McCulloughRT/CNN_Keras_TransferLearning.ipynb

## CNN_Keras_TransferLearning.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Building an Architectural Classifier - Notebook 4 - Transfer Learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The goal in this notebook is to take our pre-processed dataset of interior architectural imagery (containing images of kitchens, bathrooms, bedrooms, living rooms, etc...) and build a machine learning model that can accurately classify when it is looking at an image of a kitchen. This is the fourth notebook in a series, so I'll omit some of the explanatory notes on the boilerplate from before.\n",
    "#### Model:\n",
    "We've started to get good results with random-initialization convolutional nets, but how much more accurate can we get (and how much quicker) by retraining highly successful models initially trained for other tasks? This is transfer learning."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from keras.models import Sequential, Model\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "from keras.layers import Dense, Dropout, Flatten, Input, Merge\n",
    "from keras.layers import Conv2D, MaxPooling2D\n",
    "from keras.optimizers import SGD, Adam\n",
    "from keras import applications\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load data from numpy files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x: (4686, 244, 244, 3) | y: (4686, 2)\n"
     ]
    }
   ],
   "source": [
    "x = np.load('./all_X_shuffled_244_x4686.npy')\n",
    "y = np.load('./all_Y_shuffled_2_x4686.npy')\n",
    "\n",
    "print('x: %s | y: %s' % (x.shape, y.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test / Train / Validation Split (80% / 10% / 10%)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def split(x, y, test=0.1, train=0.8, validation=0.1):\n",
    "    assert(len(x) == len(y))\n",
    "    test_size = int(len(x) * test)\n",
    "    train_size = int(len(x) * train)\n",
    "    valid_size = int(len(x) * validation)\n",
    "    \n",
    "    x_train = np.array(x[:train_size])\n",
    "    y_train = np.array(y[:train_size])\n",
    "    x_val = np.array(x[train_size:train_size + valid_size])\n",
    "    y_val = np.array(y[train_size:train_size + valid_size])\n",
    "    x_test = np.array(x[train_size + valid_size:])\n",
    "    y_test = np.array(y[train_size + valid_size:])\n",
    "    \n",
    "    return (x_train, y_train, x_val, y_val, x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_train:  (3748, 244, 244, 3)\n",
      "y_train:  (3748, 2)\n",
      "x_val:    (468, 244, 244, 3)\n",
      "y_val:    (468, 2)\n",
      "x_test:   (470, 244, 244, 3)\n",
      "y_test:   (470, 2)\n"
     ]
    }
   ],
   "source": [
    "x_train, y_train, x_val, y_val, x_test, y_test = split(x,y)\n",
    "\n",
    "print('x_train: ', x_train.shape)\n",
    "print('y_train: ', y_train.shape)\n",
    "print('x_val:   ', x_val.shape)\n",
    "print('y_val:   ', y_val.shape)\n",
    "print('x_test:  ', x_test.shape)\n",
    "print('y_test:  ', y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check class balance on sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training balance:  0.482924226254\n",
      "Validation balance:  0.480769230769\n",
      "Testing balance:  0.436170212766\n"
     ]
    }
   ],
   "source": [
    "print('Training balance: ', np.sum(y_train, axis=0)[1] / len(y_train))\n",
    "print('Validation balance: ', np.sum(y_val, axis=0)[1] / len(y_val))\n",
    "print('Testing balance: ', np.sum(y_test, axis=0)[1] / len(y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Subtracting the mean of the imagenet dataset from each channel and reordering dimensions to (B,G,R) to match the way VGG was originally trained"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(244, 244, 3)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f64af2b97b8>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n95vCfdzHfbwn7jeF+7iP+3hP/H+kCHdkysRKWAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6512f9c2b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(x_train[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "rn_mean = np.array([123.68, 116.779, 103.939], dtype=np.float32)\n",
    "preproc = lambda x: (x - rn_mean)[-1, :, :, ::-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "proc_x_train = preproc(np.expand_dims(np.array(x_train), 0))\n",
    "proc_x_val = preproc(np.expand_dims(np.array(x_val), 0))\n",
