CNN Autoencoder Anomaly Detection.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "taken from here https://medium.com/@judewells/image-anomaly-detection-novelty-detection-using-convolutional-auto-encoders-in-keras-1c31321c10f2\n",
    "\n",
    "repo: https://github.com/JudeWells/keras_anomaly_detection/blob/master/CNN%20Autoencoder%20Anomaly%20Detection.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import keras\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Activation, Flatten, Input\n",
    "from keras.layers import Conv2D, MaxPooling2D, UpSampling2D\n",
    "import matplotlib.pyplot as plt\n",
    "from keras import backend as K\n",
    "import numpy as np\n",
    "from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img\n",
    "from PIL import Image, ImageChops\n",
    "from sklearn.neighbors import KernelDensity\n",
    "from sklearn.model_selection import StratifiedShuffleSplit\n",
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2279, 48, 48)\n"
     ]
    }
   ],
   "source": [
    "# load perfect images\n",
    "pp = np.load('./perfect_patches.npy')\n",
    "print(pp.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# numpy to jpeg\n",
    "# only run this if you haven't previously generated images\n",
    "for i in range(len(pp)):\n",
    "    filename = \"%s_perfect.jpg\" % i\n",
    "    \n",
    "    plt.imshow(pp[i])\n",
    "    plt.axis('off')\n",
    "    plt.savefig(filename, dpi=300, bbox_inches='tight')\n",
    "    plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(32, 48, 48)\n"
     ]
    }
   ],
   "source": [
    "# load defect images\n",
    "dp = np.load('./defect_patches.npy') \n",
    "print(dp.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# numpy to jpeg\n",
    "# only run this if you haven't previously generated images\n",
    "for i in range(len(dp)):\n",
    "    filename = \"%s_defect.jpg\" % i\n",
    "    \n",
    "    plt.imshow(dp[i])\n",
    "    plt.axis('off')\n",
    "    plt.savefig(filename, dpi=300, bbox_inches='tight')\n",
    "    plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 2247 images belonging to 1 classes.\n",
      "Found 32 images belonging to 1 classes.\n",
      "Found 32 images belonging to 1 classes.\n"
     ]
    }
   ],
   "source": [
    "# Create generators for training, validation and testing\n",
    "# Generate data from the images in a folder, allows use of datasets that do not fit in main memory all at once\n",
    "# Image resizing is done by the generator so a folder with any sized-images can be used\n",
    "# The named directory must contain one or more subfolders, path should look like apples_train/apple_class1/img1.jpg...\n",
    "\n",
    "batch_size = 85\n",
    "train_datagen = ImageDataGenerator(rescale=1./255, data_format='channels_last')\n",
    "train_generator = train_datagen.flow_from_directory(\n",
    "    'perfect_train/', \n",
    "    target_size=(96, 96),\n",
    "    batch_size=batch_size,\n",
    "    class_mode='input'\n",
    "    )\n",
    "\n",
    "test_datagen = ImageDataGenerator(rescale=1./255, data_format='channels_last')\n",
    "validation_generator = test_datagen.flow_from_directory(\n",
    "    'perfect_test/',\n",
    "    target_size=(96, 96),\n",
    "    batch_size=batch_size,\n",
    "    class_mode='input'\n",
    "    )\n",
    "\n",
    "anomaly_generator = test_datagen.flow_from_directory(\n",
    "    'defect/',\n",
    "    target_size=(96, 96),\n",
    "    batch_size=batch_size,\n",
    "    class_mode='input'\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d (Conv2D)             (None, 96, 96, 16)        448       \n",
      "                                                                 \n",
      " max_pooling2d (MaxPooling2D  (None, 24, 24, 16)       0         \n",
      " )                                                               \n",
      "                                                                 \n",
      " conv2d_1 (Conv2D)           (None, 24, 24, 8)         1160      \n",
      "                                                                 \n",
      " max_pooling2d_1 (MaxPooling  (None, 6, 6, 8)          0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " conv2d_2 (Conv2D)           (None, 6, 6, 3)           219       \n",
      "                                                                 \n",
      " max_pooling2d_2 (MaxPooling  (None, 3, 3, 3)          0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " conv2d_3 (Conv2D)           (None, 3, 3, 3)           84        \n",
      "                                                                 \n",
      " up_sampling2d (UpSampling2D  (None, 6, 6, 3)          0         \n",
      " )                                                               \n",
      "                                                                 \n",
      " conv2d_4 (Conv2D)           (None, 6, 6, 8)           224       \n",
      "                                                                 \n",
      " up_sampling2d_1 (UpSampling  (None, 24, 24, 8)        0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " conv2d_5 (Conv2D)           (None, 24, 24, 16)        1168      \n",
      "                                                                 \n",
      " up_sampling2d_2 (UpSampling  (None, 96, 96, 16)       0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " conv2d_6 (Conv2D)           (None, 96, 96, 3)         435       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 3,738\n",
      "Trainable params: 3,738\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Define the convolutional autoencoder model \n",
