Cifar 10 image classification CNN model.ipynb

{
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   "cell_type": "code",
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   "metadata": {},
   "outputs": [],
   "source": [
    "# import packages \n",
    "import keras\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Importing the Keras libraries and packages\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Conv2D\n",
    "from keras.layers import MaxPooling2D\n",
    "from keras.layers import Flatten\n",
    "from keras.layers import Dense\n",
    "from keras.utils import to_categorical,np_utils\n",
    "from keras.optimizers import SGD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load dataset\n",
    "from keras.datasets import cifar10\n",
    "(trainX, trainy), (testX, testy) = cifar10.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainX"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train: X=(50000, 32, 32, 3), y=(50000, 1)\n",
      "Test: X=(10000, 32, 32, 3), y=(10000, 1)\n"
     ]
    }
   ],
   "source": [
    "# trainX is an ndarray\n",
    "trainX\n",
    "trainy # class numbers\n",
    "testy\n",
    "# summarize loaded dataset\n",
    "print('Train: X=%s, y=%s' % (trainX.shape, trainy.shape))\n",
    "print('Test: X=%s, y=%s' % (testX.shape, testy.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# check images\n",
    "for i in range(9):\n",
    "    plt.subplot(330+1+i)\n",
    "    plt.imshow(trainX[i])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 1.]\n",
      " [0. 0. 0. ... 0. 0. 1.]\n",
      " ...\n",
      " [0. 0. 0. ... 0. 0. 1.]\n",
      " [0. 1. 0. ... 0. 0. 0.]\n",
      " [0. 1. 0. ... 0. 0. 0.]] \n",
      "\n",
      "[[0. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 1. 0.]\n",
      " [0. 0. 0. ... 0. 1. 0.]\n",
      " ...\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " [0. 1. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 1. 0. 0.]]\n"
     ]
    }
   ],
   "source": [
    "# one hot encode target values\n",
    "trainy = np_utils.to_categorical(trainy)\n",
    "print(trainy, '\\n')\n",
    "testy = np_utils.to_categorical(testy)\n",
    "print(testy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[[ 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",
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      " [[[154. 177. 187.]\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "\n",
      " ...\n",
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      " [[[ 35. 178. 235.]\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      " [[[235. 235. 235.]\n",
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      "\n",
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      "\n",
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      "\n",
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      " [[[158. 190. 222.]\n",
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      "\n",
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      "\n",
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      "\n",
      "\n",
      " ...\n",
      "\n",
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      " [[[ 20.  15.  12.]\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "\n",
      " [[[ 25.  40.  12.]\n",
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      "   ...\n",
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      "\n",
      "  [[ 12.  25.   6.]\n",
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      "   ...\n",
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      "\n",
      "  [[ 12.  25.  11.]\n",
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      "   ...\n",
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      "\n",
      "  ...\n",
      "\n",
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      "   ...\n",
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      "   [ 91. 121.  79.]]\n",
      "\n",
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      "   ...\n",
