cifar10_and_fashion_mnist_classification_CNN.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>CIFAR10</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Sobhan\\anaconda3\\lib\\site-packages\\pandas\\core\\computation\\expressions.py:20: UserWarning: Pandas requires version '2.7.3' or newer of 'numexpr' (version '2.7.1' currently installed).\n",
      "  from pandas.core.computation.check import NUMEXPR_INSTALLED\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras import datasets, layers, models\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "(X_train, y_train), (X_test,y_test) = datasets.cifar10.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50000, 32, 32, 3)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 32, 32, 3)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50000, 1)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[6],\n",
       "       [9],\n",
       "       [9]], dtype=uint8)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train = y_train.reshape(-1,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6, 9, 9], dtype=uint8)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3],\n",
       "       [8],\n",
       "       [8]], dtype=uint8)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test = y_test.reshape(-1,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 8, 8], dtype=uint8)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "classes = [\"airplane\",\"automobile\",\"bird\",\"cat\",\"deer\",\"dog\",\"frog\",\"horse\",\"ship\",\"truck\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_sample(X, y, index):\n",
    "    plt.figure(figsize = (5,2))\n",
    "    plt.imshow(X[index])\n",
    "    plt.xlabel(classes[y[index]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_sample(X_train, y_train, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_sample(X_train, y_train, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_sample(X_train, y_train, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[[[ 59,  62,  63],\n",
       "          [ 43,  46,  45],\n",
       "          [ 50,  48,  43],\n",
       "          ...,\n",
       "          [158, 132, 108],\n",
       "          [152, 125, 102],\n",
       "          [148, 124, 103]],\n",
       " \n",
       "         [[ 16,  20,  20],\n",
       "          [  0,   0,   0],\n",
       "          [ 18,   8,   0],\n",
       "          ...,\n",
       "          [123,  88,  55],\n",
       "          [119,  83,  50],\n",
       "          [122,  87,  57]],\n",
       " \n",
       "         [[ 25,  24,  21],\n",
       "          [ 16,   7,   0],\n",
       "          [ 49,  27,   8],\n",
       "          ...,\n",
       "          [118,  84,  50],\n",
       "          [120,  84,  50],\n",
       "          [109,  73,  42]],\n",
       " \n",
       "         ...,\n",
       " \n",
       "         [[208, 170,  96],\n",
       "          [201, 153,  34],\n",
       "          [198, 161,  26],\n",
       "          ...,\n",
       "          [160, 133,  70],\n",
       "          [ 56,  31,   7],\n",
       "          [ 53,  34,  20]],\n",
       " \n",
       "         [[180, 139,  96],\n",
       "          [173, 123,  42],\n",
       "          [186, 144,  30],\n",
       "          ...,\n",
       "          [184, 148,  94],\n",
       "          [ 97,  62,  34],\n",
       "          [ 83,  53,  34]],\n",
       " \n",
       "         [[177, 144, 116],\n",
       "          [168, 129,  94],\n",
       "          [179, 142,  87],\n",
       "          ...,\n",
       "          [216, 184, 140],\n",
       "          [151, 118,  84],\n",
       "          [123,  92,  72]]],\n",
       " \n",
       " \n",
       "        [[[154, 177, 187],\n",
       "          [126, 137, 136],\n",
       "          [105, 104,  95],\n",
       "          ...,\n",
       "          [ 91,  95,  71],\n",
       "          [ 87,  90,  71],\n",
       "          [ 79,  81,  70]],\n",
       " \n",
       "         [[140, 160, 169],\n",
       "          [145, 153, 154],\n",
       "          [125, 125, 118],\n",
       "          ...,\n",
       "          [ 96,  99,  78],\n",
       "          [ 77,  80,  62],\n",
       "          [ 71,  73,  61]],\n",
       " \n",
       "         [[140, 155, 164],\n",
       "          [139, 146, 149],\n",
       "          [115, 115, 112],\n",
       "          ...,\n",
       "          [ 79,  82,  64],\n",
       "          [ 68,  70,  55],\n",
       "          [ 67,  69,  55]],\n",
       " \n",
       "         ...,\n",
       " \n",
       "         [[175, 167, 166],\n",
       "          [156, 154, 160],\n",
       "          [154, 160, 170],\n",
       "          ...,\n",
       "          [ 42,  34,  36],\n",
       "          [ 61,  53,  57],\n",
       "          [ 93,  83,  91]],\n",
       " \n",
       "         [[165, 154, 128],\n",
       "          [156, 152, 130],\n",
       "          [159, 161, 142],\n",
       "          ...,\n",
       "          [103,  93,  96],\n",
       "          [123, 114, 120],\n",
       "          [131, 121, 131]],\n",
       " \n",
       "         [[163, 148, 120],\n",
       "          [158, 148, 122],\n",
       "          [163, 156, 133],\n",
       "          ...,\n",
       "          [143, 133, 139],\n",
       "          [143, 134, 142],\n",
       "          [143, 133, 144]]],\n",
       " \n",
       " \n",
       "        [[[255, 255, 255],\n",
       "          [253, 253, 253],\n",
       "          [253, 253, 253],\n",
       "          ...,\n",
       "          [253, 253, 253],\n",
       "          [253, 253, 253],\n",
       "          [253, 253, 253]],\n",
       " \n",
       "         [[255, 255, 255],\n",
       "          [255, 255, 255],\n",
       "          [255, 255, 255],\n",
       "          ...,\n",
       "          [255, 255, 255],\n",
       "          [255, 255, 255],\n",
       "          [255, 255, 255]],\n",
       " \n",
       "         [[255, 255, 255],\n",
       "          [254, 254, 254],\n",
       "          [254, 254, 254],\n",
