cifar10-improved-cnn.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Improved CNN on CIFAR-10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib\n",
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "from keras.callbacks import ModelCheckpoint\n",
    "from keras.constraints import maxnorm\n",
    "from keras.layers import GlobalAveragePooling2D, Lambda, Conv2D, MaxPooling2D, Dropout, Dense, Flatten, Activation\n",
    "from keras.models import load_model, Sequential\n",
    "from keras.optimizers import Adam, SGD\n",
    "from keras.preprocessing.image import ImageDataGenerator"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import decoder functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from decoder import get_class_names, get_train_data, get_test_data, plot_images\n",
    "from decoder import plot_model, predict_classes, visualize_errors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Change graph style of matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "matplotlib.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Constants"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import class names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decoding file: data/batches.meta\n",
      "['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n"
     ]
    }
   ],
   "source": [
    "class_names = get_class_names()\n",
    "print(class_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get number of classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n"
     ]
    }
   ],
   "source": [
    "num_classes = len(class_names)\n",
    "print(num_classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Height and width of the images\n",
    "IMAGE_SIZE = 32\n",
    "# 3 channels, Red, Green and Blue\n",
    "CHANNELS = 3\n",
    "# learning rate\n",
    "LEARN_RATE = 1.0e-4\n",
    "# Number of epochs\n",
    "NUM_EPOCH = 350"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decode and fetch data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the training dataset. Labels are integers whereas class is one-hot encoded vectors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decoding file: data/data_batch_1\n",
      "Decoding file: data/data_batch_2\n",
      "Decoding file: data/data_batch_3\n",
      "Decoding file: data/data_batch_4\n",
      "Decoding file: data/data_batch_5\n"
     ]
    }
   ],
   "source": [
    "images_train, labels_train, class_train = get_train_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the testing dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decoding file: data/test_batch\n"
     ]
    }
   ],
   "source": [
    "images_test, labels_test, class_test = get_test_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Print the size of the training and testing set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set size:\t 50000\n",
      "Testing set size:\t 10000\n"
     ]
    }
   ],
   "source": [
    "print(\"Training set size:\\t\",len(images_train))\n",
    "print(\"Testing set size:\\t\",len(images_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataset has been loaded."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Improving accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Define a better CNN model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def improved_cnn_model():\n",
    "    \n",
    "    model = Sequential()\n",
    "    \n",
    "    model.add(Conv2D(96, (3, 3), activation='relu', padding = 'same', input_shape=(IMAGE_SIZE,IMAGE_SIZE,CHANNELS)))    \n",
    "    model.add(Dropout(0.2))\n",
    "    \n",
    "    model.add(Conv2D(96, (3, 3), activation='relu', padding = 'same'))  \n",
    "    model.add(Conv2D(96, (3, 3), activation='relu', padding = 'same', strides = 2))    \n",
    "    model.add(Dropout(0.5))\n",
    "    \n",
    "    model.add(Conv2D(192, (3, 3), activation='relu', padding = 'same'))    \n",
    "    model.add(Conv2D(192, (3, 3), activation='relu', padding = 'same'))\n",
    "    model.add(Conv2D(192, (3, 3), activation='relu', padding = 'same', strides = 2))    \n",
    "    model.add(Dropout(0.5))    \n",
    "    \n",
    "    model.add(Conv2D(192, (3, 3), padding = 'same'))\n",
    "    model.add(Activation('relu'))\n",
    "    model.add(Conv2D(192, (1, 1),padding='valid'))\n",
    "    model.add(Activation('relu'))\n",
    "    model.add(Conv2D(10, (1, 1), padding='valid'))\n",
    "\n",
    "    model.add(GlobalAveragePooling2D())\n",
    "    \n",
    "    model.add(Activation('softmax'))\n",
    "\n",
    "    model.summary()\n",
    "    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Build the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_10 (Conv2D)           (None, 32, 32, 96)        2688      \n",
      "_________________________________________________________________\n",
      "dropout_4 (Dropout)          (None, 32, 32, 96)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_11 (Conv2D)           (None, 32, 32, 96)        83040     \n",
      "_________________________________________________________________\n",
      "conv2d_12 (Conv2D)           (None, 16, 16, 96)        83040     \n",
      "_________________________________________________________________\n",
      "dropout_5 (Dropout)          (None, 16, 16, 96)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_13 (Conv2D)           (None, 16, 16, 192)       166080    \n",
      "_________________________________________________________________\n",
      "conv2d_14 (Conv2D)           (None, 16, 16, 192)       331968    \n",
      "_________________________________________________________________\n",
      "conv2d_15 (Conv2D)           (None, 8, 8, 192)         331968    \n",
      "_________________________________________________________________\n",
      "dropout_6 (Dropout)          (None, 8, 8, 192)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_16 (Conv2D)           (None, 8, 8, 192)         331968    \n",
      "_________________________________________________________________\n",
      "activation_4 (Activation)    (None, 8, 8, 192)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_17 (Conv2D)           (None, 8, 8, 192)         37056     \n",
      "_________________________________________________________________\n",
      "activation_5 (Activation)    (None, 8, 8, 192)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_18 (Conv2D)           (None, 8, 8, 10)          1930      \n",
      "_________________________________________________________________\n",
      "global_average_pooling2d_2 ( (None, 10)                0         \n",
      "_________________________________________________________________\n",
      "activation_6 (Activation)    (None, 10)                0         \n",
      "=================================================================\n",
      "Total params: 1,369,738\n",
      "Trainable params: 1,369,738\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = improved_cnn_model()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Train model on the training data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save the model after every epoch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "checkpoint = ModelCheckpoint('cnn_improved.h5',  # model filename\n",
    "                             monitor='val_loss', # quantity to monitor - validation loss\n",
    "                             verbose=0, # verbosity - 0 or 1. 1 gives the output of the function after every epoch\n",
    "                             save_best_only= True, # The latest best model will not be overwritten\n",
    "                             mode='auto') # The decision to overwrite model is made. It can be max, min or auto.\n",
    "                                          # automatically depending on the quantity to monitor "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Configure the model for training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model.compile(loss='categorical_crossentropy', # Better loss function for neural networks\n",
    "              optimizer=Adam(lr=LEARN_RATE), # Adam optimizer with 1.0e-4 learning rate\n",
    "              metrics = ['accuracy']) # Metrics to be evaluated by the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fit the model on the data provided"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 50000 samples, validate on 10000 samples\n",
      "Epoch 1/350\n",
      "50000/50000 [==============================] - 95s - loss: 2.0042 - acc: 0.2385 - val_loss: 1.8122 - val_acc: 0.3176\n",
      "Epoch 2/350\n",
      "50000/50000 [==============================] - 92s - loss: 1.6972 - acc: 0.3663 - val_loss: 1.6534 - val_acc: 0.3903\n",
      "Epoch 3/350\n",
      "50000/50000 [==============================] - 93s - loss: 1.5731 - acc: 0.4184 - val_loss: 1.5575 - val_acc: 0.4300\n",
      "Epoch 4/350\n",
      "50000/50000 [==============================] - 93s - loss: 1.5032 - acc: 0.4480 - val_loss: 1.5217 - val_acc: 0.4372\n",
      "Epoch 5/350\n",
      "50000/50000 [==============================] - 93s - loss: 1.4448 - acc: 0.4723 - val_loss: 1.3933 - val_acc: 0.4878\n",
      "Epoch 6/350\n",
      "50000/50000 [==============================] - 93s - loss: 1.3925 - acc: 0.4948 - val_loss: 1.3786 - val_acc: 0.5099\n",
      "Epoch 7/350\n",
      "50000/50000 [==============================] - 93s - loss: 1.3506 - acc: 0.5115 - val_loss: 1.3002 - val_acc: 0.5368\n",
      "Epoch 8/350\n",
      "50000/50000 [==============================] - 93s - loss: 1.2986 - acc: 0.5307 - val_loss: 1.2381 - val_acc: 0.5628\n",
      "Epoch 9/350\n",
      "50000/50000 [==============================] - 93s - loss: 1.2505 - acc: 0.5499 - val_loss: 1.1893 - val_acc: 0.5809\n",
      "Epoch 10/350\n",
      "50000/50000 [==============================] - 93s - loss: 1.2173 - acc: 0.5657 - val_loss: 1.1476 - val_acc: 0.5895\n",
      "Epoch 11/350\n",
      "50000/50000 [==============================] - 93s - loss: 1.1813 - acc: 0.5763 - val_loss: 1.1389 - val_acc: 0.5974\n",
      "Epoch 12/350\n",
      "50000/50000 [==============================] - 93s - loss: 1.1493 - acc: 0.5900 - val_loss: 1.0917 - val_acc: 0.6136\n",
      "Epoch 13/350\n",
      "50000/50000 [==============================] - 93s - loss: 1.1211 - acc: 0.6009 - val_loss: 1.0892 - val_acc: 0.6162\n",
      "Epoch 14/350\n",
      "50000/50000 [==============================] - 93s - loss: 1.1008 - acc: 0.6070 - val_loss: 1.0934 - val_acc: 0.6118\n",
      "Epoch 15/350\n",
      "50000/50000 [==============================] - 93s - loss: 1.0738 - acc: 0.6201 - val_loss: 1.0263 - val_acc: 0.6350\n",
      "Epoch 16/350\n",
      "50000/50000 [==============================] - 93s - loss: 1.0509 - acc: 0.6296 - val_loss: 1.0201 - val_acc: 0.6386\n",
      "Epoch 17/350\n",
      "50000/50000 [==============================] - 93s - loss: 1.0333 - acc: 0.6331 - val_loss: 0.9933 - val_acc: 0.6480\n",
      "Epoch 18/350\n",
      "50000/50000 [==============================] - 93s - loss: 1.0125 - acc: 0.6405 - val_loss: 0.9838 - val_acc: 0.6552\n",
      "Epoch 19/350\n",
      "50000/50000 [==============================] - 93s - loss: 0.9887 - acc: 0.6489 - val_loss: 0.9735 - val_acc: 0.6536\n",
      "Epoch 20/350\n",
      "50000/50000 [==============================] - 93s - loss: 0.9726 - acc: 0.6572 - val_loss: 0.9817 - val_acc: 0.6501\n",
      "Epoch 21/350\n",
      "50000/50000 [==============================] - 93s - loss: 0.9558 - acc: 0.6637 - val_loss: 0.9261 - val_acc: 0.6737\n",
      "Epoch 22/350\n",
      "50000/50000 [==============================] - 93s - loss: 0.9376 - acc: 0.6685 - val_loss: 0.9240 - val_acc: 0.6678\n",
      "Epoch 23/350\n",
      "50000/50000 [==============================] - 93s - loss: 0.9331 - acc: 0.6720 - val_loss: 0.9142 - val_acc: 0.6751\n",
      "Epoch 24/350\n",
      "50000/50000 [==============================] - 93s - loss: 0.9064 - acc: 0.6813 - val_loss: 0.9189 - val_acc: 0.6717\n",
      "Epoch 25/350\n",
      "50000/50000 [==============================] - 93s - loss: 0.8913 - acc: 0.6845 - val_loss: 0.8738 - val_acc: 0.6908\n",
      "Epoch 26/350\n",
      "50000/50000 [==============================] - 93s - loss: 0.8805 - acc: 0.6899 - val_loss: 0.8706 - val_acc: 0.6887\n",
      "Epoch 27/350\n",
      "50000/50000 [==============================] - 93s - loss: 0.8642 - acc: 0.6978 - val_loss: 0.8663 - val_acc: 0.6911\n",
      "Epoch 28/350\n",
      "50000/50000 [==============================] - 93s - loss: 0.8518 - acc: 0.6995 - val_loss: 0.8401 - val_acc: 0.7024\n",
      "Epoch 29/350\n",
      "50000/50000 [==============================] - 93s - loss: 0.8415 - acc: 0.7037 - val_loss: 0.8268 - val_acc: 0.7104\n",
      "Epoch 30/350\n",
      "50000/50000 [==============================] - 93s - loss: 0.8272 - acc: 0.7090 - val_loss: 0.8339 - val_acc: 0.7075\n",
      "Epoch 31/350\n",
      "50000/50000 [==============================] - 93s - loss: 0.8131 - acc: 0.7142 - val_loss: 0.8077 - val_acc: 0.7183\n",
      "Epoch 32/350\n",
      "50000/50000 [==============================] - 93s - loss: 0.7993 - acc: 0.7188 - val_loss: 0.8632 - val_acc: 0.6934\n",
      "Epoch 33/350\n",
      "50000/50000 [==============================] - 93s - loss: 0.7936 - acc: 0.7195 - val_loss: 0.7951 - val_acc: 0.7247\n",
      "Epoch 34/350\n",
      "50000/50000 [==============================] - 93s - loss: 0.7852 - acc: 0.7229 - val_loss: 0.7753 - val_acc: 0.7267\n",
      "Epoch 35/350\n",
      "50000/50000 [==============================] - 93s - loss: 0.7719 - acc: 0.7279 - val_loss: 0.8116 - val_acc: 0.7141\n",
      "Epoch 36/350\n",
      "50000/50000 [==============================] - 93s - loss: 0.7569 - acc: 0.7343 - val_loss: 0.7882 - val_acc: 0.7218\n",
      "Epoch 37/350\n",
      "50000/50000 [==============================] - 93s - loss: 0.7486 - acc: 0.7364 - val_loss: 0.7836 - val_acc: 0.7247\n",