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      "   [ 92. 121.  79.]]\n",
      "\n",
      "  [[ 95. 126.  78.]\n",
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      "   [101. 128.  81.]\n",
      "   ...\n",
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      "   [ 95. 123.  81.]\n",
      "   [ 92. 120.  80.]]]\n",
      "\n",
      "\n",
      " [[[ 73.  78.  75.]\n",
      "   [ 98. 103. 113.]\n",
      "   [ 99. 106. 114.]\n",
      "   ...\n",
      "   [135. 150. 152.]\n",
      "   [135. 149. 154.]\n",
      "   [203. 215. 223.]]\n",
      "\n",
      "  [[ 69.  73.  70.]\n",
      "   [ 84.  89.  97.]\n",
      "   [ 68.  75.  81.]\n",
      "   ...\n",
      "   [ 85.  95.  89.]\n",
      "   [ 71.  82.  80.]\n",
      "   [120. 133. 135.]]\n",
      "\n",
      "  [[ 69.  73.  70.]\n",
      "   [ 90.  95. 100.]\n",
      "   [ 62.  71.  74.]\n",
      "   ...\n",
      "   [ 74.  81.  70.]\n",
      "   [ 53.  62.  54.]\n",
      "   [ 62.  74.  69.]]\n",
      "\n",
      "  ...\n",
      "\n",
      "  [[123. 128.  96.]\n",
      "   [132. 132. 102.]\n",
      "   [129. 128. 100.]\n",
      "   ...\n",
      "   [108. 107.  88.]\n",
      "   [ 62.  60.  55.]\n",
      "   [ 27.  27.  28.]]\n",
      "\n",
      "  [[115. 121.  91.]\n",
      "   [123. 124.  95.]\n",
      "   [129. 126.  99.]\n",
      "   ...\n",
      "   [115. 116.  94.]\n",
      "   [ 66.  65.  59.]\n",
      "   [ 27.  27.  27.]]\n",
      "\n",
      "  [[116. 120.  90.]\n",
      "   [121. 122.  94.]\n",
      "   [129. 128. 101.]\n",
      "   ...\n",
      "   [116. 115.  94.]\n",
      "   [ 68.  65.  58.]\n",
      "   [ 27.  26.  26.]]]]\n"
     ]
    }
   ],
   "source": [
    "# convert from integers to floats\n",
    "trainX = trainX.astype('float32')\n",
    "print(trainX, '\\n')\n",
    "testX = testX.astype('float32')\n",
    "print(testX)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[[0.23137255 0.24313726 0.24705882]\n",
      "   [0.16862746 0.18039216 0.1764706 ]\n",
      "   [0.19607843 0.1882353  0.16862746]\n",
      "   ...\n",
      "   [0.61960787 0.5176471  0.42352942]\n",
      "   [0.59607846 0.49019608 0.4       ]\n",
      "   [0.5803922  0.4862745  0.40392157]]\n",
      "\n",
      "  [[0.0627451  0.07843138 0.07843138]\n",
      "   [0.         0.         0.        ]\n",
      "   [0.07058824 0.03137255 0.        ]\n",
      "   ...\n",
      "   [0.48235294 0.34509805 0.21568628]\n",
      "   [0.46666667 0.3254902  0.19607843]\n",
      "   [0.47843137 0.34117648 0.22352941]]\n",
      "\n",
      "  [[0.09803922 0.09411765 0.08235294]\n",
      "   [0.0627451  0.02745098 0.        ]\n",
      "   [0.19215687 0.10588235 0.03137255]\n",
      "   ...\n",
      "   [0.4627451  0.32941177 0.19607843]\n",
      "   [0.47058824 0.32941177 0.19607843]\n",
      "   [0.42745098 0.28627452 0.16470589]]\n",
      "\n",
      "  ...\n",
      "\n",
      "  [[0.8156863  0.6666667  0.3764706 ]\n",
      "   [0.7882353  0.6        0.13333334]\n",
      "   [0.7764706  0.6313726  0.10196079]\n",
      "   ...\n",
      "   [0.627451   0.52156866 0.27450982]\n",
      "   [0.21960784 0.12156863 0.02745098]\n",
      "   [0.20784314 0.13333334 0.07843138]]\n",
      "\n",
      "  [[0.7058824  0.54509807 0.3764706 ]\n",
      "   [0.6784314  0.48235294 0.16470589]\n",
      "   [0.7294118  0.5647059  0.11764706]\n",
      "   ...\n",
      "   [0.72156864 0.5803922  0.36862746]\n",
      "   [0.38039216 0.24313726 0.13333334]\n",
      "   [0.3254902  0.20784314 0.13333334]]\n",
      "\n",
      "  [[0.69411767 0.5647059  0.45490196]\n",
      "   [0.65882355 0.5058824  0.36862746]\n",
      "   [0.7019608  0.5568628  0.34117648]\n",
      "   ...\n",
      "   [0.84705883 0.72156864 0.54901963]\n",
      "   [0.5921569  0.4627451  0.32941177]\n",
      "   [0.48235294 0.36078432 0.28235295]]]\n",
      "\n",
      "\n",
      " [[[0.6039216  0.69411767 0.73333335]\n",
      "   [0.49411765 0.5372549  0.53333336]\n",
      "   [0.4117647  0.40784314 0.37254903]\n",
      "   ...\n",
