train_vgg16.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from keras.layers import Conv2D, MaxPooling2D, ZeroPadding2D\n",
    "from keras.layers import Dense, Activation, Dropout, Flatten, GlobalAveragePooling2D\n",
    "from keras import optimizers\n",
    "from keras.models import Sequential, Model\n",
    "from keras.preprocessing.image import ImageDataGenerator, img_to_array, load_img\n",
    "from keras.applications.vgg16 import VGG16"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "IMG_SIZE = 150\n",
    "LEARNING_RATE = 0.001\n",
    "EPOCHS = 5\n",
    "BATCH_SIZE = 10\n",
    "\n",
    "TRAIN_SIZE = 2000\n",
    "TEST_SIZE = 250\n",
    "\n",
    "TRAIN_DIR = 'data/train'\n",
    "TEST_DIR = 'data/test'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 23000 images belonging to 2 classes.\n",
      "Found 2000 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "datagen = ImageDataGenerator(rescale=1./255)\n",
    "\n",
    "# augmented_gen = ImageDataGenerator(\n",
    "#     rescale=1./255,\n",
    "#     shear_range=0.2,\n",
    "#     zoom_range=0.2,\n",
    "#     horizontal_flip=True)\n",
    "\n",
    "train_gen = datagen.flow_from_directory(\n",
    "    TRAIN_DIR,\n",
    "    target_size=(IMG_SIZE, IMG_SIZE),\n",
    "    classes=['dogs', 'cats'],\n",
    "    batch_size=BATCH_SIZE)\n",
    "\n",
    "test_gen = datagen.flow_from_directory(\n",
    "    TEST_DIR,\n",
    "    target_size=(IMG_SIZE, IMG_SIZE),\n",
    "    classes=['dogs', 'cats'],\n",
    "    batch_size=BATCH_SIZE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize VGG16 Model\n",
    "vgg16_model = VGG16(include_top=False, input_shape=(IMG_SIZE, IMG_SIZE, 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_4 (InputLayer)         (None, 150, 150, 3)       0         \n",
      "_________________________________________________________________\n",
      "block1_conv1 (Conv2D)        (None, 150, 150, 64)      1792      \n",
      "_________________________________________________________________\n",
      "block1_conv2 (Conv2D)        (None, 150, 150, 64)      36928     \n",
      "_________________________________________________________________\n",
      "block1_pool (MaxPooling2D)   (None, 75, 75, 64)        0         \n",
      "_________________________________________________________________\n",
      "block2_conv1 (Conv2D)        (None, 75, 75, 128)       73856     \n",
      "_________________________________________________________________\n",
      "block2_conv2 (Conv2D)        (None, 75, 75, 128)       147584    \n",
      "_________________________________________________________________\n",
      "block2_pool (MaxPooling2D)   (None, 37, 37, 128)       0         \n",
      "_________________________________________________________________\n",
      "block3_conv1 (Conv2D)        (None, 37, 37, 256)       295168    \n",
      "_________________________________________________________________\n",
      "block3_conv2 (Conv2D)        (None, 37, 37, 256)       590080    \n",
      "_________________________________________________________________\n",
      "block3_conv3 (Conv2D)        (None, 37, 37, 256)       590080    \n",
      "_________________________________________________________________\n",
      "block3_pool (MaxPooling2D)   (None, 18, 18, 256)       0         \n",
      "_________________________________________________________________\n",
      "block4_conv1 (Conv2D)        (None, 18, 18, 512)       1180160   \n",
      "_________________________________________________________________\n",
      "block4_conv2 (Conv2D)        (None, 18, 18, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block4_conv3 (Conv2D)        (None, 18, 18, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block4_pool (MaxPooling2D)   (None, 9, 9, 512)         0         \n",
      "_________________________________________________________________\n",
      "block5_conv1 (Conv2D)        (None, 9, 9, 512)         2359808   \n",
      "_________________________________________________________________\n",
      "block5_conv2 (Conv2D)        (None, 9, 9, 512)         2359808   \n",
      "_________________________________________________________________\n",
      "block5_conv3 (Conv2D)        (None, 9, 9, 512)         2359808   \n",
      "_________________________________________________________________\n",
      "block5_pool (MaxPooling2D)   (None, 4, 4, 512)         0         \n",
      "_________________________________________________________________\n",
      "flatten_4 (Flatten)          (None, 8192)              0         \n",
      "_________________________________________________________________\n",
      "dense_7 (Dense)              (None, 64)                524352    \n",
      "_________________________________________________________________\n",
      "activation_7 (Activation)    (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dropout_4 (Dropout)          (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dense_8 (Dense)              (None, 2)                 130       \n",
      "_________________________________________________________________\n",
      "activation_8 (Activation)    (None, 2)                 0         \n",
      "=================================================================\n",
      "Total params: 15,239,170\n",
      "Trainable params: 524,482\n",
      "Non-trainable params: 14,714,688\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "for layer in vgg16_model.layers:\n",
    "    model.add(layer)\n",
    "\n",
    "# Only allow training on top layers\n",
    "for layer in model.layers:\n",
    "    layer.trainable = False\n",
    "    \n",
    "# Add top layers\n",
    "model.add(Flatten())\n",
    "model.add(Dense(64))\n",
    "model.add(Activation('relu'))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(2))\n",
    "model.add(Activation('softmax'))\n",
    "\n",
    "model.compile(\n",
    "    loss='categorical_crossentropy',\n",
    "    optimizer='adam',\n",
    "    metrics=['accuracy'])\n",
    "    \n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "200/200 [==============================] - 8s 41ms/step - loss: 0.5171 - acc: 0.7500 - val_loss: 0.3602 - val_acc: 0.9000\n",
      "Epoch 2/5\n",
      "200/200 [==============================] - 8s 40ms/step - loss: 0.3840 - acc: 0.8280 - val_loss: 0.2585 - val_acc: 0.9000\n",
      "Epoch 3/5\n",
      "200/200 [==============================] - 8s 42ms/step - loss: 0.3961 - acc: 0.8220 - val_loss: 0.2583 - val_acc: 0.9120\n",
      "Epoch 4/5\n",
      "200/200 [==============================] - 8s 41ms/step - loss: 0.3344 - acc: 0.8510 - val_loss: 0.2399 - val_acc: 0.9200\n",
      "Epoch 5/5\n",
      "200/200 [==============================] - 8s 38ms/step - loss: 0.3333 - acc: 0.8720 - val_loss: 0.2065 - val_acc: 0.9080\n"
     ]
    }
   ],
   "source": [
    "# Begin training (Rocky montage music - 'Getting stronga!')\n",
    "training = model.fit_generator(\n",
    "    generator=train_gen,\n",
    "    steps_per_epoch= TRAIN_SIZE // BATCH_SIZE,\n",
    "    epochs=EPOCHS,\n",
    "    validation_data=test_gen,\n",
    "    validation_steps= TEST_SIZE // BATCH_SIZE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save('models/vgg16-150.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}