CatsDogsFinal.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import os,shutil\n",
    "from keras import layers, models, optimizers\n",
    "from keras.preprocessing.image import ImageDataGenerator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "original_dataset_dir = '/Users/aadityasrivathsan/Downloads/dogsCats'\n",
    "base_dir = '/Users/aadityasrivathsan/Downloads/dogsCatsSmallFinal'\n",
    "\n",
    "os.mkdir(base_dir)\n",
    "\n",
    "train_dir = os.path.join(base_dir, 'train')\n",
    "os.mkdir(train_dir)\n",
    "\n",
    "validation_dir = os.path.join(base_dir, 'validation')\n",
    "os.mkdir(validation_dir)\n",
    "\n",
    "test_dir = os.path.join(base_dir, 'test')\n",
    "os.mkdir(test_dir)\n",
    "\n",
    "train_cats_dir = os.path.join(train_dir, 'cats')\n",
    "os.mkdir(train_cats_dir)\n",
    "\n",
    "train_dogs_dir = os.path.join(train_dir, 'dogs')\n",
    "os.mkdir(train_dogs_dir)\n",
    "\n",
    "validation_cats_dir = os.path.join(validation_dir, 'cats')\n",
    "os.mkdir(validation_cats_dir)\n",
    "\n",
    "validation_dogs_dir = os.path.join(validation_dir, 'dogs')\n",
    "os.mkdir(validation_dogs_dir)\n",
    "\n",
    "test_cats_dir = os.path.join(test_dir, 'cats')\n",
    "os.mkdir(test_cats_dir)\n",
    "\n",
    "test_dogs_dir = os.path.join(test_dir, 'dogs')\n",
    "os.mkdir(test_dogs_dir)\n",
    "\n",
    "fnames = ['cat.{}.jpg'.format(i) for i in range(1000)]\n",
    "for fname in fnames:\n",
    "    src = os.path.join(original_dataset_dir, fname)\n",
    "    dst = os.path.join(train_cats_dir, fname)\n",
    "    shutil.copyfile(src, dst)\n",
    "    \n",
    "fnames = ['cat.{}.jpg'.format(i) for i in range(1000, 1500)]\n",
    "for fname in fnames:\n",
    "    src = os.path.join(original_dataset_dir, fname)\n",
    "    dst = os.path.join(validation_cats_dir, fname)\n",
    "    shutil.copyfile(src, dst)\n",
    "fnames = ['cat.{}.jpg'.format(i) for i in range(1500, 2000)]\n",
    "for fname in fnames:\n",
    "    src = os.path.join(original_dataset_dir, fname)\n",
    "    dst = os.path.join(test_cats_dir, fname)\n",
    "    shutil.copyfile(src, dst)\n",
    "\n",
    "fnames = ['dog.{}.jpg'.format(i) for i in range(1000)]\n",
    "for fname in fnames:\n",
    "    src = os.path.join(original_dataset_dir, fname)\n",
    "    dst = os.path.join(train_dogs_dir, fname)\n",
    "    shutil.copyfile(src, dst)\n",
    "\n",
    "fnames = ['dog.{}.jpg'.format(i) for i in range(1000, 1500)]\n",
    "for fname in fnames:\n",
    "    src = os.path.join(original_dataset_dir, fname)\n",
    "    dst = os.path.join(validation_dogs_dir, fname)\n",
    "    shutil.copyfile(src, dst)\n",
    "\n",
    "fnames = ['dog.{}.jpg'.format(i) for i in range(1500, 2000)]\n",
    "for fname in fnames:\n",
    "    src = os.path.join(original_dataset_dir, fname)\n",
    "    dst = os.path.join(test_dogs_dir, fname)\n",
    "    shutil.copyfile(src, dst)\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create network\n",
    "network = models.Sequential()\n",
    "network.add(layers.Conv2D(32,(3,3),activation = 'relu',input_shape = (150,150,3)))\n",
    "network.add(layers.MaxPooling2D((2,2)))\n",
    "network.add(layers.Conv2D(64,(3,3),activation = 'relu'))\n",
    "network.add(layers.MaxPooling2D((2,2)))\n",
    "network.add(layers.Conv2D(128,(3,3),activation = 'relu'))\n",
    "network.add(layers.MaxPooling2D((2,2)))\n",
    "network.add(layers.Conv2D(256,(3,3),activation = 'relu'))\n",
    "network.add(layers.MaxPooling2D((2,2)))\n",
    "network.add(layers.Flatten())\n",
    "network.add(layers.Dense(512,activation='relu'))\n",
    "network.add(layers.Dense(1,activation = 'sigmoid'))\n",
    "\n",
    "# compile network\n",
    "network.compile(loss ='binary_crossentropy',optimizer = optimizers.RMSprop(lr = 1e-4),metrics = ['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 2000 images belonging to 2 classes.\n",
