DL_practical_uni.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Mnist without CNN</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "(X_train, y_train), (X_test, y_test) = keras.datasets.mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "60000\n",
      "10000\n"
     ]
    }
   ],
   "source": [
    "print(len(X_train))\n",
    "print(len(X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000, 28, 28)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10000, 28, 28)\n"
     ]
    }
   ],
   "source": [
    "print(X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(28, 28)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train[5].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "          0,   0],\n",
       "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "          0,   0],\n",
       "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "          0,   0],\n",
       "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "          0,   0],\n",
       "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "          0,   0],\n",
       "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "          0,   0,  13,  25, 100, 122,   7,   0,   0,   0,   0,   0,   0,\n",
       "          0,   0],\n",
       "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "         33, 151, 208, 252, 252, 252, 146,   0,   0,   0,   0,   0,   0,\n",
       "          0,   0],\n",
       "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,  40, 152,\n",
       "        244, 252, 253, 224, 211, 252, 232,  40,   0,   0,   0,   0,   0,\n",
       "          0,   0],\n",
       "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,  15, 152, 239, 252,\n",
       "        252, 252, 216,  31,  37, 252, 252,  60,   0,   0,   0,   0,   0,\n",
       "          0,   0],\n",
       "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,  96, 252, 252, 252,\n",
       "        252, 217,  29,   0,  37, 252, 252,  60,   0,   0,   0,   0,   0,\n",
       "          0,   0],\n",
       "       [  0,   0,   0,   0,   0,   0,   0,   0,   0, 181, 252, 252, 220,\n",
       "        167,  30,   0,   0,  77, 252, 252,  60,   0,   0,   0,   0,   0,\n",
       "          0,   0],\n",
       "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,  26, 128,  58,  22,\n",
       "          0,   0,   0,   0, 100, 252, 252,  60,   0,   0,   0,   0,   0,\n",
       "          0,   0],\n",
       "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "          0,   0,   0,   0, 157, 252, 252,  60,   0,   0,   0,   0,   0,\n",
       "          0,   0],\n",
       "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "        110, 121, 122, 121, 202, 252, 194,   3,   0,   0,   0,   0,   0,\n",
       "          0,   0],\n",
       "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,  10,  53, 179,\n",
       "        253, 253, 255, 253, 253, 228,  35,   0,   0,   0,   0,   0,   0,\n",
       "          0,   0],\n",
       "       [  0,   0,   0,   0,   0,   0,   0,   0,   5,  54, 227, 252, 243,\n",
       "        228, 170, 242, 252, 252, 231, 117,   6,   0,   0,   0,   0,   0,\n",
       "          0,   0],\n",
       "       [  0,   0,   0,   0,   0,   0,   0,   6,  78, 252, 252, 125,  59,\n",
       "          0,  18, 208, 252, 252, 252, 252,  87,   7,   0,   0,   0,   0,\n",
       "          0,   0],\n",
       "       [  0,   0,   0,   0,   0,   0,   5, 135, 252, 252, 180,  16,   0,\n",
       "         21, 203, 253, 247, 129, 173, 252, 252, 184,  66,  49,  49,   0,\n",
       "          0,   0],\n",
       "       [  0,   0,   0,   0,   0,   3, 136, 252, 241, 106,  17,   0,  53,\n",
       "        200, 252, 216,  65,   0,  14,  72, 163, 241, 252, 252, 223,   0,\n",
       "          0,   0],\n",
       "       [  0,   0,   0,   0,   0, 105, 252, 242,  88,  18,  73, 170, 244,\n",
       "        252, 126,  29,   0,   0,   0,   0,   0,  89, 180, 180,  37,   0,\n",
       "          0,   0],\n",
       "       [  0,   0,   0,   0,   0, 231, 252, 245, 205, 216, 252, 252, 252,\n",
       "        124,   3,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "          0,   0],\n",
       "       [  0,   0,   0,   0,   0, 207, 252, 252, 252, 252, 178, 116,  36,\n",
       "          4,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "          0,   0],\n",
       "       [  0,   0,   0,   0,   0,  13,  93, 143, 121,  23,   6,   0,   0,\n",
       "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "          0,   0],\n",
       "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "          0,   0],\n",
       "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "          0,   0],\n",
       "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "          0,   0],\n",
       "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "          0,   0],\n",
       "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "          0,   0]], dtype=uint8)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train[5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x14cc9f2d1f0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_train[5], cmap = \"gray\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train[5]"
   ]
  },
  {
   "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": [
