MNIST Image Classification on Digits.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### MNIST Image Classification on Digits\n",
    "* In this project I am using MNIST dataset of digits images to classify each image into a class of digits 0-9.\n",
    "* KAGGLE LINK: https://www.kaggle.com/c/digit-recognizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import seaborn as sns\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "mnist_train = pd.read_csv(\"digit_datasets/digit_train.csv\")\n",
    "mnist_test = pd.read_csv(\"digit_datasets/digit_test.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(42000, 785) (28000, 784)\n"
     ]
    }
   ],
   "source": [
    "print(mnist_train.shape, mnist_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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       "   label  pixel0  pixel1  pixel2  pixel3  pixel4  pixel5  pixel6  pixel7  \\\n",
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       "[5 rows x 785 columns]"
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     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mnist_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "      <td>253.000000</td>\n",
       "      <td>253.00000</td>\n",
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      ],
      "text/plain": [
       "              label   pixel0   pixel1   pixel2   pixel3   pixel4   pixel5  \\\n",
       "count  42000.000000  42000.0  42000.0  42000.0  42000.0  42000.0  42000.0   \n",
       "mean       4.456643      0.0      0.0      0.0      0.0      0.0      0.0   \n",
       "std        2.887730      0.0      0.0      0.0      0.0      0.0      0.0   \n",
       "min        0.000000      0.0      0.0      0.0      0.0      0.0      0.0   \n",
       "25%        2.000000      0.0      0.0      0.0      0.0      0.0      0.0   \n",
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       "max        9.000000      0.0      0.0      0.0      0.0      0.0      0.0   \n",
       "\n",
       "        pixel6   pixel7   pixel8  ...      pixel774      pixel775  \\\n",
       "count  42000.0  42000.0  42000.0  ...  42000.000000  42000.000000   \n",
       "mean       0.0      0.0      0.0  ...      0.219286      0.117095   \n",
       "std        0.0      0.0      0.0  ...      6.312890      4.633819   \n",
       "min        0.0      0.0      0.0  ...      0.000000      0.000000   \n",
       "25%        0.0      0.0      0.0  ...      0.000000      0.000000   \n",
       "50%        0.0      0.0      0.0  ...      0.000000      0.000000   \n",
       "75%        0.0      0.0      0.0  ...      0.000000      0.000000   \n",
       "max        0.0      0.0      0.0  ...    254.000000    254.000000   \n",
       "\n",
       "           pixel776     pixel777      pixel778      pixel779  pixel780  \\\n",
       "count  42000.000000  42000.00000  42000.000000  42000.000000   42000.0   \n",
       "mean       0.059024      0.02019      0.017238      0.002857       0.0   \n",
       "std        3.274488      1.75987      1.894498      0.414264       0.0   \n",
       "min        0.000000      0.00000      0.000000      0.000000       0.0   \n",
       "25%        0.000000      0.00000      0.000000      0.000000       0.0   \n",
       "50%        0.000000      0.00000      0.000000      0.000000       0.0   \n",
       "75%        0.000000      0.00000      0.000000      0.000000       0.0   \n",
       "max      253.000000    253.00000    254.000000     62.000000       0.0   \n",
       "\n",
       "       pixel781  pixel782  pixel783  \n",
       "count   42000.0   42000.0   42000.0  \n",
       "mean        0.0       0.0       0.0  \n",
       "std         0.0       0.0       0.0  \n",
       "min         0.0       0.0       0.0  \n",
       "25%         0.0       0.0       0.0  \n",
       "50%         0.0       0.0       0.0  \n",
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       "\n",
       "[8 rows x 785 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mnist_train.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Cleaning and Normalization\n",