       "          ...,\n",
       "          [254, 254, 254],\n",
       "          [254, 254, 254],\n",
       "          [254, 254, 254]],\n",
       " \n",
       "         ...,\n",
       " \n",
       "         [[113, 120, 112],\n",
       "          [111, 118, 111],\n",
       "          [105, 112, 106],\n",
       "          ...,\n",
       "          [ 72,  81,  80],\n",
       "          [ 72,  80,  79],\n",
       "          [ 72,  80,  79]],\n",
       " \n",
       "         [[111, 118, 110],\n",
       "          [104, 111, 104],\n",
       "          [ 99, 106,  98],\n",
       "          ...,\n",
       "          [ 68,  75,  73],\n",
       "          [ 70,  76,  75],\n",
       "          [ 78,  84,  82]],\n",
       " \n",
       "         [[106, 113, 105],\n",
       "          [ 99, 106,  98],\n",
       "          [ 95, 102,  94],\n",
       "          ...,\n",
       "          [ 78,  85,  83],\n",
       "          [ 79,  85,  83],\n",
       "          [ 80,  86,  84]]],\n",
       " \n",
       " \n",
       "        ...,\n",
       " \n",
       " \n",
       "        [[[ 35, 178, 235],\n",
       "          [ 40, 176, 239],\n",
       "          [ 42, 176, 241],\n",
       "          ...,\n",
       "          [ 99, 177, 219],\n",
       "          [ 79, 147, 197],\n",
       "          [ 89, 148, 189]],\n",
       " \n",
       "         [[ 57, 182, 234],\n",
       "          [ 44, 184, 250],\n",
       "          [ 50, 183, 240],\n",
       "          ...,\n",
       "          [156, 182, 200],\n",
       "          [141, 177, 206],\n",
       "          [116, 149, 175]],\n",
       " \n",
       "         [[ 98, 197, 237],\n",
       "          [ 64, 189, 252],\n",
       "          [ 69, 192, 245],\n",
       "          ...,\n",
       "          [188, 195, 206],\n",
       "          [119, 135, 147],\n",
       "          [ 61,  79,  90]],\n",
       " \n",
       "         ...,\n",
       " \n",
       "         [[ 73,  79,  77],\n",
       "          [ 53,  63,  68],\n",
       "          [ 54,  68,  80],\n",
       "          ...,\n",
       "          [ 17,  40,  64],\n",
       "          [ 21,  36,  51],\n",
       "          [ 33,  48,  49]],\n",
       " \n",
       "         [[ 61,  68,  75],\n",
       "          [ 55,  70,  86],\n",
       "          [ 57,  79, 103],\n",
       "          ...,\n",
       "          [ 24,  48,  72],\n",
       "          [ 17,  35,  53],\n",
       "          [  7,  23,  32]],\n",
       " \n",
       "         [[ 44,  56,  73],\n",
       "          [ 46,  66,  88],\n",
       "          [ 49,  77, 105],\n",
       "          ...,\n",
       "          [ 27,  52,  77],\n",
       "          [ 21,  43,  66],\n",
       "          [ 12,  31,  50]]],\n",
       " \n",
       " \n",
       "        [[[189, 211, 240],\n",
       "          [186, 208, 236],\n",
       "          [185, 207, 235],\n",
       "          ...,\n",
       "          [175, 195, 224],\n",
       "          [172, 194, 222],\n",
       "          [169, 194, 220]],\n",
       " \n",
       "         [[194, 210, 239],\n",
       "          [191, 207, 236],\n",
       "          [190, 206, 235],\n",
       "          ...,\n",
       "          [173, 192, 220],\n",
       "          [171, 191, 218],\n",
       "          [167, 190, 216]],\n",
       " \n",
       "         [[208, 219, 244],\n",
       "          [205, 216, 240],\n",
       "          [204, 215, 239],\n",
       "          ...,\n",
       "          [175, 191, 217],\n",
       "          [172, 190, 216],\n",
       "          [169, 191, 215]],\n",
       " \n",
       "         ...,\n",
       " \n",
       "         [[207, 199, 181],\n",
       "          [203, 195, 175],\n",
       "          [203, 196, 173],\n",
       "          ...,\n",
       "          [135, 132, 127],\n",
       "          [162, 158, 150],\n",
       "          [168, 163, 151]],\n",
       " \n",
       "         [[198, 190, 170],\n",
       "          [189, 181, 159],\n",
       "          [180, 172, 147],\n",
       "          ...,\n",
       "          [178, 171, 160],\n",
       "          [175, 169, 156],\n",
       "          [175, 169, 154]],\n",
       " \n",
       "         [[198, 189, 173],\n",
       "          [189, 181, 162],\n",
       "          [178, 170, 149],\n",
       "          ...,\n",
       "          [195, 184, 169],\n",
       "          [196, 189, 171],\n",
       "          [195, 190, 171]]],\n",
       " \n",
       " \n",
       "        [[[229, 229, 239],\n",
       "          [236, 237, 247],\n",
       "          [234, 236, 247],\n",
       "          ...,\n",
       "          [217, 219, 233],\n",
       "          [221, 223, 234],\n",
       "          [222, 223, 233]],\n",
       " \n",
       "         [[222, 221, 229],\n",
       "          [239, 239, 249],\n",
       "          [233, 234, 246],\n",
       "          ...,\n",
       "          [223, 223, 236],\n",
       "          [227, 228, 238],\n",
       "          [210, 211, 220]],\n",
       " \n",
       "         [[213, 206, 211],\n",
       "          [234, 232, 239],\n",
       "          [231, 233, 244],\n",
       "          ...,\n",
       "          [220, 220, 232],\n",
       "          [220, 219, 232],\n",
       "          [202, 203, 215]],\n",
       " \n",
       "         ...,\n",
       " \n",
       "         [[150, 143, 135],\n",
       "          [140, 135, 127],\n",
       "          [132, 127, 120],\n",
       "          ...,\n",
       "          [224, 222, 218],\n",
       "          [230, 228, 225],\n",
       "          [241, 241, 238]],\n",
       " \n",
       "         [[137, 132, 126],\n",
       "          [130, 127, 120],\n",
       "          [125, 121, 115],\n",
       "          ...,\n",
       "          [181, 180, 178],\n",
       "          [202, 201, 198],\n",
       "          [212, 211, 207]],\n",
       " \n",
       "         [[122, 119, 114],\n",
       "          [118, 116, 110],\n",
       "          [120, 116, 111],\n",
