first neural network.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import keras\n",
    "import numpy as np \n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "(x_train,y_train),(x_test,y_test)=keras.datasets.fashion_mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']\n"
     ]
    }
   ],
   "source": [
    "cl=['T-shirt/top','Trouser','Pullover','Dress','Coat','Sandal','Shirt','Sneaker','Bag','Ankle boot']\n",
    "print(cl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((60000, 28, 28), (60000,))"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape,y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((10000, 28, 28), (10000,))"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test.shape,y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "'greys' is not a valid value for name; supported values are 'Accent', 'Accent_r', 'Blues', 'Blues_r', 'BrBG', 'BrBG_r', 'BuGn', 'BuGn_r', 'BuPu', 'BuPu_r', 'CMRmap', 'CMRmap_r', 'Dark2', 'Dark2_r', 'GnBu', 'GnBu_r', 'Greens', 'Greens_r', 'Greys', 'Greys_r', 'OrRd', 'OrRd_r', 'Oranges', 'Oranges_r', 'PRGn', 'PRGn_r', 'Paired', 'Paired_r', 'Pastel1', 'Pastel1_r', 'Pastel2', 'Pastel2_r', 'PiYG', 'PiYG_r', 'PuBu', 'PuBuGn', 'PuBuGn_r', 'PuBu_r', 'PuOr', 'PuOr_r', 'PuRd', 'PuRd_r', 'Purples', 'Purples_r', 'RdBu', 'RdBu_r', 'RdGy', 'RdGy_r', 'RdPu', 'RdPu_r', 'RdYlBu', 'RdYlBu_r', 'RdYlGn', 'RdYlGn_r', 'Reds', 'Reds_r', 'Set1', 'Set1_r', 'Set2', 'Set2_r', 'Set3', 'Set3_r', 'Spectral', 'Spectral_r', 'Wistia', 'Wistia_r', 'YlGn', 'YlGnBu', 'YlGnBu_r', 'YlGn_r', 'YlOrBr', 'YlOrBr_r', 'YlOrRd', 'YlOrRd_r', 'afmhot', 'afmhot_r', 'autumn', 'autumn_r', 'binary', 'binary_r', 'bone', 'bone_r', 'brg', 'brg_r', 'bwr', 'bwr_r', 'cividis', 'cividis_r', 'cool', 'cool_r', 'coolwarm', 'coolwarm_r', 'copper', 'copper_r', 'cubehelix', 'cubehelix_r', 'flag', 'flag_r', 'gist_earth', 'gist_earth_r', 'gist_gray', 'gist_gray_r', 'gist_heat', 'gist_heat_r', 'gist_ncar', 'gist_ncar_r', 'gist_rainbow', 'gist_rainbow_r', 'gist_stern', 'gist_stern_r', 'gist_yarg', 'gist_yarg_r', 'gnuplot', 'gnuplot2', 'gnuplot2_r', 'gnuplot_r', 'gray', 'gray_r', 'hot', 'hot_r', 'hsv', 'hsv_r', 'icefire', 'icefire_r', 'inferno', 'inferno_r', 'jet', 'jet_r', 'magma', 'magma_r', 'mako', 'mako_r', 'nipy_spectral', 'nipy_spectral_r', 'ocean', 'ocean_r', 'pink', 'pink_r', 'plasma', 'plasma_r', 'prism', 'prism_r', 'rainbow', 'rainbow_r', 'rocket', 'rocket_r', 'seismic', 'seismic_r', 'spring', 'spring_r', 'summer', 'summer_r', 'tab10', 'tab10_r', 'tab20', 'tab20_r', 'tab20b', 'tab20b_r', 'tab20c', 'tab20c_r', 'terrain', 'terrain_r', 'twilight', 'twilight_r', 'twilight_shifted', 'twilight_shifted_r', 'viridis', 'viridis_r', 'vlag', 'vlag_r', 'winter', 'winter_r'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-8-8190ad149db1>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx_train\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mcmap\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'greys'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[1;31m#with this erroe we can determine the actual cmap value at the last\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\pyplot.py\u001b[0m in \u001b[0;36mimshow\u001b[1;34m(X, cmap, norm, aspect, interpolation, alpha, vmin, vmax, origin, extent, shape, filternorm, filterrad, imlim, resample, url, data, **kwargs)\u001b[0m\n\u001b[0;32m   2643\u001b[0m         \u001b[0mfilterrad\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m4.0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mimlim\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcbook\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdeprecation\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_deprecated_parameter\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2644\u001b[0m         resample=None, url=None, *, data=None, **kwargs):\n\u001b[1;32m-> 2645\u001b[1;33m     __ret = gca().imshow(\n\u001b[0m\u001b[0;32m   2646\u001b[0m         \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcmap\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcmap\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnorm\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnorm\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maspect\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maspect\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2647\u001b[0m         \u001b[0minterpolation\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0minterpolation\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0malpha\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvmin\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mvmin\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\__init__.py\u001b[0m in \u001b[0;36minner\u001b[1;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1563\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0minner\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1564\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mdata\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1565\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0mmap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msanitize_sequence\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1566\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1567\u001b[0m         \u001b[0mbound\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnew_sig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbind\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\cbook\\deprecation.