    "Data is totally clean in this case."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mnist_train.isna().sum().sum()\n",
    "# There is no empty field. Data is clean already."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "mnist_train_data = mnist_train.loc[:, \"pixel0\":]\n",
    "mnist_train_label = mnist_train.loc[:, \"label\"]\n",
    "\n",
    "# Data Normalization\n",
    "mnist_train_data = mnist_train_data/255.0\n",
    "mnist_test = mnist_test/255.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visulaize a single digit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "digit_array = mnist_train.loc[3, \"pixel0\":]\n",
    "arr = np.array(digit_array) #.reshape(a, (28,28))\n",
    "image_array = np.reshape(arr, (28,28))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IMAGE LABEL: 4\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "digit_img = plt.imshow(image_array, cmap=plt.cm.binary)\n",
    "plt.colorbar(digit_img)\n",
    "print(\"IMAGE LABEL: {}\".format(mnist_train.loc[3, \"label\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[4132, 4684, 4177, 4351, 4072, 3795, 4137, 4401, 4063, 4188]\n"
     ]
    },
    {
     "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(mnist_train.label)\n",
    "print(list(mnist_train.label.value_counts().sort_index()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Splitting dataset into training data and validation data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(29400, 784) (29400,) (12600, 784) (12600,)\n"
     ]
    }
   ],
   "source": [
    "X_train, X_vc, y_train, y_vc = train_test_split(mnist_train_data, mnist_train_label, test_size=0.3, random_state=42)\n",
    "print(X_train.shape, y_train.shape, X_vc.shape, y_vc.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train.reset_index(inplace = True, drop=True)\n",
    "X_vc.reset_index(inplace = True, drop=True)\n",
    "y_train = y_train.reset_index(drop=True)\n",
    "y_vc = y_vc.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training data\n",
    "Training a neural network with one input layer, one hidden layer and one output layer for learning the digits in images."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow import keras\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\paras\\Anaconda3\\lib\\site-packages\\tensorflow_core\\python\\ops\\resource_variable_ops.py:1630: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "If using Keras pass *_constraint arguments to layers.\n"
     ]
    }
   ],
   "source": [
    "model = keras.Sequential([\n",
    "    keras.layers.Flatten(input_shape=(784,)), \n",
    "    keras.layers.Dense(256, \"relu\"), \n",
    "    keras.layers.Dense(10, activation=\"softmax\")\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer=\"adam\", loss=\"sparse_categorical_crossentropy\", metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 29400 samples\n",
      "Epoch 1/500\n",
      "29400/29400 [==============================] - 1s 33us/sample - loss: 0.6542 - acc: 0.8258\n",
      "Epoch 2/500\n",
      "29400/29400 [==============================] - 1s 30us/sample - loss: 0.2726 - acc: 0.9244\n",
      "Epoch 3/500\n",
      "29400/29400 [==============================] - 1s 30us/sample - loss: 0.2162 - acc: 0.93910s - loss: 0.2151 \n",
      "Epoch 4/500\n",
      "29400/29400 [==============================] - 1s 30us/sample - loss: 0.1781 - acc: 0.95080s - loss: 0.1837 - acc: 0\n",
      "Epoch 5/500\n",
      "29400/29400 [==============================] - 1s 30us/sample - loss: 0.1514 - acc: 0.9589\n",
      "Epoch 6/500\n",
      "29400/29400 [==============================] - 1s 30us/sample - loss: 0.1301 - acc: 0.9644\n",
      "Epoch 7/500\n",
      "29400/29400 [==============================] - 1s 30us/sample - loss: 0.1130 - acc: 0.9700\n",
      "Epoch 8/500\n",
      "29400/29400 [==============================] - 1s 30us/sample - loss: 0.0993 - acc: 0.9734\n",
      "Epoch 9/500\n",
      "29400/29400 [==============================] - 1s 29us/sample - loss: 0.0879 - acc: 0.9768\n",
      "Epoch 10/500\n",