    "proc_x_test = preproc(np.expand_dims(np.array(x_test), 0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3748, 244, 244, 3)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "proc_x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3748, 244, 244, 3)\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nBz3kt8unIxEQAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f64af282550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(proc_x_train[0])\n",
    "print(proc_x_train.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculate bottleneck values for images on VGG16"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "vgg = applications.VGG16(\n",
    "    weights='imagenet', \n",
    "    include_top=False, \n",
    "    input_shape=(244,244,3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check out the VGG16 architecture:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         (None, 244, 244, 3)       0         \n",
      "_________________________________________________________________\n",
      "block1_conv1 (Conv2D)        (None, 244, 244, 64)      1792      \n",
      "_________________________________________________________________\n",
      "block1_conv2 (Conv2D)        (None, 244, 244, 64)      36928     \n",
      "_________________________________________________________________\n",
      "block1_pool (MaxPooling2D)   (None, 122, 122, 64)      0         \n",
      "_________________________________________________________________\n",
      "block2_conv1 (Conv2D)        (None, 122, 122, 128)     73856     \n",
      "_________________________________________________________________\n",
      "block2_conv2 (Conv2D)        (None, 122, 122, 128)     147584    \n",
      "_________________________________________________________________\n",
      "block2_pool (MaxPooling2D)   (None, 61, 61, 128)       0         \n",
      "_________________________________________________________________\n",
      "block3_conv1 (Conv2D)        (None, 61, 61, 256)       295168    \n",
      "_________________________________________________________________\n",
      "block3_conv2 (Conv2D)        (None, 61, 61, 256)       590080    \n",
      "_________________________________________________________________\n",
      "block3_conv3 (Conv2D)        (None, 61, 61, 256)       590080    \n",
      "_________________________________________________________________\n",
      "block3_pool (MaxPooling2D)   (None, 30, 30, 256)       0         \n",
      "_________________________________________________________________\n",
      "block4_conv1 (Conv2D)        (None, 30, 30, 512)       1180160   \n",
      "_________________________________________________________________\n",
      "block4_conv2 (Conv2D)        (None, 30, 30, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block4_conv3 (Conv2D)        (None, 30, 30, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block4_pool (MaxPooling2D)   (None, 15, 15, 512)       0         \n",
      "_________________________________________________________________\n",
      "block5_conv1 (Conv2D)        (None, 15, 15, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block5_conv2 (Conv2D)        (None, 15, 15, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block5_conv3 (Conv2D)        (None, 15, 15, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block5_pool (MaxPooling2D)   (None, 7, 7, 512)         0         \n",
      "=================================================================\n",
      "Total params: 14,714,688\n",
      "Trainable params: 14,714,688\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "vgg.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Just to make sure we don't do anything silly, freeze the layers to the bottleneck"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for layer in vgg.layers:\n",
    "    layer.trainable = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Add a flatten op to the end and then extract features from our datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "vgg_flat = vgg.output\n",
    "vgg_flat = Flatten()(vgg_flat)\n",
    "extractor = Model(inputs = vgg.input, outputs = vgg_flat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x_train_bottleneck = extractor.predict(proc_x_train)\n",
    "x_val_bottleneck = extractor.predict(proc_x_val)\n",
    "x_test_bottleneck = extractor.predict(proc_x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3748, 25088)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train_bottleneck.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "classifier = Sequential([\n",