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    356\u001b[0m                 \u001b[1;34mf\"%(removal)s.  If any parameter follows {name!r}, they \"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    357\u001b[0m                 f\"should be pass as keyword, not positionally.\")\n\u001b[1;32m--> 358\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    359\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    360\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\cbook\\deprecation.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    356\u001b[0m                 \u001b[1;34mf\"%(removal)s.  If any parameter follows {name!r}, they \"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    357\u001b[0m                 f\"should be pass as keyword, not positionally.\")\n\u001b[1;32m--> 358\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    359\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    360\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py\u001b[0m in \u001b[0;36mimshow\u001b[1;34m(self, X, cmap, norm, aspect, interpolation, alpha, vmin, vmax, origin, extent, shape, filternorm, filterrad, imlim, resample, url, **kwargs)\u001b[0m\n\u001b[0;32m   5620\u001b[0m             \u001b[0maspect\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrcParams\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'image.aspect'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5621\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_aspect\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maspect\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 5622\u001b[1;33m         im = mimage.AxesImage(self, cmap, norm, interpolation, origin, extent,\n\u001b[0m\u001b[0;32m   5623\u001b[0m                               \u001b[0mfilternorm\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfilternorm\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfilterrad\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfilterrad\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5624\u001b[0m                               resample=resample, **kwargs)\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\image.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, ax, cmap, norm, interpolation, origin, extent, filternorm, filterrad, resample, **kwargs)\u001b[0m\n\u001b[0;32m    888\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_extent\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mextent\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    889\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 890\u001b[1;33m         super().__init__(\n\u001b[0m\u001b[0;32m    891\u001b[0m             \u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    892\u001b[0m             \u001b[0mcmap\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcmap\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\image.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, ax, cmap, norm, interpolation, origin, filternorm, filterrad, resample, **kwargs)\u001b[0m\n\u001b[0;32m    246\u001b[0m                  ):\n\u001b[0;32m    247\u001b[0m         \u001b[0mmartist\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mArtist\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 248\u001b[1;33m         \u001b[0mcm\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mScalarMappable\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnorm\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcmap\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    249\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_mouseover\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    250\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0morigin\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\cm.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, norm, cmap)\u001b[0m\n\u001b[0;32m    171\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnorm\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnorm\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    172\u001b[0m         \u001b[1;31m#: The Colormap instance of this ScalarMappable.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 173\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcmap\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_cmap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcmap\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    174\u001b[0m         \u001b[1;31m#: The last colorbar associated with this ScalarMappable. May be None.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    175\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolorbar\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\cm.py\u001b[0m in \u001b[0;36mget_cmap\u001b[1;34m(name, lut)\u001b[0m\n\u001b[0;32m    132\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolors\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mColormap\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    133\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 134\u001b[1;33m     \u001b[0mcbook\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_check_in_list\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msorted\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcmap_d\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    135\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mlut\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    136\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mcmap_d\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\cbook\\__init__.py\u001b[0m in \u001b[0;36m_check_in_list\u001b[1;34m(_values, **kwargs)\u001b[0m\n\u001b[0;32m   2141\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mv\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2142\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mv\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mvalues\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2143\u001b[1;33m             raise ValueError(\n\u001b[0m\u001b[0;32m   2144\u001b[0m                 \u001b[1;34m\"{!r} is not a valid value for {}; supported values are {}\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2145\u001b[0m                 .format(v, k, ', '.join(map(repr, values))))\n",