      "29400/29400 [==============================] - 1s 31us/sample - loss: 0.0780 - acc: 0.9794\n",
      "Epoch 11/500\n",
      "29400/29400 [==============================] - 1s 35us/sample - loss: 0.0694 - acc: 0.9818\n",
      "Epoch 12/500\n",
      "29400/29400 [==============================] - 1s 34us/sample - loss: 0.0620 - acc: 0.9834\n",
      "Epoch 13/500\n",
      "29400/29400 [==============================] - 1s 33us/sample - loss: 0.0547 - acc: 0.9860\n",
      "Epoch 14/500\n",
      "29400/29400 [==============================] - 1s 33us/sample - loss: 0.0490 - acc: 0.9882\n",
      "Epoch 15/500\n",
      "29400/29400 [==============================] - 1s 32us/sample - loss: 0.0436 - acc: 0.9899\n",
      "Epoch 16/500\n",
      "29400/29400 [==============================] - 1s 33us/sample - loss: 0.0392 - acc: 0.9907\n",
      "Epoch 17/500\n",
      "29400/29400 [==============================] - 1s 32us/sample - loss: 0.0352 - acc: 0.9925\n",
      "Epoch 18/500\n",
      "29400/29400 [==============================] - 1s 32us/sample - loss: 0.0319 - acc: 0.9931\n",
      "Epoch 19/500\n",
      "29400/29400 [==============================] - 1s 33us/sample - loss: 0.0288 - acc: 0.9943\n",
      "Epoch 20/500\n",
      "29400/29400 [==============================] - 1s 32us/sample - loss: 0.0256 - acc: 0.9955\n",
      "Epoch 21/500\n",
      "29400/29400 [==============================] - 1s 32us/sample - loss: 0.0236 - acc: 0.9960\n",
      "Epoch 22/500\n",
      "29400/29400 [==============================] - 1s 33us/sample - loss: 0.0211 - acc: 0.9968\n",
      "Epoch 23/500\n",
      "29400/29400 [==============================] - 1s 32us/sample - loss: 0.0187 - acc: 0.9974\n",
      "Epoch 24/500\n",
      "29400/29400 [==============================] - 1s 36us/sample - loss: 0.0169 - acc: 0.9982\n",
      "Epoch 25/500\n",
      "29400/29400 [==============================] - 1s 35us/sample - loss: 0.0151 - acc: 0.9985\n",
      "Epoch 26/500\n",
      "29400/29400 [==============================] - 1s 33us/sample - loss: 0.0141 - acc: 0.9985\n",
      "Epoch 27/500\n",
      "29400/29400 [==============================] - 1s 33us/sample - loss: 0.0128 - acc: 0.99880s - loss: 0.0130 - acc\n",
      "Epoch 28/500\n",
      "29400/29400 [==============================] - 1s 32us/sample - loss: 0.0119 - acc: 0.9989\n",
      "Epoch 29/500\n",
      "29400/29400 [==============================] - 1s 32us/sample - loss: 0.0107 - acc: 0.9992\n",
      "Epoch 30/500\n",
      "29400/29400 [==============================] - 1s 34us/sample - loss: 0.0097 - acc: 0.9993\n",
      "Epoch 31/500\n",
      "29400/29400 [==============================] - 1s 33us/sample - loss: 0.0088 - acc: 0.9995\n",
      "Epoch 32/500\n",
      "29400/29400 [==============================] - 1s 32us/sample - loss: 0.0079 - acc: 0.9997\n",
      "Epoch 33/500\n",
      "29400/29400 [==============================] - 1s 32us/sample - loss: 0.0074 - acc: 0.9997\n",
      "Epoch 34/500\n",
      "29400/29400 [==============================] - 1s 33us/sample - loss: 0.0069 - acc: 0.9998\n",
      "Epoch 35/500\n",
      "29400/29400 [==============================] - 1s 32us/sample - loss: 0.0065 - acc: 0.9998\n",
      "Epoch 36/500\n",
      "29400/29400 [==============================] - 1s 32us/sample - loss: 0.0057 - acc: 0.9999\n",
      "Epoch 37/500\n",
      "29400/29400 [==============================] - 1s 33us/sample - loss: 0.0052 - acc: 0.9999\n",
      "Epoch 38/500\n",
      "29400/29400 [==============================] - 1s 33us/sample - loss: 0.0048 - acc: 1.0000\n",
      "Epoch 39/500\n",
      "29400/29400 [==============================] - 1s 33us/sample - loss: 0.0045 - acc: 0.9999\n",
      "Epoch 40/500\n",
      "29400/29400 [==============================] - 1s 33us/sample - loss: 0.0042 - acc: 1.0000\n",
      "Epoch 41/500\n",
      "29400/29400 [==============================] - 1s 34us/sample - loss: 0.0038 - acc: 1.0000\n",