    "# input shape must be the same size as the images that will be fed into it by the generators\n",
    "# The output layer must be the same dimensions as the original image\n",
    "model = Sequential()\n",
    "\n",
    "model.add(Conv2D(16, (3, 3), padding='same',activation='relu', input_shape=(96, 96, 3)))\n",
    "model.add(MaxPooling2D(pool_size=(4,4), padding='same')) # using pool_size (4,4) makes the layer 4x smaller in height and width\n",
    "\n",
    "model.add(Conv2D(8,(3, 3),activation='relu',  padding='same'))\n",
    "model.add(MaxPooling2D(pool_size=(4,4), padding='same'))\n",
    "\n",
    "model.add(Conv2D(3,(3, 3),activation='relu',  padding='same'))\n",
    "model.add(MaxPooling2D(pool_size=(2,2), padding='same'))\n",
    "\n",
    "#-------------------------\n",
    "model.add(Conv2D(3,(3, 3),activation='relu',  padding='same'))\n",
    "model.add(UpSampling2D((2, 2)))\n",
    "\n",
    "model.add(Conv2D(8,(3, 3),activation='relu',  padding='same'))\n",
    "model.add(UpSampling2D((4, 4)))\n",
    "\n",
    "model.add(Conv2D(16,(3, 3),activation='relu',  padding='same'))\n",
    "model.add(UpSampling2D((4, 4)))\n",
    "\n",
    "model.add(Conv2D(3,(3, 3), activation='sigmoid', padding='same'))\n",
    "#-------------------------\n",
    "\n",
    "model.summary()\n",
    "\n",
    "# Compile the model\n",
    "model.compile(optimizer='adadelta', loss='mean_squared_error')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-10-06fc8fd15423>:5: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.\n",
      "  model.fit_generator(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "11/11 [==============================] - ETA: 0s - loss: 0.0736WARNING:tensorflow:Your input ran out of data; interrupting training. Make sure that your dataset or generator can generate at least `steps_per_epoch * epochs` batches (in this case, 11 batches). You may need to use the repeat() function when building your dataset.\n",
      "11/11 [==============================] - 13s 1s/step - loss: 0.0736 - val_loss: 0.0737\n",
      "Epoch 2/100\n",
      "11/11 [==============================] - ETA: 0s - loss: 0.0735WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss\n",
      "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
      "11/11 [==============================] - 14s 1s/step - loss: 0.0735\n",
      "Epoch 3/100\n",
      "11/11 [==============================] - ETA: 0s - loss: 0.0735WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss\n",
      "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
      "11/11 [==============================] - 13s 1s/step - loss: 0.0735\n",
      "Epoch 4/100\n",
      "11/11 [==============================] - ETA: 0s - loss: 0.0735WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss\n",
      "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
      "11/11 [==============================] - 13s 1s/step - loss: 0.0735\n",
      "Epoch 5/100\n",
      "11/11 [==============================] - ETA: 0s - loss: 0.0735WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss\n",
      "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
      "11/11 [==============================] - 14s 1s/step - loss: 0.0735\n",
      "Epoch 6/100\n",
      "11/11 [==============================] - ETA: 0s - loss: 0.0735WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss\n",
      "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
      "11/11 [==============================] - 14s 1s/step - loss: 0.0735\n",
      "Epoch 7/100\n",
      "11/11 [==============================] - ETA: 0s - loss: 0.0734WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss\n",
      "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
      "11/11 [==============================] - 14s 1s/step - loss: 0.0734\n",
      "Epoch 8/100\n",
      "11/11 [==============================] - ETA: 0s - loss: 0.0735WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss\n",
      "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
      "11/11 [==============================] - 13s 1s/step - loss: 0.0735\n",
      "Epoch 9/100\n",
      "11/11 [==============================] - ETA: 0s - loss: 0.0735WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss\n",
      "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
      "11/11 [==============================] - 14s 1s/step - loss: 0.0735\n",
      "Epoch 10/100\n",
      "11/11 [==============================] - ETA: 0s - loss: 0.0735WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss\n",
      "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
      "11/11 [==============================] - 14s 1s/step - loss: 0.0735\n",
      "Epoch 11/100\n",
      "11/11 [==============================] - ETA: 0s - loss: 0.0734WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss\n",
      "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
      "11/11 [==============================] - 15s 1s/step - loss: 0.0734\n",
      "Epoch 12/100\n",
      "11/11 [==============================] - ETA: 0s - loss: 0.0734WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss\n",
      "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
      "11/11 [==============================] - 16s 1s/step - loss: 0.0734\n",
      "Epoch 13/100\n",
      "11/11 [==============================] - ETA: 0s - loss: 0.0734WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss\n",
      "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
      "11/11 [==============================] - 14s 1s/step - loss: 0.0734\n",
      "Epoch 14/100\n",
