottom_diff) {
   CUDA_KERNEL_LOOP(index, nthreads) {
     // find out the local index
     // find out the local offset
-    int w = index % width;
-    int h = (index / width) % height;
-    int c = (index / width / height) % channels;
-    int n = index / width / height / channels;
-    int phstart = (h < kernel_h) ? 0 : (h - kernel_h) / stride_h + 1;
-    int phend = min(h / stride_h + 1, pooled_height);
-    int pwstart = (w < kernel_w) ? 0 : (w - kernel_w) / stride_w + 1;
-    int pwend = min(w / stride_w + 1, pooled_width);
+    const int w = index % width;
+    const int h = (index / width) % height;
+    const int c = (index / width / height) % channels;
+    const int n = index / width / height / channels;
+    const int phstart = (h < kernel_h) ? 0 : (h - kernel_h) / stride_h + 1;
+    const int phend = min(h / stride_h + 1, pooled_height);
+    const int pwstart = (w < kernel_w) ? 0 : (w - kernel_w) / stride_w + 1;
+    const int pwend = min(w / stride_w + 1, pooled_width);
     Dtype gradient = 0;
-    rand_idx += (n * channels + c) * pooled_height * pooled_width;
-    top_diff += (n * channels + c) * pooled_height * pooled_width;
+    const Dtype* const rand_idx_slice =
+        rand_idx + (n * channels + c) * pooled_height * pooled_width;
+    const Dtype* const top_diff_slice =
+        top_diff + (n * channels + c) * pooled_height * pooled_width;
     for (int ph = phstart; ph < phend; ++ph) {
       for (int pw = pwstart; pw < pwend; ++pw) {
-        gradient += top_diff[ph * pooled_width + pw] *
-            (index == static_cast<int>(rand_idx[ph * pooled_width + pw]));
+        gradient += top_diff_slice[ph * pooled_width + pw] *
+            (index == static_cast<int>(rand_idx_slice[ph * pooled_width + pw]));
       }
     }
     bottom_diff[index] = gradient;
@@ -325,13 +333,13 @@ __global__ void StoPoolBackward(const int nthreads,
 
 template <typename Dtype>
 void PoolingLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
   if (!propagate_down[0]) {
     return;
   }
   const Dtype* top_diff = top[0]->gpu_diff();
-  Dtype* bottom_diff = (*bottom)[0]->mutable_gpu_diff();
-  const int count = (*bottom)[0]->count();
+  Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
+  const int count = bottom[0]->count();
   caffe_gpu_set(count, Dtype(0.), bottom_diff);
   // We'll output the mask to top[1] if it's of size >1.
   const bool use_top_mask = top.size() > 1;
@@ -373,7 +381,7 @@ void PoolingLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
 }
 
 
-INSTANTIATE_CLASS(PoolingLayer);
+INSTANTIATE_LAYER_GPU_FUNCS(PoolingLayer);
 
 
 }  // namespace caffe
diff --git a/src/caffe/layers/power_layer.cpp b/src/caffe/layers/power_layer.cpp
index bf61955d065..4fe34c49f32 100644
--- a/src/caffe/layers/power_layer.cpp
+++ b/src/caffe/layers/power_layer.cpp
@@ -9,7 +9,7 @@ namespace caffe {
 
 template <typename Dtype>
 void PowerLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
+      const vector<Blob<Dtype>*>& top) {
   NeuronLayer<Dtype>::LayerSetUp(bottom, top);
   power_ = this->layer_param_.power_param().power();
   scale_ = this->layer_param_.power_param().scale();
@@ -20,8 +20,8 @@ void PowerLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
 // Compute y = (shift + scale * x)^power
 template <typename Dtype>
 void PowerLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-    vector<Blob<Dtype>*>* top) {
-  Dtype* top_data = (*top)[0]->mutable_cpu_data();
+    const vector<Blob<Dtype>*>& top) {
+  Dtype* top_data = top[0]->mutable_cpu_data();
   const int count = bottom[0]->count();
   // Special case where we can ignore the input: scale or power is 0.
   if (diff_scale_ == Dtype(0)) {
@@ -45,15 +45,15 @@ void PowerLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
 template <typename Dtype>
 void PowerLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
     const vector<bool>& propagate_down,
-    vector<Blob<Dtype>*>* bottom) {
+    const vector<Blob<Dtype>*>& bottom) {
   if (propagate_down[0]) {
-    Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
-    const int count = (*bottom)[0]->count();
+    Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
+    const int count = bottom[0]->count();
     const Dtype* top_diff = top[0]->cpu_diff();
     if (diff_scale_ == Dtype(0) || power_ == Dtype(1)) {
       caffe_set(count, diff_scale_, bottom_diff);
     } else {
-      const Dtype* bottom_data = (*bottom)[0]->cpu_data();
+      const Dtype* bottom_data = bottom[0]->cpu_data();
       // Compute dy/dx = scale * power * (shift + scale * x)^(power - 1)
       //               = diff_scale * y / (shift + scale * x)
       if (power_ == Dtype(2)) {
@@ -99,6 +99,6 @@ STUB_GPU(PowerLayer);
 #endif
 
 INSTANTIATE_CLASS(PowerLayer);
-
+REGISTER_LAYER_CLASS(Power);
 
 }  // namespace caffe
diff --git a/src/caffe/layers/power_layer.cu b/src/caffe/layers/power_layer.cu
index a40bc75829d..90d944059b6 100644
--- a/src/caffe/layers/power_layer.cu
+++ b/src/caffe/layers/power_layer.cu
@@ -9,8 +9,8 @@ namespace caffe {
 
 template <typename Dtype>
 void PowerLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-    vector<Blob<Dtype>*>* top) {
-  Dtype* top_data = (*top)[0]->mutable_gpu_data();
+    const vector<Blob<Dtype>*>& top) {
+  Dtype* top_data = top[0]->mutable_gpu_data();
   const int count = bottom[0]->count();
   // Special case where we can ignore the input: scale or power is 0.
   if (diff_scale_ == Dtype(0)) {
@@ -34,15 +34,15 @@ void PowerLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
 template <typename Dtype>
 void PowerLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
     const vector<bool>& propagate_down,
-    vector<Blob<Dtype>*>* bottom) {
+    const vector<Blob<Dtype>*>& bottom) {
   if (propagate_down[0]) {
-    Dtype* bottom_diff = (*bottom)[0]->mutable_gpu_diff();
-    const int count = (*bottom)[0]->count();
+    Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
+    const int count = bottom[0]->count();
     const Dtype* top_diff = top[0]->gpu_diff();
     if (diff_scale_ == Dtype(0) || power_ == Dtype(1)) {
       caffe_gpu_set(count, diff_scale_, bottom_diff);
     } else {
-      const Dtype* bottom_data = (*bottom)[0]->gpu_data();
+      const Dtype* bottom_data = bottom[0]->gpu_data();
       // Compute dy/dx = scale * power * (shift + scale * x)^(power - 1)
       //               = diff_scale * y / (shift + scale * x)
       if (power_ == Dtype(2)) {
@@ -81,7 +81,7 @@ void PowerLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
   }
 }
 
-INSTANTIATE_CLASS(PowerLayer);
+INSTANTIATE_LAYER_GPU_FUNCS(PowerLayer);
 
 
 }  // namespace caffe
diff --git a/src/caffe/layers/prelu_layer.cpp b/src/caffe/layers/prelu_layer.cpp
new file mode 100644
index 00000000000..81831755512
--- /dev/null
+++ b/src/caffe/layers/prelu_layer.cpp
@@ -0,0 +1,140 @@
+#include <algorithm>
+#include <vector>
+
+#include "caffe/filler.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/vision_layers.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void PReLULayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+    const vector<Blob<Dtype>*>& top) {
+  CHECK_GE(bottom[0]->num_axes(), 2)
+      << "Number of axes of bottom blob must be >=2.";
+  PReLUParameter prelu_param = this->layer_param().prelu_param();
+  int channels = bottom[0]->channels();
+  channel_shared_ = prelu_param.channel_shared();
+  if (this->blobs_.size() > 0) {
+    LOG(INFO) << "Skipping parameter initialization";
+  } else {
+    this->blobs_.resize(1);
+    if (channel_shared_) {
+      this->blobs_[0].reset(new Blob<Dtype>(vector<int>(0)));
+    } else {
+      this->blobs_[0].reset(new Blob<Dtype>(vector<int>(1, channels)));
+    }
+    shared_ptr<Filler<Dtype> > filler;
+    if (prelu_param.has_filler()) {
+      filler.reset(GetFiller<Dtype>(prelu_param.filler()));
+    } else {
+      FillerParameter filler_param;
+      filler_param.set_type("constant");
+      filler_param.set_value(0.25);
+      filler.reset(GetFiller<Dtype>(filler_param));
+    }
+    filler->Fill(this->blobs_[0].get());
+  }
+  if (channel_shared_) {
+    CHECK_EQ(this->blobs_[0]->count(), 1)
+        << "Negative slope size is inconsistent with prototxt config";
+  } else {
+    CHECK_EQ(this->blobs_[0]->count(), channels)
+        << "Negative slope size is inconsistent with prototxt config";
+  }
+
+  // Propagate gradients to the parameters (as directed by backward pass).
+  this->param_propagate_down_.resize(this->blobs_.size(), true);
+  multiplier_.Reshape(vector<int>(1, bottom[0]->count(1)));
+  backward_buff_.Reshape(vector<int>(1, bottom[0]->count(1)));
+  caffe_set(multiplier_.count(), Dtype(1), multiplier_.mutable_cpu_data());
+}
+
+template <typename Dtype>
+void PReLULayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
+    const vector<Blob<Dtype>*>& top) {
+  CHECK_GE(bottom[0]->num_axes(), 2)
+      << "Number of axes of bottom blob must be >=2.";
+  top[0]->ReshapeLike(*bottom[0]);
+  if (bottom[0] == top[0]) {
+    // For in-place computation
+    bottom_memory_.ReshapeLike(*bottom[0]);
+  }
+}
+
+template <typename Dtype>
+void PReLULayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+    const vector<Blob<Dtype>*>& top) {
+  const Dtype* bottom_data = bottom[0]->cpu_data();
+  Dtype* top_data = top[0]->mutable_cpu_data();
+  const int count = bottom[0]->count();
+  const int dim = bottom[0]->count(2);
+  const int channels = bottom[0]->channels();
+  const Dtype* slope_data = this->blobs_[0]->cpu_data();
+
+  // For in-place computation
+  if (bottom[0] == top[0]) {
+    caffe_copy(count, bottom_data, bottom_memory_.mutable_cpu_data());
+  }
+
+  // if channel_shared, channel index in the following computation becomes
+  // always zero.
+  const int div_factor = channel_shared_ ? channels : 1;
+  for (int i = 0; i < count; ++i) {
+    int c = (i / dim) % channels / div_factor;
+    top_data[i] = std::max(bottom_data[i], Dtype(0))
+        + slope_data[c] * std::min(bottom_data[i], Dtype(0));
+  }
+}
+
+template <typename Dtype>
+void PReLULayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
+    const vector<bool>& propagate_down,
+    const vector<Blob<Dtype>*>& bottom) {
+  const Dtype* bottom_data = bottom[0]->cpu_data();
+  const Dtype* slope_data = this->blobs_[0]->cpu_data();
+  const Dtype* top_diff = top[0]->cpu_diff();
+  const int count = bottom[0]->count();
+  const int dim = bottom[0]->count(2);
+  const int channels = bottom[0]->channels();
+
+  // For in-place computation
+  if (top[0] == bottom[0]) {
+    bottom_data = bottom_memory_.cpu_data();
+  }
+
+  // if channel_shared, channel index in the following computation becomes
+  // always zero.
+  const int div_factor = channel_shared_ ? channels : 1;
+
+  // Propagte to param
+  // Since to write bottom diff will affect top diff if top and bottom blobs
+  // are identical (in-place computaion), we first compute param backward to
+  // keep top_diff unchanged.
+  if (this->param_propagate_down_[0]) {
+    Dtype* slope_diff = this->blobs_[0]->mutable_cpu_diff();
+    for (int i = 0; i < count; ++i) {
+      int c = (i / dim) % channels / div_factor;
+      slope_diff[c] += top_diff[i] * bottom_data[i] * (bottom_data[i] <= 0);
+    }
+  }
+  // Propagate to bottom
+  if (propagate_down[0]) {
+    Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
+    for (int i = 0; i < count; ++i) {
+      int c = (i / dim) % channels / div_factor;
+      bottom_diff[i] = top_diff[i] * ((bottom_data[i] > 0)
+          + slope_data[c] * (bottom_data[i] <= 0));
+    }
+  }
+}
+
+
+#ifdef CPU_ONLY
+STUB_GPU(PReLULayer);
+#endif
+
+INSTANTIATE_CLASS(PReLULayer);
+REGISTER_LAYER_CLASS(PReLU);
+
+}  // namespace caffe
diff --git a/src/caffe/layers/prelu_layer.cu b/src/caffe/layers/prelu_layer.cu
new file mode 100644
index 00000000000..e1f20048f60
--- /dev/null
+++ b/src/caffe/layers/prelu_layer.cu
@@ -0,0 +1,128 @@
+#include <algorithm>
+#include <vector>
+
+#include "caffe/layer.hpp"
+#include "caffe/vision_layers.hpp"
+
+namespace caffe {
+
+// CUDA kernele for forward
+template <typename Dtype>
+__global__ void PReLUForward(const int n, const int channels, const int dim,
+    const Dtype* in, Dtype* out, const Dtype* slope_data,
+    const int div_factor) {
+  CUDA_KERNEL_LOOP(index, n) {
+    int c = (index / dim) % channels / div_factor;
+    out[index] = in[index] > 0 ? in[index] : in[index] * slope_data[c];
+  }
+}
+
+// CUDA kernel for bottom backward
+template <typename Dtype>
+__global__ void PReLUBackward(const int n, const int channels, const int dim,
+    const Dtype* in_diff, const Dtype* in_data, Dtype* out_diff,
+    const Dtype* slope_data, const int div_factor) {
+  CUDA_KERNEL_LOOP(index, n) {
+    int c = (index / dim) % channels / div_factor;
+    out_diff[index] = in_diff[index] * ((in_data[index] > 0)
+        + (in_data[index] <= 0) * slope_data[c]);
+  }
+}
+
+// CUDA kernel for element-wise parameter backward
+template <typename Dtype>
+__global__ void PReLUParamBackward(const int n, const Dtype* in_diff,
+    const Dtype* in_data, Dtype* out_diff) {
+  CUDA_KERNEL_LOOP(index, n) {
+    out_diff[index] = in_diff[index] * in_data[index] * (in_data[index] <= 0);
+  }
+}
+
+template <typename Dtype>
+void PReLULayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+    const vector<Blob<Dtype>*>& top) {
+  const Dtype* bottom_data = bottom[0]->gpu_data();
+  Dtype* top_data = top[0]->mutable_gpu_data();
+  const int count = bottom[0]->count();
+  const int dim = bottom[0]->count(2);
+  const int channels = bottom[0]->channels();
+  const Dtype* slope_data = this->blobs_[0]->gpu_data();
+  const int div_factor = channel_shared_ ? channels : 1;
+
+  // For in-place computation
+  if (top[0] == bottom[0]) {
+    caffe_copy(count, bottom_data, bottom_memory_.mutable_gpu_data());
+  }
+
+  // NOLINT_NEXT_LINE(whitespace/operators)
+  PReLUForward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
+      count, channels, dim, bottom_data, top_data, slope_data, div_factor);
+  CUDA_POST_KERNEL_CHECK;
+}
+
+template <typename Dtype>
+void PReLULayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
+    const vector<bool>& propagate_down,
+    const vector<Blob<Dtype>*>& bottom) {
+  const Dtype* bottom_data = bottom[0]->gpu_data();
+  const Dtype* top_diff = top[0]->gpu_diff();
+  const int count = bottom[0]->count();
+  const int dim = bottom[0]->count(2);
+  const int channels = bottom[0]->channels();
+
+  // For in-place computation
+  if (top[0] == bottom[0]) {
+    bottom_data = bottom_memory_.gpu_data();
+  }
+
+  // Propagate to param
+  // Since to write bottom diff will affect top diff if top and bottom blobs
+  // are identical (in-place computaion), we first compute param backward to
+  // keep top_diff unchanged.
+  if (this->param_propagate_down_[0]) {
+    Dtype* slope_diff = this->blobs_[0]->mutable_gpu_diff();
+    int cdim = channels * dim;
+    Dtype dsum = 0.;
+    for (int n = 0; n < bottom[0]->num(); ++n) {
+      // compute element-wise diff
+      // NOLINT_NEXT_LINE(whitespace/operators)
+      PReLUParamBackward<Dtype><<<CAFFE_GET_BLOCKS(cdim),
+          CAFFE_CUDA_NUM_THREADS>>>(
+          cdim, top_diff + top[0]->offset(n),
+          bottom_data + bottom[0]->offset(n),
+          backward_buff_.mutable_gpu_diff());
+      CUDA_POST_KERNEL_CHECK;
+      if (channel_shared_) {
+        Dtype d;
+        caffe_gpu_dot<Dtype>(channels * dim, backward_buff_.gpu_diff(),
+            multiplier_.gpu_data(), &d);
+        dsum += d;
+      } else {
+        caffe_gpu_gemv<Dtype>(CblasNoTrans, channels, dim, 1.,
+            backward_buff_.gpu_diff(), multiplier_.gpu_data(), 1.,
+            slope_diff);
+      }
+    }
+    if (channel_shared_) {
+      caffe_gpu_add_scalar(this->blobs_[0]->count(), Dtype(dsum), slope_diff);
+    }
+  }
+  // Propagate to bottom
+  if (propagate_down[0]) {
+    Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
+    const Dtype* slope_data = this->blobs_[0]->gpu_data();
+    int div_factor = channel_shared_ ? channels : 1;
+    // NOLINT_NEXT_LINE(whitespace/operators)
+    PReLUBackward<Dtype><<<CAFFE_GET_BLOCKS(count),
+        CAFFE_CUDA_NUM_THREADS>>>(
+        count, channels, dim, top_diff, bottom_data, bottom_diff, slope_data,
+        div_factor);
+    CUDA_POST_KERNEL_CHECK;
+  }
+}
+
+
+INSTANTIATE_LAYER_GPU_FUNCS(PReLULayer);
+
+
+}  // namespace caffe
diff --git a/src/caffe/layers/reduction_layer.cpp b/src/caffe/layers/reduction_layer.cpp
new file mode 100644
index 00000000000..8ae6329ebe4
--- /dev/null
+++ b/src/caffe/layers/reduction_layer.cpp
@@ -0,0 +1,132 @@
+#include <algorithm>
+#include <cfloat>
+#include <vector>
+
+#include "caffe/layer.hpp"
+#include "caffe/util/math_functions.hpp"
+#include "caffe/vision_layers.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void ReductionLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+  op_ = this->layer_param_.reduction_param().operation();
+}
+
+template <typename Dtype>
+void ReductionLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+  axis_ = bottom[0]->CanonicalAxisIndex(
+      this->layer_param_.reduction_param().axis());
+  // In the output, we'll keep all axes up to the reduction axis, but
+  // throw away any after that.
+  // Note: currently reducing along non-tail axes is not supported; otherwise,
+  // we'd need to also copy any axes following an "end_axis".
+  vector<int> top_shape(bottom[0]->shape().begin(),
+                        bottom[0]->shape().begin() + axis_);
+  top[0]->Reshape(top_shape);
+  num_ = bottom[0]->count(0, axis_);
+  dim_ = bottom[0]->count(axis_);
+  CHECK_EQ(num_, top[0]->count());
+  if (op_ == ReductionParameter_ReductionOp_SUM ||
+      op_ == ReductionParameter_ReductionOp_MEAN) {
+    vector<int> sum_mult_shape(1, dim_);
+    sum_multiplier_.Reshape(sum_mult_shape);
+    caffe_set(dim_, Dtype(1), sum_multiplier_.mutable_cpu_data());
+  }
+  coeff_ = this->layer_param().reduction_param().coeff();
+  if (op_ == ReductionParameter_ReductionOp_MEAN) {
+    coeff_ /= dim_;
+  }
+}
+
+template <typename Dtype>
+void ReductionLayer<Dtype>::Forward_cpu(
+    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
+  const Dtype* bottom_data = bottom[0]->cpu_data();
+  const Dtype* mult_data = NULL;
+  if (sum_multiplier_.count() > 0) {
+    mult_data = sum_multiplier_.cpu_data();
+  }
+  Dtype* top_data = top[0]->mutable_cpu_data();
+  for (int i = 0; i < num_; ++i) {
+    switch (op_) {
+    case ReductionParameter_ReductionOp_SUM:
+    case ReductionParameter_ReductionOp_MEAN:
+      *top_data = caffe_cpu_dot(dim_, mult_data, bottom_data);
+      break;
+    case ReductionParameter_ReductionOp_ASUM:
+      *top_data = caffe_cpu_asum(dim_, bottom_data);
+      break;
+    case ReductionParameter_ReductionOp_SUMSQ:
+      *top_data = caffe_cpu_dot(dim_, bottom_data, bottom_data);
+      break;
+    default:
+      LOG(FATAL) << "Unknown reduction op: "
+          << ReductionParameter_ReductionOp_Name(op_);
+    }
+    bottom_data += dim_;
+    ++top_data;
+  }
+  if (coeff_ != Dtype(1)) {
+    // Reset the top_data pointer.
+    top_data = top[0]->mutable_cpu_data();
+    caffe_scal(num_, coeff_, top_data);
+  }
+}
+
+template <typename Dtype>
+void ReductionLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
+    const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+  if (!propagate_down[0]) { return; }
+  // Get bottom_data, if needed.
+  const Dtype* bottom_data = NULL;
+  switch (op_) {
+  // Operations that don't need bottom_data
+  case ReductionParameter_ReductionOp_SUM:
+  case ReductionParameter_ReductionOp_MEAN:
+    break;
+  // Operations that need bottom_data
+  case ReductionParameter_ReductionOp_ASUM:
+  case ReductionParameter_ReductionOp_SUMSQ:
+    bottom_data = bottom[0]->cpu_data();
+    break;
+  default:
+    LOG(FATAL) << "Unknown reduction op: "
+        << ReductionParameter_ReductionOp_Name(op_);
+  }
+  const Dtype* top_diff = top[0]->cpu_diff();
+  Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
+  for (int i = 0; i < num_; ++i) {
+    const Dtype bottom_coeff = (*top_diff) * coeff_;
+    switch (op_) {
+    case ReductionParameter_ReductionOp_SUM:
+    case ReductionParameter_ReductionOp_MEAN:
+      caffe_set(dim_, bottom_coeff, bottom_diff);
+      break;
+    case ReductionParameter_ReductionOp_ASUM:
+      caffe_cpu_sign(dim_, bottom_data, bottom_diff);
+      caffe_scal(dim_, bottom_coeff, bottom_diff);
+      break;
+    case ReductionParameter_ReductionOp_SUMSQ:
+      caffe_cpu_scale(dim_, 2 * bottom_coeff, bottom_data, bottom_diff);
+      break;
+    default:
+      LOG(FATAL) << "Unknown reduction op: "
+          << ReductionParameter_ReductionOp_Name(op_);
+    }
+    bottom_data += dim_;
+    bottom_diff += dim_;
+    ++top_diff;
+  }
+}
+
+#ifdef CPU_ONLY
+STUB_GPU(ReductionLayer);
+#endif
+
+INSTANTIATE_CLASS(ReductionLayer);
+REGISTER_LAYER_CLASS(Reduction);
+
+}  // namespace caffe
diff --git a/src/caffe/layers/reduction_layer.cu b/src/caffe/layers/reduction_layer.cu
new file mode 100644
index 00000000000..2dbd3bc9f94
--- /dev/null
+++ b/src/caffe/layers/reduction_layer.cu
@@ -0,0 +1,93 @@
+#include <cfloat>
+#include <vector>
+
+#include "caffe/layer.hpp"
+#include "caffe/util/math_functions.hpp"
+#include "caffe/vision_layers.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void ReductionLayer<Dtype>::Forward_gpu(
+    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
+  const Dtype* bottom_data = bottom[0]->gpu_data();
+  const Dtype* mult_data = NULL;
+  if (sum_multiplier_.count() > 0) {
+    mult_data = sum_multiplier_.gpu_data();
+  }
+  Dtype* top_data = top[0]->mutable_cpu_data();
+  for (int i = 0; i < num_; ++i) {
+    switch (op_) {
+    case ReductionParameter_ReductionOp_SUM:
+    case ReductionParameter_ReductionOp_MEAN:
+      caffe_gpu_dot(dim_, mult_data, bottom_data, top_data);
+      break;
+    case ReductionParameter_ReductionOp_ASUM:
+      caffe_gpu_asum(dim_, bottom_data, top_data);
+      break;
+    case ReductionParameter_ReductionOp_SUMSQ:
+      caffe_gpu_dot(dim_, bottom_data, bottom_data, top_data);
+      break;
+    default:
+      LOG(FATAL) << "Unknown reduction op: "
+          << ReductionParameter_ReductionOp_Name(op_);
+    }
+    bottom_data += dim_;
+    ++top_data;
+  }
+  if (coeff_ != Dtype(1)) {
+    // Reset the top_data pointer.
+    top_data = top[0]->mutable_gpu_data();
+    caffe_gpu_scal(num_, coeff_, top_data);
+  }
+}
+
+template <typename Dtype>
+void ReductionLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
+    const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+  if (!propagate_down[0]) { return; }
+  // Get bottom_data, if needed.
+  const Dtype* bottom_data = NULL;
+  switch (op_) {
+  // Operations that don't need bottom_data
+  case ReductionParameter_ReductionOp_SUM:
+  case ReductionParameter_ReductionOp_MEAN:
+    break;
+  // Operations that need bottom_data
+  case ReductionParameter_ReductionOp_ASUM:
+  case ReductionParameter_ReductionOp_SUMSQ:
+    bottom_data = bottom[0]->gpu_data();
+    break;
+  default:
+    LOG(FATAL) << "Unknown reduction op: "
+        << ReductionParameter_ReductionOp_Name(op_);
+  }
+  const Dtype* top_diff = top[0]->cpu_diff();
+  Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
+  for (int i = 0; i < num_; ++i) {
+    const Dtype bottom_coeff = (*top_diff) * coeff_;
+    switch (op_) {
+    case ReductionParameter_ReductionOp_SUM:
+    case ReductionParameter_ReductionOp_MEAN:
+      caffe_gpu_set(dim_, bottom_coeff, bottom_diff);
+      break;
+    case ReductionParameter_ReductionOp_ASUM:
+      caffe_gpu_sign(dim_, bottom_data, bottom_diff);
+      caffe_gpu_scal(dim_, bottom_coeff, bottom_diff);
+      break;
+    case ReductionParameter_ReductionOp_SUMSQ:
+      caffe_gpu_scale(dim_, 2 * bottom_coeff, bottom_data, bottom_diff);
+      break;
+    default:
+      LOG(FATAL) << "Unknown reduction op: "
+          << ReductionParameter_ReductionOp_Name(op_);
+    }
+    bottom_data += dim_;
+    bottom_diff += dim_;
+    ++top_diff;
+  }
+}
+
+INSTANTIATE_LAYER_GPU_FUNCS(ReductionLayer);
+
+}  // namespace caffe
diff --git a/src/caffe/layers/relu_layer.cpp b/src/caffe/layers/relu_layer.cpp
index b50352f8526..cc00319a578 100644
--- a/src/caffe/layers/relu_layer.cpp
+++ b/src/caffe/layers/relu_layer.cpp
@@ -8,9 +8,9 @@ namespace caffe {
 
 template <typename Dtype>
 void ReLULayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-    vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& top) {
   const Dtype* bottom_data = bottom[0]->cpu_data();
-  Dtype* top_data = (*top)[0]->mutable_cpu_data();
+  Dtype* top_data = top[0]->mutable_cpu_data();
   const int count = bottom[0]->count();
   Dtype negative_slope = this->layer_param_.relu_param().negative_slope();
   for (int i = 0; i < count; ++i) {
@@ -22,12 +22,12 @@ void ReLULayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
 template <typename Dtype>
 void ReLULayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
     const vector<bool>& propagate_down,
-    vector<Blob<Dtype>*>* bottom) {
+    const vector<Blob<Dtype>*>& bottom) {
   if (propagate_down[0]) {
-    const Dtype* bottom_data = (*bottom)[0]->cpu_data();
+    const Dtype* bottom_data = bottom[0]->cpu_data();
     const Dtype* top_diff = top[0]->cpu_diff();
-    Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
-    const int count = (*bottom)[0]->count();
+    Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
+    const int count = bottom[0]->count();
     Dtype negative_slope = this->layer_param_.relu_param().negative_slope();
     for (int i = 0; i < count; ++i) {
       bottom_diff[i] = top_diff[i] * ((bottom_data[i] > 0)
@@ -43,5 +43,4 @@ STUB_GPU(ReLULayer);
 
 INSTANTIATE_CLASS(ReLULayer);
 
-
 }  // namespace caffe
diff --git a/src/caffe/layers/relu_layer.cu b/src/caffe/layers/relu_layer.cu
index def2bbcd7b9..b8924c855e5 100644
--- a/src/caffe/layers/relu_layer.cu
+++ b/src/caffe/layers/relu_layer.cu
@@ -16,9 +16,9 @@ __global__ void ReLUForward(const int n, const Dtype* in, Dtype* out,
 
 template <typename Dtype>
 void ReLULayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-    vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& top) {
   const Dtype* bottom_data = bottom[0]->gpu_data();
-  Dtype* top_data = (*top)[0]->mutable_gpu_data();
+  Dtype* top_data = top[0]->mutable_gpu_data();
   const int count = bottom[0]->count();
   Dtype negative_slope = this->layer_param_.relu_param().negative_slope();
   // NOLINT_NEXT_LINE(whitespace/operators)
@@ -44,12 +44,12 @@ __global__ void ReLUBackward(const int n, const Dtype* in_diff,
 template <typename Dtype>
 void ReLULayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
     const vector<bool>& propagate_down,
-    vector<Blob<Dtype>*>* bottom) {
+    const vector<Blob<Dtype>*>& bottom) {
   if (propagate_down[0]) {
-    const Dtype* bottom_data = (*bottom)[0]->gpu_data();
+    const Dtype* bottom_data = bottom[0]->gpu_data();
     const Dtype* top_diff = top[0]->gpu_diff();
-    Dtype* bottom_diff = (*bottom)[0]->mutable_gpu_diff();
-    const int count = (*bottom)[0]->count();
+    Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
+    const int count = bottom[0]->count();
     Dtype negative_slope = this->layer_param_.relu_param().negative_slope();
     // NOLINT_NEXT_LINE(whitespace/operators)
     ReLUBackward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
@@ -59,7 +59,7 @@ void ReLULayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
 }
 
 
-INSTANTIATE_CLASS(ReLULayer);
+INSTANTIATE_LAYER_GPU_FUNCS(ReLULayer);
 
 
 }  // namespace caffe
diff --git a/src/caffe/layers/reshape_layer.cpp b/src/caffe/layers/reshape_layer.cpp
new file mode 100644
index 00000000000..ffe970f2689
--- /dev/null
+++ b/src/caffe/layers/reshape_layer.cpp
@@ -0,0 +1,95 @@
+#include <vector>
+
+#include "caffe/common_layers.hpp"
+#include "caffe/layer.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void ReshapeLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+    const vector<Blob<Dtype>*>& top) {
+  inferred_axis_ = -1;
+  copy_axes_.clear();
+  const BlobShape& top_blob_shape = this->layer_param_.reshape_param().shape();
+  const int top_num_axes = top_blob_shape.dim_size();
+  constant_count_ = 1;
+  for (int i = 0; i < top_num_axes; ++i) {
+    const int top_dim = top_blob_shape.dim(i);
+    if (top_dim == 0) {
+      copy_axes_.push_back(i);
+    } else if (top_dim == -1) {
+      CHECK_EQ(inferred_axis_, -1) << "new shape contains multiple "
+          << "-1 dims; at most a single (1) value of -1 may be specified";
+      inferred_axis_ = i;
+    } else {
+      constant_count_ *= top_dim;
+    }
+  }
+}
+
+template <typename Dtype>
+void ReshapeLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
+    const vector<Blob<Dtype>*>& top) {
+  const int input_start_axis = this->layer_param_.reshape_param().axis();
+  const int start_axis = (input_start_axis >= 0) ? input_start_axis :
+      bottom[0]->num_axes() + input_start_axis + 1;
+  CHECK_GE(start_axis, 0) << "axis " << input_start_axis << " out of range";
+  CHECK_LE(start_axis, bottom[0]->num_axes()) << "axis " << input_start_axis
+      << " out of range for " << bottom[0]->num_axes() << "-D input blob";
+  const int num_axes = this->layer_param_.reshape_param().num_axes();
+  CHECK_GE(num_axes, -1) << "num_axes must be >= 0, or -1 for all";
+  const int end_axis =
+      (num_axes == -1) ? bottom[0]->num_axes() : (start_axis + num_axes);
+  CHECK_LE(end_axis, bottom[0]->num_axes())
+      << "end_axis = axis + num_axes is out of range";
+  const int num_axes_replaced = end_axis - start_axis;
+  const int num_axes_retained = bottom[0]->num_axes() - num_axes_replaced;
+  const BlobShape& top_blob_shape = this->layer_param_.reshape_param().shape();
+  const int num_new_axes = top_blob_shape.dim_size();
+  vector<int> top_shape(num_axes_retained + num_new_axes);
+  int top_shape_index = 0;
+  for (int i = 0; i < start_axis; ++i) {
+    top_shape[top_shape_index++] = bottom[0]->shape(i);
+  }
+  for (int i = 0; i < num_new_axes; ++i) {
+    top_shape[top_shape_index++] = top_blob_shape.dim(i);
+  }
+  for (int i = end_axis; i < bottom[0]->num_axes(); ++i) {
+    top_shape[top_shape_index++] = bottom[0]->shape(i);
+  }
+  CHECK_EQ(top_shape_index, top_shape.size());
+  for (int i = 0; i < copy_axes_.size(); ++i) {
+    const int copy_axis_index = copy_axes_[i];
+    CHECK_GT(bottom[0]->num_axes(), start_axis + copy_axis_index)
+        << "new shape contains a 0, but there was no corresponding bottom axis "
+        << "to copy";
+    top_shape[start_axis + copy_axis_index] =
+        bottom[0]->shape(start_axis + copy_axis_index);
+  }
+  if (inferred_axis_ >= 0) {
+    // A -1 dim was specified; infer the correct dimension by computing the
+    // product of the other dimensions.
+    int explicit_count = constant_count_;
+    explicit_count *= bottom[0]->count(0, start_axis);
+    explicit_count *= bottom[0]->count(end_axis);
+    for (int i = 0; i < copy_axes_.size(); ++i) {
+      const int copy_axis_index = copy_axes_[i];
+      explicit_count *= top_shape[start_axis + copy_axis_index];
+    }
+    CHECK_EQ(0, bottom[0]->count() % explicit_count) << "bottom count ("
+        << bottom[0]->count() << ") must be divisible by the product of "
+        << "the specified dimensions (" << explicit_count << ")";
+    const int inferred_dim = bottom[0]->count() / explicit_count;
+    top_shape[start_axis + inferred_axis_] = inferred_dim;
+  }
+  top[0]->Reshape(top_shape);
+  CHECK_EQ(top[0]->count(), bottom[0]->count())
+      << "output count must match input count";
+  top[0]->ShareData(*bottom[0]);
+  top[0]->ShareDiff(*bottom[0]);
+}
+
+INSTANTIATE_CLASS(ReshapeLayer);
+REGISTER_LAYER_CLASS(Reshape);
+
+}  // namespace caffe
diff --git a/src/caffe/layers/roi_pooling_layer.cpp b/src/caffe/layers/roi_pooling_layer.cpp
new file mode 100644
index 00000000000..e734d920029
--- /dev/null
+++ b/src/caffe/layers/roi_pooling_layer.cpp
@@ -0,0 +1,141 @@
+// ------------------------------------------------------------------
+// Fast R-CNN
+// Copyright (c) 2015 Microsoft
+// Licensed under The MIT License [see fast-rcnn/LICENSE for details]
+// Written by Ross Girshick
+// ------------------------------------------------------------------
+
+#include <cfloat>
+
+#include "caffe/vision_layers.hpp"
+
+using std::max;
+using std::min;
+using std::floor;
+using std::ceil;
+
+namespace caffe {
+
+template <typename Dtype>
+void ROIPoolingLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+  ROIPoolingParameter roi_pool_param = this->layer_param_.roi_pooling_param();
+  CHECK_GT(roi_pool_param.pooled_h(), 0)
+      << "pooled_h must be > 0";
+  CHECK_GT(roi_pool_param.pooled_w(), 0)
+      << "pooled_w must be > 0";
+  pooled_height_ = roi_pool_param.pooled_h();
+  pooled_width_ = roi_pool_param.pooled_w();
+  spatial_scale_ = roi_pool_param.spatial_scale();
+  LOG(INFO) << "Spatial scale: " << spatial_scale_;
+}
+
+template <typename Dtype>
+void ROIPoolingLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+  channels_ = bottom[0]->channels();
+  height_ = bottom[0]->height();
+  width_ = bottom[0]->width();
+  top[0]->Reshape(bottom[1]->num(), channels_, pooled_height_,
+      pooled_width_);
+  max_idx_.Reshape(bottom[1]->num(), channels_, pooled_height_,
+      pooled_width_);
+}
+
+template <typename Dtype>
+void ROIPoolingLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+  const Dtype* bottom_data = bottom[0]->cpu_data();
+  const Dtype* bottom_rois = bottom[1]->cpu_data();
+  // Number of ROIs
+  int num_rois = bottom[1]->num();
+  int batch_size = bottom[0]->num();
+  int top_count = top[0]->count();
+  Dtype* top_data = top[0]->mutable_cpu_data();
+  caffe_set(top_count, Dtype(-FLT_MAX), top_data);
+  int* argmax_data = max_idx_.mutable_cpu_data();
+  caffe_set(top_count, -1, argmax_data);
+
+  // For each ROI R = [batch_index x1 y1 x2 y2]: max pool over R
+  for (int n = 0; n < num_rois; ++n) {
+    int roi_batch_ind = bottom_rois[0];
+    int roi_start_w = round(bottom_rois[1] * spatial_scale_);
+    int roi_start_h = round(bottom_rois[2] * spatial_scale_);
+    int roi_end_w = round(bottom_rois[3] * spatial_scale_);
+    int roi_end_h = round(bottom_rois[4] * spatial_scale_);
+    CHECK_GE(roi_batch_ind, 0);
+    CHECK_LT(roi_batch_ind, batch_size);
+
+    int roi_height = max(roi_end_h - roi_start_h + 1, 1);
+    int roi_width = max(roi_end_w - roi_start_w + 1, 1);
+    const Dtype bin_size_h = static_cast<Dtype>(roi_height)
+                             / static_cast<Dtype>(pooled_height_);
+    const Dtype bin_size_w = static_cast<Dtype>(roi_width)
+                             / static_cast<Dtype>(pooled_width_);
+
+    const Dtype* batch_data = bottom_data + bottom[0]->offset(roi_batch_ind);
+
+    for (int c = 0; c < channels_; ++c) {
+      for (int ph = 0; ph < pooled_height_; ++ph) {
+        for (int pw = 0; pw < pooled_width_; ++pw) {
+          // Compute pooling region for this output unit:
+          //  start (included) = floor(ph * roi_height / pooled_height_)
+          //  end (excluded) = ceil((ph + 1) * roi_height / pooled_height_)
+          int hstart = static_cast<int>(floor(static_cast<Dtype>(ph)
+                                              * bin_size_h));
+          int wstart = static_cast<int>(floor(static_cast<Dtype>(pw)
+                                              * bin_size_w));
+          int hend = static_cast<int>(ceil(static_cast<Dtype>(ph + 1)
+                                           * bin_size_h));
+          int wend = static_cast<int>(ceil(static_cast<Dtype>(pw + 1)
+                                           * bin_size_w));
+
+          hstart = min(max(hstart + roi_start_h, 0), height_);
+          hend = min(max(hend + roi_start_h, 0), height_);
+          wstart = min(max(wstart + roi_start_w, 0), width_);
+          wend = min(max(wend + roi_start_w, 0), width_);
+
+          bool is_empty = (hend <= hstart) || (wend <= wstart);
+
+          const int pool_index = ph * pooled_width_ + pw;
+          if (is_empty) {
+            top_data[pool_index] = 0;
+            argmax_data[pool_index] = -1;
+          }
+
+          for (int h = hstart; h < hend; ++h) {
+            for (int w = wstart; w < wend; ++w) {
+              const int index = h * width_ + w;
+              if (batch_data[index] > top_data[pool_index]) {
+                top_data[pool_index] = batch_data[index];
+                argmax_data[pool_index] = index;
+              }
+            }
+          }
+        }
+      }
+      // Increment all data pointers by one channel
+      batch_data += bottom[0]->offset(0, 1);
+      top_data += top[0]->offset(0, 1);
+      argmax_data += max_idx_.offset(0, 1);
+    }
+    // Increment ROI data pointer
+    bottom_rois += bottom[1]->offset(1);
+  }
+}
+
+template <typename Dtype>
+void ROIPoolingLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+  NOT_IMPLEMENTED;
+}
+
+
+#ifdef CPU_ONLY
+STUB_GPU(ROIPoolingLayer);
+#endif
+
+INSTANTIATE_CLASS(ROIPoolingLayer);
+REGISTER_LAYER_CLASS(ROIPooling);
+
+}  // namespace caffe
diff --git a/src/caffe/layers/roi_pooling_layer.cu b/src/caffe/layers/roi_pooling_layer.cu
new file mode 100644
index 00000000000..a8a7a83eaae
--- /dev/null
+++ b/src/caffe/layers/roi_pooling_layer.cu
@@ -0,0 +1,188 @@
+// ------------------------------------------------------------------
+// Fast R-CNN
+// Copyright (c) 2015 Microsoft
+// Licensed under The MIT License [see fast-rcnn/LICENSE for details]
+// Written by Ross Girshick
+// ------------------------------------------------------------------
+
+#include <cfloat>
+
+#include "caffe/vision_layers.hpp"
+
+using std::max;
+using std::min;
+
+namespace caffe {
+
+template <typename Dtype>
+__global__ void ROIPoolForward(const int nthreads, const Dtype* bottom_data,
+    const Dtype spatial_scale, const int channels, const int height,
+    const int width, const int pooled_height, const int pooled_width,
+    const Dtype* bottom_rois, Dtype* top_data, int* argmax_data) {
+  CUDA_KERNEL_LOOP(index, nthreads) {
+    // (n, c, ph, pw) is an element in the pooled output
+    int pw = index % pooled_width;
+    int ph = (index / pooled_width) % pooled_height;
+    int c = (index / pooled_width / pooled_height) % channels;
+    int n = index / pooled_width / pooled_height / channels;
+
+    bottom_rois += n * 5;
+    int roi_batch_ind = bottom_rois[0];
+    int roi_start_w = round(bottom_rois[1] * spatial_scale);
+    int roi_start_h = round(bottom_rois[2] * spatial_scale);
+    int roi_end_w = round(bottom_rois[3] * spatial_scale);
+    int roi_end_h = round(bottom_rois[4] * spatial_scale);
+
+    // Force malformed ROIs to be 1x1
+    int roi_width = max(roi_end_w - roi_start_w + 1, 1);
+    int roi_height = max(roi_end_h - roi_start_h + 1, 1);
+    Dtype bin_size_h = static_cast<Dtype>(roi_height)
+                       / static_cast<Dtype>(pooled_height);
+    Dtype bin_size_w = static_cast<Dtype>(roi_width)
+                       / static_cast<Dtype>(pooled_width);
+
+    int hstart = static_cast<int>(floor(static_cast<Dtype>(ph)
+                                        * bin_size_h));
+    int wstart = static_cast<int>(floor(static_cast<Dtype>(pw)
+                                        * bin_size_w));
+    int hend = static_cast<int>(ceil(static_cast<Dtype>(ph + 1)
+                                     * bin_size_h));
+    int wend = static_cast<int>(ceil(static_cast<Dtype>(pw + 1)
+                                     * bin_size_w));
+
+    // Add roi offsets and clip to input boundaries
+    hstart = min(max(hstart + roi_start_h, 0), height);
+    hend = min(max(hend + roi_start_h, 0), height);
+    wstart = min(max(wstart + roi_start_w, 0), width);
+    wend = min(max(wend + roi_start_w, 0), width);
+    bool is_empty = (hend <= hstart) || (wend <= wstart);
+
+    // Define an empty pooling region to be zero
+    Dtype maxval = is_empty ? 0 : -FLT_MAX;
+    // If nothing is pooled, argmax = -1 causes nothing to be backprop'd
+    int maxidx = -1;
+    bottom_data += (roi_batch_ind * channels + c) * height * width;
+    for (int h = hstart; h < hend; ++h) {
+      for (int w = wstart; w < wend; ++w) {
+        int bottom_index = h * width + w;
+        if (bottom_data[bottom_index] > maxval) {
+          maxval = bottom_data[bottom_index];
+          maxidx = bottom_index;
+        }
+      }
+    }
+    top_data[index] = maxval;
+    argmax_data[index] = maxidx;
+  }
+}
+
+template <typename Dtype>
+void ROIPoolingLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+  const Dtype* bottom_data = bottom[0]->gpu_data();
+  const Dtype* bottom_rois = bottom[1]->gpu_data();
+  Dtype* top_data = top[0]->mutable_gpu_data();
+  int* argmax_data = max_idx_.mutable_gpu_data();
+  int count = top[0]->count();
+  // NOLINT_NEXT_LINE(whitespace/operators)
+  ROIPoolForward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
+      count, bottom_data, spatial_scale_, channels_, height_, width_,
+      pooled_height_, pooled_width_, bottom_rois, top_data, argmax_data);
+  CUDA_POST_KERNEL_CHECK;
+}
+
+template <typename Dtype>
+__global__ void ROIPoolBackward(const int nthreads, const Dtype* top_diff,
+    const int* argmax_data, const int num_rois, const Dtype spatial_scale,
+    const int channels, const int height, const int width,
+    const int pooled_height, const int pooled_width, Dtype* bottom_diff,
+    const Dtype* bottom_rois) {
+  CUDA_KERNEL_LOOP(index, nthreads) {
+    // (n, c, h, w) coords in bottom data
+    int w = index % width;
+    int h = (index / width) % height;
+    int c = (index / width / height) % channels;
+    int n = index / width / height / channels;
+
+    Dtype gradient = 0;
+    // Accumulate gradient over all ROIs that pooled this element
+    for (int roi_n = 0; roi_n < num_rois; ++roi_n) {
+      const Dtype* offset_bottom_rois = bottom_rois + roi_n * 5;
+      int roi_batch_ind = offset_bottom_rois[0];
+      // Skip if ROI's batch index doesn't match n
+      if (n != roi_batch_ind) {
+        continue;
+      }
+
+      int roi_start_w = round(offset_bottom_rois[1] * spatial_scale);
+      int roi_start_h = round(offset_bottom_rois[2] * spatial_scale);
+      int roi_end_w = round(offset_bottom_rois[3] * spatial_scale);
+      int roi_end_h = round(offset_bottom_rois[4] * spatial_scale);
+
+      // Skip if ROI doesn't include (h, w)
+      const bool in_roi = (w >= roi_start_w && w <= roi_end_w &&
+                           h >= roi_start_h && h <= roi_end_h);
+      if (!in_roi) {
+        continue;
+      }
+
+      int offset = (roi_n * channels + c) * pooled_height * pooled_width;
+      const Dtype* offset_top_diff = top_diff + offset;
+      const int* offset_argmax_data = argmax_data + offset;
+
+      // Compute feasible set of pooled units that could have pooled
+      // this bottom unit
+
+      // Force malformed ROIs to be 1x1
+      int roi_width = max(roi_end_w - roi_start_w + 1, 1);
+      int roi_height = max(roi_end_h - roi_start_h + 1, 1);
+
+      Dtype bin_size_h = static_cast<Dtype>(roi_height)
+                         / static_cast<Dtype>(pooled_height);
+      Dtype bin_size_w = static_cast<Dtype>(roi_width)
+                         / static_cast<Dtype>(pooled_width);
+
+      int phstart = floor(static_cast<Dtype>(h - roi_start_h) / bin_size_h);
+      int phend = ceil(static_cast<Dtype>(h - roi_start_h + 1) / bin_size_h);
+      int pwstart = floor(static_cast<Dtype>(w - roi_start_w) / bin_size_w);
+      int pwend = ceil(static_cast<Dtype>(w - roi_start_w + 1) / bin_size_w);
+
+      phstart = min(max(phstart, 0), pooled_height);
+      phend = min(max(phend, 0), pooled_height);
+      pwstart = min(max(pwstart, 0), pooled_width);
+      pwend = min(max(pwend, 0), pooled_width);
+
+      for (int ph = phstart; ph < phend; ++ph) {
+        for (int pw = pwstart; pw < pwend; ++pw) {
+          if (offset_argmax_data[ph * pooled_width + pw] == (h * width + w)) {
+            gradient += offset_top_diff[ph * pooled_width + pw];
+          }
+        }
+      }
+    }
+    bottom_diff[index] = gradient;
+  }
+}
+
+template <typename Dtype>
+void ROIPoolingLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+  if (!propagate_down[0]) {
+    return;
+  }
+  const Dtype* bottom_rois = bottom[1]->gpu_data();
+  const Dtype* top_diff = top[0]->gpu_diff();
+  Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
+  const int count = bottom[0]->count();
+  caffe_gpu_set(count, Dtype(0.), bottom_diff);
+  const int* argmax_data = max_idx_.gpu_data();
+  // NOLINT_NEXT_LINE(whitespace/operators)
+  ROIPoolBackward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
+      count, top_diff, argmax_data, top[0]->num(), spatial_scale_, channels_,
+      height_, width_, pooled_height_, pooled_width_, bottom_diff, bottom_rois);
+  CUDA_POST_KERNEL_CHECK;
+}
+
+INSTANTIATE_LAYER_GPU_FUNCS(ROIPoolingLayer);
+
+}  // namespace caffe
diff --git a/src/caffe/layers/scale_layer.cpp b/src/caffe/layers/scale_layer.cpp
new file mode 100644
index 00000000000..f1629bad4e3
--- /dev/null
+++ b/src/caffe/layers/scale_layer.cpp
@@ -0,0 +1,219 @@
+#include <algorithm>
+#include <vector>
+
+#include "caffe/filler.hpp"
+#include "caffe/layer_factory.hpp"
+#include "caffe/common_layers.hpp"
+#include "caffe/util/math_functions.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void ScaleLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+  const ScaleParameter& param = this->layer_param_.scale_param();
+  if (bottom.size() == 1 && this->blobs_.size() > 0) {
+    LOG(INFO) << "Skipping parameter initialization";
+  } else if (bottom.size() == 1) {
+    // scale is a learned parameter; initialize it
+    axis_ = bottom[0]->CanonicalAxisIndex(param.axis());
+    const int num_axes = param.num_axes();
+    CHECK_GE(num_axes, -1) << "num_axes must be non-negative, "
+                           << "or -1 to extend to the end of bottom[0]";
+    if (num_axes >= 0) {
+      CHECK_GE(bottom[0]->num_axes(), axis_ + num_axes)
+          << "scale blob's shape extends past bottom[0]'s shape when applied "
+          << "starting with bottom[0] axis = " << axis_;
+    }
+    this->blobs_.resize(1);
+    const vector<int>::const_iterator& shape_start =
+        bottom[0]->shape().begin() + axis_;
+    const vector<int>::const_iterator& shape_end =
+        (num_axes == -1) ? bottom[0]->shape().end() : (shape_start + num_axes);
+    vector<int> scale_shape(shape_start, shape_end);
+    this->blobs_[0].reset(new Blob<Dtype>(scale_shape));
+    FillerParameter filler_param(param.filler());
+    if (!param.has_filler()) {
+      // Default to unit (1) filler for identity operation.
+      filler_param.set_type("constant");
+      filler_param.set_value(1);
+    }
+    shared_ptr<Filler<Dtype> > filler(GetFiller<Dtype>(filler_param));
+    filler->Fill(this->blobs_[0].get());
+  }
+  if (param.bias_term()) {
+    LayerParameter layer_param(this->layer_param_);
+    layer_param.set_type("Bias");
+    BiasParameter* bias_param = layer_param.mutable_bias_param();
+    bias_param->set_axis(param.axis());
+    if (bottom.size() > 1) {
+      bias_param->set_num_axes(bottom[1]->num_axes());
+    } else {
+      bias_param->set_num_axes(param.num_axes());
+    }
+    bias_param->mutable_filler()->CopyFrom(param.bias_filler());
+    bias_layer_ = LayerRegistry<Dtype>::CreateLayer(layer_param);
+    bias_bottom_vec_.resize(1);
+    bias_bottom_vec_[0] = bottom[0];
+    bias_layer_->SetUp(bias_bottom_vec_, top);
+    bias_param_id_ = this->blobs_.size();
+    this->blobs_.resize(bias_param_id_ + 1);
+    this->blobs_[bias_param_id_] = bias_layer_->blobs()[0];
+    bias_propagate_down_.resize(1, false);
+  }
+  this->param_propagate_down_.resize(this->blobs_.size(), true);
+}
+
+template <typename Dtype>
+void ScaleLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+  const ScaleParameter& param = this->layer_param_.scale_param();
+  Blob<Dtype>* scale = (bottom.size() > 1) ? bottom[1] : this->blobs_[0].get();
+  // Always set axis_ == 0 in special case where scale is a scalar
+  // (num_axes == 0). Mathematically equivalent for any choice of axis_, so the
+  // actual setting can be safely ignored; and computation is most efficient
+  // with axis_ == 0 and (therefore) outer_dim_ == 1. (Setting axis_ to
+  // bottom[0]->num_axes() - 1, giving inner_dim_ == 1, would be equally
+  // performant.)
+  axis_ = (scale->num_axes() == 0) ?
+      0 : bottom[0]->CanonicalAxisIndex(param.axis());
+  CHECK_GE(bottom[0]->num_axes(), axis_ + scale->num_axes())
+      << "scale blob's shape extends past bottom[0]'s shape when applied "
+      << "starting with bottom[0] axis = " << axis_;
+  for (int i = 0; i < scale->num_axes(); ++i) {
+    CHECK_EQ(bottom[0]->shape(axis_ + i), scale->shape(i))
+        << "dimension mismatch between bottom[0]->shape(" << axis_ + i
+        << ") and scale->shape(" << i << ")";
+  }
+  outer_dim_ = bottom[0]->count(0, axis_);
+  scale_dim_ = scale->count();
+  inner_dim_ = bottom[0]->count(axis_ + scale->num_axes());
+  if (bottom[0] == top[0]) {  // in-place computation
+    temp_.ReshapeLike(*bottom[0]);
+  } else {
+    top[0]->ReshapeLike(*bottom[0]);
+  }
+  sum_result_.Reshape(vector<int>(1, outer_dim_ * scale_dim_));
+  const int sum_mult_size = std::max(outer_dim_, inner_dim_);
+  sum_multiplier_.Reshape(vector<int>(1, sum_mult_size));
+  if (sum_multiplier_.cpu_data()[sum_mult_size - 1] != Dtype(1)) {
+    caffe_set(sum_mult_size, Dtype(1), sum_multiplier_.mutable_cpu_data());
+  }
+  if (bias_layer_) {
+    bias_bottom_vec_[0] = top[0];
+    bias_layer_->Reshape(bias_bottom_vec_, top);
+  }
+}
+
+template <typename Dtype>
+void ScaleLayer<Dtype>::Forward_cpu(
+    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
+  const Dtype* bottom_data = bottom[0]->cpu_data();
+  if (bottom[0] == top[0]) {
+    // In-place computation; need to store bottom data before overwriting it.
+    // Note that this is only necessary for Backward; we could skip this if not
+    // doing Backward, but Caffe currently provides no way of knowing whether
+    // we'll need to do Backward at the time of the Forward call.
+    caffe_copy(bottom[0]->count(), bottom[0]->cpu_data(),
+               temp_.mutable_cpu_data());
+  }
+  const Dtype* scale_data =
+      ((bottom.size() > 1) ? bottom[1] : this->blobs_[0].get())->cpu_data();
+  Dtype* top_data = top[0]->mutable_cpu_data();
+  for (int n = 0; n < outer_dim_; ++n) {
+    for (int d = 0; d < scale_dim_; ++d) {
+      const Dtype factor = scale_data[d];
+      caffe_cpu_scale(inner_dim_, factor, bottom_data, top_data);
+      bottom_data += inner_dim_;
+      top_data += inner_dim_;
+    }
+  }
+  if (bias_layer_) {
+    bias_layer_->Forward(bias_bottom_vec_, top);
+  }
+}
+
+template <typename Dtype>
+void ScaleLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
+    const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+  if (bias_layer_ &&
+      this->param_propagate_down_[this->param_propagate_down_.size() - 1]) {
+    bias_layer_->Backward(top, bias_propagate_down_, bias_bottom_vec_);
+  }
+  const bool scale_param = (bottom.size() == 1);
+  Blob<Dtype>* scale = scale_param ? this->blobs_[0].get() : bottom[1];
+  if ((!scale_param && propagate_down[1]) ||
+      (scale_param && this->param_propagate_down_[0])) {
+    const Dtype* top_diff = top[0]->cpu_diff();
+    const bool in_place = (bottom[0] == top[0]);
+    const Dtype* bottom_data = (in_place ? &temp_ : bottom[0])->cpu_data();
+    // Hack: store big eltwise product in bottom[0] diff, except in the special
+    // case where this layer itself does the eltwise product, in which case we
+    // can store it directly in the scale diff, and we're done.
+    // If we're computing in-place (and not doing eltwise computation), this
+    // hack doesn't work and we store the product in temp_.
+    const bool is_eltwise = (bottom[0]->count() == scale->count());
+    Dtype* product = (is_eltwise ? scale->mutable_cpu_diff() :
+        (in_place ? temp_.mutable_cpu_data() : bottom[0]->mutable_cpu_diff()));
+    caffe_mul(top[0]->count(), top_diff, bottom_data, product);
+    if (!is_eltwise) {
+      Dtype* sum_result = NULL;
+      if (inner_dim_ == 1) {
+        sum_result = product;
+      } else if (sum_result_.count() == 1) {
+        const Dtype* sum_mult = sum_multiplier_.cpu_data();
+        Dtype* scale_diff = scale->mutable_cpu_diff();
+        if (scale_param) {
+          Dtype result = caffe_cpu_dot(inner_dim_, product, sum_mult);
+          *scale_diff += result;
+        } else {
+          *scale_diff = caffe_cpu_dot(inner_dim_, product, sum_mult);
+        }
+      } else {
+        const Dtype* sum_mult = sum_multiplier_.cpu_data();
+        sum_result = (outer_dim_ == 1) ?
+            scale->mutable_cpu_diff() : sum_result_.mutable_cpu_data();
+        caffe_cpu_gemv(CblasNoTrans, sum_result_.count(), inner_dim_,
+                       Dtype(1), product, sum_mult, Dtype(0), sum_result);
+      }
+      if (outer_dim_ != 1) {
+        const Dtype* sum_mult = sum_multiplier_.cpu_data();
+        Dtype* scale_diff = scale->mutable_cpu_diff();
+        if (scale_dim_ == 1) {
+          if (scale_param) {
+            Dtype result = caffe_cpu_dot(outer_dim_, sum_mult, sum_result);
+            *scale_diff += result;
+          } else {
+            *scale_diff = caffe_cpu_dot(outer_dim_, sum_mult, sum_result);
+          }
+        } else {
+          caffe_cpu_gemv(CblasTrans, outer_dim_, scale_dim_,
+                         Dtype(1), sum_result, sum_mult, Dtype(scale_param),
+                         scale_diff);
+        }
+      }
+    }
+  }
+  if (propagate_down[0]) {
+    const Dtype* top_diff = top[0]->cpu_diff();
+    const Dtype* scale_data = scale->cpu_data();
+    Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
+    for (int n = 0; n < outer_dim_; ++n) {
+      for (int d = 0; d < scale_dim_; ++d) {
+        const Dtype factor = scale_data[d];
+        caffe_cpu_scale(inner_dim_, factor, top_diff, bottom_diff);
+        bottom_diff += inner_dim_;
+        top_diff += inner_dim_;
+      }
+    }
+  }
+}
+
+#ifdef CPU_ONLY
+STUB_GPU(ScaleLayer);
+#endif
+
+INSTANTIATE_CLASS(ScaleLayer);
+REGISTER_LAYER_CLASS(Scale);
+
+}  // namespace caffe
diff --git a/src/caffe/layers/scale_layer.cu b/src/caffe/layers/scale_layer.cu
new file mode 100644
index 00000000000..09e480cf6cf
--- /dev/null
+++ b/src/caffe/layers/scale_layer.cu
@@ -0,0 +1,135 @@
+#include <cfloat>
+#include <vector>
+
+#include "caffe/common_layers.hpp"
+#include "caffe/util/math_functions.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+__global__ void ScaleForward(const int n, const Dtype* in,
+    const Dtype* scale, const int scale_dim, const int inner_dim,
+    Dtype* out) {
+  CUDA_KERNEL_LOOP(index, n) {
+    const int scale_index = (index / inner_dim) % scale_dim;
+    out[index] = in[index] * scale[scale_index];
+  }
+}
+
+template <typename Dtype>
+__global__ void ScaleBiasForward(const int n, const Dtype* in,
+    const Dtype* scale, const Dtype* bias,
+    const int scale_dim, const int inner_dim, Dtype* out) {
+  CUDA_KERNEL_LOOP(index, n) {
+    const int scale_index = (index / inner_dim) % scale_dim;
+    out[index] = in[index] * scale[scale_index] + bias[scale_index];
+  }
+}
+
+template <typename Dtype>
+void ScaleLayer<Dtype>::Forward_gpu(
+    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
+  const int count = top[0]->count();
+  const Dtype* bottom_data = bottom[0]->gpu_data();
+  if (bottom[0] == top[0]) {
+    // in-place computation; need to store bottom data before overwriting it.
+    // Note that this is only necessary for Backward; we could skip this if not
+    // doing Backward, but Caffe currently provides no way of knowing whether
+    // we'll need to do Backward at the time of the Forward call.
+    caffe_copy(bottom[0]->count(), bottom[0]->gpu_data(),
+               temp_.mutable_gpu_data());
+  }
+  const Dtype* scale_data =
+      ((bottom.size() > 1) ? bottom[1] : this->blobs_[0].get())->gpu_data();
+  Dtype* top_data = top[0]->mutable_gpu_data();
+  if (bias_layer_) {
+    const Dtype* bias_data = this->blobs_[bias_param_id_]->gpu_data();
+    ScaleBiasForward<Dtype>  // NOLINT_NEXT_LINE(whitespace/operators)
+        <<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
+        count, bottom_data, scale_data, bias_data, scale_dim_, inner_dim_,
+        top_data);
+  } else {
+    ScaleForward<Dtype>  // NOLINT_NEXT_LINE(whitespace/operators)
+        <<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
+        count, bottom_data, scale_data, scale_dim_, inner_dim_, top_data);
+  }
+}
+
+template <typename Dtype>
+void ScaleLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
+    const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+  if (bias_layer_ &&
+      this->param_propagate_down_[this->param_propagate_down_.size() - 1]) {
+    bias_layer_->Backward(top, bias_propagate_down_, bias_bottom_vec_);
+  }
+  const bool scale_param = (bottom.size() == 1);
+  Blob<Dtype>* scale = scale_param ? this->blobs_[0].get() : bottom[1];
+  if ((!scale_param && propagate_down[1]) ||
+      (scale_param && this->param_propagate_down_[0])) {
+    const Dtype* top_diff = top[0]->gpu_diff();
+    const bool in_place = (bottom[0] == top[0]);
+    const Dtype* bottom_data = (in_place ? &temp_ : bottom[0])->gpu_data();
+    // Hack: store big eltwise product in bottom[0] diff, except in the special
+    // case where this layer itself does the eltwise product, in which case we
+    // can store it directly in the scale diff, and we're done.
+    // If we're computing in-place (and not doing eltwise computation), this
+    // hack doesn't work and we store the product in temp_.
+    const bool is_eltwise = (bottom[0]->count() == scale->count());
+    Dtype* product = (is_eltwise ? scale->mutable_gpu_diff() :
+        (in_place ? temp_.mutable_gpu_data() : bottom[0]->mutable_gpu_diff()));
+    caffe_gpu_mul(top[0]->count(), top_diff, bottom_data, product);
+    if (!is_eltwise) {
+      Dtype* sum_result = NULL;
+      if (inner_dim_ == 1) {
+        sum_result = product;
+      } else if (sum_result_.count() == 1) {
+        const Dtype* sum_mult = sum_multiplier_.gpu_data();
+        Dtype* scale_diff = scale->mutable_cpu_diff();
+        if (scale_param) {
+          Dtype result;
+          caffe_gpu_dot(inner_dim_, product, sum_mult, &result);
+          *scale_diff += result;
+        } else {
+          caffe_gpu_dot(inner_dim_, product, sum_mult, scale_diff);
+        }
+      } else {
+        const Dtype* sum_mult = sum_multiplier_.gpu_data();
+        sum_result = (outer_dim_ == 1) ?
+            scale->mutable_gpu_diff() : sum_result_.mutable_gpu_data();
+        caffe_gpu_gemv(CblasNoTrans, sum_result_.count(), inner_dim_,
+                       Dtype(1), product, sum_mult, Dtype(0), sum_result);
+      }
+      if (outer_dim_ != 1) {
+        const Dtype* sum_mult = sum_multiplier_.gpu_data();
+        if (scale_dim_ == 1) {
+          Dtype* scale_diff = scale->mutable_cpu_diff();
+          if (scale_param) {
+            Dtype result;
+            caffe_gpu_dot(outer_dim_, sum_mult, sum_result, &result);
+            *scale_diff += result;
+          } else {
+            caffe_gpu_dot(outer_dim_, sum_mult, sum_result, scale_diff);
+          }
+        } else {
+          Dtype* scale_diff = scale->mutable_gpu_diff();
+          caffe_gpu_gemv(CblasTrans, outer_dim_, scale_dim_,
+                         Dtype(1), sum_result, sum_mult, Dtype(scale_param),
+                         scale_diff);
+        }
+      }
+    }
+  }
+  if (propagate_down[0]) {
+    const int count = top[0]->count();
+    const Dtype* top_diff = top[0]->gpu_diff();
+    const Dtype* scale_data = scale->gpu_data();
+    Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
+    ScaleForward<Dtype>  // NOLINT_NEXT_LINE(whitespace/operators)
+        <<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
+        count, top_diff, scale_data, scale_dim_, inner_dim_, bottom_diff);
+  }
+}
+
+INSTANTIATE_LAYER_GPU_FUNCS(ScaleLayer);
+
+}  // namespace caffe
diff --git a/src/caffe/layers/scatter_layer.cpp b/src/caffe/layers/scatter_layer.cpp
new file mode 100644
index 00000000000..a848c4e32d6
--- /dev/null
+++ b/src/caffe/layers/scatter_layer.cpp
@@ -0,0 +1,79 @@
+#include <vector>
+
+#include "caffe/layer.hpp"
+#include "caffe/vision_layers.hpp"
+#include "caffe/util/math_functions.hpp"
+#include "caffe/util/mpi_functions.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void ScatterLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+    const vector<Blob<Dtype>*>& top) {
+  CHECK_EQ(bottom.size(), top.size())
+      << "The number of bottom and top blobs must be the same";
+}
+
+template <typename Dtype>
+void ScatterLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
+    const vector<Blob<Dtype>*>& top) {
+#ifdef USE_MPI
+  for (int i = 0; i < bottom.size(); ++i) {
+    vector<int> shape = bottom[i]->shape();
+    shape[0] /= (Caffe::parallel_mode()==Caffe::MPI)?Caffe::MPI_all_rank():1;
+    top[i]->Reshape(shape);
+
+    if (Caffe::parallel_mode()!=Caffe::MPI){
+      //if not in MPI mode, simply share data
+      top[i]->ShareData(*bottom[i]);
+      top[i]->ShareDiff(*bottom[i]);
+    }
+  }
+#else
+  for (int i = 0; i < bottom.size(); ++i){
+    top[i]->ReshapeLike(*bottom[i]);
+    top[i]->ShareData(*bottom[i]);
+    top[i]->ShareDiff(*bottom[i]);
+  }
+#endif
+}
+
+template <typename Dtype>
+void ScatterLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+    const vector<Blob<Dtype>*>& top) {
+#ifdef USE_MPI
+
+  for (int i = 0; i < bottom.size(); ++i) {
+    //Gather the bottom to the top
+    caffe_iscatter((Dtype*)bottom[i]->cpu_data(),top[i]->mutable_cpu_data(), top[i]->count());
+    mpi_force_synchronize();
+  }
+#else
+#endif
+}
+
+template <typename Dtype>
+void ScatterLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
+    const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+#ifdef USE_MPI
+
+  for (int i = 0; i < bottom.size(); ++i) {
+    caffe_iallgather((Dtype*)top[i]->cpu_diff(),bottom[i]->mutable_cpu_diff(), top[i]->count());
+    mpi_force_synchronize();
+    //compensate the scale on diff IMPORTANT
+    caffe_scal(bottom[i]->count(), Dtype(1)/Dtype(Caffe::MPI_all_rank()),
+               bottom[i]->mutable_cpu_diff());
+  }
+#else
+#endif
+}
+
+#ifdef CPU_ONLY
+STUB_GPU(ScatterLayer);
+#endif
+
+INSTANTIATE_CLASS(ScatterLayer);
+REGISTER_LAYER_CLASS(Scatter);
+
+} // namespace caffe
+
diff --git a/src/caffe/layers/scatter_layer.cu b/src/caffe/layers/scatter_layer.cu
new file mode 100644
index 00000000000..1fcfe5c1062
--- /dev/null
+++ b/src/caffe/layers/scatter_layer.cu
@@ -0,0 +1,49 @@
+#include <vector>
+
+#include "caffe/common_layers.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/util/math_functions.hpp"
+#include "caffe/util/mpi_functions.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void ScatterLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+const vector<Blob<Dtype>*>& top) {
+
+  #ifdef USE_MPI
+  if (Caffe::parallel_mode() == Caffe::MPI){
+    CUDA_CHECK(cudaDeviceSynchronize());
+    for (int i = 0; i < bottom.size(); ++i) {
+      //Gather the bottom to the top
+      caffe_iscatter((Dtype*)bottom[i]->gpu_data(),top[i]->mutable_gpu_data(), top[i]->count());
+      mpi_force_synchronize();
+    }
+  }
+  #endif
+  //Do nothing if not in MPI mode
+}
+
+template <typename Dtype>
+void ScatterLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
+const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+  #ifdef USE_MPI
+    if (Caffe::parallel_mode() == Caffe::MPI){
+      CUDA_CHECK(cudaDeviceSynchronize());
+      for (int i = 0; i < bottom.size(); ++i) {
+          //Scatter the top diff to buttom
+          if (propagate_down[i]) {
+          caffe_iallgather((Dtype*)top[i]->gpu_diff(),bottom[i]->mutable_gpu_diff(), top[i]->count());
+          mpi_force_synchronize();
+          //compensate the scale on diff IMPORTANT
+          caffe_gpu_scal(bottom[i]->count(), Dtype(1)/Dtype(Caffe::MPI_all_rank()),
+                         bottom[i]->mutable_gpu_diff());
+        }
+      }
+    }
+  #endif
+}
+
+INSTANTIATE_LAYER_GPU_FUNCS(ScatterLayer);
+
+}  // namespace caffe
diff --git a/src/caffe/layers/sequence_data_layer.cpp b/src/caffe/layers/sequence_data_layer.cpp
new file mode 100644
index 00000000000..5f7e9ece84e
--- /dev/null
+++ b/src/caffe/layers/sequence_data_layer.cpp
@@ -0,0 +1,240 @@
+#include <fstream>
+#include <iostream>
+#include <string>
+#include <utility>
+#include <vector>
+
+#include "caffe/data_layers.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+#include "caffe/util/io.hpp"
+#include "caffe/util/math_functions.hpp"
+#include "caffe/util/rng.hpp"
+
+#ifdef USE_MPI
+#include "mpi.h"
+#include <boost/filesystem.hpp>
+using namespace boost::filesystem;
+#endif
+
+namespace caffe{
+template <typename Dtype>
+SequenceDataLayer<Dtype>:: ~SequenceDataLayer<Dtype>(){
+	this->JoinPrefetchThread();
+}
+
+template <typename Dtype>
+void SequenceDataLayer<Dtype>:: DataLayerSetUp(const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top){
+	const int new_height  = this->layer_param_.sequence_data_param().new_height();
+	const int new_width  = this->layer_param_.sequence_data_param().new_width();
+	const int num_frames  = this->layer_param_.sequence_data_param().num_frames();
+	const int num_segments = this->layer_param_.sequence_data_param().num_segments();
+        const int num_shots = this->layer_param_.sequence_data_param().num_shots();
+	const string& video_source = this->layer_param_.sequence_data_param().video_source();
+        const string& shot_source = this->layer_param_.sequence_data_param().shot_source();
+
+	// loading video files (path, duration, label)
+        LOG(INFO) << "Opening video source file: " << video_source;
+	std:: ifstream infile(video_source.c_str());
+	string filename;
+	int label;
+	int length;
+	while (infile >> filename >> length >> label) {
+		lines_.push_back(std::make_pair(filename,label));
+		lines_duration_.push_back(length);
+	}
+
+        // loading shot files (filename, duration)
+        LOG(INFO) << "Opening shot source file: " << shot_source;
+        std:: ifstream infile_shot(shot_source.c_str());
+        while (infile_shot >> filename >> length) {
+		std:: ifstream shot_file(filename.c_str());
+                int start = 0;
+                int end = 0;
+                int tmp;
+                vector<std::pair<int, int> > tmp_vec;
+                while (shot_file >> tmp) {
+                	if (start==0) {
+				start = tmp;
+				continue;			
+			}
+                        if (this->layer_param_.sequence_data_param().modality() == SequenceDataParameter_Modality_RGB)
+				end = tmp - 1;
+                        if (this->layer_param_.sequence_data_param().modality() == SequenceDataParameter_Modality_FLOW)
+				end = tmp - 2;
+			if (end-start+1 > num_frames)
+				tmp_vec.push_back(std::make_pair(start, end));
+			start = tmp;
+                }
+               	if (this->layer_param_.sequence_data_param().modality() == SequenceDataParameter_Modality_RGB)
+			end = length;
+		if (this->layer_param_.sequence_data_param().modality() == SequenceDataParameter_Modality_FLOW)
+			end = length - 1;
+		if (end-start+1 > num_frames)
+			tmp_vec.push_back(std::make_pair(start, end));
+		lines_shot_.push_back(tmp_vec);
+	}
+ 
+	// shuffling the file_list, duration_list, shot_list	
+	if (this->layer_param_.sequence_data_param().shuffle()){
+		const unsigned int prefectch_rng_seed = caffe_rng_rand();
+		prefetch_rng_1_.reset(new Caffe::RNG(prefectch_rng_seed));
+		prefetch_rng_2_.reset(new Caffe::RNG(prefectch_rng_seed));
+		prefetch_rng_3_.reset(new Caffe::RNG(prefectch_rng_seed));
+		ShuffleSequences();
+	}
+        
+	LOG(INFO) << "A total of " << lines_.size() << " videos.";
+	LOG(INFO) << "A total of " << lines_shot_.size() << " shots.";
+        for (int i = 0; i < lines_shot_.size(); ++i)
+		LOG(INFO) << lines_[i].first << " " << lines_shot_[i].size();
+
+	//check name patter
+	if (this->layer_param_.sequence_data_param().name_pattern() == ""){
+		if (this->layer_param_.sequence_data_param().modality() == SequenceDataParameter_Modality_RGB){
+			name_pattern_ = "image_%04d.jpg";
+		}else if (this->layer_param_.sequence_data_param().modality() == SequenceDataParameter_Modality_FLOW){
+			name_pattern_ = "flow_%c_%04d.jpg";
+		}
+	}else{
+		name_pattern_ = this->layer_param_.sequence_data_param().name_pattern();
+	}
+
+        const unsigned int frame_prefectch_rng_seed = caffe_rng_rand();
+	frame_prefetch_rng_.reset(new Caffe::RNG(frame_prefectch_rng_seed));
+
+	Datum datum;
+        lines_id_ = 0;
+        vector<std::pair<int, int> > cur_shot_list = lines_shot_[lines_id_];
+        vector<int> offsets;
+
+        for (int i = 0; i < num_shots; ++i) {
+		int shot_idx = i;
+		if (i >= cur_shot_list.size()) {
+			caffe::rng_t* frame_rng = static_cast<caffe::rng_t*>(frame_prefetch_rng_->generator());
+			shot_idx = (*frame_rng)() % (cur_shot_list.size());		
+		}
+		int start_idx = cur_shot_list[shot_idx].first;
+		int end_idx = cur_shot_list[shot_idx].second;
+		int average_duration = (int) (end_idx-start_idx+1)/num_segments;
+		for (int j = 0; j < num_segments; ++j) {
+			if (average_duration < num_frames) {
+				offsets.push_back(start_idx-1);
+				continue;
+			}
+			caffe::rng_t* frame_rng1 = static_cast<caffe::rng_t*>(frame_prefetch_rng_->generator());
+			int offset = (*frame_rng1)() % (average_duration - num_frames + 1);
+			offsets.push_back(start_idx-1+offset+j*average_duration);
+		}
+	}
+	if (this->layer_param_.sequence_data_param().modality() == SequenceDataParameter_Modality_FLOW)
+		CHECK(ReadSegmentFlowToDatum(lines_[lines_id_].first, lines_[lines_id_].second,
+									 offsets, new_height, new_width, num_frames, &datum, name_pattern_.c_str()));
+	else
+		CHECK(ReadSegmentRGBToDatum(lines_[lines_id_].first, lines_[lines_id_].second,
+									offsets, new_height, new_width, num_frames, &datum, true, name_pattern_.c_str()));
+	const int crop_size = this->layer_param_.transform_param().crop_size();
+	const int batch_size = this->layer_param_.sequence_data_param().batch_size();
+	if (crop_size > 0){
+		top[0]->Reshape(batch_size, datum.channels(), crop_size, crop_size);
+		this->prefetch_data_.Reshape(batch_size, datum.channels(), crop_size, crop_size);
+	} else {
+		top[0]->Reshape(batch_size, datum.channels(), datum.height(), datum.width());
+		this->prefetch_data_.Reshape(batch_size, datum.channels(), datum.height(), datum.width());
+	}
+	LOG(INFO) << "output data size: " << top[0]->num() << "," << top[0]->channels() << "," << top[0]->height() << "," << top[0]->width();
+
+	top[1]->Reshape(batch_size, 1, 1, 1);
+	this->prefetch_label_.Reshape(batch_size, 1, 1, 1);
+
+	vector<int> top_shape = this->data_transformer_->InferBlobShape(datum);
+	this->transformed_data_.Reshape(top_shape);
+}
+
+template <typename Dtype>
+void SequenceDataLayer<Dtype>::ShuffleSequences(){
+	caffe::rng_t* prefetch_rng1 = static_cast<caffe::rng_t*>(prefetch_rng_1_->generator());
+	caffe::rng_t* prefetch_rng2 = static_cast<caffe::rng_t*>(prefetch_rng_2_->generator());
+        caffe::rng_t* prefetch_rng3 = static_cast<caffe::rng_t*>(prefetch_rng_3_->generator());
+	shuffle(lines_.begin(), lines_.end(), prefetch_rng1);
+	shuffle(lines_duration_.begin(), lines_duration_.end(), prefetch_rng2);
+	shuffle(lines_shot_.begin(), lines_shot_.end(), prefetch_rng3);
+}
+
+template <typename Dtype>
+void SequenceDataLayer<Dtype>::InternalThreadEntry(){
+
+	Datum datum;
+	CHECK(this->prefetch_data_.count());
+	Dtype* top_data = this->prefetch_data_.mutable_cpu_data();
+	Dtype* top_label = this->prefetch_label_.mutable_cpu_data();
+	SequenceDataParameter sequence_data_param = this->layer_param_.sequence_data_param();
+	const int batch_size = sequence_data_param.batch_size();
+	const int new_height = sequence_data_param.new_height();
+	const int new_width = sequence_data_param.new_width();
+	const int num_frames = sequence_data_param.num_frames();
+	const int num_segments = sequence_data_param.num_segments();
+	const int num_shots = sequence_data_param.num_shots();
+	const int lines_size = lines_.size();
+
+	for (int item_id = 0; item_id < batch_size; ++item_id){
+		CHECK_GT(lines_size, lines_id_);
+		
+		vector<std::pair<int, int> > cur_shot_list = lines_shot_[lines_id_];
+		caffe::rng_t* frame_rng3 = static_cast<caffe::rng_t*>(frame_prefetch_rng_->generator());
+		shuffle(cur_shot_list.begin(), cur_shot_list.end(), frame_rng3);
+        	vector<int> offsets;
+
+        	for (int i = 0; i < num_shots; ++i) {
+			int shot_idx = i;
+			if (i >= cur_shot_list.size()) {
+				caffe::rng_t* frame_rng1 = static_cast<caffe::rng_t*>(frame_prefetch_rng_->generator());
+				shot_idx = (*frame_rng1)() % (cur_shot_list.size());		
+			}
+			int start_idx = cur_shot_list[shot_idx].first;
+			int end_idx = cur_shot_list[shot_idx].second;
+			int average_duration = (int) (end_idx-start_idx+1)/num_segments;
+			for (int j = 0; j < num_segments; ++j) {
+				if (average_duration < num_frames) {
+					offsets.push_back(start_idx-1);
+					continue;
+				}
+				caffe::rng_t* frame_rng2 = static_cast<caffe::rng_t*>(frame_prefetch_rng_->generator());
+				int offset = (*frame_rng2)() % (average_duration - num_frames + 1);
+				offsets.push_back(start_idx-1+offset+j*average_duration);
+			}
+		}
+
+		if (this->layer_param_.sequence_data_param().modality() == SequenceDataParameter_Modality_FLOW) {
+			if(!ReadSegmentFlowToDatum(lines_[lines_id_].first, lines_[lines_id_].second,
+									   offsets, new_height, new_width, num_frames, &datum, name_pattern_.c_str())) {
+				continue;
+			}
+		} else { 
+			if(!ReadSegmentRGBToDatum(lines_[lines_id_].first, lines_[lines_id_].second,
+									  offsets, new_height, new_width, num_frames, &datum, true, name_pattern_.c_str())) {
+				continue;
+			}
+		}
+
+		int offset1 = this->prefetch_data_.offset(item_id);
+    	        this->transformed_data_.set_cpu_data(top_data + offset1);
+		this->data_transformer_->Transform(datum, &(this->transformed_data_));
+		top_label[item_id] = lines_[lines_id_].second;
+		//LOG()
+
+		//next iteration
+		lines_id_++;
+		if (lines_id_ >= lines_size) {
+			DLOG(INFO) << "Restarting data prefetching from start.";
+			lines_id_ = 0;
+			if(this->layer_param_.sequence_data_param().shuffle()){
+				ShuffleSequences();
+			}
+		}
+	}
+}
+
+INSTANTIATE_CLASS(SequenceDataLayer);
+REGISTER_LAYER_CLASS(SequenceData);
+}
diff --git a/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp b/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp
index 6a48099ae8b..cc236fe1e8e 100644
--- a/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp
+++ b/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp
@@ -10,30 +10,30 @@ namespace caffe {
 
 template <typename Dtype>
 void SigmoidCrossEntropyLossLayer<Dtype>::LayerSetUp(
-    const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
   LossLayer<Dtype>::LayerSetUp(bottom, top);
   sigmoid_bottom_vec_.clear();
   sigmoid_bottom_vec_.push_back(bottom[0]);
   sigmoid_top_vec_.clear();
   sigmoid_top_vec_.push_back(sigmoid_output_.get());
-  sigmoid_layer_->SetUp(sigmoid_bottom_vec_, &sigmoid_top_vec_);
+  sigmoid_layer_->SetUp(sigmoid_bottom_vec_, sigmoid_top_vec_);
 }
 
 template <typename Dtype>
 void SigmoidCrossEntropyLossLayer<Dtype>::Reshape(
-    const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
   LossLayer<Dtype>::Reshape(bottom, top);
   CHECK_EQ(bottom[0]->count(), bottom[1]->count()) <<
       "SIGMOID_CROSS_ENTROPY_LOSS layer inputs must have the same count.";
-  sigmoid_layer_->Reshape(sigmoid_bottom_vec_, &sigmoid_top_vec_);
+  sigmoid_layer_->Reshape(sigmoid_bottom_vec_, sigmoid_top_vec_);
 }
 
 template <typename Dtype>
 void SigmoidCrossEntropyLossLayer<Dtype>::Forward_cpu(
-    const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
   // The forward pass computes the sigmoid outputs.
   sigmoid_bottom_vec_[0] = bottom[0];
-  sigmoid_layer_->Forward(sigmoid_bottom_vec_, &sigmoid_top_vec_);
+  sigmoid_layer_->Forward(sigmoid_bottom_vec_, sigmoid_top_vec_);
   // Compute the loss (negative log likelihood)
   const int count = bottom[0]->count();
   const int num = bottom[0]->num();
@@ -45,24 +45,24 @@ void SigmoidCrossEntropyLossLayer<Dtype>::Forward_cpu(
     loss -= input_data[i] * (target[i] - (input_data[i] >= 0)) -
         log(1 + exp(input_data[i] - 2 * input_data[i] * (input_data[i] >= 0)));
   }
-  (*top)[0]->mutable_cpu_data()[0] = loss / num;
+  top[0]->mutable_cpu_data()[0] = loss / num;
 }
 
 template <typename Dtype>
 void SigmoidCrossEntropyLossLayer<Dtype>::Backward_cpu(
     const vector<Blob<Dtype>*>& top, const vector<bool>& propagate_down,
-    vector<Blob<Dtype>*>* bottom) {
+    const vector<Blob<Dtype>*>& bottom) {
   if (propagate_down[1]) {
-    LOG(FATAL) << this->type_name()
+    LOG(FATAL) << this->type()
                << " Layer cannot backpropagate to label inputs.";
   }
   if (propagate_down[0]) {
     // First, compute the diff
-    const int count = (*bottom)[0]->count();
-    const int num = (*bottom)[0]->num();
+    const int count = bottom[0]->count();
+    const int num = bottom[0]->num();
     const Dtype* sigmoid_output_data = sigmoid_output_->cpu_data();
-    const Dtype* target = (*bottom)[1]->cpu_data();
-    Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
+    const Dtype* target = bottom[1]->cpu_data();
+    Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
     caffe_sub(count, sigmoid_output_data, target, bottom_diff);
     // Scale down gradient
     const Dtype loss_weight = top[0]->cpu_diff()[0];
@@ -71,10 +71,10 @@ void SigmoidCrossEntropyLossLayer<Dtype>::Backward_cpu(
 }
 
 #ifdef CPU_ONLY
-STUB_GPU(SigmoidCrossEntropyLossLayer);
+STUB_GPU_BACKWARD(SigmoidCrossEntropyLossLayer, Backward);
 #endif
 
 INSTANTIATE_CLASS(SigmoidCrossEntropyLossLayer);
-
+REGISTER_LAYER_CLASS(SigmoidCrossEntropyLoss);
 
 }  // namespace caffe
diff --git a/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu b/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu
index 8d0fdc6fac4..547fa80c72f 100644
--- a/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu
+++ b/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu
@@ -8,41 +8,21 @@
 
 namespace caffe {
 
-template <typename Dtype>
-void SigmoidCrossEntropyLossLayer<Dtype>::Forward_gpu(
-    const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
-  // The forward pass computes the sigmoid outputs.
-  sigmoid_bottom_vec_[0] = bottom[0];
-  sigmoid_layer_->Forward(sigmoid_bottom_vec_, &sigmoid_top_vec_);
-  // Compute the loss (negative log likelihood)
-  const int count = bottom[0]->count();
-  const int num = bottom[0]->num();
-  // Stable version of loss computation from input data
-  const Dtype* input_data = bottom[0]->cpu_data();
-  const Dtype* target = bottom[1]->cpu_data();
-  Dtype loss = 0;
-  for (int i = 0; i < count; ++i) {
-    loss -= input_data[i] * (target[i] - (input_data[i] >= 0)) -
-        log(1 + exp(input_data[i] - 2 * input_data[i] * (input_data[i] >= 0)));
-  }
-  (*top)[0]->mutable_cpu_data()[0] = loss / num;
-}
-
 template <typename Dtype>
 void SigmoidCrossEntropyLossLayer<Dtype>::Backward_gpu(
     const vector<Blob<Dtype>*>& top, const vector<bool>& propagate_down,
-    vector<Blob<Dtype>*>* bottom) {
+    const vector<Blob<Dtype>*>& bottom) {
   if (propagate_down[1]) {
-    LOG(FATAL) << this->type_name()
+    LOG(FATAL) << this->type()
                << " Layer cannot backpropagate to label inputs.";
   }
   if (propagate_down[0]) {
     // First, compute the diff
-    const int count = (*bottom)[0]->count();
-    const int num = (*bottom)[0]->num();
+    const int count = bottom[0]->count();
+    const int num = bottom[0]->num();
     const Dtype* sigmoid_output_data = sigmoid_output_->gpu_data();
-    const Dtype* target = (*bottom)[1]->gpu_data();
-    Dtype* bottom_diff = (*bottom)[0]->mutable_gpu_diff();
+    const Dtype* target = bottom[1]->gpu_data();
+    Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
     caffe_copy(count, sigmoid_output_data, bottom_diff);
     caffe_gpu_axpy(count, Dtype(-1), target, bottom_diff);
     // Scale down gradient
@@ -51,7 +31,7 @@ void SigmoidCrossEntropyLossLayer<Dtype>::Backward_gpu(
   }
 }
 
-INSTANTIATE_CLASS(SigmoidCrossEntropyLossLayer);
+INSTANTIATE_LAYER_GPU_BACKWARD(SigmoidCrossEntropyLossLayer);
 
 
 }  // namespace caffe
diff --git a/src/caffe/layers/sigmoid_layer.cpp b/src/caffe/layers/sigmoid_layer.cpp
index d7bba7fbfc3..48c384905bf 100644
--- a/src/caffe/layers/sigmoid_layer.cpp
+++ b/src/caffe/layers/sigmoid_layer.cpp
@@ -14,9 +14,9 @@ inline Dtype sigmoid(Dtype x) {
 
 template <typename Dtype>
 void SigmoidLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-    vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& top) {
   const Dtype* bottom_data = bottom[0]->cpu_data();
-  Dtype* top_data = (*top)[0]->mutable_cpu_data();
+  Dtype* top_data = top[0]->mutable_cpu_data();
   const int count = bottom[0]->count();
   for (int i = 0; i < count; ++i) {
     top_data[i] = sigmoid(bottom_data[i]);
@@ -26,12 +26,12 @@ void SigmoidLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
 template <typename Dtype>
 void SigmoidLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
     const vector<bool>& propagate_down,
-    vector<Blob<Dtype>*>* bottom) {
+    const vector<Blob<Dtype>*>& bottom) {
   if (propagate_down[0]) {
     const Dtype* top_data = top[0]->cpu_data();
     const Dtype* top_diff = top[0]->cpu_diff();
-    Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
-    const int count = (*bottom)[0]->count();
+    Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
+    const int count = bottom[0]->count();
     for (int i = 0; i < count; ++i) {
       const Dtype sigmoid_x = top_data[i];
       bottom_diff[i] = top_diff[i] * sigmoid_x * (1. - sigmoid_x);
diff --git a/src/caffe/layers/sigmoid_layer.cu b/src/caffe/layers/sigmoid_layer.cu
index e1ebb1f6c41..e1af0657ec1 100644
--- a/src/caffe/layers/sigmoid_layer.cu
+++ b/src/caffe/layers/sigmoid_layer.cu
@@ -16,9 +16,9 @@ __global__ void SigmoidForward(const int n, const Dtype* in, Dtype* out) {
 
 template <typename Dtype>
 void SigmoidLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-    vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& top) {
   const Dtype* bottom_data = bottom[0]->gpu_data();
-  Dtype* top_data = (*top)[0]->mutable_gpu_data();
+  Dtype* top_data = top[0]->mutable_gpu_data();
   const int count = bottom[0]->count();
   // NOLINT_NEXT_LINE(whitespace/operators)
   SigmoidForward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
@@ -43,12 +43,12 @@ __global__ void SigmoidBackward(const int n, const Dtype* in_diff,
 template <typename Dtype>
 void SigmoidLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
     const vector<bool>& propagate_down,
-    vector<Blob<Dtype>*>* bottom) {
+    const vector<Blob<Dtype>*>& bottom) {
   if (propagate_down[0]) {
     const Dtype* top_data = top[0]->gpu_data();
     const Dtype* top_diff = top[0]->gpu_diff();
-    Dtype* bottom_diff = (*bottom)[0]->mutable_gpu_diff();
-    const int count = (*bottom)[0]->count();
+    Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
+    const int count = bottom[0]->count();
     // NOLINT_NEXT_LINE(whitespace/operators)
     SigmoidBackward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
         count, top_diff, top_data, bottom_diff);
@@ -56,7 +56,7 @@ void SigmoidLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
   }
 }
 
-INSTANTIATE_CLASS(SigmoidLayer);
+INSTANTIATE_LAYER_GPU_FUNCS(SigmoidLayer);
 
 
 }  // namespace caffe
diff --git a/src/caffe/layers/silence_layer.cpp b/src/caffe/layers/silence_layer.cpp
index 75dbbf31f0d..4abf9eff4a2 100644
--- a/src/caffe/layers/silence_layer.cpp
+++ b/src/caffe/layers/silence_layer.cpp
@@ -8,11 +8,11 @@ namespace caffe {
 
 template <typename Dtype>
 void SilenceLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
-  for (int i = 0; i < bottom->size(); ++i) {
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+  for (int i = 0; i < bottom.size(); ++i) {
     if (propagate_down[i]) {
-      caffe_set((*bottom)[i]->count(), Dtype(0),
-                (*bottom)[i]->mutable_cpu_data());
+      caffe_set(bottom[i]->count(), Dtype(0),
+                bottom[i]->mutable_cpu_data());
     }
   }
 }
@@ -22,5 +22,6 @@ STUB_GPU(SilenceLayer);
 #endif
 
 INSTANTIATE_CLASS(SilenceLayer);
+REGISTER_LAYER_CLASS(Silence);
 
 }  // namespace caffe
diff --git a/src/caffe/layers/silence_layer.cu b/src/caffe/layers/silence_layer.cu
index 735abe61eaf..8d044ee7307 100644
--- a/src/caffe/layers/silence_layer.cu
+++ b/src/caffe/layers/silence_layer.cu
@@ -8,21 +8,21 @@ namespace caffe {
 
 template <typename Dtype>
 void SilenceLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
+      const vector<Blob<Dtype>*>& top) {
   // Do nothing.
 }
 
 template <typename Dtype>
 void SilenceLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
-  for (int i = 0; i < bottom->size(); ++i) {
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+  for (int i = 0; i < bottom.size(); ++i) {
     if (propagate_down[i]) {
-      caffe_gpu_set((*bottom)[i]->count(), Dtype(0),
-                    (*bottom)[i]->mutable_gpu_data());
+      caffe_gpu_set(bottom[i]->count(), Dtype(0),
+                    bottom[i]->mutable_gpu_data());
     }
   }
 }
 
-INSTANTIATE_CLASS(SilenceLayer);
+INSTANTIATE_LAYER_GPU_FUNCS(SilenceLayer);
 
 }  // namespace caffe
diff --git a/src/caffe/layers/slice_layer.cpp b/src/caffe/layers/slice_layer.cpp
index ed679a9169e..e4418c9cf9c 100644
--- a/src/caffe/layers/slice_layer.cpp
+++ b/src/caffe/layers/slice_layer.cpp
@@ -9,11 +9,10 @@ namespace caffe {
 
 template <typename Dtype>
 void SliceLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
+      const vector<Blob<Dtype>*>& top) {
   const SliceParameter& slice_param = this->layer_param_.slice_param();
-  slice_dim_ = slice_param.slice_dim();
-  CHECK_GE(slice_dim_, 0);
-  CHECK_LE(slice_dim_, 1) << "Can only slice num and channels";
+  CHECK(!(slice_param.has_axis() && slice_param.has_slice_dim()))
+      << "Either axis or slice_dim should be specified; not both.";
   slice_point_.clear();
   std::copy(slice_param.slice_point().begin(),
       slice_param.slice_point().end(),
@@ -22,19 +21,28 @@ void SliceLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 void SliceLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
-  count_ = 0;
-  num_ = bottom[0]->num();
-  channels_ = bottom[0]->channels();
-  height_ = bottom[0]->height();
-  width_ = bottom[0]->width();
+      const vector<Blob<Dtype>*>& top) {
+  const int num_axes = bottom[0]->num_axes();
+  const SliceParameter& slice_param = this->layer_param_.slice_param();
+  if (slice_param.has_slice_dim()) {
+    slice_axis_ = static_cast<int>(slice_param.slice_dim());
+    // Don't allow negative indexing for slice_dim, a uint32 -- almost
+    // certainly unintended.
+    CHECK_GE(slice_axis_, 0) << "casting slice_dim from uint32 to int32 "
+        << "produced negative result; slice_dim must satisfy "
+        << "0 <= slice_dim < " << kMaxBlobAxes;
+    CHECK_LT(slice_axis_, num_axes) << "slice_dim out of range.";
+  } else {
+    slice_axis_ = bottom[0]->CanonicalAxisIndex(slice_param.axis());
+  }
+  vector<int> top_shape = bottom[0]->shape();
+  const int bottom_slice_axis = bottom[0]->shape(slice_axis_);
+  num_slices_ = bottom[0]->count(0, slice_axis_);
+  slice_size_ = bottom[0]->count(slice_axis_ + 1);
+  int count = 0;
   if (slice_point_.size() != 0) {
-    CHECK_EQ(slice_point_.size(), top->size() - 1);
-    if (slice_dim_ == 0) {
-      CHECK_LE(top->size(), num_);
-    } else {
-      CHECK_LE(top->size(), channels_);
-    }
+    CHECK_EQ(slice_point_.size(), top.size() - 1);
+    CHECK_LE(top.size(), bottom_slice_axis);
     int prev = 0;
     vector<int> slices;
     for (int i = 0; i < slice_point_.size(); ++i) {
@@ -42,94 +50,64 @@ void SliceLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
       slices.push_back(slice_point_[i] - prev);
       prev = slice_point_[i];
     }
-    if (slice_dim_ == 0) {
-      slices.push_back(num_ - prev);
-      for (int i = 0; i < top->size(); ++i) {
-        (*top)[i]->Reshape(slices[i], channels_, height_, width_);
-         count_ += (*top)[i]->count();
-      }
-    } else {
-      slices.push_back(channels_ - prev);
-      for (int i = 0; i < top->size(); ++i) {
-        (*top)[i]->Reshape(num_, slices[i], height_, width_);
-         count_ += (*top)[i]->count();
-      }
+    slices.push_back(bottom_slice_axis - prev);
+    for (int i = 0; i < top.size(); ++i) {
+      top_shape[slice_axis_] = slices[i];
+      top[i]->Reshape(top_shape);
+      count += top[i]->count();
     }
   } else {
-    if (slice_dim_ == 0) {
-      CHECK_EQ(num_ % top->size(), 0)
-          << "Number of top blobs (" << top->size() << ") "
-          << "should evenly divide input num ( " << num_ << ")";
-      num_ = num_ / top->size();
-    } else {
-      CHECK_EQ(channels_ % top->size(), 0)
-          << "Number of top blobs (" << top->size() << ") "
-          << "should evenly divide input channels ( " << channels_ << ")";
-      channels_ = channels_ / top->size();
-    }
-    for (int i = 0; i < top->size(); ++i) {
-      (*top)[i]->Reshape(num_, channels_, height_, width_);
-      count_ += (*top)[i]->count();
+    CHECK_EQ(bottom_slice_axis % top.size(), 0)
+        << "Number of top blobs (" << top.size() << ") should evenly "
+        << "divide input slice axis (" << bottom_slice_axis << ")";
+    top_shape[slice_axis_] = bottom_slice_axis / top.size();
+    for (int i = 0; i < top.size(); ++i) {
+      top[i]->Reshape(top_shape);
+      count += top[i]->count();
     }
   }
-  CHECK_EQ(count_, bottom[0]->count());
+  CHECK_EQ(count, bottom[0]->count());
 }
 
 template <typename Dtype>
 void SliceLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
-  const Dtype* bottom_data = bottom[0]->mutable_cpu_data();
-  if (slice_dim_ == 0) {
-    int offset_num = 0;
-    for (int i = 0; i < top->size(); ++i) {
-      Blob<Dtype>* blob = (*top)[i];
-      Dtype* top_data = blob->mutable_cpu_data();
-      caffe_copy(blob->count(), bottom_data + bottom[0]->offset(offset_num),
-                 top_data);
-      offset_num += blob->num();
+      const vector<Blob<Dtype>*>& top) {
+  int offset_slice_axis = 0;
+  const Dtype* bottom_data = bottom[0]->cpu_data();
+  const int bottom_slice_axis = bottom[0]->shape(slice_axis_);
+  for (int i = 0; i < top.size(); ++i) {
+    Dtype* top_data = top[i]->mutable_cpu_data();
+    const int top_slice_axis = top[i]->shape(slice_axis_);
+    for (int n = 0; n < num_slices_; ++n) {
+      const int top_offset = n * top_slice_axis * slice_size_;
+      const int bottom_offset =
+          (n * bottom_slice_axis + offset_slice_axis) * slice_size_;
+      caffe_copy(top_slice_axis * slice_size_,
+          bottom_data + bottom_offset, top_data + top_offset);
     }
-  } else if (slice_dim_ == 1) {
-    int offset_channel = 0;
-    for (int i = 0; i < top->size(); ++i) {
-      Blob<Dtype>* blob = (*top)[i];
-      Dtype* top_data = blob->mutable_cpu_data();
-      const int num_elem = blob->channels() * blob->height() * blob->width();
-      for (int n = 0; n < num_; ++n) {
-        caffe_copy(num_elem, bottom_data + bottom[0]->offset(n, offset_channel),
-                   top_data + blob->offset(n));
-      }
-      offset_channel += blob->channels();
-    }
-  }  // slice_dim_ is guaranteed to be 0 or 1 by SetUp.
+    offset_slice_axis += top_slice_axis;
+  }
 }
 
 template <typename Dtype>
 void SliceLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
   if (!propagate_down[0]) { return; }
-  Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
-  if (slice_dim_ == 0) {
-    int offset_num = 0;
-    for (int i = 0; i < top.size(); ++i) {
-      Blob<Dtype>* blob = top[i];
-      const Dtype* top_diff = blob->cpu_diff();
-      caffe_copy(blob->count(), top_diff,
-                 bottom_diff + (*bottom)[0]->offset(offset_num));
-      offset_num += blob->num();
+  int offset_slice_axis = 0;
+  Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
+  const int bottom_slice_axis = bottom[0]->shape(slice_axis_);
+  for (int i = 0; i < top.size(); ++i) {
+    const Dtype* top_diff = top[i]->cpu_diff();
+    const int top_slice_axis = top[i]->shape(slice_axis_);
+    for (int n = 0; n < num_slices_; ++n) {
+      const int top_offset = n * top_slice_axis * slice_size_;
+      const int bottom_offset =
+          (n * bottom_slice_axis + offset_slice_axis) * slice_size_;
+      caffe_copy(top_slice_axis * slice_size_,
+          top_diff + top_offset, bottom_diff + bottom_offset);
     }
-  } else if (slice_dim_ == 1) {
-    int offset_channel = 0;
-    for (int i = 0; i < top.size(); ++i) {
-      Blob<Dtype>* blob = top[i];
-      const Dtype* top_diff = blob->cpu_diff();
-      const int num_elem = blob->channels() * blob->height() * blob->width();
-      for (int n = 0; n < num_; ++n) {
-        caffe_copy(num_elem, top_diff + blob->offset(n),
-                   bottom_diff + (*bottom)[0]->offset(n, offset_channel));
-      }
-      offset_channel += blob->channels();
-    }
-  }  // slice_dim_ is guaranteed to be 0 or 1 by SetUp.
+    offset_slice_axis += top_slice_axis;
+  }
 }
 
 #ifdef CPU_ONLY
@@ -137,5 +115,6 @@ STUB_GPU(SliceLayer);
 #endif
 
 INSTANTIATE_CLASS(SliceLayer);
+REGISTER_LAYER_CLASS(Slice);
 
 }  // namespace caffe
diff --git a/src/caffe/layers/slice_layer.cu b/src/caffe/layers/slice_layer.cu
index f64e5754890..796841d3f52 100644
--- a/src/caffe/layers/slice_layer.cu
+++ b/src/caffe/layers/slice_layer.cu
@@ -7,62 +7,65 @@
 namespace caffe {
 
 template <typename Dtype>
-void SliceLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
-  const Dtype* bottom_data = bottom[0]->mutable_gpu_data();
-  if (slice_dim_ == 0) {
-    int offset_num = 0;
-    for (int i = 0; i < top->size(); ++i) {
-      Blob<Dtype>* blob = (*top)[i];
-      Dtype* top_data = blob->mutable_gpu_data();
-      caffe_copy(blob->count(), bottom_data + bottom[0]->offset(offset_num),
-                 top_data);
-      offset_num += blob->num();
-    }
-  } else if (slice_dim_ == 1) {
-    int offset_channel = 0;
-    for (int i = 0; i < top->size(); ++i) {
-      Blob<Dtype>* blob = (*top)[i];
-      Dtype* top_data = blob->mutable_gpu_data();
-      const int num_elem = blob->channels() * blob->height() * blob->width();
-      for (int n = 0; n < num_; ++n) {
-        caffe_copy(num_elem, bottom_data + bottom[0]->offset(n, offset_channel),
-                   top_data + blob->offset(n));
-      }
-      offset_channel += blob->channels();
+__global__ void Slice(const int nthreads, const Dtype* in_data,
+    const bool forward, const int num_slices, const int slice_size,
+    const int bottom_slice_axis, const int top_slice_axis,
+    const int offset_slice_axis, Dtype* out_data) {
+  CUDA_KERNEL_LOOP(index, nthreads) {
+    const int total_slice_size = slice_size * top_slice_axis;
+    const int slice_num = index / total_slice_size;
+    const int slice_index = index % total_slice_size;
+    const int bottom_index = slice_index +
+        (slice_num * bottom_slice_axis + offset_slice_axis) * slice_size;
+    if (forward) {
+      out_data[index] = in_data[bottom_index];
+    } else {
+      out_data[bottom_index] = in_data[index];
     }
-  }  // slice_dim_ is guaranteed to be 0 or 1 by SetUp.
+  }
+}
+
+template <typename Dtype>
+void SliceLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+  int offset_slice_axis = 0;
+  const Dtype* bottom_data = bottom[0]->gpu_data();
+  const int bottom_slice_axis = bottom[0]->shape(slice_axis_);
+  const bool kForward = true;
+  for (int i = 0; i < top.size(); ++i) {
+    Dtype* top_data = top[i]->mutable_gpu_data();
+    const int top_slice_axis = top[i]->shape(slice_axis_);
+    const int top_slice_size = top_slice_axis * slice_size_;
+    const int nthreads = top_slice_size * num_slices_;
+    Slice<Dtype>  // NOLINT_NEXT_LINE(whitespace/operators)
+        <<<CAFFE_GET_BLOCKS(nthreads), CAFFE_CUDA_NUM_THREADS>>>(
+        nthreads, bottom_data, kForward, num_slices_, slice_size_,
+        bottom_slice_axis, top_slice_axis, offset_slice_axis, top_data);
+    offset_slice_axis += top_slice_axis;
+  }
 }
 
 template <typename Dtype>
 void SliceLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
   if (!propagate_down[0]) { return; }
-  Dtype* bottom_diff = (*bottom)[0]->mutable_gpu_diff();
-  if (slice_dim_ == 0) {
-    int offset_num = 0;
-    for (int i = 0; i < top.size(); ++i) {
-      Blob<Dtype>* blob = top[i];
-      const Dtype* top_diff = blob->gpu_diff();
-      caffe_copy(blob->count(), top_diff,
-                 bottom_diff + (*bottom)[0]->offset(offset_num));
-      offset_num += blob->num();
-    }
-  } else if (slice_dim_ == 1) {
-    int offset_channel = 0;
-    for (int i = 0; i < top.size(); ++i) {
-      Blob<Dtype>* blob = top[i];
-      const Dtype* top_diff = blob->gpu_diff();
-      const int num_elem = blob->channels() * blob->height() * blob->width();
-      for (int n = 0; n < num_; ++n) {
-        caffe_copy(num_elem, top_diff + blob->offset(n),
-                   bottom_diff +  (*bottom)[0]->offset(n, offset_channel));
-      }
-      offset_channel += blob->channels();
-    }
-  }  // slice_dim_ is guaranteed to be 0 or 1 by SetUp.
+  int offset_slice_axis = 0;
+  Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
+  const int bottom_slice_axis = bottom[0]->shape(slice_axis_);
+  const bool kForward = false;
+  for (int i = 0; i < top.size(); ++i) {
+    const Dtype* top_diff = top[i]->gpu_diff();
+    const int top_slice_axis = top[i]->shape(slice_axis_);
+    const int top_slice_size = top_slice_axis * slice_size_;
+    const int nthreads = top_slice_size * num_slices_;
+    Slice<Dtype>  // NOLINT_NEXT_LINE(whitespace/operators)
+        <<<CAFFE_GET_BLOCKS(nthreads), CAFFE_CUDA_NUM_THREADS>>>(
+        nthreads, top_diff, kForward, num_slices_, slice_size_,
+        bottom_slice_axis, top_slice_axis, offset_slice_axis, bottom_diff);
+    offset_slice_axis += top_slice_axis;
+  }
 }
 
-INSTANTIATE_CLASS(SliceLayer);
+INSTANTIATE_LAYER_GPU_FUNCS(SliceLayer);
 
 }  // namespace caffe
diff --git a/src/caffe/layers/smooth_L1_loss_layer.cpp b/src/caffe/layers/smooth_L1_loss_layer.cpp
new file mode 100644
index 00000000000..37834003105
--- /dev/null
+++ b/src/caffe/layers/smooth_L1_loss_layer.cpp
@@ -0,0 +1,55 @@
+// ------------------------------------------------------------------
+// Fast R-CNN
+// Copyright (c) 2015 Microsoft
+// Licensed under The MIT License [see fast-rcnn/LICENSE for details]
+// Written by Ross Girshick
+// ------------------------------------------------------------------
+
+#include "caffe/loss_layers.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void SmoothL1LossLayer<Dtype>::LayerSetUp(
+  const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
+  has_weights_ = (bottom.size() == 3);
+}
+
+template <typename Dtype>
+void SmoothL1LossLayer<Dtype>::Reshape(
+  const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
+  LossLayer<Dtype>::Reshape(bottom, top);
+  CHECK_EQ(bottom[0]->channels(), bottom[1]->channels());
+  CHECK_EQ(bottom[0]->height(), bottom[1]->height());
+  CHECK_EQ(bottom[0]->width(), bottom[1]->width());
+  if (has_weights_) {
+    CHECK_EQ(bottom[0]->channels(), bottom[2]->channels());
+    CHECK_EQ(bottom[0]->height(), bottom[2]->height());
+    CHECK_EQ(bottom[0]->width(), bottom[2]->width());
+  }
+  diff_.Reshape(bottom[0]->num(), bottom[0]->channels(),
+      bottom[0]->height(), bottom[0]->width());
+  errors_.Reshape(bottom[0]->num(), bottom[0]->channels(),
+      bottom[0]->height(), bottom[0]->width());
+}
+
+template <typename Dtype>
+void SmoothL1LossLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+    const vector<Blob<Dtype>*>& top) {
+  NOT_IMPLEMENTED;
+}
+
+template <typename Dtype>
+void SmoothL1LossLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
+    const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+  NOT_IMPLEMENTED;
+}
+
+#ifdef CPU_ONLY
+STUB_GPU(SmoothL1LossLayer);
+#endif
+
+INSTANTIATE_CLASS(SmoothL1LossLayer);
+REGISTER_LAYER_CLASS(SmoothL1Loss);
+
+}  // namespace caffe
diff --git a/src/caffe/layers/smooth_L1_loss_layer.cu b/src/caffe/layers/smooth_L1_loss_layer.cu
new file mode 100644
index 00000000000..b1aaa4312fb
--- /dev/null
+++ b/src/caffe/layers/smooth_L1_loss_layer.cu
@@ -0,0 +1,90 @@
+// ------------------------------------------------------------------
+// Fast R-CNN
+// Copyright (c) 2015 Microsoft
+// Licensed under The MIT License [see fast-rcnn/LICENSE for details]
+// Written by Ross Girshick
+// ------------------------------------------------------------------
+
+#include "caffe/loss_layers.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+__global__ void SmoothL1Forward(const int n, const Dtype* in, Dtype* out) {
+  // f(x) = 0.5 * x^2    if |x| < 1
+  //        |x| - 0.5    otherwise
+  CUDA_KERNEL_LOOP(index, n) {
+    Dtype val = in[index];
+    Dtype abs_val = abs(val);
+    if (abs_val < 1) {
+      out[index] = 0.5 * val * val;
+    } else {
+      out[index] = abs_val - 0.5;
+    }
+  }
+}
+
+template <typename Dtype>
+void SmoothL1LossLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+    const vector<Blob<Dtype>*>& top) {
+  int count = bottom[0]->count();
+  caffe_gpu_sub(
+      count,
+      bottom[0]->gpu_data(),
+      bottom[1]->gpu_data(),
+      diff_.mutable_gpu_data());    // d := b0 - b1
+  if (has_weights_) {
+    caffe_gpu_mul(
+        count,
+        bottom[2]->gpu_data(),
+        diff_.gpu_data(),
+        diff_.mutable_gpu_data());  // d := w * (b0 - b1)
+  }
+  SmoothL1Forward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
+      count, diff_.gpu_data(), errors_.mutable_gpu_data());
+  CUDA_POST_KERNEL_CHECK;
+
+  Dtype loss;
+  caffe_gpu_asum(count, errors_.gpu_data(), &loss);
+  top[0]->mutable_cpu_data()[0] = loss / bottom[0]->num();
+}
+
+template <typename Dtype>
+__global__ void SmoothL1Backward(const int n, const Dtype* in, Dtype* out) {
+  // f'(x) = x         if |x| < 1
+  //       = sign(x)   otherwise
+  CUDA_KERNEL_LOOP(index, n) {
+    Dtype val = in[index];
+    Dtype abs_val = abs(val);
+    if (abs_val < 1) {
+      out[index] = val;
+    } else {
+      out[index] = (Dtype(0) < val) - (val < Dtype(0));
+    }
+  }
+}
+
+template <typename Dtype>
+void SmoothL1LossLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
+    const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+  int count = diff_.count();
+  SmoothL1Backward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
+      count, diff_.gpu_data(), diff_.mutable_gpu_data());
+  CUDA_POST_KERNEL_CHECK;
+  for (int i = 0; i < 2; ++i) {
+    if (propagate_down[i]) {
+      const Dtype sign = (i == 0) ? 1 : -1;
+      const Dtype alpha = sign * top[0]->cpu_diff()[0] / bottom[i]->num();
+      caffe_gpu_axpby(
+          bottom[i]->count(),              // count
+          alpha,                           // alpha
+          diff_.gpu_data(),                // x
+          Dtype(0),                        // beta
+          bottom[i]->mutable_gpu_diff());  // y
+    }
+  }
+}
+
+INSTANTIATE_LAYER_GPU_FUNCS(SmoothL1LossLayer);
+
+}  // namespace caffe
diff --git a/src/caffe/layers/softmax_layer.cpp b/src/caffe/layers/softmax_layer.cpp
index 60668a3f8ce..04712c9e653 100644
--- a/src/caffe/layers/softmax_layer.cpp
+++ b/src/caffe/layers/softmax_layer.cpp
@@ -9,51 +9,53 @@ namespace caffe {
 
 template <typename Dtype>
 void SoftmaxLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
-  (*top)[0]->Reshape(bottom[0]->num(), bottom[0]->channels(),
-      bottom[0]->height(), bottom[0]->width());
-  sum_multiplier_.Reshape(1, bottom[0]->channels(), 1, 1);
+      const vector<Blob<Dtype>*>& top) {
+  softmax_axis_ =
+      bottom[0]->CanonicalAxisIndex(this->layer_param_.softmax_param().axis());
+  top[0]->ReshapeLike(*bottom[0]);
+  vector<int> mult_dims(1, bottom[0]->shape(softmax_axis_));
+  sum_multiplier_.Reshape(mult_dims);
   Dtype* multiplier_data = sum_multiplier_.mutable_cpu_data();
-  for (int i = 0; i < sum_multiplier_.count(); ++i) {
-    multiplier_data[i] = 1.;
-  }
-  scale_.Reshape(bottom[0]->num(), 1, bottom[0]->height(), bottom[0]->width());
+  caffe_set(sum_multiplier_.count(), Dtype(1), multiplier_data);
+  outer_num_ = bottom[0]->count(0, softmax_axis_);
+  inner_num_ = bottom[0]->count(softmax_axis_ + 1);
+  vector<int> scale_dims = bottom[0]->shape();
+  scale_dims[softmax_axis_] = 1;
+  scale_.Reshape(scale_dims);
 }
 
 template <typename Dtype>
 void SoftmaxLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-    vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& top) {
   const Dtype* bottom_data = bottom[0]->cpu_data();
-  Dtype* top_data = (*top)[0]->mutable_cpu_data();
+  Dtype* top_data = top[0]->mutable_cpu_data();
   Dtype* scale_data = scale_.mutable_cpu_data();
-  int num = bottom[0]->num();
-  int channels = bottom[0]->channels();
-  int dim = bottom[0]->count() / bottom[0]->num();
-  int spatial_dim = bottom[0]->height() * bottom[0]->width();
+  int channels = bottom[0]->shape(softmax_axis_);
+  int dim = bottom[0]->count() / outer_num_;
   caffe_copy(bottom[0]->count(), bottom_data, top_data);
   // We need to subtract the max to avoid numerical issues, compute the exp,
   // and then normalize.
-  for (int i = 0; i < num; ++i) {
+  for (int i = 0; i < outer_num_; ++i) {
     // initialize scale_data to the first plane
-    caffe_copy(spatial_dim, bottom_data + i * dim, scale_data);
+    caffe_copy(inner_num_, bottom_data + i * dim, scale_data);
     for (int j = 0; j < channels; j++) {
-      for (int k = 0; k < spatial_dim; k++) {
+      for (int k = 0; k < inner_num_; k++) {
         scale_data[k] = std::max(scale_data[k],
-            bottom_data[i * dim + j * spatial_dim + k]);
+            bottom_data[i * dim + j * inner_num_ + k]);
       }
     }
     // subtraction
-    caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, channels, spatial_dim,
-        1, -1., sum_multiplier_.cpu_data(), scale_data, 1., top_data + i * dim);
+    caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, channels, inner_num_,
+        1, -1., sum_multiplier_.cpu_data(), scale_data, 1., top_data);
     // exponentiation
-    caffe_exp<Dtype>(dim, top_data + i * dim, top_data + i * dim);
+    caffe_exp<Dtype>(dim, top_data, top_data);
     // sum after exp
-    caffe_cpu_gemv<Dtype>(CblasTrans, channels, spatial_dim, 1.,
-        top_data + i * dim, sum_multiplier_.cpu_data(), 0., scale_data);
+    caffe_cpu_gemv<Dtype>(CblasTrans, channels, inner_num_, 1.,
+        top_data, sum_multiplier_.cpu_data(), 0., scale_data);
     // division
     for (int j = 0; j < channels; j++) {
-      caffe_div(spatial_dim, top_data + (*top)[0]->offset(i, j), scale_data,
-          top_data + (*top)[0]->offset(i, j));
+      caffe_div(inner_num_, top_data, scale_data, top_data);
+      top_data += inner_num_;
     }
   }
 }
@@ -61,25 +63,23 @@ void SoftmaxLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
 template <typename Dtype>
 void SoftmaxLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
     const vector<bool>& propagate_down,
-    vector<Blob<Dtype>*>* bottom) {
+    const vector<Blob<Dtype>*>& bottom) {
   const Dtype* top_diff = top[0]->cpu_diff();
   const Dtype* top_data = top[0]->cpu_data();
-  Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
+  Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
   Dtype* scale_data = scale_.mutable_cpu_data();
-  int num = top[0]->num();
-  int channels = top[0]->channels();
-  int dim = top[0]->count() / top[0]->num();
-  int spatial_dim = top[0]->height() * top[0]->width();
+  int channels = top[0]->shape(softmax_axis_);
+  int dim = top[0]->count() / outer_num_;
   caffe_copy(top[0]->count(), top_diff, bottom_diff);
-  for (int i = 0; i < num; ++i) {
+  for (int i = 0; i < outer_num_; ++i) {
     // compute dot(top_diff, top_data) and subtract them from the bottom diff
-    for (int k = 0; k < spatial_dim; ++k) {
+    for (int k = 0; k < inner_num_; ++k) {
       scale_data[k] = caffe_cpu_strided_dot<Dtype>(channels,
-          bottom_diff + i * dim + k, spatial_dim,
-          top_data + i * dim + k, spatial_dim);
+          bottom_diff + i * dim + k, inner_num_,
+          top_data + i * dim + k, inner_num_);
     }
     // subtraction
-    caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, channels, spatial_dim, 1,
+    caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, channels, inner_num_, 1,
         -1., sum_multiplier_.cpu_data(), scale_data, 1., bottom_diff + i * dim);
   }
   // elementwise multiplication
@@ -93,5 +93,4 @@ STUB_GPU(SoftmaxLayer);
 