    "        Dense(4096, activation='relu', input_shape=(25088,)),\n",
    "        Dropout(0.45),\n",
    "        Dense(4096, activation='relu'),\n",
    "        Dropout(0.45),\n",
    "        Dense(2, activation='softmax')\n",
    "    ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "classifier.compile(\n",
    "    loss='categorical_crossentropy',\n",
    "    optimizer=Adam(lr=1e-6),\n",
    "    metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 3748 samples, validate on 468 samples\n",
      "Epoch 1/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0510 - acc: 0.9965 - val_loss: 0.7819 - val_acc: 0.9423\n",
      "Epoch 2/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0528 - acc: 0.9965 - val_loss: 0.7781 - val_acc: 0.9444\n",
      "Epoch 3/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0565 - acc: 0.9963 - val_loss: 0.7780 - val_acc: 0.9466\n",
      "Epoch 4/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0573 - acc: 0.9960 - val_loss: 0.7844 - val_acc: 0.9466\n",
      "Epoch 5/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0522 - acc: 0.9960 - val_loss: 0.7965 - val_acc: 0.9466\n",
      "Epoch 6/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0570 - acc: 0.9957 - val_loss: 0.8154 - val_acc: 0.9466\n",
      "Epoch 7/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0564 - acc: 0.9963 - val_loss: 0.8205 - val_acc: 0.9466\n",
      "Epoch 8/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0543 - acc: 0.9960 - val_loss: 0.8244 - val_acc: 0.9466\n",
      "Epoch 9/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0551 - acc: 0.9960 - val_loss: 0.8173 - val_acc: 0.9466\n",
      "Epoch 10/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0537 - acc: 0.9965 - val_loss: 0.8091 - val_acc: 0.9444\n",
      "Epoch 11/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0502 - acc: 0.9965 - val_loss: 0.7983 - val_acc: 0.9444\n",
      "Epoch 12/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0516 - acc: 0.9968 - val_loss: 0.7965 - val_acc: 0.9444\n",
      "Epoch 13/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0483 - acc: 0.9968 - val_loss: 0.8003 - val_acc: 0.9444\n",
      "Epoch 14/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0539 - acc: 0.9963 - val_loss: 0.8028 - val_acc: 0.9444\n",
      "Epoch 15/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0500 - acc: 0.9968 - val_loss: 0.8053 - val_acc: 0.9444\n",
      "Epoch 16/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0512 - acc: 0.9963 - val_loss: 0.8091 - val_acc: 0.9444\n",
      "Epoch 17/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0633 - acc: 0.9957 - val_loss: 0.8088 - val_acc: 0.9444\n",
      "Epoch 18/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0516 - acc: 0.9968 - val_loss: 0.8044 - val_acc: 0.9444\n",
      "Epoch 19/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0519 - acc: 0.9965 - val_loss: 0.7987 - val_acc: 0.9444\n",
      "Epoch 20/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0517 - acc: 0.9968 - val_loss: 0.7954 - val_acc: 0.9444\n",
      "Epoch 21/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0507 - acc: 0.9965 - val_loss: 0.7988 - val_acc: 0.9444\n",
      "Epoch 22/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0482 - acc: 0.9968 - val_loss: 0.8002 - val_acc: 0.9444\n",
      "Epoch 23/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0548 - acc: 0.9963 - val_loss: 0.8142 - val_acc: 0.9444\n",
      "Epoch 24/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0474 - acc: 0.9971 - val_loss: 0.8229 - val_acc: 0.9444\n",
      "Epoch 25/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0481 - acc: 0.9968 - val_loss: 0.8301 - val_acc: 0.9423\n",
      "Epoch 26/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0500 - acc: 0.9965 - val_loss: 0.8351 - val_acc: 0.9423\n",
      "Epoch 27/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0473 - acc: 0.9971 - val_loss: 0.8349 - val_acc: 0.9423\n",
      "Epoch 28/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0490 - acc: 0.9968 - val_loss: 0.8317 - val_acc: 0.9402\n",
      "Epoch 29/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0500 - acc: 0.9968 - val_loss: 0.8347 - val_acc: 0.9423\n",
      "Epoch 30/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0516 - acc: 0.9968 - val_loss: 0.8373 - val_acc: 0.9423\n",
      "Epoch 31/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0474 - acc: 0.9971 - val_loss: 0.8411 - val_acc: 0.9423\n",