      "Epoch 42/500\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Epoch 303/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "29400/29400 [==============================] - 1s 33us/sample - loss: 3.2514e-07 - acc: 1.0000\n",
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      "Epoch 367/500\n",
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      "Epoch 368/500\n",
      "29400/29400 [==============================] - 1s 34us/sample - loss: 7.5037e-08 - acc: 1.0000\n",
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      "Epoch 370/500\n",
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      "Epoch 371/500\n",
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      "Epoch 372/500\n",
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      "Epoch 373/500\n",
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      "Epoch 374/500\n",
      "29400/29400 [==============================] - 1s 33us/sample - loss: 6.7426e-08 - acc: 1.0000\n",
      "Epoch 375/500\n",
      "29400/29400 [==============================] - 1s 32us/sample - loss: 6.5971e-08 - acc: 1.0000\n",
      "Epoch 376/500\n",
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      "Epoch 377/500\n",
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      "Epoch 378/500\n",
      "29400/29400 [==============================] - 1s 32us/sample - loss: 6.2857e-08 - acc: 1.0000\n",
      "Epoch 379/500\n",
      "29400/29400 [==============================] - 1s 33us/sample - loss: 6.1940e-08 - acc: 1.0000\n",
      "Epoch 380/500\n",
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      "Epoch 381/500\n",
      "29400/29400 [==============================] - 1s 32us/sample - loss: 5.9771e-08 - acc: 1.0000\n",
      "Epoch 382/500\n",
      "29400/29400 [==============================] - 1s 32us/sample - loss: 5.8640e-08 - acc: 1.0000\n",
      "Epoch 383/500\n",
      "29400/29400 [==============================] - 1s 32us/sample - loss: 5.8003e-08 - acc: 1.0000\n",
      "Epoch 384/500\n",
      "29400/29400 [==============================] - 1s 32us/sample - loss: 5.7014e-08 - acc: 1.0000\n",
      "Epoch 385/500\n",
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      "Epoch 386/500\n",
      "29400/29400 [==============================] - 1s 32us/sample - loss: 5.4990e-08 - acc: 1.0000\n",
      "Epoch 387/500\n",
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      "Epoch 388/500\n",
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      "Epoch 389/500\n",
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      "Epoch 390/500\n",
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      "Epoch 391/500\n",
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      "Epoch 392/500\n",
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      "Epoch 393/500\n",
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      "Epoch 394/500\n",
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      "Epoch 397/500\n",
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      "Epoch 398/500\n",
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      "Epoch 399/500\n",
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      "Epoch 400/500\n",
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      "Epoch 401/500\n",
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      "Epoch 402/500\n",
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      "Epoch 403/500\n",
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      "Epoch 407/500\n",
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      "Epoch 409/500\n",
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      "Epoch 413/500\n",
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      "Epoch 415/500\n",
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      "Epoch 416/500\n",
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      "Epoch 417/500\n",
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      "Epoch 418/500\n",
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      "Epoch 419/500\n",
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      "Epoch 420/500\n",
