candidate_2.0.0.36b.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\eric\\Anaconda2\\envs\\exts-aml2\\lib\\site-packages\\h5py\\__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import os\n",
    "import wget\n",
    "from sklearn.model_selection import train_test_split\n",
    "import tensorflow as tf\n",
    "from training_utils import download_file, get_batches, read_and_decode_single_example, load_validation_data, \\\n",
    "    download_data, evaluate_model, get_training_data, load_weights, flatten, _scale_input_data, augment, _conv2d_batch_norm, standardize\n",
    "import argparse\n",
    "from tensorboard import summary as summary_lib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## ARGUMENTS\n",
    "epochs = 1\n",
    "dataset = 10\n",
    "init_model = None\n",
    "restore_model = None\n",
    "how = \"normal\"\n",
    "action = \"eval\"\n",
    "threshold = 0.5\n",
    "freeze = False\n",
    "stop = False\n",
    "contrast = 1\n",
    "normalize = False\n",
    "weight = 7.0\n",
    "distort = False\n",
    "version = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# figure out how to label the model name\n",
    "if how == \"label\":\n",
    "    model_label = \"l\"\n",
    "elif how == \"normal\":\n",
    "    model_label = \"b\"\n",
    "else:\n",
    "    model_label = \"x\"\n",
    "\n",
    "# precalculated pixel mean of images\n",
    "mu = 104.1353\n",
    "\n",
    "# download the data\n",
    "download_data(what=dataset)\n",
    "\n",
    "## config\n",
    "batch_size = 32\n",
    "\n",
    "train_files, total_records = get_training_data(what=dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Steps per epoch: 1746\n",
      "Number of classes: 2\n"
     ]
    }
   ],
   "source": [
    "## Hyperparameters\n",
    "epsilon = 1e-8\n",
    "\n",
    "# learning rate\n",
    "epochs_per_decay = 5\n",
    "decay_factor = 0.80\n",
    "staircase = True\n",
    "\n",
    "# if we are retraining some layers start with smaller learning rate\n",
    "if not stop and not freeze:\n",
    "    starting_rate = 0.001\n",
    "else:\n",
    "    starting_rate = 0.0001\n",
    "\n",
    "# learning rate decay variables\n",
    "steps_per_epoch = int(total_records / batch_size)\n",
    "print(\"Steps per epoch:\", steps_per_epoch)\n",
    "\n",
    "# lambdas\n",
    "lamC = 0.00001\n",
    "lamF = 0.00250\n",
    "\n",
    "# use dropout\n",
    "dropout = True\n",
    "fcdropout_rate = 0.5\n",
    "convdropout_rate = 0.001\n",
    "pooldropout_rate = 0.1\n",
    "\n",
    "if how == \"label\":\n",
    "    num_classes = 5\n",
    "elif how == \"normal\":\n",
    "    num_classes = 2\n",
    "elif how == \"mass\":\n",
    "    num_classes = 3\n",
    "elif how == \"benign\":\n",
    "    num_classes = 3\n",
    "\n",
    "print(\"Number of classes:\", num_classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\eric\\Anaconda2\\envs\\exts-aml2\\lib\\site-packages\\tensorflow\\contrib\\learn\\python\\learn\\datasets\\base.py:198: retry (from tensorflow.contrib.learn.python.learn.datasets.base) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use the retry module or similar alternatives.\n",
      "Graph created...\n"
     ]
    }
   ],
   "source": [
    "## Build the graph\n",
    "graph = tf.Graph()\n",
    "\n",
    "model_name = \"model_s2.0.0.36\" + model_label + \".\" + str(dataset) + str(version)\n",
    "## Change Log\n",
    "# 0.0.0.4 - increase pool3 to 3x3 with stride 3\n",
    "# 0.0.0.6 - reduce pool 3 stride back to 2\n",
    "# 0.0.0.7 - reduce lambda for l2 reg\n",
    "# 0.0.0.8 - increase conv1 to 7x7 stride 2\n",
    "# 0.0.0.9 - disable per image normalization\n",
    "# 0.0.0.10 - commented out batch norm in conv layers, added conv4 and changed stride of convs to 1, increased FC lambda\n",
    "# 0.0.0.11 - turn dropout for conv layers on\n",
    "# 0.0.0.12 - added batch norm after pooling layers, increase pool dropout, decrease conv dropout, added extra conv layer to reduce data dimensionality\n",
    "# 0.0.0.13 - added precision and f1 summaries\n",
    "# 0.0.0.14 - fixing batch normalization, I don't think it's going to work after each pool\n",
    "# 0.0.0.15 - reduced xentropy weighting term\n",
    "# 0.0.0.17 - replaced initial 5x5 conv layers with 3 3x3 layers\n",
    "# 0.0.0.18 - changed stride of first conv to 2 from 1\n",
    "# 0.0.0.19 - doubled units in two fc layers\n",
    "# 0.0.0.20 - lowered learning rate, put a batch norm back in\n",
    "# 0.0.0.21 - put all batch norms back in\n",
    "# 0.0.0.22 - increased lambdaC, removed dropout from conv layers\n",
    "# 1.0.0.23 - added extra conv layers\n",
    "# 1.0.0.27 - updates to training code and metrics\n",
    "# 1.0.0.28 - using weighted x-entropy to improve recall\n",
    "# 1.0.0.29 - updated code to work training to classify for multiple classes\n",
    "# 1.0.0.29f - putting weighted x-entropy back\n",
    "# 1.0.0.30b - changed some hyperparameters\n",
    "# 1.0.0.31l - added decision threshold to predictions\n",
    "# 1.0.0.33 - scaling input data\n",
    "# 1.0.0.34 - centering data by 127, not by mean\n",
    "# 1.0.0.35 - not centering data, just scaling it\n",
    "# 2.0.0.35 - turning into fcn\n",
    "# 2.0.0.36 - scaling and centering data?\n",
    "\n",
    "with graph.as_default():\n",
    "    training = tf.placeholder(dtype=tf.bool, name=\"is_training\")\n",
    "    is_testing = tf.placeholder(dtype=bool, shape=(), name=\"is_testing\")\n",
    "\n",
    "    # create global step for decaying learning rate\n",
    "    global_step = tf.Variable(0, trainable=False)\n",
    "\n",
    "    learning_rate = tf.train.exponential_decay(starting_rate,\n",
    "                                               global_step,\n",
    "                                               steps_per_epoch * epochs_per_decay,\n",
    "                                               decay_factor,\n",
    "                                               staircase=staircase)\n",
    "\n",
    "    with tf.name_scope('inputs') as scope:\n",
    "        image, label = read_and_decode_single_example(train_files, label_type=how, normalize=False, distort=False)\n",
    "\n",
    "        X_def, y_def = tf.train.shuffle_batch([image, label], batch_size=batch_size, capacity=2000,\n",
    "                                              seed=None,\n",
    "                                              min_after_dequeue=1000)\n",
    "\n",
    "        # Placeholders\n",
    "        X = tf.placeholder_with_default(X_def, shape=[None, None, None, 1])\n",
    "        y = tf.placeholder_with_default(y_def, shape=[None])\n",
    "\n",
    "        # cast to float and scale input data\n",
    "        X_adj = tf.cast(X, dtype=tf.float32)\n",
    "        X_adj = _scale_input_data(X_adj, contrast=contrast, mu=127.0, scale=255.0)\n",
    "\n",
    "        # optional online data augmentation\n",
    "        if distort:\n",
    "            X_adj, y = augment(X_adj, y, horizontal_flip=True, vertical_flip=True, mixup=0)\n",
    "\n",
    "    # Convolutional layer 1\n",
    "    with tf.name_scope('conv1') as scope:\n",
    "        conv1 = tf.layers.conv2d(\n",
    "            X_adj,  # Input data\n",
    "            filters=32,  # 32 filters\n",
    "            kernel_size=(3, 3),  # Kernel size: 5x5\n",
    "            strides=(2, 2),  # Stride: 2\n",
    "            padding='SAME',  # \"same\" padding\n",
    "            activation=None,  # None\n",
    "            kernel_initializer=tf.truncated_normal_initializer(stddev=5e-2, seed=100),\n",
    "            kernel_regularizer=tf.contrib.layers.l2_regularizer(scale=lamC),\n",
    "            name='conv1'\n",
    "        )\n",
    "\n",
    "        conv1 = tf.layers.batch_normalization(\n",
    "            conv1,\n",
    "            axis=-1,\n",
    "            momentum=0.99,\n",
    "            epsilon=epsilon,\n",
    "            center=True,\n",
    "            scale=True,\n",
    "            beta_initializer=tf.zeros_initializer(),\n",
    "            gamma_initializer=tf.ones_initializer(),\n",
