Algorithm_TensorFlow_02.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers, models\n",
    "import numpy as np \n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "mnist = keras.datasets.mnist\n",
    "(train_images, train_labels), (test_images, test_labels) = mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of Train images : (60000, 28, 28)\n",
      "Shape of Train labels :  (60000,)\n",
      "\n",
      "Shape of Test images :  (10000, 28, 28)\n",
      "Shape of Test labels :  (10000,)\n"
     ]
    }
   ],
   "source": [
    "print('Shape of Train images :',train_images.shape)\n",
    "print('Shape of Train labels : ', train_labels.shape)\n",
    "print('\\nShape of Test images : ', test_images.shape)\n",
    "print(\"Shape of Test labels : \",test_labels.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train labels :  [5 0 4 ... 5 6 8]\n"
     ]
    }
   ],
   "source": [
    "print('Train labels : ',train_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  0   0   0   0   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]\n",
      " [  0   0   0   0   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]\n",
      " [  0   0   0   0   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]\n",
      " [  0   0   0   0   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]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0  51 159 253\n",
      "  159  50   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0  48 238 252 252\n",
      "  252 237   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0  54 227 253 252 239\n",
      "  233 252  57   6   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0  10  60 224 252 253 252 202\n",
      "   84 252 253 122   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0 163 252 252 252 253 252 252\n",
      "   96 189 253 167   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0  51 238 253 253 190 114 253 228\n",
      "   47  79 255 168   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0  48 238 252 252 179  12  75 121  21\n",
      "    0   0 253 243  50   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0  38 165 253 233 208  84   0   0   0   0\n",
      "    0   0 253 252 165   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   7 178 252 240  71  19  28   0   0   0   0\n",
      "    0   0 253 252 195   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0  57 252 252  63   0   0   0   0   0   0   0\n",
      "    0   0 253 252 195   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0 198 253 190   0   0   0   0   0   0   0   0\n",
      "    0   0 255 253 196   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0  76 246 252 112   0   0   0   0   0   0   0   0\n",
      "    0   0 253 252 148   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0  85 252 230  25   0   0   0   0   0   0   0   0\n",
      "    7 135 253 186  12   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0  85 252 223   0   0   0   0   0   0   0   0   7\n",
      "  131 252 225  71   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0  85 252 145   0   0   0   0   0   0   0  48 165\n",
      "  252 173   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0  86 253 225   0   0   0   0   0   0 114 238 253\n",
      "  162   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0  85 252 249 146  48  29  85 178 225 253 223 167\n",
      "   56   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0  85 252 252 252 229 215 252 252 252 196 130   0\n",
      "    0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0  28 199 252 252 253 252 252 233 145   0   0   0\n",
      "    0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0  25 128 252 253 252 141  37   0   0   0   0\n",
      "    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   0   0   0   0\n",
      "    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   0   0   0   0\n",
      "    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   0   0   0   0\n",