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      "Epoch 421/500\n",
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      "Epoch 427/500\n",
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      "Epoch 428/500\n",
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      "Epoch 429/500\n",
      "29400/29400 [==============================] - 1s 33us/sample - loss: 3.0857e-08 - acc: 1.0000\n",
      "Epoch 430/500\n",
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      "Epoch 431/500\n",
      "29400/29400 [==============================] - 1s 32us/sample - loss: 3.0228e-08 - acc: 1.0000\n",
      "Epoch 432/500\n",
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      "Epoch 433/500\n",
      "29400/29400 [==============================] - 1s 33us/sample - loss: 2.9450e-08 - acc: 1.0000\n",
      "Epoch 434/500\n",
      "29400/29400 [==============================] - 1s 33us/sample - loss: 2.9218e-08 - acc: 1.0000\n",
      "Epoch 435/500\n",
      "29400/29400 [==============================] - 1s 33us/sample - loss: 2.8975e-08 - acc: 1.0000\n",
      "Epoch 436/500\n",
      "29400/29400 [==============================] - 1s 33us/sample - loss: 2.8545e-08 - acc: 1.0000\n",
      "Epoch 437/500\n",
      "29400/29400 [==============================] - 1s 32us/sample - loss: 2.8501e-08 - acc: 1.0000\n",
      "Epoch 438/500\n",
      "29400/29400 [==============================] - 1s 32us/sample - loss: 2.8116e-08 - acc: 1.0000\n",
      "Epoch 439/500\n",
      "29400/29400 [==============================] - 1s 33us/sample - loss: 2.7771e-08 - acc: 1.0000\n",
      "Epoch 440/500\n",
      "29400/29400 [==============================] - 1s 33us/sample - loss: 2.7426e-08 - acc: 1.0000\n",
      "Epoch 441/500\n",
      "29400/29400 [==============================] - 1s 32us/sample - loss: 2.7187e-08 - acc: 1.0000\n",
      "Epoch 442/500\n",
      "29400/29400 [==============================] - 1s 33us/sample - loss: 2.6838e-08 - acc: 1.0000\n",
      "Epoch 443/500\n",
      "29400/29400 [==============================] - 1s 33us/sample - loss: 2.6725e-08 - acc: 1.0000\n",
      "Epoch 444/500\n",
      "29400/29400 [==============================] - 1s 33us/sample - loss: 2.6315e-08 - acc: 1.0000\n",
      "Epoch 445/500\n",
      "29400/29400 [==============================] - 1s 33us/sample - loss: 2.6088e-08 - acc: 1.0000\n",
      "Epoch 446/500\n",
      "29400/29400 [==============================] - 1s 32us/sample - loss: 2.5821e-08 - acc: 1.0000\n",
      "Epoch 447/500\n",
      "29400/29400 [==============================] - 1s 33us/sample - loss: 2.5484e-08 - acc: 1.0000\n",
      "Epoch 448/500\n",
      "29400/29400 [==============================] - 1s 33us/sample - loss: 2.5176e-08 - acc: 1.0000\n",
      "Epoch 449/500\n",
      "29400/29400 [==============================] - 1s 33us/sample - loss: 2.4981e-08 - acc: 1.0000\n",
      "Epoch 450/500\n",
      "29400/29400 [==============================] - 1s 33us/sample - loss: 2.4892e-08 - acc: 1.0000\n",
      "Epoch 451/500\n",
      "29400/29400 [==============================] - 1s 33us/sample - loss: 2.4620e-08 - acc: 1.0000\n",
      "Epoch 452/500\n",
      "29400/29400 [==============================] - 1s 32us/sample - loss: 2.4377e-08 - acc: 1.0000\n",
      "Epoch 453/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "29400/29400 [==============================] - 1s 32us/sample - loss: 2.4118e-08 - acc: 1.0000\n",
      "Epoch 454/500\n",
      "29400/29400 [==============================] - 1s 33us/sample - loss: 2.3899e-08 - acc: 1.0000\n",
      "Epoch 455/500\n",
      "29400/29400 [==============================] - 1s 38us/sample - loss: 2.3761e-08 - acc: 1.0000\n",
      "Epoch 456/500\n",
      "29400/29400 [==============================] - 1s 33us/sample - loss: 2.3509e-08 - acc: 1.0000\n",
      "Epoch 457/500\n",
      "29400/29400 [==============================] - 1s 33us/sample - loss: 2.3319e-08 - acc: 1.0000\n",
      "Epoch 458/500\n",
      "29400/29400 [==============================] - 1s 33us/sample - loss: 2.3031e-08 - acc: 1.0000\n",