      "Found 2000 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "trainDataGen = ImageDataGenerator(rescale = 1./255)\n",
    "testDataGen = ImageDataGenerator(rescale = 1./255)\n",
    "\n",
    "trainGenerator = trainDataGen.flow_from_directory(train_dir, target_size = (150,150),batch_size=20,class_mode = 'binary')\n",
    "validationGenerator = testDataGen.flow_from_directory(train_dir, target_size = (150,150),batch_size=20,class_mode = 'binary')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "100/100 [==============================] - 80s 798ms/step - loss: 0.2489 - acc: 0.8965 - val_loss: 0.2005 - val_acc: 0.9310\n",
      "Epoch 2/10\n",
      "100/100 [==============================] - 78s 784ms/step - loss: 0.2275 - acc: 0.9150 - val_loss: 0.1505 - val_acc: 0.9590\n",
      "Epoch 3/10\n",
      "100/100 [==============================] - 410s 4s/step - loss: 0.2006 - acc: 0.9260 - val_loss: 0.1511 - val_acc: 0.9510\n",
      "Epoch 4/10\n",
      "100/100 [==============================] - 79s 787ms/step - loss: 0.1830 - acc: 0.9305 - val_loss: 0.1267 - val_acc: 0.9690\n",
      "Epoch 5/10\n",
      "100/100 [==============================] - 78s 784ms/step - loss: 0.1502 - acc: 0.9465 - val_loss: 0.1053 - val_acc: 0.9720\n",
      "Epoch 6/10\n",
      "100/100 [==============================] - 79s 786ms/step - loss: 0.1241 - acc: 0.9575 - val_loss: 0.0648 - val_acc: 0.9870\n",
      "Epoch 7/10\n",
      "100/100 [==============================] - 78s 780ms/step - loss: 0.1260 - acc: 0.9585 - val_loss: 0.0711 - val_acc: 0.9810\n",
      "Epoch 8/10\n",
      "100/100 [==============================] - 78s 782ms/step - loss: 0.1036 - acc: 0.9655 - val_loss: 0.2468 - val_acc: 0.8780\n",
      "Epoch 9/10\n",
      "100/100 [==============================] - 78s 782ms/step - loss: 0.0788 - acc: 0.9785 - val_loss: 0.0539 - val_acc: 0.9870\n",
      "Epoch 10/10\n",
      "100/100 [==============================] - 78s 784ms/step - loss: 0.0760 - acc: 0.9745 - val_loss: 0.1943 - val_acc: 0.9140\n"
     ]
    }
   ],
   "source": [
    "history = network.fit_generator(trainGenerator,steps_per_epoch=100,epochs=10,validation_data=validationGenerator,validation_steps = 50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    " network.save('catsAndDogs1.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "acc = history.history['acc']\n",
    "val_acc = history.history['val_acc']\n",
    "loss = history.history['loss']\n",
    "val_loss = history.history['val_loss']\n",
    "epochs = range(1, len(acc) + 1)\n",
    "plt.plot(epochs, acc, 'bo', label='Training acc')\n",
    "plt.plot(epochs, val_acc, 'b', label='Validation acc')\n",
    "plt.title('Training and validation accuracy')\n",
    "plt.legend()\n",
    "plt.figure()\n",
    "plt.plot(epochs, loss, 'bo', label='Training loss')\n",
    "plt.plot(epochs, val_loss, 'b', label='Validation loss')\n",
    "plt.title('Training and validation loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "datagen = ImageDataGenerator(\n",
    "            rotation_range = 40,\n",
    "            width_shift_range = 0.2,\n",
    "            height_shift_range = 0.2,\n",
    "            shear_range = 0.2,\n",
    "            zoom_range = 0.2,\n",
    "            horizontal_flip = True,\n",
    "            fill_mode = 'nearest')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = models.Sequential()\n",
    "model.add(layers.Conv2D(32, (3, 3), activation='relu',\n",
    "                        input_shape=(150, 150, 3)))\n",
    "model.add(layers.MaxPooling2D((2, 2)))\n",
    "model.add(layers.Conv2D(64, (3, 3), activation='relu'))\n",
    "model.add(layers.MaxPooling2D((2, 2)))\n",
    "model.add(layers.Conv2D(128, (3, 3), activation='relu'))\n",
    "model.add(layers.MaxPooling2D((2, 2)))\n",
    "model.add(layers.Conv2D(128, (3, 3), activation='relu'))\n",