    "            moving_mean_initializer=tf.zeros_initializer(),\n",
    "            moving_variance_initializer=tf.ones_initializer(),\n",
    "            training=training,\n",
    "            name='bn1'\n",
    "        )\n",
    "\n",
    "        # apply relu\n",
    "        conv1_bn_relu = tf.nn.relu(conv1, name='relu1')\n",
    "\n",
    "    with tf.name_scope('conv1.1') as scope:\n",
    "        conv11 = tf.layers.conv2d(\n",
    "            conv1_bn_relu,  # Input data\n",
    "            filters=32,  # 32 filters\n",
    "            kernel_size=(3, 3),  # Kernel size: 5x5\n",
    "            strides=(1, 1),  # Stride: 2\n",
    "            padding='SAME',  # \"same\" padding\n",
    "            activation=None,  # None\n",
    "            kernel_initializer=tf.truncated_normal_initializer(stddev=5e-2, seed=101),\n",
    "            kernel_regularizer=tf.contrib.layers.l2_regularizer(scale=lamC),\n",
    "            name='conv1.1'\n",
    "        )\n",
    "\n",
    "        conv11 = tf.layers.batch_normalization(\n",
    "            conv11,\n",
    "            axis=-1,\n",
    "            momentum=0.99,\n",
    "            epsilon=epsilon,\n",
    "            center=True,\n",
    "            scale=True,\n",
    "            beta_initializer=tf.zeros_initializer(),\n",
    "            gamma_initializer=tf.ones_initializer(),\n",
    "            moving_mean_initializer=tf.zeros_initializer(),\n",
    "            moving_variance_initializer=tf.ones_initializer(),\n",
    "            training=training,\n",
    "            name='bn1.1'\n",
    "        )\n",
    "\n",
    "        # apply relu\n",
    "        conv11 = tf.nn.relu(conv11, name='relu1.1')\n",
    "\n",
    "\n",
    "    with tf.name_scope('conv1.2') as scope:\n",
    "        conv12 = tf.layers.conv2d(\n",
    "            conv11,  # Input data\n",
    "            filters=32,  # 32 filters\n",
    "            kernel_size=(3, 3),  # Kernel size: 5x5\n",
    "            strides=(1, 1),  # Stride: 2\n",
    "            padding='SAME',  # \"same\" padding\n",
    "            activation=None,  # None\n",
    "            kernel_initializer=tf.truncated_normal_initializer(stddev=5e-2, seed=1101),\n",
    "            kernel_regularizer=tf.contrib.layers.l2_regularizer(scale=lamC),\n",
    "            name='conv1.2'\n",
    "        )\n",
    "\n",
    "        conv12 = tf.layers.batch_normalization(\n",
    "            conv12,\n",
    "            axis=-1,\n",
    "            momentum=0.99,\n",
    "            epsilon=epsilon,\n",
    "            center=True,\n",
    "            scale=True,\n",
    "            beta_initializer=tf.zeros_initializer(),\n",
    "            gamma_initializer=tf.ones_initializer(),\n",
    "            moving_mean_initializer=tf.zeros_initializer(),\n",
    "            moving_variance_initializer=tf.ones_initializer(),\n",
    "            training=training,\n",
    "            name='bn1.2'\n",
    "        )\n",
    "\n",
    "        # apply relu\n",
    "        conv12 = tf.nn.relu(conv12, name='relu1.1')\n",
    "\n",
    "    # Max pooling layer 1\n",
    "    with tf.name_scope('pool1') as scope:\n",
    "        pool1 = tf.layers.max_pooling2d(\n",
    "            conv12,  # Input\n",
    "            pool_size=(3, 3),  # Pool size: 3x3\n",
    "            strides=(2, 2),  # Stride: 2\n",
    "            padding='SAME',  # \"same\" padding\n",
    "            name='pool1'\n",
    "        )\n",
    "\n",
    "        # optional dropout\n",
    "        if dropout:\n",
    "            pool1 = tf.layers.dropout(pool1, rate=pooldropout_rate, seed=103, training=training)\n",
    "\n",
    "    # Convolutional layer 2\n",
    "    with tf.name_scope('conv2.1') as scope:\n",
    "        conv2 = tf.layers.conv2d(\n",
    "            pool1,  # Input data\n",
    "            filters=64,  # 32 filters\n",
    "            kernel_size=(3, 3),  # Kernel size: 9x9\n",
    "            strides=(1, 1),  # Stride: 1\n",
    "            padding='SAME',  # \"same\" padding\n",
    "            activation=None,  # None\n",
    "            kernel_initializer=tf.truncated_normal_initializer(stddev=5e-2, seed=104),\n",
    "            kernel_regularizer=tf.contrib.layers.l2_regularizer(scale=lamC),\n",
    "            name='conv2.1'\n",
    "        )\n",
    "\n",
    "        conv2 = tf.layers.batch_normalization(\n",
    "            conv2,\n",
    "            axis=-1,\n",
    "            momentum=0.99,\n",
    "            epsilon=epsilon,\n",
    "            center=True,\n",
    "            scale=True,\n",
    "            beta_initializer=tf.zeros_initializer(),\n",
    "            gamma_initializer=tf.ones_initializer(),\n",
    "            moving_mean_initializer=tf.zeros_initializer(),\n",
    "            moving_variance_initializer=tf.ones_initializer(),\n",
    "            training=training,\n",
    "            name='bn2.1'\n",
    "        )\n",
    "\n",
    "        # apply relu\n",
    "        conv2 = tf.nn.relu(conv2, name='relu2.1')\n",
    "\n",
    "    # Convolutional layer 2\n",
    "    with tf.name_scope('conv2.2') as scope:\n",
    "        conv22 = tf.layers.conv2d(\n",
    "            conv2,  # Input data\n",
    "            filters=64,  # 32 filters\n",
    "            kernel_size=(3, 3),  # Kernel size: 9x9\n",
    "            strides=(1, 1),  # Stride: 1\n",
    "            padding='SAME',  # \"same\" padding\n",
    "            activation=None,  # None\n",
    "            kernel_initializer=tf.truncated_normal_initializer(stddev=5e-2, seed=1104),\n",
    "            kernel_regularizer=tf.contrib.layers.l2_regularizer(scale=lamC),\n",
    "            name='conv2.2'\n",
    "        )\n",
    "\n",
    "        conv22 = tf.layers.batch_normalization(\n",
    "            conv22,\n",
    "            axis=-1,\n",
    "            momentum=0.99,\n",
    "            epsilon=epsilon,\n",
    "            center=True,\n",
    "            scale=True,\n",
    "            beta_initializer=tf.zeros_initializer(),\n",
    "            gamma_initializer=tf.ones_initializer(),\n",
    "            moving_mean_initializer=tf.zeros_initializer(),\n",
    "            moving_variance_initializer=tf.ones_initializer(),\n",
    "            training=training,\n",
    "            name='bn2.2'\n",
    "        )\n",
    "\n",
    "        # apply relu\n",
    "        conv22 = tf.nn.relu(conv22, name='relu2.2')\n",
    "\n",
    "    # Max pooling layer 2\n",
    "    with tf.name_scope('pool2') as scope:\n",
    "        pool2 = tf.layers.max_pooling2d(\n",
    "            conv22,  # Input\n",
    "            pool_size=(2, 2),  # Pool size: 3x3\n",
    "            strides=(2, 2),  # Stride: 2\n",
    "            padding='SAME',  # \"same\" padding\n",
    "            name='pool2'\n",
    "        )\n",
    "\n",
    "        # optional dropout\n",
    "        if dropout:\n",
    "            pool2 = tf.layers.dropout(pool2, rate=pooldropout_rate, seed=106, training=training)\n",
    "\n",
    "    # Convolutional layer 3\n",
    "    with tf.name_scope('conv3.1') as scope:\n",
    "        conv3 = tf.layers.conv2d(\n",
    "            pool2,  # Input data\n",
    "            filters=128,  # 48 filters\n",
    "            kernel_size=(3, 3),  # Kernel size: 5x5\n",
    "            strides=(1, 1),  # Stride: 1\n",
    "            padding='SAME',  # \"same\" padding\n",
    "            activation=None,  # None\n",
    "            kernel_initializer=tf.truncated_normal_initializer(stddev=5e-2, seed=107),\n",
    "            kernel_regularizer=tf.contrib.layers.l2_regularizer(scale=lamC),\n",
    "            name='conv3.1'\n",
    "        )\n",
    "\n",
    "        conv3 = tf.layers.batch_normalization(\n",
    "            conv3,\n",
    "            axis=-1,\n",
    "            momentum=0.99,\n",
    "            epsilon=epsilon,\n",
    "            center=True,\n",
    "            scale=True,\n",
    "            beta_initializer=tf.zeros_initializer(),\n",