      "Epoch 38/350\n",
      "50000/50000 [==============================] - 93s - loss: 0.7446 - acc: 0.7409 - val_loss: 0.7781 - val_acc: 0.7215\n",
      "Epoch 39/350\n",
      "50000/50000 [==============================] - 93s - loss: 0.7346 - acc: 0.7410 - val_loss: 0.7509 - val_acc: 0.7292\n",
      "Epoch 40/350\n",
      "50000/50000 [==============================] - 93s - loss: 0.7213 - acc: 0.7458 - val_loss: 0.7410 - val_acc: 0.7415\n",
      "Epoch 41/350\n",
      "50000/50000 [==============================] - 93s - loss: 0.7103 - acc: 0.7490 - val_loss: 0.7229 - val_acc: 0.7466\n",
      "Epoch 42/350\n",
      "50000/50000 [==============================] - 93s - loss: 0.7015 - acc: 0.7541 - val_loss: 0.7380 - val_acc: 0.7401\n",
      "Epoch 43/350\n",
      "50000/50000 [==============================] - 93s - loss: 0.6990 - acc: 0.7529 - val_loss: 0.7170 - val_acc: 0.7471\n",
      "Epoch 44/350\n",
      "50000/50000 [==============================] - 93s - loss: 0.6875 - acc: 0.7590 - val_loss: 0.7139 - val_acc: 0.7516\n",
      "Epoch 45/350\n",
      "50000/50000 [==============================] - 93s - loss: 0.6794 - acc: 0.7628 - val_loss: 0.7038 - val_acc: 0.7514\n",
      "Epoch 46/350\n",
      "50000/50000 [==============================] - 93s - loss: 0.6687 - acc: 0.7642 - val_loss: 0.6959 - val_acc: 0.7565\n",
      "Epoch 47/350\n",
      "50000/50000 [==============================] - 93s - loss: 0.6634 - acc: 0.7664 - val_loss: 0.7046 - val_acc: 0.7542\n",
      "Epoch 48/350\n",
      "50000/50000 [==============================] - 93s - loss: 0.6527 - acc: 0.7699 - val_loss: 0.6942 - val_acc: 0.7569\n",
      "Epoch 49/350\n",
      "50000/50000 [==============================] - 93s - loss: 0.6489 - acc: 0.7711 - val_loss: 0.6980 - val_acc: 0.7562\n",
      "Epoch 50/350\n",
      "50000/50000 [==============================] - 93s - loss: 0.6443 - acc: 0.7743 - val_loss: 0.7010 - val_acc: 0.7571\n",
      "Epoch 51/350\n",
      "50000/50000 [==============================] - 93s - loss: 0.6344 - acc: 0.7784 - val_loss: 0.6661 - val_acc: 0.7670\n",
      "Epoch 52/350\n",
      "50000/50000 [==============================] - 93s - loss: 0.6284 - acc: 0.7799 - val_loss: 0.6616 - val_acc: 0.7710\n",
      "Epoch 53/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.6176 - acc: 0.7843 - val_loss: 0.6822 - val_acc: 0.7631\n",
      "Epoch 54/350\n",
      "50000/50000 [==============================] - 93s - loss: 0.6108 - acc: 0.7839 - val_loss: 0.6507 - val_acc: 0.7739\n",
      "Epoch 55/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.6072 - acc: 0.7861 - val_loss: 0.6573 - val_acc: 0.7722\n",
      "Epoch 56/350\n",
      "50000/50000 [==============================] - 93s - loss: 0.5988 - acc: 0.7891 - val_loss: 0.6493 - val_acc: 0.7726\n",
      "Epoch 57/350\n",
      "50000/50000 [==============================] - 93s - loss: 0.5924 - acc: 0.7916 - val_loss: 0.6314 - val_acc: 0.7810\n",
      "Epoch 58/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.5829 - acc: 0.7953 - val_loss: 0.6408 - val_acc: 0.7810\n",
      "Epoch 59/350\n",
      "50000/50000 [==============================] - 93s - loss: 0.5773 - acc: 0.7961 - val_loss: 0.6301 - val_acc: 0.7829\n",
      "Epoch 60/350\n",
      "50000/50000 [==============================] - 93s - loss: 0.5714 - acc: 0.7986 - val_loss: 0.6344 - val_acc: 0.7801\n",
      "Epoch 61/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.5677 - acc: 0.8006 - val_loss: 0.6471 - val_acc: 0.7729\n",
      "Epoch 62/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.5593 - acc: 0.8032 - val_loss: 0.6270 - val_acc: 0.7828\n",
      "Epoch 63/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.5523 - acc: 0.8049 - val_loss: 0.6107 - val_acc: 0.7918\n",
      "Epoch 64/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.5479 - acc: 0.8081 - val_loss: 0.6430 - val_acc: 0.7778\n",
      "Epoch 65/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.5440 - acc: 0.8084 - val_loss: 0.6222 - val_acc: 0.7872\n",
      "Epoch 66/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.5350 - acc: 0.8139 - val_loss: 0.6283 - val_acc: 0.7849\n",
      "Epoch 67/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.5343 - acc: 0.8111 - val_loss: 0.6106 - val_acc: 0.7896\n",
      "Epoch 68/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.5230 - acc: 0.8165 - val_loss: 0.6019 - val_acc: 0.7927\n",
      "Epoch 69/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.5184 - acc: 0.8173 - val_loss: 0.6141 - val_acc: 0.7896\n",
      "Epoch 70/350\n",
      "50000/50000 [==============================] - 93s - loss: 0.5131 - acc: 0.8198 - val_loss: 0.5987 - val_acc: 0.7937\n",
      "Epoch 71/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.5126 - acc: 0.8189 - val_loss: 0.6088 - val_acc: 0.7903\n",
      "Epoch 72/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.5034 - acc: 0.8221 - val_loss: 0.5936 - val_acc: 0.7987\n",
      "Epoch 73/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.4999 - acc: 0.8243 - val_loss: 0.5851 - val_acc: 0.8012\n",
      "Epoch 74/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.4949 - acc: 0.8259 - val_loss: 0.5772 - val_acc: 0.8009\n",
      "Epoch 75/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.4849 - acc: 0.8296 - val_loss: 0.5770 - val_acc: 0.7996\n",
      "Epoch 76/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.4863 - acc: 0.8285 - val_loss: 0.5791 - val_acc: 0.8031\n",
      "Epoch 77/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.4781 - acc: 0.8319 - val_loss: 0.6049 - val_acc: 0.7967\n",
      "Epoch 78/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.4684 - acc: 0.8347 - val_loss: 0.5797 - val_acc: 0.8055\n",
      "Epoch 79/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.4685 - acc: 0.8332 - val_loss: 0.5667 - val_acc: 0.8107\n",
      "Epoch 80/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.4625 - acc: 0.8379 - val_loss: 0.5870 - val_acc: 0.8010\n",
      "Epoch 81/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.4624 - acc: 0.8382 - val_loss: 0.5859 - val_acc: 0.8033\n",
      "Epoch 82/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.4498 - acc: 0.8396 - val_loss: 0.5777 - val_acc: 0.8060\n",
      "Epoch 83/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.4511 - acc: 0.8394 - val_loss: 0.5773 - val_acc: 0.8045\n",
      "Epoch 84/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.4417 - acc: 0.8435 - val_loss: 0.5757 - val_acc: 0.8070\n",
      "Epoch 85/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.4375 - acc: 0.8458 - val_loss: 0.5603 - val_acc: 0.8091\n",
      "Epoch 86/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.4349 - acc: 0.8453 - val_loss: 0.5862 - val_acc: 0.8017\n",
      "Epoch 87/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.4335 - acc: 0.8459 - val_loss: 0.5648 - val_acc: 0.8110\n",
      "Epoch 88/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.4226 - acc: 0.8495 - val_loss: 0.5849 - val_acc: 0.8053\n",
      "Epoch 89/350\n",
      "50000/50000 [==============================] - 93s - loss: 0.4234 - acc: 0.8502 - val_loss: 0.5581 - val_acc: 0.8156\n",
      "Epoch 90/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.4177 - acc: 0.8515 - val_loss: 0.5608 - val_acc: 0.8135\n",
      "Epoch 91/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.4152 - acc: 0.8527 - val_loss: 0.5627 - val_acc: 0.8154\n",
      "Epoch 92/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.4080 - acc: 0.8563 - val_loss: 0.5709 - val_acc: 0.8147\n",
      "Epoch 93/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.4049 - acc: 0.8569 - val_loss: 0.5975 - val_acc: 0.8005\n",
      "Epoch 94/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.4014 - acc: 0.8584 - val_loss: 0.5541 - val_acc: 0.8141\n",
      "Epoch 95/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.3930 - acc: 0.8610 - val_loss: 0.5626 - val_acc: 0.8097\n",
      "Epoch 96/350\n",
      "50000/50000 [==============================] - 93s - loss: 0.3918 - acc: 0.8613 - val_loss: 0.5510 - val_acc: 0.8154\n",
      "Epoch 97/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.3912 - acc: 0.8615 - val_loss: 0.5429 - val_acc: 0.8177\n",
      "Epoch 98/350\n",
      "50000/50000 [==============================] - 93s - loss: 0.3813 - acc: 0.8663 - val_loss: 0.5717 - val_acc: 0.8140\n",
      "Epoch 99/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.3817 - acc: 0.8642 - val_loss: 0.5754 - val_acc: 0.8109\n",
      "Epoch 100/350\n",
      "50000/50000 [==============================] - 93s - loss: 0.3748 - acc: 0.8677 - val_loss: 0.5641 - val_acc: 0.8169\n",
      "Epoch 101/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.3722 - acc: 0.8684 - val_loss: 0.5516 - val_acc: 0.8216\n",
      "Epoch 102/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.3689 - acc: 0.8690 - val_loss: 0.5633 - val_acc: 0.8149\n",
      "Epoch 103/350\n",
      "50000/50000 [==============================] - 93s - loss: 0.3676 - acc: 0.8699 - val_loss: 0.5605 - val_acc: 0.8162\n",
      "Epoch 104/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.3614 - acc: 0.8724 - val_loss: 0.5700 - val_acc: 0.8149\n",
      "Epoch 105/350\n",
      "50000/50000 [==============================] - 93s - loss: 0.3589 - acc: 0.8727 - val_loss: 0.5465 - val_acc: 0.8244\n",
      "Epoch 106/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.3581 - acc: 0.8718 - val_loss: 0.5659 - val_acc: 0.8203\n",
      "Epoch 107/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.3538 - acc: 0.8741 - val_loss: 0.5489 - val_acc: 0.8196\n",
      "Epoch 108/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.3445 - acc: 0.8768 - val_loss: 0.5492 - val_acc: 0.8170\n",
      "Epoch 109/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.3440 - acc: 0.8784 - val_loss: 0.5593 - val_acc: 0.8224\n",
      "Epoch 110/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.3412 - acc: 0.8778 - val_loss: 0.5445 - val_acc: 0.8265\n",
      "Epoch 111/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.3320 - acc: 0.8820 - val_loss: 0.5371 - val_acc: 0.8252\n",
      "Epoch 112/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.3294 - acc: 0.8836 - val_loss: 0.5409 - val_acc: 0.8249\n",
      "Epoch 113/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.3326 - acc: 0.8814 - val_loss: 0.5327 - val_acc: 0.8266\n",
      "Epoch 114/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.3290 - acc: 0.8821 - val_loss: 0.5484 - val_acc: 0.8231\n",
      "Epoch 115/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.3248 - acc: 0.8841 - val_loss: 0.5596 - val_acc: 0.8262\n",
      "Epoch 116/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.3221 - acc: 0.8856 - val_loss: 0.5542 - val_acc: 0.8237\n",
      "Epoch 117/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.3191 - acc: 0.8869 - val_loss: 0.5624 - val_acc: 0.8199\n",
      "Epoch 118/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.3188 - acc: 0.8863 - val_loss: 0.5632 - val_acc: 0.8219\n",
      "Epoch 119/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.3100 - acc: 0.8903 - val_loss: 0.5550 - val_acc: 0.8246\n",
      "Epoch 120/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.3089 - acc: 0.8891 - val_loss: 0.5314 - val_acc: 0.8290\n",
      "Epoch 121/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.3045 - acc: 0.8914 - val_loss: 0.5624 - val_acc: 0.8232\n",
      "Epoch 122/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.3032 - acc: 0.8913 - val_loss: 0.5478 - val_acc: 0.8294\n",
      "Epoch 123/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.3008 - acc: 0.8916 - val_loss: 0.5579 - val_acc: 0.8269\n",
      "Epoch 124/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.2926 - acc: 0.8943 - val_loss: 0.5533 - val_acc: 0.8263\n",
      "Epoch 125/350\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "50000/50000 [==============================] - 92s - loss: 0.2952 - acc: 0.8951 - val_loss: 0.5524 - val_acc: 0.8277\n",
      "Epoch 126/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.2953 - acc: 0.8947 - val_loss: 0.5345 - val_acc: 0.8289\n",
      "Epoch 127/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.2840 - acc: 0.8988 - val_loss: 0.5551 - val_acc: 0.8270\n",
      "Epoch 128/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.2848 - acc: 0.8968 - val_loss: 0.5613 - val_acc: 0.8241\n",
      "Epoch 129/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.2856 - acc: 0.8964 - val_loss: 0.5461 - val_acc: 0.8273\n",
      "Epoch 130/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.2766 - acc: 0.9003 - val_loss: 0.5487 - val_acc: 0.8315\n",
      "Epoch 131/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.2816 - acc: 0.8992 - val_loss: 0.5496 - val_acc: 0.8273\n",
      "Epoch 132/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.2734 - acc: 0.9023 - val_loss: 0.5642 - val_acc: 0.8269\n",
      "Epoch 133/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.2735 - acc: 0.9020 - val_loss: 0.5447 - val_acc: 0.8322\n",
      "Epoch 134/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.2661 - acc: 0.9035 - val_loss: 0.5651 - val_acc: 0.8266\n",
      "Epoch 135/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.2679 - acc: 0.9050 - val_loss: 0.5477 - val_acc: 0.8356\n",
      "Epoch 136/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.2628 - acc: 0.9044 - val_loss: 0.5560 - val_acc: 0.8339\n",
      "Epoch 137/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.2581 - acc: 0.9080 - val_loss: 0.5797 - val_acc: 0.8285\n",
      "Epoch 138/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.2619 - acc: 0.9066 - val_loss: 0.5419 - val_acc: 0.8325\n",
      "Epoch 139/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.2575 - acc: 0.9068 - val_loss: 0.5415 - val_acc: 0.8329\n",
      "Epoch 140/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.2516 - acc: 0.9089 - val_loss: 0.5311 - val_acc: 0.8345\n",