      "    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   0   0   0   0\n",
      "    0   0   0   0   0   0   0   0   0   0]]\n"
     ]
    }
   ],
   "source": [
    "print(train_images[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First 10 Train images in MNIST dataset\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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r7pXfK/7Om8O78523NMptMKmwS5dvKrSg05H8cWeSh0viOzzlKfQfnUPMycXGqyVdtkv/pzfdTlNgLKPH5hk088/l504bSfxLDBGRQ7ka0wnbPAMA6r117yk9jHsH4eK7rGdYzxdh/+9mt3U34ulU2sy6//72aU1giKwu1EijzDutkaNgQt1BvokpHZ5LYnuhJ/M/i6TZPxKwv12OmTejM9OX9GFx831VjlcLAm+eGEXz4/VfMckiAmzsLXXJz46xpWNgOvaC9JAtuRaE/TbzRzszfx2DngcvnmIMDyL3jWKSQpYy4EQEDkPO4Yjlu1V302qbvMNvH8R9RkeNZGNmdl+cI68DWGVApYJyO1VlRG7ESOu4C7JUrQg2NqR80oXkYcu4WF7KsE9n4bPmLOU5uRgGBtNx4VHe9ZDumdgbrVj39pP4btmP2t2Nxx6PojCigK1Bq2gZI6UmdhS68Zm+jdn99P/lJU73/6zKttRXtOite2uSM9LXKnZVd90MakHAaCffZIzDuzpwbYM7zVKqpi6LPW2JavILFYPWPd+fivvxQgC8zmQ16PmRdznKkI6kTtOyqtcXAPS1vVM3WGoysP9aazBmm9Gg1D2I7r2eZdx/LdWM90PZ/NzH6DVauv42nuYjLLzmn5UI0t4Ru8TYrnhcl7/c7WE4btgP/5DfTuab3UkeFs2l8lLGLHgTn2/Pca1/a0zjHNnUMZomah0BG6IA0H92FfsUKfoRr+bhtD4Pp/UwevIsPEffniU2ozFwyux+6lLtoL/Zf7ZGBJ2O/DFBuGw7hfHm/Wues2eEsW3ah2CFicAucYmsnNUKgInOGaRN1+I7Th5b3u9Vr+5QN2nCxVHl+Gp0fHWzGUCVxXYaGrzIIsA2rVtxdkJz/haxgVGN7i1tgTk5IcRH98TlCzMPtpikKCrcLo/X44JpG2tEc/kmOeFNcI24SJS39GqQJ+wPs73Qk+dODMH9U8u9G+pBqAUV1/Uamv4gz+9nbuqIRrizRmmzvVetGvlWcPOZnvCQ3oo5WPGyVBtuK8CTE/9Ni2nXGe/0r9t7dQR8PQ3ft6S1GMTymmNwj+UJmJZXfGvYtPP74TU3gfVjpbGAsY5ScHJ+yGqe6BJZ7zxjTZQ82R3nmReI913CiIORkFJVgG2aNSVrtBThb4xaVDkomSOWoim27EyRRfsHAzBkwCfoX021aJV02gxfkgbEkFiq4Z/D+tzear76Z7MKsI2PNwXBzYh4fycTG1df2m5Gdk8Sl4fgGvcbLkb5RrptBRuSHl/Jf/rYklbalAnO6VX2v3apDzsTAvF7zcr9ursQTUbZigKN4UF8EvglBpNIgbGEbj+8jn/GoxH1F7SxTCXkv2/500N3Ale1jjnuUkM0NHkkFxJb0mZTAb6nDmO6j/BamrgL0gy0yIBvAGSZGTf4g/jKgcbkOU5wq0eV/c+EJfKtx3cAGG93vcenD+ZMbDvctshbpXI/RASMxZarRFF30DN3xAZEk4kJ2yfim2p+vTCLANs0a8q1NQ5Mah1PpGPV5SinZvXmyAopB+y+6SSuN+X753nuzWX2q6EsbCrZ6GtbRm/bdACOlqqIjH8FAP2Ew/hZOd9bE0Xd5BlhL3HV0tu2EFCzq8gb/SsHrTDXqmZaxBehmVq93MncJPRrTo+x/SnoUobNFQ36lVnYXM7FpyTzkbkWFZTGNZU+fGQZe0kDP73PHqlxTCzR8fKB5/B9OQ23QuuIL0BbGzvyJnTH7XPL+PD0lr2MaJRL1/0T8H1dHr1okACXDQ6hbPo15vh+zyC7wir7csRi+m6fgf87ybjmSxdM9mXrUs+SNsaHDlFRnH76zswm/+8n0255Efqj8nd164u11zy1FsKvx4i74QFApGMWRQHN0GZeNLsdMe8anjEJeN7+/mjEujXjckwqy1t2vV3l64nMzS/TerF2cneO96r+9oAvb3iRbWjMmiNSza3vKpE2vx6zWkMVGy75eN1YjPvvtyz2opYPto0iclwMdt/LtypegwQ4/SkVqZ2+qfy+LL8t0fGDEEQB/3nn8cs5YPE8Y/m5dHynpzNserfKbXoOPtJv1ynd3QQxUL7b2+nYZaIu9melV7xsNhrC4k9HAxA5M5pmfzlDXn5n2cuuHmUqyqx2dXRiFxX3sXmnQqv3HqH1b/YET3uNL179hI5agf4nIijY25RWG7MoP5+BnwVy87XhzSTp/hjd6iiqwlKLaUqb2YkMm90NN+SLuBskwPpJvzF0UnDVbbfrOx+FAZ7/LzRdnMCfFnet9Yv86kr5+Qwu9oShBD/8YCvQYp0U5UU8NZSNvjsI/2skrs86V64hrSAPxqIiWixIYM4CqT67EedoxLlHrnfgOlRqkH7BAbDeW0Pk4I/Z71V4pBCv5iFezaNsVDntd79KfJf1iP6trO2WgoLsWHQmnILCgxCv5uE3Po9hdAP+uCkIhT8OgqkO6/IKgnAFsORrSVuZTKYmih+KH4ofih//bX5AHQVYQUFBQcF8KDlgBQUFBSuhCLCCgoKClVAEWEFBQcFKKAKsoKCgYCUUAVZQUFCwEooAKygoKFgJRYAVFBQUrIQiwAoKCgpWQhFgBQUFBSvxf93cAtPIgxJiAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Train labels match with Train label sequentialy\n",
      " [5 0 4 1 9 2 1 3 1 4]\n"
     ]
    }
   ],
   "source": [
    "print('First 10 Train images in MNIST dataset\\n')\n",
    "for i in range(10):\n",
    "    plt.subplot(1, 10, i+1)\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    plt.imshow(train_images[i])\n",
    "plt.show()\n",
    "print('\\nTrain labels match with Train label sequentialy\\n',train_labels[:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_images = tf.reshape(train_images, [-1, 28, 28, 1])\n",
    "test_images = tf.reshape(test_images, [-1, 28, 28, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def select_model(model_number):\n",
    "    if model_number == 1:\n",
    "        model = keras.models.Sequential([\n",
    "                    keras.layers.Conv2D(32, (3,3), activation = 'relu', input_shape = (28, 28,1)),  # layer 1 \n",
    "                    keras.layers.MaxPool2D((2,2)),                                                  # layer 2 \n",