       "          ...,\n",
       "          [179, 177, 173],\n",
       "          [164, 164, 162],\n",
       "          [163, 163, 161]]]], dtype=uint8),\n",
       " array([6, 9, 9, ..., 9, 1, 1], dtype=uint8),\n",
       " [5])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(X_train, y_train, [5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = X_train / 255.0\n",
    "\n",
    "X_test = X_test / 255.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "          ...,\n",
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       "          [0.04705882, 0.12156863, 0.19607843]]],\n",
       " \n",
       " \n",
       "        [[[0.74117647, 0.82745098, 0.94117647],\n",
       "          [0.72941176, 0.81568627, 0.9254902 ],\n",
       "          [0.7254902 , 0.81176471, 0.92156863],\n",
       "          ...,\n",
       "          [0.68627451, 0.76470588, 0.87843137],\n",
       "          [0.6745098 , 0.76078431, 0.87058824],\n",
       "          [0.6627451 , 0.76078431, 0.8627451 ]],\n",
       " \n",
       "         [[0.76078431, 0.82352941, 0.9372549 ],\n",
       "          [0.74901961, 0.81176471, 0.9254902 ],\n",
       "          [0.74509804, 0.80784314, 0.92156863],\n",
       "          ...,\n",
       "          [0.67843137, 0.75294118, 0.8627451 ],\n",
       "          [0.67058824, 0.74901961, 0.85490196],\n",
       "          [0.65490196, 0.74509804, 0.84705882]],\n",
       " \n",
       "         [[0.81568627, 0.85882353, 0.95686275],\n",
       "          [0.80392157, 0.84705882, 0.94117647],\n",
       "          [0.8       , 0.84313725, 0.9372549 ],\n",
       "          ...,\n",
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       "          [0.6745098 , 0.74509804, 0.84705882],\n",
       "          [0.6627451 , 0.74901961, 0.84313725]],\n",
       " \n",
       "         ...,\n",
       " \n",
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       "          [0.79607843, 0.76470588, 0.68627451],\n",
       "          [0.79607843, 0.76862745, 0.67843137],\n",
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       "          [0.63529412, 0.61960784, 0.58823529],\n",
       "          [0.65882353, 0.63921569, 0.59215686]],\n",
       " \n",
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       "          [0.74117647, 0.70980392, 0.62352941],\n",
       "          [0.70588235, 0.6745098 , 0.57647059],\n",
       "          ...,\n",
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       "          [0.68627451, 0.6627451 , 0.61176471],\n",
       "          [0.68627451, 0.6627451 , 0.60392157]],\n",
       " \n",
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       "          [0.74117647, 0.70980392, 0.63529412],\n",
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       "          ...,\n",
       "          [0.76470588, 0.72156863, 0.6627451 ],\n",
       "          [0.76862745, 0.74117647, 0.67058824],\n",
       "          [0.76470588, 0.74509804, 0.67058824]]],\n",
       " \n",
       " \n",
       "        [[[0.89803922, 0.89803922, 0.9372549 ],\n",
       "          [0.9254902 , 0.92941176, 0.96862745],\n",
       "          [0.91764706, 0.9254902 , 0.96862745],\n",
       "          ...,\n",
       "          [0.85098039, 0.85882353, 0.91372549],\n",
       "          [0.86666667, 0.8745098 , 0.91764706],\n",
       "          [0.87058824, 0.8745098 , 0.91372549]],\n",
       " \n",
       "         [[0.87058824, 0.86666667, 0.89803922],\n",
       "          [0.9372549 , 0.9372549 , 0.97647059],\n",
       "          [0.91372549, 0.91764706, 0.96470588],\n",
       "          ...,\n",
       "          [0.8745098 , 0.8745098 , 0.9254902 ],\n",
       "          [0.89019608, 0.89411765, 0.93333333],\n",
       "          [0.82352941, 0.82745098, 0.8627451 ]],\n",
       " \n",
       "         [[0.83529412, 0.80784314, 0.82745098],\n",
       "          [0.91764706, 0.90980392, 0.9372549 ],\n",
       "          [0.90588235, 0.91372549, 0.95686275],\n",
       "          ...,\n",
       "          [0.8627451 , 0.8627451 , 0.90980392],\n",
       "          [0.8627451 , 0.85882353, 0.90980392],\n",
       "          [0.79215686, 0.79607843, 0.84313725]],\n",
       " \n",
       "         ...,\n",
       " \n",
       "         [[0.58823529, 0.56078431, 0.52941176],\n",
       "          [0.54901961, 0.52941176, 0.49803922],\n",
       "          [0.51764706, 0.49803922, 0.47058824],\n",
       "          ...,\n",
       "          [0.87843137, 0.87058824, 0.85490196],\n",
       "          [0.90196078, 0.89411765, 0.88235294],\n",
       "          [0.94509804, 0.94509804, 0.93333333]],\n",
       " \n",
       "         [[0.5372549 , 0.51764706, 0.49411765],\n",
       "          [0.50980392, 0.49803922, 0.47058824],\n",
       "          [0.49019608, 0.4745098 , 0.45098039],\n",
       "          ...,\n",
       "          [0.70980392, 0.70588235, 0.69803922],\n",
       "          [0.79215686, 0.78823529, 0.77647059],\n",
       "          [0.83137255, 0.82745098, 0.81176471]],\n",
       " \n",
       "         [[0.47843137, 0.46666667, 0.44705882],\n",
       "          [0.4627451 , 0.45490196, 0.43137255],\n",
       "          [0.47058824, 0.45490196, 0.43529412],\n",
       "          ...,\n",
       "          [0.70196078, 0.69411765, 0.67843137],\n",
       "          [0.64313725, 0.64313725, 0.63529412],\n",
       "          [0.63921569, 0.63921569, 0.63137255]]]]),\n",
       " array([6, 9, 9, ..., 9, 1, 1], dtype=uint8),\n",
       " [5])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(X_train, y_train, [5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "cnn = models.Sequential([ \n",
    "    layers.Conv2D(filters=32, kernel_size=(3, 3), activation = \"relu\", input_shape=(32, 32, 3)),\n",
    "    layers.MaxPooling2D((2, 2)),\n",
    "    \n",
    "    layers.Conv2D(filters=64, kernel_size=(3, 3), activation = \"relu\"),\n",
    "    layers.MaxPooling2D((2, 2)),\n",
    "    \n",
    "    layers.Flatten(),\n",
    "    layers.Dense(64, activation = \"relu\"),\n",