    "            gamma_initializer=tf.ones_initializer(),\n",
    "            moving_mean_initializer=tf.zeros_initializer(),\n",
    "            moving_variance_initializer=tf.ones_initializer(),\n",
    "            training=training,\n",
    "            name='bn3.1'\n",
    "        )\n",
    "\n",
    "        # apply relu\n",
    "        conv3 = tf.nn.relu(conv3, name='relu3.1')\n",
    "\n",
    "    # Convolutional layer 3\n",
    "    with tf.name_scope('conv3.2') as scope:\n",
    "        conv32 = tf.layers.conv2d(\n",
    "            conv3,  # Input data\n",
    "            filters=128,  # 48 filters\n",
    "            kernel_size=(3, 3),  # Kernel size: 5x5\n",
    "            strides=(1, 1),  # Stride: 1\n",
    "            padding='SAME',  # \"same\" padding\n",
    "            activation=None,  # None\n",
    "            kernel_initializer=tf.truncated_normal_initializer(stddev=5e-2, seed=1107),\n",
    "            kernel_regularizer=tf.contrib.layers.l2_regularizer(scale=lamC),\n",
    "            name='conv3.2'\n",
    "        )\n",
    "\n",
    "        conv32 = tf.layers.batch_normalization(\n",
    "            conv32,\n",
    "            axis=-1,\n",
    "            momentum=0.99,\n",
    "            epsilon=epsilon,\n",
    "            center=True,\n",
    "            scale=True,\n",
    "            beta_initializer=tf.zeros_initializer(),\n",
    "            gamma_initializer=tf.ones_initializer(),\n",
    "            moving_mean_initializer=tf.zeros_initializer(),\n",
    "            moving_variance_initializer=tf.ones_initializer(),\n",
    "            training=training,\n",
    "            name='bn3.2'\n",
    "        )\n",
    "\n",
    "        # apply relu\n",
    "        conv32 = tf.nn.relu(conv32, name='relu3.2')\n",
    "\n",
    "    # Max pooling layer 3\n",
    "    with tf.name_scope('pool3') as scope:\n",
    "        pool3 = tf.layers.max_pooling2d(\n",
    "            conv32,  # Input\n",
    "            pool_size=(2, 2),  # Pool size: 2x2\n",
    "            strides=(2, 2),  # Stride: 2\n",
    "            padding='SAME',  # \"same\" padding\n",
    "            name='pool3'\n",
    "        )\n",
    "\n",
    "        if dropout:\n",
    "            pool3 = tf.layers.dropout(pool3, rate=pooldropout_rate, seed=109, training=training)\n",
    "\n",
    "    # Convolutional layer 4\n",
    "    with tf.name_scope('conv4') as scope:\n",
    "            conv4 = tf.layers.conv2d(\n",
    "                pool3,  # Input data\n",
    "                filters=256,  # 48 filters\n",
    "                kernel_size=(3, 3),  # Kernel size: 5x5\n",
    "                strides=(1, 1),  # Stride: 1\n",
    "                padding='SAME',  # \"same\" padding\n",
    "                activation=None,  # None\n",
    "                kernel_initializer=tf.truncated_normal_initializer(stddev=5e-2, seed=110),\n",
    "                kernel_regularizer=tf.contrib.layers.l2_regularizer(scale=lamC),\n",
    "                name='conv4'\n",
    "            )\n",
    "\n",
    "            conv4 = tf.layers.batch_normalization(\n",
    "                conv4,\n",
    "                axis=-1,\n",
    "                momentum=0.99,\n",
    "                epsilon=epsilon,\n",
    "                center=True,\n",
    "                scale=True,\n",
    "                beta_initializer=tf.zeros_initializer(),\n",
    "                gamma_initializer=tf.ones_initializer(),\n",
    "                moving_mean_initializer=tf.zeros_initializer(),\n",
    "                moving_variance_initializer=tf.ones_initializer(),\n",
    "                training=training,\n",
    "                name='bn4'\n",
    "            )\n",
    "\n",
    "            # apply relu\n",
    "            conv4_bn_relu = tf.nn.relu(conv4, name='relu4')\n",
    "\n",
    "            #if dropout:\n",
    "            #    conv4_bn_relu = tf.layers.dropout(conv4_bn_relu, rate=convdropout_rate, seed=111, training=training)\n",
    "\n",
    "    # Max pooling layer 4\n",
    "    with tf.name_scope('pool4') as scope:\n",
    "            pool4 = tf.layers.max_pooling2d(\n",
    "                conv4_bn_relu,  # Input\n",
    "                pool_size=(2, 2),  # Pool size: 2x2\n",
    "                strides=(2, 2),  # Stride: 2\n",
    "                padding='SAME',  # \"same\" padding\n",
    "                name='pool4'\n",
    "            )\n",
    "\n",
    "            if dropout:\n",
    "                pool4 = tf.layers.dropout(pool4, rate=pooldropout_rate, seed=112, training=training)\n",
    "\n",
    "            # Convolutional layer 4\n",
    "    with tf.name_scope('conv5') as scope:\n",
    "        conv5 = tf.layers.conv2d(\n",
    "            pool4,  # Input data\n",
    "            filters=512,  # 48 filters\n",
    "            kernel_size=(3, 3),  # Kernel size: 5x5\n",
    "            strides=(1, 1),  # Stride: 1\n",
    "            padding='SAME',  # \"same\" padding\n",
    "            activation=None,  # None\n",
    "            kernel_initializer=tf.truncated_normal_initializer(stddev=5e-2, seed=113),\n",
    "            kernel_regularizer=tf.contrib.layers.l2_regularizer(scale=lamC),\n",
    "            name='conv5'\n",
    "        )\n",
    "\n",
    "        conv5 = tf.layers.batch_normalization(\n",
    "            conv5,\n",
    "            axis=-1,\n",
    "            momentum=0.99,\n",
    "            epsilon=epsilon,\n",
    "            center=True,\n",
    "            scale=True,\n",
    "            beta_initializer=tf.zeros_initializer(),\n",
    "            gamma_initializer=tf.ones_initializer(),\n",
    "            moving_mean_initializer=tf.zeros_initializer(),\n",
    "            moving_variance_initializer=tf.ones_initializer(),\n",
    "            training=training,\n",
    "            name='bn5'\n",
    "        )\n",
    "\n",
    "        # apply relu\n",
    "        conv5_bn_relu = tf.nn.relu(conv5, name='relu5')\n",
    "\n",
    "    # Max pooling layer 4\n",
    "    with tf.name_scope('pool5') as scope:\n",
    "        pool5 = tf.layers.max_pooling2d(\n",
    "            conv5_bn_relu,\n",
    "            pool_size=(2, 2),\n",
    "            strides=(2, 2),\n",
    "            padding='SAME',\n",
    "            name='pool5'\n",
    "        )\n",
    "\n",
    "        if dropout:\n",
    "            pool5 = tf.layers.dropout(pool5, rate=pooldropout_rate, seed=115, training=training)\n",
    "\n",
    "    if stop:\n",
    "        pool5 = tf.stop_gradient(pool5, name=\"pool5_freeze\")\n",
    "\n",
    "    fc1 = _conv2d_batch_norm(pool5, 2048, kernel_size=(5, 5), stride=(5, 5), training=training, epsilon=1e-8,\n",
    "                             padding=\"VALID\", seed=1013, lambd=lamC, name=\"fc_1\")\n",
    "\n",
    "    fc2 = _conv2d_batch_norm(fc1, 2048, kernel_size=(1, 1), stride=(1, 1), training=training, epsilon=1e-8,\n",
    "                             padding=\"VALID\", seed=1014, lambd=lamC, name=\"fc_2\")\n",
    "\n",
    "    fc3 = tf.layers.dense(\n",
    "        fc2,\n",
    "        num_classes,  # One output unit per category\n",
    "        activation=None,  # No activation function\n",
    "        kernel_initializer=tf.variance_scaling_initializer(scale=1, seed=121),\n",
    "        bias_initializer=tf.zeros_initializer(),\n",
    "        name=\"fc_logits\"\n",
    "    )\n",
    "\n",
    "    logits = tf.squeeze(fc3, name=\"fc_flat_logits\")\n",
    "\n",
    "    # get the fully connected variables so we can only train them when retraining the network\n",
    "    fc_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, \"fc\")\n",
    "\n",
    "    with tf.variable_scope('conv1', reuse=True):\n",
    "        conv_kernels1 = tf.get_variable('kernel')\n",
    "        kernel_transposed = tf.transpose(conv_kernels1, [3, 0, 1, 2])\n",
    "\n",
    "    with tf.variable_scope('visualization'):\n",
    "        tf.summary.image('conv1/filters', kernel_transposed, max_outputs=32, collections=[\"kernels\"])\n",
    "\n",