    "                    keras.layers.Flatten(),\n",
    "                    keras.layers.Dense(10, activation = 'softmax')])                                # layer 3\n",
    "\n",
    "    if model_number == 2:\n",
    "        model = keras.models.Sequential([\n",
    "                    keras.layers.Conv2D(32, (3,3), activation = 'relu', input_shape=(28,28,1)),     # layer 1 \n",
    "                    keras.layers.MaxPool2D((2,2)),                                                  # layer 2\n",
    "                    keras.layers.Conv2D(64, (3,3), activation = 'relu'),                            # layer 3 \n",
    "                    keras.layers.MaxPool2D((2,2)),                                                  # layer 4\n",
    "                    keras.layers.Flatten(),\n",
    "                    keras.layers.Dense(10, activation = 'softmax')])                                # layer 5\n",
    "                    \n",
    "    if model_number == 3: \n",
    "        model = keras.models.Sequential([\n",
    "                    keras.layers.Conv2D(32, (3,3), activation = 'relu', input_shape = (28, 28,1)),  # layer 1\n",
    "                    keras.layers.MaxPool2D((2,2)),                                                  # layer 2\n",
    "                    keras.layers.Conv2D(64, (3,3), activation = 'relu'),                            # layer 3\n",
    "                    keras.layers.Conv2D(64, (3,3), activation = 'relu'),                            # layer 4\n",
    "                    keras.layers.MaxPool2D((2,2)),                                                  # layer 5\n",
    "                    keras.layers.Conv2D(128, (3,3), activation = 'relu'),                           # layer 6\n",
    "                    keras.layers.Flatten(),\n",
    "                    keras.layers.Dense(10, activation = 'softmax')])                                # layer 7\n",
    "    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = select_model(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d (Conv2D)              (None, 26, 26, 32)        320       \n",
      "_________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D) (None, 13, 13, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 11, 11, 64)        18496     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 5, 5, 64)          0         \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 1600)              0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 10)                16010     \n",
      "=================================================================\n",
      "Total params: 34,826\n",
      "Trainable params: 34,826\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(\n",
    "    optimizer = 'adam',\n",
    "    loss = 'sparse_categorical_crossentropy',\n",
    "    metrics = ['accuracy']\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 60000 samples\n",
      "Epoch 1/5\n",
      "60000/60000 [==============================] - 48s 808us/sample - loss: 0.3585 - accuracy: 0.9460\n",
      "Epoch 2/5\n",
      "60000/60000 [==============================] - 48s 804us/sample - loss: 0.0694 - accuracy: 0.9795\n",
      "Epoch 3/5\n",
      "60000/60000 [==============================] - 47s 784us/sample - loss: 0.0565 - accuracy: 0.9822\n",
      "Epoch 4/5\n",
      "60000/60000 [==============================] - 46s 769us/sample - loss: 0.0492 - accuracy: 0.9852\n",
      "Epoch 5/5\n",
      "60000/60000 [==============================] - 50s 830us/sample - loss: 0.0426 - accuracy: 0.9866\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x1b7835265c8>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(train_images, train_labels,  epochs = 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000/1 - 3s - loss: 0.0357 - accuracy: 0.9795\n",
      "\n",
      "Test loss :  0.07112070954719457\n",
      "Test accuracy : 0.9795\n"
     ]
    }
   ],
   "source": [
    "test_loss, accuracy = model.evaluate(test_images, test_labels, verbose = 2)\n",
