Keras_MNIST_Preprocess.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "np.random.seed(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras.utils import np_utils"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.datasets import mnist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "(x_train_image, y_train_label), (x_test_image, y_test_label) = mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of train data= 60000\n",
      "number of test data= 10000\n"
     ]
    }
   ],
   "source": [
    "print('number of train data=', len(x_train_image))\n",
    "print('number of test data=', len(x_test_image))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape of train image= (60000, 28, 28)\n",
      "shape of train label= (60000,)\n",
      "shape of test image= (60000, 28, 28)\n",
      "shape of test label= (10000,)\n"
     ]
    }
   ],
   "source": [
    "print('shape of train image=', x_train_image.shape)\n",
    "print('shape of train label=', y_train_label.shape)\n",
    "print('shape of test image=', x_train_image.shape)\n",
    "print('shape of test label=', y_test_label.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as pyplot\n",
    "def my_plot_image(input_image):\n",
    "    fig = pyplot.gcf()\n",
    "    fig.set_size_inches(2,2)\n",
    "    pyplot.imshow(input_image, cmap='binary')\n",
    "    pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 144x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 144x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "my_plot_image(x_train_image[0])\n",
    "my_plot_image(x_train_image[1])\n",
    "# my_plot_image(x_train_image[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n",
      "0\n"
     ]
    }
   ],
   "source": [
    "print(y_train_label[0])\n",
    "print(y_train_label[1])\n",
    "# print(y_train_label[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as pyplot\n",
    "def my_plot_images_labels_prediction(input_images, input_labels, input_prediction, input_index, input_num=10):\n",
    "    fig = pyplot.gcf()\n",
    "    fig.set_size_inches(12,14)\n",
    "    if input_num>10:\n",
    "        input_num=10\n",
    "    for i in range(0, input_num):\n",
    "        ax = pyplot.subplot(5,5,1+i)\n",
    "        ax.imshow(input_images[input_index], cmap='binary')\n",
    "        title = \"label=\" + str(input_labels[input_index])\n",
    "        if len(input_prediction)>0:\n",
    "            title= title + \", predict=\" + str(input_prediction[input_index])\n",
    "        ax.set_title(title,fontsize=10)\n",
    "        ax.set_xticks([])\n",
    "        ax.set_yticks([])\n",
    "        input_index = input_index + 1\n",
    "    pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x1008 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "my_plot_images_labels_prediction(x_train_image, y_train_label, [], 0, 15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x1008 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "my_plot_images_labels_prediction(x_test_image, y_test_label, [], 0, 15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape of train image= (60000, 28, 28)\n",
      "shape of train label= (60000,)\n"
     ]
    }
   ],
   "source": [
    "print('shape of train image=', x_train_image.shape)\n",
    "print('shape of train label=', y_train_label.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "reshape_dim = 28 * 28\n",
    "x_train_reshaped = x_train_image.reshape(60000, reshape_dim).astype('float32')\n",
    "x_test_reshaped = x_test_image.reshape(10000, reshape_dim).astype('float32')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_train_reshaped= (60000, 784)\n",
      "x_test_reshaped= (10000, 784)\n"
     ]
    }
   ],
   "source": [
    "print('x_train_reshaped=', x_train_reshaped.shape)\n",
    "print('x_test_reshaped=', x_test_reshaped.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
      "   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
      "   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
      "   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
      "   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
      "   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
      "   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
      "   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
      "   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
      "   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
      "   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   3.  18.\n",
      "  18.  18. 126. 136. 175.  26. 166. 255. 247. 127.   0.   0.   0.   0.\n",
      "   0.   0.   0.   0.   0.   0.   0.   0.  30.  36.  94. 154. 170. 253.\n",
      " 253. 253. 253. 253. 225. 172. 253. 242. 195.  64.   0.   0.   0.   0.\n",
      "   0.   0.   0.   0.   0.   0.   0.  49. 238. 253. 253. 253. 253. 253.\n",
      " 253. 253. 253. 251.  93.  82.  82.  56.  39.   0.   0.   0.   0.   0.\n",
      "   0.   0.   0.   0.   0.   0.   0.  18. 219. 253. 253. 253. 253. 253.\n",
      " 198. 182. 247. 241.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
      "   0.   0.   0.   0.   0.   0.   0.   0.  80. 156. 107. 253. 253. 205.\n",
      "  11.   0.  43. 154.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
      "   0.   0.   0.   0.   0.   0.   0.   0.   0.  14.   1. 154. 253.  90.\n",
      "   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
      "   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0. 139. 253. 190.\n",
      "   2.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
      "   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.  11. 190. 253.\n",
      "  70.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
      "   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.  35. 241.\n",
      " 225. 160. 108.   1.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
      "   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.  81.\n",
      " 240. 253. 253. 119.  25.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
      "   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
      "  45. 186. 253. 253. 150.  27.   0.   0.   0.   0.   0.   0.   0.   0.\n",