 INSTANTIATE_CLASS(SoftmaxLayer);
 
-
 }  // namespace caffe
diff --git a/src/caffe/layers/softmax_layer.cu b/src/caffe/layers/softmax_layer.cu
index f97eafcedb0..1f9c3a41203 100644
--- a/src/caffe/layers/softmax_layer.cu
+++ b/src/caffe/layers/softmax_layer.cu
@@ -25,14 +25,13 @@ __global__ void kernel_channel_max(const int num, const int channels,
 }
 
 template <typename Dtype>
-__global__ void kernel_channel_subtract(const int num, const int channels,
-    const int spatial_dim, Dtype* data, const Dtype* channel_max) {
-  CUDA_KERNEL_LOOP(index, num * spatial_dim) {
-    int n = index / spatial_dim;
+__global__ void kernel_channel_subtract(const int count,
+    const int num, const int channels,
+    const int spatial_dim, const Dtype* channel_max, Dtype* data) {
+  CUDA_KERNEL_LOOP(index, count) {
+    int n = index / channels / spatial_dim;
     int s = index % spatial_dim;
-    for (int c = 0; c < channels; ++c) {
-      data[(n * channels + c) * spatial_dim + s] -= channel_max[index];
-    }
+    data[index] -= channel_max[n * spatial_dim + s];
   }
 }
 
@@ -58,14 +57,13 @@ __global__ void kernel_channel_sum(const int num, const int channels,
 }
 
 template <typename Dtype>
-__global__ void kernel_channel_div(const int num, const int channels,
-    const int spatial_dim, Dtype* data, const Dtype* channel_sum) {
-  CUDA_KERNEL_LOOP(index, num * spatial_dim) {
-    int n = index / spatial_dim;
+__global__ void kernel_channel_div(const int count,
+    const int num, const int channels,
+    const int spatial_dim, const Dtype* channel_sum, Dtype* data) {
+  CUDA_KERNEL_LOOP(index, count) {
+    int n = index / channels / spatial_dim;
     int s = index % spatial_dim;
-    for (int c = 0; c < channels; ++c) {
-      data[(n * channels + c) * spatial_dim + s] /= channel_sum[index];
-    }
+    data[index] /= channel_sum[n * spatial_dim + s];
   }
 }
 
@@ -87,68 +85,65 @@ __global__ void kernel_channel_dot(const int num, const int channels,
 
 template <typename Dtype>
 void SoftmaxLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-    vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& top) {
   const Dtype* bottom_data = bottom[0]->gpu_data();
-  Dtype* top_data = (*top)[0]->mutable_gpu_data();
+  Dtype* top_data = top[0]->mutable_gpu_data();
   Dtype* scale_data = scale_.mutable_gpu_data();
-  int num = bottom[0]->num();
-  int channels = bottom[0]->channels();
-  int spatial_dim = bottom[0]->height() * bottom[0]->width();
-  caffe_copy(bottom[0]->count(), bottom_data, top_data);
+  int count = bottom[0]->count();
+  int channels = top[0]->shape(softmax_axis_);
+  caffe_copy(count, bottom_data, top_data);
   // We need to subtract the max to avoid numerical issues, compute the exp,
   // and then normalize.
   // compute max
   // NOLINT_NEXT_LINE(whitespace/operators)
-  kernel_channel_max<Dtype><<<CAFFE_GET_BLOCKS(num * spatial_dim),
-      CAFFE_CUDA_NUM_THREADS>>>(num, channels, spatial_dim, top_data,
+  kernel_channel_max<Dtype><<<CAFFE_GET_BLOCKS(outer_num_ * inner_num_),
+      CAFFE_CUDA_NUM_THREADS>>>(outer_num_, channels, inner_num_, top_data,
       scale_data);
   // subtract
   // NOLINT_NEXT_LINE(whitespace/operators)
-  kernel_channel_subtract<Dtype><<<CAFFE_GET_BLOCKS(num * spatial_dim),
-      CAFFE_CUDA_NUM_THREADS>>>(num, channels, spatial_dim, top_data,
-      scale_data);
+  kernel_channel_subtract<Dtype><<<CAFFE_GET_BLOCKS(count),
+      CAFFE_CUDA_NUM_THREADS>>>(count, outer_num_, channels, inner_num_,
+      scale_data, top_data);
   // exponentiate
   // NOLINT_NEXT_LINE(whitespace/operators)
-  kernel_exp<Dtype><<<CAFFE_GET_BLOCKS(num * channels * spatial_dim),
-      CAFFE_CUDA_NUM_THREADS>>>(num * channels * spatial_dim, top_data,
-      top_data);
+  kernel_exp<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
+      count, top_data, top_data);
   // sum after exp
   // NOLINT_NEXT_LINE(whitespace/operators)
-  kernel_channel_sum<Dtype><<<CAFFE_GET_BLOCKS(num * spatial_dim),
-      CAFFE_CUDA_NUM_THREADS>>>(num, channels, spatial_dim, top_data,
+  kernel_channel_sum<Dtype><<<CAFFE_GET_BLOCKS(outer_num_ * inner_num_),
+      CAFFE_CUDA_NUM_THREADS>>>(outer_num_, channels, inner_num_, top_data,
       scale_data);
   // divide
   // NOLINT_NEXT_LINE(whitespace/operators)
-  kernel_channel_div<Dtype><<<CAFFE_GET_BLOCKS(num * spatial_dim),
-      CAFFE_CUDA_NUM_THREADS>>>(num, channels, spatial_dim, top_data,
-      scale_data);
+  kernel_channel_div<Dtype><<<CAFFE_GET_BLOCKS(count),
+      CAFFE_CUDA_NUM_THREADS>>>(count, outer_num_, channels, inner_num_,
+      scale_data, top_data);
 }
 
 template <typename Dtype>
 void SoftmaxLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
-    const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
+    const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
   const Dtype* top_diff = top[0]->gpu_diff();
   const Dtype* top_data = top[0]->gpu_data();
-  Dtype* bottom_diff = (*bottom)[0]->mutable_gpu_diff();
+  Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
   Dtype* scale_data = scale_.mutable_gpu_data();
-  int num = top[0]->num();
-  int channels = top[0]->channels();
-  int spatial_dim = top[0]->height() * top[0]->width();
-  caffe_copy(top[0]->count(), top_diff, bottom_diff);
+  int count = top[0]->count();
+  int channels = top[0]->shape(softmax_axis_);
+  caffe_copy(count, top_diff, bottom_diff);
   // Compute inner1d(top_diff, top_data) and subtract them from the bottom diff.
   // NOLINT_NEXT_LINE(whitespace/operators)
-  kernel_channel_dot<Dtype><<<CAFFE_GET_BLOCKS(num * spatial_dim),
-      CAFFE_CUDA_NUM_THREADS>>>(num, channels, spatial_dim, top_diff, top_data,
-      scale_data);
+  kernel_channel_dot<Dtype><<<CAFFE_GET_BLOCKS(outer_num_ * inner_num_),
+      CAFFE_CUDA_NUM_THREADS>>>(outer_num_, channels, inner_num_,
+      top_diff, top_data, scale_data);
   // NOLINT_NEXT_LINE(whitespace/operators)
-  kernel_channel_subtract<Dtype><<<CAFFE_GET_BLOCKS(num * spatial_dim),
-      CAFFE_CUDA_NUM_THREADS>>>(num, channels, spatial_dim, bottom_diff,
-      scale_data);
+  kernel_channel_subtract<Dtype><<<CAFFE_GET_BLOCKS(count),
+      CAFFE_CUDA_NUM_THREADS>>>(count, outer_num_, channels, inner_num_,
+      scale_data, bottom_diff);
   // elementwise multiplication
   caffe_gpu_mul<Dtype>(top[0]->count(), bottom_diff, top_data, bottom_diff);
 }
 
-INSTANTIATE_CLASS(SoftmaxLayer);
+INSTANTIATE_LAYER_GPU_FUNCS(SoftmaxLayer);
 
 
 }  // namespace caffe
diff --git a/src/caffe/layers/softmax_loss_layer.cpp b/src/caffe/layers/softmax_loss_layer.cpp
index 55392c37ca0..ba312f67fbc 100644
--- a/src/caffe/layers/softmax_loss_layer.cpp
+++ b/src/caffe/layers/softmax_loss_layer.cpp
@@ -3,6 +3,7 @@
 #include <vector>
 
 #include "caffe/layer.hpp"
+#include "caffe/layer_factory.hpp"
 #include "caffe/util/math_functions.hpp"
 #include "caffe/vision_layers.hpp"
 
@@ -10,84 +11,120 @@ namespace caffe {
 
 template <typename Dtype>
 void SoftmaxWithLossLayer<Dtype>::LayerSetUp(
-    const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
   LossLayer<Dtype>::LayerSetUp(bottom, top);
+  LayerParameter softmax_param(this->layer_param_);
+  softmax_param.set_type("Softmax");
+  softmax_layer_ = LayerRegistry<Dtype>::CreateLayer(softmax_param);
   softmax_bottom_vec_.clear();
   softmax_bottom_vec_.push_back(bottom[0]);
   softmax_top_vec_.clear();
   softmax_top_vec_.push_back(&prob_);
-  softmax_layer_->SetUp(softmax_bottom_vec_, &softmax_top_vec_);
+  softmax_layer_->SetUp(softmax_bottom_vec_, softmax_top_vec_);
+
+  has_ignore_label_ =
+    this->layer_param_.loss_param().has_ignore_label();
+  if (has_ignore_label_) {
+    ignore_label_ = this->layer_param_.loss_param().ignore_label();
+  }
+  normalize_ = this->layer_param_.loss_param().normalize();
 }
 
 template <typename Dtype>
 void SoftmaxWithLossLayer<Dtype>::Reshape(
-    const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
   LossLayer<Dtype>::Reshape(bottom, top);
-  softmax_layer_->Reshape(softmax_bottom_vec_, &softmax_top_vec_);
-  if (top->size() >= 2) {
+  softmax_layer_->Reshape(softmax_bottom_vec_, softmax_top_vec_);
+  softmax_axis_ =
+      bottom[0]->CanonicalAxisIndex(this->layer_param_.softmax_param().axis());
+  outer_num_ = bottom[0]->count(0, softmax_axis_);
+  inner_num_ = bottom[0]->count(softmax_axis_ + 1);
+  CHECK_EQ(outer_num_ * inner_num_, bottom[1]->count())
+      << "Number of labels must match number of predictions; "
+      << "e.g., if softmax axis == 1 and prediction shape is (N, C, H, W), "
+      << "label count (number of labels) must be N*H*W, "
+      << "with integer values in {0, 1, ..., C-1}.";
+  if (top.size() >= 2) {
     // softmax output
-    (*top)[1]->ReshapeLike(*bottom[0]);
+    top[1]->ReshapeLike(*bottom[0]);
   }
 }
 
 template <typename Dtype>
 void SoftmaxWithLossLayer<Dtype>::Forward_cpu(
-    const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
   // The forward pass computes the softmax prob values.
-  softmax_layer_->Forward(softmax_bottom_vec_, &softmax_top_vec_);
+  softmax_layer_->Forward(softmax_bottom_vec_, softmax_top_vec_);
   const Dtype* prob_data = prob_.cpu_data();
   const Dtype* label = bottom[1]->cpu_data();
-  int num = prob_.num();
-  int dim = prob_.count() / num;
-  int spatial_dim = prob_.height() * prob_.width();
+  int dim = prob_.count() / outer_num_;
+  int count = 0;
   Dtype loss = 0;
-  for (int i = 0; i < num; ++i) {
-    for (int j = 0; j < spatial_dim; j++) {
-      loss -= log(std::max(prob_data[i * dim +
-          static_cast<int>(label[i * spatial_dim + j]) * spatial_dim + j],
+  for (int i = 0; i < outer_num_; ++i) {
+    for (int j = 0; j < inner_num_; j++) {
+      const int label_value = static_cast<int>(label[i * inner_num_ + j]);
+      if (has_ignore_label_ && label_value == ignore_label_) {
+        continue;
+      }
+      DCHECK_GE(label_value, 0);
+      DCHECK_LT(label_value, prob_.shape(softmax_axis_));
+      loss -= log(std::max(prob_data[i * dim + label_value * inner_num_ + j],
                            Dtype(FLT_MIN)));
+      ++count;
     }
   }
-  (*top)[0]->mutable_cpu_data()[0] = loss / num / spatial_dim;
-  if (top->size() == 2) {
-    (*top)[1]->ShareData(prob_);
+  if (normalize_) {
+    top[0]->mutable_cpu_data()[0] = loss / count;
+  } else {
+    top[0]->mutable_cpu_data()[0] = loss / outer_num_;
+  }
+  if (top.size() == 2) {
+    top[1]->ShareData(prob_);
   }
 }
 
 template <typename Dtype>
 void SoftmaxWithLossLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
-    const vector<bool>& propagate_down,
-    vector<Blob<Dtype>*>* bottom) {
+    const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
   if (propagate_down[1]) {
-    LOG(FATAL) << this->type_name()
+    LOG(FATAL) << this->type()
                << " Layer cannot backpropagate to label inputs.";
   }
   if (propagate_down[0]) {
-    Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
+    Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
     const Dtype* prob_data = prob_.cpu_data();
     caffe_copy(prob_.count(), prob_data, bottom_diff);
-    const Dtype* label = (*bottom)[1]->cpu_data();
-    int num = prob_.num();
-    int dim = prob_.count() / num;
-    int spatial_dim = prob_.height() * prob_.width();
-    for (int i = 0; i < num; ++i) {
-      for (int j = 0; j < spatial_dim; ++j) {
-        bottom_diff[i * dim + static_cast<int>(label[i * spatial_dim + j])
-            * spatial_dim + j] -= 1;
+    const Dtype* label = bottom[1]->cpu_data();
+    int dim = prob_.count() / outer_num_;
+    int count = 0;
+    for (int i = 0; i < outer_num_; ++i) {
+      for (int j = 0; j < inner_num_; ++j) {
+        const int label_value = static_cast<int>(label[i * inner_num_ + j]);
+        if (has_ignore_label_ && label_value == ignore_label_) {
+          for (int c = 0; c < bottom[0]->shape(softmax_axis_); ++c) {
+            bottom_diff[i * dim + c * inner_num_ + j] = 0;
+          }
+        } else {
+          bottom_diff[i * dim + label_value * inner_num_ + j] -= 1;
+          ++count;
+        }
       }
     }
     // Scale gradient
     const Dtype loss_weight = top[0]->cpu_diff()[0];
-    caffe_scal(prob_.count(), loss_weight / num / spatial_dim, bottom_diff);
+    if (normalize_) {
+      caffe_scal(prob_.count(), loss_weight / count, bottom_diff);
+    } else {
+      caffe_scal(prob_.count(), loss_weight / outer_num_, bottom_diff);
+    }
   }
 }
 
-
 #ifdef CPU_ONLY
 STUB_GPU(SoftmaxWithLossLayer);
 #endif
 
 INSTANTIATE_CLASS(SoftmaxWithLossLayer);
-
+REGISTER_LAYER_CLASS(SoftmaxWithLoss);
 
 }  // namespace caffe
diff --git a/src/caffe/layers/softmax_loss_layer.cu b/src/caffe/layers/softmax_loss_layer.cu
index 9ef8dd23615..7e0f3da4552 100644
--- a/src/caffe/layers/softmax_loss_layer.cu
+++ b/src/caffe/layers/softmax_loss_layer.cu
@@ -8,20 +8,118 @@
 
 namespace caffe {
 
+template <typename Dtype>
+__global__ void SoftmaxLossForwardGPU(const int nthreads,
+          const Dtype* prob_data, const Dtype* label, Dtype* loss,
+          const int num, const int dim, const int spatial_dim,
+          const bool has_ignore_label_, const int ignore_label_,
+          Dtype* counts) {
+  CUDA_KERNEL_LOOP(index, nthreads) {
+    const int n = index / spatial_dim;
+    const int s = index % spatial_dim;
+    const int label_value = static_cast<int>(label[n * spatial_dim + s]);
+    if (has_ignore_label_ && label_value == ignore_label_) {
+      loss[index] = 0;
+      counts[index] = 0;
+    } else {
+      loss[index] = -log(max(prob_data[n * dim + label_value * spatial_dim + s],
+                      Dtype(FLT_MIN)));
+      counts[index] = 1;
+    }
+  }
+}
+
 template <typename Dtype>
 void SoftmaxWithLossLayer<Dtype>::Forward_gpu(
-    const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
-  Forward_cpu(bottom, top);
+    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
+  softmax_layer_->Forward(softmax_bottom_vec_, softmax_top_vec_);
+  const Dtype* prob_data = prob_.gpu_data();
+  const Dtype* label = bottom[1]->gpu_data();
+  const int dim = prob_.count() / outer_num_;
+  const int nthreads = outer_num_ * inner_num_;
+  // Since this memory is not used for anything until it is overwritten
+  // on the backward pass, we use it here to avoid having to allocate new GPU
+  // memory to accumulate intermediate results in the kernel.
+  Dtype* loss_data = bottom[0]->mutable_gpu_diff();
+  // Similarly, this memory is never used elsewhere, and thus we can use it
+  // to avoid having to allocate additional GPU memory.
+  Dtype* counts = prob_.mutable_gpu_diff();
+  // NOLINT_NEXT_LINE(whitespace/operators)
+  SoftmaxLossForwardGPU<Dtype><<<CAFFE_GET_BLOCKS(nthreads),
+      CAFFE_CUDA_NUM_THREADS>>>(nthreads, prob_data, label, loss_data,
+      outer_num_, dim, inner_num_, has_ignore_label_, ignore_label_, counts);
+  Dtype loss;
+  caffe_gpu_asum(nthreads, loss_data, &loss);
+  if (normalize_) {
+    Dtype count;
+    caffe_gpu_asum(nthreads, counts, &count);
+    loss /= count;
+  } else {
+    loss /= outer_num_;
+  }
+  top[0]->mutable_cpu_data()[0] = loss;
+  if (top.size() == 2) {
+    top[1]->ShareData(prob_);
+  }
 }
 
 template <typename Dtype>
-void SoftmaxWithLossLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
-    const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
-  // TODO(Yangqing): implement the GPU version of softmax.
-  Backward_cpu(top, propagate_down, bottom);
+__global__ void SoftmaxLossBackwardGPU(const int nthreads, const Dtype* top,
+          const Dtype* label, Dtype* bottom_diff, const int num, const int dim,
+          const int spatial_dim, const bool has_ignore_label_,
+          const int ignore_label_, Dtype* counts) {
+  const int channels = dim / spatial_dim;
+
+  CUDA_KERNEL_LOOP(index, nthreads) {
+    const int n = index / spatial_dim;
+    const int s = index % spatial_dim;
+    const int label_value = static_cast<int>(label[n * spatial_dim + s]);
+
+    if (has_ignore_label_ && label_value == ignore_label_) {
+      for (int c = 0; c < channels; ++c) {
+        bottom_diff[n * dim + c * spatial_dim + s] = 0;
+      }
+      counts[index] = 0;
+    } else {
+      bottom_diff[n * dim + label_value * spatial_dim + s] -= 1;
+      counts[index] = 1;
+    }
+  }
 }
 
-INSTANTIATE_CLASS(SoftmaxWithLossLayer);
+template <typename Dtype>
+void SoftmaxWithLossLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
+    const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+  if (propagate_down[1]) {
+    LOG(FATAL) << this->type()
+               << " Layer cannot backpropagate to label inputs.";
+  }
+  if (propagate_down[0]) {
+    Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
+    const Dtype* prob_data = prob_.gpu_data();
+    const Dtype* top_data = top[0]->gpu_data();
+    caffe_gpu_memcpy(prob_.count() * sizeof(Dtype), prob_data, bottom_diff);
+    const Dtype* label = bottom[1]->gpu_data();
+    const int dim = prob_.count() / outer_num_;
+    const int nthreads = outer_num_ * inner_num_;
+    // Since this memory is never used for anything else,
+    // we use to to avoid allocating new GPU memory.
+    Dtype* counts = prob_.mutable_gpu_diff();
+    // NOLINT_NEXT_LINE(whitespace/operators)
+    SoftmaxLossBackwardGPU<Dtype><<<CAFFE_GET_BLOCKS(nthreads),
+        CAFFE_CUDA_NUM_THREADS>>>(nthreads, top_data, label, bottom_diff,
+        outer_num_, dim, inner_num_, has_ignore_label_, ignore_label_, counts);
+    const Dtype loss_weight = top[0]->cpu_diff()[0];
+    if (normalize_) {
+      Dtype count;
+      caffe_gpu_asum(nthreads, counts, &count);
+      caffe_gpu_scal(prob_.count(), loss_weight / count, bottom_diff);
+    } else {
+      caffe_gpu_scal(prob_.count(), loss_weight / outer_num_, bottom_diff);
+    }
+  }
+}
 
+INSTANTIATE_LAYER_GPU_FUNCS(SoftmaxWithLossLayer);
 
 }  // namespace caffe
diff --git a/src/caffe/layers/split_layer.cpp b/src/caffe/layers/split_layer.cpp
index 40d3600ff17..272cb59cd37 100644
--- a/src/caffe/layers/split_layer.cpp
+++ b/src/caffe/layers/split_layer.cpp
@@ -8,44 +8,43 @@ namespace caffe {
 
 template <typename Dtype>
 void SplitLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
+      const vector<Blob<Dtype>*>& top) {
   count_ = bottom[0]->count();
-  for (int i = 0; i < top->size(); ++i) {
+  for (int i = 0; i < top.size(); ++i) {
     // Do not allow in-place computation in the SplitLayer.  Instead, share data
     // by reference in the forward pass, and keep separate diff allocations in
     // the backward pass.  (Technically, it should be possible to share the diff
     // blob of the first split output with the input, but this seems to cause
     // some strange effects in practice...)
-    CHECK_NE((*top)[i], bottom[0]) << this->type_name() << " Layer does not "
+    CHECK_NE(top[i], bottom[0]) << this->type() << " Layer does not "
         "allow in-place computation.";
-    (*top)[i]->Reshape(bottom[0]->num(), bottom[0]->channels(),
-                       bottom[0]->height(), bottom[0]->width());
-    CHECK_EQ(count_, (*top)[i]->count());
+    top[i]->ReshapeLike(*bottom[0]);
+    CHECK_EQ(count_, top[i]->count());
   }
 }
 
 template <typename Dtype>
 void SplitLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
-  for (int i = 0; i < top->size(); ++i) {
-    (*top)[i]->ShareData(*bottom[0]);
+      const vector<Blob<Dtype>*>& top) {
+  for (int i = 0; i < top.size(); ++i) {
+    top[i]->ShareData(*bottom[0]);
   }
 }
 
 template <typename Dtype>
 void SplitLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
   if (!propagate_down[0]) { return; }
   if (top.size() == 1) {
-    caffe_copy(count_, top[0]->cpu_diff(), (*bottom)[0]->mutable_cpu_diff());
+    caffe_copy(count_, top[0]->cpu_diff(), bottom[0]->mutable_cpu_diff());
     return;
   }
   caffe_add(count_, top[0]->cpu_diff(), top[1]->cpu_diff(),
-            (*bottom)[0]->mutable_cpu_diff());
+            bottom[0]->mutable_cpu_diff());
   // Add remaining top blob diffs.
   for (int i = 2; i < top.size(); ++i) {
     const Dtype* top_diff = top[i]->cpu_diff();
-    Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
+    Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
     caffe_axpy(count_, Dtype(1.), top_diff, bottom_diff);
   }
 }
@@ -56,5 +55,6 @@ STUB_GPU(SplitLayer);
 #endif
 
 INSTANTIATE_CLASS(SplitLayer);
+REGISTER_LAYER_CLASS(Split);
 
 }  // namespace caffe
diff --git a/src/caffe/layers/split_layer.cu b/src/caffe/layers/split_layer.cu
index fcc0917e67d..a4f5df26452 100644
--- a/src/caffe/layers/split_layer.cu
+++ b/src/caffe/layers/split_layer.cu
@@ -8,31 +8,31 @@ namespace caffe {
 
 template <typename Dtype>
 void SplitLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
-  for (int i = 0; i < top->size(); ++i) {
-    (*top)[i]->ShareData(*bottom[0]);
+      const vector<Blob<Dtype>*>& top) {
+  for (int i = 0; i < top.size(); ++i) {
+    top[i]->ShareData(*bottom[0]);
   }
 }
 
 template <typename Dtype>
 void SplitLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
   if (!propagate_down[0]) { return; }
   if (top.size() == 1) {
-    caffe_copy(count_, top[0]->gpu_diff(), (*bottom)[0]->mutable_gpu_diff());
+    caffe_copy(count_, top[0]->gpu_diff(), bottom[0]->mutable_gpu_diff());
     return;
   }
   caffe_gpu_add(count_, top[0]->gpu_diff(), top[1]->gpu_diff(),
-                (*bottom)[0]->mutable_gpu_diff());
+                bottom[0]->mutable_gpu_diff());
   // Add remaining top blob diffs.
   for (int i = 2; i < top.size(); ++i) {
     const Dtype* top_diff = top[i]->gpu_diff();
-    Dtype* bottom_diff = (*bottom)[0]->mutable_gpu_diff();
+    Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
     caffe_gpu_axpy(count_, Dtype(1.), top_diff, bottom_diff);
   }
 }
 
 
-INSTANTIATE_CLASS(SplitLayer);
+INSTANTIATE_LAYER_GPU_FUNCS(SplitLayer);
 
 }  // namespace caffe
diff --git a/src/caffe/layers/spp_layer.cpp b/src/caffe/layers/spp_layer.cpp
new file mode 100644
index 00000000000..795dd71693e
--- /dev/null
+++ b/src/caffe/layers/spp_layer.cpp
@@ -0,0 +1,193 @@
+#include <algorithm>
+#include <cfloat>
+#include <vector>
+
+#include "caffe/common.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/syncedmem.hpp"
+#include "caffe/util/math_functions.hpp"
+#include "caffe/vision_layers.hpp"
+
+namespace caffe {
+
+using std::min;
+using std::max;
+
+template <typename Dtype>
+LayerParameter SPPLayer<Dtype>::GetPoolingParam(const int pyramid_level,
+      const int bottom_h, const int bottom_w, const SPPParameter spp_param) {
+  LayerParameter pooling_param;
+  int num_bins = pow(2, pyramid_level);
+
+  // find padding and kernel size so that the pooling is
+  // performed across the entire image
+  int kernel_h = ceil(bottom_h / static_cast<double>(num_bins));
+  // remainder_h is the min number of pixels that need to be padded before
+  // entire image height is pooled over with the chosen kernel dimension
+  int remainder_h = kernel_h * num_bins - bottom_h;
+  // pooling layer pads (2 * pad_h) pixels on the top and bottom of the
+  // image.
+  int pad_h = (remainder_h + 1) / 2;
+
+  // similar logic for width
+  int kernel_w = ceil(bottom_w / static_cast<double>(num_bins));
+  int remainder_w = kernel_w * num_bins - bottom_w;
+  int pad_w = (remainder_w + 1) / 2;
+
+  pooling_param.mutable_pooling_param()->set_pad_h(pad_h);
+  pooling_param.mutable_pooling_param()->set_pad_w(pad_w);
+  pooling_param.mutable_pooling_param()->set_kernel_h(kernel_h);
+  pooling_param.mutable_pooling_param()->set_kernel_w(kernel_w);
+  pooling_param.mutable_pooling_param()->set_stride_h(kernel_h);
+  pooling_param.mutable_pooling_param()->set_stride_w(kernel_w);
+
+  switch (spp_param.pool()) {
+  case SPPParameter_PoolMethod_MAX:
+    pooling_param.mutable_pooling_param()->set_pool(
+        PoolingParameter_PoolMethod_MAX);
+    break;
+  case SPPParameter_PoolMethod_AVE:
+    pooling_param.mutable_pooling_param()->set_pool(
+        PoolingParameter_PoolMethod_AVE);
+    break;
+  case SPPParameter_PoolMethod_STOCHASTIC:
+    pooling_param.mutable_pooling_param()->set_pool(
+        PoolingParameter_PoolMethod_STOCHASTIC);
+    break;
+  default:
+    LOG(FATAL) << "Unknown pooling method.";
+  }
+
+  return pooling_param;
+}
+
+template <typename Dtype>
+void SPPLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+  SPPParameter spp_param = this->layer_param_.spp_param();
+
+  bottom_h_ = bottom[0]->height();
+  bottom_w_ = bottom[0]->width();
+  CHECK_GT(bottom_h_, 0) << "Input dimensions cannot be zero.";
+  CHECK_GT(bottom_w_, 0) << "Input dimensions cannot be zero.";
+
+  pyramid_height_ = spp_param.pyramid_height();
+  split_top_vec_.clear();
+  pooling_bottom_vecs_.clear();
+  pooling_layers_.clear();
+  pooling_top_vecs_.clear();
+  pooling_outputs_.clear();
+  flatten_layers_.clear();
+  flatten_top_vecs_.clear();
+  flatten_outputs_.clear();
+  concat_bottom_vec_.clear();
+
+  // split layer output holders setup
+  for (int i = 0; i < pyramid_height_; i++) {
+    split_top_vec_.push_back(new Blob<Dtype>());
+  }
+
+  // split layer setup
+  LayerParameter split_param;
+  split_layer_.reset(new SplitLayer<Dtype>(split_param));
+  split_layer_->SetUp(bottom, split_top_vec_);
+
+  for (int i = 0; i < pyramid_height_; i++) {
+    // pooling layer input holders setup
+    pooling_bottom_vecs_.push_back(new vector<Blob<Dtype>*>);
+    pooling_bottom_vecs_[i]->push_back(split_top_vec_[i]);
+
+    // pooling layer output holders setup
+    pooling_outputs_.push_back(new Blob<Dtype>());
+    pooling_top_vecs_.push_back(new vector<Blob<Dtype>*>);
+    pooling_top_vecs_[i]->push_back(pooling_outputs_[i]);
+
+    // pooling layer setup
+    LayerParameter pooling_param = GetPoolingParam(
+        i, bottom_h_, bottom_w_, spp_param);
+
+    pooling_layers_.push_back(shared_ptr<PoolingLayer<Dtype> > (
+        new PoolingLayer<Dtype>(pooling_param)));
+    pooling_layers_[i]->SetUp(*pooling_bottom_vecs_[i], *pooling_top_vecs_[i]);
+
+    // flatten layer output holders setup
+    flatten_outputs_.push_back(new Blob<Dtype>());
+    flatten_top_vecs_.push_back(new vector<Blob<Dtype>*>);
+    flatten_top_vecs_[i]->push_back(flatten_outputs_[i]);
+
+    // flatten layer setup
+    LayerParameter flatten_param;
+    flatten_layers_.push_back(new FlattenLayer<Dtype>(flatten_param));
+    flatten_layers_[i]->SetUp(*pooling_top_vecs_[i], *flatten_top_vecs_[i]);
+
+    // concat layer input holders setup
+    concat_bottom_vec_.push_back(flatten_outputs_[i]);
+  }
+
+  // concat layer setup
+  LayerParameter concat_param;
+  concat_layer_.reset(new ConcatLayer<Dtype>(concat_param));
+  concat_layer_->SetUp(concat_bottom_vec_, top);
+}
+
+template <typename Dtype>
+void SPPLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+  CHECK_EQ(4, bottom[0]->num_axes()) << "Input must have 4 axes, "
+      << "corresponding to (num, channels, height, width)";
+  channels_ = bottom[0]->channels();
+  bottom_h_ = bottom[0]->height();
+  bottom_w_ = bottom[0]->width();
+  SPPParameter spp_param = this->layer_param_.spp_param();
+  split_layer_->Reshape(bottom, split_top_vec_);
+  for (int i = 0; i < pyramid_height_; i++) {
+    LayerParameter pooling_param = GetPoolingParam(
+        i, bottom_h_, bottom_w_, spp_param);
+
+    pooling_layers_[i].reset(
+        new PoolingLayer<Dtype>(pooling_param));
+    pooling_layers_[i]->SetUp(
+        *pooling_bottom_vecs_[i], *pooling_top_vecs_[i]);
+    pooling_layers_[i]->Reshape(
+        *pooling_bottom_vecs_[i], *pooling_top_vecs_[i]);
+    flatten_layers_[i]->Reshape(
+        *pooling_top_vecs_[i], *flatten_top_vecs_[i]);
+  }
+  concat_layer_->Reshape(concat_bottom_vec_, top);
+}
+
+template <typename Dtype>
+void SPPLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+  split_layer_->Forward(bottom, split_top_vec_);
+  for (int i = 0; i < pyramid_height_; i++) {
+    pooling_layers_[i]->Forward(
+        *pooling_bottom_vecs_[i], *pooling_top_vecs_[i]);
+    flatten_layers_[i]->Forward(
+        *pooling_top_vecs_[i], *flatten_top_vecs_[i]);
+  }
+  concat_layer_->Forward(concat_bottom_vec_, top);
+}
+
+template <typename Dtype>
+void SPPLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+  if (!propagate_down[0]) {
+    return;
+  }
+  vector<bool> concat_propagate_down(pyramid_height_, true);
+  concat_layer_->Backward(top, concat_propagate_down, concat_bottom_vec_);
+  for (int i = 0; i < pyramid_height_; i++) {
+    flatten_layers_[i]->Backward(
+        *flatten_top_vecs_[i], propagate_down, *pooling_top_vecs_[i]);
+    pooling_layers_[i]->Backward(
+        *pooling_top_vecs_[i], propagate_down, *pooling_bottom_vecs_[i]);
+  }
+  split_layer_->Backward(split_top_vec_, propagate_down, bottom);
+}
+
+
+INSTANTIATE_CLASS(SPPLayer);
+REGISTER_LAYER_CLASS(SPP);
+
+}  // namespace caffe
diff --git a/src/caffe/layers/tanh_layer.cpp b/src/caffe/layers/tanh_layer.cpp
index 8dae0054aed..ee5ed773c74 100644
--- a/src/caffe/layers/tanh_layer.cpp
+++ b/src/caffe/layers/tanh_layer.cpp
@@ -11,26 +11,24 @@ namespace caffe {
 
 template <typename Dtype>
 void TanHLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-    vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& top) {
   const Dtype* bottom_data = bottom[0]->cpu_data();
-  Dtype* top_data = (*top)[0]->mutable_cpu_data();
-  Dtype exp2x;
+  Dtype* top_data = top[0]->mutable_cpu_data();
   const int count = bottom[0]->count();
   for (int i = 0; i < count; ++i) {
-    exp2x = exp(2 * bottom_data[i]);
-    top_data[i] = (exp2x - Dtype(1)) / (exp2x + Dtype(1));
+    top_data[i] = tanh(bottom_data[i]);
   }
 }
 
 template <typename Dtype>
 void TanHLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
     const vector<bool>& propagate_down,
-    vector<Blob<Dtype>*>* bottom) {
+    const vector<Blob<Dtype>*>& bottom) {
   if (propagate_down[0]) {
     const Dtype* top_data = top[0]->cpu_data();
     const Dtype* top_diff = top[0]->cpu_diff();
-    Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
-    const int count = (*bottom)[0]->count();
+    Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
+    const int count = bottom[0]->count();
     Dtype tanhx;
     for (int i = 0; i < count; ++i) {
       tanhx = top_data[i];
diff --git a/src/caffe/layers/tanh_layer.cu b/src/caffe/layers/tanh_layer.cu
index bdb7a94978e..ccd6e63ee7c 100644
--- a/src/caffe/layers/tanh_layer.cu
+++ b/src/caffe/layers/tanh_layer.cu
@@ -12,16 +12,15 @@ namespace caffe {
 template <typename Dtype>
 __global__ void TanHForward(const int n, const Dtype* in, Dtype* out) {
   CUDA_KERNEL_LOOP(index, n) {
-    Dtype exp2x = exp(2 * in[index]);
-    out[index] = (exp2x - Dtype(1)) / (exp2x + Dtype(1));
+    out[index] = tanh(in[index]);
   }
 }
 
 template <typename Dtype>
 void TanHLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-    vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& top) {
   const Dtype* bottom_data = bottom[0]->gpu_data();
-  Dtype* top_data = (*top)[0]->mutable_gpu_data();
+  Dtype* top_data = top[0]->mutable_gpu_data();
   const int count = bottom[0]->count();
   // NOLINT_NEXT_LINE(whitespace/operators)
   TanHForward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
@@ -41,12 +40,12 @@ __global__ void TanHBackward(const int n, const Dtype* in_diff,
 template <typename Dtype>
 void TanHLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
     const vector<bool>& propagate_down,
-    vector<Blob<Dtype>*>* bottom) {
+    const vector<Blob<Dtype>*>& bottom) {
   if (propagate_down[0]) {
     const Dtype* top_data = top[0]->gpu_data();
     const Dtype* top_diff = top[0]->gpu_diff();
-    Dtype* bottom_diff = (*bottom)[0]->mutable_gpu_diff();
-    const int count = (*bottom)[0]->count();
+    Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
+    const int count = bottom[0]->count();
     // NOLINT_NEXT_LINE(whitespace/operators)
     TanHBackward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
         count, top_diff, top_data, bottom_diff);
@@ -54,7 +53,7 @@ void TanHLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
   }
 }
 
-INSTANTIATE_CLASS(TanHLayer);
+INSTANTIATE_LAYER_GPU_FUNCS(TanHLayer);
 
 
 }  // namespace caffe
diff --git a/src/caffe/layers/threshold_layer.cpp b/src/caffe/layers/threshold_layer.cpp
index 180ea6a322b..2365e7b9c72 100644
--- a/src/caffe/layers/threshold_layer.cpp
+++ b/src/caffe/layers/threshold_layer.cpp
@@ -8,16 +8,16 @@ namespace caffe {
 
 template <typename Dtype>
 void ThresholdLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
+      const vector<Blob<Dtype>*>& top) {
   NeuronLayer<Dtype>::LayerSetUp(bottom, top);
   threshold_ = this->layer_param_.threshold_param().threshold();
 }
 
 template <typename Dtype>
 void ThresholdLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-    vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& top) {
   const Dtype* bottom_data = bottom[0]->cpu_data();
-  Dtype* top_data = (*top)[0]->mutable_cpu_data();
+  Dtype* top_data = top[0]->mutable_cpu_data();
   const int count = bottom[0]->count();
   for (int i = 0; i < count; ++i) {
     top_data[i] = (bottom_data[i] > threshold_) ? Dtype(1) : Dtype(0);
@@ -29,5 +29,6 @@ STUB_GPU_FORWARD(ThresholdLayer, Forward);
 #endif
 
 INSTANTIATE_CLASS(ThresholdLayer);
+REGISTER_LAYER_CLASS(Threshold);
 
 }  // namespace caffe
diff --git a/src/caffe/layers/threshold_layer.cu b/src/caffe/layers/threshold_layer.cu
index 93430815900..bfa7f159460 100644
--- a/src/caffe/layers/threshold_layer.cu
+++ b/src/caffe/layers/threshold_layer.cu
@@ -16,9 +16,9 @@ __global__ void ThresholdForward(const int n, const Dtype threshold,
 
 template <typename Dtype>
 void ThresholdLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-    vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& top) {
   const Dtype* bottom_data = bottom[0]->gpu_data();
-  Dtype* top_data = (*top)[0]->mutable_gpu_data();
+  Dtype* top_data = top[0]->mutable_gpu_data();
   const int count = bottom[0]->count();
   // NOLINT_NEXT_LINE(whitespace/operators)
   ThresholdForward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
@@ -27,7 +27,7 @@ void ThresholdLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
 }
 
 
-INSTANTIATE_CLASS(ThresholdLayer);
+INSTANTIATE_LAYER_GPU_FORWARD(ThresholdLayer);
 
 
 }  // namespace caffe
diff --git a/src/caffe/layers/video_data_layer.cpp b/src/caffe/layers/video_data_layer.cpp
new file mode 100644
index 00000000000..69ad47726c4
--- /dev/null
+++ b/src/caffe/layers/video_data_layer.cpp
@@ -0,0 +1,173 @@
+#include <fstream>
+#include <iostream>
+#include <string>
+#include <utility>
+#include <vector>
+
+#include "caffe/data_layers.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+#include "caffe/util/io.hpp"
+#include "caffe/util/math_functions.hpp"
+#include "caffe/util/rng.hpp"
+
+#ifdef USE_MPI
+#include "mpi.h"
+#include <boost/filesystem.hpp>
+using namespace boost::filesystem;
+#endif
+
+namespace caffe{
+template <typename Dtype>
+VideoDataLayer<Dtype>:: ~VideoDataLayer<Dtype>(){
+	this->JoinPrefetchThread();
+}
+
+template <typename Dtype>
+void VideoDataLayer<Dtype>:: DataLayerSetUp(const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top){
+	const int new_height  = this->layer_param_.video_data_param().new_height();
+	const int new_width  = this->layer_param_.video_data_param().new_width();
+	const int new_length  = this->layer_param_.video_data_param().new_length();
+	const int num_segments = this->layer_param_.video_data_param().num_segments();
+	const string& source = this->layer_param_.video_data_param().source();
+
+	LOG(INFO) << "Opening file: " << source;
+	std:: ifstream infile(source.c_str());
+	string filename;
+	int label;
+	int length;
+	while (infile >> filename >> length >> label){
+		lines_.push_back(std::make_pair(filename,label));
+		lines_duration_.push_back(length);
+	}
+	if (this->layer_param_.video_data_param().shuffle()){
+		const unsigned int prefectch_rng_seed = caffe_rng_rand();
+		prefetch_rng_1_.reset(new Caffe::RNG(prefectch_rng_seed));
+		prefetch_rng_2_.reset(new Caffe::RNG(prefectch_rng_seed));
+		ShuffleVideos();
+	}
+
+	LOG(INFO) << "A total of " << lines_.size() << " videos.";
+	lines_id_ = 0;
+
+	//check name patter
+	if (this->layer_param_.video_data_param().name_pattern() == ""){
+		if (this->layer_param_.video_data_param().modality() == VideoDataParameter_Modality_RGB){
+			name_pattern_ = "image_%04d.jpg";
+		}else if (this->layer_param_.video_data_param().modality() == VideoDataParameter_Modality_FLOW){
+			name_pattern_ = "flow_%c_%04d.jpg";
+		}
+	}else{
+		name_pattern_ = this->layer_param_.video_data_param().name_pattern();
+	}
+
+	Datum datum;
+	const unsigned int frame_prefectch_rng_seed = caffe_rng_rand();
+	frame_prefetch_rng_.reset(new Caffe::RNG(frame_prefectch_rng_seed));
+	int average_duration = (int) lines_duration_[lines_id_]/num_segments;
+	vector<int> offsets;
+	for (int i = 0; i < num_segments; ++i){
+		caffe::rng_t* frame_rng = static_cast<caffe::rng_t*>(frame_prefetch_rng_->generator());
+		int offset = (*frame_rng)() % (average_duration - new_length + 1);
+		offsets.push_back(offset+i*average_duration);
+	}
+	if (this->layer_param_.video_data_param().modality() == VideoDataParameter_Modality_FLOW)
+		CHECK(ReadSegmentFlowToDatum(lines_[lines_id_].first, lines_[lines_id_].second,
+									 offsets, new_height, new_width, new_length, &datum, name_pattern_.c_str()));
+	else
+		CHECK(ReadSegmentRGBToDatum(lines_[lines_id_].first, lines_[lines_id_].second,
+									offsets, new_height, new_width, new_length, &datum, true, name_pattern_.c_str()));
+	const int crop_size = this->layer_param_.transform_param().crop_size();
+	const int batch_size = this->layer_param_.video_data_param().batch_size();
+	if (crop_size > 0){
+		top[0]->Reshape(batch_size, datum.channels(), crop_size, crop_size);
+		this->prefetch_data_.Reshape(batch_size, datum.channels(), crop_size, crop_size);
+	} else {
+		top[0]->Reshape(batch_size, datum.channels(), datum.height(), datum.width());
+		this->prefetch_data_.Reshape(batch_size, datum.channels(), datum.height(), datum.width());
+	}
+	LOG(INFO) << "output data size: " << top[0]->num() << "," << top[0]->channels() << "," << top[0]->height() << "," << top[0]->width();
+
+	top[1]->Reshape(batch_size, 1, 1, 1);
+	this->prefetch_label_.Reshape(batch_size, 1, 1, 1);
+
+	vector<int> top_shape = this->data_transformer_->InferBlobShape(datum);
+	this->transformed_data_.Reshape(top_shape);
+}
+
+template <typename Dtype>
+void VideoDataLayer<Dtype>::ShuffleVideos(){
+	caffe::rng_t* prefetch_rng1 = static_cast<caffe::rng_t*>(prefetch_rng_1_->generator());
+	caffe::rng_t* prefetch_rng2 = static_cast<caffe::rng_t*>(prefetch_rng_2_->generator());
+	shuffle(lines_.begin(), lines_.end(), prefetch_rng1);
+	shuffle(lines_duration_.begin(), lines_duration_.end(),prefetch_rng2);
+}
+
+template <typename Dtype>
+void VideoDataLayer<Dtype>::InternalThreadEntry(){
+
+	Datum datum;
+	CHECK(this->prefetch_data_.count());
+	Dtype* top_data = this->prefetch_data_.mutable_cpu_data();
+	Dtype* top_label = this->prefetch_label_.mutable_cpu_data();
+	VideoDataParameter video_data_param = this->layer_param_.video_data_param();
+	const int batch_size = video_data_param.batch_size();
+	const int new_height = video_data_param.new_height();
+	const int new_width = video_data_param.new_width();
+	const int new_length = video_data_param.new_length();
+	const int num_segments = video_data_param.num_segments();
+	const int lines_size = lines_.size();
+
+	for (int item_id = 0; item_id < batch_size; ++item_id){
+		CHECK_GT(lines_size, lines_id_);
+		vector<int> offsets;
+		int average_duration = (int) lines_duration_[lines_id_] / num_segments;
+		for (int i = 0; i < num_segments; ++i){
+			if (this->phase_==TRAIN){
+				if (average_duration >= new_length){
+					caffe::rng_t* frame_rng = static_cast<caffe::rng_t*>(frame_prefetch_rng_->generator());
+					int offset = (*frame_rng)() % (average_duration - new_length + 1);
+					offsets.push_back(offset+i*average_duration);
+				} else {
+					offsets.push_back(0);
+				}
+			} else{
+				if (average_duration >= new_length)
+				offsets.push_back(int((average_duration-new_length+1)/2 + i*average_duration));
+				else
+				offsets.push_back(0);
+			}
+		}
+		if (this->layer_param_.video_data_param().modality() == VideoDataParameter_Modality_FLOW){
+			if(!ReadSegmentFlowToDatum(lines_[lines_id_].first, lines_[lines_id_].second,
+									   offsets, new_height, new_width, new_length, &datum, name_pattern_.c_str())) {
+				continue;
+			}
+		} else{
+			if(!ReadSegmentRGBToDatum(lines_[lines_id_].first, lines_[lines_id_].second,
+									  offsets, new_height, new_width, new_length, &datum, true, name_pattern_.c_str())) {
+				continue;
+			}
+		}
+
+		int offset1 = this->prefetch_data_.offset(item_id);
+    	        this->transformed_data_.set_cpu_data(top_data + offset1);
+		this->data_transformer_->Transform(datum, &(this->transformed_data_));
+		top_label[item_id] = lines_[lines_id_].second;
+		//LOG()
+
+		//next iteration
+		lines_id_++;
+		if (lines_id_ >= lines_size) {
+			DLOG(INFO) << "Restarting data prefetching from start.";
+			lines_id_ = 0;
+			if(this->layer_param_.video_data_param().shuffle()){
+				ShuffleVideos();
+			}
+		}
+	}
+}
+
+INSTANTIATE_CLASS(VideoDataLayer);
+REGISTER_LAYER_CLASS(VideoData);
+}
diff --git a/src/caffe/layers/window_data_layer.cpp b/src/caffe/layers/window_data_layer.cpp
index afb430468cc..c127d56bc46 100644
--- a/src/caffe/layers/window_data_layer.cpp
+++ b/src/caffe/layers/window_data_layer.cpp
@@ -1,3 +1,4 @@
+#include <opencv2/highgui/highgui_c.h>
 #include <stdint.h>
 
 #include <algorithm>
@@ -13,6 +14,7 @@
 #include "caffe/common.hpp"
 #include "caffe/data_layers.hpp"
 #include "caffe/layer.hpp"
+#include "caffe/util/benchmark.hpp"
 #include "caffe/util/io.hpp"
 #include "caffe/util/math_functions.hpp"
 #include "caffe/util/rng.hpp"
@@ -30,7 +32,7 @@ WindowDataLayer<Dtype>::~WindowDataLayer<Dtype>() {
 
 template <typename Dtype>
 void WindowDataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
+      const vector<Blob<Dtype>*>& top) {
   // LayerSetUp runs through the window_file and creates two structures
   // that hold windows: one for foreground (object) windows and one
   // for background (non-object) windows. We use an overlap threshold
@@ -52,7 +54,14 @@ void WindowDataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
       << "  background (non-object) overlap threshold: "
       << this->layer_param_.window_data_param().bg_threshold() << std::endl
       << "  foreground sampling fraction: "
-      << this->layer_param_.window_data_param().fg_fraction();
+      << this->layer_param_.window_data_param().fg_fraction() << std::endl
+      << "  cache_images: "
+      << this->layer_param_.window_data_param().cache_images() << std::endl
+      << "  root_folder: "
+      << this->layer_param_.window_data_param().root_folder();
+
+  cache_images_ = this->layer_param_.window_data_param().cache_images();
+  string root_folder = this->layer_param_.window_data_param().root_folder();
 
   const bool prefetch_needs_rand =
       this->transform_param_.mirror() ||
@@ -81,12 +90,21 @@ void WindowDataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
     // read image path
     string image_path;
     infile >> image_path;
+    image_path = root_folder + image_path;
     // read image dimensions
     vector<int> image_size(3);
     infile >> image_size[0] >> image_size[1] >> image_size[2];
     channels = image_size[0];
     image_database_.push_back(std::make_pair(image_path, image_size));
 
+    if (cache_images_) {
+      Datum datum;
+      if (!ReadFileToDatum(image_path, &datum)) {
+        LOG(ERROR) << "Could not open or find file " << image_path;
+        return;
+      }
+      image_database_cache_.push_back(std::make_pair(image_path, datum));
+    }
     // read each box
     int num_windows;
     infile >> num_windows;
@@ -152,21 +170,43 @@ void WindowDataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
   const int crop_size = this->transform_param_.crop_size();
   CHECK_GT(crop_size, 0);
   const int batch_size = this->layer_param_.window_data_param().batch_size();
-  (*top)[0]->Reshape(batch_size, channels, crop_size, crop_size);
+  top[0]->Reshape(batch_size, channels, crop_size, crop_size);
   this->prefetch_data_.Reshape(batch_size, channels, crop_size, crop_size);
 
-  LOG(INFO) << "output data size: " << (*top)[0]->num() << ","
-      << (*top)[0]->channels() << "," << (*top)[0]->height() << ","
-      << (*top)[0]->width();
-  // datum size
-  this->datum_channels_ = (*top)[0]->channels();
-  this->datum_height_ = (*top)[0]->height();
-  this->datum_width_ = (*top)[0]->width();
-  this->datum_size_ =
-      (*top)[0]->channels() * (*top)[0]->height() * (*top)[0]->width();
+  LOG(INFO) << "output data size: " << top[0]->num() << ","
+      << top[0]->channels() << "," << top[0]->height() << ","
+      << top[0]->width();
   // label
-  (*top)[1]->Reshape(batch_size, 1, 1, 1);
-  this->prefetch_label_.Reshape(batch_size, 1, 1, 1);
+  vector<int> label_shape(1, batch_size);
+  top[1]->Reshape(label_shape);
+  this->prefetch_label_.Reshape(label_shape);
+
+  // data mean
+  has_mean_file_ = this->transform_param_.has_mean_file();
+  has_mean_values_ = this->transform_param_.mean_value_size() > 0;
+  if (has_mean_file_) {
+    const string& mean_file =
+          this->transform_param_.mean_file();
+    LOG(INFO) << "Loading mean file from: " << mean_file;
+    BlobProto blob_proto;
+    ReadProtoFromBinaryFileOrDie(mean_file.c_str(), &blob_proto);
+    data_mean_.FromProto(blob_proto);
+  }
+  if (has_mean_values_) {
+    CHECK(has_mean_file_ == false) <<
+      "Cannot specify mean_file and mean_value at the same time";
+    for (int c = 0; c < this->transform_param_.mean_value_size(); ++c) {
+      mean_values_.push_back(this->transform_param_.mean_value(c));
+    }
+    CHECK(mean_values_.size() == 1 || mean_values_.size() == channels) <<
+     "Specify either 1 mean_value or as many as channels: " << channels;
+    if (channels > 1 && mean_values_.size() == 1) {
+      // Replicate the mean_value for simplicity
+      for (int c = 1; c < channels; ++c) {
+        mean_values_.push_back(mean_values_[0]);
+      }
+    }
+  }
 }
 
 template <typename Dtype>
@@ -182,7 +222,11 @@ template <typename Dtype>
 void WindowDataLayer<Dtype>::InternalThreadEntry() {
   // At each iteration, sample N windows where N*p are foreground (object)
   // windows and N*(1-p) are background (non-object) windows
-
+  CPUTimer batch_timer;
+  batch_timer.Start();
+  double read_time = 0;
+  double trans_time = 0;
+  CPUTimer timer;
   Dtype* top_data = this->prefetch_data_.mutable_cpu_data();
   Dtype* top_label = this->prefetch_label_.mutable_cpu_data();
   const Dtype scale = this->layer_param_.window_data_param().scale();
@@ -192,10 +236,16 @@ void WindowDataLayer<Dtype>::InternalThreadEntry() {
   const bool mirror = this->transform_param_.mirror();
   const float fg_fraction =
       this->layer_param_.window_data_param().fg_fraction();
-  const Dtype* mean = this->data_mean_.cpu_data();
-  const int mean_off = (this->data_mean_.width() - crop_size) / 2;
-  const int mean_width = this->data_mean_.width();
-  const int mean_height = this->data_mean_.height();
+  Dtype* mean = NULL;
+  int mean_off = 0;
+  int mean_width = 0;
+  int mean_height = 0;
+  if (this->has_mean_file_) {
+    mean = this->data_mean_.mutable_cpu_data();
+    mean_off = (this->data_mean_.width() - crop_size) / 2;
+    mean_width = this->data_mean_.width();
+    mean_height = this->data_mean_.height();
+  }
   cv::Size cv_crop_size(crop_size, crop_size);
   const string& crop_mode = this->layer_param_.window_data_param().crop_mode();
 
@@ -213,25 +263,32 @@ void WindowDataLayer<Dtype>::InternalThreadEntry() {
   for (int is_fg = 0; is_fg < 2; ++is_fg) {
     for (int dummy = 0; dummy < num_samples[is_fg]; ++dummy) {
       // sample a window
+      timer.Start();
       const unsigned int rand_index = PrefetchRand();
       vector<float> window = (is_fg) ?
           fg_windows_[rand_index % fg_windows_.size()] :
           bg_windows_[rand_index % bg_windows_.size()];
 
-      bool do_mirror = false;
-      if (mirror && PrefetchRand() % 2) {
-        do_mirror = true;
-      }
+      bool do_mirror = mirror && PrefetchRand() % 2;
 
       // load the image containing the window
       pair<std::string, vector<int> > image =
           image_database_[window[WindowDataLayer<Dtype>::IMAGE_INDEX]];
 
-      cv::Mat cv_img = cv::imread(image.first, CV_LOAD_IMAGE_COLOR);
-      if (!cv_img.data) {
-        LOG(ERROR) << "Could not open or find file " << image.first;
-        return;
+      cv::Mat cv_img;
+      if (this->cache_images_) {
+        pair<std::string, Datum> image_cached =
+          image_database_cache_[window[WindowDataLayer<Dtype>::IMAGE_INDEX]];
+        cv_img = DecodeDatumToCVMat(image_cached.second, true);
+      } else {
+        cv_img = cv::imread(image.first, CV_LOAD_IMAGE_COLOR);
+        if (!cv_img.data) {
+          LOG(ERROR) << "Could not open or find file " << image.first;
+          return;
+        }
       }
+      read_time += timer.MicroSeconds();
+      timer.Start();
       const int channels = cv_img.channels();
 
       // crop window out of image and warp it
@@ -334,22 +391,30 @@ void WindowDataLayer<Dtype>::InternalThreadEntry() {
       }
 
       // copy the warped window into top_data
-      for (int c = 0; c < channels; ++c) {
-        for (int h = 0; h < cv_cropped_img.rows; ++h) {
-          for (int w = 0; w < cv_cropped_img.cols; ++w) {
-            Dtype pixel =
-                static_cast<Dtype>(cv_cropped_img.at<cv::Vec3b>(h, w)[c]);
-
-            top_data[((item_id * channels + c) * crop_size + h + pad_h)
-                     * crop_size + w + pad_w]
-                = (pixel
-                    - mean[(c * mean_height + h + mean_off + pad_h)
-                           * mean_width + w + mean_off + pad_w])
-                  * scale;
+      for (int h = 0; h < cv_cropped_img.rows; ++h) {
+        const uchar* ptr = cv_cropped_img.ptr<uchar>(h);
+        int img_index = 0;
+        for (int w = 0; w < cv_cropped_img.cols; ++w) {
+          for (int c = 0; c < channels; ++c) {
+            int top_index = ((item_id * channels + c) * crop_size + h + pad_h)
+                     * crop_size + w + pad_w;
+            // int top_index = (c * height + h) * width + w;
+            Dtype pixel = static_cast<Dtype>(ptr[img_index++]);
+            if (this->has_mean_file_) {
+              int mean_index = (c * mean_height + h + mean_off + pad_h)
+                           * mean_width + w + mean_off + pad_w;
+              top_data[top_index] = (pixel - mean[mean_index]) * scale;
+            } else {
+              if (this->has_mean_values_) {
+                top_data[top_index] = (pixel - this->mean_values_[c]) * scale;
+              } else {
+                top_data[top_index] = pixel * scale;
+              }
+            }
           }
         }
       }
-
+      trans_time += timer.MicroSeconds();
       // get window label
       top_label[item_id] = window[WindowDataLayer<Dtype>::LABEL];
 
@@ -389,8 +454,13 @@ void WindowDataLayer<Dtype>::InternalThreadEntry() {
       item_id++;
     }
   }
+  batch_timer.Stop();
+  DLOG(INFO) << "Prefetch batch: " << batch_timer.MilliSeconds() << " ms.";
+  DLOG(INFO) << "     Read time: " << read_time / 1000 << " ms.";
+  DLOG(INFO) << "Transform time: " << trans_time / 1000 << " ms.";
 }
 
 INSTANTIATE_CLASS(WindowDataLayer);
+REGISTER_LAYER_CLASS(WindowData);
 
 }  // namespace caffe
diff --git a/src/caffe/net.cpp b/src/caffe/net.cpp
index 6f4a651fb10..dc9d7aea4d6 100644
--- a/src/caffe/net.cpp
+++ b/src/caffe/net.cpp
@@ -14,7 +14,11 @@
 #include "caffe/util/math_functions.hpp"
 #include "caffe/util/upgrade_proto.hpp"
 
+#include "caffe/util/channel.hpp"
+#include "caffe/util/mpi_functions.hpp"
+
 #include "caffe/test/test_caffe_main.hpp"
+#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
@@ -24,14 +28,17 @@ Net<Dtype>::Net(const NetParameter& param) {
 }
 
 template <typename Dtype>
-Net<Dtype>::Net(const string& param_file) {
+Net<Dtype>::Net(const string& param_file, Phase phase) {
   NetParameter param;
   ReadNetParamsFromTextFileOrDie(param_file, &param);
+  param.mutable_state()->set_phase(phase);
   Init(param);
 }
 
 template <typename Dtype>
 void Net<Dtype>::Init(const NetParameter& in_param) {
+  // Set phase from the state.
+  phase_ = in_param.state().phase();
   // Filter layers based on their include/exclude rules and
   // the current NetState.
   NetParameter filtered_param;
@@ -41,39 +48,99 @@ void Net<Dtype>::Init(const NetParameter& in_param) {
   // Create a copy of filtered_param with splits added where necessary.
   NetParameter param;
   InsertSplits(filtered_param, &param);
-  // Basically, build all the layers and set up its connections.
+  // Basically, build all the layers and set up their connections.
   name_ = param.name();
   map<string, int> blob_name_to_idx;
   set<string> available_blobs;
-  CHECK_EQ(param.input_size() * 4, param.input_dim_size())
-      << "Incorrect input blob dimension specifications.";
+  CHECK(param.input_dim_size() == 0 || param.input_shape_size() == 0)
+      << "Must specify either input_shape OR deprecated input_dim, not both.";
+  if (param.input_dim_size() > 0) {
+    // Deprecated 4D dimensions.
+    CHECK_EQ(param.input_size() * 4, param.input_dim_size())
+        << "Incorrect input blob dimension specifications.";
+  } else {
+    CHECK_EQ(param.input_size(), param.input_shape_size())
+        << "Exactly one input_shape must be specified per input.";
+  }
   memory_used_ = 0;
   // set the input blobs
   for (int input_id = 0; input_id < param.input_size(); ++input_id) {
     const int layer_id = -1;  // inputs have fake layer ID -1
     AppendTop(param, layer_id, input_id, &available_blobs, &blob_name_to_idx);
+
+    // input blobs are excluded from memory optimization by default
+    excluded_blob_names_.insert(param.input(input_id));
   }
   DLOG(INFO) << "Memory required for data: " << memory_used_ * sizeof(Dtype);
-  // For each layer, set up their input and output
-  bottom_vecs_.resize(param.layers_size());
-  top_vecs_.resize(param.layers_size());
-  bottom_id_vecs_.resize(param.layers_size());
-  top_id_vecs_.resize(param.layers_size());
-  bottom_need_backward_.resize(param.layers_size());
-  for (int layer_id = 0; layer_id < param.layers_size(); ++layer_id) {
-    const LayerParameter& layer_param = param.layers(layer_id);
-    layers_.push_back(shared_ptr<Layer<Dtype> >(GetLayer<Dtype>(layer_param)));
+  // For each layer, set up its input and output
+  bottom_vecs_.resize(param.layer_size());
+  top_vecs_.resize(param.layer_size());
+  bottom_id_vecs_.resize(param.layer_size());
+  param_id_vecs_.resize(param.layer_size());
+  top_id_vecs_.resize(param.layer_size());
+  bottom_need_backward_.resize(param.layer_size());
+
+  for (int layer_id = 0; layer_id < param.layer_size(); ++layer_id) {
+    // Inherit phase from net if unset.
+    if (!param.layer(layer_id).has_phase()) {
+      param.mutable_layer(layer_id)->set_phase(phase_);
+    }
+
+    // Setup layer.
+    const LayerParameter& layer_param = param.layer(layer_id);
+    if (layer_param.propagate_down_size() > 0) {
+      CHECK_EQ(layer_param.propagate_down_size(),
+          layer_param.bottom_size())
+          << "propagate_down param must be specified "
+          << "either 0 or bottom_size times ";
+    }
+    layers_.push_back(LayerRegistry<Dtype>::CreateLayer(layer_param));
     layer_names_.push_back(layer_param.name());
     LOG(INFO) << "Creating Layer " << layer_param.name();
     bool need_backward = false;
+
     // Figure out this layer's input and output
+    #ifdef USE_MPI
+    vector<bool> source_layer_need_sync;
     for (int bottom_id = 0; bottom_id < layer_param.bottom_size();
          ++bottom_id) {
+
       const int blob_id = AppendBottom(param, layer_id, bottom_id,
                                        &available_blobs, &blob_name_to_idx);
+      int src_layer_id = top_layer_indices_[blob_id].first;
+      if (src_layer_id>=0) source_layer_need_sync.push_back(layers_[src_layer_id]->need_sync());
+      if (source_layer_need_sync.size()>0){
+        CHECK_EQ(source_layer_need_sync.back(), source_layer_need_sync[0])
+          <<" blob "<<layer_param.bottom(0)
+          <<" and blob "<< layer_param.bottom(bottom_id)
+          <<" are from layers with different paralle mode. This is not supported.";
+      }
       // If a blob needs backward, this layer should provide it.
       need_backward |= blob_need_backward_[blob_id];
     }
+
+    if (layers_[layer_id]->is_gathering()){
+      layers_[layer_id]->set_need_sync(false);
+    } else {
+      if(layers_[layer_id]->is_scattering()){
+        layers_[layer_id]->set_need_sync(true);
+      } else {
+        if ((source_layer_need_sync.size() > 0)) {
+          layers_[layer_id]->set_need_sync(source_layer_need_sync[0]);
+          LOG(INFO) << "This layer is inheriting previous layer's sync mode: " << source_layer_need_sync[0];
+        }
+      }
+    }
+    #else
+    for (int bottom_id = 0; bottom_id < layer_param.bottom_size();
+         ++bottom_id) {
+      const int blob_id = AppendBottom(param, layer_id, bottom_id,
+                                       &available_blobs, &blob_name_to_idx);
+      // If a blob needs backward, this layer should provide it.
+      need_backward |= blob_need_backward_[blob_id];
+    }
+    #endif
+
     int num_top = layer_param.top_size();
     for (int top_id = 0; top_id < num_top; ++top_id) {
       AppendTop(param, layer_id, top_id, &available_blobs, &blob_name_to_idx);
@@ -94,52 +161,32 @@ void Net<Dtype>::Init(const NetParameter& in_param) {
     }
     // After this layer is connected, set it up.
     LOG(INFO) << "Setting up " << layer_names_[layer_id];
-    layers_[layer_id]->SetUp(bottom_vecs_[layer_id], &top_vecs_[layer_id]);
+    layers_[layer_id]->SetUp(bottom_vecs_[layer_id], top_vecs_[layer_id]);
     for (int top_id = 0; top_id < top_vecs_[layer_id].size(); ++top_id) {
       if (blob_loss_weights_.size() <= top_id_vecs_[layer_id][top_id]) {
         blob_loss_weights_.resize(top_id_vecs_[layer_id][top_id] + 1, Dtype(0));
       }
       blob_loss_weights_[top_id_vecs_[layer_id][top_id]] = layer->loss(top_id);
-      LOG(INFO) << "Top shape: " << top_vecs_[layer_id][top_id]->num() << " "
-          << top_vecs_[layer_id][top_id]->channels() << " "
-          << top_vecs_[layer_id][top_id]->height() << " "
-          << top_vecs_[layer_id][top_id]->width() << " ("
-          << top_vecs_[layer_id][top_id]->count() << ")";
+      LOG(INFO) << "Top shape: " << top_vecs_[layer_id][top_id]->shape_string();
       if (layer->loss(top_id)) {
         LOG(INFO) << "    with loss weight " << layer->loss(top_id);
       }
       memory_used_ += top_vecs_[layer_id][top_id]->count();
     }
     DLOG(INFO) << "Memory required for data: " << memory_used_ * sizeof(Dtype);
-    const int blobs_lr_size = layer_param.blobs_lr_size();
+    const int param_size = layer_param.param_size();
     const int num_param_blobs = layers_[layer_id]->blobs().size();
-    CHECK(blobs_lr_size == num_param_blobs || blobs_lr_size == 0)
-        << "Incorrect blobs lr size: should be either 0 "
-        << "or the same as the number of the layer's parameter blobs.";
-    if (blobs_lr_size) {
-      // Check if this layer needs backward operation itself
-      for (int param_id = 0; param_id < blobs_lr_size; ++param_id) {
-        const bool param_need_backward = layer_param.blobs_lr(param_id) > 0;
-        need_backward |= param_need_backward;
-        layers_[layer_id]->set_param_propagate_down(param_id,
-                                                    param_need_backward);
-      }
-    } else if (layers_[layer_id]->blobs().size()) {
-      // catch: if a layer param does not specify blobs_lr, we should assume the
-      // learning rate to be 1. Thus we will need to perform backward.
-      need_backward = true;
-      for (int param_id = 0; param_id < blobs_lr_size; ++param_id) {
-        layers_[layer_id]->set_param_propagate_down(param_id, true);
-      }
+    CHECK_LE(param_size, num_param_blobs)
+        << "Too many params specified for layer " << layer_param.name();
+    ParamSpec default_param_spec;
+    for (int param_id = 0; param_id < num_param_blobs; ++param_id) {
+      const ParamSpec* param_spec = (param_id < param_size) ?
+          &layer_param.param(param_id) : &default_param_spec;
+      const bool param_need_backward = param_spec->lr_mult() > 0;
+      need_backward |= param_need_backward;
+      layers_[layer_id]->set_param_propagate_down(param_id,
+                                                  param_need_backward);
     }
-    const int param_size = layer_param.param_size();
-    CHECK(param_size == num_param_blobs || param_size == 0)
-        << "Incorrect param size: should be either 0 or the same as "
-           "the number of the layer's parameter blobs: " << num_param_blobs;
-    const int blob_share_mode_size = layer_param.blob_share_mode_size();
-    CHECK(blob_share_mode_size == num_param_blobs || blob_share_mode_size == 0)
-        << "Incorrect blob_share_mode size: should be either 0 or the same as "
-           "the number of the layer's parameter blobs: " << num_param_blobs;
     for (int param_id = 0; param_id < num_param_blobs; ++param_id) {
       AppendParam(param, layer_id, param_id);
     }
@@ -148,21 +195,46 @@ void Net<Dtype>::Init(const NetParameter& in_param) {
     if (need_backward) {
       for (int top_id = 0; top_id < top_id_vecs_[layer_id].size(); ++top_id) {
         blob_need_backward_[top_id_vecs_[layer_id][top_id]] = true;
+
+        //special treatment for "Gather" layer
+        //This layer should be transparent to bp inferring.
+        if (strcmp(layers_[layer_id]->type(), "Gather")==0){
+          blob_need_backward_[top_id_vecs_[layer_id][top_id]]
+              = blob_need_backward_[bottom_id_vecs_[layer_id][top_id]];
+        }
       }
     }
   }
   // Go through the net backwards to determine which blobs contribute to the
   // loss.  We can skip backward computation for blobs that don't contribute
   // to the loss.
+  // Also checks if all bottom blobs don't need backward computation (possible
+  // because the skip_propagate_down param) and so we can skip backward
+  // computation for the entire layer
   set<string> blobs_under_loss;
+  set<string> blobs_skip_backp;
   for (int layer_id = layers_.size() - 1; layer_id >= 0; --layer_id) {
     bool layer_contributes_loss = false;
+    bool layer_skip_propagate_down = true;
     for (int top_id = 0; top_id < top_vecs_[layer_id].size(); ++top_id) {
       const string& blob_name = blob_names_[top_id_vecs_[layer_id][top_id]];
       if (layers_[layer_id]->loss(top_id) ||
           (blobs_under_loss.find(blob_name) != blobs_under_loss.end())) {
         layer_contributes_loss = true;
+      }
+      if (blobs_skip_backp.find(blob_name) == blobs_skip_backp.end()) {
+        layer_skip_propagate_down = false;
+      }
+      if (layer_contributes_loss && !layer_skip_propagate_down)
         break;
+    }
+    // If this layer can skip backward computation, also all his bottom blobs
+    // don't need backpropagation
+    if (layer_need_backward_[layer_id] && layer_skip_propagate_down) {
+      layer_need_backward_[layer_id] = false;
+      for (int bottom_id = 0; bottom_id < bottom_vecs_[layer_id].size();
+               ++bottom_id) {
+        bottom_need_backward_[layer_id][bottom_id] = false;
       }
     }
     if (!layer_contributes_loss) { layer_need_backward_[layer_id] = false; }
@@ -181,6 +253,11 @@ void Net<Dtype>::Init(const NetParameter& in_param) {
       } else {
         bottom_need_backward_[layer_id][bottom_id] = false;
       }
+      if (!bottom_need_backward_[layer_id][bottom_id]) {
+        const string& blob_name =
+                   blob_names_[bottom_id_vecs_[layer_id][bottom_id]];
+        blobs_skip_backp.insert(blob_name);
+      }
     }
   }
   // Handle force_backward if needed.
@@ -208,6 +285,9 @@ void Net<Dtype>::Init(const NetParameter& in_param) {
     LOG(INFO) << "This network produces output " << *it;
     net_output_blobs_.push_back(blobs_[blob_name_to_idx[*it]].get());
     net_output_blob_indices_.push_back(blob_name_to_idx[*it]);
+
+    // add output blob name to default excluded blobs
+    excluded_blob_names_.insert(*it);
   }
   for (size_t blob_id = 0; blob_id < blob_names_.size(); ++blob_id) {
     blob_names_index_[blob_names_[blob_id]] = blob_id;
@@ -216,34 +296,34 @@ void Net<Dtype>::Init(const NetParameter& in_param) {
     layer_names_index_[layer_names_[layer_id]] = layer_id;
   }
   GetLearningRateAndWeightDecay();
+  debug_info_ = param.debug_info();
   LOG(INFO) << "Network initialization done.";
   LOG(INFO) << "Memory required for data: " << memory_used_ * sizeof(Dtype);
-  // Don't display debug info by default.
-  debug_info_ = false;
+
+  // optimize memory
+  const bool need_optimze_mem =
+    (param.mem_param().optimize_train() && phase_ == TRAIN)
+    || (param.mem_param().optimize_test() && phase_ == TEST);
+
+  // add additional specified blobs to the exclusion list
+  for (int ex_id = 0; ex_id < param.mem_param().exclude_blob_size(); ++ex_id){
+    excluded_blob_names_.insert(param.mem_param().exclude_blob(ex_id));
+  }
+
+  // launch memory optimization if necessary
+  if (!debug_info_ && need_optimze_mem) {
+    MemoryOptimize();
+  }
 }
 
 template <typename Dtype>
 void Net<Dtype>::FilterNet(const NetParameter& param,
     NetParameter* param_filtered) {
   NetState net_state(param.state());
-  // Let the phase of the net be the current global phase provided in the Caffe
-  // singleton, unless explicitly provided by the state.
-  if (!net_state.has_phase()) {
-    switch (Caffe::phase()) {
-      case Caffe::TRAIN:
-        net_state.set_phase(TRAIN);
-        break;
-      case Caffe::TEST:
-        net_state.set_phase(TEST);
-        break;
-      default:
-        LOG(FATAL) << "Unknown phase: " << Caffe::phase();
-    }
-  }
   param_filtered->CopyFrom(param);
-  param_filtered->clear_layers();
-  for (int i = 0; i < param.layers_size(); ++i) {
-    const LayerParameter& layer_param = param.layers(i);
+  param_filtered->clear_layer();
+  for (int i = 0; i < param.layer_size(); ++i) {
+    const LayerParameter& layer_param = param.layer(i);
     const string& layer_name = layer_param.name();
     CHECK(layer_param.include_size() == 0 || layer_param.exclude_size() == 0)
           << "Specify either include rules or exclude rules; not both.";
@@ -261,7 +341,7 @@ void Net<Dtype>::FilterNet(const NetParameter& param,
       }
     }
     if (layer_included) {
-      param_filtered->add_layers()->CopyFrom(layer_param);
+      param_filtered->add_layer()->CopyFrom(layer_param);
     }
   }
 }
@@ -334,7 +414,7 @@ void Net<Dtype>::AppendTop(const NetParameter& param, const int layer_id,
                            const int top_id, set<string>* available_blobs,
                            map<string, int>* blob_name_to_idx) {
   shared_ptr<LayerParameter> layer_param((layer_id >= 0) ?
-    (new LayerParameter(param.layers(layer_id))) : NULL);
+    (new LayerParameter(param.layer(layer_id))) : NULL);
   const string& blob_name = layer_param ?
       (layer_param->top_size() > top_id ?
           layer_param->top(top_id) : "(automatic)") : param.input(top_id);
@@ -362,29 +442,35 @@ void Net<Dtype>::AppendTop(const NetParameter& param, const int layer_id,
     blobs_.push_back(blob_pointer);
     blob_names_.push_back(blob_name);
     blob_need_backward_.push_back(false);
+    top_layer_indices_.push_back(make_pair(layer_id, blob_id));
     if (blob_name_to_idx) { (*blob_name_to_idx)[blob_name] = blob_id; }
     if (layer_id == -1) {
       // Set the (explicitly specified) dimensions of the input blob.
-      blob_pointer->Reshape(param.input_dim(top_id * 4),
-                            param.input_dim(top_id * 4 + 1),
-                            param.input_dim(top_id * 4 + 2),
-                            param.input_dim(top_id * 4 + 3));
+      if (param.input_dim_size() > 0) {
+        blob_pointer->Reshape(param.input_dim(top_id * 4),
+                              param.input_dim(top_id * 4 + 1),
+                              param.input_dim(top_id * 4 + 2),
+                              param.input_dim(top_id * 4 + 3));
+      } else {
+        blob_pointer->Reshape(param.input_shape(top_id));
+      }
       net_input_blob_indices_.push_back(blob_id);
       net_input_blobs_.push_back(blob_pointer.get());
     } else {
       top_id_vecs_[layer_id].push_back(blob_id);
       top_vecs_[layer_id].push_back(blob_pointer.get());
     }
+
   }
   if (available_blobs) { available_blobs->insert(blob_name); }
 }
 
 // Helper for Net::Init: add a new bottom blob to the net.
 template <typename Dtype>
-int Net<Dtype>::AppendBottom(const NetParameter& param,
-    const int layer_id, const int bottom_id,
-    set<string>* available_blobs, map<string, int>* blob_name_to_idx) {
-  const LayerParameter& layer_param = param.layers(layer_id);
+int Net<Dtype>::AppendBottom(const NetParameter& param, const int layer_id,
+    const int bottom_id, set<string>* available_blobs,
+    map<string, int>* blob_name_to_idx) {
+  const LayerParameter& layer_param = param.layer(layer_id);
   const string& blob_name = layer_param.bottom(bottom_id);
   if (available_blobs->find(blob_name) == available_blobs->end()) {
     LOG(FATAL) << "Unknown blob input " << blob_name
@@ -395,7 +481,12 @@ int Net<Dtype>::AppendBottom(const NetParameter& param,
   bottom_vecs_[layer_id].push_back(blobs_[blob_id].get());
   bottom_id_vecs_[layer_id].push_back(blob_id);
   available_blobs->erase(blob_name);
-  const bool need_backward = blob_need_backward_[blob_id];
+  bool propagate_down = true;
+  // Check if the backpropagation on bottom_id should be skipped
+  if (layer_param.propagate_down_size() > 0)
+    propagate_down = layer_param.propagate_down(bottom_id);
+  const bool need_backward = blob_need_backward_[blob_id] &&
+                          propagate_down;
   bottom_need_backward_[layer_id].push_back(need_backward);
   return blob_id;
 }
@@ -405,7 +496,8 @@ void Net<Dtype>::AppendParam(const NetParameter& param, const int layer_id,
                              const int param_id) {
   const LayerParameter& layer_param = layers_[layer_id]->layer_param();
   const int param_size = layer_param.param_size();
-  string param_name = param_size ? layer_param.param(param_id) : "";
+  string param_name =
+      (param_size > param_id) ? layer_param.param(param_id).name() : "";
   if (param_name.size()) {
     param_display_names_.push_back(param_name);
   } else {
@@ -415,6 +507,7 @@ void Net<Dtype>::AppendParam(const NetParameter& param, const int layer_id,
   }
   const int net_param_id = params_.size();
   params_.push_back(layers_[layer_id]->blobs()[param_id]);
+  param_id_vecs_[layer_id].push_back(net_param_id);
   param_layer_indices_.push_back(make_pair(layer_id, param_id));
   if (!param_size || !param_name.size() || (param_name.size() &&
       param_names_index_.find(param_name) == param_names_index_.end())) {
@@ -422,7 +515,7 @@ void Net<Dtype>::AppendParam(const NetParameter& param, const int layer_id,
     // (i.e., not given a param_name) or explicitly given a name that we
     // haven't already seen.
     param_owners_.push_back(-1);
-    if (param_size) {
+    if (param_name.size()) {
       param_names_index_[param_name] = net_param_id;
     }
   } else {
@@ -439,23 +532,15 @@ void Net<Dtype>::AppendParam(const NetParameter& param, const int layer_id,
     Blob<Dtype>* this_blob = layers_[layer_id]->blobs()[param_id].get();
     Blob<Dtype>* owner_blob =
         layers_[owner_layer_id]->blobs()[owner_param_id].get();
-    const int blob_share_mode_size = layer_param.blob_share_mode_size();
-    if (blob_share_mode_size > param_id &&
-        (layer_param.blob_share_mode(param_id) ==
-         LayerParameter_DimCheckMode_PERMISSIVE)) {
+    const int param_size = layer_param.param_size();
+    if (param_size > param_id && (layer_param.param(param_id).share_mode() ==
+                                  ParamSpec_DimCheckMode_PERMISSIVE)) {
       // Permissive dimension checking -- only check counts are the same.
       CHECK_EQ(this_blob->count(), owner_blob->count())
           << "Shared parameter blobs must have the same count.";
     } else {
       // Strict dimension checking -- all dims must be the same.
-      CHECK_EQ(this_blob->num(), owner_blob->num())
-          << "Shared parameter blobs must have the same num.";
-      CHECK_EQ(this_blob->channels(), owner_blob->channels())
-          << "Shared parameter blobs must have the same channels.";
-      CHECK_EQ(this_blob->height(), owner_blob->height())
-          << "Shared parameter blobs must have the same height.";
-      CHECK_EQ(this_blob->width(), owner_blob->width())
-          << "Shared parameter blobs must have the same width.";
+      CHECK(this_blob->shape() == owner_blob->shape());
     }
     layers_[layer_id]->blobs()[param_id]->ShareData(
         *layers_[owner_layer_id]->blobs()[owner_param_id]);
@@ -465,34 +550,15 @@ void Net<Dtype>::AppendParam(const NetParameter& param, const int layer_id,
 template <typename Dtype>
 void Net<Dtype>::GetLearningRateAndWeightDecay() {
   LOG(INFO) << "Collecting Learning Rate and Weight Decay.";
+  ParamSpec default_param_spec;
   for (int i = 0; i < layers_.size(); ++i) {
     vector<shared_ptr<Blob<Dtype> > >& layer_blobs = layers_[i]->blobs();
-    // push the learning rate mutlipliers
-    if (layers_[i]->layer_param().blobs_lr_size()) {
-      CHECK_EQ(layers_[i]->layer_param().blobs_lr_size(), layer_blobs.size());
-      for (int j = 0; j < layer_blobs.size(); ++j) {
-        float local_lr = layers_[i]->layer_param().blobs_lr(j);
-        CHECK_GE(local_lr, 0.);
-        params_lr_.push_back(local_lr);
-      }
-    } else {
-      for (int j = 0; j < layer_blobs.size(); ++j) {
-        params_lr_.push_back(1.);
-      }
-    }
-    // push the weight decay multipliers
-    if (layers_[i]->layer_param().weight_decay_size()) {
-      CHECK_EQ(layers_[i]->layer_param().weight_decay_size(),
-          layer_blobs.size());
-      for (int j = 0; j < layer_blobs.size(); ++j) {
-        float local_decay = layers_[i]->layer_param().weight_decay(j);
-        CHECK_GE(local_decay, 0.);
-        params_weight_decay_.push_back(local_decay);
-      }
-    } else {
-      for (int j = 0; j < layer_blobs.size(); ++j) {
-        params_weight_decay_.push_back(1.);
-      }
+    for (int j = 0; j < layer_blobs.size(); ++j) {
+      const ParamSpec* param_spec =
+          (layers_[i]->layer_param().param_size() > j) ?
+          &layers_[i]->layer_param().param(j) : &default_param_spec;
+      params_lr_.push_back(param_spec->lr_mult());
+      params_weight_decay_.push_back(param_spec->decay_mult());
     }
   }
 }
@@ -502,13 +568,22 @@ Dtype Net<Dtype>::ForwardFromTo(int start, int end) {
   CHECK_GE(start, 0);
   CHECK_LT(end, layers_.size());
   Dtype loss = 0;
+  if (debug_info_) {
+    for (int i = 0; i < net_input_blobs_.size(); ++i) {
+      InputDebugInfo(i);
+    }
+  }
   for (int i = start; i <= end; ++i) {
     // LOG(ERROR) << "Forwarding " << layer_names_[i];
-    layers_[i]->Reshape(bottom_vecs_[i], &top_vecs_[i]);
-    Dtype layer_loss = layers_[i]->Forward(bottom_vecs_[i], &top_vecs_[i]);
+    Dtype layer_loss = layers_[i]->Forward(bottom_vecs_[i], top_vecs_[i]);
     loss += layer_loss;
     if (debug_info_) { ForwardDebugInfo(i); }
   }
+
+#ifdef USE_CUDNN
+  if (Caffe::mode() == Caffe::GPU)
+    CuDNNConvolutionLayer<Dtype>::RuntimeOptimize(1000);
+#endif
   return loss;
 }
 
@@ -567,15 +642,74 @@ template <typename Dtype>
 void Net<Dtype>::BackwardFromTo(int start, int end) {
   CHECK_GE(end, 0);
   CHECK_LT(start, layers_.size());
+
   for (int i = start; i >= end; --i) {
     if (layer_need_backward_[i]) {
+
+      //DEBUG USE
+//      for (int x = 0; x < top_vecs_[i].size(); ++x){
+//        LOG(INFO)<<"Layer "<<i<<" name "<<layer_names_[i]
+//          <<" top blob "<<x<<" ptr: "<<top_vecs_[i][x]->gpu_diff();
+//      }
+//
+//      for (int x = 0; x < bottom_vecs_[i].size(); ++x){
+//        LOG(INFO)<<"Layer "<<i<<" name "<<layer_names_[i]
+//          <<" bottom blob "<<x<<" ptr: "<<bottom_vecs_[i][x]->mutable_gpu_diff();
+//      }
+      //END DEBUG
+
       layers_[i]->Backward(
-          top_vecs_[i], bottom_need_backward_[i], &bottom_vecs_[i]);
+          top_vecs_[i], bottom_need_backward_[i], bottom_vecs_[i]);
+
       if (debug_info_) { BackwardDebugInfo(i); }
+
+#ifdef USE_MPI
+      if ((Caffe::parallel_mode() == Caffe::MPI) && (Caffe::remaining_sub_iter() == 0)) {
+        for (int n = 0; n < param_layer_indices_.size(); ++n) {
+          bool ready_for_sync = false;
+
+          //decide whether we need to sync the gradient of this blob
+          if ((param_layer_indices_[n].first == i)) {
+            if (param_owners_[n] == -1) {
+              ready_for_sync = true;
+            } else {
+              // this blob is a shared one, we need to make sure no more gradients will be
+              // accumulated to it before transmission
+              int owner_id = param_owners_[n];
+              ready_for_sync = true;
+              for (int m = n - 1; m >= 0; --m) {
+                if ((param_owners_[m] == owner_id) && (param_layer_indices_[m].first >= end)) {
+                  // there are still layers holding this shared blob,
+                  // not secure the do the transmission
+                  ready_for_sync = false;
+                  break;
+                }
+              }
+            }
+          }
+          //sync gradient
+          if (ready_for_sync && layers_[i]->need_sync())
+            caffe_iallreduce(
+                this->params_[n]->mutable_cpu_diff(),
+                this->params_[n]->count()
+            );
+        }
+      }
+#endif //USE_MPI
+
     }
   }
 }
 
+template <typename Dtype>
+void Net<Dtype>::InputDebugInfo(const int input_id) {
+  const Blob<Dtype>& blob = *net_input_blobs_[input_id];
+  const string& blob_name = blob_names_[net_input_blob_indices_[input_id]];
+  const Dtype data_abs_val_mean = blob.asum_data() / blob.count();
+  LOG(INFO) << "    [Forward] "
+     << "Input " << blob_name << " data: " << data_abs_val_mean;
+}
+
 template <typename Dtype>
 void Net<Dtype>::ForwardDebugInfo(const int layer_id) {
   for (int top_id = 0; top_id < top_vecs_[layer_id].size(); ++top_id) {
@@ -586,6 +720,16 @@ void Net<Dtype>::ForwardDebugInfo(const int layer_id) {
        << "Layer " << layer_names_[layer_id] << ", top blob " << blob_name
        << " data: " << data_abs_val_mean;
   }
+  for (int param_id = 0; param_id < layers_[layer_id]->blobs().size();
+       ++param_id) {
+    const Blob<Dtype>& blob = *layers_[layer_id]->blobs()[param_id];
+    const int net_param_id = param_id_vecs_[layer_id][param_id];
+    const string& blob_name = param_display_names_[net_param_id];
+    const Dtype data_abs_val_mean = blob.asum_data() / blob.count();
+    LOG(INFO) << "    [Forward] "
+       << "Layer " << layer_names_[layer_id] << ", param blob " << blob_name
+       << " data: " << data_abs_val_mean;
+  }
 }
 
 template <typename Dtype>
@@ -635,7 +779,7 @@ void Net<Dtype>::UpdateDebugInfo(const int param_id) {
 }
 
 template <typename Dtype>
-void Net<Dtype>::ShareTrainedLayersWith(Net* other) {
+void Net<Dtype>::ShareTrainedLayersWith(const Net* other) {
   int num_source_layers = other->layers().size();
   for (int i = 0; i < num_source_layers; ++i) {
     Layer<Dtype>* source_layer = other->layers()[i].get();
@@ -656,10 +800,7 @@ void Net<Dtype>::ShareTrainedLayersWith(Net* other) {
         << "Incompatible number of blobs for layer " << source_layer_name;
     for (int j = 0; j < target_blobs.size(); ++j) {
       Blob<Dtype>* source_blob = source_layer->blobs()[j].get();
-      CHECK_EQ(target_blobs[j]->num(), source_blob->num());
-      CHECK_EQ(target_blobs[j]->channels(), source_blob->channels());
-      CHECK_EQ(target_blobs[j]->height(), source_blob->height());
-      CHECK_EQ(target_blobs[j]->width(), source_blob->width());
+      CHECK(target_blobs[j]->shape() == source_blob->shape());
       target_blobs[j]->ShareData(*source_blob);
     }
   }
@@ -678,20 +819,40 @@ void Net<Dtype>::BackwardTo(int end) {
 template <typename Dtype>
 void Net<Dtype>::Backward() {
   BackwardFromTo(layers_.size() - 1, 0);
+  if (debug_info_) {
+    Dtype asum_data = 0, asum_diff = 0, sumsq_data = 0, sumsq_diff = 0;
+    for (int i = 0; i < params_.size(); ++i) {
+      if (param_owners_[i] >= 0) { continue; }
+      asum_data += params_[i]->asum_data();
+      asum_diff += params_[i]->asum_diff();
+      sumsq_data += params_[i]->sumsq_data();
+      sumsq_diff += params_[i]->sumsq_diff();
+    }
+    const Dtype l2norm_data = std::sqrt(sumsq_data);
+    const Dtype l2norm_diff = std::sqrt(sumsq_diff);
+    LOG(ERROR) << "    [Backward] All net params (data, diff): "
+        << "L1 norm = (" << asum_data << ", " << asum_diff << "); "
+        << "L2 norm = (" << l2norm_data << ", " << l2norm_diff << ")";
+  }
 }
 
 template <typename Dtype>
 void Net<Dtype>::Reshape() {
   for (int i = 0; i < layers_.size(); ++i) {
-    layers_[i]->Reshape(bottom_vecs_[i], &top_vecs_[i]);
+    layers_[i]->Reshape(bottom_vecs_[i], top_vecs_[i]);
   }
+
+#ifdef USE_CUDNN
+  if (Caffe::mode() == Caffe::GPU)
+    CuDNNConvolutionLayer<Dtype>::RuntimeOptimize(1000);
+#endif
 }
 
 template <typename Dtype>
 void Net<Dtype>::CopyTrainedLayersFrom(const NetParameter& param) {
-  int num_source_layers = param.layers_size();
+  int num_source_layers = param.layer_size();
   for (int i = 0; i < num_source_layers; ++i) {
-    const LayerParameter& source_layer = param.layers(i);
+    const LayerParameter& source_layer = param.layer(i);
     const string& source_layer_name = source_layer.name();
     int target_layer_id = 0;
     while (target_layer_id != layer_names_.size() &&
@@ -708,11 +869,8 @@ void Net<Dtype>::CopyTrainedLayersFrom(const NetParameter& param) {
     CHECK_EQ(target_blobs.size(), source_layer.blobs_size())
         << "Incompatible number of blobs for layer " << source_layer_name;
     for (int j = 0; j < target_blobs.size(); ++j) {
-      CHECK_EQ(target_blobs[j]->num(), source_layer.blobs(j).num());
-      CHECK_EQ(target_blobs[j]->channels(), source_layer.blobs(j).channels());
-      CHECK_EQ(target_blobs[j]->height(), source_layer.blobs(j).height());
-      CHECK_EQ(target_blobs[j]->width(), source_layer.blobs(j).width());
-      target_blobs[j]->FromProto(source_layer.blobs(j));
+      const bool kReshape = false;
+      target_blobs[j]->FromProto(source_layer.blobs(j), kReshape);
     }
   }
 }
@@ -725,7 +883,7 @@ void Net<Dtype>::CopyTrainedLayersFrom(const string trained_filename) {
 }
 
 template <typename Dtype>
-void Net<Dtype>::ToProto(NetParameter* param, bool write_diff) {
+void Net<Dtype>::ToProto(NetParameter* param, bool write_diff) const {
   param->Clear();
   param->set_name(name_);
   // Add bottom and top
@@ -734,7 +892,7 @@ void Net<Dtype>::ToProto(NetParameter* param, bool write_diff) {
   }
   DLOG(INFO) << "Serializing " << layers_.size() << " layers";
   for (int i = 0; i < layers_.size(); ++i) {
-    LayerParameter* layer_param = param->add_layers();
+    LayerParameter* layer_param = param->add_layer();
     for (int j = 0; j < bottom_id_vecs_[i].size(); ++j) {
       layer_param->add_bottom(blob_names_[bottom_id_vecs_[i][j]]);
     }
@@ -762,15 +920,15 @@ void Net<Dtype>::Update() {
       owner_diff = params_[param_owners_[i]]->mutable_cpu_diff();
       caffe_add(count, this_diff, owner_diff, owner_diff);
       break;
-#ifndef CPU_ONLY
     case Caffe::GPU:
+#ifndef CPU_ONLY
       this_diff = params_[i]->gpu_diff();
       owner_diff = params_[param_owners_[i]]->mutable_gpu_diff();
       caffe_gpu_add(count, this_diff, owner_diff, owner_diff);
-      break;
 #else
       NO_GPU;
 #endif
+      break;
     default:
       LOG(FATAL) << "Unknown caffe mode: " << Caffe::mode();
     }
@@ -784,16 +942,16 @@ void Net<Dtype>::Update() {
 }
 
 template <typename Dtype>
-bool Net<Dtype>::has_blob(const string& blob_name) {
+bool Net<Dtype>::has_blob(const string& blob_name) const {
   return blob_names_index_.find(blob_name) != blob_names_index_.end();
 }
 
 template <typename Dtype>
 const shared_ptr<Blob<Dtype> > Net<Dtype>::blob_by_name(
-    const string& blob_name) {
+    const string& blob_name) const {
   shared_ptr<Blob<Dtype> > blob_ptr;
   if (has_blob(blob_name)) {
-    blob_ptr = blobs_[blob_names_index_[blob_name]];
+    blob_ptr = blobs_[blob_names_index_.find(blob_name)->second];
   } else {
     blob_ptr.reset((Blob<Dtype>*)(NULL));
     LOG(WARNING) << "Unknown blob name " << blob_name;
@@ -802,16 +960,16 @@ const shared_ptr<Blob<Dtype> > Net<Dtype>::blob_by_name(
 }
 
 template <typename Dtype>
-bool Net<Dtype>::has_layer(const string& layer_name) {
+bool Net<Dtype>::has_layer(const string& layer_name) const {
   return layer_names_index_.find(layer_name) != layer_names_index_.end();
 }
 
 template <typename Dtype>
 const shared_ptr<Layer<Dtype> > Net<Dtype>::layer_by_name(
-    const string& layer_name) {
+    const string& layer_name) const {
   shared_ptr<Layer<Dtype> > layer_ptr;
   if (has_layer(layer_name)) {
-    layer_ptr = layers_[layer_names_index_[layer_name]];
+    layer_ptr = layers_[layer_names_index_.find(layer_name)->second];
   } else {
     layer_ptr.reset((Layer<Dtype>*)(NULL));
     LOG(WARNING) << "Unknown layer name " << layer_name;
@@ -819,6 +977,271 @@ const shared_ptr<Layer<Dtype> > Net<Dtype>::layer_by_name(
   return layer_ptr;
 }
 
+/**
+ * This class is the core of memory optimization
+ * It simulates a abstract ``slot'' with preemption.
+ * During the dry-run process, a dynamic number of slots are created when deemed necessary (no empty slot is available
+ * when we are acquring one).
+ * By keeping track of which blob used which slot, we can safely make a series of blobs share the underlying storage
+ * without the risk of data corruption.
+ */
+class SlotMeta {
+public:
+    SlotMeta()
+      : key_(), ref_(0) { }
+
+    SlotMeta(const string& key, int ref)
+      : key_(key), ref_(ref) { }
+
+    inline const string& key() const { return key_; }
+    inline int ref() const { return ref_; }
+
+    inline void DerefOne(){
+      CHECK_GT(ref_, 0);
+      ref_ -= 1;
+      if (ref_ == 0){
+        key_.clear();
+      }
+    }
+
+    inline void IncRef(){ref_ += 1;}
+
+    void RefSlot(const string& key, int ref) {
+      CHECK(key_.empty());
+      CHECK_NE(key_, key);
+      CHECK_GT(ref, 0);
+      key_ = key;
+      ref_ = ref;
+    }
+
+    inline bool Empty(){
+      return key_.empty();
+    }
+
+    inline bool isSlot(const string& key){return key_ == key;}
+
+private:
+    string key_;
+    int ref_;
+};
+
+size_t AcquireSlot(vector<SlotMeta>& slot_vec, const string& key, int ref) {
+  for (size_t i = 0 ; i < slot_vec.size(); ++i) {
+    if (slot_vec[i].Empty()) {
+      slot_vec[i].RefSlot(key, ref);
+      return i;
+    }
+  }
+
+  // no available slot, need a new one
+  slot_vec.push_back(SlotMeta(key, ref));
+
+  return slot_vec.size() - 1;
+}
+
+int FindSlot(vector<SlotMeta>& slot_vec, const string& key){
+  for (int i = 0; i < slot_vec.size(); ++i){
+    if (slot_vec[i].isSlot(key)){
+      return i;
+    }
+  }
+  return -1;
+}
+
+inline bool check_exclude(const std::set<string>& exclude_list, const string& blob_name){
+  return exclude_list.find(blob_name) != exclude_list.end();
+}
+
+template <typename Dtype>
+void Net<Dtype>::MemoryOptimize() {
+  // Dry run phase
+  // In this phase, we assume the network topology has been setup
+  boost::unordered_map<string, int> slot_index;
+
+  vector<SlotMeta> slots;
+
+  // Forward pass, try to reuse blobs' data memory
+  for (int i = 0; i < layers_.size(); ++i) {
+    const vector<Blob<Dtype>* >& layer_top = top_vecs_[i];
+    const vector<Blob<Dtype>* >& layer_bottom = bottom_vecs_[i];
+    LOG(INFO) << "layer " << i << " " << layer_names_[i];
+    // Find slot for each top blob's data
+    for (int i_top = 0; i_top < layer_top.size(); ++i_top) {
+      const string& top_name = blob_names_[top_id_vecs_[i][i_top]];
+
+      if (check_exclude(excluded_blob_names_, top_name)) continue;
+
+      int idx = FindSlot(slots, top_name + "_data");
+      if (idx == -1) {
+        // Detect share data conditions
+        bool sharing_data = false;
+        for (int i_bottom = 0; i_bottom < layer_bottom.size(); ++i_bottom) {
+          if (layers_[i]->is_sharing_data(i_top, i_bottom)) {
+            sharing_data = true;
+            const string& bottom_name = blob_names_[bottom_id_vecs_[i][i_bottom]];
+            idx = FindSlot(slots, bottom_name + "_data");
+            LOG(INFO) << "top " << top_name
+                      << " shares data with bottom " << bottom_name
+                      << " slot " << idx;
+            break;
+          }
+        }
+        if (!sharing_data) {
+          if (!layers_[i]->loss(i_top)) {
+            idx = (int)AcquireSlot(slots, top_name + "_data", 1);
+            slot_index[top_name + "_data"] = idx;
+            LOG(INFO) << "top " << top_name << " acquires data slot " << idx;
+          }
+        } else {
+          if (idx != -1) {
+            // idx == -1 means the top blob is (recursively) sharing data with an excluded bottom blob
+            // This makes this blob itself excluded from the optimization
+            slots[idx].IncRef();
+            slot_index[top_name + "_data"] = idx;
+          }
+        }
+      } else {
+        // Top data blob is already assigned a slot (maybe inplace layer).
+        slots[idx].IncRef();
+        LOG(INFO) << "top " << top_name << " refers to data slot " << idx;
+      }
+    }
+    // Deref bottom blob's data slot if this layer does not propagate down.
+    if (phase_ == TRAIN && layer_need_backward_[i]) continue;
+    for (int i_bottom = 0; i_bottom < layer_bottom.size(); ++i_bottom) {
+      const string& bottom_name = blob_names_[bottom_id_vecs_[i][i_bottom]];
+
+      if (check_exclude(excluded_blob_names_, bottom_name)) continue;
+
+      int idx = FindSlot(slots, bottom_name + "_data");
+      if (slot_index.find(bottom_name + "_data") != slot_index.end()) {
+        idx = slot_index[bottom_name + "_data"];
+      }
+      if (idx >= 0) {
+        // idx == -1 if this is an input blob
+        // as a good practice, we do not touch the input blobs' data memory cause leads to unwanted behaviors.
+        slots[idx].DerefOne();
+      }
+      LOG(INFO) << "bottom " << bottom_name << " derefs data slot " << idx;
+    }
+  }
+
+  // backward pass, try to reuse blobs' diff memory
+  for (int i = (layers_.size() -1); i >=0; --i){
+    vector<Blob<Dtype>* >& layer_top = top_vecs_[i];
+    vector<int> layer_top_idx = top_id_vecs_[i];
+    vector<Blob<Dtype>* >& layer_bottom = bottom_vecs_[i];
+    vector<int> layer_bottom_idx = bottom_id_vecs_[i];
+
+    LOG(INFO)<<"layer id: "<<i<<" name: "<<layer_names_[i];
+
+    // first deal with bottoms
+    for (int i_bottom = 0; i_bottom < layer_bottom.size(); ++i_bottom){
+      const string& bottom_name = blob_names_[layer_bottom_idx[i_bottom]];
+
+      if (check_exclude(excluded_blob_names_, bottom_name)) continue;
+
+      int idx = FindSlot(slots, bottom_name + "_diff");
+      if (idx == -1){
+        //detect share diff conditions
+        bool sharing_diff = false;
+        for (int i_top = 0; i_top < layer_top.size(); ++i_top){
+          if(layers_[i]->is_sharing_diff(i_top, i_bottom)){
+            const string& top_name = blob_names_[layer_top_idx[i_top]];
+            sharing_diff = true;
+            idx = FindSlot(slots, top_name + "_diff");
+          }
+        }
+        if (!sharing_diff) {
+          idx = (int) AcquireSlot(slots, bottom_name + "_diff", 1);
+          slot_index[bottom_name + "_diff"] = idx;
+          LOG(INFO) << "acquired slot for new blob";
+        }else{
+          LOG(INFO) << "sharing diff using slot "<<idx;
+          if(idx != -1) {
+            // idx == -1 means the bottom blob is (recursively) sharing diff with an excluded top blob
+            // This makes this blob itself excluded from the optimization
+            slots[idx].IncRef();
+            slot_index[bottom_name + "_diff"] = idx;
+          }
+        }
+      }else{
+        // bottom is already registered
+        // usually this means in-place operation
+        slots[idx].IncRef();
+      }
+      LOG(INFO)<<"bottom blob "<<i_bottom<<" name "
+               <<bottom_name<<" slot id "<<idx;
+    }
+
+    // then deal with top
+    for (int i_top = 0; i_top < layer_top.size(); ++i_top){
+      const string& top_name = blob_names_[layer_top_idx[i_top]];
+
+      if (check_exclude(excluded_blob_names_, top_name)) continue;
+
+      // find the top in the slots
+      int idx = FindSlot(slots, top_name + "_diff");
+
+      // look for shared diff
+      if (slot_index.find(top_name + "_diff") != slot_index.end()){
+        idx = slot_index[top_name + "_diff"];
+      }
+      //after the layer's operation, the refcount of top should be decreased by 1
+      if (idx != -1)
+        slots[idx].DerefOne();
+
+      LOG(INFO) << "top blob " << i_top
+                << " name " << top_name << " slot id " << idx;
+    }
+  }
+
+  // Memory assignment phase
+  shared_storage_.resize(slots.size());
+  for (int i_mem = 0; i_mem < shared_storage_.size(); i_mem++){
+    shared_storage_[i_mem].reset(new SyncedMemory(1));
+  }
+
+  size_t count_raw = 0;
+  size_t count_opt = 0;
+  for (int i_blob = 0; i_blob < blobs_.size(); ++i_blob){
+    const string& name = blob_names_[i_blob];
+    const size_t bytes = blobs_[i_blob]->count() * sizeof(Dtype);
+    count_raw += bytes * 2;
+    int idx = -1;
+
+    // all blobs in the same slot share a same externally hosted SyncedMem instance
+    // we will keep track of the estimated memory usage reduction while linking them to the SyncedMem
+    if (slot_index.find(name + "_data") != slot_index.end()) {
+      idx = slot_index[name + "_data"];
+      blobs_[i_blob]->SetDataStorage(shared_storage_[idx]);
+      shared_storage_[idx]->Resize(bytes);
+    } else {
+      count_opt += bytes;
+    }
+    LOG(INFO) << "blob " << i_blob
+              << " name " << blob_names_[i_blob]
+              << " data idx " << idx;
+    if (slot_index.find(name + "_diff") != slot_index.end()) {
+      idx = slot_index[name + "_diff"];
+      blobs_[i_blob]->SetDiffStorage(shared_storage_[idx]);
+      shared_storage_[idx]->Resize(bytes);
+    } else {
+      count_opt += bytes;
+    }
+    LOG(INFO) << "blob " << i_blob
+              << " name " << blob_names_[i_blob]
+              << " diff idx " << idx;
+  }
+
+  for (int i_mem = 0; i_mem < shared_storage_.size(); i_mem++){
+    LOG(INFO) << "storage memory slot " << i_mem
+              << " size " << shared_storage_[i_mem]->size();
+    count_opt += shared_storage_[i_mem]->size();
+  }
+
+  LOG(INFO) << "raw memory " << count_raw << " opt memory " << count_opt;
+}
 INSTANTIATE_CLASS(Net);
 
 }  // namespace caffe
diff --git a/src/caffe/proto/CMakeLists.txt b/src/caffe/proto/CMakeLists.txt
deleted file mode 100644
index 186a856509b..00000000000
--- a/src/caffe/proto/CMakeLists.txt
+++ /dev/null
@@ -1,37 +0,0 @@
-project( Proto )
-
-#	Google Protocol Buffers
-find_package( Protobuf REQUIRED )
-
-# As of Ubuntu 14.04 protoc is no longer a part of libprotobuf-dev package and should be installed
-# separately as in: sudo apt-get install protobuf-compiler
-if(PROTOBUF_PROTOC_EXECUTABLE)
-	message(STATUS "Found PROTOBUF Compiler: ${PROTOBUF_PROTOC_EXECUTABLE}")
-else()
-	message(FATAL_ERROR "Could not find PROTOBUF Compiler")
-endif()
-
-include_directories(${PROTOBUF_INCLUDE_DIR})
-file(GLOB ProtoFiles "${CMAKE_CURRENT_SOURCE_DIR}/*.proto")
-PROTOBUF_GENERATE_CPP(ProtoSources ProtoHeaders ${ProtoFiles})
-
-add_library(proto
-        ${ProtoSources}
-        ${ProtoHeaders}
-)
-
-target_link_libraries(proto ${PROTOBUF_LIBRARIES})
-
-# Create proto include directory
-file(MAKE_DIRECTORY ${CMAKE_SOURCE_DIR}/include/caffe/proto)
-
-# Copy proto headers to include/caffe/proto/
-foreach(header ${ProtoHeaders})
-
-    ADD_CUSTOM_COMMAND(TARGET proto
-        COMMAND cmake -E copy ${header}
-        ${Caffe_INCLUDE_DIRS}/caffe/proto/
-        DEPENDS ${header}
-)
-
-endforeach(header)
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index 9395c38f3e9..e45c28221cc 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -2,13 +2,21 @@ syntax = "proto2";
 
 package caffe;
 
+// Specifies the shape (dimensions) of a Blob.
+message BlobShape {
+  repeated int64 dim = 1 [packed = true];
+}
+
 message BlobProto {
+  optional BlobShape shape = 7;
+  repeated float data = 5 [packed = true];
+  repeated float diff = 6 [packed = true];
+
+  // 4D dimensions -- deprecated.  Use "shape" instead.
   optional int32 num = 1 [default = 0];
   optional int32 channels = 2 [default = 0];
   optional int32 height = 3 [default = 0];
   optional int32 width = 4 [default = 0];
-  repeated float data = 5 [packed = true];
-  repeated float diff = 6 [packed = true];
 }
 