      "11/11 [==============================] - ETA: 0s - loss: 0.0734WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss\n",
      "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
      "11/11 [==============================] - 16s 1s/step - loss: 0.0734\n",
      "Epoch 15/100\n",
      "11/11 [==============================] - ETA: 0s - loss: 0.0733WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss\n",
      "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
      "11/11 [==============================] - 15s 1s/step - loss: 0.0733\n",
      "Epoch 16/100\n",
      "11/11 [==============================] - ETA: 0s - loss: 0.0733WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss\n",
      "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
      "11/11 [==============================] - 15s 1s/step - loss: 0.0733\n",
      "Epoch 17/100\n",
      "11/11 [==============================] - ETA: 0s - loss: 0.0734WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss\n",
      "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
      "11/11 [==============================] - 14s 1s/step - loss: 0.0734\n",
      "Epoch 18/100\n",
      "11/11 [==============================] - ETA: 0s - loss: 0.0733WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss\n",
      "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
      "11/11 [==============================] - 15s 1s/step - loss: 0.0733\n",
      "Epoch 19/100\n",
      "11/11 [==============================] - ETA: 0s - loss: 0.0733WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss\n",
      "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
      "11/11 [==============================] - 15s 1s/step - loss: 0.0733\n",
      "Epoch 20/100\n",
      "11/11 [==============================] - ETA: 0s - loss: 0.0733WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss\n",
      "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
      "11/11 [==============================] - 16s 1s/step - loss: 0.0733\n",
      "Epoch 21/100\n",
      "11/11 [==============================] - ETA: 0s - loss: 0.0733WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss\n",
      "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
      "11/11 [==============================] - 14s 1s/step - loss: 0.0733\n",
      "Epoch 22/100\n",
      "11/11 [==============================] - ETA: 0s - loss: 0.0732WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss\n",
      "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
      "11/11 [==============================] - 15s 1s/step - loss: 0.0732\n",
      "Epoch 23/100\n",
      "11/11 [==============================] - ETA: 0s - loss: 0.0732WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss\n",
      "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
      "11/11 [==============================] - 14s 1s/step - loss: 0.0732\n",
      "Epoch 24/100\n",
      "11/11 [==============================] - ETA: 0s - loss: 0.0732WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss\n",
      "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11/11 [==============================] - 18s 2s/step - loss: 0.0732\n",
      "Epoch 25/100\n",
      "11/11 [==============================] - ETA: 0s - loss: 0.0732WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss\n",
      "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
      "11/11 [==============================] - 15s 1s/step - loss: 0.0732\n",
      "Epoch 26/100\n",
      "11/11 [==============================] - ETA: 0s - loss: 0.0732WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss\n",
      "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
      "11/11 [==============================] - 16s 1s/step - loss: 0.0732\n",
      "Epoch 27/100\n",
      "11/11 [==============================] - ETA: 0s - loss: 0.0732WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss\n",
      "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
      "11/11 [==============================] - 17s 2s/step - loss: 0.0732\n",
      "Epoch 28/100\n",
      "11/11 [==============================] - ETA: 0s - loss: 0.0731WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss\n",
      "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
      "11/11 [==============================] - 17s 2s/step - loss: 0.0731\n",
      "Epoch 29/100\n",
      "11/11 [==============================] - ETA: 0s - loss: 0.0732WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss\n",
      "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
      "11/11 [==============================] - 14s 1s/step - loss: 0.0732\n",
      "Epoch 30/100\n",
      "11/11 [==============================] - ETA: 0s - loss: 0.0731WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss\n",
      "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
      "11/11 [==============================] - 15s 1s/step - loss: 0.0731\n",
      "Epoch 31/100\n",
      "11/11 [==============================] - ETA: 0s - loss: 0.0731WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss\n",
      "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
      "11/11 [==============================] - 26s 2s/step - loss: 0.0731\n",
      "Epoch 32/100\n",
      "11/11 [==============================] - ETA: 0s - loss: 0.0731WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss\n",
      "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
      "11/11 [==============================] - 24s 2s/step - loss: 0.0731\n",
      "Epoch 33/100\n",
      "11/11 [==============================] - ETA: 0s - loss: 0.0731WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss\n",
