keras_model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import ticker\n",
    "\n",
    "import seaborn as sns\n",
    "\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Convolution2D, MaxPooling2D\n",
    "from keras.layers import Activation, Dropout, Flatten, Dense\n",
    "from keras.callbacks import ModelCheckpoint, Callback, EarlyStopping, TensorBoard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# dimensions of our images.\n",
    "img_width, img_height = 50, 50\n",
    "\n",
    "train_data_dir = 'data/symlinks/train'\n",
    "validation_data_dir = 'data/symlinks/validation'\n",
    "test_data_dir = 'data/symlinks/test'\n",
    "nb_epoch = 10\n",
    "batch_size=128"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# this is the augmentation configuration we will use for training\n",
    "train_datagen = ImageDataGenerator(\n",
    "        samplewise_center=True,\n",
    "        rescale=1./255,\n",
    "        shear_range=0.2,\n",
    "        zoom_range=0.2,\n",
    "        horizontal_flip=True)\n",
    "\n",
    "# this is the augmentation configuration we will use for testing:\n",
    "# only rescaling\n",
    "test_datagen = ImageDataGenerator(\n",
    "        samplewise_center=True,\n",
    "        rescale=1./255\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 325298 images belonging to 2 classes.\n",
      "Found 219720 images belonging to 2 classes.\n",
      "Found 150833 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "train_generator = train_datagen.flow_from_directory(\n",
    "        train_data_dir,\n",
    "        target_size=(img_width, img_height),\n",
    "        batch_size=batch_size,\n",
    "        class_mode='binary',\n",
    "        follow_links=True)\n",
    "\n",
    "validation_generator = test_datagen.flow_from_directory(\n",
    "        validation_data_dir,\n",
    "        target_size=(img_width, img_height),\n",
    "        batch_size=batch_size,\n",
    "        class_mode='binary',\n",
    "        follow_links=True)\n",
    "\n",
    "test_generator = test_datagen.flow_from_directory(\n",
    "        test_data_dir,\n",
    "        target_size=(img_width, img_height),\n",
    "        batch_size=batch_size,\n",
    "        shuffle=False,\n",
    "        class_mode='binary',\n",
    "        follow_links=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_model():\n",
    "    model = Sequential()\n",
    "    model.add(Convolution2D(32, (3, 3), input_shape=(img_width, img_height, 3)))\n",
    "    model.add(Activation('relu'))\n",
    "    model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "    \n",
    "    model.add(Convolution2D(32, (3, 3)))\n",
    "    model.add(Activation('relu'))\n",
    "    model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "    \n",
    "    model.add(Convolution2D(64, (3, 3)))\n",
    "    model.add(Activation('relu'))\n",
    "    model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "    \n",
    "    model.add(Flatten())\n",
    "    model.add(Dense(64))\n",
    "    model.add(Activation('relu'))\n",
    "    model.add(Dropout(0.5))\n",
    "    model.add(Dense(1))\n",
    "    model.add(Activation('sigmoid'))\n",
    "    \n",
    "    return model\n",
    "\n",
    "model = get_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_1 (Conv2D)            (None, 48, 48, 32)        896       \n",
      "_________________________________________________________________\n",
      "activation_1 (Activation)    (None, 48, 48, 32)        0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 24, 24, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 22, 22, 32)        9248      \n",
      "_________________________________________________________________\n",
      "activation_2 (Activation)    (None, 22, 22, 32)        0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 11, 11, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 9, 9, 64)          18496     \n",
      "_________________________________________________________________\n",
      "activation_3 (Activation)    (None, 9, 9, 64)          0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2 (None, 4, 4, 64)          0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 1024)              0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 64)                65600     \n",
      "_________________________________________________________________\n",
      "activation_4 (Activation)    (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 1)                 65        \n",
      "_________________________________________________________________\n",
      "activation_5 (Activation)    (None, 1)                 0         \n",
      "=================================================================\n",
      "Total params: 94,305.0\n",
      "Trainable params: 94,305.0\n",
      "Non-trainable params: 0.0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## Callback for loss logging per epoch\n",