 // The BlobProtoVector is simply a way to pass multiple blobproto instances
@@ -26,6 +34,8 @@ message Datum {
   optional int32 label = 5;
   // Optionally, the datum could also hold float data.
   repeated float float_data = 6;
+  // If true data contains an encoded image that need to be decoded
+  optional bool encoded = 7 [default = false];
 }
 
 message FillerParameter {
@@ -36,20 +46,32 @@ message FillerParameter {
   optional float max = 4 [default = 1]; // the max value in uniform filler
   optional float mean = 5 [default = 0]; // the mean value in Gaussian filler
   optional float std = 6 [default = 1]; // the std value in Gaussian filler
-  // The expected number of non-zero input weights for a given output in
+  // The expected number of non-zero output weights for a given input in
   // Gaussian filler -- the default -1 means don't perform sparsification.
   optional int32 sparse = 7 [default = -1];
+  // Normalize the filler variance by fan_in, fan_out, or their average.
+  // Applies to 'xavier' and 'msra' fillers.
+  enum VarianceNorm {
+    FAN_IN = 0;
+    FAN_OUT = 1;
+    AVERAGE = 2;
+  }
+  optional VarianceNorm variance_norm = 8 [default = FAN_IN];
 }
 
 message NetParameter {
   optional string name = 1; // consider giving the network a name
-  repeated LayerParameter layers = 2; // a bunch of layers.
   // The input blobs to the network.
   repeated string input = 3;
-  // The dim of the input blobs. For each input blob there should be four
+  // The shape of the input blobs.
+  repeated BlobShape input_shape = 8;
+
+  // 4D input dimensions -- deprecated.  Use "shape" instead.
+  // If specified, for each input blob there should be four
   // values specifying the num, channels, height and width of the input blob.
   // Thus, there should be a total of (4 * #input) numbers.
   repeated int32 input_dim = 4;
+
   // Whether the network will force every layer to carry out backward operation.
   // If set False, then whether to carry out backward is determined
   // automatically according to the net structure and learning rates.
@@ -58,12 +80,26 @@ message NetParameter {
   // Some layers may be included/excluded depending on this state and the states
   // specified in the layers' include and exclude fields.
   optional NetState state = 6;
+
+  // Print debugging information about results while running Net::Forward,
+  // Net::Backward, and Net::Update.
+  optional bool debug_info = 7 [default = false];
+
+  // The layers that make up the net.  Each of their configurations, including
+  // connectivity and behavior, is specified as a LayerParameter.
+  repeated LayerParameter layer = 100;  // ID 100 so layers are printed last.
+
+  // The configuration of memory optimization
+  optional MemoryOptimizationParameter mem_param = 200;
+
+  // DEPRECATED: use 'layer' instead.
+  repeated V1LayerParameter layers = 2;
 }
 
 // NOTE
 // Update the next available ID when you add a new SolverParameter field.
 //
-// SolverParameter next available ID: 33 (last added: test_initialization)
+// SolverParameter next available ID: 39 (last added: group_id)
 message SolverParameter {
   //////////////////////////////////////////////////////////////////////////////
   // Specifying the train and test networks
@@ -113,7 +149,11 @@ message SolverParameter {
   // the number of iterations between displaying info. If display = 0, no info
   // will be displayed.
   optional int32 display = 6;
+  // Display the loss averaged over the last average_loss iterations
+  optional int32 average_loss = 33 [default = 1];
   optional int32 max_iter = 7; // the maximum number of iterations
+  // accumulate gradients over `iter_size` x `batch_size` instances
+  optional int32 iter_size = 36 [default = 1];
   optional string lr_policy = 8; // The learning rate decay policy.
   optional float gamma = 9; // The parameter to compute the learning rate.
   optional float power = 10; // The parameter to compute the learning rate.
@@ -122,7 +162,15 @@ message SolverParameter {
   // regularization types supported: L1 and L2
   // controlled by weight_decay
   optional string regularization_type = 29 [default = "L2"];
-  optional int32 stepsize = 13; // the stepsize for learning rate policy "step"
+  // the stepsize for learning rate policy "step"
+  optional int32 stepsize = 13;
+  // the stepsize for learning rate policy "multistep"
+  repeated int32 stepvalue = 34;
+
+  // Set clip_gradients to >= 0 to clip parameter gradients to that L2 norm,
+  // whenever their actual L2 norm is larger.
+  optional float clip_gradients = 35 [default = -1];
+
   optional int32 snapshot = 14 [default = 0]; // The snapshot interval
   optional string snapshot_prefix = 15; // The prefix for the snapshot.
   // whether to snapshot diff in the results or not. Snapshotting diff will help
@@ -135,7 +183,8 @@ message SolverParameter {
   }
   optional SolverMode solver_mode = 17 [default = GPU];
   // the device_id will that be used in GPU mode. Use device_id = 0 in default.
-  optional int32 device_id = 18 [default = 0];
+  repeated int32 device_id = 18 ;
+  repeated int32 group_id = 38;
   // If non-negative, the seed with which the Solver will initialize the Caffe
   // random number generator -- useful for reproducible results. Otherwise,
   // (and by default) initialize using a seed derived from the system clock.
@@ -157,6 +206,10 @@ message SolverParameter {
 
   // If false, don't save a snapshot after training finishes.
   optional bool snapshot_after_train = 28 [default = true];
+
+  // Total memory allowed to be used for workspaces in cudnn's convolution, in MBs. default is 300MB.
+  // The framework will try to find the fastest setup given this limit.
+  optional int32 richness = 37 [default = 300];
 }
 
 // A message that stores the solver snapshots
@@ -164,6 +217,7 @@ message SolverState {
   optional int32 iter = 1; // The current iteration
   optional string learned_net = 2; // The file that stores the learned net.
   repeated BlobProto history = 3; // The history for sgd solvers
+  optional int32 current_step = 4 [default = 0]; // The current step for learning rate
 }
 
 enum Phase {
@@ -195,141 +249,123 @@ message NetStateRule {
   repeated string not_stage = 5;
 }
 
-// NOTE
-// Update the next available ID when you add a new LayerParameter field.
-//
-// LayerParameter next available ID: 41 (last added: contrastive_loss_param)
-message LayerParameter {
-  repeated string bottom = 2; // the name of the bottom blobs
-  repeated string top = 3; // the name of the top blobs
-  optional string name = 4; // the layer name
-
-  // Rules controlling whether and when a layer is included in the network,
-  // based on the current NetState.  You may specify a non-zero number of rules
-  // to include OR exclude, but not both.  If no include or exclude rules are
-  // specified, the layer is always included.  If the current NetState meets
-  // ANY (i.e., one or more) of the specified rules, the layer is
-  // included/excluded.
-  repeated NetStateRule include = 32;
-  repeated NetStateRule exclude = 33;
-
-  // NOTE
-  // Add new LayerTypes to the enum below in lexicographical order (other than
-  // starting with NONE), starting with the next available ID in the comment
-  // line above the enum. Update the next available ID when you add a new
-  // LayerType.
-  //
-  // LayerType next available ID: 38 (last added: CONTRASTIVE_LOSS)
-  enum LayerType {
-    // "NONE" layer type is 0th enum element so that we don't cause confusion
-    // by defaulting to an existent LayerType (instead, should usually error if
-    // the type is unspecified).
-    NONE = 0;
-    ABSVAL = 35;
-    ACCURACY = 1;
-    ARGMAX = 30;
-    BNLL = 2;
-    CONCAT = 3;
-    CONTRASTIVE_LOSS = 37;
-    CONVOLUTION = 4;
-    DATA = 5;
-    DROPOUT = 6;
-    DUMMY_DATA = 32;
-    EUCLIDEAN_LOSS = 7;
-    ELTWISE = 25;
-    FLATTEN = 8;
-    HDF5_DATA = 9;
-    HDF5_OUTPUT = 10;
-    HINGE_LOSS = 28;
-    IM2COL = 11;
-    IMAGE_DATA = 12;
-    INFOGAIN_LOSS = 13;
-    INNER_PRODUCT = 14;
-    LRN = 15;
-    MEMORY_DATA = 29;
-    MULTINOMIAL_LOGISTIC_LOSS = 16;
-    MVN = 34;
-    POOLING = 17;
-    POWER = 26;
-    RELU = 18;
-    SIGMOID = 19;
-    SIGMOID_CROSS_ENTROPY_LOSS = 27;
-    SILENCE = 36;
-    SOFTMAX = 20;
-    SOFTMAX_LOSS = 21;
-    SPLIT = 22;
-    SLICE = 33;
-    TANH = 23;
-    WINDOW_DATA = 24;
-    THRESHOLD = 31;
-  }
-  optional LayerType type = 5; // the layer type from the enum above
-
-  // The blobs containing the numeric parameters of the layer
-  repeated BlobProto blobs = 6;
+// Specifies training parameters (multipliers on global learning constants,
+// and the name and other settings used for weight sharing).
+message ParamSpec {
   // The names of the parameter blobs -- useful for sharing parameters among
-  // layers (but never required).
-  repeated string param = 1001;
+  // layers, but never required otherwise.  To share a parameter between two
+  // layers, give it a (non-empty) name.
+  optional string name = 1;
+
   // Whether to require shared weights to have the same shape, or just the same
   // count -- defaults to STRICT if unspecified.
-  repeated DimCheckMode blob_share_mode = 1002;
+  optional DimCheckMode share_mode = 2;
   enum DimCheckMode {
     // STRICT (default) requires that num, channels, height, width each match.
     STRICT = 0;
     // PERMISSIVE requires only the count (num*channels*height*width) to match.
     PERMISSIVE = 1;
   }
-  // The ratio that is multiplied on the global learning rate. If you want to
-  // set the learning ratio for one blob, you need to set it for all blobs.
-  repeated float blobs_lr = 7;
-  // The weight decay that is multiplied on the global weight decay.
-  repeated float weight_decay = 8;
+
+  // The multiplier on the global learning rate for this parameter.
+  optional float lr_mult = 3 [default = 1.0];
+
+  // The multiplier on the global weight decay for this parameter.
+  optional float decay_mult = 4 [default = 1.0];
+}
+
+// NOTE
+// Update the next available ID when you add a new LayerParameter field.
+//
+// LayerParameter next available layer-specific ID: 163 (last added: batch_reduction_param)
+message LayerParameter {
+  optional string name = 1; // the layer name
+  optional string type = 2; // the layer type
+  repeated string bottom = 3; // the name of each bottom blob
+  repeated string top = 4; // the name of each top blob
+
+  // The train / test phase for computation.
+  optional Phase phase = 10;
 
   // The amount of weight to assign each top blob in the objective.
   // Each layer assigns a default value, usually of either 0 or 1,
   // to each top blob.
-  repeated float loss_weight = 35;
+  repeated float loss_weight = 5;
 
-  optional AccuracyParameter accuracy_param = 27;
-  optional ArgMaxParameter argmax_param = 23;
-  optional ConcatParameter concat_param = 9;
-  optional ContrastiveLossParameter contrastive_loss_param = 40;
-  optional ConvolutionParameter convolution_param = 10;
-  optional DataParameter data_param = 11;
-  optional DropoutParameter dropout_param = 12;
-  optional DummyDataParameter dummy_data_param = 26;
-  optional EltwiseParameter eltwise_param = 24;
-  optional HDF5DataParameter hdf5_data_param = 13;
-  optional HDF5OutputParameter hdf5_output_param = 14;
-  optional HingeLossParameter hinge_loss_param = 29;
-  optional ImageDataParameter image_data_param = 15;
-  optional InfogainLossParameter infogain_loss_param = 16;
-  optional InnerProductParameter inner_product_param = 17;
-  optional LRNParameter lrn_param = 18;
-  optional MemoryDataParameter memory_data_param = 22;
-  optional MVNParameter mvn_param = 34;
-  optional PoolingParameter pooling_param = 19;
-  optional PowerParameter power_param = 21;
-  optional ReLUParameter relu_param = 30;
-  optional SigmoidParameter sigmoid_param = 38;
-  optional SoftmaxParameter softmax_param = 39;
-  optional SliceParameter slice_param = 31;
-  optional TanHParameter tanh_param = 37;
-  optional ThresholdParameter threshold_param = 25;
-  optional WindowDataParameter window_data_param = 20;
+  // Specifies training parameters (multipliers on global learning constants,
+  // and the name and other settings used for weight sharing).
+  repeated ParamSpec param = 6;
+
+  // The blobs containing the numeric parameters of the layer.
+  repeated BlobProto blobs = 7;
+
+  // Specifies on which bottoms the backpropagation should be skipped.
+  // The size must be either 0 or equal to the number of bottoms.
+  repeated bool propagate_down = 11;
+
+  // Rules controlling whether and when a layer is included in the network,
+  // based on the current NetState.  You may specify a non-zero number of rules
+  // to include OR exclude, but not both.  If no include or exclude rules are
+  // specified, the layer is always included.  If the current NetState meets
+  // ANY (i.e., one or more) of the specified rules, the layer is
+  // included/excluded.
+  repeated NetStateRule include = 8;
+  repeated NetStateRule exclude = 9;
 
   // Parameters for data pre-processing.
-  optional TransformationParameter transform_param = 36;
+  optional TransformationParameter transform_param = 100;
 
+  // Parameters shared by loss layers.
+  optional LossParameter loss_param = 101;
+
+  // Layer type-specific parameters.
+  //
   // Note: certain layers may have more than one computational engine
   // for their implementation. These layers include an Engine type and
   // engine parameter for selecting the implementation.
   // The default for the engine is set by the ENGINE switch at compile-time.
-
-  // DEPRECATED: The layer parameters specified as a V0LayerParameter.
-  // This should never be used by any code except to upgrade to the new
-  // LayerParameter specification.
-  optional V0LayerParameter layer = 1;
+  optional AccuracyParameter accuracy_param = 102;
+  optional ArgMaxParameter argmax_param = 103;
+  optional BNParameter bn_param = 137;
+  optional ConcatParameter concat_param = 104;
+  optional ContrastiveLossParameter contrastive_loss_param = 105;
+  optional ConvolutionParameter convolution_param = 106;
+  optional DataParameter data_param = 107;
+  optional DropoutParameter dropout_param = 108;
+  optional DummyDataParameter dummy_data_param = 109;
+  optional EltwiseParameter eltwise_param = 110;
+  optional ExpParameter exp_param = 111;
+  optional FlattenParameter flatten_param = 135;
+  optional HDF5DataParameter hdf5_data_param = 112;
+  optional HDF5OutputParameter hdf5_output_param = 113;
+  optional HingeLossParameter hinge_loss_param = 114;
+  optional ImageDataParameter image_data_param = 115;
+  optional InfogainLossParameter infogain_loss_param = 116;
+  optional InnerProductParameter inner_product_param = 117;
+  optional LogParameter log_param = 134;
+  optional LRNParameter lrn_param = 118;
+  optional MemoryDataParameter memory_data_param = 119;
+  optional MVNParameter mvn_param = 120;
+  optional PoolingParameter pooling_param = 121;
+  optional PowerParameter power_param = 122;
+  optional PReLUParameter prelu_param = 131;
+  optional PythonParameter python_param = 130;
+  optional ReductionParameter reduction_param = 136;
+  optional ReLUParameter relu_param = 123;
+  optional ReshapeParameter reshape_param = 133;
+  optional SigmoidParameter sigmoid_param = 124;
+  optional SoftmaxParameter softmax_param = 125;
+  optional SPPParameter spp_param = 132;
+  optional SliceParameter slice_param = 126;
+  optional TanHParameter tanh_param = 127;
+  optional ThresholdParameter threshold_param = 128;
+  optional WindowDataParameter window_data_param = 129;
+  optional VideoDataParameter video_data_param = 140;
+  optional ROIPoolingParameter roi_pooling_param = 150;
+  optional ScaleParameter scale_param = 160;
+  optional BiasParameter bias_param = 161;
+  optional BatchReductionParameter batch_reduction_param = 162;
+  optional SequenceDataParameter sequence_data_param = 163;
 }
 
 // Message that stores parameters used to apply transformation
@@ -343,39 +379,115 @@ message TransformationParameter {
   optional bool mirror = 2 [default = false];
   // Specify if we would like to randomly crop an image.
   optional uint32 crop_size = 3 [default = 0];
+  // mean_file and mean_value cannot be specified at the same time
   optional string mean_file = 4;
+  // if specified can be repeated once (would substract it from all the channels)
+  // or can be repeated the same number of times as channels
+  // (would subtract them from the corresponding channel)
+  repeated float mean_value = 5;
+  // Force the decoded image to have 3 color channels.
+  optional bool force_color = 6 [default = false];
+  // Force the decoded image to have 1 color channels.
+  optional bool force_gray = 7 [default = false];
+
+  //if set to true,
+  //instead using a fixed position cropping (center, left-up, left-down, right-up, right-down)
+  optional bool fix_crop = 10 [default = false];
+  //turn on to use stronger augmentation
+  optional bool more_fix_crop = 15 [default = false];
+
+  //if set to true, use multi-scale data augmentation
+  optional bool multi_scale = 11 [default = false];
+
+  // a list of float numbers less than 1 for multiple scales
+  repeated float scale_ratios = 12;
+
+  // maximal tolerated aspect ratio mismatch in the ratio list
+  optional int32 max_distort = 13 [default = 1];
+
+  //if set to true, it is optical flow field
+  optional bool is_flow = 14 [default = false];
+
+  //if set to true, will use original image processing mode
+  // i.e. will use Inception style data augmentation
+  optional bool original_image = 20 [ default = false];
+}
+
+// Message that stores parameters shared by loss layers
+message LossParameter {
+  // If specified, ignore instances with the given label.
+  optional int32 ignore_label = 1;
+  // If true, normalize each batch across all instances (including spatial
+  // dimesions, but not ignored instances); else, divide by batch size only.
+  optional bool normalize = 2 [default = true];
 }
 
-// Message that stores parameters used by AccuracyLayer
+// Messages that store parameters used by individual layer types follow, in
+// alphabetical order.
+
 message AccuracyParameter {
   // When computing accuracy, count as correct by comparing the true label to
   // the top k scoring classes.  By default, only compare to the top scoring
   // class (i.e. argmax).
   optional uint32 top_k = 1 [default = 1];
+
+  // The "label" axis of the prediction blob, whose argmax corresponds to the
+  // predicted label -- may be negative to index from the end (e.g., -1 for the
+  // last axis).  For example, if axis == 1 and the predictions are
+  // (N x C x H x W), the label blob is expected to contain N*H*W ground truth
+  // labels with integer values in {0, 1, ..., C-1}.
+  optional int32 axis = 2 [default = 1];
+
+  // If specified, ignore instances with the given label.
+  optional int32 ignore_label = 3;
 }
 
-// Message that stores parameters used by ArgMaxLayer
 message ArgMaxParameter {
   // If true produce pairs (argmax, maxval)
   optional bool out_max_val = 1 [default = false];
   optional uint32 top_k = 2 [default = 1];
 }
 
-// Message that stores parameters used by ConcatLayer
+message BNParameter {
+  optional FillerParameter slope_filler = 1;
+  optional FillerParameter bias_filler = 2;
+  optional float momentum = 3 [default = 0.9];
+  optional float eps = 4 [default = 1e-5];
+  // If true, will use the moving average mean and std for training and test.
+  // Will override the lr_param and freeze all the parameters.
+  // Make sure to initialize the layer properly with pretrained parameters.
+  optional bool frozen = 5 [default = false];
+  enum Engine {
+    DEFAULT = 0;
+    CAFFE = 1;
+    CUDNN = 2;
+  }
+  optional Engine engine = 6 [default = DEFAULT];
+}
+
 message ConcatParameter {
-  // Concat Layer needs to specify the dimension along the concat will happen,
-  // the other dimensions must be the same for all the bottom blobs
-  // By default it will concatenate blobs along channels dimension
+  // The axis along which to concatenate -- may be negative to index from the
+  // end (e.g., -1 for the last axis).  Other axes must have the
+  // same dimension for all the bottom blobs.
+  // By default, ConcatLayer concatenates blobs along the "channels" axis (1).
+  optional int32 axis = 2 [default = 1];
+
+  // DEPRECATED: alias for "axis" -- does not support negative indexing.
   optional uint32 concat_dim = 1 [default = 1];
 }
 
-// Message that stores parameters used by ContrastiveLossLayer
 message ContrastiveLossParameter {
-  //margin for dissimilar pair
+  // margin for dissimilar pair
   optional float margin = 1 [default = 1.0];
+  // The first implementation of this cost did not exactly match the cost of
+  // Hadsell et al 2006 -- using (margin - d^2) instead of (margin - d)^2.
+  // legacy_version = false (the default) uses (margin - d)^2 as proposed in the
+  // Hadsell paper. New models should probably use this version.
+  // legacy_version = true uses (margin - d^2). This is kept to support /
+  // reproduce existing models and results
+  optional bool legacy_version = 2 [default = false];
 }
 
-// Message that stores parameters used by ConvolutionLayer
 message ConvolutionParameter {
   optional uint32 num_output = 1; // The number of outputs for the layer
   optional bool bias_term = 2 [default = true]; // whether to have bias terms
@@ -401,7 +513,6 @@ message ConvolutionParameter {
   optional Engine engine = 15 [default = DEFAULT];
 }
 
-// Message that stores parameters used by DataLayer
 message DataParameter {
   enum DB {
     LEVELDB = 0;
@@ -414,7 +525,7 @@ message DataParameter {
   // The rand_skip variable is for the data layer to skip a few data points
   // to avoid all asynchronous sgd clients to start at the same point. The skip
   // point would be set as rand_skip * rand(0,1). Note that rand_skip should not
-  // be larger than the number of keys in the leveldb.
+  // be larger than the number of keys in the database.
   optional uint32 rand_skip = 7 [default = 0];
   optional DB backend = 8 [default = LEVELDB];
   // DEPRECATED. See TransformationParameter. For data pre-processing, we can do
@@ -428,37 +539,41 @@ message DataParameter {
   // DEPRECATED. See TransformationParameter. Specify if we want to randomly mirror
   // data.
   optional bool mirror = 6 [default = false];
+  // Force the encoded image to have 3 color channels
+  optional bool force_encoded_color = 9 [default = false];
+  // Enable within-shared shuffling
+  optional bool shuffle = 10 [default = false];
 }
 
-// Message that stores parameters used by DropoutLayer
 message DropoutParameter {
   optional float dropout_ratio = 1 [default = 0.5]; // dropout ratio
 }
 
-// Message that stores parameters used by DummyDataLayer.
 // DummyDataLayer fills any number of arbitrarily shaped blobs with random
 // (or constant) data generated by "Fillers" (see "message FillerParameter").
 message DummyDataParameter {
   // This layer produces N >= 1 top blobs.  DummyDataParameter must specify 1 or N
-  // num, N channels, N height, and N width fields, and must specify 0, 1 or N
-  // data_fillers.
+  // shape fields, and 0, 1 or N data_fillers.
   //
   // If 0 data_fillers are specified, ConstantFiller with a value of 0 is used.
   // If 1 data_filler is specified, it is applied to all top blobs.  If N are
   // specified, the ith is applied to the ith top blob.
   repeated FillerParameter data_filler = 1;
+  repeated BlobShape shape = 6;
+
+  // 4D dimensions -- deprecated.  Use "shape" instead.
   repeated uint32 num = 2;
   repeated uint32 channels = 3;
   repeated uint32 height = 4;
   repeated uint32 width = 5;
 }
 
-// Message that stores parameters used by EltwiseLayer
 message EltwiseParameter {
   enum EltwiseOp {
     PROD = 0;
     SUM = 1;
     MAX = 2;
+    STOCHASTIC_SUM = 3;
   }
   optional EltwiseOp operation = 1 [default = SUM]; // element-wise operation
   repeated float coeff = 2; // blob-wise coefficient for SUM operation
@@ -468,9 +583,25 @@ message EltwiseParameter {
   optional bool stable_prod_grad = 3 [default = true];
 }
 
-// Message that stores parameters used by ThresholdLayer
-message ThresholdParameter {
-  optional float threshold = 1 [default = 0]; // Strictly Positive values
+message ExpParameter {
+  // ExpLayer computes outputs y = base ^ (shift + scale * x), for base > 0.
+  // Or if base is set to the default (-1), base is set to e,
+  // so y = exp(shift + scale * x).
+  optional float base = 1 [default = -1.0];
+  optional float scale = 2 [default = 1.0];
+  optional float shift = 3 [default = 0.0];
+}
+
+/// Message that stores parameters used by FlattenLayer
+message FlattenParameter {
+  // The first axis to flatten: all preceding axes are retained in the output.
+  // May be negative to index from the end (e.g., -1 for the last axis).
+  optional int32 axis = 1 [default = 1];
+
+  // The last axis to flatten: all following axes are retained in the output.
+  // May be negative to index from the end (e.g., the default -1 for the last
+  // axis).
+  optional int32 end_axis = 2 [default = -1];
 }
 
 // Message that stores parameters used by HDF5DataLayer
@@ -479,9 +610,15 @@ message HDF5DataParameter {
   optional string source = 1;
   // Specify the batch size.
   optional uint32 batch_size = 2;
+
+  // Specify whether to shuffle the data.
+  // If shuffle == true, the ordering of the HDF5 files is shuffled,
+  // and the ordering of data within any given HDF5 file is shuffled,
+  // but data between different files are not interleaved; all of a file's
+  // data are output (in a random order) before moving onto another file.
+  optional bool shuffle = 3 [default = false];
 }
 
-// Message that stores parameters used by HDF5OutputLayer
 message HDF5OutputParameter {
   optional string file_name = 1;
 }
@@ -495,7 +632,6 @@ message HingeLossParameter {
   optional Norm norm = 1 [default = L1];
 }
 
-// Message that stores parameters used by ImageDataLayer
 message ImageDataParameter {
   // Specify the data source.
   optional string source = 1;
@@ -504,6 +640,81 @@ message ImageDataParameter {
   // The rand_skip variable is for the data layer to skip a few data points
   // to avoid all asynchronous sgd clients to start at the same point. The skip
   // point would be set as rand_skip * rand(0,1). Note that rand_skip should not
+  // be larger than the number of keys in the database.
+  optional uint32 rand_skip = 7 [default = 0];
+  // Whether or not ImageLayer should shuffle the list of files at every epoch.
+  optional bool shuffle = 8 [default = false];
+  // It will also resize images if new_height or new_width are not zero.
+  optional uint32 new_height = 9 [default = 0];
+  optional uint32 new_width = 10 [default = 0];
+  // Specify if the images are color or gray
+  optional bool is_color = 11 [default = true];
+  // DEPRECATED. See TransformationParameter. For data pre-processing, we can do
+  // simple scaling and subtracting the data mean, if provided. Note that the
+  // mean subtraction is always carried out before scaling.
+  optional float scale = 2 [default = 1];
+  optional string mean_file = 3;
+  // DEPRECATED. See TransformationParameter. Specify if we would like to randomly
+  // crop an image.
+  optional uint32 crop_size = 5 [default = 0];
+  // DEPRECATED. See TransformationParameter. Specify if we want to randomly mirror
+  // data.
+  optional bool mirror = 6 [default = false];
+  optional string root_folder = 12 [default = ""];
+}
+
+message VideoDataParameter{
+  // Specify the data source.
+  optional string source = 1;
+  // Specify the batch size.
+  optional uint32 batch_size = 4;
+  // The rand_skip variable is for the data layer to skip a few data points
+  // to avoid all asynchronous sgd clients to start at the same point. The skip
+  // point would be set as rand_skip * rand(0,1). Note that rand_skip should not
+  // be larger than the number of keys in the leveldb.
+  optional uint32 rand_skip = 7 [default = 0];
+  // Whether or not ImageLayer should shuffle the list of files at every epoch.
+  optional bool shuffle = 8 [default = false];
+  // It will also resize images if new_height or new_width are not zero.
+  optional uint32 new_height = 9 [default = 0];
+  optional uint32 new_width = 10 [default = 0];
+  optional uint32 new_length = 11 [default = 1];
+  optional uint32 num_segments = 12 [default = 1];
+  // DEPRECATED. See TransformationParameter. For data pre-processing, we can do
+  // simple scaling and subtracting the data mean, if provided. Note that the
+  // mean subtraction is always carried out before scaling.
+  optional float scale = 2 [default = 1];
+  optional string mean_file = 3;
+  // DEPRECATED. See TransformationParameter. Specify if we would like to randomly
+  // crop an image.
+  optional uint32 crop_size = 5 [default = 0];
+  // DEPRECATED. See TransformationParameter. Specify if we want to randomly mirror
+  // data.
+  optional bool mirror = 6 [default = false];
+  enum Modality {
+    RGB = 0;
+    FLOW = 1;
+  }
+  optional Modality modality = 13 [default = FLOW];
+
+  // the name pattern for frame images,
+  // for RGB modality it is default to "img_%04d.jpg", for FLOW "flow_x_%04d" and "flow_y_%04d"
+  optional string name_pattern = 14;
+
+  // The type of input
+  optional bool encoded = 15 [default = false];
+}
+
+message SequenceDataParameter{
+  // Specify the video source.
+  optional string video_source = 1;
+  // Specify the shot source.
+  optional string shot_source = 16;
+  // Specify the batch size.
+  optional uint32 batch_size = 4;
+  // The rand_skip variable is for the data layer to skip a few data points
+  // to avoid all asynchronous sgd clients to start at the same point. The skip
+  // point would be set as rand_skip * rand(0,1). Note that rand_skip should not
   // be larger than the number of keys in the leveldb.
   optional uint32 rand_skip = 7 [default = 0];
   // Whether or not ImageLayer should shuffle the list of files at every epoch.
@@ -511,6 +722,9 @@ message ImageDataParameter {
   // It will also resize images if new_height or new_width are not zero.
   optional uint32 new_height = 9 [default = 0];
   optional uint32 new_width = 10 [default = 0];
+  optional uint32 num_frames = 11 [default = 1];
+  optional uint32 num_segments = 12 [default = 1];
+  optional uint32 num_shots = 17 [default = 1];
   // DEPRECATED. See TransformationParameter. For data pre-processing, we can do
   // simple scaling and subtracting the data mean, if provided. Note that the
   // mean subtraction is always carried out before scaling.
@@ -522,20 +736,45 @@ message ImageDataParameter {
   // DEPRECATED. See TransformationParameter. Specify if we want to randomly mirror
   // data.
   optional bool mirror = 6 [default = false];
+  enum Modality {
+    RGB = 0;
+    FLOW = 1;
+  }
+  optional Modality modality = 13 [default = FLOW];
+
+  // the name pattern for frame images,
+  // for RGB modality it is default to "img_%04d.jpg", for FLOW "flow_x_%04d" and "flow_y_%04d"
+  optional string name_pattern = 14;
+
+  // The type of input
+  optional bool encoded = 15 [default = false];
 }
 
-// Message that stores parameters InfogainLossLayer
 message InfogainLossParameter {
   // Specify the infogain matrix source.
   optional string source = 1;
 }
 
-// Message that stores parameters used by InnerProductLayer
 message InnerProductParameter {
   optional uint32 num_output = 1; // The number of outputs for the layer
   optional bool bias_term = 2 [default = true]; // whether to have bias terms
   optional FillerParameter weight_filler = 3; // The filler for the weight
   optional FillerParameter bias_filler = 4; // The filler for the bias
+
+  // The first axis to be lumped into a single inner product computation;
+  // all preceding axes are retained in the output.
+  // May be negative to index from the end (e.g., -1 for the last axis).
+  optional int32 axis = 5 [default = 1];
+}
+
+// Message that stores parameters used by LogLayer
+message LogParameter {
+  // LogLayer computes outputs y = log_base(shift + scale * x), for base > 0.
+  // Or if base is set to the default (-1), base is set to e,
+  // so y = ln(shift + scale * x) = log_e(shift + scale * x)
+  optional float base = 1 [default = -1.0];
+  optional float scale = 2 [default = 1.0];
+  optional float shift = 3 [default = 0.0];
 }
 
 // Message that stores parameters used by LRNLayer
@@ -548,9 +787,9 @@ message LRNParameter {
     WITHIN_CHANNEL = 1;
   }
   optional NormRegion norm_region = 4 [default = ACROSS_CHANNELS];
+  optional float k = 5 [default = 1.];
 }
 
-// Message that stores parameters used by MemoryDataLayer
 message MemoryDataParameter {
   optional uint32 batch_size = 1;
   optional uint32 channels = 2;
@@ -558,16 +797,17 @@ message MemoryDataParameter {
   optional uint32 width = 4;
 }
 
-// Message that stores parameters used by MVNLayer
 message MVNParameter {
   // This parameter can be set to false to normalize mean only
   optional bool normalize_variance = 1 [default = true];
 
   // This parameter can be set to true to perform DNN-like MVN
   optional bool across_channels = 2 [default = false];
+
+  // Epsilon for not dividing by zero while normalizing variance
+  optional float eps = 3 [default = 1e-9];
 }
 
-// Message that stores parameters used by PoolingLayer
 message PoolingParameter {
   enum PoolMethod {
     MAX = 0;
@@ -592,9 +832,11 @@ message PoolingParameter {
     CUDNN = 2;
   }
   optional Engine engine = 11 [default = DEFAULT];
+  // If global_pooling then it will pool over the size of the bottom by doing
+  // kernel_h = bottom->height and kernel_w = bottom->width
+  optional bool global_pooling = 12 [default = false];
 }
 
-// Message that stores parameters used by PowerLayer
 message PowerParameter {
   // PowerLayer computes outputs y = (shift + scale * x) ^ power.
   optional float power = 1 [default = 1.0];
@@ -602,6 +844,46 @@ message PowerParameter {
   optional float shift = 3 [default = 0.0];
 }
 
+message PythonParameter {
+  optional string module = 1;
+  optional string layer = 2;
+
+  //param_str to be sent to the python layer
+  optional string param_str = 3;
+}
+
+// Message that stores parameters used by ReductionLayer
+message ReductionParameter {
+  enum ReductionOp {
+    SUM = 1;
+    ASUM = 2;
+    SUMSQ = 3;
+    MEAN = 4;
+    TOPK = 5;  // top-k reduction output the average of the largest k elements
+  }
+
+  optional ReductionOp operation = 1 [default = SUM]; // reduction operation
+
+  // The first axis to reduce to a scalar -- may be negative to index from the
+  // end (e.g., -1 for the last axis).
+  // (Currently, only reduction along ALL "tail" axes is supported; reduction
+  // of axis M through N, where N < num_axes - 1, is unsupported.)
+  // Suppose we have an n-axis bottom Blob with shape:
+  //     (d0, d1, d2, ..., d(m-1), dm, d(m+1), ..., d(n-1)).
+  // If axis == m, the output Blob will have shape
+  //     (d0, d1, d2, ..., d(m-1)),
+  // and the ReductionOp operation is performed (d0 * d1 * d2 * ... * d(m-1))
+  // times, each including (dm * d(m+1) * ... * d(n-1)) individual data.
+  // If axis == 0 (the default), the output Blob always has the empty shape
+  // (count 1), performing reduction across the entire input --
+  // often useful for creating new loss functions.
+  optional int32 axis = 2 [default = 0];
+
+  optional float coeff = 3 [default = 1.0]; // coefficient for output
+
+  optional int32 k = 4 [default = 1]; // k for top k reduction (only used in batch reduction layer)
+}
+
 // Message that stores parameters used by ReLULayer
 message ReLUParameter {
   // Allow non-zero slope for negative inputs to speed up optimization
@@ -618,7 +900,70 @@ message ReLUParameter {
   optional Engine engine = 2 [default = DEFAULT];
 }
 
-// Message that stores parameters used by SigmoidLayer
+message ReshapeParameter {
+  // Specify the output dimensions. If some of the dimensions are set to 0,
+  // the corresponding dimension from the bottom layer is used (unchanged).
+  // Exactly one dimension may be set to -1, in which case its value is
+  // inferred from the count of the bottom blob and the remaining dimensions.
+  // For example, suppose we want to reshape a 2D blob "input" with shape 2 x 8:
+  //
+  //   layer {
+  //     type: "Reshape" bottom: "input" top: "output"
+  //     reshape_param { ... }
+  //   }
+  //
+  // If "input" is 2D with shape 2 x 8, then the following reshape_param
+  // specifications are all equivalent, producing a 3D blob "output" with shape
+  // 2 x 2 x 4:
+  //
+  //   reshape_param { shape { dim:  2  dim: 2  dim:  4 } }
+  //   reshape_param { shape { dim:  0  dim: 2  dim:  4 } }
+  //   reshape_param { shape { dim:  0  dim: 2  dim: -1 } }
+  //   reshape_param { shape { dim: -1  dim: 0  dim:  2 } }
+  //
+  optional BlobShape shape = 1;
+
+  // axis and num_axes control the portion of the bottom blob's shape that are
+  // replaced by (included in) the reshape. By default (axis == 0 and
+  // num_axes == -1), the entire bottom blob shape is included in the reshape,
+  // and hence the shape field must specify the entire output shape.
+  //
+  // axis may be non-zero to retain some portion of the beginning of the input
+  // shape (and may be negative to index from the end; e.g., -1 to begin the
+  // reshape after the last axis, including nothing in the reshape,
+  // -2 to include only the last axis, etc.).
+  //
+  // For example, suppose "input" is a 2D blob with shape 2 x 8.
+  // Then the following ReshapeLayer specifications are all equivalent,
+  // producing a blob "output" with shape 2 x 2 x 4:
+  //
+  //   reshape_param { shape { dim: 2  dim: 2  dim: 4 } }
+  //   reshape_param { shape { dim: 2  dim: 4 } axis:  1 }
+  //   reshape_param { shape { dim: 2  dim: 4 } axis: -3 }
+  //
+  // num_axes specifies the extent of the reshape.
+  // If num_axes >= 0 (and axis >= 0), the reshape will be performed only on
+  // input axes in the range [axis, axis+num_axes].
+  // num_axes may also be -1, the default, to include all remaining axes
+  // (starting from axis).
+  //
+  // For example, suppose "input" is a 2D blob with shape 2 x 8.
+  // Then the following ReshapeLayer specifications are equivalent,
+  // producing a blob "output" with shape 1 x 2 x 8.
+  //
+  //   reshape_param { shape { dim:  1  dim: 2  dim:  8 } }
+  //   reshape_param { shape { dim:  1  dim: 2  }  num_axes: 1 }
+  //   reshape_param { shape { dim:  1  }  num_axes: 0 }
+  //
+  // On the other hand, these would produce output blob shape 2 x 1 x 8:
+  //
+  //   reshape_param { shape { dim: 2  dim: 1  dim: 8  }  }
+  //   reshape_param { shape { dim: 1 }  axis: 1  num_axes: 0 }
+  //
+  optional int32 axis = 2 [default = 0];
+  optional int32 num_axes = 3 [default = -1];
+}
+
 message SigmoidParameter {
   enum Engine {
     DEFAULT = 0;
@@ -628,17 +973,18 @@ message SigmoidParameter {
   optional Engine engine = 1 [default = DEFAULT];
 }
 
-// Message that stores parameters used by SliceLayer
 message SliceParameter {
-  // SliceLayer needs to know which dimension to slice across.
-  // Currently, SliceLayer only supports slicing across num (dim 0)
-  // and channels (dim 1).
-  // By default, SliceLayer slices across channels.
-  optional uint32 slice_dim = 1 [default = 1];
+  // The axis along which to slice -- may be negative to index from the end
+  // (e.g., -1 for the last axis).
+  // By default, SliceLayer concatenates blobs along the "channels" axis (1).
+  optional int32 axis = 3 [default = 1];
   repeated uint32 slice_point = 2;
+
+  // DEPRECATED: alias for "axis" -- does not support negative indexing.
+  optional uint32 slice_dim = 1 [default = 1];
 }
 
-// Message that stores parameters used by SoftmaxLayer, SoftMaxWithLossLayer
+// Message that stores parameters used by SoftmaxLayer, SoftmaxWithLossLayer
 message SoftmaxParameter {
   enum Engine {
     DEFAULT = 0;
@@ -646,9 +992,13 @@ message SoftmaxParameter {
     CUDNN = 2;
   }
   optional Engine engine = 1 [default = DEFAULT];
+
+  // The axis along which to perform the softmax -- may be negative to index
+  // from the end (e.g., -1 for the last axis).
+  // Any other axes will be evaluated as independent softmaxes.
+  optional int32 axis = 2 [default = 1];
 }
 
-// Message that stores parameters used by SigmoidLayer
 message TanHParameter {
   enum Engine {
     DEFAULT = 0;
@@ -658,7 +1008,10 @@ message TanHParameter {
   optional Engine engine = 1 [default = DEFAULT];
 }
 
-// Message that stores parameters used by WindowDataLayer
+message ThresholdParameter {
+  optional float threshold = 1 [default = 0]; // Strictly positive values
+}
+
 message WindowDataParameter {
   // Specify the data source.
   optional string source = 1;
@@ -686,6 +1039,132 @@ message WindowDataParameter {
   // warp: cropped window is warped to a fixed size and aspect ratio
   // square: the tightest square around the window is cropped
   optional string crop_mode = 11 [default = "warp"];
+  // cache_images: will load all images in memory for faster access
+  optional bool cache_images = 12 [default = false];
+  // append root_folder to locate images
+  optional string root_folder = 13 [default = ""];
+}
+
+
+message SPPParameter {
+  enum PoolMethod {
+    MAX = 0;
+    AVE = 1;
+    STOCHASTIC = 2;
+  }
+  optional uint32 pyramid_height = 1;
+  optional PoolMethod pool = 2 [default = MAX]; // The pooling method
+  enum Engine {
+    DEFAULT = 0;
+    CAFFE = 1;
+    CUDNN = 2;
+  }
+  optional Engine engine = 6 [default = DEFAULT];
+}
+
+// Message that stores parameters used by ROIPoolingLayer
+message ROIPoolingParameter {
+  // Pad, kernel size, and stride are all given as a single value for equal
+  // dimensions in height and width or as Y, X pairs.
+  optional uint32 pooled_h = 1 [default = 0]; // The pooled output height
+  optional uint32 pooled_w = 2 [default = 0]; // The pooled output width
+  // Multiplicative spatial scale factor to translate ROI coords from their
+  // input scale to the scale used when pooling
+  optional float spatial_scale = 3 [default = 1];
+}
+
+
+// DEPRECATED: use LayerParameter.
+message V1LayerParameter {
+  repeated string bottom = 2;
+  repeated string top = 3;
+  optional string name = 4;
+  repeated NetStateRule include = 32;
+  repeated NetStateRule exclude = 33;
+  enum LayerType {
+    NONE = 0;
+    ABSVAL = 35;
+    ACCURACY = 1;
+    ARGMAX = 30;
+    BNLL = 2;
+    CONCAT = 3;
+    CONTRASTIVE_LOSS = 37;
+    CONVOLUTION = 4;
+    DATA = 5;
+    DECONVOLUTION = 39;
+    DROPOUT = 6;
+    DUMMY_DATA = 32;
+    EUCLIDEAN_LOSS = 7;
+    ELTWISE = 25;
+    EXP = 38;
+    FLATTEN = 8;
+    HDF5_DATA = 9;
+    HDF5_OUTPUT = 10;
+    HINGE_LOSS = 28;
+    IM2COL = 11;
+    IMAGE_DATA = 12;
+    INFOGAIN_LOSS = 13;
+    INNER_PRODUCT = 14;
+    LRN = 15;
+    MEMORY_DATA = 29;
+    MULTINOMIAL_LOGISTIC_LOSS = 16;
+    MVN = 34;
+    POOLING = 17;
+    POWER = 26;
+    RELU = 18;
+    SIGMOID = 19;
+    SIGMOID_CROSS_ENTROPY_LOSS = 27;
+    SILENCE = 36;
+    SOFTMAX = 20;
+    SOFTMAX_LOSS = 21;
+    SPLIT = 22;
+    SLICE = 33;
+    TANH = 23;
+    WINDOW_DATA = 24;
+    THRESHOLD = 31;
+  }
+  optional LayerType type = 5;
+  repeated BlobProto blobs = 6;
+  repeated string param = 1001;
+  repeated DimCheckMode blob_share_mode = 1002;
+  enum DimCheckMode {
+    STRICT = 0;
+    PERMISSIVE = 1;
+  }
+  repeated float blobs_lr = 7;
+  repeated float weight_decay = 8;
+  repeated float loss_weight = 35;
+  optional AccuracyParameter accuracy_param = 27;
+  optional ArgMaxParameter argmax_param = 23;
+  optional ConcatParameter concat_param = 9;
+  optional ContrastiveLossParameter contrastive_loss_param = 40;
+  optional ConvolutionParameter convolution_param = 10;
+  optional DataParameter data_param = 11;
+  optional DropoutParameter dropout_param = 12;
+  optional DummyDataParameter dummy_data_param = 26;
+  optional EltwiseParameter eltwise_param = 24;
+  optional ExpParameter exp_param = 41;
+  optional HDF5DataParameter hdf5_data_param = 13;
+  optional HDF5OutputParameter hdf5_output_param = 14;
+  optional HingeLossParameter hinge_loss_param = 29;
+  optional ImageDataParameter image_data_param = 15;
+  optional InfogainLossParameter infogain_loss_param = 16;
+  optional InnerProductParameter inner_product_param = 17;
+  optional LRNParameter lrn_param = 18;
+  optional MemoryDataParameter memory_data_param = 22;
+  optional MVNParameter mvn_param = 34;
+  optional PoolingParameter pooling_param = 19;
+  optional PowerParameter power_param = 21;
+  optional ReLUParameter relu_param = 30;
+  optional SigmoidParameter sigmoid_param = 38;
+  optional SoftmaxParameter softmax_param = 39;
+  optional SliceParameter slice_param = 31;
+  optional TanHParameter tanh_param = 37;
+  optional ThresholdParameter threshold_param = 25;
+  optional WindowDataParameter window_data_param = 20;
+  optional TransformationParameter transform_param = 36;
+  optional LossParameter loss_param = 42;
+  optional V0LayerParameter layer = 1;
 }
 
 // DEPRECATED: V0LayerParameter is the old way of specifying layer parameters
@@ -715,6 +1194,7 @@ message V0LayerParameter {
   optional uint32 local_size = 13 [default = 5]; // for local response norm
   optional float alpha = 14 [default = 1.]; // for local response norm
   optional float beta = 15 [default = 0.75]; // for local response norm
+  optional float k = 22 [default = 1.];
 
   // For data layers, specify the data source
   optional string source = 16;
@@ -741,7 +1221,7 @@ message V0LayerParameter {
   // The rand_skip variable is for the data layer to skip a few data points
   // to avoid all asynchronous sgd clients to start at the same point. The skip
   // point would be set as rand_skip * rand(0,1). Note that rand_skip should not
-  // be larger than the number of keys in the leveldb.
+  // be larger than the number of keys in the database.
   optional uint32 rand_skip = 53 [default = 0];
 
   // Fields related to detection (det_*)
@@ -780,3 +1260,103 @@ message V0LayerParameter {
 
   optional HDF5OutputParameter hdf5_output_param = 1001;
 }
+
+message PReLUParameter {
+  // Parametric ReLU described in K. He et al, Delving Deep into Rectifiers:
+  // Surpassing Human-Level Performance on ImageNet Classification, 2015.
+
+  // Initial value of a_i. Default is a_i=0.25 for all i.
+  optional FillerParameter filler = 1;
+  // Whether or not slope paramters are shared across channels.
+  optional bool channel_shared = 2 [default = false];
+}
+
+message ScaleParameter {
+  // The first axis of bottom[0] (the first input Blob) along which to apply
+  // bottom[1] (the second input Blob).  May be negative to index from the end
+  // (e.g., -1 for the last axis).
+  //
+  // For example, if bottom[0] is 4D with shape 100x3x40x60, the output
+  // top[0] will have the same shape, and bottom[1] may have any of the
+  // following shapes (for the given value of axis):
+  //    (axis == 0 == -4) 100; 100x3; 100x3x40; 100x3x40x60
+  //    (axis == 1 == -3)          3;     3x40;     3x40x60
+  //    (axis == 2 == -2)                   40;       40x60
+  //    (axis == 3 == -1)                                60
+  // Furthermore, bottom[1] may have the empty shape (regardless of the value of
+  // "axis") -- a scalar multiplier.
+  optional int32 axis = 1 [default = 1];
+
+  // (num_axes is ignored unless just one bottom is given and the scale is
+  // a learned parameter of the layer.  Otherwise, num_axes is determined by the
+  // number of axes by the second bottom.)
+  // The number of axes of the input (bottom[0]) covered by the scale
+  // parameter, or -1 to cover all axes of bottom[0] starting from `axis`.
+  // Set num_axes := 0, to multiply with a zero-axis Blob: a scalar.
+  optional int32 num_axes = 2 [default = 1];
+
+  // (filler is ignored unless just one bottom is given and the scale is
+  // a learned parameter of the layer.)
+  // The initialization for the learned scale parameter.
+  // Default is the unit (1) initialization, resulting in the ScaleLayer
+  // initially performing the identity operation.
+  optional FillerParameter filler = 3;
+
+  // Whether to also learn a bias (equivalent to a ScaleLayer+BiasLayer, but
+  // may be more efficient).  Initialized with bias_filler (defaults to 0).
+  optional bool bias_term = 4 [default = false];
+  optional FillerParameter bias_filler = 5;
+}
+
+message BiasParameter {
+  // The first axis of bottom[0] (the first input Blob) along which to apply
+  // bottom[1] (the second input Blob).  May be negative to index from the end
+  // (e.g., -1 for the last axis).
+  //
+  // For example, if bottom[0] is 4D with shape 100x3x40x60, the output
+  // top[0] will have the same shape, and bottom[1] may have any of the
+  // following shapes (for the given value of axis):
+  //    (axis == 0 == -4) 100; 100x3; 100x3x40; 100x3x40x60
+  //    (axis == 1 == -3)          3;     3x40;     3x40x60
+  //    (axis == 2 == -2)                   40;       40x60
+  //    (axis == 3 == -1)                                60
+  // Furthermore, bottom[1] may have the empty shape (regardless of the value of
+  // "axis") -- a scalar bias.
+  optional int32 axis = 1 [default = 1];
+
+  // (num_axes is ignored unless just one bottom is given and the bias is
+  // a learned parameter of the layer.  Otherwise, num_axes is determined by the
+  // number of axes by the second bottom.)
+  // The number of axes of the input (bottom[0]) covered by the bias
+  // parameter, or -1 to cover all axes of bottom[0] starting from `axis`.
+  // Set num_axes := 0, to add a zero-axis Blob: a scalar.
+  optional int32 num_axes = 2 [default = 1];
+
+  // (filler is ignored unless just one bottom is given and the bias is
+  // a learned parameter of the layer.)
+  // The initialization for the learned bias parameter.
+  // Default is the zero (0) initialization, resulting in the BiasLayer
+  // initially performing the identity operation.
+  optional FillerParameter filler = 3;
+}
+
+message BatchReductionParameter {
+    repeated int32 level = 1;
+    optional ReductionParameter reduction_param = 2;
+}
+
+message MemoryOptimizationParameter {
+  // Mode of optimization
+
+  // whether to optimize for all nets specified in train phase
+  optional bool optimize_train = 1 [default = true];
+
+  // whether to optimize for all nets specified in test phase
+  optional bool optimize_test = 2 [default = false];
+
+  // By default, all blobs for input and output are excluded from the optimization for safety.
+  // However, one can also claim a few blobs to be excluded.
+  // This is rather helpful when extracting features from intermediate blobs or debugging problems.
+  repeated string exclude_blob = 3;
+
+}
diff --git a/src/caffe/proto/caffe_pretty_print.proto b/src/caffe/proto/caffe_pretty_print.proto
deleted file mode 100644
index 6f0a5f6b8df..00000000000
--- a/src/caffe/proto/caffe_pretty_print.proto
+++ /dev/null
@@ -1,18 +0,0 @@
-syntax = "proto2";
-
-package caffe;
-
-import "caffe.proto";
-
-// A near-duplicate of NetParameter with fields re-numbered to beautify
-// automatic prototext dumps.  The main practical purpose is to print inputs
-// before layers, because having inputs at the end looks weird.
-// NetParameterPrettyPrint should never be used in code except for conversion
-// FROM NetParameter and subsequent dumping to proto text file.
-message NetParameterPrettyPrint {
-  optional string name = 1;
-  optional bool force_backward = 2 [default = false];
-  repeated string input = 3;
-  repeated int32 input_dim = 4;
-  repeated LayerParameter layers = 5;
-}
diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp
index ba262920a9a..a29e1035ccc 100644
--- a/src/caffe/solver.cpp
+++ b/src/caffe/solver.cpp
@@ -10,6 +10,8 @@
 #include "caffe/util/io.hpp"
 #include "caffe/util/math_functions.hpp"
 #include "caffe/util/upgrade_proto.hpp"
+#include "caffe/util/mpi_functions.hpp"
+#include "caffe/util/channel.hpp"
 
 namespace caffe {
 
@@ -32,13 +34,19 @@ void Solver<Dtype>::Init(const SolverParameter& param) {
   LOG(INFO) << "Initializing solver from parameters: " << std::endl
             << param.DebugString();
   param_ = param;
+  CHECK_GE(param_.average_loss(), 1) << "average_loss should be non-negative.";
   if (param_.random_seed() >= 0) {
     Caffe::set_random_seed(param_.random_seed());
   }
+#ifdef USE_CUDNN
+  Caffe::set_cudnn_mem_richness(param_.richness());
+#endif
   // Scaffolding code
   InitTrainNet();
   InitTestNets();
   LOG(INFO) << "Solver scaffolding done.";
+  iter_ = 0;
+  current_step_ = 0;
 }
 
 template <typename Dtype>
@@ -151,44 +159,85 @@ void Solver<Dtype>::InitTestNets() {
     LOG(INFO)
         << "Creating test net (#" << i << ") specified by " << sources[i];
     test_nets_[i].reset(new Net<Dtype>(net_params[i]));
+    test_nets_[i]->set_debug_info(param_.debug_info());
   }
 }
 
 template <typename Dtype>
-void Solver<Dtype>::Solve(const char* resume_file) {
-  Caffe::set_phase(Caffe::TRAIN);
-  LOG(INFO) << "Solving " << net_->name();
-  PreSolve();
-
-  iter_ = 0;
-  if (resume_file) {
-    LOG(INFO) << "Restoring previous solver status from " << resume_file;
-    Restore(resume_file);
-  }
-  // Remember the initial iter_ value; will be non-zero if we loaded from a
-  // resume_file above.
-  const int start_iter = iter_;
-
-  // For a network that is trained by the solver, no bottom or top vecs
-  // should be given, and we will just provide dummy vecs.
+void Solver<Dtype>::Step(int iters) {
   vector<Blob<Dtype>*> bottom_vec;
-  for (; iter_ < param_.max_iter(); ++iter_) {
-    // Save a snapshot if needed.
-    if (param_.snapshot() && iter_ > start_iter &&
-        iter_ % param_.snapshot() == 0) {
-      Snapshot();
+  const int start_iter = iter_;
+  const int stop_iter = iter_ + iters;
+  int average_loss = this->param_.average_loss();
+  vector<Dtype> losses;
+  Dtype smoothed_loss = 0;
+
+  while (iter_ < stop_iter) {
+    // zero-init the params
+    for (int i = 0; i < net_->params().size(); ++i) {
+      shared_ptr<Blob<Dtype> > blob = net_->params()[i];
+      switch (Caffe::mode()) {
+      case Caffe::CPU:
+        caffe_set(blob->count(), static_cast<Dtype>(0),
+            blob->mutable_cpu_diff());
+        break;
+      case Caffe::GPU:
+#ifndef CPU_ONLY
+        caffe_gpu_set(blob->count(), static_cast<Dtype>(0),
+            blob->mutable_gpu_diff());
+#else
+        NO_GPU;
+#endif
+        break;
+      }
     }
 
     if (param_.test_interval() && iter_ % param_.test_interval() == 0
         && (iter_ > 0 || param_.test_initialization())) {
+#ifdef USE_MPI
+      if (Caffe::parallel_mode()==Caffe::MPI){
+          SyncData();
+      }
+#endif
       TestAll();
     }
 
     const bool display = param_.display() && iter_ % param_.display() == 0;
     net_->set_debug_info(display && param_.debug_info());
-    Dtype loss = net_->ForwardBackward(bottom_vec);
+    // accumulate the loss and gradient
+    Dtype loss = 0;
+    for (int i = 0; i < param_.iter_size(); ++i) {
+#ifdef USE_MPI
+      Caffe::set_remaining_sub_iter(param_.iter_size() - i - 1);
+#endif
+      loss += net_->ForwardBackward(bottom_vec);
+    }
+
+    #ifdef USE_MPI
+    if (Caffe::parallel_mode() == Caffe::MPI) {
+      DLOG(INFO)<<"Communication";
+
+      SyncGradient();
+
+      SyncOutput(this->net_);
+
+      loss = SyncLoss(loss);
+    }
+    #endif
+
+    loss /= param_.iter_size();
+    // average the loss across iterations for smoothed reporting
+    if (losses.size() < average_loss) {
+      losses.push_back(loss);
+      int size = losses.size();
+      smoothed_loss = (smoothed_loss * (size - 1) + loss) / size;
+    } else {
+      int idx = (iter_ - start_iter) % average_loss;
+      smoothed_loss += (loss - losses[idx]) / average_loss;
+      losses[idx] = loss;
+    }
     if (display) {
-      LOG(INFO) << "Iteration " << iter_ << ", loss = " << loss;
+      LOG(INFO) << "Iteration " << iter_ << ", loss = " << smoothed_loss;
       const vector<Blob<Dtype>*>& result = net_->output_blobs();
       int score_index = 0;
       for (int j = 0; j < result.size(); ++j) {
@@ -209,13 +258,151 @@ void Solver<Dtype>::Solve(const char* resume_file) {
         }
       }
     }
+    ApplyUpdate();
+
+    // Increment the internal iter_ counter -- its value should always indicate
+    // the number of times the weights have been updated.
+    ++iter_;
+
+    // Save a snapshot if needed.
+    if (param_.snapshot() && iter_ % param_.snapshot() == 0) {
+      Snapshot();
+#ifdef USE_MPI
+    if (Caffe::parallel_mode() == Caffe::MPI){
+      //Stop the world to wait for the master process to finish snapshot
+      //TODO: Send this to queue in blocking mode
+      MPIComm::Syncrhonize();
+      MPI_Barrier(MPI_COMM_WORLD);
+    }
+#endif
+
+    }
+  }
+}
+
+#ifdef USE_MPI
+template <typename Dtype>
+void Solver<Dtype>::SyncGradient(){
+
+  const vector<int>& param_owners = this->net_->param_owners();
+  const vector<shared_ptr<Blob<Dtype> > >& net_params = this->net_->params();
+  shared_ptr<Layer<Dtype> > layer;
+  double t1, t2;
+  t1 = MPI_Wtime();
+
+  mpi_force_synchronize();
+  for (int param_id = 0; param_id < net_params.size(); ++param_id) {
+    int param_owner = param_owners[param_id];
+
+
+    // is_self is a flag for whether we need to sync this blob
+    // if not, this blob has already been synced.
+    bool is_self = (param_owner == -1);
+
+    // need_sync is a flag for whether we need sync the gradient
+    layer = this->net_->layer_by_param(param_id);
+    bool need_sync = layer->need_sync();
+
+    // conduct gradient synchronization here
+    if (is_self && need_sync){
+
+#ifndef CPU_ONLY
+      caffe_gpu_scal(net_params[param_id]->count(),
+                     Dtype(1.)/Dtype(Caffe::MPI_all_rank()),
+                     net_params[param_id]->mutable_gpu_diff());
+#else
+      caffe_scal(net_params[param_id]->count(),
+                     Dtype(1.)/Dtype(Caffe::MPI_all_rank()),
+                     net_params[param_id]->mutable_cpu_diff());
+#endif
+    }
+  }
+  t2 = MPI_Wtime();
+  DLOG(INFO)<<"Communication time "<<t2-t1<<" second";
+}
 
-    ComputeUpdateValue();
-    net_->Update();
+template <typename Dtype>
+void Solver<Dtype>::SyncData(){
+
+  const vector<int>& param_owners = this->net_->param_owners();
+  const vector<shared_ptr<Blob<Dtype> > >& net_params = this->net_->params();
+  double t1, t2;
+  t1 = MPI_Wtime();
+  for (int param_id = 0; param_id < net_params.size(); ++param_id) {
+    int param_owner = param_owners[param_id];
+    // is_self is a flag for whether we need to sync this blob
+    // if not, this blob has already been synced.
+    bool is_self = (param_owner == -1);
+    // conduct data synchronization here
+    if (is_self){
+      Dtype* data = net_params[param_id]->mutable_cpu_data();
+      caffe_ibcast(data, net_params[param_id]->count());
+    }
+  }
+  mpi_force_synchronize();
+  t2 = MPI_Wtime();
+  LOG(INFO)<<"Model Synchronization Communication time "<<t2-t1<<" second";
+}
+template <typename Dtype>
+void Solver<Dtype>::SyncOutput(shared_ptr<Net<Dtype> > net){
+  const vector<Blob<Dtype>*>& result = net->output_blobs();
+  for (int j = 0; j < result.size(); ++j) {
+    caffe_iallreduce<Dtype>(result[j]->mutable_cpu_data(),
+                            result[j]->count());
+  }
+  mpi_force_synchronize();
+  for( int j = 0; j < result.size(); ++j){
+    caffe_scal(result[j]->count(),
+                   Dtype(1.)/Dtype(Caffe::MPI_all_rank()),
+                   result[j]->mutable_cpu_data());
   }
-  // Always save a snapshot after optimization, unless overridden by setting
-  // snapshot_after_train := false.
-  if (param_.snapshot_after_train()) { Snapshot(); }
+
+}
+template <typename Dtype>
+Dtype Solver<Dtype>::SyncLoss(Dtype loss){
+  Dtype sum_loss;
+  caffe_iallreduce<Dtype>(&loss, &sum_loss, 1);
+  mpi_force_synchronize();
+  return sum_loss / Caffe::MPI_all_rank();
+}
+#endif
+
+template <typename Dtype>
+void Solver<Dtype>::Solve(const char* resume_file) {
+  LOG(INFO) << "Solving " << net_->name();
+  LOG(INFO) << "Learning Rate Policy: " << param_.lr_policy();
+
+  if (resume_file) {
+    LOG(INFO) << "Restoring previous solver status from " << resume_file;
+    Restore(resume_file);
+  }
+  #ifdef USE_MPI
+  else{
+    SyncData();
+  }
+  #endif
+
+
+  // For a network that is trained by the solver, no bottom or top vecs
+  // should be given, and we will just provide dummy vecs.
+  Step(param_.max_iter() - iter_);
+  // If we haven't already, save a snapshot after optimization, unless
+  // overridden by setting snapshot_after_train := false
+  if (param_.snapshot_after_train()
+      && (!param_.snapshot() || iter_ % param_.snapshot() != 0)) {
+
+    Snapshot();
+    #ifdef USE_MPI
+    if (Caffe::parallel_mode() == Caffe::MPI){
+      //Stop the world to wait for the master process to finish snapshot
+      //TODO: Send this to queue in blocking mode
+      MPIComm::Syncrhonize();
+      MPI_Barrier(MPI_COMM_WORLD);
+    }
+    #endif
+  }
+
+
   // After the optimization is done, run an additional train and test pass to
   // display the train and test loss/outputs if appropriate (based on the
   // display and test_interval settings, respectively).  Unlike in the rest of
@@ -224,7 +411,7 @@ void Solver<Dtype>::Solve(const char* resume_file) {
   // display the loss, which is computed in the forward pass.
   if (param_.display() && iter_ % param_.display() == 0) {
     Dtype loss;
-    net_->Forward(bottom_vec, &loss);
+    net_->ForwardPrefilled(&loss);
     LOG(INFO) << "Iteration " << iter_ << ", loss = " << loss;
   }
   if (param_.test_interval() && iter_ % param_.test_interval() == 0) {
@@ -241,13 +428,10 @@ void Solver<Dtype>::TestAll() {
   }
 }
 
-
 template <typename Dtype>
 void Solver<Dtype>::Test(const int test_net_id) {
   LOG(INFO) << "Iteration " << iter_
             << ", Testing net (#" << test_net_id << ")";
-  // We need to set phase to test before running.
-  Caffe::set_phase(Caffe::TEST);
   CHECK_NOTNULL(test_nets_[test_net_id].get())->
       ShareTrainedLayersWith(net_.get());
   vector<Dtype> test_score;
@@ -259,6 +443,12 @@ void Solver<Dtype>::Test(const int test_net_id) {
     Dtype iter_loss;
     const vector<Blob<Dtype>*>& result =
         test_net->Forward(bottom_vec, &iter_loss);
+#ifdef USE_MPI
+    if (Caffe::parallel_mode() == Caffe::MPI) {
+      SyncOutput(test_net);
+      iter_loss = SyncLoss(iter_loss);
+    }
+#endif
     if (param_.test_compute_loss()) {
       loss += iter_loss;
     }
@@ -284,13 +474,15 @@ void Solver<Dtype>::Test(const int test_net_id) {
     loss /= param_.test_iter(test_net_id);
     LOG(INFO) << "Test loss: " << loss;
   }
+
   for (int i = 0; i < test_score.size(); ++i) {
     const int output_blob_index =
         test_net->output_blob_indices()[test_score_output_id[i]];
     const string& output_name = test_net->blob_names()[output_blob_index];
     const Dtype loss_weight = test_net->blob_loss_weights()[output_blob_index];
     ostringstream loss_msg_stream;
-    const Dtype mean_score = test_score[i] / param_.test_iter(test_net_id);
+    Dtype mean_score = test_score[i] / param_.test_iter(test_net_id);
+
     if (loss_weight) {
       loss_msg_stream << " (* " << loss_weight
                       << " = " << loss_weight * mean_score << " loss)";
@@ -298,12 +490,14 @@ void Solver<Dtype>::Test(const int test_net_id) {
     LOG(INFO) << "    Test net output #" << i << ": " << output_name << " = "
         << mean_score << loss_msg_stream.str();
   }
-  Caffe::set_phase(Caffe::TRAIN);
 }
 
 
 template <typename Dtype>
 void Solver<Dtype>::Snapshot() {
+#ifdef USE_MPI
+  if (Caffe::MPI_my_rank() != 0) return;
+#endif
   NetParameter net_param;
   // For intermediate results, we will also dump the gradient values.
   net_->ToProto(&net_param, param_.snapshot_diff());
@@ -320,6 +514,7 @@ void Solver<Dtype>::Snapshot() {
   SnapshotSolverState(&state);
   state.set_iter(iter_);
   state.set_learned_net(model_filename);
+  state.set_current_step(current_step_);
   snapshot_filename = filename + ".solverstate";
   LOG(INFO) << "Snapshotting solver state to " << snapshot_filename;
   WriteProtoToBinaryFile(state, snapshot_filename.c_str());
@@ -331,10 +526,11 @@ void Solver<Dtype>::Restore(const char* state_file) {
   NetParameter net_param;
   ReadProtoFromBinaryFile(state_file, &state);
   if (state.has_learned_net()) {
-    ReadProtoFromBinaryFile(state.learned_net().c_str(), &net_param);
+    ReadNetParamsFromBinaryFileOrDie(state.learned_net().c_str(), &net_param);
     net_->CopyTrainedLayersFrom(net_param);
   }
   iter_ = state.iter();
+  current_step_ = state.current_step();
   RestoreSolverState(state);
 }
 
@@ -345,8 +541,16 @@ void Solver<Dtype>::Restore(const char* state_file) {
 //    - step: return base_lr * gamma ^ (floor(iter / step))
 //    - exp: return base_lr * gamma ^ iter
 //    - inv: return base_lr * (1 + gamma * iter) ^ (- power)
-// where base_lr, gamma, step and power are defined in the solver parameter
-// protocol buffer, and iter is the current iteration.
+//    - multistep: similar to step but it allows non uniform steps defined by
+//      stepvalue
+//    - poly: the effective learning rate follows a polynomial decay, to be
+//      zero by the max_iter. return base_lr (1 - iter/max_iter) ^ (power)
+//    - sigmoid: the effective learning rate follows a sigmod decay
+//      return base_lr ( 1/(1 + exp(-gamma * (iter - stepsize))))
+//    - exp10: return base_lr * (-iter/stepsize)
+//
+// where base_lr, max_iter, gamma, step, stepvalue and power are defined
+// in the solver parameter protocol buffer, and iter is the current iteration.
 template <typename Dtype>
 Dtype SGDSolver<Dtype>::GetLearningRate() {
   Dtype rate;
@@ -354,132 +558,214 @@ Dtype SGDSolver<Dtype>::GetLearningRate() {
   if (lr_policy == "fixed") {
     rate = this->param_.base_lr();
   } else if (lr_policy == "step") {
-    int current_step = this->iter_ / this->param_.stepsize();
+    this->current_step_ = this->iter_ / this->param_.stepsize();
     rate = this->param_.base_lr() *
-        pow(this->param_.gamma(), current_step);
+        pow(this->param_.gamma(), this->current_step_);
   } else if (lr_policy == "exp") {
     rate = this->param_.base_lr() * pow(this->param_.gamma(), this->iter_);
   } else if (lr_policy == "inv") {
     rate = this->param_.base_lr() *
         pow(Dtype(1) + this->param_.gamma() * this->iter_,
             - this->param_.power());
-  } else {
+  } else if (lr_policy == "multistep") {
+    if (this->current_step_ < this->param_.stepvalue_size() &&
+          this->iter_ >= this->param_.stepvalue(this->current_step_)) {
+      this->current_step_++;
+      LOG(INFO) << "MultiStep Status: Iteration " <<
+      this->iter_ << ", step = " << this->current_step_;
+    }
+    rate = this->param_.base_lr() *
+        pow(this->param_.gamma(), this->current_step_);
+  } else if (lr_policy == "poly") {
+    rate = this->param_.base_lr() * pow(Dtype(1.) -
+        (Dtype(this->iter_) / Dtype(this->param_.max_iter())),
+        this->param_.power());
+  } else if (lr_policy == "sigmoid") {
+    rate = this->param_.base_lr() * (Dtype(1.) /
+        (Dtype(1.) + exp(-this->param_.gamma() * (Dtype(this->iter_) -
+          Dtype(this->param_.stepsize())))));
+  } else if (lr_policy == "exp10"){
+    rate = this->param_.base_lr()
+           * pow(Dtype(10.), Dtype(-1 * this->iter_) / Dtype(this->param_.stepsize()));
+  }else {
     LOG(FATAL) << "Unknown learning rate policy: " << lr_policy;
   }
   return rate;
 }
 
-
 template <typename Dtype>
 void SGDSolver<Dtype>::PreSolve() {
   // Initialize the history
-  vector<shared_ptr<Blob<Dtype> > >& net_params = this->net_->params();
+  const vector<shared_ptr<Blob<Dtype> > >& net_params = this->net_->params();
   history_.clear();
   update_.clear();
   temp_.clear();
   for (int i = 0; i < net_params.size(); ++i) {
-    const Blob<Dtype>* net_param = net_params[i].get();
-    history_.push_back(shared_ptr<Blob<Dtype> >(new Blob<Dtype>(
-        net_param->num(), net_param->channels(), net_param->height(),
-        net_param->width())));
-    update_.push_back(shared_ptr<Blob<Dtype> >(new Blob<Dtype>(
-        net_param->num(), net_param->channels(), net_param->height(),
-        net_param->width())));
-    temp_.push_back(shared_ptr<Blob<Dtype> >(new Blob<Dtype>(
-        net_param->num(), net_param->channels(), net_param->height(),
-        net_param->width())));
+    const vector<int>& shape = net_params[i]->shape();
+    history_.push_back(shared_ptr<Blob<Dtype> >(new Blob<Dtype>(shape)));
+    update_.push_back(shared_ptr<Blob<Dtype> >(new Blob<Dtype>(shape)));
+    temp_.push_back(shared_ptr<Blob<Dtype> >(new Blob<Dtype>(shape)));
   }
 }
 
+template <typename Dtype>
+void SGDSolver<Dtype>::ClipGradients() {
+  const Dtype clip_gradients = this->param_.clip_gradients();
+  if (clip_gradients < 0) { return; }
+  const vector<shared_ptr<Blob<Dtype> > >& net_params = this->net_->params();
+  Dtype sumsq_diff = 0;
+  for (int i = 0; i < net_params.size(); ++i) {
+    if (this->net_->param_owners()[i] < 0) {
+      sumsq_diff += net_params[i]->sumsq_diff();
+    }
+  }
+  const Dtype l2norm_diff = std::sqrt(sumsq_diff);
+  if (l2norm_diff > clip_gradients) {
+    Dtype scale_factor = clip_gradients / l2norm_diff;
+    LOG(INFO) << "Gradient clipping: scaling down gradients (L2 norm "
+        << l2norm_diff << " > " << clip_gradients << ") "
+        << "by scale factor " << scale_factor;
+    for (int i = 0; i < net_params.size(); ++i) {
+      if (this->net_->param_owners()[i] < 0) {
+        net_params[i]->scale_diff(scale_factor);
+      }
+    }
+  }
+}
 
 template <typename Dtype>
-void SGDSolver<Dtype>::ComputeUpdateValue() {
-  vector<shared_ptr<Blob<Dtype> > >& net_params = this->net_->params();
-  vector<float>& net_params_lr = this->net_->params_lr();
-  vector<float>& net_params_weight_decay = this->net_->params_weight_decay();
-  // get the learning rate
+void SGDSolver<Dtype>::ApplyUpdate() {
   Dtype rate = GetLearningRate();
   if (this->param_.display() && this->iter_ % this->param_.display() == 0) {
     LOG(INFO) << "Iteration " << this->iter_ << ", lr = " << rate;
   }
-  Dtype momentum = this->param_.momentum();
+  ClipGradients();
+  for (int param_id = 0; param_id < this->net_->params().size(); ++param_id) {
+    Normalize(param_id);
+    Regularize(param_id);
+    ComputeUpdateValue(param_id, rate);
+  }
+  this->net_->Update();
+}
+
+template <typename Dtype>
+void SGDSolver<Dtype>::Normalize(int param_id) {
+  if (this->param_.iter_size() == 1) { return; }
+  // Scale gradient to counterbalance accumulation.
+  const vector<shared_ptr<Blob<Dtype> > >& net_params = this->net_->params();
+  const Dtype accum_normalization = Dtype(1.) / this->param_.iter_size();
+  switch (Caffe::mode()) {
+  case Caffe::CPU: {
+    caffe_scal(net_params[param_id]->count(), accum_normalization,
+        net_params[param_id]->mutable_cpu_diff());
+    break;
+  }
+  case Caffe::GPU: {
+#ifndef CPU_ONLY
+    caffe_gpu_scal(net_params[param_id]->count(), accum_normalization,
+        net_params[param_id]->mutable_gpu_diff());
+#else
+    NO_GPU;
+#endif
+    break;
+  }
+  default:
+    LOG(FATAL) << "Unknown caffe mode: " << Caffe::mode();
+  }
+}
+
+template <typename Dtype>
+void SGDSolver<Dtype>::Regularize(int param_id) {
+  const vector<shared_ptr<Blob<Dtype> > >& net_params = this->net_->params();
+  const vector<float>& net_params_weight_decay =
+      this->net_->params_weight_decay();
   Dtype weight_decay = this->param_.weight_decay();
   string regularization_type = this->param_.regularization_type();
+  Dtype local_decay = weight_decay * net_params_weight_decay[param_id];
   switch (Caffe::mode()) {
-  case Caffe::CPU:
-    for (int param_id = 0; param_id < net_params.size(); ++param_id) {
-      // Compute the value to history, and then copy them to the blob's diff.
-      Dtype local_rate = rate * net_params_lr[param_id];
-      Dtype local_decay = weight_decay * net_params_weight_decay[param_id];
-
-      if (local_decay) {
-        if (regularization_type == "L2") {
-          // add weight decay
-          caffe_axpy(net_params[param_id]->count(),
-              local_decay,
-              net_params[param_id]->cpu_data(),
-              net_params[param_id]->mutable_cpu_diff());
-        } else if (regularization_type == "L1") {
-          caffe_cpu_sign(net_params[param_id]->count(),
-              net_params[param_id]->cpu_data(),
-              temp_[param_id]->mutable_cpu_data());
-          caffe_axpy(net_params[param_id]->count(),
-              local_decay,
-              temp_[param_id]->cpu_data(),
-              net_params[param_id]->mutable_cpu_diff());
-        } else {
-          LOG(FATAL) << "Unknown regularization type: " << regularization_type;
-        }
+  case Caffe::CPU: {
+    if (local_decay) {
+      if (regularization_type == "L2") {
+        // add weight decay
+        caffe_axpy(net_params[param_id]->count(),
+            local_decay,
+            net_params[param_id]->cpu_data(),
+            net_params[param_id]->mutable_cpu_diff());
+      } else if (regularization_type == "L1") {
+        caffe_cpu_sign(net_params[param_id]->count(),
+            net_params[param_id]->cpu_data(),
+            temp_[param_id]->mutable_cpu_data());
+        caffe_axpy(net_params[param_id]->count(),
+            local_decay,
+            temp_[param_id]->cpu_data(),
+            net_params[param_id]->mutable_cpu_diff());
+      } else {
+        LOG(FATAL) << "Unknown regularization type: " << regularization_type;
       }
-
-      caffe_cpu_axpby(net_params[param_id]->count(), local_rate,
-                net_params[param_id]->cpu_diff(), momentum,
-                history_[param_id]->mutable_cpu_data());
-      // copy
-      caffe_copy(net_params[param_id]->count(),
-          history_[param_id]->cpu_data(),
-          net_params[param_id]->mutable_cpu_diff());
     }
     break;
-  case Caffe::GPU:
+  }
+  case Caffe::GPU: {
 #ifndef CPU_ONLY
-    for (int param_id = 0; param_id < net_params.size(); ++param_id) {
-      // Compute the value to history, and then copy them to the blob's diff.
-      Dtype local_rate = rate * net_params_lr[param_id];
-      Dtype local_decay = weight_decay * net_params_weight_decay[param_id];
-
-      if (local_decay) {
-        if (regularization_type == "L2") {
-          // add weight decay
-          caffe_gpu_axpy(net_params[param_id]->count(),
-              local_decay,
-              net_params[param_id]->gpu_data(),
-              net_params[param_id]->mutable_gpu_diff());
-        } else if (regularization_type == "L1") {
-          caffe_gpu_sign(net_params[param_id]->count(),
-              net_params[param_id]->gpu_data(),
-              temp_[param_id]->mutable_gpu_data());
-          caffe_gpu_axpy(net_params[param_id]->count(),
-              local_decay,
-              temp_[param_id]->gpu_data(),
-              net_params[param_id]->mutable_gpu_diff());
-        } else {
-          LOG(FATAL) << "Unknown regularization type: " << regularization_type;
-        }
+    if (local_decay) {
+      if (regularization_type == "L2") {
+        // add weight decay
+        caffe_gpu_axpy(net_params[param_id]->count(),
+            local_decay,
+            net_params[param_id]->gpu_data(),
+            net_params[param_id]->mutable_gpu_diff());
+      } else if (regularization_type == "L1") {
+        caffe_gpu_sign(net_params[param_id]->count(),
+            net_params[param_id]->gpu_data(),
+            temp_[param_id]->mutable_gpu_data());
+        caffe_gpu_axpy(net_params[param_id]->count(),
+            local_decay,
+            temp_[param_id]->gpu_data(),
+            net_params[param_id]->mutable_gpu_diff());
+      } else {
+        LOG(FATAL) << "Unknown regularization type: " << regularization_type;
       }
-
-      caffe_gpu_axpby(net_params[param_id]->count(), local_rate,
-                net_params[param_id]->gpu_diff(), momentum,
-                history_[param_id]->mutable_gpu_data());
-      // copy
-      caffe_copy(net_params[param_id]->count(),
-          history_[param_id]->gpu_data(),
-          net_params[param_id]->mutable_gpu_diff());
     }
 #else
     NO_GPU;
 #endif
     break;
+  }
+  default:
+    LOG(FATAL) << "Unknown caffe mode: " << Caffe::mode();
+  }
+}
+
+template <typename Dtype>
+void SGDSolver<Dtype>::ComputeUpdateValue(int param_id, Dtype rate) {
+  const vector<shared_ptr<Blob<Dtype> > >& net_params = this->net_->params();
+  const vector<float>& net_params_lr = this->net_->params_lr();
+  Dtype momentum = this->param_.momentum();
+  Dtype local_rate = rate * net_params_lr[param_id];
+  // Compute the update to history, then copy it to the parameter diff.
+  switch (Caffe::mode()) {
+  case Caffe::CPU: {
+    caffe_cpu_axpby(net_params[param_id]->count(), local_rate,
+              net_params[param_id]->cpu_diff(), momentum,
+              history_[param_id]->mutable_cpu_data());
+    caffe_copy(net_params[param_id]->count(),
+        history_[param_id]->cpu_data(),
+        net_params[param_id]->mutable_cpu_diff());
+    break;
+  }
+  case Caffe::GPU: {
+#ifndef CPU_ONLY
+    caffe_gpu_axpby(net_params[param_id]->count(), local_rate,
+              net_params[param_id]->gpu_diff(), momentum,
+              history_[param_id]->mutable_gpu_data());
+    caffe_copy(net_params[param_id]->count(),
+        history_[param_id]->gpu_data(),
+        net_params[param_id]->mutable_gpu_diff());
+#else
+    NO_GPU;
+#endif
+    break;
+  }
   default:
     LOG(FATAL) << "Unknown caffe mode: " << Caffe::mode();
   }
@@ -506,248 +792,138 @@ void SGDSolver<Dtype>::RestoreSolverState(const SolverState& state) {
 }
 
 template <typename Dtype>
-void NesterovSolver<Dtype>::ComputeUpdateValue() {
-  vector<shared_ptr<Blob<Dtype> > >& net_params = this->net_->params();
-  vector<float>& net_params_lr = this->net_->params_lr();
-  vector<float>& net_params_weight_decay = this->net_->params_weight_decay();
-  // get the learning rate
-  Dtype rate = this->GetLearningRate();
-  if (this->param_.display() && this->iter_ % this->param_.display() == 0) {
-    LOG(INFO) << "Iteration " << this->iter_ << ", lr = " << rate;
-  }
+void NesterovSolver<Dtype>::ComputeUpdateValue(int param_id, Dtype rate) {
+  const vector<shared_ptr<Blob<Dtype> > >& net_params = this->net_->params();
+  const vector<float>& net_params_lr = this->net_->params_lr();
   Dtype momentum = this->param_.momentum();
-  Dtype weight_decay = this->param_.weight_decay();
-  string regularization_type = this->param_.regularization_type();
+  Dtype local_rate = rate * net_params_lr[param_id];
   switch (Caffe::mode()) {
-  case Caffe::CPU:
-    for (int param_id = 0; param_id < net_params.size(); ++param_id) {
-      // save history momentum for stepping back
-      caffe_copy(net_params[param_id]->count(),
-          this->history_[param_id]->cpu_data(),
-          this->update_[param_id]->mutable_cpu_data());
-
-      Dtype local_rate = rate * net_params_lr[param_id];
-      Dtype local_decay = weight_decay * net_params_weight_decay[param_id];
-
-      if (local_decay) {
-        if (regularization_type == "L2") {
-          // add weight decay
-          caffe_axpy(net_params[param_id]->count(),
-              local_decay,
-              net_params[param_id]->cpu_data(),
-              net_params[param_id]->mutable_cpu_diff());
-        } else if (regularization_type == "L1") {
-          caffe_cpu_sign(net_params[param_id]->count(),
-              net_params[param_id]->cpu_data(),
-              this->temp_[param_id]->mutable_cpu_data());
-          caffe_axpy(net_params[param_id]->count(),
-              local_decay,
-              this->temp_[param_id]->cpu_data(),
-              net_params[param_id]->mutable_cpu_diff());
-        } else {
-          LOG(FATAL) << "Unknown regularization type: " << regularization_type;
-        }
-      }
-
-      // update history
-      caffe_cpu_axpby(net_params[param_id]->count(), local_rate,
-                net_params[param_id]->cpu_diff(), momentum,
-                this->history_[param_id]->mutable_cpu_data());
-
-      // compute udpate: step back then over step
-      caffe_cpu_axpby(net_params[param_id]->count(), Dtype(1) + momentum,
-          this->history_[param_id]->cpu_data(), -momentum,
-          this->update_[param_id]->mutable_cpu_data());
-
-      // copy
-      caffe_copy(net_params[param_id]->count(),
-          this->update_[param_id]->cpu_data(),
-          net_params[param_id]->mutable_cpu_diff());
-    }
+  case Caffe::CPU: {
+    // save history momentum for stepping back
+    caffe_copy(net_params[param_id]->count(),
+        this->history_[param_id]->cpu_data(),
+        this->update_[param_id]->mutable_cpu_data());
+
+    // update history
+    caffe_cpu_axpby(net_params[param_id]->count(), local_rate,
+              net_params[param_id]->cpu_diff(), momentum,
+              this->history_[param_id]->mutable_cpu_data());
+
+    // compute update: step back then over step
+    caffe_cpu_axpby(net_params[param_id]->count(), Dtype(1) + momentum,
+        this->history_[param_id]->cpu_data(), -momentum,
+        this->update_[param_id]->mutable_cpu_data());
+
+    // copy
+    caffe_copy(net_params[param_id]->count(),
+        this->update_[param_id]->cpu_data(),
+        net_params[param_id]->mutable_cpu_diff());
     break;
-  case Caffe::GPU:
+  }
+  case Caffe::GPU: {
 #ifndef CPU_ONLY
-    for (int param_id = 0; param_id < net_params.size(); ++param_id) {
-      // save history momentum for stepping back
-      caffe_copy(net_params[param_id]->count(),
-          this->history_[param_id]->gpu_data(),
-          this->update_[param_id]->mutable_gpu_data());
-
-      Dtype local_rate = rate * net_params_lr[param_id];
-      Dtype local_decay = weight_decay * net_params_weight_decay[param_id];
-
-      if (local_decay) {
-        if (regularization_type == "L2") {
-          // add weight decay
-          caffe_gpu_axpy(net_params[param_id]->count(),
-              local_decay,
-              net_params[param_id]->gpu_data(),
-              net_params[param_id]->mutable_gpu_diff());
-        } else if (regularization_type == "L1") {
-          caffe_gpu_sign(net_params[param_id]->count(),
-              net_params[param_id]->gpu_data(),
-              this->temp_[param_id]->mutable_gpu_data());
-          caffe_gpu_axpy(net_params[param_id]->count(),
-              local_decay,
-              this->temp_[param_id]->gpu_data(),
-              net_params[param_id]->mutable_gpu_diff());
-        } else {
-          LOG(FATAL) << "Unknown regularization type: " << regularization_type;
-        }
-      }
-
-      // update history
-      caffe_gpu_axpby(net_params[param_id]->count(), local_rate,
-                net_params[param_id]->gpu_diff(), momentum,
-                this->history_[param_id]->mutable_gpu_data());
-
-      // compute udpate: step back then over step
-      caffe_gpu_axpby(net_params[param_id]->count(), Dtype(1) + momentum,
-          this->history_[param_id]->gpu_data(), -momentum,
-          this->update_[param_id]->mutable_gpu_data());
-
-      // copy
-      caffe_copy(net_params[param_id]->count(),
-          this->update_[param_id]->gpu_data(),
-          net_params[param_id]->mutable_gpu_diff());
-    }
+    // save history momentum for stepping back
+    caffe_copy(net_params[param_id]->count(),
+        this->history_[param_id]->gpu_data(),
+        this->update_[param_id]->mutable_gpu_data());
+
+    // update history
+    caffe_gpu_axpby(net_params[param_id]->count(), local_rate,
+              net_params[param_id]->gpu_diff(), momentum,
+              this->history_[param_id]->mutable_gpu_data());
+
+    // compute update: step back then over step
+    caffe_gpu_axpby(net_params[param_id]->count(), Dtype(1) + momentum,
+        this->history_[param_id]->gpu_data(), -momentum,
+        this->update_[param_id]->mutable_gpu_data());
+
+    // copy
+    caffe_copy(net_params[param_id]->count(),
+        this->update_[param_id]->gpu_data(),
+        net_params[param_id]->mutable_gpu_diff());
 #else
     NO_GPU;
 #endif
     break;
+  }
   default:
     LOG(FATAL) << "Unknown caffe mode: " << Caffe::mode();
   }
 }
 
 template <typename Dtype>
-void AdaGradSolver<Dtype>::ComputeUpdateValue() {
-  vector<shared_ptr<Blob<Dtype> > >& net_params = this->net_->params();
-  vector<float>& net_params_lr = this->net_->params_lr();
-  vector<float>& net_params_weight_decay = this->net_->params_weight_decay();
-  // get the learning rate
-  Dtype rate = this->GetLearningRate();
+void AdaGradSolver<Dtype>::ComputeUpdateValue(int param_id, Dtype rate) {
+  const vector<shared_ptr<Blob<Dtype> > >& net_params = this->net_->params();
+  const vector<float>& net_params_lr = this->net_->params_lr();
   Dtype delta = this->param_.delta();
-  if (this->param_.display() && this->iter_ % this->param_.display() == 0) {
-    LOG(INFO) << "Iteration " << this->iter_ << ", lr = " << rate;
-  }
-  Dtype weight_decay = this->param_.weight_decay();
-  string regularization_type = this->param_.regularization_type();
+  Dtype local_rate = rate * net_params_lr[param_id];
   switch (Caffe::mode()) {
-  case Caffe::CPU:
-    for (int param_id = 0; param_id < net_params.size(); ++param_id) {
-      Dtype local_rate = rate * net_params_lr[param_id];
-      Dtype local_decay = weight_decay * net_params_weight_decay[param_id];
-
-      if (local_decay) {
-        if (regularization_type == "L2") {
-          // add weight decay
-          caffe_axpy(net_params[param_id]->count(),
-              local_decay,
-              net_params[param_id]->cpu_data(),
-              net_params[param_id]->mutable_cpu_diff());
-        } else if (regularization_type == "L1") {
-          caffe_cpu_sign(net_params[param_id]->count(),
-              net_params[param_id]->cpu_data(),
-              this->temp_[param_id]->mutable_cpu_data());
-          caffe_axpy(net_params[param_id]->count(),
-              local_decay,
-              this->temp_[param_id]->cpu_data(),
-              net_params[param_id]->mutable_cpu_diff());
-        } else {
-          LOG(FATAL) << "Unknown regularization type: " << regularization_type;
-        }
-      }
-
-      // compute square of gradient in update
-      caffe_powx(net_params[param_id]->count(),
-          net_params[param_id]->cpu_diff(), Dtype(2),
-          this->update_[param_id]->mutable_cpu_data());
-
-      // update history
-      caffe_add(net_params[param_id]->count(),
-          this->update_[param_id]->cpu_data(),
-          this->history_[param_id]->cpu_data(),
-          this->history_[param_id]->mutable_cpu_data());
-
-      // prepare update
-      caffe_powx(net_params[param_id]->count(),
-                this->history_[param_id]->cpu_data(), Dtype(0.5),
-                this->update_[param_id]->mutable_cpu_data());
-
-      caffe_add_scalar(net_params[param_id]->count(),
-                delta, this->update_[param_id]->mutable_cpu_data());
-
-      caffe_div(net_params[param_id]->count(),
-                net_params[param_id]->cpu_diff(),
-                this->update_[param_id]->cpu_data(),
-                this->update_[param_id]->mutable_cpu_data());
-
-      // scale and copy
-      caffe_cpu_axpby(net_params[param_id]->count(), local_rate,
-          this->update_[param_id]->cpu_data(), Dtype(0),
-          net_params[param_id]->mutable_cpu_diff());
-    }
+  case Caffe::CPU: {
+    // compute square of gradient in update
+    caffe_powx(net_params[param_id]->count(),
+        net_params[param_id]->cpu_diff(), Dtype(2),
+        this->update_[param_id]->mutable_cpu_data());
+
+    // update history
+    caffe_add(net_params[param_id]->count(),
+        this->update_[param_id]->cpu_data(),
+        this->history_[param_id]->cpu_data(),
+        this->history_[param_id]->mutable_cpu_data());
+
+    // prepare update
+    caffe_powx(net_params[param_id]->count(),
+              this->history_[param_id]->cpu_data(), Dtype(0.5),
+              this->update_[param_id]->mutable_cpu_data());
+
+    caffe_add_scalar(net_params[param_id]->count(),
+              delta, this->update_[param_id]->mutable_cpu_data());
+
+    caffe_div(net_params[param_id]->count(),
+              net_params[param_id]->cpu_diff(),
+              this->update_[param_id]->cpu_data(),
+              this->update_[param_id]->mutable_cpu_data());
+
+    // scale and copy
+    caffe_cpu_axpby(net_params[param_id]->count(), local_rate,
+        this->update_[param_id]->cpu_data(), Dtype(0),
+        net_params[param_id]->mutable_cpu_diff());
     break;
-  case Caffe::GPU:
+  }
+  case Caffe::GPU: {
 #ifndef CPU_ONLY
-    for (int param_id = 0; param_id < net_params.size(); ++param_id) {
-      Dtype local_rate = rate * net_params_lr[param_id];
-      Dtype local_decay = weight_decay * net_params_weight_decay[param_id];
-
-      if (local_decay) {
-        if (regularization_type == "L2") {
-          // add weight decay
-          caffe_gpu_axpy(net_params[param_id]->count(),
-              local_decay,
-              net_params[param_id]->gpu_data(),
-              net_params[param_id]->mutable_gpu_diff());
-        } else if (regularization_type == "L1") {
-          caffe_gpu_sign(net_params[param_id]->count(),
-              net_params[param_id]->gpu_data(),
-              this->temp_[param_id]->mutable_gpu_data());
-          caffe_gpu_axpy(net_params[param_id]->count(),
-              local_decay,
-              this->temp_[param_id]->gpu_data(),
-              net_params[param_id]->mutable_gpu_diff());
-        } else {
-          LOG(FATAL) << "Unknown regularization type: " << regularization_type;
-        }
-      }
-
-      // compute square of gradient in update
-      caffe_gpu_powx(net_params[param_id]->count(),
-          net_params[param_id]->gpu_diff(), Dtype(2),
-          this->update_[param_id]->mutable_gpu_data());
-
-      // update history
-      caffe_gpu_add(net_params[param_id]->count(),
-          this->update_[param_id]->gpu_data(),
-          this->history_[param_id]->gpu_data(),
-          this->history_[param_id]->mutable_gpu_data());
-
-      // prepare update
-      caffe_gpu_powx(net_params[param_id]->count(),
-                this->history_[param_id]->gpu_data(), Dtype(0.5),
-                this->update_[param_id]->mutable_gpu_data());
-
-      caffe_gpu_add_scalar(net_params[param_id]->count(),
-                delta, this->update_[param_id]->mutable_gpu_data());
-
-      caffe_gpu_div(net_params[param_id]->count(),
-                net_params[param_id]->gpu_diff(),
-                this->update_[param_id]->gpu_data(),
-                this->update_[param_id]->mutable_gpu_data());
-
-      // scale and copy
-      caffe_gpu_axpby(net_params[param_id]->count(), local_rate,
-          this->update_[param_id]->gpu_data(), Dtype(0),
-          net_params[param_id]->mutable_gpu_diff());
-    }
+    // compute square of gradient in update
+    caffe_gpu_powx(net_params[param_id]->count(),
+        net_params[param_id]->gpu_diff(), Dtype(2),
+        this->update_[param_id]->mutable_gpu_data());
+
+    // update history
+    caffe_gpu_add(net_params[param_id]->count(),
+        this->update_[param_id]->gpu_data(),
+        this->history_[param_id]->gpu_data(),
+        this->history_[param_id]->mutable_gpu_data());
+
+    // prepare update
+    caffe_gpu_powx(net_params[param_id]->count(),
+              this->history_[param_id]->gpu_data(), Dtype(0.5),
+              this->update_[param_id]->mutable_gpu_data());
+
+    caffe_gpu_add_scalar(net_params[param_id]->count(),
+              delta, this->update_[param_id]->mutable_gpu_data());
+
+    caffe_gpu_div(net_params[param_id]->count(),
+              net_params[param_id]->gpu_diff(),
+              this->update_[param_id]->gpu_data(),
+              this->update_[param_id]->mutable_gpu_data());
+
+    // scale and copy
+    caffe_gpu_axpby(net_params[param_id]->count(), local_rate,
+        this->update_[param_id]->gpu_data(), Dtype(0),
+        net_params[param_id]->mutable_gpu_diff());
 #else
     NO_GPU;
 #endif
     break;
+  }
   default:
     LOG(FATAL) << "Unknown caffe mode: " << Caffe::mode();
   }
diff --git a/src/caffe/syncedmem.cpp b/src/caffe/syncedmem.cpp
index 7617ccfb27f..791e29565ff 100644
--- a/src/caffe/syncedmem.cpp
+++ b/src/caffe/syncedmem.cpp
@@ -108,6 +108,33 @@ void* SyncedMemory::mutable_gpu_data() {
 #endif
 }
 
+void SyncedMemory::Resize(size_t new_size) {
+  if (new_size <= size_){
+    // do nothing if the new size requirement is already fulfilled
+    return;
+  }else{
+    // we need to enlarge the underlying memory
+    // For this we just discard currently allocated memory blocks and set the new size
+    size_ = new_size;
+    head_ = UNINITIALIZED;
+
+    if (cpu_ptr_ && own_cpu_data_) {
+      CaffeFreeHost(cpu_ptr_);
+    }
+    cpu_ptr_ = NULL;
+
+#ifndef CPU_ONLY
+    if (gpu_ptr_) {
+      CUDA_CHECK(cudaFree(gpu_ptr_));
+    }
+    gpu_ptr_ = NULL;
+#endif  // CPU_ONLY
+
+    own_cpu_data_ = false;
+
+  }
+}
+
 
 }  // namespace caffe
 
diff --git a/src/caffe/test/CMakeLists.txt b/src/caffe/test/CMakeLists.txt
index 20a1d084d6a..35a803f2f41 100644
--- a/src/caffe/test/CMakeLists.txt
+++ b/src/caffe/test/CMakeLists.txt
@@ -1,105 +1,36 @@
-#
-#
-#    All test files' names must begin with a "test_" prefix
-#
-#
-project( Test )
-
-# Configuration
-set(TEST_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/test)    # test executables are going to be placed there
-set(TEST_EXT .testbin)           # test executable extension
-set(ALL_TEST test${TEST_EXT})    # name of an executable comprising of all tests
-set(RUN_TEST runtest)            # dummy target for running tests
-set(TEST_MAIN test_caffe_main.cpp)     # main test file (with main function)
-
-#    Generate config files
-add_definitions(-DCMAKE_BUILD)    # definition needed in order to include CMake's generated files
-set(IN_EXT .in)            # generator input file extension
-set(GEN_EXT .gen.cmake)    # generated output file extension
-set(TEST_DEFINES_FILE ${CMAKE_CURRENT_SOURCE_DIR}/cmake_test_defines.hpp)
-set(TEST_DATA_FILE ${CMAKE_CURRENT_SOURCE_DIR}/test_data/sample_data_list.txt)
-
-# Function prepares name of a test executable
-#    @output_name -  output variable's name
-#    @filename    -  test_*.cpp file path
-function(test_name output_name filename)
-    get_filename_component(name ${filename} NAME_WE)
-    set(${output_name} ${name}${TEST_EXT} PARENT_SCOPE)
-endfunction()
-
-set(IN_FILES             # generator input files
-    ${TEST_DEFINES_FILE}
-    ${TEST_DATA_FILE}
-)
-
-foreach(in_file ${IN_FILES})    
-    configure_file(
-        ${in_file}${IN_EXT}
-        ${in_file}${GEN_EXT}
-    )    
-endforeach()       
-
-include_directories(
-        ${Caffe_SOURCE_DIR}
-        ${CMAKE_CURRENT_SOURCE_DIR}
-)
-
-# Remove main from test sources and prepare an Object lib with main
-file(GLOB TEST_MAIN ${TEST_MAIN}) 
-list(REMOVE_ITEM TEST_CPP_SOURCES ${TEST_MAIN})
-add_library(main_obj EXCLUDE_FROM_ALL OBJECT ${TEST_MAIN} ../common.cpp)
-
-#    Build each test separately from *.cpp files
-foreach(source ${TEST_CPP_SOURCES})
-    test_name(TEST_NAME ${source})
-    
-    #
-    add_library(${TEST_NAME}.obj EXCLUDE_FROM_ALL OBJECT ${source})
-    set(TEST_OBJ_LIB $<TARGET_OBJECTS:${TEST_NAME}.obj>)
-    
-    add_executable(${TEST_NAME} EXCLUDE_FROM_ALL ${TEST_OBJ_LIB} $<TARGET_OBJECTS:main_obj>)     
-    target_link_libraries(${TEST_NAME} gtest caffe)
-    
-    #    output dir
-    set_target_properties(${TEST_NAME} PROPERTIES RUNTIME_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/test)
-    
-    # Targets and object libs
-    set(TEST_TARGETS ${TEST_TARGETS} ${TEST_NAME})
-    set(TEST_OBJ_LIBS ${TEST_OBJ_LIBS} ${TEST_OBJ_LIB})
-endforeach()
-
-#    Build each test separately from *.cu files
-foreach(source ${TEST_CU_SOURCES})
-    test_name(TEST_NAME ${source})
-    
-    cuda_add_library(${TEST_NAME}.lib EXCLUDE_FROM_ALL ${source})
-    
-    add_executable(${TEST_NAME} EXCLUDE_FROM_ALL $<TARGET_OBJECTS:main_obj>)     
-    target_link_libraries(${TEST_NAME} ${TEST_NAME}.lib gtest caffe)
-    
-    #    output dir
-    set_target_properties(${TEST_NAME} PROPERTIES RUNTIME_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/test)
-    
-    # Targets and object libs
-    set(TEST_TARGETS ${TEST_TARGETS} ${TEST_NAME})
-    set(TEST_CU_LIBS ${TEST_CU_LIBS} ${TEST_NAME}.lib)
-endforeach()
-
-#    Build a compound test excluded from the ALL target
-add_executable(${ALL_TEST} EXCLUDE_FROM_ALL ${TEST_OBJ_LIBS} $<TARGET_OBJECTS:main_obj>)
-if(NOT CPU_ONLY)
-    target_link_libraries(${ALL_TEST} ${TEST_CU_LIBS})
+# The option allows to include in build only selected test files and exclude all others
+# Usage example:
+#  cmake -DBUILD_only_tests="common,net,blob,im2col_kernel"
+set(BUILD_only_tests "" CACHE STRING "Blank or comma-separated list of test files to build without 'test_' prefix and extention")
+caffe_leave_only_selected_tests(test_srcs ${BUILD_only_tests})
+caffe_leave_only_selected_tests(test_cuda ${BUILD_only_tests})
+
+# For 'make runtest' target we don't need to embed test data paths to
+# source files, because test target is executed in source directory
+# That's why the lines below are commented. TODO: remove them
+
+# definition needed to include CMake generated files
+#add_definitions(-DCMAKE_BUILD)
+
+# generates test_data/sample_data_list.txt.gen.cmake
+#caffe_configure_testdatafile(test_data/sample_data_list.txt)
+
+set(the_target test.testbin)
+set(test_args --gtest_shuffle)
+
+if(HAVE_CUDA)
+  caffe_cuda_compile(test_cuda_objs ${test_cuda})
+  list(APPEND test_srcs ${test_cuda_objs} ${test_cuda})
+else()
+  list(APPEND test_args --gtest_filter="-*GPU*")
 endif()
-target_link_libraries(${ALL_TEST} gtest caffe)
-add_dependencies(${ALL_TEST} ${TEST_TARGETS})
-
-#    Output directory
-set_target_properties(${ALL_TEST} PROPERTIES RUNTIME_OUTPUT_DIRECTORY ${TEST_OUTPUT_DIRECTORY})
 
-#    Test command
-set(TEST_ARGS --gtest_shuffle)
-if(CPU_ONLY)
-    set(TEST_ARGS ${TEST_ARGS} --gtest_filter="-*GPU*")
-endif()
+# ---[ Adding test target
+add_executable(${the_target} EXCLUDE_FROM_ALL ${test_srcs})
+target_link_libraries(${the_target} gtest ${Caffe_LINK})
+caffe_default_properties(${the_target})
+caffe_set_runtime_directory(${the_target} "${PROJECT_BINARY_DIR}/test")
 
-add_custom_target(${RUN_TEST} COMMAND ${ALL_TEST} ${TEST_ARGS})
+# ---[ Adding runtest
+add_custom_target(runtest COMMAND ${the_target} ${test_args}
+                          WORKING_DIRECTORY ${PROJECT_SOURCE_DIR})
diff --git a/src/caffe/test/cmake_test_defines.hpp.in b/src/caffe/test/cmake_test_defines.hpp.in
deleted file mode 100644
index 870eaf5c26e..00000000000
--- a/src/caffe/test/cmake_test_defines.hpp.in
+++ /dev/null
@@ -1,4 +0,0 @@
-#define CUDA_TEST_DEVICE @CUDA_TEST_DEVICE@
-#define CMAKE_SOURCE_DIR "@CMAKE_SOURCE_DIR@/src/"
-#define EXAMPLES_SOURCE_DIR "@CMAKE_SOURCE_DIR@/examples/"
-#define CMAKE_EXT ".gen.cmake"
diff --git a/src/caffe/test/test_accuracy_layer.cpp b/src/caffe/test/test_accuracy_layer.cpp
index e11e3f2a981..c14b67cc0e9 100644
--- a/src/caffe/test/test_accuracy_layer.cpp
+++ b/src/caffe/test/test_accuracy_layer.cpp
@@ -16,13 +16,27 @@
 namespace caffe {
 
 template <typename Dtype>
-class AccuracyLayerTest : public ::testing::Test {
+class AccuracyLayerTest : public CPUDeviceTest<Dtype> {
  protected:
   AccuracyLayerTest()
-      : blob_bottom_data_(new Blob<Dtype>(100, 10, 1, 1)),
-        blob_bottom_label_(new Blob<Dtype>(100, 1, 1, 1)),
+      : blob_bottom_data_(new Blob<Dtype>()),
+        blob_bottom_label_(new Blob<Dtype>()),
         blob_top_(new Blob<Dtype>()),
         top_k_(3) {
+    vector<int> shape(2);
+    shape[0] = 100;
+    shape[1] = 10;
+    blob_bottom_data_->Reshape(shape);
+    shape.resize(1);
+    blob_bottom_label_->Reshape(shape);
+    FillBottoms();
+
+    blob_bottom_vec_.push_back(blob_bottom_data_);
+    blob_bottom_vec_.push_back(blob_bottom_label_);
+    blob_top_vec_.push_back(blob_top_);
+  }
+
+  virtual void FillBottoms() {
     // fill the probability values
     FillerParameter filler_param;
     GaussianFiller<Dtype> filler(filler_param);
@@ -33,14 +47,11 @@ class AccuracyLayerTest : public ::testing::Test {
     caffe::rng_t* prefetch_rng =
           static_cast<caffe::rng_t*>(rng->generator());
     Dtype* label_data = blob_bottom_label_->mutable_cpu_data();
-    for (int i = 0; i < 100; ++i) {
+    for (int i = 0; i < blob_bottom_label_->count(); ++i) {
       label_data[i] = (*prefetch_rng)() % 10;
     }
-
-    blob_bottom_vec_.push_back(blob_bottom_data_);
-    blob_bottom_vec_.push_back(blob_bottom_label_);
-    blob_top_vec_.push_back(blob_top_);
   }
+
   virtual ~AccuracyLayerTest() {
     delete blob_bottom_data_;
     delete blob_bottom_label_;
@@ -59,7 +70,7 @@ TYPED_TEST_CASE(AccuracyLayerTest, TestDtypes);
 TYPED_TEST(AccuracyLayerTest, TestSetup) {
   LayerParameter layer_param;
   AccuracyLayer<TypeParam> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
   EXPECT_EQ(this->blob_top_->num(), 1);
   EXPECT_EQ(this->blob_top_->channels(), 1);
   EXPECT_EQ(this->blob_top_->height(), 1);
@@ -72,7 +83,7 @@ TYPED_TEST(AccuracyLayerTest, TestSetupTopK) {
       layer_param.mutable_accuracy_param();
   accuracy_param->set_top_k(5);
   AccuracyLayer<TypeParam> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
   EXPECT_EQ(this->blob_top_->num(), 1);
   EXPECT_EQ(this->blob_top_->channels(), 1);
   EXPECT_EQ(this->blob_top_->height(), 1);
@@ -81,10 +92,9 @@ TYPED_TEST(AccuracyLayerTest, TestSetupTopK) {
 
 TYPED_TEST(AccuracyLayerTest, TestForwardCPU) {
   LayerParameter layer_param;
-  Caffe::set_mode(Caffe::CPU);
   AccuracyLayer<TypeParam> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
 
   TypeParam max_value;
   int max_id;
@@ -106,13 +116,94 @@ TYPED_TEST(AccuracyLayerTest, TestForwardCPU) {
               num_correct_labels / 100.0, 1e-4);
 }
 
+TYPED_TEST(AccuracyLayerTest, TestForwardWithSpatialAxes) {
+  this->blob_bottom_data_->Reshape(2, 10, 4, 5);
+  vector<int> label_shape(3);
+  label_shape[0] = 2; label_shape[1] = 4; label_shape[2] = 5;
+  this->blob_bottom_label_->Reshape(label_shape);
+  this->FillBottoms();
+  LayerParameter layer_param;
+  layer_param.mutable_accuracy_param()->set_axis(1);
+  AccuracyLayer<TypeParam> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+
+  TypeParam max_value;
+  const int num_labels = this->blob_bottom_label_->count();
+  int max_id;
+  int num_correct_labels = 0;
+  vector<int> label_offset(3);
+  for (int n = 0; n < this->blob_bottom_data_->num(); ++n) {
+    for (int h = 0; h < this->blob_bottom_data_->height(); ++h) {
+      for (int w = 0; w < this->blob_bottom_data_->width(); ++w) {
+        max_value = -FLT_MAX;
+        max_id = 0;
+        for (int c = 0; c < this->blob_bottom_data_->channels(); ++c) {
+          const TypeParam pred_value =
+              this->blob_bottom_data_->data_at(n, c, h, w);
+          if (pred_value > max_value) {
+            max_value = pred_value;
+            max_id = c;
+          }
+        }
+        label_offset[0] = n; label_offset[1] = h; label_offset[2] = w;
+        const int correct_label =
+            static_cast<int>(this->blob_bottom_label_->data_at(label_offset));
+        if (max_id == correct_label) {
+          ++num_correct_labels;
+        }
+      }
+    }
+  }
+  EXPECT_NEAR(this->blob_top_->data_at(0, 0, 0, 0),
+              num_correct_labels / TypeParam(num_labels), 1e-4);
+}
+
+TYPED_TEST(AccuracyLayerTest, TestForwardIgnoreLabel) {
+  LayerParameter layer_param;
+  const TypeParam kIgnoreLabelValue = -1;
+  layer_param.mutable_accuracy_param()->set_ignore_label(kIgnoreLabelValue);
+  AccuracyLayer<TypeParam> layer(layer_param);
+  // Manually set some labels to the ignore label value (-1).
+  this->blob_bottom_label_->mutable_cpu_data()[2] = kIgnoreLabelValue;
+  this->blob_bottom_label_->mutable_cpu_data()[5] = kIgnoreLabelValue;
+  this->blob_bottom_label_->mutable_cpu_data()[32] = kIgnoreLabelValue;
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+
+  TypeParam max_value;
+  int max_id;
+  int num_correct_labels = 0;
+  int count = 0;
+  for (int i = 0; i < 100; ++i) {
+    if (kIgnoreLabelValue == this->blob_bottom_label_->data_at(i, 0, 0, 0)) {
+      continue;
+    }
+    ++count;
+    max_value = -FLT_MAX;
+    max_id = 0;
+    for (int j = 0; j < 10; ++j) {
+      if (this->blob_bottom_data_->data_at(i, j, 0, 0) > max_value) {
+        max_value = this->blob_bottom_data_->data_at(i, j, 0, 0);
+        max_id = j;
+      }
+    }
+    if (max_id == this->blob_bottom_label_->data_at(i, 0, 0, 0)) {
+      ++num_correct_labels;
+    }
+  }
+  EXPECT_EQ(count, 97);  // We set 3 out of 100 labels to kIgnoreLabelValue.
+  EXPECT_NEAR(this->blob_top_->data_at(0, 0, 0, 0),
+              num_correct_labels / TypeParam(count), 1e-4);
+}
+
 TYPED_TEST(AccuracyLayerTest, TestForwardCPUTopK) {
   LayerParameter layer_param;
   AccuracyParameter* accuracy_param = layer_param.mutable_accuracy_param();
   accuracy_param->set_top_k(this->top_k_);
   AccuracyLayer<TypeParam> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
 
   TypeParam current_value;
   int current_rank;
diff --git a/src/caffe/test/test_argmax_layer.cpp b/src/caffe/test/test_argmax_layer.cpp
index fb3951c3098..895c3d372ff 100644
--- a/src/caffe/test/test_argmax_layer.cpp
+++ b/src/caffe/test/test_argmax_layer.cpp
@@ -13,13 +13,12 @@
 namespace caffe {
 
 template <typename Dtype>
-class ArgMaxLayerTest : public ::testing::Test {
+class ArgMaxLayerTest : public CPUDeviceTest<Dtype> {
  protected:
   ArgMaxLayerTest()
       : blob_bottom_(new Blob<Dtype>(10, 20, 1, 1)),
         blob_top_(new Blob<Dtype>()),
         top_k_(5) {
-    Caffe::set_mode(Caffe::CPU);
     Caffe::set_random_seed(1701);
     // fill the values
     FillerParameter filler_param;
@@ -41,7 +40,7 @@ TYPED_TEST_CASE(ArgMaxLayerTest, TestDtypes);
 TYPED_TEST(ArgMaxLayerTest, TestSetup) {
   LayerParameter layer_param;
   ArgMaxLayer<TypeParam> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
   EXPECT_EQ(this->blob_top_->num(), this->blob_bottom_->num());
   EXPECT_EQ(this->blob_top_->channels(), 1);
 }
@@ -51,7 +50,7 @@ TYPED_TEST(ArgMaxLayerTest, TestSetupMaxVal) {
   ArgMaxParameter* argmax_param = layer_param.mutable_argmax_param();
   argmax_param->set_out_max_val(true);
   ArgMaxLayer<TypeParam> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
   EXPECT_EQ(this->blob_top_->num(), this->blob_bottom_->num());
   EXPECT_EQ(this->blob_top_->channels(), 2);
 }
@@ -59,8 +58,8 @@ TYPED_TEST(ArgMaxLayerTest, TestSetupMaxVal) {
 TYPED_TEST(ArgMaxLayerTest, TestCPU) {
   LayerParameter layer_param;
   ArgMaxLayer<TypeParam> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   // Now, check values
   const TypeParam* bottom_data = this->blob_bottom_->cpu_data();
   const TypeParam* top_data = this->blob_top_->cpu_data();
@@ -84,8 +83,8 @@ TYPED_TEST(ArgMaxLayerTest, TestCPUMaxVal) {
   ArgMaxParameter* argmax_param = layer_param.mutable_argmax_param();
   argmax_param->set_out_max_val(true);
   ArgMaxLayer<TypeParam> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   // Now, check values
   const TypeParam* bottom_data = this->blob_bottom_->cpu_data();
   const TypeParam* top_data = this->blob_top_->cpu_data();
@@ -110,8 +109,8 @@ TYPED_TEST(ArgMaxLayerTest, TestCPUTopK) {
   ArgMaxParameter* argmax_param = layer_param.mutable_argmax_param();
   argmax_param->set_top_k(this->top_k_);
   ArgMaxLayer<TypeParam> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   // Now, check values
   int max_ind;
   TypeParam max_val;
@@ -140,8 +139,8 @@ TYPED_TEST(ArgMaxLayerTest, TestCPUMaxValTopK) {
   argmax_param->set_out_max_val(true);
   argmax_param->set_top_k(this->top_k_);
   ArgMaxLayer<TypeParam> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   // Now, check values
   int max_ind;
   TypeParam max_val;
diff --git a/src/caffe/test/test_benchmark.cpp b/src/caffe/test/test_benchmark.cpp
index dbbee08d667..43aaa639b3c 100644
--- a/src/caffe/test/test_benchmark.cpp
+++ b/src/caffe/test/test_benchmark.cpp
@@ -9,6 +9,8 @@
 
 namespace caffe {
 
+const float kMillisecondsThreshold = 30;
+
 template <typename TypeParam>
 class BenchmarkTest : public MultiDeviceTest<TypeParam> {};
 