    "X_train_flattened = X_train.reshape(len(X_train), 28*28)\n",
    "X_test_flattened = X_test.reshape(len(X_test), 28*28)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(784,)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_flattened[5].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.05098039, 0.09803922, 0.39215686, 0.47843137, 0.02745098,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.12941176, 0.59215686, 0.81568627, 0.98823529,\n",
       "       0.98823529, 0.98823529, 0.57254902, 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.15686275, 0.59607843, 0.95686275,\n",
       "       0.98823529, 0.99215686, 0.87843137, 0.82745098, 0.98823529,\n",
       "       0.90980392, 0.15686275, 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.05882353, 0.59607843,\n",
       "       0.9372549 , 0.98823529, 0.98823529, 0.98823529, 0.84705882,\n",
       "       0.12156863, 0.14509804, 0.98823529, 0.98823529, 0.23529412,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.37647059, 0.98823529, 0.98823529, 0.98823529,\n",
       "       0.98823529, 0.85098039, 0.11372549, 0.        , 0.14509804,\n",
       "       0.98823529, 0.98823529, 0.23529412, 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.70980392,\n",
       "       0.98823529, 0.98823529, 0.8627451 , 0.65490196, 0.11764706,\n",
       "       0.        , 0.        , 0.30196078, 0.98823529, 0.98823529,\n",
       "       0.23529412, 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.10196078, 0.50196078, 0.22745098,\n",
       "       0.08627451, 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.39215686, 0.98823529, 0.98823529, 0.23529412, 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.61568627, 0.98823529,\n",
       "       0.98823529, 0.23529412, 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.43137255, 0.4745098 , 0.47843137,\n",
       "       0.4745098 , 0.79215686, 0.98823529, 0.76078431, 0.01176471,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.03921569, 0.20784314, 0.70196078,\n",
       "       0.99215686, 0.99215686, 1.        , 0.99215686, 0.99215686,\n",
       "       0.89411765, 0.1372549 , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.01960784, 0.21176471,\n",
       "       0.89019608, 0.98823529, 0.95294118, 0.89411765, 0.66666667,\n",
       "       0.94901961, 0.98823529, 0.98823529, 0.90588235, 0.45882353,\n",
       "       0.02352941, 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.02352941, 0.30588235, 0.98823529, 0.98823529, 0.49019608,\n",
       "       0.23137255, 0.        , 0.07058824, 0.81568627, 0.98823529,\n",
       "       0.98823529, 0.98823529, 0.98823529, 0.34117647, 0.02745098,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.01960784, 0.52941176, 0.98823529,\n",
       "       0.98823529, 0.70588235, 0.0627451 , 0.        , 0.08235294,\n",
       "       0.79607843, 0.99215686, 0.96862745, 0.50588235, 0.67843137,\n",
       "       0.98823529, 0.98823529, 0.72156863, 0.25882353, 0.19215686,\n",
       "       0.19215686, 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.01176471,\n",
       "       0.53333333, 0.98823529, 0.94509804, 0.41568627, 0.06666667,\n",
       "       0.        , 0.20784314, 0.78431373, 0.98823529, 0.84705882,\n",
       "       0.25490196, 0.        , 0.05490196, 0.28235294, 0.63921569,\n",
       "       0.94509804, 0.98823529, 0.98823529, 0.8745098 , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.41176471, 0.98823529, 0.94901961,\n",
       "       0.34509804, 0.07058824, 0.28627451, 0.66666667, 0.95686275,\n",
       "       0.98823529, 0.49411765, 0.11372549, 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.34901961, 0.70588235,\n",
       "       0.70588235, 0.14509804, 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.90588235, 0.98823529, 0.96078431, 0.80392157, 0.84705882,\n",
       "       0.98823529, 0.98823529, 0.98823529, 0.48627451, 0.01176471,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.81176471, 0.98823529,\n",
       "       0.98823529, 0.98823529, 0.98823529, 0.69803922, 0.45490196,\n",
       "       0.14117647, 0.01568627, 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.05098039, 0.36470588, 0.56078431, 0.4745098 ,\n",
       "       0.09019608, 0.02352941, 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        ])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_flattened[5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(784,)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_flattened[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = keras.Sequential([\n",
    "        keras.layers.Dense(10, input_shape = (784,), activation = \"sigmoid\")\n",
    "        ])\n",
    "\n",
    "\n",
    "model.compile(optimizer = \"adam\",\n",
    "             loss = \"sparse_categorical_crossentropy\",\n",
    "             metrics = [\"accuracy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense (Dense)               (None, 10)                7850      \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 7,850\n",
      "Trainable params: 7,850\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 0.4719 - accuracy: 0.8759\n",
      "Epoch 2/10\n",
      "1875/1875 [==============================] - 5s 2ms/step - loss: 0.3037 - accuracy: 0.9152\n",
      "Epoch 3/10\n",