      "Epoch 141/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.2457 - acc: 0.9123 - val_loss: 0.5604 - val_acc: 0.8276\n",
      "Epoch 142/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.2535 - acc: 0.9084 - val_loss: 0.5466 - val_acc: 0.8327\n",
      "Epoch 143/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.2477 - acc: 0.9114 - val_loss: 0.5667 - val_acc: 0.8311\n",
      "Epoch 144/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.2447 - acc: 0.9111 - val_loss: 0.5431 - val_acc: 0.8346\n",
      "Epoch 145/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.2419 - acc: 0.9141 - val_loss: 0.5668 - val_acc: 0.8340\n",
      "Epoch 146/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.2402 - acc: 0.9127 - val_loss: 0.5616 - val_acc: 0.8321\n",
      "Epoch 147/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.2416 - acc: 0.9127 - val_loss: 0.5525 - val_acc: 0.8342\n",
      "Epoch 148/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.2349 - acc: 0.9157 - val_loss: 0.5451 - val_acc: 0.8361\n",
      "Epoch 149/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.2370 - acc: 0.9134 - val_loss: 0.5544 - val_acc: 0.8337\n",
      "Epoch 150/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.2277 - acc: 0.9184 - val_loss: 0.5793 - val_acc: 0.8320\n",
      "Epoch 151/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.2312 - acc: 0.9164 - val_loss: 0.5551 - val_acc: 0.8361\n",
      "Epoch 152/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.2301 - acc: 0.9172 - val_loss: 0.5539 - val_acc: 0.8358\n",
      "Epoch 153/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.2273 - acc: 0.9179 - val_loss: 0.5858 - val_acc: 0.8273\n",
      "Epoch 154/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.2177 - acc: 0.9210 - val_loss: 0.5663 - val_acc: 0.8340\n",
      "Epoch 155/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.2216 - acc: 0.9200 - val_loss: 0.5668 - val_acc: 0.8355\n",
      "Epoch 156/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.2192 - acc: 0.9210 - val_loss: 0.5643 - val_acc: 0.8327\n",
      "Epoch 157/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.2162 - acc: 0.9223 - val_loss: 0.5952 - val_acc: 0.8260\n",
      "Epoch 158/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.2152 - acc: 0.9218 - val_loss: 0.5705 - val_acc: 0.8363\n",
      "Epoch 159/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.2162 - acc: 0.9212 - val_loss: 0.5829 - val_acc: 0.8278\n",
      "Epoch 160/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.2140 - acc: 0.9232 - val_loss: 0.5511 - val_acc: 0.8359\n",
      "Epoch 161/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.2093 - acc: 0.9236 - val_loss: 0.5714 - val_acc: 0.8355\n",
      "Epoch 162/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.2079 - acc: 0.9248 - val_loss: 0.5636 - val_acc: 0.8359\n",
      "Epoch 163/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.2062 - acc: 0.9246 - val_loss: 0.5744 - val_acc: 0.8316\n",
      "Epoch 164/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.2020 - acc: 0.9262 - val_loss: 0.5714 - val_acc: 0.8345\n",
      "Epoch 165/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.2025 - acc: 0.9273 - val_loss: 0.5749 - val_acc: 0.8321\n",
      "Epoch 166/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.1986 - acc: 0.9281 - val_loss: 0.5825 - val_acc: 0.8362\n",
      "Epoch 167/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.1984 - acc: 0.9282 - val_loss: 0.6042 - val_acc: 0.8321\n",
      "Epoch 168/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.1958 - acc: 0.9281 - val_loss: 0.5854 - val_acc: 0.8352\n",
      "Epoch 169/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.1980 - acc: 0.9286 - val_loss: 0.5865 - val_acc: 0.8400\n",
      "Epoch 170/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.1962 - acc: 0.9292 - val_loss: 0.5832 - val_acc: 0.8369\n",
      "Epoch 171/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.1929 - acc: 0.9307 - val_loss: 0.5767 - val_acc: 0.8416\n",
      "Epoch 172/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.1887 - acc: 0.9294 - val_loss: 0.5681 - val_acc: 0.8391\n",
      "Epoch 173/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.1964 - acc: 0.9289 - val_loss: 0.5921 - val_acc: 0.8398\n",
      "Epoch 174/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.1881 - acc: 0.9311 - val_loss: 0.6142 - val_acc: 0.8283\n",
      "Epoch 175/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.1875 - acc: 0.9311 - val_loss: 0.5754 - val_acc: 0.8372\n",
      "Epoch 176/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.1846 - acc: 0.9321 - val_loss: 0.6213 - val_acc: 0.8342\n",
      "Epoch 177/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.1818 - acc: 0.9336 - val_loss: 0.5922 - val_acc: 0.8324\n",
      "Epoch 178/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.1839 - acc: 0.9339 - val_loss: 0.5896 - val_acc: 0.8371\n",
      "Epoch 179/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.1822 - acc: 0.9334 - val_loss: 0.6121 - val_acc: 0.8365\n",
      "Epoch 180/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.1789 - acc: 0.9353 - val_loss: 0.5709 - val_acc: 0.8385\n",
      "Epoch 181/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.1816 - acc: 0.9346 - val_loss: 0.5820 - val_acc: 0.8379\n",
      "Epoch 182/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.1785 - acc: 0.9351 - val_loss: 0.5989 - val_acc: 0.8381\n",
      "Epoch 183/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.1764 - acc: 0.9369 - val_loss: 0.5926 - val_acc: 0.8373\n",
      "Epoch 184/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.1712 - acc: 0.9364 - val_loss: 0.5842 - val_acc: 0.8415\n",
      "Epoch 185/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.1706 - acc: 0.9395 - val_loss: 0.5914 - val_acc: 0.8404\n",
      "Epoch 186/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.1731 - acc: 0.9371 - val_loss: 0.5997 - val_acc: 0.8363\n",
      "Epoch 187/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.1717 - acc: 0.9382 - val_loss: 0.5938 - val_acc: 0.8376\n",
      "Epoch 188/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.1675 - acc: 0.9383 - val_loss: 0.6077 - val_acc: 0.8358\n",
      "Epoch 189/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.1694 - acc: 0.9385 - val_loss: 0.5811 - val_acc: 0.8388\n",
      "Epoch 190/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.1629 - acc: 0.9400 - val_loss: 0.6183 - val_acc: 0.8336\n",
      "Epoch 191/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.1666 - acc: 0.9404 - val_loss: 0.6177 - val_acc: 0.8342\n",
      "Epoch 192/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.1676 - acc: 0.9393 - val_loss: 0.5861 - val_acc: 0.8410\n",
      "Epoch 193/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.1584 - acc: 0.9407 - val_loss: 0.6432 - val_acc: 0.8341\n",
      "Epoch 194/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.1630 - acc: 0.9407 - val_loss: 0.6130 - val_acc: 0.8393\n",
      "Epoch 195/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.1557 - acc: 0.9440 - val_loss: 0.6087 - val_acc: 0.8359\n",
      "Epoch 196/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.1583 - acc: 0.9424 - val_loss: 0.6358 - val_acc: 0.8351\n",
      "Epoch 197/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.1574 - acc: 0.9434 - val_loss: 0.6256 - val_acc: 0.8384\n",
      "Epoch 198/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.1535 - acc: 0.9447 - val_loss: 0.6227 - val_acc: 0.8442\n",
      "Epoch 199/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.1581 - acc: 0.9433 - val_loss: 0.6158 - val_acc: 0.8424\n",
      "Epoch 200/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.1494 - acc: 0.9473 - val_loss: 0.6381 - val_acc: 0.8303\n",
      "Epoch 201/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.1514 - acc: 0.9458 - val_loss: 0.6174 - val_acc: 0.8386\n",
      "Epoch 202/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.1475 - acc: 0.9471 - val_loss: 0.6705 - val_acc: 0.8273\n",
      "Epoch 203/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.1547 - acc: 0.9437 - val_loss: 0.6054 - val_acc: 0.8438\n",
      "Epoch 204/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.1484 - acc: 0.9470 - val_loss: 0.6308 - val_acc: 0.8362\n",
      "Epoch 205/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.1480 - acc: 0.9460 - val_loss: 0.5994 - val_acc: 0.8419\n",
      "Epoch 206/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.1479 - acc: 0.9458 - val_loss: 0.6151 - val_acc: 0.8432\n",
      "Epoch 207/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.1473 - acc: 0.9471 - val_loss: 0.6082 - val_acc: 0.8412\n",
      "Epoch 208/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.1427 - acc: 0.9475 - val_loss: 0.6300 - val_acc: 0.8411\n",
      "Epoch 209/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.1395 - acc: 0.9495 - val_loss: 0.6133 - val_acc: 0.8428\n",
      "Epoch 210/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.1432 - acc: 0.9473 - val_loss: 0.6292 - val_acc: 0.8416\n",
      "Epoch 211/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.1398 - acc: 0.9487 - val_loss: 0.6096 - val_acc: 0.8401\n",
      "Epoch 212/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.1395 - acc: 0.9489 - val_loss: 0.6714 - val_acc: 0.8384\n",
      "Epoch 213/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.1384 - acc: 0.9498 - val_loss: 0.6223 - val_acc: 0.8472\n",
      "Epoch 214/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.1377 - acc: 0.9498 - val_loss: 0.6438 - val_acc: 0.8385\n",
      "Epoch 215/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.1364 - acc: 0.9504 - val_loss: 0.6605 - val_acc: 0.8371\n",
      "Epoch 216/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.1380 - acc: 0.9496 - val_loss: 0.6185 - val_acc: 0.8435\n",
      "Epoch 217/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.1416 - acc: 0.9484 - val_loss: 0.6113 - val_acc: 0.8434\n",
      "Epoch 218/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.1332 - acc: 0.9517 - val_loss: 0.6213 - val_acc: 0.8412\n",
      "Epoch 219/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.1364 - acc: 0.9493 - val_loss: 0.6487 - val_acc: 0.8378\n",
      "Epoch 220/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.1356 - acc: 0.9514 - val_loss: 0.6333 - val_acc: 0.8406\n",
      "Epoch 221/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.1323 - acc: 0.9527 - val_loss: 0.6325 - val_acc: 0.8410\n",
      "Epoch 222/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.1327 - acc: 0.9520 - val_loss: 0.6374 - val_acc: 0.8449\n",
      "Epoch 223/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.1319 - acc: 0.9524 - val_loss: 0.6459 - val_acc: 0.8432\n",
      "Epoch 224/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.1311 - acc: 0.9514 - val_loss: 0.6455 - val_acc: 0.8391\n",
      "Epoch 225/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.1305 - acc: 0.9523 - val_loss: 0.6514 - val_acc: 0.8385\n",
      "Epoch 226/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.1289 - acc: 0.9535 - val_loss: 0.6287 - val_acc: 0.8457\n",
      "Epoch 227/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.1257 - acc: 0.9543 - val_loss: 0.6612 - val_acc: 0.8379\n",
      "Epoch 228/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.1285 - acc: 0.9529 - val_loss: 0.6384 - val_acc: 0.8438\n",
      "Epoch 229/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.1235 - acc: 0.9537 - val_loss: 0.6327 - val_acc: 0.8416\n",
      "Epoch 230/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.1273 - acc: 0.9541 - val_loss: 0.6348 - val_acc: 0.8437\n",
      "Epoch 231/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.1217 - acc: 0.9561 - val_loss: 0.6386 - val_acc: 0.8437\n",
      "Epoch 232/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.1220 - acc: 0.9553 - val_loss: 0.6514 - val_acc: 0.8386\n",
      "Epoch 233/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.1232 - acc: 0.9558 - val_loss: 0.6436 - val_acc: 0.8439\n",
      "Epoch 234/350\n",
      "50000/50000 [==============================] - 90s - loss: 0.1242 - acc: 0.9550 - val_loss: 0.6398 - val_acc: 0.8414\n",
      "Epoch 235/350\n",
      "50000/50000 [==============================] - 90s - loss: 0.1220 - acc: 0.9545 - val_loss: 0.6335 - val_acc: 0.8422\n",
      "Epoch 236/350\n",
      "50000/50000 [==============================] - 89s - loss: 0.1197 - acc: 0.9575 - val_loss: 0.6330 - val_acc: 0.8403\n",
      "Epoch 237/350\n",
      "50000/50000 [==============================] - 89s - loss: 0.1164 - acc: 0.9576 - val_loss: 0.6734 - val_acc: 0.8368\n",
      "Epoch 238/350\n",
      "50000/50000 [==============================] - 89s - loss: 0.1196 - acc: 0.9570 - val_loss: 0.6270 - val_acc: 0.8407\n",
      "Epoch 239/350\n",
      "50000/50000 [==============================] - 89s - loss: 0.1212 - acc: 0.9563 - val_loss: 0.6449 - val_acc: 0.8446\n",
      "Epoch 240/350\n",
      "50000/50000 [==============================] - 89s - loss: 0.1151 - acc: 0.9578 - val_loss: 0.6420 - val_acc: 0.8458\n",
      "Epoch 241/350\n",
      "50000/50000 [==============================] - 89s - loss: 0.1130 - acc: 0.9587 - val_loss: 0.6580 - val_acc: 0.8429\n",
      "Epoch 242/350\n",
      "50000/50000 [==============================] - 89s - loss: 0.1172 - acc: 0.9574 - val_loss: 0.6744 - val_acc: 0.8401\n",
      "Epoch 243/350\n",
      "50000/50000 [==============================] - 89s - loss: 0.1162 - acc: 0.9580 - val_loss: 0.6335 - val_acc: 0.8485\n",
      "Epoch 244/350\n",
      "50000/50000 [==============================] - 89s - loss: 0.1138 - acc: 0.9581 - val_loss: 0.6201 - val_acc: 0.8468\n",
      "Epoch 245/350\n",
      "50000/50000 [==============================] - 89s - loss: 0.1185 - acc: 0.9569 - val_loss: 0.6458 - val_acc: 0.8421\n",