      "   [0.35686275 0.37254903 0.2784314 ]\n",
      "   [0.34117648 0.3529412  0.2784314 ]\n",
      "   [0.30980393 0.31764707 0.27450982]]\n",
      "\n",
      "  [[0.54901963 0.627451   0.6627451 ]\n",
      "   [0.5686275  0.6        0.6039216 ]\n",
      "   [0.49019608 0.49019608 0.4627451 ]\n",
      "   ...\n",
      "   [0.3764706  0.3882353  0.30588236]\n",
      "   [0.3019608  0.3137255  0.24313726]\n",
      "   [0.2784314  0.28627452 0.23921569]]\n",
      "\n",
      "  [[0.54901963 0.60784316 0.6431373 ]\n",
      "   [0.54509807 0.57254905 0.58431375]\n",
      "   [0.4509804  0.4509804  0.4392157 ]\n",
      "   ...\n",
      "   [0.30980393 0.32156864 0.2509804 ]\n",
      "   [0.26666668 0.27450982 0.21568628]\n",
      "   [0.2627451  0.27058825 0.21568628]]\n",
      "\n",
      "  ...\n",
      "\n",
      "  [[0.6862745  0.654902   0.6509804 ]\n",
      "   [0.6117647  0.6039216  0.627451  ]\n",
      "   [0.6039216  0.627451   0.6666667 ]\n",
      "   ...\n",
      "   [0.16470589 0.13333334 0.14117648]\n",
      "   [0.23921569 0.20784314 0.22352941]\n",
      "   [0.3647059  0.3254902  0.35686275]]\n",
      "\n",
      "  [[0.64705884 0.6039216  0.5019608 ]\n",
      "   [0.6117647  0.59607846 0.50980395]\n",
      "   [0.62352943 0.6313726  0.5568628 ]\n",
      "   ...\n",
      "   [0.40392157 0.3647059  0.3764706 ]\n",
      "   [0.48235294 0.44705883 0.47058824]\n",
      "   [0.5137255  0.4745098  0.5137255 ]]\n",
      "\n",
      "  [[0.6392157  0.5803922  0.47058824]\n",
      "   [0.61960787 0.5803922  0.47843137]\n",
      "   [0.6392157  0.6117647  0.52156866]\n",
      "   ...\n",
      "   [0.56078434 0.52156866 0.54509807]\n",
      "   [0.56078434 0.5254902  0.5568628 ]\n",
      "   [0.56078434 0.52156866 0.5647059 ]]]\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",
      "  [[0.44313726 0.47058824 0.4392157 ]\n",
      "   [0.43529412 0.4627451  0.43529412]\n",
      "   [0.4117647  0.4392157  0.41568628]\n",
      "   ...\n",
      "   [0.28235295 0.31764707 0.3137255 ]\n",
      "   [0.28235295 0.3137255  0.30980393]\n",
      "   [0.28235295 0.3137255  0.30980393]]\n",
      "\n",
      "  [[0.43529412 0.4627451  0.43137255]\n",
      "   [0.40784314 0.43529412 0.40784314]\n",
      "   [0.3882353  0.41568628 0.38431373]\n",
      "   ...\n",
      "   [0.26666668 0.29411766 0.28627452]\n",
      "   [0.27450982 0.29803923 0.29411766]\n",
      "   [0.30588236 0.32941177 0.32156864]]\n",
      "\n",
      "  [[0.41568628 0.44313726 0.4117647 ]\n",
      "   [0.3882353  0.41568628 0.38431373]\n",
      "   [0.37254903 0.4        0.36862746]\n",
      "   ...\n",
      "   [0.30588236 0.33333334 0.3254902 ]\n",
      "   [0.30980393 0.33333334 0.3254902 ]\n",
      "   [0.3137255  0.3372549  0.32941177]]]\n",
      "\n",
      "\n",
      " ...\n",
      "\n",
      "\n",
      " [[[0.13725491 0.69803923 0.92156863]\n",
      "   [0.15686275 0.6901961  0.9372549 ]\n",
      "   [0.16470589 0.6901961  0.94509804]\n",
      "   ...\n",
      "   [0.3882353  0.69411767 0.85882354]\n",
      "   [0.30980393 0.5764706  0.77254903]\n",
      "   [0.34901962 0.5803922  0.7411765 ]]\n",
      "\n",
      "  [[0.22352941 0.7137255  0.91764706]\n",
      "   [0.17254902 0.72156864 0.98039216]\n",
      "   [0.19607843 0.7176471  0.9411765 ]\n",
      "   ...\n",
      "   [0.6117647  0.7137255  0.78431374]\n",
      "   [0.5529412  0.69411767 0.80784315]\n",
      "   [0.45490196 0.58431375 0.6862745 ]]\n",
      "\n",
      "  [[0.38431373 0.77254903 0.92941177]\n",
      "   [0.2509804  0.7411765  0.9882353 ]\n",
      "   [0.27058825 0.7529412  0.9607843 ]\n",
      "   ...\n",
      "   [0.7372549  0.7647059  0.80784315]\n",
      "   [0.46666667 0.5294118  0.5764706 ]\n",
      "   [0.23921569 0.30980393 0.3529412 ]]\n",
      "\n",
      "  ...\n",
      "\n",
      "  [[0.28627452 0.30980393 0.3019608 ]\n",
      "   [0.20784314 0.24705882 0.26666668]\n",
      "   [0.21176471 0.26666668 0.3137255 ]\n",
      "   ...\n",
      "   [0.06666667 0.15686275 0.2509804 ]\n",
