From a6a16acdae547a95d685d570b11f2ddbd4c4f252 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E6=9D=A8=E5=9F=B9=E6=96=87?= <915505626@qq.com>
Date: Fri, 24 Mar 2017 12:13:51 +0800
Subject: [PATCH] 1
---
.gitignore | 7 +
Preprocessing train dataset gap.ipynb | 94 +
gap.ipynb | 205 +
gap_train.ipynb | 410 +
sample_submission.csv | 12501 ++++++++++++++++++++++++
5 files changed, 13217 insertions(+)
create mode 100644 .gitignore
create mode 100644 Preprocessing train dataset gap.ipynb
create mode 100644 gap.ipynb
create mode 100644 gap_train.ipynb
create mode 100644 sample_submission.csv
diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..88cb0d4
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,7 @@
+train*
+test*
+*.h5
+*.zip
+*.png
+pred.csv
+.ipy*
diff --git a/Preprocessing train dataset gap.ipynb b/Preprocessing train dataset gap.ipynb
new file mode 100644
index 0000000..f2de7b7
--- /dev/null
+++ b/Preprocessing train dataset gap.ipynb
@@ -0,0 +1,94 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "collapsed": false,
+ "deletable": true,
+ "editable": true
+ },
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "import shutil"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "collapsed": false,
+ "deletable": true,
+ "editable": true
+ },
+ "outputs": [],
+ "source": [
+ "train_filenames = os.listdir('train')\n",
+ "train_cat = filter(lambda x:x[:3] == 'cat', train_filenames)\n",
+ "train_dog = filter(lambda x:x[:3] == 'dog', train_filenames)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "collapsed": false,
+ "deletable": true,
+ "editable": true
+ },
+ "outputs": [],
+ "source": [
+ "def rmrf_mkdir(dirname):\n",
+ " if os.path.exists(dirname):\n",
+ " shutil.rmtree(dirname)\n",
+ " os.mkdir(dirname)\n",
+ "\n",
+ "rmrf_mkdir('train2')\n",
+ "os.mkdir('train2/cat')\n",
+ "os.mkdir('train2/dog')\n",
+ "\n",
+ "rmrf_mkdir('test2')\n",
+ "os.symlink('../test/', 'test2/test')\n",
+ "\n",
+ "for filename in train_cat:\n",
+ " os.symlink('../../train/'+filename, 'train2/cat/'+filename)\n",
+ "\n",
+ "for filename in train_dog:\n",
+ " os.symlink('../../train/'+filename, 'train2/dog/'+filename)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": true,
+ "deletable": true,
+ "editable": true
+ },
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "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": "2.7.13"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/gap.ipynb b/gap.ipynb
new file mode 100644
index 0000000..0b21114
--- /dev/null
+++ b/gap.ipynb
@@ -0,0 +1,205 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "collapsed": false,
+ "deletable": true,
+ "editable": true
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Using TensorFlow backend.\n"
+ ]
+ }
+ ],
+ "source": [
+ "from keras.models import *\n",
+ "from keras.layers import *\n",
+ "from keras.applications import *\n",
+ "from keras.preprocessing.image import *\n",
+ "\n",
+ "import h5py"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "collapsed": false,
+ "deletable": true,
+ "editable": true
+ },
+ "outputs": [],
+ "source": [
+ "def write_gap(MODEL, image_size, lambda_func=None):\n",
+ " width = image_size[0]\n",
+ " height = image_size[1]\n",
+ " input_tensor = Input((height, width, 3))\n",
+ " x = input_tensor\n",
+ " if lambda_func:\n",
+ " x = Lambda(lambda_func)(x)\n",
+ " base_model = MODEL(input_tensor=x, weights='imagenet', include_top=False)\n",
+ " model = Model(base_model.input, GlobalAveragePooling2D()(base_model.output))\n",
+ "\n",
+ " gen = ImageDataGenerator()\n",
+ " train_generator = gen.flow_from_directory(\"train2\", image_size, shuffle=False, \n",
+ " batch_size=16)\n",
+ " test_generator = gen.flow_from_directory(\"test2\", image_size, shuffle=False, \n",
+ " batch_size=16, class_mode=None)\n",
+ "\n",
+ " train = model.predict_generator(train_generator, train_generator.nb_sample)\n",
+ " test = model.predict_generator(test_generator, test_generator.nb_sample)\n",
+ " with h5py.File(\"gap_%s.h5\"%MODEL.func_name) as h:\n",
+ " h.create_dataset(\"train\", data=train)\n",
+ " h.create_dataset(\"test\", data=test)\n",
+ " h.create_dataset(\"label\", data=train_generator.classes)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "collapsed": false,
+ "deletable": true,
+ "editable": true,
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Found 25000 images belonging to 2 classes.\n",
+ "Found 12500 images belonging to 1 classes.\n"
+ ]
+ }
+ ],
+ "source": [
+ "write_gap(ResNet50, (224, 224))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "collapsed": false,
+ "deletable": true,
+ "editable": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Found 25000 images belonging to 2 classes.\n",
+ "Found 12500 images belonging to 1 classes.\n"
+ ]
+ }
+ ],
+ "source": [
+ "write_gap(Xception, (299, 299), xception.preprocess_input)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "collapsed": false,
+ "deletable": true,
+ "editable": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Found 25000 images belonging to 2 classes.\n",
+ "Found 12500 images belonging to 1 classes.\n"
+ ]
+ }
+ ],
+ "source": [