      "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.0731\n",
      "Epoch 34/100\n",
      "11/11 [==============================] - ETA: 0s - loss: 0.0731WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss\n",
      "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
      "11/11 [==============================] - 24s 2s/step - loss: 0.0731\n",
      "Epoch 35/100\n",
      "11/11 [==============================] - ETA: 0s - loss: 0.0730WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss\n",
      "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
      "11/11 [==============================] - 21s 2s/step - loss: 0.0730\n",
      "Epoch 36/100\n",
      "11/11 [==============================] - ETA: 0s - loss: 0.0731WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss\n",
      "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.0731\n",
      "Epoch 37/100\n",
      "11/11 [==============================] - ETA: 0s - loss: 0.0730WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss\n",
      "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
      "11/11 [==============================] - 15s 1s/step - loss: 0.0730\n",
      "Epoch 38/100\n",
      "11/11 [==============================] - ETA: 0s - loss: 0.0730WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss\n",
      "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
      "11/11 [==============================] - 16s 1s/step - loss: 0.0730\n",
      "Epoch 39/100\n",
      "11/11 [==============================] - ETA: 0s - loss: 0.0730WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss\n",
      "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
      "11/11 [==============================] - 16s 1s/step - loss: 0.0730\n",
      "Epoch 40/100\n",
      "11/11 [==============================] - ETA: 0s - loss: 0.0729WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss\n",
      "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
      "11/11 [==============================] - 17s 2s/step - loss: 0.0729\n",
      "Epoch 41/100\n",
      "11/11 [==============================] - ETA: 0s - loss: 0.0730WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss\n",
      "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
      "11/11 [==============================] - 16s 1s/step - loss: 0.0730\n",
      "Epoch 42/100\n",
      "11/11 [==============================] - ETA: 0s - loss: 0.0729WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss\n",
      "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
      "11/11 [==============================] - 16s 1s/step - loss: 0.0729\n",
      "Epoch 43/100\n",
      "11/11 [==============================] - ETA: 0s - loss: 0.0729WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss\n",
      "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
      "11/11 [==============================] - 16s 1s/step - loss: 0.0729\n",
      "Epoch 44/100\n",
      "11/11 [==============================] - ETA: 0s - loss: 0.0729WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss\n",
      "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
      "11/11 [==============================] - 15s 1s/step - loss: 0.0729\n",
      "Epoch 45/100\n",
      "11/11 [==============================] - ETA: 0s - loss: 0.0729WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss\n",
      "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
      "11/11 [==============================] - 15s 1s/step - loss: 0.0729\n",
      "Epoch 46/100\n",
      "11/11 [==============================] - ETA: 0s - loss: 0.0729WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss\n",
      "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.0729\n",
      "Epoch 47/100\n",
      "11/11 [==============================] - ETA: 0s - loss: 0.0729WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss\n",
      "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
      "11/11 [==============================] - 15s 1s/step - loss: 0.0729\n",
      "Epoch 48/100\n",
      "11/11 [==============================] - ETA: 0s - loss: 0.0728WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss\n",
      "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11/11 [==============================] - 19s 2s/step - loss: 0.0728\n",
      "Epoch 49/100\n",
      "11/11 [==============================] - ETA: 0s - loss: 0.0728WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss\n",
      "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.0728\n",
      "Epoch 50/100\n",
      "11/11 [==============================] - ETA: 0s - loss: 0.0728WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss\n",
      "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
      "11/11 [==============================] - 16s 1s/step - loss: 0.0728\n",
      "Epoch 51/100\n",
      "11/11 [==============================] - ETA: 0s - loss: 0.0728WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss\n",
      "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.0728\n",
      "Epoch 52/100\n",
      "11/11 [==============================] - ETA: 0s - loss: 0.0728WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss\n",
      "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
      "11/11 [==============================] - 14s 1s/step - loss: 0.0728\n",
      "Epoch 53/100\n",
      "11/11 [==============================] - ETA: 0s - loss: 0.0727WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss\n",
      "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