      "Epoch 32/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0487 - acc: 0.9968 - val_loss: 0.8414 - val_acc: 0.9423\n",
      "Epoch 33/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0524 - acc: 0.9965 - val_loss: 0.8387 - val_acc: 0.9423\n",
      "Epoch 34/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0473 - acc: 0.9971 - val_loss: 0.8389 - val_acc: 0.9423\n",
      "Epoch 35/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0473 - acc: 0.9971 - val_loss: 0.8382 - val_acc: 0.9423\n",
      "Epoch 36/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0485 - acc: 0.9965 - val_loss: 0.8282 - val_acc: 0.9402\n",
      "Epoch 37/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0473 - acc: 0.9971 - val_loss: 0.8223 - val_acc: 0.9423\n",
      "Epoch 38/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0516 - acc: 0.9968 - val_loss: 0.8200 - val_acc: 0.9423\n",
      "Epoch 39/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0473 - acc: 0.9971 - val_loss: 0.8189 - val_acc: 0.9444\n",
      "Epoch 40/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0473 - acc: 0.9971 - val_loss: 0.8185 - val_acc: 0.9444\n",
      "Epoch 41/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0473 - acc: 0.9971 - val_loss: 0.8183 - val_acc: 0.9444\n",
      "Epoch 42/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0476 - acc: 0.9968 - val_loss: 0.8137 - val_acc: 0.9444\n",
      "Epoch 43/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0476 - acc: 0.9968 - val_loss: 0.8142 - val_acc: 0.9444\n",
      "Epoch 44/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0486 - acc: 0.9968 - val_loss: 0.8146 - val_acc: 0.9444\n",
      "Epoch 45/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0493 - acc: 0.9965 - val_loss: 0.8219 - val_acc: 0.9402\n",
      "Epoch 46/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0515 - acc: 0.9968 - val_loss: 0.8329 - val_acc: 0.9402\n",
      "Epoch 47/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0473 - acc: 0.9971 - val_loss: 0.8438 - val_acc: 0.9423\n",
      "Epoch 48/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0505 - acc: 0.9968 - val_loss: 0.8531 - val_acc: 0.9423\n",
      "Epoch 49/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0488 - acc: 0.9968 - val_loss: 0.8643 - val_acc: 0.9423\n",
      "Epoch 50/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0498 - acc: 0.9963 - val_loss: 0.8753 - val_acc: 0.9423\n",
      "Epoch 51/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0473 - acc: 0.9971 - val_loss: 0.8820 - val_acc: 0.9423\n",
      "Epoch 52/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0475 - acc: 0.9968 - val_loss: 0.8898 - val_acc: 0.9402\n",
      "Epoch 53/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0481 - acc: 0.9968 - val_loss: 0.8907 - val_acc: 0.9402\n",
      "Epoch 54/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0554 - acc: 0.9965 - val_loss: 0.8836 - val_acc: 0.9402\n",
      "Epoch 55/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0473 - acc: 0.9971 - val_loss: 0.8817 - val_acc: 0.9402\n",
      "Epoch 56/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0473 - acc: 0.9971 - val_loss: 0.8808 - val_acc: 0.9423\n",
      "Epoch 57/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0477 - acc: 0.9968 - val_loss: 0.8755 - val_acc: 0.9423\n",
      "Epoch 58/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0527 - acc: 0.9965 - val_loss: 0.8774 - val_acc: 0.9423\n",
      "Epoch 59/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0485 - acc: 0.9968 - val_loss: 0.8751 - val_acc: 0.9423\n",
      "Epoch 60/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0504 - acc: 0.9965 - val_loss: 0.8553 - val_acc: 0.9423\n",
      "Epoch 61/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0485 - acc: 0.9968 - val_loss: 0.8421 - val_acc: 0.9402\n",
      "Epoch 62/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0516 - acc: 0.9968 - val_loss: 0.8354 - val_acc: 0.9402\n",
      "Epoch 63/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0516 - acc: 0.9968 - val_loss: 0.8329 - val_acc: 0.9402\n",
      "Epoch 64/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0473 - acc: 0.9971 - val_loss: 0.8318 - val_acc: 0.9402\n",
      "Epoch 65/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0473 - acc: 0.9971 - val_loss: 0.8314 - val_acc: 0.9402\n",
      "Epoch 66/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0473 - acc: 0.9971 - val_loss: 0.8315 - val_acc: 0.9402\n",
