MNIST_digit_recog.ipynb

{
  "cells": [
    {
      "metadata": {
        "_uuid": "4f03da6b561f148faa30cbff42ec5b211e6cb6bd"
      },
      "cell_type": "markdown",
      "source": "MNIST is the \"Hello World\" of computer vision. \nIn this notebook, let's classify the MNIST digits with deap learning CNN, with tf.Keras part of the TensorFlow core API."
    },
    {
      "metadata": {
        "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
        "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
        "trusted": true
      },
      "cell_type": "code",
      "source": "# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load in \n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the \"../input/\" directory.\n# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n\nimport os\nprint(os.listdir(\"../input\"))\n\n# Any results you write to the current directory are saved as output.",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
        "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a",
        "trusted": true
      },
      "cell_type": "code",
      "source": "import tensorflow as tf\nimport pandas as pd\nfrom tensorflow import keras\nprint(tf.__version__)",
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": "1.11.0-rc1\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "_uuid": "76da6cf7a1e3c10a93668002d9d7b9b6f6833488"
      },
      "cell_type": "markdown",
      "source": "**Load train dataset**"
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "804e4cfa252c0b96303b4ebb3765f0f35e820b2c"
      },
      "cell_type": "code",
      "source": "train = pd.read_csv(\"../input/train.csv\")\nprint(\"train dataset shape is \", train.shape)\ntrain.head()\n",
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": "train dataset shape is  (42000, 785)\n",
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "execution_count": 3,
          "data": {
            "text/plain": "   label  pixel0  pixel1    ...     pixel781  pixel782  pixel783\n0      1       0       0    ...            0         0         0\n1      0       0       0    ...            0         0         0\n2      1       0       0    ...            0         0         0\n3      4       0       0    ...            0         0         0\n4      0       0       0    ...            0         0         0\n\n[5 rows x 785 columns]",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>label</th>\n      <th>pixel0</th>\n      <th>pixel1</th>\n      <th>pixel2</th>\n      <th>pixel3</th>\n      <th>pixel4</th>\n      <th>pixel5</th>\n      <th>pixel6</th>\n      <th>pixel7</th>\n      <th>pixel8</th>\n      <th>pixel9</th>\n      <th>pixel10</th>\n      <th>pixel11</th>\n      <th>pixel12</th>\n      <th>pixel13</th>\n      <th>pixel14</th>\n      <th>pixel15</th>\n      <th>pixel16</th>\n      <th>pixel17</th>\n      <th>pixel18</th>\n      <th>pixel19</th>\n      <th>pixel20</th>\n      <th>pixel21</th>\n      <th>pixel22</th>\n      <th>pixel23</th>\n      <th>pixel24</th>\n      <th>pixel25</th>\n      <th>pixel26</th>\n      <th>pixel27</th>\n      <th>pixel28</th>\n      <th>pixel29</th>\n      <th>pixel30</th>\n      <th>pixel31</th>\n      <th>pixel32</th>\n      <th>pixel33</th>\n      <th>pixel34</th>\n      <th>pixel35</th>\n      <th>pixel36</th>\n      <th>pixel37</th>\n      <th>pixel38</th>\n      <th>...</th>\n      <th>pixel744</th>\n      <th>pixel745</th>\n      <th>pixel746</th>\n      <th>pixel747</th>\n      <th>pixel748</th>\n      <th>pixel749</th>\n      <th>pixel750</th>\n      <th>pixel751</th>\n      <th>pixel752</th>\n      <th>pixel753</th>\n      <th>pixel754</th>\n      <th>pixel755</th>\n      <th>pixel756</th>\n      <th>pixel757</th>\n      <th>pixel758</th>\n      <th>pixel759</th>\n      <th>pixel760</th>\n      <th>pixel761</th>\n      <th>pixel762</th>\n      <th>pixel763</th>\n      <th>pixel764</th>\n      <th>pixel765</th>\n      <th>pixel766</th>\n      <th>pixel767</th>\n      <th>pixel768</th>\n      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<td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      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<td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "_uuid": "0059533243234a6c9346abed7cd7651344abd06e"