      "   [0.08235294 0.14117648 0.2       ]\n",
      "   [0.12941177 0.1882353  0.19215687]]\n",
      "\n",
      "  [[0.23921569 0.26666668 0.29411766]\n",
      "   [0.21568628 0.27450982 0.3372549 ]\n",
      "   [0.22352941 0.30980393 0.40392157]\n",
      "   ...\n",
      "   [0.09411765 0.1882353  0.28235295]\n",
      "   [0.06666667 0.13725491 0.20784314]\n",
      "   [0.02745098 0.09019608 0.1254902 ]]\n",
      "\n",
      "  [[0.17254902 0.21960784 0.28627452]\n",
      "   [0.18039216 0.25882354 0.34509805]\n",
      "   [0.19215687 0.3019608  0.4117647 ]\n",
      "   ...\n",
      "   [0.10588235 0.20392157 0.3019608 ]\n",
      "   [0.08235294 0.16862746 0.25882354]\n",
      "   [0.04705882 0.12156863 0.19607843]]]\n",
      "\n",
      "\n",
      " [[[0.7411765  0.827451   0.9411765 ]\n",
      "   [0.7294118  0.8156863  0.9254902 ]\n",
      "   [0.7254902  0.8117647  0.92156863]\n",
      "   ...\n",
      "   [0.6862745  0.7647059  0.8784314 ]\n",
      "   [0.6745098  0.7607843  0.87058824]\n",
      "   [0.6627451  0.7607843  0.8627451 ]]\n",
      "\n",
      "  [[0.7607843  0.8235294  0.9372549 ]\n",
      "   [0.7490196  0.8117647  0.9254902 ]\n",
      "   [0.74509805 0.80784315 0.92156863]\n",
      "   ...\n",
      "   [0.6784314  0.7529412  0.8627451 ]\n",
      "   [0.67058825 0.7490196  0.85490197]\n",
      "   [0.654902   0.74509805 0.84705883]]\n",
      "\n",
      "  [[0.8156863  0.85882354 0.95686275]\n",
      "   [0.8039216  0.84705883 0.9411765 ]\n",
      "   [0.8        0.84313726 0.9372549 ]\n",
      "   ...\n",
      "   [0.6862745  0.7490196  0.8509804 ]\n",
      "   [0.6745098  0.74509805 0.84705883]\n",
      "   [0.6627451  0.7490196  0.84313726]]\n",
      "\n",
      "  ...\n",
      "\n",
      "  [[0.8117647  0.78039217 0.70980394]\n",
      "   [0.79607844 0.7647059  0.6862745 ]\n",
      "   [0.79607844 0.76862746 0.6784314 ]\n",
      "   ...\n",
      "   [0.5294118  0.5176471  0.49803922]\n",
      "   [0.63529414 0.61960787 0.5882353 ]\n",
      "   [0.65882355 0.6392157  0.5921569 ]]\n",
      "\n",
      "  [[0.7764706  0.74509805 0.6666667 ]\n",
      "   [0.7411765  0.70980394 0.62352943]\n",
      "   [0.7058824  0.6745098  0.5764706 ]\n",
      "   ...\n",
      "   [0.69803923 0.67058825 0.627451  ]\n",
      "   [0.6862745  0.6627451  0.6117647 ]\n",
      "   [0.6862745  0.6627451  0.6039216 ]]\n",
      "\n",
      "  [[0.7764706  0.7411765  0.6784314 ]\n",
      "   [0.7411765  0.70980394 0.63529414]\n",
      "   [0.69803923 0.6666667  0.58431375]\n",
      "   ...\n",
      "   [0.7647059  0.72156864 0.6627451 ]\n",
      "   [0.76862746 0.7411765  0.67058825]\n",
      "   [0.7647059  0.74509805 0.67058825]]]\n",
      "\n",
      "\n",
      " [[[0.8980392  0.8980392  0.9372549 ]\n",
      "   [0.9254902  0.92941177 0.96862745]\n",
      "   [0.91764706 0.9254902  0.96862745]\n",
      "   ...\n",
      "   [0.8509804  0.85882354 0.9137255 ]\n",
      "   [0.8666667  0.8745098  0.91764706]\n",
      "   [0.87058824 0.8745098  0.9137255 ]]\n",
      "\n",
      "  [[0.87058824 0.8666667  0.8980392 ]\n",
      "   [0.9372549  0.9372549  0.9764706 ]\n",
      "   [0.9137255  0.91764706 0.9647059 ]\n",
      "   ...\n",
      "   [0.8745098  0.8745098  0.9254902 ]\n",
      "   [0.8901961  0.89411765 0.93333334]\n",
      "   [0.8235294  0.827451   0.8627451 ]]\n",
      "\n",
      "  [[0.8352941  0.80784315 0.827451  ]\n",
      "   [0.91764706 0.9098039  0.9372549 ]\n",
      "   [0.90588236 0.9137255  0.95686275]\n",
      "   ...\n",
      "   [0.8627451  0.8627451  0.9098039 ]\n",
      "   [0.8627451  0.85882354 0.9098039 ]\n",
      "   [0.7921569  0.79607844 0.84313726]]\n",
      "\n",
      "  ...\n",
      "\n",
      "  [[0.5882353  0.56078434 0.5294118 ]\n",
      "   [0.54901963 0.5294118  0.49803922]\n",
      "   [0.5176471  0.49803922 0.47058824]\n",
      "   ...\n",
      "   [0.8784314  0.87058824 0.85490197]\n",
      "   [0.9019608  0.89411765 0.88235295]\n",