      "\u001b[1;31mValueError\u001b[0m: 'greys' is not a valid value for name; supported values are 'Accent', 'Accent_r', 'Blues', 'Blues_r', 'BrBG', 'BrBG_r', 'BuGn', 'BuGn_r', 'BuPu', 'BuPu_r', 'CMRmap', 'CMRmap_r', 'Dark2', 'Dark2_r', 'GnBu', 'GnBu_r', 'Greens', 'Greens_r', 'Greys', 'Greys_r', 'OrRd', 'OrRd_r', 'Oranges', 'Oranges_r', 'PRGn', 'PRGn_r', 'Paired', 'Paired_r', 'Pastel1', 'Pastel1_r', 'Pastel2', 'Pastel2_r', 'PiYG', 'PiYG_r', 'PuBu', 'PuBuGn', 'PuBuGn_r', 'PuBu_r', 'PuOr', 'PuOr_r', 'PuRd', 'PuRd_r', 'Purples', 'Purples_r', 'RdBu', 'RdBu_r', 'RdGy', 'RdGy_r', 'RdPu', 'RdPu_r', 'RdYlBu', 'RdYlBu_r', 'RdYlGn', 'RdYlGn_r', 'Reds', 'Reds_r', 'Set1', 'Set1_r', 'Set2', 'Set2_r', 'Set3', 'Set3_r', 'Spectral', 'Spectral_r', 'Wistia', 'Wistia_r', 'YlGn', 'YlGnBu', 'YlGnBu_r', 'YlGn_r', 'YlOrBr', 'YlOrBr_r', 'YlOrRd', 'YlOrRd_r', 'afmhot', 'afmhot_r', 'autumn', 'autumn_r', 'binary', 'binary_r', 'bone', 'bone_r', 'brg', 'brg_r', 'bwr', 'bwr_r', 'cividis', 'cividis_r', 'cool', 'cool_r', 'coolwarm', 'coolwarm_r', 'copper', 'copper_r', 'cubehelix', 'cubehelix_r', 'flag', 'flag_r', 'gist_earth', 'gist_earth_r', 'gist_gray', 'gist_gray_r', 'gist_heat', 'gist_heat_r', 'gist_ncar', 'gist_ncar_r', 'gist_rainbow', 'gist_rainbow_r', 'gist_stern', 'gist_stern_r', 'gist_yarg', 'gist_yarg_r', 'gnuplot', 'gnuplot2', 'gnuplot2_r', 'gnuplot_r', 'gray', 'gray_r', 'hot', 'hot_r', 'hsv', 'hsv_r', 'icefire', 'icefire_r', 'inferno', 'inferno_r', 'jet', 'jet_r', 'magma', 'magma_r', 'mako', 'mako_r', 'nipy_spectral', 'nipy_spectral_r', 'ocean', 'ocean_r', 'pink', 'pink_r', 'plasma', 'plasma_r', 'prism', 'prism_r', 'rainbow', 'rainbow_r', 'rocket', 'rocket_r', 'seismic', 'seismic_r', 'spring', 'spring_r', 'summer', 'summer_r', 'tab10', 'tab10_r', 'tab20', 'tab20_r', 'tab20b', 'tab20b_r', 'tab20c', 'tab20c_r', 'terrain', 'terrain_r', 'twilight', 'twilight_r', 'twilight_shifted', 'twilight_shifted_r', 'viridis', 'viridis_r', 'vlag', 'vlag_r', 'winter', 'winter_r'"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plt.imshow(x_train[0],cmap='greys')\n",
    "#with this erroe we can determine the actual cmap value at the last "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x22f3839f160>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(x_train[0],cmap='Accent')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-10-0ee2c3dd1bc0>:2: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
      "  plt.figure(figsize=(16,16))\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1152 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1152 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x1152 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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RbDtdRS1Haz6gtSCssyBT8rbX9anYMyfHVcyqQwnwy28cKNWnokzPyLjOYFlZmdsH7Vqad2yP+pBBUIoQZOCKEGTgihBk4IoQZOAKScvM2ZtdpKbL0xaNuikNBfVNqkmdnTyvV+Nt7tJzHIfH7XTkVmPVX1CelgbsD4YYIQNnhCADV4QgA1eEIANXLGtqgmC1iREycEUIMnBFCDJwRQgycEUIMnBFCDJwxf8CBZBpBx2qEWYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1152x1152 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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uQmCC\n",
      "text/plain": [
       "<Figure size 1152x1152 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1152 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x1152 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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QnSqhEVK4QoIUrpAghSskSOGKqrmGttZ43tyLj14VxXY+4wZ+go7FUWjfk48fdL/y0tcXp876Eum0ImnCwdCQOGkD2fIukE7RdC6Aw4+MU6qPXfc870RHnP7d4SvHRLFLpiTmjhZEI6RwhQQpXCFBCldIkMIVNU2FTBhDFi6lypuQuZNvvZXI6hA6yRzF1ub8GRVmFBrozEWf9BJT1tTI+z9ll7iUzGOpbfhWxntrz/jGnsU6VwCNkMIVEqRwhQQpXCFBCldIkMIVNXXZLJ31uytOoG3Ht8aOfIs24tITDEs4yk8KqTQhY5tR5HNtHcUbk6KtjbXIk2ZohBSukCCFKyRI4QoJUrjCmPEQol5ohBSukCCFKyRI4QoJUrhCghSukCCFK/4HWRtFlwcgbzgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1152x1152 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x1152 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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VZgUAYxJO6B8575umnk/Se9sV+4gX2/4ri+33tXaYzoyNqNLrbrYnTCgrMTJQnORFeg00JHEFDUlcQUMSV9CQxBWuo2yyY8Iom/QaaEjiChqSuIKGJK4oGNQQ0t1whCSuoCGJK2hI4goakriChiSuoCGJK/4HZhrYu749A4YAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1152x1152 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Zte35Un5aIh1n1fAR0jGFC9IxhQvSMYUL0jGFGaemVPafih9mAujri/Mjdna2qGXPnBmNbJpTMJlwCrRIiZbNIuXUaGsn+/pK61PqvJpTNaTYANpb4x3C0utBPVLjvI1xQTqmcEE6pnBBOqZwQTqmWHBe9nFltwXQw4FNifyQ/f2xR9vZ2RrZJhPrGUvdG1vbbQH0fbCH+uO1m21teuhwWvH+Nc+/Vwmngu5lW8FHSMcULkjHFC5IxxQuSMcUC86p2XdkULVrYbaZRDhsbDgOM7Zf1BnZNEcpxeBA7Ch1dOihy2bF2RoZHIlso6N6fkctRYz2VkeVdZvW8RHSMYUL0jGFC9IxhQvSMYUL0jHFgvOytSfmQN8xQVs0C9DQFJdtaog9X+3pQoBRxUsfOBMnDL1wywq1/lgipFhMKmVLvfIoYKqvpVJidpiq4yOkYwoXpGMKF6RjChekY4oF59RoYTdITfT19YwrVy2N6ys5E1Nby01NxiHFOsXZWpHYr3pYCUm2LInDjCmnpq6utHEkld9SPaeRjRl8hHRM4YJ0TOGCdEzhgnRMseCcmmdfOa3atVQqjY36962/L157ePZsbJua0NdDDp5WEsz3n4pMPz2wWq3f2xOngxnctzuyLbrqOrW+lqBfS4XSWMZ+3VZYeD123tK4IB1TuCAdU7ggHVO4IB1TmPGyS93C7LrL16r2w2djL/vCRMLSVmWz9I1KmK8hEU97+Uzskb/UEz91+KFL4ycZAcam47WLzxy7IrKlwnntLfHH9vD+nsjWoaRsSVHrLeRS+AjpmMIF6ZjCBemYwgXpmELS2fcdp/b4COmYwgXpmMIF6ZjCBemYwgXpmMIF6Zji/wGydyw6mNoSYwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1152x1152 