      },
      "cell_type": "markdown",
      "source": "**Train dataset **- split into image pixel data and labels"
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "e9aa728ef859d987236dad0fc6c833e684e15909"
      },
      "cell_type": "code",
      "source": "# All pixel values - all rows and column 1 (pixel0) to column 785 (pixel 783)\nx_train = (train.iloc[:,1:].values).astype('float32') \n# Take a look at x_train\nx_train",
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 4,
          "data": {
            "text/plain": "array([[0., 0., 0., ..., 0., 0., 0.],\n       [0., 0., 0., ..., 0., 0., 0.],\n       [0., 0., 0., ..., 0., 0., 0.],\n       ...,\n       [0., 0., 0., ..., 0., 0., 0.],\n       [0., 0., 0., ..., 0., 0., 0.],\n       [0., 0., 0., ..., 0., 0., 0.]], dtype=float32)"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "a85b0798846d1ea80271ab07005a9ed4ff03db73",
        "scrolled": true
      },
      "cell_type": "code",
      "source": "# Labels - all rows and column 0\ny_train = (train.iloc[:,0].values).astype('int32') \n\n# Take a look at y_train\ny_train",
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 5,
          "data": {
            "text/plain": "array([1, 0, 1, ..., 7, 6, 9], dtype=int32)"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "_uuid": "bd521a9508a7c2c6d9f8cc7d574f94d260b9bd4f"
      },
      "cell_type": "markdown",
      "source": "**Load test dataset**"
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "0b93484fc8799f376b5eee52eb69eae1ce9f2d40"
      },
      "cell_type": "code",
      "source": "test = pd.read_csv(\"../input/test.csv\")\nprint(\"test dataset shape is \", test.shape)\ntest.head()",
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": "test dataset shape is  (28000, 784)\n",
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "execution_count": 6,
          "data": {
            "text/plain": "   pixel0  pixel1  pixel2    ...     pixel781  pixel782  pixel783\n0       0       0       0    ...            0         0         0\n1       0       0       0    ...            0         0         0\n2       0       0       0    ...            0         0         0\n3       0       0       0    ...            0         0         0\n4       0       0       0    ...            0         0         0\n\n[5 rows x 784 columns]",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>pixel0</th>\n      <th>pixel1</th>\n      <th>pixel2</th>\n      <th>pixel3</th>\n      <th>pixel4</th>\n      <th>pixel5</th>\n      <th>pixel6</th>\n      <th>pixel7</th>\n      <th>pixel8</th>\n      <th>pixel9</th>\n      <th>pixel10</th>\n      <th>pixel11</th>\n      <th>pixel12</th>\n      <th>pixel13</th>\n      <th>pixel14</th>\n      <th>pixel15</th>\n      <th>pixel16</th>\n      <th>pixel17</th>\n      <th>pixel18</th>\n      <th>pixel19</th>\n      <th>pixel20</th>\n      <th>pixel21</th>\n      <th>pixel22</th>\n      <th>pixel23</th>\n      <th>pixel24</th>\n      <th>pixel25</th>\n      <th>pixel26</th>\n      <th>pixel27</th>\n      <th>pixel28</th>\n      <th>pixel29</th>\n      <th>pixel30</th>\n      <th>pixel31</th>\n      <th>pixel32</th>\n      <th>pixel33</th>\n      <th>pixel34</th>\n      <th>pixel35</th>\n      <th>pixel36</th>\n      <th>pixel37</th>\n      <th>pixel38</th>\n      <th>pixel39</th>\n      <th>...