    "    ## Loss function options\n",
    "    # Regular mean cross entropy\n",
    "    #mean_ce = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y, logits=logits))\n",
    "\n",
    "    # Different weighting method\n",
    "    # This will weight the positive examples higher so as to improve recall\n",
    "    weights = tf.multiply(weight, tf.cast(tf.greater(y, 0), tf.float32)) + 1\n",
    "    mean_ce = tf.reduce_mean(tf.losses.sparse_softmax_cross_entropy(labels=y, logits=logits, weights=weights))\n",
    "\n",
    "    # Add in l2 loss\n",
    "    loss = mean_ce + tf.losses.get_regularization_loss()\n",
    "\n",
    "    # Adam optimizer\n",
    "    optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)\n",
    "\n",
    "    # Minimize cross-entropy - freeze certain layers depending on input\n",
    "    if freeze:\n",
    "        train_op = optimizer.minimize(loss, global_step=global_step, var_list=fc_vars)\n",
    "    else:\n",
    "        train_op = optimizer.minimize(loss, global_step=global_step)\n",
    "\n",
    "    # get the probabilites for the classes\n",
    "    probabilities = tf.nn.softmax(logits, name=\"probabilities\")\n",
    "    abnormal_probability = 1 - probabilities[:,0]\n",
    "\n",
    "    # Compute predictions from the probabilities\n",
    "    if threshold == 0.5:\n",
    "        predictions = tf.argmax(probabilities, axis=1, output_type=tf.int32)\n",
    "    else:\n",
    "        predictions = tf.cast(tf.greater(abnormal_probability, threshold), tf.int32)\n",
    "\n",
    "    # get the accuracy\n",
    "    accuracy, acc_op = tf.metrics.accuracy(\n",
    "        labels=y,\n",
    "        predictions=predictions,\n",
    "        updates_collections=tf.GraphKeys.UPDATE_OPS,\n",
    "        name=\"accuracy\",\n",
    "    )\n",
    "\n",
    "    # calculate recall\n",
    "    if num_classes > 2:\n",
    "        # collapse the predictions down to normal or not for our pr metrics\n",
    "        zero = tf.constant(0, dtype=tf.int64)\n",
    "        collapsed_predictions = tf.cast(tf.greater(abnormal_probability, threshold), tf.int32)\n",
    "        collapsed_labels = tf.greater(y, 0)\n",
    "\n",
    "        recall, rec_op = tf.metrics.recall(labels=collapsed_labels, predictions=collapsed_predictions, updates_collections=tf.GraphKeys.UPDATE_OPS, name=\"recall\")\n",
    "        precision, prec_op = tf.metrics.precision(labels=collapsed_labels, predictions=collapsed_predictions, updates_collections=tf.GraphKeys.UPDATE_OPS, name=\"precision\")\n",
    "\n",
    "    else:\n",
    "        recall, rec_op = tf.metrics.recall(labels=y, predictions=predictions, updates_collections=tf.GraphKeys.UPDATE_OPS, name=\"recall\")\n",
    "        precision, prec_op = tf.metrics.precision(labels=y, predictions=predictions, updates_collections=tf.GraphKeys.UPDATE_OPS, name=\"precision\")\n",
    "\n",
    "    f1_score = 2 * ((precision * recall) / (precision + recall))\n",
    "    _, update_op = summary_lib.pr_curve_streaming_op(name='pr_curve',\n",
    "                                                     predictions=abnormal_probability,\n",
    "                                                     labels=y,\n",
    "                                                     updates_collections=tf.GraphKeys.UPDATE_OPS,\n",
    "                                                     num_thresholds=20)\n",
    "\n",
    "    tf.summary.scalar('recall_1', recall, collections=[\"summaries\"])\n",
    "    tf.summary.scalar('precision_1', precision, collections=[\"summaries\"])\n",
    "    tf.summary.scalar('f1_score', f1_score, collections=[\"summaries\"])\n",
    "\n",
    "    # Create summary hooks\n",
    "    tf.summary.scalar('accuracy', accuracy, collections=[\"summaries\"])\n",
    "    tf.summary.scalar('cross_entropy', mean_ce, collections=[\"summaries\"])\n",
    "    tf.summary.scalar('learning_rate', learning_rate, collections=[\"summaries\"])\n",
    "\n",
    "    # add this so that the batch norm gets run\n",
    "    extra_update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)\n",
    "\n",
    "    # Merge all the summaries\n",
    "    merged = tf.summary.merge_all(\"summaries\")\n",
    "    kernel_summaries = tf.summary.merge_all(\"kernels\")\n",
    "    per_epoch_summaries = [[]]\n",
    "\n",
    "    print(\"Graph created...\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## CONFIGURE OPTIONS\n",
    "if init_model is not None:\n",
    "    if os.path.exists(os.path.join(\"model\", init_model + '.ckpt.index')):\n",
    "        init = False\n",
    "    else:\n",
    "        init = True\n",
    "elif restore_model is not None:\n",
    "    if os.path.exists(os.path.join(\"model\", restore_model + '.ckpt.index')):\n",
    "        init = False\n",
    "    else:\n",
    "        init = True\n",
    "else:\n",
    "    if os.path.exists(os.path.join(\"model\", model_name + '.ckpt.index')):\n",
    "        init = False\n",
    "    else:\n",
    "        init = True\n",
    "\n",
    "meta_data_every = 1\n",
    "log_to_tensorboard = True\n",
    "print_every = 5  # how often to print metrics\n",
    "checkpoint_every = 1  # how often to save model in epochs\n",
    "use_gpu = False  # whether or not to use the GPU\n",
    "print_metrics = True  # whether to print or plot metrics, if False a plot will be created and updated every epoch\n",
    "\n",
    "# Initialize metrics or load them from disk if they exist\n",
    "if os.path.exists(os.path.join(\"data\", model_name + \"train_acc.npy\")):\n",
    "    train_acc_values = np.load(os.path.join(\"data\", model_name + \"train_acc.npy\")).tolist()\n",
    "else:\n",
    "    train_acc_values = []\n",
    "\n",
    "if os.path.exists(os.path.join(\"data\", model_name + \"train_loss.npy\")):\n",
    "    train_cost_values = np.load(os.path.join(\"data\", model_name + \"train_loss.npy\")).tolist()\n",
    "else:\n",
    "    train_cost_values = []\n",
    "\n",
    "if os.path.exists(os.path.join(\"data\", model_name + \"train_lr.npy\")):\n",
    "    train_lr_values = np.load(os.path.join(\"data\", model_name + \"train_lr.npy\")).tolist()\n",
    "else:\n",
    "    train_lr_values = []\n",
    "\n",
    "if os.path.exists(os.path.join(\"data\", model_name + \"train_recall.npy\")):\n",
    "    train_recall_values = np.load(os.path.join(\"data\", model_name + \"train_recall.npy\")).tolist()\n",
    "else:\n",
    "    train_recall_values = []\n",
    "\n",
    "if os.path.exists(os.path.join(\"data\", model_name + \"cv_acc.npy\")):\n",
    "    valid_acc_values = np.load(os.path.join(\"data\", model_name + \"cv_acc.npy\")).tolist()\n",
    "else:\n",
    "    valid_acc_values = []\n",
    "\n",
    "if os.path.exists(os.path.join(\"data\", model_name + \"cv_loss.npy\")):\n",
    "    valid_cost_values = np.load(os.path.join(\"data\", model_name + \"cv_loss.npy\")).tolist()\n",
    "else:\n",
    "    valid_cost_values = []\n",
    "\n",
    "if os.path.exists(os.path.join(\"data\", model_name + \"cv_recall.npy\")):\n",
    "    valid_recall_values = np.load(os.path.join(\"data\", model_name + \"cv_recall.npy\")).tolist()\n",
    "else:\n",
    "    valid_recall_values = []\n",
    "\n",
    "config = tf.ConfigProto(device_count = {'GPU': 0})\n",
    "# config = tf.ConfigProto()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from ./model/model_s2.0.0.36b.101.ckpt\n",
      "Restoring model model_s2.0.0.36b.101\n",
      "Evaluating on test data\n",
      "Accuracy: 0.9520958\n",
      "Recall: 0.96007985\n",
      "Mean Test Accuracy: 0.94831157\n",
      "Mean Test Recall: 0.9515739\n",
      "\n",
      "Evaluating on MIAS data\n",
      "Mean MIAS Accuracy: 0.69985956\n",
      "Mean MIAS Recall: 0.9222879\n"
     ]
    }
   ],
   "source": [
    "config = tf.ConfigProto()\n",
    "with tf.Session(graph=graph, config=config) as sess:\n",
    "    # create the saver\n",
    "    saver = tf.train.Saver()\n",
    "    \n",
    "    saver.restore(sess, './model/' + model_name + '.ckpt')\n",
    "    print(\"Restoring model\", model_name)\n",