@@ -63,8 +65,8 @@ TYPED_TEST(BenchmarkTest, TestTimerMilliSeconds) {
   EXPECT_FALSE(timer.has_run_at_least_once());
   timer.Start();
   usleep(300 * 1000);
-  EXPECT_GE(timer.MilliSeconds(), 290);
-  EXPECT_LE(timer.MilliSeconds(), 310);
+  EXPECT_GE(timer.MilliSeconds(), 300 - kMillisecondsThreshold);
+  EXPECT_LE(timer.MilliSeconds(), 300 + kMillisecondsThreshold);
   EXPECT_TRUE(timer.initted());
   EXPECT_FALSE(timer.running());
   EXPECT_TRUE(timer.has_run_at_least_once());
@@ -78,8 +80,8 @@ TYPED_TEST(BenchmarkTest, TestTimerSeconds) {
   EXPECT_FALSE(timer.has_run_at_least_once());
   timer.Start();
   usleep(300 * 1000);
-  EXPECT_GE(timer.Seconds(), 0.290);
-  EXPECT_LE(timer.Seconds(), 0.310);
+  EXPECT_GE(timer.Seconds(), 0.3 - kMillisecondsThreshold / 1000.);
+  EXPECT_LE(timer.Seconds(), 0.3 + kMillisecondsThreshold / 1000.);
   EXPECT_TRUE(timer.initted());
   EXPECT_FALSE(timer.running());
   EXPECT_TRUE(timer.has_run_at_least_once());
diff --git a/src/caffe/test/test_blob.cpp b/src/caffe/test/test_blob.cpp
index adf7a4d38e9..7da6423b67c 100644
--- a/src/caffe/test/test_blob.cpp
+++ b/src/caffe/test/test_blob.cpp
@@ -1,4 +1,5 @@
 #include <cstring>
+#include <vector>
 
 #include "gtest/gtest.h"
 
@@ -31,10 +32,7 @@ TYPED_TEST(BlobSimpleTest, TestInitialization) {
   EXPECT_EQ(this->blob_preshaped_->height(), 4);
   EXPECT_EQ(this->blob_preshaped_->width(), 5);
   EXPECT_EQ(this->blob_preshaped_->count(), 120);
-  EXPECT_EQ(this->blob_->num(), 0);
-  EXPECT_EQ(this->blob_->channels(), 0);
-  EXPECT_EQ(this->blob_->height(), 0);
-  EXPECT_EQ(this->blob_->width(), 0);
+  EXPECT_EQ(this->blob_->num_axes(), 0);
   EXPECT_EQ(this->blob_->count(), 0);
 }
 
@@ -54,4 +52,243 @@ TYPED_TEST(BlobSimpleTest, TestReshape) {
   EXPECT_EQ(this->blob_->count(), 120);
 }
 
+TYPED_TEST(BlobSimpleTest, TestLegacyBlobProtoShapeEquals) {
+  BlobProto blob_proto;
+
+  // Reshape to (3 x 2).
+  vector<int> shape(2);
+  shape[0] = 3;
+  shape[1] = 2;
+  this->blob_->Reshape(shape);
+
+  // (3 x 2) blob == (1 x 1 x 3 x 2) legacy blob
+  blob_proto.set_num(1);
+  blob_proto.set_channels(1);
+  blob_proto.set_height(3);
+  blob_proto.set_width(2);
+  EXPECT_TRUE(this->blob_->ShapeEquals(blob_proto));
+
+  // (3 x 2) blob != (0 x 1 x 3 x 2) legacy blob
+  blob_proto.set_num(0);
+  blob_proto.set_channels(1);
+  blob_proto.set_height(3);
+  blob_proto.set_width(2);
+  EXPECT_FALSE(this->blob_->ShapeEquals(blob_proto));
+
+  // (3 x 2) blob != (3 x 1 x 3 x 2) legacy blob
+  blob_proto.set_num(3);
+  blob_proto.set_channels(1);
+  blob_proto.set_height(3);
+  blob_proto.set_width(2);
+  EXPECT_FALSE(this->blob_->ShapeEquals(blob_proto));
+
+  // Reshape to (1 x 3 x 2).
+  shape.insert(shape.begin(), 1);
+  this->blob_->Reshape(shape);
+
+  // (1 x 3 x 2) blob == (1 x 1 x 3 x 2) legacy blob
+  blob_proto.set_num(1);
+  blob_proto.set_channels(1);
+  blob_proto.set_height(3);
+  blob_proto.set_width(2);
+  EXPECT_TRUE(this->blob_->ShapeEquals(blob_proto));
+
+  // Reshape to (2 x 3 x 2).
+  shape[0] = 2;
+  this->blob_->Reshape(shape);
+
+  // (2 x 3 x 2) blob != (1 x 1 x 3 x 2) legacy blob
+  blob_proto.set_num(1);
+  blob_proto.set_channels(1);
+  blob_proto.set_height(3);
+  blob_proto.set_width(2);
+  EXPECT_FALSE(this->blob_->ShapeEquals(blob_proto));
+}
+
+template <typename TypeParam>
+class BlobMathTest : public MultiDeviceTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
+ protected:
+  BlobMathTest()
+      : blob_(new Blob<Dtype>(2, 3, 4, 5)),
+        epsilon_(1e-6) {}
+
+  virtual ~BlobMathTest() { delete blob_; }
+  Blob<Dtype>* const blob_;
+  Dtype epsilon_;
+};
+
+TYPED_TEST_CASE(BlobMathTest, TestDtypesAndDevices);
+
+TYPED_TEST(BlobMathTest, TestSumOfSquares) {
+  typedef typename TypeParam::Dtype Dtype;
+
+  // Uninitialized Blob should have sum of squares == 0.
+  EXPECT_EQ(0, this->blob_->sumsq_data());
+  EXPECT_EQ(0, this->blob_->sumsq_diff());
+  FillerParameter filler_param;
+  filler_param.set_min(-3);
+  filler_param.set_max(3);
+  UniformFiller<Dtype> filler(filler_param);
+  filler.Fill(this->blob_);
+  Dtype expected_sumsq = 0;
+  const Dtype* data = this->blob_->cpu_data();
+  for (int i = 0; i < this->blob_->count(); ++i) {
+    expected_sumsq += data[i] * data[i];
+  }
+  // Do a mutable access on the current device,
+  // so that the sumsq computation is done on that device.
+  // (Otherwise, this would only check the CPU sumsq implementation.)
+  switch (TypeParam::device) {
+  case Caffe::CPU:
+    this->blob_->mutable_cpu_data();
+    break;
+  case Caffe::GPU:
+    this->blob_->mutable_gpu_data();
+    break;
+  default:
+    LOG(FATAL) << "Unknown device: " << TypeParam::device;
+  }
+  EXPECT_NEAR(expected_sumsq, this->blob_->sumsq_data(),
+              this->epsilon_ * expected_sumsq);
+  EXPECT_EQ(0, this->blob_->sumsq_diff());
+
+  // Check sumsq_diff too.
+  const Dtype kDiffScaleFactor = 7;
+  caffe_cpu_scale(this->blob_->count(), kDiffScaleFactor, data,
+                  this->blob_->mutable_cpu_diff());
+  switch (TypeParam::device) {
+  case Caffe::CPU:
+    this->blob_->mutable_cpu_diff();
+    break;
+  case Caffe::GPU:
+    this->blob_->mutable_gpu_diff();
+    break;
+  default:
+    LOG(FATAL) << "Unknown device: " << TypeParam::device;
+  }
+  EXPECT_NEAR(expected_sumsq, this->blob_->sumsq_data(),
+              this->epsilon_ * expected_sumsq);
+  const Dtype expected_sumsq_diff =
+      expected_sumsq * kDiffScaleFactor * kDiffScaleFactor;
+  EXPECT_NEAR(expected_sumsq_diff, this->blob_->sumsq_diff(),
+              this->epsilon_ * expected_sumsq_diff);
+}
+
+TYPED_TEST(BlobMathTest, TestAsum) {
+  typedef typename TypeParam::Dtype Dtype;
+
+  // Uninitialized Blob should have asum == 0.
+  EXPECT_EQ(0, this->blob_->asum_data());
+  EXPECT_EQ(0, this->blob_->asum_diff());
+  FillerParameter filler_param;
+  filler_param.set_min(-3);
+  filler_param.set_max(3);
+  UniformFiller<Dtype> filler(filler_param);
+  filler.Fill(this->blob_);
+  Dtype expected_asum = 0;
+  const Dtype* data = this->blob_->cpu_data();
+  for (int i = 0; i < this->blob_->count(); ++i) {
+    expected_asum += std::fabs(data[i]);
+  }
+  // Do a mutable access on the current device,
+  // so that the asum computation is done on that device.
+  // (Otherwise, this would only check the CPU asum implementation.)
+  switch (TypeParam::device) {
+  case Caffe::CPU:
+    this->blob_->mutable_cpu_data();
+    break;
+  case Caffe::GPU:
+    this->blob_->mutable_gpu_data();
+    break;
+  default:
+    LOG(FATAL) << "Unknown device: " << TypeParam::device;
+  }
+  EXPECT_NEAR(expected_asum, this->blob_->asum_data(),
+              this->epsilon_ * expected_asum);
+  EXPECT_EQ(0, this->blob_->asum_diff());
+
+  // Check asum_diff too.
+  const Dtype kDiffScaleFactor = 7;
+  caffe_cpu_scale(this->blob_->count(), kDiffScaleFactor, data,
+                  this->blob_->mutable_cpu_diff());
+  switch (TypeParam::device) {
+  case Caffe::CPU:
+    this->blob_->mutable_cpu_diff();
+    break;
+  case Caffe::GPU:
+    this->blob_->mutable_gpu_diff();
+    break;
+  default:
+    LOG(FATAL) << "Unknown device: " << TypeParam::device;
+  }
+  EXPECT_NEAR(expected_asum, this->blob_->asum_data(),
+              this->epsilon_ * expected_asum);
+  const Dtype expected_diff_asum = expected_asum * kDiffScaleFactor;
+  EXPECT_NEAR(expected_diff_asum, this->blob_->asum_diff(),
+              this->epsilon_ * expected_diff_asum);
+}
+
+TYPED_TEST(BlobMathTest, TestScaleData) {
+  typedef typename TypeParam::Dtype Dtype;
+
+  EXPECT_EQ(0, this->blob_->asum_data());
+  EXPECT_EQ(0, this->blob_->asum_diff());
+  FillerParameter filler_param;
+  filler_param.set_min(-3);
+  filler_param.set_max(3);
+  UniformFiller<Dtype> filler(filler_param);
+  filler.Fill(this->blob_);
+  const Dtype asum_before_scale = this->blob_->asum_data();
+  // Do a mutable access on the current device,
+  // so that the asum computation is done on that device.
+  // (Otherwise, this would only check the CPU asum implementation.)
+  switch (TypeParam::device) {
+  case Caffe::CPU:
+    this->blob_->mutable_cpu_data();
+    break;
+  case Caffe::GPU:
+    this->blob_->mutable_gpu_data();
+    break;
+  default:
+    LOG(FATAL) << "Unknown device: " << TypeParam::device;
+  }
+  const Dtype kDataScaleFactor = 3;
+  this->blob_->scale_data(kDataScaleFactor);
+  EXPECT_NEAR(asum_before_scale * kDataScaleFactor, this->blob_->asum_data(),
+              this->epsilon_ * asum_before_scale * kDataScaleFactor);
+  EXPECT_EQ(0, this->blob_->asum_diff());
+
+  // Check scale_diff too.
+  const Dtype kDataToDiffScaleFactor = 7;
+  const Dtype* data = this->blob_->cpu_data();
+  caffe_cpu_scale(this->blob_->count(), kDataToDiffScaleFactor, data,
+                  this->blob_->mutable_cpu_diff());
+  const Dtype expected_asum_before_scale = asum_before_scale * kDataScaleFactor;
+  EXPECT_NEAR(expected_asum_before_scale, this->blob_->asum_data(),
+              this->epsilon_ * expected_asum_before_scale);
+  const Dtype expected_diff_asum_before_scale =
+      asum_before_scale * kDataScaleFactor * kDataToDiffScaleFactor;
+  EXPECT_NEAR(expected_diff_asum_before_scale, this->blob_->asum_diff(),
+              this->epsilon_ * expected_diff_asum_before_scale);
+  switch (TypeParam::device) {
+  case Caffe::CPU:
+    this->blob_->mutable_cpu_diff();
+    break;
+  case Caffe::GPU:
+    this->blob_->mutable_gpu_diff();
+    break;
+  default:
+    LOG(FATAL) << "Unknown device: " << TypeParam::device;
+  }
+  const Dtype kDiffScaleFactor = 3;
+  this->blob_->scale_diff(kDiffScaleFactor);
+  EXPECT_NEAR(asum_before_scale * kDataScaleFactor, this->blob_->asum_data(),
+              this->epsilon_ * asum_before_scale * kDataScaleFactor);
+  const Dtype expected_diff_asum =
+      expected_diff_asum_before_scale * kDiffScaleFactor;
+  EXPECT_NEAR(expected_diff_asum, this->blob_->asum_diff(),
+              this->epsilon_ * expected_diff_asum);
+}
+
 }  // namespace caffe
diff --git a/src/caffe/test/test_bn_layer.cpp b/src/caffe/test/test_bn_layer.cpp
new file mode 100644
index 00000000000..bdf76a2d6ce
--- /dev/null
+++ b/src/caffe/test/test_bn_layer.cpp
@@ -0,0 +1,405 @@
+#include <algorithm>
+#include <cstring>
+#include <vector>
+
+#include "gtest/gtest.h"
+
+#include "caffe/blob.hpp"
+#include "caffe/common.hpp"
+#include "caffe/filler.hpp"
+
+#include "caffe/common_layers.hpp"
+
+#include "caffe/test/test_caffe_main.hpp"
+#include "caffe/test/test_gradient_check_util.hpp"
+
+#define BATCH_SIZE 2
+#define INPUT_DATA_SIZE 3
+
+namespace caffe {
+
+template <typename TypeParam>
+class BNLayerTest : public MultiDeviceTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
+ protected:
+  BNLayerTest()
+      : blob_bottom_(new Blob<Dtype>(5, 2, 3, 4)),
+        blob_top_(new Blob<Dtype>()) {
+    // fill the values
+    FillerParameter filler_param;
+    GaussianFiller<Dtype> filler(filler_param);
+    filler.Fill(this->blob_bottom_);
+    blob_bottom_vec_.push_back(blob_bottom_);
+    blob_top_vec_.push_back(blob_top_);
+  }
+  virtual ~BNLayerTest() { delete blob_bottom_; delete blob_top_; }
+  Blob<Dtype>* const blob_bottom_;
+  Blob<Dtype>* const blob_top_;
+  vector<Blob<Dtype>*> blob_bottom_vec_;
+  vector<Blob<Dtype>*> blob_top_vec_;
+};
+
+TYPED_TEST_CASE(BNLayerTest, TestDtypesAndDevices);
+
+TYPED_TEST(BNLayerTest, TestForward) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+
+  BNParameter* bn_param = layer_param.mutable_bn_param();
+  FillerParameter *slope_param = bn_param->mutable_slope_filler();
+  slope_param->set_value(1);
+  FillerParameter *bias_param = bn_param->mutable_bias_filler();
+  bias_param->set_value(0);
+  bn_param->set_eps(0.);
+  bn_param->set_frozen(false);
+
+  BNLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Reshape(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+
+  // Test mean
+  int num = this->blob_bottom_->num();
+  int channels = this->blob_bottom_->channels();
+  int height = this->blob_bottom_->height();
+  int width = this->blob_bottom_->width();
+
+  for (int j = 0; j < channels; ++j) {
+    Dtype sum = 0, var = 0;
+    for (int i = 0; i < num; ++i) {
+      for ( int k = 0; k < height; ++k ) {
+        for ( int l = 0; l < width; ++l ) {
+          Dtype data = this->blob_top_->data_at(i, j, k, l);
+          sum += data;
+          var += data * data;
+        }
+      }
+    }
+    sum /= height * width * num;
+    var /= height * width * num;
+
+    const Dtype kErrorBound = 0.001;
+    // expect zero mean
+    EXPECT_NEAR(0, sum, kErrorBound);
+    // expect unit variance
+    EXPECT_NEAR(1, var, kErrorBound);
+  }
+}
+
+TYPED_TEST(BNLayerTest, TestBackward) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+
+  BNParameter* bn_param = layer_param.mutable_bn_param();
+  FillerParameter *slope_param = bn_param->mutable_slope_filler();
+  slope_param->set_value(1);
+  FillerParameter *bias_param = bn_param->mutable_bias_filler();
+  bias_param->set_value(0);
+  bn_param->set_eps(0.);
+  bn_param->set_frozen(false);
+
+  BNLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-4);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
+}
+
+TYPED_TEST(BNLayerTest, TestForwardFrozen) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+
+  BNParameter* bn_param = layer_param.mutable_bn_param();
+  FillerParameter *slope_param = bn_param->mutable_slope_filler();
+  slope_param->set_value(1);
+  FillerParameter *bias_param = bn_param->mutable_bias_filler();
+  bias_param->set_value(0);
+  bn_param->set_eps(0.);
+  bn_param->set_frozen(true);
+
+  BNLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  // Init running mean and var
+  shared_ptr<Blob<Dtype> > running_mean = layer.blobs()[2];
+  Dtype* running_mean_data = running_mean->mutable_cpu_data();
+  for (int c = 0; c < running_mean->count(); ++c) {
+    running_mean_data[c] = Dtype(c);
+  }
+  shared_ptr<Blob<Dtype> > running_var = layer.blobs()[3];
+  Dtype* running_var_data = running_var->mutable_cpu_data();
+  for (int c = 0; c < running_var->count(); ++c) {
+    running_var_data[c] = Dtype(c + 1);
+  }
+
+  layer.Reshape(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+
+  int num = this->blob_bottom_->num();
+  int channels = this->blob_bottom_->channels();
+  int height = this->blob_bottom_->height();
+  int width = this->blob_bottom_->width();
+
+  const Dtype kErrorBound = 0.001;
+  for (int j = 0; j < channels; ++j) {
+    for (int i = 0; i < num; ++i) {
+      for ( int k = 0; k < height; ++k ) {
+        for ( int l = 0; l < width; ++l ) {
+          Dtype input = this->blob_bottom_->data_at(i, j, k, l);
+          Dtype output = this->blob_top_->data_at(i, j, k, l);
+          Dtype expect_output = (input - j) / sqrt(j + 1);
+          EXPECT_NEAR(expect_output, output, kErrorBound);
+        }
+      }
+    }
+  }
+}
+
+TYPED_TEST(BNLayerTest, TestBackwardFrozen) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+
+  BNParameter* bn_param = layer_param.mutable_bn_param();
+  FillerParameter *slope_param = bn_param->mutable_slope_filler();
+  slope_param->set_value(1);
+  FillerParameter *bias_param = bn_param->mutable_bias_filler();
+  bias_param->set_value(0);
+  bn_param->set_eps(0.);
+  bn_param->set_frozen(true);
+
+  BNLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  // Init running mean and var
+  shared_ptr<Blob<Dtype> > running_mean = layer.blobs()[2];
+  Dtype* running_mean_data = running_mean->mutable_cpu_data();
+  for (int c = 0; c < running_mean->count(); ++c) {
+    running_mean_data[c] = Dtype(c);
+  }
+  shared_ptr<Blob<Dtype> > running_var = layer.blobs()[3];
+  Dtype* running_var_data = running_var->mutable_cpu_data();
+  for (int c = 0; c < running_var->count(); ++c) {
+    running_var_data[c] = Dtype(c + 1);
+  }
+
+  layer.Reshape(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+
+  Dtype* top_diff_data = this->blob_top_->mutable_cpu_diff();
+  for (int i = 0; i < this->blob_top_->count(); ++i) {
+    top_diff_data[i] = Dtype(i);
+  }
+  vector<bool> propagate_down(1, true);
+  layer.Backward(this->blob_top_vec_, propagate_down, this->blob_bottom_vec_);
+
+  int num = this->blob_bottom_->num();
+  int channels = this->blob_bottom_->channels();
+  int height = this->blob_bottom_->height();
+  int width = this->blob_bottom_->width();
+
+  const Dtype kErrorBound = 0.001;
+  for (int j = 0; j < channels; ++j) {
+    for (int i = 0; i < num; ++i) {
+      for ( int k = 0; k < height; ++k ) {
+        for ( int l = 0; l < width; ++l ) {
+          Dtype input = this->blob_top_->diff_at(i, j, k, l);
+          Dtype output = this->blob_bottom_->diff_at(i, j, k, l);
+          Dtype expect_output = input / sqrt(j + 1);
+          EXPECT_NEAR(expect_output, output, kErrorBound);
+        }
+      }
+    }
+  }
+}
+
+#ifdef USE_CUDNN
+template <typename Dtype>
+class CuDNNBNLayerTest : public GPUDeviceTest<Dtype> {
+ protected:
+  CuDNNBNLayerTest()
+      : blob_bottom_(new Blob<Dtype>(2, 3, 4, 5)),
+        blob_top_(new Blob<Dtype>()) {
+    // fill the values
+    FillerParameter filler_param;
+    filler_param.set_mean(-10);
+    filler_param.set_std(5);
+    GaussianFiller<Dtype> filler(filler_param);
+    filler.Fill(this->blob_bottom_);
+    blob_bottom_vec_.push_back(blob_bottom_);
+    blob_top_vec_.push_back(blob_top_);
+  }
+  virtual ~CuDNNBNLayerTest() { delete blob_bottom_; delete blob_top_; }
+  Blob<Dtype>* const blob_bottom_;
+  Blob<Dtype>* const blob_top_;
+  vector<Blob<Dtype>*> blob_bottom_vec_;
+  vector<Blob<Dtype>*> blob_top_vec_;
+};
+
+TYPED_TEST_CASE(CuDNNBNLayerTest, TestDtypes);
+
+TYPED_TEST(CuDNNBNLayerTest, TestForward) {
+  Caffe::set_random_seed(1701);
+  typedef TypeParam Dtype;
+  LayerParameter layer_param;
+  BNParameter* bn_param = layer_param.mutable_bn_param();
+  FillerParameter *slope_param = bn_param->mutable_slope_filler();
+  slope_param->set_value(1);
+  FillerParameter *bias_param = bn_param->mutable_bias_filler();
+  bias_param->set_value(0);
+  bn_param->set_eps(0.);
+  bn_param->set_frozen(false);
+
+  CuDNNBNLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Reshape(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+
+  // Test mean
+  Dtype mean, var;
+  int num = this->blob_bottom_->num();
+  int channels = this->blob_bottom_->channels();
+  int height = this->blob_bottom_->height();
+  int width = this->blob_bottom_->width();
+
+  for (int j = 0; j < channels; ++j) {
+    Dtype mean = 0, var = 0;
+    for (int i = 0; i < num; ++i) {
+      for (int k = 0; k < height; ++k) {
+        for (int l = 0; l < width; ++l) {
+          Dtype data = this->blob_top_->data_at(i, j, k, l);
+          mean += data;
+          var += data * data;
+        }
+      }
+    }
+    mean /= num * height * width;
+    var /= num * height * width;
+
+    const Dtype kErrorBound = 0.001;
+    EXPECT_NEAR(0, mean, kErrorBound);
+    EXPECT_NEAR(1, var, kErrorBound);
+  }
+}
+
+TYPED_TEST(CuDNNBNLayerTest, TestGradient) {
+  typedef TypeParam Dtype;
+  LayerParameter layer_param;
+  BNParameter* bn_param = layer_param.mutable_bn_param();
+  FillerParameter *slope_param = bn_param->mutable_slope_filler();
+  slope_param->set_value(1);
+  FillerParameter *bias_param = bn_param->mutable_bias_filler();
+  bias_param->set_value(0);
+  bn_param->set_eps(0.);
+  bn_param->set_frozen(false);
+
+  CuDNNBNLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 4e-4);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
+}
+
+TYPED_TEST(CuDNNBNLayerTest, TestForwardFrozen) {
+  typedef TypeParam Dtype;
+  LayerParameter layer_param;
+
+  BNParameter* bn_param = layer_param.mutable_bn_param();
+  FillerParameter *slope_param = bn_param->mutable_slope_filler();
+  slope_param->set_value(1);
+  FillerParameter *bias_param = bn_param->mutable_bias_filler();
+  bias_param->set_value(0);
+  bn_param->set_eps(0.);
+  bn_param->set_frozen(true);
+
+  CuDNNBNLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  // Init running mean and var
+  shared_ptr<Blob<Dtype> > running_mean = layer.blobs()[2];
+  Dtype* running_mean_data = running_mean->mutable_cpu_data();
+  for (int c = 0; c < running_mean->count(); ++c) {
+    running_mean_data[c] = Dtype(c);
+  }
+  shared_ptr<Blob<Dtype> > running_var = layer.blobs()[3];
+  Dtype* running_var_data = running_var->mutable_cpu_data();
+  for (int c = 0; c < running_var->count(); ++c) {
+    running_var_data[c] = Dtype(c + 1);
+  }
+
+  layer.Reshape(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+
+  int num = this->blob_bottom_->num();
+  int channels = this->blob_bottom_->channels();
+  int height = this->blob_bottom_->height();
+  int width = this->blob_bottom_->width();
+
+  const Dtype kErrorBound = 0.001;
+  for (int j = 0; j < channels; ++j) {
+    for (int i = 0; i < num; ++i) {
+      for ( int k = 0; k < height; ++k ) {
+        for ( int l = 0; l < width; ++l ) {
+          Dtype input = this->blob_bottom_->data_at(i, j, k, l);
+          Dtype output = this->blob_top_->data_at(i, j, k, l);
+          Dtype expect_output = (input - j) / sqrt(j + 1);
+          EXPECT_NEAR(expect_output, output, kErrorBound);
+        }
+      }
+    }
+  }
+}
+
+TYPED_TEST(CuDNNBNLayerTest, TestBackwardFrozen) {
+  typedef TypeParam Dtype;
+  LayerParameter layer_param;
+
+  BNParameter* bn_param = layer_param.mutable_bn_param();
+  FillerParameter *slope_param = bn_param->mutable_slope_filler();
+  slope_param->set_value(1);
+  FillerParameter *bias_param = bn_param->mutable_bias_filler();
+  bias_param->set_value(0);
+  bn_param->set_eps(0.);
+  bn_param->set_frozen(true);
+
+  BNLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  // Init running mean and var
+  shared_ptr<Blob<Dtype> > running_mean = layer.blobs()[2];
+  Dtype* running_mean_data = running_mean->mutable_cpu_data();
+  for (int c = 0; c < running_mean->count(); ++c) {
+    running_mean_data[c] = Dtype(c);
+  }
+  shared_ptr<Blob<Dtype> > running_var = layer.blobs()[3];
+  Dtype* running_var_data = running_var->mutable_cpu_data();
+  for (int c = 0; c < running_var->count(); ++c) {
+    running_var_data[c] = Dtype(c + 1);
+  }
+
+  layer.Reshape(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+
+  Dtype* top_diff_data = this->blob_top_->mutable_cpu_diff();
+  for (int i = 0; i < this->blob_top_->count(); ++i) {
+    top_diff_data[i] = Dtype(i);
+  }
+  vector<bool> propagate_down(1, true);
+  layer.Backward(this->blob_top_vec_, propagate_down, this->blob_bottom_vec_);
+
+  int num = this->blob_bottom_->num();
+  int channels = this->blob_bottom_->channels();
+  int height = this->blob_bottom_->height();
+  int width = this->blob_bottom_->width();
+
+  const Dtype kErrorBound = 0.001;
+  for (int j = 0; j < channels; ++j) {
+    for (int i = 0; i < num; ++i) {
+      for ( int k = 0; k < height; ++k ) {
+        for ( int l = 0; l < width; ++l ) {
+          Dtype input = this->blob_top_->diff_at(i, j, k, l);
+          Dtype output = this->blob_bottom_->diff_at(i, j, k, l);
+          Dtype expect_output = input / sqrt(j + 1);
+          EXPECT_NEAR(expect_output, output, kErrorBound);
+        }
+      }
+    }
+  }
+}
+#endif
+
+}
+
diff --git a/src/caffe/test/test_caffe_main.cpp b/src/caffe/test/test_caffe_main.cpp
index bff0c4ed20e..336f16f300e 100644
--- a/src/caffe/test/test_caffe_main.cpp
+++ b/src/caffe/test/test_caffe_main.cpp
@@ -1,6 +1,7 @@
 // The main caffe test code. Your test cpp code should include this hpp
 // to allow a main function to be compiled into the binary.
 
+#include "caffe/caffe.hpp"
 #include "caffe/test/test_caffe_main.hpp"
 
 namespace caffe {
@@ -15,7 +16,7 @@ using caffe::CAFFE_TEST_CUDA_PROP;
 
 int main(int argc, char** argv) {
   ::testing::InitGoogleTest(&argc, argv);
-  ::google::InitGoogleLogging(argv[0]);
+  caffe::GlobalInit(&argc, &argv);
 #ifndef CPU_ONLY
   // Before starting testing, let's first print out a few cuda defice info.
   int device;
@@ -24,11 +25,13 @@ int main(int argc, char** argv) {
   if (argc > 1) {
     // Use the given device
     device = atoi(argv[1]);
-    cudaSetDevice(device);
+    caffe::Caffe::SetDevice(0);
     cout << "Setting to use device " << device << endl;
   } else if (CUDA_TEST_DEVICE >= 0) {
     // Use the device assigned in build configuration; but with a lower priority
-    device = CUDA_TEST_DEVICE;
+    caffe::Caffe::SetDevice(CUDA_TEST_DEVICE);
+  } else {
+    caffe::Caffe::SetDevice(0);
   }
   cudaGetDevice(&device);
   cout << "Current device id: " << device << endl;
diff --git a/src/caffe/test/test_common.cpp b/src/caffe/test/test_common.cpp
index 0b3639c7706..b3a61b0fd25 100644
--- a/src/caffe/test/test_common.cpp
+++ b/src/caffe/test/test_common.cpp
@@ -29,13 +29,6 @@ TEST_F(CommonTest, TestBrewMode) {
   EXPECT_EQ(Caffe::mode(), Caffe::GPU);
 }
 
-TEST_F(CommonTest, TestPhase) {
-  Caffe::set_phase(Caffe::TRAIN);
-  EXPECT_EQ(Caffe::phase(), Caffe::TRAIN);
-  Caffe::set_phase(Caffe::TEST);
-  EXPECT_EQ(Caffe::phase(), Caffe::TEST);
-}
-
 TEST_F(CommonTest, TestRandSeedCPU) {
   SyncedMemory data_a(10 * sizeof(int));
   SyncedMemory data_b(10 * sizeof(int));
diff --git a/src/caffe/test/test_concat_layer.cpp b/src/caffe/test/test_concat_layer.cpp
index c60b7f744cc..662a50fa23b 100644
--- a/src/caffe/test/test_concat_layer.cpp
+++ b/src/caffe/test/test_concat_layer.cpp
@@ -19,9 +19,9 @@ class ConcatLayerTest : public MultiDeviceTest<TypeParam> {
 
  protected:
   ConcatLayerTest()
-      : blob_bottom_0(new Blob<Dtype>(2, 3, 6, 5)),
-        blob_bottom_1(new Blob<Dtype>(2, 5, 6, 5)),
-        blob_bottom_2(new Blob<Dtype>(5, 3, 6, 5)),
+      : blob_bottom_0_(new Blob<Dtype>(2, 3, 6, 5)),
+        blob_bottom_1_(new Blob<Dtype>(2, 5, 6, 5)),
+        blob_bottom_2_(new Blob<Dtype>(5, 3, 6, 5)),
         blob_top_(new Blob<Dtype>()) {}
   virtual void SetUp() {
     // fill the values
@@ -29,30 +29,30 @@ class ConcatLayerTest : public MultiDeviceTest<TypeParam> {
     FillerParameter filler_param;
     filler_param.set_value(1.);
     filler.reset(new ConstantFiller<Dtype>(filler_param));
-    filler->Fill(this->blob_bottom_0);
+    filler->Fill(this->blob_bottom_0_);
     filler_param.set_value(2.);
     filler.reset(new ConstantFiller<Dtype>(filler_param));
-    filler->Fill(this->blob_bottom_1);
+    filler->Fill(this->blob_bottom_1_);
     filler_param.set_value(3.);
     filler.reset(new ConstantFiller<Dtype>(filler_param));
-    filler->Fill(this->blob_bottom_2);
-    blob_bottom_vec_0.push_back(blob_bottom_0);
-    blob_bottom_vec_0.push_back(blob_bottom_1);
-    blob_bottom_vec_1.push_back(blob_bottom_0);
-    blob_bottom_vec_1.push_back(blob_bottom_2);
+    filler->Fill(this->blob_bottom_2_);
+    blob_bottom_vec_0_.push_back(blob_bottom_0_);
+    blob_bottom_vec_0_.push_back(blob_bottom_1_);
+    blob_bottom_vec_1_.push_back(blob_bottom_0_);
+    blob_bottom_vec_1_.push_back(blob_bottom_2_);
     blob_top_vec_.push_back(blob_top_);
   }
 
   virtual ~ConcatLayerTest() {
-    delete blob_bottom_0; delete blob_bottom_1;
-    delete blob_bottom_2; delete blob_top_;
+    delete blob_bottom_0_; delete blob_bottom_1_;
+    delete blob_bottom_2_; delete blob_top_;
   }
 
-  Blob<Dtype>* const blob_bottom_0;
-  Blob<Dtype>* const blob_bottom_1;
-  Blob<Dtype>* const blob_bottom_2;
+  Blob<Dtype>* const blob_bottom_0_;
+  Blob<Dtype>* const blob_bottom_1_;
+  Blob<Dtype>* const blob_bottom_2_;
   Blob<Dtype>* const blob_top_;
-  vector<Blob<Dtype>*> blob_bottom_vec_0, blob_bottom_vec_1;
+  vector<Blob<Dtype>*> blob_bottom_vec_0_, blob_bottom_vec_1_;
   vector<Blob<Dtype>*> blob_top_vec_;
 };
 
@@ -61,62 +61,116 @@ TYPED_TEST_CASE(ConcatLayerTest, TestDtypesAndDevices);
 TYPED_TEST(ConcatLayerTest, TestSetupNum) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  layer_param.mutable_concat_param()->set_concat_dim(0);
+  layer_param.mutable_concat_param()->set_axis(0);
   ConcatLayer<Dtype> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_1, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_1_, this->blob_top_vec_);
   EXPECT_EQ(this->blob_top_->num(),
-    this->blob_bottom_0->num() + this->blob_bottom_2->num());
-  EXPECT_EQ(this->blob_top_->channels(), this->blob_bottom_0->channels());
-  EXPECT_EQ(this->blob_top_->height(), this->blob_bottom_0->height());
-  EXPECT_EQ(this->blob_top_->width(), this->blob_bottom_0->width());
+      this->blob_bottom_0_->num() + this->blob_bottom_2_->num());
+  EXPECT_EQ(this->blob_top_->channels(), this->blob_bottom_0_->channels());
+  EXPECT_EQ(this->blob_top_->height(), this->blob_bottom_0_->height());
+  EXPECT_EQ(this->blob_top_->width(), this->blob_bottom_0_->width());
 }
 
 TYPED_TEST(ConcatLayerTest, TestSetupChannels) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
   ConcatLayer<Dtype> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_0, &(this->blob_top_vec_));
-  EXPECT_EQ(this->blob_top_->num(), this->blob_bottom_0->num());
+  layer.SetUp(this->blob_bottom_vec_0_, this->blob_top_vec_);
+  EXPECT_EQ(this->blob_top_->num(), this->blob_bottom_0_->num());
   EXPECT_EQ(this->blob_top_->channels(),
-    this->blob_bottom_0->channels()+this->blob_bottom_1->channels());
-  EXPECT_EQ(this->blob_top_->height(), this->blob_bottom_0->height());
-  EXPECT_EQ(this->blob_top_->width(), this->blob_bottom_0->width());
+      this->blob_bottom_0_->channels() + this->blob_bottom_1_->channels());
+  EXPECT_EQ(this->blob_top_->height(), this->blob_bottom_0_->height());
+  EXPECT_EQ(this->blob_top_->width(), this->blob_bottom_0_->width());
 }
 
+TYPED_TEST(ConcatLayerTest, TestSetupChannelsNegativeIndexing) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  ConcatLayer<Dtype> layer(layer_param);
+  // "channels" index is the third one from the end -- test negative indexing
+  // by setting axis to -3 and checking that we get the same results as above in
+  // TestSetupChannels.
+  layer_param.mutable_concat_param()->set_axis(-3);
+  layer.SetUp(this->blob_bottom_vec_0_, this->blob_top_vec_);
+  EXPECT_EQ(this->blob_top_->num(), this->blob_bottom_0_->num());
+  EXPECT_EQ(this->blob_top_->channels(),
+      this->blob_bottom_0_->channels() + this->blob_bottom_1_->channels());
+  EXPECT_EQ(this->blob_top_->height(), this->blob_bottom_0_->height());
+  EXPECT_EQ(this->blob_top_->width(), this->blob_bottom_0_->width());
+}
 
-TYPED_TEST(ConcatLayerTest, TestNum) {
+TYPED_TEST(ConcatLayerTest, TestForwardNum) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
+  layer_param.mutable_concat_param()->set_axis(0);
   ConcatLayer<Dtype> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_0, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_0, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_1_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_1_, this->blob_top_vec_);
+  for (int n = 0; n < this->blob_bottom_vec_1_[0]->num(); ++n) {
+    for (int c = 0; c < this->blob_top_->channels(); ++c) {
+      for (int h = 0; h < this->blob_top_->height(); ++h) {
+        for (int w = 0; w < this->blob_top_->width(); ++w) {
+          EXPECT_EQ(this->blob_top_->data_at(n, c, h, w),
+              this->blob_bottom_vec_1_[0]->data_at(n, c, h, w));
+        }
+      }
+    }
+  }
+  for (int n = 0; n < this->blob_bottom_vec_1_[1]->num(); ++n) {
+    for (int c = 0; c < this->blob_top_->channels(); ++c) {
+      for (int h = 0; h < this->blob_top_->height(); ++h) {
+        for (int w = 0; w < this->blob_top_->width(); ++w) {
+          EXPECT_EQ(this->blob_top_->data_at(n + 2, c, h, w),
+              this->blob_bottom_vec_1_[1]->data_at(n, c, h, w));
+        }
+      }
+    }
+  }
+}
+
+TYPED_TEST(ConcatLayerTest, TestForwardChannels) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  ConcatLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_0_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_0_, this->blob_top_vec_);
   for (int n = 0; n < this->blob_top_->num(); ++n) {
-    for (int c = 0; c < this->blob_bottom_0->channels(); ++c) {
+    for (int c = 0; c < this->blob_bottom_0_->channels(); ++c) {
       for (int h = 0; h < this->blob_top_->height(); ++h) {
         for (int w = 0; w < this->blob_top_->width(); ++w) {
           EXPECT_EQ(this->blob_top_->data_at(n, c, h, w),
-            this->blob_bottom_vec_0[0]->data_at(n, c, h, w));
+              this->blob_bottom_vec_0_[0]->data_at(n, c, h, w));
         }
       }
     }
-    for (int c = 0; c < this->blob_bottom_1->channels(); ++c) {
+    for (int c = 0; c < this->blob_bottom_1_->channels(); ++c) {
       for (int h = 0; h < this->blob_top_->height(); ++h) {
         for (int w = 0; w < this->blob_top_->width(); ++w) {
-          EXPECT_EQ(this->blob_top_->data_at(n, c+3, h, w),
-            this->blob_bottom_vec_0[1]->data_at(n, c, h, w));
+          EXPECT_EQ(this->blob_top_->data_at(n, c + 3, h, w),
+              this->blob_bottom_vec_0_[1]->data_at(n, c, h, w));
         }
       }
     }
   }
 }
 
-TYPED_TEST(ConcatLayerTest, TestGradient) {
+TYPED_TEST(ConcatLayerTest, TestGradientNum) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  layer_param.mutable_concat_param()->set_axis(0);
+  ConcatLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-2);
+  checker.CheckGradient(&layer, this->blob_bottom_vec_1_,
+    this->blob_top_vec_);
+}
+
+TYPED_TEST(ConcatLayerTest, TestGradientChannels) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
   ConcatLayer<Dtype> layer(layer_param);
   GradientChecker<Dtype> checker(1e-2, 1e-2);
-  checker.CheckGradient(&layer, &(this->blob_bottom_vec_0),
-    &(this->blob_top_vec_));
+  checker.CheckGradient(&layer, this->blob_bottom_vec_0_,
+    this->blob_top_vec_);
 }
 
 }  // namespace caffe
diff --git a/src/caffe/test/test_contrastive_loss_layer.cpp b/src/caffe/test/test_contrastive_loss_layer.cpp
index a5bef4c9826..1e9447cbc51 100644
--- a/src/caffe/test/test_contrastive_loss_layer.cpp
+++ b/src/caffe/test/test_contrastive_loss_layer.cpp
@@ -22,15 +22,15 @@ class ContrastiveLossLayerTest : public MultiDeviceTest<TypeParam> {
 
  protected:
   ContrastiveLossLayerTest()
-      : blob_bottom_data_i_(new Blob<Dtype>(128, 10, 1, 1)),
-        blob_bottom_data_j_(new Blob<Dtype>(128, 10, 1, 1)),
-        blob_bottom_y_(new Blob<Dtype>(128, 1, 1, 1)),
+      : blob_bottom_data_i_(new Blob<Dtype>(512, 2, 1, 1)),
+        blob_bottom_data_j_(new Blob<Dtype>(512, 2, 1, 1)),
+        blob_bottom_y_(new Blob<Dtype>(512, 1, 1, 1)),
         blob_top_loss_(new Blob<Dtype>()) {
     // fill the values
     FillerParameter filler_param;
-    filler_param.set_mean(0.0);
-    filler_param.set_std(0.3);  // distances~=1.0 to test both sides of margin
-    GaussianFiller<Dtype> filler(filler_param);
+    filler_param.set_min(-1.0);
+    filler_param.set_max(1.0);  // distances~=1.0 to test both sides of margin
+    UniformFiller<Dtype> filler(filler_param);
     filler.Fill(this->blob_bottom_data_i_);
     blob_bottom_vec_.push_back(blob_bottom_data_i_);
     filler.Fill(this->blob_bottom_data_j_);
@@ -62,8 +62,8 @@ TYPED_TEST(ContrastiveLossLayerTest, TestForward) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
   ContrastiveLossLayer<Dtype> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
-  layer.Forward(this->blob_bottom_vec_, &this->blob_top_vec_);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   // manually compute to compare
   const Dtype margin = layer_param.contrastive_loss_param().margin();
   const int num = this->blob_bottom_data_i_->num();
@@ -79,7 +79,8 @@ TYPED_TEST(ContrastiveLossLayerTest, TestForward) {
     if (this->blob_bottom_y_->cpu_data()[i]) {  // similar pairs
       loss += dist_sq;
     } else {
-      loss += std::max(margin-dist_sq, Dtype(0));
+      Dtype dist = std::max(margin - sqrt(dist_sq), 0.0);
+      loss += dist*dist;
     }
   }
   loss /= static_cast<Dtype>(num) * Dtype(2);
@@ -90,13 +91,56 @@ TYPED_TEST(ContrastiveLossLayerTest, TestGradient) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
   ContrastiveLossLayer<Dtype> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
   GradientChecker<Dtype> checker(1e-2, 1e-2, 1701);
   // check the gradient for the first two bottom layers
-  checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_), 0);
-  checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_), 1);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_, 0);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_, 1);
+}
+
+TYPED_TEST(ContrastiveLossLayerTest, TestForwardLegacy) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  layer_param.mutable_contrastive_loss_param()->set_legacy_version(true);
+  ContrastiveLossLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  // manually compute to compare
+  const Dtype margin = layer_param.contrastive_loss_param().margin();
+  const int num = this->blob_bottom_data_i_->num();
+  const int channels = this->blob_bottom_data_i_->channels();
+  Dtype loss(0);
+  for (int i = 0; i < num; ++i) {
+    Dtype dist_sq(0);
+    for (int j = 0; j < channels; ++j) {
+      Dtype diff = this->blob_bottom_data_i_->cpu_data()[i*channels+j] -
+          this->blob_bottom_data_j_->cpu_data()[i*channels+j];
+      dist_sq += diff*diff;
+    }
+    if (this->blob_bottom_y_->cpu_data()[i]) {  // similar pairs
+      loss += dist_sq;
+    } else {
+      loss += std::max(margin - dist_sq, Dtype(0.0));
+    }
+  }
+  loss /= static_cast<Dtype>(num) * Dtype(2);
+  EXPECT_NEAR(this->blob_top_loss_->cpu_data()[0], loss, 1e-6);
+}
+
+TYPED_TEST(ContrastiveLossLayerTest, TestGradientLegacy) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  layer_param.mutable_contrastive_loss_param()->set_legacy_version(true);
+  ContrastiveLossLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  GradientChecker<Dtype> checker(1e-2, 1e-2, 1701);
+  // check the gradient for the first two bottom layers
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_, 0);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_, 1);
 }
 
 }  // namespace caffe
diff --git a/src/caffe/test/test_convolution_layer.cpp b/src/caffe/test/test_convolution_layer.cpp
index a38ad3fd1a8..67d41fff844 100644
--- a/src/caffe/test/test_convolution_layer.cpp
+++ b/src/caffe/test/test_convolution_layer.cpp
@@ -157,7 +157,7 @@ TYPED_TEST(ConvolutionLayerTest, TestSetup) {
   this->blob_top_vec_.push_back(this->blob_top_2_);
   shared_ptr<Layer<Dtype> > layer(
       new ConvolutionLayer<Dtype>(layer_param));
-  layer->SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
   EXPECT_EQ(this->blob_top_->num(), 2);
   EXPECT_EQ(this->blob_top_->channels(), 4);
   EXPECT_EQ(this->blob_top_->height(), 2);
@@ -170,7 +170,7 @@ TYPED_TEST(ConvolutionLayerTest, TestSetup) {
   convolution_param->set_num_output(3);
   convolution_param->set_group(3);
   layer.reset(new ConvolutionLayer<Dtype>(layer_param));
-  layer->SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
   EXPECT_EQ(this->blob_top_->num(), 2);
   EXPECT_EQ(this->blob_top_->channels(), 3);
   EXPECT_EQ(this->blob_top_->height(), 2);
@@ -182,7 +182,6 @@ TYPED_TEST(ConvolutionLayerTest, TestSetup) {
 }
 
 TYPED_TEST(ConvolutionLayerTest, TestSimpleConvolution) {
-  // We will simply see if the convolution layer carries out averaging well.
   typedef typename TypeParam::Dtype Dtype;
   this->blob_bottom_vec_.push_back(this->blob_bottom_2_);
   this->blob_top_vec_.push_back(this->blob_top_2_);
@@ -197,8 +196,8 @@ TYPED_TEST(ConvolutionLayerTest, TestSimpleConvolution) {
   convolution_param->mutable_bias_filler()->set_value(0.1);
   shared_ptr<Layer<Dtype> > layer(
       new ConvolutionLayer<Dtype>(layer_param));
-  layer->SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer->Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   // Check against reference convolution.
   const Dtype* top_data;
   const Dtype* ref_top_data;
@@ -218,8 +217,34 @@ TYPED_TEST(ConvolutionLayerTest, TestSimpleConvolution) {
   }
 }
 
+TYPED_TEST(ConvolutionLayerTest, Test1x1Convolution) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  ConvolutionParameter* convolution_param =
+      layer_param.mutable_convolution_param();
+  convolution_param->set_kernel_size(1);
+  convolution_param->set_stride(1);
+  convolution_param->set_num_output(4);
+  convolution_param->mutable_weight_filler()->set_type("gaussian");
+  convolution_param->mutable_bias_filler()->set_type("constant");
+  convolution_param->mutable_bias_filler()->set_value(0.1);
+  shared_ptr<Layer<Dtype> > layer(
+      new ConvolutionLayer<Dtype>(layer_param));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  // Check against reference convolution.
+  const Dtype* top_data;
+  const Dtype* ref_top_data;
+  caffe_conv(this->blob_bottom_, convolution_param, layer->blobs(),
+      this->MakeReferenceTop(this->blob_top_));
+  top_data = this->blob_top_->cpu_data();
+  ref_top_data = this->ref_blob_top_->cpu_data();
+  for (int i = 0; i < this->blob_top_->count(); ++i) {
+    EXPECT_NEAR(top_data[i], ref_top_data[i], 1e-4);
+  }
+}
+
 TYPED_TEST(ConvolutionLayerTest, TestSimpleConvolutionGroup) {
-  // We will simply see if the convolution layer carries out averaging well.
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
   ConvolutionParameter* convolution_param =
@@ -233,8 +258,8 @@ TYPED_TEST(ConvolutionLayerTest, TestSimpleConvolutionGroup) {
   convolution_param->mutable_bias_filler()->set_value(0.1);
   shared_ptr<Layer<Dtype> > layer(
       new ConvolutionLayer<Dtype>(layer_param));
-  layer->SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer->Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   // Check against reference convolution.
   const Dtype* top_data;
   const Dtype* ref_top_data;
@@ -284,8 +309,8 @@ TYPED_TEST(ConvolutionLayerTest, TestSobelConvolution) {
     weights[i +  7] =  0;
     weights[i +  8] =  1;
   }
-  layer->SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer->Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   // Compute Sobel G_x operator as separable 3 x 1 and 1 x 3 convolutions.
   // (1) the [1 2 1] column filter
   vector<Blob<Dtype>*> sep_blob_bottom_vec;
@@ -311,8 +336,8 @@ TYPED_TEST(ConvolutionLayerTest, TestSobelConvolution) {
     weights_1[i +  1] = 2;
     weights_1[i +  2] = 1;
   }
-  layer->SetUp(sep_blob_bottom_vec, &(sep_blob_top_vec));
-  layer->Forward(sep_blob_bottom_vec, &(sep_blob_top_vec));
+  layer->SetUp(sep_blob_bottom_vec, sep_blob_top_vec);
+  layer->Forward(sep_blob_bottom_vec, sep_blob_top_vec);
   // (2) the [-1 0 1] row filter
   blob_sep->CopyFrom(*this->blob_top_2_, false, true);
   sep_blob_bottom_vec.clear();
@@ -333,8 +358,8 @@ TYPED_TEST(ConvolutionLayerTest, TestSobelConvolution) {
     weights_2[i +  1] =  0;
     weights_2[i +  2] =  1;
   }
-  layer->SetUp(sep_blob_bottom_vec, &(sep_blob_top_vec));
-  layer->Forward(sep_blob_bottom_vec, &(sep_blob_top_vec));
+  layer->SetUp(sep_blob_bottom_vec, sep_blob_top_vec);
+  layer->Forward(sep_blob_bottom_vec, sep_blob_top_vec);
   // Test equivalence of full and separable filters.
   const Dtype* top_data = this->blob_top_->cpu_data();
   const Dtype* sep_top_data = this->blob_top_2_->cpu_data();
@@ -357,8 +382,26 @@ TYPED_TEST(ConvolutionLayerTest, TestGradient) {
   convolution_param->mutable_bias_filler()->set_type("gaussian");
   ConvolutionLayer<Dtype> layer(layer_param);
   GradientChecker<Dtype> checker(1e-2, 1e-3);
-  checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_));
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
+}
+
+TYPED_TEST(ConvolutionLayerTest, Test1x1Gradient) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  ConvolutionParameter* convolution_param =
+      layer_param.mutable_convolution_param();
+  this->blob_bottom_vec_.push_back(this->blob_bottom_2_);
+  this->blob_top_vec_.push_back(this->blob_top_2_);
+  convolution_param->set_kernel_size(1);
+  convolution_param->set_stride(1);
+  convolution_param->set_num_output(2);
+  convolution_param->mutable_weight_filler()->set_type("gaussian");
+  convolution_param->mutable_bias_filler()->set_type("gaussian");
+  ConvolutionLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
 }
 
 TYPED_TEST(ConvolutionLayerTest, TestGradientGroup) {
@@ -374,14 +417,14 @@ TYPED_TEST(ConvolutionLayerTest, TestGradientGroup) {
   convolution_param->mutable_bias_filler()->set_type("gaussian");
   ConvolutionLayer<Dtype> layer(layer_param);
   GradientChecker<Dtype> checker(1e-2, 1e-3);
-  checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_));
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
 }
 
 #ifdef USE_CUDNN
 
 template <typename Dtype>
-class CuDNNConvolutionLayerTest : public ::testing::Test {
+class CuDNNConvolutionLayerTest : public GPUDeviceTest<Dtype> {
  protected:
   CuDNNConvolutionLayerTest()
       : blob_bottom_(new Blob<Dtype>(2, 3, 6, 4)),
@@ -424,7 +467,6 @@ class CuDNNConvolutionLayerTest : public ::testing::Test {
 TYPED_TEST_CASE(CuDNNConvolutionLayerTest, TestDtypes);
 
 TYPED_TEST(CuDNNConvolutionLayerTest, TestSetupCuDNN) {
-  Caffe::set_mode(Caffe::GPU);
   this->blob_bottom_vec_.push_back(this->blob_bottom_2_);
   this->blob_top_vec_.push_back(this->blob_top_2_);
   LayerParameter layer_param;
@@ -437,7 +479,7 @@ TYPED_TEST(CuDNNConvolutionLayerTest, TestSetupCuDNN) {
   this->blob_top_vec_.push_back(this->blob_top_2_);
   shared_ptr<Layer<TypeParam> > layer(
       new CuDNNConvolutionLayer<TypeParam>(layer_param));
-  layer->SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
   EXPECT_EQ(this->blob_top_->num(), 2);
   EXPECT_EQ(this->blob_top_->channels(), 4);
   EXPECT_EQ(this->blob_top_->height(), 2);
@@ -450,7 +492,7 @@ TYPED_TEST(CuDNNConvolutionLayerTest, TestSetupCuDNN) {
   convolution_param->set_num_output(3);
   convolution_param->set_group(3);
   layer.reset(new CuDNNConvolutionLayer<TypeParam>(layer_param));
-  layer->SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
   EXPECT_EQ(this->blob_top_->num(), 2);
   EXPECT_EQ(this->blob_top_->channels(), 3);
   EXPECT_EQ(this->blob_top_->height(), 2);
@@ -462,8 +504,6 @@ TYPED_TEST(CuDNNConvolutionLayerTest, TestSetupCuDNN) {
 }
 
 TYPED_TEST(CuDNNConvolutionLayerTest, TestSimpleConvolutionCuDNN) {
-  // We will simply see if the convolution layer carries out averaging well.
-  Caffe::set_mode(Caffe::GPU);
   this->blob_bottom_vec_.push_back(this->blob_bottom_2_);
   this->blob_top_vec_.push_back(this->blob_top_2_);
   LayerParameter layer_param;
@@ -477,8 +517,8 @@ TYPED_TEST(CuDNNConvolutionLayerTest, TestSimpleConvolutionCuDNN) {
   convolution_param->mutable_bias_filler()->set_value(0.1);
   shared_ptr<Layer<TypeParam> > layer(
       new CuDNNConvolutionLayer<TypeParam>(layer_param));
-  layer->SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer->Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   // Check against reference convolution.
   const TypeParam* top_data;
   const TypeParam* ref_top_data;
@@ -499,8 +539,6 @@ TYPED_TEST(CuDNNConvolutionLayerTest, TestSimpleConvolutionCuDNN) {
 }
 
 TYPED_TEST(CuDNNConvolutionLayerTest, TestSimpleConvolutionGroupCuDNN) {
-  // We will simply see if the convolution layer carries out averaging well.
-  Caffe::set_mode(Caffe::GPU);
   LayerParameter layer_param;
   ConvolutionParameter* convolution_param =
       layer_param.mutable_convolution_param();
@@ -513,8 +551,8 @@ TYPED_TEST(CuDNNConvolutionLayerTest, TestSimpleConvolutionGroupCuDNN) {
   convolution_param->mutable_bias_filler()->set_value(0.1);
   shared_ptr<Layer<TypeParam> > layer(
       new CuDNNConvolutionLayer<TypeParam>(layer_param));
-  layer->SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer->Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   // Check against reference convolution.
   const TypeParam* top_data;
   const TypeParam* ref_top_data;
@@ -531,7 +569,7 @@ TYPED_TEST(CuDNNConvolutionLayerTest, TestSobelConvolutionCuDNN) {
   // Test separable convolution by computing the Sobel operator
   // as a single filter then comparing the result
   // as the convolution of two rectangular filters.
-  Caffe::set_mode(Caffe::GPU);
+
   // Fill bottoms with identical Gaussian noise.
   shared_ptr<GaussianFiller<TypeParam> > filler;
   FillerParameter filler_param;
@@ -564,8 +602,8 @@ TYPED_TEST(CuDNNConvolutionLayerTest, TestSobelConvolutionCuDNN) {
     weights[i +  7] =  0;
     weights[i +  8] =  1;
   }
-  layer->SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer->Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   // Compute Sobel G_x operator as separable 3 x 1 and 1 x 3 convolutions.
   // (1) the [1 2 1] column filter
   vector<Blob<TypeParam>*> sep_blob_bottom_vec;
@@ -591,8 +629,8 @@ TYPED_TEST(CuDNNConvolutionLayerTest, TestSobelConvolutionCuDNN) {
     weights_1[i +  1] = 2;
     weights_1[i +  2] = 1;
   }
-  layer->SetUp(sep_blob_bottom_vec, &(sep_blob_top_vec));
-  layer->Forward(sep_blob_bottom_vec, &(sep_blob_top_vec));
+  layer->SetUp(sep_blob_bottom_vec, sep_blob_top_vec);
+  layer->Forward(sep_blob_bottom_vec, sep_blob_top_vec);
   // (2) the [-1 0 1] row filter
   blob_sep->CopyFrom(*this->blob_top_2_, false, true);
   sep_blob_bottom_vec.clear();
@@ -613,8 +651,8 @@ TYPED_TEST(CuDNNConvolutionLayerTest, TestSobelConvolutionCuDNN) {
     weights_2[i +  1] =  0;
     weights_2[i +  2] =  1;
   }
-  layer->SetUp(sep_blob_bottom_vec, &(sep_blob_top_vec));
-  layer->Forward(sep_blob_bottom_vec, &(sep_blob_top_vec));
+  layer->SetUp(sep_blob_bottom_vec, sep_blob_top_vec);
+  layer->Forward(sep_blob_bottom_vec, sep_blob_top_vec);
   // Test equivalence of full and separable filters.
   const TypeParam* top_data = this->blob_top_->cpu_data();
   const TypeParam* sep_top_data = this->blob_top_2_->cpu_data();
@@ -624,7 +662,6 @@ TYPED_TEST(CuDNNConvolutionLayerTest, TestSobelConvolutionCuDNN) {
 }
 
 TYPED_TEST(CuDNNConvolutionLayerTest, TestGradientCuDNN) {
-  Caffe::set_mode(Caffe::GPU);
   LayerParameter layer_param;
   ConvolutionParameter* convolution_param =
       layer_param.mutable_convolution_param();
@@ -637,12 +674,11 @@ TYPED_TEST(CuDNNConvolutionLayerTest, TestGradientCuDNN) {
   convolution_param->mutable_bias_filler()->set_type("gaussian");
   CuDNNConvolutionLayer<TypeParam> layer(layer_param);
   GradientChecker<TypeParam> checker(1e-2, 1e-3);
-  checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_));
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
 }
 
 TYPED_TEST(CuDNNConvolutionLayerTest, TestGradientGroupCuDNN) {
-  Caffe::set_mode(Caffe::GPU);
   LayerParameter layer_param;
   ConvolutionParameter* convolution_param =
       layer_param.mutable_convolution_param();
@@ -654,8 +690,8 @@ TYPED_TEST(CuDNNConvolutionLayerTest, TestGradientGroupCuDNN) {
   convolution_param->mutable_bias_filler()->set_type("gaussian");
   CuDNNConvolutionLayer<TypeParam> layer(layer_param);
   GradientChecker<TypeParam> checker(1e-2, 1e-3);
-  checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_));
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
 }
 
 #endif
diff --git a/src/caffe/test/test_data/generate_sample_data.py b/src/caffe/test/test_data/generate_sample_data.py
index 2ab238611d8..ab5572685cb 100644
--- a/src/caffe/test/test_data/generate_sample_data.py
+++ b/src/caffe/test/test_data/generate_sample_data.py
@@ -5,6 +5,8 @@
 import numpy as np
 import h5py
 
+script_dir = os.path.dirname(os.path.abspath(__file__))
+
 num_cols = 8
 num_rows = 10
 height = 6
@@ -20,14 +22,19 @@
 label = 1 + np.arange(num_rows)[:, np.newaxis]
 label = label.astype('float32')
 
+# We add an extra label2 dataset to test HDF5 layer's ability
+# to handle arbitrary number of output ("top") Blobs.
+label2 = label + 1
+
 print data
 print label
 
-with h5py.File(os.path.dirname(__file__) + '/sample_data.h5', 'w') as f:
+with h5py.File(script_dir + '/sample_data.h5', 'w') as f:
     f['data'] = data
     f['label'] = label
+    f['label2'] = label2
 
-with h5py.File(os.path.dirname(__file__) + '/sample_data_2_gzip.h5', 'w') as f:
+with h5py.File(script_dir + '/sample_data_2_gzip.h5', 'w') as f:
     f.create_dataset(
         'data', data=data + total_size,
         compression='gzip', compression_opts=1
@@ -36,7 +43,11 @@
         'label', data=label,
         compression='gzip', compression_opts=1
     )
+    f.create_dataset(
+        'label2', data=label2,
+        compression='gzip', compression_opts=1
+    )
 
-with open(os.path.dirname(__file__) + '/sample_data_list.txt', 'w') as f:
-    f.write(os.path.dirname(__file__) + '/sample_data.h5\n')
-    f.write(os.path.dirname(__file__) + '/sample_data_2_gzip.h5\n')
+with open(script_dir + '/sample_data_list.txt', 'w') as f:
+    f.write(script_dir + '/sample_data.h5\n')
+    f.write(script_dir + '/sample_data_2_gzip.h5\n')
diff --git a/src/caffe/test/test_data/sample_data.h5 b/src/caffe/test/test_data/sample_data.h5
index 90eaaa56ec7..236e66b0d12 100644
Binary files a/src/caffe/test/test_data/sample_data.h5 and b/src/caffe/test/test_data/sample_data.h5 differ
diff --git a/src/caffe/test/test_data/sample_data_2_gzip.h5 b/src/caffe/test/test_data/sample_data_2_gzip.h5
index ff49db4ea74..a138e0367be 100644
Binary files a/src/caffe/test/test_data/sample_data_2_gzip.h5 and b/src/caffe/test/test_data/sample_data_2_gzip.h5 differ
diff --git a/src/caffe/test/test_data/sample_data_list.txt.in b/src/caffe/test/test_data/sample_data_list.txt.in
deleted file mode 100644
index 9860ef583ab..00000000000
--- a/src/caffe/test/test_data/sample_data_list.txt.in
+++ /dev/null
@@ -1,2 +0,0 @@
-@CMAKE_SOURCE_DIR@/src/caffe/test/test_data/sample_data.h5
-@CMAKE_SOURCE_DIR@/src/caffe/test/test_data/sample_data_2_gzip.h5
\ No newline at end of file
diff --git a/src/caffe/test/test_data_layer.cpp b/src/caffe/test/test_data_layer.cpp
index 887124aa5bc..afe2a40d227 100644
--- a/src/caffe/test/test_data_layer.cpp
+++ b/src/caffe/test/test_data_layer.cpp
@@ -1,20 +1,23 @@
 #include <string>
 #include <vector>
 
+#include "boost/scoped_ptr.hpp"
 #include "gtest/gtest.h"
-#include "leveldb/db.h"
 
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
+#include "caffe/data_layers.hpp"
 #include "caffe/filler.hpp"
 #include "caffe/proto/caffe.pb.h"
+#include "caffe/util/db.hpp"
 #include "caffe/util/io.hpp"
-#include "caffe/vision_layers.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
 
 namespace caffe {
 
+using boost::scoped_ptr;
+
 template <typename TypeParam>
 class DataLayerTest : public MultiDeviceTest<TypeParam> {
   typedef typename TypeParam::Dtype Dtype;
@@ -33,56 +36,15 @@ class DataLayerTest : public MultiDeviceTest<TypeParam> {
     blob_top_vec_.push_back(blob_top_label_);
   }
 
-  // Fill the LevelDB with data: if unique_pixels, each pixel is unique but
+  // Fill the DB with data: if unique_pixels, each pixel is unique but
   // all images are the same; else each image is unique but all pixels within
   // an image are the same.
-  void FillLevelDB(const bool unique_pixels) {
-    backend_ = DataParameter_DB_LEVELDB;
-    LOG(INFO) << "Using temporary leveldb " << *filename_;
-    leveldb::DB* db;
-    leveldb::Options options;
-    options.error_if_exists = true;
-    options.create_if_missing = true;
-    leveldb::Status status =
-        leveldb::DB::Open(options, filename_->c_str(), &db);
-    CHECK(status.ok());
-    for (int i = 0; i < 5; ++i) {
-      Datum datum;
-      datum.set_label(i);
-      datum.set_channels(2);
-      datum.set_height(3);
-      datum.set_width(4);
-      std::string* data = datum.mutable_data();
-      for (int j = 0; j < 24; ++j) {
-        int datum = unique_pixels ? j : i;
-        data->push_back(static_cast<uint8_t>(datum));
-      }
-      stringstream ss;
-      ss << i;
-      db->Put(leveldb::WriteOptions(), ss.str(), datum.SerializeAsString());
-    }
-    delete db;
-  }
-
-  // Fill the LMDB with data: unique_pixels has same meaning as in FillLevelDB.
-  void FillLMDB(const bool unique_pixels) {
-    backend_ = DataParameter_DB_LMDB;
-    LOG(INFO) << "Using temporary lmdb " << *filename_;
-    CHECK_EQ(mkdir(filename_->c_str(), 0744), 0) << "mkdir " << filename_
-                                                 << "failed";
-    MDB_env *env;
-    MDB_dbi dbi;
-    MDB_val mdbkey, mdbdata;
-    MDB_txn *txn;
-    CHECK_EQ(mdb_env_create(&env), MDB_SUCCESS) << "mdb_env_create failed";
-    CHECK_EQ(mdb_env_set_mapsize(env, 1099511627776), MDB_SUCCESS)  // 1TB
-        << "mdb_env_set_mapsize failed";
-    CHECK_EQ(mdb_env_open(env, filename_->c_str(), 0, 0664), MDB_SUCCESS)
-        << "mdb_env_open failed";
-    CHECK_EQ(mdb_txn_begin(env, NULL, 0, &txn), MDB_SUCCESS)
-        << "mdb_txn_begin failed";
-    CHECK_EQ(mdb_open(txn, NULL, 0, &dbi), MDB_SUCCESS) << "mdb_open failed";
-
+  void Fill(const bool unique_pixels, DataParameter_DB backend) {
+    backend_ = backend;
+    LOG(INFO) << "Using temporary dataset " << *filename_;
+    scoped_ptr<db::DB> db(db::GetDB(backend));
+    db->Open(*filename_, db::NEW);
+    scoped_ptr<db::Transaction> txn(db->NewTransaction());
     for (int i = 0; i < 5; ++i) {
       Datum datum;
       datum.set_label(i);
@@ -96,25 +58,18 @@ class DataLayerTest : public MultiDeviceTest<TypeParam> {
       }
       stringstream ss;
       ss << i;
-
-      string value;
-      datum.SerializeToString(&value);
-      mdbdata.mv_size = value.size();
-      mdbdata.mv_data = reinterpret_cast<void*>(&value[0]);
-      string keystr = ss.str();
-      mdbkey.mv_size = keystr.size();
-      mdbkey.mv_data = reinterpret_cast<void*>(&keystr[0]);
-      CHECK_EQ(mdb_put(txn, dbi, &mdbkey, &mdbdata, 0), MDB_SUCCESS)
-          << "mdb_put failed";
+      string out;
+      CHECK(datum.SerializeToString(&out));
+      txn->Put(ss.str(), out);
     }
-    CHECK_EQ(mdb_txn_commit(txn), MDB_SUCCESS) << "mdb_txn_commit failed";
-    mdb_close(env, dbi);
-    mdb_env_close(env);
+    txn->Commit();
+    db->Close();
   }
 
   void TestRead() {
     const Dtype scale = 3;
     LayerParameter param;
+    param.set_phase(TRAIN);
     DataParameter* data_param = param.mutable_data_param();
     data_param->set_batch_size(5);
     data_param->set_source(filename_->c_str());
@@ -125,7 +80,7 @@ class DataLayerTest : public MultiDeviceTest<TypeParam> {
     transform_param->set_scale(scale);
 
     DataLayer<Dtype> layer(param);
-    layer.SetUp(blob_bottom_vec_, &blob_top_vec_);
+    layer.SetUp(blob_bottom_vec_, blob_top_vec_);
     EXPECT_EQ(blob_top_data_->num(), 5);
     EXPECT_EQ(blob_top_data_->channels(), 2);
     EXPECT_EQ(blob_top_data_->height(), 3);
@@ -136,7 +91,7 @@ class DataLayerTest : public MultiDeviceTest<TypeParam> {
     EXPECT_EQ(blob_top_label_->width(), 1);
 
     for (int iter = 0; iter < 100; ++iter) {
-      layer.Forward(blob_bottom_vec_, &blob_top_vec_);
+      layer.Forward(blob_bottom_vec_, blob_top_vec_);
       for (int i = 0; i < 5; ++i) {
         EXPECT_EQ(i, blob_top_label_->cpu_data()[i]);
       }
@@ -149,9 +104,75 @@ class DataLayerTest : public MultiDeviceTest<TypeParam> {
     }
   }
 
-  void TestReadCrop() {
+  void TestReshape(DataParameter_DB backend) {
+    const int num_inputs = 5;
+    // Save data of varying shapes.
+    LOG(INFO) << "Using temporary dataset " << *filename_;
+    scoped_ptr<db::DB> db(db::GetDB(backend));
+    db->Open(*filename_, db::NEW);
+    scoped_ptr<db::Transaction> txn(db->NewTransaction());
+    for (int i = 0; i < num_inputs; ++i) {
+      Datum datum;
+      datum.set_label(i);
+      datum.set_channels(2);
+      datum.set_height(i % 2 + 1);
+      datum.set_width(i % 4 + 1);
+      std::string* data = datum.mutable_data();
+      const int data_size = datum.channels() * datum.height() * datum.width();
+      for (int j = 0; j < data_size; ++j) {
+        data->push_back(static_cast<uint8_t>(j));
+      }
+      stringstream ss;
+      ss << i;
+      string out;
+      CHECK(datum.SerializeToString(&out));
+      txn->Put(ss.str(), out);
+    }
+    txn->Commit();
+    db->Close();
+
+    // Load and check data of various shapes.
+    LayerParameter param;
+    param.set_phase(TEST);
+    DataParameter* data_param = param.mutable_data_param();
+    data_param->set_batch_size(1);
+    data_param->set_source(filename_->c_str());
+    data_param->set_backend(backend);
+
+    DataLayer<Dtype> layer(param);
+    layer.SetUp(blob_bottom_vec_, blob_top_vec_);
+    EXPECT_EQ(blob_top_data_->num(), 1);
+    EXPECT_EQ(blob_top_data_->channels(), 2);
+    EXPECT_EQ(blob_top_label_->num(), 1);
+    EXPECT_EQ(blob_top_label_->channels(), 1);
+    EXPECT_EQ(blob_top_label_->height(), 1);
+    EXPECT_EQ(blob_top_label_->width(), 1);
+
+    for (int iter = 0; iter < num_inputs; ++iter) {
+      layer.Forward(blob_bottom_vec_, blob_top_vec_);
+      EXPECT_EQ(blob_top_data_->height(), iter % 2 + 1);
+      EXPECT_EQ(blob_top_data_->width(), iter % 4 + 1);
+      EXPECT_EQ(iter, blob_top_label_->cpu_data()[0]);
+      const int channels = blob_top_data_->channels();
+      const int height = blob_top_data_->height();
+      const int width = blob_top_data_->width();
+      for (int c = 0; c < channels; ++c) {
+        for (int h = 0; h < height; ++h) {
+          for (int w = 0; w < width; ++w) {
+            const int idx = (c * height + h) * width + w;
+            EXPECT_EQ(idx, static_cast<int>(blob_top_data_->cpu_data()[idx]))
+                << "debug: iter " << iter << " c " << c
+                << " h " << h << " w " << w;
+          }
+        }
+      }
+    }
+  }
+
+  void TestReadCrop(Phase phase) {
     const Dtype scale = 3;
     LayerParameter param;
+    param.set_phase(phase);
     Caffe::set_random_seed(1701);
 
     DataParameter* data_param = param.mutable_data_param();
@@ -165,7 +186,7 @@ class DataLayerTest : public MultiDeviceTest<TypeParam> {
     transform_param->set_crop_size(1);
 
     DataLayer<Dtype> layer(param);
-    layer.SetUp(blob_bottom_vec_, &blob_top_vec_);
+    layer.SetUp(blob_bottom_vec_, blob_top_vec_);
     EXPECT_EQ(blob_top_data_->num(), 5);
     EXPECT_EQ(blob_top_data_->channels(), 2);
     EXPECT_EQ(blob_top_data_->height(), 1);
@@ -176,7 +197,7 @@ class DataLayerTest : public MultiDeviceTest<TypeParam> {
     EXPECT_EQ(blob_top_label_->width(), 1);
 
     for (int iter = 0; iter < 2; ++iter) {
-      layer.Forward(blob_bottom_vec_, &blob_top_vec_);
+      layer.Forward(blob_bottom_vec_, blob_top_vec_);
       for (int i = 0; i < 5; ++i) {
         EXPECT_EQ(i, blob_top_label_->cpu_data()[i]);
       }
@@ -187,7 +208,7 @@ class DataLayerTest : public MultiDeviceTest<TypeParam> {
           num_with_center_value +=
               (center_value == blob_top_data_->cpu_data()[i * 2 + j]);
           // At TEST time, check that we always get center value.
-          if (Caffe::phase() == Caffe::TEST) {
+          if (phase == caffe::TEST) {
             EXPECT_EQ(center_value, this->blob_top_data_->cpu_data()[i * 2 + j])
                 << "debug: iter " << iter << " i " << i << " j " << j;
           }
@@ -196,7 +217,7 @@ class DataLayerTest : public MultiDeviceTest<TypeParam> {
       // At TRAIN time, check that we did not get the center crop all 10 times.
       // (This check fails with probability 1-1/12^10 in a correct
       // implementation, so we call set_random_seed.)
-      if (Caffe::phase() == Caffe::TRAIN) {
+      if (phase == caffe::TRAIN) {
         EXPECT_LT(num_with_center_value, 10);
       }
     }
@@ -204,6 +225,7 @@ class DataLayerTest : public MultiDeviceTest<TypeParam> {
 
   void TestReadCropTrainSequenceSeeded() {
     LayerParameter param;
+    param.set_phase(TRAIN);
     DataParameter* data_param = param.mutable_data_param();
     data_param->set_batch_size(5);
     data_param->set_source(filename_->c_str());
@@ -219,9 +241,9 @@ class DataLayerTest : public MultiDeviceTest<TypeParam> {
     vector<vector<Dtype> > crop_sequence;
     {
       DataLayer<Dtype> layer1(param);
-      layer1.SetUp(blob_bottom_vec_, &blob_top_vec_);
+      layer1.SetUp(blob_bottom_vec_, blob_top_vec_);
       for (int iter = 0; iter < 2; ++iter) {
-        layer1.Forward(blob_bottom_vec_, &blob_top_vec_);
+        layer1.Forward(blob_bottom_vec_, blob_top_vec_);
         for (int i = 0; i < 5; ++i) {
           EXPECT_EQ(i, blob_top_label_->cpu_data()[i]);
         }
@@ -234,15 +256,15 @@ class DataLayerTest : public MultiDeviceTest<TypeParam> {
         }
         crop_sequence.push_back(iter_crop_sequence);
       }
-    }  // destroy 1st data layer and unlock the leveldb
+    }  // destroy 1st data layer and unlock the db
 
     // Get crop sequence after reseeding Caffe with 1701.
     // Check that the sequence is the same as the original.
     Caffe::set_random_seed(seed_);
     DataLayer<Dtype> layer2(param);
-    layer2.SetUp(blob_bottom_vec_, &blob_top_vec_);
+    layer2.SetUp(blob_bottom_vec_, blob_top_vec_);
     for (int iter = 0; iter < 2; ++iter) {
-      layer2.Forward(blob_bottom_vec_, &blob_top_vec_);
+      layer2.Forward(blob_bottom_vec_, blob_top_vec_);
       for (int i = 0; i < 5; ++i) {
         EXPECT_EQ(i, blob_top_label_->cpu_data()[i]);
       }
@@ -258,6 +280,7 @@ class DataLayerTest : public MultiDeviceTest<TypeParam> {
 
   void TestReadCropTrainSequenceUnseeded() {
     LayerParameter param;
+    param.set_phase(TRAIN);
     DataParameter* data_param = param.mutable_data_param();
     data_param->set_batch_size(5);
     data_param->set_source(filename_->c_str());
@@ -274,9 +297,9 @@ class DataLayerTest : public MultiDeviceTest<TypeParam> {
     vector<vector<Dtype> > crop_sequence;
     {
       DataLayer<Dtype> layer1(param);
-      layer1.SetUp(blob_bottom_vec_, &blob_top_vec_);
+      layer1.SetUp(blob_bottom_vec_, blob_top_vec_);
       for (int iter = 0; iter < 2; ++iter) {
-        layer1.Forward(blob_bottom_vec_, &blob_top_vec_);
+        layer1.Forward(blob_bottom_vec_, blob_top_vec_);
         for (int i = 0; i < 5; ++i) {
           EXPECT_EQ(i, blob_top_label_->cpu_data()[i]);
         }
@@ -289,15 +312,15 @@ class DataLayerTest : public MultiDeviceTest<TypeParam> {
         }
         crop_sequence.push_back(iter_crop_sequence);
       }
-    }  // destroy 1st data layer and unlock the leveldb
+    }  // destroy 1st data layer and unlock the db
 
     // Get crop sequence continuing from previous Caffe RNG state; reseed
     // srand with 1701. Check that the sequence differs from the original.
     srand(seed_);
     DataLayer<Dtype> layer2(param);
-    layer2.SetUp(blob_bottom_vec_, &blob_top_vec_);
+    layer2.SetUp(blob_bottom_vec_, blob_top_vec_);
     for (int iter = 0; iter < 2; ++iter) {
-      layer2.Forward(blob_bottom_vec_, &blob_top_vec_);
+      layer2.Forward(blob_bottom_vec_, blob_top_vec_);
       for (int i = 0; i < 5; ++i) {
         EXPECT_EQ(i, blob_top_label_->cpu_data()[i]);
       }
@@ -327,78 +350,78 @@ TYPED_TEST_CASE(DataLayerTest, TestDtypesAndDevices);
 
 TYPED_TEST(DataLayerTest, TestReadLevelDB) {
   const bool unique_pixels = false;  // all pixels the same; images different
-  this->FillLevelDB(unique_pixels);
+  this->Fill(unique_pixels, DataParameter_DB_LEVELDB);
   this->TestRead();
 }
 
+TYPED_TEST(DataLayerTest, TestReshapeLevelDB) {
+  this->TestReshape(DataParameter_DB_LEVELDB);
+}
+
 TYPED_TEST(DataLayerTest, TestReadCropTrainLevelDB) {
-  Caffe::set_phase(Caffe::TRAIN);
   const bool unique_pixels = true;  // all images the same; pixels different
-  this->FillLevelDB(unique_pixels);
-  this->TestReadCrop();
+  this->Fill(unique_pixels, DataParameter_DB_LEVELDB);
+  this->TestReadCrop(TRAIN);
 }
 
 // Test that the sequence of random crops is consistent when using
 // Caffe::set_random_seed.
 TYPED_TEST(DataLayerTest, TestReadCropTrainSequenceSeededLevelDB) {
-  Caffe::set_phase(Caffe::TRAIN);
   const bool unique_pixels = true;  // all images the same; pixels different
-  this->FillLevelDB(unique_pixels);
+  this->Fill(unique_pixels, DataParameter_DB_LEVELDB);
   this->TestReadCropTrainSequenceSeeded();
 }
 
 // Test that the sequence of random crops differs across iterations when
 // Caffe::set_random_seed isn't called (and seeds from srand are ignored).
 TYPED_TEST(DataLayerTest, TestReadCropTrainSequenceUnseededLevelDB) {
-  Caffe::set_phase(Caffe::TRAIN);
   const bool unique_pixels = true;  // all images the same; pixels different
-  this->FillLevelDB(unique_pixels);
+  this->Fill(unique_pixels, DataParameter_DB_LEVELDB);
   this->TestReadCropTrainSequenceUnseeded();
 }
 
 TYPED_TEST(DataLayerTest, TestReadCropTestLevelDB) {
-  Caffe::set_phase(Caffe::TEST);
   const bool unique_pixels = true;  // all images the same; pixels different
-  this->FillLevelDB(unique_pixels);
-  this->TestReadCrop();
+  this->Fill(unique_pixels, DataParameter_DB_LEVELDB);
+  this->TestReadCrop(TEST);
 }
 
 TYPED_TEST(DataLayerTest, TestReadLMDB) {
   const bool unique_pixels = false;  // all pixels the same; images different
-  this->FillLMDB(unique_pixels);
+  this->Fill(unique_pixels, DataParameter_DB_LMDB);
   this->TestRead();
 }
 
+TYPED_TEST(DataLayerTest, TestReshapeLMDB) {
+  this->TestReshape(DataParameter_DB_LMDB);
+}
+
 TYPED_TEST(DataLayerTest, TestReadCropTrainLMDB) {
-  Caffe::set_phase(Caffe::TRAIN);
   const bool unique_pixels = true;  // all images the same; pixels different
-  this->FillLMDB(unique_pixels);
-  this->TestReadCrop();
+  this->Fill(unique_pixels, DataParameter_DB_LMDB);
+  this->TestReadCrop(TRAIN);
 }
 
 // Test that the sequence of random crops is consistent when using
 // Caffe::set_random_seed.
 TYPED_TEST(DataLayerTest, TestReadCropTrainSequenceSeededLMDB) {
-  Caffe::set_phase(Caffe::TRAIN);
   const bool unique_pixels = true;  // all images the same; pixels different
-  this->FillLMDB(unique_pixels);
+  this->Fill(unique_pixels, DataParameter_DB_LMDB);
   this->TestReadCropTrainSequenceSeeded();
 }
 
 // Test that the sequence of random crops differs across iterations when
 // Caffe::set_random_seed isn't called (and seeds from srand are ignored).
 TYPED_TEST(DataLayerTest, TestReadCropTrainSequenceUnseededLMDB) {
-  Caffe::set_phase(Caffe::TRAIN);
   const bool unique_pixels = true;  // all images the same; pixels different
-  this->FillLMDB(unique_pixels);
+  this->Fill(unique_pixels, DataParameter_DB_LMDB);
   this->TestReadCropTrainSequenceUnseeded();
 }
 
 TYPED_TEST(DataLayerTest, TestReadCropTestLMDB) {
-  Caffe::set_phase(Caffe::TEST);
   const bool unique_pixels = true;  // all images the same; pixels different
-  this->FillLMDB(unique_pixels);
-  this->TestReadCrop();
+  this->Fill(unique_pixels, DataParameter_DB_LMDB);
+  this->TestReadCrop(TEST);
 }
 
 }  // namespace caffe
diff --git a/src/caffe/test/test_data_transformer.cpp b/src/caffe/test/test_data_transformer.cpp
new file mode 100644
index 00000000000..16570e20356
--- /dev/null
+++ b/src/caffe/test/test_data_transformer.cpp
@@ -0,0 +1,355 @@
+#include <string>
+#include <vector>
+
+#include "gtest/gtest.h"
+#include "leveldb/db.h"
+
+#include "caffe/blob.hpp"
+#include "caffe/common.hpp"
+#include "caffe/data_transformer.hpp"
+#include "caffe/filler.hpp"
+#include "caffe/proto/caffe.pb.h"
+#include "caffe/util/io.hpp"
+
+#include "caffe/test/test_caffe_main.hpp"
+
+namespace caffe {
+
+void FillDatum(const int label, const int channels, const int height,
+  const int width, const bool unique_pixels, Datum * datum) {
+  datum->set_label(label);
+  datum->set_channels(channels);
+  datum->set_height(height);
+  datum->set_width(width);
+  int size = channels * height * width;
+  std::string* data = datum->mutable_data();
+  for (int j = 0; j < size; ++j) {
+    int datum = unique_pixels ? j : label;
+    data->push_back(static_cast<uint8_t>(datum));
+  }
+}
+
+template <typename Dtype>
+class DataTransformTest : public ::testing::Test {
+ protected:
+  DataTransformTest()
+      : seed_(1701),
+      num_iter_(10) {}
+
+  int NumSequenceMatches(const TransformationParameter transform_param,
+      const Datum& datum, Phase phase) {
+    // Get crop sequence with Caffe seed 1701.
+    DataTransformer<Dtype>* transformer =
+        new DataTransformer<Dtype>(transform_param, phase);
+    const int crop_size = transform_param.crop_size();
+    Caffe::set_random_seed(seed_);
+    transformer->InitRand();
+    Blob<Dtype>* blob =
+        new Blob<Dtype>(1, datum.channels(), datum.height(), datum.width());
+    if (transform_param.crop_size() > 0) {
+      blob->Reshape(1, datum.channels(), crop_size, crop_size);
+    }
+
+    vector<vector<Dtype> > crop_sequence;
+    for (int iter = 0; iter < this->num_iter_; ++iter) {
+      vector<Dtype> iter_crop_sequence;
+      transformer->Transform(datum, blob);
+      for (int j = 0; j < blob->count(); ++j) {
+        iter_crop_sequence.push_back(blob->cpu_data()[j]);
+      }
+      crop_sequence.push_back(iter_crop_sequence);
+    }
+    // Check if the sequence differs from the previous
+    int num_sequence_matches = 0;
+    for (int iter = 0; iter < this->num_iter_; ++iter) {
+      vector<Dtype> iter_crop_sequence = crop_sequence[iter];
+      transformer->Transform(datum, blob);
+      for (int j = 0; j < blob->count(); ++j) {
+        num_sequence_matches +=
+            (crop_sequence[iter][j] == blob->cpu_data()[j]);
+      }
+    }
+    return num_sequence_matches;
+  }
+
+  virtual ~DataTransformTest() { }
+
+  int seed_;
+  int num_iter_;
+};
+
+TYPED_TEST_CASE(DataTransformTest, TestDtypes);
+
+TYPED_TEST(DataTransformTest, TestEmptyTransform) {
+  TransformationParameter transform_param;
+  const bool unique_pixels = false;  // all pixels the same equal to label
+  const int label = 0;
+  const int channels = 3;
+  const int height = 4;
+  const int width = 5;
+
+  Datum datum;
+  FillDatum(label, channels, height, width, unique_pixels, &datum);
+  Blob<TypeParam>* blob = new Blob<TypeParam>(1, channels, height, width);
+  DataTransformer<TypeParam>* transformer =
+      new DataTransformer<TypeParam>(transform_param, TEST);
+  transformer->InitRand();
+  transformer->Transform(datum, blob);
+  EXPECT_EQ(blob->num(), 1);
+  EXPECT_EQ(blob->channels(), datum.channels());
+  EXPECT_EQ(blob->height(), datum.height());
+  EXPECT_EQ(blob->width(), datum.width());
+  for (int j = 0; j < blob->count(); ++j) {
+    EXPECT_EQ(blob->cpu_data()[j], label);
+  }
+}
+
+TYPED_TEST(DataTransformTest, TestEmptyTransformUniquePixels) {
+  TransformationParameter transform_param;
+  const bool unique_pixels = true;  // pixels are consecutive ints [0,size]
+  const int label = 0;
+  const int channels = 3;
+  const int height = 4;
+  const int width = 5;
+
+  Datum datum;
+  FillDatum(label, channels, height, width, unique_pixels, &datum);
+  Blob<TypeParam>* blob = new Blob<TypeParam>(1, 3, 4, 5);
+  DataTransformer<TypeParam>* transformer =
+      new DataTransformer<TypeParam>(transform_param, TEST);
+  transformer->InitRand();
+  transformer->Transform(datum, blob);
+  EXPECT_EQ(blob->num(), 1);
+  EXPECT_EQ(blob->channels(), datum.channels());
+  EXPECT_EQ(blob->height(), datum.height());
+  EXPECT_EQ(blob->width(), datum.width());
+  for (int j = 0; j < blob->count(); ++j) {
+    EXPECT_EQ(blob->cpu_data()[j], j);
+  }
+}
+
+TYPED_TEST(DataTransformTest, TestCropSize) {
+  TransformationParameter transform_param;
+  const bool unique_pixels = false;  // all pixels the same equal to label
+  const int label = 0;
+  const int channels = 3;
+  const int height = 4;
+  const int width = 5;
+  const int crop_size = 2;
+
+  transform_param.set_crop_size(crop_size);
+  Datum datum;
+  FillDatum(label, channels, height, width, unique_pixels, &datum);
+  DataTransformer<TypeParam>* transformer =
+      new DataTransformer<TypeParam>(transform_param, TEST);
+  transformer->InitRand();
+  Blob<TypeParam>* blob =
+      new Blob<TypeParam>(1, channels, crop_size, crop_size);
+  for (int iter = 0; iter < this->num_iter_; ++iter) {
+    transformer->Transform(datum, blob);
+    EXPECT_EQ(blob->num(), 1);
+    EXPECT_EQ(blob->channels(), datum.channels());
+    EXPECT_EQ(blob->height(), crop_size);
+    EXPECT_EQ(blob->width(), crop_size);
+    for (int j = 0; j < blob->count(); ++j) {
+      EXPECT_EQ(blob->cpu_data()[j], label);
+    }
+  }
+}
+
+TYPED_TEST(DataTransformTest, TestCropTrain) {
+  TransformationParameter transform_param;
+  const bool unique_pixels = true;  // pixels are consecutive ints [0,size]
+  const int label = 0;
+  const int channels = 3;
+  const int height = 4;
+  const int width = 5;
+  const int crop_size = 2;
+  const int size = channels * crop_size * crop_size;
+
+  transform_param.set_crop_size(crop_size);
+  Datum datum;
+  FillDatum(label, channels, height, width, unique_pixels, &datum);
+  int num_matches = this->NumSequenceMatches(transform_param, datum, TRAIN);
+  EXPECT_LT(num_matches, size * this->num_iter_);
+}
+
+TYPED_TEST(DataTransformTest, TestCropTest) {
+  TransformationParameter transform_param;
+  const bool unique_pixels = true;  // pixels are consecutive ints [0,size]
+  const int label = 0;
+  const int channels = 3;
+  const int height = 4;
+  const int width = 5;
+  const int crop_size = 2;
+  const int size = channels * crop_size * crop_size;
+
+  transform_param.set_crop_size(crop_size);
+  Datum datum;
+  FillDatum(label, channels, height, width, unique_pixels, &datum);
+  int num_matches = this->NumSequenceMatches(transform_param, datum, TEST);
+  EXPECT_EQ(num_matches, size * this->num_iter_);
+}
+
+TYPED_TEST(DataTransformTest, TestMirrorTrain) {
+  TransformationParameter transform_param;
+  const bool unique_pixels = true;  // pixels are consecutive ints [0,size]
+  const int label = 0;
+  const int channels = 3;
+  const int height = 4;
+  const int width = 5;
+  const int size = channels * height * width;
+
+  transform_param.set_mirror(true);
+  Datum datum;
+  FillDatum(label, channels, height, width, unique_pixels, &datum);
+  int num_matches = this->NumSequenceMatches(transform_param, datum, TRAIN);
+  EXPECT_LT(num_matches, size * this->num_iter_);
+}
+
+TYPED_TEST(DataTransformTest, TestMirrorTest) {
+  TransformationParameter transform_param;
+  const bool unique_pixels = true;  // pixels are consecutive ints [0,size]
+  const int label = 0;
+  const int channels = 3;
+  const int height = 4;
+  const int width = 5;
+  const int size = channels * height * width;
+
+  transform_param.set_mirror(true);
+  Datum datum;
+  FillDatum(label, channels, height, width, unique_pixels, &datum);
+  int num_matches = this->NumSequenceMatches(transform_param, datum, TEST);
+  EXPECT_LT(num_matches, size * this->num_iter_);
+}
+
+TYPED_TEST(DataTransformTest, TestCropMirrorTrain) {
+  TransformationParameter transform_param;
+  const bool unique_pixels = true;  // pixels are consecutive ints [0,size]
+  const int label = 0;
+  const int channels = 3;
+  const int height = 4;
+  const int width = 5;
+  const int crop_size = 2;
+
+  Datum datum;
+  FillDatum(label, channels, height, width, unique_pixels, &datum);
+  transform_param.set_crop_size(crop_size);
+  int num_matches_crop = this->NumSequenceMatches(
+      transform_param, datum, TRAIN);
+
+  transform_param.set_mirror(true);
+  int num_matches_crop_mirror =
+      this->NumSequenceMatches(transform_param, datum, TRAIN);
+  // When doing crop and mirror we expect less num_matches than just crop
+  EXPECT_LE(num_matches_crop_mirror, num_matches_crop);
+}
+
+TYPED_TEST(DataTransformTest, TestCropMirrorTest) {
+  TransformationParameter transform_param;
+  const bool unique_pixels = true;  // pixels are consecutive ints [0,size]
+  const int label = 0;
+  const int channels = 3;
+  const int height = 4;
+  const int width = 5;
+  const int crop_size = 2;
+
+  Datum datum;
+  FillDatum(label, channels, height, width, unique_pixels, &datum);
+  transform_param.set_crop_size(crop_size);
+  int num_matches_crop = this->NumSequenceMatches(transform_param, datum, TEST);
+
+  transform_param.set_mirror(true);
+  int num_matches_crop_mirror =
+      this->NumSequenceMatches(transform_param, datum, TEST);
+  // When doing crop and mirror we expect less num_matches than just crop
+  EXPECT_LT(num_matches_crop_mirror, num_matches_crop);
+}
+
+
+TYPED_TEST(DataTransformTest, TestMeanValue) {
+  TransformationParameter transform_param;
+  const bool unique_pixels = false;  // pixels are equal to label
+  const int label = 0;
+  const int channels = 3;
+  const int height = 4;
+  const int width = 5;
+  const int mean_value = 2;
+
+  transform_param.add_mean_value(mean_value);
+  Datum datum;
+  FillDatum(label, channels, height, width, unique_pixels, &datum);
+  Blob<TypeParam>* blob = new Blob<TypeParam>(1, channels, height, width);
+  DataTransformer<TypeParam>* transformer =
+      new DataTransformer<TypeParam>(transform_param, TEST);
+  transformer->InitRand();
+  transformer->Transform(datum, blob);
+  for (int j = 0; j < blob->count(); ++j) {
+    EXPECT_EQ(blob->cpu_data()[j], label - mean_value);
+  }
+}
+
+TYPED_TEST(DataTransformTest, TestMeanValues) {
+  TransformationParameter transform_param;
+  const bool unique_pixels = false;  // pixels are equal to label
+  const int label = 0;
+  const int channels = 3;
+  const int height = 4;
+  const int width = 5;
+
+  transform_param.add_mean_value(0);
+  transform_param.add_mean_value(1);
+  transform_param.add_mean_value(2);
+  Datum datum;
+  FillDatum(label, channels, height, width, unique_pixels, &datum);
+  Blob<TypeParam>* blob = new Blob<TypeParam>(1, channels, height, width);
+  DataTransformer<TypeParam>* transformer =
+      new DataTransformer<TypeParam>(transform_param, TEST);
+  transformer->InitRand();
+  transformer->Transform(datum, blob);
+  for (int c = 0; c < channels; ++c) {
+    for (int j = 0; j < height * width; ++j) {
+      EXPECT_EQ(blob->cpu_data()[blob->offset(0, c) + j], label - c);
+    }
+  }
+}
+
+TYPED_TEST(DataTransformTest, TestMeanFile) {
+  TransformationParameter transform_param;
+  const bool unique_pixels = true;  // pixels are consecutive ints [0,size]
+  const int label = 0;
+  const int channels = 3;
+  const int height = 4;
+  const int width = 5;
+  const int size = channels * height * width;
+
+  // Create a mean file
+  string* mean_file = new string();
+  MakeTempFilename(mean_file);
+  BlobProto blob_mean;
+  blob_mean.set_num(1);
+  blob_mean.set_channels(channels);
+  blob_mean.set_height(height);
+  blob_mean.set_width(width);
+
+  for (int j = 0; j < size; ++j) {
+      blob_mean.add_data(j);
+  }
+
+  LOG(INFO) << "Using temporary mean_file " << *mean_file;
+  WriteProtoToBinaryFile(blob_mean, *mean_file);
+
+  transform_param.set_mean_file(*mean_file);
+  Datum datum;
+  FillDatum(label, channels, height, width, unique_pixels, &datum);
+  Blob<TypeParam>* blob = new Blob<TypeParam>(1, channels, height, width);
+  DataTransformer<TypeParam>* transformer =
+      new DataTransformer<TypeParam>(transform_param, TEST);
+  transformer->InitRand();
+  transformer->Transform(datum, blob);
+  for (int j = 0; j < blob->count(); ++j) {
+      EXPECT_EQ(blob->cpu_data()[j], 0);
+  }
+}
+
+}  // namespace caffe
diff --git a/src/caffe/test/test_db.cpp b/src/caffe/test/test_db.cpp
new file mode 100644
index 00000000000..5b2ac230a0b
--- /dev/null
+++ b/src/caffe/test/test_db.cpp
@@ -0,0 +1,134 @@
+#include <string>
+
+#include "boost/scoped_ptr.hpp"
+#include "gtest/gtest.h"
+
+#include "caffe/common.hpp"
+#include "caffe/proto/caffe.pb.h"
+#include "caffe/util/db.hpp"
+#include "caffe/util/io.hpp"
+
+#include "caffe/test/test_caffe_main.hpp"
+
+namespace caffe {
+
+using boost::scoped_ptr;
+
+template <typename TypeParam>
+class DBTest : public ::testing::Test {
+ protected:
+  DBTest()
+      : backend_(TypeParam::backend),
+      root_images_(string(EXAMPLES_SOURCE_DIR) + string("images/")) {}
+
+  virtual void SetUp() {
+    MakeTempDir(&source_);
+    source_ += "/db";
+    string keys[] = {"cat.jpg", "fish-bike.jpg"};
+    LOG(INFO) << "Using temporary db " << source_;
+    scoped_ptr<db::DB> db(db::GetDB(TypeParam::backend));
+    db->Open(this->source_, db::NEW);
+    scoped_ptr<db::Transaction> txn(db->NewTransaction());
+    for (int i = 0; i < 2; ++i) {
+      Datum datum;
+      ReadImageToDatum(root_images_ + keys[i], i, &datum);
+      string out;
+      CHECK(datum.SerializeToString(&out));
+      txn->Put(keys[i], out);
+    }
+    txn->Commit();
+  }
+
+  virtual ~DBTest() { }
+
+  DataParameter_DB backend_;
+  string source_;
+  string root_images_;
+};
+
+struct TypeLevelDB {
+  static DataParameter_DB backend;
+};
+DataParameter_DB TypeLevelDB::backend = DataParameter_DB_LEVELDB;
+
+struct TypeLMDB {
+  static DataParameter_DB backend;
+};
+DataParameter_DB TypeLMDB::backend = DataParameter_DB_LMDB;
+
+// typedef ::testing::Types<TypeLmdb> TestTypes;
+typedef ::testing::Types<TypeLevelDB, TypeLMDB> TestTypes;
+
+TYPED_TEST_CASE(DBTest, TestTypes);
+
+TYPED_TEST(DBTest, TestGetDB) {
+  scoped_ptr<db::DB> db(db::GetDB(TypeParam::backend));
+}
+
+TYPED_TEST(DBTest, TestNext) {
+  scoped_ptr<db::DB> db(db::GetDB(TypeParam::backend));
+  db->Open(this->source_, db::READ);
+  scoped_ptr<db::Cursor> cursor(db->NewCursor());
+  EXPECT_TRUE(cursor->valid());
+  cursor->Next();
+  EXPECT_TRUE(cursor->valid());
+  cursor->Next();
+  EXPECT_FALSE(cursor->valid());
+}
+
+TYPED_TEST(DBTest, TestSeekToFirst) {
+  scoped_ptr<db::DB> db(db::GetDB(TypeParam::backend));
+  db->Open(this->source_, db::READ);
+  scoped_ptr<db::Cursor> cursor(db->NewCursor());
+  cursor->Next();
+  cursor->SeekToFirst();
+  EXPECT_TRUE(cursor->valid());
+  string key = cursor->key();
+  Datum datum;
+  datum.ParseFromString(cursor->value());
+  EXPECT_EQ(key, "cat.jpg");
+  EXPECT_EQ(datum.channels(), 3);
+  EXPECT_EQ(datum.height(), 360);
+  EXPECT_EQ(datum.width(), 480);
+}
+
+TYPED_TEST(DBTest, TestKeyValue) {
+  scoped_ptr<db::DB> db(db::GetDB(TypeParam::backend));
+  db->Open(this->source_, db::READ);
+  scoped_ptr<db::Cursor> cursor(db->NewCursor());
+  EXPECT_TRUE(cursor->valid());
+  string key = cursor->key();
+  Datum datum;
+  datum.ParseFromString(cursor->value());
+  EXPECT_EQ(key, "cat.jpg");
+  EXPECT_EQ(datum.channels(), 3);
+  EXPECT_EQ(datum.height(), 360);
+  EXPECT_EQ(datum.width(), 480);
+  cursor->Next();
+  EXPECT_TRUE(cursor->valid());
+  key = cursor->key();
+  datum.ParseFromString(cursor->value());
+  EXPECT_EQ(key, "fish-bike.jpg");
+  EXPECT_EQ(datum.channels(), 3);
+  EXPECT_EQ(datum.height(), 323);
+  EXPECT_EQ(datum.width(), 481);
+  cursor->Next();
+  EXPECT_FALSE(cursor->valid());
+}
+
+TYPED_TEST(DBTest, TestWrite) {
+  scoped_ptr<db::DB> db(db::GetDB(TypeParam::backend));
+  db->Open(this->source_, db::WRITE);
+  scoped_ptr<db::Transaction> txn(db->NewTransaction());
+  Datum datum;
+  ReadFileToDatum(this->root_images_ + "cat.jpg", 0, &datum);
+  string out;
+  CHECK(datum.SerializeToString(&out));
+  txn->Put("cat.jpg", out);
+  ReadFileToDatum(this->root_images_ + "fish-bike.jpg", 1, &datum);
+  CHECK(datum.SerializeToString(&out));
+  txn->Put("fish-bike.jpg", out);
+  txn->Commit();
+}
+
+}  // namespace caffe
diff --git a/src/caffe/test/test_deconvolution_layer.cpp b/src/caffe/test/test_deconvolution_layer.cpp
new file mode 100644
index 00000000000..fc63d5efbe3
--- /dev/null
+++ b/src/caffe/test/test_deconvolution_layer.cpp
@@ -0,0 +1,158 @@
+#include <cstring>
+#include <vector>
+
+#include "gtest/gtest.h"
+
+#include "caffe/blob.hpp"
+#include "caffe/common.hpp"
+#include "caffe/filler.hpp"
+#include "caffe/vision_layers.hpp"
+
+#include "caffe/test/test_caffe_main.hpp"
+#include "caffe/test/test_gradient_check_util.hpp"
+
+namespace caffe {
+
+// Since ConvolutionLayerTest checks the shared conv/deconv code in detail,
+// we'll just do a simple forward test and a gradient check.
+template <typename TypeParam>
+class DeconvolutionLayerTest : public MultiDeviceTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
+
+ protected:
+  DeconvolutionLayerTest()
+      : blob_bottom_(new Blob<Dtype>(2, 3, 6, 4)),
+        blob_bottom_2_(new Blob<Dtype>(2, 3, 6, 4)),
+        blob_top_(new Blob<Dtype>()),
+        blob_top_2_(new Blob<Dtype>()) {}
+  virtual void SetUp() {
+    // fill the values
+    FillerParameter filler_param;
+    filler_param.set_value(1.);
+    GaussianFiller<Dtype> filler(filler_param);
+    filler.Fill(this->blob_bottom_);
+    filler.Fill(this->blob_bottom_2_);
+    blob_bottom_vec_.push_back(blob_bottom_);
+    blob_top_vec_.push_back(blob_top_);
+  }
+
+  virtual ~DeconvolutionLayerTest() {
+    delete blob_bottom_;
+    delete blob_bottom_2_;
+    delete blob_top_;
+    delete blob_top_2_;
+  }
+
+  Blob<Dtype>* const blob_bottom_;
+  Blob<Dtype>* const blob_bottom_2_;
+  Blob<Dtype>* const blob_top_;
+  Blob<Dtype>* const blob_top_2_;
+  vector<Blob<Dtype>*> blob_bottom_vec_;
+  vector<Blob<Dtype>*> blob_top_vec_;
+};
+
+TYPED_TEST_CASE(DeconvolutionLayerTest, TestDtypesAndDevices);
+
+TYPED_TEST(DeconvolutionLayerTest, TestSetup) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  ConvolutionParameter* convolution_param =
+      layer_param.mutable_convolution_param();
+  convolution_param->set_kernel_size(3);
+  convolution_param->set_stride(2);
+  convolution_param->set_num_output(4);
+  this->blob_bottom_vec_.push_back(this->blob_bottom_2_);
+  this->blob_top_vec_.push_back(this->blob_top_2_);
+  shared_ptr<Layer<Dtype> > layer(
+      new DeconvolutionLayer<Dtype>(layer_param));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  EXPECT_EQ(this->blob_top_->num(), 2);
+  EXPECT_EQ(this->blob_top_->channels(), 4);
+  EXPECT_EQ(this->blob_top_->height(), 13);
+  EXPECT_EQ(this->blob_top_->width(), 9);
+  EXPECT_EQ(this->blob_top_2_->num(), 2);
+  EXPECT_EQ(this->blob_top_2_->channels(), 4);
+  EXPECT_EQ(this->blob_top_2_->height(), 13);
+  EXPECT_EQ(this->blob_top_2_->width(), 9);
+  // setting group should not change the shape
+  convolution_param->set_num_output(3);
+  convolution_param->set_group(3);
+  layer.reset(new DeconvolutionLayer<Dtype>(layer_param));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  EXPECT_EQ(this->blob_top_->num(), 2);
+  EXPECT_EQ(this->blob_top_->channels(), 3);
+  EXPECT_EQ(this->blob_top_->height(), 13);
+  EXPECT_EQ(this->blob_top_->width(), 9);
+  EXPECT_EQ(this->blob_top_2_->num(), 2);
+  EXPECT_EQ(this->blob_top_2_->channels(), 3);
+  EXPECT_EQ(this->blob_top_2_->height(), 13);
+  EXPECT_EQ(this->blob_top_2_->width(), 9);
+}
+
+TYPED_TEST(DeconvolutionLayerTest, TestSimpleDeconvolution) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->blob_bottom_vec_.push_back(this->blob_bottom_2_);
+  this->blob_top_vec_.push_back(this->blob_top_2_);
+  LayerParameter layer_param;
+  ConvolutionParameter* convolution_param =
+      layer_param.mutable_convolution_param();
+  convolution_param->set_kernel_size(3);
+  convolution_param->set_stride(2);
+  convolution_param->set_num_output(4);
+  convolution_param->mutable_weight_filler()->set_type("constant");
+  convolution_param->mutable_weight_filler()->set_value(1);
+  convolution_param->mutable_bias_filler()->set_type("constant");
+  convolution_param->mutable_bias_filler()->set_value(0.1);
+  shared_ptr<Layer<Dtype> > layer(
+      new DeconvolutionLayer<Dtype>(layer_param));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  // constant-fill the bottom blobs
+  FillerParameter filler_param;
+  filler_param.set_value(1.);
+  ConstantFiller<Dtype> filler(filler_param);
+  filler.Fill(this->blob_bottom_);
+  filler.Fill(this->blob_bottom_2_);
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  // simply check that accumulation works with overlapping filters
+  const Dtype* top_data = this->blob_top_->cpu_data();
+  for (int n = 0; n < this->blob_top_->num(); ++n) {
+    for (int c = 0; c < this->blob_top_->channels(); ++c) {
+      for (int h = 0; h < this->blob_top_->height(); ++h) {
+        for (int w = 0; w < this->blob_top_->width(); ++w) {
+          Dtype expected = 3.1;
+          bool h_overlap = h % 2 == 0 && h > 0
+            && h < this->blob_top_->height() - 1;
+          bool w_overlap = w % 2 == 0 && w > 0
+            && w < this->blob_top_->width() - 1;
+          if (h_overlap && w_overlap) {
+            expected += 9;
+          } else if (h_overlap || w_overlap) {
+            expected += 3;
+          }
+          EXPECT_NEAR(top_data[this->blob_top_->offset(n, c, h, w)],
+              expected, 1e-4);
+        }
+      }
+    }
+  }
+}
+
+TYPED_TEST(DeconvolutionLayerTest, TestGradient) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  ConvolutionParameter* convolution_param =
+      layer_param.mutable_convolution_param();
+  this->blob_bottom_vec_.push_back(this->blob_bottom_2_);
+  this->blob_top_vec_.push_back(this->blob_top_2_);
+  convolution_param->set_kernel_size(2);
+  convolution_param->set_stride(1);
+  convolution_param->set_num_output(1);
+  convolution_param->mutable_weight_filler()->set_type("gaussian");
+  convolution_param->mutable_bias_filler()->set_type("gaussian");
+  DeconvolutionLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
+}
+
+}  // namespace caffe
diff --git a/src/caffe/test/test_dummy_data_layer.cpp b/src/caffe/test/test_dummy_data_layer.cpp
index 4188bb68c9e..c9ed38db3a5 100644
--- a/src/caffe/test/test_dummy_data_layer.cpp
+++ b/src/caffe/test/test_dummy_data_layer.cpp
@@ -10,13 +10,10 @@
 
 #include "caffe/test/test_caffe_main.hpp"
 
-using std::string;
-using std::stringstream;
-
 namespace caffe {
 
 template <typename Dtype>
-class DummyDataLayerTest : public ::testing::Test {
+class DummyDataLayerTest : public CPUDeviceTest<Dtype> {
  protected:
   DummyDataLayerTest()
       : blob_top_a_(new Blob<Dtype>()),
@@ -47,7 +44,6 @@ class DummyDataLayerTest : public ::testing::Test {
 TYPED_TEST_CASE(DummyDataLayerTest, TestDtypes);
 
 TYPED_TEST(DummyDataLayerTest, TestOneTopConstant) {
-  Caffe::set_mode(Caffe::CPU);
   LayerParameter param;
   DummyDataParameter* dummy_data_param = param.mutable_dummy_data_param();
   dummy_data_param->add_num(5);
@@ -56,7 +52,7 @@ TYPED_TEST(DummyDataLayerTest, TestOneTopConstant) {
   dummy_data_param->add_width(4);
   this->blob_top_vec_.resize(1);
   DummyDataLayer<TypeParam> layer(param);
-  layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
   EXPECT_EQ(this->blob_top_a_->num(), 5);
   EXPECT_EQ(this->blob_top_a_->channels(), 3);
   EXPECT_EQ(this->blob_top_a_->height(), 2);
@@ -68,7 +64,7 @@ TYPED_TEST(DummyDataLayerTest, TestOneTopConstant) {
       EXPECT_EQ(0, this->blob_top_vec_[i]->cpu_data()[j]);
     }
   }
-  layer.Forward(this->blob_bottom_vec_, &this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   for (int i = 0; i < this->blob_top_vec_.size(); ++i) {
     for (int j = 0; j < this->blob_top_vec_[i]->count(); ++j) {
       EXPECT_EQ(0, this->blob_top_vec_[i]->cpu_data()[j]);
@@ -77,7 +73,6 @@ TYPED_TEST(DummyDataLayerTest, TestOneTopConstant) {
 }
 
 TYPED_TEST(DummyDataLayerTest, TestTwoTopConstant) {
-  Caffe::set_mode(Caffe::CPU);
   LayerParameter param;
   DummyDataParameter* dummy_data_param = param.mutable_dummy_data_param();
   dummy_data_param->add_num(5);
@@ -92,7 +87,7 @@ TYPED_TEST(DummyDataLayerTest, TestTwoTopConstant) {
   data_filler_param->set_value(7);
   this->blob_top_vec_.resize(2);
   DummyDataLayer<TypeParam> layer(param);
-  layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
   EXPECT_EQ(this->blob_top_a_->num(), 5);
   EXPECT_EQ(this->blob_top_a_->channels(), 3);
   EXPECT_EQ(this->blob_top_a_->height(), 2);
@@ -107,7 +102,7 @@ TYPED_TEST(DummyDataLayerTest, TestTwoTopConstant) {
       EXPECT_EQ(7, this->blob_top_vec_[i]->cpu_data()[j]);
     }
   }
-  layer.Forward(this->blob_bottom_vec_, &this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   for (int i = 0; i < this->blob_top_vec_.size(); ++i) {
     for (int j = 0; j < this->blob_top_vec_[i]->count(); ++j) {
       EXPECT_EQ(7, this->blob_top_vec_[i]->cpu_data()[j]);
@@ -116,7 +111,6 @@ TYPED_TEST(DummyDataLayerTest, TestTwoTopConstant) {
 }
 
 TYPED_TEST(DummyDataLayerTest, TestThreeTopConstantGaussianConstant) {
-  Caffe::set_mode(Caffe::CPU);
   LayerParameter param;
   DummyDataParameter* dummy_data_param = param.mutable_dummy_data_param();
   dummy_data_param->add_num(5);
@@ -134,7 +128,7 @@ TYPED_TEST(DummyDataLayerTest, TestThreeTopConstantGaussianConstant) {
   FillerParameter* data_filler_param_c = dummy_data_param->add_data_filler();
   data_filler_param_c->set_value(9);
   DummyDataLayer<TypeParam> layer(param);
-  layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
   EXPECT_EQ(this->blob_top_a_->num(), 5);
   EXPECT_EQ(this->blob_top_a_->channels(), 3);
   EXPECT_EQ(this->blob_top_a_->height(), 2);
@@ -160,7 +154,7 @@ TYPED_TEST(DummyDataLayerTest, TestThreeTopConstantGaussianConstant) {
   }
 
   // Do a Forward pass to fill in Blob b with Gaussian data.
-  layer.Forward(this->blob_bottom_vec_, &this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   for (int i = 0; i < this->blob_top_a_->count(); ++i) {
     EXPECT_EQ(7, this->blob_top_a_->cpu_data()[i]);
   }
@@ -180,7 +174,7 @@ TYPED_TEST(DummyDataLayerTest, TestThreeTopConstantGaussianConstant) {
 
   // Do another Forward pass to fill in Blob b with Gaussian data again,
   // checking that we get different values.
-  layer.Forward(this->blob_bottom_vec_, &this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   for (int i = 0; i < this->blob_top_a_->count(); ++i) {
     EXPECT_EQ(7, this->blob_top_a_->cpu_data()[i]);
   }
diff --git a/src/caffe/test/test_eltwise_layer.cpp b/src/caffe/test/test_eltwise_layer.cpp
index d5cf08229ab..be0c1347709 100644
--- a/src/caffe/test/test_eltwise_layer.cpp
+++ b/src/caffe/test/test_eltwise_layer.cpp
@@ -58,7 +58,7 @@ TYPED_TEST(EltwiseLayerTest, TestSetUp) {
   eltwise_param->set_operation(EltwiseParameter_EltwiseOp_PROD);
   shared_ptr<EltwiseLayer<Dtype> > layer(
       new EltwiseLayer<Dtype>(layer_param));
-  layer->SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
   EXPECT_EQ(this->blob_top_->num(), 2);
   EXPECT_EQ(this->blob_top_->channels(), 3);
   EXPECT_EQ(this->blob_top_->height(), 4);
@@ -72,8 +72,8 @@ TYPED_TEST(EltwiseLayerTest, TestProd) {
   eltwise_param->set_operation(EltwiseParameter_EltwiseOp_PROD);
   shared_ptr<EltwiseLayer<Dtype> > layer(
       new EltwiseLayer<Dtype>(layer_param));
-  layer->SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer->Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   const Dtype* data = this->blob_top_->cpu_data();
   const int count = this->blob_top_->count();
   const Dtype* in_data_a = this->blob_bottom_a_->cpu_data();
@@ -91,8 +91,8 @@ TYPED_TEST(EltwiseLayerTest, TestSum) {
   eltwise_param->set_operation(EltwiseParameter_EltwiseOp_SUM);
   shared_ptr<EltwiseLayer<Dtype> > layer(
       new EltwiseLayer<Dtype>(layer_param));
-  layer->SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer->Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   const Dtype* data = this->blob_top_->cpu_data();
   const int count = this->blob_top_->count();
   const Dtype* in_data_a = this->blob_bottom_a_->cpu_data();
@@ -113,8 +113,8 @@ TYPED_TEST(EltwiseLayerTest, TestSumCoeff) {
   eltwise_param->add_coeff(2);
   shared_ptr<EltwiseLayer<Dtype> > layer(
       new EltwiseLayer<Dtype>(layer_param));
-  layer->SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer->Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   const Dtype* data = this->blob_top_->cpu_data();
   const int count = this->blob_top_->count();
   const Dtype* in_data_a = this->blob_bottom_a_->cpu_data();
@@ -134,8 +134,8 @@ TYPED_TEST(EltwiseLayerTest, TestStableProdGradient) {
   eltwise_param->set_stable_prod_grad(true);
   EltwiseLayer<Dtype> layer(layer_param);
   GradientChecker<Dtype> checker(1e-2, 1e-3);
-  checker.CheckGradientEltwise(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_));
+  checker.CheckGradientEltwise(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
 }
 