      "11/11 [==============================] - 14s 1s/step - loss: 0.0727\n",
      "Epoch 54/100\n",
      "11/11 [==============================] - ETA: 0s - loss: 0.0727WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss\n",
      "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
      "11/11 [==============================] - 16s 1s/step - loss: 0.0727\n",
      "Epoch 55/100\n",
      "11/11 [==============================] - ETA: 0s - loss: 0.0727WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss\n",
      "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
      "11/11 [==============================] - 15s 1s/step - loss: 0.0727\n",
      "Epoch 56/100\n",
      "11/11 [==============================] - ETA: 0s - loss: 0.0727WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss\n",
      "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
      "11/11 [==============================] - 16s 1s/step - loss: 0.0727\n",
      "Epoch 57/100\n",
      "11/11 [==============================] - ETA: 0s - loss: 0.0727WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss\n",
      "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
      "11/11 [==============================] - 17s 2s/step - loss: 0.0727\n",
      "Epoch 58/100\n",
      "11/11 [==============================] - ETA: 0s - loss: 0.0726WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss\n",
      "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
      "11/11 [==============================] - 15s 1s/step - loss: 0.0726\n",
      "Epoch 59/100\n",
      "11/11 [==============================] - ETA: 0s - loss: 0.0727WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss\n",
      "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
      "11/11 [==============================] - 15s 1s/step - loss: 0.0727\n",
      "Epoch 60/100\n",
      "11/11 [==============================] - ETA: 0s - loss: 0.0726WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss\n",
      "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
      "11/11 [==============================] - 15s 1s/step - loss: 0.0726\n",
      "Epoch 61/100\n",
      "11/11 [==============================] - ETA: 0s - loss: 0.0726WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss\n",
      "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
      "11/11 [==============================] - 16s 1s/step - loss: 0.0726\n",
      "Epoch 62/100\n",
      "11/11 [==============================] - ETA: 0s - loss: 0.0726WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss\n",
      "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
      "11/11 [==============================] - 17s 2s/step - loss: 0.0726\n",
      "Epoch 63/100\n",
      "11/11 [==============================] - ETA: 0s - loss: 0.0726WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss\n",
      "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
      "11/11 [==============================] - 16s 1s/step - loss: 0.0726\n",
      "Epoch 64/100\n",
      "11/11 [==============================] - ETA: 0s - loss: 0.0725WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss\n",
      "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
      "11/11 [==============================] - 16s 1s/step - loss: 0.0725\n",
      "Epoch 65/100\n",
      "11/11 [==============================] - ETA: 0s - loss: 0.0725WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss\n",
      "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
      "11/11 [==============================] - 17s 2s/step - loss: 0.0725\n",
      "Epoch 66/100\n",
      "11/11 [==============================] - ETA: 0s - loss: 0.0725WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss\n",
      "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
      "11/11 [==============================] - 14s 1s/step - loss: 0.0725\n",
      "Epoch 67/100\n",
      "11/11 [==============================] - ETA: 0s - loss: 0.0725WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss\n",
      "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
      "11/11 [==============================] - 16s 1s/step - loss: 0.0725\n",
      "Epoch 68/100\n",
      "11/11 [==============================] - ETA: 0s - loss: 0.0725WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss\n",
      "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.0725\n",
      "Epoch 69/100\n",
      "11/11 [==============================] - ETA: 0s - loss: 0.0725WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss\n",
      "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
      "11/11 [==============================] - 24s 2s/step - loss: 0.0725\n",
      "Epoch 70/100\n",
      "11/11 [==============================] - ETA: 0s - loss: 0.0724WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss\n",
      "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
      "11/11 [==============================] - 17s 1s/step - loss: 0.0724\n",
      "Epoch 71/100\n",
      "11/11 [==============================] - ETA: 0s - loss: 0.0724WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss\n",
      "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
      "11/11 [==============================] - 21s 2s/step - loss: 0.0724\n",
      "Epoch 72/100\n",
      "11/11 [==============================] - ETA: 0s - loss: 0.0724WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss\n",
      "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11/11 [==============================] - 17s 2s/step - loss: 0.0724\n",
      "Epoch 73/100\n",