    "            \n",
    "    sess.run(tf.local_variables_initializer())\n",
    "    print(\"Evaluating on test data\")\n",
    "    \n",
    "    #######################################################################################\n",
    "    ## evaluate on the test data\n",
    "    X_te, y_te = load_validation_data(how=how, data=\"test\", which=dataset)\n",
    "\n",
    "    test_accuracy = []\n",
    "    test_recall = []\n",
    "    test_predictions = []\n",
    "    ground_truth = []\n",
    "    for X_batch, y_batch in get_batches(X_te, y_te, batch_size, distort=False):\n",
    "        _, yhat, test_acc_value, test_recall_value = sess.run([extra_update_ops, predictions, accuracy, rec_op], feed_dict=\n",
    "        {\n",
    "            X: X_batch,\n",
    "            y: y_batch,\n",
    "            training: False\n",
    "        })\n",
    "\n",
    "        test_accuracy.append(test_acc_value)\n",
    "        test_recall.append(test_recall_value)\n",
    "        test_predictions.append(yhat)\n",
    "        ground_truth.append(y_batch)\n",
    "        \n",
    "    accuracy_value, recall_value = sess.run([accuracy, recall], feed_dict=\n",
    "        {\n",
    "            X: X_te[0:1],\n",
    "            y: y_te[0:1],\n",
    "            training: False\n",
    "        })\n",
    "        \n",
    "    print(\"Accuracy:\", accuracy_value)\n",
    "    print(\"Recall:\", recall_value)\n",
    "        \n",
    "    # print the results\n",
    "    print(\"Mean Test Accuracy:\", np.mean(test_accuracy))\n",
    "    print(\"Mean Test Recall:\", np.mean(test_recall))\n",
    "\n",
    "    sess.run(tf.local_variables_initializer())\n",
    "    \n",
    "    ########################################################################################\n",
    "    ## Evaluate on MIAS Data\n",
    "    print()\n",
    "    print(\"Evaluating on MIAS data\")\n",
    "\n",
    "    ## evaluate on MIAS  dataset 9 which is the closest to raw images we have\n",
    "    X_te, y_te = load_validation_data(how=how, data=\"mias\", which=9)\n",
    "\n",
    "    mias_test_accuracy = []\n",
    "    mias_test_recall = []\n",
    "    mias_test_predictions = []\n",
    "    mias_ground_truth = []\n",
    "    for X_batch, y_batch in get_batches(X_te, y_te, batch_size, distort=False):\n",
    "        _, yhat, test_acc_value, test_recall_value = sess.run([extra_update_ops, predictions, accuracy, rec_op], feed_dict=\n",
    "        {\n",
    "            X: X_batch,\n",
    "            y: y_batch,\n",
    "            training: False\n",
    "        })\n",
    "\n",
    "        mias_test_accuracy.append(test_acc_value)\n",
    "        mias_test_recall.append(test_recall_value)\n",
    "        mias_test_predictions.append(yhat)\n",
    "        mias_ground_truth.append(y_batch)\n",
    "\n",
    "    # print the results\n",
    "    print(\"Mean MIAS Accuracy:\", np.mean(mias_test_accuracy))\n",
    "    print(\"Mean MIAS Recall:\", np.mean(mias_test_recall))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluate on Whole Images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import PIL\n",
    "from PIL import ImageMath\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.misc import imresize\n",
    "%matplotlib inline\n",
    "\n",
    "def trim_edges_from_image(image):\n",
    "    # trim from the left side\n",
    "    h = image.shape[0]\n",
    "    \n",
    "    # figure if a column contains almost all black pixels\n",
    "    black_cols = np.sum(image <= 9, axis=0) == h\n",
    "    \n",
    "    return image[:,~black_cols]\n",
    "\n",
    "def softmax(x):\n",
    "    \"\"\"\n",
    "    Compute softmax values for each sets of scores in x.\n",
    "    \n",
    "    Rows are scores for each class. \n",
    "    Columns are predictions (samples).\n",
    "    \"\"\"\n",
    "    scoreMatExp = np.exp(np.asarray(x))\n",
    "    return scoreMatExp / scoreMatExp.sum(0)\n",
    "\n",
    "def scale_data(input_data, new_min=0.0, new_max=255.0):\n",
    "    old_max = np.max(input_data)\n",
    "    old_min = np.min(input_data)\n",
    "    \n",
    "    adjustment_factor = (new_max - new_min) / (old_max - old_min)\n",
    "    scaled_data = adjustment_factor * (input_data - old_min) + new_min\n",
    "    \n",
    "    return scaled_data\n",
    "\n",
    "def summarize(data):\n",
    "    print(\"Mean:\", np.mean(data))\n",
    "    print(\"Min:\", np.min(data))\n",
    "    print(\"Max:\", np.max(data))\n",
    "    print(\"Std:\", np.std(data))\n",
    "    \n",
    "def resize_image(image, factor):\n",
    "    h = image.shape[0]\n",
    "    w = image.shape[1]\n",
    "    \n",
    "    new_size = (int(h * factor), int(w * factor))\n",
    "    \n",
    "    image = imresize(image, new_size)\n",
    "    \n",
    "    return image\n",
    "\n",
    "def load_image(image, mask=None, scale=0.5, trim=True, margin=0.15, contrast=1.0, normalize=False, base_path= \"C:\\\\Users\\\\eric\\\\Documents\\\\Courses\\\\Applied ML 2\\\\mammography\\\\data\\\\cbis-ddsm\\\\\"):\n",
    "    image_path = os.path.join(base_path, image)\n",
    "    image = PIL.Image.open(image_path)\n",
    "   \n",
    "    # if the image is a png convert it to RGB \n",
    "    if \".png\" in image_path:\n",
    "        image = ImageMath.eval('im/256', {'im':image}).convert('L')\n",
    "        \n",
    "    image = np.array(image)\n",
    "\n",
    "    # try to lose the extra channels, if there is an error they don't exist\n",
    "    try:\n",
    "        image = image[:,:,0]\n",
    "    except:\n",
    "        pass\n",
    "    \n",
    "    h = image.shape[0]\n",
    "    w = image.shape[1]\n",
    "    \n",
    "    new_h = int(h * scale)\n",
    "    new_w = int(w * scale)\n",
    "    \n",
    "    if mask is not None:\n",
    "        mask_path = base_path + mask\n",
    "        mask = PIL.Image.open(mask_path)\n",
    "        mask = np.array(mask)\n",
    "        mask = mask[:,:,0]\n",
    "        mask = imresize(mask, (new_h, new_w))\n",
    "   \n",
    "    image = imresize(image, (new_h, new_w))\n",
    "    \n",
    "    if trim:\n",
    "        image = trim_edges_from_image(image)\n",
    "    \n",
    "    # this used to crop off margin % off each side, but now we will reshape the image so its dimensions are multiples of 299\n",
    "    if margin != 0:\n",
    "        h = image.shape[0]\n",
    "        w = image.shape[1]\n",
    "        \n",
    "        new_h = (h // 299) * 299\n",
    "        new_w = (w // 299) * 299\n",
    "        \n",
    "        h_border = (h - new_h) // 2\n",
    "        w_border = (w - new_w) // 2\n",
    "        \n",
    "        # center crop the image\n",
    "        image = image[h_border:h_border+new_h,w_border:w_border+new_w]\n",
    "#         # trim margin off each side\n",
    "#         h_border = int(h * margin)\n",
    "#         w_border = int(w * margin)\n",
    "\n",
    "#         image = image[h_border:(h-h_border),w_border:(w-w_border)]\n",
    "    \n",
    "    if contrast != 1.0:\n",
    "        mu = np.mean(image)\n",
    "        image = (image - mu) * contrast + mu\n",
    "    \n",
    "    if normalize:\n",
    "        image = scale_data(image)\n",
    "    \n",
    "    return image, mask\n",
    "\n",
    "def load_images(dir_name, base_path= \"C:\\\\Users\\\\eric\\\\Documents\\\\Courses\\\\Applied ML 2\\\\mammography\\\\data\\\\cbis-ddsm\\\\\"):\n",
    "    directory = base_path + dir_name + \"\\\\\"\n",
    "    files = os.listdir(directory)\n",
    "    \n",
    "    images = []\n",
    "    \n",
    "    for file in files:\n",
    "        image, _ = load_image(file, base_path = directory)\n",
    "        images.append(image)\n",
    "        \n",
    "    return images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from ./model/model_s2.0.0.36b.10.ckpt\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\eric\\Anaconda2\\envs\\exts-aml2\\lib\\site-packages\\ipykernel_launcher.py:80: DeprecationWarning: `imresize` is deprecated!\n",
      "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n",