    "model.add(layers.MaxPooling2D((2, 2)))\n",
    "model.add(layers.Flatten())\n",
    "model.add(layers.Dropout(0.5))\n",
    "model.add(layers.Dense(512, activation='relu'))\n",
    "model.add(layers.Dense(1, activation='sigmoid'))\n",
    "model.compile(loss='binary_crossentropy',\n",
    "              optimizer=optimizers.RMSprop(lr=1e-4),\n",
    "              metrics=['acc'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 2000 images belonging to 2 classes.\n",
      "Found 1000 images belonging to 2 classes.\n",
      "Epoch 1/100\n",
      "100/100 [==============================] - 122s 1s/step - loss: 0.6925 - acc: 0.5175 - val_loss: 0.6871 - val_acc: 0.5298\n",
      "Epoch 2/100\n",
      "100/100 [==============================] - 121s 1s/step - loss: 0.6808 - acc: 0.5606 - val_loss: 0.6827 - val_acc: 0.5238\n",
      "Epoch 3/100\n",
      "100/100 [==============================] - 121s 1s/step - loss: 0.6699 - acc: 0.5850 - val_loss: 0.6646 - val_acc: 0.5850\n",
      "Epoch 4/100\n",
      "100/100 [==============================] - 118s 1s/step - loss: 0.6645 - acc: 0.5847 - val_loss: 0.6554 - val_acc: 0.6012\n",
      "Epoch 5/100\n",
      "100/100 [==============================] - 119s 1s/step - loss: 0.6416 - acc: 0.6150 - val_loss: 0.6207 - val_acc: 0.6440\n",
      "Epoch 6/100\n",
      "100/100 [==============================] - 120s 1s/step - loss: 0.6342 - acc: 0.6303 - val_loss: 0.6184 - val_acc: 0.6308\n",
      "Epoch 7/100\n",
      "100/100 [==============================] - 118s 1s/step - loss: 0.6089 - acc: 0.6641 - val_loss: 0.6108 - val_acc: 0.6523\n",
      "Epoch 8/100\n",
      "100/100 [==============================] - 118s 1s/step - loss: 0.6104 - acc: 0.6603 - val_loss: 0.5945 - val_acc: 0.6804\n",
      "Epoch 9/100\n",
      "100/100 [==============================] - 118s 1s/step - loss: 0.6056 - acc: 0.6588 - val_loss: 0.5861 - val_acc: 0.6701\n",
      "Epoch 10/100\n",
      "100/100 [==============================] - 119s 1s/step - loss: 0.5983 - acc: 0.6725 - val_loss: 1.1528 - val_acc: 0.5558\n",
      "Epoch 11/100\n",
      "100/100 [==============================] - 117s 1s/step - loss: 0.5999 - acc: 0.6772 - val_loss: 0.5912 - val_acc: 0.6675\n",
      "Epoch 12/100\n",
      "100/100 [==============================] - 2575s 26s/step - loss: 0.5984 - acc: 0.6806 - val_loss: 0.5624 - val_acc: 0.7030\n",
      "Epoch 13/100\n",
      "100/100 [==============================] - 131s 1s/step - loss: 0.5747 - acc: 0.6956 - val_loss: 0.5612 - val_acc: 0.7049\n",
      "Epoch 14/100\n",
      "100/100 [==============================] - 144s 1s/step - loss: 0.5893 - acc: 0.6794 - val_loss: 0.5537 - val_acc: 0.7189\n",
      "Epoch 15/100\n",
      "100/100 [==============================] - 147s 1s/step - loss: 0.5785 - acc: 0.6906 - val_loss: 0.6508 - val_acc: 0.6534\n",
      "Epoch 16/100\n",
      "100/100 [==============================] - 149s 1s/step - loss: 0.5708 - acc: 0.6962 - val_loss: 0.6260 - val_acc: 0.6656\n",
      "Epoch 17/100\n",
      "100/100 [==============================] - 152s 2s/step - loss: 0.5670 - acc: 0.6966 - val_loss: 0.5565 - val_acc: 0.7119\n",
      "Epoch 18/100\n",
      "100/100 [==============================] - 158s 2s/step - loss: 0.5670 - acc: 0.7081 - val_loss: 0.5494 - val_acc: 0.7113\n",
      "Epoch 19/100\n",
      "100/100 [==============================] - 191s 2s/step - loss: 0.5632 - acc: 0.7003 - val_loss: 0.5418 - val_acc: 0.7208\n",
      "Epoch 20/100\n",
      "100/100 [==============================] - 218s 2s/step - loss: 0.5658 - acc: 0.7059 - val_loss: 0.5543 - val_acc: 0.6965\n",
      "Epoch 21/100\n",
      "100/100 [==============================] - 206s 2s/step - loss: 0.5629 - acc: 0.7109 - val_loss: 0.5472 - val_acc: 0.7030\n",
      "Epoch 22/100\n",
      "100/100 [==============================] - 197s 2s/step - loss: 0.5526 - acc: 0.7194 - val_loss: 0.6096 - val_acc: 0.6649\n",
