MNIST.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense,Activation,Dropout,MaxPool2D,Conv2D,Flatten,ReLU\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "from keras.optimizers import Adam\n",
    "from keras.utils import np_utils\n",
    "import numpy as np\n",
    "from keras.datasets import mnist\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from livelossplot.keras import PlotLossesCallback\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on module keras.datasets.mnist in keras.datasets:\n",
      "\n",
      "NAME\n",
      "    keras.datasets.mnist - MNIST handwritten digits dataset.\n",
      "\n",
      "FUNCTIONS\n",
      "    load_data(path='mnist.npz')\n",
      "        Loads the MNIST dataset.\n",
      "        \n",
      "        # Arguments\n",
      "            path: path where to cache the dataset locally\n",
      "                (relative to ~/.keras/datasets).\n",
      "        \n",
      "        # Returns\n",
      "            Tuple of Numpy arrays: `(x_train, y_train), (x_test, y_test)`.\n",
      "\n",
      "DATA\n",
      "    absolute_import = _Feature((2, 5, 0, 'alpha', 1), (3, 0, 0, 'alpha', 0...\n",
      "    division = _Feature((2, 2, 0, 'alpha', 2), (3, 0, 0, 'alpha', 0), 8192...\n",
      "    print_function = _Feature((2, 6, 0, 'alpha', 2), (3, 0, 0, 'alpha', 0)...\n",
      "\n",
      "FILE\n",
      "    c:\\users\\singh\\anaconda3\\lib\\site-packages\\keras\\datasets\\mnist.py\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(mnist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "(x_train, y_train), (x_test, y_test)=mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2a00e429908>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(x_train[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000, 28, 28)\n",
      "(60000,)\n",
      "(10000, 28, 28)\n",
      "(10000,)\n"
     ]
    }
   ],
   "source": [
    "print( x_train.shape)\n",
    "print(y_train.shape)\n",
    "print(x_test.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train=x_train.reshape(x_train.shape[0],28,28,1).astype('float32')\n",
    "x_test=x_test.reshape(x_test.shape[0],28,28,1).astype('float32')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000, 28, 28, 1)\n",
      "(10000, 28, 28, 1)\n"
     ]
    }
   ],
   "source": [
    "print( x_train.shape)\n",
    "print(x_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train=x_train/255\n",
    "x_test=x_test/255"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train=np_utils.to_categorical(y_train)\n",
    "y_test=np_utils.to_categorical(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_classes = y_test.shape[1]\n",
    "num_classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def myModel():\n",
    "    model=Sequential()\n",
    "    model.add(Conv2D(32,(3,3),input_shape=(28,28,1),activation='relu'))\n",
    "    model.add(Conv2D(64,(3,3),activation='relu'))\n",
    "    model.add(MaxPool2D(pool_size=(2, 2)))\n",
    "    model.add(Dropout(0.25))\n",
    "    model.add(Conv2D(32,(3,3),activation='relu'))\n",
    "    model.add(Conv2D(64,(3,3),activation='relu'))\n",
    "    model.add(MaxPool2D(pool_size=(2, 2)))\n",
    "    model.add(Dropout(0.25))\n",
    "    model.add(Flatten())\n",
    "    model.add(Dense(128, activation='relu'))\n",
    "    model.add(Dropout(0.25))\n",
    "    model.add(Dense(256, activation='relu'))\n",
    "    model.add(Dense(num_classes, activation='softmax'))\n",
    "    model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "model=myModel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Log-loss (cost function):\n",
      "training   (min:    0.019, max:    0.303, cur:    0.019)\n",
      "validation (min:    0.018, max:    0.061, cur:    0.018)\n",
      "\n",
      "accuracy:\n",
      "training   (min:    0.902, max:    0.994, cur:    0.994)\n",
      "validation (min:    0.979, max:    0.995, cur:    0.995)\n",
      "CNN Error: 0.54%\n"
     ]
    }
   ],
   "source": [
    "model.fit(x_train, y_train, validation_data=(x_test, y_test), epochs=12, batch_size=200,callbacks=[PlotLossesCallback()], verbose=2)\n",
    "scores = model.evaluate(x_test, y_test, verbose=0)\n",
    "print(\"CNN Error: %.2f%%\" % (100-scores[1]*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\Users\\\\singh'"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}