      "Use ``skimage.transform.resize`` instead.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 Image: P_01471_RIGHT_MLO000000.jpg  - Shape: (1, 2392, 897, 1) - Probability: 0.99955577\n",
      "2 Image: P_01476_RIGHT_CC000000.jpg  - Shape: (1, 2392, 1196, 1) - Probability: 0.99991274\n",
      "3 Image: P_01476_RIGHT_MLO000000.jpg  - Shape: (1, 2691, 1196, 1) - Probability: 0.99971753\n",
      "4 Image: P_01483_LEFT_CC_FULL.jpg  - Shape: (1, 2990, 1495, 1) - Probability: 0.9983375\n",
      "5 Image: P_01483_LEFT_MLO000000.jpg  - Shape: (1, 2990, 1495, 1) - Probability: 0.9982774\n",
      "6 Image: P_01490_LEFT_CC000000.jpg  - Shape: (1, 1794, 1495, 1) - Probability: 0.9918643\n",
      "7 Image: P_01490_LEFT_MLO000000.jpg  - Shape: (1, 2093, 897, 1) - Probability: 0.9697852\n",
      "8 Image: P_01502_LEFT_CC000000.jpg  - Shape: (1, 2691, 1794, 1) - Probability: 0.9991698\n",
      "9 Image: P_01502_LEFT_MLO000000.jpg  - Shape: (1, 2691, 1794, 1) - Probability: 0.99942976\n",
      "10 Image: P_01523_LEFT_MLO000000.jpg  - Shape: (1, 2691, 1495, 1) - Probability: 0.99777657\n",
      "11 Image: P_01534_LEFT_CC000000.jpg  - Shape: (1, 3289, 1794, 1) - Probability: 0.99935657\n",
      "12 Image: P_01534_LEFT_CC000000_1.jpg  - Shape: (1, 3289, 1794, 1) - Probability: 0.99935657\n",
      "13 Image: P_01534_LEFT_MLO000000.jpg  - Shape: (1, 2990, 1794, 1) - Probability: 0.999818\n",
      "14 Image: P_01535_LEFT_CC000000.jpg  - Shape: (1, 2392, 1196, 1) - Probability: 0.9995555\n",
      "15 Image: P_01535_LEFT_MLO000000.jpg  - Shape: (1, 2691, 1196, 1) - Probability: 0.99937683\n",
      "16 Image: P_01536_RIGHT_CC000000.jpg  - Shape: (1, 2093, 598, 1) - Probability: 0.9998118\n",
      "17 Image: P_01536_RIGHT_MLO000000.jpg  - Shape: (1, 2392, 598, 1) - Probability: 0.9878319\n",
      "18 Image: P_01538_RIGHT_MLO000000.jpg  - Shape: (1, 2691, 1196, 1) - Probability: 0.9999684\n",
      "19 Image: P_01547_LEFT_CC000000.jpg  - Shape: (1, 2093, 1196, 1) - Probability: 0.99272835\n",
      "20 Image: P_01547_LEFT_MLO000000.jpg  - Shape: (1, 2093, 1196, 1) - Probability: 0.9989309\n",
      "21 Image: P_01562_LEFT_CC000000.jpg  - Shape: (1, 2691, 1794, 1) - Probability: 0.9989496\n",
      "22 Image: P_01562_LEFT_MLO000000.jpg  - Shape: (1, 2691, 1495, 1) - Probability: 0.99511296\n",
      "23 Image: P_01602_RIGHT_CC000000.jpg  - Shape: (1, 2093, 1196, 1) - Probability: 0.9944483\n",
      "24 Image: P_01621_LEFT_CC000000.jpg  - Shape: (1, 2691, 1794, 1) - Probability: 0.9979114\n",
      "25 Image: P_01621_LEFT_MLO000000.jpg  - Shape: (1, 2691, 1495, 1) - Probability: 0.99771607\n",
      "26 Image: P_01635_RIGHT_CC000000.jpg  - Shape: (1, 2392, 1495, 1) - Probability: 0.9950669\n",
      "27 Image: P_01635_RIGHT_MLO_FULL.jpg  - Shape: (1, 2093, 1495, 1) - Probability: 0.97589964\n",
      "28 Image: P_01643_LEFT_CC000000.jpg  - Shape: (1, 2093, 1196, 1) - Probability: 0.9952832\n",
      "29 Image: P_01643_LEFT_MLO000000.jpg  - Shape: (1, 2093, 1196, 1) - Probability: 0.99407613\n",
      "30 Image: P_01643_RIGHT_CC_FULL.jpg  - Shape: (1, 2093, 1196, 1) - Probability: 0.74536175\n",
      "31 Image: P_01643_RIGHT_MLO000000.jpg  - Shape: (1, 2093, 1196, 1) - Probability: 0.9977229\n",
      "32 Image: P_01670_LEFT_CC000000.jpg  - Shape: (1, 2093, 598, 1) - Probability: 0.9789049\n",
      "33 Image: P_01670_LEFT_MLO_FULL.jpg  - Shape: (1, 2093, 897, 1) - Probability: 0.99867165\n",
      "34 Image: P_01711_RIGHT_CC000000.jpg  - Shape: (1, 2392, 897, 1) - Probability: 0.999539\n",
      "35 Image: P_01711_RIGHT_MLO000000.jpg  - Shape: (1, 2691, 1196, 1) - Probability: 0.9997048\n",
      "36 Image: P_01713_RIGHT_MLO000000.jpg  - Shape: (1, 2093, 897, 1) - Probability: 0.99822265\n",
      "37 Image: P_01728_LEFT_CC000000.jpg  - Shape: (1, 2392, 1196, 1) - Probability: 0.9970444\n",
      "38 Image: P_01743_RIGHT_CC000000.jpg  - Shape: (1, 2093, 299, 1) - Probability: 0.9999491\n",
      "39 Image: P_01743_RIGHT_MLO000000.jpg  - Shape: (1, 2392, 1196, 1) - Probability: 0.9998454\n",
      "40 Image: P_01752_LEFT_CC000000.jpg  - Shape: (1, 2093, 598, 1) - Probability: 0.99947804\n",
      "41 Image: P_01752_LEFT_MLO000000.jpg  - Shape: (1, 2691, 897, 1) - Probability: 0.99951255\n",
      "42 Image: P_01773_RIGHT_CC000000.jpg  - Shape: (1, 2691, 1495, 1) - Probability: 0.99495643\n",
      "43 Image: P_01773_RIGHT_MLO_FULL.jpg  - Shape: (1, 2691, 1495, 1) - Probability: 0.99836546\n",
      "44 Image: P_01803_RIGHT_CC_FULL.jpg  - Shape: (1, 2093, 1196, 1) - Probability: 0.9979373\n",
      "45 Image: P_01803_RIGHT_MLO000000.jpg  - Shape: (1, 2093, 1196, 1) - Probability: 0.9970497\n",
      "46 Image: P_01820_LEFT_MLO000000.jpg  - Shape: (1, 2691, 1196, 1) - Probability: 0.9988059\n",
      "47 Image: P_01834_LEFT_MLO000000.jpg  - Shape: (1, 2392, 897, 1) - Probability: 0.9988181\n",
      "48 Image: P_01835_RIGHT_CC000000.jpg  - Shape: (1, 2093, 1196, 1) - Probability: 0.99971133\n",
      "49 Image: P_01842_LEFT_MLO_FULL.jpg  - Shape: (1, 2990, 1794, 1) - Probability: 0.9997851\n",
      "50 Image: P_01845_RIGHT_CC000000.jpg  - Shape: (1, 2093, 1196, 1) - Probability: 0.99971133\n",
      "51 Image: P_01845_RIGHT_MLO000000.jpg  - Shape: (1, 2392, 1196, 1) - Probability: 0.99998116\n",
      "52 Image: P_01861_LEFT_CC000000.jpg  - Shape: (1, 2392, 1196, 1) - Probability: 0.9983718\n",
      "53 Image: P_01861_LEFT_MLO_FULL.jpg  - Shape: (1, 2691, 1196, 1) - Probability: 0.9972568\n",
      "54 Image: P_01867#_LEFT_CC000000.jpg  - Shape: (1, 2093, 1495, 1) - Probability: 0.97401583\n",
      "55 Image: P_01867_LEFT_CC000000.jpg  - Shape: (1, 2093, 1495, 1) - Probability: 0.97401583\n",
      "56 Image: P_01867_LEFT_MLO000000.jpg  - Shape: (1, 2093, 1196, 1) - Probability: 0.95149183\n",
      "57 Image: P_01868_LEFT_CC000000.jpg  - Shape: (1, 2093, 1196, 1) - Probability: 0.9726123\n",
      "58 Image: P_01868_LEFT_MLO000000.jpg  - Shape: (1, 2093, 1196, 1) - Probability: 0.9967084\n",
      "59 Image: P_01872_LEFT_CC000000.jpg  - Shape: (1, 2691, 897, 1) - Probability: 0.99938893\n",
      "60 Image: P_01872_LEFT_MLO000000.jpg  - Shape: (1, 2691, 1196, 1) - Probability: 0.99913603\n",
      "61 Image: P_01882_LEFT_CC_FULL.jpg  - Shape: (1, 2093, 1196, 1) - Probability: 0.9914854\n",
      "62 Image: P_01882_LEFT_MLO_FULL.jpg  - Shape: (1, 2392, 1196, 1) - Probability: 0.9844785\n",
      "63 Image: P_01883_RIGHT_CC000000.jpg  - Shape: (1, 2691, 1794, 1) - Probability: 0.9961636\n",
      "64 Image: P_01883_RIGHT_MLO000000.jpg  - Shape: (1, 2691, 1794, 1) - Probability: 0.98481536\n",
      "65 Image: P_02102_LEFT_CC000000.jpg  - Shape: (1, 2691, 1196, 1) - Probability: 0.99772125\n",
      "66 Image: P_02102_LEFT_MLO000000.jpg  - Shape: (1, 2392, 1196, 1) - Probability: 0.99983656\n",
      "67 Image: P_02139_LEFT_CC000000.jpg  - Shape: (1, 2392, 897, 1) - Probability: 0.99948573\n",
      "68 Image: P_02139_LEFT_MLO000000.jpg  - Shape: (1, 2392, 598, 1) - Probability: 0.9992124\n",
      "69 Image: P_02153_RIGHT_CC000000.jpg  - Shape: (1, 2392, 1196, 1) - Probability: 0.99434304\n",
      "70 Image: P_02153_RIGHT_MLO000000.jpg  - Shape: (1, 2392, 1196, 1) - Probability: 0.99904865\n",
      "71 Image: P_02159_LEFT_CC000000.jpg  - Shape: (1, 2990, 2093, 1) - Probability: 0.9981029\n",
      "72 Image: P_02159_LEFT_MLO000000.jpg  - Shape: (1, 2990, 1794, 1) - Probability: 0.99644524\n",
      "73 Image: P_02176_RIGHT_CC000000.jpg  - Shape: (1, 2990, 1196, 1) - Probability: 0.9873253\n",
      "74 Image: P_02176_RIGHT_MLO000000.jpg  - Shape: (1, 2691, 1196, 1) - Probability: 0.9986461\n",
      "75 Image: P_02198_LEFT_CC000000.jpg  - Shape: (1, 2392, 897, 1) - Probability: 0.99969923\n",
      "76 Image: P_02198_LEFT_MLO000000.jpg  - Shape: (1, 2392, 1196, 1) - Probability: 0.99418455\n",
      "77 Image: P_02275_LEFT_CC000000.jpg  - Shape: (1, 2990, 1794, 1) - Probability: 0.9997358\n",
      "78 Image: P_02275_LEFT_MLO000000.jpg  - Shape: (1, 3289, 1794, 1) - Probability: 0.9994904\n",
      "79 Image: P_02418_RIGHT_CC_FULL.jpg  - Shape: (1, 2392, 897, 1) - Probability: 0.9987691\n",