      "Epoch 23/100\n",
      "100/100 [==============================] - 195s 2s/step - loss: 0.5490 - acc: 0.7200 - val_loss: 0.5913 - val_acc: 0.7081\n",
      "Epoch 24/100\n",
      "100/100 [==============================] - 202s 2s/step - loss: 0.5510 - acc: 0.7122 - val_loss: 0.5329 - val_acc: 0.7197\n",
      "Epoch 25/100\n",
      "100/100 [==============================] - 212s 2s/step - loss: 0.5509 - acc: 0.7184 - val_loss: 0.5528 - val_acc: 0.7120\n",
      "Epoch 26/100\n",
      "100/100 [==============================] - 224s 2s/step - loss: 0.5468 - acc: 0.7134 - val_loss: 0.5376 - val_acc: 0.7278\n",
      "Epoch 27/100\n",
      "100/100 [==============================] - 231s 2s/step - loss: 0.5349 - acc: 0.7303 - val_loss: 0.5515 - val_acc: 0.7133\n",
      "Epoch 28/100\n",
      "100/100 [==============================] - 227s 2s/step - loss: 0.5472 - acc: 0.7263 - val_loss: 0.5508 - val_acc: 0.7240\n",
      "Epoch 29/100\n",
      "100/100 [==============================] - 224s 2s/step - loss: 0.5325 - acc: 0.7359 - val_loss: 0.5303 - val_acc: 0.7313\n",
      "Epoch 30/100\n",
      "100/100 [==============================] - 174s 2s/step - loss: 0.5348 - acc: 0.7259 - val_loss: 0.5481 - val_acc: 0.7227\n",
      "Epoch 31/100\n",
      "100/100 [==============================] - 119s 1s/step - loss: 0.5246 - acc: 0.7337 - val_loss: 0.5356 - val_acc: 0.7242\n",
      "Epoch 32/100\n",
      "100/100 [==============================] - 119s 1s/step - loss: 0.5280 - acc: 0.7344 - val_loss: 0.5469 - val_acc: 0.7236\n",
      "Epoch 33/100\n",
      "100/100 [==============================] - 119s 1s/step - loss: 0.5238 - acc: 0.7441 - val_loss: 0.5347 - val_acc: 0.7272\n",
      "Epoch 34/100\n",
      "100/100 [==============================] - 119s 1s/step - loss: 0.5258 - acc: 0.7334 - val_loss: 0.5268 - val_acc: 0.7339\n",
      "Epoch 35/100\n",
      "100/100 [==============================] - 119s 1s/step - loss: 0.5384 - acc: 0.7291 - val_loss: 0.5375 - val_acc: 0.7284\n",
      "Epoch 36/100\n",
      "100/100 [==============================] - 1254s 13s/step - loss: 0.5203 - acc: 0.7453 - val_loss: 0.4905 - val_acc: 0.7506\n",
      "Epoch 37/100\n",
      "100/100 [==============================] - 121s 1s/step - loss: 0.5218 - acc: 0.7497 - val_loss: 0.5547 - val_acc: 0.7253\n",
      "Epoch 38/100\n",
      "100/100 [==============================] - 120s 1s/step - loss: 0.5132 - acc: 0.7478 - val_loss: 0.5225 - val_acc: 0.7545\n",
      "Epoch 39/100\n",
      "100/100 [==============================] - 121s 1s/step - loss: 0.5174 - acc: 0.7444 - val_loss: 0.4820 - val_acc: 0.7576\n",
      "Epoch 40/100\n",
      "100/100 [==============================] - 120s 1s/step - loss: 0.5116 - acc: 0.7409 - val_loss: 0.5101 - val_acc: 0.7397\n",
      "Epoch 41/100\n",
      "100/100 [==============================] - 120s 1s/step - loss: 0.5148 - acc: 0.7459 - val_loss: 0.5128 - val_acc: 0.7545\n",
      "Epoch 42/100\n",
      "100/100 [==============================] - 121s 1s/step - loss: 0.5050 - acc: 0.7450 - val_loss: 0.5348 - val_acc: 0.7354\n",
      "Epoch 43/100\n",
      "100/100 [==============================] - 119s 1s/step - loss: 0.4933 - acc: 0.7619 - val_loss: 0.5093 - val_acc: 0.7532\n",
      "Epoch 44/100\n",
      "100/100 [==============================] - 121s 1s/step - loss: 0.5017 - acc: 0.7534 - val_loss: 0.5576 - val_acc: 0.7297\n",
      "Epoch 45/100\n",
      "100/100 [==============================] - 120s 1s/step - loss: 0.5042 - acc: 0.7591 - val_loss: 0.5083 - val_acc: 0.7287\n",
      "Epoch 46/100\n",
      "100/100 [==============================] - 120s 1s/step - loss: 0.5191 - acc: 0.7431 - val_loss: 0.5003 - val_acc: 0.7671\n",
      "Epoch 47/100\n",
      "100/100 [==============================] - 120s 1s/step - loss: 0.5132 - acc: 0.7453 - val_loss: 0.5068 - val_acc: 0.7461\n",