+ "write_gap(InceptionV3, (299, 299), inception_v3.preprocess_input)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "collapsed": false,
+ "deletable": true,
+ "editable": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Found 25000 images belonging to 2 classes.\n",
+ "Found 12500 images belonging to 1 classes.\n"
+ ]
+ }
+ ],
+ "source": [
+ "write_gap(VGG16, (224, 224))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {
+ "collapsed": false,
+ "deletable": true,
+ "editable": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Found 25000 images belonging to 2 classes.\n",
+ "Found 12500 images belonging to 1 classes.\n"
+ ]
+ }
+ ],
+ "source": [
+ "write_gap(VGG19, (224, 224))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "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": "2.7.13"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/gap_train.ipynb b/gap_train.ipynb
new file mode 100644
index 0000000..35e0d7b
--- /dev/null
+++ b/gap_train.ipynb
@@ -0,0 +1,410 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "collapsed": false,
+ "deletable": true,
+ "editable": true
+ },
+ "outputs": [],
+ "source": [
+ "import h5py\n",
+ "import numpy as np\n",
+ "from sklearn.utils import shuffle\n",
+ "\n",
+ "\n",
+ "X_train = []\n",
+ "X_test = []\n",
+ "\n",
+ "for filename in [\"gap_ResNet50.h5\", \"gap_Xception.h5\", \"gap_InceptionV3.h5\"]:\n",
+ " with h5py.File(filename, 'r') as h:\n",
+ " X_train.append(np.array(h['train']))\n",
+ " X_test.append(np.array(h['test']))\n",
+ " y_train = np.array(h['label'])\n",
+ "\n",
+ "X_train = np.concatenate(X_train, axis=1)\n",
+ "X_test = np.concatenate(X_test, axis=1)\n",
+ "\n",
+ "X_train, y_train = shuffle(X_train, y_train)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "train (25000, 2048)\n",
+ "test (12500, 2048)\n",
+ "label (25000,)\n"
+ ]
+ }
+ ],
+ "source": [
+ "with h5py.File(\"gap_ResNet50.h5\", 'r') as h:\n",
+ " for k in h.keys()[::-1]:\n",
+ " print k, h[k].shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "collapsed": false,
+ "deletable": true,
+ "editable": true
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Using TensorFlow backend.\n"
+ ]
+ }
+ ],
+ "source": [
+ "from keras.models import *\n",
+ "from keras.layers import *\n",
+ "\n",
+ "input_tensor = Input(X_train.shape[1:])\n",
+ "x = input_tensor\n",
+ "x = Dropout(0.5)(x)\n",
+ "x = Dense(1, activation='sigmoid')(x)\n",
+ "model = Model(input_tensor, x)\n",
+ "\n",
+ "model.compile(optimizer='adadelta',\n",
+ " loss='binary_crossentropy',\n",
+ " metrics=['accuracy'])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "collapsed": false,
+ "deletable": true,
+ "editable": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/svg+xml": [
+ ""
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from IPython.display import SVG\n",
+ "from keras.utils.visualize_util import model_to_dot, plot\n",
+ "\n",
+ "SVG(model_to_dot(model, show_shapes=True).create(prog='dot', format='svg'))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "collapsed": false,
+ "deletable": true,
+ "editable": true,
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Train on 20000 samples, validate on 5000 samples\n",
+ "Epoch 1/8\n",
+ "20000/20000 [==============================] - 1s - loss: 0.1193 - acc: 0.9591 - val_loss: 0.0283 - val_acc: 0.9936\n",
+ "Epoch 2/8\n",
+ "20000/20000 [==============================] - 0s - loss: 0.0319 - acc: 0.9898 - val_loss: 0.0181 - val_acc: 0.9952\n",
+ "Epoch 3/8\n",
+ "20000/20000 [==============================] - 0s - loss: 0.0252 - acc: 0.9916 - val_loss: 0.0172 - val_acc: 0.9934\n",
+ "Epoch 4/8\n",
+ "20000/20000 [==============================] - 0s - loss: 0.0214 - acc: 0.9936 - val_loss: 0.0140 - val_acc: 0.9956\n",
+ "Epoch 5/8\n",
+ "20000/20000 [==============================] - 0s - loss: 0.0200 - acc: 0.9926 - val_loss: 0.0139 - val_acc: 0.9954\n",
+ "Epoch 6/8\n",
+ "20000/20000 [==============================] - 0s - loss: 0.0189 - acc: 0.9933 - val_loss: 0.0129 - val_acc: 0.9956\n",
+ "Epoch 7/8\n",
+ "20000/20000 [==============================] - 0s - loss: 0.0170 - acc: 0.9946 - val_loss: 0.0123 - val_acc: 0.9960\n",
+ "Epoch 8/8\n",
+ "20000/20000 [==============================] - 0s - loss: 0.0163 - acc: 0.9945 - val_loss: 0.0119 - val_acc: 0.9958\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "model.fit(X_train, y_train, batch_size=128, nb_epoch=8, validation_split=0.2)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "collapsed": false,
+ "deletable": true,
+ "editable": true
+ },
+ "outputs": [],
+ "source": [
+ "model.save('model.h5')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "collapsed": false,
+ "deletable": true,
+ "editable": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "11936/12500 [===========================>..] - ETA: 0s"
+ ]
+ }
+ ],
+ "source": [
+ "y_pred = model.predict(X_test, verbose=1)\n",
+ "y_pred = y_pred.clip(min=0.005, max=0.995)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Found 12500 images belonging to 1 classes.\n"
+ ]
+ },
+ {
+ "data": {
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