      "80 Image: P_02418_RIGHT_MLO000000.jpg  - Shape: (1, 2392, 1196, 1) - Probability: 0.9977099\n",
      "81 Image: P_02420_RIGHT_CC000000.jpg  - Shape: (1, 3289, 1794, 1) - Probability: 0.99691004\n",
      "82 Image: P_02420_RIGHT_MLO000000.jpg  - Shape: (1, 3289, 1794, 1) - Probability: 0.997658\n",
      "83 Image: P_02432_LEFT_CC000000.jpg  - Shape: (1, 2392, 1495, 1) - Probability: 0.9977944\n",
      "84 Image: P_02432_LEFT_MLO000000.jpg  - Shape: (1, 2392, 1495, 1) - Probability: 0.99944335\n",
      "85 Image: P_02464_RIGHT_CC000000.jpg  - Shape: (1, 3289, 2093, 1) - Probability: 0.9994267\n",
      "86 Image: P_02464_RIGHT_MLO000000.jpg  - Shape: (1, 3289, 2392, 1) - Probability: 0.99108434\n",
      "87 Image: P_02498_RIGHT_CC000000.jpg  - Shape: (1, 2392, 897, 1) - Probability: 0.99533904\n",
      "88 Image: P_02498_RIGHT_MLO000000.jpg  - Shape: (1, 2392, 897, 1) - Probability: 0.9977969\n",
      "89 Image: P_02501_RIGHT_CC_FULL.jpg  - Shape: (1, 2093, 598, 1) - Probability: 0.9896884\n",
      "90 Image: P_02501_RIGHT_MLO000000.jpg  - Shape: (1, 2392, 897, 1) - Probability: 0.9964605\n",
      "Positive Predictions: 89 out of 90\n",
      "Mean Max Prob: 0.9923933\n",
      "Min Max Prob: 0.74536175\n",
      "Max Max Prob: 0.99998116\n"
     ]
    }
   ],
   "source": [
    "## Predictions for directory of images\n",
    "# directory = \"C:\\\\Users\\\\eric\\\\Documents\\\\Courses\\\\Applied ML 2\\\\mammography\\\\data\\\\ddsm\\\\Howtek pngs\"\n",
    "# directory = \"C:\\\\Users\\\\eric\\\\Documents\\\\Courses\\\\Applied ML 2\\\\mammography\\\\data\\\\MIAS\"\n",
    "directory = \"C:\\\\Users\\\\eric\\\\Documents\\\\Courses\\\\Applied ML 2\\\\mammography\\\\data\\\\cbis-ddsm\\\\Calc Test All Full JPEGs\"\n",
    "files = os.listdir(directory)[201:]\n",
    "\n",
    "threshold = 0.75\n",
    "\n",
    "# scale = 2.58\n",
    "scale = 0.5\n",
    "how = \"normal\"\n",
    "dataset = 10\n",
    "model_name = \"model_s2.0.0.36b.10\"\n",
    "\n",
    "config = tf.ConfigProto()\n",
    "config.gpu_options.allocator_type = 'BFC'\n",
    "config.gpu_options.per_process_gpu_memory_fraction = 0.7\n",
    "\n",
    "config = tf.ConfigProto(device_count = {\"GPU\": 0, \"CPU\": 1})\n",
    "\n",
    "## Evaluate on test data multi-class\n",
    "with tf.Session(graph=graph, config=config) as sess:\n",
    "    # create the saver\n",
    "    saver = tf.train.Saver()\n",
    "    \n",
    "    # restore the model\n",
    "    sess.run(tf.local_variables_initializer())\n",
    "    saver.restore(sess, './model/' + model_name + '.ckpt')\n",
    "    \n",
    "    max_probs = []\n",
    "    all_probs = []\n",
    "    count = 0\n",
    "    i = 0\n",
    "    for file in files:\n",
    "        i += 1\n",
    "        image, _ = load_image(file, base_path=directory, margin=0.10, contrast=1.0, scale=scale, normalize=False)\n",
    "        \n",
    "        # reshape the input so tensorflow can handle it\n",
    "        image = image.reshape(1,image.shape[0], image.shape[1], 1)\n",
    "\n",
    "        yhat = sess.run(probabilities, feed_dict = {\n",
    "            X: image,\n",
    "            training: False,\n",
    "            })\n",
    "        \n",
    "        try:\n",
    "            abnormal_probs = yhat[:,:,1]\n",
    "        except:\n",
    "            abnormal_probs = yhat[:,1]\n",
    "            \n",
    "        all_probs.append(abnormal_probs)\n",
    "        max_prob = np.max(abnormal_probs)\n",
    "        max_probs.append(max_prob)\n",
    "        print(i, \"Image:\", file, \" - Shape:\", image.shape, \"- Probability:\", max_prob)\n",
    "        \n",
    "        if max_prob > threshold:\n",
    "            count += 1\n",
    "            \n",
    "print(\"Positive Predictions:\", count, \"out of\", len(files))\n",
    "print(\"Mean Max Prob:\", np.mean(max_probs))        \n",
    "print(\"Min Max Prob:\", np.min(max_probs))        \n",
    "print(\"Max Max Prob:\", np.max(max_probs))   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TP: 11\n",
      "FN: 4\n",
      "Positive Predictions: 34\n",
      "Accuracy: 0.46\n",
      "Recall: 0.7333333333333333\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "all_cases_df = pd.read_table('data/Info.txt', delimiter=' ')\n",
    "all_cases_df.drop_duplicates(subset=['REFNUM'], keep='first', inplace=True)\n",
    "actual_abnormal = all_cases_df.CLASS != \"NORM\"\n",
    "classes = np.array(actual_abnormal * 1)[:50]\n",
    "\n",
    "max_probs = np.array(max_probs)\n",
    "preds = max_probs > threshold\n",
    "correct = (preds == classes)\n",
    "tp = (classes & preds)\n",
    "fn = (classes & ~preds)\n",
    "\n",
    "print(\"TP:\",np.sum(tp))\n",
    "print(\"FN:\",np.sum(fn))\n",
    "print(\"Positive Predictions:\",np.sum(preds))\n",
    "print(\"Accuracy:\", np.sum(correct) / len(preds))\n",
    "print(\"Recall:\", np.sum(tp) / (np.sum(tp) + np.sum(fn)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from ./model/model_s2.0.0.36b.101.ckpt\n",
      "Restoring model model_s2.0.0.36b.101\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "Cannot feed value of shape (2985, 2190) for Tensor 'inputs/PlaceholderWithDefault:0', which has shape '(?, ?, ?, 1)'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-10-f006482604d2>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     14\u001b[0m     {\n\u001b[0;32m     15\u001b[0m         \u001b[0mX\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mimage\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 16\u001b[1;33m         \u001b[0mtraining\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     17\u001b[0m     })\n\u001b[0;32m     18\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda2\\envs\\exts-aml2\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36mrun\u001b[1;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[0;32m    903\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    904\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[1;32m--> 905\u001b[1;33m                          run_metadata_ptr)\n\u001b[0m\u001b[0;32m    906\u001b[0m       \u001b[1;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    907\u001b[0m         \u001b[0mproto_data\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda2\\envs\\exts-aml2\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_run\u001b[1;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[0;32m   1114\u001b[0m                              \u001b[1;34m'which has shape %r'\u001b[0m \u001b[1;33m%\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1115\u001b[0m                              (np_val.shape, subfeed_t.name,\n\u001b[1;32m-> 1116\u001b[1;33m                               str(subfeed_t.get_shape())))\n\u001b[0m\u001b[0;32m   1117\u001b[0m           \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgraph\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_feedable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msubfeed_t\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1118\u001b[0m             \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Tensor %s may not be fed.'\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0msubfeed_t\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: Cannot feed value of shape (2985, 2190) for Tensor 'inputs/PlaceholderWithDefault:0', which has shape '(?, ?, ?, 1)'"
     ]
    }
   ],
   "source": [
    "# reshape the input so tensorflow can handle it\n",
    "if len(image.shape) != 4:\n",
    "    image = image.reshape(1,image.shape[0], image.shape[1], 1)\n",
    "\n",
    "## EVALUATE ON SINGLE IMAGE\n",
    "with tf.Session(graph=graph, config=config) as sess:\n",
    "    # create the saver\n",
    "    saver = tf.train.Saver()\n",
    "    \n",
    "    saver.restore(sess, './model/' + model_name + '.ckpt')\n",
    "    print(\"Restoring model\", model_name)\n",
    "            \n",
    "    sess.run(tf.local_variables_initializer())\n",
    "    \n",
    "    #######################################################################################\n",