    "    layers.Dense(10, activation = \"softmax\")\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "cnn.compile(optimizer = \"adam\",\n",
    "              loss = \"sparse_categorical_crossentropy\",\n",
    "              metrics = [\"accuracy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/15\n",
      "1563/1563 [==============================] - 26s 16ms/step - loss: 1.4637 - accuracy: 0.4759\n",
      "Epoch 2/15\n",
      "1563/1563 [==============================] - 26s 17ms/step - loss: 1.1031 - accuracy: 0.6100\n",
      "Epoch 3/15\n",
      "1563/1563 [==============================] - 32s 21ms/step - loss: 0.9867 - accuracy: 0.6545\n",
      "Epoch 4/15\n",
      "1563/1563 [==============================] - 36s 23ms/step - loss: 0.9071 - accuracy: 0.6836\n",
      "Epoch 5/15\n",
      "1563/1563 [==============================] - 40s 26ms/step - loss: 0.8386 - accuracy: 0.7079\n",
      "Epoch 6/15\n",
      "1563/1563 [==============================] - 36s 23ms/step - loss: 0.7858 - accuracy: 0.7264\n",
      "Epoch 7/15\n",
      "1563/1563 [==============================] - 38s 24ms/step - loss: 0.7395 - accuracy: 0.7406\n",
      "Epoch 8/15\n",
      "1563/1563 [==============================] - 33s 21ms/step - loss: 0.6965 - accuracy: 0.7589\n",
      "Epoch 9/15\n",
      "1563/1563 [==============================] - 39s 25ms/step - loss: 0.6565 - accuracy: 0.7699\n",
      "Epoch 10/15\n",
      "1563/1563 [==============================] - 34s 22ms/step - loss: 0.6219 - accuracy: 0.7817\n",
      "Epoch 11/15\n",
      "1563/1563 [==============================] - 34s 22ms/step - loss: 0.5901 - accuracy: 0.7919\n",
      "Epoch 12/15\n",
      "1563/1563 [==============================] - 35s 23ms/step - loss: 0.5595 - accuracy: 0.8026\n",
      "Epoch 13/15\n",
      "1563/1563 [==============================] - 35s 22ms/step - loss: 0.5273 - accuracy: 0.8146\n",
      "Epoch 14/15\n",
      "1563/1563 [==============================] - 35s 23ms/step - loss: 0.5014 - accuracy: 0.8220\n",
      "Epoch 15/15\n",
      "1563/1563 [==============================] - 36s 23ms/step - loss: 0.4758 - accuracy: 0.8313\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x228b9924730>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cnn.fit(X_train, y_train, epochs = 15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 3s 9ms/step - loss: 1.0883 - accuracy: 0.6784\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[1.0882939100265503, 0.6783999800682068]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cnn.evaluate(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 3s 9ms/step\n"
     ]
    }
   ],
   "source": [
    "y_pred = cnn.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5.58206625e-02, 2.58684508e-04, 1.89728048e-02, 8.28229129e-01,\n",
       "        1.27841288e-03, 8.13186020e-02, 1.36538604e-02, 3.38614744e-04,\n",
       "        6.41366796e-06, 1.22766491e-04],\n",
       "       [1.11692597e-03, 1.04698986e-01, 3.04424333e-07, 5.64680391e-08,\n",
       "        1.32834743e-10, 2.25921681e-09, 2.17248108e-09, 3.80054668e-12,\n",
       "        8.94125998e-01, 5.76996645e-05],\n",
       "       [3.66991535e-02, 2.14875743e-01, 8.54897685e-03, 7.86694139e-03,\n",
       "        8.49939606e-05, 1.75245383e-04, 1.01748132e-03, 2.75231923e-05,\n",
       "        7.28018045e-01, 2.68593524e-03],\n",
       "       [9.55308080e-01, 1.90182909e-04, 3.04105529e-03, 5.76653809e-04,\n",
       "        1.40102420e-04, 1.32076439e-05, 3.27012676e-05, 2.02279966e-06,\n",
       "        4.06895094e-02, 6.48607738e-06],\n",
       "       [1.23936547e-06, 6.91561155e-08, 6.45598695e-02, 2.42278531e-01,\n",
       "        3.72003093e-02, 5.45893470e-03, 6.50494695e-01, 5.16681837e-07,\n",
       "        5.87620525e-06, 9.43986334e-10],\n",
       "       [7.12098918e-05, 1.46323218e-04, 1.71643100e-03, 2.70501208e-02,\n",
       "        2.35074127e-04, 2.38729734e-02, 9.46784794e-01, 4.21680597e-05,\n",
       "        2.56081080e-06, 7.82259231e-05]], dtype=float32)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred[:6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[3, 8, 8, 0, 6, 6]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_classes = [np.argmax(element) for element in y_pred]\n",
    "y_classes[:6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 8, 8, 0, 6, 6], dtype=uint8)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test[:6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_sample(X_test, y_test,8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'cat'"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classes[y_classes[8]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Fashion MNIST</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "fashion_mnist = tf.keras.datasets.fashion_mnist\n",
    "\n",
    "(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 28, 28)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_images.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 28, 28)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_images.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000,)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_labels.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[  0,   0,   0, ...,   0,   0,   0],\n",
       "        [  0,   0,   0, ...,   0,   0,   0],\n",
       "        [  0,   0,   0, ...,   0,   0,   0],\n",
       "        ...,\n",
       "        [  0,   0,   0, ...,   0,   0,   0],\n",
       "        [  0,   0,   0, ...,   0,   0,   0],\n",
       "        [  0,   0,   0, ...,   0,   0,   0]],\n",
       "\n",
       "       [[  0,   0,   0, ...,   0,   0,   0],\n",
       "        [  0,   0,   0, ...,   0,   0,   0],\n",
       "        [  0,   0,   0, ...,   0,   0,   0],\n",
       "        ...,\n",
       "        [  0,   0,   0, ...,   0,   0,   0],\n",
       "        [  0,   0,   0, ...,   0,   0,   0],\n",
       "        [  0,   0,   0, ...,   0,   0,   0]],\n",