</th>\n      <th>pixel744</th>\n      <th>pixel745</th>\n      <th>pixel746</th>\n      <th>pixel747</th>\n      <th>pixel748</th>\n      <th>pixel749</th>\n      <th>pixel750</th>\n      <th>pixel751</th>\n      <th>pixel752</th>\n      <th>pixel753</th>\n      <th>pixel754</th>\n      <th>pixel755</th>\n      <th>pixel756</th>\n      <th>pixel757</th>\n      <th>pixel758</th>\n      <th>pixel759</th>\n      <th>pixel760</th>\n      <th>pixel761</th>\n      <th>pixel762</th>\n      <th>pixel763</th>\n      <th>pixel764</th>\n      <th>pixel765</th>\n      <th>pixel766</th>\n      <th>pixel767</th>\n      <th>pixel768</th>\n      <th>pixel769</th>\n      <th>pixel770</th>\n      <th>pixel771</th>\n      <th>pixel772</th>\n      <th>pixel773</th>\n      <th>pixel774</th>\n      <th>pixel775</th>\n      <th>pixel776</th>\n      <th>pixel777</th>\n      <th>pixel778</th>\n      <th>pixel779</th>\n      <th>pixel780</th>\n      <th>pixel781</th>\n      <th>pixel782</th>\n      <th>pixel783</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "70a5c1f6587fc719816d7a4640b73e9191223ca7"
      },
      "cell_type": "code",
      "source": "x_test = test.values.astype('float32')",
      "execution_count": 7,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "0c32362091a9c6aa2a7f56a1e0de6c97f8ce60c4"
      },
      "cell_type": "code",
      "source": "x_test",
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 8,
          "data": {
            "text/plain": "array([[0., 0., 0., ..., 0., 0., 0.],\n       [0., 0., 0., ..., 0., 0., 0.],\n       [0., 0., 0., ..., 0., 0., 0.],\n       ...,\n       [0., 0., 0., ..., 0., 0., 0.],\n       [0., 0., 0., ..., 0., 0., 0.],\n       [0., 0., 0., ..., 0., 0., 0.]], dtype=float32)"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "_uuid": "a2144d881722593bfaae48ccf46a252823a323c5"
      },
      "cell_type": "markdown",
      "source": "### Preprocess data"
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "032c16b0b811ffdf7bfef0887973399a4ebcd66f"
      },
      "cell_type": "code",
      "source": "num_classes = 10\n\n# Normalize the input data\nx_train = x_train.astype('float32') / 255\nx_test = x_test.astype('float32') / 255\n\n# Reshape input data from (28, 28) to (28, 28, 1)\nw, h = 28, 28\nx_train = x_train.reshape(-1, w, h, 1)\nx_test = x_test.reshape(-1, w, h, 1)\n\n# One-hot encode the labels\ny_train = keras.utils.to_categorical(y_train, num_classes)\n\n# Take a look at the dataset shape after conversion with keras.utils.to_categorical\nprint(\"x_train shape:\", x_train.shape, \"y_train shape:\", y_train.shape)",
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": "x_train shape: (42000, 28, 28, 1) y_train shape: (42000, 10)\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "_uuid": "a2ef2f84eb30a9b3c6fda88c6eee5f42dceeaa53"
      },
      "cell_type": "markdown",
      "source": "### Define the model architecture"
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "194dc7fba2e2e2d831a7ed1903ef025e7562db38"
      },
      "cell_type": "code",
      "source": "model = keras.Sequential()\n\n# Must define the input shape in the first layer of the neural network\nmodel.add(keras.layers.Conv2D(filters=32, kernel_size=3, padding='same', activation='relu', input_shape=(28,28,1))) \nmodel.add(keras.layers.BatchNormalization())\nmodel.add(keras.layers.MaxPooling2D(pool_size=2))\nmodel.add(keras.layers.Dropout(0.3))\n\nmodel.add(keras.layers.Conv2D(filters=64, kernel_size=3, padding='same', activation='relu'))\nmodel.add(keras.layers.BatchNormalization())\nmodel.add(keras.layers.MaxPooling2D(pool_size=2))\nmodel.add(keras.layers.Dropout(0.3))\n\nmodel.add(keras.layers.Flatten())\nmodel.add(keras.layers.Dense(128, activation='relu'))\nmodel.add(keras.layers.Dropout(0.5))\nmodel.add(keras.layers.Dense(10, activation='softmax'))\n\n# Take a look at the model summary\nmodel.summary()",