 TYPED_TEST(EltwiseLayerTest, TestUnstableProdGradient) {
@@ -146,8 +146,8 @@ TYPED_TEST(EltwiseLayerTest, TestUnstableProdGradient) {
   eltwise_param->set_stable_prod_grad(false);
   EltwiseLayer<Dtype> layer(layer_param);
   GradientChecker<Dtype> checker(1e-2, 1e-3);
-  checker.CheckGradientEltwise(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_));
+  checker.CheckGradientEltwise(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
 }
 
 TYPED_TEST(EltwiseLayerTest, TestSumGradient) {
@@ -157,8 +157,8 @@ TYPED_TEST(EltwiseLayerTest, TestSumGradient) {
   eltwise_param->set_operation(EltwiseParameter_EltwiseOp_SUM);
   EltwiseLayer<Dtype> layer(layer_param);
   GradientChecker<Dtype> checker(1e-2, 1e-3);
-  checker.CheckGradientEltwise(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_));
+  checker.CheckGradientEltwise(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
 }
 
 TYPED_TEST(EltwiseLayerTest, TestSumCoeffGradient) {
@@ -171,8 +171,8 @@ TYPED_TEST(EltwiseLayerTest, TestSumCoeffGradient) {
   eltwise_param->add_coeff(2);
   EltwiseLayer<Dtype> layer(layer_param);
   GradientChecker<Dtype> checker(1e-2, 1e-3);
-  checker.CheckGradientEltwise(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_));
+  checker.CheckGradientEltwise(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
 }
 
 TYPED_TEST(EltwiseLayerTest, TestMax) {
@@ -182,8 +182,8 @@ TYPED_TEST(EltwiseLayerTest, TestMax) {
   eltwise_param->set_operation(EltwiseParameter_EltwiseOp_MAX);
   shared_ptr<EltwiseLayer<Dtype> > layer(
       new EltwiseLayer<Dtype>(layer_param));
-  layer->SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer->Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   const Dtype* data = this->blob_top_->cpu_data();
   const int count = this->blob_top_->count();
   const Dtype* in_data_a = this->blob_bottom_a_->cpu_data();
@@ -202,8 +202,8 @@ TYPED_TEST(EltwiseLayerTest, TestMaxGradient) {
   eltwise_param->set_operation(EltwiseParameter_EltwiseOp_MAX);
   EltwiseLayer<Dtype> layer(layer_param);
   GradientChecker<Dtype> checker(1e-4, 1e-3);
-  checker.CheckGradientEltwise(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_));
+  checker.CheckGradientEltwise(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
 }
 
 }  // namespace caffe
diff --git a/src/caffe/test/test_euclidean_loss_layer.cpp b/src/caffe/test/test_euclidean_loss_layer.cpp
index d7d2de7e9d4..1949742bbcb 100644
--- a/src/caffe/test/test_euclidean_loss_layer.cpp
+++ b/src/caffe/test/test_euclidean_loss_layer.cpp
@@ -44,18 +44,18 @@ class EuclideanLossLayerTest : public MultiDeviceTest<TypeParam> {
     // equivalent to explicitly specifiying a weight of 1.
     LayerParameter layer_param;
     EuclideanLossLayer<Dtype> layer_weight_1(layer_param);
-    layer_weight_1.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
+    layer_weight_1.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
     const Dtype loss_weight_1 =
-        layer_weight_1.Forward(this->blob_bottom_vec_, &this->blob_top_vec_);
+        layer_weight_1.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
 
     // Get the loss again with a different objective weight; check that it is
     // scaled appropriately.
     const Dtype kLossWeight = 3.7;
     layer_param.add_loss_weight(kLossWeight);
     EuclideanLossLayer<Dtype> layer_weight_2(layer_param);
-    layer_weight_2.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
+    layer_weight_2.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
     const Dtype loss_weight_2 =
-        layer_weight_2.Forward(this->blob_bottom_vec_, &this->blob_top_vec_);
+        layer_weight_2.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
     const Dtype kErrorMargin = 1e-5;
     EXPECT_NEAR(loss_weight_1 * kLossWeight, loss_weight_2, kErrorMargin);
     // Make sure the loss is non-trivial.
@@ -82,10 +82,10 @@ TYPED_TEST(EuclideanLossLayerTest, TestGradient) {
   const Dtype kLossWeight = 3.7;
   layer_param.add_loss_weight(kLossWeight);
   EuclideanLossLayer<Dtype> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
   GradientChecker<Dtype> checker(1e-2, 1e-2, 1701);
-  checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_));
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
 }
 
 }  // namespace caffe
diff --git a/src/caffe/test/test_filler.cpp b/src/caffe/test/test_filler.cpp
index e04b0fd22af..728b8dc5f0d 100644
--- a/src/caffe/test/test_filler.cpp
+++ b/src/caffe/test/test_filler.cpp
@@ -142,4 +142,102 @@ TYPED_TEST(GaussianFillerTest, TestFill) {
   EXPECT_LE(var, target_var * 5.);
 }
 
+template <typename Dtype>
+class XavierFillerTest : public ::testing::Test {
+ protected:
+  XavierFillerTest()
+      : blob_(new Blob<Dtype>(1000, 2, 4, 5)),
+        filler_param_() {
+  }
+  virtual void test_params(FillerParameter_VarianceNorm variance_norm,
+      Dtype n) {
+    this->filler_param_.set_variance_norm(variance_norm);
+    this->filler_.reset(new XavierFiller<Dtype>(this->filler_param_));
+    this->filler_->Fill(blob_);
+    EXPECT_TRUE(this->blob_);
+    const int count = this->blob_->count();
+    const Dtype* data = this->blob_->cpu_data();
+    Dtype mean = 0.;
+    Dtype ex2 = 0.;
+    for (int i = 0; i < count; ++i) {
+      mean += data[i];
+      ex2 += data[i] * data[i];
+    }
+    mean /= count;
+    ex2 /= count;
+    Dtype std = sqrt(ex2 - mean*mean);
+    Dtype target_std = sqrt(2.0 / n);
+    EXPECT_NEAR(mean, 0.0, 0.1);
+    EXPECT_NEAR(std, target_std, 0.1);
+  }
+  virtual ~XavierFillerTest() { delete blob_; }
+  Blob<Dtype>* const blob_;
+  FillerParameter filler_param_;
+  shared_ptr<XavierFiller<Dtype> > filler_;
+};
+
+TYPED_TEST_CASE(XavierFillerTest, TestDtypes);
+
+TYPED_TEST(XavierFillerTest, TestFillFanIn) {
+  TypeParam n = 2*4*5;
+  this->test_params(FillerParameter_VarianceNorm_FAN_IN, n);
+}
+TYPED_TEST(XavierFillerTest, TestFillFanOut) {
+  TypeParam n = 1000*4*5;
+  this->test_params(FillerParameter_VarianceNorm_FAN_OUT, n);
+}
+TYPED_TEST(XavierFillerTest, TestFillAverage) {
+  TypeParam n = (2*4*5 + 1000*4*5) / 2.0;
+  this->test_params(FillerParameter_VarianceNorm_AVERAGE, n);
+}
+
+template <typename Dtype>
+class MSRAFillerTest : public ::testing::Test {
+ protected:
+  MSRAFillerTest()
+      : blob_(new Blob<Dtype>(1000, 2, 4, 5)),
+        filler_param_() {
+  }
+  virtual void test_params(FillerParameter_VarianceNorm variance_norm,
+      Dtype n) {
+    this->filler_param_.set_variance_norm(variance_norm);
+    this->filler_.reset(new MSRAFiller<Dtype>(this->filler_param_));
+    this->filler_->Fill(blob_);
+    EXPECT_TRUE(this->blob_);
+    const int count = this->blob_->count();
+    const Dtype* data = this->blob_->cpu_data();
+    Dtype mean = 0.;
+    Dtype ex2 = 0.;
+    for (int i = 0; i < count; ++i) {
+      mean += data[i];
+      ex2 += data[i] * data[i];
+    }
+    mean /= count;
+    ex2 /= count;
+    Dtype std = sqrt(ex2 - mean*mean);
+    Dtype target_std = sqrt(2.0 / n);
+    EXPECT_NEAR(mean, 0.0, 0.1);
+    EXPECT_NEAR(std, target_std, 0.1);
+  }
+  virtual ~MSRAFillerTest() { delete blob_; }
+  Blob<Dtype>* const blob_;
+  FillerParameter filler_param_;
+  shared_ptr<MSRAFiller<Dtype> > filler_;
+};
+
+TYPED_TEST_CASE(MSRAFillerTest, TestDtypes);
+
+TYPED_TEST(MSRAFillerTest, TestFillFanIn) {
+  TypeParam n = 2*4*5;
+  this->test_params(FillerParameter_VarianceNorm_FAN_IN, n);
+}
+TYPED_TEST(MSRAFillerTest, TestFillFanOut) {
+  TypeParam n = 1000*4*5;
+  this->test_params(FillerParameter_VarianceNorm_FAN_OUT, n);
+}
+TYPED_TEST(MSRAFillerTest, TestFillAverage) {
+  TypeParam n = (2*4*5 + 1000*4*5) / 2.0;
+  this->test_params(FillerParameter_VarianceNorm_AVERAGE, n);
+}
+
 }  // namespace caffe
diff --git a/src/caffe/test/test_filter_layer.cpp b/src/caffe/test/test_filter_layer.cpp
new file mode 100644
index 00000000000..c641b6ef6e8
--- /dev/null
+++ b/src/caffe/test/test_filter_layer.cpp
@@ -0,0 +1,128 @@
+#include <cstring>
+#include <limits>
+#include <vector>
+
+#include "gtest/gtest.h"
+
+#include "caffe/blob.hpp"
+#include "caffe/common.hpp"
+#include "caffe/filler.hpp"
+#include "caffe/vision_layers.hpp"
+
+#include "caffe/test/test_caffe_main.hpp"
+#include "caffe/test/test_gradient_check_util.hpp"
+
+namespace caffe {
+
+template <typename TypeParam>
+class FilterLayerTest : public MultiDeviceTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
+
+ protected:
+  FilterLayerTest()
+      : blob_bottom_data_(new Blob<Dtype>(4, 3, 6, 4)),
+        blob_bottom_labels_(new Blob<Dtype>(4, 1, 1, 1)),
+        blob_bottom_selector_(new Blob<Dtype>(4, 1, 1, 1)),
+        blob_top_data_(new Blob<Dtype>()),
+        blob_top_labels_(new Blob<Dtype>()) {}
+  virtual void SetUp() {
+    // fill the values
+    Caffe::set_random_seed(1890);
+    FillerParameter filler_param;
+    GaussianFiller<Dtype> filler(filler_param);
+    // fill the selector blob
+    Dtype* bottom_data_selector_ = blob_bottom_selector_->mutable_cpu_data();
+    bottom_data_selector_[0] = 0;
+    bottom_data_selector_[1] = 1;
+    bottom_data_selector_[2] = 1;
+    bottom_data_selector_[3] = 0;
+    // fill the other bottom blobs
+    filler.Fill(blob_bottom_data_);
+    for (int i = 0; i < blob_bottom_labels_->count(); ++i) {
+      blob_bottom_labels_->mutable_cpu_data()[i] = caffe_rng_rand() % 5;
+    }
+    blob_bottom_vec_.push_back(blob_bottom_data_);
+    blob_bottom_vec_.push_back(blob_bottom_labels_);
+    blob_bottom_vec_.push_back(blob_bottom_selector_);
+    blob_top_vec_.push_back(blob_top_data_);
+    blob_top_vec_.push_back(blob_top_labels_);
+  }
+  virtual ~FilterLayerTest() {
+    delete blob_bottom_data_;
+    delete blob_bottom_labels_;
+    delete blob_bottom_selector_;
+    delete blob_top_data_;
+    delete blob_top_labels_;
+  }
+  Blob<Dtype>* const blob_bottom_data_;
+  Blob<Dtype>* const blob_bottom_labels_;
+  Blob<Dtype>* const blob_bottom_selector_;
+  // blobs for the top of FilterLayer
+  Blob<Dtype>* const blob_top_data_;
+  Blob<Dtype>* const blob_top_labels_;
+  vector<Blob<Dtype>*> blob_bottom_vec_;
+  vector<Blob<Dtype>*> blob_top_vec_;
+};
+
+TYPED_TEST_CASE(FilterLayerTest, TestDtypesAndDevices);
+
+TYPED_TEST(FilterLayerTest, TestReshape) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  FilterLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Reshape(this->blob_bottom_vec_, this->blob_top_vec_);
+  // In the test first and last items should have been filtered
+  // so we just expect 2 remaining items
+  EXPECT_EQ(this->blob_top_data_->shape(0), 2);
+  EXPECT_EQ(this->blob_top_labels_->shape(0), 2);
+  EXPECT_GT(this->blob_bottom_data_->shape(0),
+      this->blob_top_data_->shape(0));
+  EXPECT_GT(this->blob_bottom_labels_->shape(0),
+      this->blob_top_labels_->shape(0));
+  for (int i = 1; i < this->blob_bottom_labels_->num_axes(); i++) {
+    EXPECT_EQ(this->blob_bottom_labels_->shape(i),
+        this->blob_top_labels_->shape(i));
+  }
+}
+
+TYPED_TEST(FilterLayerTest, TestForward) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  FilterLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Reshape(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  EXPECT_EQ(this->blob_top_labels_->data_at(0, 0, 0, 0),
+      this->blob_bottom_labels_->data_at(1, 0, 0, 0));
+  EXPECT_EQ(this->blob_top_labels_->data_at(1, 0, 0, 0),
+      this->blob_bottom_labels_->data_at(2, 0, 0, 0));
+
+  int dim = this->blob_top_data_->count() /
+      this->blob_top_data_->shape(0);
+  const Dtype* top_data = this->blob_top_data_->cpu_data();
+  const Dtype* bottom_data = this->blob_bottom_data_->cpu_data();
+  // selector is 0 1 1 0, so we need to compare bottom(1,c,h,w)
+  // with top(0,c,h,w) and bottom(2,c,h,w) with top(1,c,h,w)
+  bottom_data += dim;  // bottom(1,c,h,w)
+  for (size_t n = 0; n < dim; n++)
+    EXPECT_EQ(top_data[n], bottom_data[n]);
+
+  bottom_data += dim;  // bottom(2,c,h,w)
+  top_data += dim;  // top(1,c,h,w)
+  for (size_t n = 0; n < dim; n++)
+    EXPECT_EQ(top_data[n], bottom_data[n]);
+}
+
+TYPED_TEST(FilterLayerTest, TestGradient) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  FilterLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
+  // check only input 0 (data) because labels and selector
+  // don't need backpropagation
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_, 0);
+}
+
+}  // namespace caffe
diff --git a/src/caffe/test/test_flatten_layer.cpp b/src/caffe/test/test_flatten_layer.cpp
index cbd01f245f2..7b6757cba32 100644
--- a/src/caffe/test/test_flatten_layer.cpp
+++ b/src/caffe/test/test_flatten_layer.cpp
@@ -41,19 +41,54 @@ TYPED_TEST(FlattenLayerTest, TestSetup) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
   FlattenLayer<Dtype> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  EXPECT_EQ(this->blob_top_->num(), 2);
-  EXPECT_EQ(this->blob_top_->channels(), 3 * 6 * 5);
-  EXPECT_EQ(this->blob_top_->height(), 1);
-  EXPECT_EQ(this->blob_top_->width(), 1);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  ASSERT_EQ(this->blob_top_->num_axes(), 2);
+  EXPECT_EQ(this->blob_top_->shape(0), 2);
+  EXPECT_EQ(this->blob_top_->shape(1), 3 * 6 * 5);
 }
 
-TYPED_TEST(FlattenLayerTest, Test) {
+TYPED_TEST(FlattenLayerTest, TestSetupWithAxis) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
+  layer_param.mutable_flatten_param()->set_axis(2);
   FlattenLayer<Dtype> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  ASSERT_EQ(this->blob_top_->num_axes(), 3);
+  EXPECT_EQ(this->blob_top_->shape(0), 2);
+  EXPECT_EQ(this->blob_top_->shape(1), 3);
+  EXPECT_EQ(this->blob_top_->shape(2), 6 * 5);
+}
+
+TYPED_TEST(FlattenLayerTest, TestSetupWithEndAxis) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  layer_param.mutable_flatten_param()->set_end_axis(-2);
+  FlattenLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  ASSERT_EQ(this->blob_top_->num_axes(), 3);
+  EXPECT_EQ(this->blob_top_->shape(0), 2);
+  EXPECT_EQ(this->blob_top_->shape(1), 3 * 6);
+  EXPECT_EQ(this->blob_top_->shape(2), 5);
+}
+
+TYPED_TEST(FlattenLayerTest, TestSetupWithStartAndEndAxis) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  layer_param.mutable_flatten_param()->set_axis(0);
+  layer_param.mutable_flatten_param()->set_end_axis(-2);
+  FlattenLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  ASSERT_EQ(this->blob_top_->num_axes(), 2);
+  EXPECT_EQ(this->blob_top_->shape(0), 2 * 3 * 6);
+  EXPECT_EQ(this->blob_top_->shape(1), 5);
+}
+
+TYPED_TEST(FlattenLayerTest, TestForward) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  FlattenLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   for (int c = 0; c < 3 * 6 * 5; ++c) {
     EXPECT_EQ(this->blob_top_->data_at(0, c, 0, 0),
         this->blob_bottom_->data_at(0, c / (6 * 5), (c / 5) % 6, c % 5));
@@ -67,9 +102,8 @@ TYPED_TEST(FlattenLayerTest, TestGradient) {
   LayerParameter layer_param;
   FlattenLayer<Dtype> layer(layer_param);
   GradientChecker<Dtype> checker(1e-2, 1e-2);
-  checker.CheckGradientEltwise(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_));
+  checker.CheckGradientEltwise(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
 }
 
-
 }  // namespace caffe
diff --git a/src/caffe/test/test_gradient_based_solver.cpp b/src/caffe/test/test_gradient_based_solver.cpp
index 3040eb134a4..c9135d64e70 100644
--- a/src/caffe/test/test_gradient_based_solver.cpp
+++ b/src/caffe/test/test_gradient_based_solver.cpp
@@ -23,7 +23,7 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
 
  protected:
   GradientBasedSolverTest() :
-      seed_(1701), num_(5), channels_(3), height_(10), width_(10) {}
+      seed_(1701), num_(4), channels_(3), height_(10), width_(10) {}
 
   shared_ptr<SGDSolver<Dtype> > solver_;
   int seed_;
@@ -56,19 +56,21 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
   }
 
   void RunLeastSquaresSolver(const Dtype learning_rate,
-      const Dtype weight_decay, const Dtype momentum, const int num_iters) {
+      const Dtype weight_decay, const Dtype momentum, const int num_iters,
+      const int iter_size = 1) {
     ostringstream proto;
     proto <<
        "max_iter: " << num_iters << " "
        "base_lr: " << learning_rate << " "
        "lr_policy: 'fixed' "
+       "iter_size: " << iter_size << " "
        "net_param { "
        "  name: 'TestNetwork' "
-       "  layers: { "
+       "  layer { "
        "    name: 'data' "
-       "    type: DUMMY_DATA "
+       "    type: 'DummyData' "
        "    dummy_data_param { "
-       "      num: " << num_ << " "
+       "      num: " << num_ / iter_size << " "
        "      channels: " << channels_ << " "
        "      height: " << height_ << " "
        "      width: " << width_ << " "
@@ -76,6 +78,10 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
        "      height: 1 "
        "      width: 1 "
        "      data_filler { "
+       "        type: 'constant' "
+       "        value: 1.0 "
+       "      } "
+       "      data_filler { "
        "        type: 'gaussian' "
        "        std: 1.0 "
        "      } "
@@ -83,9 +89,9 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
        "    top: 'data' "
        "    top: 'targets' "
        "  } "
-       "  layers: { "
+       "  layer { "
        "    name: 'innerprod' "
-       "    type: INNER_PRODUCT "
+       "    type: 'InnerProduct' "
        "    inner_product_param { "
        "      num_output: 1 "
        "      weight_filler { "
@@ -100,9 +106,9 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
        "    bottom: 'data' "
        "    top: 'innerprod' "
        "  } "
-       "  layers: { "
+       "  layer { "
        "    name: 'loss' "
-       "    type: EUCLIDEAN_LOSS "
+       "    type: 'EuclideanLoss' "
        "    bottom: 'innerprod' "
        "    bottom: 'targets' "
        "  } "
@@ -270,6 +276,45 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
     }
   }
 
+  void CheckAccumulation(const Dtype kLearningRate, const Dtype kWeightDecay,
+      const Dtype kMomentum, const int kNumIters, const int kIterSize) {
+    const double kPrecision = 1e-2;
+    const double kMinPrecision = 1e-7;
+    // Solve without accumulation and save parameters.
+    this->RunLeastSquaresSolver(kLearningRate, kWeightDecay, kMomentum,
+        kNumIters);
+    // Save parameters for comparison.
+    Net<Dtype>& net = *this->solver_->net();
+    const vector<shared_ptr<Blob<Dtype> > >& param_blobs =
+        net.layer_by_name("innerprod")->blobs();
+    vector<shared_ptr<Blob<Dtype> > > noaccum_params(param_blobs.size());
+    for (int i = 0; i < param_blobs.size(); ++i) {
+      noaccum_params[i].reset(new Blob<Dtype>());
+      noaccum_params[i]->CopyFrom(*param_blobs[i], false, true);
+    }
+    // Solve by equivalent accumulation of gradients over divided batches.
+    this->RunLeastSquaresSolver(kLearningRate, kWeightDecay, kMomentum,
+        kNumIters, kIterSize);
+    Net<Dtype>& net_accum = *this->solver_->net();
+    const vector<shared_ptr<Blob<Dtype> > >& accum_params =
+        net_accum.layer_by_name("innerprod")->blobs();
+    // Compare accumulated parameters against no accumulation standard.
+    const int D = this->channels_ * this->height_ * this->width_;
+    for (int i = 0; i < D; ++i) {
+      const Dtype expected_param = noaccum_params[0]->cpu_data()[i];
+      const Dtype accum_param = accum_params[0]->cpu_data()[i];
+      const Dtype error_margin = std::max(kMinPrecision, kPrecision *
+          std::min(fabs(expected_param), fabs(accum_param)));
+      EXPECT_NEAR(expected_param, accum_param, error_margin);
+    }
+    ASSERT_EQ(1, accum_params[1]->count());
+    const Dtype expected_bias = noaccum_params[1]->cpu_data()[0];
+    const Dtype accum_bias = accum_params[1]->cpu_data()[0];
+    const Dtype error_margin = std::max(kMinPrecision, kPrecision *
+        std::min(fabs(expected_bias), fabs(accum_bias)));
+    EXPECT_NEAR(expected_bias, accum_bias, error_margin);
+  }
+
   // Test that the correct update is computed for a regularized least squares
   // problem:
   //
@@ -323,7 +368,6 @@ class SGDSolverTest : public GradientBasedSolverTest<TypeParam> {
 TYPED_TEST_CASE(SGDSolverTest, TestDtypesAndDevices);
 
 TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdate) {
-  typedef typename TypeParam::Dtype Dtype;
   this->TestLeastSquaresUpdate();
 }
 
@@ -373,6 +417,16 @@ TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdateWithEverything) {
   }
 }
 
+TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdateWithEverythingAccum) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 0.01;
+  const Dtype kWeightDecay = 0.1;
+  const Dtype kMomentum = 0.9;
+  const int kNumIters = 4;
+  const int kIterSize = 2;
+  this->CheckAccumulation(kLearningRate, kWeightDecay, kMomentum, kNumIters,
+      kIterSize);
+}
 
 template <typename TypeParam>
 class AdaGradSolverTest : public GradientBasedSolverTest<TypeParam> {
@@ -390,7 +444,6 @@ class AdaGradSolverTest : public GradientBasedSolverTest<TypeParam> {
 TYPED_TEST_CASE(AdaGradSolverTest, TestDtypesAndDevices);
 
 TYPED_TEST(AdaGradSolverTest, TestAdaGradLeastSquaresUpdate) {
-  typedef typename TypeParam::Dtype Dtype;
   this->TestLeastSquaresUpdate();
 }
 
@@ -418,6 +471,16 @@ TYPED_TEST(AdaGradSolverTest, TestAdaGradLeastSquaresUpdateWithEverything) {
   }
 }
 
+TYPED_TEST(AdaGradSolverTest, TestLeastSquaresUpdateWithEverythingAccum) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 0.01;
+  const Dtype kWeightDecay = 0.1;
+  const Dtype kMomentum = 0.0;
+  const int kNumIters = 4;
+  const int kIterSize = 2;
+  this->CheckAccumulation(kLearningRate, kWeightDecay, kMomentum, kNumIters,
+      kIterSize);
+}
 
 template <typename TypeParam>
 class NesterovSolverTest : public GradientBasedSolverTest<TypeParam> {
@@ -435,7 +498,6 @@ class NesterovSolverTest : public GradientBasedSolverTest<TypeParam> {
 TYPED_TEST_CASE(NesterovSolverTest, TestDtypesAndDevices);
 
 TYPED_TEST(NesterovSolverTest, TestNesterovLeastSquaresUpdate) {
-  typedef typename TypeParam::Dtype Dtype;
   this->TestLeastSquaresUpdate();
 }
 
@@ -485,4 +547,15 @@ TYPED_TEST(NesterovSolverTest, TestNesterovLeastSquaresUpdateWithEverything) {
   }
 }
 
+TYPED_TEST(NesterovSolverTest, TestLeastSquaresUpdateWithEverythingAccum) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 0.01;
+  const Dtype kWeightDecay = 0.1;
+  const Dtype kMomentum = 0.9;
+  const int kNumIters = 4;
+  const int kIterSize = 2;
+  this->CheckAccumulation(kLearningRate, kWeightDecay, kMomentum, kNumIters,
+      kIterSize);
+}
+
 }  // namespace caffe
diff --git a/src/caffe/test/test_hdf5_output_layer.cpp b/src/caffe/test/test_hdf5_output_layer.cpp
index eb09c8d1f3a..a23034f284a 100644
--- a/src/caffe/test/test_hdf5_output_layer.cpp
+++ b/src/caffe/test/test_hdf5_output_layer.cpp
@@ -11,9 +11,6 @@
 
 #include "caffe/test/test_caffe_main.hpp"
 
-using std::string;
-using std::vector;
-
 namespace caffe {
 
 template<typename TypeParam>
@@ -95,9 +92,9 @@ TYPED_TEST(HDF5OutputLayerTest, TestForward) {
   //   the output hdf5 file is closed.
   {
     HDF5OutputLayer<Dtype> layer(param);
+    layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
     EXPECT_EQ(layer.file_name(), this->output_file_name_);
-    layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
-    layer.Forward(this->blob_bottom_vec_, &this->blob_top_vec_);
+    layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   }
   file_id = H5Fopen(this->output_file_name_.c_str(), H5F_ACC_RDONLY,
                           H5P_DEFAULT);
diff --git a/src/caffe/test/test_hdf5data_layer.cpp b/src/caffe/test/test_hdf5data_layer.cpp
index 29e70c9417f..c9b027f88cf 100644
--- a/src/caffe/test/test_hdf5data_layer.cpp
+++ b/src/caffe/test/test_hdf5data_layer.cpp
@@ -1,8 +1,6 @@
 #include <string>
 #include <vector>
 
-#include "leveldb/db.h"
-
 #include "gtest/gtest.h"
 
 #include "caffe/blob.hpp"
@@ -13,8 +11,6 @@
 
 #include "caffe/test/test_caffe_main.hpp"
 
-using std::string;
-
 namespace caffe {
 
 template <typename TypeParam>
@@ -25,10 +21,12 @@ class HDF5DataLayerTest : public MultiDeviceTest<TypeParam> {
   HDF5DataLayerTest()
       : filename(NULL),
         blob_top_data_(new Blob<Dtype>()),
-        blob_top_label_(new Blob<Dtype>()) {}
+        blob_top_label_(new Blob<Dtype>()),
+        blob_top_label2_(new Blob<Dtype>()) {}
   virtual void SetUp() {
     blob_top_vec_.push_back(blob_top_data_);
     blob_top_vec_.push_back(blob_top_label_);
+    blob_top_vec_.push_back(blob_top_label2_);
 
     // Check out generate_sample_data.py in the same directory.
     filename = new string(
@@ -39,12 +37,14 @@ class HDF5DataLayerTest : public MultiDeviceTest<TypeParam> {
   virtual ~HDF5DataLayerTest() {
     delete blob_top_data_;
     delete blob_top_label_;
+    delete blob_top_label2_;
     delete filename;
   }
 
   string* filename;
   Blob<Dtype>* const blob_top_data_;
   Blob<Dtype>* const blob_top_label_;
+  Blob<Dtype>* const blob_top_label2_;
   vector<Blob<Dtype>*> blob_bottom_vec_;
   vector<Blob<Dtype>*> blob_top_vec_;
 };
@@ -57,6 +57,10 @@ TYPED_TEST(HDF5DataLayerTest, TestRead) {
   // The data file we are reading has 10 rows and 8 columns,
   // with values from 0 to 10*8 reshaped in row-major order.
   LayerParameter param;
+  param.add_top("data");
+  param.add_top("label");
+  param.add_top("label2");
+
   HDF5DataParameter* hdf5_data_param = param.mutable_hdf5_data_param();
   int batch_size = 5;
   hdf5_data_param->set_batch_size(batch_size);
@@ -67,28 +71,32 @@ TYPED_TEST(HDF5DataLayerTest, TestRead) {
 
   // Test that the layer setup got the correct parameters.
   HDF5DataLayer<Dtype> layer(param);
-  layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
   EXPECT_EQ(this->blob_top_data_->num(), batch_size);
   EXPECT_EQ(this->blob_top_data_->channels(), num_cols);
   EXPECT_EQ(this->blob_top_data_->height(), height);
   EXPECT_EQ(this->blob_top_data_->width(), width);
 
-  EXPECT_EQ(this->blob_top_label_->num(), batch_size);
-  EXPECT_EQ(this->blob_top_label_->channels(), 1);
-  EXPECT_EQ(this->blob_top_label_->height(), 1);
-  EXPECT_EQ(this->blob_top_label_->width(), 1);
+  EXPECT_EQ(this->blob_top_label_->num_axes(), 2);
+  EXPECT_EQ(this->blob_top_label_->shape(0), batch_size);
+  EXPECT_EQ(this->blob_top_label_->shape(1), 1);
 
-  layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
+  EXPECT_EQ(this->blob_top_label2_->num_axes(), 2);
+  EXPECT_EQ(this->blob_top_label2_->shape(0), batch_size);
+  EXPECT_EQ(this->blob_top_label2_->shape(1), 1);
+
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
 
   // Go through the data 10 times (5 batches).
   const int data_size = num_cols * height * width;
   for (int iter = 0; iter < 10; ++iter) {
-    layer.Forward(this->blob_bottom_vec_, &this->blob_top_vec_);
+    layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
 
     // On even iterations, we're reading the first half of the data.
     // On odd iterations, we're reading the second half of the data.
     // NB: label is 1-indexed
     int label_offset = 1 + ((iter % 2 == 0) ? 0 : batch_size);
+    int label2_offset = 1 + label_offset;
     int data_offset = (iter % 2 == 0) ? 0 : batch_size * data_size;
 
     // Every two iterations we are reading the second file,
@@ -100,6 +108,9 @@ TYPED_TEST(HDF5DataLayerTest, TestRead) {
       EXPECT_EQ(
         label_offset + i,
         this->blob_top_label_->cpu_data()[i]);
+      EXPECT_EQ(
+        label2_offset + i,
+        this->blob_top_label2_->cpu_data()[i]);
     }
     for (int i = 0; i < batch_size; ++i) {
       for (int j = 0; j < num_cols; ++j) {
diff --git a/src/caffe/test/test_hinge_loss_layer.cpp b/src/caffe/test/test_hinge_loss_layer.cpp
index 3c11b9ac491..b6a99022905 100644
--- a/src/caffe/test/test_hinge_loss_layer.cpp
+++ b/src/caffe/test/test_hinge_loss_layer.cpp
@@ -57,8 +57,8 @@ TYPED_TEST(HingeLossLayerTest, TestGradientL1) {
   LayerParameter layer_param;
   HingeLossLayer<Dtype> layer(layer_param);
   GradientChecker<Dtype> checker(1e-2, 2e-3, 1701, 1, 0.01);
-  checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_), 0);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_, 0);
 }
 
 TYPED_TEST(HingeLossLayerTest, TestGradientL2) {
@@ -69,8 +69,8 @@ TYPED_TEST(HingeLossLayerTest, TestGradientL2) {
   hinge_loss_param->set_norm(HingeLossParameter_Norm_L2);
   HingeLossLayer<Dtype> layer(layer_param);
   GradientChecker<Dtype> checker(1e-2, 1e-2, 1701);
-  checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_), 0);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_, 0);
 }
 
 }  // namespace caffe
diff --git a/src/caffe/test/test_im2col_kernel.cu b/src/caffe/test/test_im2col_kernel.cu
index ee684c00255..0017ac23e69 100644
--- a/src/caffe/test/test_im2col_kernel.cu
+++ b/src/caffe/test/test_im2col_kernel.cu
@@ -25,7 +25,7 @@ __global__ void im2col_gpu_kernel(const int n, const Dtype* data_im,
 extern cudaDeviceProp CAFFE_TEST_CUDA_PROP;
 
 template <typename Dtype>
-class Im2colKernelTest : public ::testing::Test {
+class Im2colKernelTest : public GPUDeviceTest<Dtype> {
  protected:
   Im2colKernelTest()
         // big so launches > 1024 threads
@@ -68,8 +68,6 @@ class Im2colKernelTest : public ::testing::Test {
 TYPED_TEST_CASE(Im2colKernelTest, TestDtypes);
 
 TYPED_TEST(Im2colKernelTest, TestGPU) {
-  Caffe::set_mode(Caffe::GPU);
-
   // Reshape the blobs to correct size for im2col output
   this->blob_top_->Reshape(this->blob_bottom_->num(),
           this->channels_ * this->kernel_size_ * this->kernel_size_,
diff --git a/src/caffe/test/test_im2col_layer.cpp b/src/caffe/test/test_im2col_layer.cpp
index 32cf6369361..f50abe103f8 100644
--- a/src/caffe/test/test_im2col_layer.cpp
+++ b/src/caffe/test/test_im2col_layer.cpp
@@ -44,7 +44,7 @@ TYPED_TEST(Im2colLayerTest, TestSetup) {
   convolution_param->set_kernel_size(3);
   convolution_param->set_stride(2);
   Im2colLayer<Dtype> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
   EXPECT_EQ(this->blob_top_->num(), 2);
   EXPECT_EQ(this->blob_top_->channels(), 27);
   EXPECT_EQ(this->blob_top_->height(), 2);
@@ -59,8 +59,8 @@ TYPED_TEST(Im2colLayerTest, TestForward) {
   convolution_param->set_kernel_size(3);
   convolution_param->set_stride(2);
   Im2colLayer<Dtype> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   // We are lazy and will only check the top left block
   for (int c = 0; c < 27; ++c) {
     EXPECT_EQ(this->blob_bottom_->data_at(0, (c / 9), (c / 3) % 3, c % 3),
@@ -77,8 +77,8 @@ TYPED_TEST(Im2colLayerTest, TestGradient) {
   convolution_param->set_stride(2);
   Im2colLayer<Dtype> layer(layer_param);
   GradientChecker<Dtype> checker(1e-2, 1e-2);
-  checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_));
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
 }
 
 
@@ -91,8 +91,8 @@ TYPED_TEST(Im2colLayerTest, TestRect) {
   convolution_param->set_kernel_w(3);
   convolution_param->set_stride(2);
   Im2colLayer<Dtype> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   // We are lazy and will only check the top left block
   for (int c = 0; c < 45; ++c) {
     EXPECT_EQ(this->blob_top_->data_at(0, c, 0, 0),
@@ -111,8 +111,8 @@ TYPED_TEST(Im2colLayerTest, TestRectGradient) {
   convolution_param->set_stride(2);
   Im2colLayer<Dtype> layer(layer_param);
   GradientChecker<Dtype> checker(1e-2, 1e-2);
-  checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_));
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
 }
 
 }  // namespace caffe
diff --git a/src/caffe/test/test_image_data_layer.cpp b/src/caffe/test/test_image_data_layer.cpp
index d098c765258..931a5ebf137 100644
--- a/src/caffe/test/test_image_data_layer.cpp
+++ b/src/caffe/test/test_image_data_layer.cpp
@@ -25,17 +25,24 @@ class ImageDataLayerTest : public MultiDeviceTest<TypeParam> {
         blob_top_data_(new Blob<Dtype>()),
         blob_top_label_(new Blob<Dtype>()) {}
   virtual void SetUp() {
-    MakeTempFilename(&filename_);
     blob_top_vec_.push_back(blob_top_data_);
     blob_top_vec_.push_back(blob_top_label_);
     Caffe::set_random_seed(seed_);
-    // Create a Vector of files with labels
+    // Create test input file.
+    MakeTempFilename(&filename_);
     std::ofstream outfile(filename_.c_str(), std::ofstream::out);
     LOG(INFO) << "Using temporary file " << filename_;
     for (int i = 0; i < 5; ++i) {
       outfile << EXAMPLES_SOURCE_DIR "images/cat.jpg " << i;
     }
     outfile.close();
+    // Create test input file for images of distinct sizes.
+    MakeTempFilename(&filename_reshape_);
+    std::ofstream reshapefile(filename_reshape_.c_str(), std::ofstream::out);
+    LOG(INFO) << "Using temporary file " << filename_reshape_;
+    reshapefile << EXAMPLES_SOURCE_DIR "images/cat.jpg " << 0;
+    reshapefile << EXAMPLES_SOURCE_DIR "images/fish-bike.jpg " << 1;
+    reshapefile.close();
   }
 
   virtual ~ImageDataLayerTest() {
@@ -45,6 +52,7 @@ class ImageDataLayerTest : public MultiDeviceTest<TypeParam> {
 
   int seed_;
   string filename_;
+  string filename_reshape_;
   Blob<Dtype>* const blob_top_data_;
   Blob<Dtype>* const blob_top_label_;
   vector<Blob<Dtype>*> blob_bottom_vec_;
@@ -61,7 +69,7 @@ TYPED_TEST(ImageDataLayerTest, TestRead) {
   image_data_param->set_source(this->filename_.c_str());
   image_data_param->set_shuffle(false);
   ImageDataLayer<Dtype> layer(param);
-  layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
   EXPECT_EQ(this->blob_top_data_->num(), 5);
   EXPECT_EQ(this->blob_top_data_->channels(), 3);
   EXPECT_EQ(this->blob_top_data_->height(), 360);
@@ -72,7 +80,7 @@ TYPED_TEST(ImageDataLayerTest, TestRead) {
   EXPECT_EQ(this->blob_top_label_->width(), 1);
   // Go through the data twice
   for (int iter = 0; iter < 2; ++iter) {
-    layer.Forward(this->blob_bottom_vec_, &this->blob_top_vec_);
+    layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
     for (int i = 0; i < 5; ++i) {
       EXPECT_EQ(i, this->blob_top_label_->cpu_data()[i]);
     }
@@ -89,7 +97,7 @@ TYPED_TEST(ImageDataLayerTest, TestResize) {
   image_data_param->set_new_width(256);
   image_data_param->set_shuffle(false);
   ImageDataLayer<Dtype> layer(param);
-  layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
   EXPECT_EQ(this->blob_top_data_->num(), 5);
   EXPECT_EQ(this->blob_top_data_->channels(), 3);
   EXPECT_EQ(this->blob_top_data_->height(), 256);
@@ -100,13 +108,40 @@ TYPED_TEST(ImageDataLayerTest, TestResize) {
   EXPECT_EQ(this->blob_top_label_->width(), 1);
   // Go through the data twice
   for (int iter = 0; iter < 2; ++iter) {
-    layer.Forward(this->blob_bottom_vec_, &this->blob_top_vec_);
+    layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
     for (int i = 0; i < 5; ++i) {
       EXPECT_EQ(i, this->blob_top_label_->cpu_data()[i]);
     }
   }
 }
 
+TYPED_TEST(ImageDataLayerTest, TestReshape) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter param;
+  ImageDataParameter* image_data_param = param.mutable_image_data_param();
+  image_data_param->set_batch_size(1);
+  image_data_param->set_source(this->filename_reshape_.c_str());
+  image_data_param->set_shuffle(false);
+  ImageDataLayer<Dtype> layer(param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  EXPECT_EQ(this->blob_top_label_->num(), 1);
+  EXPECT_EQ(this->blob_top_label_->channels(), 1);
+  EXPECT_EQ(this->blob_top_label_->height(), 1);
+  EXPECT_EQ(this->blob_top_label_->width(), 1);
+  // cat.jpg
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  EXPECT_EQ(this->blob_top_data_->num(), 1);
+  EXPECT_EQ(this->blob_top_data_->channels(), 3);
+  EXPECT_EQ(this->blob_top_data_->height(), 360);
+  EXPECT_EQ(this->blob_top_data_->width(), 480);
+  // fish-bike.jpg
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  EXPECT_EQ(this->blob_top_data_->num(), 1);
+  EXPECT_EQ(this->blob_top_data_->channels(), 3);
+  EXPECT_EQ(this->blob_top_data_->height(), 323);
+  EXPECT_EQ(this->blob_top_data_->width(), 481);
+}
+
 TYPED_TEST(ImageDataLayerTest, TestShuffle) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter param;
@@ -115,7 +150,7 @@ TYPED_TEST(ImageDataLayerTest, TestShuffle) {
   image_data_param->set_source(this->filename_.c_str());
   image_data_param->set_shuffle(true);
   ImageDataLayer<Dtype> layer(param);
-  layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
   EXPECT_EQ(this->blob_top_data_->num(), 5);
   EXPECT_EQ(this->blob_top_data_->channels(), 3);
   EXPECT_EQ(this->blob_top_data_->height(), 360);
@@ -126,7 +161,7 @@ TYPED_TEST(ImageDataLayerTest, TestShuffle) {
   EXPECT_EQ(this->blob_top_label_->width(), 1);
   // Go through the data twice
   for (int iter = 0; iter < 2; ++iter) {
-    layer.Forward(this->blob_bottom_vec_, &this->blob_top_vec_);
+    layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
     map<Dtype, int> values_to_indices;
     int num_in_order = 0;
     for (int i = 0; i < 5; ++i) {
diff --git a/src/caffe/test/test_infogain_loss_layer.cpp b/src/caffe/test/test_infogain_loss_layer.cpp
index de2f901af31..7ec2f8073c1 100644
--- a/src/caffe/test/test_infogain_loss_layer.cpp
+++ b/src/caffe/test/test_infogain_loss_layer.cpp
@@ -63,8 +63,8 @@ TYPED_TEST(InfogainLossLayerTest, TestGradient) {
   LayerParameter layer_param;
   InfogainLossLayer<Dtype> layer(layer_param);
   GradientChecker<Dtype> checker(1e-4, 2e-2, 1701, 1, 0.01);
-  checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_), 0);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_, 0);
 }
 
 }  // namespace caffe
diff --git a/src/caffe/test/test_inner_product_layer.cpp b/src/caffe/test/test_inner_product_layer.cpp
index 5f9729c4f90..c03df17383a 100644
--- a/src/caffe/test/test_inner_product_layer.cpp
+++ b/src/caffe/test/test_inner_product_layer.cpp
@@ -48,7 +48,7 @@ TYPED_TEST(InnerProductLayerTest, TestSetUp) {
   inner_product_param->set_num_output(10);
   shared_ptr<InnerProductLayer<Dtype> > layer(
       new InnerProductLayer<Dtype>(layer_param));
-  layer->SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
   EXPECT_EQ(this->blob_top_->num(), 2);
   EXPECT_EQ(this->blob_top_->height(), 1);
   EXPECT_EQ(this->blob_top_->width(), 1);
@@ -73,8 +73,8 @@ TYPED_TEST(InnerProductLayerTest, TestForward) {
     inner_product_param->mutable_bias_filler()->set_max(2);
     shared_ptr<InnerProductLayer<Dtype> > layer(
         new InnerProductLayer<Dtype>(layer_param));
-    layer->SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-    layer->Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+    layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+    layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
     const Dtype* data = this->blob_top_->cpu_data();
     const int count = this->blob_top_->count();
     for (int i = 0; i < count; ++i) {
@@ -103,8 +103,8 @@ TYPED_TEST(InnerProductLayerTest, TestGradient) {
     inner_product_param->mutable_bias_filler()->set_max(2);
     InnerProductLayer<Dtype> layer(layer_param);
     GradientChecker<Dtype> checker(1e-2, 1e-3);
-    checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
-        &(this->blob_top_vec_));
+    checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+        this->blob_top_vec_);
   } else {
     LOG(ERROR) << "Skipping test due to old architecture.";
   }
diff --git a/src/caffe/test/test_io.cpp b/src/caffe/test/test_io.cpp
new file mode 100644
index 00000000000..4ab96311bbc
--- /dev/null
+++ b/src/caffe/test/test_io.cpp
@@ -0,0 +1,422 @@
+#include <opencv2/core/core.hpp>
+#include <opencv2/highgui/highgui.hpp>
+#include <opencv2/highgui/highgui_c.h>
+#include <opencv2/imgproc/imgproc.hpp>
+
+#include <string>
+
+#include "gtest/gtest.h"
+
+#include "caffe/common.hpp"
+#include "caffe/util/io.hpp"
+
+#include "caffe/test/test_caffe_main.hpp"
+
+namespace caffe {
+
+class IOTest : public ::testing::Test {};
+
+bool ReadImageToDatumReference(const string& filename, const int label,
+    const int height, const int width, const bool is_color, Datum* datum) {
+  cv::Mat cv_img;
+  int cv_read_flag = (is_color ? CV_LOAD_IMAGE_COLOR :
+    CV_LOAD_IMAGE_GRAYSCALE);
+
+  cv::Mat cv_img_origin = cv::imread(filename, cv_read_flag);
+  if (!cv_img_origin.data) {
+    LOG(ERROR) << "Could not open or find file " << filename;
+    return false;
+  }
+  if (height > 0 && width > 0) {
+    cv::resize(cv_img_origin, cv_img, cv::Size(width, height));
+  } else {
+    cv_img = cv_img_origin;
+  }
+
+  int num_channels = (is_color ? 3 : 1);
+  datum->set_channels(num_channels);
+  datum->set_height(cv_img.rows);
+  datum->set_width(cv_img.cols);
+  datum->set_label(label);
+  datum->clear_data();
+  datum->clear_float_data();
+  string* datum_string = datum->mutable_data();
+  if (is_color) {
+    for (int c = 0; c < num_channels; ++c) {
+      for (int h = 0; h < cv_img.rows; ++h) {
+        for (int w = 0; w < cv_img.cols; ++w) {
+          datum_string->push_back(
+            static_cast<char>(cv_img.at<cv::Vec3b>(h, w)[c]));
+        }
+      }
+    }
+  } else {  // Faster than repeatedly testing is_color for each pixel w/i loop
+    for (int h = 0; h < cv_img.rows; ++h) {
+      for (int w = 0; w < cv_img.cols; ++w) {
+        datum_string->push_back(
+          static_cast<char>(cv_img.at<uchar>(h, w)));
+        }
+      }
+  }
+  return true;
+}
+
+TEST_F(IOTest, TestReadImageToDatum) {
+  string filename = EXAMPLES_SOURCE_DIR "images/cat.jpg";
+  Datum datum;
+  ReadImageToDatum(filename, 0, &datum);
+  EXPECT_EQ(datum.channels(), 3);
+  EXPECT_EQ(datum.height(), 360);
+  EXPECT_EQ(datum.width(), 480);
+}
+
+TEST_F(IOTest, TestReadImageToDatumReference) {
+  string filename = EXAMPLES_SOURCE_DIR "images/cat.jpg";
+  Datum datum, datum_ref;
+  ReadImageToDatum(filename, 0, 0, 0, true, &datum);
+  ReadImageToDatumReference(filename, 0, 0, 0, true, &datum_ref);
+  EXPECT_EQ(datum.channels(), datum_ref.channels());
+  EXPECT_EQ(datum.height(), datum_ref.height());
+  EXPECT_EQ(datum.width(), datum_ref.width());
+  EXPECT_EQ(datum.data().size(), datum_ref.data().size());
+
+  const string& data = datum.data();
+  const string& data_ref = datum.data();
+
+  for (int i = 0; i < datum.data().size(); ++i) {
+    EXPECT_TRUE(data[i] == data_ref[i]);
+  }
+}
+
+
+TEST_F(IOTest, TestReadImageToDatumReferenceResized) {
+  string filename = EXAMPLES_SOURCE_DIR "images/cat.jpg";
+  Datum datum, datum_ref;
+  ReadImageToDatum(filename, 0, 100, 200, true, &datum);
+  ReadImageToDatumReference(filename, 0, 100, 200, true, &datum_ref);
+  EXPECT_EQ(datum.channels(), datum_ref.channels());
+  EXPECT_EQ(datum.height(), datum_ref.height());
+  EXPECT_EQ(datum.width(), datum_ref.width());
+  EXPECT_EQ(datum.data().size(), datum_ref.data().size());
+
+  const string& data = datum.data();
+  const string& data_ref = datum.data();
+
+  for (int i = 0; i < datum.data().size(); ++i) {
+    EXPECT_TRUE(data[i] == data_ref[i]);
+  }
+}
+
+TEST_F(IOTest, TestReadImageToDatumContent) {
+  string filename = EXAMPLES_SOURCE_DIR "images/cat.jpg";
+  Datum datum;
+  ReadImageToDatum(filename, 0, &datum);
+  cv::Mat cv_img = ReadImageToCVMat(filename);
+  EXPECT_EQ(datum.channels(), cv_img.channels());
+  EXPECT_EQ(datum.height(), cv_img.rows);
+  EXPECT_EQ(datum.width(), cv_img.cols);
+
+  const string& data = datum.data();
+  int index = 0;
+  for (int c = 0; c < datum.channels(); ++c) {
+    for (int h = 0; h < datum.height(); ++h) {
+      for (int w = 0; w < datum.width(); ++w) {
+        EXPECT_TRUE(data[index++] ==
+          static_cast<char>(cv_img.at<cv::Vec3b>(h, w)[c]));
+      }
+    }
+  }
+}
+
+TEST_F(IOTest, TestReadImageToDatumContentGray) {
+  string filename = EXAMPLES_SOURCE_DIR "images/cat.jpg";
+  Datum datum;
+  const bool is_color = false;
+  ReadImageToDatum(filename, 0, is_color, &datum);
+  cv::Mat cv_img = ReadImageToCVMat(filename, is_color);
+  EXPECT_EQ(datum.channels(), cv_img.channels());
+  EXPECT_EQ(datum.height(), cv_img.rows);
+  EXPECT_EQ(datum.width(), cv_img.cols);
+
+  const string& data = datum.data();
+  int index = 0;
+  for (int h = 0; h < datum.height(); ++h) {
+    for (int w = 0; w < datum.width(); ++w) {
+      EXPECT_TRUE(data[index++] == static_cast<char>(cv_img.at<uchar>(h, w)));
+    }
+  }
+}
+
+TEST_F(IOTest, TestReadImageToDatumResized) {
+  string filename = EXAMPLES_SOURCE_DIR "images/cat.jpg";
+  Datum datum;
+  ReadImageToDatum(filename, 0, 100, 200, &datum);
+  EXPECT_EQ(datum.channels(), 3);
+  EXPECT_EQ(datum.height(), 100);
+  EXPECT_EQ(datum.width(), 200);
+}
+
+
+TEST_F(IOTest, TestReadImageToDatumResizedSquare) {
+  string filename = EXAMPLES_SOURCE_DIR "images/cat.jpg";
+  Datum datum;
+  ReadImageToDatum(filename, 0, 256, 256, &datum);
+  EXPECT_EQ(datum.channels(), 3);
+  EXPECT_EQ(datum.height(), 256);
+  EXPECT_EQ(datum.width(), 256);
+}
+
+TEST_F(IOTest, TestReadImageToDatumGray) {
+  string filename = EXAMPLES_SOURCE_DIR "images/cat.jpg";
+  Datum datum;
+  const bool is_color = false;
+  ReadImageToDatum(filename, 0, is_color, &datum);
+  EXPECT_EQ(datum.channels(), 1);
+  EXPECT_EQ(datum.height(), 360);
+  EXPECT_EQ(datum.width(), 480);
+}
+
+TEST_F(IOTest, TestReadImageToDatumResizedGray) {
+  string filename = EXAMPLES_SOURCE_DIR "images/cat.jpg";
+  Datum datum;
+  const bool is_color = false;
+  ReadImageToDatum(filename, 0, 256, 256, is_color, &datum);
+  EXPECT_EQ(datum.channels(), 1);
+  EXPECT_EQ(datum.height(), 256);
+  EXPECT_EQ(datum.width(), 256);
+}
+
+TEST_F(IOTest, TestReadImageToCVMat) {
+  string filename = EXAMPLES_SOURCE_DIR "images/cat.jpg";
+  cv::Mat cv_img = ReadImageToCVMat(filename);
+  EXPECT_EQ(cv_img.channels(), 3);
+  EXPECT_EQ(cv_img.rows, 360);
+  EXPECT_EQ(cv_img.cols, 480);
+}
+
+TEST_F(IOTest, TestReadImageToCVMatResized) {
+  string filename = EXAMPLES_SOURCE_DIR "images/cat.jpg";
+  cv::Mat cv_img = ReadImageToCVMat(filename, 100, 200);
+  EXPECT_EQ(cv_img.channels(), 3);
+  EXPECT_EQ(cv_img.rows, 100);
+  EXPECT_EQ(cv_img.cols, 200);
+}
+
+TEST_F(IOTest, TestReadImageToCVMatResizedSquare) {
+  string filename = EXAMPLES_SOURCE_DIR "images/cat.jpg";
+  cv::Mat cv_img = ReadImageToCVMat(filename, 256, 256);
+  EXPECT_EQ(cv_img.channels(), 3);
+  EXPECT_EQ(cv_img.rows, 256);
+  EXPECT_EQ(cv_img.cols, 256);
+}
+
+TEST_F(IOTest, TestReadImageToCVMatGray) {
+  string filename = EXAMPLES_SOURCE_DIR "images/cat.jpg";
+  const bool is_color = false;
+  cv::Mat cv_img = ReadImageToCVMat(filename, is_color);
+  EXPECT_EQ(cv_img.channels(), 1);
+  EXPECT_EQ(cv_img.rows, 360);
+  EXPECT_EQ(cv_img.cols, 480);
+}
+
+TEST_F(IOTest, TestReadImageToCVMatResizedGray) {
+  string filename = EXAMPLES_SOURCE_DIR "images/cat.jpg";
+  const bool is_color = false;
+  cv::Mat cv_img = ReadImageToCVMat(filename, 256, 256, is_color);
+  EXPECT_EQ(cv_img.channels(), 1);
+  EXPECT_EQ(cv_img.rows, 256);
+  EXPECT_EQ(cv_img.cols, 256);
+}
+
+TEST_F(IOTest, TestCVMatToDatum) {
+  string filename = EXAMPLES_SOURCE_DIR "images/cat.jpg";
+  cv::Mat cv_img = ReadImageToCVMat(filename);
+  Datum datum;
+  CVMatToDatum(cv_img, &datum);
+  EXPECT_EQ(datum.channels(), 3);
+  EXPECT_EQ(datum.height(), 360);
+  EXPECT_EQ(datum.width(), 480);
+}
+
+TEST_F(IOTest, TestCVMatToDatumContent) {
+  string filename = EXAMPLES_SOURCE_DIR "images/cat.jpg";
+  cv::Mat cv_img = ReadImageToCVMat(filename);
+  Datum datum;
+  CVMatToDatum(cv_img, &datum);
+  Datum datum_ref;
+  ReadImageToDatum(filename, 0, &datum_ref);
+  EXPECT_EQ(datum.channels(), datum_ref.channels());
+  EXPECT_EQ(datum.height(), datum_ref.height());
+  EXPECT_EQ(datum.width(), datum_ref.width());
+  EXPECT_EQ(datum.data().size(), datum_ref.data().size());
+
+  const string& data = datum.data();
+  const string& data_ref = datum_ref.data();
+  for (int i = 0; i < datum.data().size(); ++i) {
+    EXPECT_TRUE(data[i] == data_ref[i]);
+  }
+}
+
+TEST_F(IOTest, TestCVMatToDatumReference) {
+  string filename = EXAMPLES_SOURCE_DIR "images/cat.jpg";
+  cv::Mat cv_img = ReadImageToCVMat(filename);
+  Datum datum;
+  CVMatToDatum(cv_img, &datum);
+  Datum datum_ref;
+  ReadImageToDatumReference(filename, 0, 0, 0, true, &datum_ref);
+  EXPECT_EQ(datum.channels(), datum_ref.channels());
+  EXPECT_EQ(datum.height(), datum_ref.height());
+  EXPECT_EQ(datum.width(), datum_ref.width());
+  EXPECT_EQ(datum.data().size(), datum_ref.data().size());
+
+  const string& data = datum.data();
+  const string& data_ref = datum_ref.data();
+  for (int i = 0; i < datum.data().size(); ++i) {
+    EXPECT_TRUE(data[i] == data_ref[i]);
+  }
+}
+
+TEST_F(IOTest, TestReadFileToDatum) {
+  string filename = EXAMPLES_SOURCE_DIR "images/cat.jpg";
+  Datum datum;
+  EXPECT_TRUE(ReadFileToDatum(filename, &datum));
+  EXPECT_TRUE(datum.encoded());
+  EXPECT_EQ(datum.label(), -1);
+  EXPECT_EQ(datum.data().size(), 140391);
+}
+
+TEST_F(IOTest, TestDecodeDatum) {
+  string filename = EXAMPLES_SOURCE_DIR "images/cat.jpg";
+  Datum datum;
+  EXPECT_TRUE(ReadFileToDatum(filename, &datum));
+  EXPECT_TRUE(DecodeDatum(&datum, true));
+  EXPECT_FALSE(DecodeDatum(&datum, true));
+  Datum datum_ref;
+  ReadImageToDatumReference(filename, 0, 0, 0, true, &datum_ref);
+  EXPECT_EQ(datum.channels(), datum_ref.channels());
+  EXPECT_EQ(datum.height(), datum_ref.height());
+  EXPECT_EQ(datum.width(), datum_ref.width());
+  EXPECT_EQ(datum.data().size(), datum_ref.data().size());
+
+  const string& data = datum.data();
+  const string& data_ref = datum_ref.data();
+  for (int i = 0; i < datum.data().size(); ++i) {
+    EXPECT_TRUE(data[i] == data_ref[i]);
+  }
+}
+
+TEST_F(IOTest, TestDecodeDatumToCVMat) {
+  string filename = EXAMPLES_SOURCE_DIR "images/cat.jpg";
+  Datum datum;
+  EXPECT_TRUE(ReadFileToDatum(filename, &datum));
+  cv::Mat cv_img = DecodeDatumToCVMat(datum, true);
+  EXPECT_EQ(cv_img.channels(), 3);
+  EXPECT_EQ(cv_img.rows, 360);
+  EXPECT_EQ(cv_img.cols, 480);
+  cv_img = DecodeDatumToCVMat(datum, false);
+  EXPECT_EQ(cv_img.channels(), 1);
+  EXPECT_EQ(cv_img.rows, 360);
+  EXPECT_EQ(cv_img.cols, 480);
+}
+
+TEST_F(IOTest, TestDecodeDatumToCVMatContent) {
+  string filename = EXAMPLES_SOURCE_DIR "images/cat.jpg";
+  Datum datum;
+  EXPECT_TRUE(ReadImageToDatum(filename, 0, std::string("jpg"), &datum));
+  cv::Mat cv_img = DecodeDatumToCVMat(datum, true);
+  cv::Mat cv_img_ref = ReadImageToCVMat(filename);
+  EXPECT_EQ(cv_img_ref.channels(), cv_img.channels());
+  EXPECT_EQ(cv_img_ref.rows, cv_img.rows);
+  EXPECT_EQ(cv_img_ref.cols, cv_img.cols);
+
+  for (int c = 0; c < datum.channels(); ++c) {
+    for (int h = 0; h < datum.height(); ++h) {
+      for (int w = 0; w < datum.width(); ++w) {
+        EXPECT_TRUE(cv_img.at<cv::Vec3b>(h, w)[c]==
+          cv_img_ref.at<cv::Vec3b>(h, w)[c]);
+      }
+    }
+  }
+}
+
+TEST_F(IOTest, TestDecodeDatumNative) {
+  string filename = EXAMPLES_SOURCE_DIR "images/cat.jpg";
+  Datum datum;
+  EXPECT_TRUE(ReadFileToDatum(filename, &datum));
+  EXPECT_TRUE(DecodeDatumNative(&datum));
+  EXPECT_FALSE(DecodeDatumNative(&datum));
+  Datum datum_ref;
+  ReadImageToDatumReference(filename, 0, 0, 0, true, &datum_ref);
+  EXPECT_EQ(datum.channels(), datum_ref.channels());
+  EXPECT_EQ(datum.height(), datum_ref.height());
+  EXPECT_EQ(datum.width(), datum_ref.width());
+  EXPECT_EQ(datum.data().size(), datum_ref.data().size());
+
+  const string& data = datum.data();
+  const string& data_ref = datum_ref.data();
+  for (int i = 0; i < datum.data().size(); ++i) {
+    EXPECT_TRUE(data[i] == data_ref[i]);
+  }
+}
+
+TEST_F(IOTest, TestDecodeDatumToCVMatNative) {
+  string filename = EXAMPLES_SOURCE_DIR "images/cat.jpg";
+  Datum datum;
+  EXPECT_TRUE(ReadFileToDatum(filename, &datum));
+  cv::Mat cv_img = DecodeDatumToCVMatNative(datum);
+  EXPECT_EQ(cv_img.channels(), 3);
+  EXPECT_EQ(cv_img.rows, 360);
+  EXPECT_EQ(cv_img.cols, 480);
+}
+
+TEST_F(IOTest, TestDecodeDatumNativeGray) {
+  string filename = EXAMPLES_SOURCE_DIR "images/cat_gray.jpg";
+  Datum datum;
+  EXPECT_TRUE(ReadFileToDatum(filename, &datum));
+  EXPECT_TRUE(DecodeDatumNative(&datum));
+  EXPECT_FALSE(DecodeDatumNative(&datum));
+  Datum datum_ref;
+  ReadImageToDatumReference(filename, 0, 0, 0, false, &datum_ref);
+  EXPECT_EQ(datum.channels(), datum_ref.channels());
+  EXPECT_EQ(datum.height(), datum_ref.height());
+  EXPECT_EQ(datum.width(), datum_ref.width());
+  EXPECT_EQ(datum.data().size(), datum_ref.data().size());
+
+  const string& data = datum.data();
+  const string& data_ref = datum_ref.data();
+  for (int i = 0; i < datum.data().size(); ++i) {
+    EXPECT_TRUE(data[i] == data_ref[i]);
+  }
+}
+
+TEST_F(IOTest, TestDecodeDatumToCVMatNativeGray) {
+  string filename = EXAMPLES_SOURCE_DIR "images/cat_gray.jpg";
+  Datum datum;
+  EXPECT_TRUE(ReadFileToDatum(filename, &datum));
+  cv::Mat cv_img = DecodeDatumToCVMatNative(datum);
+  EXPECT_EQ(cv_img.channels(), 1);
+  EXPECT_EQ(cv_img.rows, 360);
+  EXPECT_EQ(cv_img.cols, 480);
+}
+
+TEST_F(IOTest, TestDecodeDatumToCVMatContentNative) {
+  string filename = EXAMPLES_SOURCE_DIR "images/cat.jpg";
+  Datum datum;
+  EXPECT_TRUE(ReadImageToDatum(filename, 0, std::string("jpg"), &datum));
+  cv::Mat cv_img = DecodeDatumToCVMatNative(datum);
+  cv::Mat cv_img_ref = ReadImageToCVMat(filename);
+  EXPECT_EQ(cv_img_ref.channels(), cv_img.channels());
+  EXPECT_EQ(cv_img_ref.rows, cv_img.rows);
+  EXPECT_EQ(cv_img_ref.cols, cv_img.cols);
+
+  for (int c = 0; c < datum.channels(); ++c) {
+    for (int h = 0; h < datum.height(); ++h) {
+      for (int w = 0; w < datum.width(); ++w) {
+        EXPECT_TRUE(cv_img.at<cv::Vec3b>(h, w)[c]==
+          cv_img_ref.at<cv::Vec3b>(h, w)[c]);
+      }
+    }
+  }
+}
+
+}  // namespace caffe
diff --git a/src/caffe/test/test_layer_factory.cpp b/src/caffe/test/test_layer_factory.cpp
new file mode 100644
index 00000000000..efb1b37ac42
--- /dev/null
+++ b/src/caffe/test/test_layer_factory.cpp
@@ -0,0 +1,35 @@
+#include <map>
+#include <string>
+
+#include "gtest/gtest.h"
+
+#include "caffe/common.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/layer_factory.hpp"
+
+#include "caffe/test/test_caffe_main.hpp"
+
+namespace caffe {
+
+template <typename TypeParam>
+class LayerFactoryTest : public MultiDeviceTest<TypeParam> {};
+
+TYPED_TEST_CASE(LayerFactoryTest, TestDtypesAndDevices);
+
+TYPED_TEST(LayerFactoryTest, TestCreateLayer) {
+  typedef typename TypeParam::Dtype Dtype;
+  typename LayerRegistry<Dtype>::CreatorRegistry& registry =
+      LayerRegistry<Dtype>::Registry();
+  shared_ptr<Layer<Dtype> > layer;
+  LayerParameter layer_param;
+  for (typename LayerRegistry<Dtype>::CreatorRegistry::iterator iter =
+       registry.begin(); iter != registry.end(); ++iter) {
+    // Special case: PythonLayer is checked by pytest
+    if (iter->first == "Python") { continue; }
+    layer_param.set_type(iter->first);
+    layer = LayerRegistry<Dtype>::CreateLayer(layer_param);
+    EXPECT_EQ(iter->first, layer->type());
+  }
+}
+
+}  // namespace caffe
diff --git a/src/caffe/test/test_lrn_layer.cpp b/src/caffe/test/test_lrn_layer.cpp
index 3bd62fd9e18..c4e2f8ea7f2 100644
--- a/src/caffe/test/test_lrn_layer.cpp
+++ b/src/caffe/test/test_lrn_layer.cpp
@@ -116,7 +116,7 @@ TYPED_TEST(LRNLayerTest, TestSetupAcrossChannels) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
   LRNLayer<Dtype> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
   EXPECT_EQ(this->blob_top_->num(), 2);
   EXPECT_EQ(this->blob_top_->channels(), 7);
   EXPECT_EQ(this->blob_top_->height(), 3);
@@ -127,8 +127,24 @@ TYPED_TEST(LRNLayerTest, TestForwardAcrossChannels) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
   LRNLayer<Dtype> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  Blob<Dtype> top_reference;
+  this->ReferenceLRNForward(*(this->blob_bottom_), layer_param,
+      &top_reference);
+  for (int i = 0; i < this->blob_bottom_->count(); ++i) {
+    EXPECT_NEAR(this->blob_top_->cpu_data()[i], top_reference.cpu_data()[i],
+                this->epsilon_);
+  }
+}
+
+TYPED_TEST(LRNLayerTest, TestForwardAcrossChannelsLargeRegion) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  layer_param.mutable_lrn_param()->set_local_size(15);
+  LRNLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   Blob<Dtype> top_reference;
   this->ReferenceLRNForward(*(this->blob_bottom_), layer_param,
       &top_reference);
@@ -143,20 +159,42 @@ TYPED_TEST(LRNLayerTest, TestGradientAcrossChannels) {
   LayerParameter layer_param;
   LRNLayer<Dtype> layer(layer_param);
   GradientChecker<Dtype> checker(1e-2, 1e-2);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  for (int i = 0; i < this->blob_top_->count(); ++i) {
+    this->blob_top_->mutable_cpu_diff()[i] = 1.;
+  }
+  vector<bool> propagate_down(this->blob_bottom_vec_.size(), true);
+  layer.Backward(this->blob_top_vec_, propagate_down,
+                 this->blob_bottom_vec_);
+  // for (int i = 0; i < this->blob_bottom_->count(); ++i) {
+  //   std::cout << "CPU diff " << this->blob_bottom_->cpu_diff()[i]
+  //       << std::endl;
+  // }
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
+}
+
+TYPED_TEST(LRNLayerTest, TestGradientAcrossChannelsLargeRegion) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  layer_param.mutable_lrn_param()->set_local_size(15);
+  LRNLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-2);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   for (int i = 0; i < this->blob_top_->count(); ++i) {
     this->blob_top_->mutable_cpu_diff()[i] = 1.;
   }
   vector<bool> propagate_down(this->blob_bottom_vec_.size(), true);
   layer.Backward(this->blob_top_vec_, propagate_down,
-                 &(this->blob_bottom_vec_));
+                 this->blob_bottom_vec_);
   // for (int i = 0; i < this->blob_bottom_->count(); ++i) {
   //   std::cout << "CPU diff " << this->blob_bottom_->cpu_diff()[i]
   //       << std::endl;
   // }
-  checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_));
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
 }
 
 TYPED_TEST(LRNLayerTest, TestSetupWithinChannel) {
@@ -166,7 +204,7 @@ TYPED_TEST(LRNLayerTest, TestSetupWithinChannel) {
       LRNParameter_NormRegion_WITHIN_CHANNEL);
   layer_param.mutable_lrn_param()->set_local_size(3);
   LRNLayer<Dtype> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
   EXPECT_EQ(this->blob_top_->num(), 2);
   EXPECT_EQ(this->blob_top_->channels(), 7);
   EXPECT_EQ(this->blob_top_->height(), 3);
@@ -180,8 +218,8 @@ TYPED_TEST(LRNLayerTest, TestForwardWithinChannel) {
       LRNParameter_NormRegion_WITHIN_CHANNEL);
   layer_param.mutable_lrn_param()->set_local_size(3);
   LRNLayer<Dtype> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   Blob<Dtype> top_reference;
   this->ReferenceLRNForward(*(this->blob_bottom_), layer_param,
       &top_reference);
@@ -199,13 +237,13 @@ TYPED_TEST(LRNLayerTest, TestGradientWithinChannel) {
   layer_param.mutable_lrn_param()->set_local_size(3);
   LRNLayer<Dtype> layer(layer_param);
   GradientChecker<Dtype> checker(1e-2, 1e-2);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   for (int i = 0; i < this->blob_top_->count(); ++i) {
     this->blob_top_->mutable_cpu_diff()[i] = 1.;
   }
-  checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_));
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
 }
 
 
diff --git a/src/caffe/test/test_math_functions.cpp b/src/caffe/test/test_math_functions.cpp
index 667f744bdd7..a095b544e17 100644
--- a/src/caffe/test/test_math_functions.cpp
+++ b/src/caffe/test/test_math_functions.cpp
@@ -15,8 +15,10 @@
 
 namespace caffe {
 
-template<typename Dtype>
-class MathFunctionsTest : public ::testing::Test {
+template <typename TypeParam>
+class MathFunctionsTest : public MultiDeviceTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
+
  protected:
   MathFunctionsTest()
       : blob_bottom_(new Blob<Dtype>()),
@@ -64,14 +66,19 @@ class MathFunctionsTest : public ::testing::Test {
   Blob<Dtype>* const blob_top_;
 };
 
-TYPED_TEST_CASE(MathFunctionsTest, TestDtypes);
+template <typename Dtype>
+class CPUMathFunctionsTest
+  : public MathFunctionsTest<CPUDevice<Dtype> > {
+};
+
+TYPED_TEST_CASE(CPUMathFunctionsTest, TestDtypes);
 
-TYPED_TEST(MathFunctionsTest, TestNothing) {
+TYPED_TEST(CPUMathFunctionsTest, TestNothing) {
   // The first test case of a test suite takes the longest time
   //   due to the set up overhead.
 }
 
-TYPED_TEST(MathFunctionsTest, TestHammingDistanceCPU) {
+TYPED_TEST(CPUMathFunctionsTest, TestHammingDistance) {
   int n = this->blob_bottom_->count();
   const TypeParam* x = this->blob_bottom_->cpu_data();
   const TypeParam* y = this->blob_top_->cpu_data();
@@ -79,7 +86,7 @@ TYPED_TEST(MathFunctionsTest, TestHammingDistanceCPU) {
             caffe_cpu_hamming_distance<TypeParam>(n, x, y));
 }
 
-TYPED_TEST(MathFunctionsTest, TestAsumCPU) {
+TYPED_TEST(CPUMathFunctionsTest, TestAsum) {
   int n = this->blob_bottom_->count();
   const TypeParam* x = this->blob_bottom_->cpu_data();
   TypeParam std_asum = 0;
@@ -90,7 +97,7 @@ TYPED_TEST(MathFunctionsTest, TestAsumCPU) {
   EXPECT_LT((cpu_asum - std_asum) / std_asum, 1e-2);
 }
 
-TYPED_TEST(MathFunctionsTest, TestSignCPU) {
+TYPED_TEST(CPUMathFunctionsTest, TestSign) {
   int n = this->blob_bottom_->count();
   const TypeParam* x = this->blob_bottom_->cpu_data();
   caffe_cpu_sign<TypeParam>(n, x, this->blob_bottom_->mutable_cpu_diff());
@@ -100,7 +107,7 @@ TYPED_TEST(MathFunctionsTest, TestSignCPU) {
   }
 }
 
-TYPED_TEST(MathFunctionsTest, TestSgnbitCPU) {
+TYPED_TEST(CPUMathFunctionsTest, TestSgnbit) {
   int n = this->blob_bottom_->count();
   const TypeParam* x = this->blob_bottom_->cpu_data();
   caffe_cpu_sgnbit<TypeParam>(n, x, this->blob_bottom_->mutable_cpu_diff());
@@ -110,7 +117,7 @@ TYPED_TEST(MathFunctionsTest, TestSgnbitCPU) {
   }
 }
 
-TYPED_TEST(MathFunctionsTest, TestFabsCPU) {
+TYPED_TEST(CPUMathFunctionsTest, TestFabs) {
   int n = this->blob_bottom_->count();
   const TypeParam* x = this->blob_bottom_->cpu_data();
   caffe_abs<TypeParam>(n, x, this->blob_bottom_->mutable_cpu_diff());
@@ -120,7 +127,7 @@ TYPED_TEST(MathFunctionsTest, TestFabsCPU) {
   }
 }
 
-TYPED_TEST(MathFunctionsTest, TestScaleCPU) {
+TYPED_TEST(CPUMathFunctionsTest, TestScale) {
   int n = this->blob_bottom_->count();
   TypeParam alpha = this->blob_bottom_->cpu_diff()[caffe_rng_rand() %
                                                    this->blob_bottom_->count()];
@@ -133,11 +140,10 @@ TYPED_TEST(MathFunctionsTest, TestScaleCPU) {
   }
 }
 
-TYPED_TEST(MathFunctionsTest, TestCopyCPU) {
+TYPED_TEST(CPUMathFunctionsTest, TestCopy) {
   const int n = this->blob_bottom_->count();
   const TypeParam* bottom_data = this->blob_bottom_->cpu_data();
   TypeParam* top_data = this->blob_top_->mutable_cpu_data();
-  Caffe::set_mode(Caffe::CPU);
   caffe_copy(n, bottom_data, top_data);
   for (int i = 0; i < n; ++i) {
     EXPECT_EQ(bottom_data[i], top_data[i]);
@@ -146,8 +152,14 @@ TYPED_TEST(MathFunctionsTest, TestCopyCPU) {
 
 #ifndef CPU_ONLY
 
+template <typename Dtype>
+class GPUMathFunctionsTest : public MathFunctionsTest<GPUDevice<Dtype> > {
+};
+
+TYPED_TEST_CASE(GPUMathFunctionsTest, TestDtypes);
+
 // TODO: Fix caffe_gpu_hamming_distance and re-enable this test.
-TYPED_TEST(MathFunctionsTest, DISABLED_TestHammingDistanceGPU) {
+TYPED_TEST(GPUMathFunctionsTest, DISABLED_TestHammingDistance) {
   int n = this->blob_bottom_->count();
   const TypeParam* x = this->blob_bottom_->cpu_data();
   const TypeParam* y = this->blob_top_->cpu_data();
@@ -158,7 +170,7 @@ TYPED_TEST(MathFunctionsTest, DISABLED_TestHammingDistanceGPU) {
   EXPECT_EQ(reference_distance, computed_distance);
 }
 
-TYPED_TEST(MathFunctionsTest, TestAsumGPU) {
+TYPED_TEST(GPUMathFunctionsTest, TestAsum) {
   int n = this->blob_bottom_->count();
   const TypeParam* x = this->blob_bottom_->cpu_data();
   TypeParam std_asum = 0;
@@ -170,7 +182,7 @@ TYPED_TEST(MathFunctionsTest, TestAsumGPU) {
   EXPECT_LT((gpu_asum - std_asum) / std_asum, 1e-2);
 }
 
-TYPED_TEST(MathFunctionsTest, TestSignGPU) {
+TYPED_TEST(GPUMathFunctionsTest, TestSign) {
   int n = this->blob_bottom_->count();
   caffe_gpu_sign<TypeParam>(n, this->blob_bottom_->gpu_data(),
                             this->blob_bottom_->mutable_gpu_diff());
@@ -181,7 +193,7 @@ TYPED_TEST(MathFunctionsTest, TestSignGPU) {
   }
 }
 
-TYPED_TEST(MathFunctionsTest, TestSgnbitGPU) {
+TYPED_TEST(GPUMathFunctionsTest, TestSgnbit) {
   int n = this->blob_bottom_->count();
   caffe_gpu_sgnbit<TypeParam>(n, this->blob_bottom_->gpu_data(),
                             this->blob_bottom_->mutable_gpu_diff());
@@ -192,7 +204,7 @@ TYPED_TEST(MathFunctionsTest, TestSgnbitGPU) {
   }
 }
 
-TYPED_TEST(MathFunctionsTest, TestFabsGPU) {
+TYPED_TEST(GPUMathFunctionsTest, TestFabs) {
   int n = this->blob_bottom_->count();
   caffe_gpu_abs<TypeParam>(n, this->blob_bottom_->gpu_data(),
                             this->blob_bottom_->mutable_gpu_diff());
@@ -203,7 +215,7 @@ TYPED_TEST(MathFunctionsTest, TestFabsGPU) {
   }
 }
 
-TYPED_TEST(MathFunctionsTest, TestScaleGPU) {
+TYPED_TEST(GPUMathFunctionsTest, TestScale) {
   int n = this->blob_bottom_->count();
   TypeParam alpha = this->blob_bottom_->cpu_diff()[caffe_rng_rand() %
                                                    this->blob_bottom_->count()];
@@ -216,11 +228,10 @@ TYPED_TEST(MathFunctionsTest, TestScaleGPU) {
   }
 }
 
-TYPED_TEST(MathFunctionsTest, TestCopyGPU) {
+TYPED_TEST(GPUMathFunctionsTest, TestCopy) {
   const int n = this->blob_bottom_->count();
   const TypeParam* bottom_data = this->blob_bottom_->gpu_data();
   TypeParam* top_data = this->blob_top_->mutable_gpu_data();
-  Caffe::set_mode(Caffe::GPU);
   caffe_copy(n, bottom_data, top_data);
   bottom_data = this->blob_bottom_->cpu_data();
   top_data = this->blob_top_->mutable_cpu_data();
diff --git a/src/caffe/test/test_maxpool_dropout_layers.cpp b/src/caffe/test/test_maxpool_dropout_layers.cpp
index 311c7781be5..611d9790863 100644
--- a/src/caffe/test/test_maxpool_dropout_layers.cpp
+++ b/src/caffe/test/test_maxpool_dropout_layers.cpp
@@ -47,9 +47,9 @@ TYPED_TEST(MaxPoolingDropoutTest, TestSetup) {
   pooling_param->set_kernel_size(3);
   pooling_param->set_stride(2);
   PoolingLayer<Dtype> max_layer(layer_param);
-  max_layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  max_layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
   DropoutLayer<Dtype> dropout_layer(layer_param);
-  dropout_layer.SetUp(this->blob_top_vec_, &(this->blob_top_vec_));
+  dropout_layer.SetUp(this->blob_top_vec_, this->blob_top_vec_);
   EXPECT_EQ(this->blob_top_->num(), this->blob_bottom_->num());
   EXPECT_EQ(this->blob_top_->channels(), this->blob_bottom_->channels());
   EXPECT_EQ(this->blob_top_->height(), 3);
@@ -64,8 +64,8 @@ TYPED_TEST(MaxPoolingDropoutTest, TestForward) {
   pooling_param->set_kernel_size(3);
   pooling_param->set_stride(2);
   PoolingLayer<Dtype> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   const Dtype* top_data = this->blob_top_->cpu_data();
   Dtype sum = 0.;
   for (int i = 0; i < this->blob_top_->count(); ++i) {
@@ -74,8 +74,8 @@ TYPED_TEST(MaxPoolingDropoutTest, TestForward) {
   EXPECT_EQ(sum, this->blob_top_->count());
   // Dropout in-place
   DropoutLayer<Dtype> dropout_layer(layer_param);
-  dropout_layer.SetUp(this->blob_top_vec_, &(this->blob_top_vec_));
-  dropout_layer.Forward(this->blob_top_vec_, &(this->blob_top_vec_));
+  dropout_layer.SetUp(this->blob_top_vec_, this->blob_top_vec_);
+  dropout_layer.Forward(this->blob_top_vec_, this->blob_top_vec_);
   sum = 0.;
   Dtype scale = 1. / (1. - layer_param.dropout_param().dropout_ratio());
   top_data = this->blob_top_->cpu_data();
@@ -88,20 +88,20 @@ TYPED_TEST(MaxPoolingDropoutTest, TestForward) {
 
 TYPED_TEST(MaxPoolingDropoutTest, TestBackward) {
   typedef typename TypeParam::Dtype Dtype;
-  Caffe::set_phase(Caffe::TRAIN);
   LayerParameter layer_param;
+  layer_param.set_phase(TRAIN);
   PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
   pooling_param->set_kernel_size(3);
   pooling_param->set_stride(2);
   PoolingLayer<Dtype> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   for (int i = 0; i < this->blob_top_->count(); ++i) {
     this->blob_top_->mutable_cpu_diff()[i] = 1.;
   }
   vector<bool> propagate_down(this->blob_bottom_vec_.size(), true);
   layer.Backward(this->blob_top_vec_, propagate_down,
-                 &(this->blob_bottom_vec_));
+                 this->blob_bottom_vec_);
   const Dtype* bottom_diff = this->blob_bottom_->cpu_diff();
   Dtype sum = 0.;
   for (int i = 0; i < this->blob_bottom_->count(); ++i) {
@@ -110,12 +110,12 @@ TYPED_TEST(MaxPoolingDropoutTest, TestBackward) {
   EXPECT_EQ(sum, this->blob_top_->count());
   // Dropout in-place
   DropoutLayer<Dtype> dropout_layer(layer_param);
-  dropout_layer.SetUp(this->blob_top_vec_, &(this->blob_top_vec_));
-  dropout_layer.Forward(this->blob_top_vec_, &(this->blob_top_vec_));
+  dropout_layer.SetUp(this->blob_top_vec_, this->blob_top_vec_);
+  dropout_layer.Forward(this->blob_top_vec_, this->blob_top_vec_);
   dropout_layer.Backward(this->blob_top_vec_, propagate_down,
-                         &(this->blob_top_vec_));
+                         this->blob_top_vec_);
   layer.Backward(this->blob_top_vec_, propagate_down,
-                 &(this->blob_bottom_vec_));
+                 this->blob_bottom_vec_);
   Dtype sum_with_dropout = 0.;
   bottom_diff = this->blob_bottom_->cpu_diff();
   for (int i = 0; i < this->blob_bottom_->count(); ++i) {
diff --git a/src/caffe/test/test_memory_data_layer.cpp b/src/caffe/test/test_memory_data_layer.cpp
index 3dc00345743..a79033f59f1 100644
--- a/src/caffe/test/test_memory_data_layer.cpp
+++ b/src/caffe/test/test_memory_data_layer.cpp
@@ -1,3 +1,5 @@
+#include <opencv2/core/core.hpp>
+
 #include <string>
 #include <vector>
 
@@ -70,7 +72,7 @@ TYPED_TEST(MemoryDataLayerTest, TestSetup) {
   md_param->set_width(this->width_);
   shared_ptr<Layer<Dtype> > layer(
       new MemoryDataLayer<Dtype>(layer_param));
-  layer->SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
   EXPECT_EQ(this->data_blob_->num(), this->batch_size_);
   EXPECT_EQ(this->data_blob_->channels(), this->channels_);
   EXPECT_EQ(this->data_blob_->height(), this->height_);
@@ -93,12 +95,12 @@ TYPED_TEST(MemoryDataLayerTest, TestForward) {
   md_param->set_width(this->width_);
   shared_ptr<MemoryDataLayer<Dtype> > layer(
       new MemoryDataLayer<Dtype>(layer_param));
-  layer->DataLayerSetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer->DataLayerSetUp(this->blob_bottom_vec_, this->blob_top_vec_);
   layer->Reset(this->data_->mutable_cpu_data(),
       this->labels_->mutable_cpu_data(), this->data_->num());
   for (int i = 0; i < this->batches_ * 6; ++i) {
     int batch_num = i % this->batches_;
-    layer->Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+    layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
     for (int j = 0; j < this->data_blob_->count(); ++j) {
       EXPECT_EQ(this->data_blob_->cpu_data()[j],
           this->data_->cpu_data()[
@@ -121,13 +123,14 @@ TYPED_TEST(MemoryDataLayerTest, AddDatumVectorDefaultTransform) {
   memory_data_param->set_height(this->height_);
   memory_data_param->set_width(this->width_);
   MemoryDataLayer<Dtype> layer(param);
-  layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
-
-  vector<Datum> datum_vector(this->batch_size_);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  // We add batch_size*num_iter items, then for each iteration
+  // we forward batch_size elements
+  int num_iter = 5;
+  vector<Datum> datum_vector(this->batch_size_ * num_iter);
   const size_t count = this->channels_ * this->height_ * this->width_;
   size_t pixel_index = 0;
-  for (int i = 0; i < this->batch_size_; ++i) {
-    LOG(ERROR) << "i " << i;
+  for (int i = 0; i < this->batch_size_ * num_iter; ++i) {
     datum_vector[i].set_channels(this->channels_);
     datum_vector[i].set_height(this->height_);
     datum_vector[i].set_width(this->width_);
@@ -138,18 +141,18 @@ TYPED_TEST(MemoryDataLayerTest, AddDatumVectorDefaultTransform) {
     }
     datum_vector[i].set_data(&(pixels[0]), count);
   }
-
   layer.AddDatumVector(datum_vector);
 
   int data_index;
   // Go through the data 5 times
-  for (int iter = 0; iter < 5; ++iter) {
-    layer.Forward(this->blob_bottom_vec_, &this->blob_top_vec_);
+  for (int iter = 0; iter < num_iter; ++iter) {
+    int offset = this->batch_size_ * iter;
+    layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
     const Dtype* data = this->data_blob_->cpu_data();
     size_t index = 0;
     for (int i = 0; i < this->batch_size_; ++i) {
-      const string& data_string = datum_vector[i].data();
-      EXPECT_EQ(i, this->label_blob_->cpu_data()[i]);
+      const string& data_string = datum_vector[offset + i].data();
+      EXPECT_EQ(offset + i, this->label_blob_->cpu_data()[i]);
       for (int c = 0; c < this->channels_; ++c) {
         for (int h = 0; h < this->height_; ++h) {
           for (int w = 0; w < this->width_; ++w) {
@@ -164,4 +167,130 @@ TYPED_TEST(MemoryDataLayerTest, AddDatumVectorDefaultTransform) {
   }
 }
 
+TYPED_TEST(MemoryDataLayerTest, AddMatVectorDefaultTransform) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter param;
+  MemoryDataParameter* memory_data_param = param.mutable_memory_data_param();
+  memory_data_param->set_batch_size(this->batch_size_);
+  memory_data_param->set_channels(this->channels_);
+  memory_data_param->set_height(this->height_);
+  memory_data_param->set_width(this->width_);
+  MemoryDataLayer<Dtype> layer(param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  // We add batch_size*num_iter items, then for each iteration
+  // we forward batch_size elements
+  int num_iter = 5;
+  vector<cv::Mat> mat_vector(this->batch_size_ * num_iter);
+  vector<int> label_vector(this->batch_size_ * num_iter);
+  for (int i = 0; i < this->batch_size_*num_iter; ++i) {
+    mat_vector[i] = cv::Mat(this->height_, this->width_, CV_8UC4);
+    label_vector[i] = i;
+    cv::randu(mat_vector[i], cv::Scalar::all(0), cv::Scalar::all(255));
+  }
+  layer.AddMatVector(mat_vector, label_vector);
+
+  int data_index;
+  const size_t count = this->channels_ * this->height_ * this->width_;
+  for (int iter = 0; iter < num_iter; ++iter) {
+    int offset = this->batch_size_ * iter;
+    layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+    const Dtype* data = this->data_blob_->cpu_data();
+    for (int i = 0; i < this->batch_size_; ++i) {
+      EXPECT_EQ(offset + i, this->label_blob_->cpu_data()[i]);
+      for (int h = 0; h < this->height_; ++h) {
+        const unsigned char* ptr_mat = mat_vector[offset + i].ptr<uchar>(h);
+        int index = 0;
+        for (int w = 0; w < this->width_; ++w) {
+          for (int c = 0; c < this->channels_; ++c) {
+            data_index = (i*count) + (c * this->height_ + h) * this->width_ + w;
+            Dtype pixel = static_cast<Dtype>(ptr_mat[index++]);
+            EXPECT_EQ(static_cast<int>(pixel),
+                      data[data_index]);
+          }
+        }
+      }
+    }
+  }
+}
+
+TYPED_TEST(MemoryDataLayerTest, TestSetBatchSize) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter param;
+  MemoryDataParameter* memory_data_param = param.mutable_memory_data_param();
+  memory_data_param->set_batch_size(this->batch_size_);
+  memory_data_param->set_channels(this->channels_);
+  memory_data_param->set_height(this->height_);
+  memory_data_param->set_width(this->width_);
+  MemoryDataLayer<Dtype> layer(param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  // first add data as usual
+  int num_iter = 5;
+  vector<cv::Mat> mat_vector(this->batch_size_ * num_iter);
+  vector<int> label_vector(this->batch_size_ * num_iter);
+  for (int i = 0; i < this->batch_size_*num_iter; ++i) {
+    mat_vector[i] = cv::Mat(this->height_, this->width_, CV_8UC4);
+    label_vector[i] = i;
+    cv::randu(mat_vector[i], cv::Scalar::all(0), cv::Scalar::all(255));
+  }
+  layer.AddMatVector(mat_vector, label_vector);
+  // then consume the data
+  int data_index;
+  const size_t count = this->channels_ * this->height_ * this->width_;
+  for (int iter = 0; iter < num_iter; ++iter) {
+    int offset = this->batch_size_ * iter;
+    layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+    const Dtype* data = this->data_blob_->cpu_data();
+    for (int i = 0; i < this->batch_size_; ++i) {
+      EXPECT_EQ(offset + i, this->label_blob_->cpu_data()[i]);
+      for (int h = 0; h < this->height_; ++h) {
+        const unsigned char* ptr_mat = mat_vector[offset + i].ptr<uchar>(h);
+        int index = 0;
+        for (int w = 0; w < this->width_; ++w) {
+          for (int c = 0; c < this->channels_; ++c) {
+            data_index = (i*count) + (c * this->height_ + h) * this->width_ + w;
+            Dtype pixel = static_cast<Dtype>(ptr_mat[index++]);
+            EXPECT_EQ(static_cast<int>(pixel), data[data_index]);
+          }
+        }
+      }
+    }
+  }
+  // and then add new data with different batch_size
+  int new_batch_size = 16;
+  layer.set_batch_size(new_batch_size);
+  mat_vector.clear();
+  mat_vector.resize(new_batch_size * num_iter);
+  label_vector.clear();
+  label_vector.resize(new_batch_size * num_iter);
+  for (int i = 0; i < new_batch_size*num_iter; ++i) {
+    mat_vector[i] = cv::Mat(this->height_, this->width_, CV_8UC4);
+    label_vector[i] = i;
+    cv::randu(mat_vector[i], cv::Scalar::all(0), cv::Scalar::all(255));
+  }
+  layer.AddMatVector(mat_vector, label_vector);
+
+  // finally consume new data and check if everything is fine
+  for (int iter = 0; iter < num_iter; ++iter) {
+    int offset = new_batch_size * iter;
+    layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+    EXPECT_EQ(new_batch_size, this->blob_top_vec_[0]->num());
+    EXPECT_EQ(new_batch_size, this->blob_top_vec_[1]->num());
+    const Dtype* data = this->data_blob_->cpu_data();
+    for (int i = 0; i < new_batch_size; ++i) {
+      EXPECT_EQ(offset + i, this->label_blob_->cpu_data()[i]);
+      for (int h = 0; h < this->height_; ++h) {
+        const unsigned char* ptr_mat = mat_vector[offset + i].ptr<uchar>(h);
+        int index = 0;
+        for (int w = 0; w < this->width_; ++w) {
+          for (int c = 0; c < this->channels_; ++c) {
+            data_index = (i*count) + (c * this->height_ + h) * this->width_ + w;
+            Dtype pixel = static_cast<Dtype>(ptr_mat[index++]);
+            EXPECT_EQ(static_cast<int>(pixel), data[data_index]);
+          }
+        }
+      }
+    }
+  }
+}
+
 }  // namespace caffe
diff --git a/src/caffe/test/test_multinomial_logistic_loss_layer.cpp b/src/caffe/test/test_multinomial_logistic_loss_layer.cpp
index 1fc4c42f0f4..b2db984feb1 100644
--- a/src/caffe/test/test_multinomial_logistic_loss_layer.cpp
+++ b/src/caffe/test/test_multinomial_logistic_loss_layer.cpp
@@ -16,7 +16,7 @@
 namespace caffe {
 
 template <typename Dtype>
-class MultinomialLogisticLossLayerTest : public ::testing::Test {
+class MultinomialLogisticLossLayerTest : public CPUDeviceTest<Dtype> {
  protected:
   MultinomialLogisticLossLayerTest()
       : blob_bottom_data_(new Blob<Dtype>(10, 5, 1, 1)),
@@ -51,12 +51,11 @@ TYPED_TEST_CASE(MultinomialLogisticLossLayerTest, TestDtypes);
 
 TYPED_TEST(MultinomialLogisticLossLayerTest, TestGradientCPU) {
   LayerParameter layer_param;
-  Caffe::set_mode(Caffe::CPU);
   MultinomialLogisticLossLayer<TypeParam> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
   GradientChecker<TypeParam> checker(1e-2, 2*1e-2, 1701, 0, 0.05);
-  checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_), 0);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_, 0);
 }
 
 }  // namespace caffe
diff --git a/src/caffe/test/test_mvn_layer.cpp b/src/caffe/test/test_mvn_layer.cpp
index d3d8012f09f..933b4326417 100644
--- a/src/caffe/test/test_mvn_layer.cpp
+++ b/src/caffe/test/test_mvn_layer.cpp
@@ -40,8 +40,8 @@ TYPED_TEST(MVNLayerTest, TestForward) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
   MVNLayer<Dtype> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   // Test mean
   int num = this->blob_bottom_->num();
   int channels = this->blob_bottom_->channels();
@@ -75,8 +75,8 @@ TYPED_TEST(MVNLayerTest, TestForwardMeanOnly) {
   LayerParameter layer_param;
   layer_param.ParseFromString("mvn_param{normalize_variance: false}");
   MVNLayer<Dtype> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   // Test mean
   int num = this->blob_bottom_->num();
   int channels = this->blob_bottom_->channels();
@@ -107,8 +107,8 @@ TYPED_TEST(MVNLayerTest, TestForwardAcrossChannels) {
   LayerParameter layer_param;
   layer_param.ParseFromString("mvn_param{across_channels: true}");
   MVNLayer<Dtype> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   // Test mean
   int num = this->blob_bottom_->num();
   int channels = this->blob_bottom_->channels();
@@ -142,8 +142,8 @@ TYPED_TEST(MVNLayerTest, TestGradient) {
   LayerParameter layer_param;
   MVNLayer<Dtype> layer(layer_param);
   GradientChecker<Dtype> checker(1e-2, 1e-3);
-  checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_));
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
 }
 
 TYPED_TEST(MVNLayerTest, TestGradientMeanOnly) {
@@ -152,8 +152,8 @@ TYPED_TEST(MVNLayerTest, TestGradientMeanOnly) {
   layer_param.ParseFromString("mvn_param{normalize_variance: false}");
   MVNLayer<Dtype> layer(layer_param);
   GradientChecker<Dtype> checker(1e-2, 1e-3);
-  checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_));
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
 }
 
 TYPED_TEST(MVNLayerTest, TestGradientAcrossChannels) {
@@ -162,8 +162,8 @@ TYPED_TEST(MVNLayerTest, TestGradientAcrossChannels) {
   layer_param.ParseFromString("mvn_param{across_channels: true}");
   MVNLayer<Dtype> layer(layer_param);
   GradientChecker<Dtype> checker(1e-2, 1e-3);
-  checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_));
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
 }
 
 }  // namespace caffe
diff --git a/src/caffe/test/test_net.cpp b/src/caffe/test/test_net.cpp
index 9b10d100afb..56959f4793b 100644
--- a/src/caffe/test/test_net.cpp
+++ b/src/caffe/test/test_net.cpp
@@ -59,29 +59,34 @@ class NetTest : public MultiDeviceTest<TypeParam> {
                            const bool accuracy_layer = false) {
     string proto =
         "name: 'TinyTestNetwork' "
-        "layers: { "
+        "layer { "
         "  name: 'data' "
-        "  type: DUMMY_DATA "
+        "  type: 'DummyData' "
         "  dummy_data_param { "
-        "    num: 5 "
-        "    channels: 2 "
-        "    height: 3 "
-        "    width: 4 "
-        "    num: 5 "
-        "    channels: 1 "
-        "    height: 1 "
-        "    width: 1 "
+        "    shape { "
+        "      dim: 5 "
+        "      dim: 2 "
+        "      dim: 3 "
+        "      dim: 4 "
+        "    } "
         "    data_filler { "
         "      type: 'gaussian' "
         "      std: 0.01 "
         "    } "
+        "    shape { "
+        "      dim: 5 "
+        "    } "
+        "    data_filler { "
+        "      type: 'constant' "
+        "      value: 0 "
+        "    } "
         "  } "
         "  top: 'data' "
         "  top: 'label' "
         "} "
-        "layers: { "
+        "layer { "
         "  name: 'innerproduct' "
-        "  type: INNER_PRODUCT "
+        "  type: 'InnerProduct' "
         "  inner_product_param { "
         "    num_output: 1000 "
         "    weight_filler { "
@@ -93,25 +98,29 @@ class NetTest : public MultiDeviceTest<TypeParam> {
         "      value: 0 "
         "    } "
         "  } "
-        "  blobs_lr: 1. "
-        "  blobs_lr: 2. "
-        "  weight_decay: 1. "
-        "  weight_decay: 0. "
+        "  param { "
+        "    lr_mult: 1 "
+        "    decay_mult: 1 "
+        "  } "
+        "  param { "
+        "    lr_mult: 2 "
+        "    decay_mult: 0 "
+        "  } "
         "  bottom: 'data' "
         "  top: 'innerproduct' "
         "} "
-        "layers: { "
+        "layer { "
         "  name: 'loss' "
-        "  type: SOFTMAX_LOSS "
+        "  type: 'SoftmaxWithLoss' "
         "  bottom: 'innerproduct' "
         "  bottom: 'label' "
         "  top: 'top_loss' "
         "} ";
     if (accuracy_layer) {
       proto +=
-          "layers: { "
+          "layer { "
           "  name: 'loss' "
-          "  type: ACCURACY "
+          "  type: 'Accuracy' "
           "  bottom: 'innerproduct' "
           "  bottom: 'label' "
           "  top: 'accuracy' "
@@ -126,9 +135,9 @@ class NetTest : public MultiDeviceTest<TypeParam> {
   virtual void InitTinyNetEuclidean(const bool force_backward = false) {
     string proto =
         "name: 'TinyTestEuclidLossNetwork' "
-        "layers: { "
+        "layer { "
         "  name: 'data' "
-        "  type: DUMMY_DATA "
+        "  type: 'DummyData' "
         "  dummy_data_param { "
         "    num: 5 "
         "    channels: 2 "
@@ -146,9 +155,9 @@ class NetTest : public MultiDeviceTest<TypeParam> {
         "  top: 'data' "
         "  top: 'label' "
         "} "
-        "layers: { "
+        "layer { "
         "  name: 'innerproduct' "
-        "  type: INNER_PRODUCT "
+        "  type: 'InnerProduct' "
         "  inner_product_param { "
         "    num_output: 1 "
         "    weight_filler { "
@@ -160,16 +169,20 @@ class NetTest : public MultiDeviceTest<TypeParam> {
         "      value: 0 "
         "    } "
         "  } "
-        "  blobs_lr: 1. "
-        "  blobs_lr: 2. "
-        "  weight_decay: 1. "
-        "  weight_decay: 0. "
+        "  param { "
+        "    lr_mult: 1 "
+        "    decay_mult: 1 "
+        "  } "
+        "  param { "
+        "    lr_mult: 2 "
+        "    decay_mult: 0 "
+        "  } "
         "  bottom: 'data' "
         "  top: 'innerproduct' "
         "} "
-        "layers: { "
+        "layer { "
         "  name: 'loss' "
-        "  type: EUCLIDEAN_LOSS "
+        "  type: 'EuclideanLoss' "
         "  bottom: 'innerproduct' "
         "  bottom: 'label' "
         "} ";
@@ -186,9 +199,9 @@ class NetTest : public MultiDeviceTest<TypeParam> {
     }
     const string& proto =
         "name: 'TrickyTestNetwork' "
-        "layers: { "
+        "layer { "
         "  name: 'data' "
-        "  type: DUMMY_DATA "
+        "  type: 'DummyData' "
         "  dummy_data_param { "
         "    num: 5 "
         "    channels: 2 "
@@ -206,9 +219,9 @@ class NetTest : public MultiDeviceTest<TypeParam> {
         "  top: 'data' "
         "  top: 'label' "
         "} "
-        "layers: { "
+        "layer { "
         "  name: 'innerproduct' "
-        "  type: INNER_PRODUCT "
+        "  type: 'InnerProduct' "
         "  inner_product_param { "
         "    num_output: 1000 "
         "    weight_filler { "
@@ -220,16 +233,20 @@ class NetTest : public MultiDeviceTest<TypeParam> {
         "      value: 0 "
         "    } "
         "  } "
-        "  blobs_lr: 1. "
-        "  blobs_lr: 2. "
-        "  weight_decay: 1. "
-        "  weight_decay: 0. "
+        "  param { "
+        "    lr_mult: 1 "
+        "    decay_mult: 1 "
+        "  } "
+        "  param { "
+        "    lr_mult: 2 "
+        "    decay_mult: 0 "
+        "  } "
         "  bottom: 'data' "
         "  top: 'transformed_data' "
         "} "
-        "layers: { "
+        "layer { "
         "  name: 'innerproduct' "
-        "  type: INNER_PRODUCT "
+        "  type: 'InnerProduct' "
         "  inner_product_param { "
         "    num_output: 1 "
         "    weight_filler { "
@@ -241,16 +258,20 @@ class NetTest : public MultiDeviceTest<TypeParam> {
         "      value: 0 "
         "    } "
         "  } "
-        "  blobs_lr: 1. "
-        "  blobs_lr: 2. "
-        "  weight_decay: 1. "
-        "  weight_decay: 0. "
+        "  param { "
+        "    lr_mult: 1 "
+        "    decay_mult: 1 "
+        "  } "
+        "  param { "
+        "    lr_mult: 2 "
+        "    decay_mult: 0 "
+        "  } "
         "  bottom: 'label' "
         "  top: 'transformed_label' "
         "} "
-        "layers: { "
+        "layer { "
         "  name: 'loss' "
-        "  type: SOFTMAX_LOSS " +
+        "  type: 'SoftmaxWithLoss' " +
         loss_weight_stream.str() +
         "  bottom: 'transformed_data' "
         "  bottom: 'transformed_label' "
@@ -258,8 +279,8 @@ class NetTest : public MultiDeviceTest<TypeParam> {
     InitNetFromProtoString(proto);
   }
 
-  // loss_weight is the loss weight for the EUCLIDEAN_LOSS layer output.
-  // midnet_loss_weight is the loss weight for the first INNER_PRODUCT layer
+  // loss_weight is the loss weight for the 'EuclideanLoss' layer output.
+  // midnet_loss_weight is the loss weight for the first 'InnerProduct' layer
   // output.  Should both default to 0.0 if unspecified (i.e., if NULL is
   // passed to this function).
   virtual void InitUnsharedWeightsNet(const Dtype* loss_weight = NULL,
@@ -267,15 +288,16 @@ class NetTest : public MultiDeviceTest<TypeParam> {
       const bool force_backward = false, const bool bias_term = false,
       const Dtype blobs_lr_w1 = 1, const Dtype blobs_lr_b1 = 2,
       const Dtype blobs_lr_w2 = 1, const Dtype blobs_lr_b2 = 2) {
+    string bias_str = bias_term ? "true ":"false ";
     ostringstream proto;
     proto << "name: 'UnsharedWeightsNetwork' ";
     if (force_backward) {
       proto << "force_backward: true ";
     }
     proto <<
-        "layers: { "
+        "layer { "
         "  name: 'data' "
-        "  type: DUMMY_DATA "
+        "  type: 'DummyData' "
         "  dummy_data_param { "
         "    num: 5 "
         "    channels: 2 "
@@ -288,25 +310,23 @@ class NetTest : public MultiDeviceTest<TypeParam> {
         "  } "
         "  top: 'data' "
         "} "
-        "layers: { "
+        "layer { "
         "  name: 'innerproduct1' "
-       "  type: INNER_PRODUCT "
+        "  type: 'InnerProduct' "
         "  inner_product_param { "
         "    num_output: 10 "
-        "    bias_term: " << bias_term <<
+        "    bias_term: " << bias_str <<
         "    weight_filler { "
         "      type: 'gaussian' "
         "      std: 10 "
         "    } "
         "  } "
-        "  param: 'unsharedweights1' ";
-    if (bias_term) {
-      proto << "  param: '' ";
-    }
-    proto <<
-        "  blobs_lr: " << blobs_lr_w1;
+        "  param { "
+        "    name: 'unsharedweights1' "
+        "    lr_mult: " << blobs_lr_w1 <<
+        "  } ";
     if (bias_term) {
-      proto << "  blobs_lr: " << blobs_lr_b1;
+      proto << "  param { lr_mult: " << blobs_lr_b1 << " } ";
     }
     proto <<
         "  bottom: 'data' "
@@ -316,33 +336,31 @@ class NetTest : public MultiDeviceTest<TypeParam> {
     }
     proto <<
         "} "
-        "layers: { "
+        "layer { "
         "  name: 'innerproduct2' "
-        "  type: INNER_PRODUCT "
+        "  type: 'InnerProduct' "
         "  inner_product_param { "
         "    num_output: 10 "
-        "    bias_term: " << bias_term <<
+        "    bias_term: " << bias_str <<
         "    weight_filler { "
         "      type: 'gaussian' "
         "      std: 10 "
         "    } "
         "  } "
-        "  param: 'unsharedweights2' ";
+        "  param { "
+        "    name: 'unsharedweights2' "
+        "    lr_mult: " << blobs_lr_w2 <<
+        "  } ";
     if (bias_term) {
-      proto << "  param: '' ";
+      proto << "  param { lr_mult: " << blobs_lr_b2 << " } ";
     }
     proto <<
         "  bottom: 'data' "
-        "  blobs_lr: " << blobs_lr_w2;
-    if (bias_term) {
-      proto << "  blobs_lr: " << blobs_lr_b2;
-    }
-    proto <<
         "  top: 'innerproduct2' "
         "} "
-        "layers: { "
+        "layer { "
         "  name: 'loss' "
-        "  type: EUCLIDEAN_LOSS ";
+        "  type: 'EuclideanLoss' ";
     if (loss_weight) {
       proto << "  loss_weight: " << *loss_weight << " ";
     }
@@ -356,9 +374,9 @@ class NetTest : public MultiDeviceTest<TypeParam> {
   virtual void InitSharedWeightsNet() {
     const string& proto =
         "name: 'SharedWeightsNetwork' "
-        "layers: { "
+        "layer { "
         "  name: 'data' "
-        "  type: DUMMY_DATA "
+        "  type: 'DummyData' "
         "  dummy_data_param { "
         "    num: 5 "
         "    channels: 2 "
@@ -371,9 +389,9 @@ class NetTest : public MultiDeviceTest<TypeParam> {
         "  } "
         "  top: 'data' "
         "} "
-        "layers: { "
+        "layer { "
         "  name: 'innerproduct1' "
-        "  type: INNER_PRODUCT "
+        "  type: 'InnerProduct' "
         "  inner_product_param { "
         "    num_output: 10 "
         "    bias_term: false "
@@ -382,13 +400,13 @@ class NetTest : public MultiDeviceTest<TypeParam> {
         "      std: 10 "
         "    } "
         "  } "
-        "  param: 'sharedweights' "
+        "  param { name: 'sharedweights' } "
         "  bottom: 'data' "
         "  top: 'innerproduct1' "
         "} "
-        "layers: { "
+        "layer { "
         "  name: 'innerproduct2' "
-        "  type: INNER_PRODUCT "
+        "  type: 'InnerProduct' "
         "  inner_product_param { "
         "    num_output: 10 "
         "    bias_term: false "
@@ -397,13 +415,13 @@ class NetTest : public MultiDeviceTest<TypeParam> {
         "      std: 10 "
         "    } "
         "  } "
-        "  param: 'sharedweights' "
+        "  param { name: 'sharedweights' } "
         "  bottom: 'data' "
         "  top: 'innerproduct2' "
         "} "
-        "layers: { "
+        "layer { "
         "  name: 'loss' "
-        "  type: EUCLIDEAN_LOSS "
+        "  type: 'EuclideanLoss' "
         "  bottom: 'innerproduct1' "
         "  bottom: 'innerproduct2' "
         "} ";
@@ -413,9 +431,9 @@ class NetTest : public MultiDeviceTest<TypeParam> {
   virtual void InitDiffDataUnsharedWeightsNet() {
     const string& proto =
         "name: 'DiffDataUnsharedWeightsNetwork' "
-        "layers: { "
+        "layer { "
         "  name: 'data' "
-        "  type: DUMMY_DATA "
+        "  type: 'DummyData' "
         "  dummy_data_param { "
         "    num: 10 "
         "    channels: 10 "
@@ -433,9 +451,9 @@ class NetTest : public MultiDeviceTest<TypeParam> {
         "  top: 'data1' "
         "  top: 'data2' "
         "} "
-        "layers: { "
+        "layer { "
         "  name: 'innerproduct1' "
-        "  type: INNER_PRODUCT "
+        "  type: 'InnerProduct' "
         "  inner_product_param { "
         "    num_output: 10 "
         "    bias_term: false "
@@ -444,13 +462,13 @@ class NetTest : public MultiDeviceTest<TypeParam> {
         "      value: 0.5 "
         "    } "
         "  } "
-        "  param: 'unsharedweights1' "
+        "  param { name: 'unsharedweights1' } "
         "  bottom: 'data1' "
         "  top: 'innerproduct1' "
         "} "
-        "layers: { "
+        "layer { "
         "  name: 'innerproduct2' "
-        "  type: INNER_PRODUCT "
+        "  type: 'InnerProduct' "
         "  inner_product_param { "
         "    num_output: 10 "
         "    bias_term: false "
@@ -459,13 +477,13 @@ class NetTest : public MultiDeviceTest<TypeParam> {
         "      value: 0.5 "
         "    } "
         "  } "
-        "  param: 'unsharedweights2' "
+        "  param { name: 'unsharedweights2' } "
         "  bottom: 'innerproduct1' "
         "  top: 'innerproduct2' "
         "} "
-        "layers: { "
+        "layer { "
         "  name: 'loss' "
-        "  type: EUCLIDEAN_LOSS "
+        "  type: 'EuclideanLoss' "
         "  bottom: 'data2' "
         "  bottom: 'innerproduct2' "
         "} ";
@@ -475,9 +493,9 @@ class NetTest : public MultiDeviceTest<TypeParam> {
   virtual void InitDiffDataSharedWeightsNet() {
     const string& proto =
         "name: 'DiffDataSharedWeightsNetwork' "
-        "layers: { "
+        "layer { "
         "  name: 'data' "
-        "  type: DUMMY_DATA "
+        "  type: 'DummyData' "
         "  dummy_data_param { "
         "    num: 10 "
         "    channels: 10 "
@@ -495,9 +513,9 @@ class NetTest : public MultiDeviceTest<TypeParam> {
         "  top: 'data1' "
         "  top: 'data2' "
         "} "
-        "layers: { "
+        "layer { "
         "  name: 'innerproduct1' "
-        "  type: INNER_PRODUCT "
+        "  type: 'InnerProduct' "
         "  inner_product_param { "
         "    num_output: 10 "
         "    bias_term: false "
@@ -506,13 +524,13 @@ class NetTest : public MultiDeviceTest<TypeParam> {
         "      value: 0.5 "
         "    } "
         "  } "
-        "  param: 'sharedweights' "
+        "  param { name: 'sharedweights' } "
         "  bottom: 'data1' "
         "  top: 'innerproduct1' "
         "} "
-        "layers: { "
+        "layer { "
         "  name: 'innerproduct2' "
-        "  type: INNER_PRODUCT "
+        "  type: 'InnerProduct' "
         "  inner_product_param { "
         "    num_output: 10 "
         "    bias_term: false "
@@ -521,13 +539,13 @@ class NetTest : public MultiDeviceTest<TypeParam> {
         "      value: 0.5 "
         "    } "
         "  } "
-        "  param: 'sharedweights' "
+        "  param { name: 'sharedweights' } "
         "  bottom: 'innerproduct1' "
         "  top: 'innerproduct2' "
         "} "
-        "layers: { "
+        "layer { "
         "  name: 'loss' "
-        "  type: EUCLIDEAN_LOSS "
+        "  type: 'EuclideanLoss' "
         "  bottom: 'data2' "
         "  bottom: 'innerproduct2' "
         "} ";
@@ -542,9 +560,9 @@ class NetTest : public MultiDeviceTest<TypeParam> {
         "input_dim: 3 "
         "input_dim: 100 "
         "input_dim: 100 "
-        "layers: { "
+        "layer { "
         "  name: 'conv1' "
-        "  type: CONVOLUTION "
+        "  type: 'Convolution' "
         "  bottom: 'data' "
         "  top: 'conv1' "
         "  convolution_param { "
@@ -561,15 +579,15 @@ class NetTest : public MultiDeviceTest<TypeParam> {
         "    } "
         "  } "
         "} "
-        "layers: { "
+        "layer { "
         "  name: 'relu1' "
-        "  type: RELU "
+        "  type: 'ReLU' "
         "  bottom: 'conv1' "
         "  top: 'conv1' "
         "} "
-        "layers: { "
+        "layer { "
         "  name: 'pool1' "
-        "  type: POOLING "
+        "  type: 'Pooling' "
         "  bottom: 'conv1' "
         "  top: 'pool1' "
         "  pooling_param { "
@@ -578,24 +596,123 @@ class NetTest : public MultiDeviceTest<TypeParam> {
         "    stride: 2 "
         "  } "
         "} "
-        "layers: { "
+        "layer { "
         "  name: 'norm1' "
-        "  type: LRN "
+        "  type: 'LRN' "
         "  bottom: 'pool1' "
         "  top: 'norm1' "
         "  lrn_param { "
         "    local_size: 3 "
         "  } "
         "} "
-        "layers: { "
+        "layer { "
         "  name: 'softmax' "
-        "  type: SOFTMAX "
+        "  type: 'Softmax' "
         "  bottom: 'norm1' "
         "  top: 'softmax' "
         "} ";
     InitNetFromProtoString(proto);
   }
 