    "    ## evaluate on the test data\n",
    "    yhat = sess.run(probabilities, feed_dict=\n",
    "    {\n",
    "        X: image,\n",
    "        training: False\n",
    "    })\n",
    "    \n",
    "try:\n",
    "    abnormal_probs = yhat[:,:,1]\n",
    "except:\n",
    "    abnormal_probs = yhat[1]\n",
    "    \n",
    "print(\"Max Abnormal Prob:\", np.max(abnormal_probs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\eric\\Anaconda2\\envs\\exts-aml2\\lib\\site-packages\\ipykernel_launcher.py:47: DeprecationWarning: `imresize` is deprecated!\n",
      "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n",
      "Use ``skimage.transform.resize`` instead.\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ae3b7b5710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image: (2970, 1695)\n"
     ]
    }
   ],
   "source": [
    "# load an image to classify\n",
    "# Calc Train Positive Full Image\n",
    "image_path = \"C:\\\\Users\\\\eric\\\\Documents\\\\Courses\\\\Applied ML 2\\\\mammography\\\\data\\\\cbis-ddsm\\\\Calc Train All Full JPEGs\\\\P_00019_RIGHT_CC_FULL.jpg\"\n",
    "\n",
    "# Mass Train Positive Full Image\n",
    "# image_path = \"C:\\\\Users\\\\eric\\\\Documents\\\\Courses\\\\Applied ML 2\\\\mammography\\\\data\\\\cbis-ddsm\\\\Mass Train All JPEGs Full\\\\P_00064_RIGHT_MLO_FULL.jpg\"\n",
    "\n",
    "# image_path = \"C:\\\\Users\\\\eric\\\\Documents\\\\Courses\\\\Applied ML 2\\\\mammography\\\\data\\\\cbis-ddsm\\\\Mass Test All Full JPEGs\\\\P_00498_LEFT_CC_FULL.jpg \"\n",
    "\n",
    "# negative howtek image\n",
    "# image_path = \"C:\\\\Users\\\\eric\\\\Documents\\\\Courses\\\\Applied ML 2\\\\mammography\\\\data\\\\ddsm\\\\Howtek pngs\\\\A_1969_1.LEFT_CC.LJPEG.png\"\n",
    "\n",
    "# negative lumisys\n",
    "# image_path = \"C:\\\\Users\\\\eric\\\\Documents\\\\Courses\\\\Applied ML 2\\\\mammography\\\\data\\\\ddsm\\\\Lumisys pngs\\\\B_3635_1.LEFT_CC.LJPEG.png\"\n",
    "\n",
    "# negative dba image\n",
    "# image_path = \"E:\\\\Documents\\\\Mammography\\\\DDSM\\\\PNGs\\\\DBA pngs\\\\A_0026_1.LEFT_MLO.LJPEG.png\"\n",
    "\n",
    "# mias image\n",
    "# image_path = \"E:\\\\Documents\\\\Mammography\\\\MIAS\\\\JPEG\\\\mdb004.jpg\"\n",
    "\n",
    "# single tile\n",
    "# image_path = \"C:\\\\Users\\\\eric\\\\Documents\\\\Courses\\\\Applied ML 2\\\\mammography\\\\data\\\\cbis-images\\\\1_P_00010_LEFT_CC_2.png\"\n",
    "\n",
    "# open the image\n",
    "image = PIL.Image.open(image_path)\n",
    "\n",
    "# convert to numpy array\n",
    "image = np.array(image)\n",
    "\n",
    "# try to lose the extra channels, if there is an error they don't exist\n",
    "try:\n",
    "    image = image[:,:,0]\n",
    "except:\n",
    "    pass\n",
    "\n",
    "# size it down by half\n",
    "image = resize_image(image, 0.5)\n",
    "# image = resize_image(image, 2.58)\n",
    "\n",
    "h = image.shape[0]\n",
    "w = image.shape[1]\n",
    "\n",
    "trim_borders = False\n",
    "if trim_borders:\n",
    "    # trim 15% off each side\n",
    "    h_border = int(h * 0.15)\n",
    "    w_border = int(w * 0.15)\n",
    "\n",
    "    image = image[h_border:(h-h_border),w_border:(w-w_border)]\n",
    "\n",
    "image = trim_edges_from_image(image)    \n",
    "    \n",
    "plt.imshow(image)\n",
    "plt.show()\n",
    "\n",
    "\n",
    "print(\"Image:\", image.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image: (1, 1680, 1162, 1)\n",
      "INFO:tensorflow:Restoring parameters from ./model/model_s2.0.0.36b.101.ckpt\n",
      "Restoring model model_s2.0.0.36b.101\n",
      "Yhat: (5, 3, 2)\n",
      "Max Abnormal Prob: 0.3580893\n"
     ]
    }
   ],
   "source": [
    "## Predictions for single image\n",
    "how = \"normal\"\n",
    "dataset = 10\n",
    "model_name = \"model_s2.0.0.36b.101\"\n",
    "# config = tf.ConfigProto(device_count = {'GPU': 0})\n",
    "config = tf.ConfigProto()\n",
    "\n",
    "# reshape the input so tensorflow can handle it\n",
    "if len(image.shape) != 4:\n",
    "    image = image.reshape(1,image.shape[0], image.shape[1], 1)\n",
    "\n",
    "print(\"Image:\", image.shape)\n",
    "\n",
    "## Evaluate on test data multi-class\n",
    "with tf.Session(graph=graph, config=config) as sess:\n",
    "    # create the saver\n",
    "    saver = tf.train.Saver()\n",
    "    \n",
    "    saver.restore(sess, './model/' + model_name + '.ckpt')\n",
    "    print(\"Restoring model\", model_name)\n",
    "            \n",
    "    sess.run(tf.local_variables_initializer())\n",
    "    \n",
    "    yhat = sess.run(probabilities, feed_dict = {\n",
    "        X: image,\n",
    "        training: False,\n",
    "        })\n",
    "\n",
    "print(\"Yhat:\", yhat.shape)\n",
    "try:\n",
    "    abnormal_probs = yhat[:,:,1]\n",
    "    print(\"Max Abnormal Prob:\", np.max(abnormal_probs))\n",
    "except:\n",
    "    print(yhat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Positive - R: 3  C: 2 Prob: 0.95513994\n",
      "Positive - R: 4  C: 0 Prob: 0.5420265\n",
      "Positive - R: 5  C: 2 Prob: 0.6033908\n",
      "Positive - R: 6  C: 1 Prob: 0.86668366\n",
      "Positive - R: 6  C: 2 Prob: 0.90205646\n",
      "Max Prob: 0.95513994\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1aa92ea3128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "h = yhat.shape[0]\n",
    "w = yhat.shape[1]\n",
    "probs = np.zeros_like(yhat)\n",
    "max_prob = 0\n",
    "\n",
    "for y in range(h):\n",
    "    for x in range(w):\n",
    "        if yhat[y,x,1] > 0.5:\n",
    "            print(\"Positive - R:\", y, \" C:\", x, \"Prob:\", yhat[y,x,1])\n",
    "        \n",
    "        if yhat[y,x,1] > max_prob:\n",
    "            max_prob = yhat[y,x,1]\n",
    "            \n",
    "print(\"Max Prob:\", max_prob)\n",
    "\n",
    "vertlines = image.shape[1] / yhat.shape[0]\n",
    "horlines = image.shape[2] / yhat.shape[1]\n",
    "\n",
    "yticks = np.arange(0, image.shape[1], vertlines)\n",
    "xticks = np.arange(0, image.shape[2], horlines)\n",
    "\n",
    "plt.imshow(image[0,:,:,0])\n",
    "plt.grid(True)\n",
    "plt.yticks(yticks)\n",
    "plt.xticks(xticks)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "_, h,w, _ = image.shape\n",
    "new_h = (h // 299) * 299\n",
    "new_w = (w // 299) * 299\n",
    "h_border = (h - new_h) // 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ae3b69de10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1ae3b77d240>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ae12ac7198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# image1 = image.copy()\n",
    "foo = np.sum(image1 <= 10, axis=1)\n",
    "plt.plot(foo)\n",
    "plt.show()\n",
    "plt.imshow(image1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1ae3a46e9b0>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ae3a222550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ae121b5f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ae3b6c1a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "image2 = image.copy()\n",
    "foo = np.sum(image <= 10, axis=1)\n",
    "plt.plot(foo)\n",
    "plt.show()\n",
    "plt.imshow(image)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ae3a499a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ae51d41940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "image4 = image.copy()\n",
    "foo = np.sum(image <= 10, axis=1)\n",
    "plt.plot(foo)\n",
    "plt.show()\n",
    "plt.imshow(image)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1ae564f8dd8>]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ae51d73cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "foo = np.sum(image <= 10, axis=1)\n",
    "plt.plot(foo[250:1000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ae3b648f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ae3b8bcf60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "image3 = image.copy()\n",
    "foo = np.sum(image <= 10, axis=1)\n",
    "plt.plot(foo)\n",
    "plt.show()\n",
    "plt.imshow(image)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}