      "Epoch 48/100\n",
      "100/100 [==============================] - 172s 2s/step - loss: 0.4998 - acc: 0.7547 - val_loss: 0.4785 - val_acc: 0.7687\n",
      "Epoch 49/100\n",
      "100/100 [==============================] - 25602s 256s/step - loss: 0.5055 - acc: 0.7491 - val_loss: 0.5316 - val_acc: 0.7329\n",
      "Epoch 50/100\n",
      "100/100 [==============================] - 18392s 184s/step - loss: 0.5029 - acc: 0.7522 - val_loss: 0.4720 - val_acc: 0.7803\n",
      "Epoch 51/100\n",
      " 99/100 [============================>.] - ETA: 1s - loss: 0.5099 - acc: 0.7509"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-18-061d61b72bc6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     24\u001b[0m       \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     25\u001b[0m       \u001b[0mvalidation_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvalidation_generator\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 26\u001b[0;31m       validation_steps=50)\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/keras/legacy/interfaces.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     89\u001b[0m                 warnings.warn('Update your `' + object_name + '` call to the ' +\n\u001b[1;32m     90\u001b[0m                               'Keras 2 API: ' + signature, stacklevel=2)\n\u001b[0;32m---> 91\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     92\u001b[0m         \u001b[0mwrapper\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_original_function\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     93\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit_generator\u001b[0;34m(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)\u001b[0m\n\u001b[1;32m   1416\u001b[0m             \u001b[0muse_multiprocessing\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0muse_multiprocessing\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1417\u001b[0m             \u001b[0mshuffle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mshuffle\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1418\u001b[0;31m             initial_epoch=initial_epoch)\n\u001b[0m\u001b[1;32m   1419\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1420\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0minterfaces\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlegacy_generator_methods_support\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/keras/engine/training_generator.py\u001b[0m in \u001b[0;36mfit_generator\u001b[0;34m(model, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)\u001b[0m\n\u001b[1;32m    232\u001b[0m                             \u001b[0mval_enqueuer_gen\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    233\u001b[0m                             \u001b[0mvalidation_steps\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 234\u001b[0;31m                             workers=0)\n\u001b[0m\u001b[1;32m    235\u001b[0m                     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    236\u001b[0m                         \u001b[0;31m# No need for try/except because\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/keras/legacy/interfaces.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     89\u001b[0m                 warnings.warn('Update your `' + object_name + '` call to the ' +\n\u001b[1;32m     90\u001b[0m                               'Keras 2 API: ' + signature, stacklevel=2)\n\u001b[0;32m---> 91\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     92\u001b[0m         \u001b[0mwrapper\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_original_function\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     93\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mevaluate_generator\u001b[0;34m(self, generator, steps, max_queue_size, workers, use_multiprocessing, verbose)\u001b[0m\n\u001b[1;32m   1470\u001b[0m             \u001b[0mworkers\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mworkers\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1471\u001b[0m             \u001b[0muse_multiprocessing\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0muse_multiprocessing\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1472\u001b[0;31m             verbose=verbose)\n\u001b[0m\u001b[1;32m   1473\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1474\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0minterfaces\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlegacy_generator_methods_support\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/keras/engine/training_generator.py\u001b[0m in \u001b[0;36mevaluate_generator\u001b[0;34m(model, generator, steps, max_queue_size, workers, use_multiprocessing, verbose)\u001b[0m\n\u001b[1;32m    344\u001b[0m                                  \u001b[0;34m'or (x, y). Found: '\u001b[0m \u001b[0;34m+\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    345\u001b[0m                                  str(generator_output))\n\u001b[0;32m--> 346\u001b[0;31m             \u001b[0mouts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtest_on_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    347\u001b[0m             \u001b[0mouts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mto_list\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mouts\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    348\u001b[0m             \u001b[0mouts_per_batch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mouts\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mtest_on_batch\u001b[0;34m(self, x, y, sample_weight)\u001b[0m\n\u001b[1;32m   1254\u001b[0m             \u001b[0mins\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0msample_weights\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1255\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_test_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1256\u001b[0;31m         \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtest_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mins\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1257\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0munpack_singleton\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1258\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   2713\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_legacy_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2714\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2715\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2716\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2717\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mpy_any\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mis_tensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   2673\u001b[0m             \u001b[0mfetched\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_callable_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0marray_vals\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_metadata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2674\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2675\u001b[0;31m             \u001b[0mfetched\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_callable_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0marray_vals\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2676\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mfetched\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2677\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1397\u001b[0m           ret = tf_session.TF_SessionRunCallable(\n\u001b[1;32m   1398\u001b[0m               \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_handle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstatus\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1399\u001b[0;31m               run_metadata_ptr)\n\u001b[0m\u001b[1;32m   1400\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1401\u001b[0m           \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "train_datagen = ImageDataGenerator(\n",