      "   [0.94509804 0.94509804 0.93333334]]\n",
      "\n",
      "  [[0.5372549  0.5176471  0.49411765]\n",
      "   [0.50980395 0.49803922 0.47058824]\n",
      "   [0.49019608 0.4745098  0.4509804 ]\n",
      "   ...\n",
      "   [0.70980394 0.7058824  0.69803923]\n",
      "   [0.7921569  0.7882353  0.7764706 ]\n",
      "   [0.83137256 0.827451   0.8117647 ]]\n",
      "\n",
      "  [[0.47843137 0.46666667 0.44705883]\n",
      "   [0.4627451  0.45490196 0.43137255]\n",
      "   [0.47058824 0.45490196 0.43529412]\n",
      "   ...\n",
      "   [0.7019608  0.69411767 0.6784314 ]\n",
      "   [0.6431373  0.6431373  0.63529414]\n",
      "   [0.6392157  0.6392157  0.6313726 ]]]] \n",
      "\n",
      "[[[[0.61960787 0.4392157  0.19215687]\n",
      "   [0.62352943 0.43529412 0.18431373]\n",
      "   [0.64705884 0.45490196 0.2       ]\n",
      "   ...\n",
      "   [0.5372549  0.37254903 0.14117648]\n",
      "   [0.49411765 0.35686275 0.14117648]\n",
      "   [0.45490196 0.33333334 0.12941177]]\n",
      "\n",
      "  [[0.59607846 0.4392157  0.2       ]\n",
      "   [0.5921569  0.43137255 0.15686275]\n",
      "   [0.62352943 0.44705883 0.1764706 ]\n",
      "   ...\n",
      "   [0.53333336 0.37254903 0.12156863]\n",
      "   [0.49019608 0.35686275 0.1254902 ]\n",
      "   [0.46666667 0.34509805 0.13333334]]\n",
      "\n",
      "  [[0.5921569  0.43137255 0.18431373]\n",
      "   [0.5921569  0.42745098 0.12941177]\n",
      "   [0.61960787 0.43529412 0.14117648]\n",
      "   ...\n",
      "   [0.54509807 0.38431373 0.13333334]\n",
      "   [0.50980395 0.37254903 0.13333334]\n",
      "   [0.47058824 0.34901962 0.12941177]]\n",
      "\n",
      "  ...\n",
      "\n",
      "  [[0.26666668 0.4862745  0.69411767]\n",
      "   [0.16470589 0.39215687 0.5803922 ]\n",
      "   [0.12156863 0.34509805 0.5372549 ]\n",
      "   ...\n",
      "   [0.14901961 0.38039216 0.57254905]\n",
      "   [0.05098039 0.2509804  0.42352942]\n",
      "   [0.15686275 0.33333334 0.49803922]]\n",
      "\n",
      "  [[0.23921569 0.45490196 0.65882355]\n",
      "   [0.19215687 0.4        0.5803922 ]\n",
      "   [0.13725491 0.33333334 0.5176471 ]\n",
      "   ...\n",
      "   [0.10196079 0.32156864 0.50980395]\n",
      "   [0.11372549 0.32156864 0.49411765]\n",
      "   [0.07843138 0.2509804  0.41960785]]\n",
      "\n",
      "  [[0.21176471 0.41960785 0.627451  ]\n",
      "   [0.21960784 0.4117647  0.58431375]\n",
      "   [0.1764706  0.34901962 0.5176471 ]\n",
      "   ...\n",
      "   [0.09411765 0.3019608  0.4862745 ]\n",
      "   [0.13333334 0.32941177 0.5058824 ]\n",
      "   [0.08235294 0.2627451  0.43137255]]]\n",
      "\n",
      "\n",
      " [[[0.92156863 0.92156863 0.92156863]\n",
      "   [0.90588236 0.90588236 0.90588236]\n",
      "   [0.9098039  0.9098039  0.9098039 ]\n",
      "   ...\n",
      "   [0.9137255  0.9137255  0.9137255 ]\n",
      "   [0.9137255  0.9137255  0.9137255 ]\n",
      "   [0.9098039  0.9098039  0.9098039 ]]\n",
      "\n",
      "  [[0.93333334 0.93333334 0.93333334]\n",
      "   [0.92156863 0.92156863 0.92156863]\n",
      "   [0.92156863 0.92156863 0.92156863]\n",
      "   ...\n",
      "   [0.9254902  0.9254902  0.9254902 ]\n",
      "   [0.9254902  0.9254902  0.9254902 ]\n",
      "   [0.92156863 0.92156863 0.92156863]]\n",
      "\n",
      "  [[0.92941177 0.92941177 0.92941177]\n",
      "   [0.91764706 0.91764706 0.91764706]\n",
      "   [0.91764706 0.91764706 0.91764706]\n",
      "   ...\n",
      "   [0.92156863 0.92156863 0.92156863]\n",
      "   [0.92156863 0.92156863 0.92156863]\n",
      "   [0.91764706 0.91764706 0.91764706]]\n",
      "\n",
      "  ...\n",
      "\n",
      "  [[0.34117648 0.3882353  0.34901962]\n",
      "   [0.16862746 0.2        0.14509805]\n",
      "   [0.07450981 0.09019608 0.04313726]\n",
      "   ...\n",
      "   [0.6627451  0.72156864 0.7019608 ]\n",
      "   [0.7137255  0.77254903 0.75686276]\n",
      "   [0.7372549  0.7921569  0.7882353 ]]\n",
      "\n",
      "  [[0.32156864 0.3764706  0.32156864]\n",