      "11/11 [==============================] - ETA: 0s - loss: 0.0724WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss\n",
      "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
      "11/11 [==============================] - 17s 2s/step - loss: 0.0724\n",
      "Epoch 74/100\n",
      "11/11 [==============================] - ETA: 0s - loss: 0.0723WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss\n",
      "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.0723\n",
      "Epoch 75/100\n",
      "11/11 [==============================] - ETA: 0s - loss: 0.0724WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss\n",
      "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
      "11/11 [==============================] - 16s 1s/step - loss: 0.0724\n",
      "Epoch 76/100\n",
      "11/11 [==============================] - ETA: 0s - loss: 0.0724WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss\n",
      "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
      "11/11 [==============================] - 16s 1s/step - loss: 0.0724\n",
      "Epoch 77/100\n",
      "11/11 [==============================] - ETA: 0s - loss: 0.0724WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss\n",
      "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
      "11/11 [==============================] - 16s 1s/step - loss: 0.0724\n",
      "Epoch 78/100\n",
      "11/11 [==============================] - ETA: 0s - loss: 0.0723WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss\n",
      "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
      "11/11 [==============================] - 17s 2s/step - loss: 0.0723\n",
      "Epoch 79/100\n",
      "11/11 [==============================] - ETA: 0s - loss: 0.0723WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss\n",
      "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
      "11/11 [==============================] - 17s 1s/step - loss: 0.0723\n",
      "Epoch 80/100\n",
      "11/11 [==============================] - ETA: 0s - loss: 0.0722WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss\n",
      "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
      "11/11 [==============================] - 17s 2s/step - loss: 0.0722\n",
      "Epoch 81/100\n",
      "11/11 [==============================] - ETA: 0s - loss: 0.0722WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss\n",
      "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.0722\n",
      "Epoch 82/100\n",
      "11/11 [==============================] - ETA: 0s - loss: 0.0722WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss\n",
      "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
      "11/11 [==============================] - 20s 2s/step - loss: 0.0722\n",
      "Epoch 83/100\n",
      "11/11 [==============================] - ETA: 0s - loss: 0.0722WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss\n",
      "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
      "11/11 [==============================] - 16s 1s/step - loss: 0.0722\n",
      "Epoch 84/100\n",
      "11/11 [==============================] - ETA: 0s - loss: 0.0722WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss\n",
      "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
      "11/11 [==============================] - 20s 2s/step - loss: 0.0722\n",
      "Epoch 85/100\n",
      "11/11 [==============================] - ETA: 0s - loss: 0.0722WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss\n",
      "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
      "11/11 [==============================] - 16s 1s/step - loss: 0.0722\n",
      "Epoch 86/100\n",
      "11/11 [==============================] - ETA: 0s - loss: 0.0722WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss\n",
      "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.0722\n",
      "Epoch 87/100\n",
      "11/11 [==============================] - ETA: 0s - loss: 0.0722WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss\n",
      "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
      "11/11 [==============================] - 20s 2s/step - loss: 0.0722\n",
      "Epoch 88/100\n",
      "11/11 [==============================] - ETA: 0s - loss: 0.0721WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss\n",
      "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
      "11/11 [==============================] - 14s 1s/step - loss: 0.0721\n",
      "Epoch 89/100\n",
      "11/11 [==============================] - ETA: 0s - loss: 0.0721WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss\n",
      "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
      "11/11 [==============================] - 15s 1s/step - loss: 0.0721\n",
      "Epoch 90/100\n",
      "11/11 [==============================] - ETA: 0s - loss: 0.0721WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss\n",
      "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
      "11/11 [==============================] - 15s 1s/step - loss: 0.0721\n",
      "Epoch 91/100\n",
      "11/11 [==============================] - ETA: 0s - loss: 0.0721WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss\n",
      "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
      "11/11 [==============================] - 15s 1s/step - loss: 0.0721\n",
      "Epoch 92/100\n",
      "11/11 [==============================] - ETA: 0s - loss: 0.0721WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss\n",
      "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.0721\n",
      "Epoch 93/100\n",