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1152 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x1152 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAKQAAAC5CAYAAACm/ZQEAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAMoklEQVR4nO2de5BWZR3HPz/2wgILKysqosCimUiRZRcrNbGyiSnpotXkOGZO2h813ZtqKrv6X1PUVFNNGYyVTdNNulqOqGlmTXkhxQsgKgruArq7LOzCsk9/nLPN2/v8DuwbsPvb+n5m3ln4vc9zzvOe9/M+5/zO85xzLKWEEFGYMtENEKIWCSlCISFFKCSkCIWEFKGQkCIUEvIQMbNkZjcf4jK6yuWsOjytmryEE9LMmszsCjO7xcx2mtk+M+s2s3vN7LtmtmKi2ziZMLNVpexVr8UT3cZamie6AbWYWRPwa+C1wDPAb4AtQCdwMnAxsBhYM1FtnMR8lWKb1rN9vBtyIEIJCbydQsZ7gHNTSr21b5rZdODMiWjY/wArU0qbJ7oRByPaLvvl5d9V9TICpJR2p5TWjv7fzDrM7KNmdpOZbTGzvWbWY2ZrzOyl3gpGj/nMbI6ZfcfMtprZkJndZ2bvrKjTamafNrONZdlHzOyLZja1ovw8M7vKzG43s21lu540sx+Z2Wn/xXb5vyFaD7mj/PvsMZY/DbgauJVi9/40sABYASw3swtSSr936h0F3A7sBX4KtAEXAdeY2UhKafVoQTMz4CfAG4CNwNeBVuByYGlFu14BfBxYC/wM2AWcUq5jhZmdlVK6Z4yf8XCx3MxmAfuBDcBNKaW+cW7DQbFIkyvM7AXAnRQ/lB8CvwD+nlJ6tKJ8B9CSUtpeFz8R+CvQm1I6re690Q/8PeDdKaX9ZXwJcC/wUEppSU35i8u2/AU4L6U0WMY7gb8BJwG3pJSW1dQ5FtiTUuqvW/fpFD+EP6WUltfEu4BHgNUppctq4s8H3uhvrUpWppT+faxYZu7vcMr1A59IKX2jweUfWVJKoV7AW4GtQKp57aCQ84IGlvO1su6CungCBoBZTp1byvdn1sT+WMbOc8pfVr53cwPtWgMMUvyQRmNd5XJWVSy/kVdX3TIuL7fpAoo9wUnAh4G+svyVE/2d/0d7J7oBFV9aC/Aa4PPAryh2xaMbfDVlz16WPYtil/o4MOR8QS9zhLy7Yr0/KN+fXxPbQbGba3HKj4qUCQm8rmz7VmCf067jDybkEd7Gry/X2QM0TfR3PvqKdgwJQEppH/CH8jV6OuhC4BrgUore8pdm9iaKY8BBip5sI0XvNwIsA84FvMTDO/0BMFz+baqJdQA7yzbVs81biJm9j+I0y9Nlux4DdlMI8Ebg9Ip2jRsppV+b2RPACcASYN1EtmeUkELWk4rjvJ+Y2VLgU8ArgV8CX6BITF6UUlpfW8fMvk0h5KHSC3SaWYsj5dz6wmbWDHyOQtYzUkpb695/2VhXfDiOIQ9CD4WQMxpcxxFjUghZw2iSYOXfZwH3OTJOAc4+TOv8B/Dqcnlr695b5pSfQ5HF/9yRsR04o4F1Px/4TAPlAVZRvQeobUsHxSBDAjY3uI4jRqjzkGb2djM7vxSq/r25wBXlf28t/24GTjGzeTXljOJLXMLh4fvl36vNrK1mPZ0UvXU93RS75xeWAo6Wb6HYjc8Z64pTSqtSStbga3PNOuea2bPql1u2axVFknNjSsk99JgIovWQZwLvB7aZ2W0Up0IAFlEkCdOA6ymOGwG+AnwLuMvMfkaRPJxFIeOvgAsOQ5uuA95GcW7zn2Z2PUXSdRHFaZ+TawunlEbM7GsU5yHXleVbgfMohkDXlv8eDxYDa83sDmA9xY/lBOB8isONTcC7xqktY2Ois6q6zG8+8B6KpOVBilMTeyky1d8ClwBT6upcBtxNkcxsL+suBT5LsTtaVle+8jQNRa/hnTppBa6i+AKHKHrmqykSk2x5FD/0DwH3A3sojievBRZ66+AIZdnl9vw2xWFHD8UPtpfiHO0nqTm9FeUV6sS4EKGOIYWQkCIUElKEQkKKUEhIEYoDnoccHEYpuDjstDX/e6QtQz2kCIWEFKGQkCIUElKEQkKKUEhIEQoJKUIhIUUoJKQIhYQUoZCQIhQSUoRCQopQSEgRCgkpQiEhRSgkpAiFhBShkJAiFBJShEJCilBISBEKCSlCISFFKCSkCIWEFKGQkCIUElKEQkKKUEhIEQoJKUIhIUUoJKQIhYQUoZCQIhQSUoRCQopQSEgRCgkpQiEhRSgkpAiFhBShOOCj5SY7VQ+nN6t8stmYWHnrxix23sKjs9jsGa1u/Tse357F5rVPy2KnHjfLrX/srKkHa+IR5arfP5jFzu06yi37qsXHNbRs9ZAiFBJShEJCilBISBGK/+mkZv+In9Q0N40tqblz4043fukZ87PY7BktWeyG9dvc+jc8kC+3vS3/Kv5y3T1u/VMXdWaxFUuPyWIvPiEvB9B1zIws9uTTe9yyf9z4VBa7/4neLNY3OOzWV1IjJjUSUoRCQopQSEgRCgkpQjHpsuyRisx5ypQ8c25u8n9vm3sGstg373wsiz28tc+tP7s9H7rzhvPamv31Lzm+PYtNbc7bf+zLF7j1587MM/p123ZnsTXretz6C46ensVaKs48XPHivA0PdA9msbs27XDrN4p6SBEKCSlCISFFKCSkCMW4JzVVcxS9sFeyyUleqrjt4XzeIcB7V/89i335khdksZfMn+kv95F86Mxj8/Y80QDwpmP279mXxToq5lPuH8nnTs5qa3LK+dt69vT8a/eGQwG8zf3Qk3myt3OnP/TY3ZsnQAuObnPLgnpIEQwJKUIhIUUoJKQIxQQkNX7cG2nxRmUerUgUFs7JRx8u+/ptbtkbP3V+Ftuzd38W6+rM5w0CXHT6iW78UPCSmps2dLtlb3gwn0+5/tE8NtMZUQI4Y25+8dgzA3vdsj0DQ1lsl9PW1tY8qQLo7svrK6kRkwYJKUIhIUUoJKQIhYQUobCqoTyAwWF39M7FW04jtyy5f0s+HPX0YJ75ffBHd7v133JOVxa78syFbtltz+TDWed87BdZ7I4vvdmtf/Jx+XzG4f0jWaxqPuaRYONTu7LYx3+z3i071ZmnOTSctx/gcWeYcK9zRqKzMx/OBFh54fOy2IsWdVSKoR5ShEJCilBISBEKCSlCccChQ2/oripPGWsC860/b3Ljd23JD8pfeUp+z8E3n+UnKj++OV/uGypu4/E7Z0judcuXZrH5zsVQVXifv5H7Ux5qUviRNfdlsS1P9rtl9zsJ2K7+PNEDGOjLL4hrPypP6trb/bmbc2Y2di9L9ZAiFBJShEJCilBISBGKAyY13hzFQ+XZnfkBMcDZ8/ObxnsXKT1njt+mcxbOzmJbev0Lj7wb1N+/NU+qWivuPOHRyMVnHo0kMLdvyC9eu/k712axRctXuPUfffiJLNZxdIdbtm16PnexoyMflWmu2FZzZvrJThXqIUUoJKQIhYQUoZCQIhQSUoRi3K86vH69f8/C7oqMuJ4nuvNsGGDatPyeiaee6GeOm57Kh9Q2bHDub/i208fUJvDnQ+7Y5V/JNzCUzyfc2pd//v69+dV9AA/vzK+8vG71J7PYwg7/qsmZzraa1uL3Td6cTu8pFoP7/PmUjc4JVQ8pQiEhRSgkpAiFhBShOOBFXn/d1Ju9ufL2R9yyJx2Tzx2cNysfNtq8M7+1BsAW59FmA4P5QX1vxS0/+vvzeNXQpzfPc4bzaLirli926+8byQ/gW6bkv+2+iqSkyRkm9Op7F2MBNFse95a5Y9Df1s8M5dtq0EnKAAb35duqrSVf18bt/ro+cPaiLDbvqFZd5CUmBxJShEJCilBISBGKAyY1Pf35EW3VPQsf6MlHD/Y4Z+9Prrg3oHdCv38or9+9y08UHnduuu7d8xGgb3d+UL/f2Q4PPeiPKnU5z6seGMjbNVxxNwgP73uo+mo2rMsvaJvekd+gv73Dn3uKk1IM7/Ofd+3x3Occn8Ue2+I/COCGjy7LYgs6pyqpEZMDCSlCISFFKCSkCIWEFKE4bPeHHCtVjzvb62SkXpZclTmPOJ9jtzPvEKDFGZLz5jNWsWswz0i9j1V1xZ13NaO3XYYq5hh66x926lc9A9sb0vSGIwFmTs2nzLa15E9cOMZ5XjhAx/R8SLat2cvzC9RDilBISBEKCSlCISFFKMY9qRFCSY2YNEhIEQoJKUIhIUUoJKQIhYQUoZCQIhQSUoRCQopQSEgRCgkpQiEhRSgkpAiFhBShkJAiFBJShEJCilBISBEKCSlCISFFKCSkCIWEFKGQkCIUElKEQkKKUEhIEQoJKUIhIUUoJKQIhYQUoZCQIhQSUoRCQopQSEgRCgkpQiEhRSgkpAiFhBShkJAiFBJShEJCilBISBGKAz5aTojxRj2kCIWEFKGQkCIUElKEQkKKUEhIEYp/AXF8YbzrqUVUAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1152x1152 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1152x1152 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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bVoeactfdL667UK1+8/dG6+0WmD6n36nqZX3DY6XdCQnoc1p79wv/Bzs2blbrb33i0kCrLI/sDBwhiTFoSGIKGpKYgoYkpsg5qImlmLQbf/LltfVh6u35DfocwZfXbgu0mhp97ubO+nDdSY3Y8iIXnnZYoM2cNCqrNnMh32P9yMqPVL1SSYmWRW5oq1LmlPYqD7XRSjoyRnlxbJEYjpDEGDQkMQUNSUxBQxJT0JDEFAVLHRKSLYyyyX4DDUlMQUMSU9CQxBQ0JDEFDUlMQUMSU9CQxBQ0JDEFDUlMQUMSU9CQxBQ0JDEFDUlMQUMSU9CQxBQ0JDEFDUlMQUMSU9CQxBQZb/Ii5POGIyQxBQ1JTEFDElPQkMQUNCQxBQ1JTPE/9Z3Cheyf0TYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1152x1152 