      "Epoch 246/350\n",
      "50000/50000 [==============================] - 89s - loss: 0.1149 - acc: 0.9594 - val_loss: 0.6516 - val_acc: 0.8447\n",
      "Epoch 247/350\n",
      "50000/50000 [==============================] - 89s - loss: 0.1126 - acc: 0.9584 - val_loss: 0.6875 - val_acc: 0.8345\n",
      "Epoch 248/350\n",
      "50000/50000 [==============================] - 89s - loss: 0.1104 - acc: 0.9598 - val_loss: 0.6549 - val_acc: 0.8446\n",
      "Epoch 249/350\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "50000/50000 [==============================] - 89s - loss: 0.1126 - acc: 0.9598 - val_loss: 0.6285 - val_acc: 0.8425\n",
      "Epoch 250/350\n",
      "50000/50000 [==============================] - 89s - loss: 0.1092 - acc: 0.9603 - val_loss: 0.6685 - val_acc: 0.8401\n",
      "Epoch 251/350\n",
      "50000/50000 [==============================] - 89s - loss: 0.1073 - acc: 0.9610 - val_loss: 0.6560 - val_acc: 0.8472\n",
      "Epoch 252/350\n",
      "50000/50000 [==============================] - 89s - loss: 0.1126 - acc: 0.9594 - val_loss: 0.6392 - val_acc: 0.8446\n",
      "Epoch 253/350\n",
      "50000/50000 [==============================] - 89s - loss: 0.1077 - acc: 0.9608 - val_loss: 0.6499 - val_acc: 0.8453\n",
      "Epoch 254/350\n",
      "50000/50000 [==============================] - 89s - loss: 0.1080 - acc: 0.9612 - val_loss: 0.6430 - val_acc: 0.8471\n",
      "Epoch 255/350\n",
      "50000/50000 [==============================] - 89s - loss: 0.1056 - acc: 0.9627 - val_loss: 0.6512 - val_acc: 0.8454\n",
      "Epoch 256/350\n",
      "50000/50000 [==============================] - 89s - loss: 0.1055 - acc: 0.9619 - val_loss: 0.6502 - val_acc: 0.8449\n",
      "Epoch 257/350\n",
      "50000/50000 [==============================] - 89s - loss: 0.1077 - acc: 0.9602 - val_loss: 0.6729 - val_acc: 0.8414\n",
      "Epoch 258/350\n",
      "50000/50000 [==============================] - 89s - loss: 0.1034 - acc: 0.9624 - val_loss: 0.6405 - val_acc: 0.8481\n",
      "Epoch 259/350\n",
      "50000/50000 [==============================] - 89s - loss: 0.1038 - acc: 0.9627 - val_loss: 0.6261 - val_acc: 0.8512\n",
      "Epoch 260/350\n",
      "50000/50000 [==============================] - 89s - loss: 0.1021 - acc: 0.9630 - val_loss: 0.6408 - val_acc: 0.8448\n",
      "Epoch 261/350\n",
      "50000/50000 [==============================] - 89s - loss: 0.1024 - acc: 0.9626 - val_loss: 0.6540 - val_acc: 0.8446\n",
      "Epoch 262/350\n",
      "50000/50000 [==============================] - 89s - loss: 0.1019 - acc: 0.9623 - val_loss: 0.6779 - val_acc: 0.8429\n",
      "Epoch 263/350\n",
      "50000/50000 [==============================] - 89s - loss: 0.1037 - acc: 0.9620 - val_loss: 0.6564 - val_acc: 0.8476\n",
      "Epoch 264/350\n",
      "50000/50000 [==============================] - 89s - loss: 0.1039 - acc: 0.9626 - val_loss: 0.6613 - val_acc: 0.8460\n",
      "Epoch 265/350\n",
      "50000/50000 [==============================] - 89s - loss: 0.0984 - acc: 0.9642 - val_loss: 0.6737 - val_acc: 0.8465\n",
      "Epoch 266/350\n",
      "50000/50000 [==============================] - 89s - loss: 0.1022 - acc: 0.9633 - val_loss: 0.6873 - val_acc: 0.8403\n",
      "Epoch 267/350\n",
      "50000/50000 [==============================] - 89s - loss: 0.0999 - acc: 0.9638 - val_loss: 0.6452 - val_acc: 0.8483\n",
      "Epoch 268/350\n",
      "50000/50000 [==============================] - 89s - loss: 0.1053 - acc: 0.9613 - val_loss: 0.6521 - val_acc: 0.8448\n",
      "Epoch 269/350\n",
      "50000/50000 [==============================] - 89s - loss: 0.0994 - acc: 0.9637 - val_loss: 0.6268 - val_acc: 0.8469\n",
      "Epoch 270/350\n",
      "50000/50000 [==============================] - 89s - loss: 0.0999 - acc: 0.9644 - val_loss: 0.6922 - val_acc: 0.8385\n",
      "Epoch 271/350\n",
      "50000/50000 [==============================] - 89s - loss: 0.1007 - acc: 0.9628 - val_loss: 0.6256 - val_acc: 0.8477\n",
      "Epoch 272/350\n",
      "50000/50000 [==============================] - 89s - loss: 0.1001 - acc: 0.9637 - val_loss: 0.6827 - val_acc: 0.8450\n",
      "Epoch 273/350\n",
      "50000/50000 [==============================] - 89s - loss: 0.0979 - acc: 0.9653 - val_loss: 0.6530 - val_acc: 0.8437\n",
      "Epoch 274/350\n",
      "50000/50000 [==============================] - 89s - loss: 0.0994 - acc: 0.9640 - val_loss: 0.6634 - val_acc: 0.8446\n",
      "Epoch 275/350\n",
      "50000/50000 [==============================] - 89s - loss: 0.0961 - acc: 0.9663 - val_loss: 0.6528 - val_acc: 0.8473\n",
      "Epoch 276/350\n",
      "50000/50000 [==============================] - 89s - loss: 0.0958 - acc: 0.9657 - val_loss: 0.6477 - val_acc: 0.8496\n",
      "Epoch 277/350\n",
      "50000/50000 [==============================] - 89s - loss: 0.0922 - acc: 0.9665 - val_loss: 0.6732 - val_acc: 0.8483\n",
      "Epoch 278/350\n",
      "50000/50000 [==============================] - 89s - loss: 0.0943 - acc: 0.9653 - val_loss: 0.6514 - val_acc: 0.8486\n",
      "Epoch 279/350\n",
      "50000/50000 [==============================] - 89s - loss: 0.0903 - acc: 0.9675 - val_loss: 0.6771 - val_acc: 0.8428\n",
      "Epoch 280/350\n",
      "50000/50000 [==============================] - 89s - loss: 0.0973 - acc: 0.9657 - val_loss: 0.6811 - val_acc: 0.8471\n",
      "Epoch 281/350\n",
      "50000/50000 [==============================] - 89s - loss: 0.0946 - acc: 0.9650 - val_loss: 0.6726 - val_acc: 0.8479\n",
      "Epoch 282/350\n",
      "50000/50000 [==============================] - 89s - loss: 0.0957 - acc: 0.9660 - val_loss: 0.6505 - val_acc: 0.8479\n",
      "Epoch 283/350\n",
      "50000/50000 [==============================] - 89s - loss: 0.0944 - acc: 0.9660 - val_loss: 0.6901 - val_acc: 0.8442\n",
      "Epoch 284/350\n",
      "50000/50000 [==============================] - 89s - loss: 0.0913 - acc: 0.9668 - val_loss: 0.6532 - val_acc: 0.8479\n",
      "Epoch 285/350\n",
      "50000/50000 [==============================] - 89s - loss: 0.0910 - acc: 0.9672 - val_loss: 0.6872 - val_acc: 0.8457\n",
      "Epoch 286/350\n",
      "50000/50000 [==============================] - 89s - loss: 0.0916 - acc: 0.9669 - val_loss: 0.6833 - val_acc: 0.8463\n",
      "Epoch 287/350\n",
      "50000/50000 [==============================] - 89s - loss: 0.0913 - acc: 0.9677 - val_loss: 0.6862 - val_acc: 0.8414\n",
      "Epoch 288/350\n",
      "50000/50000 [==============================] - 89s - loss: 0.0893 - acc: 0.9680 - val_loss: 0.6681 - val_acc: 0.8462\n",
      "Epoch 289/350\n",
      "50000/50000 [==============================] - 89s - loss: 0.0879 - acc: 0.9685 - val_loss: 0.7099 - val_acc: 0.8453\n",
      "Epoch 290/350\n",
      "50000/50000 [==============================] - 89s - loss: 0.0914 - acc: 0.9673 - val_loss: 0.6410 - val_acc: 0.8477\n",
      "Epoch 291/350\n",
      "50000/50000 [==============================] - 89s - loss: 0.0858 - acc: 0.9687 - val_loss: 0.6655 - val_acc: 0.8458\n",
      "Epoch 292/350\n",
      "50000/50000 [==============================] - 89s - loss: 0.0884 - acc: 0.9684 - val_loss: 0.6761 - val_acc: 0.8493\n",
      "Epoch 293/350\n",
      "50000/50000 [==============================] - 89s - loss: 0.0877 - acc: 0.9681 - val_loss: 0.6866 - val_acc: 0.8483\n",
      "Epoch 294/350\n",
      "50000/50000 [==============================] - 89s - loss: 0.0879 - acc: 0.9688 - val_loss: 0.6640 - val_acc: 0.8512\n",
      "Epoch 295/350\n",
      "50000/50000 [==============================] - 89s - loss: 0.0873 - acc: 0.9686 - val_loss: 0.6879 - val_acc: 0.8438\n",
      "Epoch 296/350\n",
      "50000/50000 [==============================] - 89s - loss: 0.0884 - acc: 0.9681 - val_loss: 0.6866 - val_acc: 0.8498\n",
      "Epoch 297/350\n",
      "50000/50000 [==============================] - 89s - loss: 0.0836 - acc: 0.9693 - val_loss: 0.7283 - val_acc: 0.8439\n",
      "Epoch 298/350\n",
      "50000/50000 [==============================] - 89s - loss: 0.0818 - acc: 0.9712 - val_loss: 0.7063 - val_acc: 0.8439\n",
      "Epoch 299/350\n",
      "50000/50000 [==============================] - 89s - loss: 0.0876 - acc: 0.9684 - val_loss: 0.6757 - val_acc: 0.8423\n",
      "Epoch 300/350\n",
      "50000/50000 [==============================] - 89s - loss: 0.0869 - acc: 0.9690 - val_loss: 0.6929 - val_acc: 0.8479\n",
      "Epoch 301/350\n",
      "50000/50000 [==============================] - 89s - loss: 0.0878 - acc: 0.9685 - val_loss: 0.6819 - val_acc: 0.8455\n",
      "Epoch 302/350\n",
      "50000/50000 [==============================] - 89s - loss: 0.0806 - acc: 0.9706 - val_loss: 0.6702 - val_acc: 0.8478\n",
      "Epoch 303/350\n",
      "50000/50000 [==============================] - 89s - loss: 0.0857 - acc: 0.9694 - val_loss: 0.6633 - val_acc: 0.8499\n",
      "Epoch 304/350\n",
      "50000/50000 [==============================] - 89s - loss: 0.0802 - acc: 0.9716 - val_loss: 0.7191 - val_acc: 0.8430\n",
      "Epoch 305/350\n",
      "50000/50000 [==============================] - 89s - loss: 0.0877 - acc: 0.9687 - val_loss: 0.6628 - val_acc: 0.8492\n",
      "Epoch 306/350\n",
      "50000/50000 [==============================] - 89s - loss: 0.0803 - acc: 0.9716 - val_loss: 0.6711 - val_acc: 0.8491\n",
      "Epoch 307/350\n",
      "50000/50000 [==============================] - 89s - loss: 0.0830 - acc: 0.9706 - val_loss: 0.6971 - val_acc: 0.8436\n",
      "Epoch 308/350\n",
      "50000/50000 [==============================] - 89s - loss: 0.0830 - acc: 0.9705 - val_loss: 0.6570 - val_acc: 0.8476\n",
      "Epoch 309/350\n",
      "50000/50000 [==============================] - 90s - loss: 0.0816 - acc: 0.9714 - val_loss: 0.6913 - val_acc: 0.8427\n",
      "Epoch 310/350\n",
      "50000/50000 [==============================] - 91s - loss: 0.0823 - acc: 0.9712 - val_loss: 0.6845 - val_acc: 0.8448\n",
      "Epoch 311/350\n",
      "50000/50000 [==============================] - 91s - loss: 0.0795 - acc: 0.9706 - val_loss: 0.6899 - val_acc: 0.8467\n",
      "Epoch 312/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.0855 - acc: 0.9694 - val_loss: 0.6879 - val_acc: 0.8488\n",
      "Epoch 313/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.0809 - acc: 0.9714 - val_loss: 0.6743 - val_acc: 0.8499\n",
      "Epoch 314/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.0759 - acc: 0.9737 - val_loss: 0.6672 - val_acc: 0.8517\n",
      "Epoch 315/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.0787 - acc: 0.9718 - val_loss: 0.6892 - val_acc: 0.8470\n",
      "Epoch 316/350\n",
      "50000/50000 [==============================] - 91s - loss: 0.0779 - acc: 0.9714 - val_loss: 0.6854 - val_acc: 0.8505\n",
      "Epoch 317/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.0811 - acc: 0.9706 - val_loss: 0.6608 - val_acc: 0.8505\n",
      "Epoch 318/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.0765 - acc: 0.9726 - val_loss: 0.6994 - val_acc: 0.8458\n",
      "Epoch 319/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.0797 - acc: 0.9718 - val_loss: 0.6663 - val_acc: 0.8511\n",
      "Epoch 320/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.0791 - acc: 0.9722 - val_loss: 0.6816 - val_acc: 0.8514\n",
      "Epoch 321/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.0810 - acc: 0.9715 - val_loss: 0.6982 - val_acc: 0.8463\n",
      "Epoch 322/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.0772 - acc: 0.9719 - val_loss: 0.6967 - val_acc: 0.8492\n",
      "Epoch 323/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.0760 - acc: 0.9721 - val_loss: 0.6865 - val_acc: 0.8487\n",
      "Epoch 324/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.0766 - acc: 0.9729 - val_loss: 0.6872 - val_acc: 0.8469\n",
      "Epoch 325/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.0772 - acc: 0.9723 - val_loss: 0.6707 - val_acc: 0.8480\n",
      "Epoch 326/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.0705 - acc: 0.9746 - val_loss: 0.6955 - val_acc: 0.8483\n",
      "Epoch 327/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.0790 - acc: 0.9713 - val_loss: 0.6830 - val_acc: 0.8501\n",
      "Epoch 328/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.0754 - acc: 0.9732 - val_loss: 0.6807 - val_acc: 0.8478\n",
      "Epoch 329/350\n",
      "50000/50000 [==============================] - 91s - loss: 0.0749 - acc: 0.9734 - val_loss: 0.7042 - val_acc: 0.8450\n",
      "Epoch 330/350\n",
      "50000/50000 [==============================] - 91s - loss: 0.0758 - acc: 0.9727 - val_loss: 0.7086 - val_acc: 0.8480\n",
      "Epoch 331/350\n",
      "50000/50000 [==============================] - 91s - loss: 0.0744 - acc: 0.9733 - val_loss: 0.6931 - val_acc: 0.8495\n",
      "Epoch 332/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.0773 - acc: 0.9723 - val_loss: 0.7019 - val_acc: 0.8481\n",
      "Epoch 333/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.0727 - acc: 0.9743 - val_loss: 0.6886 - val_acc: 0.8499\n",
      "Epoch 334/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.0761 - acc: 0.9724 - val_loss: 0.6710 - val_acc: 0.8512\n",
      "Epoch 335/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.0714 - acc: 0.9748 - val_loss: 0.7063 - val_acc: 0.8509\n",
      "Epoch 336/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.0693 - acc: 0.9749 - val_loss: 0.7140 - val_acc: 0.8522\n",
      "Epoch 337/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.0744 - acc: 0.9732 - val_loss: 0.6983 - val_acc: 0.8499\n",
      "Epoch 338/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.0728 - acc: 0.9736 - val_loss: 0.7125 - val_acc: 0.8439\n",
      "Epoch 339/350\n",
      "50000/50000 [==============================] - 91s - loss: 0.0724 - acc: 0.9733 - val_loss: 0.7075 - val_acc: 0.8481\n",
      "Epoch 340/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.0763 - acc: 0.9722 - val_loss: 0.6878 - val_acc: 0.8486\n",
