5.3_convnet_pretrained.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using a pretrained convnet\n",
    "A pretrained network is a saved network (ie. weights & bias) that was previously trained on a large dataset. For instance, you might train a network on ImageNet (where classes are mostly animals and everyday objects) and then repurpose this trained network for something as remote as identifying furniture items in images.\n",
    "\n",
    "Demonstrate this method with cats vs dogs example."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "VGG16 is a network structure that comes with keras.\n",
    "\n",
    "- `weights`, to specify which weight to initialize the model from.\n",
    "- `include_top`, which refers to include or not the densely-connected classifier on top of the network. Since we intend to use our own densely-connected classifier (with only two classes, cat and dog), we don't need to include it.\n",
    "- `input_shape`, the shape of the image tensors that we will feed to the network. This argument is purely optional: if we don't pass it, then the network will be able to process inputs of any size."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.applications import VGG16\n",
    "\n",
    "conv_base = VGG16(weights='imagenet',\n",
    "                  include_top=False,\n",
    "                  input_shape=(150, 150, 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Model: \"vgg16\"\n_________________________________________________________________\nLayer (type)                 Output Shape              Param #   \n=================================================================\ninput_1 (InputLayer)         [(None, 150, 150, 3)]     0         \n_________________________________________________________________\nblock1_conv1 (Conv2D)        (None, 150, 150, 64)      1792      \n_________________________________________________________________\nblock1_conv2 (Conv2D)        (None, 150, 150, 64)      36928     \n_________________________________________________________________\nblock1_pool (MaxPooling2D)   (None, 75, 75, 64)        0         \n_________________________________________________________________\nblock2_conv1 (Conv2D)        (None, 75, 75, 128)       73856     \n_________________________________________________________________\nblock2_conv2 (Conv2D)        (None, 75, 75, 128)       147584    \n_________________________________________________________________\nblock2_pool (MaxPooling2D)   (None, 37, 37, 128)       0         \n_________________________________________________________________\nblock3_conv1 (Conv2D)        (None, 37, 37, 256)       295168    \n_________________________________________________________________\nblock3_conv2 (Conv2D)        (None, 37, 37, 256)       590080    \n_________________________________________________________________\nblock3_conv3 (Conv2D)        (None, 37, 37, 256)       590080    \n_________________________________________________________________\nblock3_pool (MaxPooling2D)   (None, 18, 18, 256)       0         \n_________________________________________________________________\nblock4_conv1 (Conv2D)        (None, 18, 18, 512)       1180160   \n_________________________________________________________________\nblock4_conv2 (Conv2D)        (None, 18, 18, 512)       2359808   \n_________________________________________________________________\nblock4_conv3 (Conv2D)        (None, 18, 18, 512)       2359808   \n_________________________________________________________________\nblock4_pool (MaxPooling2D)   (None, 9, 9, 512)         0         \n_________________________________________________________________\nblock5_conv1 (Conv2D)        (None, 9, 9, 512)         2359808   \n_________________________________________________________________\nblock5_conv2 (Conv2D)        (None, 9, 9, 512)         2359808   \n_________________________________________________________________\nblock5_conv3 (Conv2D)        (None, 9, 9, 512)         2359808   \n_________________________________________________________________\nblock5_pool (MaxPooling2D)   (None, 4, 4, 512)         0         \n=================================================================\nTotal params: 14,714,688\nTrainable params: 14,714,688\nNon-trainable params: 0\n_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "conv_base.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Extraction with a pretrained convnet\n",
    "Use the convolutional base as it is. Run my input through the base to get an output. Use the output as input to a standalone densely-connected classifier. Fast and cheap to run.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "\n",
    "base_dir = './cats_and_dogs_small'\n",
    "\n",
    "train_dir = os.path.join(base_dir, 'train')\n",
    "validation_dir = os.path.join(base_dir, 'validation')\n",
    "test_dir = os.path.join(base_dir, 'test')\n",
    "\n",
    "datagen = ImageDataGenerator(rescale=1./255)\n",
    "batch_size = 20\n",
    "\n",
    "def extract_features(directory, sample_count):\n",
    "    features = np.zeros(shape=(sample_count, 4, 4, 512))\n",
    "    labels = np.zeros(shape=(sample_count))\n",
    "    generator = datagen.flow_from_directory(\n",
    "        directory,\n",
    "        target_size=(150, 150),\n",
    "        batch_size=batch_size,\n",
    "        class_mode='binary')\n",
    "    i = 0\n",
    "    for inputs_batch, labels_batch in generator:\n",
    "        features_batch = conv_base.predict(inputs_batch)\n",
    "        features[i * batch_size : (i + 1) * batch_size] = features_batch\n",
    "        labels[i * batch_size : (i + 1) * batch_size] = labels_batch\n",
    "        i += 1\n",
    "        if i * batch_size >= sample_count:\n",
    "            # Note that since generators yield data indefinitely in a loop,\n",
    "            # we must `break` after every image has been seen once.\n",
    "            break\n",
    "    return features, labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Found 2000 images belonging to 2 classes.\n",