      "Epoch 67/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0473 - acc: 0.9971 - val_loss: 0.8318 - val_acc: 0.9402\n",
      "Epoch 68/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0531 - acc: 0.9965 - val_loss: 0.8330 - val_acc: 0.9402\n",
      "Epoch 69/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0474 - acc: 0.9971 - val_loss: 0.8363 - val_acc: 0.9402\n",
      "Epoch 70/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0473 - acc: 0.9971 - val_loss: 0.8418 - val_acc: 0.9402\n",
      "Epoch 71/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0473 - acc: 0.9971 - val_loss: 0.8440 - val_acc: 0.9402\n",
      "Epoch 72/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0512 - acc: 0.9965 - val_loss: 0.8434 - val_acc: 0.9402\n",
      "Epoch 73/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0502 - acc: 0.9968 - val_loss: 0.8529 - val_acc: 0.9402\n",
      "Epoch 74/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0473 - acc: 0.9971 - val_loss: 0.8580 - val_acc: 0.9402\n",
      "Epoch 75/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0495 - acc: 0.9968 - val_loss: 0.8678 - val_acc: 0.9402\n",
      "Epoch 76/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0488 - acc: 0.9968 - val_loss: 0.8617 - val_acc: 0.9402\n",
      "Epoch 77/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0473 - acc: 0.9971 - val_loss: 0.8591 - val_acc: 0.9402\n",
      "Epoch 78/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0473 - acc: 0.9971 - val_loss: 0.8581 - val_acc: 0.9402\n",
      "Epoch 79/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0473 - acc: 0.9971 - val_loss: 0.8576 - val_acc: 0.9402\n",
      "Epoch 80/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0473 - acc: 0.9971 - val_loss: 0.8574 - val_acc: 0.9402\n",
      "Epoch 81/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0473 - acc: 0.9971 - val_loss: 0.8573 - val_acc: 0.9402\n",
      "Epoch 82/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0478 - acc: 0.9968 - val_loss: 0.8646 - val_acc: 0.9402\n",
      "Epoch 83/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0516 - acc: 0.9968 - val_loss: 0.8655 - val_acc: 0.9402\n",
      "Epoch 84/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0517 - acc: 0.9968 - val_loss: 0.8668 - val_acc: 0.9402\n",
      "Epoch 85/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0473 - acc: 0.9971 - val_loss: 0.8678 - val_acc: 0.9402\n",
      "Epoch 86/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0473 - acc: 0.9971 - val_loss: 0.8682 - val_acc: 0.9402\n",
      "Epoch 87/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0519 - acc: 0.9965 - val_loss: 0.8617 - val_acc: 0.9402\n",
      "Epoch 88/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0505 - acc: 0.9968 - val_loss: 0.8535 - val_acc: 0.9402\n",
      "Epoch 89/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0484 - acc: 0.9968 - val_loss: 0.8372 - val_acc: 0.9402\n",
      "Epoch 90/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0473 - acc: 0.9971 - val_loss: 0.8303 - val_acc: 0.9402\n",
      "Epoch 91/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0473 - acc: 0.9971 - val_loss: 0.8274 - val_acc: 0.9402\n",
      "Epoch 92/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0473 - acc: 0.9971 - val_loss: 0.8262 - val_acc: 0.9402\n",
      "Epoch 93/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0473 - acc: 0.9971 - val_loss: 0.8257 - val_acc: 0.9402\n",
      "Epoch 94/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0473 - acc: 0.9971 - val_loss: 0.8262 - val_acc: 0.9402\n",
      "Epoch 95/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0473 - acc: 0.9971 - val_loss: 0.8264 - val_acc: 0.9402\n",
      "Epoch 96/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0473 - acc: 0.9971 - val_loss: 0.8269 - val_acc: 0.9402\n",
      "Epoch 97/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0473 - acc: 0.9971 - val_loss: 0.8275 - val_acc: 0.9402\n",
      "Epoch 98/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0473 - acc: 0.9971 - val_loss: 0.8277 - val_acc: 0.9402\n",
      "Epoch 99/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0473 - acc: 0.9971 - val_loss: 0.8279 - val_acc: 0.9402\n",
      "Epoch 100/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0473 - acc: 0.9971 - val_loss: 0.8279 - val_acc: 0.9402\n",
      "Epoch 101/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0473 - acc: 0.9971 - val_loss: 0.8289 - val_acc: 0.9402\n",