      "Epoch 341/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.0709 - acc: 0.9752 - val_loss: 0.7004 - val_acc: 0.8497\n",
      "Epoch 342/350\n",
      "50000/50000 [==============================] - 91s - loss: 0.0695 - acc: 0.9754 - val_loss: 0.6897 - val_acc: 0.8520\n",
      "Epoch 343/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.0677 - acc: 0.9757 - val_loss: 0.7112 - val_acc: 0.8456\n",
      "Epoch 344/350\n",
      "50000/50000 [==============================] - 91s - loss: 0.0724 - acc: 0.9744 - val_loss: 0.7068 - val_acc: 0.8503\n",
      "Epoch 345/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.0697 - acc: 0.9749 - val_loss: 0.7034 - val_acc: 0.8504\n",
      "Epoch 346/350\n",
      "50000/50000 [==============================] - 91s - loss: 0.0739 - acc: 0.9745 - val_loss: 0.6881 - val_acc: 0.8445\n",
      "Epoch 347/350\n",
      "50000/50000 [==============================] - 91s - loss: 0.0694 - acc: 0.9758 - val_loss: 0.7061 - val_acc: 0.8487\n",
      "Epoch 348/350\n",
      "50000/50000 [==============================] - 91s - loss: 0.0697 - acc: 0.9748 - val_loss: 0.7144 - val_acc: 0.8509\n",
      "Epoch 349/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.0678 - acc: 0.9759 - val_loss: 0.7058 - val_acc: 0.8495\n",
      "Epoch 350/350\n",
      "50000/50000 [==============================] - 92s - loss: 0.0710 - acc: 0.9752 - val_loss: 0.7187 - val_acc: 0.8496\n"
     ]
    }
   ],
   "source": [
    "model_details = model.fit(images_train, class_train,\n",
    "                    batch_size = 128, # number of samples per gradient update\n",
    "                    epochs = NUM_EPOCH, # number of iterations\n",
    "                    validation_data= (images_test, class_test),\n",
    "                    callbacks=[checkpoint],\n",
    "                    verbose=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Evaluate the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 84.96%\n"
     ]
    }
   ],
   "source": [
    "scores = model.evaluate(images_test, class_test, verbose=0)\n",
    "print(\"Accuracy: %.2f%%\" % (scores[1]*100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model accuracy and loss of every epoch on the plots\n",
    "From the model it is seen that the model stopped improving its accuracy after about 60 epochs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA34AAAFRCAYAAAArTH/eAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff1dc9d9d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_model(model_details)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Augment the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "datagen = ImageDataGenerator(\n",
    "    featurewise_center=False,  # set input mean to 0 over the dataset\n",
    "    samplewise_center=False,  # set each sample mean to 0\n",
    "    featurewise_std_normalization=False,  # divide inputs by std of the dataset\n",
    "    samplewise_std_normalization=False,  # divide each input by its std\n",
    "    zca_whitening=False,  # apply ZCA whitening\n",
    "    rotation_range=45,  # randomly rotate images in the range (degrees, 0 to 180)\n",
    "    width_shift_range=0.2,  # randomly shift images horizontally (fraction of total width)\n",
    "    height_shift_range=0.2,  # randomly shift images vertically (fraction of total height)\n",
    "    horizontal_flip=True,  # randomly flip images\n",
    "    vertical_flip=False)  # randomly flip images\n",
    "\n",
    "datagen.fit(images_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Build model again"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_19 (Conv2D)           (None, 32, 32, 96)        2688      \n",
      "_________________________________________________________________\n",
      "dropout_7 (Dropout)          (None, 32, 32, 96)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_20 (Conv2D)           (None, 32, 32, 96)        83040     \n",
      "_________________________________________________________________\n",
      "conv2d_21 (Conv2D)           (None, 16, 16, 96)        83040     \n",
      "_________________________________________________________________\n",
      "dropout_8 (Dropout)          (None, 16, 16, 96)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_22 (Conv2D)           (None, 16, 16, 192)       166080    \n",
      "_________________________________________________________________\n",
      "conv2d_23 (Conv2D)           (None, 16, 16, 192)       331968    \n",
      "_________________________________________________________________\n",
      "conv2d_24 (Conv2D)           (None, 8, 8, 192)         331968    \n",
      "_________________________________________________________________\n",
      "dropout_9 (Dropout)          (None, 8, 8, 192)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_25 (Conv2D)           (None, 8, 8, 192)         331968    \n",
      "_________________________________________________________________\n",
      "activation_7 (Activation)    (None, 8, 8, 192)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_26 (Conv2D)           (None, 8, 8, 192)         37056     \n",
      "_________________________________________________________________\n",
      "activation_8 (Activation)    (None, 8, 8, 192)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_27 (Conv2D)           (None, 8, 8, 10)          1930      \n",
      "_________________________________________________________________\n",
      "global_average_pooling2d_3 ( (None, 10)                0         \n",
      "_________________________________________________________________\n",
      "activation_9 (Activation)    (None, 10)                0         \n",
      "=================================================================\n",
      "Total params: 1,369,738\n",
      "Trainable params: 1,369,738\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "augmented_model = improved_cnn_model()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Train model on the training data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save the model after every epoch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "augmented_checkpoint = ModelCheckpoint('cnn_improved_augmented.h5',  # model filename\n",
    "                             monitor='val_loss', # quantity to monitor\n",
    "                             verbose=0, # verbosity - 0 or 1\n",
    "                             save_best_only= True, # The latest best model will not be overwritten\n",
    "                             mode='auto') # The decision to overwrite model is made \n",
    "                                          # automatically depending on the quantity to monitor "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Configure the model for training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "augmented_model.compile(loss='categorical_crossentropy', # Better loss function for neural networks\n",
    "              optimizer=Adam(lr=LEARN_RATE), # Adam optimizer with 1.0e-4 learning rate\n",
    "              metrics = ['accuracy']) # Metrics to be evaluated by the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fit the model on the data provided"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/350\n",
      "1563/1562 [==============================] - 107s - loss: 1.9689 - acc: 0.2589 - val_loss: 1.6575 - val_acc: 0.3786\n",
      "Epoch 2/350\n",
      "1563/1562 [==============================] - 106s - loss: 1.7189 - acc: 0.3684 - val_loss: 1.5286 - val_acc: 0.4464\n",
      "Epoch 3/350\n",
      "1563/1562 [==============================] - 106s - loss: 1.6102 - acc: 0.4114 - val_loss: 1.4007 - val_acc: 0.4890\n",
      "Epoch 4/350\n",
      "1563/1562 [==============================] - 106s - loss: 1.5129 - acc: 0.4503 - val_loss: 1.3066 - val_acc: 0.5261\n",
      "Epoch 5/350\n",
      "1563/1562 [==============================] - 106s - loss: 1.4430 - acc: 0.4780 - val_loss: 1.2456 - val_acc: 0.5559\n",
      "Epoch 6/350\n",
      "1563/1562 [==============================] - 106s - loss: 1.3788 - acc: 0.5016 - val_loss: 1.1631 - val_acc: 0.5806\n",
      "Epoch 7/350\n",
      "1563/1562 [==============================] - 106s - loss: 1.3361 - acc: 0.5168 - val_loss: 1.1373 - val_acc: 0.5938\n",
      "Epoch 8/350\n",
      "1563/1562 [==============================] - 106s - loss: 1.3005 - acc: 0.5305 - val_loss: 1.1376 - val_acc: 0.5951\n",
      "Epoch 9/350\n",
      "1563/1562 [==============================] - 106s - loss: 1.2734 - acc: 0.5428 - val_loss: 1.0886 - val_acc: 0.6090\n",
      "Epoch 10/350\n",
      "1563/1562 [==============================] - 106s - loss: 1.2414 - acc: 0.5546 - val_loss: 1.1023 - val_acc: 0.6093\n",
      "Epoch 11/350\n",
      "1563/1562 [==============================] - 106s - loss: 1.2222 - acc: 0.5649 - val_loss: 1.0647 - val_acc: 0.6253\n",
      "Epoch 12/350\n",
      "1563/1562 [==============================] - 106s - loss: 1.1988 - acc: 0.5700 - val_loss: 1.0680 - val_acc: 0.6242\n",
      "Epoch 13/350\n",
      "1563/1562 [==============================] - 106s - loss: 1.1748 - acc: 0.5838 - val_loss: 1.0642 - val_acc: 0.6246\n",
      "Epoch 14/350\n",
      "1563/1562 [==============================] - 106s - loss: 1.1596 - acc: 0.5861 - val_loss: 0.9812 - val_acc: 0.6453\n",
      "Epoch 15/350\n",
      "1563/1562 [==============================] - 106s - loss: 1.1411 - acc: 0.5958 - val_loss: 0.9417 - val_acc: 0.6695\n",
      "Epoch 16/350\n",
      "1563/1562 [==============================] - 106s - loss: 1.1209 - acc: 0.6035 - val_loss: 0.9407 - val_acc: 0.6682\n",
      "Epoch 17/350\n",
      "1563/1562 [==============================] - 106s - loss: 1.1083 - acc: 0.6053 - val_loss: 0.9901 - val_acc: 0.6606\n",
      "Epoch 18/350\n",
      "1563/1562 [==============================] - 106s - loss: 1.0900 - acc: 0.6135 - val_loss: 0.9502 - val_acc: 0.6693\n",
      "Epoch 19/350\n",
      "1563/1562 [==============================] - 106s - loss: 1.0718 - acc: 0.6192 - val_loss: 0.9558 - val_acc: 0.6719\n",
      "Epoch 20/350\n",
      "1563/1562 [==============================] - 106s - loss: 1.0609 - acc: 0.6211 - val_loss: 0.8963 - val_acc: 0.6818\n",
      "Epoch 21/350\n",
      "1563/1562 [==============================] - 106s - loss: 1.0495 - acc: 0.6267 - val_loss: 0.8861 - val_acc: 0.6889\n",
      "Epoch 22/350\n",
      "1563/1562 [==============================] - 106s - loss: 1.0368 - acc: 0.6332 - val_loss: 0.8915 - val_acc: 0.6813\n",
      "Epoch 23/350\n",
      "1563/1562 [==============================] - 106s - loss: 1.0223 - acc: 0.6377 - val_loss: 0.8900 - val_acc: 0.6872\n",
      "Epoch 24/350\n",
      "1563/1562 [==============================] - 106s - loss: 1.0204 - acc: 0.6396 - val_loss: 0.8309 - val_acc: 0.7074\n",
      "Epoch 25/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.9972 - acc: 0.6490 - val_loss: 0.8353 - val_acc: 0.7068\n",
      "Epoch 26/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.9936 - acc: 0.6514 - val_loss: 0.8420 - val_acc: 0.7086\n",
      "Epoch 27/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.9826 - acc: 0.6540 - val_loss: 0.8441 - val_acc: 0.7101\n",
      "Epoch 28/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.9618 - acc: 0.6597 - val_loss: 0.8333 - val_acc: 0.7092\n",
      "Epoch 29/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.9592 - acc: 0.6620 - val_loss: 0.8251 - val_acc: 0.7212\n",
      "Epoch 30/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.9447 - acc: 0.6660 - val_loss: 0.8106 - val_acc: 0.7202\n",
      "Epoch 31/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.9332 - acc: 0.6715 - val_loss: 0.7898 - val_acc: 0.7246\n",
      "Epoch 32/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.9260 - acc: 0.6734 - val_loss: 0.7786 - val_acc: 0.7334\n",
      "Epoch 33/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.9128 - acc: 0.6795 - val_loss: 0.7982 - val_acc: 0.7214\n",
      "Epoch 34/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.9074 - acc: 0.6825 - val_loss: 0.7505 - val_acc: 0.7361\n",
      "Epoch 35/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.8946 - acc: 0.6872 - val_loss: 0.7615 - val_acc: 0.7358\n",
      "Epoch 36/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.8872 - acc: 0.6886 - val_loss: 0.7302 - val_acc: 0.7502\n",
      "Epoch 37/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.8776 - acc: 0.6911 - val_loss: 0.7350 - val_acc: 0.7487\n",
      "Epoch 38/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.8656 - acc: 0.6943 - val_loss: 0.7372 - val_acc: 0.7511\n",
      "Epoch 39/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.8593 - acc: 0.6984 - val_loss: 0.7601 - val_acc: 0.7426\n",
      "Epoch 40/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.8577 - acc: 0.6997 - val_loss: 0.7086 - val_acc: 0.7566\n",
      "Epoch 41/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.8446 - acc: 0.7032 - val_loss: 0.7057 - val_acc: 0.7598\n",
      "Epoch 42/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.8336 - acc: 0.7056 - val_loss: 0.6953 - val_acc: 0.7598\n",
      "Epoch 43/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.8233 - acc: 0.7113 - val_loss: 0.6960 - val_acc: 0.7662\n",
      "Epoch 44/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.8164 - acc: 0.7123 - val_loss: 0.7206 - val_acc: 0.7517\n",
      "Epoch 45/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.8079 - acc: 0.7149 - val_loss: 0.6861 - val_acc: 0.7672\n",
      "Epoch 46/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.7960 - acc: 0.7204 - val_loss: 0.6587 - val_acc: 0.7722\n",
      "Epoch 47/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.7916 - acc: 0.7204 - val_loss: 0.7090 - val_acc: 0.7582\n",
      "Epoch 48/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.7858 - acc: 0.7240 - val_loss: 0.6967 - val_acc: 0.7642\n",
