CNN.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "import keras\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv('train.csv')\n",
    "test = pd.read_csv('test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "   label  pixel0  pixel1  pixel2  pixel3  pixel4  pixel5  pixel6  pixel7  \\\n",
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       "\n",
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       "3       0  ...         0         0         0         0         0         0   \n",
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       "\n",
       "   pixel780  pixel781  pixel782  pixel783  \n",
       "0         0         0         0         0  \n",
       "1         0         0         0         0  \n",
       "2         0         0         0         0  \n",
       "3         0         0         0         0  \n",
       "4         0         0         0         0  \n",
       "\n",
       "[5 rows x 785 columns]"
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     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "   pixel0  pixel1  pixel2  pixel3  pixel4  pixel5  pixel6  pixel7  pixel8  \\\n",
       "0       0       0       0       0       0       0       0       0       0   \n",
       "1       0       0       0       0       0       0       0       0       0   \n",
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       "3       0       0       0       0       0       0       0       0       0   \n",
       "4       0       0       0       0       0       0       0       0       0   \n",
       "\n",
       "   pixel9  ...  pixel774  pixel775  pixel776  pixel777  pixel778  pixel779  \\\n",
       "0       0  ...         0         0         0         0         0         0   \n",
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       "4       0  ...         0         0         0         0         0         0   \n",
       "\n",
       "   pixel780  pixel781  pixel782  pixel783  \n",
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       "\n",
       "[5 rows x 784 columns]"
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     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "test.head()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "Index(['label', 'pixel0', 'pixel1', 'pixel2', 'pixel3', 'pixel4', 'pixel5',\n",
       "       'pixel6', 'pixel7', 'pixel8',\n",
       "       ...\n",
       "       'pixel774', 'pixel775', 'pixel776', 'pixel777', 'pixel778', 'pixel779',\n",
       "       'pixel780', 'pixel781', 'pixel782', 'pixel783'],\n",
       "      dtype='object', length=785)"
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     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
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    "train.columns"
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   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "Index(['pixel0', 'pixel1', 'pixel2', 'pixel3', 'pixel4', 'pixel5', 'pixel6',\n",
       "       'pixel7', 'pixel8', 'pixel9',\n",
       "       ...\n",
       "       'pixel774', 'pixel775', 'pixel776', 'pixel777', 'pixel778', 'pixel779',\n",
       "       'pixel780', 'pixel781', 'pixel782', 'pixel783'],\n",
       "      dtype='object', length=784)"
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     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
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    "test.columns"
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  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "train['label'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(42000, 785)"
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     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "train.shape"
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  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
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       "      <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",
       "    </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>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <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>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <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",
       "<p>5 rows × 784 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   pixel0  pixel1  pixel2  pixel3  pixel4  pixel5  pixel6  pixel7  pixel8  \\\n",
       "0       0       0       0       0       0       0       0       0       0   \n",
       "1       0       0       0       0       0       0       0       0       0   \n",
       "2       0       0       0       0       0       0       0       0       0   \n",
       "3       0       0       0       0       0       0       0       0       0   \n",
       "4       0       0       0       0       0       0       0       0       0   \n",
       "\n",
       "   pixel9  ...  pixel774  pixel775  pixel776  pixel777  pixel778  pixel779  \\\n",
       "0       0  ...         0         0         0         0         0         0   \n",
       "1       0  ...         0         0         0         0         0         0   \n",
       "2       0  ...         0         0         0         0         0         0   \n",
       "3       0  ...         0         0         0         0         0         0   \n",
       "4       0  ...         0         0         0         0         0         0   \n",
       "\n",
       "   pixel780  pixel781  pixel782  pixel783  \n",
       "0         0         0         0         0  \n",
       "1         0         0         0         0  \n",
       "2         0         0         0         0  \n",
       "3         0         0         0         0  \n",
       "4         0         0         0         0  \n",
       "\n",
       "[5 rows x 784 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = train.drop(['label'], axis =1)\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 1., 0., ..., 0., 0., 0.],\n",
       "       [1., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 1., 0., ..., 0., 0., 0.],\n",
       "       ...,\n",