      "Epoch 102/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0473 - acc: 0.9971 - val_loss: 0.8294 - val_acc: 0.9402\n",
      "Epoch 103/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0473 - acc: 0.9971 - val_loss: 0.8296 - val_acc: 0.9402\n",
      "Epoch 104/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0481 - acc: 0.9968 - val_loss: 0.8231 - val_acc: 0.9402\n",
      "Epoch 105/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0477 - acc: 0.9968 - val_loss: 0.8134 - val_acc: 0.9423\n",
      "Epoch 106/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0473 - acc: 0.9971 - val_loss: 0.8043 - val_acc: 0.9423\n",
      "Epoch 107/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0500 - acc: 0.9968 - val_loss: 0.8038 - val_acc: 0.9423\n",
      "Epoch 108/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0473 - acc: 0.9971 - val_loss: 0.8072 - val_acc: 0.9423\n",
      "Epoch 109/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0473 - acc: 0.9971 - val_loss: 0.8080 - val_acc: 0.9423\n",
      "Epoch 110/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0473 - acc: 0.9971 - val_loss: 0.8084 - val_acc: 0.9423\n",
      "Epoch 111/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0473 - acc: 0.9971 - val_loss: 0.8087 - val_acc: 0.9423\n",
      "Epoch 112/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0491 - acc: 0.9968 - val_loss: 0.8187 - val_acc: 0.9402\n",
      "Epoch 113/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0503 - acc: 0.9968 - val_loss: 0.8315 - val_acc: 0.9402\n",
      "Epoch 114/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0473 - acc: 0.9971 - val_loss: 0.8412 - val_acc: 0.9402\n",
      "Epoch 115/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0473 - acc: 0.9971 - val_loss: 0.8455 - val_acc: 0.9402\n",
      "Epoch 116/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0473 - acc: 0.9971 - val_loss: 0.8473 - val_acc: 0.9402\n",
      "Epoch 117/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0473 - acc: 0.9971 - val_loss: 0.8481 - val_acc: 0.9402\n",
      "Epoch 118/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0473 - acc: 0.9971 - val_loss: 0.8481 - val_acc: 0.9402\n",
      "Epoch 119/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0493 - acc: 0.9968 - val_loss: 0.8370 - val_acc: 0.9402\n",
      "Epoch 120/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0473 - acc: 0.9971 - val_loss: 0.8311 - val_acc: 0.9402\n",
      "Epoch 121/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0473 - acc: 0.9971 - val_loss: 0.8296 - val_acc: 0.9402\n",
      "Epoch 122/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0473 - acc: 0.9971 - val_loss: 0.8293 - val_acc: 0.9402\n",
      "Epoch 123/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0473 - acc: 0.9971 - val_loss: 0.8290 - val_acc: 0.9402\n",
      "Epoch 124/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0493 - acc: 0.9968 - val_loss: 0.8312 - val_acc: 0.9402\n",
      "Epoch 125/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0511 - acc: 0.9965 - val_loss: 0.8385 - val_acc: 0.9402\n",
      "Epoch 126/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0475 - acc: 0.9971 - val_loss: 0.8692 - val_acc: 0.9402\n",
      "Epoch 127/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0473 - acc: 0.9971 - val_loss: 0.8844 - val_acc: 0.9402\n",
      "Epoch 128/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0513 - acc: 0.9963 - val_loss: 0.9002 - val_acc: 0.9423\n",
      "Epoch 129/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0473 - acc: 0.9971 - val_loss: 0.9037 - val_acc: 0.9423\n",
      "Epoch 130/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0473 - acc: 0.9971 - val_loss: 0.9048 - val_acc: 0.9423\n",
      "Epoch 131/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0473 - acc: 0.9971 - val_loss: 0.9052 - val_acc: 0.9423\n",
      "Epoch 132/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0473 - acc: 0.9971 - val_loss: 0.9054 - val_acc: 0.9423\n",
      "Epoch 133/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0473 - acc: 0.9971 - val_loss: 0.9056 - val_acc: 0.9423\n",
      "Epoch 134/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0473 - acc: 0.9971 - val_loss: 0.9056 - val_acc: 0.9423\n",
      "Epoch 135/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0475 - acc: 0.9971 - val_loss: 0.9051 - val_acc: 0.9423\n",