      "Epoch 49/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.7833 - acc: 0.7258 - val_loss: 0.6718 - val_acc: 0.7704\n",
      "Epoch 50/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.7696 - acc: 0.7297 - val_loss: 0.6761 - val_acc: 0.7724\n",
      "Epoch 51/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.7659 - acc: 0.7340 - val_loss: 0.6658 - val_acc: 0.7739\n",
      "Epoch 52/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.7538 - acc: 0.7385 - val_loss: 0.6617 - val_acc: 0.7823\n",
      "Epoch 53/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.7533 - acc: 0.7372 - val_loss: 0.6424 - val_acc: 0.7823\n",
      "Epoch 54/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.7452 - acc: 0.7378 - val_loss: 0.6266 - val_acc: 0.7845\n",
      "Epoch 55/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.7387 - acc: 0.7408 - val_loss: 0.6430 - val_acc: 0.7781\n",
      "Epoch 56/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.7318 - acc: 0.7438 - val_loss: 0.6123 - val_acc: 0.7919\n",
      "Epoch 57/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.7314 - acc: 0.7435 - val_loss: 0.6038 - val_acc: 0.7910\n",
      "Epoch 58/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.7214 - acc: 0.7460 - val_loss: 0.6280 - val_acc: 0.7839\n",
      "Epoch 59/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.7171 - acc: 0.7481 - val_loss: 0.5900 - val_acc: 0.7988\n",
      "Epoch 60/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.7115 - acc: 0.7505 - val_loss: 0.5882 - val_acc: 0.8002\n",
      "Epoch 61/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.7049 - acc: 0.7540 - val_loss: 0.6137 - val_acc: 0.7947\n",
      "Epoch 62/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.6997 - acc: 0.7559 - val_loss: 0.6107 - val_acc: 0.7932\n",
      "Epoch 63/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.6893 - acc: 0.7599 - val_loss: 0.5877 - val_acc: 0.8007\n",
      "Epoch 64/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.6906 - acc: 0.7570 - val_loss: 0.6062 - val_acc: 0.7925\n",
      "Epoch 65/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.6895 - acc: 0.7592 - val_loss: 0.5761 - val_acc: 0.8039\n",
      "Epoch 66/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.6711 - acc: 0.7670 - val_loss: 0.5705 - val_acc: 0.8041\n",
      "Epoch 67/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.6723 - acc: 0.7655 - val_loss: 0.5790 - val_acc: 0.8032\n",
      "Epoch 68/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.6631 - acc: 0.7687 - val_loss: 0.5643 - val_acc: 0.8058\n",
      "Epoch 69/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.6628 - acc: 0.7686 - val_loss: 0.6267 - val_acc: 0.7880\n",
      "Epoch 70/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.6580 - acc: 0.7692 - val_loss: 0.5905 - val_acc: 0.8063\n",
      "Epoch 71/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.6564 - acc: 0.7687 - val_loss: 0.6011 - val_acc: 0.8001\n",
      "Epoch 72/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.6526 - acc: 0.7731 - val_loss: 0.5556 - val_acc: 0.8131\n",
      "Epoch 73/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.6471 - acc: 0.7729 - val_loss: 0.5458 - val_acc: 0.8138\n",
      "Epoch 74/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.6412 - acc: 0.7747 - val_loss: 0.5606 - val_acc: 0.8113\n",
      "Epoch 75/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.6380 - acc: 0.7757 - val_loss: 0.5466 - val_acc: 0.8149\n",
      "Epoch 76/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.6350 - acc: 0.7794 - val_loss: 0.5470 - val_acc: 0.8135\n",
      "Epoch 77/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.6321 - acc: 0.7798 - val_loss: 0.5291 - val_acc: 0.8203\n",
      "Epoch 78/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.6265 - acc: 0.7803 - val_loss: 0.5703 - val_acc: 0.8076\n",
      "Epoch 79/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.6227 - acc: 0.7810 - val_loss: 0.5734 - val_acc: 0.8101\n",
      "Epoch 80/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.6186 - acc: 0.7843 - val_loss: 0.5286 - val_acc: 0.8221\n",
      "Epoch 81/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.6134 - acc: 0.7850 - val_loss: 0.5338 - val_acc: 0.8209\n",
      "Epoch 82/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.6112 - acc: 0.7876 - val_loss: 0.5356 - val_acc: 0.8180\n",
      "Epoch 83/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.6106 - acc: 0.7843 - val_loss: 0.5434 - val_acc: 0.8199\n",
      "Epoch 84/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.6057 - acc: 0.7897 - val_loss: 0.5360 - val_acc: 0.8205\n",
      "Epoch 85/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.5969 - acc: 0.7916 - val_loss: 0.5213 - val_acc: 0.8234\n",
      "Epoch 86/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.5951 - acc: 0.7915 - val_loss: 0.5162 - val_acc: 0.8261\n",
      "Epoch 87/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.5962 - acc: 0.7930 - val_loss: 0.5173 - val_acc: 0.8268\n",
      "Epoch 88/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.5846 - acc: 0.7951 - val_loss: 0.4977 - val_acc: 0.8312\n",
      "Epoch 89/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.5855 - acc: 0.7943 - val_loss: 0.5180 - val_acc: 0.8265\n",
      "Epoch 90/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.5830 - acc: 0.7953 - val_loss: 0.5129 - val_acc: 0.8266\n",
      "Epoch 91/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.5783 - acc: 0.7988 - val_loss: 0.5373 - val_acc: 0.8180\n",
      "Epoch 92/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.5765 - acc: 0.7991 - val_loss: 0.5398 - val_acc: 0.8183\n",
      "Epoch 93/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.5731 - acc: 0.7990 - val_loss: 0.5348 - val_acc: 0.8192\n",
      "Epoch 94/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.5757 - acc: 0.7998 - val_loss: 0.5329 - val_acc: 0.8217\n",
      "Epoch 95/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.5658 - acc: 0.8035 - val_loss: 0.5068 - val_acc: 0.8284\n",
      "Epoch 96/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.5642 - acc: 0.8055 - val_loss: 0.4896 - val_acc: 0.8378\n",
      "Epoch 97/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.5601 - acc: 0.8045 - val_loss: 0.5216 - val_acc: 0.8249\n",
      "Epoch 98/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.5550 - acc: 0.8069 - val_loss: 0.5084 - val_acc: 0.8304\n",
      "Epoch 99/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.5620 - acc: 0.8042 - val_loss: 0.4852 - val_acc: 0.8370\n",
      "Epoch 100/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.5504 - acc: 0.8082 - val_loss: 0.4913 - val_acc: 0.8376\n",
      "Epoch 101/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.5546 - acc: 0.8054 - val_loss: 0.4988 - val_acc: 0.8336\n",
      "Epoch 102/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.5439 - acc: 0.8107 - val_loss: 0.5035 - val_acc: 0.8332\n",
      "Epoch 103/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.5405 - acc: 0.8103 - val_loss: 0.4831 - val_acc: 0.8354\n",
      "Epoch 104/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.5447 - acc: 0.8100 - val_loss: 0.4964 - val_acc: 0.8315\n",
      "Epoch 105/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.5365 - acc: 0.8128 - val_loss: 0.4935 - val_acc: 0.8338\n",
      "Epoch 106/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.5355 - acc: 0.8138 - val_loss: 0.5012 - val_acc: 0.8303\n",
      "Epoch 107/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.5310 - acc: 0.8130 - val_loss: 0.4862 - val_acc: 0.8362\n",
      "Epoch 108/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.5295 - acc: 0.8160 - val_loss: 0.4751 - val_acc: 0.8440\n",
      "Epoch 109/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.5270 - acc: 0.8158 - val_loss: 0.5261 - val_acc: 0.8278\n",
      "Epoch 110/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.5259 - acc: 0.8159 - val_loss: 0.4817 - val_acc: 0.8397\n",
      "Epoch 111/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.5223 - acc: 0.8170 - val_loss: 0.5033 - val_acc: 0.8348\n",
      "Epoch 112/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.5163 - acc: 0.8202 - val_loss: 0.4833 - val_acc: 0.8377\n",
      "Epoch 113/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.5182 - acc: 0.8198 - val_loss: 0.4873 - val_acc: 0.8430\n",
      "Epoch 114/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.5180 - acc: 0.8197 - val_loss: 0.4653 - val_acc: 0.8429\n",
      "Epoch 115/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.5181 - acc: 0.8200 - val_loss: 0.4765 - val_acc: 0.8424\n",
      "Epoch 116/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.5025 - acc: 0.8250 - val_loss: 0.4582 - val_acc: 0.8440\n",
      "Epoch 117/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.5118 - acc: 0.8208 - val_loss: 0.4797 - val_acc: 0.8421\n",
      "Epoch 118/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.5106 - acc: 0.8214 - val_loss: 0.4725 - val_acc: 0.8417\n",
      "Epoch 119/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.5054 - acc: 0.8227 - val_loss: 0.4363 - val_acc: 0.8537\n",
      "Epoch 120/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.5027 - acc: 0.8240 - val_loss: 0.4607 - val_acc: 0.8477\n",
      "Epoch 121/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.4983 - acc: 0.8261 - val_loss: 0.4584 - val_acc: 0.8479\n",
      "Epoch 122/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.4972 - acc: 0.8277 - val_loss: 0.4820 - val_acc: 0.8406\n",
      "Epoch 123/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.4958 - acc: 0.8258 - val_loss: 0.4867 - val_acc: 0.8444\n",
      "Epoch 124/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.4896 - acc: 0.8286 - val_loss: 0.4528 - val_acc: 0.8496\n",
      "Epoch 125/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.4846 - acc: 0.8317 - val_loss: 0.4632 - val_acc: 0.8490\n",
      "Epoch 126/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.4923 - acc: 0.8283 - val_loss: 0.4706 - val_acc: 0.8461\n",
      "Epoch 127/350\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1563/1562 [==============================] - 106s - loss: 0.4824 - acc: 0.8316 - val_loss: 0.4491 - val_acc: 0.8495\n",
      "Epoch 128/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.4841 - acc: 0.8300 - val_loss: 0.4684 - val_acc: 0.8458\n",
      "Epoch 129/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.4797 - acc: 0.8308 - val_loss: 0.4674 - val_acc: 0.8513\n",
      "Epoch 130/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.4819 - acc: 0.8332 - val_loss: 0.4468 - val_acc: 0.8512\n",
      "Epoch 131/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.4786 - acc: 0.8324 - val_loss: 0.4394 - val_acc: 0.8527\n",
      "Epoch 132/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.4737 - acc: 0.8361 - val_loss: 0.4551 - val_acc: 0.8535\n",
      "Epoch 133/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.4705 - acc: 0.8362 - val_loss: 0.4711 - val_acc: 0.8496\n",
      "Epoch 134/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.4726 - acc: 0.8343 - val_loss: 0.4874 - val_acc: 0.8454\n",
      "Epoch 135/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.4690 - acc: 0.8374 - val_loss: 0.4422 - val_acc: 0.8532\n",
      "Epoch 136/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.4701 - acc: 0.8350 - val_loss: 0.4306 - val_acc: 0.8574\n",
      "Epoch 137/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.4648 - acc: 0.8385 - val_loss: 0.4817 - val_acc: 0.8447\n",
      "Epoch 138/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.4661 - acc: 0.8374 - val_loss: 0.4540 - val_acc: 0.8521\n",
      "Epoch 139/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.4660 - acc: 0.8369 - val_loss: 0.4501 - val_acc: 0.8512\n",
      "Epoch 140/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.4588 - acc: 0.8377 - val_loss: 0.4384 - val_acc: 0.8485\n",
      "Epoch 141/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.4596 - acc: 0.8407 - val_loss: 0.4609 - val_acc: 0.8469\n",
      "Epoch 142/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.4583 - acc: 0.8393 - val_loss: 0.4689 - val_acc: 0.8468\n",
      "Epoch 143/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.4531 - acc: 0.8429 - val_loss: 0.4422 - val_acc: 0.8574\n",
      "Epoch 144/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.4574 - acc: 0.8396 - val_loss: 0.4536 - val_acc: 0.8515\n",
      "Epoch 145/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.4507 - acc: 0.8417 - val_loss: 0.4442 - val_acc: 0.8566\n",
      "Epoch 146/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.4518 - acc: 0.8405 - val_loss: 0.4482 - val_acc: 0.8527\n",
      "Epoch 147/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.4474 - acc: 0.8428 - val_loss: 0.4389 - val_acc: 0.8559\n",
      "Epoch 148/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.4486 - acc: 0.8428 - val_loss: 0.4426 - val_acc: 0.8577\n",
      "Epoch 149/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.4497 - acc: 0.8429 - val_loss: 0.4362 - val_acc: 0.8565\n",
      "Epoch 150/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.4361 - acc: 0.8465 - val_loss: 0.4598 - val_acc: 0.8523\n",
      "Epoch 151/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.4396 - acc: 0.8458 - val_loss: 0.4435 - val_acc: 0.8518\n",
      "Epoch 152/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.4380 - acc: 0.8463 - val_loss: 0.4443 - val_acc: 0.8595\n",
      "Epoch 153/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.4375 - acc: 0.8479 - val_loss: 0.4315 - val_acc: 0.8557\n",
      "Epoch 154/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.4327 - acc: 0.8485 - val_loss: 0.4429 - val_acc: 0.8549\n",
      "Epoch 155/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.4418 - acc: 0.8458 - val_loss: 0.4634 - val_acc: 0.8504\n",