      "   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
      "   0.  16.  93. 252. 253. 187.   0.   0.   0.   0.   0.   0.   0.   0.\n",
      "   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
      "   0.   0.   0. 249. 253. 249.  64.   0.   0.   0.   0.   0.   0.   0.\n",
      "   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
      "  46. 130. 183. 253. 253. 207.   2.   0.   0.   0.   0.   0.   0.   0.\n",
      "   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.  39. 148.\n",
      " 229. 253. 253. 253. 250. 182.   0.   0.   0.   0.   0.   0.   0.   0.\n",
      "   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.  24. 114. 221. 253.\n",
      " 253. 253. 253. 201.  78.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
      "   0.   0.   0.   0.   0.   0.   0.   0.  23.  66. 213. 253. 253. 253.\n",
      " 253. 198.  81.   2.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
      "   0.   0.   0.   0.   0.   0.  18. 171. 219. 253. 253. 253. 253. 195.\n",
      "  80.   9.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
      "   0.   0.   0.   0.  55. 172. 226. 253. 253. 253. 253. 244. 133.  11.\n",
      "   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
      "   0.   0.   0.   0. 136. 253. 253. 253. 212. 135. 132.  16.   0.   0.\n",
      "   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
      "   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
      "   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
      "   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
      "   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
      "   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
      "   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.]\n"
     ]
    }
   ],
   "source": [
    "print(x_train_reshaped[0])\n",
    "# print(x_train_reshaped[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train_normalized = x_train_reshaped / 255\n",
    "x_test_normalized = x_test_reshaped / 255"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.01176471 0.07058824 0.07058824 0.07058824\n",
      " 0.49411765 0.53333336 0.6862745  0.10196079 0.6509804  1.\n",
      " 0.96862745 0.49803922 0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.11764706 0.14117648 0.36862746 0.6039216\n",
      " 0.6666667  0.99215686 0.99215686 0.99215686 0.99215686 0.99215686\n",
      " 0.88235295 0.6745098  0.99215686 0.9490196  0.7647059  0.2509804\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.19215687\n",
      " 0.93333334 0.99215686 0.99215686 0.99215686 0.99215686 0.99215686\n",
      " 0.99215686 0.99215686 0.99215686 0.9843137  0.3647059  0.32156864\n",
      " 0.32156864 0.21960784 0.15294118 0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.07058824 0.85882354 0.99215686\n",
      " 0.99215686 0.99215686 0.99215686 0.99215686 0.7764706  0.7137255\n",
      " 0.96862745 0.94509804 0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.3137255  0.6117647  0.41960785 0.99215686\n",
      " 0.99215686 0.8039216  0.04313726 0.         0.16862746 0.6039216\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.05490196 0.00392157 0.6039216  0.99215686 0.3529412\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.54509807 0.99215686 0.74509805 0.00784314 0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.04313726\n",
      " 0.74509805 0.99215686 0.27450982 0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.13725491 0.94509804\n",
      " 0.88235295 0.627451   0.42352942 0.00392157 0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.31764707 0.9411765  0.99215686\n",
      " 0.99215686 0.46666667 0.09803922 0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.1764706  0.7294118  0.99215686 0.99215686\n",
      " 0.5882353  0.10588235 0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.0627451  0.3647059  0.9882353  0.99215686 0.73333335\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.9764706  0.99215686 0.9764706  0.2509804  0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.18039216 0.50980395 0.7176471  0.99215686\n",
      " 0.99215686 0.8117647  0.00784314 0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.15294118 0.5803922\n",
      " 0.8980392  0.99215686 0.99215686 0.99215686 0.98039216 0.7137255\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.09411765 0.44705883 0.8666667  0.99215686 0.99215686 0.99215686\n",
      " 0.99215686 0.7882353  0.30588236 0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.09019608 0.25882354 0.8352941  0.99215686\n",
      " 0.99215686 0.99215686 0.99215686 0.7764706  0.31764707 0.00784314\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.07058824 0.67058825\n",
      " 0.85882354 0.99215686 0.99215686 0.99215686 0.99215686 0.7647059\n",
      " 0.3137255  0.03529412 0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.21568628 0.6745098  0.8862745  0.99215686 0.99215686 0.99215686\n",
      " 0.99215686 0.95686275 0.52156866 0.04313726 0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.53333336 0.99215686\n",
      " 0.99215686 0.99215686 0.83137256 0.5294118  0.5176471  0.0627451\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.        ]\n"
     ]
    }
   ],
   "source": [
    "print(x_train_normalized[0])\n",
    "# print(x_train_normalized[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5, 0, 4, 1, 9], dtype=uint8)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train_label[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_oneHotEncoding = np_utils.to_categorical(y_train_label)\n",
    "y_test_oneHotEncoding = np_utils.to_categorical(y_test_label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., 0., 0., 1., 0., 0., 0., 0.],\n",
       "       [1., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 1., 0., 0., 0., 0., 0.],\n",
       "       [0., 1., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0., 0., 0., 0., 0., 1.]], dtype=float32)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train_oneHotEncoding[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
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