      "Epoch 459/500\n",
      "29400/29400 [==============================] - 1s 34us/sample - loss: 2.2804e-08 - acc: 1.0000\n",
      "Epoch 460/500\n",
      "29400/29400 [==============================] - 1s 33us/sample - loss: 2.2629e-08 - acc: 1.0000\n",
      "Epoch 461/500\n",
      "29400/29400 [==============================] - 1s 33us/sample - loss: 2.2374e-08 - acc: 1.0000\n",
      "Epoch 462/500\n",
      "29400/29400 [==============================] - 1s 32us/sample - loss: 2.2200e-08 - acc: 1.0000\n",
      "Epoch 463/500\n",
      "29400/29400 [==============================] - 1s 32us/sample - loss: 2.1965e-08 - acc: 1.0000\n",
      "Epoch 464/500\n",
      "29400/29400 [==============================] - 1s 32us/sample - loss: 2.1827e-08 - acc: 1.0000\n",
      "Epoch 465/500\n",
      "29400/29400 [==============================] - 1s 32us/sample - loss: 2.1737e-08 - acc: 1.0000\n",
      "Epoch 466/500\n",
      "29400/29400 [==============================] - 1s 32us/sample - loss: 2.1531e-08 - acc: 1.0000\n",
      "Epoch 467/500\n",
      "29400/29400 [==============================] - 1s 32us/sample - loss: 2.1308e-08 - acc: 1.0000\n",
      "Epoch 468/500\n",
      "29400/29400 [==============================] - 1s 32us/sample - loss: 2.1008e-08 - acc: 1.0000\n",
      "Epoch 469/500\n",
      "29400/29400 [==============================] - 1s 33us/sample - loss: 2.0947e-08 - acc: 1.0000\n",
      "Epoch 470/500\n",
      "29400/29400 [==============================] - 1s 32us/sample - loss: 2.0772e-08 - acc: 1.0000\n",
      "Epoch 471/500\n",
      "29400/29400 [==============================] - 1s 33us/sample - loss: 2.0529e-08 - acc: 1.0000\n",
      "Epoch 472/500\n",
      "29400/29400 [==============================] - 1s 34us/sample - loss: 2.0570e-08 - acc: 1.0000\n",
      "Epoch 473/500\n",
      "29400/29400 [==============================] - 1s 33us/sample - loss: 2.0262e-08 - acc: 1.0000\n",
      "Epoch 474/500\n",
      "29400/29400 [==============================] - 1s 32us/sample - loss: 2.0152e-08 - acc: 1.0000\n",
      "Epoch 475/500\n",
      "29400/29400 [==============================] - 1s 33us/sample - loss: 1.9998e-08 - acc: 1.0000\n",
      "Epoch 476/500\n",
      "29400/29400 [==============================] - 1s 33us/sample - loss: 1.9852e-08 - acc: 1.0000\n",
      "Epoch 477/500\n",
      "29400/29400 [==============================] - 1s 33us/sample - loss: 1.9670e-08 - acc: 1.0000\n",
      "Epoch 478/500\n",
      "29400/29400 [==============================] - 1s 32us/sample - loss: 1.9633e-08 - acc: 1.0000\n",
      "Epoch 479/500\n",
      "29400/29400 [==============================] - 1s 33us/sample - loss: 1.9353e-08 - acc: 1.0000\n",
      "Epoch 480/500\n",
      "29400/29400 [==============================] - 1s 33us/sample - loss: 1.9195e-08 - acc: 1.0000\n",
      "Epoch 481/500\n",
      "29400/29400 [==============================] - 1s 32us/sample - loss: 1.9118e-08 - acc: 1.0000\n",
      "Epoch 482/500\n",
      "29400/29400 [==============================] - 1s 33us/sample - loss: 1.8956e-08 - acc: 1.0000\n",
      "Epoch 483/500\n",
      "29400/29400 [==============================] - 1s 32us/sample - loss: 1.8818e-08 - acc: 1.0000\n",
      "Epoch 484/500\n",
      "29400/29400 [==============================] - 1s 34us/sample - loss: 1.8688e-08 - acc: 1.0000\n",
      "Epoch 485/500\n",
      "29400/29400 [==============================] - 1s 34us/sample - loss: 1.8644e-08 - acc: 1.0000\n",
      "Epoch 486/500\n",
      "29400/29400 [==============================] - 1s 36us/sample - loss: 1.8421e-08 - acc: 1.0000\n",
      "Epoch 487/500\n",
      "29400/29400 [==============================] - 1s 33us/sample - loss: 1.8258e-08 - acc: 1.0000\n",
      "Epoch 488/500\n",
      "29400/29400 [==============================] - 1s 32us/sample - loss: 1.8173e-08 - acc: 1.0000\n",
      "Epoch 489/500\n",
      "29400/29400 [==============================] - 1s 32us/sample - loss: 1.8048e-08 - acc: 1.0000\n",
      "Epoch 490/500\n",
