30-5-17-DL-GermanTrafficSignsClassifier-Sandbox.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Pushing German Traffic Signs Recognition Benchmark (GTSRB) beyond human performance level(98.84%)\n",
    "#### User is assumed to download data from this link [Dataset](https://d17h27t6h515a5.cloudfront.net/topher/2016/November/581faac4_traffic-signs-data/traffic-signs-data.zip)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1-  Checking system config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.5.3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Vendor:  Continuum Analytics, Inc.\n",
      "Package: mkl\n",
      "Message: trial mode expires in 30 days\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'1.1.0'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Verify python version\n",
    "import platform\n",
    "print(platform.python_version())\n",
    "# Verify tensorflow version\n",
    "import tensorflow as tf\n",
    "tf.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2- Loading independencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "#from __future__ import print_function\n",
    "#from sklearn.model_selection import train_test_split\n",
    "#from sklearn import metrics \n",
    "#from tensorflow.contrib import learn\n",
    "from sklearn.utils import shuffle #Shuffle the X and Y train data to get rid of biases\n",
    "#from tensorflow.contrib.layers import flatten\n",
    "import tensorflow as tf\n",
    "import numpy as np \n",
    "import matplotlib.pyplot as plt \n",
    "#import tensorflow.contrib.learn as skflow \n",
    "import pickle\n",
    "#import cv2\n",
    "#from tflearn.data_utils import shuffle, to_categorical\n",
    "import os\n",
    "import random\n",
    "#import skimage.data\n",
    "#import skimage.transform\n",
    "#import matplotlib\n",
    "#import matplotlib.pyplot as plt\n",
    "#import numpy as np\n",
    "#import tensorflow as tf\n",
    "# Allow image embeding in notebookb\n",
    "%matplotlib inline \n",
    "# Load pickled data\n",
    "import pickle\n",
    "import pandas as pd\n",
    "# Load data into a pandas dataframe\n",
    "from IPython.display import IFrame\n",
    "#import matplotlib.image as mpimg\n",
    "#import time\n",
    "# Load keras features\n",
    "#from __future__ import print_function\n",
    "import keras\n",
    "#from keras.datasets import mnist\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout, Flatten\n",
    "from keras.layers import Conv2D, MaxPooling2D\n",
    "from keras import backend as K\n",
    "#from keras.utils import to_categorical\n",
    "#from keras.utils import np_utils\n",
    "#import keras\n",
    "#from keras.models import Sequential\n",
    "#from keras.layers import Flatten, Dense, Lambda\n",
    "from keras.layers.convolutional import Convolution2D, Cropping2D\n",
    "#from keras.layers.pooling import MaxPooling2D\n",
    "#from keras.layers import Dense, Dropout, Activation, Flatten, Merge\n",
    "from keras.models import load_model\n",
    "# For compatibility\n",
    "#from __future__ import absolute_import\n",
    "#from __future__ import print_function\n",
    "# For manipulating data\n",
    "#import pandas as pd\n",
    "#import numpy as np\n",
    "from keras.utils import np_utils # For y values\n",
    "# For plotting\n",
    "#%matplotlib inline\n",
    "#import seaborn as sns\n",
    "# For Keras\n",
    "#from keras.models import Sequential\n",
    "#from keras.layers.core import Dense, Dropout\n",
    "#from keras.models import Model\n",
    "#from keras.applications.resnet50 import ResNet50\n",
    "#from keras.preprocessing import image\n",
    "#from keras.applications.resnet50 import preprocess_input, decode_predictions\n",
    "#import numpy as np\n",
    "#slim = tf.contrib.slim"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  3- Defining functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Define a function to extract features from a single image window\n",
    "# This function is very similar to extract_features()\n",
    "# just for a single image rather than list of images\n",
    "def single_img_features(img, vis=False):    \n",
    "    #1) Define an empty list to receive features\n",
    "    img_features = []\n",
    "    processed_image = []\n",
    "    if vis == True:\n",
    "        return img_features, processed_image\n",
    "    else:\n",
    "        img_features = img\n",
    "        return img_features\n",
    "    \n",
    "# Define a function to extract features from a list of images\n",
    "def extract_features(imgs):\n",
    "    # Create a list to append features vectors to\n",
    "    features = []\n",
    "    # Iterate through the list of images\n",
    "    for files in imgs:\n",
    "        file_features = []\n",
    "        image = files\n",
    "        file_features.append(image)\n",
    "        features.append(np.concatenate(file_features))\n",
    "    # Return list of feature vectors\n",
    "    return features    \n",
    "\n",
    "## Define a function for plotting multiple images\n",
    "def visualize(fig, rows, cols, imgs, titles):\n",
    "    for i, img in enumerate(imgs):\n",
    "        plt.subplot(rows, cols, i+1)\n",
    "        plt.title(i+1)\n",
    "        img_dims = len(img.shape)\n",
    "        if img_dims < 3:\n",
    "            plt.imshow(img, cmap='hot')\n",
    "            plt.title(titles[i])\n",
    "        else:\n",
    "            plt.imshow(img)\n",
    "            plt.title(titles[i])        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4- Loading GTSRB, 4 input data files, training, test, validation, label files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "43"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Annotes Udacity original data Log location \n",
    "csv_path = './input-data/signnames.csv' # ./ means root directory\n",
    "dframe = pd.read_csv(csv_path)\n",
    "assert dframe is not None\n",
    "len(dframe)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** observation 43 entries starting with 0 **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ClassId</th>\n",