      "Epoch 136/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0473 - acc: 0.9971 - val_loss: 0.9026 - val_acc: 0.9423\n",
      "Epoch 137/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0473 - acc: 0.9971 - val_loss: 0.9015 - val_acc: 0.9402\n",
      "Epoch 138/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0473 - acc: 0.9971 - val_loss: 0.9011 - val_acc: 0.9402\n",
      "Epoch 139/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0473 - acc: 0.9971 - val_loss: 0.9010 - val_acc: 0.9402\n",
      "Epoch 140/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0473 - acc: 0.9971 - val_loss: 0.9008 - val_acc: 0.9402\n",
      "Epoch 141/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0473 - acc: 0.9971 - val_loss: 0.9006 - val_acc: 0.9402\n",
      "Epoch 142/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0473 - acc: 0.9971 - val_loss: 0.9005 - val_acc: 0.9402\n",
      "Epoch 143/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0502 - acc: 0.9968 - val_loss: 0.8958 - val_acc: 0.9402\n",
      "Epoch 144/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0473 - acc: 0.9971 - val_loss: 0.8883 - val_acc: 0.9402\n",
      "Epoch 145/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0504 - acc: 0.9968 - val_loss: 0.8916 - val_acc: 0.9402\n",
      "Epoch 146/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0473 - acc: 0.9971 - val_loss: 0.8944 - val_acc: 0.9402\n",
      "Epoch 147/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0473 - acc: 0.9971 - val_loss: 0.8956 - val_acc: 0.9402\n",
      "Epoch 148/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0473 - acc: 0.9971 - val_loss: 0.8961 - val_acc: 0.9402\n",
      "Epoch 149/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0473 - acc: 0.9971 - val_loss: 0.8964 - val_acc: 0.9402\n",
      "Epoch 150/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0473 - acc: 0.9971 - val_loss: 0.8964 - val_acc: 0.9402\n",
      "Epoch 151/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0473 - acc: 0.9971 - val_loss: 0.8965 - val_acc: 0.9402\n",
      "Epoch 152/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0487 - acc: 0.9965 - val_loss: 0.8869 - val_acc: 0.9402\n",
      "Epoch 153/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0524 - acc: 0.9965 - val_loss: 0.8873 - val_acc: 0.9402\n",
      "Epoch 154/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0474 - acc: 0.9971 - val_loss: 0.8878 - val_acc: 0.9402\n",
      "Epoch 155/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0499 - acc: 0.9968 - val_loss: 0.8945 - val_acc: 0.9402\n",
      "Epoch 156/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0473 - acc: 0.9971 - val_loss: 0.9007 - val_acc: 0.9402\n",
      "Epoch 157/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0473 - acc: 0.9971 - val_loss: 0.9034 - val_acc: 0.9402\n",
      "Epoch 158/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0488 - acc: 0.9968 - val_loss: 0.8930 - val_acc: 0.9402\n",
      "Epoch 159/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0473 - acc: 0.9971 - val_loss: 0.8879 - val_acc: 0.9402\n",
      "Epoch 160/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0473 - acc: 0.9971 - val_loss: 0.8854 - val_acc: 0.9402\n",
      "Epoch 161/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0473 - acc: 0.9971 - val_loss: 0.8846 - val_acc: 0.9402\n",
      "Epoch 162/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0473 - acc: 0.9971 - val_loss: 0.8860 - val_acc: 0.9402\n",
      "Epoch 163/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0473 - acc: 0.9971 - val_loss: 0.8869 - val_acc: 0.9402\n",
      "Epoch 164/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0473 - acc: 0.9971 - val_loss: 0.8875 - val_acc: 0.9402\n",
      "Epoch 165/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0516 - acc: 0.9968 - val_loss: 0.8878 - val_acc: 0.9402\n",
      "Epoch 166/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0473 - acc: 0.9971 - val_loss: 0.8879 - val_acc: 0.9402\n",
      "Epoch 167/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0473 - acc: 0.9971 - val_loss: 0.8879 - val_acc: 0.9402\n",
      "Epoch 168/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0482 - acc: 0.9968 - val_loss: 0.8827 - val_acc: 0.9402\n",
      "Epoch 169/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0473 - acc: 0.9971 - val_loss: 0.8771 - val_acc: 0.9402\n",
      "Epoch 170/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0473 - acc: 0.9971 - val_loss: 0.8746 - val_acc: 0.9402\n",