       "       [0., 0., 0., ..., 1., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 1.]], dtype=float32)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from keras.utils.np_utils import to_categorical\n",
    "y = train['label']\n",
    "y1 = train['label']\n",
    "y = to_categorical(y)\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "#EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20299072470>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(y1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## NORMILIZATION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>...</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>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <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>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <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>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <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>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <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>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <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",
       "<p>5 rows × 784 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   pixel0  pixel1  pixel2  pixel3  pixel4  pixel5  pixel6  pixel7  pixel8  \\\n",
       "0       0       0       0       0       0       0       0       0       0   \n",
       "1       0       0       0       0       0       0       0       0       0   \n",
       "2       0       0       0       0       0       0       0       0       0   \n",
       "3       0       0       0       0       0       0       0       0       0   \n",
       "4       0       0       0       0       0       0       0       0       0   \n",
       "\n",
       "   pixel9  ...  pixel774  pixel775  pixel776  pixel777  pixel778  pixel779  \\\n",
       "0       0  ...         0         0         0         0         0         0   \n",
       "1       0  ...         0         0         0         0         0         0   \n",
       "2       0  ...         0         0         0         0         0         0   \n",
       "3       0  ...         0         0         0         0         0         0   \n",
       "4       0  ...         0         0         0         0         0         0   \n",
       "\n",
       "   pixel780  pixel781  pixel782  pixel783  \n",
       "0         0         0         0         0  \n",
       "1         0         0         0         0  \n",
       "2         0         0         0         0  \n",
       "3         0         0         0         0  \n",
       "4         0         0         0         0  \n",
       "\n",
       "[5 rows x 784 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>...</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>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <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>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <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>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <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>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <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>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <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",
       "<p>5 rows × 784 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   pixel0  pixel1  pixel2  pixel3  pixel4  pixel5  pixel6  pixel7  pixel8  \\\n",
       "0       0       0       0       0       0       0       0       0       0   \n",
       "1       0       0       0       0       0       0       0       0       0   \n",
       "2       0       0       0       0       0       0       0       0       0   \n",
       "3       0       0       0       0       0       0       0       0       0   \n",
       "4       0       0       0       0       0       0       0       0       0   \n",
       "\n",
       "   pixel9  ...  pixel774  pixel775  pixel776  pixel777  pixel778  pixel779  \\\n",
       "0       0  ...         0         0         0         0         0         0   \n",
       "1       0  ...         0         0         0         0         0         0   \n",
       "2       0  ...         0         0         0         0         0         0   \n",
       "3       0  ...         0         0         0         0         0         0   \n",
       "4       0  ...         0         0         0         0         0         0   \n",
       "\n",
       "   pixel780  pixel781  pixel782  pixel783  \n",
       "0         0         0         0         0  \n",
       "1         0         0         0         0  \n",
       "2         0         0         0         0  \n",
       "3         0         0         0         0  \n",
       "4         0         0         0         0  \n",
       "\n",
       "[5 rows x 784 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = X/255.0\n",
    "test = test/255.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reshaping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(42000, 28, 28, 1)\n",
      "(28000, 28, 28, 1)\n"
     ]
    }
   ],
   "source": [
    "X = np.array(X).reshape(-1, 28, 28, 1)\n",
    "test = np.array(test).reshape(-1, 28, 28, 1)\n",
    "print(X.shape)\n",
    "print(test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((33600, 28, 28, 1), (8400, 28, 28, 1), (33600, 10), (8400, 10))"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state =4)\n",
    "\n",
    "X_train.shape, X_test.shape, y_train.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x1440 with 100 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axis = plt.subplots(10, 10, figsize=(20, 20))\n",
    "\n",
    "for i, ax in enumerate(axis.flat):\n",
    "    ax.imshow(X_train[i].reshape(28,28))\n",
    "    ax.axis('off')\n",
    "    ax.set_title(f'Real NUmber is {y_train[i].argmax()}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CNN(Convolutional Neural Networks)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* size of image will be height * width * 3(R:red,G:green,B:blue)\n",