       "      <th>SignName</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Speed limit (20km/h)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Speed limit (30km/h)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Speed limit (50km/h)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Speed limit (60km/h)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Speed limit (70km/h)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>Speed limit (80km/h)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>End of speed limit (80km/h)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>Speed limit (100km/h)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>Speed limit (120km/h)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>No passing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>No passing for vehicles over 3.5 metric tons</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>Right-of-way at the next intersection</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>Priority road</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>Yield</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>Stop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>No vehicles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>Vehicles over 3.5 metric tons prohibited</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>No entry</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>General caution</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>Dangerous curve to the left</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20</td>\n",
       "      <td>Dangerous curve to the right</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21</td>\n",
       "      <td>Double curve</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>22</td>\n",
       "      <td>Bumpy road</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>23</td>\n",
       "      <td>Slippery road</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>24</td>\n",
       "      <td>Road narrows on the right</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>25</td>\n",
       "      <td>Road work</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>26</td>\n",
       "      <td>Traffic signals</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>27</td>\n",
       "      <td>Pedestrians</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>28</td>\n",
       "      <td>Children crossing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>29</td>\n",
       "      <td>Bicycles crossing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>30</td>\n",
       "      <td>Beware of ice/snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>31</td>\n",
       "      <td>Wild animals crossing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>32</td>\n",
       "      <td>End of all speed and passing limits</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>33</td>\n",
       "      <td>Turn right ahead</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>34</td>\n",
       "      <td>Turn left ahead</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>35</td>\n",
       "      <td>Ahead only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>36</td>\n",
       "      <td>Go straight or right</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>37</td>\n",
       "      <td>Go straight or left</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>38</td>\n",
       "      <td>Keep right</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>39</td>\n",
       "      <td>Keep left</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>40</td>\n",
       "      <td>Roundabout mandatory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>41</td>\n",
       "      <td>End of no passing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>42</td>\n",
       "      <td>End of no passing by vehicles over 3.5 metric ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    ClassId                                           SignName\n",
       "0         0                               Speed limit (20km/h)\n",
       "1         1                               Speed limit (30km/h)\n",
       "2         2                               Speed limit (50km/h)\n",
       "3         3                               Speed limit (60km/h)\n",
       "4         4                               Speed limit (70km/h)\n",
       "5         5                               Speed limit (80km/h)\n",
       "6         6                        End of speed limit (80km/h)\n",
       "7         7                              Speed limit (100km/h)\n",
       "8         8                              Speed limit (120km/h)\n",
       "9         9                                         No passing\n",
       "10       10       No passing for vehicles over 3.5 metric tons\n",
       "11       11              Right-of-way at the next intersection\n",
       "12       12                                      Priority road\n",
       "13       13                                              Yield\n",
       "14       14                                               Stop\n",
       "15       15                                        No vehicles\n",
       "16       16           Vehicles over 3.5 metric tons prohibited\n",
       "17       17                                           No entry\n",
       "18       18                                    General caution\n",
       "19       19                        Dangerous curve to the left\n",
       "20       20                       Dangerous curve to the right\n",
       "21       21                                       Double curve\n",
       "22       22                                         Bumpy road\n",
       "23       23                                      Slippery road\n",
       "24       24                          Road narrows on the right\n",
       "25       25                                          Road work\n",
       "26       26                                    Traffic signals\n",
       "27       27                                        Pedestrians\n",
       "28       28                                  Children crossing\n",
       "29       29                                  Bicycles crossing\n",
       "30       30                                 Beware of ice/snow\n",
       "31       31                              Wild animals crossing\n",
       "32       32                End of all speed and passing limits\n",
       "33       33                                   Turn right ahead\n",
       "34       34                                    Turn left ahead\n",
       "35       35                                         Ahead only\n",
       "36       36                               Go straight or right\n",
       "37       37                                Go straight or left\n",
       "38       38                                         Keep right\n",
       "39       39                                          Keep left\n",
       "40       40                               Roundabout mandatory\n",
       "41       41                                  End of no passing\n",
       "42       42  End of no passing by vehicles over 3.5 metric ..."