+  virtual void InitSkipPropNet(bool test_skip_true) {
+    string proto =
+      "name: 'SkipPropTestNetwork' "
+      "layer { "
+      "  name: 'data' "
+      "  type: 'DummyData' "
+      "  dummy_data_param { "
+      "    shape { "
+      "      dim: 5 "
+      "      dim: 2 "
+      "      dim: 3 "
+      "      dim: 4 "
+      "    } "
+      "    data_filler { "
+      "      type: 'gaussian' "
+      "      std: 0.01 "
+      "    } "
+      "    shape { "
+      "      dim: 5 "
+      "    } "
+      "    data_filler { "
+      "      type: 'constant' "
+      "      value: 0 "
+      "    } "
+      "  } "
+      "  top: 'data' "
+      "  top: 'label' "
+      "} "
+      "layer { "
+      "  name: 'silence' "
+      "  bottom: 'label' "
+      "  type: 'Silence' "
+      "} "
+      "layer { "
+      "  name: 'innerproduct' "
+      "  type: 'InnerProduct' "
+      "  inner_product_param { "
+      "    num_output: 1 "
+      "    weight_filler { "
+      "      type: 'gaussian' "
+      "      std: 0.01 "
+      "    } "
+      "    bias_filler { "
+      "      type: 'constant' "
+      "      value: 0 "
+      "    } "
+      "  } "
+      "  param { "
+      "    lr_mult: 1 "
+      "    decay_mult: 1 "
+      "  } "
+      "  param { "
+      "    lr_mult: 2 "
+      "    decay_mult: 0 "
+      "  } "
+      "  bottom: 'data' "
+      "  top: 'innerproduct' "
+      "} "
+      "layer { "
+      "  name: 'ip_fake_labels' "
+      "  type: 'InnerProduct' "
+      "  inner_product_param { "
+      "    num_output: 1 "
+      "    weight_filler { "
+      "      type: 'gaussian' "
+      "      std: 0.01 "
+      "    } "
+      "    bias_filler { "
+      "      type: 'constant' "
+      "      value: 0 "
+      "    } "
+      "  } "
+      "  bottom: 'data' "
+      "  top: 'fake_labels' "
+      "} "
+      "layer { "
+      "  name: 'argmax' "
+      "  bottom: 'fake_labels' "
+      "  top: 'label_argmax' "
+      "  type: 'ArgMax' "
+      "} "
+      "layer { "
+      "  name: 'loss' "
+      "  bottom: 'innerproduct' "
+      "  bottom: 'label_argmax' ";
+    if (test_skip_true)
+      proto += "  propagate_down: true "
+               "  propagate_down: false ";
+    else
+      proto += "  propagate_down: true "
+               "  propagate_down: true ";
+    proto +=
+      "  top: 'cross_entropy_loss' "
+      "  type: 'SigmoidCrossEntropyLoss' "
+      "  loss_weight: 0.1 "
+      "} ";
+    InitNetFromProtoString(proto);
+  }
+
   int seed_;
   shared_ptr<Net<Dtype> > net_;
 };
@@ -698,7 +815,7 @@ TYPED_TEST(NetTest, TestBottomNeedBackwardTricky) {
 TYPED_TEST(NetTest, TestLossWeight) {
   typedef typename TypeParam::Dtype Dtype;
   // First, compute the loss and gradients with no loss_weight specified.
-  // In this case, the loss weight for the EUCLIDEAN_LOSS layer should default
+  // In this case, the loss weight for the 'EuclideanLoss' layer should default
   // to 1.
   vector<Blob<Dtype>*> bottom;
   Caffe::set_random_seed(this->seed_);
@@ -792,8 +909,8 @@ TYPED_TEST(NetTest, TestComboLossWeight) {
   const bool kForceBackward = true;
   const Dtype kErrorMargin = 1e-4;
 
-  // Get the loss and gradients with EUCLIDEAN_LOSS weight 1,
-  // INNER_PRODUCT weight 1.
+  // Get the loss and gradients with 'EuclideanLoss' weight 1,
+  // 'InnerProduct' weight 1.
   loss_weight = 1;
   midnet_loss_weight = 1;
   Caffe::set_random_seed(this->seed_);
@@ -921,7 +1038,7 @@ TYPED_TEST(NetTest, TestBackwardWithAccuracyLayer) {
   this->InitTinyNet(kForceBackward, kAccuracyLayer);
   EXPECT_TRUE(this->net_->has_blob("accuracy"));
   vector<Blob<Dtype>*> bottom;
-  // Test that we can do Backward even though we have an ACCURACY layer.
+  // Test that we can do Backward even though we have an 'Accuracy' layer.
   this->net_->ForwardBackward(bottom);
 }
 
@@ -993,7 +1110,7 @@ TYPED_TEST(NetTest, TestSharedWeightsUpdate) {
   // Check that data blobs of shared weights share the same location in memory.
   EXPECT_EQ(ip1_weights->cpu_data(), ip2_weights->cpu_data());
   // Check that diff blobs of shared weights are at different locations in
-  // locations.  (The diffs should be accumulated at update time.)
+  // memory.  (The diffs should be accumulated at update time.)
   EXPECT_NE(ip1_weights->cpu_diff(), ip2_weights->cpu_diff());
   this->net_->Forward(bottom);
   this->net_->Backward();
@@ -1068,6 +1185,54 @@ TYPED_TEST(NetTest, TestSharedWeightsUpdate) {
   }
 }
 
+TYPED_TEST(NetTest, TestSharedWeightsResume) {
+  typedef typename TypeParam::Dtype Dtype;
+
+  // Create a net with weight sharing; Update it once.
+  Caffe::set_random_seed(this->seed_);
+  this->InitDiffDataSharedWeightsNet();
+  vector<Blob<Dtype>*> bottom;
+  EXPECT_EQ(this->net_->layer_names()[1], "innerproduct1");
+  EXPECT_EQ(this->net_->layer_names()[2], "innerproduct2");
+  Blob<Dtype>* ip1_weights = this->net_->layers()[1]->blobs()[0].get();
+  Blob<Dtype>* ip2_weights = this->net_->layers()[2]->blobs()[0].get();
+  // Check that data blobs of shared weights share the same location in memory.
+  EXPECT_EQ(ip1_weights->cpu_data(), ip2_weights->cpu_data());
+  // Check that diff blobs of shared weights are at different locations in
+  // memory.  (The diffs should be accumulated at update time.)
+  EXPECT_NE(ip1_weights->cpu_diff(), ip2_weights->cpu_diff());
+  this->net_->ForwardBackward(bottom);
+  this->net_->Update();
+  Blob<Dtype> shared_params;
+  const bool kReshape = true;
+  const bool kCopyDiff = false;
+  shared_params.CopyFrom(*ip1_weights, kCopyDiff, kReshape);
+  const int count = ip1_weights->count();
+
+  // Write the net to a NetParameter, as in Solver::Snapshot.
+  NetParameter net_param;
+  this->net_->ToProto(&net_param);
+
+  // Reinitialize the net and copy parameters from net_param, as in
+  // Solver::Restore.
+  Caffe::set_random_seed(this->seed_);
+  this->InitDiffDataSharedWeightsNet();
+  this->net_->CopyTrainedLayersFrom(net_param);
+  ip1_weights = this->net_->layers()[1]->blobs()[0].get();
+  ip2_weights = this->net_->layers()[2]->blobs()[0].get();
+  ASSERT_FALSE(NULL == ip1_weights);
+  ASSERT_FALSE(NULL == ip2_weights);
+  EXPECT_NE(ip1_weights, ip2_weights);
+  // Check that data blobs of shared weights share the same location in memory.
+  EXPECT_EQ(ip1_weights->cpu_data(), ip2_weights->cpu_data());
+  for (int i = 0; i < count; ++i) {
+    EXPECT_FLOAT_EQ(shared_params.cpu_data()[i], ip1_weights->cpu_data()[i]);
+  }
+  // Check that diff blobs of shared weights are at different locations in
+  // memory.  (The diffs should be accumulated at update time.)
+  EXPECT_NE(ip1_weights->cpu_diff(), ip2_weights->cpu_diff());
+}
+
 TYPED_TEST(NetTest, TestParamPropagateDown) {
   typedef typename TypeParam::Dtype Dtype;
   vector<Blob<Dtype>*> bottom;
@@ -1107,7 +1272,7 @@ TYPED_TEST(NetTest, TestParamPropagateDown) {
   for (int i = 0; i < num_params; ++i) {
     const Dtype param_asum =
        caffe_cpu_asum(params2[i]->count(), params2[i]->cpu_diff());
-    EXPECT_EQ(param_asum, param_asums[i]);
+    EXPECT_FLOAT_EQ(param_asum, param_asums[i]);
   }
 
   // Change a subset of the learning rates to zero; check that we see zero
@@ -1124,9 +1289,9 @@ TYPED_TEST(NetTest, TestParamPropagateDown) {
     const Dtype param_asum =
        caffe_cpu_asum(params3[i]->count(), params3[i]->cpu_diff());
     if (i == 1 || i == 2) {
-      EXPECT_EQ(0, param_asum);
+      EXPECT_FLOAT_EQ(0, param_asum);
     } else {
-      EXPECT_EQ(param_asum, param_asums[i]);
+      EXPECT_FLOAT_EQ(param_asum, param_asums[i]);
     }
   }
 
@@ -1143,9 +1308,9 @@ TYPED_TEST(NetTest, TestParamPropagateDown) {
     const Dtype param_asum =
        caffe_cpu_asum(params4[i]->count(), params4[i]->cpu_diff());
     if (i == 0 || i == 3) {
-      EXPECT_EQ(0, param_asum);
+      EXPECT_FLOAT_EQ(0, param_asum);
     } else {
-      EXPECT_EQ(param_asum, param_asums[i]);
+      EXPECT_FLOAT_EQ(param_asum, param_asums[i]);
     }
   }
 }
@@ -1209,21 +1374,21 @@ class FilterNetTest : public ::testing::Test {
 TEST_F(FilterNetTest, TestNoFilter) {
   const string& input_proto =
       "name: 'TestNetwork' "
-      "layers: { "
+      "layer { "
       "  name: 'data' "
-      "  type: DATA "
+      "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'innerprod' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  bottom: 'data' "
       "  top: 'innerprod' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'loss' "
-      "  type: SOFTMAX_LOSS "
+      "  type: 'SoftmaxWithLoss' "
       "  bottom: 'innerprod' "
       "  bottom: 'label' "
       "} ";
@@ -1233,9 +1398,9 @@ TEST_F(FilterNetTest, TestNoFilter) {
 TEST_F(FilterNetTest, TestFilterLeNetTrainTest) {
   const string& input_proto =
       "name: 'LeNet' "
-      "layers { "
+      "layer { "
       "  name: 'mnist' "
-      "  type: DATA "
+      "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "  data_param { "
@@ -1247,9 +1412,9 @@ TEST_F(FilterNetTest, TestFilterLeNetTrainTest) {
       "  } "
       "  include: { phase: TRAIN } "
       "} "
-      "layers { "
+      "layer { "
       "  name: 'mnist' "
-      "  type: DATA "
+      "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "  data_param { "
@@ -1261,13 +1426,17 @@ TEST_F(FilterNetTest, TestFilterLeNetTrainTest) {
       "  } "
       "  include: { phase: TEST } "
       "} "
-      "layers { "
+      "layer { "
       "  name: 'conv1' "
-      "  type: CONVOLUTION "
+      "  type: 'Convolution' "
       "  bottom: 'data' "
       "  top: 'conv1' "
-      "  blobs_lr: 1 "
-      "  blobs_lr: 2 "
+      "  param { "
+      "    lr_mult: 1 "
+      "  } "
+      "  param { "
+      "    lr_mult: 2 "
+      "  } "
       "  convolution_param { "
       "    num_output: 20 "
       "    kernel_size: 5 "
@@ -1280,13 +1449,17 @@ TEST_F(FilterNetTest, TestFilterLeNetTrainTest) {
       "    } "
       "  } "
       "} "
-      "layers { "
+      "layer { "
       "  name: 'ip1' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  bottom: 'conv1' "
       "  top: 'ip1' "
-      "  blobs_lr: 1 "
-      "  blobs_lr: 2 "
+      "  param { "
+      "    lr_mult: 1 "
+      "  } "
+      "  param { "
+      "    lr_mult: 2 "
+      "  } "
       "  inner_product_param { "
       "    num_output: 10 "
       "    weight_filler { "
@@ -1297,17 +1470,17 @@ TEST_F(FilterNetTest, TestFilterLeNetTrainTest) {
       "    } "
       "  } "
       "} "
-      "layers { "
+      "layer { "
       "  name: 'accuracy' "
-      "  type: ACCURACY "
+      "  type: 'Accuracy' "
       "  bottom: 'ip1' "
       "  bottom: 'label' "
       "  top: 'accuracy' "
       "  include: { phase: TEST } "
       "} "
-      "layers { "
+      "layer { "
       "  name: 'loss' "
-      "  type: SOFTMAX_LOSS "
+      "  type: 'SoftmaxWithLoss' "
       "  bottom: 'ip2' "
       "  bottom: 'label' "
       "  top: 'loss' "
@@ -1316,9 +1489,9 @@ TEST_F(FilterNetTest, TestFilterLeNetTrainTest) {
   const string input_proto_test = "state: { phase: TEST } " + input_proto;
   const string output_proto_train =
       "name: 'LeNet' "
-      "layers { "
+      "layer { "
       "  name: 'mnist' "
-      "  type: DATA "
+      "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "  data_param { "
@@ -1330,13 +1503,17 @@ TEST_F(FilterNetTest, TestFilterLeNetTrainTest) {
       "  } "
       "  include: { phase: TRAIN } "
       "} "
-      "layers { "
+      "layer { "
       "  name: 'conv1' "
-      "  type: CONVOLUTION "
+      "  type: 'Convolution' "
       "  bottom: 'data' "
       "  top: 'conv1' "
-      "  blobs_lr: 1 "
-      "  blobs_lr: 2 "
+      "  param { "
+      "    lr_mult: 1 "
+      "  } "
+      "  param { "
+      "    lr_mult: 2 "
+      "  } "
       "  convolution_param { "
       "    num_output: 20 "
       "    kernel_size: 5 "
@@ -1349,13 +1526,17 @@ TEST_F(FilterNetTest, TestFilterLeNetTrainTest) {
       "    } "
       "  } "
       "} "
-      "layers { "
+      "layer { "
       "  name: 'ip1' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  bottom: 'conv1' "
       "  top: 'ip1' "
-      "  blobs_lr: 1 "
-      "  blobs_lr: 2 "
+      "  param { "
+      "    lr_mult: 1 "
+      "  } "
+      "  param { "
+      "    lr_mult: 2 "
+      "  } "
       "  inner_product_param { "
       "    num_output: 10 "
       "    weight_filler { "
@@ -1366,18 +1547,18 @@ TEST_F(FilterNetTest, TestFilterLeNetTrainTest) {
       "    } "
       "  } "
       "} "
-      "layers { "
+      "layer { "
       "  name: 'loss' "
-      "  type: SOFTMAX_LOSS "
+      "  type: 'SoftmaxWithLoss' "
       "  bottom: 'ip2' "
       "  bottom: 'label' "
       "  top: 'loss' "
       "} ";
   const string& output_proto_test =
       "name: 'LeNet' "
-      "layers { "
+      "layer { "
       "  name: 'mnist' "
-      "  type: DATA "
+      "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "  data_param { "
@@ -1389,13 +1570,17 @@ TEST_F(FilterNetTest, TestFilterLeNetTrainTest) {
       "  } "
       "  include: { phase: TEST } "
       "} "
-      "layers { "
+      "layer { "
       "  name: 'conv1' "
-      "  type: CONVOLUTION "
+      "  type: 'Convolution' "
       "  bottom: 'data' "
       "  top: 'conv1' "
-      "  blobs_lr: 1 "
-      "  blobs_lr: 2 "
+      "  param { "
+      "    lr_mult: 1 "
+      "  } "
+      "  param { "
+      "    lr_mult: 2 "
+      "  } "
       "  convolution_param { "
       "    num_output: 20 "
       "    kernel_size: 5 "
@@ -1408,13 +1593,17 @@ TEST_F(FilterNetTest, TestFilterLeNetTrainTest) {
       "    } "
       "  } "
       "} "
-      "layers { "
+      "layer { "
       "  name: 'ip1' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  bottom: 'conv1' "
       "  top: 'ip1' "
-      "  blobs_lr: 1 "
-      "  blobs_lr: 2 "
+      "  param { "
+      "    lr_mult: 1 "
+      "  } "
+      "  param { "
+      "    lr_mult: 2 "
+      "  } "
       "  inner_product_param { "
       "    num_output: 10 "
       "    weight_filler { "
@@ -1425,17 +1614,17 @@ TEST_F(FilterNetTest, TestFilterLeNetTrainTest) {
       "    } "
       "  } "
       "} "
-      "layers { "
+      "layer { "
       "  name: 'accuracy' "
-      "  type: ACCURACY "
+      "  type: 'Accuracy' "
       "  bottom: 'ip1' "
       "  bottom: 'label' "
       "  top: 'accuracy' "
       "  include: { phase: TEST } "
       "} "
-      "layers { "
+      "layer { "
       "  name: 'loss' "
-      "  type: SOFTMAX_LOSS "
+      "  type: 'SoftmaxWithLoss' "
       "  bottom: 'ip2' "
       "  bottom: 'label' "
       "  top: 'loss' "
@@ -1446,55 +1635,41 @@ TEST_F(FilterNetTest, TestFilterLeNetTrainTest) {
       output_proto_test + " state: { phase: TEST } ";
   this->RunFilterNetTest(input_proto_train, output_proto_train_explicit);
   this->RunFilterNetTest(input_proto_test, output_proto_test_explicit);
-
-  // Also check that nets are filtered according to the Caffe singleton phase,
-  // if not explicitly specified in the input proto.
-  Caffe::set_phase(Caffe::TRAIN);
-  this->RunFilterNetTest(input_proto, output_proto_train);
-  Caffe::set_phase(Caffe::TEST);
-  this->RunFilterNetTest(input_proto, output_proto_test);
-
-  // Finally, check that the current Caffe singleton phase is ignored if the
-  // phase is explicitly specified in the input proto.
-  Caffe::set_phase(Caffe::TEST);
-  this->RunFilterNetTest(input_proto_train, output_proto_train_explicit);
-  Caffe::set_phase(Caffe::TRAIN);
-  this->RunFilterNetTest(input_proto_test, output_proto_test_explicit);
 }
 
 TEST_F(FilterNetTest, TestFilterOutByStage) {
   const string& input_proto =
       "name: 'TestNetwork' "
-      "layers: { "
+      "layer { "
       "  name: 'data' "
-      "  type: DATA "
+      "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "  include: { stage: 'mystage' } "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'innerprod' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  bottom: 'data' "
       "  top: 'innerprod' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'loss' "
-      "  type: SOFTMAX_LOSS "
+      "  type: 'SoftmaxWithLoss' "
       "  bottom: 'innerprod' "
       "  bottom: 'label' "
       "} ";
   const string& output_proto =
       "name: 'TestNetwork' "
-      "layers: { "
+      "layer { "
       "  name: 'innerprod' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  bottom: 'data' "
       "  top: 'innerprod' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'loss' "
-      "  type: SOFTMAX_LOSS "
+      "  type: 'SoftmaxWithLoss' "
       "  bottom: 'innerprod' "
       "  bottom: 'label' "
       "} ";
@@ -1504,36 +1679,36 @@ TEST_F(FilterNetTest, TestFilterOutByStage) {
 TEST_F(FilterNetTest, TestFilterOutByStage2) {
   const string& input_proto =
       "name: 'TestNetwork' "
-      "layers: { "
+      "layer { "
       "  name: 'data' "
-      "  type: DATA "
+      "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'innerprod' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  bottom: 'data' "
       "  top: 'innerprod' "
       "  include: { stage: 'mystage' } "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'loss' "
-      "  type: SOFTMAX_LOSS "
+      "  type: 'SoftmaxWithLoss' "
       "  bottom: 'innerprod' "
       "  bottom: 'label' "
       "} ";
   const string& output_proto =
       "name: 'TestNetwork' "
-      "layers: { "
+      "layer { "
       "  name: 'data' "
-      "  type: DATA "
+      "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'loss' "
-      "  type: SOFTMAX_LOSS "
+      "  type: 'SoftmaxWithLoss' "
       "  bottom: 'innerprod' "
       "  bottom: 'label' "
       "} ";
@@ -1544,22 +1719,22 @@ TEST_F(FilterNetTest, TestFilterInByStage) {
   const string& input_proto =
       "state: { stage: 'mystage' } "
       "name: 'TestNetwork' "
-      "layers: { "
+      "layer { "
       "  name: 'data' "
-      "  type: DATA "
+      "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'innerprod' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  bottom: 'data' "
       "  top: 'innerprod' "
       "  include: { stage: 'mystage' } "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'loss' "
-      "  type: SOFTMAX_LOSS "
+      "  type: 'SoftmaxWithLoss' "
       "  bottom: 'innerprod' "
       "  bottom: 'label' "
       "} ";
@@ -1569,22 +1744,22 @@ TEST_F(FilterNetTest, TestFilterInByStage) {
 TEST_F(FilterNetTest, TestFilterInByStage2) {
   const string& input_proto =
       "name: 'TestNetwork' "
-      "layers: { "
+      "layer { "
       "  name: 'data' "
-      "  type: DATA "
+      "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'innerprod' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  bottom: 'data' "
       "  top: 'innerprod' "
       "  exclude: { stage: 'mystage' } "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'loss' "
-      "  type: SOFTMAX_LOSS "
+      "  type: 'SoftmaxWithLoss' "
       "  bottom: 'innerprod' "
       "  bottom: 'label' "
       "} ";
@@ -1595,22 +1770,22 @@ TEST_F(FilterNetTest, TestFilterOutByMultipleStage) {
   const string& input_proto =
       "state: { stage: 'mystage' } "
       "name: 'TestNetwork' "
-      "layers: { "
+      "layer { "
       "  name: 'data' "
-      "  type: DATA "
+      "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'innerprod' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  bottom: 'data' "
       "  top: 'innerprod' "
       "  include: { stage: 'mystage' stage: 'myotherstage' } "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'loss' "
-      "  type: SOFTMAX_LOSS "
+      "  type: 'SoftmaxWithLoss' "
       "  bottom: 'innerprod' "
       "  bottom: 'label' "
       "  include: { stage: 'mystage' } "
@@ -1618,15 +1793,15 @@ TEST_F(FilterNetTest, TestFilterOutByMultipleStage) {
   const string& output_proto =
       "state: { stage: 'mystage' } "
       "name: 'TestNetwork' "
-      "layers: { "
+      "layer { "
       "  name: 'data' "
-      "  type: DATA "
+      "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'loss' "
-      "  type: SOFTMAX_LOSS "
+      "  type: 'SoftmaxWithLoss' "
       "  bottom: 'innerprod' "
       "  bottom: 'label' "
       "  include: { stage: 'mystage' } "
@@ -1638,23 +1813,23 @@ TEST_F(FilterNetTest, TestFilterInByMultipleStage) {
   const string& input_proto =
       "state: { stage: 'mystage' } "
       "name: 'TestNetwork' "
-      "layers: { "
+      "layer { "
       "  name: 'data' "
-      "  type: DATA "
+      "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'innerprod' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  bottom: 'data' "
       "  top: 'innerprod' "
       "  include: { stage: 'myotherstage' } "
       "  include: { stage: 'mystage' } "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'loss' "
-      "  type: SOFTMAX_LOSS "
+      "  type: 'SoftmaxWithLoss' "
       "  bottom: 'innerprod' "
       "  bottom: 'label' "
       "  include: { stage: 'mystage' } "
@@ -1666,22 +1841,22 @@ TEST_F(FilterNetTest, TestFilterInByMultipleStage2) {
   const string& input_proto =
       "state: { stage: 'mystage' stage: 'myotherstage' } "
       "name: 'TestNetwork' "
-      "layers: { "
+      "layer { "
       "  name: 'data' "
-      "  type: DATA "
+      "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'innerprod' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  bottom: 'data' "
       "  top: 'innerprod' "
       "  include: { stage: 'mystage' stage: 'myotherstage' } "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'loss' "
-      "  type: SOFTMAX_LOSS "
+      "  type: 'SoftmaxWithLoss' "
       "  bottom: 'innerprod' "
       "  bottom: 'label' "
       "  include: { stage: 'mystage' } "
@@ -1693,22 +1868,22 @@ TEST_F(FilterNetTest, TestFilterInByNotStage) {
   const string& input_proto =
       "state: { stage: 'mystage' } "
       "name: 'TestNetwork' "
-      "layers: { "
+      "layer { "
       "  name: 'data' "
-      "  type: DATA "
+      "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'innerprod' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  bottom: 'data' "
       "  top: 'innerprod' "
       "  include: { not_stage: 'myotherstage' } "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'loss' "
-      "  type: SOFTMAX_LOSS "
+      "  type: 'SoftmaxWithLoss' "
       "  bottom: 'innerprod' "
       "  bottom: 'label' "
       "  include: { not_stage: 'myotherstage' } "
@@ -1720,22 +1895,22 @@ TEST_F(FilterNetTest, TestFilterOutByNotStage) {
   const string& input_proto =
       "state: { stage: 'mystage' } "
       "name: 'TestNetwork' "
-      "layers: { "
+      "layer { "
       "  name: 'data' "
-      "  type: DATA "
+      "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'innerprod' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  bottom: 'data' "
       "  top: 'innerprod' "
       "  include: { not_stage: 'mystage' } "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'loss' "
-      "  type: SOFTMAX_LOSS "
+      "  type: 'SoftmaxWithLoss' "
       "  bottom: 'innerprod' "
       "  bottom: 'label' "
       "  include: { not_stage: 'mystage' } "
@@ -1743,9 +1918,9 @@ TEST_F(FilterNetTest, TestFilterOutByNotStage) {
   const string& output_proto =
       "state: { stage: 'mystage' } "
       "name: 'TestNetwork' "
-      "layers: { "
+      "layer { "
       "  name: 'data' "
-      "  type: DATA "
+      "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "} ";
@@ -1755,36 +1930,36 @@ TEST_F(FilterNetTest, TestFilterOutByNotStage) {
 TEST_F(FilterNetTest, TestFilterOutByMinLevel) {
   const string& input_proto =
       "name: 'TestNetwork' "
-      "layers: { "
+      "layer { "
       "  name: 'data' "
-      "  type: DATA "
+      "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'innerprod' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  bottom: 'data' "
       "  top: 'innerprod' "
       "  include: { min_level: 3 } "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'loss' "
-      "  type: SOFTMAX_LOSS "
+      "  type: 'SoftmaxWithLoss' "
       "  bottom: 'innerprod' "
       "  bottom: 'label' "
       "} ";
   const string& output_proto =
       "name: 'TestNetwork' "
-      "layers: { "
+      "layer { "
       "  name: 'data' "
-      "  type: DATA "
+      "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'loss' "
-      "  type: SOFTMAX_LOSS "
+      "  type: 'SoftmaxWithLoss' "
       "  bottom: 'innerprod' "
       "  bottom: 'label' "
       "} ";
@@ -1794,36 +1969,36 @@ TEST_F(FilterNetTest, TestFilterOutByMinLevel) {
 TEST_F(FilterNetTest, TestFilterOutByMaxLevel) {
   const string& input_proto =
       "name: 'TestNetwork' "
-      "layers: { "
+      "layer { "
       "  name: 'data' "
-      "  type: DATA "
+      "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'innerprod' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  bottom: 'data' "
       "  top: 'innerprod' "
       "  include: { max_level: -3 } "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'loss' "
-      "  type: SOFTMAX_LOSS "
+      "  type: 'SoftmaxWithLoss' "
       "  bottom: 'innerprod' "
       "  bottom: 'label' "
       "} ";
   const string& output_proto =
       "name: 'TestNetwork' "
-      "layers: { "
+      "layer { "
       "  name: 'data' "
-      "  type: DATA "
+      "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'loss' "
-      "  type: SOFTMAX_LOSS "
+      "  type: 'SoftmaxWithLoss' "
       "  bottom: 'innerprod' "
       "  bottom: 'label' "
       "} ";
@@ -1833,22 +2008,22 @@ TEST_F(FilterNetTest, TestFilterOutByMaxLevel) {
 TEST_F(FilterNetTest, TestFilterInByMinLevel) {
   const string& input_proto =
       "name: 'TestNetwork' "
-      "layers: { "
+      "layer { "
       "  name: 'data' "
-      "  type: DATA "
+      "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'innerprod' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  bottom: 'data' "
       "  top: 'innerprod' "
       "  include: { min_level: 0 } "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'loss' "
-      "  type: SOFTMAX_LOSS "
+      "  type: 'SoftmaxWithLoss' "
       "  bottom: 'innerprod' "
       "  bottom: 'label' "
       "} ";
@@ -1859,22 +2034,22 @@ TEST_F(FilterNetTest, TestFilterInByMinLevel2) {
   const string& input_proto =
       "state: { level: 7 } "
       "name: 'TestNetwork' "
-      "layers: { "
+      "layer { "
       "  name: 'data' "
-      "  type: DATA "
+      "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'innerprod' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  bottom: 'data' "
       "  top: 'innerprod' "
       "  include: { min_level: 3 } "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'loss' "
-      "  type: SOFTMAX_LOSS "
+      "  type: 'SoftmaxWithLoss' "
       "  bottom: 'innerprod' "
       "  bottom: 'label' "
       "} ";
@@ -1884,22 +2059,22 @@ TEST_F(FilterNetTest, TestFilterInByMinLevel2) {
 TEST_F(FilterNetTest, TestFilterInByMaxLevel) {
   const string& input_proto =
       "name: 'TestNetwork' "
-      "layers: { "
+      "layer { "
       "  name: 'data' "
-      "  type: DATA "
+      "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'innerprod' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  bottom: 'data' "
       "  top: 'innerprod' "
       "  include: { max_level: 0 } "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'loss' "
-      "  type: SOFTMAX_LOSS "
+      "  type: 'SoftmaxWithLoss' "
       "  bottom: 'innerprod' "
       "  bottom: 'label' "
       "} ";
@@ -1910,22 +2085,22 @@ TEST_F(FilterNetTest, TestFilterInByMaxLevel2) {
   const string& input_proto =
       "state: { level: -7 } "
       "name: 'TestNetwork' "
-      "layers: { "
+      "layer { "
       "  name: 'data' "
-      "  type: DATA "
+      "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'innerprod' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  bottom: 'data' "
       "  top: 'innerprod' "
       "  include: { max_level: -3 } "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'loss' "
-      "  type: SOFTMAX_LOSS "
+      "  type: 'SoftmaxWithLoss' "
       "  bottom: 'innerprod' "
       "  bottom: 'label' "
       "} ";
@@ -1935,22 +2110,22 @@ TEST_F(FilterNetTest, TestFilterInByMaxLevel2) {
 TEST_F(FilterNetTest, TestFilterInOutByIncludeMultiRule) {
   const string& input_proto =
       "name: 'TestNetwork' "
-      "layers: { "
+      "layer { "
       "  name: 'data' "
-      "  type: DATA "
+      "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'innerprod' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  bottom: 'data' "
       "  top: 'innerprod' "
       "  include: { min_level: 2  phase: TRAIN } "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'loss' "
-      "  type: SOFTMAX_LOSS "
+      "  type: 'SoftmaxWithLoss' "
       "  bottom: 'innerprod' "
       "  bottom: 'label' "
       "  include: { min_level: 2  phase: TEST } "
@@ -1962,15 +2137,15 @@ TEST_F(FilterNetTest, TestFilterInOutByIncludeMultiRule) {
   const string& output_proto_train =
       "state: { level: 4  phase: TRAIN } "
       "name: 'TestNetwork' "
-      "layers: { "
+      "layer { "
       "  name: 'data' "
-      "  type: DATA "
+      "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'innerprod' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  bottom: 'data' "
       "  top: 'innerprod' "
       "  include: { min_level: 2  phase: TRAIN } "
@@ -1978,15 +2153,15 @@ TEST_F(FilterNetTest, TestFilterInOutByIncludeMultiRule) {
   const string& output_proto_test =
       "state: { level: 4  phase: TEST } "
       "name: 'TestNetwork' "
-      "layers: { "
+      "layer { "
       "  name: 'data' "
-      "  type: DATA "
+      "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'loss' "
-      "  type: SOFTMAX_LOSS "
+      "  type: 'SoftmaxWithLoss' "
       "  bottom: 'innerprod' "
       "  bottom: 'label' "
       "  include: { min_level: 2  phase: TEST } "
@@ -1998,23 +2173,23 @@ TEST_F(FilterNetTest, TestFilterInOutByIncludeMultiRule) {
 TEST_F(FilterNetTest, TestFilterInByIncludeMultiRule) {
   const string& input_proto =
       "name: 'TestNetwork' "
-      "layers: { "
+      "layer { "
       "  name: 'data' "
-      "  type: DATA "
+      "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'innerprod' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  bottom: 'data' "
       "  top: 'innerprod' "
       "  include: { min_level: 2  phase: TRAIN } "
       "  include: { phase: TEST } "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'loss' "
-      "  type: SOFTMAX_LOSS "
+      "  type: 'SoftmaxWithLoss' "
       "  bottom: 'innerprod' "
       "  bottom: 'label' "
       "  include: { min_level: 2  phase: TEST } "
@@ -2031,22 +2206,22 @@ TEST_F(FilterNetTest, TestFilterInByIncludeMultiRule) {
 TEST_F(FilterNetTest, TestFilterInOutByExcludeMultiRule) {
   const string& input_proto =
       "name: 'TestNetwork' "
-      "layers: { "
+      "layer { "
       "  name: 'data' "
-      "  type: DATA "
+      "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'innerprod' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  bottom: 'data' "
       "  top: 'innerprod' "
       "  exclude: { min_level: 2  phase: TRAIN } "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'loss' "
-      "  type: SOFTMAX_LOSS "
+      "  type: 'SoftmaxWithLoss' "
       "  bottom: 'innerprod' "
       "  bottom: 'label' "
       "  exclude: { min_level: 2  phase: TEST } "
@@ -2058,15 +2233,15 @@ TEST_F(FilterNetTest, TestFilterInOutByExcludeMultiRule) {
   const string& output_proto_train =
       "state: { level: 4  phase: TRAIN } "
       "name: 'TestNetwork' "
-      "layers: { "
+      "layer { "
       "  name: 'data' "
-      "  type: DATA "
+      "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'loss' "
-      "  type: SOFTMAX_LOSS "
+      "  type: 'SoftmaxWithLoss' "
       "  bottom: 'innerprod' "
       "  bottom: 'label' "
       "  exclude: { min_level: 2  phase: TEST } "
@@ -2074,15 +2249,15 @@ TEST_F(FilterNetTest, TestFilterInOutByExcludeMultiRule) {
   const string& output_proto_test =
       "state: { level: 4  phase: TEST } "
       "name: 'TestNetwork' "
-      "layers: { "
+      "layer { "
       "  name: 'data' "
-      "  type: DATA "
+      "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'innerprod' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  bottom: 'data' "
       "  top: 'innerprod' "
       "  exclude: { min_level: 2  phase: TRAIN } "
@@ -2149,4 +2324,52 @@ TYPED_TEST(NetTest, TestReshape) {
   }
 }
 
+TYPED_TEST(NetTest, TestSkipPropagateDown) {
+  // check bottom_need_backward if propagate_down is true
+  this->InitSkipPropNet(false);
+  vector<bool> vec_layer_need_backward = this->net_->layer_need_backward();
+  for (int layer_id = 0; layer_id < this->net_->layers().size(); ++layer_id) {
+    string layer_name = this->net_->layer_names()[layer_id];
+    if (layer_name == "loss") {
+      // access to bottom_need_backward coresponding to label's blob
+      bool need_back = this->net_->bottom_need_backward()[layer_id][1];
+      // if propagate_down is true, the loss layer will try to
+      // backpropagate on labels
+      EXPECT_TRUE(need_back) << "bottom_need_backward should be True";
+    }
+    // layer_need_backward should be True except for data and silence layers
+    if (layer_name.find("data") != std::string::npos ||
+          layer_name == "silence") {
+      EXPECT_FALSE(vec_layer_need_backward[layer_id])
+          << "layer_need_backward for " << layer_name << " should be False";
+    } else {
+      EXPECT_TRUE(vec_layer_need_backward[layer_id])
+          << "layer_need_backward for " << layer_name << " should be True";
+    }
+  }
+  // check bottom_need_backward if propagat_down is false
+  this->InitSkipPropNet(true);
+  vec_layer_need_backward.clear();
+  vec_layer_need_backward = this->net_->layer_need_backward();
+  for (int layer_id = 0; layer_id < this->net_->layers().size(); ++layer_id) {
+    string layer_name = this->net_->layer_names()[layer_id];
+    if (layer_name == "loss") {
+      // access to bottom_need_backward coresponding to label's blob
+      bool need_back = this->net_->bottom_need_backward()[layer_id][1];
+      // if propagate_down is false, the loss layer will not try to
+      // backpropagate on labels
+      EXPECT_FALSE(need_back) << "bottom_need_backward should be False";
+    }
+    // layer_need_backward should be False except for innerproduct and
+    // loss layers
+    if (layer_name == "innerproduct" || layer_name == "loss") {
+      EXPECT_TRUE(vec_layer_need_backward[layer_id])
+          << "layer_need_backward for " << layer_name << " should be True";
+    } else {
+      EXPECT_FALSE(vec_layer_need_backward[layer_id])
+          << "layer_need_backward for " << layer_name << " should be False";
+    }
+  }
+}
+
 }  // namespace caffe
diff --git a/src/caffe/test/test_neuron_layer.cpp b/src/caffe/test/test_neuron_layer.cpp
index 4c19d3f9bae..c6e4d27b903 100644
--- a/src/caffe/test/test_neuron_layer.cpp
+++ b/src/caffe/test/test_neuron_layer.cpp
@@ -1,6 +1,8 @@
+#include <algorithm>
 #include <cstring>
 #include <vector>
 
+#include "google/protobuf/text_format.h"
 #include "gtest/gtest.h"
 
 #include "caffe/blob.hpp"
@@ -42,10 +44,10 @@ class NeuronLayerTest : public MultiDeviceTest<TypeParam> {
     if (dropout_ratio != 0.5) {
       layer_param.mutable_dropout_param()->set_dropout_ratio(dropout_ratio);
     }
-    Caffe::set_phase(Caffe::TRAIN);
     DropoutLayer<Dtype> layer(layer_param);
-    layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-    layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+    layer_param.set_phase(TRAIN);
+    layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+    layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
     // Now, check values
     const Dtype* bottom_data = this->blob_bottom_->cpu_data();
     const Dtype* top_data = this->blob_top_->cpu_data();
@@ -66,6 +68,98 @@ class NeuronLayerTest : public MultiDeviceTest<TypeParam> {
     const Dtype empirical_dropout_ratio = 1 - num_kept / Dtype(count);
     EXPECT_NEAR(empirical_dropout_ratio, dropout_ratio, 1.96 * std_error);
   }
+
+  void TestExpForward(const float base, const float scale, const float shift) {
+    LayerParameter layer_param;
+    layer_param.mutable_exp_param()->set_base(base);
+    layer_param.mutable_exp_param()->set_scale(scale);
+    layer_param.mutable_exp_param()->set_shift(shift);
+    ExpLayer<Dtype> layer(layer_param);
+    layer.SetUp(blob_bottom_vec_, blob_top_vec_);
+    layer.Forward(blob_bottom_vec_, blob_top_vec_);
+    const Dtype kDelta = 2e-4;
+    const Dtype* bottom_data = blob_bottom_->cpu_data();
+    const Dtype* top_data = blob_top_->cpu_data();
+    for (int i = 0; i < blob_bottom_->count(); ++i) {
+      const Dtype bottom_val = bottom_data[i];
+      const Dtype top_val = top_data[i];
+      if (base == -1) {
+        EXPECT_NEAR(top_val, exp(shift + scale * bottom_val), kDelta);
+      } else {
+        EXPECT_NEAR(top_val, pow(base, shift + scale * bottom_val), kDelta);
+      }
+    }
+  }
+
+  void TestExpGradient(const float base, const float scale, const float shift) {
+    LayerParameter layer_param;
+    layer_param.mutable_exp_param()->set_base(base);
+    layer_param.mutable_exp_param()->set_scale(scale);
+    layer_param.mutable_exp_param()->set_shift(shift);
+    ExpLayer<Dtype> layer(layer_param);
+    GradientChecker<Dtype> checker(1e-2, 1e-3);
+    checker.CheckGradientEltwise(&layer, blob_bottom_vec_, blob_top_vec_);
+  }
+
+  void TestPReLU(PReLULayer<Dtype> *layer) {
+    layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  // Now, check values
+    const Dtype* bottom_data = this->blob_bottom_->cpu_data();
+    const Dtype* top_data = this->blob_top_->cpu_data();
+    const Dtype* slope_data = layer->blobs()[0]->cpu_data();
+    int hw = this->blob_bottom_->height() * this->blob_bottom_->width();
+    int channels = this->blob_bottom_->channels();
+    bool channel_shared = layer->layer_param().prelu_param().channel_shared();
+    for (int i = 0; i < this->blob_bottom_->count(); ++i) {
+      int c = channel_shared ? 0 : (i / hw) % channels;
+      EXPECT_EQ(top_data[i],
+          std::max(bottom_data[i], (Dtype)(0))
+          + slope_data[c] * std::min(bottom_data[i], (Dtype)(0)));
+    }
+  }
+
+  void LogBottomInit() {
+    FillerParameter filler_param;
+    GaussianFiller<Dtype> filler(filler_param);
+    filler.Fill(this->blob_bottom_);
+    Dtype* bottom_data = this->blob_bottom_->mutable_cpu_data();
+    caffe_exp(this->blob_bottom_->count(), bottom_data, bottom_data);
+  }
+
+  void TestLogForward(const float base, const float scale, const float shift) {
+    LogBottomInit();
+    LayerParameter layer_param;
+    layer_param.mutable_log_param()->set_base(base);
+    layer_param.mutable_log_param()->set_scale(scale);
+    layer_param.mutable_log_param()->set_shift(shift);
+    LogLayer<Dtype> layer(layer_param);
+    layer.SetUp(blob_bottom_vec_, blob_top_vec_);
+    layer.Forward(blob_bottom_vec_, blob_top_vec_);
+    const Dtype kDelta = 2e-4;
+    const Dtype* bottom_data = blob_bottom_->cpu_data();
+    const Dtype* top_data = blob_top_->cpu_data();
+    for (int i = 0; i < blob_bottom_->count(); ++i) {
+      const Dtype bottom_val = bottom_data[i];
+      const Dtype top_val = top_data[i];
+      if (base == -1) {
+        EXPECT_NEAR(top_val, log(shift + scale * bottom_val), kDelta);
+      } else {
+        EXPECT_NEAR(top_val, log(shift + scale * bottom_val) / log(base),
+                    kDelta);
+      }
+    }
+  }
+
+  void TestLogGradient(const float base, const float scale, const float shift) {
+    LogBottomInit();
+    LayerParameter layer_param;
+    layer_param.mutable_log_param()->set_base(base);
+    layer_param.mutable_log_param()->set_scale(scale);
+    layer_param.mutable_log_param()->set_shift(shift);
+    LogLayer<Dtype> layer(layer_param);
+    GradientChecker<Dtype> checker(1e-2, 1e-2);
+    checker.CheckGradientEltwise(&layer, blob_bottom_vec_, blob_top_vec_);
+  }
 };
 
 TYPED_TEST_CASE(NeuronLayerTest, TestDtypesAndDevices);
@@ -74,8 +168,8 @@ TYPED_TEST(NeuronLayerTest, TestAbsVal) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
   AbsValLayer<Dtype> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   const Dtype* bottom_data = this->blob_bottom_->cpu_data();
   const Dtype* top_data    = this->blob_top_->cpu_data();
   const int count = this->blob_bottom_->count();
@@ -89,16 +183,16 @@ TYPED_TEST(NeuronLayerTest, TestAbsGradient) {
   LayerParameter layer_param;
   AbsValLayer<Dtype> layer(layer_param);
   GradientChecker<Dtype> checker(1e-2, 1e-3, 1701, 0., 0.01);
-  checker.CheckGradientEltwise(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_));
+  checker.CheckGradientEltwise(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
 }
 
 TYPED_TEST(NeuronLayerTest, TestReLU) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
   ReLULayer<Dtype> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   // Now, check values
   const Dtype* bottom_data = this->blob_bottom_->cpu_data();
   const Dtype* top_data = this->blob_top_->cpu_data();
@@ -113,42 +207,47 @@ TYPED_TEST(NeuronLayerTest, TestReLUGradient) {
   LayerParameter layer_param;
   ReLULayer<Dtype> layer(layer_param);
   GradientChecker<Dtype> checker(1e-2, 1e-3, 1701, 0., 0.01);
-  checker.CheckGradientEltwise(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_));
+  checker.CheckGradientEltwise(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
 }
 
 TYPED_TEST(NeuronLayerTest, TestReLUWithNegativeSlope) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  layer_param.ParseFromString("relu_param{negative_slope:0.01}");
+  CHECK(google::protobuf::TextFormat::ParseFromString(
+      "relu_param { negative_slope: 0.01 }", &layer_param));
   ReLULayer<Dtype> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   // Now, check values
   const Dtype* bottom_data = this->blob_bottom_->cpu_data();
   const Dtype* top_data = this->blob_top_->cpu_data();
   for (int i = 0; i < this->blob_bottom_->count(); ++i) {
-    EXPECT_GE(top_data[i], 0.);
-    EXPECT_TRUE(top_data[i] == 0 || top_data[i] == bottom_data[i]);
+    if (top_data[i] >= 0) {
+      EXPECT_FLOAT_EQ(top_data[i], bottom_data[i]);
+    } else {
+      EXPECT_FLOAT_EQ(top_data[i], bottom_data[i] * 0.01);
+    }
   }
 }
 
 TYPED_TEST(NeuronLayerTest, TestReLUGradientWithNegativeSlope) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  layer_param.ParseFromString("relu_param{negative_slope:0.01}");
+  CHECK(google::protobuf::TextFormat::ParseFromString(
+      "relu_param { negative_slope: 0.01 }", &layer_param));
   ReLULayer<Dtype> layer(layer_param);
   GradientChecker<Dtype> checker(1e-2, 1e-3, 1701, 0., 0.01);
-  checker.CheckGradientEltwise(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_));
+  checker.CheckGradientEltwise(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
 }
 
 TYPED_TEST(NeuronLayerTest, TestSigmoid) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
   SigmoidLayer<Dtype> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   // Now, check values
   const Dtype* bottom_data = this->blob_bottom_->cpu_data();
   const Dtype* top_data = this->blob_top_->cpu_data();
@@ -165,16 +264,16 @@ TYPED_TEST(NeuronLayerTest, TestSigmoidGradient) {
   LayerParameter layer_param;
   SigmoidLayer<Dtype> layer(layer_param);
   GradientChecker<Dtype> checker(1e-2, 1e-3, 1701, 0., 0.01);
-  checker.CheckGradientEltwise(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_));
+  checker.CheckGradientEltwise(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
 }
 
 TYPED_TEST(NeuronLayerTest, TestTanH) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
   TanHLayer<Dtype> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   // Test exact values
   for (int i = 0; i < this->blob_bottom_->num(); ++i) {
     for (int j = 0; j < this->blob_bottom_->channels(); ++j) {
@@ -197,8 +296,172 @@ TYPED_TEST(NeuronLayerTest, TestTanHGradient) {
   LayerParameter layer_param;
   TanHLayer<Dtype> layer(layer_param);
   GradientChecker<Dtype> checker(1e-2, 1e-3);
-  checker.CheckGradientEltwise(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_));
+  checker.CheckGradientEltwise(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
+}
+
+TYPED_TEST(NeuronLayerTest, TestExpLayer) {
+  typedef typename TypeParam::Dtype Dtype;
+  // Test default base of "-1" -- should actually set base := e.
+  const Dtype kBase = -1;
+  const Dtype kScale = 1;
+  const Dtype kShift = 0;
+  this->TestExpForward(kBase, kScale, kShift);
+}
+
+TYPED_TEST(NeuronLayerTest, TestExpGradient) {
+  typedef typename TypeParam::Dtype Dtype;
+  // Test default base of "-1" -- should actually set base := e.
+  const Dtype kBase = -1;
+  const Dtype kScale = 1;
+  const Dtype kShift = 0;
+  this->TestExpGradient(kBase, kScale, kShift);
+}
+
+TYPED_TEST(NeuronLayerTest, TestExpLayerBase2) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kBase = 2;
+  const Dtype kScale = 1;
+  const Dtype kShift = 0;
+  this->TestExpForward(kBase, kScale, kShift);
+}
+
+TYPED_TEST(NeuronLayerTest, TestExpGradientBase2) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kBase = 2;
+  const Dtype kScale = 1;
+  const Dtype kShift = 0;
+  this->TestExpGradient(kBase, kScale, kShift);
+}
+
+TYPED_TEST(NeuronLayerTest, TestExpLayerBase2Shift1) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kBase = 2;
+  const Dtype kScale = 1;
+  const Dtype kShift = 1;
+  this->TestExpForward(kBase, kScale, kShift);
+}
+
+TYPED_TEST(NeuronLayerTest, TestExpGradientBase2Shift1) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kBase = 2;
+  const Dtype kScale = 1;
+  const Dtype kShift = 1;
+  this->TestExpGradient(kBase, kScale, kShift);
+}
+
+TYPED_TEST(NeuronLayerTest, TestExpLayerBase2Scale3) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kBase = 2;
+  const Dtype kScale = 3;
+  const Dtype kShift = 0;
+  this->TestExpForward(kBase, kScale, kShift);
+}
+
+TYPED_TEST(NeuronLayerTest, TestExpGradientBase2Scale3) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kBase = 2;
+  const Dtype kScale = 3;
+  const Dtype kShift = 0;
+  this->TestExpGradient(kBase, kScale, kShift);
+}
+
+TYPED_TEST(NeuronLayerTest, TestExpLayerBase2Shift1Scale3) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kBase = 2;
+  const Dtype kScale = 3;
+  const Dtype kShift = 1;
+  this->TestExpForward(kBase, kScale, kShift);
+}
+
+TYPED_TEST(NeuronLayerTest, TestExpGradientBase2Shift1Scale3) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kBase = 2;
+  const Dtype kScale = 3;
+  const Dtype kShift = 1;
+  this->TestExpGradient(kBase, kScale, kShift);
+}
+
+TYPED_TEST(NeuronLayerTest, TestLogLayer) {
+  typedef typename TypeParam::Dtype Dtype;
+  // Test default base of "-1" -- should actually set base := e.
+  const Dtype kBase = -1;
+  const Dtype kScale = 1;
+  const Dtype kShift = 0;
+  this->TestLogForward(kBase, kScale, kShift);
+}
+
+TYPED_TEST(NeuronLayerTest, TestLogGradient) {
+  typedef typename TypeParam::Dtype Dtype;
+  // Test default base of "-1" -- should actually set base := e.
+  const Dtype kBase = -1;
+  const Dtype kScale = 1;
+  const Dtype kShift = 0;
+  this->TestLogGradient(kBase, kScale, kShift);
+}
+
+TYPED_TEST(NeuronLayerTest, TestLogLayerBase2) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kBase = 2;
+  const Dtype kScale = 1;
+  const Dtype kShift = 0;
+  this->TestLogForward(kBase, kScale, kShift);
+}
+
+TYPED_TEST(NeuronLayerTest, TestLogGradientBase2) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kBase = 2;
+  const Dtype kScale = 1;
+  const Dtype kShift = 0;
+  this->TestLogGradient(kBase, kScale, kShift);
+}
+
+TYPED_TEST(NeuronLayerTest, TestLogLayerBase2Shift1) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kBase = 2;
+  const Dtype kScale = 1;
+  const Dtype kShift = 1;
+  this->TestLogForward(kBase, kScale, kShift);
+}
+
+TYPED_TEST(NeuronLayerTest, TestLogGradientBase2Shift1) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kBase = 2;
+  const Dtype kScale = 1;
+  const Dtype kShift = 1;
+  this->TestLogGradient(kBase, kScale, kShift);
+}
+
+TYPED_TEST(NeuronLayerTest, TestLogLayerBase2Scale3) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kBase = 2;
+  const Dtype kScale = 3;
+  const Dtype kShift = 0;
+  this->TestLogForward(kBase, kScale, kShift);
+}
+
+TYPED_TEST(NeuronLayerTest, TestLogGradientBase2Scale3) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kBase = 2;
+  const Dtype kScale = 3;
+  const Dtype kShift = 0;
+  this->TestLogGradient(kBase, kScale, kShift);
+}
+
+TYPED_TEST(NeuronLayerTest, TestLogLayerBase2Shift1Scale3) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kBase = 2;
+  const Dtype kScale = 3;
+  const Dtype kShift = 1;
+  this->TestLogForward(kBase, kScale, kShift);
+}
+
+TYPED_TEST(NeuronLayerTest, TestLogGradientBase2Shift1Scale3) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kBase = 2;
+  const Dtype kScale = 3;
+  const Dtype kShift = 1;
+  this->TestLogGradient(kBase, kScale, kShift);
 }
 
 TYPED_TEST(NeuronLayerTest, TestDropoutHalf) {
@@ -214,10 +477,10 @@ TYPED_TEST(NeuronLayerTest, TestDropoutThreeQuarters) {
 TYPED_TEST(NeuronLayerTest, TestDropoutTestPhase) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  Caffe::set_phase(Caffe::TEST);
+  layer_param.set_phase(TEST);
   DropoutLayer<Dtype> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   // Now, check values
   const Dtype* bottom_data = this->blob_bottom_->cpu_data();
   const Dtype* top_data = this->blob_top_->cpu_data();
@@ -231,29 +494,29 @@ TYPED_TEST(NeuronLayerTest, TestDropoutTestPhase) {
 TYPED_TEST(NeuronLayerTest, TestDropoutGradient) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  Caffe::set_phase(Caffe::TRAIN);
+  layer_param.set_phase(TRAIN);
   DropoutLayer<Dtype> layer(layer_param);
   GradientChecker<Dtype> checker(1e-2, 1e-3);
-  checker.CheckGradientEltwise(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_));
+  checker.CheckGradientEltwise(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
 }
 
 TYPED_TEST(NeuronLayerTest, TestDropoutGradientTest) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  Caffe::set_phase(Caffe::TEST);
+  layer_param.set_phase(TEST);
   DropoutLayer<Dtype> layer(layer_param);
   GradientChecker<Dtype> checker(1e-2, 1e-3);
-  checker.CheckGradientEltwise(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_));
+  checker.CheckGradientEltwise(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
 }
 
 TYPED_TEST(NeuronLayerTest, TestBNLL) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
   BNLLLayer<Dtype> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   // Now, check values
   const Dtype* bottom_data = this->blob_bottom_->cpu_data();
   const Dtype* top_data = this->blob_top_->cpu_data();
@@ -268,13 +531,187 @@ TYPED_TEST(NeuronLayerTest, TestBNLLGradient) {
   LayerParameter layer_param;
   BNLLLayer<Dtype> layer(layer_param);
   GradientChecker<Dtype> checker(1e-2, 1e-3);
-  checker.CheckGradientEltwise(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_));
+  checker.CheckGradientEltwise(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
+}
+
+TYPED_TEST(NeuronLayerTest, TestPReLUParam) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  PReLULayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  const Dtype* slopes = layer.blobs()[0]->cpu_data();
+  int count = layer.blobs()[0]->count();
+  for (int i = 0; i < count; ++i, ++slopes) {
+    EXPECT_EQ(*slopes, 0.25);
+  }
+}
+
+TYPED_TEST(NeuronLayerTest, TestPReLUForward) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  PReLULayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  FillerParameter filler_param;
+  GaussianFiller<Dtype> filler(filler_param);
+  filler.Fill(layer.blobs()[0].get());
+  this->TestPReLU(&layer);
+}
+
+TYPED_TEST(NeuronLayerTest, TestPReLUForwardChannelShared) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  layer_param.mutable_prelu_param()->set_channel_shared(true);
+  PReLULayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  this->TestPReLU(&layer);
+}
+
+TYPED_TEST(NeuronLayerTest, TestPReLUGradient) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  PReLULayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  FillerParameter filler_param;
+  GaussianFiller<Dtype> filler(filler_param);
+  filler.Fill(layer.blobs()[0].get());
+  GradientChecker<Dtype> checker(1e-2, 1e-3, 1701, 0., 0.01);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
+}
+
+TYPED_TEST(NeuronLayerTest, TestPReLUGradientChannelShared) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  layer_param.mutable_prelu_param()->set_channel_shared(true);
+  PReLULayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  GradientChecker<Dtype> checker(1e-2, 1e-3, 1701, 0., 0.01);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
+}
+
+TYPED_TEST(NeuronLayerTest, TestPReLUConsistencyReLU) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter prelu_layer_param;
+  LayerParameter relu_layer_param;
+  relu_layer_param.mutable_relu_param()->set_negative_slope(0.25);
+  PReLULayer<Dtype> prelu(prelu_layer_param);
+  ReLULayer<Dtype> relu(relu_layer_param);
+  // Set up blobs
+  vector<Blob<Dtype>*> blob_bottom_vec_2;
+  vector<Blob<Dtype>*> blob_top_vec_2;
+  shared_ptr<Blob<Dtype> > blob_bottom_2(new Blob<Dtype>());
+  shared_ptr<Blob<Dtype> > blob_top_2(new Blob<Dtype>());
+  blob_bottom_vec_2.push_back(blob_bottom_2.get());
+  blob_top_vec_2.push_back(blob_top_2.get());
+  blob_bottom_2->CopyFrom(*this->blob_bottom_, false, true);
+  // SetUp layers
+  prelu.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  relu.SetUp(blob_bottom_vec_2, blob_top_vec_2);
+  // Check forward
+  prelu.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  relu.Forward(this->blob_bottom_vec_, blob_top_vec_2);
+  for (int s = 0; s < blob_top_2->count(); ++s) {
+    EXPECT_EQ(this->blob_top_->cpu_data()[s], blob_top_2->cpu_data()[s]);
+  }
+  // Check backward
+  shared_ptr<Blob<Dtype> > tmp_blob(new Blob<Dtype>());
+  tmp_blob->ReshapeLike(*blob_top_2.get());
+  FillerParameter filler_param;
+  GaussianFiller<Dtype> filler(filler_param);
+  filler.Fill(tmp_blob.get());
+  caffe_copy(blob_top_2->count(), tmp_blob->cpu_data(),
+      this->blob_top_->mutable_cpu_diff());
+  caffe_copy(blob_top_2->count(), tmp_blob->cpu_data(),
+      blob_top_2->mutable_cpu_diff());
+  vector<bool> propagate_down;
+  propagate_down.push_back(true);
+  prelu.Backward(this->blob_top_vec_, propagate_down, this->blob_bottom_vec_);
+  relu.Backward(blob_top_vec_2, propagate_down, blob_bottom_vec_2);
+  for (int s = 0; s < blob_bottom_2->count(); ++s) {
+    EXPECT_EQ(this->blob_bottom_->cpu_diff()[s], blob_bottom_2->cpu_diff()[s]);
+  }
+}
+
+TYPED_TEST(NeuronLayerTest, TestPReLUInPlace) {
+  typedef typename TypeParam::Dtype Dtype;
+  // Set layer parameters
+  LayerParameter ip_layer_param;
+  LayerParameter prelu_layer_param;
+  InnerProductParameter *ip_param =
+      ip_layer_param.mutable_inner_product_param();
+  ip_param->mutable_weight_filler()->set_type("gaussian");
+  ip_param->set_num_output(3);
+  InnerProductLayer<Dtype> ip(ip_layer_param);
+  PReLULayer<Dtype> prelu(prelu_layer_param);
+  InnerProductLayer<Dtype> ip2(ip_layer_param);
+  PReLULayer<Dtype> prelu2(prelu_layer_param);
+  // Set up blobs
+  vector<Blob<Dtype>*> blob_bottom_vec_2;
+  vector<Blob<Dtype>*> blob_middle_vec_2;
+  vector<Blob<Dtype>*> blob_top_vec_2;
+  shared_ptr<Blob<Dtype> > blob_bottom_2(new Blob<Dtype>());
+  shared_ptr<Blob<Dtype> > blob_middle_2(new Blob<Dtype>());
+  shared_ptr<Blob<Dtype> > blob_top_2(new Blob<Dtype>());
+  blob_bottom_vec_2.push_back(blob_bottom_2.get());
+  blob_middle_vec_2.push_back(blob_middle_2.get());
+  blob_top_vec_2.push_back(blob_top_2.get());
+  blob_bottom_2->CopyFrom(*this->blob_bottom_, false, true);
+  // SetUp layers
+  ip.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  prelu.SetUp(this->blob_top_vec_, this->blob_top_vec_);
+  ip2.SetUp(blob_bottom_vec_2, blob_middle_vec_2);
+  prelu2.SetUp(blob_middle_vec_2, blob_top_vec_2);
+  caffe_copy(ip2.blobs()[0]->count(), ip.blobs()[0]->cpu_data(),
+      ip2.blobs()[0]->mutable_cpu_data());
+  // Forward in-place
+  ip.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  prelu.Forward(this->blob_top_vec_, this->blob_top_vec_);
+  // Forward non-in-place
+  ip2.Forward(blob_bottom_vec_2, blob_middle_vec_2);
+  prelu2.Forward(blob_middle_vec_2, blob_top_vec_2);
+  // Check numbers
+  for (int s = 0; s < blob_top_2->count(); ++s) {
+    EXPECT_EQ(this->blob_top_->cpu_data()[s], blob_top_2->cpu_data()[s]);
+  }
+  // Fill top diff with random numbers
+  shared_ptr<Blob<Dtype> > tmp_blob(new Blob<Dtype>());
+  tmp_blob->ReshapeLike(*blob_top_2.get());
+  FillerParameter filler_param;
+  GaussianFiller<Dtype> filler(filler_param);
+  filler.Fill(tmp_blob.get());
+  caffe_copy(blob_top_2->count(), tmp_blob->cpu_data(),
+      this->blob_top_->mutable_cpu_diff());
+  caffe_copy(blob_top_2->count(), tmp_blob->cpu_data(),
+      blob_top_2->mutable_cpu_diff());
+  // Backward in-place
+  vector<bool> propagate_down;
+  propagate_down.push_back(true);
+  prelu.Backward(this->blob_top_vec_, propagate_down, this->blob_top_vec_);
+  ip.Backward(this->blob_top_vec_, propagate_down, this->blob_bottom_vec_);
+  // Backward non-in-place
+  prelu2.Backward(blob_top_vec_2, propagate_down, blob_middle_vec_2);
+  ip2.Backward(blob_middle_vec_2, propagate_down, blob_bottom_vec_2);
+  // Check numbers
+  for (int s = 0; s < blob_bottom_2->count(); ++s) {
+    EXPECT_EQ(this->blob_bottom_->cpu_diff()[s], blob_bottom_2->cpu_diff()[s]);
+  }
+  for (int s = 0; s < ip.blobs()[0]->count(); ++s) {
+    EXPECT_EQ(ip.blobs()[0]->cpu_diff()[s], ip2.blobs()[0]->cpu_diff()[s]);
+  }
+  for (int s = 0; s < ip.blobs()[1]->count(); ++s) {
+    EXPECT_EQ(ip.blobs()[1]->cpu_diff()[s], ip2.blobs()[1]->cpu_diff()[s]);
+  }
+  for (int s = 0; s < prelu.blobs()[0]->count(); ++s) {
+    EXPECT_EQ(prelu.blobs()[0]->cpu_diff()[s],
+        prelu2.blobs()[0]->cpu_diff()[s]);
+  }
 }
 
 #ifdef USE_CUDNN
 template <typename Dtype>
-class CuDNNNeuronLayerTest : public ::testing::Test {
+class CuDNNNeuronLayerTest : public GPUDeviceTest<Dtype> {
  protected:
   CuDNNNeuronLayerTest()
       : blob_bottom_(new Blob<Dtype>(2, 3, 4, 5)),
@@ -297,11 +734,10 @@ class CuDNNNeuronLayerTest : public ::testing::Test {
 TYPED_TEST_CASE(CuDNNNeuronLayerTest, TestDtypes);
 
 TYPED_TEST(CuDNNNeuronLayerTest, TestReLUCuDNN) {
-  Caffe::set_mode(Caffe::GPU);
   LayerParameter layer_param;
   CuDNNReLULayer<TypeParam> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   // Now, check values
   const TypeParam* bottom_data = this->blob_bottom_->cpu_data();
   const TypeParam* top_data = this->blob_top_->cpu_data();
@@ -312,46 +748,47 @@ TYPED_TEST(CuDNNNeuronLayerTest, TestReLUCuDNN) {
 }
 
 TYPED_TEST(CuDNNNeuronLayerTest, TestReLUGradientCuDNN) {
-  Caffe::set_mode(Caffe::GPU);
   LayerParameter layer_param;
   CuDNNReLULayer<TypeParam> layer(layer_param);
   GradientChecker<TypeParam> checker(1e-2, 1e-3, 1701, 0., 0.01);
-  checker.CheckGradientEltwise(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_));
+  checker.CheckGradientEltwise(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
 }
 
 TYPED_TEST(CuDNNNeuronLayerTest, TestReLUWithNegativeSlopeCuDNN) {
-  Caffe::set_mode(Caffe::GPU);
   LayerParameter layer_param;
-  layer_param.ParseFromString("relu_param{negative_slope:0.01}");
+  CHECK(google::protobuf::TextFormat::ParseFromString(
+      "relu_param { negative_slope: 0.01 }", &layer_param));
   CuDNNReLULayer<TypeParam> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   // Now, check values
   const TypeParam* bottom_data = this->blob_bottom_->cpu_data();
   const TypeParam* top_data = this->blob_top_->cpu_data();
   for (int i = 0; i < this->blob_bottom_->count(); ++i) {
-    EXPECT_GE(top_data[i], 0.);
-    EXPECT_TRUE(top_data[i] == 0 || top_data[i] == bottom_data[i]);
+    if (top_data[i] >= 0) {
+      EXPECT_FLOAT_EQ(top_data[i], bottom_data[i]);
+    } else {
+      EXPECT_FLOAT_EQ(top_data[i], bottom_data[i] * 0.01);
+    }
   }
 }
 
 TYPED_TEST(CuDNNNeuronLayerTest, TestReLUGradientWithNegativeSlopeCuDNN) {
-  Caffe::set_mode(Caffe::GPU);
   LayerParameter layer_param;
-  layer_param.ParseFromString("relu_param{negative_slope:0.01}");
+  CHECK(google::protobuf::TextFormat::ParseFromString(
+      "relu_param { negative_slope: 0.01 }", &layer_param));
   CuDNNReLULayer<TypeParam> layer(layer_param);
   GradientChecker<TypeParam> checker(1e-2, 1e-3, 1701, 0., 0.01);
-  checker.CheckGradientEltwise(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_));
+  checker.CheckGradientEltwise(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
 }
 
 TYPED_TEST(CuDNNNeuronLayerTest, TestSigmoidCuDNN) {
-  Caffe::set_mode(Caffe::GPU);
   LayerParameter layer_param;
   CuDNNSigmoidLayer<TypeParam> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   // Now, check values
   const TypeParam* bottom_data = this->blob_bottom_->cpu_data();
   const TypeParam* top_data = this->blob_top_->cpu_data();
@@ -364,20 +801,18 @@ TYPED_TEST(CuDNNNeuronLayerTest, TestSigmoidCuDNN) {
 }
 
 TYPED_TEST(CuDNNNeuronLayerTest, TestSigmoidGradientCuDNN) {
-  Caffe::set_mode(Caffe::GPU);
   LayerParameter layer_param;
   CuDNNSigmoidLayer<TypeParam> layer(layer_param);
   GradientChecker<TypeParam> checker(1e-2, 1e-3, 1701, 0., 0.01);
-  checker.CheckGradientEltwise(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_));
+  checker.CheckGradientEltwise(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
 }
 
 TYPED_TEST(CuDNNNeuronLayerTest, TestTanHCuDNN) {
-  Caffe::set_mode(Caffe::GPU);
   LayerParameter layer_param;
   CuDNNTanHLayer<TypeParam> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   // Test exact values
   for (int i = 0; i < this->blob_bottom_->num(); ++i) {
     for (int j = 0; j < this->blob_bottom_->channels(); ++j) {
@@ -396,12 +831,11 @@ TYPED_TEST(CuDNNNeuronLayerTest, TestTanHCuDNN) {
 }
 
 TYPED_TEST(CuDNNNeuronLayerTest, TestTanHGradientCuDNN) {
-  Caffe::set_mode(Caffe::GPU);
   LayerParameter layer_param;
   CuDNNTanHLayer<TypeParam> layer(layer_param);
   GradientChecker<TypeParam> checker(1e-2, 1e-3);
-  checker.CheckGradientEltwise(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_));
+  checker.CheckGradientEltwise(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
 }
 #endif
 
diff --git a/src/caffe/test/test_pooling_layer.cpp b/src/caffe/test/test_pooling_layer.cpp
index ec23a682c2f..69f2d5c1135 100644
--- a/src/caffe/test/test_pooling_layer.cpp
+++ b/src/caffe/test/test_pooling_layer.cpp
@@ -73,7 +73,7 @@ class PoolingLayerTest : public MultiDeviceTest<TypeParam> {
       blob_bottom_->mutable_cpu_data()[i + 14] = 3;
     }
     PoolingLayer<Dtype> layer(layer_param);
-    layer.SetUp(blob_bottom_vec_, &blob_top_vec_);
+    layer.SetUp(blob_bottom_vec_, blob_top_vec_);
     EXPECT_EQ(blob_top_->num(), num);
     EXPECT_EQ(blob_top_->channels(), channels);
     EXPECT_EQ(blob_top_->height(), 2);
@@ -84,7 +84,7 @@ class PoolingLayerTest : public MultiDeviceTest<TypeParam> {
       EXPECT_EQ(blob_top_mask_->height(), 2);
       EXPECT_EQ(blob_top_mask_->width(), 4);
     }
-    layer.Forward(blob_bottom_vec_, &blob_top_vec_);
+    layer.Forward(blob_bottom_vec_, blob_top_vec_);
     // Expected output: 2x 2 channels of:
     //     [9 5 5 8]
     //     [9 5 5 8]
@@ -171,7 +171,7 @@ class PoolingLayerTest : public MultiDeviceTest<TypeParam> {
       blob_bottom_->mutable_cpu_data()[i + 35] = 11;
     }
     PoolingLayer<Dtype> layer(layer_param);
-    layer.SetUp(blob_bottom_vec_, &blob_top_vec_);
+    layer.SetUp(blob_bottom_vec_, blob_top_vec_);
     EXPECT_EQ(blob_top_->num(), num);
     EXPECT_EQ(blob_top_->channels(), channels);
     EXPECT_EQ(blob_top_->height(), 4);
@@ -182,7 +182,7 @@ class PoolingLayerTest : public MultiDeviceTest<TypeParam> {
       EXPECT_EQ(blob_top_mask_->height(), 4);
       EXPECT_EQ(blob_top_mask_->width(), 5);
     }
-    layer.Forward(blob_bottom_vec_, &blob_top_vec_);
+    layer.Forward(blob_bottom_vec_, blob_top_vec_);
     // Expected output: 2x 2 channels of:
     // [35    32    26    27    27]
     // [32    33    33    27    27]
@@ -296,7 +296,7 @@ class PoolingLayerTest : public MultiDeviceTest<TypeParam> {
       blob_bottom_->mutable_cpu_data()[i + 35] = 11;
     }
     PoolingLayer<Dtype> layer(layer_param);
-    layer.SetUp(blob_bottom_vec_, &blob_top_vec_);
+    layer.SetUp(blob_bottom_vec_, blob_top_vec_);
     EXPECT_EQ(blob_top_->num(), num);
     EXPECT_EQ(blob_top_->channels(), channels);
     EXPECT_EQ(blob_top_->height(), 5);
@@ -307,7 +307,7 @@ class PoolingLayerTest : public MultiDeviceTest<TypeParam> {
       EXPECT_EQ(blob_top_mask_->height(), 5);
       EXPECT_EQ(blob_top_mask_->width(), 4);
     }
-    layer.Forward(blob_bottom_vec_, &blob_top_vec_);
+    layer.Forward(blob_bottom_vec_, blob_top_vec_);
     // Expected output: 2x 2 channels of:
     // [35    32    26    26]
     // [32    32    27    27]
@@ -377,7 +377,7 @@ TYPED_TEST(PoolingLayerTest, TestSetup) {
   pooling_param->set_kernel_size(3);
   pooling_param->set_stride(2);
   PoolingLayer<Dtype> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
   EXPECT_EQ(this->blob_top_->num(), this->blob_bottom_->num());
   EXPECT_EQ(this->blob_top_->channels(), this->blob_bottom_->channels());
   EXPECT_EQ(this->blob_top_->height(), 3);
@@ -393,13 +393,27 @@ TYPED_TEST(PoolingLayerTest, TestSetupPadded) {
   pooling_param->set_pad(1);
   pooling_param->set_pool(PoolingParameter_PoolMethod_AVE);
   PoolingLayer<Dtype> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
   EXPECT_EQ(this->blob_top_->num(), this->blob_bottom_->num());
   EXPECT_EQ(this->blob_top_->channels(), this->blob_bottom_->channels());
   EXPECT_EQ(this->blob_top_->height(), 4);
   EXPECT_EQ(this->blob_top_->width(), 3);
 }
 
+TYPED_TEST(PoolingLayerTest, TestSetupGlobalPooling) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
+  pooling_param->set_global_pooling(true);
+  pooling_param->set_pool(PoolingParameter_PoolMethod_AVE);
+  PoolingLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  EXPECT_EQ(this->blob_top_->num(), this->blob_bottom_->num());
+  EXPECT_EQ(this->blob_top_->channels(), this->blob_bottom_->channels());
+  EXPECT_EQ(this->blob_top_->height(), 1);
+  EXPECT_EQ(this->blob_top_->width(), 1);
+}
+
 /*
 TYPED_TEST(PoolingLayerTest, PrintBackward) {
   LayerParameter layer_param;
@@ -407,8 +421,8 @@ TYPED_TEST(PoolingLayerTest, PrintBackward) {
   layer_param.set_stride(2);
   layer_param.set_pool(LayerParameter_PoolMethod_MAX);
   PoolingLayer<TypeParam> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   for (int i = 0; i < this->blob_bottom_->count(); ++i) {
     cout << "bottom data " << i << " " << this->blob_bottom_->cpu_data()[i] << endl;
   }
@@ -419,7 +433,7 @@ TYPED_TEST(PoolingLayerTest, PrintBackward) {
   for (int i = 0; i < this->blob_top_->count(); ++i) {
     this->blob_top_->mutable_cpu_diff()[i] = i;
   }
-  layer.Backward(this->blob_top_vec_, true, &(this->blob_bottom_vec_));
+  layer.Backward(this->blob_top_vec_, true, this->blob_bottom_vec_);
   for (int i = 0; i < this->blob_bottom_->count(); ++i) {
     cout << "bottom diff " << i << " " << this->blob_bottom_->cpu_diff()[i] << endl;
   }
@@ -452,8 +466,8 @@ TYPED_TEST(PoolingLayerTest, TestGradientMax) {
       pooling_param->set_pool(PoolingParameter_PoolMethod_MAX);
       PoolingLayer<Dtype> layer(layer_param);
       GradientChecker<Dtype> checker(1e-4, 1e-2);
-      checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
-          &(this->blob_top_vec_));
+      checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+          this->blob_top_vec_);
     }
   }
 }
@@ -481,12 +495,12 @@ TYPED_TEST(PoolingLayerTest, TestForwardMaxPadded) {
   this->blob_bottom_->mutable_cpu_data()[7] = 2;
   this->blob_bottom_->mutable_cpu_data()[8] = 1;
   PoolingLayer<Dtype> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
   EXPECT_EQ(this->blob_top_->num(), 1);
   EXPECT_EQ(this->blob_top_->channels(), 1);
   EXPECT_EQ(this->blob_top_->height(), 3);
   EXPECT_EQ(this->blob_top_->width(), 3);
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   Dtype epsilon = 1e-8;
   // Output:
   //     [ 1 4 4 ]
@@ -516,8 +530,8 @@ TYPED_TEST(PoolingLayerTest, TestGradientMaxTopMask) {
       this->blob_top_vec_.push_back(this->blob_top_mask_);
       PoolingLayer<Dtype> layer(layer_param);
       GradientChecker<Dtype> checker(1e-4, 1e-2);
-      checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
-          &(this->blob_top_vec_));
+      checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+          this->blob_top_vec_);
       this->blob_top_vec_.pop_back();
     }
   }
@@ -537,12 +551,12 @@ TYPED_TEST(PoolingLayerTest, TestForwardAve) {
   ConstantFiller<Dtype> filler(filler_param);
   filler.Fill(this->blob_bottom_);
   PoolingLayer<Dtype> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
   EXPECT_EQ(this->blob_top_->num(), 1);
   EXPECT_EQ(this->blob_top_->channels(), 1);
   EXPECT_EQ(this->blob_top_->height(), 3);
   EXPECT_EQ(this->blob_top_->width(), 3);
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   Dtype epsilon = 1e-5;
   EXPECT_NEAR(this->blob_top_->cpu_data()[0], 8.0 / 9, epsilon);
   EXPECT_NEAR(this->blob_top_->cpu_data()[1], 4.0 / 3, epsilon);
@@ -567,8 +581,8 @@ TYPED_TEST(PoolingLayerTest, TestGradientAve) {
       pooling_param->set_pool(PoolingParameter_PoolMethod_AVE);
       PoolingLayer<Dtype> layer(layer_param);
       GradientChecker<Dtype> checker(1e-2, 1e-2);
-      checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
-          &(this->blob_top_vec_));
+      checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+          this->blob_top_vec_);
     }
   }
 }
@@ -586,15 +600,15 @@ TYPED_TEST(PoolingLayerTest, TestGradientAvePadded) {
       pooling_param->set_pool(PoolingParameter_PoolMethod_AVE);
       PoolingLayer<Dtype> layer(layer_param);
       GradientChecker<Dtype> checker(1e-2, 1e-2);
-      checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
-          &(this->blob_top_vec_));
+      checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+          this->blob_top_vec_);
     }
   }
 }
 
 #ifdef USE_CUDNN
 template <typename Dtype>
-class CuDNNPoolingLayerTest : public ::testing::Test {
+class CuDNNPoolingLayerTest : public GPUDeviceTest<Dtype> {
  protected:
   CuDNNPoolingLayerTest()
       : blob_bottom_(new Blob<Dtype>()),
@@ -651,7 +665,7 @@ class CuDNNPoolingLayerTest : public ::testing::Test {
       blob_bottom_->mutable_cpu_data()[i + 14] = 3;
     }
     CuDNNPoolingLayer<Dtype> layer(layer_param);
-    layer.SetUp(blob_bottom_vec_, &blob_top_vec_);
+    layer.SetUp(blob_bottom_vec_, blob_top_vec_);
     EXPECT_EQ(blob_top_->num(), num);
     EXPECT_EQ(blob_top_->channels(), channels);
     EXPECT_EQ(blob_top_->height(), 2);
@@ -662,7 +676,7 @@ class CuDNNPoolingLayerTest : public ::testing::Test {
       EXPECT_EQ(blob_top_mask_->height(), 2);
       EXPECT_EQ(blob_top_mask_->width(), 4);
     }
-    layer.Forward(blob_bottom_vec_, &blob_top_vec_);
+    layer.Forward(blob_bottom_vec_, blob_top_vec_);
     // Expected output: 2x 2 channels of:
     //     [9 5 5 8]
     //     [9 5 5 8]
@@ -749,7 +763,7 @@ class CuDNNPoolingLayerTest : public ::testing::Test {
       blob_bottom_->mutable_cpu_data()[i + 35] = 11;
     }
     CuDNNPoolingLayer<Dtype> layer(layer_param);
-    layer.SetUp(blob_bottom_vec_, &blob_top_vec_);
+    layer.SetUp(blob_bottom_vec_, blob_top_vec_);
     EXPECT_EQ(blob_top_->num(), num);
     EXPECT_EQ(blob_top_->channels(), channels);
     EXPECT_EQ(blob_top_->height(), 4);
@@ -760,7 +774,7 @@ class CuDNNPoolingLayerTest : public ::testing::Test {
       EXPECT_EQ(blob_top_mask_->height(), 4);
       EXPECT_EQ(blob_top_mask_->width(), 5);
     }
-    layer.Forward(blob_bottom_vec_, &blob_top_vec_);
+    layer.Forward(blob_bottom_vec_, blob_top_vec_);
     // Expected output: 2x 2 channels of:
     // [35    32    26    27    27]
     // [32    33    33    27    27]
@@ -874,7 +888,7 @@ class CuDNNPoolingLayerTest : public ::testing::Test {
       blob_bottom_->mutable_cpu_data()[i + 35] = 11;
     }
     CuDNNPoolingLayer<Dtype> layer(layer_param);
-    layer.SetUp(blob_bottom_vec_, &blob_top_vec_);
+    layer.SetUp(blob_bottom_vec_, blob_top_vec_);
     EXPECT_EQ(blob_top_->num(), num);
     EXPECT_EQ(blob_top_->channels(), channels);
     EXPECT_EQ(blob_top_->height(), 5);
@@ -885,7 +899,7 @@ class CuDNNPoolingLayerTest : public ::testing::Test {
       EXPECT_EQ(blob_top_mask_->height(), 5);
       EXPECT_EQ(blob_top_mask_->width(), 4);
     }
-    layer.Forward(blob_bottom_vec_, &blob_top_vec_);
+    layer.Forward(blob_bottom_vec_, blob_top_vec_);
     // Expected output: 2x 2 channels of:
     // [35    32    26    26]
     // [32    32    27    27]
@@ -949,13 +963,12 @@ class CuDNNPoolingLayerTest : public ::testing::Test {
 TYPED_TEST_CASE(CuDNNPoolingLayerTest, TestDtypes);
 
 TYPED_TEST(CuDNNPoolingLayerTest, TestSetupCuDNN) {
-  Caffe::set_mode(Caffe::GPU);
   LayerParameter layer_param;
   PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
   pooling_param->set_kernel_size(3);
   pooling_param->set_stride(2);
   CuDNNPoolingLayer<TypeParam> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
   EXPECT_EQ(this->blob_top_->num(), this->blob_bottom_->num());
   EXPECT_EQ(this->blob_top_->channels(), this->blob_bottom_->channels());
   EXPECT_EQ(this->blob_top_->height(), 3);
@@ -963,7 +976,6 @@ TYPED_TEST(CuDNNPoolingLayerTest, TestSetupCuDNN) {
 }
 
 TYPED_TEST(CuDNNPoolingLayerTest, TestSetupPaddedCuDNN) {
-  Caffe::set_mode(Caffe::GPU);
   LayerParameter layer_param;
   PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
   pooling_param->set_kernel_size(3);
@@ -971,7 +983,7 @@ TYPED_TEST(CuDNNPoolingLayerTest, TestSetupPaddedCuDNN) {
   pooling_param->set_pad(1);
   pooling_param->set_pool(PoolingParameter_PoolMethod_AVE);
   CuDNNPoolingLayer<TypeParam> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
   EXPECT_EQ(this->blob_top_->num(), this->blob_bottom_->num());
   EXPECT_EQ(this->blob_top_->channels(), this->blob_bottom_->channels());
   EXPECT_EQ(this->blob_top_->height(), 4);
@@ -980,14 +992,13 @@ TYPED_TEST(CuDNNPoolingLayerTest, TestSetupPaddedCuDNN) {
 
 /*
 TYPED_TEST(CuDNNPoolingLayerTest, PrintBackwardCuDNN) {
-  Caffe::set_mode(Caffe::GPU);
   LayerParameter layer_param;
   layer_param.set_kernelsize(3);
   layer_param.set_stride(2);
   layer_param.set_pool(LayerParameter_PoolMethod_MAX);
   CuDNNPoolingLayer<TypeParam> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   for (int i = 0; i < this->blob_bottom_->count(); ++i) {
     cout << "bottom data " << i << " " << this->blob_bottom_->cpu_data()[i] << endl;
   }
@@ -998,7 +1009,7 @@ TYPED_TEST(CuDNNPoolingLayerTest, PrintBackwardCuDNN) {
   for (int i = 0; i < this->blob_top_->count(); ++i) {
     this->blob_top_->mutable_cpu_diff()[i] = i;
   }
-  layer.Backward(this->blob_top_vec_, true, &(this->blob_bottom_vec_));
+  layer.Backward(this->blob_top_vec_, true, this->blob_bottom_vec_);
   for (int i = 0; i < this->blob_bottom_->count(); ++i) {
     cout << "bottom diff " << i << " " << this->blob_bottom_->cpu_diff()[i] << endl;
   }
@@ -1006,22 +1017,23 @@ TYPED_TEST(CuDNNPoolingLayerTest, PrintBackwardCuDNN) {
 */
 
 TYPED_TEST(CuDNNPoolingLayerTest, TestForwardMaxCuDNN) {
-  Caffe::set_mode(Caffe::GPU);
   this->TestForwardSquare();
   this->TestForwardRectHigh();
   this->TestForwardRectWide();
 }
 
+// Currently, cuDNN does not support a top mask, so we comment this and
+// the corresponding backward test.
+/*
 TYPED_TEST(CuDNNPoolingLayerTest, TestForwardMaxTopMaskCuDNN) {
-  Caffe::set_mode(Caffe::GPU);
   this->blob_top_vec_.push_back(this->blob_top_mask_);
   this->TestForwardSquare();
   this->TestForwardRectHigh();
   this->TestForwardRectWide();
 }
+*/
 
 TYPED_TEST(CuDNNPoolingLayerTest, TestGradientMaxCuDNN) {
-  Caffe::set_mode(Caffe::GPU);
   for (int kernel_h = 3; kernel_h <= 4; kernel_h++) {
     for (int kernel_w = 3; kernel_w <= 4; kernel_w++) {
       LayerParameter layer_param;
@@ -1029,18 +1041,18 @@ TYPED_TEST(CuDNNPoolingLayerTest, TestGradientMaxCuDNN) {
       pooling_param->set_kernel_h(kernel_h);
       pooling_param->set_kernel_w(kernel_w);
       pooling_param->set_stride(2);
-      pooling_param->set_pad(1);
+      // currenty, cuDNN pooling does not support padding
+      pooling_param->set_pad(0);
       pooling_param->set_pool(PoolingParameter_PoolMethod_MAX);
       CuDNNPoolingLayer<TypeParam> layer(layer_param);
       GradientChecker<TypeParam> checker(1e-4, 1e-2);
-      checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
-          &(this->blob_top_vec_));
+      checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+          this->blob_top_vec_);
     }
   }
 }
 
 TYPED_TEST(CuDNNPoolingLayerTest, TestForwardMaxPaddedCuDNN) {
-  Caffe::set_mode(Caffe::GPU);
   LayerParameter layer_param;
   PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
   pooling_param->set_kernel_size(3);
@@ -1062,12 +1074,12 @@ TYPED_TEST(CuDNNPoolingLayerTest, TestForwardMaxPaddedCuDNN) {
   this->blob_bottom_->mutable_cpu_data()[7] = 2;
   this->blob_bottom_->mutable_cpu_data()[8] = 1;
   CuDNNPoolingLayer<TypeParam> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
   EXPECT_EQ(this->blob_top_->num(), 1);
   EXPECT_EQ(this->blob_top_->channels(), 1);
   EXPECT_EQ(this->blob_top_->height(), 3);
   EXPECT_EQ(this->blob_top_->width(), 3);
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   TypeParam epsilon = 1e-8;
   // Output:
   //     [ 1 4 4 ]
@@ -1084,8 +1096,8 @@ TYPED_TEST(CuDNNPoolingLayerTest, TestForwardMaxPaddedCuDNN) {
   EXPECT_NEAR(this->blob_top_->cpu_data()[8], 1, epsilon);
 }
 
+/*
 TYPED_TEST(CuDNNPoolingLayerTest, TestGradientMaxTopMaskCuDNN) {
-  Caffe::set_mode(Caffe::GPU);
   for (int kernel_h = 3; kernel_h <= 4; kernel_h++) {
     for (int kernel_w = 3; kernel_w <= 4; kernel_w++) {
       LayerParameter layer_param;
@@ -1097,20 +1109,22 @@ TYPED_TEST(CuDNNPoolingLayerTest, TestGradientMaxTopMaskCuDNN) {
       this->blob_top_vec_.push_back(this->blob_top_mask_);
       CuDNNPoolingLayer<TypeParam> layer(layer_param);
       GradientChecker<TypeParam> checker(1e-4, 1e-2);
-      checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
-          &(this->blob_top_vec_));
+      checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+          this->blob_top_vec_);
       this->blob_top_vec_.pop_back();
     }
   }
 }
+*/
 
 TYPED_TEST(CuDNNPoolingLayerTest, TestForwardAveCuDNN) {
-  Caffe::set_mode(Caffe::GPU);
   LayerParameter layer_param;
   PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
   pooling_param->set_kernel_size(3);
   pooling_param->set_stride(1);
-  pooling_param->set_pad(1);
+  // Currently, cuDNN pooling does not support padding, so we use
+  // a simplified version of this test.
+  pooling_param->set_pad(0);
   pooling_param->set_pool(PoolingParameter_PoolMethod_AVE);
   this->blob_bottom_->Reshape(1, 1, 3, 3);
   FillerParameter filler_param;
@@ -1118,26 +1132,17 @@ TYPED_TEST(CuDNNPoolingLayerTest, TestForwardAveCuDNN) {
   ConstantFiller<TypeParam> filler(filler_param);
   filler.Fill(this->blob_bottom_);
   CuDNNPoolingLayer<TypeParam> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
   EXPECT_EQ(this->blob_top_->num(), 1);
   EXPECT_EQ(this->blob_top_->channels(), 1);
-  EXPECT_EQ(this->blob_top_->height(), 3);
-  EXPECT_EQ(this->blob_top_->width(), 3);
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  EXPECT_EQ(this->blob_top_->height(), 1);
+  EXPECT_EQ(this->blob_top_->width(), 1);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   TypeParam epsilon = 1e-5;
-  EXPECT_NEAR(this->blob_top_->cpu_data()[0], 8.0 / 9, epsilon);
-  EXPECT_NEAR(this->blob_top_->cpu_data()[1], 4.0 / 3, epsilon);
-  EXPECT_NEAR(this->blob_top_->cpu_data()[2], 8.0 / 9, epsilon);
-  EXPECT_NEAR(this->blob_top_->cpu_data()[3], 4.0 / 3, epsilon);
-  EXPECT_NEAR(this->blob_top_->cpu_data()[4], 2.0    , epsilon);
-  EXPECT_NEAR(this->blob_top_->cpu_data()[5], 4.0 / 3, epsilon);
-  EXPECT_NEAR(this->blob_top_->cpu_data()[6], 8.0 / 9, epsilon);
-  EXPECT_NEAR(this->blob_top_->cpu_data()[7], 4.0 / 3, epsilon);
-  EXPECT_NEAR(this->blob_top_->cpu_data()[8], 8.0 / 9, epsilon);
+  EXPECT_NEAR(this->blob_top_->cpu_data()[0], 2.0, epsilon);
 }
 
 TYPED_TEST(CuDNNPoolingLayerTest, TestGradientAveCuDNN) {
-  Caffe::set_mode(Caffe::GPU);
   for (int kernel_h = 3; kernel_h <= 4; kernel_h++) {
     for (int kernel_w = 3; kernel_w <= 4; kernel_w++) {
       LayerParameter layer_param;
@@ -1148,14 +1153,13 @@ TYPED_TEST(CuDNNPoolingLayerTest, TestGradientAveCuDNN) {
       pooling_param->set_pool(PoolingParameter_PoolMethod_AVE);
       CuDNNPoolingLayer<TypeParam> layer(layer_param);
       GradientChecker<TypeParam> checker(1e-2, 1e-2);
-      checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
-          &(this->blob_top_vec_));
+      checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+          this->blob_top_vec_);
     }
   }
 }
 
 TYPED_TEST(CuDNNPoolingLayerTest, TestGradientAvePaddedCuDNN) {
-  Caffe::set_mode(Caffe::GPU);
   for (int kernel_h = 3; kernel_h <= 4; kernel_h++) {
     for (int kernel_w = 3; kernel_w <= 4; kernel_w++) {
       LayerParameter layer_param;
@@ -1167,8 +1171,8 @@ TYPED_TEST(CuDNNPoolingLayerTest, TestGradientAvePaddedCuDNN) {
       pooling_param->set_pool(PoolingParameter_PoolMethod_AVE);
       CuDNNPoolingLayer<TypeParam> layer(layer_param);
       GradientChecker<TypeParam> checker(1e-2, 1e-2);
-      checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
-          &(this->blob_top_vec_));
+      checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+          this->blob_top_vec_);
     }
   }
 }
diff --git a/src/caffe/test/test_power_layer.cpp b/src/caffe/test/test_power_layer.cpp
index 0c104c20b7c..76c9e857f36 100644
--- a/src/caffe/test/test_power_layer.cpp
+++ b/src/caffe/test/test_power_layer.cpp
@@ -37,8 +37,8 @@ class PowerLayerTest : public MultiDeviceTest<TypeParam> {
     layer_param.mutable_power_param()->set_scale(scale);
     layer_param.mutable_power_param()->set_shift(shift);
     PowerLayer<Dtype> layer(layer_param);
-    layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-    layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+    layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+    layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
     // Now, check values
     const Dtype* bottom_data = this->blob_bottom_->cpu_data();
     const Dtype* top_data = this->blob_top_->cpu_data();
@@ -74,9 +74,9 @@ class PowerLayerTest : public MultiDeviceTest<TypeParam> {
         }
       }
     }
-    GradientChecker<Dtype> checker(1e-2, 1e-2, 1701, 0., 0.01);
-    checker.CheckGradientEltwise(&layer, &(this->blob_bottom_vec_),
-        &(this->blob_top_vec_));
+    GradientChecker<Dtype> checker(1e-3, 1e-2, 1701, 0., 0.01);
+    checker.CheckGradientEltwise(&layer, this->blob_bottom_vec_,
+        this->blob_top_vec_);
   }
 