      "1875/1875 [==============================] - 5s 2ms/step - loss: 0.2834 - accuracy: 0.9205\n",
      "Epoch 4/10\n",
      "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2735 - accuracy: 0.9234\n",
      "Epoch 5/10\n",
      "1875/1875 [==============================] - 5s 2ms/step - loss: 0.2668 - accuracy: 0.9257\n",
      "Epoch 6/10\n",
      "1875/1875 [==============================] - 5s 2ms/step - loss: 0.2618 - accuracy: 0.9266\n",
      "Epoch 7/10\n",
      "1875/1875 [==============================] - 5s 2ms/step - loss: 0.2583 - accuracy: 0.9281\n",
      "Epoch 8/10\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2552 - accuracy: 0.9297\n",
      "Epoch 9/10\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2527 - accuracy: 0.9296\n",
      "Epoch 10/10\n",
      "1875/1875 [==============================] - 5s 2ms/step - loss: 0.2511 - accuracy: 0.9304\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x14cc99892e0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train_flattened, y_train, epochs = 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x14cc9bc22b0>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "image_index = 15\n",
    "plt.imshow(X_test[image_index], cmap = \"Greys\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 142ms/step\n",
      "5\n"
     ]
    }
   ],
   "source": [
    "test = X_test[image_index].reshape(1, 28*28, 1)\n",
    "prediction = model.predict((test))\n",
    "print(prediction.argmax())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Mnist with CNN</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "import matplotlib.pyplot as plt\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Conv2D, Dropout, Flatten, MaxPooling2D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "(X_train, y_train), (X_test, y_test) = keras.datasets.mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000, 28, 28)\n",
      "(10000, 28, 28)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape)\n",
    "print(X_test.shape)\n",
    "input_shape = (28, 28, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = X_train / 255\n",
    "X_test = X_test / 255"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(Conv2D(28, kernel_size = (3,3), input_shape = input_shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.add(MaxPooling2D(pool_size = (2,2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.add(Flatten())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.add(Dense(128, activation = \"relu\"))\n",
    "model.add(Dense(10,activation = \"softmax\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer = \"adam\", loss = \"sparse_categorical_crossentropy\", metrics = [\"accuracy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d (Conv2D)             (None, 26, 26, 28)        280       \n",
      "                                                                 \n",
      " max_pooling2d (MaxPooling2D  (None, 13, 13, 28)       0         \n",
      " )                                                               \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 4732)              0         \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 128)               605824    \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 10)                1290      \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 607,394\n",
      "Trainable params: 607,394\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1875/1875 [==============================] - 29s 15ms/step - loss: 0.1715 - accuracy: 0.9488\n",
      "Epoch 2/10\n",
      "1875/1875 [==============================] - 27s 15ms/step - loss: 0.0594 - accuracy: 0.9816\n",
      "Epoch 3/10\n",
      "1875/1875 [==============================] - 27s 15ms/step - loss: 0.0356 - accuracy: 0.9887\n",
      "Epoch 4/10\n",
      "1875/1875 [==============================] - 27s 15ms/step - loss: 0.0230 - accuracy: 0.9926\n",
      "Epoch 5/10\n",
      "1875/1875 [==============================] - 27s 15ms/step - loss: 0.0183 - accuracy: 0.9940\n",
      "Epoch 6/10\n",
      "1875/1875 [==============================] - 27s 14ms/step - loss: 0.0130 - accuracy: 0.9953\n",
      "Epoch 7/10\n",
      "1875/1875 [==============================] - 27s 14ms/step - loss: 0.0108 - accuracy: 0.9963\n",
      "Epoch 8/10\n",
      "1875/1875 [==============================] - 27s 14ms/step - loss: 0.0097 - accuracy: 0.9967\n",
      "Epoch 9/10\n",
      "1875/1875 [==============================] - 27s 14ms/step - loss: 0.0075 - accuracy: 0.9976\n",
      "Epoch 10/10\n",
      "1875/1875 [==============================] - 27s 14ms/step - loss: 0.0079 - accuracy: 0.9973\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x14cca764730>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, y = y_train, epochs = 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x14cc9de1c70>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "image_index = 10\n",
    "plt.imshow(X_test[image_index], cmap = \"Greys\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 110ms/step\n",
      "0\n"
     ]
    }
   ],
   "source": [
    "test = X_test[image_index].reshape(1, 28, 28, 1)\n",
    "prediction = model.predict((test))\n",
    "print(prediction.argmax())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}