       "\n",
       "       [[  0,   0,   0, ...,   0,   0,   0],\n",
       "        [  0,   0,   0, ...,   0,   0,   0],\n",
       "        [  0,   0,   0, ...,   0,   0,   0],\n",
       "        ...,\n",
       "        [  0,   0,   0, ...,   0,   0,   0],\n",
       "        [  0,   0,   0, ...,   0,   0,   0],\n",
       "        [  0,   0,   0, ...,   0,   0,   0]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[  0,   0,   0, ...,   0,   0,   0],\n",
       "        [  0,   0,   0, ...,   0,   0,   0],\n",
       "        [  0,   0,   0, ...,   0,   0,   0],\n",
       "        ...,\n",
       "        [  0,   0,   0, ..., 180,   0,   0],\n",
       "        [  0,   0,   0, ...,  72,   0,   0],\n",
       "        [  0,   0,   0, ...,  70,   0,   0]],\n",
       "\n",
       "       [[  0,   0,   0, ...,   0,   0,   0],\n",
       "        [  0,   0,   0, ...,   0,   0,   0],\n",
       "        [  0,   0,   0, ...,  39,   1,   0],\n",
       "        ...,\n",
       "        [  0,   0,   0, ..., 238,   0,   0],\n",
       "        [  0,   0,   0, ..., 131,   0,   0],\n",
       "        [  0,   0,   0, ...,   0,   0,   0]],\n",
       "\n",
       "       [[  0,   0,   0, ...,   0,   0,   0],\n",
       "        [  0,   0,   0, ...,   0,   0,   0],\n",
       "        [  0,   0,   0, ...,   7,   0,   0],\n",
       "        ...,\n",
       "        [  0,   0,   0, ...,   0,   9,   0],\n",
       "        [  0,   0,   0, ...,   0,   3,   0],\n",
       "        [  0,   0,   0, ...,   0,   0,   0]]], dtype=uint8)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_images[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([9, 0, 0, 3, 0, 2, 7, 2, 5, 5], dtype=uint8)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_labels[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "classes = [\"T-shirt/top\", \"Trouser\", \"Pullover\", \"Dress\", \"Coat\", \"Sandal\", \"Shirt\", \"Sneaker\", \"Bag\", \"Ankle boot\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "plt.figure()\n",
    "plt.imshow(X_train[10])\n",
    "plt.colorbar()\n",
    "plt.grid(False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[[[0.23137255, 0.24313725, 0.24705882],\n",
       "          [0.16862745, 0.18039216, 0.17647059],\n",
       "          [0.19607843, 0.18823529, 0.16862745],\n",
       "          ...,\n",
       "          [0.61960784, 0.51764706, 0.42352941],\n",
       "          [0.59607843, 0.49019608, 0.4       ],\n",
       "          [0.58039216, 0.48627451, 0.40392157]],\n",
       " \n",
       "         [[0.0627451 , 0.07843137, 0.07843137],\n",
       "          [0.        , 0.        , 0.        ],\n",
       "          [0.07058824, 0.03137255, 0.        ],\n",
       "          ...,\n",
       "          [0.48235294, 0.34509804, 0.21568627],\n",
       "          [0.46666667, 0.3254902 , 0.19607843],\n",
       "          [0.47843137, 0.34117647, 0.22352941]],\n",
       " \n",
       "         [[0.09803922, 0.09411765, 0.08235294],\n",
       "          [0.0627451 , 0.02745098, 0.        ],\n",
       "          [0.19215686, 0.10588235, 0.03137255],\n",
       "          ...,\n",
       "          [0.4627451 , 0.32941176, 0.19607843],\n",
       "          [0.47058824, 0.32941176, 0.19607843],\n",
       "          [0.42745098, 0.28627451, 0.16470588]],\n",
       " \n",
       "         ...,\n",
       " \n",
       "         [[0.81568627, 0.66666667, 0.37647059],\n",
       "          [0.78823529, 0.6       , 0.13333333],\n",
       "          [0.77647059, 0.63137255, 0.10196078],\n",
       "          ...,\n",
       "          [0.62745098, 0.52156863, 0.2745098 ],\n",
       "          [0.21960784, 0.12156863, 0.02745098],\n",
       "          [0.20784314, 0.13333333, 0.07843137]],\n",
       " \n",
       "         [[0.70588235, 0.54509804, 0.37647059],\n",
       "          [0.67843137, 0.48235294, 0.16470588],\n",
       "          [0.72941176, 0.56470588, 0.11764706],\n",
       "          ...,\n",
       "          [0.72156863, 0.58039216, 0.36862745],\n",
       "          [0.38039216, 0.24313725, 0.13333333],\n",
       "          [0.3254902 , 0.20784314, 0.13333333]],\n",
       " \n",
       "         [[0.69411765, 0.56470588, 0.45490196],\n",
       "          [0.65882353, 0.50588235, 0.36862745],\n",
       "          [0.70196078, 0.55686275, 0.34117647],\n",
       "          ...,\n",
       "          [0.84705882, 0.72156863, 0.54901961],\n",
       "          [0.59215686, 0.4627451 , 0.32941176],\n",
       "          [0.48235294, 0.36078431, 0.28235294]]],\n",
       " \n",
       " \n",
       "        [[[0.60392157, 0.69411765, 0.73333333],\n",
       "          [0.49411765, 0.5372549 , 0.53333333],\n",
       "          [0.41176471, 0.40784314, 0.37254902],\n",
       "          ...,\n",
       "          [0.35686275, 0.37254902, 0.27843137],\n",
       "          [0.34117647, 0.35294118, 0.27843137],\n",
       "          [0.30980392, 0.31764706, 0.2745098 ]],\n",
       " \n",
       "         [[0.54901961, 0.62745098, 0.6627451 ],\n",
       "          [0.56862745, 0.6       , 0.60392157],\n",
       "          [0.49019608, 0.49019608, 0.4627451 ],\n",
       "          ...,\n",
       "          [0.37647059, 0.38823529, 0.30588235],\n",
       "          [0.30196078, 0.31372549, 0.24313725],\n",
       "          [0.27843137, 0.28627451, 0.23921569]],\n",
       " \n",
       "         [[0.54901961, 0.60784314, 0.64313725],\n",
       "          [0.54509804, 0.57254902, 0.58431373],\n",
       "          [0.45098039, 0.45098039, 0.43921569],\n",
       "          ...,\n",
       "          [0.30980392, 0.32156863, 0.25098039],\n",
       "          [0.26666667, 0.2745098 , 0.21568627],\n",
       "          [0.2627451 , 0.27058824, 0.21568627]],\n",
       " \n",
       "         ...,\n",
       " \n",
       "         [[0.68627451, 0.65490196, 0.65098039],\n",
       "          [0.61176471, 0.60392157, 0.62745098],\n",
       "          [0.60392157, 0.62745098, 0.66666667],\n",
       "          ...,\n",