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": "_________________________________________________________________\nLayer (type)                 Output Shape              Param #   \n=================================================================\nconv2d (Conv2D)              (None, 28, 28, 32)        320       \n_________________________________________________________________\nbatch_normalization (BatchNo (None, 28, 28, 32)        128       \n_________________________________________________________________\nmax_pooling2d (MaxPooling2D) (None, 14, 14, 32)        0         \n_________________________________________________________________\ndropout (Dropout)            (None, 14, 14, 32)        0         \n_________________________________________________________________\nconv2d_1 (Conv2D)            (None, 14, 14, 64)        18496     \n_________________________________________________________________\nbatch_normalization_1 (Batch (None, 14, 14, 64)        256       \n_________________________________________________________________\nmax_pooling2d_1 (MaxPooling2 (None, 7, 7, 64)          0         \n_________________________________________________________________\ndropout_1 (Dropout)          (None, 7, 7, 64)          0         \n_________________________________________________________________\nflatten (Flatten)            (None, 3136)              0         \n_________________________________________________________________\ndense (Dense)                (None, 128)               401536    \n_________________________________________________________________\ndropout_2 (Dropout)          (None, 128)               0         \n_________________________________________________________________\ndense_1 (Dense)              (None, 10)                1290      \n=================================================================\nTotal params: 422,026\nTrainable params: 421,834\nNon-trainable params: 192\n_________________________________________________________________\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "_uuid": "8f626e553fcffe7230b683410738053e5e3f7620"
      },
      "cell_type": "markdown",
      "source": "**Model Compilation**"
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "f2725efd3a5e07e71e8731c0a9491eb41bc48e0f"
      },
      "cell_type": "code",
      "source": "model.compile(loss=keras.losses.categorical_crossentropy,\n             optimizer=keras.optimizers.Adam(),\n             metrics=['accuracy'])",
      "execution_count": 11,
      "outputs": []
    },
    {
      "metadata": {
        "_uuid": "c4674e9f7aa3abb8f7f3991239f175956c0137dc"
      },
      "cell_type": "markdown",
      "source": "**Fitting data into Model**"
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "6cbc99bdb5c366e52da68613bcaae12954b0fa07"
      },
      "cell_type": "code",
      "source": "model.fit(x_train,\n         y_train,\n         batch_size=64,\n         epochs=40)",
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": "Epoch 1/40\n42000/42000 [==============================] - 8s 186us/step - loss: 0.0436 - acc: 0.9865\nEpoch 2/40\n42000/42000 [==============================] - 8s 179us/step - loss: 0.0369 - acc: 0.9885\nEpoch 3/40\n42000/42000 [==============================] - 7s 178us/step - loss: 0.0396 - acc: 0.9882\nEpoch 4/40\n42000/42000 [==============================] - 7s 178us/step - loss: 0.0392 - acc: 0.9879\nEpoch 5/40\n42000/42000 [==============================] - 7s 177us/step - loss: 0.0406 - acc: 0.9880\nEpoch 6/40\n42000/42000 [==============================] - 7s 177us/step - loss: 0.0343 - acc: 0.9895\nEpoch 7/40\n42000/42000 [==============================] - 7s 177us/step - loss: 0.0352 - acc: 0.9897\nEpoch 8/40\n42000/42000 [==============================] - 7s 177us/step - loss: 0.0339 - acc: 0.9893\nEpoch 9/40\n42000/42000 [==============================] - 7s 177us/step - loss: 0.0325 - acc: 0.9899\nEpoch 10/40\n42000/42000 [==============================] - 7s 175us/step - loss: 0.0320 - acc: 0.9899\nEpoch 11/40\n42000/42000 [==============================] - 7s 177us/step - loss: 0.0297 - acc: 0.9910\nEpoch 12/40\n42000/42000 [==============================] - 7s 177us/step - loss: 0.0311 - acc: 0.9907\nEpoch 13/40\n42000/42000 [==============================] - 7s 176us/step - loss: 0.0310 - acc: 0.9908\nEpoch 14/40\n42000/42000 [==============================] - 7s 176us/step - loss: 0.0266 - acc: 0.9916\nEpoch 15/40\n42000/42000 [==============================] - 7s 176us/step - loss: 0.0291 - acc: 0.9913\nEpoch 16/40\n42000/42000 [==============================] - 7s 176us/step - loss: 0.0299 - acc: 0.9910\nEpoch 17/40\n42000/42000 [==============================] - 7s 178us/step - loss: 0.0283 - acc: 0.9912\nEpoch 18/40\n42000/42000 [==============================] - 7s 178us/step - loss: 0.0263 - acc: 0.9919\nEpoch 19/40\n42000/42000 [==============================] - 7s 178us/step - loss: 0.0267 - acc: 0.9915\nEpoch 20/40\n42000/42000 [==============================] - 7s 177us/step - loss: 0.0260 - acc: 0.9913\nEpoch 21/40\n42000/42000 [==============================] - 7s 176us/step - loss: 0.0273 - acc: 0.9923\nEpoch 22/40\n42000/42000 [==============================] - 7s 177us/step - loss: 0.0240 - acc: 0.9923\nEpoch 23/40\n42000/42000 [==============================] - 7s 176us/step - loss: 0.0258 - acc: 0.9924\nEpoch 24/40\n42000/42000 [==============================] - 7s 178us/step - loss: 0.0253 - acc: 0.9926\nEpoch 25/40\n42000/42000 [==============================] - 7s 178us/step - loss: 0.0248 - acc: 0.9920\nEpoch 26/40\n42000/42000 [==============================] - 7s 176us/step - loss: 0.0232 - acc: 0.9932\nEpoch 27/40\n42000/42000 [==============================] - 7s 176us/step - loss: 0.0230 - acc: 0.9929\nEpoch 28/40\n42000/42000 [==============================] - 7s 177us/step - loss: 0.0238 - acc: 0.9923\nEpoch 29/40\n42000/42000 [==============================] - 7s 177us/step - loss: 0.0242 - acc: 0.9923\nEpoch 30/40\n42000/42000 [==============================] - 7s 177us/step - loss: 0.0227 - acc: 0.9935\nEpoch 31/40\n42000/42000 [==============================] - 7s 176us/step - loss: 0.0219 - acc: 0.9931\nEpoch 32/40\n42000/42000 [==============================] - 7s 177us/step - loss: 0.0211 - acc: 0.9934\nEpoch 33/40\n42000/42000 [==============================] - 7s 176us/step - loss: 0.0225 - acc: 0.9932\nEpoch 34/40\n42000/42000 [==============================] - 7s 177us/step - loss: 0.0214 - acc: 0.9930\nEpoch 35/40\n42000/42000 [==============================] - 7s 177us/step - loss: 0.0207 - acc: 0.9935\nEpoch 36/40\n42000/42000 [==============================] - 7s 177us/step - loss: 0.0202 - acc: 0.9937\nEpoch 37/40\n42000/42000 [==============================] - 7s 177us/step - loss: 0.0183 - acc: 0.9942\nEpoch 38/40\n42000/42000 [==============================] - 7s 177us/step - loss: 0.0221 - acc: 0.9934\nEpoch 39/40\n42000/42000 [==============================] - 7s 176us/step - loss: 0.0218 - acc: 0.9932\nEpoch 40/40\n42000/42000 [==============================] - 7s 176us/step - loss: 0.0192 - acc: 0.9942\n",
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "execution_count": 14,
          "data": {
            "text/plain": "<tensorflow.python.keras.callbacks.History at 0x7fd4101330b8>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "456c034bdfc0fa34d6761b8e5a4c27b305de8f94"
      },
      "cell_type": "code",
      "source": "predictions = model.predict_classes(x_test, verbose=0)\n\nsubmissions=pd.DataFrame({\"ImageId\": list(range(1,len(predictions)+1)),\n                         \"Label\": predictions})\nsubmissions.to_csv(\"mnist_digi_recog.csv\", index=False, header=True)",
      "execution_count": 15,
      "outputs": []
    }
  ],
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      "name": "python3"
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