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dframe.head(43) # See all the classess description"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Step 3: Load training and test data **\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of training sample: 34799\n",
      "Shape of training sample: (32, 32, 3)\n",
      "Number of valid sample: 4410\n",
      "Shape of valid sample: (32, 32, 3)\n",
      "Number of testing sample: 12630\n",
      "Shape of test sample: (32, 32, 3)\n"
     ]
    }
   ],
   "source": [
    "# TODO: Fill this in based on where you saved the training and testing data\n",
    "training_file = './input-data/train.p'\n",
    "testing_file = './input-data/test.p'\n",
    "valid_file = './input-data/valid.p'\n",
    "# Open 3 input data files, train, valid, test\n",
    "with open(training_file, mode='rb') as f:\n",
    "    train = pickle.load(f) \n",
    "with open(valid_file, mode='rb') as f:\n",
    "    valid = pickle.load(f)\n",
    "with open(testing_file, mode='rb') as f:\n",
    "    test = pickle.load(f)\n",
    "\n",
    "X_train, y_train = train['features'], train['labels']\n",
    "X_valid, y_valid = valid['features'], valid['labels']\n",
    "X_test, y_test = test['features'], test['labels']\n",
    "# Assert length \n",
    "assert(len(X_train) == len(y_train))\n",
    "assert(len(X_valid) == len(y_valid))\n",
    "assert(len(X_test) == len(y_test))\n",
    "# Shuffle training data\n",
    "X_train, y_train = shuffle(X_train, y_train)\n",
    "X_test, y_test = shuffle(X_test, y_test)\n",
    "X_valid, y_valid = shuffle(X_valid, y_valid)\n",
    "\n",
    "print(\"Number of training sample:\",len(X_train))\n",
    "print(\"Shape of training sample:\", X_train[0].shape)\n",
    "print(\"Number of valid sample:\", len(X_valid))\n",
    "print(\"Shape of valid sample:\", X_valid[0].shape)\n",
    "print(\"Number of testing sample:\", len(X_test))\n",
    "print(\"Shape of test sample:\", X_test[0].shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Step 4: Visualize training and test datasets **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set\n",
      "Labels: 43\n",
      "Total Images: 34799\n",
      "Validation set\n",
      "Labels: 43\n",
      "Total Images: 4410\n",
      "Test set\n",
      "Labels: 43\n",
      "Total Images: 12630\n"
     ]
    },
    {
     "data": {
      "image/png": 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CCy94Xl5enSCQqFvIs846y6+88so685szZ46vWbPG3d0ffPBB32effSLfJ0yY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\nl5oEzxhk2isuMjYIDD388D3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      "text/plain": [
       "<matplotlib.figure.Figure at 0x17cc7f787b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Data exploration visualization goes here.\n",
    "### Feel free to use as many code cells as needed.\n",
    "import matplotlib.pyplot as plt\n",
    "# Visualizations will be shown in the notebook.\n",
    "%matplotlib inline\n",
    "# Add detailed data summary \n",
    "print(\"Training set\\nLabels: {0}\\nTotal Images: {1}\".format(len(set(y_train)), len(X_train)))\n",
    "print(\"Validation set\\nLabels: {0}\\nTotal Images: {1}\".format(len(set(y_valid)), len(X_valid)))\n",
    "print(\"Test set\\nLabels: {0}\\nTotal Images: {1}\".format(len(set(y_test)), len(X_test)))\n",
    "# Histogram \n",
    "plt.title(\"Histogram: training datasets vs test datasets\")\n",
    "plt.hist(y_train, bins=43, label=\"training dataset\", color=\"cyan\")\n",
    "plt.hist(y_valid, bins=43, label=\"valid dataset\", color=\"red\")\n",
    "plt.hist(y_test, bins=43, label=\"test dataset\", color=\"yellow\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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DqP/FTYkg5wnavA+1atrV2lmxiV/XzFAHOPmA9fHI9a/9cdnYTpU01Pw7P+Ne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\nPbVND3ZLwaL5Z5DpZmwgl8B/yrmsbFRAxVnCDD6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ro+jEq8BTsgrf9Gp8Q2MvwjYB0Jw4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\n/n+bd2B20FoAPwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x17cdb562cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Find random training and test traffic signs indices\n",
    "trainingset_index = np.random.randint(0, len(X_train))\n",
    "validset_index = np.random.randint(0, len(X_valid))\n",
    "testset_index = np.random.randint(0, len(X_test))\n",
    "# Read in training/test set \n",
    "trainingset_image = X_train[trainingset_index]\n",
    "validset_image = X_valid[validset_index]\n",
    "testset_image = X_test[testset_index]\n",
    "# Display random single images for training and test sets\n",
    "trainingset_features = single_img_features(trainingset_image)\n",
    "validset_features = single_img_features(validset_image)\n",
    "testset_features = single_img_features(testset_image)\n",
    "images = [trainingset_features, validset_features, testset_features]\n",