      "   [0.18039216 0.22352941 0.14117648]\n",
      "   [0.14117648 0.17254902 0.08627451]\n",
      "   ...\n",
      "   [0.68235296 0.7411765  0.7176471 ]\n",
      "   [0.7254902  0.78431374 0.76862746]\n",
      "   [0.73333335 0.7921569  0.78431374]]\n",
      "\n",
      "  [[0.33333334 0.39607844 0.3254902 ]\n",
      "   [0.24313726 0.29411766 0.1882353 ]\n",
      "   [0.22745098 0.2627451  0.14901961]\n",
      "   ...\n",
      "   [0.65882355 0.7176471  0.69803923]\n",
      "   [0.7058824  0.7647059  0.7490196 ]\n",
      "   [0.7294118  0.78431374 0.78039217]]]\n",
      "\n",
      "\n",
      " [[[0.61960787 0.74509805 0.87058824]\n",
      "   [0.61960787 0.73333335 0.85490197]\n",
      "   [0.54509807 0.6509804  0.7607843 ]\n",
      "   ...\n",
      "   [0.89411765 0.90588236 0.91764706]\n",
      "   [0.92941177 0.9372549  0.9529412 ]\n",
      "   [0.93333334 0.94509804 0.9647059 ]]\n",
      "\n",
      "  [[0.6666667  0.78431374 0.8980392 ]\n",
      "   [0.6745098  0.78039217 0.8862745 ]\n",
      "   [0.5921569  0.6901961  0.7882353 ]\n",
      "   ...\n",
      "   [0.9098039  0.9098039  0.9254902 ]\n",
      "   [0.9647059  0.9647059  0.98039216]\n",
      "   [0.9647059  0.96862745 0.9843137 ]]\n",
      "\n",
      "  [[0.68235296 0.7882353  0.88235295]\n",
      "   [0.6901961  0.78431374 0.87058824]\n",
      "   [0.6156863  0.7019608  0.78039217]\n",
      "   ...\n",
      "   [0.9019608  0.8980392  0.9098039 ]\n",
      "   [0.98039216 0.9764706  0.9843137 ]\n",
      "   [0.9607843  0.95686275 0.96862745]]\n",
      "\n",
      "  ...\n",
      "\n",
      "  [[0.12156863 0.15686275 0.1764706 ]\n",
      "   [0.11764706 0.15294118 0.17254902]\n",
      "   [0.10196079 0.13725491 0.15686275]\n",
      "   ...\n",
      "   [0.14509805 0.15686275 0.18039216]\n",
      "   [0.03529412 0.05098039 0.05490196]\n",
      "   [0.01568628 0.02745098 0.01960784]]\n",
      "\n",
      "  [[0.09019608 0.13333334 0.15294118]\n",
      "   [0.10588235 0.14901961 0.16862746]\n",
      "   [0.09803922 0.14117648 0.16078432]\n",
      "   ...\n",
      "   [0.07450981 0.07843138 0.09411765]\n",
      "   [0.01568628 0.02352941 0.01176471]\n",
      "   [0.01960784 0.02745098 0.01176471]]\n",
      "\n",
      "  [[0.10980392 0.16078432 0.18431373]\n",
      "   [0.11764706 0.16862746 0.19607843]\n",
      "   [0.1254902  0.1764706  0.20392157]\n",
      "   ...\n",
      "   [0.01960784 0.02352941 0.03137255]\n",
      "   [0.01568628 0.01960784 0.01176471]\n",
      "   [0.02745098 0.03137255 0.02745098]]]\n",
      "\n",
      "\n",
      " ...\n",
      "\n",
      "\n",
      " [[[0.07843138 0.05882353 0.04705882]\n",
      "   [0.07450981 0.05490196 0.04313726]\n",
      "   [0.05882353 0.05490196 0.04313726]\n",
      "   ...\n",
      "   [0.03921569 0.03529412 0.02745098]\n",
      "   [0.04705882 0.04313726 0.03529412]\n",
      "   [0.05098039 0.04705882 0.03921569]]\n",
      "\n",
      "  [[0.08235294 0.0627451  0.05098039]\n",
      "   [0.07843138 0.0627451  0.05098039]\n",
      "   [0.07058824 0.06666667 0.04705882]\n",
      "   ...\n",
      "   [0.03921569 0.03529412 0.02745098]\n",
      "   [0.03921569 0.03529412 0.02745098]\n",
      "   [0.04705882 0.04313726 0.03529412]]\n",
      "\n",
      "  [[0.08235294 0.0627451  0.05098039]\n",
      "   [0.08235294 0.06666667 0.04705882]\n",
      "   [0.07843138 0.07058824 0.04313726]\n",
      "   ...\n",
      "   [0.04705882 0.04313726 0.03529412]\n",
      "   [0.04705882 0.04313726 0.03529412]\n",
      "   [0.05098039 0.04705882 0.03921569]]\n",
      "\n",
      "  ...\n",
      "\n",
      "  [[0.12941177 0.09803922 0.05098039]\n",
      "   [0.13333334 0.10196079 0.05882353]\n",
      "   [0.13333334 0.10196079 0.05882353]\n",
      "   ...\n",
      "   [0.10980392 0.09803922 0.20392157]\n",
      "   [0.11372549 0.09803922 0.22745098]\n",
      "   [0.09019608 0.07843138 0.16470589]]\n",
      "\n",
      "  [[0.12941177 0.09803922 0.05490196]\n",
      "   [0.13333334 0.10196079 0.05882353]\n",