    "class LossHistory(Callback):\n",
    "    def on_train_begin(self, logs={}):\n",
    "        self.losses = []\n",
    "        self.val_losses = []\n",
    "\n",
    "    def on_epoch_end(self, batch, logs={}):\n",
    "        self.losses.append(logs.get('loss'))\n",
    "        self.val_losses.append(logs.get('val_loss'))\n",
    "        \n",
    "history = LossHistory()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tensorboard = TensorBoard(log_dir='./logs', histogram_freq=1, write_graph=True, write_images=False)\n",
    "checkpointer = ModelCheckpoint(filepath=\"weights.hdf5\", verbose=0, save_best_only=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "2542/2541 [==============================] - 144s - loss: 0.0697 - acc: 0.9773 - val_loss: 0.0529 - val_acc: 0.9825\n",
      "Epoch 2/10\n",
      "2542/2541 [==============================] - 161s - loss: 0.0343 - acc: 0.9901 - val_loss: 0.0371 - val_acc: 0.9876\n",
      "Epoch 3/10\n",
      "2542/2541 [==============================] - 183s - loss: 0.0287 - acc: 0.9920 - val_loss: 0.0381 - val_acc: 0.9881\n",
      "Epoch 4/10\n",
      "2542/2541 [==============================] - 201s - loss: 0.0232 - acc: 0.9936 - val_loss: 0.0298 - val_acc: 0.9901\n",
      "Epoch 5/10\n",
      "2542/2541 [==============================] - 202s - loss: 0.0215 - acc: 0.9941 - val_loss: 0.0328 - val_acc: 0.9898\n",
      "Epoch 6/10\n",
      "2542/2541 [==============================] - 195s - loss: 0.0204 - acc: 0.9947 - val_loss: 0.0307 - val_acc: 0.9906\n",
      "Epoch 7/10\n",
      "2412/2541 [===========================>..] - ETA: 8s - loss: 0.0189 - acc: 0.9950"
     ]
    }
   ],
   "source": [
    "model.compile(loss='binary_crossentropy',\n",
    "              optimizer='adam',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "model.fit_generator(\n",
    "        train_generator,\n",
    "        steps_per_epoch=train_generator.samples/batch_size,\n",
    "        epochs=nb_epoch,\n",
    "        validation_data=validation_generator,\n",
    "        verbose=1,\n",
    "        validation_steps=validation_generator.samples/batch_size,\n",
    "        workers=8,\n",
    "        pickle_safe=True,\n",
    "        callbacks=[history, tensorboard, checkpointer])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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9C/Gd/cCtan3x93+S779fTkBAD2rWrEWlf26+8f77s/j440+oU8eLBQs+vO/8/72F6q1b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\nkUYw1ImIiDSCoU5ERKQR/wch7RmlWSlUDAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f48f4422588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "loss = history.losses\n",
    "val_loss = history.val_losses\n",
    "\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.title('Loss Trend')\n",
    "plt.plot(loss, 'blue', label='Training Loss')\n",
    "plt.plot(val_loss, 'green', label='Validation Loss')\n",
    "plt.xticks(range(0,nb_epoch)[0::2])\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test_loss: 0.0122 - test_acc: 0.9961\n"
     ]
    }
   ],
   "source": [
    "test_loss, test_acc = model.evaluate_generator(\n",
    "         test_generator,\n",
    "         workers=8,\n",
    "         pickle_safe=True,\n",
    "         steps=test_generator.samples/batch_size)\n",
    "\n",
    "print(\"test_loss: %.4f - test_acc: %.4f\"%(test_loss, test_acc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<svg height=\"1300pt\" viewBox=\"0.00 0.00 388.00 1300.00\" width=\"388pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
       "<g class=\"graph\" id=\"graph0\" transform=\"scale(1 1) rotate(0) translate(4 1296)\">\n",
       "<title>G</title>\n",
       "<polygon fill=\"white\" points=\"-4,4 -4,-1296 384,-1296 384,4 -4,4\" stroke=\"none\"/>\n",
       "<!-- 139951372596448 -->\n",
       "<g class=\"node\" id=\"node1\"><title>139951372596448</title>\n",
       "<polygon fill=\"none\" points=\"21,-1245.5 21,-1291.5 359,-1291.5 359,-1245.5 21,-1245.5\" stroke=\"black\"/>\n",
       "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"107\" y=\"-1264.8\">conv2d_1_input: InputLayer</text>\n",
       "<polyline fill=\"none\" points=\"193,-1245.5 193,-1291.5 \" stroke=\"black\"/>\n",
       "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"220.5\" y=\"-1276.3\">input:</text>\n",
       "<polyline fill=\"none\" points=\"193,-1268.5 248,-1268.5 \" stroke=\"black\"/>\n",
       "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"220.5\" y=\"-1253.3\">output:</text>\n",
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     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import SVG\n",
    "from keras.utils.vis_utils import model_to_dot\n",
    "\n",
    "SVG(model_to_dot(model,show_shapes=True, show_layer_names=True).create(prog='dot', format='svg'))"
   ]
  },
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   "outputs": [],
   "source": []
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