       "          [0.16470588, 0.13333333, 0.14117647],\n",
       "          [0.23921569, 0.20784314, 0.22352941],\n",
       "          [0.36470588, 0.3254902 , 0.35686275]],\n",
       " \n",
       "         [[0.64705882, 0.60392157, 0.50196078],\n",
       "          [0.61176471, 0.59607843, 0.50980392],\n",
       "          [0.62352941, 0.63137255, 0.55686275],\n",
       "          ...,\n",
       "          [0.40392157, 0.36470588, 0.37647059],\n",
       "          [0.48235294, 0.44705882, 0.47058824],\n",
       "          [0.51372549, 0.4745098 , 0.51372549]],\n",
       " \n",
       "         [[0.63921569, 0.58039216, 0.47058824],\n",
       "          [0.61960784, 0.58039216, 0.47843137],\n",
       "          [0.63921569, 0.61176471, 0.52156863],\n",
       "          ...,\n",
       "          [0.56078431, 0.52156863, 0.54509804],\n",
       "          [0.56078431, 0.5254902 , 0.55686275],\n",
       "          [0.56078431, 0.52156863, 0.56470588]]],\n",
       " \n",
       " \n",
       "        [[[1.        , 1.        , 1.        ],\n",
       "          [0.99215686, 0.99215686, 0.99215686],\n",
       "          [0.99215686, 0.99215686, 0.99215686],\n",
       "          ...,\n",
       "          [0.99215686, 0.99215686, 0.99215686],\n",
       "          [0.99215686, 0.99215686, 0.99215686],\n",
       "          [0.99215686, 0.99215686, 0.99215686]],\n",
       " \n",
       "         [[1.        , 1.        , 1.        ],\n",
       "          [1.        , 1.        , 1.        ],\n",
       "          [1.        , 1.        , 1.        ],\n",
       "          ...,\n",
       "          [1.        , 1.        , 1.        ],\n",
       "          [1.        , 1.        , 1.        ],\n",
       "          [1.        , 1.        , 1.        ]],\n",
       " \n",
       "         [[1.        , 1.        , 1.        ],\n",
       "          [0.99607843, 0.99607843, 0.99607843],\n",
       "          [0.99607843, 0.99607843, 0.99607843],\n",
       "          ...,\n",
       "          [0.99607843, 0.99607843, 0.99607843],\n",
       "          [0.99607843, 0.99607843, 0.99607843],\n",
       "          [0.99607843, 0.99607843, 0.99607843]],\n",
       " \n",
       "         ...,\n",
       " \n",
       "         [[0.44313725, 0.47058824, 0.43921569],\n",
       "          [0.43529412, 0.4627451 , 0.43529412],\n",
       "          [0.41176471, 0.43921569, 0.41568627],\n",
       "          ...,\n",
       "          [0.28235294, 0.31764706, 0.31372549],\n",
       "          [0.28235294, 0.31372549, 0.30980392],\n",
       "          [0.28235294, 0.31372549, 0.30980392]],\n",
       " \n",
       "         [[0.43529412, 0.4627451 , 0.43137255],\n",
       "          [0.40784314, 0.43529412, 0.40784314],\n",
       "          [0.38823529, 0.41568627, 0.38431373],\n",
       "          ...,\n",
       "          [0.26666667, 0.29411765, 0.28627451],\n",
       "          [0.2745098 , 0.29803922, 0.29411765],\n",
       "          [0.30588235, 0.32941176, 0.32156863]],\n",
       " \n",
       "         [[0.41568627, 0.44313725, 0.41176471],\n",
       "          [0.38823529, 0.41568627, 0.38431373],\n",
       "          [0.37254902, 0.4       , 0.36862745],\n",
       "          ...,\n",
       "          [0.30588235, 0.33333333, 0.3254902 ],\n",
       "          [0.30980392, 0.33333333, 0.3254902 ],\n",
       "          [0.31372549, 0.3372549 , 0.32941176]]],\n",
       " \n",
       " \n",
       "        ...,\n",
       " \n",
       " \n",
       "        [[[0.1372549 , 0.69803922, 0.92156863],\n",
       "          [0.15686275, 0.69019608, 0.9372549 ],\n",
       "          [0.16470588, 0.69019608, 0.94509804],\n",
       "          ...,\n",
       "          [0.38823529, 0.69411765, 0.85882353],\n",
       "          [0.30980392, 0.57647059, 0.77254902],\n",
       "          [0.34901961, 0.58039216, 0.74117647]],\n",
       " \n",
       "         [[0.22352941, 0.71372549, 0.91764706],\n",
       "          [0.17254902, 0.72156863, 0.98039216],\n",
       "          [0.19607843, 0.71764706, 0.94117647],\n",
       "          ...,\n",
       "          [0.61176471, 0.71372549, 0.78431373],\n",
       "          [0.55294118, 0.69411765, 0.80784314],\n",
       "          [0.45490196, 0.58431373, 0.68627451]],\n",
       " \n",
       "         [[0.38431373, 0.77254902, 0.92941176],\n",
       "          [0.25098039, 0.74117647, 0.98823529],\n",
       "          [0.27058824, 0.75294118, 0.96078431],\n",
       "          ...,\n",
       "          [0.7372549 , 0.76470588, 0.80784314],\n",
       "          [0.46666667, 0.52941176, 0.57647059],\n",
       "          [0.23921569, 0.30980392, 0.35294118]],\n",
       " \n",
       "         ...,\n",
       " \n",
       "         [[0.28627451, 0.30980392, 0.30196078],\n",
       "          [0.20784314, 0.24705882, 0.26666667],\n",
       "          [0.21176471, 0.26666667, 0.31372549],\n",
       "          ...,\n",
       "          [0.06666667, 0.15686275, 0.25098039],\n",
       "          [0.08235294, 0.14117647, 0.2       ],\n",
       "          [0.12941176, 0.18823529, 0.19215686]],\n",
       " \n",
       "         [[0.23921569, 0.26666667, 0.29411765],\n",
       "          [0.21568627, 0.2745098 , 0.3372549 ],\n",
       "          [0.22352941, 0.30980392, 0.40392157],\n",
       "          ...,\n",
       "          [0.09411765, 0.18823529, 0.28235294],\n",
       "          [0.06666667, 0.1372549 , 0.20784314],\n",
       "          [0.02745098, 0.09019608, 0.1254902 ]],\n",
       " \n",
       "         [[0.17254902, 0.21960784, 0.28627451],\n",
       "          [0.18039216, 0.25882353, 0.34509804],\n",
       "          [0.19215686, 0.30196078, 0.41176471],\n",
       "          ...,\n",
       "          [0.10588235, 0.20392157, 0.30196078],\n",
       "          [0.08235294, 0.16862745, 0.25882353],\n",
       "          [0.04705882, 0.12156863, 0.19607843]]],\n",
       " \n",
       " \n",
       "        [[[0.74117647, 0.82745098, 0.94117647],\n",