    "*  cnn is feed-forward artifical neutral net work it has three layer namely mentioned below\n",
    "*      1 convlution layer\n",
    "*      2 relu layer\n",
    "*      3 pooling layer\n",
    "*      4 fully connected\n",
    "* Now in a traditional convolutional neural network architecture, there are other layers that are interspersed between these conv layers. I’d strongly encourage those interested to read up on them and understand their function and effects, but in a general sense, they provide nonlinearities and preservation of dimension that help to improve the robustness of the network and control overfitting. A classic CNN architecture would look like this.The last layer, however, is an important one and one that we will go into later on. Let’s just take a step back and review what we’ve learned so far. We talked about what the filters in the first conv layer are designed to detect. They detect low level features such as edges and curves. As one would imagine, in order to predict whether an image is a type of object, we need the network to be able to recognize higher level features such as hands or paws or ears. So let’s think about what the output of the network is after the first conv layer. It would be a 28 x 28 x 3 volume (assuming we use three 5 x 5 x 3 filters).  When we go through another conv layer, the output of the first conv layer becomes the input of the 2nd conv layer.  Now, this is a little bit harder to visualize. When we were talking about the first layer, the input was just the original image. However, when we’re talking about the 2nd conv layer, the input is the activation map(s) that result from the first layer. So each layer of the input is basically describing the locations in the original image for where certain low level features appear. Now when you apply a set of filters on top of that (pass it through the 2nd conv layer), the output will be activations that represent higher level features. Types of these features could be semicircles (combination of a curve and straight edge) or squares (combination of several straight edges). As you go through the network and go through more conv layers, you get activation maps that represent more and more complex features. By the end of the network, you may have some filters that activate when there is handwriting in the image, filters that activate when they see pink objects, etc. If you want more information about visualizing filters in ConvNets, Matt Zeiler and Rob Fergus had an excellent research paper discussing the topic. Jason Yosinski also has a video on YouTube that provides a great visual representation. Another interesting thing to note is that as you go deeper into the network, the filters begin to have a larger and larger receptive field, which means that they are able to consider information from a larger area of the original input volume (another way of putting it is that they are more responsive to a larger region of pixel space).\n",
    "Now that we can detect these high level features, the icing on the cake is attaching a fully connected layer to the end of the network. This layer basically takes an input volume (whatever the output is of the conv or ReLU or pool layer preceding it) and outputs an N dimensional vector where N is the number of classes that the program has to choose from. For example, if you wanted a digit classification program, N would be 10 since there are 10 digits. Each number in this N dimensional vector represents the probability of a certain class. For example, if the resulting vector for a digit classification program is [0 .1 .1 .75 0 0 0 0 0 .05], then this represents a 10% probability that the image is a 1, a 10% probability that the image is a 2, a 75% probability that the image is a 3, and a 5% probability that the image is a 9 (Side note: There are other ways that you can represent the output, but I am just showing the softmax approach). The way this fully connected layer works is that it looks at the output of the previous layer (which as we remember should represent the activation maps of high level features) and determines which features most correlate to a particular class. For example, if the program is predicting that some image is a dog, it will have high values in the activation maps that represent high level features like a paw or 4 legs, etc. Similarly, if the program is predicting that some image is a bird, it will have high values in the activation maps that represent high level features like wings or a beak, etc. Basically, a FC layer looks at what high level features most strongly correlate to a particular class and has particular weights so that when you compute the products between the weights and the previous layer, you get the correct probabilities for the different classes.\n",
    "*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.optimizers import Adam\n",
    "from keras.utils.np_utils import to_categorical\n",
    "from keras.layers import Dropout, Flatten ,BatchNormalization , MaxPool2D\n",
    "from keras.layers.convolutional import Conv2D\n",
    "from keras.preprocessing.image import ImageDataGenerator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#FIRST\n",
    "model = Sequential()\n",
    "\n",
    "model.add(Conv2D(filters=64, kernel_size=(3,3), activation='relu', input_shape=(28, 28, 1)))\n",
    "model.add(Conv2D(filters=48, kernel_size=(3,3), activation='relu'))\n",
    "model.add(BatchNormalization())\n",
    "model.add(MaxPool2D(pool_size=(2,2)))\n",
    "model.add(Dropout(0.2))\n",
    "\n",
    "#Second\n",
    "model.add(Conv2D(filters = 64, kernel_size = (3,3),activation ='relu'))\n",
    "model.add(Conv2D(filters = 48, kernel_size = (3,3),activation ='relu'))\n",
    "model.add(Conv2D(filters = 32, kernel_size = (3,3),activation ='relu'))\n",
    "model.add(BatchNormalization())\n",
    "model.add(MaxPool2D(pool_size=(2,2)))\n",
    "model.add(Dropout(0.2))\n",
    "\n",
    "\n",
    "#Third\n",
    "model.add(Flatten())\n",
    "model.add(Dense(256, activation = \"relu\"))\n",
    "model.add(Dense(128, activation = \"relu\"))\n",
    "model.add(Dense(64, activation = \"relu\"))\n",
    "model.add(Dropout(0.4))\n",