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x1152 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1152 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1152 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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4ALQRR8x2UajEObOVlACf+zmTaIQUrpAghSskSOEKCVK44qTzIYWoNhohhSskSOEKCVK4QoIUrpAghSskSOGK/wCbjngo/agJiQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1152x1152 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x1152 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x1152 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Tsnbzgvly89neKxar/ZX9YMhXTEXF2fp536WFcpPaiFI0/+LNW9T+Tcel2dp42xVq22TDGZKYgoIkpqAgiSkoSGIKCpKYwozL/vxi6VJnzpTOMytLLw/y6PUXTOr65cVy7aIWS4RlynrOqSJTWbu5eKH85gEAChWXfuG5enHXZMMZkpiCgiSmoCCJKShIYgqXyPFnhEw1nCGJKShIYgoKkpiCgiSmoCCJKShIYor/Aj0nWMveE/0lAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1152x1152 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x1152 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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PrzrXbLtqiZ2qPRaaskbF1wqcIYkrKEjiCgqSuIKCJK6Y0NQQkjacIYkrKEjiCgqSuIKCJK6gIIkrKEjiiv8CkFM3mdphfSYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1152x1152 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(25):\n",
    "    plt.figure(figsize=(16,16))\n",
    "    plt.subplot(5,5,i+1)\n",
    "    plt.imshow(x_train[i],cmap=\"Blues\")\n",
    "    plt.axis(\"off\")\n",
    "    plt.title(cl[y_train[i]]+\"=\"+str(y_train[i]),fontsize=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Feature Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "        0.86666667, 0.90196078, 0.2627451 ],\n",
       "       [0.18823529, 0.79607843, 0.71764706, 0.76078431, 0.83529412,\n",
       "        0.77254902, 0.7254902 , 0.74509804, 0.76078431, 0.75294118,\n",
       "        0.79215686, 0.83921569, 0.85882353, 0.86666667, 0.8627451 ,\n",
       "        0.9254902 , 0.88235294, 0.84705882, 0.78039216, 0.80784314,\n",
       "        0.72941176, 0.70980392, 0.69411765, 0.6745098 , 0.70980392,\n",
       "        0.80392157, 0.80784314, 0.45098039],\n",
       "       [0.        , 0.47843137, 0.85882353, 0.75686275, 0.70196078,\n",
       "        0.67058824, 0.71764706, 0.76862745, 0.8       , 0.82352941,\n",
       "        0.83529412, 0.81176471, 0.82745098, 0.82352941, 0.78431373,\n",
       "        0.76862745, 0.76078431, 0.74901961, 0.76470588, 0.74901961,\n",
       "        0.77647059, 0.75294118, 0.69019608, 0.61176471, 0.65490196,\n",
       "        0.69411765, 0.82352941, 0.36078431],\n",
       "       [0.        , 0.        , 0.29019608, 0.74117647, 0.83137255,\n",
       "        0.74901961, 0.68627451, 0.6745098 , 0.68627451, 0.70980392,\n",
       "        0.7254902 , 0.7372549 , 0.74117647, 0.7372549 , 0.75686275,\n",
       "        0.77647059, 0.8       , 0.81960784, 0.82352941, 0.82352941,\n",
       "        0.82745098, 0.7372549 , 0.7372549 , 0.76078431, 0.75294118,\n",
       "        0.84705882, 0.66666667, 0.        ],\n",
       "       [0.00784314, 0.        , 0.        , 0.        , 0.25882353,\n",
       "        0.78431373, 0.87058824, 0.92941176, 0.9372549 , 0.94901961,\n",
       "        0.96470588, 0.95294118, 0.95686275, 0.86666667, 0.8627451 ,\n",
       "        0.75686275, 0.74901961, 0.70196078, 0.71372549, 0.71372549,\n",
       "        0.70980392, 0.69019608, 0.65098039, 0.65882353, 0.38823529,\n",
       "        0.22745098, 0.        , 0.        ],\n",
       "       [0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.15686275, 0.23921569, 0.17254902,\n",
       "        0.28235294, 0.16078431, 0.1372549 , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        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.        ],\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.        ]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#basicly this feature scaling is just to scale your data within the range of 0 to 1\n",
    "#since we have color max value of 255\n",
    "x_train=x_train/255\n",
    "x_test=x_test/255\n",
    "\n",
    "x_train[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#lets  buit the keras model\n",
    "model= keras.models.Sequential([\n",
    "    keras.layers.Flatten(input_shape=[28,28]),\n",
    "    keras.layers.Dense(units=32,activation='relu'),\n",
    "    keras.layers.Dense(units=10,activation='softmax')\n",
    "    \n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "flatten_1 (Flatten)          (None, 784)               0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 32)                25120     \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 10)                330       \n",
      "=================================================================\n",
      "Total params: 25,450\n",
      "Trainable params: 25,450\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1875/1875 [==============================] - 3s 2ms/step - loss: 0.5410 - accuracy: 0.8124\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x22f374808b0>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.compile(optimizer='adam',loss='sparse_categorical_crossentropy',metrics=['accuracy'])\n",
    "model.fit(x_train,y_train,epochs=1)\n",