   Blob<Dtype>* const blob_bottom_;
diff --git a/src/caffe/test/test_protobuf.cpp b/src/caffe/test/test_protobuf.cpp
index 0c502d6dd36..01de461afdf 100644
--- a/src/caffe/test/test_protobuf.cpp
+++ b/src/caffe/test/test_protobuf.cpp
@@ -16,7 +16,7 @@ class ProtoTest : public ::testing::Test {};
 TEST_F(ProtoTest, TestSerialization) {
   LayerParameter param;
   param.set_name("test");
-  param.set_type(LayerParameter_LayerType_NONE);
+  param.set_type("Test");
   std::cout << "Printing in binary format." << std::endl;
   std::cout << param.SerializeAsString() << std::endl;
   std::cout << "Printing in text format." << std::endl;
diff --git a/src/caffe/test/test_reduction_layer.cpp b/src/caffe/test/test_reduction_layer.cpp
new file mode 100644
index 00000000000..f568a18089a
--- /dev/null
+++ b/src/caffe/test/test_reduction_layer.cpp
@@ -0,0 +1,297 @@
+#include <algorithm>
+#include <vector>
+
+#include "gtest/gtest.h"
+
+#include "caffe/blob.hpp"
+#include "caffe/common.hpp"
+#include "caffe/filler.hpp"
+#include "caffe/vision_layers.hpp"
+
+#include "caffe/test/test_caffe_main.hpp"
+#include "caffe/test/test_gradient_check_util.hpp"
+
+namespace caffe {
+
+template <typename TypeParam>
+class ReductionLayerTest : public MultiDeviceTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
+
+ protected:
+  ReductionLayerTest()
+      : blob_bottom_(new Blob<Dtype>(2, 3, 4, 5)),
+        blob_top_(new Blob<Dtype>()) {
+    // fill the values
+    Caffe::set_random_seed(1701);
+    FillerParameter filler_param;
+    UniformFiller<Dtype> filler(filler_param);
+    filler.Fill(this->blob_bottom_);
+    blob_bottom_vec_.push_back(blob_bottom_);
+    blob_top_vec_.push_back(blob_top_);
+  }
+  virtual ~ReductionLayerTest() {
+    delete blob_bottom_;
+    delete blob_top_;
+  }
+
+  void TestForward(ReductionParameter_ReductionOp op,
+                   float coeff = 1, int axis = 0) {
+    LayerParameter layer_param;
+    ReductionParameter* reduction_param = layer_param.mutable_reduction_param();
+    reduction_param->set_operation(op);
+    if (coeff != 1.0) { reduction_param->set_coeff(coeff); }
+    if (axis != 0) { reduction_param->set_axis(axis); }
+    shared_ptr<ReductionLayer<Dtype> > layer(
+        new ReductionLayer<Dtype>(layer_param));
+    layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+    layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+    const Dtype* in_data = this->blob_bottom_->cpu_data();
+    const int num = this->blob_bottom_->count(0, axis);
+    const int dim = this->blob_bottom_->count(axis);
+    for (int n = 0; n < num; ++n) {
+      Dtype expected_result = 0;
+      for (int d = 0; d < dim; ++d) {
+        switch (op) {
+          case ReductionParameter_ReductionOp_SUM:
+            expected_result += *in_data;
+            break;
+          case ReductionParameter_ReductionOp_MEAN:
+            expected_result += *in_data / dim;
+            break;
+          case ReductionParameter_ReductionOp_ASUM:
+            expected_result += fabs(*in_data);
+            break;
+          case ReductionParameter_ReductionOp_SUMSQ:
+            expected_result += (*in_data) * (*in_data);
+            break;
+          default:
+            LOG(FATAL) << "Unknown reduction op: "
+                << ReductionParameter_ReductionOp_Name(op);
+        }
+        ++in_data;
+      }
+      expected_result *= coeff;
+      const Dtype computed_result = this->blob_top_->cpu_data()[n];
+      EXPECT_FLOAT_EQ(expected_result, computed_result)
+          << "Incorrect result computed with op "
+          << ReductionParameter_ReductionOp_Name(op) << ", coeff " << coeff;
+    }
+  }
+
+  void TestGradient(ReductionParameter_ReductionOp op,
+                    float coeff = 1, int axis = 0) {
+    typedef typename TypeParam::Dtype Dtype;
+    LayerParameter layer_param;
+    ReductionParameter* reduction_param = layer_param.mutable_reduction_param();
+    reduction_param->set_operation(op);
+    reduction_param->set_coeff(coeff);
+    reduction_param->set_axis(axis);
+    ReductionLayer<Dtype> layer(layer_param);
+    GradientChecker<Dtype> checker(1e-2, 2e-3);
+    checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+        this->blob_top_vec_);
+  }
+
+  Blob<Dtype>* const blob_bottom_;
+  Blob<Dtype>* const blob_top_;
+  vector<Blob<Dtype>*> blob_bottom_vec_;
+  vector<Blob<Dtype>*> blob_top_vec_;
+};
+
+TYPED_TEST_CASE(ReductionLayerTest, TestDtypesAndDevices);
+
+TYPED_TEST(ReductionLayerTest, TestSetUp) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  shared_ptr<ReductionLayer<Dtype> > layer(
+      new ReductionLayer<Dtype>(layer_param));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  ASSERT_EQ(this->blob_top_->num_axes(), 0);
+}
+
+TYPED_TEST(ReductionLayerTest, TestSetUpWithAxis1) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  layer_param.mutable_reduction_param()->set_axis(1);
+  shared_ptr<ReductionLayer<Dtype> > layer(
+      new ReductionLayer<Dtype>(layer_param));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  ASSERT_EQ(this->blob_top_->num_axes(), 1);
+  EXPECT_EQ(this->blob_top_->shape(0), 2);
+}
+
+TYPED_TEST(ReductionLayerTest, TestSetUpWithAxis2) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  layer_param.mutable_reduction_param()->set_axis(2);
+  shared_ptr<ReductionLayer<Dtype> > layer(
+      new ReductionLayer<Dtype>(layer_param));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  ASSERT_EQ(this->blob_top_->num_axes(), 2);
+  EXPECT_EQ(this->blob_top_->shape(0), 2);
+  EXPECT_EQ(this->blob_top_->shape(1), 3);
+}
+
+TYPED_TEST(ReductionLayerTest, TestSum) {
+  const ReductionParameter_ReductionOp kOp = ReductionParameter_ReductionOp_SUM;
+  this->TestForward(kOp);
+}
+
+TYPED_TEST(ReductionLayerTest, TestSumCoeff) {
+  const ReductionParameter_ReductionOp kOp = ReductionParameter_ReductionOp_SUM;
+  const float kCoeff = 2.3;
+  this->TestForward(kOp, kCoeff);
+}
+
+TYPED_TEST(ReductionLayerTest, TestSumCoeffAxis1) {
+  const ReductionParameter_ReductionOp kOp = ReductionParameter_ReductionOp_SUM;
+  const float kCoeff = 2.3;
+  const int kAxis = 1;
+  this->TestForward(kOp, kCoeff, kAxis);
+}
+
+TYPED_TEST(ReductionLayerTest, TestSumGradient) {
+  const ReductionParameter_ReductionOp kOp = ReductionParameter_ReductionOp_SUM;
+  this->TestGradient(kOp);
+}
+
+TYPED_TEST(ReductionLayerTest, TestSumCoeffGradient) {
+  const ReductionParameter_ReductionOp kOp = ReductionParameter_ReductionOp_SUM;
+  const float kCoeff = 2.3;
+  this->TestGradient(kOp, kCoeff);
+}
+
+TYPED_TEST(ReductionLayerTest, TestSumCoeffAxis1Gradient) {
+  const ReductionParameter_ReductionOp kOp = ReductionParameter_ReductionOp_SUM;
+  const float kCoeff = 2.3;
+  const int kAxis = 1;
+  this->TestGradient(kOp, kCoeff, kAxis);
+}
+
+TYPED_TEST(ReductionLayerTest, TestMean) {
+  const ReductionParameter_ReductionOp kOp =
+      ReductionParameter_ReductionOp_MEAN;
+  this->TestForward(kOp);
+}
+
+TYPED_TEST(ReductionLayerTest, TestMeanCoeff) {
+  const ReductionParameter_ReductionOp kOp =
+      ReductionParameter_ReductionOp_MEAN;
+  const float kCoeff = 2.3;
+  this->TestForward(kOp, kCoeff);
+}
+
+TYPED_TEST(ReductionLayerTest, TestMeanCoeffAxis1) {
+  const ReductionParameter_ReductionOp kOp =
+      ReductionParameter_ReductionOp_MEAN;
+  const float kCoeff = 2.3;
+  const int kAxis = 1;
+  this->TestForward(kOp, kCoeff, kAxis);
+}
+
+TYPED_TEST(ReductionLayerTest, TestMeanGradient) {
+  const ReductionParameter_ReductionOp kOp =
+      ReductionParameter_ReductionOp_MEAN;
+  this->TestGradient(kOp);
+}
+
+TYPED_TEST(ReductionLayerTest, TestMeanCoeffGradient) {
+  const ReductionParameter_ReductionOp kOp =
+      ReductionParameter_ReductionOp_MEAN;
+  const float kCoeff = 2.3;
+  this->TestGradient(kOp, kCoeff);
+}
+
+TYPED_TEST(ReductionLayerTest, TestMeanCoeffGradientAxis1) {
+  const ReductionParameter_ReductionOp kOp =
+      ReductionParameter_ReductionOp_MEAN;
+  const float kCoeff = 2.3;
+  const int kAxis = 1;
+  this->TestGradient(kOp, kCoeff, kAxis);
+}
+
+TYPED_TEST(ReductionLayerTest, TestAbsSum) {
+  const ReductionParameter_ReductionOp kOp =
+      ReductionParameter_ReductionOp_ASUM;
+  this->TestForward(kOp);
+}
+
+TYPED_TEST(ReductionLayerTest, TestAbsSumCoeff) {
+  const ReductionParameter_ReductionOp kOp =
+      ReductionParameter_ReductionOp_ASUM;
+  const float kCoeff = 2.3;
+  this->TestForward(kOp, kCoeff);
+}
+
+TYPED_TEST(ReductionLayerTest, TestAbsSumCoeffAxis1) {
+  const ReductionParameter_ReductionOp kOp =
+      ReductionParameter_ReductionOp_ASUM;
+  const float kCoeff = 2.3;
+  const int kAxis = 1;
+  this->TestForward(kOp, kCoeff, kAxis);
+}
+
+TYPED_TEST(ReductionLayerTest, TestAbsSumGradient) {
+  const ReductionParameter_ReductionOp kOp =
+      ReductionParameter_ReductionOp_ASUM;
+  this->TestGradient(kOp);
+}
+
+TYPED_TEST(ReductionLayerTest, TestAbsSumCoeffGradient) {
+  const ReductionParameter_ReductionOp kOp =
+      ReductionParameter_ReductionOp_ASUM;
+  const float kCoeff = 2.3;
+  this->TestGradient(kOp, kCoeff);
+}
+
+TYPED_TEST(ReductionLayerTest, TestAbsSumCoeffAxis1Gradient) {
+  const ReductionParameter_ReductionOp kOp =
+      ReductionParameter_ReductionOp_ASUM;
+  const float kCoeff = 2.3;
+  const int kAxis = 1;
+  this->TestGradient(kOp, kCoeff, kAxis);
+}
+
+TYPED_TEST(ReductionLayerTest, TestSumOfSquares) {
+  const ReductionParameter_ReductionOp kOp =
+      ReductionParameter_ReductionOp_SUMSQ;
+  this->TestForward(kOp);
+}
+
+TYPED_TEST(ReductionLayerTest, TestSumOfSquaresCoeff) {
+  const ReductionParameter_ReductionOp kOp =
+      ReductionParameter_ReductionOp_SUMSQ;
+  const float kCoeff = 2.3;
+  this->TestForward(kOp, kCoeff);
+}
+
+TYPED_TEST(ReductionLayerTest, TestSumOfSquaresCoeffAxis1) {
+  const ReductionParameter_ReductionOp kOp =
+      ReductionParameter_ReductionOp_SUMSQ;
+  const float kCoeff = 2.3;
+  const int kAxis = 1;
+  this->TestForward(kOp, kCoeff, kAxis);
+}
+
+TYPED_TEST(ReductionLayerTest, TestSumOfSquaresGradient) {
+  const ReductionParameter_ReductionOp kOp =
+      ReductionParameter_ReductionOp_SUMSQ;
+  this->TestGradient(kOp);
+}
+
+TYPED_TEST(ReductionLayerTest, TestSumOfSquaresCoeffGradient) {
+  const ReductionParameter_ReductionOp kOp =
+      ReductionParameter_ReductionOp_SUMSQ;
+  const float kCoeff = 2.3;
+  this->TestGradient(kOp, kCoeff);
+}
+
+TYPED_TEST(ReductionLayerTest, TestSumOfSquaresCoeffAxis1Gradient) {
+  const ReductionParameter_ReductionOp kOp =
+      ReductionParameter_ReductionOp_SUMSQ;
+  const float kCoeff = 2.3;
+  const int kAxis = 1;
+  this->TestGradient(kOp, kCoeff, kAxis);
+}
+
+}  // namespace caffe
diff --git a/src/caffe/test/test_reshape_layer.cpp b/src/caffe/test/test_reshape_layer.cpp
new file mode 100644
index 00000000000..9d08ec60d4e
--- /dev/null
+++ b/src/caffe/test/test_reshape_layer.cpp
@@ -0,0 +1,280 @@
+#include <cstring>
+#include <vector>
+
+#include "gtest/gtest.h"
+
+#include "caffe/blob.hpp"
+#include "caffe/common.hpp"
+#include "caffe/common_layers.hpp"
+#include "caffe/filler.hpp"
+
+#include "caffe/test/test_caffe_main.hpp"
+#include "caffe/test/test_gradient_check_util.hpp"
+
+namespace caffe {
+
+template <typename TypeParam>
+class ReshapeLayerTest : public MultiDeviceTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
+ protected:
+  ReshapeLayerTest()
+    : blob_bottom_(new Blob<Dtype>(2, 3, 6, 5)),
+      blob_top_(new Blob<Dtype>()) {
+    // fill the values
+    FillerParameter filler_param;
+    GaussianFiller<Dtype> filler(filler_param);
+    filler.Fill(this->blob_bottom_);
+    blob_bottom_vec_.push_back(blob_bottom_);
+    blob_top_vec_.push_back(blob_top_);
+  }
+
+  virtual ~ReshapeLayerTest() { delete blob_bottom_; delete blob_top_; }
+
+  Blob<Dtype>* const blob_bottom_;
+  Blob<Dtype>* const blob_top_;
+  vector<Blob<Dtype>*> blob_bottom_vec_;
+  vector<Blob<Dtype>*> blob_top_vec_;
+};
+
+TYPED_TEST_CASE(ReshapeLayerTest, TestDtypesAndDevices);
+
+TYPED_TEST(ReshapeLayerTest, TestFlattenOutputSizes) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  BlobShape* blob_shape = layer_param.mutable_reshape_param()->mutable_shape();
+  blob_shape->add_dim(0);
+  blob_shape->add_dim(-1);
+  blob_shape->add_dim(1);
+  blob_shape->add_dim(1);
+
+  ReshapeLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  EXPECT_EQ(this->blob_top_->num(), 2);
+  EXPECT_EQ(this->blob_top_->channels(), 3 * 6 * 5);
+  EXPECT_EQ(this->blob_top_->height(), 1);
+  EXPECT_EQ(this->blob_top_->width(), 1);
+}
+
+TYPED_TEST(ReshapeLayerTest, TestFlattenValues) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  BlobShape* blob_shape = layer_param.mutable_reshape_param()->mutable_shape();
+  blob_shape->add_dim(0);
+  blob_shape->add_dim(-1);
+  blob_shape->add_dim(1);
+  blob_shape->add_dim(1);
+  ReshapeLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  for (int c = 0; c < 3 * 6 * 5; ++c) {
+    EXPECT_EQ(this->blob_top_->data_at(0, c, 0, 0),
+        this->blob_bottom_->data_at(0, c / (6 * 5), (c / 5) % 6, c % 5));
+    EXPECT_EQ(this->blob_top_->data_at(1, c, 0, 0),
+        this->blob_bottom_->data_at(1, c / (6 * 5), (c / 5) % 6, c % 5));
+  }
+}
+
+// Test whether setting output dimensions to 0 either explicitly or implicitly
+// copies the respective dimension of the input layer.
+TYPED_TEST(ReshapeLayerTest, TestCopyDimensions) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  BlobShape* blob_shape = layer_param.mutable_reshape_param()->mutable_shape();
+  blob_shape->add_dim(0);
+  blob_shape->add_dim(0);
+  blob_shape->add_dim(0);
+  blob_shape->add_dim(0);
+  ReshapeLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+
+  EXPECT_EQ(this->blob_top_->num(), 2);
+  EXPECT_EQ(this->blob_top_->channels(), 3);
+  EXPECT_EQ(this->blob_top_->height(), 6);
+  EXPECT_EQ(this->blob_top_->width(), 5);
+}
+
+// When a dimension is set to -1, we should infer its value from the other
+// dimensions (including those that get copied from below).
+TYPED_TEST(ReshapeLayerTest, TestInferenceOfUnspecified) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  BlobShape* blob_shape = layer_param.mutable_reshape_param()->mutable_shape();
+  blob_shape->add_dim(0);
+  blob_shape->add_dim(3);
+  blob_shape->add_dim(10);
+  blob_shape->add_dim(-1);
+
+  // Count is 180, thus height should be 180 / (2*3*10) = 3.
+
+  ReshapeLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+
+  EXPECT_EQ(this->blob_top_->num(), 2);
+  EXPECT_EQ(this->blob_top_->channels(), 3);
+  EXPECT_EQ(this->blob_top_->height(), 10);
+  EXPECT_EQ(this->blob_top_->width(), 3);
+}
+
+TYPED_TEST(ReshapeLayerTest, TestInferenceOfUnspecifiedWithStartAxis) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  layer_param.mutable_reshape_param()->set_axis(1);
+  BlobShape* blob_shape = layer_param.mutable_reshape_param()->mutable_shape();
+  blob_shape->add_dim(3);
+  blob_shape->add_dim(10);
+  blob_shape->add_dim(-1);
+
+  ReshapeLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+
+  ASSERT_EQ(this->blob_top_->num_axes(), 4);
+  EXPECT_EQ(this->blob_top_->num(), 2);
+  EXPECT_EQ(this->blob_top_->channels(), 3);
+  EXPECT_EQ(this->blob_top_->height(), 10);
+  EXPECT_EQ(this->blob_top_->width(), 3);
+}
+
+TYPED_TEST(ReshapeLayerTest, TestInsertSingletonAxesStart) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  layer_param.mutable_reshape_param()->set_axis(0);
+  layer_param.mutable_reshape_param()->set_num_axes(0);
+  BlobShape* blob_shape = layer_param.mutable_reshape_param()->mutable_shape();
+  blob_shape->add_dim(1);
+  blob_shape->add_dim(1);
+  blob_shape->add_dim(1);
+
+  ReshapeLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+
+  ASSERT_EQ(this->blob_top_->num_axes(), 7);
+  EXPECT_EQ(this->blob_top_->shape(0), 1);
+  EXPECT_EQ(this->blob_top_->shape(1), 1);
+  EXPECT_EQ(this->blob_top_->shape(2), 1);
+  EXPECT_EQ(this->blob_top_->shape(3), 2);
+  EXPECT_EQ(this->blob_top_->shape(4), 3);
+  EXPECT_EQ(this->blob_top_->shape(5), 6);
+  EXPECT_EQ(this->blob_top_->shape(6), 5);
+}
+
+TYPED_TEST(ReshapeLayerTest, TestInsertSingletonAxesMiddle) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  layer_param.mutable_reshape_param()->set_axis(2);
+  layer_param.mutable_reshape_param()->set_num_axes(0);
+  BlobShape* blob_shape = layer_param.mutable_reshape_param()->mutable_shape();
+  blob_shape->add_dim(1);
+  blob_shape->add_dim(1);
+  blob_shape->add_dim(1);
+
+  ReshapeLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+
+  ASSERT_EQ(this->blob_top_->num_axes(), 7);
+  EXPECT_EQ(this->blob_top_->shape(0), 2);
+  EXPECT_EQ(this->blob_top_->shape(1), 3);
+  EXPECT_EQ(this->blob_top_->shape(2), 1);
+  EXPECT_EQ(this->blob_top_->shape(3), 1);
+  EXPECT_EQ(this->blob_top_->shape(4), 1);
+  EXPECT_EQ(this->blob_top_->shape(5), 6);
+  EXPECT_EQ(this->blob_top_->shape(6), 5);
+}
+
+TYPED_TEST(ReshapeLayerTest, TestInsertSingletonAxesEnd) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  layer_param.mutable_reshape_param()->set_axis(-1);
+  layer_param.mutable_reshape_param()->set_num_axes(0);
+  BlobShape* blob_shape = layer_param.mutable_reshape_param()->mutable_shape();
+  blob_shape->add_dim(1);
+  blob_shape->add_dim(1);
+  blob_shape->add_dim(1);
+
+  ReshapeLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+
+  ASSERT_EQ(this->blob_top_->num_axes(), 7);
+  EXPECT_EQ(this->blob_top_->shape(0), 2);
+  EXPECT_EQ(this->blob_top_->shape(1), 3);
+  EXPECT_EQ(this->blob_top_->shape(2), 6);
+  EXPECT_EQ(this->blob_top_->shape(3), 5);
+  EXPECT_EQ(this->blob_top_->shape(4), 1);
+  EXPECT_EQ(this->blob_top_->shape(5), 1);
+  EXPECT_EQ(this->blob_top_->shape(6), 1);
+}
+
+TYPED_TEST(ReshapeLayerTest, TestFlattenMiddle) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  layer_param.mutable_reshape_param()->set_axis(1);
+  layer_param.mutable_reshape_param()->set_num_axes(2);
+  BlobShape* blob_shape = layer_param.mutable_reshape_param()->mutable_shape();
+  blob_shape->add_dim(-1);
+
+  ReshapeLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+
+  ASSERT_EQ(this->blob_top_->num_axes(), 3);
+  EXPECT_EQ(this->blob_top_->shape(0), 2);
+  EXPECT_EQ(this->blob_top_->shape(1), 3 * 6);
+  EXPECT_EQ(this->blob_top_->shape(2), 5);
+}
+
+TYPED_TEST(ReshapeLayerTest, TestForward) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  BlobShape* shape = layer_param.mutable_reshape_param()->mutable_shape();
+  shape->add_dim(6);
+  shape->add_dim(2);
+  shape->add_dim(3);
+  shape->add_dim(5);
+  ReshapeLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  for (int i = 0; i < this->blob_bottom_->count(); ++i) {
+    EXPECT_EQ(this->blob_top_->cpu_data()[i],
+              this->blob_bottom_->cpu_data()[i]);
+  }
+}
+
+TYPED_TEST(ReshapeLayerTest, TestForwardAfterReshape) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  BlobShape* shape = layer_param.mutable_reshape_param()->mutable_shape();
+  shape->add_dim(6);
+  shape->add_dim(2);
+  shape->add_dim(3);
+  shape->add_dim(5);
+  ReshapeLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  // We know the above produced the correct result from TestForward.
+  // Reshape the bottom and call layer.Reshape, then try again.
+  vector<int> new_bottom_shape(1, 2 * 3 * 6 * 5);
+  this->blob_bottom_->Reshape(new_bottom_shape);
+  layer.Reshape(this->blob_bottom_vec_, this->blob_top_vec_);
+  FillerParameter filler_param;
+  GaussianFiller<Dtype> filler(filler_param);
+  filler.Fill(this->blob_bottom_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  for (int i = 0; i < this->blob_bottom_->count(); ++i) {
+    EXPECT_EQ(this->blob_top_->cpu_data()[i],
+              this->blob_bottom_->cpu_data()[i]);
+  }
+}
+
+TYPED_TEST(ReshapeLayerTest, TestGradient) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  BlobShape* shape = layer_param.mutable_reshape_param()->mutable_shape();
+  shape->add_dim(6);
+  shape->add_dim(2);
+  shape->add_dim(3);
+  shape->add_dim(5);
+  ReshapeLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-2);
+  checker.CheckGradientEltwise(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
+}
+
+}  // namespace caffe
diff --git a/src/caffe/test/test_roi_pooling_layer.cpp b/src/caffe/test/test_roi_pooling_layer.cpp
new file mode 100644
index 00000000000..13d335f5626
--- /dev/null
+++ b/src/caffe/test/test_roi_pooling_layer.cpp
@@ -0,0 +1,105 @@
+// ------------------------------------------------------------------
+// Fast R-CNN
+// Copyright (c) 2015 Microsoft
+// Licensed under The MIT License [see fast-rcnn/LICENSE for details]
+// Written by Ross Girshick
+// ------------------------------------------------------------------
+#ifndef CPU_ONLY // ROIPooling only has GPU-based unit tests
+
+#include <cmath>
+#include <cstdlib>
+#include <cstring>
+#include <vector>
+
+#include "boost/scoped_ptr.hpp"
+#include "gtest/gtest.h"
+
+#include "caffe/blob.hpp"
+#include "caffe/common.hpp"
+#include "caffe/filler.hpp"
+#include "caffe/vision_layers.hpp"
+
+#include "caffe/test/test_caffe_main.hpp"
+#include "caffe/test/test_gradient_check_util.hpp"
+
+using boost::scoped_ptr;
+
+namespace caffe {
+
+typedef ::testing::Types<GPUDevice<float>, GPUDevice<double> > TestDtypesGPUOnly;
+
+template <typename TypeParam>
+class ROIPoolingLayerTest : public MultiDeviceTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
+
+ protected:
+  ROIPoolingLayerTest()
+      : blob_bottom_data_(new Blob<Dtype>(4, 3, 12, 8)),
+        blob_bottom_rois_(new Blob<Dtype>(4, 5, 1, 1)),
+        blob_top_data_(new Blob<Dtype>()) {
+    // fill the values
+    FillerParameter filler_param;
+    filler_param.set_std(10);
+    GaussianFiller<Dtype> filler(filler_param);
+    filler.Fill(this->blob_bottom_data_);
+    //for (int i = 0; i < blob_bottom_data_->count(); ++i) {
+    //  blob_bottom_data_->mutable_cpu_data()[i] = i;
+    //}
+    blob_bottom_vec_.push_back(blob_bottom_data_);
+    int i = 0;
+    blob_bottom_rois_->mutable_cpu_data()[0 + 5*i] = 0; //caffe_rng_rand() % 4;
+    blob_bottom_rois_->mutable_cpu_data()[1 + 5*i] = 1; // x1 < 8
+    blob_bottom_rois_->mutable_cpu_data()[2 + 5*i] = 1; // y1 < 12
+    blob_bottom_rois_->mutable_cpu_data()[3 + 5*i] = 6; // x2 < 8
+    blob_bottom_rois_->mutable_cpu_data()[4 + 5*i] = 6; // y2 < 12
+    i = 1;
+    blob_bottom_rois_->mutable_cpu_data()[0 + 5*i] = 2;
+    blob_bottom_rois_->mutable_cpu_data()[1 + 5*i] = 6; // x1 < 8
+    blob_bottom_rois_->mutable_cpu_data()[2 + 5*i] = 2; // y1 < 12
+    blob_bottom_rois_->mutable_cpu_data()[3 + 5*i] = 7; // x2 < 8
+    blob_bottom_rois_->mutable_cpu_data()[4 + 5*i] = 11; // y2 < 12
+    i = 2;
+    blob_bottom_rois_->mutable_cpu_data()[0 + 5*i] = 1;
+    blob_bottom_rois_->mutable_cpu_data()[1 + 5*i] = 3; // x1 < 8
+    blob_bottom_rois_->mutable_cpu_data()[2 + 5*i] = 1; // y1 < 12
+    blob_bottom_rois_->mutable_cpu_data()[3 + 5*i] = 5; // x2 < 8
+    blob_bottom_rois_->mutable_cpu_data()[4 + 5*i] = 10; // y2 < 12
+    i = 3;
+    blob_bottom_rois_->mutable_cpu_data()[0 + 5*i] = 0;
+    blob_bottom_rois_->mutable_cpu_data()[1 + 5*i] = 3; // x1 < 8
+    blob_bottom_rois_->mutable_cpu_data()[2 + 5*i] = 3; // y1 < 12
+    blob_bottom_rois_->mutable_cpu_data()[3 + 5*i] = 3; // x2 < 8
+    blob_bottom_rois_->mutable_cpu_data()[4 + 5*i] = 3; // y2 < 12
+
+    blob_bottom_vec_.push_back(blob_bottom_rois_);
+    blob_top_vec_.push_back(blob_top_data_);
+  }
+  virtual ~ROIPoolingLayerTest() {
+    delete blob_bottom_data_;
+    delete blob_bottom_rois_;
+    delete blob_top_data_;
+  }
+  Blob<Dtype>* const blob_bottom_data_;
+  Blob<Dtype>* const blob_bottom_rois_;
+  Blob<Dtype>* const blob_top_data_;
+  vector<Blob<Dtype>*> blob_bottom_vec_;
+  vector<Blob<Dtype>*> blob_top_vec_;
+};
+
+TYPED_TEST_CASE(ROIPoolingLayerTest, TestDtypesGPUOnly);
+
+TYPED_TEST(ROIPoolingLayerTest, TestGradient) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  ROIPoolingParameter* roi_pooling_param =
+      layer_param.mutable_roi_pooling_param();
+  roi_pooling_param->set_pooled_h(6);
+  roi_pooling_param->set_pooled_w(6);
+  ROIPoolingLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-4, 1e-2);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_, 0);
+}
+
+}  // namespace caffe
+#endif
\ No newline at end of file
diff --git a/src/caffe/test/test_scale_layer.cpp b/src/caffe/test/test_scale_layer.cpp
new file mode 100644
index 00000000000..4c0266bdc34
--- /dev/null
+++ b/src/caffe/test/test_scale_layer.cpp
@@ -0,0 +1,507 @@
+#include <algorithm>
+#include <vector>
+
+#include "gtest/gtest.h"
+
+#include "caffe/blob.hpp"
+#include "caffe/common.hpp"
+#include "caffe/filler.hpp"
+#include "caffe/common_layers.hpp"
+
+#include "caffe/test/test_caffe_main.hpp"
+#include "caffe/test/test_gradient_check_util.hpp"
+
+namespace caffe {
+
+template <typename TypeParam>
+class ScaleLayerTest : public MultiDeviceTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
+
+ protected:
+  ScaleLayerTest()
+      : blob_bottom_(new Blob<Dtype>(2, 3, 4, 5)),
+        blob_bottom_eltwise_(new Blob<Dtype>(2, 3, 4, 5)),
+        blob_bottom_broadcast_0_(new Blob<Dtype>()),
+        blob_bottom_broadcast_1_(new Blob<Dtype>()),
+        blob_bottom_broadcast_2_(new Blob<Dtype>()),
+        blob_bottom_scale_(new Blob<Dtype>(vector<int>())),
+        blob_top_(new Blob<Dtype>()) {
+    Caffe::set_random_seed(1701);
+    vector<int> broadcast_shape(2);
+    broadcast_shape[0] = 2; broadcast_shape[1] = 3;
+    this->blob_bottom_broadcast_0_->Reshape(broadcast_shape);
+    broadcast_shape[0] = 3; broadcast_shape[1] = 4;
+    this->blob_bottom_broadcast_1_->Reshape(broadcast_shape);
+    broadcast_shape[0] = 4; broadcast_shape[1] = 5;
+    this->blob_bottom_broadcast_2_->Reshape(broadcast_shape);
+    FillerParameter filler_param;
+    filler_param.set_min(1);
+    filler_param.set_max(10);
+    UniformFiller<Dtype> filler(filler_param);
+    filler.Fill(this->blob_bottom_);
+    filler.Fill(this->blob_bottom_eltwise_);
+    filler.Fill(this->blob_bottom_broadcast_0_);
+    filler.Fill(this->blob_bottom_broadcast_1_);
+    filler.Fill(this->blob_bottom_broadcast_2_);
+    filler.Fill(this->blob_bottom_scale_);
+    blob_bottom_vec_.push_back(blob_bottom_);
+    blob_top_vec_.push_back(blob_top_);
+  }
+  virtual ~ScaleLayerTest() {
+    delete blob_bottom_;
+    delete blob_bottom_eltwise_;
+    delete blob_bottom_broadcast_0_;
+    delete blob_bottom_broadcast_1_;
+    delete blob_bottom_broadcast_2_;
+    delete blob_bottom_scale_;
+    delete blob_top_;
+  }
+  Blob<Dtype>* const blob_bottom_;
+  Blob<Dtype>* const blob_bottom_eltwise_;
+  Blob<Dtype>* const blob_bottom_broadcast_0_;
+  Blob<Dtype>* const blob_bottom_broadcast_1_;
+  Blob<Dtype>* const blob_bottom_broadcast_2_;
+  Blob<Dtype>* const blob_bottom_scale_;
+  Blob<Dtype>* const blob_top_;
+  vector<Blob<Dtype>*> blob_bottom_vec_;
+  vector<Blob<Dtype>*> blob_top_vec_;
+};
+
+TYPED_TEST_CASE(ScaleLayerTest, TestDtypesAndDevices);
+
+TYPED_TEST(ScaleLayerTest, TestForwardEltwise) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->blob_bottom_vec_.push_back(this->blob_bottom_eltwise_);
+  LayerParameter layer_param;
+  layer_param.mutable_scale_param()->set_axis(0);
+  shared_ptr<ScaleLayer<Dtype> > layer(new ScaleLayer<Dtype>(layer_param));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  ASSERT_EQ(this->blob_bottom_->shape(), this->blob_top_->shape());
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  const Dtype* data = this->blob_top_->cpu_data();
+  const int count = this->blob_top_->count();
+  const Dtype* in_data_a = this->blob_bottom_->cpu_data();
+  const Dtype* in_data_b = this->blob_bottom_eltwise_->cpu_data();
+  for (int i = 0; i < count; ++i) {
+    EXPECT_NEAR(data[i], in_data_a[i] * in_data_b[i], 1e-5);
+  }
+}
+
+TYPED_TEST(ScaleLayerTest, TestForwardEltwiseInPlace) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->blob_top_vec_[0] = this->blob_bottom_;  // in-place computation
+  Blob<Dtype> orig_bottom(this->blob_bottom_->shape());
+  orig_bottom.CopyFrom(*this->blob_bottom_);
+  this->blob_bottom_vec_.push_back(this->blob_bottom_eltwise_);
+  LayerParameter layer_param;
+  layer_param.mutable_scale_param()->set_axis(0);
+  shared_ptr<ScaleLayer<Dtype> > layer(new ScaleLayer<Dtype>(layer_param));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  const Dtype* data = this->blob_bottom_->cpu_data();
+  const int count = this->blob_bottom_->count();
+  const Dtype* in_data_a = orig_bottom.cpu_data();
+  const Dtype* in_data_b = this->blob_bottom_eltwise_->cpu_data();
+  for (int i = 0; i < count; ++i) {
+    EXPECT_NEAR(data[i], in_data_a[i] * in_data_b[i], 1e-5);
+  }
+}
+
+TYPED_TEST(ScaleLayerTest, TestBackwardEltwiseInPlace) {
+  typedef typename TypeParam::Dtype Dtype;
+  Blob<Dtype> orig_bottom(this->blob_bottom_->shape());
+  orig_bottom.CopyFrom(*this->blob_bottom_);
+  this->blob_bottom_vec_.push_back(this->blob_bottom_eltwise_);
+  LayerParameter layer_param;
+  layer_param.mutable_scale_param()->set_axis(0);
+  shared_ptr<ScaleLayer<Dtype> > layer(new ScaleLayer<Dtype>(layer_param));
+  Blob<Dtype> top_diff(this->blob_bottom_->shape());
+  FillerParameter filler_param;
+  filler_param.set_type("gaussian");
+  filler_param.set_std(1);
+  GaussianFiller<Dtype> filler(filler_param);
+  filler.Fill(&top_diff);
+  vector<bool> propagate_down(2, true);
+  // Run forward + backward without in-place computation;
+  // save resulting bottom diffs.
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  caffe_copy(top_diff.count(), top_diff.cpu_data(),
+             this->blob_top_->mutable_cpu_diff());
+  layer->Backward(this->blob_top_vec_, propagate_down, this->blob_bottom_vec_);
+  const bool kReshape = true;
+  const bool kCopyDiff = true;
+  Blob<Dtype> orig_bottom_diff;
+  orig_bottom_diff.CopyFrom(*this->blob_bottom_, kCopyDiff, kReshape);
+  Blob<Dtype> orig_scale_diff;
+  orig_scale_diff.CopyFrom(*this->blob_bottom_eltwise_,
+                            kCopyDiff, kReshape);
+  // Rerun forward + backward with in-place computation;
+  // check that resulting bottom diffs are the same.
+  this->blob_top_vec_[0] = this->blob_bottom_;  // in-place computation
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  caffe_copy(top_diff.count(), top_diff.cpu_data(),
+             this->blob_bottom_->mutable_cpu_diff());
+  layer->Backward(this->blob_top_vec_, propagate_down, this->blob_bottom_vec_);
+  for (int i = 0; i < this->blob_bottom_->count(); ++i) {
+    EXPECT_NEAR(orig_bottom_diff.cpu_diff()[i],
+                this->blob_bottom_->cpu_diff()[i], 1e-5);
+  }
+  for (int i = 0; i < this->blob_bottom_eltwise_->count(); ++i) {
+    EXPECT_NEAR(orig_scale_diff.cpu_diff()[i],
+                this->blob_bottom_eltwise_->cpu_diff()[i], 1e-5);
+  }
+}
+
+TYPED_TEST(ScaleLayerTest, TestForwardEltwiseWithParam) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  ScaleParameter* scale_param = layer_param.mutable_scale_param();
+  scale_param->set_axis(0);
+  scale_param->set_num_axes(-1);
+  scale_param->mutable_filler()->set_type("gaussian");
+  shared_ptr<ScaleLayer<Dtype> > layer(new ScaleLayer<Dtype>(layer_param));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  ASSERT_EQ(this->blob_bottom_->shape(), this->blob_top_->shape());
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  const Dtype* data = this->blob_top_->cpu_data();
+  const int count = this->blob_top_->count();
+  const Dtype* in_data_a = this->blob_bottom_->cpu_data();
+  const Dtype* in_data_b = layer->blobs()[0]->cpu_data();
+  for (int i = 0; i < count; ++i) {
+    EXPECT_NEAR(data[i], in_data_a[i] * in_data_b[i], 1e-5);
+  }
+}
+
+TYPED_TEST(ScaleLayerTest, TestForwardBroadcastBegin) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->blob_bottom_vec_.push_back(this->blob_bottom_broadcast_0_);
+  LayerParameter layer_param;
+  layer_param.mutable_scale_param()->set_axis(0);
+  shared_ptr<ScaleLayer<Dtype> > layer(new ScaleLayer<Dtype>(layer_param));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  ASSERT_EQ(this->blob_bottom_->shape(), this->blob_top_->shape());
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  for (int n = 0; n < this->blob_bottom_->num(); ++n) {
+    for (int c = 0; c < this->blob_bottom_->channels(); ++c) {
+      for (int h = 0; h < this->blob_bottom_->height(); ++h) {
+        for (int w = 0; w < this->blob_bottom_->width(); ++w) {
+          EXPECT_NEAR(this->blob_top_->data_at(n, c, h, w),
+                      this->blob_bottom_->data_at(n, c, h, w) *
+                      this->blob_bottom_broadcast_0_->data_at(n, c, 0, 0),
+                      1e-5);
+        }
+      }
+    }
+  }
+}
+
+TYPED_TEST(ScaleLayerTest, TestForwardBroadcastMiddle) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->blob_bottom_vec_.push_back(this->blob_bottom_broadcast_1_);
+  LayerParameter layer_param;
+  layer_param.mutable_scale_param()->set_axis(1);
+  shared_ptr<ScaleLayer<Dtype> > layer(new ScaleLayer<Dtype>(layer_param));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  ASSERT_EQ(this->blob_bottom_->shape(), this->blob_top_->shape());
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  for (int n = 0; n < this->blob_bottom_->num(); ++n) {
+    for (int c = 0; c < this->blob_bottom_->channels(); ++c) {
+      for (int h = 0; h < this->blob_bottom_->height(); ++h) {
+        for (int w = 0; w < this->blob_bottom_->width(); ++w) {
+          EXPECT_NEAR(this->blob_top_->data_at(n, c, h, w),
+                      this->blob_bottom_->data_at(n, c, h, w) *
+                      this->blob_bottom_broadcast_1_->data_at(c, h, 0, 0),
+                      1e-5);
+        }
+      }
+    }
+  }
+}
+
+TYPED_TEST(ScaleLayerTest, TestForwardBroadcastMiddleInPlace) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->blob_top_vec_[0] = this->blob_bottom_;  // in-place computation
+  Blob<Dtype> orig_bottom(this->blob_bottom_->shape());
+  orig_bottom.CopyFrom(*this->blob_bottom_);
+  this->blob_bottom_vec_.push_back(this->blob_bottom_broadcast_1_);
+  LayerParameter layer_param;
+  layer_param.mutable_scale_param()->set_axis(1);
+  shared_ptr<ScaleLayer<Dtype> > layer(new ScaleLayer<Dtype>(layer_param));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  for (int n = 0; n < this->blob_bottom_->num(); ++n) {
+    for (int c = 0; c < this->blob_bottom_->channels(); ++c) {
+      for (int h = 0; h < this->blob_bottom_->height(); ++h) {
+        for (int w = 0; w < this->blob_bottom_->width(); ++w) {
+          EXPECT_NEAR(this->blob_bottom_->data_at(n, c, h, w),
+                      orig_bottom.data_at(n, c, h, w) *
+                      this->blob_bottom_broadcast_1_->data_at(c, h, 0, 0),
+                      1e-5);
+        }
+      }
+    }
+  }
+}
+
+TYPED_TEST(ScaleLayerTest, TestBackwardBroadcastMiddleInPlace) {
+  typedef typename TypeParam::Dtype Dtype;
+  Blob<Dtype> orig_bottom(this->blob_bottom_->shape());
+  orig_bottom.CopyFrom(*this->blob_bottom_);
+  this->blob_bottom_vec_.push_back(this->blob_bottom_broadcast_1_);
+  LayerParameter layer_param;
+  layer_param.mutable_scale_param()->set_axis(1);
+  shared_ptr<ScaleLayer<Dtype> > layer(new ScaleLayer<Dtype>(layer_param));
+  Blob<Dtype> top_diff(this->blob_bottom_->shape());
+  FillerParameter filler_param;
+  filler_param.set_type("gaussian");
+  filler_param.set_std(1);
+  GaussianFiller<Dtype> filler(filler_param);
+  filler.Fill(&top_diff);
+  vector<bool> propagate_down(2, true);
+  // Run forward + backward without in-place computation;
+  // save resulting bottom diffs.
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  caffe_copy(top_diff.count(), top_diff.cpu_data(),
+             this->blob_top_->mutable_cpu_diff());
+  layer->Backward(this->blob_top_vec_, propagate_down, this->blob_bottom_vec_);
+  const bool kReshape = true;
+  const bool kCopyDiff = true;
+  Blob<Dtype> orig_bottom_diff;
+  orig_bottom_diff.CopyFrom(*this->blob_bottom_, kCopyDiff, kReshape);
+  Blob<Dtype> orig_scale_diff;
+  orig_scale_diff.CopyFrom(*this->blob_bottom_broadcast_1_,
+                            kCopyDiff, kReshape);
+  // Rerun forward + backward with in-place computation;
+  // check that resulting bottom diffs are the same.
+  this->blob_top_vec_[0] = this->blob_bottom_;  // in-place computation
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  caffe_copy(top_diff.count(), top_diff.cpu_data(),
+             this->blob_bottom_->mutable_cpu_diff());
+  layer->Backward(this->blob_top_vec_, propagate_down, this->blob_bottom_vec_);
+  for (int i = 0; i < this->blob_bottom_->count(); ++i) {
+    EXPECT_NEAR(orig_bottom_diff.cpu_diff()[i],
+                this->blob_bottom_->cpu_diff()[i], 1e-5);
+  }
+  for (int i = 0; i < this->blob_bottom_broadcast_1_->count(); ++i) {
+    EXPECT_NEAR(orig_scale_diff.cpu_diff()[i],
+                this->blob_bottom_broadcast_1_->cpu_diff()[i], 1e-5);
+  }
+}
+
+TYPED_TEST(ScaleLayerTest, TestForwardBroadcastMiddleWithParam) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  ScaleParameter* scale_param = layer_param.mutable_scale_param();
+  scale_param->set_axis(1);
+  scale_param->set_num_axes(2);
+  scale_param->mutable_filler()->set_type("gaussian");
+  shared_ptr<ScaleLayer<Dtype> > layer(new ScaleLayer<Dtype>(layer_param));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  ASSERT_EQ(this->blob_bottom_->shape(), this->blob_top_->shape());
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  for (int n = 0; n < this->blob_bottom_->num(); ++n) {
+    for (int c = 0; c < this->blob_bottom_->channels(); ++c) {
+      for (int h = 0; h < this->blob_bottom_->height(); ++h) {
+        for (int w = 0; w < this->blob_bottom_->width(); ++w) {
+          EXPECT_NEAR(this->blob_top_->data_at(n, c, h, w),
+                      this->blob_bottom_->data_at(n, c, h, w) *
+                      layer->blobs()[0]->data_at(c, h, 0, 0), 1e-5);
+        }
+      }
+    }
+  }
+}
+
+TYPED_TEST(ScaleLayerTest, TestForwardBroadcastMiddleWithParamAndBias) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  ScaleParameter* scale_param = layer_param.mutable_scale_param();
+  scale_param->set_axis(1);
+  scale_param->set_num_axes(2);
+  scale_param->mutable_filler()->set_type("gaussian");
+  scale_param->set_bias_term(true);
+  scale_param->mutable_bias_filler()->set_type("gaussian");
+  shared_ptr<ScaleLayer<Dtype> > layer(new ScaleLayer<Dtype>(layer_param));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  ASSERT_EQ(this->blob_bottom_->shape(), this->blob_top_->shape());
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  for (int n = 0; n < this->blob_bottom_->num(); ++n) {
+    for (int c = 0; c < this->blob_bottom_->channels(); ++c) {
+      for (int h = 0; h < this->blob_bottom_->height(); ++h) {
+        for (int w = 0; w < this->blob_bottom_->width(); ++w) {
+          EXPECT_NEAR(this->blob_top_->data_at(n, c, h, w),
+                      this->blob_bottom_->data_at(n, c, h, w) *
+                      layer->blobs()[0]->data_at(c, h, 0, 0) +
+                      layer->blobs()[1]->data_at(c, h, 0, 0), 1e-5);
+        }
+      }
+    }
+  }
+}
+
+TYPED_TEST(ScaleLayerTest, TestForwardBroadcastEnd) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->blob_bottom_vec_.push_back(this->blob_bottom_broadcast_2_);
+  LayerParameter layer_param;
+  layer_param.mutable_scale_param()->set_axis(2);
+  shared_ptr<ScaleLayer<Dtype> > layer(new ScaleLayer<Dtype>(layer_param));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  ASSERT_EQ(this->blob_bottom_->shape(), this->blob_top_->shape());
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  for (int n = 0; n < this->blob_bottom_->num(); ++n) {
+    for (int c = 0; c < this->blob_bottom_->channels(); ++c) {
+      for (int h = 0; h < this->blob_bottom_->height(); ++h) {
+        for (int w = 0; w < this->blob_bottom_->width(); ++w) {
+          EXPECT_NEAR(this->blob_top_->data_at(n, c, h, w),
+                      this->blob_bottom_->data_at(n, c, h, w) *
+                      this->blob_bottom_broadcast_2_->data_at(h, w, 0, 0),
+                      1e-5);
+        }
+      }
+    }
+  }
+}
+
+TYPED_TEST(ScaleLayerTest, TestForwardScale) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->blob_bottom_vec_.push_back(this->blob_bottom_scale_);
+  LayerParameter layer_param;
+  shared_ptr<ScaleLayer<Dtype> > layer(new ScaleLayer<Dtype>(layer_param));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  ASSERT_EQ(this->blob_bottom_->shape(), this->blob_top_->shape());
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  const Dtype* data = this->blob_top_->cpu_data();
+  const int count = this->blob_top_->count();
+  const Dtype* in_data = this->blob_bottom_->cpu_data();
+  const Dtype scale = *this->blob_bottom_scale_->cpu_data();
+  for (int i = 0; i < count; ++i) {
+    EXPECT_NEAR(data[i], in_data[i] * scale, 1e-5);
+  }
+}
+
+TYPED_TEST(ScaleLayerTest, TestForwardScaleAxis2) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->blob_bottom_vec_.push_back(this->blob_bottom_scale_);
+  LayerParameter layer_param;
+  layer_param.mutable_scale_param()->set_axis(2);
+  shared_ptr<ScaleLayer<Dtype> > layer(new ScaleLayer<Dtype>(layer_param));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  ASSERT_EQ(this->blob_bottom_->shape(), this->blob_top_->shape());
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  const Dtype* data = this->blob_top_->cpu_data();
+  const int count = this->blob_top_->count();
+  const Dtype* in_data = this->blob_bottom_->cpu_data();
+  const Dtype scale = *this->blob_bottom_scale_->cpu_data();
+  for (int i = 0; i < count; ++i) {
+    EXPECT_NEAR(data[i], in_data[i] * scale, 1e-5);
+  }
+}
+
+TYPED_TEST(ScaleLayerTest, TestGradientEltwise) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->blob_bottom_vec_.push_back(this->blob_bottom_eltwise_);
+  LayerParameter layer_param;
+  layer_param.mutable_scale_param()->set_axis(0);
+  ScaleLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
+  checker.CheckGradientEltwise(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
+}
+
+TYPED_TEST(ScaleLayerTest, TestGradientEltwiseWithParam) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  ScaleParameter* scale_param = layer_param.mutable_scale_param();
+  scale_param->set_axis(0);
+  scale_param->set_num_axes(-1);
+  scale_param->mutable_filler()->set_type("gaussian");
+  ScaleLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
+}
+
+TYPED_TEST(ScaleLayerTest, TestGradientBroadcastBegin) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->blob_bottom_vec_.push_back(this->blob_bottom_broadcast_0_);
+  LayerParameter layer_param;
+  layer_param.mutable_scale_param()->set_axis(0);
+  ScaleLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
+}
+
+TYPED_TEST(ScaleLayerTest, TestGradientBroadcastMiddle) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->blob_bottom_vec_.push_back(this->blob_bottom_broadcast_1_);
+  LayerParameter layer_param;
+  layer_param.mutable_scale_param()->set_axis(1);
+  ScaleLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
+}
+
+TYPED_TEST(ScaleLayerTest, TestGradientBroadcastMiddleWithParam) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->blob_bottom_vec_.push_back(this->blob_bottom_broadcast_1_);
+  LayerParameter layer_param;
+  ScaleParameter* scale_param = layer_param.mutable_scale_param();
+  scale_param->set_axis(1);
+  scale_param->set_num_axes(2);
+  scale_param->mutable_filler()->set_type("gaussian");
+  ScaleLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
+}
+
+TYPED_TEST(ScaleLayerTest, TestGradientBroadcastEnd) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->blob_bottom_vec_.push_back(this->blob_bottom_broadcast_2_);
+  LayerParameter layer_param;
+  layer_param.mutable_scale_param()->set_axis(2);
+  ScaleLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
+}
+
+TYPED_TEST(ScaleLayerTest, TestGradientScale) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->blob_bottom_vec_.push_back(this->blob_bottom_scale_);
+  LayerParameter layer_param;
+  ScaleLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
+}
+
+TYPED_TEST(ScaleLayerTest, TestGradientScaleAndBias) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->blob_bottom_vec_.push_back(this->blob_bottom_scale_);
+  LayerParameter layer_param;
+  ScaleParameter* scale_param = layer_param.mutable_scale_param();
+  scale_param->set_bias_term(true);
+  scale_param->mutable_bias_filler()->set_type("gaussian");
+  ScaleLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
+}
+
+TYPED_TEST(ScaleLayerTest, TestGradientScaleAxis2) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->blob_bottom_vec_.push_back(this->blob_bottom_scale_);
+  LayerParameter layer_param;
+  layer_param.mutable_scale_param()->set_axis(2);
+  ScaleLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
+}
+
+}  // namespace caffe
diff --git a/src/caffe/test/test_sigmoid_cross_entropy_loss_layer.cpp b/src/caffe/test/test_sigmoid_cross_entropy_loss_layer.cpp
index 47ccdea1538..e5737e43f6e 100644
--- a/src/caffe/test/test_sigmoid_cross_entropy_loss_layer.cpp
+++ b/src/caffe/test/test_sigmoid_cross_entropy_loss_layer.cpp
@@ -79,9 +79,9 @@ class SigmoidCrossEntropyLossLayerTest : public MultiDeviceTest<TypeParam> {
       // Fill the targets vector
       targets_filler.Fill(this->blob_bottom_targets_);
       SigmoidCrossEntropyLossLayer<Dtype> layer(layer_param);
-      layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+      layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
       Dtype layer_loss =
-          layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+          layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
       const int count = this->blob_bottom_data_->count();
       const int num = this->blob_bottom_data_->num();
       const Dtype* blob_bottom_data = this->blob_bottom_data_->cpu_data();
@@ -112,10 +112,10 @@ TYPED_TEST(SigmoidCrossEntropyLossLayerTest, TestGradient) {
   const Dtype kLossWeight = 3.7;
   layer_param.add_loss_weight(kLossWeight);
   SigmoidCrossEntropyLossLayer<Dtype> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
   GradientChecker<Dtype> checker(1e-2, 1e-2, 1701);
-  checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_), 0);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_, 0);
 }
 
 
diff --git a/src/caffe/test/test_slice_layer.cpp b/src/caffe/test/test_slice_layer.cpp
index ee8818781f5..ccd03646d19 100644
--- a/src/caffe/test/test_slice_layer.cpp
+++ b/src/caffe/test/test_slice_layer.cpp
@@ -62,9 +62,9 @@ TYPED_TEST_CASE(SliceLayerTest, TestDtypesAndDevices);
 TYPED_TEST(SliceLayerTest, TestSetupNum) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  layer_param.mutable_slice_param()->set_slice_dim(0);
+  layer_param.mutable_slice_param()->set_axis(0);
   SliceLayer<Dtype> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_1_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_1_);
   EXPECT_EQ(this->blob_bottom_->num(), 3 * this->blob_top_0_->num());
   EXPECT_EQ(this->blob_top_0_->num(), this->blob_top_1_->num());
   EXPECT_EQ(this->blob_top_0_->num(), this->blob_top_2_->num());
@@ -78,7 +78,7 @@ TYPED_TEST(SliceLayerTest, TestSetupChannels) {
   LayerParameter layer_param;
   layer_param.mutable_slice_param()->add_slice_point(3);
   SliceLayer<Dtype> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_0_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_0_);
   EXPECT_EQ(this->blob_top_0_->num(), this->blob_bottom_->num());
   EXPECT_EQ(this->blob_top_0_->channels(), 3);
   EXPECT_EQ(this->blob_top_1_->channels(), 9);
@@ -91,13 +91,13 @@ TYPED_TEST(SliceLayerTest, TestSetupChannels) {
 TYPED_TEST(SliceLayerTest, TestSliceAcrossNum) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  layer_param.mutable_slice_param()->set_slice_dim(0);
+  layer_param.mutable_slice_param()->set_axis(0);
   SliceLayer<Dtype> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_0_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_0_);
   const int top_num = this->blob_bottom_->num() / 2;
   ASSERT_EQ(top_num, this->blob_top_0_->num());
   ASSERT_EQ(top_num, this->blob_top_1_->num());
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_0_));
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_0_);
   for (int n = 0; n < top_num; ++n) {
     for (int c = 0; c < this->blob_top_0_->channels(); ++c) {
       for (int h = 0; h < this->blob_bottom_->height(); ++h) {
@@ -127,12 +127,12 @@ TYPED_TEST(SliceLayerTest, TestSliceAcrossChannels) {
   layer_param.mutable_slice_param()->add_slice_point(kSlicePoint0);
   layer_param.mutable_slice_param()->add_slice_point(kSlicePoint1);
   SliceLayer<Dtype> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_1_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_1_);
   ASSERT_EQ(kSlicePoint0, this->blob_top_0_->channels());
   ASSERT_EQ(kSlicePoint1 - kSlicePoint0, this->blob_top_1_->channels());
   ASSERT_EQ(this->blob_bottom_->channels() - kSlicePoint1,
             this->blob_top_2_->channels());
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_1_));
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_1_);
   for (int n = 0; n < this->blob_bottom_->num(); ++n) {
     for (int c = 0; c < this->blob_top_0_->channels(); ++c) {
       for (int h = 0; h < this->blob_bottom_->height(); ++h) {
@@ -166,11 +166,11 @@ TYPED_TEST(SliceLayerTest, TestGradientAcrossNum) {
   // Gradient checks are slow; reduce blob size.
   this->ReduceBottomBlobSize();
   LayerParameter layer_param;
-  layer_param.mutable_slice_param()->set_slice_dim(0);
+  layer_param.mutable_slice_param()->set_axis(0);
   SliceLayer<Dtype> layer(layer_param);
   GradientChecker<Dtype> checker(1e-2, 1e-3);
-  checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
-    &(this->blob_top_vec_0_));
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+    this->blob_top_vec_0_);
 }
 
 TYPED_TEST(SliceLayerTest, TestGradientAcrossChannels) {
@@ -182,8 +182,8 @@ TYPED_TEST(SliceLayerTest, TestGradientAcrossChannels) {
   layer_param.mutable_slice_param()->add_slice_point(kSlicePoint);
   SliceLayer<Dtype> layer(layer_param);
   GradientChecker<Dtype> checker(1e-2, 1e-3);
-  checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
-    &(this->blob_top_vec_0_));
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+    this->blob_top_vec_0_);
 }
 
 }  // namespace caffe
diff --git a/src/caffe/test/test_softmax_layer.cpp b/src/caffe/test/test_softmax_layer.cpp
index 41f643f4e2a..996da4b8f7c 100644
--- a/src/caffe/test/test_softmax_layer.cpp
+++ b/src/caffe/test/test_softmax_layer.cpp
@@ -41,8 +41,8 @@ TYPED_TEST(SoftmaxLayerTest, TestForward) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
   SoftmaxLayer<Dtype> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   // Test sum
   for (int i = 0; i < this->blob_bottom_->num(); ++i) {
     for (int k = 0; k < this->blob_bottom_->height(); ++k) {
@@ -76,13 +76,13 @@ TYPED_TEST(SoftmaxLayerTest, TestGradient) {
   LayerParameter layer_param;
   SoftmaxLayer<Dtype> layer(layer_param);
   GradientChecker<Dtype> checker(1e-2, 1e-3);
-  checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_));
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
 }
 
 #ifdef USE_CUDNN
 template <typename Dtype>
-class CuDNNSoftmaxLayerTest : public ::testing::Test {
+class CuDNNSoftmaxLayerTest : public GPUDeviceTest<Dtype> {
  protected:
   CuDNNSoftmaxLayerTest()
       : blob_bottom_(new Blob<Dtype>(2, 10, 2, 3)),
@@ -104,11 +104,10 @@ class CuDNNSoftmaxLayerTest : public ::testing::Test {
 TYPED_TEST_CASE(CuDNNSoftmaxLayerTest, TestDtypes);
 
 TYPED_TEST(CuDNNSoftmaxLayerTest, TestForwardCuDNN) {
-  Caffe::set_mode(Caffe::GPU);
   LayerParameter layer_param;
   CuDNNSoftmaxLayer<TypeParam> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   // Test sum
   for (int i = 0; i < this->blob_bottom_->num(); ++i) {
     for (int k = 0; k < this->blob_bottom_->height(); ++k) {
@@ -138,12 +137,11 @@ TYPED_TEST(CuDNNSoftmaxLayerTest, TestForwardCuDNN) {
 }
 
 TYPED_TEST(CuDNNSoftmaxLayerTest, TestGradientCuDNN) {
-  Caffe::set_mode(Caffe::GPU);
   LayerParameter layer_param;
   CuDNNSoftmaxLayer<TypeParam> layer(layer_param);
   GradientChecker<TypeParam> checker(1e-2, 1e-3);
-  checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_));
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
 }
 
 #endif
diff --git a/src/caffe/test/test_softmax_with_loss_layer.cpp b/src/caffe/test/test_softmax_with_loss_layer.cpp
index 246d64e116a..1498d5c5ce1 100644
--- a/src/caffe/test/test_softmax_with_loss_layer.cpp
+++ b/src/caffe/test/test_softmax_with_loss_layer.cpp
@@ -3,6 +3,7 @@
 #include <cstring>
 #include <vector>
 
+#include "boost/scoped_ptr.hpp"
 #include "gtest/gtest.h"
 
 #include "caffe/blob.hpp"
@@ -13,6 +14,8 @@
 #include "caffe/test/test_caffe_main.hpp"
 #include "caffe/test/test_gradient_check_util.hpp"
 
+using boost::scoped_ptr;
+
 namespace caffe {
 
 template <typename TypeParam>
@@ -50,15 +53,58 @@ class SoftmaxWithLossLayerTest : public MultiDeviceTest<TypeParam> {
 
 TYPED_TEST_CASE(SoftmaxWithLossLayerTest, TestDtypesAndDevices);
 
-
 TYPED_TEST(SoftmaxWithLossLayerTest, TestGradient) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
   layer_param.add_loss_weight(3);
   SoftmaxWithLossLayer<Dtype> layer(layer_param);
   GradientChecker<Dtype> checker(1e-2, 1e-2, 1701);
-  checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_), 0);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_, 0);
+}
+
+TYPED_TEST(SoftmaxWithLossLayerTest, TestForwardIgnoreLabel) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  layer_param.mutable_loss_param()->set_normalize(false);
+  // First, compute the loss with all labels
+  scoped_ptr<SoftmaxWithLossLayer<Dtype> > layer(
+      new SoftmaxWithLossLayer<Dtype>(layer_param));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  Dtype full_loss = this->blob_top_loss_->cpu_data()[0];
+  // Now, accumulate the loss, ignoring each label in {0, ..., 4} in turn.
+  Dtype accum_loss = 0;
+  for (int label = 0; label < 5; ++label) {
+    layer_param.mutable_loss_param()->set_ignore_label(label);
+    layer.reset(new SoftmaxWithLossLayer<Dtype>(layer_param));
+    layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+    layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+    accum_loss += this->blob_top_loss_->cpu_data()[0];
+  }
+  // Check that each label was included all but once.
+  EXPECT_NEAR(4 * full_loss, accum_loss, 1e-4);
+}
+
+TYPED_TEST(SoftmaxWithLossLayerTest, TestGradientIgnoreLabel) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  // labels are in {0, ..., 4}, so we'll ignore about a fifth of them
+  layer_param.mutable_loss_param()->set_ignore_label(0);
+  SoftmaxWithLossLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-2, 1701);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_, 0);
+}
+
+TYPED_TEST(SoftmaxWithLossLayerTest, TestGradientUnnormalized) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  layer_param.mutable_loss_param()->set_normalize(false);
+  SoftmaxWithLossLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-2, 1701);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_, 0);
 }
 
 }  // namespace caffe
diff --git a/src/caffe/test/test_solver.cpp b/src/caffe/test/test_solver.cpp
index a7dbf77fd95..ceabc9cdd2c 100644
--- a/src/caffe/test/test_solver.cpp
+++ b/src/caffe/test/test_solver.cpp
@@ -51,42 +51,43 @@ TYPED_TEST(SolverTest, TestInitTrainTestNets) {
      "test_state: {}"
      "net_param { "
      "  name: 'TestNetwork' "
-     "  layers: { "
+     "  layer { "
      "    name: 'data' "
-     "    type: DUMMY_DATA "
+     "    type: 'DummyData' "
      "    dummy_data_param { "
-     "      num: 5 "
-     "      channels: 3 "
-     "      height: 10 "
-     "      width: 10 "
-     "      num: 5 "
-     "      channels: 1 "
-     "      height: 1 "
-     "      width: 1 "
+     "      shape { "
+     "        dim: 5 "
+     "        dim: 2 "
+     "        dim: 3 "
+     "        dim: 4 "
+     "      } "
+     "      shape { "
+     "        dim: 5 "
+     "      } "
      "    } "
      "    top: 'data' "
      "    top: 'label' "
      "  } "
-     "  layers: { "
+     "  layer { "
      "    name: 'innerprod' "
-     "    type: INNER_PRODUCT "
+     "    type: 'InnerProduct' "
      "    inner_product_param { "
      "      num_output: 10 "
      "    } "
      "    bottom: 'data' "
      "    top: 'innerprod' "
      "  } "
-     "  layers: { "
+     "  layer { "
      "    name: 'accuracy' "
-     "    type: ACCURACY "
+     "    type: 'Accuracy' "
      "    bottom: 'innerprod' "
      "    bottom: 'label' "
      "    top: 'accuracy' "
      "    exclude: { phase: TRAIN } "
      "  } "
-     "  layers: { "
+     "  layer { "
      "    name: 'loss' "
-     "    type: SOFTMAX_LOSS "
+     "    type: 'SoftmaxWithLoss' "
      "    bottom: 'innerprod' "
      "    bottom: 'label' "
      "    include: { phase: TRAIN } "
diff --git a/src/caffe/test/test_split_layer.cpp b/src/caffe/test/test_split_layer.cpp
index e9b942c5c51..be5204bfc3e 100644
--- a/src/caffe/test/test_split_layer.cpp
+++ b/src/caffe/test/test_split_layer.cpp
@@ -52,7 +52,7 @@ TYPED_TEST(SplitLayerTest, TestSetup) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
   SplitLayer<Dtype> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
   EXPECT_EQ(this->blob_top_a_->num(), 2);
   EXPECT_EQ(this->blob_top_a_->channels(), 3);
   EXPECT_EQ(this->blob_top_a_->height(), 6);
@@ -67,8 +67,8 @@ TYPED_TEST(SplitLayerTest, Test) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
   SplitLayer<Dtype> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   for (int i = 0; i < this->blob_bottom_->count(); ++i) {
     Dtype bottom_value = this->blob_bottom_->cpu_data()[i];
     EXPECT_EQ(bottom_value, this->blob_top_a_->cpu_data()[i]);
@@ -81,8 +81,8 @@ TYPED_TEST(SplitLayerTest, TestGradient) {
   LayerParameter layer_param;
   SplitLayer<Dtype> layer(layer_param);
   GradientChecker<Dtype> checker(1e-2, 1e-2);
-  checker.CheckGradientEltwise(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_));
+  checker.CheckGradientEltwise(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
 }
 
 
@@ -114,21 +114,21 @@ class SplitLayerInsertionTest : public ::testing::Test {
 TEST_F(SplitLayerInsertionTest, TestNoInsertion1) {
   const string& input_proto =
       "name: 'TestNetwork' "
-      "layers: { "
+      "layer { "
       "  name: 'data' "
-      "  type: DATA "
+      "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'innerprod' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  bottom: 'data' "
       "  top: 'innerprod' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'loss' "
-      "  type: SOFTMAX_LOSS "
+      "  type: 'SoftmaxWithLoss' "
       "  bottom: 'innerprod' "
       "  bottom: 'label' "
       "} ";
@@ -138,34 +138,34 @@ TEST_F(SplitLayerInsertionTest, TestNoInsertion1) {
 TEST_F(SplitLayerInsertionTest, TestNoInsertion2) {
   const string& input_proto =
       "name: 'TestNetwork' "
-      "layers: { "
+      "layer { "
       "  name: 'data' "
-      "  type: DATA "
+      "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'data_split' "
-      "  type: SPLIT "
+      "  type: 'Split' "
       "  bottom: 'data' "
       "  top: 'data_split_0' "
       "  top: 'data_split_1' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'innerprod1' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  bottom: 'data_split_0' "
       "  top: 'innerprod1' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'innerprod2' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  bottom: 'data_split_1' "
       "  top: 'innerprod2' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'loss' "
-      "  type: EUCLIDEAN_LOSS "
+      "  type: 'EuclideanLoss' "
       "  bottom: 'innerprod1' "
       "  bottom: 'innerprod2' "
       "} ";
@@ -175,9 +175,9 @@ TEST_F(SplitLayerInsertionTest, TestNoInsertion2) {
 TEST_F(SplitLayerInsertionTest, TestNoInsertionImageNet) {
   const string& input_proto =
       "name: 'CaffeNet' "
-      "layers { "
+      "layer { "
       "  name: 'data' "
-      "  type: DATA "
+      "  type: 'Data' "
       "  data_param { "
       "    source: '/home/jiayq/Data/ILSVRC12/train-leveldb' "
       "    batch_size: 256 "
@@ -190,9 +190,9 @@ TEST_F(SplitLayerInsertionTest, TestNoInsertionImageNet) {
       "  top: 'data' "
       "  top: 'label' "
       "} "
-      "layers { "
+      "layer { "
       "  name: 'conv1' "
-      "  type: CONVOLUTION "
+      "  type: 'Convolution' "
       "  convolution_param { "
       "    num_output: 96 "
       "    kernel_size: 11 "
@@ -206,22 +206,26 @@ TEST_F(SplitLayerInsertionTest, TestNoInsertionImageNet) {
       "      value: 0. "
       "    } "
       "  } "
-      "  blobs_lr: 1. "
-      "  blobs_lr: 2. "
-      "  weight_decay: 1. "
-      "  weight_decay: 0. "
+      "  param { "
+      "    lr_mult: 1 "
+      "    decay_mult: 1 "
+      "  } "
+      "  param { "
+      "    lr_mult: 2 "
+      "    decay_mult: 0 "
+      "  } "
       "  bottom: 'data' "
       "  top: 'conv1' "
       "} "
-      "layers { "
+      "layer { "
       "  name: 'relu1' "
-      "  type: RELU "
+      "  type: 'ReLU' "
       "  bottom: 'conv1' "
       "  top: 'conv1' "
       "} "
-      "layers { "
+      "layer { "
       "  name: 'pool1' "
-      "  type: POOLING "
+      "  type: 'Pooling' "
       "  pooling_param { "
       "    pool: MAX "
       "    kernel_size: 3 "
@@ -230,9 +234,9 @@ TEST_F(SplitLayerInsertionTest, TestNoInsertionImageNet) {
       "  bottom: 'conv1' "
       "  top: 'pool1' "
       "} "
-      "layers { "
+      "layer { "
       "  name: 'norm1' "
-      "  type: LRN "
+      "  type: 'LRN' "
       "  lrn_param { "
       "    local_size: 5 "
       "    alpha: 0.0001 "
@@ -241,9 +245,9 @@ TEST_F(SplitLayerInsertionTest, TestNoInsertionImageNet) {
       "  bottom: 'pool1' "
       "  top: 'norm1' "
       "} "
-      "layers { "
+      "layer { "
       "  name: 'conv2' "
-      "  type: CONVOLUTION "
+      "  type: 'Convolution' "
       "  convolution_param { "
       "    num_output: 256 "
       "    group: 2 "
@@ -258,22 +262,26 @@ TEST_F(SplitLayerInsertionTest, TestNoInsertionImageNet) {
       "      value: 1. "
       "    } "
       "  } "
-      "  blobs_lr: 1. "
-      "  blobs_lr: 2. "
-      "  weight_decay: 1. "
-      "  weight_decay: 0. "
+      "  param { "
+      "    lr_mult: 1 "
+      "    decay_mult: 1 "
+      "  } "
+      "  param { "
+      "    lr_mult: 2 "
+      "    decay_mult: 0 "
+      "  } "
       "  bottom: 'norm1' "
       "  top: 'conv2' "
       "} "
-      "layers { "
+      "layer { "
       "  name: 'relu2' "
-      "  type: RELU "
+      "  type: 'ReLU' "
       "  bottom: 'conv2' "
       "  top: 'conv2' "
       "} "
-      "layers { "
+      "layer { "
       "  name: 'pool2' "
-      "  type: POOLING "
+      "  type: 'Pooling' "
       "  pooling_param { "
       "    pool: MAX "
       "    kernel_size: 3 "
@@ -282,9 +290,9 @@ TEST_F(SplitLayerInsertionTest, TestNoInsertionImageNet) {
       "  bottom: 'conv2' "
       "  top: 'pool2' "
       "} "
-      "layers { "
+      "layer { "
       "  name: 'norm2' "
-      "  type: LRN "
+      "  type: 'LRN' "
       "  lrn_param { "
       "    local_size: 5 "
       "    alpha: 0.0001 "
@@ -293,9 +301,9 @@ TEST_F(SplitLayerInsertionTest, TestNoInsertionImageNet) {
       "  bottom: 'pool2' "
       "  top: 'norm2' "
       "} "
-      "layers { "
+      "layer { "
       "  name: 'conv3' "
-      "  type: CONVOLUTION "
+      "  type: 'Convolution' "
       "  convolution_param { "
       "    num_output: 384 "
       "    kernel_size: 3 "
@@ -309,22 +317,26 @@ TEST_F(SplitLayerInsertionTest, TestNoInsertionImageNet) {
       "      value: 0. "
       "    } "
       "  } "
-      "  blobs_lr: 1. "
-      "  blobs_lr: 2. "
-      "  weight_decay: 1. "
-      "  weight_decay: 0. "
+      "  param { "
+      "    lr_mult: 1 "
+      "    decay_mult: 1 "
+      "  } "
+      "  param { "
+      "    lr_mult: 2 "
+      "    decay_mult: 0 "
+      "  } "
       "  bottom: 'norm2' "
       "  top: 'conv3' "
       "} "
-      "layers { "
+      "layer { "
       "  name: 'relu3' "
-      "  type: RELU "
+      "  type: 'ReLU' "
       "  bottom: 'conv3' "
       "  top: 'conv3' "
       "} "
-      "layers { "
+      "layer { "
       "  name: 'conv4' "
-      "  type: CONVOLUTION "
+      "  type: 'Convolution' "
       "  convolution_param { "
       "    num_output: 384 "
       "    group: 2 "
@@ -339,22 +351,26 @@ TEST_F(SplitLayerInsertionTest, TestNoInsertionImageNet) {
       "      value: 1. "
       "    } "
       "  } "
-      "  blobs_lr: 1. "
-      "  blobs_lr: 2. "
-      "  weight_decay: 1. "
-      "  weight_decay: 0. "
+      "  param { "
+      "    lr_mult: 1 "
+      "    decay_mult: 1 "
+      "  } "
+      "  param { "
+      "    lr_mult: 2 "
+      "    decay_mult: 0 "
+      "  } "
       "  bottom: 'conv3' "
       "  top: 'conv4' "
       "} "
-      "layers { "
+      "layer { "
       "  name: 'relu4' "
-      "  type: RELU "
+      "  type: 'ReLU' "
       "  bottom: 'conv4' "
       "  top: 'conv4' "
       "} "
-      "layers { "
+      "layer { "
       "  name: 'conv5' "
-      "  type: CONVOLUTION "
+      "  type: 'Convolution' "
       "  convolution_param { "
       "    num_output: 256 "
       "    group: 2 "
@@ -369,22 +385,26 @@ TEST_F(SplitLayerInsertionTest, TestNoInsertionImageNet) {
       "      value: 1. "
       "    } "
       "  } "
-      "  blobs_lr: 1. "
-      "  blobs_lr: 2. "
-      "  weight_decay: 1. "
-      "  weight_decay: 0. "
+      "  param { "
+      "    lr_mult: 1 "
+      "    decay_mult: 1 "
+      "  } "
+      "  param { "
+      "    lr_mult: 2 "
+      "    decay_mult: 0 "
+      "  } "
       "  bottom: 'conv4' "
       "  top: 'conv5' "
       "} "
-      "layers { "
+      "layer { "
       "  name: 'relu5' "
-      "  type: RELU "
+      "  type: 'ReLU' "
       "  bottom: 'conv5' "
       "  top: 'conv5' "
       "} "
-      "layers { "
+      "layer { "
       "  name: 'pool5' "
-      "  type: POOLING "
+      "  type: 'Pooling' "
       "  pooling_param { "
       "    kernel_size: 3 "
       "    pool: MAX "
@@ -393,9 +413,9 @@ TEST_F(SplitLayerInsertionTest, TestNoInsertionImageNet) {
       "  bottom: 'conv5' "
       "  top: 'pool5' "
       "} "
-      "layers { "
+      "layer { "
       "  name: 'fc6' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  inner_product_param { "
       "    num_output: 4096 "
       "    weight_filler { "
@@ -407,31 +427,35 @@ TEST_F(SplitLayerInsertionTest, TestNoInsertionImageNet) {
       "      value: 1. "
       "    } "
       "  } "
-      "  blobs_lr: 1. "
-      "  blobs_lr: 2. "
-      "  weight_decay: 1. "
-      "  weight_decay: 0. "
+      "  param { "
+      "    lr_mult: 1 "
+      "    decay_mult: 1 "
+      "  } "
+      "  param { "
+      "    lr_mult: 2 "
+      "    decay_mult: 0 "
+      "  } "
       "  bottom: 'pool5' "
       "  top: 'fc6' "
       "} "
-      "layers { "
+      "layer { "
       "  name: 'relu6' "
-      "  type: RELU "
+      "  type: 'ReLU' "
       "  bottom: 'fc6' "
       "  top: 'fc6' "
       "} "
-      "layers { "
+      "layer { "
       "  name: 'drop6' "
-      "  type: DROPOUT "
+      "  type: 'Dropout' "
       "  dropout_param { "
       "    dropout_ratio: 0.5 "
       "  } "
       "  bottom: 'fc6' "
       "  top: 'fc6' "
       "} "
-      "layers { "
+      "layer { "
       "  name: 'fc7' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  inner_product_param { "
       "    num_output: 4096 "
       "    weight_filler { "
@@ -443,31 +467,35 @@ TEST_F(SplitLayerInsertionTest, TestNoInsertionImageNet) {
       "      value: 1. "
       "    } "
       "  } "
-      "  blobs_lr: 1. "
-      "  blobs_lr: 2. "
-      "  weight_decay: 1. "
-      "  weight_decay: 0. "
+      "  param { "
+      "    lr_mult: 1 "
+      "    decay_mult: 1 "
+      "  } "
+      "  param { "
+      "    lr_mult: 2 "
+      "    decay_mult: 0 "
+      "  } "
       "  bottom: 'fc6' "
       "  top: 'fc7' "
       "} "
-      "layers { "
+      "layer { "
       "  name: 'relu7' "
-      "  type: RELU "
+      "  type: 'ReLU' "
       "  bottom: 'fc7' "
       "  top: 'fc7' "
       "} "
-      "layers { "
+      "layer { "
       "  name: 'drop7' "
-      "  type: DROPOUT "
+      "  type: 'Dropout' "
       "  dropout_param { "
       "    dropout_ratio: 0.5 "
       "  } "
       "  bottom: 'fc7' "
       "  top: 'fc7' "
       "} "
-      "layers { "
+      "layer { "
       "  name: 'fc8' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  inner_product_param { "
       "    num_output: 1000 "
       "    weight_filler { "
@@ -479,16 +507,20 @@ TEST_F(SplitLayerInsertionTest, TestNoInsertionImageNet) {
       "      value: 0 "
       "    } "
       "  } "
-      "  blobs_lr: 1. "
-      "  blobs_lr: 2. "
-      "  weight_decay: 1. "
-      "  weight_decay: 0. "
+      "  param { "
+      "    lr_mult: 1 "
+      "    decay_mult: 1 "
+      "  } "
+      "  param { "
+      "    lr_mult: 2 "
+      "    decay_mult: 0 "
+      "  } "
       "  bottom: 'fc7' "
       "  top: 'fc8' "
       "} "
-      "layers { "
+      "layer { "
       "  name: 'loss' "
-      "  type: SOFTMAX_LOSS "
+      "  type: 'SoftmaxWithLoss' "
       "  bottom: 'fc8' "
       "  bottom: 'label' "
       "} ";
@@ -498,27 +530,27 @@ TEST_F(SplitLayerInsertionTest, TestNoInsertionImageNet) {
 TEST_F(SplitLayerInsertionTest, TestNoInsertionWithInPlace) {
   const string& input_proto =
       "name: 'TestNetwork' "
-      "layers: { "
+      "layer { "
       "  name: 'data' "
-      "  type: DATA "
+      "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'innerprod' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  bottom: 'data' "
       "  top: 'innerprod' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'relu' "
-      "  type: RELU "
+      "  type: 'ReLU' "
       "  bottom: 'innerprod' "
       "  top: 'innerprod' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'loss' "
-      "  type: SOFTMAX_LOSS "
+      "  type: 'SoftmaxWithLoss' "
       "  bottom: 'innerprod' "
       "  bottom: 'label' "
       "} ";
@@ -529,9 +561,9 @@ TEST_F(SplitLayerInsertionTest, TestLossInsertion) {
   const string& input_proto =
       "name: 'UnsharedWeightsNetwork' "
       "force_backward: true "
-      "layers: { "
+      "layer { "
       "  name: 'data' "
-      "  type: DUMMY_DATA "
+      "  type: 'DummyData' "
       "  dummy_data_param { "
       "    num: 5 "
       "    channels: 2 "
@@ -544,9 +576,9 @@ TEST_F(SplitLayerInsertionTest, TestLossInsertion) {
       "  } "
       "  top: 'data' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'innerproduct1' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  inner_product_param { "
       "    num_output: 10 "
       "    bias_term: false "
@@ -555,14 +587,14 @@ TEST_F(SplitLayerInsertionTest, TestLossInsertion) {
       "      std: 10 "
       "    } "
       "  } "
-      "  param: 'unsharedweights1' "
+      "  param { name: 'unsharedweights1' } "
       "  bottom: 'data' "
       "  top: 'innerproduct1' "
       "  loss_weight: 2.5 "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'innerproduct2' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  inner_product_param { "
       "    num_output: 10 "
       "    bias_term: false "
@@ -571,22 +603,22 @@ TEST_F(SplitLayerInsertionTest, TestLossInsertion) {
       "      std: 10 "
       "    } "
       "  } "
-      "  param: 'unsharedweights2' "
+      "  param { name: 'unsharedweights2' } "
       "  bottom: 'data' "
       "  top: 'innerproduct2' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'loss' "
-      "  type: EUCLIDEAN_LOSS "
+      "  type: 'EuclideanLoss' "
       "  bottom: 'innerproduct1' "
       "  bottom: 'innerproduct2' "
       "} ";
   const string& expected_output_proto =
       "name: 'UnsharedWeightsNetwork' "
       "force_backward: true "
-      "layers: { "
+      "layer { "
       "  name: 'data' "
-      "  type: DUMMY_DATA "
+      "  type: 'DummyData' "
       "  dummy_data_param { "
       "    num: 5 "
       "    channels: 2 "
@@ -599,16 +631,16 @@ TEST_F(SplitLayerInsertionTest, TestLossInsertion) {
       "  } "
       "  top: 'data' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'data_data_0_split' "
-      "  type: SPLIT "
+      "  type: 'Split' "
       "  bottom: 'data' "
       "  top: 'data_data_0_split_0' "
       "  top: 'data_data_0_split_1' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'innerproduct1' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  inner_product_param { "
       "    num_output: 10 "
       "    bias_term: false "
@@ -617,22 +649,22 @@ TEST_F(SplitLayerInsertionTest, TestLossInsertion) {
       "      std: 10 "
       "    } "
       "  } "
-      "  param: 'unsharedweights1' "
+      "  param { name: 'unsharedweights1' } "
       "  bottom: 'data_data_0_split_0' "
       "  top: 'innerproduct1' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'innerproduct1_innerproduct1_0_split' "
-      "  type: SPLIT "
+      "  type: 'Split' "
       "  bottom: 'innerproduct1' "
       "  top: 'innerproduct1_innerproduct1_0_split_0' "
       "  top: 'innerproduct1_innerproduct1_0_split_1' "
       "  loss_weight: 2.5 "
       "  loss_weight: 0 "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'innerproduct2' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  inner_product_param { "
       "    num_output: 10 "
       "    bias_term: false "
@@ -641,13 +673,13 @@ TEST_F(SplitLayerInsertionTest, TestLossInsertion) {
       "      std: 10 "
       "    } "
       "  } "
-      "  param: 'unsharedweights2' "
+      "  param { name: 'unsharedweights2' } "
       "  bottom: 'data_data_0_split_1' "
       "  top: 'innerproduct2' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'loss' "
-      "  type: EUCLIDEAN_LOSS "
+      "  type: 'EuclideanLoss' "
       "  bottom: 'innerproduct1_innerproduct1_0_split_1' "
       "  bottom: 'innerproduct2' "
       "} ";
@@ -657,92 +689,92 @@ TEST_F(SplitLayerInsertionTest, TestLossInsertion) {
 TEST_F(SplitLayerInsertionTest, TestInsertion) {
   const string& input_proto =
       "name: 'TestNetwork' "
-      "layers: { "
+      "layer { "
       "  name: 'data' "
-      "  type: DATA "
+      "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'innerprod1' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  bottom: 'data' "
       "  top: 'innerprod1' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'innerprod2' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  bottom: 'data' "
       "  top: 'innerprod2' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'innerprod3' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  bottom: 'data' "
       "  top: 'innerprod3' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'loss1' "
-      "  type: EUCLIDEAN_LOSS "
+      "  type: 'EuclideanLoss' "
       "  bottom: 'innerprod1' "
       "  bottom: 'innerprod2' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'loss2' "
-      "  type: EUCLIDEAN_LOSS "
+      "  type: 'EuclideanLoss' "
       "  bottom: 'innerprod2' "
       "  bottom: 'innerprod3' "
       "} ";
   const string& expected_output_proto =
       "name: 'TestNetwork' "
-      "layers: { "
+      "layer { "
       "  name: 'data' "
-      "  type: DATA "
+      "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'data_data_0_split' "
-      "  type: SPLIT "
+      "  type: 'Split' "
       "  bottom: 'data' "
       "  top: 'data_data_0_split_0' "
       "  top: 'data_data_0_split_1' "
       "  top: 'data_data_0_split_2' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'innerprod1' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  bottom: 'data_data_0_split_0' "
       "  top: 'innerprod1' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'innerprod2' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  bottom: 'data_data_0_split_1' "
       "  top: 'innerprod2' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'innerprod2_innerprod2_0_split' "
-      "  type: SPLIT "
+      "  type: 'Split' "
       "  bottom: 'innerprod2' "
       "  top: 'innerprod2_innerprod2_0_split_0' "
       "  top: 'innerprod2_innerprod2_0_split_1' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'innerprod3' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  bottom: 'data_data_0_split_2' "
       "  top: 'innerprod3' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'loss1' "
-      "  type: EUCLIDEAN_LOSS "
+      "  type: 'EuclideanLoss' "
       "  bottom: 'innerprod1' "
       "  bottom: 'innerprod2_innerprod2_0_split_0' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'loss2' "
-      "  type: EUCLIDEAN_LOSS "
+      "  type: 'EuclideanLoss' "
       "  bottom: 'innerprod2_innerprod2_0_split_1' "
       "  bottom: 'innerprod3' "
       "} ";
@@ -752,103 +784,103 @@ TEST_F(SplitLayerInsertionTest, TestInsertion) {
 TEST_F(SplitLayerInsertionTest, TestInsertionTwoTop) {
   const string& input_proto =
       "name: 'TestNetwork' "
-      "layers: { "
+      "layer { "
       "  name: 'data' "
-      "  type: DATA "
+      "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'innerprod1' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  bottom: 'data' "
       "  top: 'innerprod1' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'innerprod2' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  bottom: 'label' "
       "  top: 'innerprod2' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'innerprod3' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  bottom: 'data' "
       "  top: 'innerprod3' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'innerprod4' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  bottom: 'label' "
       "  top: 'innerprod4' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'loss1' "
-      "  type: EUCLIDEAN_LOSS "
+      "  type: 'EuclideanLoss' "
       "  bottom: 'innerprod1' "
       "  bottom: 'innerprod3' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'loss2' "
-      "  type: EUCLIDEAN_LOSS "
+      "  type: 'EuclideanLoss' "
       "  bottom: 'innerprod2' "
       "  bottom: 'innerprod4' "
       "} ";
   const string& expected_output_proto =
       "name: 'TestNetwork' "
-      "layers: { "
+      "layer { "
       "  name: 'data' "
-      "  type: DATA "
+      "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'data_data_0_split' "
-      "  type: SPLIT "
+      "  type: 'Split' "
       "  bottom: 'data' "
       "  top: 'data_data_0_split_0' "
       "  top: 'data_data_0_split_1' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'label_data_1_split' "
-      "  type: SPLIT "
+      "  type: 'Split' "
       "  bottom: 'label' "
       "  top: 'label_data_1_split_0' "
       "  top: 'label_data_1_split_1' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'innerprod1' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  bottom: 'data_data_0_split_0' "
       "  top: 'innerprod1' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'innerprod2' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  bottom: 'label_data_1_split_0' "
       "  top: 'innerprod2' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'innerprod3' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  bottom: 'data_data_0_split_1' "
       "  top: 'innerprod3' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'innerprod4' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  bottom: 'label_data_1_split_1' "
       "  top: 'innerprod4' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'loss1' "
-      "  type: EUCLIDEAN_LOSS "
+      "  type: 'EuclideanLoss' "
       "  bottom: 'innerprod1' "
       "  bottom: 'innerprod3' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'loss2' "
-      "  type: EUCLIDEAN_LOSS "
+      "  type: 'EuclideanLoss' "
       "  bottom: 'innerprod2' "
       "  bottom: 'innerprod4' "
       "} ";
@@ -863,21 +895,21 @@ TEST_F(SplitLayerInsertionTest, TestInputInsertion) {
       "input_dim: 3 "
       "input_dim: 227 "
       "input_dim: 227 "
-      "layers: { "
+      "layer { "
       "  name: 'innerprod1' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  bottom: 'data' "
       "  top: 'innerprod1' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'innerprod2' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  bottom: 'data' "
       "  top: 'innerprod2' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'loss' "
-      "  type: EUCLIDEAN_LOSS "
+      "  type: 'EuclideanLoss' "
       "  bottom: 'innerprod1' "
       "  bottom: 'innerprod2' "
       "} ";
@@ -888,28 +920,28 @@ TEST_F(SplitLayerInsertionTest, TestInputInsertion) {
       "input_dim: 3 "
       "input_dim: 227 "
       "input_dim: 227 "
-      "layers: { "
+      "layer { "
       "  name: 'data_input_0_split' "
-      "  type: SPLIT "
+      "  type: 'Split' "
       "  bottom: 'data' "
       "  top: 'data_input_0_split_0' "
       "  top: 'data_input_0_split_1' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'innerprod1' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  bottom: 'data_input_0_split_0' "
       "  top: 'innerprod1' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'innerprod2' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  bottom: 'data_input_0_split_1' "
       "  top: 'innerprod2' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'loss' "
-      "  type: EUCLIDEAN_LOSS "
+      "  type: 'EuclideanLoss' "
       "  bottom: 'innerprod1' "
       "  bottom: 'innerprod2' "
       "} ";
@@ -919,91 +951,91 @@ TEST_F(SplitLayerInsertionTest, TestInputInsertion) {
 TEST_F(SplitLayerInsertionTest, TestWithInPlace) {
   const string& input_proto =
       "name: 'TestNetwork' "
-      "layers: { "
+      "layer { "
       "  name: 'data' "
-      "  type: DATA "
+      "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'innerprod1' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  bottom: 'data' "
       "  top: 'innerprod1' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'relu1' "
-      "  type: RELU "
+      "  type: 'ReLU' "
       "  bottom: 'innerprod1' "
       "  top: 'innerprod1' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'innerprod2' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  bottom: 'innerprod1' "
       "  top: 'innerprod2' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'loss1' "
-      "  type: EUCLIDEAN_LOSS "
+      "  type: 'EuclideanLoss' "
       "  bottom: 'innerprod1' "
       "  bottom: 'label' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'loss2' "
-      "  type: EUCLIDEAN_LOSS "
+      "  type: 'EuclideanLoss' "
       "  bottom: 'innerprod2' "
       "  bottom: 'data' "
       "} ";
   const string& expected_output_proto =
       "name: 'TestNetwork' "
-      "layers: { "
+      "layer { "
       "  name: 'data' "
-      "  type: DATA "
+      "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'data_data_0_split' "
-      "  type: SPLIT "
+      "  type: 'Split' "
       "  bottom: 'data' "
       "  top: 'data_data_0_split_0' "
       "  top: 'data_data_0_split_1' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'innerprod1' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  bottom: 'data_data_0_split_0' "
       "  top: 'innerprod1' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'relu1' "
-      "  type: RELU "
+      "  type: 'ReLU' "
       "  bottom: 'innerprod1' "
       "  top: 'innerprod1' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'innerprod1_relu1_0_split' "
-      "  type: SPLIT "
+      "  type: 'Split' "
       "  bottom: 'innerprod1' "
       "  top: 'innerprod1_relu1_0_split_0' "
       "  top: 'innerprod1_relu1_0_split_1' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'innerprod2' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  bottom: 'innerprod1_relu1_0_split_0' "
       "  top: 'innerprod2' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'loss1' "
-      "  type: EUCLIDEAN_LOSS "
+      "  type: 'EuclideanLoss' "
       "  bottom: 'innerprod1_relu1_0_split_1' "
       "  bottom: 'label' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'loss2' "
-      "  type: EUCLIDEAN_LOSS "
+      "  type: 'EuclideanLoss' "
       "  bottom: 'innerprod2' "
       "  bottom: 'data_data_0_split_1' "
       "} ";
diff --git a/src/caffe/test/test_spp_layer.cpp b/src/caffe/test/test_spp_layer.cpp
new file mode 100644
index 00000000000..b2585f1a5fa
--- /dev/null
+++ b/src/caffe/test/test_spp_layer.cpp
@@ -0,0 +1,131 @@
+#include <algorithm>
+#include <cstring>
+#include <vector>
+
+#include "gtest/gtest.h"
+
+#include "caffe/blob.hpp"
+#include "caffe/common.hpp"
+#include "caffe/filler.hpp"
+#include "caffe/vision_layers.hpp"
+
+#include "caffe/test/test_caffe_main.hpp"
+#include "caffe/test/test_gradient_check_util.hpp"
+
+namespace caffe {
+
+template <typename TypeParam>
+class SPPLayerTest : public MultiDeviceTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
+
+ protected:
+  SPPLayerTest()
+      : blob_bottom_(new Blob<Dtype>()),
+        blob_bottom_2_(new Blob<Dtype>()),
+        blob_bottom_3_(new Blob<Dtype>()),
+        blob_top_(new Blob<Dtype>()) {}
+  virtual void SetUp() {
+    Caffe::set_random_seed(1701);
+    blob_bottom_->Reshape(2, 3, 9, 8);
+    blob_bottom_2_->Reshape(4, 3, 1024, 765);
+    blob_bottom_3_->Reshape(10, 3, 7, 7);
+    // fill the values
+    FillerParameter filler_param;
+    GaussianFiller<Dtype> filler(filler_param);
+    filler.Fill(this->blob_bottom_);
+    blob_bottom_vec_.push_back(blob_bottom_);
+    blob_bottom_vec_2_.push_back(blob_bottom_2_);
+    blob_bottom_vec_3_.push_back(blob_bottom_3_);
+    blob_top_vec_.push_back(blob_top_);
+  }
+  virtual ~SPPLayerTest() { delete blob_bottom_; delete blob_top_; }
+
+  Blob<Dtype>* const blob_bottom_;
+  Blob<Dtype>* const blob_bottom_2_;
+  Blob<Dtype>* const blob_bottom_3_;
+  Blob<Dtype>* const blob_top_;
+  vector<Blob<Dtype>*> blob_bottom_vec_;
+  vector<Blob<Dtype>*> blob_bottom_vec_2_;
+  vector<Blob<Dtype>*> blob_bottom_vec_3_;
+  vector<Blob<Dtype>*> blob_top_vec_;
+};
+
+TYPED_TEST_CASE(SPPLayerTest, TestDtypesAndDevices);
+
+TYPED_TEST(SPPLayerTest, TestSetup) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  layer_param.mutable_spp_param()->set_pyramid_height(3);
+  SPPLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  // expected number of pool results is geometric sum
+  // (1 - r ** n)/(1 - r) where r = 4 and n = pyramid_height
+  // (1 - 4 ** 3)/(1 - 4) = 21
+  // multiply bottom num_channels * expected_pool_results
+  // to get expected num_channels (3 * 21 = 63)
+  EXPECT_EQ(this->blob_top_->num(), 2);
+  EXPECT_EQ(this->blob_top_->channels(), 63);
+  EXPECT_EQ(this->blob_top_->height(), 1);
+  EXPECT_EQ(this->blob_top_->width(), 1);
+}
+
+TYPED_TEST(SPPLayerTest, TestEqualOutputDims) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  layer_param.mutable_spp_param()->set_pyramid_height(5);
+  SPPLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_2_, this->blob_top_vec_);
+  // expected number of pool results is geometric sum
+  // (1 - r ** n)/(1 - r) where r = 4 and n = pyramid_height
+  // (1 - 4 ** 5)/(1 - 4) = 341
+  // multiply bottom num_channels * expected_pool_results
+  // to get expected num_channels (3 * 341 = 1023)
+  EXPECT_EQ(this->blob_top_->num(), 4);
+  EXPECT_EQ(this->blob_top_->channels(), 1023);
+  EXPECT_EQ(this->blob_top_->height(), 1);
+  EXPECT_EQ(this->blob_top_->width(), 1);
+}
+
+TYPED_TEST(SPPLayerTest, TestEqualOutputDims2) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  layer_param.mutable_spp_param()->set_pyramid_height(3);
+  SPPLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_3_, this->blob_top_vec_);
+  // expected number of pool results is geometric sum
+  // (1 - r ** n)/(1 - r) where r = 4 and n = pyramid_height
+  // (1 - 4 ** 3)/(1 - 4) = 21
+  // multiply bottom num_channels * expected_pool_results
+  // to get expected num_channels (3 * 21 = 63)
+  EXPECT_EQ(this->blob_top_->num(), 10);
+  EXPECT_EQ(this->blob_top_->channels(), 63);
+  EXPECT_EQ(this->blob_top_->height(), 1);
+  EXPECT_EQ(this->blob_top_->width(), 1);
+}
+
+TYPED_TEST(SPPLayerTest, TestForwardBackward) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  layer_param.mutable_spp_param()->set_pyramid_height(3);
+  SPPLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  vector<bool> propagate_down(this->blob_bottom_vec_.size(), true);
+  layer.Backward(this->blob_top_vec_, propagate_down,
+                 this->blob_bottom_vec_);
+}
+
+TYPED_TEST(SPPLayerTest, TestGradient) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  SPPParameter* spp_param = layer_param.mutable_spp_param();
+  spp_param->set_pyramid_height(3);
+  SPPLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-4, 1e-2);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
+}
+
+
+}  // namespace caffe
diff --git a/src/caffe/test/test_stochastic_pooling.cpp b/src/caffe/test/test_stochastic_pooling.cpp
index 4f13981bd82..f84464c322c 100644
--- a/src/caffe/test/test_stochastic_pooling.cpp
+++ b/src/caffe/test/test_stochastic_pooling.cpp
@@ -16,8 +16,10 @@ using std::min;
 
 namespace caffe {
 
-template <typename Dtype>
-class StochasticPoolingLayerTest : public ::testing::Test {
+template <typename TypeParam>
+class StochasticPoolingLayerTest : public MultiDeviceTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
+
  protected:
   StochasticPoolingLayerTest()
       : blob_bottom_(new Blob<Dtype>()),
@@ -45,32 +47,45 @@ class StochasticPoolingLayerTest : public ::testing::Test {
   vector<Blob<Dtype>*> blob_top_vec_;
 };
 
-TYPED_TEST_CASE(StochasticPoolingLayerTest, TestDtypes);
+template <typename Dtype>
+class CPUStochasticPoolingLayerTest
+  : public StochasticPoolingLayerTest<CPUDevice<Dtype> > {
+};
+
+TYPED_TEST_CASE(CPUStochasticPoolingLayerTest, TestDtypes);
 
-TYPED_TEST(StochasticPoolingLayerTest, TestSetup) {
+TYPED_TEST(CPUStochasticPoolingLayerTest, TestSetup) {
   LayerParameter layer_param;
   PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
   pooling_param->set_kernel_size(3);
   pooling_param->set_stride(2);
   PoolingLayer<TypeParam> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
   EXPECT_EQ(this->blob_top_->num(), this->blob_bottom_->num());
   EXPECT_EQ(this->blob_top_->channels(), this->blob_bottom_->channels());
   EXPECT_EQ(this->blob_top_->height(), 3);
   EXPECT_EQ(this->blob_top_->width(), 2);
 }
 
-TYPED_TEST(StochasticPoolingLayerTest, TestStochasticGPU) {
-  Caffe::set_mode(Caffe::GPU);
-  Caffe::set_phase(Caffe::TRAIN);
+#ifndef CPU_ONLY
+
+template <typename Dtype>
+class GPUStochasticPoolingLayerTest
+  : public StochasticPoolingLayerTest<GPUDevice<Dtype> > {
+};
+
+TYPED_TEST_CASE(GPUStochasticPoolingLayerTest, TestDtypes);
+
+TYPED_TEST(GPUStochasticPoolingLayerTest, TestStochastic) {
   LayerParameter layer_param;
+  layer_param.set_phase(TRAIN);
   PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
   pooling_param->set_kernel_size(3);
   pooling_param->set_stride(2);
   pooling_param->set_pool(PoolingParameter_PoolMethod_STOCHASTIC);
   PoolingLayer<TypeParam> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
 
   // Check if the output is correct - it should do random sampling
   const TypeParam* bottom_data = this->blob_bottom_->cpu_data();
@@ -104,17 +119,16 @@ TYPED_TEST(StochasticPoolingLayerTest, TestStochasticGPU) {
   EXPECT_GE(total / this->blob_top_->count(), 0.55);
 }
 
-TYPED_TEST(StochasticPoolingLayerTest, TestStochasticGPUTestPhase) {
-  Caffe::set_mode(Caffe::GPU);
-  Caffe::set_phase(Caffe::TEST);
+TYPED_TEST(GPUStochasticPoolingLayerTest, TestStochasticTestPhase) {
   LayerParameter layer_param;
+  layer_param.set_phase(TEST);
   PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
   pooling_param->set_kernel_size(3);
   pooling_param->set_stride(2);
   pooling_param->set_pool(PoolingParameter_PoolMethod_STOCHASTIC);
   PoolingLayer<TypeParam> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
 
   // Check if the output is correct - it should do random sampling
   const TypeParam* bottom_data = this->blob_bottom_->cpu_data();
@@ -142,10 +156,9 @@ TYPED_TEST(StochasticPoolingLayerTest, TestStochasticGPUTestPhase) {
   }
 }
 
-TYPED_TEST(StochasticPoolingLayerTest, TestGradientGPU) {
-  Caffe::set_mode(Caffe::GPU);
-  Caffe::set_phase(Caffe::TRAIN);
+TYPED_TEST(GPUStochasticPoolingLayerTest, TestGradient) {
   LayerParameter layer_param;
+  layer_param.set_phase(TRAIN);
   PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
   pooling_param->set_kernel_size(3);
   pooling_param->set_stride(2);
@@ -154,10 +167,10 @@ TYPED_TEST(StochasticPoolingLayerTest, TestGradientGPU) {
   GradientChecker<TypeParam> checker(1e-4, 1e-2);
   // it is too expensive to call curand multiple times, so we don't do an
   // exhaustive gradient check.
-  checker.CheckGradient(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_));
+  checker.CheckGradient(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
 }
 
-
+#endif
 
 }  // namespace caffe
diff --git a/src/caffe/test/test_tanh_layer.cpp b/src/caffe/test/test_tanh_layer.cpp
new file mode 100644
index 00000000000..5dc92832fc8
--- /dev/null
+++ b/src/caffe/test/test_tanh_layer.cpp
@@ -0,0 +1,101 @@
+#include <algorithm>
+#include <vector>
+
+#include "gtest/gtest.h"
+
+#include "caffe/blob.hpp"
+#include "caffe/common.hpp"
+#include "caffe/common_layers.hpp"
+#include "caffe/filler.hpp"
+
+#include "caffe/test/test_caffe_main.hpp"
+#include "caffe/test/test_gradient_check_util.hpp"
+
+namespace caffe {
+
+double tanh_naive(double x) {
+  if (x < -40) {
+    // avoid negative overflow
+    return -1;
+  } else if (x > 40) {
+    // avoid positive overflow
+    return 1;
+  } else {
+    // exact expression for tanh, which is unstable for large x
+    double exp2x = exp(2 * x);
+    return (exp2x - 1.0) / (exp2x + 1.0);
+  }
+}
+
+template <typename TypeParam>
+class TanHLayerTest : public MultiDeviceTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
+
+ protected:
+  TanHLayerTest()
+      : blob_bottom_(new Blob<Dtype>(2, 3, 4, 5)),
+        blob_top_(new Blob<Dtype>()) {
+    Caffe::set_random_seed(1701);
+    FillerParameter filler_param;
+    blob_bottom_vec_.push_back(blob_bottom_);
+    blob_top_vec_.push_back(blob_top_);
+  }
+  virtual ~TanHLayerTest() { delete blob_bottom_; delete blob_top_; }
+
+  void TestForward(Dtype filler_std) {
+    FillerParameter filler_param;
+    filler_param.set_std(filler_std);
+    GaussianFiller<Dtype> filler(filler_param);
+    filler.Fill(this->blob_bottom_);
+
+    LayerParameter layer_param;
+    TanHLayer<Dtype> layer(layer_param);
+    layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+    layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+    // Now, check values
+    const Dtype* bottom_data = this->blob_bottom_->cpu_data();
+    const Dtype* top_data = this->blob_top_->cpu_data();
+    const Dtype min_precision = 1e-5;
+    for (int i = 0; i < this->blob_bottom_->count(); ++i) {
+      Dtype expected_value = tanh_naive(bottom_data[i]);
+      Dtype precision = std::max(
+        Dtype(std::abs(expected_value * Dtype(1e-4))), min_precision);
+      EXPECT_NEAR(expected_value, top_data[i], precision);
+    }
+  }
+
+  void TestBackward(Dtype filler_std) {
+    FillerParameter filler_param;
+    filler_param.set_std(filler_std);
+    GaussianFiller<Dtype> filler(filler_param);
+    filler.Fill(this->blob_bottom_);
+
+    LayerParameter layer_param;
+    TanHLayer<Dtype> layer(layer_param);
+    GradientChecker<Dtype> checker(1e-2, 1e-2, 1701);
+    checker.CheckGradientEltwise(&layer, this->blob_bottom_vec_,
+        this->blob_top_vec_);
+  }
+
+  Blob<Dtype>* const blob_bottom_;
+  Blob<Dtype>* const blob_top_;
+  vector<Blob<Dtype>*> blob_bottom_vec_;
+  vector<Blob<Dtype>*> blob_top_vec_;
+};
+
+TYPED_TEST_CASE(TanHLayerTest, TestDtypesAndDevices);
+
+TYPED_TEST(TanHLayerTest, TestTanH) {
+  this->TestForward(1.0);
+}
+
+TYPED_TEST(TanHLayerTest, TestTanHOverflow) {
+  // this will fail if tanh overflow is not properly handled
+  this->TestForward(10000.0);
+}
+
+TYPED_TEST(TanHLayerTest, TestTanHGradient) {
+  this->TestBackward(1.0);
+}
+
+}  // namespace caffe
diff --git a/src/caffe/test/test_threshold_layer.cpp b/src/caffe/test/test_threshold_layer.cpp
index 32dfbeeac92..05ce82120e6 100644
--- a/src/caffe/test/test_threshold_layer.cpp
+++ b/src/caffe/test/test_threshold_layer.cpp
@@ -40,7 +40,7 @@ TYPED_TEST(ThresholdLayerTest, TestSetup) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
   ThresholdLayer<Dtype> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
   EXPECT_EQ(this->blob_top_->num(), this->blob_bottom_->num());
   EXPECT_EQ(this->blob_top_->channels(), this->blob_bottom_->channels());
   EXPECT_EQ(this->blob_top_->height(), this->blob_bottom_->height());
@@ -51,8 +51,8 @@ TYPED_TEST(ThresholdLayerTest, Test) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
   ThresholdLayer<Dtype> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   // Now, check values
   const Dtype* bottom_data = this->blob_bottom_->cpu_data();
   const Dtype* top_data = this->blob_top_->cpu_data();
@@ -76,8 +76,8 @@ TYPED_TEST(ThresholdLayerTest, Test2) {
     layer_param.mutable_threshold_param();
   threshold_param->set_threshold(0.5);
   ThresholdLayer<Dtype> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   // Now, check values
   const Dtype* bottom_data = this->blob_bottom_->cpu_data();
   const Dtype* top_data = this->blob_top_->cpu_data();
diff --git a/src/caffe/test/test_upgrade_proto.cpp b/src/caffe/test/test_upgrade_proto.cpp
index f46a0e823c1..eec627656ef 100644
--- a/src/caffe/test/test_upgrade_proto.cpp
+++ b/src/caffe/test/test_upgrade_proto.cpp
@@ -7,12 +7,11 @@
 
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
+#include "caffe/layer.hpp"
 #include "caffe/util/upgrade_proto.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
 
-using std::string;
-
 namespace caffe {
 
 class PaddingLayerUpgradeTest : public ::testing::Test {
@@ -1085,7 +1084,7 @@ TEST_F(PaddingLayerUpgradeTest, TestImageNet) {
   this->RunPaddingUpgradeTest(input_proto, expected_output_proto);
 }
 
-class V0UpgradeTest : public ::testing::Test {
+class NetUpgradeTest : public ::testing::Test {
  protected:
   void RunV0UpgradeTest(
       const string& input_param_string, const string& output_param_string) {
@@ -1103,10 +1102,27 @@ class V0UpgradeTest : public ::testing::Test {
     EXPECT_EQ(expected_output_param.DebugString(),
         actual_output_param.DebugString());
   }
+
+  void RunV1UpgradeTest(
+      const string& input_param_string, const string& output_param_string) {
+    // Test that UpgradeV0Net called on the NetParameter proto specified by
+    // input_param_string results in the NetParameter proto specified by
+    // output_param_string.
+    NetParameter input_param;
+    CHECK(google::protobuf::TextFormat::ParseFromString(
+        input_param_string, &input_param));
+    NetParameter expected_output_param;
+    CHECK(google::protobuf::TextFormat::ParseFromString(
+        output_param_string, &expected_output_param));
+    NetParameter actual_output_param;
+    UpgradeV1Net(input_param, &actual_output_param);
+    EXPECT_EQ(expected_output_param.DebugString(),
+        actual_output_param.DebugString());
+  }
 };
 
-TEST_F(V0UpgradeTest, TestSimple) {
-  const string& input_proto =
+TEST_F(NetUpgradeTest, TestSimple) {
+  const string& v0_proto =
       "name: 'CaffeNet' "
       "layers { "
       "  layer { "
@@ -1182,7 +1198,7 @@ TEST_F(V0UpgradeTest, TestSimple) {
       "  bottom: 'fc8' "
       "  bottom: 'label' "
       "} ";
-  const string& expected_output_proto =
+  const string& expected_v1_proto =
       "name: 'CaffeNet' "
       "layers { "
       "  name: 'data' "
@@ -1250,11 +1266,89 @@ TEST_F(V0UpgradeTest, TestSimple) {
       "  bottom: 'fc8' "
       "  bottom: 'label' "
       "} ";
-  this->RunV0UpgradeTest(input_proto, expected_output_proto);
+  this->RunV0UpgradeTest(v0_proto, expected_v1_proto);
+
+  const string& expected_v2_proto =
+      "name: 'CaffeNet' "
+      "layer { "
+      "  name: 'data' "
+      "  type: 'Data' "
+      "  data_param { "
+      "    source: '/home/jiayq/Data/ILSVRC12/train-leveldb' "
+      "    batch_size: 256 "
+      "  } "
+      "  transform_param { "
+      "    crop_size: 227 "
+      "    mirror: true "
+      "    mean_file: '/home/jiayq/Data/ILSVRC12/image_mean.binaryproto' "
+      "  } "
+      "  top: 'data' "
+      "  top: 'label' "
+      "} "
+      "layer { "
+      "  name: 'conv1' "
+      "  type: 'Convolution' "
+      "  convolution_param { "
+      "    num_output: 96 "
+      "    kernel_size: 11 "
+      "    stride: 4 "
+      "    pad: 2 "
+      "    weight_filler { "
+      "      type: 'gaussian' "
+      "      std: 0.01 "
+      "    } "
+      "    bias_filler { "
+      "      type: 'constant' "
+      "      value: 0. "
+      "    } "
+      "  } "
+      "  param { "
+      "    lr_mult: 1 "
+      "    decay_mult: 1 "
+      "  } "
+      "  param { "
+      "    lr_mult: 2 "
+      "    decay_mult: 0 "
+      "  } "
+      "  bottom: 'data' "
+      "  top: 'conv1' "
+      "} "
+      "layer { "
+      "  name: 'fc8' "
+      "  type: 'InnerProduct' "
+      "  inner_product_param { "
+      "    num_output: 1000 "
+      "    weight_filler { "
+      "      type: 'gaussian' "
+      "      std: 0.01 "
+      "    } "
+      "    bias_filler { "
+      "      type: 'constant' "
+      "      value: 0 "
+      "    } "
+      "  } "
+      "  param { "
+      "    lr_mult: 1 "
+      "    decay_mult: 1 "
+      "  } "
+      "  param { "
+      "    lr_mult: 2 "
+      "    decay_mult: 0 "
+      "  } "
+      "  bottom: 'conv1' "
+      "  top: 'fc8' "
+      "} "
+      "layer { "
+      "  name: 'loss' "
+      "  type: 'SoftmaxWithLoss' "
+      "  bottom: 'fc8' "
+      "  bottom: 'label' "
+      "} ";
+  this->RunV1UpgradeTest(expected_v1_proto, expected_v2_proto);
 }
 
 // Test any layer or parameter upgrades not covered by other tests.
-TEST_F(V0UpgradeTest, TestAllParams) {
+TEST_F(NetUpgradeTest, TestAllParams) {
   const string& input_proto =
       "name: 'CaffeNet' "
       "input: 'input_data' "
@@ -1754,8 +1848,8 @@ TEST_F(V0UpgradeTest, TestAllParams) {
   this->RunV0UpgradeTest(input_proto, expected_output_proto);
 }
 
-TEST_F(V0UpgradeTest, TestImageNet) {
-  const string& input_proto =
+TEST_F(NetUpgradeTest, TestImageNet) {
+  const string& v0_proto =
       "name: 'CaffeNet' "
       "layers { "
       "  layer { "
@@ -2120,7 +2214,7 @@ TEST_F(V0UpgradeTest, TestImageNet) {
       "  bottom: 'fc8' "
       "  bottom: 'label' "
       "} ";
-  const string& expected_output_proto =
+  const string& expected_v1_proto =
       "name: 'CaffeNet' "
       "layers { "
       "  name: 'data' "
@@ -2439,7 +2533,377 @@ TEST_F(V0UpgradeTest, TestImageNet) {
       "  bottom: 'fc8' "
       "  bottom: 'label' "
       "} ";
-  this->RunV0UpgradeTest(input_proto, expected_output_proto);
+  this->RunV0UpgradeTest(v0_proto, expected_v1_proto);
+
+  const string& expected_v2_proto =
+      "name: 'CaffeNet' "
+      "layer { "
+      "  name: 'data' "
+      "  type: 'Data' "
+      "  data_param { "
+      "    source: '/home/jiayq/Data/ILSVRC12/train-leveldb' "
+      "    batch_size: 256 "
+      "  } "
+      "  transform_param { "
+      "    crop_size: 227 "
+      "    mirror: true "
+      "    mean_file: '/home/jiayq/Data/ILSVRC12/image_mean.binaryproto' "
+      "  } "
+      "  top: 'data' "
+      "  top: 'label' "
+      "} "
+      "layer { "
+      "  name: 'conv1' "
+      "  type: 'Convolution' "
+      "  convolution_param { "
+      "    num_output: 96 "
+      "    kernel_size: 11 "
+      "    stride: 4 "
+      "    weight_filler { "
+      "      type: 'gaussian' "
+      "      std: 0.01 "
+      "    } "
+      "    bias_filler { "
+      "      type: 'constant' "
+      "      value: 0. "
+      "    } "
+      "  } "
+      "  param { "
+      "    lr_mult: 1 "
+      "    decay_mult: 1 "
+      "  } "
+      "  param { "
+      "    lr_mult: 2 "
+      "    decay_mult: 0 "
+      "  } "
+      "  bottom: 'data' "
+      "  top: 'conv1' "
+      "} "
+      "layer { "
+      "  name: 'relu1' "
+      "  type: 'ReLU' "
+      "  bottom: 'conv1' "
+      "  top: 'conv1' "
+      "} "
+      "layer { "
+      "  name: 'pool1' "
+      "  type: 'Pooling' "
+      "  pooling_param { "
+      "    pool: MAX "
+      "    kernel_size: 3 "
+      "    stride: 2 "
+      "  } "
+      "  bottom: 'conv1' "
+      "  top: 'pool1' "
+      "} "
+      "layer { "
+      "  name: 'norm1' "
+      "  type: 'LRN' "
+      "  lrn_param { "
+      "    local_size: 5 "
+      "    alpha: 0.0001 "
+      "    beta: 0.75 "
+      "  } "
+      "  bottom: 'pool1' "
+      "  top: 'norm1' "
+      "} "
+      "layer { "
+      "  name: 'conv2' "
+      "  type: 'Convolution' "
+      "  convolution_param { "
+      "    num_output: 256 "
+      "    group: 2 "
+      "    kernel_size: 5 "
+      "    pad: 2 "
+      "    weight_filler { "
+      "      type: 'gaussian' "
+      "      std: 0.01 "
+      "    } "
+      "    bias_filler { "
+      "      type: 'constant' "
+      "      value: 1. "
+      "    } "
+      "  } "
+      "  param { "
+      "    lr_mult: 1 "
+      "    decay_mult: 1 "
+      "  } "
+      "  param { "
+      "    lr_mult: 2 "
+      "    decay_mult: 0 "
+      "  } "
+      "  bottom: 'norm1' "
+      "  top: 'conv2' "
+      "} "
+      "layer { "
+      "  name: 'relu2' "
+      "  type: 'ReLU' "
+      "  bottom: 'conv2' "
+      "  top: 'conv2' "
+      "} "
+      "layer { "
+      "  name: 'pool2' "
+      "  type: 'Pooling' "
+      "  pooling_param { "
+      "    pool: MAX "
+      "    kernel_size: 3 "
+      "    stride: 2 "
+      "  } "
+      "  bottom: 'conv2' "
+      "  top: 'pool2' "
+      "} "
+      "layer { "
+      "  name: 'norm2' "
+      "  type: 'LRN' "
+      "  lrn_param { "
+      "    local_size: 5 "
+      "    alpha: 0.0001 "
+      "    beta: 0.75 "
+      "  } "
+      "  bottom: 'pool2' "
+      "  top: 'norm2' "
+      "} "
+      "layer { "
+      "  name: 'conv3' "
+      "  type: 'Convolution' "
+      "  convolution_param { "
+      "    num_output: 384 "
+      "    kernel_size: 3 "
+      "    pad: 1 "
+      "    weight_filler { "
+      "      type: 'gaussian' "
+      "      std: 0.01 "
+      "    } "
+      "    bias_filler { "
+      "      type: 'constant' "
+      "      value: 0. "
+      "    } "
+      "  } "
+      "  param { "
+      "    lr_mult: 1 "
+      "    decay_mult: 1 "
+      "  } "
+      "  param { "
+      "    lr_mult: 2 "
+      "    decay_mult: 0 "
+      "  } "
+      "  bottom: 'norm2' "
+      "  top: 'conv3' "
+      "} "
+      "layer { "
+      "  name: 'relu3' "
+      "  type: 'ReLU' "
+      "  bottom: 'conv3' "
+      "  top: 'conv3' "
+      "} "
+      "layer { "
+      "  name: 'conv4' "
+      "  type: 'Convolution' "
+      "  convolution_param { "
+      "    num_output: 384 "
+      "    group: 2 "
+      "    kernel_size: 3 "
+      "    pad: 1 "
+      "    weight_filler { "
+      "      type: 'gaussian' "
+      "      std: 0.01 "
+      "    } "
+      "    bias_filler { "
+      "      type: 'constant' "
+      "      value: 1. "
+      "    } "
+      "  } "
+      "  param { "
+      "    lr_mult: 1 "
+      "    decay_mult: 1 "
+      "  } "
+      "  param { "
+      "    lr_mult: 2 "
+      "    decay_mult: 0 "
+      "  } "
+      "  bottom: 'conv3' "
+      "  top: 'conv4' "
+      "} "
+      "layer { "
+      "  name: 'relu4' "
+      "  type: 'ReLU' "
+      "  bottom: 'conv4' "
+      "  top: 'conv4' "
+      "} "
+      "layer { "
+      "  name: 'conv5' "
+      "  type: 'Convolution' "
+      "  convolution_param { "
+      "    num_output: 256 "
+      "    group: 2 "
+      "    kernel_size: 3 "
+      "    pad: 1 "
+      "    weight_filler { "
+      "      type: 'gaussian' "
+      "      std: 0.01 "
+      "    } "
+      "    bias_filler { "
+      "      type: 'constant' "
+      "      value: 1. "
+      "    } "
+      "  } "
+      "  param { "
+      "    lr_mult: 1 "
+      "    decay_mult: 1 "
+      "  } "
+      "  param { "
+      "    lr_mult: 2 "
+      "    decay_mult: 0 "
+      "  } "
+      "  bottom: 'conv4' "
+      "  top: 'conv5' "
+      "} "
+      "layer { "
+      "  name: 'relu5' "
+      "  type: 'ReLU' "
+      "  bottom: 'conv5' "
+      "  top: 'conv5' "
+      "} "
+      "layer { "
+      "  name: 'pool5' "
+      "  type: 'Pooling' "
+      "  pooling_param { "
+      "    kernel_size: 3 "
+      "    pool: MAX "
+      "    stride: 2 "
+      "  } "
+      "  bottom: 'conv5' "
+      "  top: 'pool5' "
+      "} "
+      "layer { "
+      "  name: 'fc6' "
+      "  type: 'InnerProduct' "
+      "  inner_product_param { "
+      "    num_output: 4096 "
+      "    weight_filler { "
+      "      type: 'gaussian' "
+      "      std: 0.005 "
+      "    } "
+      "    bias_filler { "
+      "      type: 'constant' "
+      "      value: 1. "
+      "    } "
+      "  } "
+      "  param { "
+      "    lr_mult: 1 "
+      "    decay_mult: 1 "
+      "  } "
+      "  param { "
+      "    lr_mult: 2 "
+      "    decay_mult: 0 "
+      "  } "
+      "  bottom: 'pool5' "
+      "  top: 'fc6' "
+      "} "
+      "layer { "
+      "  name: 'relu6' "
+      "  type: 'ReLU' "
+      "  bottom: 'fc6' "
+      "  top: 'fc6' "
+      "} "
+      "layer { "
+      "  name: 'drop6' "
+      "  type: 'Dropout' "
+      "  dropout_param { "
+      "    dropout_ratio: 0.5 "
+      "  } "
+      "  bottom: 'fc6' "
+      "  top: 'fc6' "
+      "} "
+      "layer { "
+      "  name: 'fc7' "
+      "  type: 'InnerProduct' "
+      "  inner_product_param { "
+      "    num_output: 4096 "
+      "    weight_filler { "
+      "      type: 'gaussian' "
+      "      std: 0.005 "
+      "    } "
+      "    bias_filler { "
+      "      type: 'constant' "
+      "      value: 1. "
+      "    } "
+      "  } "
+      "  param { "
+      "    lr_mult: 1 "
+      "    decay_mult: 1 "
+      "  } "
+      "  param { "
+      "    lr_mult: 2 "
+      "    decay_mult: 0 "
+      "  } "
+      "  bottom: 'fc6' "
+      "  top: 'fc7' "
+      "} "
+      "layer { "
+      "  name: 'relu7' "
+      "  type: 'ReLU' "
+      "  bottom: 'fc7' "
+      "  top: 'fc7' "
+      "} "
+      "layer { "
+      "  name: 'drop7' "
+      "  type: 'Dropout' "
+      "  dropout_param { "
+      "    dropout_ratio: 0.5 "
+      "  } "
+      "  bottom: 'fc7' "
+      "  top: 'fc7' "
+      "} "
+      "layer { "
+      "  name: 'fc8' "
+      "  type: 'InnerProduct' "
+      "  inner_product_param { "
+      "    num_output: 1000 "
+      "    weight_filler { "
+      "      type: 'gaussian' "
+      "      std: 0.01 "
+      "    } "
+      "    bias_filler { "
+      "      type: 'constant' "
+      "      value: 0 "
+      "    } "
+      "  } "
+      "  param { "
+      "    lr_mult: 1 "
+      "    decay_mult: 1 "
+      "  } "
+      "  param { "
+      "    lr_mult: 2 "
+      "    decay_mult: 0 "
+      "  } "
+      "  bottom: 'fc7' "
+      "  top: 'fc8' "
+      "} "
+      "layer { "
+      "  name: 'loss' "
+      "  type: 'SoftmaxWithLoss' "
+      "  bottom: 'fc8' "
+      "  bottom: 'label' "
+      "} ";
+  this->RunV1UpgradeTest(expected_v1_proto, expected_v2_proto);
+}  // NOLINT(readability/fn_size)
+
+TEST_F(NetUpgradeTest, TestUpgradeV1LayerType) {
+  LayerParameter layer_param;
+  shared_ptr<Layer<float> > layer;
+  for (int i = 0; i < V1LayerParameter_LayerType_LayerType_ARRAYSIZE; ++i) {
+    ASSERT_TRUE(V1LayerParameter_LayerType_IsValid(i));
+    V1LayerParameter_LayerType v1_type = V1LayerParameter_LayerType(i);
+    string v2_layer_type(UpgradeV1LayerType(v1_type));
+    if (v2_layer_type == "") {
+      EXPECT_EQ(V1LayerParameter_LayerType_NONE, v1_type);
+      continue;  // Empty string isn't actually a valid layer type.
+    }
+    layer_param.set_type(v2_layer_type);
+    layer = LayerRegistry<float>::CreateLayer(layer_param);
+    EXPECT_EQ(v2_layer_type, layer->type());
+  }
 }
 
-}  // namespace caffe
+}  // NOLINT(readability/fn_size)  // namespace caffe
diff --git a/src/caffe/util/benchmark.cpp b/src/caffe/util/benchmark.cpp
index 566d06a8f5f..1d269c351c1 100644
--- a/src/caffe/util/benchmark.cpp
+++ b/src/caffe/util/benchmark.cpp
@@ -55,6 +55,30 @@ void Timer::Stop() {
   }
 }
 
+
+float Timer::MicroSeconds() {
+  if (!has_run_at_least_once()) {
+    LOG(WARNING) << "Timer has never been run before reading time.";
+    return 0;
+  }
+  if (running()) {
+    Stop();
+  }
+  if (Caffe::mode() == Caffe::GPU) {
+#ifndef CPU_ONLY
+    CUDA_CHECK(cudaEventElapsedTime(&elapsed_milliseconds_, start_gpu_,
+                                    stop_gpu_));
+    // Cuda only measure milliseconds
+    elapsed_microseconds_ = elapsed_milliseconds_ * 1000;
+#else
+      NO_GPU;
+#endif
+  } else {
+    elapsed_microseconds_ = (stop_cpu_ - start_cpu_).total_microseconds();
+  }
+  return elapsed_microseconds_;
+}
+
 float Timer::MilliSeconds() {
   if (!has_run_at_least_once()) {
     LOG(WARNING) << "Timer has never been run before reading time.";
@@ -94,4 +118,51 @@ void Timer::Init() {
   }
 }
 