      "Found 1000 images belonging to 2 classes.\n",
      "Found 1000 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "train_features, train_labels = extract_features(train_dir, 2000)\n",
    "validation_features, validation_labels = extract_features(validation_dir, 1000)\n",
    "test_features, test_labels = extract_features(test_dir, 1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Epoch 1/30\n",
      "100/100 [==============================] - 0s 4ms/step - loss: 0.5904 - acc: 0.6730 - val_loss: 0.4363 - val_acc: 0.8230\n",
      "Epoch 2/30\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 0.4190 - acc: 0.8110 - val_loss: 0.3564 - val_acc: 0.8550\n",
      "Epoch 3/30\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 0.3485 - acc: 0.8600 - val_loss: 0.3198 - val_acc: 0.8670\n",
      "Epoch 4/30\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 0.3165 - acc: 0.8680 - val_loss: 0.2952 - val_acc: 0.8810\n",
      "Epoch 5/30\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 0.2818 - acc: 0.8850 - val_loss: 0.2901 - val_acc: 0.8800\n",
      "Epoch 6/30\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 0.2596 - acc: 0.8975 - val_loss: 0.2687 - val_acc: 0.8960\n",
      "Epoch 7/30\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 0.2438 - acc: 0.9085 - val_loss: 0.2602 - val_acc: 0.8960\n",
      "Epoch 8/30\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 0.2263 - acc: 0.9160 - val_loss: 0.2543 - val_acc: 0.8980\n",
      "Epoch 9/30\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 0.2125 - acc: 0.9175 - val_loss: 0.2507 - val_acc: 0.8990\n",
      "Epoch 10/30\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 0.2095 - acc: 0.9215 - val_loss: 0.2513 - val_acc: 0.8970\n",
      "Epoch 11/30\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 0.1936 - acc: 0.9320 - val_loss: 0.2440 - val_acc: 0.9010\n",
      "Epoch 12/30\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 0.1848 - acc: 0.9315 - val_loss: 0.2565 - val_acc: 0.8880\n",
      "Epoch 13/30\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 0.1684 - acc: 0.9375 - val_loss: 0.2427 - val_acc: 0.8990\n",
      "Epoch 14/30\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.1701 - acc: 0.9365 - val_loss: 0.2371 - val_acc: 0.9030\n",
      "Epoch 15/30\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 0.1567 - acc: 0.9440 - val_loss: 0.2461 - val_acc: 0.8950\n",
      "Epoch 16/30\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 0.1488 - acc: 0.9455 - val_loss: 0.2463 - val_acc: 0.8930\n",
      "Epoch 17/30\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 0.1460 - acc: 0.9495 - val_loss: 0.2352 - val_acc: 0.9100\n",
      "Epoch 18/30\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 0.1326 - acc: 0.9545 - val_loss: 0.2362 - val_acc: 0.9110\n",
      "Epoch 19/30\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 0.1355 - acc: 0.9550 - val_loss: 0.2431 - val_acc: 0.8980\n",
      "Epoch 20/30\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 0.1259 - acc: 0.9565 - val_loss: 0.2373 - val_acc: 0.9000\n",
      "Epoch 21/30\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 0.1231 - acc: 0.9575 - val_loss: 0.2350 - val_acc: 0.9020\n",
      "Epoch 22/30\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 0.1174 - acc: 0.9640 - val_loss: 0.2448 - val_acc: 0.8940\n",
      "Epoch 23/30\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 0.1115 - acc: 0.9625 - val_loss: 0.2361 - val_acc: 0.9010\n",
      "Epoch 24/30\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 0.1127 - acc: 0.9625 - val_loss: 0.2426 - val_acc: 0.9020\n",
      "Epoch 25/30\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 0.1052 - acc: 0.9660 - val_loss: 0.2347 - val_acc: 0.9030\n",
      "Epoch 26/30\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 0.0978 - acc: 0.9710 - val_loss: 0.2357 - val_acc: 0.9020\n",
      "Epoch 27/30\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 0.1002 - acc: 0.9670 - val_loss: 0.2362 - val_acc: 0.9040\n",
      "Epoch 28/30\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 0.0903 - acc: 0.9735 - val_loss: 0.2420 - val_acc: 0.9020\n",
      "Epoch 29/30\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 0.0864 - acc: 0.9755 - val_loss: 0.2388 - val_acc: 0.9030\n",
      "Epoch 30/30\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 0.0863 - acc: 0.9750 - val_loss: 0.2380 - val_acc: 0.9050\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "Now I have the output of convolutional base. Feed them into the densely-connected classifier\n",
    "\"\"\"\n",
    "train_features = np.reshape(train_features, (2000, 4 * 4 * 512))\n",
    "validation_features = np.reshape(validation_features, (1000, 4 * 4 * 512))\n",
    "test_features = np.reshape(test_features, (1000, 4 * 4 * 512))\n",
    "\n",
    "from keras import models\n",
    "from keras import layers\n",
    "from keras import optimizers\n",
    "\n",
    "model = models.Sequential()\n",
    "model.add(layers.Dense(256, activation='relu', input_dim=4 * 4 * 512))\n",
    "model.add(layers.Dropout(0.5))\n",
    "model.add(layers.Dense(1, activation='sigmoid'))\n",
    "\n",
    "model.compile(optimizer=optimizers.RMSprop(lr=2e-5),\n",
    "              loss='binary_crossentropy',\n",
    "              metrics=['acc'])\n",
    "\n",
    "history = model.fit(train_features, train_labels,\n",
    "                    epochs=30,\n",
    "                    batch_size=20,\n",
    "                    validation_data=(validation_features, validation_labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\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",
    "\n",
    "epochs = range(len(acc))\n",
    "\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",
    "\n",
    "plt.figure()\n",
    "\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",
    "\n",
    "plt.show()"
   ]
  },
  {
   "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": "3.6.8-final"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}