      "Epoch 156/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.4339 - acc: 0.8469 - val_loss: 0.4407 - val_acc: 0.8559\n",
      "Epoch 157/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.4303 - acc: 0.8491 - val_loss: 0.4344 - val_acc: 0.8561\n",
      "Epoch 158/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.4328 - acc: 0.8488 - val_loss: 0.4264 - val_acc: 0.8587\n",
      "Epoch 159/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.4271 - acc: 0.8508 - val_loss: 0.4380 - val_acc: 0.8577\n",
      "Epoch 160/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.4254 - acc: 0.8510 - val_loss: 0.4499 - val_acc: 0.8524\n",
      "Epoch 161/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.4236 - acc: 0.8524 - val_loss: 0.4161 - val_acc: 0.8639\n",
      "Epoch 162/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.4234 - acc: 0.8525 - val_loss: 0.4149 - val_acc: 0.8614\n",
      "Epoch 163/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.4226 - acc: 0.8519 - val_loss: 0.4266 - val_acc: 0.8607\n",
      "Epoch 164/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.4243 - acc: 0.8512 - val_loss: 0.4209 - val_acc: 0.8578\n",
      "Epoch 165/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.4238 - acc: 0.8525 - val_loss: 0.4212 - val_acc: 0.8625\n",
      "Epoch 166/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.4250 - acc: 0.8516 - val_loss: 0.4148 - val_acc: 0.8633\n",
      "Epoch 167/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.4174 - acc: 0.8521 - val_loss: 0.4350 - val_acc: 0.8592\n",
      "Epoch 168/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.4177 - acc: 0.8524 - val_loss: 0.4307 - val_acc: 0.8624\n",
      "Epoch 169/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.4147 - acc: 0.8541 - val_loss: 0.4374 - val_acc: 0.8611\n",
      "Epoch 170/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.4131 - acc: 0.8563 - val_loss: 0.4317 - val_acc: 0.8618\n",
      "Epoch 171/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.4120 - acc: 0.8549 - val_loss: 0.4372 - val_acc: 0.8598\n",
      "Epoch 172/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.4126 - acc: 0.8553 - val_loss: 0.4380 - val_acc: 0.8594\n",
      "Epoch 173/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.4096 - acc: 0.8557 - val_loss: 0.4231 - val_acc: 0.8654\n",
      "Epoch 174/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.4110 - acc: 0.8556 - val_loss: 0.4070 - val_acc: 0.8658\n",
      "Epoch 175/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.4052 - acc: 0.8555 - val_loss: 0.4334 - val_acc: 0.8612\n",
      "Epoch 176/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.4021 - acc: 0.8580 - val_loss: 0.4298 - val_acc: 0.8623\n",
      "Epoch 177/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.4014 - acc: 0.8590 - val_loss: 0.4279 - val_acc: 0.8573\n",
      "Epoch 178/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.4010 - acc: 0.8591 - val_loss: 0.4123 - val_acc: 0.8637\n",
      "Epoch 179/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.4054 - acc: 0.8576 - val_loss: 0.4093 - val_acc: 0.8657\n",
      "Epoch 180/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.3965 - acc: 0.8603 - val_loss: 0.4225 - val_acc: 0.8617\n",
      "Epoch 181/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.3970 - acc: 0.8603 - val_loss: 0.4281 - val_acc: 0.8615\n",
      "Epoch 182/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.3936 - acc: 0.8617 - val_loss: 0.4198 - val_acc: 0.8634\n",
      "Epoch 183/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.3958 - acc: 0.8598 - val_loss: 0.4423 - val_acc: 0.8614\n",
      "Epoch 184/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.3951 - acc: 0.8617 - val_loss: 0.4537 - val_acc: 0.8560\n",
      "Epoch 185/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.3949 - acc: 0.8617 - val_loss: 0.4122 - val_acc: 0.8655\n",
      "Epoch 186/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.3876 - acc: 0.8655 - val_loss: 0.4166 - val_acc: 0.8645\n",
      "Epoch 187/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.3910 - acc: 0.8635 - val_loss: 0.4117 - val_acc: 0.8673\n",
      "Epoch 188/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.3872 - acc: 0.8657 - val_loss: 0.4159 - val_acc: 0.8695\n",
      "Epoch 189/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.3905 - acc: 0.8636 - val_loss: 0.4340 - val_acc: 0.8583\n",
      "Epoch 190/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.3865 - acc: 0.8644 - val_loss: 0.4276 - val_acc: 0.8652\n",
      "Epoch 191/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.3856 - acc: 0.8648 - val_loss: 0.4184 - val_acc: 0.8648\n",
      "Epoch 192/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.3879 - acc: 0.8644 - val_loss: 0.4054 - val_acc: 0.8698\n",
      "Epoch 193/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.3838 - acc: 0.8664 - val_loss: 0.4318 - val_acc: 0.8647\n",
      "Epoch 194/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.3767 - acc: 0.8690 - val_loss: 0.4060 - val_acc: 0.8722\n",
      "Epoch 195/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.3808 - acc: 0.8658 - val_loss: 0.4219 - val_acc: 0.8648\n",
      "Epoch 196/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.3797 - acc: 0.8671 - val_loss: 0.4133 - val_acc: 0.8688\n",
      "Epoch 197/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.3815 - acc: 0.8659 - val_loss: 0.4236 - val_acc: 0.8616\n",
      "Epoch 198/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.3809 - acc: 0.8675 - val_loss: 0.4157 - val_acc: 0.8680\n",
      "Epoch 199/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.3726 - acc: 0.8706 - val_loss: 0.4150 - val_acc: 0.8673\n",
      "Epoch 200/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.3764 - acc: 0.8675 - val_loss: 0.4285 - val_acc: 0.8650\n",
      "Epoch 201/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.3805 - acc: 0.8649 - val_loss: 0.4380 - val_acc: 0.8621\n",
      "Epoch 202/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.3763 - acc: 0.8689 - val_loss: 0.4069 - val_acc: 0.8687\n",
      "Epoch 203/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.3789 - acc: 0.8667 - val_loss: 0.4291 - val_acc: 0.8679\n",
      "Epoch 204/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.3746 - acc: 0.8683 - val_loss: 0.4241 - val_acc: 0.8678\n",
      "Epoch 205/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.3705 - acc: 0.8698 - val_loss: 0.4264 - val_acc: 0.8666\n",
      "Epoch 206/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.3720 - acc: 0.8694 - val_loss: 0.4439 - val_acc: 0.8571\n",
      "Epoch 207/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.3709 - acc: 0.8691 - val_loss: 0.4088 - val_acc: 0.8698\n",
      "Epoch 208/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.3711 - acc: 0.8690 - val_loss: 0.4095 - val_acc: 0.8667\n",
      "Epoch 209/350\n",
      "1563/1562 [==============================] - 106s - loss: 0.3689 - acc: 0.8693 - val_loss: 0.4006 - val_acc: 0.8720\n",
      "Epoch 210/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.3644 - acc: 0.8703 - val_loss: 0.4407 - val_acc: 0.8613\n",
      "Epoch 211/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.3626 - acc: 0.8727 - val_loss: 0.4435 - val_acc: 0.8639\n",
      "Epoch 212/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.3644 - acc: 0.8707 - val_loss: 0.4355 - val_acc: 0.8654\n",
      "Epoch 213/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.3632 - acc: 0.8725 - val_loss: 0.4145 - val_acc: 0.8710\n",
      "Epoch 214/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.3646 - acc: 0.8732 - val_loss: 0.4306 - val_acc: 0.8669\n",
      "Epoch 215/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.3571 - acc: 0.8759 - val_loss: 0.4254 - val_acc: 0.8650\n",
      "Epoch 216/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.3644 - acc: 0.8726 - val_loss: 0.4101 - val_acc: 0.8680\n",
      "Epoch 217/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.3620 - acc: 0.8733 - val_loss: 0.4054 - val_acc: 0.8743\n",
      "Epoch 218/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.3601 - acc: 0.8735 - val_loss: 0.4278 - val_acc: 0.8672\n",
      "Epoch 219/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.3568 - acc: 0.8749 - val_loss: 0.4237 - val_acc: 0.8664\n",
      "Epoch 220/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.3541 - acc: 0.8759 - val_loss: 0.4127 - val_acc: 0.8677\n",
      "Epoch 221/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.3627 - acc: 0.8732 - val_loss: 0.4108 - val_acc: 0.8719\n",
      "Epoch 222/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.3545 - acc: 0.8756 - val_loss: 0.4178 - val_acc: 0.8696\n",
      "Epoch 223/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.3532 - acc: 0.8756 - val_loss: 0.4162 - val_acc: 0.8664\n",
      "Epoch 224/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.3540 - acc: 0.8757 - val_loss: 0.4271 - val_acc: 0.8652\n",
      "Epoch 225/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.3539 - acc: 0.8753 - val_loss: 0.4146 - val_acc: 0.8694\n",
      "Epoch 226/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.3552 - acc: 0.8750 - val_loss: 0.4221 - val_acc: 0.8707\n",
      "Epoch 227/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.3514 - acc: 0.8751 - val_loss: 0.4082 - val_acc: 0.8693\n",
      "Epoch 228/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.3530 - acc: 0.8767 - val_loss: 0.4246 - val_acc: 0.8645\n",
      "Epoch 229/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.3442 - acc: 0.8798 - val_loss: 0.3922 - val_acc: 0.8723\n",
      "Epoch 230/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.3482 - acc: 0.8786 - val_loss: 0.4189 - val_acc: 0.8696\n",
      "Epoch 231/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.3484 - acc: 0.8772 - val_loss: 0.3937 - val_acc: 0.8720\n",
      "Epoch 232/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.3498 - acc: 0.8775 - val_loss: 0.4114 - val_acc: 0.8644\n",
      "Epoch 233/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.3472 - acc: 0.8781 - val_loss: 0.4472 - val_acc: 0.8619\n",
      "Epoch 234/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.3471 - acc: 0.8781 - val_loss: 0.3882 - val_acc: 0.8762\n",
      "Epoch 235/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.3431 - acc: 0.8780 - val_loss: 0.4042 - val_acc: 0.8749\n",
      "Epoch 236/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.3432 - acc: 0.8789 - val_loss: 0.4625 - val_acc: 0.8582\n",
      "Epoch 237/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.3422 - acc: 0.8796 - val_loss: 0.3998 - val_acc: 0.8753\n",
      "Epoch 238/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.3414 - acc: 0.8794 - val_loss: 0.4220 - val_acc: 0.8654\n",
      "Epoch 239/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.3389 - acc: 0.8804 - val_loss: 0.4484 - val_acc: 0.8644\n",
      "Epoch 240/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.3422 - acc: 0.8792 - val_loss: 0.4274 - val_acc: 0.8665\n",
      "Epoch 241/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.3396 - acc: 0.8807 - val_loss: 0.3991 - val_acc: 0.8718\n",
      "Epoch 242/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.3374 - acc: 0.8801 - val_loss: 0.4025 - val_acc: 0.8735\n",
      "Epoch 243/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.3391 - acc: 0.8802 - val_loss: 0.4307 - val_acc: 0.8702\n",
      "Epoch 244/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.3392 - acc: 0.8805 - val_loss: 0.4054 - val_acc: 0.8704\n",
      "Epoch 245/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.3357 - acc: 0.8814 - val_loss: 0.3937 - val_acc: 0.8756\n",
      "Epoch 246/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.3332 - acc: 0.8820 - val_loss: 0.4110 - val_acc: 0.8740\n",
      "Epoch 247/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.3357 - acc: 0.8820 - val_loss: 0.4089 - val_acc: 0.8696\n",
      "Epoch 248/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.3340 - acc: 0.8816 - val_loss: 0.4034 - val_acc: 0.8727\n",
      "Epoch 249/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.3331 - acc: 0.8841 - val_loss: 0.4214 - val_acc: 0.8674\n",
      "Epoch 250/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.3332 - acc: 0.8806 - val_loss: 0.4150 - val_acc: 0.8734\n",
      "Epoch 251/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.3305 - acc: 0.8822 - val_loss: 0.4213 - val_acc: 0.8694\n",
      "Epoch 252/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.3330 - acc: 0.8837 - val_loss: 0.4088 - val_acc: 0.8739\n",
      "Epoch 253/350\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1563/1562 [==============================] - 105s - loss: 0.3264 - acc: 0.8857 - val_loss: 0.4460 - val_acc: 0.8613\n",
      "Epoch 254/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.3294 - acc: 0.8854 - val_loss: 0.4340 - val_acc: 0.8667\n",
      "Epoch 255/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.3278 - acc: 0.8829 - val_loss: 0.4130 - val_acc: 0.8709\n",
      "Epoch 256/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.3278 - acc: 0.8844 - val_loss: 0.3942 - val_acc: 0.8737\n",
      "Epoch 257/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.3278 - acc: 0.8838 - val_loss: 0.4112 - val_acc: 0.8672\n",
      "Epoch 258/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.3277 - acc: 0.8849 - val_loss: 0.4011 - val_acc: 0.8703\n",
      "Epoch 259/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.3205 - acc: 0.8860 - val_loss: 0.4127 - val_acc: 0.8720\n",
      "Epoch 260/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.3274 - acc: 0.8846 - val_loss: 0.4122 - val_acc: 0.8738\n",
      "Epoch 261/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.3298 - acc: 0.8842 - val_loss: 0.4023 - val_acc: 0.8730\n",
      "Epoch 262/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.3219 - acc: 0.8856 - val_loss: 0.4268 - val_acc: 0.8685\n",