      "   [0.13333334 0.10196079 0.05882353]\n",
      "   ...\n",
      "   [0.10588235 0.09411765 0.20392157]\n",
      "   [0.10588235 0.09411765 0.21960784]\n",
      "   [0.09803922 0.08627451 0.18431373]]\n",
      "\n",
      "  [[0.12156863 0.09019608 0.04705882]\n",
      "   [0.1254902  0.09411765 0.05098039]\n",
      "   [0.12941177 0.09803922 0.05490196]\n",
      "   ...\n",
      "   [0.09411765 0.09019608 0.19607843]\n",
      "   [0.10196079 0.09019608 0.20784314]\n",
      "   [0.09803922 0.07843138 0.18431373]]]\n",
      "\n",
      "\n",
      " [[[0.09803922 0.15686275 0.04705882]\n",
      "   [0.05882353 0.14117648 0.01176471]\n",
      "   [0.09019608 0.16078432 0.07058824]\n",
      "   ...\n",
      "   [0.23921569 0.32156864 0.30588236]\n",
      "   [0.36078432 0.44313726 0.4392157 ]\n",
      "   [0.29411766 0.34901962 0.36078432]]\n",
      "\n",
      "  [[0.04705882 0.09803922 0.02352941]\n",
      "   [0.07843138 0.14509805 0.02745098]\n",
      "   [0.09411765 0.14117648 0.05882353]\n",
      "   ...\n",
      "   [0.4509804  0.5254902  0.5411765 ]\n",
      "   [0.58431375 0.65882355 0.69411767]\n",
      "   [0.40784314 0.45882353 0.5137255 ]]\n",
      "\n",
      "  [[0.04705882 0.09803922 0.04313726]\n",
      "   [0.05882353 0.11372549 0.02352941]\n",
      "   [0.13333334 0.15686275 0.09411765]\n",
      "   ...\n",
      "   [0.6039216  0.6745098  0.7137255 ]\n",
      "   [0.6156863  0.6862745  0.7529412 ]\n",
      "   [0.45490196 0.5058824  0.5921569 ]]\n",
      "\n",
      "  ...\n",
      "\n",
      "  [[0.39215687 0.5058824  0.31764707]\n",
      "   [0.40392157 0.5176471  0.32941177]\n",
      "   [0.40784314 0.5254902  0.3372549 ]\n",
      "   ...\n",
      "   [0.38039216 0.5019608  0.32941177]\n",
      "   [0.38431373 0.49411765 0.32941177]\n",
      "   [0.35686275 0.4745098  0.30980393]]\n",
      "\n",
      "  [[0.40392157 0.5176471  0.3254902 ]\n",
      "   [0.40784314 0.5137255  0.3254902 ]\n",
      "   [0.41960785 0.5294118  0.34117648]\n",
      "   ...\n",
      "   [0.39607844 0.5176471  0.34117648]\n",
      "   [0.3882353  0.49803922 0.32941177]\n",
      "   [0.36078432 0.4745098  0.30980393]]\n",
      "\n",
      "  [[0.37254903 0.49411765 0.30588236]\n",
      "   [0.37254903 0.48235294 0.29803923]\n",
      "   [0.39607844 0.5019608  0.31764707]\n",
      "   ...\n",
      "   [0.3647059  0.4862745  0.3137255 ]\n",
      "   [0.37254903 0.48235294 0.31764707]\n",
      "   [0.36078432 0.47058824 0.3137255 ]]]\n",
      "\n",
      "\n",
      " [[[0.28627452 0.30588236 0.29411766]\n",
      "   [0.38431373 0.40392157 0.44313726]\n",
      "   [0.3882353  0.41568628 0.44705883]\n",
      "   ...\n",
      "   [0.5294118  0.5882353  0.59607846]\n",
      "   [0.5294118  0.58431375 0.6039216 ]\n",
      "   [0.79607844 0.84313726 0.8745098 ]]\n",
      "\n",
      "  [[0.27058825 0.28627452 0.27450982]\n",
      "   [0.32941177 0.34901962 0.38039216]\n",
      "   [0.26666668 0.29411766 0.31764707]\n",
      "   ...\n",
      "   [0.33333334 0.37254903 0.34901962]\n",
      "   [0.2784314  0.32156864 0.3137255 ]\n",
      "   [0.47058824 0.52156866 0.5294118 ]]\n",
      "\n",
      "  [[0.27058825 0.28627452 0.27450982]\n",
      "   [0.3529412  0.37254903 0.39215687]\n",
      "   [0.24313726 0.2784314  0.2901961 ]\n",
      "   ...\n",
      "   [0.2901961  0.31764707 0.27450982]\n",
      "   [0.20784314 0.24313726 0.21176471]\n",
      "   [0.24313726 0.2901961  0.27058825]]\n",
      "\n",
      "  ...\n",
      "\n",
      "  [[0.48235294 0.5019608  0.3764706 ]\n",
      "   [0.5176471  0.5176471  0.4       ]\n",
      "   [0.5058824  0.5019608  0.39215687]\n",
      "   ...\n",
      "   [0.42352942 0.41960785 0.34509805]\n",
      "   [0.24313726 0.23529412 0.21568628]\n",
      "   [0.10588235 0.10588235 0.10980392]]\n",
      "\n",
      "  [[0.4509804  0.4745098  0.35686275]\n",
      "   [0.48235294 0.4862745  0.37254903]\n",
      "   [0.5058824  0.49411765 0.3882353 ]\n",
      "   ...\n",
      "   [0.4509804  0.45490196 0.36862746]\n",