      "11/11 [==============================] - ETA: 0s - loss: 0.0721WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss\n",
      "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
      "11/11 [==============================] - 16s 1s/step - loss: 0.0721\n",
      "Epoch 94/100\n",
      "11/11 [==============================] - ETA: 0s - loss: 0.0721WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss\n",
      "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
      "11/11 [==============================] - 16s 1s/step - loss: 0.0721\n",
      "Epoch 95/100\n",
      "11/11 [==============================] - ETA: 0s - loss: 0.0720WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss\n",
      "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.0720\n",
      "Epoch 96/100\n",
      "11/11 [==============================] - ETA: 0s - loss: 0.0720WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss\n",
      "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11/11 [==============================] - 18s 2s/step - loss: 0.0720\n",
      "Epoch 97/100\n",
      "11/11 [==============================] - ETA: 0s - loss: 0.0720WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss\n",
      "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
      "11/11 [==============================] - 16s 1s/step - loss: 0.0720\n",
      "Epoch 98/100\n",
      "11/11 [==============================] - ETA: 0s - loss: 0.0719WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss\n",
      "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
      "11/11 [==============================] - 17s 2s/step - loss: 0.0719\n",
      "Epoch 99/100\n",
      "11/11 [==============================] - ETA: 0s - loss: 0.0720WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss\n",
      "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.0720\n",
      "Epoch 100/100\n",
      "11/11 [==============================] - ETA: 0s - loss: 0.0719WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss\n",
      "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
      "11/11 [==============================] - 15s 1s/step - loss: 0.0719\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x2cca0b59df0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Training the model\n",
    "es = keras.callbacks.EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=30) # Early stopping (stops training when validation doesn't improve for {patience} epochs)\n",
    "model_filepath = 'models/model1.keras'\n",
    "save_best = keras.callbacks.ModelCheckpoint(model_filepath, monitor='val_loss', save_best_only=True, mode='min') # Saves the best version of the model to disk (as measured on the validation data set)\n",
    "model.fit_generator(\n",
    "        train_generator,\n",
    "        steps_per_epoch=1000 // batch_size,\n",
    "        epochs=100,\n",
    "        validation_data=validation_generator,\n",
    "        validation_steps=1000 // batch_size,\n",
    "        shuffle = True,\n",
    "        callbacks=[es, save_best])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Training continues after improvement stops for the number of epochs equivalent to the 'patience' hyper-parameter\n",
    "# To get back the model that performed best on the validation set we load the checkpointed model from disk:\n",
    "model = keras.models.load_model(model_filepath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'models/model1.keras'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_filepath"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x576 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Test the model by viewing a sample of original and reconstructed images\n",
    "data_list = []\n",
    "batch_index = 0\n",
    "while batch_index <= train_generator.batch_index:\n",
    "    data = train_generator.next()\n",
    "    data_list.append(data[0])\n",
    "    batch_index = batch_index + 1\n",
    "\n",
    "predicted = model.predict(data_list[0])\n",
    "no_of_samples = 4\n",
    "_, axs = plt.subplots(no_of_samples, 2, figsize=(5, 8))\n",
    "axs = axs.flatten()\n",
    "imgs = []\n",
    "for i in range(no_of_samples):\n",
    "    imgs.append(data_list[i][i])\n",
    "    imgs.append(predicted[i])\n",
    "for img, ax in zip(imgs, axs):\n",
    "    ax.imshow(img)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We want the difference the difference in error between the validation (normal) images and anomalous images to be as high as possible\n",
    "print(f\"Error on validation set:{model.evaluate_generator(validation_generator)}, error on anomaly set:{model.evaluate_generator(anomaly_generator)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Extracting the encoded image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We build a replica of the first half of the auto-encoder (the encoder only)\n",
    "# This network takes a an image which is 96x96x3 and compresses it down to a 3x3x3 tensor\n",
    "encoder_replica = Sequential()\n",
    "encoder_replica.add(Conv2D(16, (3, 3), padding='same',activation='relu', input_shape=(96, 96, 3), weights=model.layers[0].get_weights()) )\n",
    "encoder_replica.add(MaxPooling2D(pool_size=(4,4), padding='same'))\n",
    "encoder_replica.add(Conv2D(8,(3, 3),activation='relu',  padding='same', weights=model.layers[2].get_weights()))\n",
    "encoder_replica.add(MaxPooling2D(pool_size=(4,4), padding='same'))\n",
    "encoder_replica.add(Conv2D(3,(3, 3),activation='relu',  padding='same', weights=model.layers[4].get_weights()))\n",
    "encoder_replica.add(MaxPooling2D(pool_size=(2,2), padding='same'))\n",