    "titles = ['trainingset_features', 'validset_features', 'testset_features']\n",
    "fig = plt.figure(figsize=(12,3))#,dpi=80) # 12,3\n",
    "visualize(fig, 1, 3, images, titles)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5- Training 1.6M parameters network, 2 convnets layers from scratch: 92% accuracy in 3 epochs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "channels_last\n",
      "x_train shape: (34799, 32, 32, 3)\n",
      "X_train shape: (34799, 32, 32, 3)\n",
      "34799 train samples\n",
      "12630 test samples\n"
     ]
    }
   ],
   "source": [
    "from __future__ import print_function\n",
    "import keras\n",
    "from keras.datasets import mnist\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout, Flatten\n",
    "from keras.layers import Conv2D, MaxPooling2D\n",
    "from keras import backend as K\n",
    "\n",
    "batch_size = 128\n",
    "num_classes = 43 # 10\n",
    "epochs = 12\n",
    "\n",
    "# input image dimensions\n",
    "img_rows, img_cols = 32, 32 #28, 28\n",
    "\n",
    "# the data, shuffled and split between train and test sets\n",
    "#(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
    "\n",
    "print(K.image_data_format()) # Explains explictly the function \n",
    "print('x_train shape:', X_train.shape)\n",
    "\n",
    "if K.image_data_format() == 'channels_first':\n",
    "    X_train = X_train.reshape(X_train.shape[0], 3, img_rows, img_cols)\n",
    "    X_test = X_test.reshape(X_test.shape[0], 3, img_rows, img_cols)\n",
    "    input_shape = (3, img_rows, img_cols)\n",
    "else:\n",
    "    X_train = X_train.reshape(X_train.shape[0], img_rows, img_cols, 3)\n",
    "    X_test = X_test.reshape(X_test.shape[0], img_rows, img_cols, 3)\n",
    "    input_shape = (img_rows, img_cols, 3)\n",
    "\n",
    "X_train = X_train.astype('float32')\n",
    "X_test = X_test.astype('float32')\n",
    "X_train /= 255\n",
    "X_test /= 255\n",
    "print('X_train shape:', X_train.shape)\n",
    "print(X_train.shape[0], 'train samples')\n",
    "print(X_test.shape[0], 'test samples')\n",
    "\n",
    "# convert class vectors to binary class matrices\n",
    "y_train = keras.utils.to_categorical(y_train, num_classes)\n",
    "y_test = keras.utils.to_categorical(y_test, num_classes)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_1 (Conv2D)            (None, 30, 30, 32)        896       \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 28, 28, 64)        18496     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 14, 14, 64)        0         \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 14, 14, 64)        0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 12544)             0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 128)               1605760   \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 43)                5547      \n",
      "=================================================================\n",
      "Total params: 1,630,699.0\n",
      "Trainable params: 1,630,699.0\n",
      "Non-trainable params: 0.0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(Conv2D(32, kernel_size=(3, 3),\n",
    "                 activation='relu',\n",
    "                 input_shape=input_shape))\n",
    "model.add(Conv2D(64, (3, 3), activation='relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "model.add(Dropout(0.25))\n",
    "model.add(Flatten())\n",
    "model.add(Dense(128, activation='relu'))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(num_classes, activation='softmax'))\n",
    "\n",
    "model.compile(loss=keras.losses.categorical_crossentropy,\n",
    "              optimizer=keras.optimizers.Adadelta(),\n",
    "              metrics=['accuracy'])\n",
    "model.summary()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 34799 samples, validate on 12630 samples\n",
      "Epoch 1/3\n",
      "34799/34799 [==============================] - 239s - loss: 1.8064 - acc: 0.5183 - val_loss: 0.6360 - val_acc: 0.8285\n",
      "Epoch 2/3\n",
      "34799/34799 [==============================] - 237s - loss: 0.4828 - acc: 0.8638 - val_loss: 0.3780 - val_acc: 0.9072\n",
      "Epoch 3/3\n",
      "34799/34799 [==============================] - 249s - loss: 0.2774 - acc: 0.9234 - val_loss: 0.3202 - val_acc: 0.9222\n",
      "Test loss: 0.320217017333\n",
      "Test accuracy: 0.922169437809\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(X_train, y_train,\n",
    "                    batch_size=batch_size,\n",
    "                    epochs=3,\n",
    "                    verbose=1,\n",
    "                    validation_data=(X_test, y_test))\n",
    "score = model.evaluate(X_test, y_test, verbose=0)\n",
    "print('Test loss:', score[0])\n",
    "print('Test accuracy:', score[1])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.3"
  },
  "widgets": {
   "state": {},
   "version": "1.1.2"
  }
 },
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}