+CPUTimer::CPUTimer() {
+  this->initted_ = true;
+  this->running_ = false;
+  this->has_run_at_least_once_ = false;
+}
+
+void CPUTimer::Start() {
+  if (!running()) {
+    this->start_cpu_ = boost::posix_time::microsec_clock::local_time();
+    this->running_ = true;
+    this->has_run_at_least_once_ = true;
+  }
+}
+
+void CPUTimer::Stop() {
+  if (running()) {
+    this->stop_cpu_ = boost::posix_time::microsec_clock::local_time();
+    this->running_ = false;
+  }
+}
+
+float CPUTimer::MilliSeconds() {
+  if (!has_run_at_least_once()) {
+    LOG(WARNING) << "Timer has never been run before reading time.";
+    return 0;
+  }
+  if (running()) {
+    Stop();
+  }
+  this->elapsed_milliseconds_ = (this->stop_cpu_ -
+                                this->start_cpu_).total_milliseconds();
+  return this->elapsed_milliseconds_;
+}
+
+float CPUTimer::MicroSeconds() {
+  if (!has_run_at_least_once()) {
+    LOG(WARNING) << "Timer has never been run before reading time.";
+    return 0;
+  }
+  if (running()) {
+    Stop();
+  }
+  this->elapsed_microseconds_ = (this->stop_cpu_ -
+                                this->start_cpu_).total_microseconds();
+  return this->elapsed_microseconds_;
+}
+
 }  // namespace caffe
diff --git a/src/caffe/util/channel.cpp b/src/caffe/util/channel.cpp
new file mode 100644
index 00000000000..cd91d6e9a9d
--- /dev/null
+++ b/src/caffe/util/channel.cpp
@@ -0,0 +1,164 @@
+//
+// Created by alex on 8/25/15.
+//
+
+#ifdef USE_MPI
+
+#include <boost/thread.hpp>
+#include <boost/atomic.hpp>
+#include <boost/lockfree/queue.hpp>
+
+
+#include "caffe/util/channel.hpp"
+
+#include "caffe/common.hpp"
+
+#include "mpi.h"
+
+namespace caffe {
+
+shared_ptr<MPIComm> MPIComm::singleton_;
+
+MPIComm::MPIComm() :
+    running_(false), started_(false){}
+
+MPIComm::~MPIComm() {
+  if (IsRunning()){
+    EndProcessing();
+  }
+}
+
+bool MPIComm::IsRunning(){
+ return running_.load();
+}
+
+bool MPIComm::IsIdle(){
+  mutex::scoped_lock lock(queue_mutex_);
+  return task_queue_.empty();
+}
+
+void MPIComm::WaitAll() {
+
+  mutex::scoped_lock lock(queue_mutex_);
+  while (task_queue_.size()){
+    DLOG(INFO)<<"Waiting for tasks to finish, task size "<<task_queue_.size()<<"\n";
+    cond_finish_.wait(lock);
+  }
+  DLOG(INFO)<<"all task done on "<<Caffe::MPI_my_rank()<<"\n";
+}
+
+void MPIComm::StartProcessing() {
+
+  running_.store(true);
+  // start the transmission thread
+  try {
+    thread_.reset(
+        new boost::thread(&MPIComm::ThreadFunc, this));
+  } catch (...) {
+    LOG(FATAL)<<"Cannot start MPI comminication thread";
+  }
+}
+
+void MPIComm::EndProcessing(){
+  if (IsRunning()) {
+    try {
+      cond_work_.notify_one();
+      running_.store(false); //notify the transmission thread to finish and shutdown
+      thread_->join();
+    } catch (...) {
+      LOG(FATAL)<<"Cannot destroy MPI comminication thread";
+    }
+  }
+}
+
+void MPIComm::AddJob(MPIJob new_job) {
+  if (IsRunning()) {
+    while(!started_.load());
+    mutex::scoped_lock lock(queue_mutex_);
+    DLOG(INFO) << "adding job on " << Caffe::MPI_my_rank() << " task queue size " << task_queue_.size() << " \n";
+    task_queue_.push(new_job);
+    lock.unlock();
+    cond_work_.notify_one();
+  }else{
+    LOG(FATAL)<<"Cannot push job while MPI Comm is shutting down";
+  }
+}
+
+void MPIComm::DispatchJob(MPIJob &job) {
+  MPI_Datatype data_type = (job.dtype_size_ == 4) ? MPI_FLOAT : MPI_DOUBLE;
+
+  // call MPI APIs for real works
+  switch (job.op_) {
+    case OP_SUM_ALL: {
+      DLOG(INFO)<<"Running all reduce\n";
+      MPI_CHECK(MPI_Allreduce((job.src_ptr_ == job.dst_ptr_) ? MPI_IN_PLACE : job.src_ptr_,
+                              job.dst_ptr_, job.count_, data_type,
+                              MPI_SUM, MPI_COMM_WORLD
+      ));
+      break;
+    }
+    case OP_GATHER: {
+      MPI_CHECK(MPI_Allgather(job.src_ptr_, job.count_, data_type,
+                              job.dst_ptr_, job.count_, data_type,
+                              MPI_COMM_WORLD));
+      break;
+    }
+    case OP_SCATTER: {
+      MPI_CHECK(MPI_Scatter(job.src_ptr_, job.count_, data_type,
+                            job.dst_ptr_, job.count_, data_type,
+                            0, MPI_COMM_WORLD));
+      break;
+    }
+    case OP_BROADCAST: {
+      CHECK_EQ(job.src_ptr_, job.dst_ptr_);
+      MPI_CHECK(MPI_Bcast(job.src_ptr_, job.count_, data_type,
+                          0, MPI_COMM_WORLD));
+      break;
+    }
+    default: {
+      LOG(FATAL)<<"Unknown MPI job type";
+    }
+  }
+}
+void MPIComm::ThreadFunc(){
+#ifndef CPU_ONLY
+  CUDA_CHECK(cudaSetDevice(Caffe::device_id()));
+#endif
+  started_.store(true);
+  MPIJob job;
+  while (true){
+    mutex::scoped_lock lock(queue_mutex_);
+    while( task_queue_.empty() && IsRunning()){
+      DLOG(INFO)<<"no job running, waiting on cond";
+      cond_work_.wait(lock);
+    }
+    lock.unlock();
+
+    DLOG(INFO)<<"Cond fulfilled, dispatching job";
+    if (IsRunning()){
+      job = task_queue_.front();
+      DLOG(INFO)<<task_queue_.size();
+      DispatchJob(job);
+      mutex::scoped_lock pop_lock(queue_mutex_);
+      task_queue_.pop();
+      pop_lock.unlock();
+      cond_finish_.notify_one();
+      DLOG(INFO)<<"job finished, poped taskqueue";
+    }else{
+      break;
+    }
+
+  }
+
+  // finish remaining jobs
+  while (!task_queue_.empty()){
+    boost::lock_guard<mutex> lock(queue_mutex_);
+    job = task_queue_.front();
+    task_queue_.pop();
+    DispatchJob(job);
+  }
+}
+
+}
+
+#endif //USE_MPI
diff --git a/src/caffe/util/cudnn.cpp b/src/caffe/util/cudnn.cpp
new file mode 100644
index 00000000000..1772f0099ce
--- /dev/null
+++ b/src/caffe/util/cudnn.cpp
@@ -0,0 +1,23 @@
+#ifdef USE_CUDNN
+#include "caffe/util/cudnn.hpp"
+
+namespace caffe {
+namespace cudnn {
+
+float dataType<float>::oneval = 1.0;
+float dataType<float>::zeroval = 0.0;
+const void* dataType<float>::one =
+    static_cast<void *>(&dataType<float>::oneval);
+const void* dataType<float>::zero =
+    static_cast<void *>(&dataType<float>::zeroval);
+
+double dataType<double>::oneval = 1.0;
+double dataType<double>::zeroval = 0.0;
+const void* dataType<double>::one =
+    static_cast<void *>(&dataType<double>::oneval);
+const void* dataType<double>::zero =
+    static_cast<void *>(&dataType<double>::zeroval);
+
+}  // namespace cudnn
+}  // namespace caffe
+#endif
diff --git a/src/caffe/util/db.cpp b/src/caffe/util/db.cpp
new file mode 100644
index 00000000000..f55420e9840
--- /dev/null
+++ b/src/caffe/util/db.cpp
@@ -0,0 +1,31 @@
+#include "caffe/util/db.hpp"
+#include "caffe/util/db_leveldb.hpp"
+#include "caffe/util/db_lmdb.hpp"
+
+#include <string>
+
+namespace caffe { namespace db {
+
+DB* GetDB(DataParameter::DB backend) {
+  switch (backend) {
+  case DataParameter_DB_LEVELDB:
+    return new LevelDB();
+  case DataParameter_DB_LMDB:
+    return new LMDB();
+  default:
+    LOG(FATAL) << "Unknown database backend";
+  }
+}
+
+DB* GetDB(const string& backend) {
+  if (backend == "leveldb") {
+    return new LevelDB();
+  } else if (backend == "lmdb") {
+    return new LMDB();
+  } else {
+    LOG(FATAL) << "Unknown database backend";
+  }
+}
+
+}  // namespace db
+}  // namespace caffe
diff --git a/src/caffe/util/db_leveldb.cpp b/src/caffe/util/db_leveldb.cpp
new file mode 100644
index 00000000000..06c46627d31
--- /dev/null
+++ b/src/caffe/util/db_leveldb.cpp
@@ -0,0 +1,21 @@
+#include "caffe/util/db_leveldb.hpp"
+
+#include <string>
+
+namespace caffe { namespace db {
+
+void LevelDB::Open(const string& source, Mode mode) {
+  leveldb::Options options;
+  options.block_size = 65536;
+  options.write_buffer_size = 268435456;
+  options.max_open_files = 100;
+  options.error_if_exists = mode == NEW;
+  options.create_if_missing = mode != READ;
+  leveldb::Status status = leveldb::DB::Open(options, source, &db_);
+  CHECK(status.ok()) << "Failed to open leveldb " << source
+                     << std::endl << status.ToString();
+  LOG(INFO) << "Opened leveldb " << source;
+}
+
+}  // namespace db
+}  // namespace caffe
diff --git a/src/caffe/util/db_lmdb.cpp b/src/caffe/util/db_lmdb.cpp
new file mode 100644
index 00000000000..a054b796806
--- /dev/null
+++ b/src/caffe/util/db_lmdb.cpp
@@ -0,0 +1,51 @@
+#include "caffe/util/db_lmdb.hpp"
+
+#include <sys/stat.h>
+
+#include <string>
+
+namespace caffe { namespace db {
+
+const size_t LMDB_MAP_SIZE = 1099511627776;  // 1 TB
+
+void LMDB::Open(const string& source, Mode mode) {
+  MDB_CHECK(mdb_env_create(&mdb_env_));
+  MDB_CHECK(mdb_env_set_mapsize(mdb_env_, LMDB_MAP_SIZE));
+  if (mode == NEW) {
+    CHECK_EQ(mkdir(source.c_str(), 0744), 0) << "mkdir " << source << "failed";
+  }
+  int flags = 0;
+  if (mode == READ) {
+    flags = MDB_RDONLY | MDB_NOTLS;
+  }
+  MDB_CHECK(mdb_env_open(mdb_env_, source.c_str(), flags, 0664));
+  LOG(INFO) << "Opened lmdb " << source;
+}
+
+LMDBCursor* LMDB::NewCursor() {
+  MDB_txn* mdb_txn;
+  MDB_cursor* mdb_cursor;
+  MDB_CHECK(mdb_txn_begin(mdb_env_, NULL, MDB_RDONLY, &mdb_txn));
+  MDB_CHECK(mdb_dbi_open(mdb_txn, NULL, 0, &mdb_dbi_));
+  MDB_CHECK(mdb_cursor_open(mdb_txn, mdb_dbi_, &mdb_cursor));
+  return new LMDBCursor(mdb_txn, mdb_cursor);
+}
+
+LMDBTransaction* LMDB::NewTransaction() {
+  MDB_txn* mdb_txn;
+  MDB_CHECK(mdb_txn_begin(mdb_env_, NULL, 0, &mdb_txn));
+  MDB_CHECK(mdb_dbi_open(mdb_txn, NULL, 0, &mdb_dbi_));
+  return new LMDBTransaction(&mdb_dbi_, mdb_txn);
+}
+
+void LMDBTransaction::Put(const string& key, const string& value) {
+  MDB_val mdb_key, mdb_value;
+  mdb_key.mv_data = const_cast<char*>(key.data());
+  mdb_key.mv_size = key.size();
+  mdb_value.mv_data = const_cast<char*>(value.data());
+  mdb_value.mv_size = value.size();
+  MDB_CHECK(mdb_put(mdb_txn_, *mdb_dbi_, &mdb_key, &mdb_value, 0));
+}
+
+}  // namespace db
+}  // namespace caffe
diff --git a/src/caffe/util/insert_splits.cpp b/src/caffe/util/insert_splits.cpp
index f20efdae8c6..416f80ab3c2 100644
--- a/src/caffe/util/insert_splits.cpp
+++ b/src/caffe/util/insert_splits.cpp
@@ -12,7 +12,7 @@ namespace caffe {
 void InsertSplits(const NetParameter& param, NetParameter* param_split) {
   // Initialize by copying from the input NetParameter.
   param_split->CopyFrom(param);
-  param_split->clear_layers();
+  param_split->clear_layer();
   map<string, pair<int, int> > blob_name_to_last_top_idx;
   map<pair<int, int>, pair<int, int> > bottom_idx_to_source_top_idx;
   map<pair<int, int>, int> top_idx_to_bottom_count;
@@ -25,8 +25,8 @@ void InsertSplits(const NetParameter& param, NetParameter* param_split) {
     const string& blob_name = param.input(i);
     blob_name_to_last_top_idx[blob_name] = make_pair(-1, i);
   }
-  for (int i = 0; i < param.layers_size(); ++i) {
-    const LayerParameter& layer_param = param.layers(i);
+  for (int i = 0; i < param.layer_size(); ++i) {
+    const LayerParameter& layer_param = param.layer(i);
     layer_idx_to_layer_name[i] = layer_param.name();
     for (int j = 0; j < layer_param.bottom_size(); ++j) {
       const string& blob_name = layer_param.bottom(j);
@@ -56,22 +56,22 @@ void InsertSplits(const NetParameter& param, NetParameter* param_split) {
       }
     }
   }
-  // Create split layer for any input blobs used by other layers as bottom
+  // Create split layer for any input blobs used by other layer as bottom
   // blobs more than once.
   for (int i = 0; i < param.input_size(); ++i) {
     const int split_count = top_idx_to_bottom_count[make_pair(-1, i)];
     if (split_count > 1) {
       const string& layer_name = layer_idx_to_layer_name[-1];
       const string& blob_name = param.input(i);
-      LayerParameter* split_layer_param = param_split->add_layers();
+      LayerParameter* split_layer_param = param_split->add_layer();
       const float kZeroLossWeight = 0;
       ConfigureSplitLayer(layer_name, blob_name, i, split_count,
           kZeroLossWeight, split_layer_param);
     }
   }
-  for (int i = 0; i < param.layers_size(); ++i) {
-    LayerParameter* layer_param = param_split->add_layers();
-    layer_param->CopyFrom(param.layers(i));
+  for (int i = 0; i < param.layer_size(); ++i) {
+    LayerParameter* layer_param = param_split->add_layer();
+    layer_param->CopyFrom(param.layer(i));
     // Replace any shared bottom blobs with split layer outputs.
     for (int j = 0; j < layer_param->bottom_size(); ++j) {
       const pair<int, int>& top_idx =
@@ -84,7 +84,7 @@ void InsertSplits(const NetParameter& param, NetParameter* param_split) {
             blob_name, top_idx.second, top_idx_to_bottom_split_idx[top_idx]++));
       }
     }
-    // Create split layer for any top blobs used by other layers as bottom
+    // Create split layer for any top blobs used by other layer as bottom
     // blobs more than once.
     for (int j = 0; j < layer_param->top_size(); ++j) {
       const pair<int, int>& top_idx = make_pair(i, j);
@@ -92,7 +92,7 @@ void InsertSplits(const NetParameter& param, NetParameter* param_split) {
       if (split_count > 1) {
         const string& layer_name = layer_idx_to_layer_name[i];
         const string& blob_name = layer_param->top(j);
-        LayerParameter* split_layer_param = param_split->add_layers();
+        LayerParameter* split_layer_param = param_split->add_layer();
         const float loss_weight = top_idx_to_loss_weight[top_idx];
         ConfigureSplitLayer(layer_name, blob_name, j, split_count,
             loss_weight, split_layer_param);
@@ -111,7 +111,7 @@ void ConfigureSplitLayer(const string& layer_name, const string& blob_name,
   split_layer_param->Clear();
   split_layer_param->add_bottom(blob_name);
   split_layer_param->set_name(SplitLayerName(layer_name, blob_name, blob_idx));
-  split_layer_param->set_type(LayerParameter_LayerType_SPLIT);
+  split_layer_param->set_type("Split");
   for (int k = 0; k < split_count; ++k) {
     split_layer_param->add_top(
         SplitBlobName(layer_name, blob_name, blob_idx, k));
diff --git a/src/caffe/util/io.cpp b/src/caffe/util/io.cpp
index 4a22e18725d..df4778fce08 100644
--- a/src/caffe/util/io.cpp
+++ b/src/caffe/util/io.cpp
@@ -2,7 +2,6 @@
 #include <google/protobuf/io/coded_stream.h>
 #include <google/protobuf/io/zero_copy_stream_impl.h>
 #include <google/protobuf/text_format.h>
-#include <leveldb/db.h>
 #include <opencv2/core/core.hpp>
 #include <opencv2/highgui/highgui.hpp>
 #include <opencv2/highgui/highgui_c.h>
@@ -18,6 +17,8 @@
 #include "caffe/proto/caffe.pb.h"
 #include "caffe/util/io.hpp"
 
+const int kProtoReadBytesLimit = INT_MAX;  // Max size of 2 GB minus 1 byte.
+
 namespace caffe {
 
 using google::protobuf::io::FileInputStream;
@@ -51,7 +52,7 @@ bool ReadProtoFromBinaryFile(const char* filename, Message* proto) {
   CHECK_NE(fd, -1) << "File not found: " << filename;
   ZeroCopyInputStream* raw_input = new FileInputStream(fd);
   CodedInputStream* coded_input = new CodedInputStream(raw_input);
-  coded_input->SetTotalBytesLimit(1073741824, 536870912);
+  coded_input->SetTotalBytesLimit(kProtoReadBytesLimit, 536870912);
 
   bool success = proto->ParseFromCodedStream(coded_input);
 
@@ -66,57 +67,165 @@ void WriteProtoToBinaryFile(const Message& proto, const char* filename) {
   CHECK(proto.SerializeToOstream(&output));
 }
 
-bool ReadImageToDatum(const string& filename, const int label,
-    const int height, const int width, const bool is_color, Datum* datum) {
+cv::Mat ReadImageToCVMat(const string& filename,
+    const int height, const int width, const bool is_color) {
   cv::Mat cv_img;
   int cv_read_flag = (is_color ? CV_LOAD_IMAGE_COLOR :
     CV_LOAD_IMAGE_GRAYSCALE);
-
   cv::Mat cv_img_origin = cv::imread(filename, cv_read_flag);
   if (!cv_img_origin.data) {
     LOG(ERROR) << "Could not open or find file " << filename;
-    return false;
+    return cv_img_origin;
   }
   if (height > 0 && width > 0) {
     cv::resize(cv_img_origin, cv_img, cv::Size(width, height));
   } else {
     cv_img = cv_img_origin;
   }
+  return cv_img;
+}
+
+cv::Mat ReadImageToCVMat(const string& filename,
+    const int height, const int width) {
+  return ReadImageToCVMat(filename, height, width, true);
+}
+
+cv::Mat ReadImageToCVMat(const string& filename,
+    const bool is_color) {
+  return ReadImageToCVMat(filename, 0, 0, is_color);
+}
+
+cv::Mat ReadImageToCVMat(const string& filename) {
+  return ReadImageToCVMat(filename, 0, 0, true);
+}
+// Do the file extension and encoding match?
+static bool matchExt(const std::string & fn,
+                     std::string en) {
+  size_t p = fn.rfind('.');
+  std::string ext = p != fn.npos ? fn.substr(p) : fn;
+  std::transform(ext.begin(), ext.end(), ext.begin(), ::tolower);
+  std::transform(en.begin(), en.end(), en.begin(), ::tolower);
+  if ( ext == en )
+    return true;
+  if ( en == "jpg" && ext == "jpeg" )
+    return true;
+  return false;
+}
+bool ReadImageToDatum(const string& filename, const int label,
+    const int height, const int width, const bool is_color,
+    const std::string & encoding, Datum* datum) {
+  cv::Mat cv_img = ReadImageToCVMat(filename, height, width, is_color);
+  if (cv_img.data) {
+    if (encoding.size()) {
+      if ( (cv_img.channels() == 3) == is_color && !height && !width &&
+          matchExt(filename, encoding) )
+        return ReadFileToDatum(filename, label, datum);
+      std::vector<uchar> buf;
+      cv::imencode("."+encoding, cv_img, buf);
+      datum->set_data(std::string(reinterpret_cast<char*>(&buf[0]),
+                      buf.size()));
+      datum->set_label(label);
+      datum->set_encoded(true);
+      return true;
+    }
+    CVMatToDatum(cv_img, datum);
+    datum->set_label(label);
+    return true;
+  } else {
+    return false;
+  }
+}
+
+bool ReadFileToDatum(const string& filename, const int label,
+    Datum* datum) {
+  std::streampos size;
+
+  fstream file(filename.c_str(), ios::in|ios::binary|ios::ate);
+  if (file.is_open()) {
+    size = file.tellg();
+    std::string buffer(size, ' ');
+    file.seekg(0, ios::beg);
+    file.read(&buffer[0], size);
+    file.close();
+    datum->set_data(buffer);
+    datum->set_label(label);
+    datum->set_encoded(true);
+    return true;
+  } else {
+    return false;
+  }
+}
+
+cv::Mat DecodeDatumToCVMatNative(const Datum& datum) {
+  cv::Mat cv_img;
+  CHECK(datum.encoded()) << "Datum not encoded";
+  const string& data = datum.data();
+  std::vector<char> vec_data(data.c_str(), data.c_str() + data.size());
+  cv_img = cv::imdecode(vec_data, -1);
+  if (!cv_img.data) {
+    LOG(ERROR) << "Could not decode datum ";
+  }
+  return cv_img;
+}
+cv::Mat DecodeDatumToCVMat(const Datum& datum, bool is_color) {
+  cv::Mat cv_img;
+  CHECK(datum.encoded()) << "Datum not encoded";
+  const string& data = datum.data();
+  std::vector<char> vec_data(data.c_str(), data.c_str() + data.size());
+  int cv_read_flag = (is_color ? CV_LOAD_IMAGE_COLOR :
+    CV_LOAD_IMAGE_GRAYSCALE);
+  cv_img = cv::imdecode(vec_data, cv_read_flag);
+  if (!cv_img.data) {
+    LOG(ERROR) << "Could not decode datum ";
+  }
+  return cv_img;
+}
 
-  int num_channels = (is_color ? 3 : 1);
-  datum->set_channels(num_channels);
+// If Datum is encoded will decoded using DecodeDatumToCVMat and CVMatToDatum
+// If Datum is not encoded will do nothing
+bool DecodeDatumNative(Datum* datum) {
+  if (datum->encoded()) {
+    cv::Mat cv_img = DecodeDatumToCVMatNative((*datum));
+    CVMatToDatum(cv_img, datum);
+    return true;
+  } else {
+    return false;
+  }
+}
+bool DecodeDatum(Datum* datum, bool is_color) {
+  if (datum->encoded()) {
+    cv::Mat cv_img = DecodeDatumToCVMat((*datum), is_color);
+    CVMatToDatum(cv_img, datum);
+    return true;
+  } else {
+    return false;
+  }
+}
+
+void CVMatToDatum(const cv::Mat& cv_img, Datum* datum) {
+  CHECK(cv_img.depth() == CV_8U) << "Image data type must be unsigned byte";
+  datum->set_channels(cv_img.channels());
   datum->set_height(cv_img.rows);
   datum->set_width(cv_img.cols);
-  datum->set_label(label);
   datum->clear_data();
   datum->clear_float_data();
-  string* datum_string = datum->mutable_data();
-  if (is_color) {
-    for (int c = 0; c < num_channels; ++c) {
-      for (int h = 0; h < cv_img.rows; ++h) {
-        for (int w = 0; w < cv_img.cols; ++w) {
-          datum_string->push_back(
-            static_cast<char>(cv_img.at<cv::Vec3b>(h, w)[c]));
-        }
+  datum->set_encoded(false);
+  int datum_channels = datum->channels();
+  int datum_height = datum->height();
+  int datum_width = datum->width();
+  int datum_size = datum_channels * datum_height * datum_width;
+  std::string buffer(datum_size, ' ');
+  for (int h = 0; h < datum_height; ++h) {
+    const uchar* ptr = cv_img.ptr<uchar>(h);
+    int img_index = 0;
+    for (int w = 0; w < datum_width; ++w) {
+      for (int c = 0; c < datum_channels; ++c) {
+        int datum_index = (c * datum_height + h) * datum_width + w;
+        buffer[datum_index] = static_cast<char>(ptr[img_index++]);
       }
     }
-  } else {  // Faster than repeatedly testing is_color for each pixel w/i loop
-    for (int h = 0; h < cv_img.rows; ++h) {
-      for (int w = 0; w < cv_img.cols; ++w) {
-        datum_string->push_back(
-          static_cast<char>(cv_img.at<uchar>(h, w)));
-        }
-      }
   }
-  return true;
-}
-
-leveldb::Options GetLevelDBOptions() {
-  // In default, we will return the leveldb option and set the max open files
-  // in order to avoid using up the operating system's limit.
-  leveldb::Options options;
-  options.max_open_files = 100;
-  return options;
+  datum->set_data(buffer);
 }
 
 // Verifies format of data stored in HDF5 file and reshapes blob accordingly.
@@ -124,6 +233,9 @@ template <typename Dtype>
 void hdf5_load_nd_dataset_helper(
     hid_t file_id, const char* dataset_name_, int min_dim, int max_dim,
     Blob<Dtype>* blob) {
+  // Verify that the dataset exists.
+  CHECK(H5LTfind_dataset(file_id, dataset_name_))
+      << "Failed to find HDF5 dataset " << dataset_name_;
   // Verify that the number of dimensions is in the accepted range.
   herr_t status;
   int ndims;
@@ -140,11 +252,11 @@ void hdf5_load_nd_dataset_helper(
   CHECK_GE(status, 0) << "Failed to get dataset info for " << dataset_name_;
   CHECK_EQ(class_, H5T_FLOAT) << "Expected float or double data";
 
-  blob->Reshape(
-    dims[0],
-    (dims.size() > 1) ? dims[1] : 1,
-    (dims.size() > 2) ? dims[2] : 1,
-    (dims.size() > 3) ? dims[3] : 1);
+  vector<int> blob_dims(dims.size());
+  for (int i = 0; i < dims.size(); ++i) {
+    blob_dims[i] = dims[i];
+  }
+  blob->Reshape(blob_dims);
 }
 
 template <>
@@ -167,7 +279,7 @@ void hdf5_load_nd_dataset<double>(hid_t file_id, const char* dataset_name_,
 
 template <>
 void hdf5_save_nd_dataset<float>(
-    const hid_t file_id, const string dataset_name, const Blob<float>& blob) {
+    const hid_t file_id, const string& dataset_name, const Blob<float>& blob) {
   hsize_t dims[HDF5_NUM_DIMS];
   dims[0] = blob.num();
   dims[1] = blob.channels();
@@ -180,7 +292,7 @@ void hdf5_save_nd_dataset<float>(
 
 template <>
 void hdf5_save_nd_dataset<double>(
-    const hid_t file_id, const string dataset_name, const Blob<double>& blob) {
+    const hid_t file_id, const string& dataset_name, const Blob<double>& blob) {
   hsize_t dims[HDF5_NUM_DIMS];
   dims[0] = blob.num();
   dims[1] = blob.channels();
@@ -191,4 +303,110 @@ void hdf5_save_nd_dataset<double>(
   CHECK_GE(status, 0) << "Failed to make double dataset " << dataset_name;
 }
 
+bool ReadSegmentRGBToDatum(const string& filename, const int label,
+    const vector<int> offsets, const int height, const int width, const int length, Datum* datum, bool is_color,
+    const char* name_pattern ){
+	cv::Mat cv_img;
+	string* datum_string;
+	char tmp[30];
+	int cv_read_flag = (is_color ? CV_LOAD_IMAGE_COLOR :
+	    CV_LOAD_IMAGE_GRAYSCALE);
+	for (int i = 0; i < offsets.size(); ++i){
+		int offset = offsets[i];
+		for (int file_id = 1; file_id < length+1; ++file_id){
+			sprintf(tmp, name_pattern, int(file_id+offset));
+			string filename_t = filename + "/" + tmp;
+			cv::Mat cv_img_origin = cv::imread(filename_t, cv_read_flag);
+			if (!cv_img_origin.data){
+				LOG(ERROR) << "Could not load file " << filename_t;
+				return false;
+			}
+			if (height > 0 && width > 0){
+				cv::resize(cv_img_origin, cv_img, cv::Size(width, height));
+			}else{
+				cv_img = cv_img_origin;
+			}
+			int num_channels = (is_color ? 3 : 1);
+			if (file_id==1 && i==0){
+				datum->set_channels(num_channels*length*offsets.size());
+				datum->set_height(cv_img.rows);
+				datum->set_width(cv_img.cols);
+				datum->set_label(label);
+				datum->clear_data();
+				datum->clear_float_data();
+				datum_string = datum->mutable_data();
+			}
+			if (is_color) {
+			    for (int c = 0; c < num_channels; ++c) {
+			      for (int h = 0; h < cv_img.rows; ++h) {
+			        for (int w = 0; w < cv_img.cols; ++w) {
+			          datum_string->push_back(
+			            static_cast<char>(cv_img.at<cv::Vec3b>(h, w)[c]));
+			        }
+			      }
+			    }
+			  } else {  // Faster than repeatedly testing is_color for each pixel w/i loop
+			    for (int h = 0; h < cv_img.rows; ++h) {
+			      for (int w = 0; w < cv_img.cols; ++w) {
+			        datum_string->push_back(
+			          static_cast<char>(cv_img.at<uchar>(h, w)));
+			        }
+			      }
+			  }
+		}
+	}
+	return true;
+}
+
+bool ReadSegmentFlowToDatum(const string& filename, const int label,
+    const vector<int> offsets, const int height, const int width, const int length, Datum* datum,
+    const char* name_pattern ){
+	cv::Mat cv_img_x, cv_img_y;
+	string* datum_string;
+	char tmp[30];
+	for (int i = 0; i < offsets.size(); ++i){
+		int offset = offsets[i];
+		for (int file_id = 1; file_id < length+1; ++file_id){
+			sprintf(tmp,name_pattern, 'x', int(file_id+offset));
+			string filename_x = filename + "/" + tmp;
+			cv::Mat cv_img_origin_x = cv::imread(filename_x, CV_LOAD_IMAGE_GRAYSCALE);
+			sprintf(tmp, name_pattern, 'y', int(file_id+offset));
+			string filename_y = filename + "/" + tmp;
+			cv::Mat cv_img_origin_y = cv::imread(filename_y, CV_LOAD_IMAGE_GRAYSCALE);
+			if (!cv_img_origin_x.data || !cv_img_origin_y.data){
+				LOG(ERROR) << "Could not load file " << filename_x << " or " << filename_y;
+				return false;
+			}
+			if (height > 0 && width > 0){
+				cv::resize(cv_img_origin_x, cv_img_x, cv::Size(width, height));
+				cv::resize(cv_img_origin_y, cv_img_y, cv::Size(width, height));
+			}else{
+				cv_img_x = cv_img_origin_x;
+				cv_img_y = cv_img_origin_y;
+			}
+			if (file_id==1 && i==0){
+				int num_channels = 2;
+				datum->set_channels(num_channels*length*offsets.size());
+				datum->set_height(cv_img_x.rows);
+				datum->set_width(cv_img_x.cols);
+				datum->set_label(label);
+				datum->clear_data();
+				datum->clear_float_data();
+				datum_string = datum->mutable_data();
+			}
+			for (int h = 0; h < cv_img_x.rows; ++h){
+				for (int w = 0; w < cv_img_x.cols; ++w){
+					datum_string->push_back(static_cast<char>(cv_img_x.at<uchar>(h,w)));
+				}
+			}
+			for (int h = 0; h < cv_img_y.rows; ++h){
+				for (int w = 0; w < cv_img_y.cols; ++w){
+					datum_string->push_back(static_cast<char>(cv_img_y.at<uchar>(h,w)));
+				}
+			}
+		}
+	}
+	return true;
+}
+
 }  // namespace caffe
diff --git a/src/caffe/util/math_functions.cpp b/src/caffe/util/math_functions.cpp
index c3afd2ffbc5..0aab6b17b85 100644
--- a/src/caffe/util/math_functions.cpp
+++ b/src/caffe/util/math_functions.cpp
@@ -206,6 +206,16 @@ void caffe_exp<double>(const int n, const double* a, double* y) {
   vdExp(n, a, y);
 }
 
+template <>
+void caffe_log<float>(const int n, const float* a, float* y) {
+  vsLn(n, a, y);
+}
+
+template <>
+void caffe_log<double>(const int n, const double* a, double* y) {
+  vdLn(n, a, y);
+}
+
 template <>
 void caffe_abs<float>(const int n, const float* a, float* y) {
     vsAbs(n, a, y);
@@ -370,9 +380,6 @@ double caffe_cpu_asum<double>(const int n, const double* x) {
   return cblas_dasum(n, x, 1);
 }
 
-INSTANTIATE_CAFFE_CPU_UNARY_FUNC(sign);
-INSTANTIATE_CAFFE_CPU_UNARY_FUNC(sgnbit);
-
 template <>
 void caffe_cpu_scale<float>(const int n, const float alpha, const float *x,
                             float* y) {
diff --git a/src/caffe/util/math_functions.cu b/src/caffe/util/math_functions.cu
index 4ae4bba6029..2631a0740d6 100644
--- a/src/caffe/util/math_functions.cu
+++ b/src/caffe/util/math_functions.cu
@@ -303,6 +303,48 @@ void caffe_gpu_abs<double>(const int N, const double* a, double* y) {
 }
 
 
+template <typename Dtype>
+__global__ void exp_kernel(const int n, const Dtype* a, Dtype* y) {
+  CUDA_KERNEL_LOOP(index, n) {
+    y[index] = exp(a[index]);
+  }
+}
+
+template <>
+void caffe_gpu_exp<float>(const int N, const float* a, float* y) {
+  // NOLINT_NEXT_LINE(whitespace/operators)
+  exp_kernel<float><<<CAFFE_GET_BLOCKS(N), CAFFE_CUDA_NUM_THREADS>>>(
+      N, a, y);
+}
+
+template <>
+void caffe_gpu_exp<double>(const int N, const double* a, double* y) {
+  // NOLINT_NEXT_LINE(whitespace/operators)
+  exp_kernel<double><<<CAFFE_GET_BLOCKS(N), CAFFE_CUDA_NUM_THREADS>>>(
+      N, a, y);
+}
+
+template <typename Dtype>
+__global__ void log_kernel(const int n, const Dtype* a, Dtype* y) {
+  CUDA_KERNEL_LOOP(index, n) {
+    y[index] = log(a[index]);
+  }
+}
+
+template <>
+void caffe_gpu_log<float>(const int N, const float* a, float* y) {
+  // NOLINT_NEXT_LINE(whitespace/operators)
+  log_kernel<float><<<CAFFE_GET_BLOCKS(N), CAFFE_CUDA_NUM_THREADS>>>(
+      N, a, y);
+}
+
+template <>
+void caffe_gpu_log<double>(const int N, const double* a, double* y) {
+  // NOLINT_NEXT_LINE(whitespace/operators)
+  log_kernel<double><<<CAFFE_GET_BLOCKS(N), CAFFE_CUDA_NUM_THREADS>>>(
+      N, a, y);
+}
+
 template <typename Dtype>
 __global__ void powx_kernel(const int n, const Dtype* a,
     const Dtype alpha, Dtype* y) {
diff --git a/src/caffe/util/mpi_functions.cpp b/src/caffe/util/mpi_functions.cpp
new file mode 100644
index 00000000000..c65f4bb0e9b
--- /dev/null
+++ b/src/caffe/util/mpi_functions.cpp
@@ -0,0 +1,60 @@
+//
+// Created by alex on 8/25/15.
+//
+
+#ifdef USE_MPI
+
+#include "caffe/caffe.hpp"
+#include "caffe/util/mpi_functions.hpp"
+#include "caffe/util/channel.hpp"
+
+namespace caffe {
+  template <typename Dtype>
+  void caffe_iallreduce(Dtype* data, int count){
+    MPIJob job = {data, data, count, sizeof(Dtype), OP_SUM_ALL};
+    MPIComm::AddMPIJob(job);
+  }
+
+  template void caffe_iallreduce<float>(float* data, int count);
+  template void caffe_iallreduce<double>(double* data, int count);
+
+  template <typename Dtype>
+  void caffe_iallreduce(Dtype* src_data, Dtype* dst_data, int count){
+    MPIJob job = {src_data, dst_data, count, sizeof(Dtype), OP_SUM_ALL};
+    MPIComm::AddMPIJob(job);
+  }
+
+  template void caffe_iallreduce<float>(float* src_data, float* dst_data, int count);
+  template void caffe_iallreduce<double>(double* src_data, double* dst_data, int count);
+
+  template <typename Dtype>
+  void caffe_iallgather(Dtype* src_data, Dtype* dst_data, int count){
+    MPIJob job = {src_data, dst_data, count, sizeof(Dtype), OP_GATHER};
+    MPIComm::AddMPIJob(job);
+  }
+  template void caffe_iallgather<float>(float*, float*, int);
+  template void caffe_iallgather<double>(double*, double*, int);
+
+  template <typename Dtype>
+  void caffe_iscatter(Dtype* src_data, Dtype* dst_data, int count){
+    MPIJob job = {src_data, dst_data, count, sizeof(Dtype), OP_SCATTER};
+    MPIComm::AddMPIJob(job);
+  }
+
+  template void caffe_iscatter<float>(float*, float*, int);
+  template void caffe_iscatter<double>(double*, double*, int);
+
+  template <typename Dtype>
+  void caffe_ibcast(Dtype* data, int count){
+    MPIJob job = {data, data, count, sizeof(Dtype), OP_BROADCAST};
+    MPIComm::AddMPIJob(job);
+  }
+  template void caffe_ibcast<float>(float* data, int count);
+  template void caffe_ibcast<double>(double* data, int count);
+
+  void mpi_force_synchronize(){
+    MPIComm::Syncrhonize();
+  }
+}
+
+#endif //USE_MPI
diff --git a/src/caffe/util/upgrade_proto.cpp b/src/caffe/util/upgrade_proto.cpp
index c69c58eb340..38a06026adf 100644
--- a/src/caffe/util/upgrade_proto.cpp
+++ b/src/caffe/util/upgrade_proto.cpp
@@ -13,6 +13,10 @@
 namespace caffe {
 
 bool NetNeedsUpgrade(const NetParameter& net_param) {
+  return NetNeedsV0ToV1Upgrade(net_param) || NetNeedsV1ToV2Upgrade(net_param);
+}
+
+bool NetNeedsV0ToV1Upgrade(const NetParameter& net_param) {
   for (int i = 0; i < net_param.layers_size(); ++i) {
     if (net_param.layers(i).has_layer()) {
       return true;
@@ -21,6 +25,10 @@ bool NetNeedsUpgrade(const NetParameter& net_param) {
   return false;
 }
 
+bool NetNeedsV1ToV2Upgrade(const NetParameter& net_param) {
+  return net_param.layers_size() > 0;
+}
+
 bool UpgradeV0Net(const NetParameter& v0_net_param_padding_layers,
                   NetParameter* net_param) {
   // First upgrade padding layers to padded conv layers.
@@ -33,8 +41,8 @@ bool UpgradeV0Net(const NetParameter& v0_net_param_padding_layers,
     net_param->set_name(v0_net_param.name());
   }
   for (int i = 0; i < v0_net_param.layers_size(); ++i) {
-    is_fully_compatible &= UpgradeLayerParameter(v0_net_param.layers(i),
-                                                 net_param->add_layers());
+    is_fully_compatible &= UpgradeV0LayerParameter(v0_net_param.layers(i),
+                                                   net_param->add_layers());
   }
   for (int i = 0; i < v0_net_param.input_size(); ++i) {
     net_param->add_input(v0_net_param.input(i));
@@ -61,7 +69,7 @@ void UpgradeV0PaddingLayers(const NetParameter& param,
     blob_name_to_last_top_idx[blob_name] = -1;
   }
   for (int i = 0; i < param.layers_size(); ++i) {
-    const LayerParameter& layer_connection = param.layers(i);
+    const V1LayerParameter& layer_connection = param.layers(i);
     const V0LayerParameter& layer_param = layer_connection.layer();
     // Add the layer to the new net, unless it's a padding layer.
     if (layer_param.type() != "padding") {
@@ -77,7 +85,7 @@ void UpgradeV0PaddingLayers(const NetParameter& param,
       if (top_idx == -1) {
         continue;
       }
-      LayerParameter source_layer = param.layers(top_idx);
+      const V1LayerParameter& source_layer = param.layers(top_idx);
       if (source_layer.layer().type() == "padding") {
         // This layer has a padding layer as input -- check that it is a conv
         // layer or a pooling layer and takes only one input.  Also check that
@@ -107,8 +115,8 @@ void UpgradeV0PaddingLayers(const NetParameter& param,
   }
 }
 
-bool UpgradeLayerParameter(const LayerParameter& v0_layer_connection,
-                           LayerParameter* layer_param) {
+bool UpgradeV0LayerParameter(const V1LayerParameter& v0_layer_connection,
+                             V1LayerParameter* layer_param) {
   bool is_fully_compatible = true;
   layer_param->Clear();
   for (int i = 0; i < v0_layer_connection.bottom_size(); ++i) {
@@ -285,6 +293,14 @@ bool UpgradeLayerParameter(const LayerParameter& v0_layer_connection,
         is_fully_compatible = false;
       }
     }
+    if (v0_layer_param.has_k()) {
+      if (type == "lrn") {
+        layer_param->mutable_lrn_param()->set_k(v0_layer_param.k());
+      } else {
+        LOG(ERROR) << "Unknown parameter k for layer type " << type;
+        is_fully_compatible = false;
+      }
+    }
     if (v0_layer_param.has_source()) {
       if (type == "data") {
         layer_param->mutable_data_param()->set_source(v0_layer_param.source());
@@ -451,78 +467,78 @@ bool UpgradeLayerParameter(const LayerParameter& v0_layer_connection,
   return is_fully_compatible;
 }
 
-LayerParameter_LayerType UpgradeV0LayerType(const string& type) {
+V1LayerParameter_LayerType UpgradeV0LayerType(const string& type) {
   if (type == "accuracy") {
-    return LayerParameter_LayerType_ACCURACY;
+    return V1LayerParameter_LayerType_ACCURACY;
   } else if (type == "bnll") {
-    return LayerParameter_LayerType_BNLL;
+    return V1LayerParameter_LayerType_BNLL;
   } else if (type == "concat") {
-    return LayerParameter_LayerType_CONCAT;
+    return V1LayerParameter_LayerType_CONCAT;
   } else if (type == "conv") {
-    return LayerParameter_LayerType_CONVOLUTION;
+    return V1LayerParameter_LayerType_CONVOLUTION;
   } else if (type == "data") {
-    return LayerParameter_LayerType_DATA;
+    return V1LayerParameter_LayerType_DATA;
   } else if (type == "dropout") {
-    return LayerParameter_LayerType_DROPOUT;
+    return V1LayerParameter_LayerType_DROPOUT;
   } else if (type == "euclidean_loss") {
-    return LayerParameter_LayerType_EUCLIDEAN_LOSS;
+    return V1LayerParameter_LayerType_EUCLIDEAN_LOSS;
   } else if (type == "flatten") {
-    return LayerParameter_LayerType_FLATTEN;
+    return V1LayerParameter_LayerType_FLATTEN;
   } else if (type == "hdf5_data") {
-    return LayerParameter_LayerType_HDF5_DATA;
+    return V1LayerParameter_LayerType_HDF5_DATA;
   } else if (type == "hdf5_output") {
-    return LayerParameter_LayerType_HDF5_OUTPUT;
+    return V1LayerParameter_LayerType_HDF5_OUTPUT;
   } else if (type == "im2col") {
-    return LayerParameter_LayerType_IM2COL;
+    return V1LayerParameter_LayerType_IM2COL;
   } else if (type == "images") {
-    return LayerParameter_LayerType_IMAGE_DATA;
+    return V1LayerParameter_LayerType_IMAGE_DATA;
   } else if (type == "infogain_loss") {
-    return LayerParameter_LayerType_INFOGAIN_LOSS;
+    return V1LayerParameter_LayerType_INFOGAIN_LOSS;
   } else if (type == "innerproduct") {
-    return LayerParameter_LayerType_INNER_PRODUCT;
+    return V1LayerParameter_LayerType_INNER_PRODUCT;
   } else if (type == "lrn") {
-    return LayerParameter_LayerType_LRN;
+    return V1LayerParameter_LayerType_LRN;
   } else if (type == "multinomial_logistic_loss") {
-    return LayerParameter_LayerType_MULTINOMIAL_LOGISTIC_LOSS;
+    return V1LayerParameter_LayerType_MULTINOMIAL_LOGISTIC_LOSS;
   } else if (type == "pool") {
-    return LayerParameter_LayerType_POOLING;
+    return V1LayerParameter_LayerType_POOLING;
   } else if (type == "relu") {
-    return LayerParameter_LayerType_RELU;
+    return V1LayerParameter_LayerType_RELU;
   } else if (type == "sigmoid") {
-    return LayerParameter_LayerType_SIGMOID;
+    return V1LayerParameter_LayerType_SIGMOID;
   } else if (type == "softmax") {
-    return LayerParameter_LayerType_SOFTMAX;
+    return V1LayerParameter_LayerType_SOFTMAX;
   } else if (type == "softmax_loss") {
-    return LayerParameter_LayerType_SOFTMAX_LOSS;
+    return V1LayerParameter_LayerType_SOFTMAX_LOSS;
   } else if (type == "split") {
-    return LayerParameter_LayerType_SPLIT;
+    return V1LayerParameter_LayerType_SPLIT;
   } else if (type == "tanh") {
-    return LayerParameter_LayerType_TANH;
+    return V1LayerParameter_LayerType_TANH;
   } else if (type == "window_data") {
-    return LayerParameter_LayerType_WINDOW_DATA;
+    return V1LayerParameter_LayerType_WINDOW_DATA;
   } else {
     LOG(FATAL) << "Unknown layer name: " << type;
-    return LayerParameter_LayerType_NONE;
+    return V1LayerParameter_LayerType_NONE;
   }
 }
 
 bool NetNeedsDataUpgrade(const NetParameter& net_param) {
   for (int i = 0; i < net_param.layers_size(); ++i) {
-    if (net_param.layers(i).type() == LayerParameter_LayerType_DATA) {
+    if (net_param.layers(i).type() == V1LayerParameter_LayerType_DATA) {
       DataParameter layer_param = net_param.layers(i).data_param();
       if (layer_param.has_scale()) { return true; }
       if (layer_param.has_mean_file()) { return true; }
       if (layer_param.has_crop_size()) { return true; }
       if (layer_param.has_mirror()) { return true; }
     }
-    if (net_param.layers(i).type() == LayerParameter_LayerType_IMAGE_DATA) {
+    if (net_param.layers(i).type() == V1LayerParameter_LayerType_IMAGE_DATA) {
       ImageDataParameter layer_param = net_param.layers(i).image_data_param();
       if (layer_param.has_scale()) { return true; }
       if (layer_param.has_mean_file()) { return true; }
       if (layer_param.has_crop_size()) { return true; }
       if (layer_param.has_mirror()) { return true; }
     }
-    if (net_param.layers(i).type() == LayerParameter_LayerType_WINDOW_DATA) {
+    if (net_param.layers(i).type() == V1LayerParameter_LayerType_WINDOW_DATA) {
       WindowDataParameter layer_param = net_param.layers(i).window_data_param();
       if (layer_param.has_scale()) { return true; }
       if (layer_param.has_mean_file()) { return true; }
@@ -535,7 +551,7 @@ bool NetNeedsDataUpgrade(const NetParameter& net_param) {
 
 #define CONVERT_LAYER_TRANSFORM_PARAM(TYPE, Name, param_name) \
   do { \
-    if (net_param->layers(i).type() == LayerParameter_LayerType_##TYPE) { \
+    if (net_param->layers(i).type() == V1LayerParameter_LayerType_##TYPE) { \
       Name##Parameter* layer_param = \
           net_param->mutable_layers(i)->mutable_##param_name##_param(); \
       TransformationParameter* transform_param = \
@@ -567,34 +583,16 @@ void UpgradeNetDataTransformation(NetParameter* net_param) {
   }
 }
 
-void NetParameterToPrettyPrint(const NetParameter& param,
-                               NetParameterPrettyPrint* pretty_param) {
-  pretty_param->Clear();
-  if (param.has_name()) {
-    pretty_param->set_name(param.name());
-  }
-  if (param.has_force_backward()) {
-    pretty_param->set_force_backward(param.force_backward());
-  }
-  for (int i = 0; i < param.input_size(); ++i) {
-    pretty_param->add_input(param.input(i));
-  }
-  for (int i = 0; i < param.input_dim_size(); ++i) {
-    pretty_param->add_input_dim(param.input_dim(i));
-  }
-  for (int i = 0; i < param.layers_size(); ++i) {
-    pretty_param->add_layers()->CopyFrom(param.layers(i));
-  }
-}
-
-void UpgradeNetAsNeeded(const string& param_file, NetParameter* param) {
-  if (NetNeedsUpgrade(*param)) {
+bool UpgradeNetAsNeeded(const string& param_file, NetParameter* param) {
+  bool success = true;
+  if (NetNeedsV0ToV1Upgrade(*param)) {
     // NetParameter was specified using the old style (V0LayerParameter); try to
     // upgrade it.
     LOG(ERROR) << "Attempting to upgrade input file specified using deprecated "
                << "V0LayerParameter: " << param_file;
     NetParameter original_param(*param);
     if (!UpgradeV0Net(original_param, param)) {
+      success = false;
       LOG(ERROR) << "Warning: had one or more problems upgrading "
           << "V0NetParameter to NetParameter (see above); continuing anyway.";
     } else {
@@ -616,6 +614,313 @@ void UpgradeNetAsNeeded(const string& param_file, NetParameter* param) {
     LOG(ERROR) << "Note that future Caffe releases will only support "
                << "transform_param messages for transformation fields.";
   }
+  if (NetNeedsV1ToV2Upgrade(*param)) {
+    LOG(ERROR) << "Attempting to upgrade input file specified using deprecated "
+               << "V1LayerParameter: " << param_file;
+    NetParameter original_param(*param);
+    if (!UpgradeV1Net(original_param, param)) {
+      success = false;
+      LOG(ERROR) << "Warning: had one or more problems upgrading "
+          << "V1LayerParameter (see above); continuing anyway.";
+    } else {
+      LOG(INFO) << "Successfully upgraded file specified using deprecated "
+                << "V1LayerParameter";
+    }
+  }
+  return success;
+}
+
+bool UpgradeV1Net(const NetParameter& v1_net_param, NetParameter* net_param) {
+  bool is_fully_compatible = true;
+  if (v1_net_param.layer_size() > 0) {
+    LOG(ERROR) << "Input NetParameter to be upgraded already specifies 'layer' "
+               << "fields; these will be ignored for the upgrade.";
+    is_fully_compatible = false;
+  }
+  net_param->CopyFrom(v1_net_param);
+  net_param->clear_layers();
+  net_param->clear_layer();
+  for (int i = 0; i < v1_net_param.layers_size(); ++i) {
+    if (!UpgradeV1LayerParameter(v1_net_param.layers(i),
+                                 net_param->add_layer())) {
+      LOG(ERROR) << "Upgrade of input layer " << i << " failed.";
+      is_fully_compatible = false;
+    }
+  }
+  return is_fully_compatible;
+}
+
+bool UpgradeV1LayerParameter(const V1LayerParameter& v1_layer_param,
+                             LayerParameter* layer_param) {
+  layer_param->Clear();
+  bool is_fully_compatible = true;
+  for (int i = 0; i < v1_layer_param.bottom_size(); ++i) {
+    layer_param->add_bottom(v1_layer_param.bottom(i));
+  }
+  for (int i = 0; i < v1_layer_param.top_size(); ++i) {
+    layer_param->add_top(v1_layer_param.top(i));
+  }
+  if (v1_layer_param.has_name()) {
+    layer_param->set_name(v1_layer_param.name());
+  }
+  for (int i = 0; i < v1_layer_param.include_size(); ++i) {
+    layer_param->add_include()->CopyFrom(v1_layer_param.include(i));
+  }
+  for (int i = 0; i < v1_layer_param.exclude_size(); ++i) {
+    layer_param->add_exclude()->CopyFrom(v1_layer_param.exclude(i));
+  }
+  if (v1_layer_param.has_type()) {
+    layer_param->set_type(UpgradeV1LayerType(v1_layer_param.type()));
+  }
+  for (int i = 0; i < v1_layer_param.blobs_size(); ++i) {
+    layer_param->add_blobs()->CopyFrom(v1_layer_param.blobs(i));
+  }
+  for (int i = 0; i < v1_layer_param.param_size(); ++i) {
+    while (layer_param->param_size() <= i) { layer_param->add_param(); }
+    layer_param->mutable_param(i)->set_name(v1_layer_param.param(i));
+  }
+  ParamSpec_DimCheckMode mode;
+  for (int i = 0; i < v1_layer_param.blob_share_mode_size(); ++i) {
+    while (layer_param->param_size() <= i) { layer_param->add_param(); }
+    switch (v1_layer_param.blob_share_mode(i)) {
+    case V1LayerParameter_DimCheckMode_STRICT:
+      mode = ParamSpec_DimCheckMode_STRICT;
+      break;
+    case V1LayerParameter_DimCheckMode_PERMISSIVE:
+      mode = ParamSpec_DimCheckMode_PERMISSIVE;
+      break;
+    default:
+      LOG(FATAL) << "Unknown blob_share_mode: "
+                 << v1_layer_param.blob_share_mode(i);
+      break;
+    }
+    layer_param->mutable_param(i)->set_share_mode(mode);
+  }
+  for (int i = 0; i < v1_layer_param.blobs_lr_size(); ++i) {
+    while (layer_param->param_size() <= i) { layer_param->add_param(); }
+    layer_param->mutable_param(i)->set_lr_mult(v1_layer_param.blobs_lr(i));
+  }
+  for (int i = 0; i < v1_layer_param.weight_decay_size(); ++i) {
+    while (layer_param->param_size() <= i) { layer_param->add_param(); }
+    layer_param->mutable_param(i)->set_decay_mult(
+        v1_layer_param.weight_decay(i));
+  }
+  for (int i = 0; i < v1_layer_param.loss_weight_size(); ++i) {
+    layer_param->add_loss_weight(v1_layer_param.loss_weight(i));
+  }
+  if (v1_layer_param.has_accuracy_param()) {
+    layer_param->mutable_accuracy_param()->CopyFrom(
+        v1_layer_param.accuracy_param());
+  }
+  if (v1_layer_param.has_argmax_param()) {
+    layer_param->mutable_argmax_param()->CopyFrom(
+        v1_layer_param.argmax_param());
+  }
+  if (v1_layer_param.has_concat_param()) {
+    layer_param->mutable_concat_param()->CopyFrom(
+        v1_layer_param.concat_param());
+  }
+  if (v1_layer_param.has_contrastive_loss_param()) {
+    layer_param->mutable_contrastive_loss_param()->CopyFrom(
+        v1_layer_param.contrastive_loss_param());
+  }
+  if (v1_layer_param.has_convolution_param()) {
+    layer_param->mutable_convolution_param()->CopyFrom(
+        v1_layer_param.convolution_param());
+  }
+  if (v1_layer_param.has_data_param()) {
+    layer_param->mutable_data_param()->CopyFrom(
+        v1_layer_param.data_param());
+  }
+  if (v1_layer_param.has_dropout_param()) {
+    layer_param->mutable_dropout_param()->CopyFrom(
+        v1_layer_param.dropout_param());
+  }
+  if (v1_layer_param.has_dummy_data_param()) {
+    layer_param->mutable_dummy_data_param()->CopyFrom(
+        v1_layer_param.dummy_data_param());
+  }
+  if (v1_layer_param.has_eltwise_param()) {
+    layer_param->mutable_eltwise_param()->CopyFrom(
+        v1_layer_param.eltwise_param());
+  }
+  if (v1_layer_param.has_exp_param()) {
+    layer_param->mutable_exp_param()->CopyFrom(
+        v1_layer_param.exp_param());
+  }
+  if (v1_layer_param.has_hdf5_data_param()) {
+    layer_param->mutable_hdf5_data_param()->CopyFrom(
+        v1_layer_param.hdf5_data_param());
+  }
+  if (v1_layer_param.has_hdf5_output_param()) {
+    layer_param->mutable_hdf5_output_param()->CopyFrom(
+        v1_layer_param.hdf5_output_param());
+  }
+  if (v1_layer_param.has_hinge_loss_param()) {
+    layer_param->mutable_hinge_loss_param()->CopyFrom(
+        v1_layer_param.hinge_loss_param());
+  }
+  if (v1_layer_param.has_image_data_param()) {
+    layer_param->mutable_image_data_param()->CopyFrom(
+        v1_layer_param.image_data_param());
+  }
+  if (v1_layer_param.has_infogain_loss_param()) {
+    layer_param->mutable_infogain_loss_param()->CopyFrom(
+        v1_layer_param.infogain_loss_param());
+  }
+  if (v1_layer_param.has_inner_product_param()) {
+    layer_param->mutable_inner_product_param()->CopyFrom(
+        v1_layer_param.inner_product_param());
+  }
+  if (v1_layer_param.has_lrn_param()) {
+    layer_param->mutable_lrn_param()->CopyFrom(
+        v1_layer_param.lrn_param());
+  }
+  if (v1_layer_param.has_memory_data_param()) {
+    layer_param->mutable_memory_data_param()->CopyFrom(
+        v1_layer_param.memory_data_param());
+  }
+  if (v1_layer_param.has_mvn_param()) {
+    layer_param->mutable_mvn_param()->CopyFrom(
+        v1_layer_param.mvn_param());
+  }
+  if (v1_layer_param.has_pooling_param()) {
+    layer_param->mutable_pooling_param()->CopyFrom(
+        v1_layer_param.pooling_param());
+  }
+  if (v1_layer_param.has_power_param()) {
+    layer_param->mutable_power_param()->CopyFrom(
+        v1_layer_param.power_param());
+  }
+  if (v1_layer_param.has_relu_param()) {
+    layer_param->mutable_relu_param()->CopyFrom(
+        v1_layer_param.relu_param());
+  }
+  if (v1_layer_param.has_sigmoid_param()) {
+    layer_param->mutable_sigmoid_param()->CopyFrom(
+        v1_layer_param.sigmoid_param());
+  }
+  if (v1_layer_param.has_softmax_param()) {
+    layer_param->mutable_softmax_param()->CopyFrom(
+        v1_layer_param.softmax_param());
+  }
+  if (v1_layer_param.has_slice_param()) {
+    layer_param->mutable_slice_param()->CopyFrom(
+        v1_layer_param.slice_param());
+  }
+  if (v1_layer_param.has_tanh_param()) {
+    layer_param->mutable_tanh_param()->CopyFrom(
+        v1_layer_param.tanh_param());
+  }
+  if (v1_layer_param.has_threshold_param()) {
+    layer_param->mutable_threshold_param()->CopyFrom(
+        v1_layer_param.threshold_param());
+  }
+  if (v1_layer_param.has_window_data_param()) {
+    layer_param->mutable_window_data_param()->CopyFrom(
+        v1_layer_param.window_data_param());
+  }
+  if (v1_layer_param.has_transform_param()) {
+    layer_param->mutable_transform_param()->CopyFrom(
+        v1_layer_param.transform_param());
+  }
+  if (v1_layer_param.has_loss_param()) {
+    layer_param->mutable_loss_param()->CopyFrom(
+        v1_layer_param.loss_param());
+  }
+  if (v1_layer_param.has_layer()) {
+    LOG(ERROR) << "Input NetParameter has V0 layer -- ignoring.";
+    is_fully_compatible = false;
+  }
+  return is_fully_compatible;
+}
+
+const char* UpgradeV1LayerType(const V1LayerParameter_LayerType type) {
+  switch (type) {
+  case V1LayerParameter_LayerType_NONE:
+    return "";
+  case V1LayerParameter_LayerType_ABSVAL:
+    return "AbsVal";
+  case V1LayerParameter_LayerType_ACCURACY:
+    return "Accuracy";
+  case V1LayerParameter_LayerType_ARGMAX:
+    return "ArgMax";
+  case V1LayerParameter_LayerType_BNLL:
+    return "BNLL";
+  case V1LayerParameter_LayerType_CONCAT:
+    return "Concat";
+  case V1LayerParameter_LayerType_CONTRASTIVE_LOSS:
+    return "ContrastiveLoss";
+  case V1LayerParameter_LayerType_CONVOLUTION:
+    return "Convolution";
+  case V1LayerParameter_LayerType_DECONVOLUTION:
+    return "Deconvolution";
+  case V1LayerParameter_LayerType_DATA:
+    return "Data";
+  case V1LayerParameter_LayerType_DROPOUT:
+    return "Dropout";
+  case V1LayerParameter_LayerType_DUMMY_DATA:
+    return "DummyData";
+  case V1LayerParameter_LayerType_EUCLIDEAN_LOSS:
+    return "EuclideanLoss";
+  case V1LayerParameter_LayerType_ELTWISE:
+    return "Eltwise";
+  case V1LayerParameter_LayerType_EXP:
+    return "Exp";
+  case V1LayerParameter_LayerType_FLATTEN:
+    return "Flatten";
+  case V1LayerParameter_LayerType_HDF5_DATA:
+    return "HDF5Data";
+  case V1LayerParameter_LayerType_HDF5_OUTPUT:
+    return "HDF5Output";
+  case V1LayerParameter_LayerType_HINGE_LOSS:
+    return "HingeLoss";
+  case V1LayerParameter_LayerType_IM2COL:
+    return "Im2col";
+  case V1LayerParameter_LayerType_IMAGE_DATA:
+    return "ImageData";
+  case V1LayerParameter_LayerType_INFOGAIN_LOSS:
+    return "InfogainLoss";
+  case V1LayerParameter_LayerType_INNER_PRODUCT:
+    return "InnerProduct";
+  case V1LayerParameter_LayerType_LRN:
+    return "LRN";
+  case V1LayerParameter_LayerType_MEMORY_DATA:
+    return "MemoryData";
+  case V1LayerParameter_LayerType_MULTINOMIAL_LOGISTIC_LOSS:
+    return "MultinomialLogisticLoss";
+  case V1LayerParameter_LayerType_MVN:
+    return "MVN";
+  case V1LayerParameter_LayerType_POOLING:
+    return "Pooling";
+  case V1LayerParameter_LayerType_POWER:
+    return "Power";
+  case V1LayerParameter_LayerType_RELU:
+    return "ReLU";
+  case V1LayerParameter_LayerType_SIGMOID:
+    return "Sigmoid";
+  case V1LayerParameter_LayerType_SIGMOID_CROSS_ENTROPY_LOSS:
+    return "SigmoidCrossEntropyLoss";
+  case V1LayerParameter_LayerType_SILENCE:
+    return "Silence";
+  case V1LayerParameter_LayerType_SOFTMAX:
+    return "Softmax";
+  case V1LayerParameter_LayerType_SOFTMAX_LOSS:
+    return "SoftmaxWithLoss";
+  case V1LayerParameter_LayerType_SPLIT:
+    return "Split";
+  case V1LayerParameter_LayerType_SLICE:
+    return "Slice";
+  case V1LayerParameter_LayerType_TANH:
+    return "TanH";
+  case V1LayerParameter_LayerType_WINDOW_DATA:
+    return "WindowData";
+  case V1LayerParameter_LayerType_THRESHOLD:
+    return "Threshold";
+  default:
+    LOG(FATAL) << "Unknown V1LayerParameter layer type: " << type;
+    return "";
+  }
 }
 
 void ReadNetParamsFromTextFileOrDie(const string& param_file,
diff --git a/src/gtest/CMakeLists.txt b/src/gtest/CMakeLists.txt
index 82a4120ca3f..ef7ff7ed14b 100644
--- a/src/gtest/CMakeLists.txt
+++ b/src/gtest/CMakeLists.txt
@@ -1,6 +1,5 @@
-project(gtest CXX C)
-cmake_minimum_required(VERSION 2.6.2)
+add_library(gtest STATIC EXCLUDE_FROM_ALL gtest.h gtest-all.cpp)
+caffe_default_properties(gtest)
 
-add_library(gtest gtest-all.cpp)
-add_library(gtest_main gtest_main.cc)
-target_link_libraries(gtest_main gtest)
\ No newline at end of file
+#add_library(gtest_main gtest_main.cc)
+#target_link_libraries(gtest_main gtest)
diff --git a/tools/CMakeLists.txt b/tools/CMakeLists.txt
index 0da19fbadc8..02fbd5cadd8 100644
--- a/tools/CMakeLists.txt
+++ b/tools/CMakeLists.txt
@@ -1,19 +1,29 @@
-project( Tools )
+# Collect source files
+file(GLOB_RECURSE srcs ${CMAKE_CURRENT_SOURCE_DIR}/*.cpp)
 
-#    Find all source files
-file(GLOB_RECURSE TOOLS_SOURCES ${CMAKE_CURRENT_SOURCE_DIR}/*.cpp)
+# Build each source file independently
+foreach(source ${srcs})
+  get_filename_component(name ${source} NAME_WE)
 
-#    Build each source file independently
-foreach(source ${TOOLS_SOURCES})
-    get_filename_component(name ${source} NAME_WE)
-    add_executable(${name}.bin ${source})
-    set_target_properties(${name}.bin PROPERTIES OUTPUT_NAME ${name})
-    target_link_libraries(${name}.bin caffe)
+  # caffe target already exits
+  if(name MATCHES "caffe")
+    set(name ${name}.bin)
+  endif()
 
-###    Install    #################################################################################
+  # target
+  add_executable(${name} ${source})
+  target_link_libraries(${name} ${Caffe_LINK})
+  caffe_default_properties(${name})
 
-    install(TARGETS ${name}.bin DESTINATION tools)
+  # set back RUNTIME_OUTPUT_DIRECTORY
+  caffe_set_runtime_directory(${name} "${PROJECT_BINARY_DIR}/tools")
+  caffe_set_solution_folder(${name} tools)
 
+  # restore output name without suffix
+  if(name MATCHES "caffe.bin")
+    set_target_properties(${name} PROPERTIES OUTPUT_NAME caffe)
+  endif()
 
+  # Install
+  install(TARGETS ${name} DESTINATION bin)
 endforeach(source)
-
diff --git a/tools/caffe.cpp b/tools/caffe.cpp
index c8c8c1a6b4c..3210a943c1c 100644
--- a/tools/caffe.cpp
+++ b/tools/caffe.cpp
@@ -5,6 +5,7 @@
 #include <string>
 #include <vector>
 
+#include "boost/algorithm/string.hpp"
 #include "caffe/caffe.hpp"
 
 using caffe::Blob;
@@ -76,6 +77,19 @@ int device_query() {
 }
 RegisterBrewFunction(device_query);
 
+// Load the weights from the specified caffemodel(s) into the train and
+// test nets.
+void CopyLayers(caffe::Solver<float>* solver, const std::string& model_list) {
+  std::vector<std::string> model_names;
+  boost::split(model_names, model_list, boost::is_any_of(",") );
+  for (int i = 0; i < model_names.size(); ++i) {
+    LOG(INFO) << "Finetuning from " << model_names[i];
+    solver->net()->CopyTrainedLayersFrom(model_names[i]);
+    for (int j = 0; j < solver->test_nets().size(); ++j) {
+      solver->test_nets()[j]->CopyTrainedLayersFrom(model_names[i]);
+    }
+  }
+}
 
 // Train / Finetune a model.
 int train() {
@@ -87,13 +101,8 @@ int train() {
   caffe::SolverParameter solver_param;
   caffe::ReadProtoFromTextFileOrDie(FLAGS_solver, &solver_param);
 
-  // If the gpu flag is not provided, allow the mode and device to be set
-  // in the solver prototxt.
-  if (FLAGS_gpu < 0
-      && solver_param.solver_mode() == caffe::SolverParameter_SolverMode_GPU) {
-    FLAGS_gpu = solver_param.device_id();
-  }
 
+#ifndef USE_MPI
   // Set device id and mode
   if (FLAGS_gpu >= 0) {
     LOG(INFO) << "Use GPU with device ID " << FLAGS_gpu;
@@ -104,6 +113,80 @@ int train() {
     Caffe::set_mode(Caffe::CPU);
   }
 
+  // If the gpu flag is not provided, allow the mode and device to be set
+  // in the solver prototxt.
+  if (FLAGS_gpu < 0
+      && solver_param.solver_mode() == caffe::SolverParameter_SolverMode_GPU) {
+    LOG(INFO) <<"Swtiching to GPU 0";
+    Caffe::set_mode(Caffe::GPU);
+    if (solver_param.device_id_size() == 0){
+      Caffe::SetDevice(0);
+    }else{
+      Caffe::SetDevice(solver_param.device_id(0));
+    }
+  }
+  #else
+  if (Caffe::parallel_mode() == Caffe::MPI){
+    if (FLAGS_gpu >= 0 ){
+      LOG(WARNING)<<"We detect that you are setting device id in command line flags. This will be ignored in parallel mode";
+      LOG(WARNING)<<"Please set a list of usable devices in the solver file.";
+    }
+    if (solver_param.solver_mode() == caffe::SolverParameter_SolverMode_GPU){
+      Caffe::set_mode(Caffe::GPU);
+      if (solver_param.device_id_size() == 0){
+        LOG(INFO)<<"Using the automatic ordinal info for device id. Possible risk of over number";
+        Caffe::SetDevice(Caffe::MPI_my_rank());
+      }else {
+        CHECK_GE(solver_param.device_id_size(), Caffe::MPI_all_rank())
+          <<"If you would like to specify device id, please specify equal or more number of ids than the number of jobs";
+        Caffe::SetDevice(solver_param.device_id(Caffe::MPI_my_rank()));
+      }
+      // Check if group_id is specified.
+      if (solver_param.group_id_size() > 0) {
+        CHECK_GE(solver_param.group_id_size(), Caffe::MPI_all_rank())
+          << "If you would like to specifiy group id, please specify equal or more number of ids than the number of jobs";
+        std::map<int, int> count; // count how many processes in each group
+        int index; // index of the current process inside its group
+        for (int i = 0; i < solver_param.group_id_size(); ++i) {
+          if (i == Caffe::MPI_my_rank()) {
+            index = count[solver_param.group_id(i)];
+          }
+          ++count[solver_param.group_id(i)];
+        }
+        if (count.size() > 1) {
+          Caffe::MPI_split_comm(solver_param.group_id(Caffe::MPI_my_rank()), index);
+        }
+      }
+    }else{
+      Caffe::set_mode(Caffe::CPU);
+    }
+  }else{
+    if (FLAGS_gpu >= 0) {
+      LOG(INFO) << "Use GPU with device ID " << FLAGS_gpu;
+      Caffe::SetDevice(FLAGS_gpu);
+      Caffe::set_mode(Caffe::GPU);
+    } else {
+      LOG(INFO) << "Use CPU.";
+      Caffe::set_mode(Caffe::CPU);
+    }
+
+    // If the gpu flag is not provided, allow the mode and device to be set
+    // in the solver prototxt.
+    if (FLAGS_gpu < 0
+        && solver_param.solver_mode() == caffe::SolverParameter_SolverMode_GPU) {
+      LOG(INFO) <<"Swtiching to GPU 0";
+      Caffe::set_mode(Caffe::GPU);
+      if (solver_param.device_id_size() == 0){
+        Caffe::SetDevice(0);
+      }else{
+        Caffe::SetDevice(solver_param.device_id(0));
+      }
+    }
+  }
+
+
+  #endif
+
   LOG(INFO) << "Starting Optimization";
   shared_ptr<caffe::Solver<float> >
     solver(caffe::GetSolver<float>(solver_param));
@@ -112,8 +195,7 @@ int train() {
     LOG(INFO) << "Resuming from " << FLAGS_snapshot;
     solver->Solve(FLAGS_snapshot);
   } else if (FLAGS_weights.size()) {
-    LOG(INFO) << "Finetuning from " << FLAGS_weights;
-    solver->net()->CopyTrainedLayersFrom(FLAGS_weights);
+    CopyLayers(&*solver, FLAGS_weights);
     solver->Solve();
   } else {
     solver->Solve();
@@ -139,8 +221,7 @@ int test() {
     Caffe::set_mode(Caffe::CPU);
   }
   // Instantiate the caffe net.
-  Caffe::set_phase(Caffe::TEST);
-  Net<float> caffe_net(FLAGS_model);
+  Net<float> caffe_net(FLAGS_model, caffe::TEST);
   caffe_net.CopyTrainedLayersFrom(FLAGS_weights);
   LOG(INFO) << "Running for " << FLAGS_iterations << " iterations.";
 
@@ -175,8 +256,8 @@ int test() {
   for (int i = 0; i < test_score.size(); ++i) {
     const std::string& output_name = caffe_net.blob_names()[
         caffe_net.output_blob_indices()[test_score_output_id[i]]];
-    const float loss_weight =
-        caffe_net.blob_loss_weights()[caffe_net.output_blob_indices()[i]];
+    const float loss_weight = caffe_net.blob_loss_weights()[
+        caffe_net.output_blob_indices()[test_score_output_id[i]]];
     std::ostringstream loss_msg_stream;
     const float mean_score = test_score[i] / FLAGS_iterations;
     if (loss_weight) {
@@ -205,8 +286,7 @@ int time() {
     Caffe::set_mode(Caffe::CPU);
   }
   // Instantiate the caffe net.
-  Caffe::set_phase(Caffe::TRAIN);
-  Net<float> caffe_net(FLAGS_model);
+  Net<float> caffe_net(FLAGS_model, caffe::TRAIN);
 
   // Do a clean forward and backward pass, so that memory allocation are done
   // and future iterations will be more stable.
@@ -220,8 +300,8 @@ int time() {
   caffe_net.Backward();
 
   const vector<shared_ptr<Layer<float> > >& layers = caffe_net.layers();
-  vector<vector<Blob<float>*> >& bottom_vecs = caffe_net.bottom_vecs();
-  vector<vector<Blob<float>*> >& top_vecs = caffe_net.top_vecs();
+  const vector<vector<Blob<float>*> >& bottom_vecs = caffe_net.bottom_vecs();
+  const vector<vector<Blob<float>*> >& top_vecs = caffe_net.top_vecs();
   const vector<vector<bool> >& bottom_need_backward =
       caffe_net.bottom_need_backward();
   LOG(INFO) << "*** Benchmark begins ***";
@@ -229,38 +309,51 @@ int time() {
   Timer total_timer;
   total_timer.Start();
   Timer forward_timer;
-  forward_timer.Start();
+  Timer backward_timer;
   Timer timer;
-  for (int i = 0; i < layers.size(); ++i) {
-    const caffe::string& layername = layers[i]->layer_param().name();
-    timer.Start();
-    for (int j = 0; j < FLAGS_iterations; ++j) {
-      // Although Reshape should be essentially free, we include it here
-      // so that we will notice Reshape performance bugs.
-      layers[i]->Reshape(bottom_vecs[i], &top_vecs[i]);
-      layers[i]->Forward(bottom_vecs[i], &top_vecs[i]);
+  std::vector<double> forward_time_per_layer(layers.size(), 0.0);
+  std::vector<double> backward_time_per_layer(layers.size(), 0.0);
+  double forward_time = 0.0;
+  double backward_time = 0.0;
+  for (int j = 0; j < FLAGS_iterations; ++j) {
+    Timer iter_timer;
+    iter_timer.Start();
+    forward_timer.Start();
+    for (int i = 0; i < layers.size(); ++i) {
+      timer.Start();
+      layers[i]->Forward(bottom_vecs[i], top_vecs[i]);
+      forward_time_per_layer[i] += timer.MicroSeconds();
     }
-    LOG(INFO) << layername << "\tforward: " << timer.MilliSeconds() <<
-        " milliseconds.";
-  }
-  LOG(INFO) << "Forward pass: " << forward_timer.MilliSeconds() <<
-      " milliseconds.";
-  Timer backward_timer;
-  backward_timer.Start();
-  for (int i = layers.size() - 1; i >= 0; --i) {
-    const caffe::string& layername = layers[i]->layer_param().name();
-    timer.Start();
-    for (int j = 0; j < FLAGS_iterations; ++j) {
+    forward_time += forward_timer.MicroSeconds();
+    backward_timer.Start();
+    for (int i = layers.size() - 1; i >= 0; --i) {
+      timer.Start();
       layers[i]->Backward(top_vecs[i], bottom_need_backward[i],
-                          &bottom_vecs[i]);
+                          bottom_vecs[i]);
+      backward_time_per_layer[i] += timer.MicroSeconds();
     }
-    LOG(INFO) << layername << "\tbackward: "
-        << timer.MilliSeconds() << " milliseconds.";
+    backward_time += backward_timer.MicroSeconds();
+    LOG(INFO) << "Iteration: " << j + 1 << " forward-backward time: "
+      << iter_timer.MilliSeconds() << " ms.";
+  }
+  LOG(INFO) << "Average time per layer: ";
+  for (int i = 0; i < layers.size(); ++i) {
+    const caffe::string& layername = layers[i]->layer_param().name();
+    LOG(INFO) << std::setfill(' ') << std::setw(10) << layername <<
+      "\tforward: " << forward_time_per_layer[i] / 1000 /
+      FLAGS_iterations << " ms.";
+    LOG(INFO) << std::setfill(' ') << std::setw(10) << layername  <<
+      "\tbackward: " << backward_time_per_layer[i] / 1000 /
+      FLAGS_iterations << " ms.";
   }
-  LOG(INFO) << "Backward pass: " << backward_timer.MilliSeconds() <<
-      " milliseconds.";
-  LOG(INFO) << "Total Time: " << total_timer.MilliSeconds() <<
-      " milliseconds.";
+  total_timer.Stop();
+  LOG(INFO) << "Average Forward pass: " << forward_time / 1000 /
+    FLAGS_iterations << " ms.";
+  LOG(INFO) << "Average Backward pass: " << backward_time / 1000 /
+    FLAGS_iterations << " ms.";
+  LOG(INFO) << "Average Forward-Backward: " << total_timer.MilliSeconds() /
+    FLAGS_iterations << " ms.";
+  LOG(INFO) << "Total Time: " << total_timer.MilliSeconds() << " ms.";
   LOG(INFO) << "*** Benchmark ends ***";
   return 0;
 }
@@ -279,9 +372,17 @@ int main(int argc, char** argv) {
       "  time            benchmark model execution time");
   // Run tool or show usage.
   caffe::GlobalInit(&argc, &argv);
+
   if (argc == 2) {
-    return GetBrewFunction(caffe::string(argv[1]))();
+    int ret = GetBrewFunction(caffe::string(argv[1]))();
+    //Clean up after use.
+    caffe::GlobalFinalize();
+    return ret;
   } else {
     gflags::ShowUsageWithFlagsRestrict(argv[0], "tools/caffe");
+    //Clean up after use.
+    caffe::GlobalFinalize();
   }
+
+
 }
diff --git a/tools/compute_image_mean.cpp b/tools/compute_image_mean.cpp
index 20f1ff81f1c..b1fc7cae38f 100644
--- a/tools/compute_image_mean.cpp
+++ b/tools/compute_image_mean.cpp
@@ -1,82 +1,57 @@
-#include <glog/logging.h>
-#include <leveldb/db.h>
-#include <lmdb.h>
 #include <stdint.h>
-
 #include <algorithm>
 #include <string>
+#include <utility>
+#include <vector>
+
+#include "boost/scoped_ptr.hpp"
+#include "gflags/gflags.h"
+#include "glog/logging.h"
 
 #include "caffe/proto/caffe.pb.h"
+#include "caffe/util/db.hpp"
 #include "caffe/util/io.hpp"
 
-using caffe::Datum;
-using caffe::BlobProto;
-using std::string;
+using namespace caffe;  // NOLINT(build/namespaces)
+
 using std::max;
+using std::pair;
+using boost::scoped_ptr;
+
+DEFINE_string(backend, "lmdb",
+        "The backend {leveldb, lmdb} containing the images");
 
 int main(int argc, char** argv) {
   ::google::InitGoogleLogging(argv[0]);
-  if (argc < 3 || argc > 4) {
-    LOG(ERROR) << "Usage: compute_image_mean input_db output_file"
-               << " db_backend[leveldb or lmdb]";
-    return 1;
-  }
 
-  string db_backend = "lmdb";
-  if (argc == 4) {
-    db_backend = string(argv[3]);
-  }
+#ifndef GFLAGS_GFLAGS_H_
+  namespace gflags = google;
+#endif
+
+  gflags::SetUsageMessage("Compute the mean_image of a set of images given by"
+        " a leveldb/lmdb\n"
+        "Usage:\n"
+        "    compute_image_mean [FLAGS] INPUT_DB [OUTPUT_FILE]\n");
 
-  // leveldb
-  leveldb::DB* db;
-  leveldb::Options options;
-  options.create_if_missing = false;
-  leveldb::Iterator* it = NULL;
-  // lmdb
-  MDB_env* mdb_env;
-  MDB_dbi mdb_dbi;
-  MDB_val mdb_key, mdb_value;
-  MDB_txn* mdb_txn;
-  MDB_cursor* mdb_cursor;
-
-  // Open db
-  if (db_backend == "leveldb") {  // leveldb
-    LOG(INFO) << "Opening leveldb " << argv[1];
-    leveldb::Status status = leveldb::DB::Open(
-        options, argv[1], &db);
-    CHECK(status.ok()) << "Failed to open leveldb " << argv[1];
-    leveldb::ReadOptions read_options;
-    read_options.fill_cache = false;
-    it = db->NewIterator(read_options);
-    it->SeekToFirst();
-  } else if (db_backend == "lmdb") {  // lmdb
-    LOG(INFO) << "Opening lmdb " << argv[1];
-    CHECK_EQ(mdb_env_create(&mdb_env), MDB_SUCCESS) << "mdb_env_create failed";
-    CHECK_EQ(mdb_env_set_mapsize(mdb_env, 1099511627776), MDB_SUCCESS);  // 1TB
-    CHECK_EQ(mdb_env_open(mdb_env, argv[1], MDB_RDONLY, 0664),
-        MDB_SUCCESS) << "mdb_env_open failed";
-    CHECK_EQ(mdb_txn_begin(mdb_env, NULL, MDB_RDONLY, &mdb_txn), MDB_SUCCESS)
-        << "mdb_txn_begin failed";
-    CHECK_EQ(mdb_open(mdb_txn, NULL, 0, &mdb_dbi), MDB_SUCCESS)
-        << "mdb_open failed";
-    CHECK_EQ(mdb_cursor_open(mdb_txn, mdb_dbi, &mdb_cursor), MDB_SUCCESS)
-        << "mdb_cursor_open failed";
-    CHECK_EQ(mdb_cursor_get(mdb_cursor, &mdb_key, &mdb_value, MDB_FIRST),
-        MDB_SUCCESS);
-  } else {
-    LOG(FATAL) << "Unknown db backend " << db_backend;
+  gflags::ParseCommandLineFlags(&argc, &argv, true);
+
+  if (argc < 2 || argc > 3) {
+    gflags::ShowUsageWithFlagsRestrict(argv[0], "tools/compute_image_mean");
+    return 1;
   }
 
-  Datum datum;
+  scoped_ptr<db::DB> db(db::GetDB(FLAGS_backend));
+  db->Open(argv[1], db::READ);
+  scoped_ptr<db::Cursor> cursor(db->NewCursor());
+
   BlobProto sum_blob;
   int count = 0;
   // load first datum
-  if (db_backend == "leveldb") {
-    datum.ParseFromString(it->value().ToString());
-  } else if (db_backend == "lmdb") {
-    datum.ParseFromArray(mdb_value.mv_data, mdb_value.mv_size);
-  } else {
-    LOG(FATAL) << "Unknown db backend " << db_backend;
+  Datum datum;
+  datum.ParseFromString(cursor->value());
+
+  if (DecodeDatumNative(&datum)) {
+    LOG(INFO) << "Decoding Datum";
   }
 
   sum_blob.set_num(1);
@@ -90,81 +65,55 @@ int main(int argc, char** argv) {
     sum_blob.add_data(0.);
   }
   LOG(INFO) << "Starting Iteration";
-  if (db_backend == "leveldb") {  // leveldb
-    for (it->SeekToFirst(); it->Valid(); it->Next()) {
-      // just a dummy operation
-      datum.ParseFromString(it->value().ToString());
-      const string& data = datum.data();
-      size_in_datum = std::max<int>(datum.data().size(),
-          datum.float_data_size());
-      CHECK_EQ(size_in_datum, data_size) << "Incorrect data field size " <<
-          size_in_datum;
-      if (data.size() != 0) {
-        for (int i = 0; i < size_in_datum; ++i) {
-          sum_blob.set_data(i, sum_blob.data(i) + (uint8_t)data[i]);
-        }
-      } else {
-        for (int i = 0; i < size_in_datum; ++i) {
-          sum_blob.set_data(i, sum_blob.data(i) +
-              static_cast<float>(datum.float_data(i)));
-        }
+  while (cursor->valid()) {
+    Datum datum;
+    datum.ParseFromString(cursor->value());
+    DecodeDatumNative(&datum);
+
+    const std::string& data = datum.data();
+    size_in_datum = std::max<int>(datum.data().size(),
+        datum.float_data_size());
+    CHECK_EQ(size_in_datum, data_size) << "Incorrect data field size " <<
+        size_in_datum;
+    if (data.size() != 0) {
+      CHECK_EQ(data.size(), size_in_datum);
+      for (int i = 0; i < size_in_datum; ++i) {
+        sum_blob.set_data(i, sum_blob.data(i) + (uint8_t)data[i]);
       }
-      ++count;
-      if (count % 10000 == 0) {
-        LOG(ERROR) << "Processed " << count << " files.";
+    } else {
+      CHECK_EQ(datum.float_data_size(), size_in_datum);
+      for (int i = 0; i < size_in_datum; ++i) {
+        sum_blob.set_data(i, sum_blob.data(i) +
+            static_cast<float>(datum.float_data(i)));
       }
     }
-  } else if (db_backend == "lmdb") {  // lmdb
-    CHECK_EQ(mdb_cursor_get(mdb_cursor, &mdb_key, &mdb_value, MDB_FIRST),
-        MDB_SUCCESS);
-    do {
-      // just a dummy operation
-      datum.ParseFromArray(mdb_value.mv_data, mdb_value.mv_size);
-      const string& data = datum.data();
-      size_in_datum = std::max<int>(datum.data().size(),
-          datum.float_data_size());
-      CHECK_EQ(size_in_datum, data_size) << "Incorrect data field size " <<
-          size_in_datum;
-      if (data.size() != 0) {
-        for (int i = 0; i < size_in_datum; ++i) {
-          sum_blob.set_data(i, sum_blob.data(i) + (uint8_t)data[i]);
-        }
-      } else {
-        for (int i = 0; i < size_in_datum; ++i) {
-          sum_blob.set_data(i, sum_blob.data(i) +
-              static_cast<float>(datum.float_data(i)));
-        }
-      }
-      ++count;
-      if (count % 10000 == 0) {
-        LOG(ERROR) << "Processed " << count << " files.";
-      }
-    } while (mdb_cursor_get(mdb_cursor, &mdb_key, &mdb_value, MDB_NEXT)
-        == MDB_SUCCESS);
-  } else {
-    LOG(FATAL) << "Unknown db backend " << db_backend;
+    ++count;
+    if (count % 10000 == 0) {
+      LOG(INFO) << "Processed " << count << " files.";
+    }
+    cursor->Next();
   }
 
   if (count % 10000 != 0) {
-    LOG(ERROR) << "Processed " << count << " files.";
+    LOG(INFO) << "Processed " << count << " files.";
   }
   for (int i = 0; i < sum_blob.data_size(); ++i) {
     sum_blob.set_data(i, sum_blob.data(i) / count);
   }
   // Write to disk
-  LOG(INFO) << "Write to " << argv[2];
-  WriteProtoToBinaryFile(sum_blob, argv[2]);
-
-  // Clean up
-  if (db_backend == "leveldb") {
-    delete db;
-  } else if (db_backend == "lmdb") {
-    mdb_cursor_close(mdb_cursor);
-    mdb_close(mdb_env, mdb_dbi);
-    mdb_txn_abort(mdb_txn);
-    mdb_env_close(mdb_env);
-  } else {
-    LOG(FATAL) << "Unknown db backend " << db_backend;
+  if (argc == 3) {
+    LOG(INFO) << "Write to " << argv[2];
+    WriteProtoToBinaryFile(sum_blob, argv[2]);
+  }
+  const int channels = sum_blob.channels();
+  const int dim = sum_blob.height() * sum_blob.width();
+  std::vector<float> mean_values(channels, 0.0);
+  LOG(INFO) << "Number of channels: " << channels;
+  for (int c = 0; c < channels; ++c) {
+    for (int i = 0; i < dim; ++i) {
+      mean_values[c] += sum_blob.data(dim * c + i);
+    }
+    LOG(INFO) << "mean_value channel [" << c << "]:" << mean_values[c] / dim;
   }
   return 0;
 }
diff --git a/tools/convert_imageset.cpp b/tools/convert_imageset.cpp
index 7e1e83d9d0c..816a91f971b 100644
--- a/tools/convert_imageset.cpp
+++ b/tools/convert_imageset.cpp
@@ -8,34 +8,39 @@
 //   subfolder1/file1.JPEG 7
 //   ....
 
-#include <gflags/gflags.h>
-#include <glog/logging.h>
-#include <leveldb/db.h>
-#include <leveldb/write_batch.h>
-#include <lmdb.h>
-#include <sys/stat.h>
-
 #include <algorithm>
 #include <fstream>  // NOLINT(readability/streams)
 #include <string>
 #include <utility>
 #include <vector>
 
+#include "boost/scoped_ptr.hpp"
+#include "gflags/gflags.h"
+#include "glog/logging.h"
+
 #include "caffe/proto/caffe.pb.h"
+#include "caffe/util/db.hpp"
 #include "caffe/util/io.hpp"
 #include "caffe/util/rng.hpp"
 
 using namespace caffe;  // NOLINT(build/namespaces)
 using std::pair;
-using std::string;
+using boost::scoped_ptr;
 
 DEFINE_bool(gray, false,
     "When this option is on, treat images as grayscale ones");
 DEFINE_bool(shuffle, false,
     "Randomly shuffle the order of images and their labels");
-DEFINE_string(backend, "lmdb", "The backend for storing the result");
+DEFINE_string(backend, "lmdb",
+        "The backend {lmdb, leveldb} for storing the result");
 DEFINE_int32(resize_width, 0, "Width images are resized to");
 DEFINE_int32(resize_height, 0, "Height images are resized to");
+DEFINE_bool(check_size, false,
+    "When this option is on, check that all the datum have the same size");
+DEFINE_bool(encoded, false,
+    "When this option is on, the encoded image will be save in datum");
+DEFINE_string(encode_type, "",
+    "Optional: What type should we encode the image as ('png','jpg',...).");
 
 int main(int argc, char** argv) {
   ::google::InitGoogleLogging(argv[0]);
@@ -52,15 +57,19 @@ int main(int argc, char** argv) {
         "    http://www.image-net.org/download-images\n");
   gflags::ParseCommandLineFlags(&argc, &argv, true);
 
-  if (argc != 4) {
+  if (argc < 4) {
     gflags::ShowUsageWithFlagsRestrict(argv[0], "tools/convert_imageset");
     return 1;
   }
 
-  bool is_color = !FLAGS_gray;
+  const bool is_color = !FLAGS_gray;
+  const bool check_size = FLAGS_check_size;
+  const bool encoded = FLAGS_encoded;
+  const string encode_type = FLAGS_encode_type;
+
   std::ifstream infile(argv[2]);
-  std::vector<std::pair<string, int> > lines;
-  string filename;
+  std::vector<std::pair<std::string, int> > lines;
+  std::string filename;
   int label;
   while (infile >> filename >> label) {
     lines.push_back(std::make_pair(filename, label));
@@ -72,124 +81,71 @@ int main(int argc, char** argv) {
   }
   LOG(INFO) << "A total of " << lines.size() << " images.";
 
-  const string& db_backend = FLAGS_backend;
-  const char* db_path = argv[3];
+  if (encode_type.size() && !encoded)
+    LOG(INFO) << "encode_type specified, assuming encoded=true.";
 
   int resize_height = std::max<int>(0, FLAGS_resize_height);
   int resize_width = std::max<int>(0, FLAGS_resize_width);
 
-  // Open new db
-  // lmdb
-  MDB_env *mdb_env;
-  MDB_dbi mdb_dbi;
-  MDB_val mdb_key, mdb_data;
-  MDB_txn *mdb_txn;
-  // leveldb
-  leveldb::DB* db;
-  leveldb::Options options;
-  options.error_if_exists = true;
-  options.create_if_missing = true;
-  options.write_buffer_size = 268435456;
-  leveldb::WriteBatch* batch = NULL;
-
-  // Open db
-  if (db_backend == "leveldb") {  // leveldb
-    LOG(INFO) << "Opening leveldb " << db_path;
-    leveldb::Status status = leveldb::DB::Open(
-        options, db_path, &db);
-    CHECK(status.ok()) << "Failed to open leveldb " << db_path
-        << ". Is it already existing?";
-    batch = new leveldb::WriteBatch();
-  } else if (db_backend == "lmdb") {  // lmdb
-    LOG(INFO) << "Opening lmdb " << db_path;
-    CHECK_EQ(mkdir(db_path, 0744), 0)
-        << "mkdir " << db_path << "failed";
-    CHECK_EQ(mdb_env_create(&mdb_env), MDB_SUCCESS) << "mdb_env_create failed";
-    CHECK_EQ(mdb_env_set_mapsize(mdb_env, 1099511627776), MDB_SUCCESS)  // 1TB
-        << "mdb_env_set_mapsize failed";
-    CHECK_EQ(mdb_env_open(mdb_env, db_path, 0, 0664), MDB_SUCCESS)
-        << "mdb_env_open failed";
-    CHECK_EQ(mdb_txn_begin(mdb_env, NULL, 0, &mdb_txn), MDB_SUCCESS)
-        << "mdb_txn_begin failed";
-    CHECK_EQ(mdb_open(mdb_txn, NULL, 0, &mdb_dbi), MDB_SUCCESS)
-        << "mdb_open failed. Does the lmdb already exist? ";
-  } else {
-    LOG(FATAL) << "Unknown db backend " << db_backend;
-  }
+  // Create new DB
+  scoped_ptr<db::DB> db(db::GetDB(FLAGS_backend));
+  db->Open(argv[3], db::NEW);
+  scoped_ptr<db::Transaction> txn(db->NewTransaction());
 
   // Storing to db
-  string root_folder(argv[1]);
+  std::string root_folder(argv[1]);
   Datum datum;
   int count = 0;
   const int kMaxKeyLength = 256;
   char key_cstr[kMaxKeyLength];
-  int data_size;
+  int data_size = 0;
   bool data_size_initialized = false;
 
   for (int line_id = 0; line_id < lines.size(); ++line_id) {
-    if (!ReadImageToDatum(root_folder + lines[line_id].first,
-        lines[line_id].second, resize_height, resize_width, is_color, &datum)) {
-      continue;
+    bool status;
+    std::string enc = encode_type;
+    if (encoded && !enc.size()) {
+      // Guess the encoding type from the file name
+      string fn = lines[line_id].first;
+      size_t p = fn.rfind('.');
+      if ( p == fn.npos )
+        LOG(WARNING) << "Failed to guess the encoding of '" << fn << "'";
+      enc = fn.substr(p);
+      std::transform(enc.begin(), enc.end(), enc.begin(), ::tolower);
     }
-    if (!data_size_initialized) {
-      data_size = datum.channels() * datum.height() * datum.width();
-      data_size_initialized = true;
-    } else {
-      const string& data = datum.data();
-      CHECK_EQ(data.size(), data_size) << "Incorrect data field size "
-          << data.size();
+    status = ReadImageToDatum(root_folder + lines[line_id].first,
+        lines[line_id].second, resize_height, resize_width, is_color,
+        enc, &datum);
+    if (status == false) continue;
+    if (check_size) {
+      if (!data_size_initialized) {
+        data_size = datum.channels() * datum.height() * datum.width();
+        data_size_initialized = true;
+      } else {
+        const std::string& data = datum.data();
+        CHECK_EQ(data.size(), data_size) << "Incorrect data field size "
+            << data.size();
+      }
     }
     // sequential
-    snprintf(key_cstr, kMaxKeyLength, "%08d_%s", line_id,
+    int length = snprintf(key_cstr, kMaxKeyLength, "%08d_%s", line_id,
         lines[line_id].first.c_str());
-    string value;
-    datum.SerializeToString(&value);
-    string keystr(key_cstr);
 
     // Put in db
-    if (db_backend == "leveldb") {  // leveldb
-      batch->Put(keystr, value);
-    } else if (db_backend == "lmdb") {  // lmdb
-      mdb_data.mv_size = value.size();
-      mdb_data.mv_data = reinterpret_cast<void*>(&value[0]);
-      mdb_key.mv_size = keystr.size();
-      mdb_key.mv_data = reinterpret_cast<void*>(&keystr[0]);
-      CHECK_EQ(mdb_put(mdb_txn, mdb_dbi, &mdb_key, &mdb_data, 0), MDB_SUCCESS)
-          << "mdb_put failed";
-    } else {
-      LOG(FATAL) << "Unknown db backend " << db_backend;
-    }
+    string out;
+    CHECK(datum.SerializeToString(&out));
+    txn->Put(string(key_cstr, length), out);
 
     if (++count % 1000 == 0) {
-      // Commit txn
-      if (db_backend == "leveldb") {  // leveldb
-        db->Write(leveldb::WriteOptions(), batch);
-        delete batch;
-        batch = new leveldb::WriteBatch();
-      } else if (db_backend == "lmdb") {  // lmdb
-        CHECK_EQ(mdb_txn_commit(mdb_txn), MDB_SUCCESS)
-            << "mdb_txn_commit failed";
-        CHECK_EQ(mdb_txn_begin(mdb_env, NULL, 0, &mdb_txn), MDB_SUCCESS)
-            << "mdb_txn_begin failed";
-      } else {
-        LOG(FATAL) << "Unknown db backend " << db_backend;
-      }
+      // Commit db
+      txn->Commit();
+      txn.reset(db->NewTransaction());
       LOG(ERROR) << "Processed " << count << " files.";
     }
   }
   // write the last batch
   if (count % 1000 != 0) {
-    if (db_backend == "leveldb") {  // leveldb
-      db->Write(leveldb::WriteOptions(), batch);
-      delete batch;
-      delete db;
-    } else if (db_backend == "lmdb") {  // lmdb
-      CHECK_EQ(mdb_txn_commit(mdb_txn), MDB_SUCCESS) << "mdb_txn_commit failed";
-      mdb_close(mdb_env, mdb_dbi);
-      mdb_env_close(mdb_env);
-    } else {
-      LOG(FATAL) << "Unknown db backend " << db_backend;
-    }
+    txn->Commit();
     LOG(ERROR) << "Processed " << count << " files.";
   }
   return 0;
diff --git a/tools/dump_network.cpp b/tools/dump_network.cpp
deleted file mode 100644
index 90895fdc969..00000000000
--- a/tools/dump_network.cpp
+++ /dev/null
@@ -1,77 +0,0 @@
-// This program takes in a trained network and an input blob, and then dumps
-// all the intermediate blobs produced by the net to individual binary
-// files stored in protobuffer binary formats.
-// Usage:
-//    dump_network input_net_param trained_net_param
-//        input_blob output_prefix 0/1
-// if input_net_param is 'none', we will directly load the network from
-// trained_net_param. If the last argv is 1, we will do a forward-backward pass
-// before dumping everyting, and also dump the who network.
-
-#include <string>
-#include <vector>
-
-#include "fcntl.h"
-#include "google/protobuf/text_format.h"
-
-#include "caffe/blob.hpp"
-#include "caffe/common.hpp"
-#include "caffe/filler.hpp"
-#include "caffe/net.hpp"
-#include "caffe/proto/caffe.pb.h"
-#include "caffe/solver.hpp"
-#include "caffe/util/io.hpp"
-
-using namespace caffe;  // NOLINT(build/namespaces)
-
-int main(int argc, char** argv) {
-  Caffe::set_mode(Caffe::GPU);
-  Caffe::set_phase(Caffe::TEST);
-
-  shared_ptr<Net<float> > caffe_net;
-  if (strcmp(argv[1], "none") == 0) {
-    // We directly load the net param from trained file
-    caffe_net.reset(new Net<float>(argv[2]));
-  } else {
-    caffe_net.reset(new Net<float>(argv[1]));
-  }
-  caffe_net->CopyTrainedLayersFrom(argv[2]);
-
-  vector<Blob<float>* > input_vec;
-  shared_ptr<Blob<float> > input_blob(new Blob<float>());
-  if (strcmp(argv[3], "none") != 0) {
-    BlobProto input_blob_proto;
-    ReadProtoFromBinaryFile(argv[3], &input_blob_proto);
-    input_blob->FromProto(input_blob_proto);
-    input_vec.push_back(input_blob.get());
-  }
-
-  string output_prefix(argv[4]);
-  // Run the network without training.
-  LOG(ERROR) << "Performing Forward";
-  caffe_net->Forward(input_vec);
-  if (argc > 5 && strcmp(argv[5], "1") == 0) {
-    LOG(ERROR) << "Performing Backward";
-    Caffe::set_phase(Caffe::TRAIN);
-    caffe_net->Backward();
-    // Dump the network
-    NetParameter output_net_param;
-    caffe_net->ToProto(&output_net_param, true);
-    WriteProtoToBinaryFile(output_net_param,
-        output_prefix + output_net_param.name());
-  }
-  // Now, let's dump all the layers
-
-  const vector<string>& blob_names = caffe_net->blob_names();
-  const vector<shared_ptr<Blob<float> > >& blobs = caffe_net->blobs();
-  for (int blobid = 0; blobid < caffe_net->blobs().size(); ++blobid) {
-    // Serialize blob
-    LOG(ERROR) << "Dumping " << blob_names[blobid];
-    BlobProto output_blob_proto;
-    blobs[blobid]->ToProto(&output_blob_proto);
-    WriteProtoToBinaryFile(output_blob_proto,
-        output_prefix + blob_names[blobid]);
-  }
-
-  return 0;
-}
diff --git a/tools/extra/extract_seconds.py b/tools/extra/extract_seconds.py
index f791afa32a2..591a51f96bd 100755
--- a/tools/extra/extract_seconds.py
+++ b/tools/extra/extract_seconds.py
@@ -18,18 +18,39 @@ def extract_datetime_from_line(line, year):
     dt = datetime.datetime(year, month, day, hour, minute, second, microsecond)
     return dt
 
+
+def get_log_created_year(input_file):
+    """Get year from log file system timestamp
+    """
+
+    log_created_time = os.path.getctime(input_file)
+    log_created_year = datetime.datetime.fromtimestamp(log_created_time).year
+    return log_created_year
+
+
+def get_start_time(line_iterable, year):
+    """Find start time from group of lines
+    """
+
+    start_datetime = None
+    for line in line_iterable:
+        line = line.strip()
+        if line.find('Solving') != -1:
+            start_datetime = extract_datetime_from_line(line, year)
+            break
+    return start_datetime
+
+
 def extract_seconds(input_file, output_file):
     with open(input_file, 'r') as f:
         lines = f.readlines()
-    log_created_time = os.path.getctime(input_file)
-    log_created_year = datetime.datetime.fromtimestamp(log_created_time).year
-    start_time_found = False
+    log_created_year = get_log_created_year(input_file)
+    start_datetime = get_start_time(lines, log_created_year)
+    assert start_datetime, 'Start time not found'
+
     out = open(output_file, 'w')
     for line in lines:
         line = line.strip()
-        if not start_time_found and line.find('Solving') != -1:
-            start_time_found = True
-            start_datetime = extract_datetime_from_line(line, log_created_year)
         if line.find('Iteration') != -1:
             dt = extract_datetime_from_line(line, log_created_year)
             elapsed_seconds = (dt - start_datetime).total_seconds()
diff --git a/tools/extra/parse_log.py b/tools/extra/parse_log.py
new file mode 100755
index 00000000000..09ea216ced3
--- /dev/null
+++ b/tools/extra/parse_log.py
@@ -0,0 +1,196 @@
+#!/usr/bin/env python
+
+"""
+Parse training log
+
+Evolved from parse_log.sh
+"""
+
+import os
+import re
+import extract_seconds
+import argparse
+import csv
+from collections import OrderedDict
+
+
+def parse_log(path_to_log):
+    """Parse log file
+    Returns (train_dict_list, train_dict_names, test_dict_list, test_dict_names)
+
+    train_dict_list and test_dict_list are lists of dicts that define the table
+    rows
+
+    train_dict_names and test_dict_names are ordered tuples of the column names
+    for the two dict_lists
+    """
+
+    regex_iteration = re.compile('Iteration (\d+)')
+    regex_train_output = re.compile('Train net output #(\d+): (\S+) = ([\.\deE+-]+)')
+    regex_test_output = re.compile('Test net output #(\d+): (\S+) = ([\.\deE+-]+)')
+    regex_learning_rate = re.compile('lr = ([\.\d]+)')
+
+    # Pick out lines of interest
+    iteration = -1
+    learning_rate = float('NaN')
+    train_dict_list = []
+    test_dict_list = []
+    train_row = None
+    test_row = None
+
+    logfile_year = extract_seconds.get_log_created_year(path_to_log)
+    with open(path_to_log) as f:
+        start_time = extract_seconds.get_start_time(f, logfile_year)
+
+        for line in f:
+            iteration_match = regex_iteration.search(line)
+            if iteration_match:
+                iteration = float(iteration_match.group(1))
+            if iteration == -1:
+                # Only start parsing for other stuff if we've found the first
+                # iteration
+                continue
+
+            time = extract_seconds.extract_datetime_from_line(line,
+                                                              logfile_year)
+            seconds = (time - start_time).total_seconds()
+
+            learning_rate_match = regex_learning_rate.search(line)
+            if learning_rate_match:
+                learning_rate = float(learning_rate_match.group(1))
+
+            train_dict_list, train_row = parse_line_for_net_output(
+                regex_train_output, train_row, train_dict_list,
+                line, iteration, seconds, learning_rate
+            )
+            test_dict_list, test_row = parse_line_for_net_output(
+                regex_test_output, test_row, test_dict_list,
+                line, iteration, seconds, learning_rate
+            )
+
+    fix_initial_nan_learning_rate(train_dict_list)
+    fix_initial_nan_learning_rate(test_dict_list)
+
+    return train_dict_list, test_dict_list
+
+
+def parse_line_for_net_output(regex_obj, row, row_dict_list,
+                              line, iteration, seconds, learning_rate):
+    """Parse a single line for training or test output
+
+    Returns a a tuple with (row_dict_list, row)
+    row: may be either a new row or an augmented version of the current row
+    row_dict_list: may be either the current row_dict_list or an augmented
+    version of the current row_dict_list
+    """
+
+    output_match = regex_obj.search(line)
+    if output_match:
+        if not row or row['NumIters'] != iteration:
+            # Push the last row and start a new one
+            if row:
+                # If we're on a new iteration, push the last row
+                # This will probably only happen for the first row; otherwise
+                # the full row checking logic below will push and clear full
+                # rows
+                row_dict_list.append(row)
+
+            row = OrderedDict([
+                ('NumIters', iteration),
+                ('Seconds', seconds),
+                ('LearningRate', learning_rate)
+            ])
+
+        # output_num is not used; may be used in the future
+        # output_num = output_match.group(1)
+        output_name = output_match.group(2)
+        output_val = output_match.group(3)
+        row[output_name] = float(output_val)
+
+    if row and len(row_dict_list) >= 1 and len(row) == len(row_dict_list[0]):
+        # The row is full, based on the fact that it has the same number of
+        # columns as the first row; append it to the list
+        row_dict_list.append(row)
+        row = None
+
+    return row_dict_list, row
+
+
+def fix_initial_nan_learning_rate(dict_list):
+    """Correct initial value of learning rate
+
+    Learning rate is normally not printed until after the initial test and
+    training step, which means the initial testing and training rows have
+    LearningRate = NaN. Fix this by copying over the LearningRate from the
+    second row, if it exists.
+    """
+
+    if len(dict_list) > 1:
+        dict_list[0]['LearningRate'] = dict_list[1]['LearningRate']
+
+
+def save_csv_files(logfile_path, output_dir, train_dict_list, test_dict_list,
+                   delimiter=',', verbose=False):
+    """Save CSV files to output_dir
+
+    If the input log file is, e.g., caffe.INFO, the names will be
+    caffe.INFO.train and caffe.INFO.test
+    """
+
+    log_basename = os.path.basename(logfile_path)
+    train_filename = os.path.join(output_dir, log_basename + '.train')
+    write_csv(train_filename, train_dict_list, delimiter, verbose)
+
+    test_filename = os.path.join(output_dir, log_basename + '.test')
+    write_csv(test_filename, test_dict_list, delimiter, verbose)
+
+
+def write_csv(output_filename, dict_list, delimiter, verbose=False):
+    """Write a CSV file
+    """
+
+    dialect = csv.excel
+    dialect.delimiter = delimiter
+
+    with open(output_filename, 'w') as f:
+        dict_writer = csv.DictWriter(f, fieldnames=dict_list[0].keys(),
+                                     dialect=dialect)
+        dict_writer.writeheader()
+        dict_writer.writerows(dict_list)
+    if verbose:
+        print 'Wrote %s' % output_filename
+
+
+def parse_args():
+    description = ('Parse a Caffe training log into two CSV files '
+                   'containing training and testing information')
+    parser = argparse.ArgumentParser(description=description)
+
+    parser.add_argument('logfile_path',
+                        help='Path to log file')
+
+    parser.add_argument('output_dir',
+                        help='Directory in which to place output CSV files')
+
+    parser.add_argument('--verbose',
+                        action='store_true',
+                        help='Print some extra info (e.g., output filenames)')
+
+    parser.add_argument('--delimiter',
+                        default=',',
+                        help=('Column delimiter in output files '
+                              '(default: \'%(default)s\')'))
+
+    args = parser.parse_args()
+    return args
+
+
+def main():
+    args = parse_args()
+    train_dict_list, test_dict_list = parse_log(args.logfile_path)
+    save_csv_files(args.logfile_path, args.output_dir, train_dict_list,
+                   test_dict_list, delimiter=args.delimiter)
+
+
+if __name__ == '__main__':
+    main()
diff --git a/tools/extra/plot_log.gnuplot.example b/tools/extra/plot_log.gnuplot.example
index 334ff1f2858..748b96e6925 100644
--- a/tools/extra/plot_log.gnuplot.example
+++ b/tools/extra/plot_log.gnuplot.example
@@ -39,8 +39,8 @@ set key right
 
 # Training loss vs. training iterations
 set title "Training loss vs. training iterations"
-set xlabel "Training loss"
-set ylabel "Training iterations"
+set xlabel "Training iterations"
+set ylabel "Training loss"
 plot "mnist.log.train" using 1:3 title "mnist"
 
 # Training loss vs. training time
diff --git a/tools/extract_features.cpp b/tools/extract_features.cpp
index 49e8f98971c..365dd495bbf 100644
--- a/tools/extract_features.cpp
+++ b/tools/extract_features.cpp
@@ -4,17 +4,22 @@
 
 #include "boost/algorithm/string.hpp"
 #include "google/protobuf/text_format.h"
-#include "leveldb/db.h"
-#include "leveldb/write_batch.h"
 
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/net.hpp"
 #include "caffe/proto/caffe.pb.h"
+#include "caffe/util/db.hpp"
 #include "caffe/util/io.hpp"
 #include "caffe/vision_layers.hpp"
 
-using namespace caffe;  // NOLINT(build/namespaces)
+using caffe::Blob;
+using caffe::Caffe;
+using caffe::Datum;
+using caffe::Net;
+using boost::shared_ptr;
+using std::string;
+namespace db = caffe::db;
 
 template<typename Dtype>
 int feature_extraction_pipeline(int argc, char** argv);
@@ -27,19 +32,19 @@ int main(int argc, char** argv) {
 template<typename Dtype>
 int feature_extraction_pipeline(int argc, char** argv) {
   ::google::InitGoogleLogging(argv[0]);
-  const int num_required_args = 6;
+  const int num_required_args = 7;
   if (argc < num_required_args) {
     LOG(ERROR)<<
     "This program takes in a trained network and an input data layer, and then"
     " extract features of the input data produced by the net.\n"
     "Usage: extract_features  pretrained_net_param"
     "  feature_extraction_proto_file  extract_feature_blob_name1[,name2,...]"
-    "  save_feature_leveldb_name1[,name2,...]  num_mini_batches  [CPU/GPU]"
-    "  [DEVICE_ID=0]\n"
+    "  save_feature_dataset_name1[,name2,...]  num_mini_batches  db_type"
+    "  [CPU/GPU] [DEVICE_ID=0]\n"
     "Note: you can extract multiple features in one pass by specifying"
-    " multiple feature blob names and leveldb names seperated by ','."
+    " multiple feature blob names and dataset names seperated by ','."
     " The names cannot contain white space characters and the number of blobs"
-    " and leveldbs must be equal.";
+    " and datasets must be equal.";
     return 1;
   }
   int arg_pos = num_required_args;
@@ -59,10 +64,9 @@ int feature_extraction_pipeline(int argc, char** argv) {
     LOG(ERROR) << "Using CPU";
     Caffe::set_mode(Caffe::CPU);
   }
-  Caffe::set_phase(Caffe::TEST);
 
   arg_pos = 0;  // the name of the executable
-  string pretrained_binary_proto(argv[++arg_pos]);
+  std::string pretrained_binary_proto(argv[++arg_pos]);
 
   // Expected prototxt contains at least one data layer such as
   //  the layer data_layer_name and one feature blob such as the
@@ -91,21 +95,21 @@ int feature_extraction_pipeline(int argc, char** argv) {
      top: "fc7"
    }
    */
-  string feature_extraction_proto(argv[++arg_pos]);
+  std::string feature_extraction_proto(argv[++arg_pos]);
   shared_ptr<Net<Dtype> > feature_extraction_net(
-      new Net<Dtype>(feature_extraction_proto));
+      new Net<Dtype>(feature_extraction_proto, caffe::TEST));
   feature_extraction_net->CopyTrainedLayersFrom(pretrained_binary_proto);
 
-  string extract_feature_blob_names(argv[++arg_pos]);
-  vector<string> blob_names;
+  std::string extract_feature_blob_names(argv[++arg_pos]);
+  std::vector<std::string> blob_names;
   boost::split(blob_names, extract_feature_blob_names, boost::is_any_of(","));
 
-  string save_feature_leveldb_names(argv[++arg_pos]);
-  vector<string> leveldb_names;
-  boost::split(leveldb_names, save_feature_leveldb_names,
+  std::string save_feature_dataset_names(argv[++arg_pos]);
+  std::vector<std::string> dataset_names;
+  boost::split(dataset_names, save_feature_dataset_names,
                boost::is_any_of(","));
-  CHECK_EQ(blob_names.size(), leveldb_names.size()) <<
-      " the number of blob names and leveldb names must be equal";
+  CHECK_EQ(blob_names.size(), dataset_names.size()) <<
+      " the number of blob names and dataset names must be equal";
   size_t num_features = blob_names.size();
 
   for (size_t i = 0; i < num_features; i++) {
@@ -114,33 +118,27 @@ int feature_extraction_pipeline(int argc, char** argv) {
         << " in the network " << feature_extraction_proto;
   }
 
-  leveldb::Options options;
-  options.error_if_exists = true;
-  options.create_if_missing = true;
-  options.write_buffer_size = 268435456;
-  vector<shared_ptr<leveldb::DB> > feature_dbs;
+  int num_mini_batches = atoi(argv[++arg_pos]);
+
+  std::vector<shared_ptr<db::DB> > feature_dbs;
+  std::vector<shared_ptr<db::Transaction> > txns;
+  const char* db_type = argv[++arg_pos];
   for (size_t i = 0; i < num_features; ++i) {
-    LOG(INFO)<< "Opening leveldb " << leveldb_names[i];
-    leveldb::DB* db;
-    leveldb::Status status = leveldb::DB::Open(options,
-                                               leveldb_names[i].c_str(),
-                                               &db);
-    CHECK(status.ok()) << "Failed to open leveldb " << leveldb_names[i];
-    feature_dbs.push_back(shared_ptr<leveldb::DB>(db));
+    LOG(INFO)<< "Opening dataset " << dataset_names[i];
+    shared_ptr<db::DB> db(db::GetDB(db_type));
+    db->Open(dataset_names.at(i), db::NEW);
+    feature_dbs.push_back(db);
+    shared_ptr<db::Transaction> txn(db->NewTransaction());
+    txns.push_back(txn);
   }
 
-  int num_mini_batches = atoi(argv[++arg_pos]);
-
   LOG(ERROR)<< "Extacting Features";
 
   Datum datum;
-  vector<shared_ptr<leveldb::WriteBatch> > feature_batches(
-      num_features,
-      shared_ptr<leveldb::WriteBatch>(new leveldb::WriteBatch()));
   const int kMaxKeyStrLength = 100;
   char key_str[kMaxKeyStrLength];
-  vector<Blob<float>*> input_vec;
-  vector<int> image_indices(num_features, 0);
+  std::vector<Blob<float>*> input_vec;
+  std::vector<int> image_indices(num_features, 0);
   for (int batch_index = 0; batch_index < num_mini_batches; ++batch_index) {
     feature_extraction_net->Forward(input_vec);
     for (int i = 0; i < num_features; ++i) {
@@ -148,29 +146,29 @@ int feature_extraction_pipeline(int argc, char** argv) {
           ->blob_by_name(blob_names[i]);
       int batch_size = feature_blob->num();
       int dim_features = feature_blob->count() / batch_size;
-      Dtype* feature_blob_data;
+      const Dtype* feature_blob_data;
       for (int n = 0; n < batch_size; ++n) {
-        datum.set_height(dim_features);
-        datum.set_width(1);
-        datum.set_channels(1);
+        datum.set_height(feature_blob->height());
+        datum.set_width(feature_blob->width());
+        datum.set_channels(feature_blob->channels());
         datum.clear_data();
         datum.clear_float_data();
-        feature_blob_data = feature_blob->mutable_cpu_data() +
+        feature_blob_data = feature_blob->cpu_data() +
             feature_blob->offset(n);
         for (int d = 0; d < dim_features; ++d) {
           datum.add_float_data(feature_blob_data[d]);
         }
-        string value;
-        datum.SerializeToString(&value);
-        snprintf(key_str, kMaxKeyStrLength, "%d", image_indices[i]);
-        feature_batches[i]->Put(string(key_str), value);
+        int length = snprintf(key_str, kMaxKeyStrLength, "%010d",
+            image_indices[i]);
+        string out;
+        CHECK(datum.SerializeToString(&out));
+        txns.at(i)->Put(std::string(key_str, length), out);
         ++image_indices[i];
         if (image_indices[i] % 1000 == 0) {
-          feature_dbs[i]->Write(leveldb::WriteOptions(),
-                                feature_batches[i].get());
+          txns.at(i)->Commit();
+          txns.at(i).reset(feature_dbs.at(i)->NewTransaction());
           LOG(ERROR)<< "Extracted features of " << image_indices[i] <<
               " query images for feature blob " << blob_names[i];
-          feature_batches[i].reset(new leveldb::WriteBatch());
         }
       }  // for (int n = 0; n < batch_size; ++n)
     }  // for (int i = 0; i < num_features; ++i)
@@ -178,10 +176,11 @@ int feature_extraction_pipeline(int argc, char** argv) {
   // write the last batch
   for (int i = 0; i < num_features; ++i) {
     if (image_indices[i] % 1000 != 0) {
-      feature_dbs[i]->Write(leveldb::WriteOptions(), feature_batches[i].get());
+      txns.at(i)->Commit();
     }
     LOG(ERROR)<< "Extracted features of " << image_indices[i] <<
         " query images for feature blob " << blob_names[i];
+    feature_dbs.at(i)->Close();
   }
 
   LOG(ERROR)<< "Successfully extracted the features!";
diff --git a/tools/upgrade_net_proto_binary.cpp b/tools/upgrade_net_proto_binary.cpp
index d7a62e32441..8a0dd7af743 100644
--- a/tools/upgrade_net_proto_binary.cpp
+++ b/tools/upgrade_net_proto_binary.cpp
@@ -5,6 +5,7 @@
 #include <cstring>
 #include <fstream>  // NOLINT(readability/streams)
 #include <iostream>  // NOLINT(readability/streams)
+#include <string>
 
 #include "caffe/caffe.hpp"
 #include "caffe/util/io.hpp"
@@ -23,16 +24,20 @@ int main(int argc, char** argv) {
   }
 
   NetParameter net_param;
-  if (!ReadProtoFromBinaryFile(argv[1], &net_param)) {
+  string input_filename(argv[1]);
+  if (!ReadProtoFromBinaryFile(input_filename, &net_param)) {
     LOG(ERROR) << "Failed to parse input binary file as NetParameter: "
-               << argv[1];
+               << input_filename;
     return 2;
   }
   bool need_upgrade = NetNeedsUpgrade(net_param);
   bool success = true;
   if (need_upgrade) {
-    NetParameter v0_net_param(net_param);
-    success = UpgradeV0Net(v0_net_param, &net_param);
+    success = UpgradeNetAsNeeded(input_filename, &net_param);
+    if (!success) {
+      LOG(ERROR) << "Encountered error(s) while upgrading prototxt; "
+                 << "see details above.";
+    }
   } else {
     LOG(ERROR) << "File already in V1 proto format: " << argv[1];
   }
diff --git a/tools/upgrade_net_proto_text.cpp b/tools/upgrade_net_proto_text.cpp
index 2f290fc5e14..9200431bc27 100644
--- a/tools/upgrade_net_proto_text.cpp
+++ b/tools/upgrade_net_proto_text.cpp
@@ -5,6 +5,7 @@
 #include <cstring>
 #include <fstream>  // NOLINT(readability/streams)
 #include <iostream>  // NOLINT(readability/streams)
+#include <string>
 
 #include "caffe/caffe.hpp"
 #include "caffe/util/io.hpp"
@@ -23,32 +24,31 @@ int main(int argc, char** argv) {
   }
 
   NetParameter net_param;
-  if (!ReadProtoFromTextFile(argv[1], &net_param)) {
+  string input_filename(argv[1]);
+  if (!ReadProtoFromTextFile(input_filename, &net_param)) {
     LOG(ERROR) << "Failed to parse input text file as NetParameter: "
-               << argv[1];
+               << input_filename;
     return 2;
   }
   bool need_upgrade = NetNeedsUpgrade(net_param);
   bool need_data_upgrade = NetNeedsDataUpgrade(net_param);
   bool success = true;
   if (need_upgrade) {
-    NetParameter v0_net_param(net_param);
-    success = UpgradeV0Net(v0_net_param, &net_param);
+    success = UpgradeNetAsNeeded(input_filename, &net_param);
+    if (!success) {
+      LOG(ERROR) << "Encountered error(s) while upgrading prototxt; "
+                 << "see details above.";
+    }
   } else {
-    LOG(ERROR) << "File already in V1 proto format: " << argv[1];
+    LOG(ERROR) << "File already in latest proto format: " << input_filename;
   }
 
   if (need_data_upgrade) {
     UpgradeNetDataTransformation(&net_param);
   }
 
-  // Convert to a NetParameterPrettyPrint to print fields in desired
-  // order.
-  NetParameterPrettyPrint net_param_pretty;
-  NetParameterToPrettyPrint(net_param, &net_param_pretty);
-
   // Save new format prototxt.
-  WriteProtoToTextFile(net_param_pretty, argv[2]);
+  WriteProtoToTextFile(net_param, argv[2]);
 
   LOG(ERROR) << "Wrote upgraded NetParameter text proto to " << argv[2];
   return !success;