    "    rescale=1./255,\n",
    "    rotation_range=40,\n",
    "    width_shift_range=0.2,\n",
    "    height_shift_range=0.2,\n",
    "    shear_range=0.2,\n",
    "    zoom_range=0.2,\n",
    "    horizontal_flip=True,)\n",
    "test_datagen = ImageDataGenerator(rescale=1./255)\n",
    "train_generator = train_datagen.flow_from_directory(\n",
    "\n",
    "train_dir,\n",
    "target_size=(150, 150),\n",
    "batch_size=32,\n",
    "class_mode='binary')\n",
    "validation_generator = test_datagen.flow_from_directory(\n",
    "        validation_dir,\n",
    "        target_size=(150, 150),\n",
    "        batch_size=32,\n",
    "        class_mode='binary')\n",
    "history = model.fit_generator(\n",
    "      train_generator,\n",
    "      steps_per_epoch=100,\n",
    "      epochs=100,\n",
    "      validation_data=validation_generator,\n",
    "      validation_steps=50)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "`steps=None` is only valid for a generator based on the `keras.utils.Sequence` class. Please specify `steps` or use the `keras.utils.Sequence` class.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-21-adff750f3154>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnetwork\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict_generator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalidationGenerator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/keras/legacy/interfaces.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     89\u001b[0m                 warnings.warn('Update your `' + object_name + '` call to the ' +\n\u001b[1;32m     90\u001b[0m                               'Keras 2 API: ' + signature, stacklevel=2)\n\u001b[0;32m---> 91\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     92\u001b[0m         \u001b[0mwrapper\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_original_function\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     93\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mpredict_generator\u001b[0;34m(self, generator, steps, max_queue_size, workers, use_multiprocessing, verbose)\u001b[0m\n\u001b[1;32m   1520\u001b[0m             \u001b[0mworkers\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mworkers\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1521\u001b[0m             \u001b[0muse_multiprocessing\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0muse_multiprocessing\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1522\u001b[0;31m             verbose=verbose)\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/keras/engine/training_generator.py\u001b[0m in \u001b[0;36mpredict_generator\u001b[0;34m(model, generator, steps, max_queue_size, workers, use_multiprocessing, verbose)\u001b[0m\n\u001b[1;32m    404\u001b[0m             \u001b[0msteps\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgenerator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    405\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 406\u001b[0;31m             raise ValueError('`steps=None` is only valid for a generator'\n\u001b[0m\u001b[1;32m    407\u001b[0m                              \u001b[0;34m' based on the `keras.utils.Sequence` class.'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    408\u001b[0m                              \u001b[0;34m' Please specify `steps` or use the'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: `steps=None` is only valid for a generator based on the `keras.utils.Sequence` class. Please specify `steps` or use the `keras.utils.Sequence` class."
     ]
    }
   ],
   "source": [
    "network.predict_generator(validationGenerator)"
   ]
  }
 ],
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