       "          [0.72941176, 0.81568627, 0.9254902 ],\n",
       "          [0.7254902 , 0.81176471, 0.92156863],\n",
       "          ...,\n",
       "          [0.68627451, 0.76470588, 0.87843137],\n",
       "          [0.6745098 , 0.76078431, 0.87058824],\n",
       "          [0.6627451 , 0.76078431, 0.8627451 ]],\n",
       " \n",
       "         [[0.76078431, 0.82352941, 0.9372549 ],\n",
       "          [0.74901961, 0.81176471, 0.9254902 ],\n",
       "          [0.74509804, 0.80784314, 0.92156863],\n",
       "          ...,\n",
       "          [0.67843137, 0.75294118, 0.8627451 ],\n",
       "          [0.67058824, 0.74901961, 0.85490196],\n",
       "          [0.65490196, 0.74509804, 0.84705882]],\n",
       " \n",
       "         [[0.81568627, 0.85882353, 0.95686275],\n",
       "          [0.80392157, 0.84705882, 0.94117647],\n",
       "          [0.8       , 0.84313725, 0.9372549 ],\n",
       "          ...,\n",
       "          [0.68627451, 0.74901961, 0.85098039],\n",
       "          [0.6745098 , 0.74509804, 0.84705882],\n",
       "          [0.6627451 , 0.74901961, 0.84313725]],\n",
       " \n",
       "         ...,\n",
       " \n",
       "         [[0.81176471, 0.78039216, 0.70980392],\n",
       "          [0.79607843, 0.76470588, 0.68627451],\n",
       "          [0.79607843, 0.76862745, 0.67843137],\n",
       "          ...,\n",
       "          [0.52941176, 0.51764706, 0.49803922],\n",
       "          [0.63529412, 0.61960784, 0.58823529],\n",
       "          [0.65882353, 0.63921569, 0.59215686]],\n",
       " \n",
       "         [[0.77647059, 0.74509804, 0.66666667],\n",
       "          [0.74117647, 0.70980392, 0.62352941],\n",
       "          [0.70588235, 0.6745098 , 0.57647059],\n",
       "          ...,\n",
       "          [0.69803922, 0.67058824, 0.62745098],\n",
       "          [0.68627451, 0.6627451 , 0.61176471],\n",
       "          [0.68627451, 0.6627451 , 0.60392157]],\n",
       " \n",
       "         [[0.77647059, 0.74117647, 0.67843137],\n",
       "          [0.74117647, 0.70980392, 0.63529412],\n",
       "          [0.69803922, 0.66666667, 0.58431373],\n",
       "          ...,\n",
       "          [0.76470588, 0.72156863, 0.6627451 ],\n",
       "          [0.76862745, 0.74117647, 0.67058824],\n",
       "          [0.76470588, 0.74509804, 0.67058824]]],\n",
       " \n",
       " \n",
       "        [[[0.89803922, 0.89803922, 0.9372549 ],\n",
       "          [0.9254902 , 0.92941176, 0.96862745],\n",
       "          [0.91764706, 0.9254902 , 0.96862745],\n",
       "          ...,\n",
       "          [0.85098039, 0.85882353, 0.91372549],\n",
       "          [0.86666667, 0.8745098 , 0.91764706],\n",
       "          [0.87058824, 0.8745098 , 0.91372549]],\n",
       " \n",
       "         [[0.87058824, 0.86666667, 0.89803922],\n",
       "          [0.9372549 , 0.9372549 , 0.97647059],\n",
       "          [0.91372549, 0.91764706, 0.96470588],\n",
       "          ...,\n",
       "          [0.8745098 , 0.8745098 , 0.9254902 ],\n",
       "          [0.89019608, 0.89411765, 0.93333333],\n",
       "          [0.82352941, 0.82745098, 0.8627451 ]],\n",
       " \n",
       "         [[0.83529412, 0.80784314, 0.82745098],\n",
       "          [0.91764706, 0.90980392, 0.9372549 ],\n",
       "          [0.90588235, 0.91372549, 0.95686275],\n",
       "          ...,\n",
       "          [0.8627451 , 0.8627451 , 0.90980392],\n",
       "          [0.8627451 , 0.85882353, 0.90980392],\n",
       "          [0.79215686, 0.79607843, 0.84313725]],\n",
       " \n",
       "         ...,\n",
       " \n",
       "         [[0.58823529, 0.56078431, 0.52941176],\n",
       "          [0.54901961, 0.52941176, 0.49803922],\n",
       "          [0.51764706, 0.49803922, 0.47058824],\n",
       "          ...,\n",
       "          [0.87843137, 0.87058824, 0.85490196],\n",
       "          [0.90196078, 0.89411765, 0.88235294],\n",
       "          [0.94509804, 0.94509804, 0.93333333]],\n",
       " \n",
       "         [[0.5372549 , 0.51764706, 0.49411765],\n",
       "          [0.50980392, 0.49803922, 0.47058824],\n",
       "          [0.49019608, 0.4745098 , 0.45098039],\n",
       "          ...,\n",
       "          [0.70980392, 0.70588235, 0.69803922],\n",
       "          [0.79215686, 0.78823529, 0.77647059],\n",
       "          [0.83137255, 0.82745098, 0.81176471]],\n",
       " \n",
       "         [[0.47843137, 0.46666667, 0.44705882],\n",
       "          [0.4627451 , 0.45490196, 0.43137255],\n",
       "          [0.47058824, 0.45490196, 0.43529412],\n",
       "          ...,\n",
       "          [0.70196078, 0.69411765, 0.67843137],\n",
       "          [0.64313725, 0.64313725, 0.63529412],\n",
       "          [0.63921569, 0.63921569, 0.63137255]]]]),\n",
       " array([6, 9, 9, ..., 9, 1, 1], dtype=uint8),\n",
       " [10])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(X_train, y_train, [10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = X_train / 255.0\n",
    "\n",
    "X_test = X_test / 255.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x720 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (5, 10))\n",
    "\n",
    "for i in range(20):\n",
    "    plt.subplot(5, 5, i+1)\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    plt.grid(False)\n",
    "    plt.imshow(train_images[i])\n",
    "    plt.xlabel(classes[train_labels[i]])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = tf.keras.Sequential([\n",
    "    tf.keras.layers.Flatten(input_shape=(28, 28)),\n",
    "    tf.keras.layers.Dense(128, activation='relu'),\n",
    "    tf.keras.layers.Dense(10)\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer = \"adam\",\n",
    "              loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits = True),\n",
    "              metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 3.3690 - accuracy: 0.6888\n",
      "Epoch 2/20\n",
      "1875/1875 [==============================] - 5s 2ms/step - loss: 0.6976 - accuracy: 0.7415\n",