    "encoder_replica.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating encoded (compressed versions of the training, validation and anomalous images) and getting their density\n",
    "# The SKLearn kernel density function only works with 1D arrays so we need to flatten the tensors created by the encoder\n",
    "encoded_images = encoder_replica.predict_generator(train_generator)\n",
    "encoded_images_flat = [np.reshape(img, (27)) for img in encoded_images]\n",
    "\n",
    "validation_encoded = encoder_replica.predict_generator(validation_generator)\n",
    "val_enc_flat = [np.reshape(img, (27)) for img in validation_encoded]\n",
    "\n",
    "anom_encoded = encoder_replica.predict_generator(anomaly_generator)\n",
    "anom_enc_flat = [np.reshape(img, (27)) for img in anom_encoded]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Kernel Density Estimation of the encoded vectors\n",
    "kde = KernelDensity(kernel='gaussian', bandwidth=0.2).fit(encoded_images_flat)\n",
    "training_density_scores = kde.score_samples(encoded_images_flat) \n",
    "validation_density_scores = kde.score_samples(val_enc_flat)\n",
    "anomaly_density_scores = kde.score_samples(anom_enc_flat)\n",
    "\n",
    "# Plotting the density distributions of the training (normal), validation (normal) and anomalous images\n",
    "# Ideally we want to see high separation between the normal and anomalous classes\n",
    "plt.figure(figsize = (10,7))\n",
    "plt.title('Distribution of Density Scores')\n",
    "plt.hist(training_density_scores, 12, alpha=0.5, label='Training Normal')\n",
    "plt.hist(validation_density_scores, 12, alpha=0.5, label='Validation Normal')\n",
    "plt.hist(anomaly_density_scores, 12, alpha=0.5, label='Anomalies')\n",
    "plt.legend(loc='upper right')\n",
    "plt.xlabel('Density Score')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creat a function that will classify whether an unseen image is normal or an anomaly\n",
    "# Low density and high reconstruction error mean that the image is likely to be anomalous\n",
    "def check_anomaly(img_path):\n",
    "    density_threshold = 0 # This threshold was chosen based on looking at the distribution of the density scores of the normal class (validation set)\n",
    "    reconstruction_error_threshold = 0.04 # This threshold was chosen based on looking at the distribution of reconstruction errors of the normal class\n",
    "    img  = Image.open(img_path)\n",
    "    img = np.array(img.resize((96,96), Image.ANTIALIAS))\n",
    "    img = img / 255\n",
    "    encoded_img = encoder_replica.predict([[img]]) # Create a compressed version of the image using the encoder\n",
    "    encoded_img = [np.reshape(img, (27)) for img in encoded_img] # Flatten the compressed image\n",
    "    density = kde.score_samples(encoded_img)[0] # get a density score for the new image\n",
    "#     print(f'density: {density}')\n",
    "    reconstruction = model.predict([[img]])\n",
    "    reconstruction_error = model.evaluate([reconstruction],[[img]], batch_size = 1)\n",
    "#     print(f'reconstruction_error: {reconstruction_error}')\n",
    "    if density < density_threshold or reconstruction_error > reconstruction_error_threshold:\n",
    "        return True\n",
    "    else:\n",
    "        return False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Check what proportion of onion images are classified as anomalous\n",
    "onion_files = []\n",
    "for (dirpath, dirnames, filenames) in os.walk('onions'):\n",
    "    for x in filenames:\n",
    "        if x.endswith(\".jpg\"):\n",
    "            onion_files.append(os.path.join(dirpath, x))\n",
    "\n",
    "onion_predictions = []\n",
    "for file in onion_files:\n",
    "    onion_predictions.append(check_anomaly(file))\n",
    "print(sum(onion_predictions)/len(onion_predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Check what proportion of apples are classified as anomalous (we hope that this will be close to zero)\n",
    "apples_test = []\n",
    "for (dirpath, dirnames, filenames) in os.walk('apples_test'):\n",
    "    for x in filenames:\n",
    "        if x.endswith(\".jpg\"):\n",
    "            apples_test.append(os.path.join(dirpath, x))\n",
    "\n",
    "apples_predictions = []\n",
    "for file in apples_test:\n",
    "    apples_predictions.append(check_anomaly(file))\n",
    "print(sum(apples_predictions)/len(apples_predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Check what proportion of aubergines/eggplants get classed as anomalous.\n",
    "eggplant_test = []\n",
    "for (dirpath, dirnames, filenames) in os.walk('eggplant'):\n",
    "    for x in filenames:\n",
    "        if x.endswith(\".jpg\"):\n",
    "            eggplant_test.append(os.path.join(dirpath, x))\n",
    "\n",
    "eggplant_predictions = []\n",
    "for file in eggplant_test:\n",
    "    eggplant_predictions.append(check_anomaly(file))\n",
    "print(sum(eggplant_predictions)/len(eggplant_predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "peppers_test = []\n",
    "for (dirpath, dirnames, filenames) in os.walk('peppers'):\n",
    "    for x in filenames:\n",
    "        if x.endswith(\".jpg\"):\n",
    "            peppers_test.append(os.path.join(dirpath, x))\n",
    "\n",
    "peppers_predictions = []\n",
    "for file in peppers_test:\n",
    "    peppers_predictions.append(check_anomaly(file))\n",
    "print(sum(peppers_predictions)/len(peppers_predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "len(peppers_predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO - AUC score "
   ]
  }
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