    "#epoch is used to increase the effeciency the data is train multiple time as the number of epoch."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.4085 - accuracy: 0.8555\n",
      "Epoch 2/10\n",
      "1875/1875 [==============================] - 3s 2ms/step - loss: 0.3736 - accuracy: 0.8676\n",
      "Epoch 3/10\n",
      "1875/1875 [==============================] - 3s 2ms/step - loss: 0.3524 - accuracy: 0.8734\n",
      "Epoch 4/10\n",
      "1875/1875 [==============================] - 3s 2ms/step - loss: 0.3331 - accuracy: 0.8809\n",
      "Epoch 5/10\n",
      "1875/1875 [==============================] - 3s 2ms/step - loss: 0.3222 - accuracy: 0.8838\n",
      "Epoch 6/10\n",
      "1875/1875 [==============================] - 3s 2ms/step - loss: 0.3099 - accuracy: 0.8887\n",
      "Epoch 7/10\n",
      "1875/1875 [==============================] - 3s 2ms/step - loss: 0.3019 - accuracy: 0.8909\n",
      "Epoch 8/10\n",
      "1875/1875 [==============================] - 3s 2ms/step - loss: 0.2935 - accuracy: 0.8931\n",
      "Epoch 9/10\n",
      "1875/1875 [==============================] - 3s 2ms/step - loss: 0.2880 - accuracy: 0.8947\n",
      "Epoch 10/10\n",
      "1875/1875 [==============================] - 3s 2ms/step - loss: 0.2817 - accuracy: 0.8968\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x22f3868c970>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x_train,y_train,epochs=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Test and evaluate network."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 0s 1ms/step - loss: 0.3629 - accuracy: 0.8709\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.3629361689090729, 0.8708999752998352]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(x_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#since we can see the network is showing the decreace in accuracy by 2% , so we can asume our model in a genralize model and not a overfit model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0., 0., 0., 0., 0., 0., 0., 1.], dtype=float32)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predict=model.predict(x_test)\n",
    "y_predict[0].round(2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argmax(y_predict[0].round(2))\n",
    "#theindex 9 has a ankleboot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test[0]\n",
    "#which is actual value, but what we want is image of the prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-22-07fba9a5ca64>:2: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
      "  plt.figure(figsize=(16,16))\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1152 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1152 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Js/Ye/eFSiTXDZUrxgPwTFsOVmEocsedQg8yMepIhZQyX4ja5Eda2/aE/EKKVGVOziW8PtXLkyC+IzT+h/8fOsfzqdmYwNdPkUL3FroNzTDdb1JOUNMuY8xsuzZzUx8GcJ0/Y5VG9Winm1KEap45VWTdSYXzIOXzLjfYNfnX7pwRJxIKJkTLjQyWafl6emdEALMlIfBwgki3w3vPpYTWOqJQjJmtVThmtctrEkNsFFBdndlxIyc+R/+5+vZX6oI876s2UZpJRb6YkqdH0zl2EGKnGlOOIkVrJbfcqR5TjyG370tGv2h1I/k8R+fxcgpHq/GOmmVEpJTRaGZFEK80oJfO7ckdrJaqliPEh59gVSdI7cUJL/gDHF8X9rAc4Igbkr2IMyF/FGJC/ijEgfxVjQP4qxv8D4Zursfn2HmUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1152x1152 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1152 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1152 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1152 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1152 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1152 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ccfE8jz++eCs7+3PYkVqXsIWKGzBecvGCkLGSy0BnikLaadrxHE5B0tcK8eWBUeBM3Hyi7PJkucKh8RlSKYeefIruXBKnI81gR5qBzrn//ZuIMF318YKQauADKRK21bQ573AKkr6QVr5aWJbMiiuOLagKpZrP5IzLF+55kpobcNmuAYanquwfLfEXv3kGl+7onZPlOsem6N8gNFE4E4I9MRvXE6cc6WsJwTSiUJXDxRqjpRozdd+kRHkh/YUUgx1ptnZmKSyT7BiqEWJgabGmGdCypMc9hh+E3PLACI8eKtHfmabmBjwyPMWbLt7Ma8/dsuT4bBqOIXyi7kbeozkjcQ2c0qSrmmyVQBUv0rC9QCmkHdIrTEo8OFFlouwyNuPh+iFHijX6OtK86eLNnNvfNYfA+dFLEZmdG7hByHQtIAibMpg4B6c46SbjpR69G151A2bqPlv7sism/f6RSf5vzyQTpTpBqBweLbG5J8trz93ylB6rymxihIhgN1KiFWpBwHjFa5O+3ghCZWLGvBNerHiEmGwVL1D8IFwy380kNwY8Mlbh4QNTzMy4FAoprn/Fmezoyi+qhYvIrEuPo+IHjBTdU+KjBKc26aqUaz4VN+BwtUbSssgmbMLopQZriZm+64eU6wF7x6uMjEwTBEo2m+CKnf10ZheftDUIn5NeDdT9gIlSHT9o9/R1RdK2OK03SxAq24MsRC43nbBJ2kvHvn81XOQrPx/mx/cNMzlW5D1veB4Xb+4ivYQcutD1Go3gaMVn/8g0VS9Yi6qtK05p0i1LyKcd/CCk5pmlV2Pc9UPF4alENdKgH5kozRKOwoWbOjm9P3/ckbSqG1Iq1XHb7v3pwb6jFd7/Pw9TLNeZnq7zqktO44qt3Zy7uesprnpkssp3Hh3lptv3cfCee9h2yfM5+4w+Tu/LM9CRWvVySzETuRk3YKZUxQvbpB8XKnXzxab4pKkxPi80kXL9kCeHp5kYLzM1NsXPBvLkkjZDfea9Mtsy6U81z3wM6I7dk4yPmZSmzZs7uGhHF7mUfdzpTfHU6VNg8t6cpO8eLWOJeVPEsYWEbZFKmJf+E7Zl3kSxj4keIpBIWNSrdfzhx/nRHcIjj41x2eYecimHQtqh6gbsHi3zjYdGufVbPyHd3cXA+c/jz68c4qJtPSte4s1HY+1uCTgJhyaPywBNSnqx7pmAS+QqBSFhWSRsIW3bOLPxbdMwnixWyGYTdPYUqG05nf5NfWzYkGPa9Rgr1TkyXadS97lreIInjpRx8nm27hzk3DP62FTIkE6a15GP5+NARkoV+vIJNgwUyDlN+UjnoCktPFiuMOOG7B6rUa75TJbrVN2AmuvjOBaOZdGdT86qWWGobOjK0F1IMbSjh6HBAtt7Uhyt1jhcrXHbo5McKVbZ88QEtm2x88wt/MGVO7j6rEF68uZFBT8IIRJgVgMRc84Z3Vkuf/bgsjH6ZkBTkv68wR6CUDl/Q0A9DKj5AV4Y4oeKGyUs+NELiVUvpOaHjM/4eFFINu1YlOoBI9MeVTfg0FQF3w/ZsaObga4M55/WweWbe+jIJEhG/tiKhVRXA8Eoa/uKVX7wixFes2twTZ/FeqApST9zU2HRspm6j+eHTFU887qwG1D2PEZnaijGRR8ouhwpezw2Mk1ppk657FEoJLlgqJcLtxR4/tZe8imHXPpY9Y83KcMS81Lkw4crPHbnTzl4zVlcQPdxXevpQlOSvhRSjcmcbR37nEio7PTNf6+mmK9F+aFSc4PZYxxLKGQcMkmbjkyCxCrd+GJIOBZd2QQvHuriFy+5lG2F3PInnWSccqQ31tHJJnl7xLaETNJmR2eeS581QCHd/I+0/bLDGmGm5lOq+XRmm/87cm3S1xlt0ttoCjTHwNjG04o26S2INuktiDbpLYg26S2INuktiP8H8nDxYl6nN/cAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1152x1152 