      "   [0.25882354 0.25490198 0.23137255]\n",
      "   [0.10588235 0.10588235 0.10588235]]\n",
      "\n",
      "  [[0.45490196 0.47058824 0.3529412 ]\n",
      "   [0.4745098  0.47843137 0.36862746]\n",
      "   [0.5058824  0.5019608  0.39607844]\n",
      "   ...\n",
      "   [0.45490196 0.4509804  0.36862746]\n",
      "   [0.26666668 0.25490198 0.22745098]\n",
      "   [0.10588235 0.10196079 0.10196079]]]]\n"
     ]
    }
   ],
   "source": [
    "# normalize to range 0-1\n",
    "trainX = trainX / 255.0\n",
    "print(trainX, '\\n')\n",
    "testX = testX / 255.0\n",
    "print(testX)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "# example of a 3-block vgg style architecture\n",
    "model = Sequential()\n",
    "model.add(Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same', input_shape=(32, 32, 3)))\n",
    "model.add(Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))\n",
    "model.add(MaxPooling2D((2, 2)))\n",
    "model.add(Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))\n",
    "model.add(Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))\n",
    "model.add(MaxPooling2D((2, 2)))\n",
    "model.add(Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))\n",
    "model.add(Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))\n",
    "model.add(MaxPooling2D((2, 2)))\n",
    "model.add(Flatten())\n",
    "model.add(Dense(128, activation='relu', kernel_initializer='he_uniform'))\n",
    "model.add(Dense(10, activation='softmax'))\n",
    "opt = SGD(lr=0.001, momentum=0.9)\n",
    "model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_4\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_19 (Conv2D)           (None, 30, 30, 32)        896       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_10 (MaxPooling (None, 15, 15, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_20 (Conv2D)           (None, 13, 13, 64)        18496     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_11 (MaxPooling (None, 6, 6, 64)          0         \n",
      "_________________________________________________________________\n",
      "conv2d_21 (Conv2D)           (None, 4, 4, 128)         73856     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_12 (MaxPooling (None, 2, 2, 128)         0         \n",
      "_________________________________________________________________\n",
      "flatten_4 (Flatten)          (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "dense_7 (Dense)              (None, 128)               65664     \n",
      "_________________________________________________________________\n",
      "dense_8 (Dense)              (None, 10)                1290      \n",
      "=================================================================\n",
      "Total params: 160,202\n",
      "Trainable params: 160,202\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# model summary\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # fit model\n",
    "classifier = model.fit(trainX, trainy, epochs=50, batch_size=32,validation_data=(testX, testy), verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# evaluate model\n",
    "_, acc = model.evaluate(testX, testy, verbose=0)\n",
    "print('> %.3f' % (acc * 100.0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# to predict the class of an image \n",
    "plt.imshow(trainX[18])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.preprocessing.image import img_to_array\n",
    "# convert to array\n",
    "img = img_to_array(trainX[18])\n",
    "# reshape into a single sample with 3 channels\n",
    "img = img.reshape(1, 32, 32, 3)\n",
    "# prepare pixel data\n",
    "img = img.astype('float32')\n",
    "img = img / 255.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# predict the class\n",
    "# pass img path or image that you want to predict\n",
    "result = model.predict_classes(img)"
   ]
  },
  {
   "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": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
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