    "print('\\nTest loss : ', test_loss)\n",
    "print('Test accuracy :', accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_images = tf.cast(test_images, tf.float32)\n",
    "pred = model.predict(test_images)\n",
    "Number = [0,1,2,3,4,5,6,7,8,9]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction :  (10000, 10)\n",
      "Test labels :  (10000,)\n"
     ]
    }
   ],
   "source": [
    "print('Prediction : ', pred.shape)\n",
    "print('Test labels : ', test_labels.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_image(i, predictions_array, true_label, img):\n",
    "  predictions_array, true_label, img = predictions_array[i], true_label[i], img[i]\n",
    "  plt.grid(False)\n",
    "  plt.xticks([])\n",
    "  plt.yticks([])\n",
    "\n",
    "  plt.imshow(img, cmap=plt.cm.binary)\n",
    "\n",
    "  predicted_label = np.argmax(predictions_array)\n",
    "  if predicted_label == true_label:\n",
    "    color = 'blue'\n",
    "  else:\n",
    "    color = 'red'\n",
    "\n",
    "  plt.xlabel(\"{} {:2.0f}% ({})\".format(Number[predicted_label],\n",
    "                                100*np.max(predictions_array),\n",
    "                                Number[true_label]),\n",
    "                                color=color)\n",
    "\n",
    "def plot_value_array(i, predictions_array, true_label):\n",
    "  predictions_array, true_label = predictions_array[i], true_label[i]\n",
    "  plt.grid(False)\n",
    "  plt.xticks([])\n",
    "  plt.yticks([])\n",
    "  thisplot = plt.bar(range(10), predictions_array, color=\"#777777\")\n",
    "  plt.ylim([0, 1])\n",
    "  predicted_label = np.argmax(predictions_array)\n",
    "  plt.xticks(Number)\n",
    "\n",
    "  thisplot[predicted_label].set_color('red')\n",
    "  thisplot[true_label].set_color('blue')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "(train_images, train_labels), (test_images, test_labels) = mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "i = 1\n",
    "plt.figure(figsize=(6,3))\n",
    "plt.subplot(1,2,1)\n",
    "plot_image(i, pred, test_labels, test_images)\n",
    "plt.subplot(1,2,2)\n",
    "plot_value_array(i, pred,  test_labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 30 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_rows = 5\n",
    "num_cols = 3\n",
    "num_images = num_rows*num_cols\n",
    "plt.figure(figsize=(2*2*num_cols, 2*num_rows))\n",
    "for i in range(num_images):\n",
    "  plt.subplot(num_rows, 2*num_cols, 2*i+1)\n",
    "  plot_image(i, pred, test_labels, test_images)\n",
    "  plt.subplot(num_rows, 2*num_cols, 2*i+2)\n",
    "  plot_value_array(i, pred, test_labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def error_mnist(prediction_array, true_label):\n",
    "    error_index = []\n",
    "    \n",
    "    for i in range(true_label.shape[0]):\n",
    "        if np.argmax(prediction_array[i]) != true_label[i]:\n",
    "            error_index.append(i)\n",
    "    return error_index\n",
    "\n",
    "# change num_cols, num_rows if you want to see more result.  \n",
    "def plot_error(index, prediction_array, true_label):\n",
    "    num_cols = 5\n",
    "    num_rows = 5\n",
    "    plt.figure(figsize=(2*2*num_cols, 2*num_rows))\n",
    "\n",
    "    assert len(index) < num_cols * num_rows\n",
    "    for i in range(len(index)):\n",
    "        plt.subplot(num_rows, 2*num_cols, 2*i+1)\n",
    "        idx = index[i]\n",
    "        plt.imshow(test_images[idx])\n",
    "        plt.subplot(num_rows, 2*num_cols, 2*i+2)\n",
    "        plt.bar(range(10), prediction_array[idx])\n",
    "        plt.xticks(Number)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[15, 95, 111, 115, 121, 211, 247, 250, 266, 282]\n"
     ]
    }
   ],
   "source": [
    "index = error_mnist(pred, test_labels)\n",
    "index_slice = index[:10]\n",
    "print(index[:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_error(index_slice, pred, test_labels)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}