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1152 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1152 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1152 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1152 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1152 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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M4M3tP+Fk86k6r53lgj93HHeuMXDyROwRlcVg4mJZRf4CUoaRk5dh5ORlGDl5GUZOXoaRk5dh5ORlGDl5GUZOXoaRk5dh5ORlGDl5GUZOXoaRk5dh5ORlGPn/Vcgwcs3LMHLyMoycvAwjJy/DyMnLMHLyMoz/B1BagdCDZTn5AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1152x1152 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1152 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1152 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1152 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1152 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1152 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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GqSnnYwgCF5Dq8DeIPGnFK/gSskhnu9nmkV2T/OCJQxyYcvkPxYzpMqC0ZCiaiAePtLhvzzhTU02SJMEPfIIgmOd08jwP3/dztQHkKXLWqLPjqXFu+/EeDky1jvftHBeUlgwwty7YdaTOf2+boNlo4/tuEZgRJcuUxsAP/DxvIfNBZBFNhkZozjbZ9uRhDrfKSYaTwrQcb7bZe2jGpb2rU9cXJUgxrpGXSf/4vk/YDpmamGE2KuRDlAillgwZJhsxExMN4nQSs7iDH/gdkiKzNLJUuMwxle37vu/qdK0TOsizinFSkKEZJjQaYUdGdGZJdAQc0qyneUGI8qwRl0Rp1UQR47MRRw4fAdxTH4XOvKwF6b/qzjyPIk9ygbl3JczMJddGMY2ZBs0onvcGdxlQOsmwkLiOYstfqsnL2AKm4TKSPsuMDpOEOCmHaiiidGSA+YRoRTFho5kntubRSm9xP0FOFM1tZ5bHkWZMox0TJwMyrDoMVXxqoyN5fmM2wRmKzqfiQtKdpGPb4pjZMKEdJZSMCycHGdYNB4ytG8u9jlnia0eKXDqxWbAKulLoMpM0iqi3E5phZ4yiDCgdGbrVuJkRpmuGbJ2QJbZAp4TIUt6KdTMzdC4BJmG6FVNvRQM1sRqQ+4vMSAzCNM1trgAdE9yR0zCvrTnSZOVn2zHNMCEeLCBPfGQPcWIQxQlhbB0Jrh0BqiKsc/G5UKY1lhBGCWGczJNCqx2lI0OHpZh9vW2JkHP3sW6SdLuqs2NlkwpQcqeTcyCRv/6WvSNRTHfLjrsKc2Wy/bxe8T2MwKPme5TM51Q+yVCESNPkvQVyIhdSAUtgLu/Bw08TbEvGhXKTwfPcSy+Bp87Q9RKzmMcmligzVPGpVfyCxJlvxaxGlFpNZAgK2UuLSYKO7OjsDW3ITU0r2JxDgUc18PBKpidODjL4mktzS2zZJ7+I7tR5fJ81NZ+R6txLNWXhRKnVRIZ6O6Ex4/IZcg/kYjO4gHlZjFHg+VQD9wJuLjwGamL1oN6KmJ1xHwDNk1Y8b75PoWA95FOtufPyhHyfmi8CT6WRCBlKSYbup36k6jM8Okyj3iCJIqhW3Nfa0u9Ahs0WRBFELaKgBkEABekhOTWTxAkWxwsGqMpAjNKpiYXEf9X3qNQq7lxBGsSx+2obSQxx6P4mkSNGHGNRhEVR5+v5C+iDMhABSioZunHqaMD6U9cQtSNaTS//mtvQyFCqNiAKY6Iowvf9DkLJE0mc0G613dfewoj1IwFDlfK9lX1SkKHmi1otyJNbs8TWWi3A993b2WEYE4YBnue+IpsJAM8TYRh3fCGu4jnHU9nS3lbFB74GOD4o3ZphgKPHgAwD5BiQYYAcAzIMkGNAhgFyDMgwQI7/B6Fmn2qsxKopAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1152x1152 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1152 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1152 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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SzrQjzpzpcvRoi5kZn1GUkecFoWcTpTkXLw7ohS5JlnNkvsrRmYDNfky7HXF0oYZtCU+WF1a16qFUwXYn4mLVw7WENMvRWjMeZxSFJkkUeV6QZQUi4AcueZ7vnsd00j7FxI+V6bylpRoLzZAXHa5z4uKQtY1hSbhmaamKbVs0az6Nisf5fsqdi1Xu+ra7ONWO2B4kDAYJ43HG58YZQeDwgtvmWJmpcN/RGmlekCrNK2+ZpbvcoD1M2B4kXFjbpFKrcPstsxxfrPHyw1ViVRArzW3LDWbrPo9lbaIoYzRM0FpTFAWNZoXZ2RCROp5ns1qrUJtS52469xpAkhfkBdRCl0bFZaHm4toWShUoVZDnGs9zCAIH17HQWrM1VFR9m5cs1ZiremitiWNFHJltV1FollshK02PpufiWsIoNXv9ozM+jm2ZURwl5CqnVfM40vK5pVml4Tu720jXtnBdC9sW8jwnV+YlAr7v0Kr7LLVCfMfCsQ4COHvC2W7KKM2ZqfqkWcFfn+pzdnvEaJRhWSYOPy6Fm+3tyMT18wLr3lW+4cgcs5URlgjtzR5JlPDd33wft84FtAKHU52Y3/jMGp1+TL+f8o33HubFyxXuv63F2lzIFz4XUqlXuOdQjcWaQ6xyPnGyx8MnNhgOjV8xN1ehVvPJsoI4Soj6EVE1JEkUq3NVjswGFBriLL/2yd4ETC3xdd9MRjvhcUvAd21qNZckMd52mubYttBs+uS5JooyUpVzcRTTHitUoWnO1imKGsdnfVqBQzvKONdLWd8aMxqljMcpvXFGL8lZrLos1Vzml2epVj0GiSFtlBb0RilFofH9Z7usKDRZptCFxvVdLEuM76FyorRA5cXUbuem9haqTzzZ1uvjmI88uk49dDk+GzDOCqK04KGnt9ncHDEaJTSbIe980+3YIpzvp5zcinj6fA8RwbaF++9a4PiMz7FGhYujmF/585P0+wmDfmzkWUs4cqTF0kzIW166RM21+eL6iHO9hMfXuoxGJlizvFxnsRVy22IVzxE++sgFtrbGbJzbIqgELB+exbYF27YIAocwcHjnA8c4XKtw363NqZvvp3bE+7ZFrjVn1gfUKh6FhoWay3LDZbbuk2W5ceyaAfOhxyhTrHUTNnpmm6VK6Ta9fd7E5i2Luudw/FCDi0G064WLCM2aR6vmE5cO5c4sMx5nJElOlhVkWU6qchZrLnMVh0fnqmgNnU2XaqPK8dUG7X5CpxMRx4qxZ5PkxYFXv1e4tkWich57dI2wGrK50uK1Lz7Eyw4FrC/VqAQO3WFKq+axXAk52R/y8IkN48zFin67TxIlXLh7iZWGi9uyWAwDvuslS3xpfcSfJWo3E+fWpTp3zAd0IkVeKKKsICoDQ6p06JIkpz/OONzwOVavcu+xlKdClwsX+hw6VOONL5znL05s88zJLVSqsGyLscqnNr1raonfGif0k5z55Vkcx8LzbEZxxlo/YbnhsVR3GSQ5rmVxZjhiY5RxaLFGUXb0+mbAcJiyOhvQCGweXu8ySHKe2hiTF5pbVhrkuUblBd1RyhdSQ5JjWSw1XHzXwnFM/MCyhDA0u4entiPGKqfqWdy2UEHfd5TQt+lGOVGaoyfW9F6k6MTZzerCq2Jqib84jhkkOcvLdYqiwLIshrHiVDvhVUdqzAUeqtBEec4Tm2NGacHqXJXQMxLq01WP9iDhSMun6jr8+ZObrHcj1tZ6rKw0eMM9i0RZQZwVfGV9QGeQ4Lo2Fd/hUKOFa1s4jtm2+b6D61q4tsWT6yMu9FNeslJhoepy10KFbpJxYiMmyUxAaQftSNFJkpvYi1fG1BJ/rF5Fa02W5WbEBQ6LzYAXLAR8dm3IKM44PBNQ82xWGh4nNiI++cg5ZmcrHFmscbETMRgkfOpkl6rvcH57TK8X09nqE4Yuw8Tsuz1HePypbc49c4Fb7zrC7GzIxjBjaxDTbo+xbQvft0mSHKVy7rlrkcCziTPNKMl46Ewf37FYKPfu8a3znDq5xag/YrnucaxevfbJ3gRMLfFVz6bqOiaxwrbwXZu6b9MMHM5tj7iwNaLQMyzUPY6W6VgbFzooVVAJHIbDlCjKOLc1wndt4jgjTXPS2ARzxllB4Fi4ttBrDyjOPkF8fJk89xklinGiiEYRtm2T5x6j/ogkSohvmzOaf6HpRoonTnVoNHxaVY+K77A0V+XsWpdc5dQ9m9Czb3JPXh5TS3xeaKquw30vWESVCtnGwMTiv3hik35nsCuJepbFXNVh8dAMea45vdZDqWLXeQs8mzvuXKQ/ThmPM8LQZWuQmOncFuqtGsmtL+LuO+dZaoVImRypMoVKFWmSkiUZRVEwW/M53PI51U5Y2xry2KcewW7MsLW1yrHVBnetNMhfskJ3MIeIMEhuyP9puuGYauKB3TSouMyS6Y1SslSRq9wkRxYF/TRjkJjtXZ4bHR3AsoQ0VUSpTcW1KAKXRsOnWvVwyoxdgFYrQOtZGqGH59iME4XKNY7rUOTFczzzvNCkyjiFqSogHpIDnXaLpaUqdd9mvu7jOyY/L82Lrzq3acDUEn9xHJMWBc3AxleCI0IUuuRac3i1yWimwmzdR+Wa3/rMOfr9hE57hOu5hKGD49jYtrB2poMuNLZlEi5efPs8c3WPOxdCstwkXt6xcJQs15xYH3J6c0hvmJDnBbfevkgUKUajlH5nyHg45omT22z3Y77x7kUCt8Hn73whKlVEw8hEFLVmturRrLh4loWe0p381BK/o20UGrJcE6uC0LXwGgHjWDGoZNgiqMIINpYl1OoBlmWiZ45jRJSw4u/KuADzDZ+mb2OJyYy1LYu8AMfWu9s7MIEdrTW2LQSBQ1YLsWwLrSGKMrbGilTl1BrGeatUXFp1n1RpktzcnuVaFqE9nV08nVYBNdcFzBTeGSu2hikvXK5yfCbgWMujHSm+sjEmTnNm6z52M8BdbdIZpmx3olI9s7jztjlcx0LlBa5jcfdiSFZo2mNF3bdp+DYXhxndSJFkZvloVD3GiWJjY4Tn2cZ5awWICKNRSpYVfOl0GxFhebnG6kKNb7l7jgv9lDOdhH6UofKCquvQCKezi6fTKp4Nm6pc040yTm8MyPKCzWFGN8qI0pwLbfM00FuW6lR8h/mKw5fzAWfO9mg0fILAYr4RUPUdktIn+PQzPVSuSVROLXCpBQ69cUaS5dRDl7lGwGrT50I/ZXs7IklMJHBlpc7KTIWg9NKfONvbFXlEhIfWfDb7MRvdCNu28ByLSOVk+cFUvydYpWed5CbN+ZmTbdbXh5wIXYrCeOvDYUKl4vEP7pjjcNPjaCPkbC+h3xtTrXqEnsMtcyFzVYdYFZxqJ/zJJ09RFAWOYxOGLtWqSxwbz/v1L1/h9vmAe5dmeKY/5KETGwwGCf12n9uOz/Cy1TovWqhjW8J7z/Xo901coNc1MYJ+P2bUHzG/1GJursJIqd3bvaYNU0t83Xco0ASORavqs7hUx3FMLn0cm/Dq7bfOstQKefVqA0uEYaaoBw7Hb5ljdaHGfCPAtoRYmbtrAGZmjGa+k7ZlRBizC+hHio1hxpnKiG6sWF2uUa26dKoevmOxMczYqCX4tkWr5pPOPbuDiGNFkZv0bde18X2Hhuse7OP3Cs+xCJXJu6v6DvOzIVlelDp8gdZwfLHOrfMBq/UKiSrYGCdUXIvVhRqrsyFLddc4hplxDrWGRsNnOBSycj3Psnw3334YZ7THDhcGCeOsYKEZ4rsmjdqxLbqRYmuUEnoW1cChUfPpVWKyrCBNFXmel3Kwmer9MkA0jZha4s/3Y04PRvzhX59Fa3adNcexqFbNdP/pRy/wOd/mdDuhGdosVF02hylnN4dsDWJCz+Hlx5rUfZsvnx/THaX0ejFFoQkCd1eWjWOFUooL22OSLGeh6tCNc5461zP/jlME37WZq7qc6iQMk5wnT3eJY4VIeUdPuWsQS+h2I7Is5/PrfW5JMu45fEP/fc8NwdQSP8wytkeK7e0xrms8axEoCsFxLEQ0va5x7p5sBSy1Quq+TZTkjEYZcawYuRbjQ3Uqrs0wzhjH2a4O77r2rgNp20JRWGSZUdhiVTBOcgaDBMsSfN9M164l9CNFd5SW3n2+ewEhYFkWOOxqDJ2xYjZMb1IPXh1TS3zNdTnaCnjtyw+bvXqhWe9EdDoRrmuk0mPHZ3Fdi1rgYmESJ4dxxnic7u7nHz/Xpx66eI5NveIRRWo3Euf7Nq5rs7pcJ/RMmxbCxiClP87QGpIkZzhMCQIzyySlxj4/XyFNzUUG4LoOYcOlVnOZa4XM1nxesVJnIfRvZjdeEVNLPIBjCfMNnyQrSPOCja7ZXtlllutcMyD0bELPwbUt4qzYvXNFa5MTN4pNQuZcPSDwbAZlcoVJsLCwLKERurSq5h72vNAkaieIY+zYCQDl5QUjIlRDF88z0cE4NrNDreayOFthrh4wU3FxpvixG1NL/CDLyPKC5dJBS/OCp85BrzOk0Zhnphlw77EWCzWHudBjO0r59MkemTI3VJrbnwrG4wzbtnj1cfOv6D9jCb1xymYn2r3jdq7uc6Tp0Qpt8gJOtuMysdJGa8hz48y1Kt7uXTzLrRDftWgFNqe2I/7my+scW27w+jtnUOV2s5ukDDKF+V9N04WpJb7lu4wti7ajcC1N4FostkJ6KzPMtgKaFY9xltONhNBRJEoTeg4LDTMLbHTGDAapubnRschyXT7swNzVqhoa37UJPCPg7MC2oBk45AWsLtaJUsUwymhWPXzXApxdAUlrTcWzqAYuzaaP55hZpxfnpLmmGTgEB7dJ7w0rjZBxkrMdmwwWW4RXHm9ybL5Crs2UfK4Tc16EXuzj2sJSw6URhMxVHP7fE22ezns0az710KUzVtiWUPNtKr7NoaZJyar7Nrk2+vo4K0w7dZelusudCwH9OGdjmJXP34HZ0BB/phOT5oJnW8xXTBJn4NmsdVPOdSKGccY9SxXmgulc46c2vfpcN9WZKuiNjfMkAqkqyEoBRANjpRCEWpmwAeaBQ64trA9jtqKEquvgWTYzgbv7xKydByY5toVdPlwB2JVpd/4a2VeTqWK3/R3xZufRKnXfReVGGnYtC8+y6KeKtMi5ZaZG4FrcsVSZusV+aok/eIjx1xfTuQAd4OuOA+L3KQ6I36c4IH6fYmqduwN8fXEw4vcpDojfpzggfp/igPh9igPi9ykOiN+n+P825lLPJa7oSwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1152x1152 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x1152 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(25):\n",
    "    plt.figure(figsize=(16,16))\n",
    "    plt.subplot(5,15,i+1)\n",
    "    plt.imshow(x_train[i],cmap=\"Blues\")\n",
    "    plt.axis(\"off\")\n",
    "    plt.title(\"Actual={}\\nPrediction={}\".format(cl[y_test[i]], cl[np.argmax(y_predict[i])]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "cm= confusion_matrix(y_test,[np.argmax(i) for i in y_predict])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22f3919fd60>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,9))\n",
    "sns.heatmap(cm, annot=True,fmt=\"d\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "#classification report\n",
    "from sklearn.metrics import classification_report\n",
    "cr= classification_report(y_test, [np.argmax(i) for i in y_predict],target_names=cl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      " T-shirt/top       0.85      0.78      0.82      1000\n",
      "     Trouser       0.98      0.97      0.97      1000\n",
      "    Pullover       0.83      0.73      0.78      1000\n",
      "       Dress       0.87      0.88      0.88      1000\n",
      "        Coat       0.73      0.87      0.79      1000\n",
      "      Sandal       0.96      0.95      0.95      1000\n",
      "       Shirt       0.68      0.70      0.69      1000\n",
      "     Sneaker       0.95      0.90      0.92      1000\n",
      "         Bag       0.98      0.95      0.97      1000\n",
      "  Ankle boot       0.91      0.98      0.94      1000\n",
      "\n",
      "    accuracy                           0.87     10000\n",
      "   macro avg       0.87      0.87      0.87     10000\n",
      "weighted avg       0.87      0.87      0.87     10000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(cr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save(\"Fashion_mnist.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[7.89220940e-06, 6.14217788e-09, 4.76943706e-06, ...,\n",
       "        7.87288998e-04, 5.81015047e-06, 9.97131944e-01],\n",
       "       [1.15122166e-05, 1.42757290e-12, 9.90176141e-01, ...,\n",
       "        4.77749108e-22, 4.10434246e-13, 2.12029631e-16],\n",
       "       [9.77585569e-06, 9.99988556e-01, 6.12729878e-07, ...,\n",
       "        1.25740478e-22, 2.77488818e-12, 3.75246505e-14],\n",
       "       ...,\n",
       "       [9.31175891e-03, 3.34751249e-08, 4.93237935e-03, ...,\n",
       "        9.33869515e-09, 9.76328552e-01, 1.12115324e-10],\n",
       "       [1.21805340e-06, 9.99891281e-01, 1.15886851e-05, ...,\n",
       "        1.24586956e-18, 1.01741549e-07, 1.69354930e-08],\n",
       "       [4.03197220e-07, 2.28439934e-09, 4.84832981e-07, ...,\n",
       "        1.09061737e-04, 9.82480401e-07, 1.47950277e-06]], dtype=float32)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model=keras.models.load_model(\"fashion_mnist.h5\")\n",
    "model.predict(x_test)"
   ]
  }
 ],
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