    "\n",
    "#Output\n",
    "model.add(Dense(10, activation = \"softmax\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_1 (Conv2D)            (None, 26, 26, 64)        640       \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 24, 24, 48)        27696     \n",
      "_________________________________________________________________\n",
      "batch_normalization_1 (Batch (None, 24, 24, 48)        192       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 12, 12, 48)        0         \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 12, 12, 48)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 10, 10, 64)        27712     \n",
      "_________________________________________________________________\n",
      "conv2d_4 (Conv2D)            (None, 8, 8, 48)          27696     \n",
      "_________________________________________________________________\n",
      "conv2d_5 (Conv2D)            (None, 6, 6, 32)          13856     \n",
      "_________________________________________________________________\n",
      "batch_normalization_2 (Batch (None, 6, 6, 32)          128       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 3, 3, 32)          0         \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 3, 3, 32)          0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 288)               0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 256)               73984     \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 128)               32896     \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 64)                8256      \n",
      "_________________________________________________________________\n",
      "dropout_3 (Dropout)          (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 10)                650       \n",
      "=================================================================\n",
      "Total params: 213,706\n",
      "Trainable params: 213,546\n",
      "Non-trainable params: 160\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(Adam(lr=0.001),loss='categorical_crossentropy',metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "##IMAGE DATA GENERATOR\n",
    "\n",
    "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=10,  # randomly rotate images in the range (degrees, 0 to 180)\n",
    "        zoom_range = 0.1, # Randomly zoom image \n",
    "        width_shift_range=0.1,  # randomly shift images horizontally (fraction of total width)\n",
    "        height_shift_range=0.1,  # randomly shift images vertically (fraction of total height)\n",
    "        horizontal_flip=False,  # randomly flip images\n",
    "        vertical_flip=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<keras.preprocessing.image.ImageDataGenerator at 0x2029c0e8828>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datagen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      " - 184s - loss: 0.5189 - accuracy: 0.8350 - val_loss: 0.0802 - val_accuracy: 0.9762\n",
      "Epoch 2/10\n",
      " - 177s - loss: 0.1451 - accuracy: 0.9598 - val_loss: 0.0627 - val_accuracy: 0.9820\n",
      "Epoch 3/10\n",
      " - 196s - loss: 0.1104 - accuracy: 0.9700 - val_loss: 0.1157 - val_accuracy: 0.9683\n",
      "Epoch 4/10\n",
      " - 194s - loss: 0.0925 - accuracy: 0.9748 - val_loss: 0.0526 - val_accuracy: 0.9873\n",
      "Epoch 5/10\n",
      " - 184s - loss: 0.0834 - accuracy: 0.9779 - val_loss: 0.0997 - val_accuracy: 0.9749\n",
      "Epoch 6/10\n",
      " - 179s - loss: 0.0767 - accuracy: 0.9800 - val_loss: 0.0426 - val_accuracy: 0.9877\n",
      "Epoch 7/10\n",
      " - 176s - loss: 0.0705 - accuracy: 0.9818 - val_loss: 0.0404 - val_accuracy: 0.9902\n",
      "Epoch 8/10\n",
      " - 177s - loss: 0.0661 - accuracy: 0.9809 - val_loss: 0.0441 - val_accuracy: 0.9879\n",
      "Epoch 9/10\n",
      " - 177s - loss: 0.0618 - accuracy: 0.9840 - val_loss: 0.0474 - val_accuracy: 0.9864\n",
      "Epoch 10/10\n",
      " - 178s - loss: 0.0567 - accuracy: 0.9846 - val_loss: 0.0628 - val_accuracy: 0.9851\n"
     ]
    }
   ],
   "source": [
    "history = model.fit_generator(datagen.flow(X_train,y_train, batch_size=56),\n",
    "                              epochs = 10, validation_data = (X_test,y_test),\n",
    "                              verbose = 2, steps_per_epoch=660)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Score: 0.06280354030205108\n",
      "Test Accuracy: 0.9851190447807312\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1368x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "fig,(ax1, ax2)=plt.subplots(1,2,figsize=(19,7))\n",
    "ax1.plot(history.history['loss'])\n",
    "ax1.plot(history.history['val_loss'])\n",
    "ax1.legend(['training','validation'])\n",
    "ax1.set_title('loss')\n",
    "ax1.set_xlabel('epoch')\n",
    "\n",
    "ax2.plot(history.history['accuracy'])\n",
    "ax2.plot(history.history['val_accuracy'])\n",
    "ax2.legend(['training','validation'])\n",
    "ax2.set_title('Acurracy')\n",
    "ax2.set_xlabel('epoch')\n",
    "\n",
    "\n",
    "\n",
    "score =model.evaluate(X_test,y_test,verbose=0)\n",
    "print('Test Score:',score[0])\n",
    "print('Test Accuracy:',score[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 25 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred = model.predict(X_test)\n",
    "X_test__ = X_test.reshape(X_test.shape[0], 28, 28)\n",
    "\n",
    "fig, axis = plt.subplots(5, 5, figsize=(20, 20))\n",
    "for i, ax in enumerate(axis.flat):\n",
    "    ax.imshow(X_test__[i])\n",
    "    ax.axis('off')\n",
    "    ax.set(title = f\"Real Number is {y_test[i].argmax()}\\nPredict Number is {y_pred[i].argmax()}\");\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "results = model.predict(test)\n",
    "\n",
    "results = np.argmax(results,axis = 1)\n",
    "\n",
    "results = pd.Series(results,name=\"Label\")\n",
    "\n",
    "submission = pd.concat([pd.Series(range(1,28001),name = \"ImageId\"),results],axis = 1)\n",
    "\n",
    "submission.to_csv(\"CNN_Digit_Recognizer.csv\",index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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