      "Epoch 3/20\n",
      "1875/1875 [==============================] - 3s 2ms/step - loss: 0.6365 - accuracy: 0.7626\n",
      "Epoch 4/20\n",
      "1875/1875 [==============================] - 3s 2ms/step - loss: 0.5982 - accuracy: 0.7771\n",
      "Epoch 5/20\n",
      "1875/1875 [==============================] - 3s 2ms/step - loss: 0.5490 - accuracy: 0.8029\n",
      "Epoch 6/20\n",
      "1875/1875 [==============================] - 3s 2ms/step - loss: 0.5127 - accuracy: 0.8228\n",
      "Epoch 7/20\n",
      "1875/1875 [==============================] - 3s 2ms/step - loss: 0.5030 - accuracy: 0.8263\n",
      "Epoch 8/20\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.4902 - accuracy: 0.8309\n",
      "Epoch 9/20\n",
      "1875/1875 [==============================] - 3s 2ms/step - loss: 0.4846 - accuracy: 0.8324\n",
      "Epoch 10/20\n",
      "1875/1875 [==============================] - 5s 2ms/step - loss: 0.4842 - accuracy: 0.8335\n",
      "Epoch 11/20\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.4754 - accuracy: 0.8360\n",
      "Epoch 12/20\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.4675 - accuracy: 0.8388\n",
      "Epoch 13/20\n",
      "1875/1875 [==============================] - 3s 2ms/step - loss: 0.4638 - accuracy: 0.8392\n",
      "Epoch 14/20\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.4660 - accuracy: 0.8398\n",
      "Epoch 15/20\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.4593 - accuracy: 0.8419\n",
      "Epoch 16/20\n",
      "1875/1875 [==============================] - 5s 2ms/step - loss: 0.4720 - accuracy: 0.8399\n",
      "Epoch 17/20\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.4550 - accuracy: 0.8442\n",
      "Epoch 18/20\n",
      "1875/1875 [==============================] - 3s 1ms/step - loss: 0.4599 - accuracy: 0.8413\n",
      "Epoch 19/20\n",
      "1875/1875 [==============================] - 3s 2ms/step - loss: 0.4467 - accuracy: 0.8459\n",
      "Epoch 20/20\n",
      "1875/1875 [==============================] - 5s 2ms/step - loss: 0.4461 - accuracy: 0.8455\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x22906e92c10>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(train_images, train_labels, epochs = 20)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 1s 2ms/step - loss: 0.5768 - accuracy: 0.8033\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.5767979621887207, 0.8033000230789185]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(test_images,test_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 0s 1ms/step\n"
     ]
    }
   ],
   "source": [
    "prediction = model.predict(test_images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ -38.750835 ,   20.870192 , -217.34789  ,   16.322361 ,\n",
       "        -186.78377  ,   54.25183  , -136.9245   ,   54.964138 ,\n",
       "          32.175858 ,   59.902832 ],\n",
       "       [   7.756601 ,    6.345602 ,   12.354261 ,    7.258774 ,\n",
       "           9.737389 ,  -33.520752 ,   10.888173 , -165.74918  ,\n",
       "           2.971644 ,  -32.593384 ],\n",
       "       [ 118.47221  ,  151.29126  ,   62.790585 ,  126.85719  ,\n",
       "         113.60918  ,  -67.06069  ,  109.41269  , -658.7454   ,\n",
       "         109.55555  , -381.9534   ],\n",
       "       [ 112.36636  ,  179.7366   ,   70.22263  ,  137.67906  ,\n",
       "         122.884415 ,  -57.41133  ,  116.91693  , -632.9451   ,\n",
       "          90.89701  , -476.01068  ],\n",
       "       [   5.932323 ,    2.4957683,    6.8935065,    4.725526 ,\n",
       "           3.6889095,   -6.4714956,    6.7341638,  -61.98672  ,\n",
       "           3.3309255,   -8.641227 ],\n",
       "       [  93.77914  ,  117.5229   ,   50.651863 ,   98.70926  ,\n",
       "          90.09835  ,  -75.84     ,   87.30076  , -644.4699   ,\n",
       "          84.491    , -320.84177  ]], dtype=float32)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction[:6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argmax(prediction[3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_labels[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_image(i, predictions_array, true_label, img):\n",
    "  true_label, img = true_label[i], img[i]\n",
    "  plt.grid(False)\n",
    "  plt.xticks([])\n",
    "  plt.yticks([])\n",
    "\n",
    "  plt.imshow(img, cmap=plt.cm.binary)\n",
    "\n",
    "  predicted_label = np.argmax(predictions_array)\n",
    "  if predicted_label == true_label:\n",
    "    color = \"blue\"\n",
    "  else:\n",
    "    color = \"red\"\n",
    "\n",
    "  plt.xlabel(\"{} {:2.0f}% ({})\".format(classes[predicted_label],\n",
    "                                100*np.max(predictions_array),\n",
    "                                classes[true_label]),\n",
    "                                color=color)\n",
    "\n",
    "def plot_value_array(i, predictions_array, true_label):\n",
    "  true_label = true_label[i]\n",
    "  plt.grid(False)\n",
    "  plt.xticks(range(10))\n",
    "  plt.yticks([])\n",
    "  thisplot = plt.bar(range(10), predictions_array, color = \"#777777\")\n",
    "  plt.ylim([0, 1])\n",
    "  predicted_label = np.argmax(predictions_array)\n",
    "\n",
    "  thisplot[predicted_label].set_color(\"red\")\n",
    "  thisplot[true_label].set_color(\"blue\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "i = 3\n",
    "plt.figure(figsize=(6,3))\n",
    "plt.subplot(1,2,1)\n",
    "plot_image(i, prediction[i], test_labels, test_images)\n",
    "plt.subplot(1,2,2)\n",
    "plot_value_array(i, prediction[i],  test_labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "i = 8\n",
    "plt.figure(figsize=(6,3))\n",
    "plt.subplot(1,2,1)\n",
    "plot_image(i, prediction[i], test_labels, test_images)\n",
    "plt.subplot(1,2,2)\n",
    "plot_value_array(i, prediction[i],  test_labels)\n",
    "plt.show()"
   ]
  },
  {
   "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": []
  },
  {
   "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
}