      "29400/29400 [==============================] - 1s 34us/sample - loss: 1.7873e-08 - acc: 1.0000\n",
      "Epoch 491/500\n",
      "29400/29400 [==============================] - 1s 33us/sample - loss: 1.7691e-08 - acc: 1.0000\n",
      "Epoch 492/500\n",
      "29400/29400 [==============================] - 1s 33us/sample - loss: 1.7614e-08 - acc: 1.0000\n",
      "Epoch 493/500\n",
      "29400/29400 [==============================] - 1s 33us/sample - loss: 1.7549e-08 - acc: 1.0000\n",
      "Epoch 494/500\n",
      "29400/29400 [==============================] - 1s 32us/sample - loss: 1.7338e-08 - acc: 1.0000\n",
      "Epoch 495/500\n",
      "29400/29400 [==============================] - 1s 32us/sample - loss: 1.7233e-08 - acc: 1.0000\n",
      "Epoch 496/500\n",
      "29400/29400 [==============================] - 1s 33us/sample - loss: 1.7135e-08 - acc: 1.0000\n",
      "Epoch 497/500\n",
      "29400/29400 [==============================] - 1s 35us/sample - loss: 1.7062e-08 - acc: 1.0000\n",
      "Epoch 498/500\n",
      "29400/29400 [==============================] - 1s 34us/sample - loss: 1.6908e-08 - acc: 1.0000\n",
      "Epoch 499/500\n",
      "29400/29400 [==============================] - 1s 33us/sample - loss: 1.6847e-08 - acc: 1.0000\n",
      "Epoch 500/500\n",
      "29400/29400 [==============================] - 1s 32us/sample - loss: 1.6750e-08 - acc: 1.0000\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x1f6f8404400>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(np.array(X_train), np.array(y_train), shuffle=True, batch_size=500, epochs=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Evaluation of model over validation dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12600/12600 [==============================] - 0s 25us/sample - loss: 0.2349 - acc: 0.9759\n",
      "\n",
      "LOSS: 0.23491768245077102\n",
      "ACCURACY: 0.9758729934692383\n"
     ]
    }
   ],
   "source": [
    "vc_loss, vc_accuracy = model.evaluate(X_vc, y_vc)\n",
    "print(\"\\nLOSS: {}\\nACCURACY: {}\".format(vc_loss, vc_accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Making prediction over test dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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+f13ac5foa4lKeN74hh8QFN/wA4Ii/EBQhB8IivADQRF+ICjCDwRF+IGgCD8Q1P8DqkoYlPwbXhwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PREDICTION DIGIT = 2\n"
     ]
    },
    {
     "data": {
      "image/png": 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,
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PREDICTION DIGIT = 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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+ICjCDwRF+IGgCD8QFOEHgvo/Ygv9SLjKP6YAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PREDICTION DIGIT = 9\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PREDICTION DIGIT = 9\n"
     ]
    },
    {
     "data": {
      "image/png": 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vkl6T9Lakz7PND2vs9XVpj12ir+Uq4XHjE35AUHzCDwiK8ANBEX4gKMIPBEX4gaAIPxAU4QeCIvxAUH8FdqQhq6iJ3eIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PREDICTION DIGIT = 3\n"
     ]
    }
   ],
   "source": [
    "predictions = model.predict(mnist_test)\n",
    "\n",
    "for i in range(5):\n",
    "    plt.imshow(np.reshape(np.array(mnist_test.loc[i]), (28, 28)), cmap = plt.cm.binary)\n",
    "    plt.show()\n",
    "    print(\"PREDICTION DIGIT = {}\".format(np.argmax(predictions[i])))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Submissions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions_test = []\n",
    "\n",
    "for i in predictions:\n",
    "    predictions_test.append(np.argmax(i))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission =  pd.DataFrame({\n",
    "        \"ImageId\": mnist_test.index+1,\n",
    "        \"Label\": predictions_test\n",
    "    })\n",
    "\n",
    "submission.to_csv('digit_submission.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Final Result\n",
    "* LOSS: 9%\n",
    "* ACCURACY: 97.23%"
   ]
  }
 ],
 "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}