      "Epoch 171/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0473 - acc: 0.9971 - val_loss: 0.8736 - val_acc: 0.9402\n",
      "Epoch 172/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0473 - acc: 0.9971 - val_loss: 0.8732 - val_acc: 0.9402\n",
      "Epoch 173/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0473 - acc: 0.9971 - val_loss: 0.8728 - val_acc: 0.9402\n",
      "Epoch 174/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0473 - acc: 0.9971 - val_loss: 0.8723 - val_acc: 0.9402\n",
      "Epoch 175/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0473 - acc: 0.9971 - val_loss: 0.8724 - val_acc: 0.9402\n",
      "Epoch 176/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0473 - acc: 0.9971 - val_loss: 0.8724 - val_acc: 0.9402\n",
      "Epoch 177/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0473 - acc: 0.9971 - val_loss: 0.8725 - val_acc: 0.9402\n",
      "Epoch 178/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0497 - acc: 0.9965 - val_loss: 0.8688 - val_acc: 0.9402\n",
      "Epoch 179/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0487 - acc: 0.9968 - val_loss: 0.8729 - val_acc: 0.9402\n",
      "Epoch 180/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0473 - acc: 0.9971 - val_loss: 0.8763 - val_acc: 0.9402\n",
      "Epoch 181/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0473 - acc: 0.9971 - val_loss: 0.8778 - val_acc: 0.9402\n",
      "Epoch 182/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0473 - acc: 0.9971 - val_loss: 0.8784 - val_acc: 0.9402\n",
      "Epoch 183/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0473 - acc: 0.9971 - val_loss: 0.8786 - val_acc: 0.9402\n",
      "Epoch 184/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0473 - acc: 0.9971 - val_loss: 0.8787 - val_acc: 0.9402\n",
      "Epoch 185/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0473 - acc: 0.9971 - val_loss: 0.8788 - val_acc: 0.9402\n",
      "Epoch 186/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0473 - acc: 0.9971 - val_loss: 0.8788 - val_acc: 0.9402\n",
      "Epoch 187/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0510 - acc: 0.9968 - val_loss: 0.8710 - val_acc: 0.9402\n",
      "Epoch 188/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0498 - acc: 0.9968 - val_loss: 0.8520 - val_acc: 0.9402\n",
      "Epoch 189/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0473 - acc: 0.9971 - val_loss: 0.8391 - val_acc: 0.9402\n",
      "Epoch 190/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0473 - acc: 0.9971 - val_loss: 0.8331 - val_acc: 0.9402\n",
      "Epoch 191/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0473 - acc: 0.9971 - val_loss: 0.8306 - val_acc: 0.9402\n",
      "Epoch 192/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0473 - acc: 0.9971 - val_loss: 0.8295 - val_acc: 0.9402\n",
      "Epoch 193/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0489 - acc: 0.9968 - val_loss: 0.8342 - val_acc: 0.9402\n",
      "Epoch 194/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0473 - acc: 0.9971 - val_loss: 0.8469 - val_acc: 0.9402\n",
      "Epoch 195/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0473 - acc: 0.9971 - val_loss: 0.8523 - val_acc: 0.9402\n",
      "Epoch 196/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0473 - acc: 0.9971 - val_loss: 0.8546 - val_acc: 0.9402\n",
      "Epoch 197/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0474 - acc: 0.9971 - val_loss: 0.8568 - val_acc: 0.9402\n",
      "Epoch 198/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0479 - acc: 0.9968 - val_loss: 0.8397 - val_acc: 0.9402\n",
      "Epoch 199/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0473 - acc: 0.9971 - val_loss: 0.8300 - val_acc: 0.9402\n",
      "Epoch 200/200\n",
      "3748/3748 [==============================] - 2s - loss: 0.0476 - acc: 0.9968 - val_loss: 0.8345 - val_acc: 0.9402\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f64a87f0dd8>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier.fit(x_train_bottleneck, y_train, batch_size=512, epochs=200, validation_data=(x_val_bottleneck, y_val))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "416/470 [=========================>....] - ETA: 0s"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[1.2108385070088379, 0.91914893667748632]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier.evaluate(x_test_bottleneck, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda root]",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}