      "Epoch 263/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.3255 - acc: 0.8863 - val_loss: 0.4007 - val_acc: 0.8749\n",
      "Epoch 264/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.3285 - acc: 0.8835 - val_loss: 0.4044 - val_acc: 0.8736\n",
      "Epoch 265/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.3213 - acc: 0.8847 - val_loss: 0.3972 - val_acc: 0.8744\n",
      "Epoch 266/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.3191 - acc: 0.8873 - val_loss: 0.4240 - val_acc: 0.8699\n",
      "Epoch 267/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.3264 - acc: 0.8835 - val_loss: 0.4207 - val_acc: 0.8707\n",
      "Epoch 268/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.3223 - acc: 0.8865 - val_loss: 0.4088 - val_acc: 0.8756\n",
      "Epoch 269/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.3190 - acc: 0.8869 - val_loss: 0.4186 - val_acc: 0.8717\n",
      "Epoch 270/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.3194 - acc: 0.8872 - val_loss: 0.4214 - val_acc: 0.8703\n",
      "Epoch 271/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.3200 - acc: 0.8853 - val_loss: 0.3878 - val_acc: 0.8762\n",
      "Epoch 272/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.3166 - acc: 0.8898 - val_loss: 0.3989 - val_acc: 0.8736\n",
      "Epoch 273/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.3149 - acc: 0.8900 - val_loss: 0.4174 - val_acc: 0.8704\n",
      "Epoch 274/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.3148 - acc: 0.8882 - val_loss: 0.4438 - val_acc: 0.8620\n",
      "Epoch 275/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.3168 - acc: 0.8865 - val_loss: 0.4102 - val_acc: 0.8731\n",
      "Epoch 276/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.3095 - acc: 0.8905 - val_loss: 0.4147 - val_acc: 0.8749\n",
      "Epoch 277/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.3137 - acc: 0.8890 - val_loss: 0.4202 - val_acc: 0.8723\n",
      "Epoch 278/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.3123 - acc: 0.8895 - val_loss: 0.4278 - val_acc: 0.8706\n",
      "Epoch 279/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.3164 - acc: 0.8891 - val_loss: 0.4079 - val_acc: 0.8731\n",
      "Epoch 280/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.3149 - acc: 0.8886 - val_loss: 0.4206 - val_acc: 0.8696\n",
      "Epoch 281/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.3118 - acc: 0.8903 - val_loss: 0.4394 - val_acc: 0.8679\n",
      "Epoch 282/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.3131 - acc: 0.8914 - val_loss: 0.3975 - val_acc: 0.8725\n",
      "Epoch 283/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.3123 - acc: 0.8905 - val_loss: 0.4093 - val_acc: 0.8731\n",
      "Epoch 284/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.3096 - acc: 0.8907 - val_loss: 0.4063 - val_acc: 0.8749\n",
      "Epoch 285/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.3116 - acc: 0.8896 - val_loss: 0.4037 - val_acc: 0.8755\n",
      "Epoch 286/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.3095 - acc: 0.8904 - val_loss: 0.4140 - val_acc: 0.8744\n",
      "Epoch 287/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.3086 - acc: 0.8898 - val_loss: 0.4122 - val_acc: 0.8700\n",
      "Epoch 288/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.3109 - acc: 0.8908 - val_loss: 0.4250 - val_acc: 0.8739\n",
      "Epoch 289/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.3103 - acc: 0.8889 - val_loss: 0.4147 - val_acc: 0.8699\n",
      "Epoch 290/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.3043 - acc: 0.8927 - val_loss: 0.4208 - val_acc: 0.8697\n",
      "Epoch 291/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.3075 - acc: 0.8916 - val_loss: 0.4077 - val_acc: 0.8752\n",
      "Epoch 292/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.3075 - acc: 0.8911 - val_loss: 0.4165 - val_acc: 0.8743\n",
      "Epoch 293/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.3042 - acc: 0.8934 - val_loss: 0.4192 - val_acc: 0.8683\n",
      "Epoch 294/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.3051 - acc: 0.8923 - val_loss: 0.4098 - val_acc: 0.8720\n",
      "Epoch 295/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.3032 - acc: 0.8928 - val_loss: 0.4086 - val_acc: 0.8706\n",
      "Epoch 296/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.3019 - acc: 0.8945 - val_loss: 0.3995 - val_acc: 0.8753\n",
      "Epoch 297/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.2982 - acc: 0.8948 - val_loss: 0.4446 - val_acc: 0.8689\n",
      "Epoch 298/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.3090 - acc: 0.8911 - val_loss: 0.4127 - val_acc: 0.8787\n",
      "Epoch 299/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.3016 - acc: 0.8948 - val_loss: 0.4372 - val_acc: 0.8691\n",
      "Epoch 300/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.3036 - acc: 0.8917 - val_loss: 0.4032 - val_acc: 0.8757\n",
      "Epoch 301/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.3003 - acc: 0.8936 - val_loss: 0.4092 - val_acc: 0.8732\n",
      "Epoch 302/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.3018 - acc: 0.8921 - val_loss: 0.3898 - val_acc: 0.8775\n",
      "Epoch 303/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.3009 - acc: 0.8922 - val_loss: 0.4045 - val_acc: 0.8743\n",
      "Epoch 304/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.2995 - acc: 0.8946 - val_loss: 0.4158 - val_acc: 0.8754\n",
      "Epoch 305/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.3028 - acc: 0.8930 - val_loss: 0.3936 - val_acc: 0.8766\n",
      "Epoch 306/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.2999 - acc: 0.8941 - val_loss: 0.4090 - val_acc: 0.8759\n",
      "Epoch 307/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.3040 - acc: 0.8925 - val_loss: 0.4139 - val_acc: 0.8760\n",
      "Epoch 308/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.3021 - acc: 0.8934 - val_loss: 0.4051 - val_acc: 0.8757\n",
      "Epoch 309/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.2977 - acc: 0.8947 - val_loss: 0.3833 - val_acc: 0.8789\n",
      "Epoch 310/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.2881 - acc: 0.8987 - val_loss: 0.4181 - val_acc: 0.8750\n",
      "Epoch 311/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.2950 - acc: 0.8954 - val_loss: 0.4130 - val_acc: 0.8749\n",
      "Epoch 312/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.2956 - acc: 0.8960 - val_loss: 0.4032 - val_acc: 0.8775\n",
      "Epoch 313/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.2893 - acc: 0.8966 - val_loss: 0.4205 - val_acc: 0.8744\n",
      "Epoch 314/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.2930 - acc: 0.8971 - val_loss: 0.4083 - val_acc: 0.8781\n",
      "Epoch 315/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.2960 - acc: 0.8961 - val_loss: 0.4095 - val_acc: 0.8757\n",
      "Epoch 316/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.2956 - acc: 0.8960 - val_loss: 0.4079 - val_acc: 0.8764\n",
      "Epoch 317/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.2934 - acc: 0.8964 - val_loss: 0.4111 - val_acc: 0.8750\n",
      "Epoch 318/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.2931 - acc: 0.8966 - val_loss: 0.4439 - val_acc: 0.8681\n",
      "Epoch 319/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.2939 - acc: 0.8957 - val_loss: 0.4227 - val_acc: 0.8692\n",
      "Epoch 320/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.2929 - acc: 0.8969 - val_loss: 0.4244 - val_acc: 0.8786\n",
      "Epoch 321/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.2902 - acc: 0.8980 - val_loss: 0.4408 - val_acc: 0.8707\n",
      "Epoch 322/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.2910 - acc: 0.8964 - val_loss: 0.4418 - val_acc: 0.8702\n",
      "Epoch 323/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.2924 - acc: 0.8960 - val_loss: 0.4137 - val_acc: 0.8776\n",
      "Epoch 324/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.2875 - acc: 0.8985 - val_loss: 0.4455 - val_acc: 0.8679\n",
      "Epoch 325/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.2902 - acc: 0.8971 - val_loss: 0.4118 - val_acc: 0.8781\n",
      "Epoch 326/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.2868 - acc: 0.8987 - val_loss: 0.4291 - val_acc: 0.8734\n",
      "Epoch 327/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.2853 - acc: 0.8977 - val_loss: 0.4115 - val_acc: 0.8759\n",
      "Epoch 328/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.2876 - acc: 0.8973 - val_loss: 0.4282 - val_acc: 0.8748\n",
      "Epoch 329/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.2875 - acc: 0.8993 - val_loss: 0.4202 - val_acc: 0.8742\n",
      "Epoch 330/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.2848 - acc: 0.8999 - val_loss: 0.4235 - val_acc: 0.8710\n",
      "Epoch 331/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.2881 - acc: 0.8980 - val_loss: 0.4144 - val_acc: 0.8785\n",
      "Epoch 332/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.2855 - acc: 0.8984 - val_loss: 0.4383 - val_acc: 0.8726\n",
      "Epoch 333/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.2868 - acc: 0.8989 - val_loss: 0.4320 - val_acc: 0.8709\n",
      "Epoch 334/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.2861 - acc: 0.8985 - val_loss: 0.4165 - val_acc: 0.8714\n",
      "Epoch 335/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.2829 - acc: 0.8993 - val_loss: 0.4358 - val_acc: 0.8712\n",
      "Epoch 336/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.2837 - acc: 0.8982 - val_loss: 0.4180 - val_acc: 0.8717\n",
      "Epoch 337/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.2847 - acc: 0.8981 - val_loss: 0.4331 - val_acc: 0.8681\n",
      "Epoch 338/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.2821 - acc: 0.9014 - val_loss: 0.4294 - val_acc: 0.8753\n",
      "Epoch 339/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.2801 - acc: 0.8994 - val_loss: 0.4193 - val_acc: 0.8774\n",
      "Epoch 340/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.2785 - acc: 0.9013 - val_loss: 0.4315 - val_acc: 0.8734\n",
      "Epoch 341/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.2780 - acc: 0.9022 - val_loss: 0.4006 - val_acc: 0.8793\n",
      "Epoch 342/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.2855 - acc: 0.8991 - val_loss: 0.4078 - val_acc: 0.8775\n",
      "Epoch 343/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.2858 - acc: 0.9004 - val_loss: 0.3926 - val_acc: 0.8797\n",
      "Epoch 344/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.2776 - acc: 0.9013 - val_loss: 0.4074 - val_acc: 0.8750\n",
      "Epoch 345/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.2818 - acc: 0.9007 - val_loss: 0.4279 - val_acc: 0.8755\n",
      "Epoch 346/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.2834 - acc: 0.8986 - val_loss: 0.4003 - val_acc: 0.8760\n",
      "Epoch 347/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.2812 - acc: 0.9013 - val_loss: 0.4039 - val_acc: 0.8771\n",
      "Epoch 348/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.2780 - acc: 0.9021 - val_loss: 0.4160 - val_acc: 0.8751\n",
      "Epoch 349/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.2835 - acc: 0.9000 - val_loss: 0.4493 - val_acc: 0.8697\n",
      "Epoch 350/350\n",
      "1563/1562 [==============================] - 105s - loss: 0.2795 - acc: 0.9029 - val_loss: 0.4150 - val_acc: 0.8794\n"
     ]
    }
   ],
   "source": [
    "augmented_model_details = augmented_model.fit_generator(datagen.flow(images_train, class_train, batch_size = 32),\n",
    "                    steps_per_epoch = len(images_train) / 32, # number of samples per gradient update\n",
    "                    epochs = NUM_EPOCH, # number of iterations\n",
    "                    validation_data= (images_test, class_test),\n",
    "                    callbacks=[augmented_checkpoint],\n",
    "                    verbose=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Evaluate the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 87.94%\n"
     ]
    }
   ],
   "source": [
    "scores = augmented_model.evaluate(images_test, class_test, verbose=0)\n",
    "print(\"Accuracy: %.2f%%\" % (scores[1]*100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model accuracy and loss plots\n",
    "It does not look like the model overfits the data, looking into \"Model Loss\" graph, so the model can be run for larger time to get even better accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA34AAAFRCAYAAAArTH/eAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nhOjnJPETQgghhBBCiH7u/wNut97elGtPZgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa2754ceeb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_model(augmented_model_details)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Predict class for test set images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "correct, labels_pred = predict_classes(augmented_model, images_test, labels_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate accuracy using manual calculation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 87.94%\n"
     ]
    }
   ],
   "source": [
    "num_images = len(correct)\n",
    "print(\"Accuracy: %.2f%%\" % ((sum(correct)*100)/num_images))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Show some mis-classifications"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the first 9 mis-classified images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAcQAAAHkCAYAAACg818pAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQEGIBQoUKFCgAFAQYoECBQoUKADA/wOr84bXMhEG6AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa26546da90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualize_errors(images_test, labels_test, class_names, labels_pred, correct)"
   ]
  }
 ],
 "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}