Advanced AIML Lab/Advanced AI-ML Lab 9 20.04.2021.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Abhishek Sharma\n",
    "### CS 2nd Year | Section : \"I\" | Roll No.: 01\n",
    "\n",
    "### Enrollment No.: 12019009001127\n",
    "\n",
    "## Advanced Artificial Intelligence and Machine Learning Lab 9\n",
    "\n",
    "#### Faculty : PB, MB, BM | Date : 20.04.2021"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## import library"
   ]
  },
  {
   "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": "markdown",
   "metadata": {},
   "source": [
    "## Get MNIST Data. \n",
    "### MNIST data loacted in tensorflow > keras > datasets > mnist \n",
    "### Split data to (train images, train labels) and (test images, test labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
      "11493376/11490434 [==============================] - 7s 1us/step\n"
     ]
    }
   ],
   "source": [
    "mnist = keras.datasets.mnist\n",
    "(train_images, train_labels), (test_images, test_labels) = mnist.load_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### There are Total 60000 Train images and Train labels. (6000 images for single class)\n",
    "### Shape of single image is 28 x 28 (pixel)\n",
    "### "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "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": 73,
   "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": "markdown",
   "metadata": {},
   "source": [
    "## Plot first train image. \n",
    "### when value is close to 0 : dark \n",
    "### when value is close to 255 : white"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "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": "markdown",
   "metadata": {},
   "source": [
    "### Plot First 10 Train images and Corresponding labels  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First 10 Train images in MNIST dataset\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\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])\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Important\n",
    "### Change data shape (60000 x 28 x 28) to (60000 x 28 x 28 x 1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "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": "markdown",
   "metadata": {},
   "source": [
    "# Select one convolution model below \n",
    "## There are 3 example models. \n",
    "## 3, 5, 7 layer each \n",
    "## MODEL 1 : 3 Layers with 1 Convolution layer  \n",
    "## MODEL 2 : 5 Layers with 2 Convolution layer \n",
    "## MODEL 3 : 7 Layers with 4 Convolution layer "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "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 \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = select_model(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## If you want to see information of model, model.summary() will help\n",
    "### summary() is also built in function "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_7\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_15 (Conv2D)           (None, 26, 26, 32)        320       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_11 (MaxPooling (None, 13, 13, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_16 (Conv2D)           (None, 11, 11, 64)        18496     \n",
      "_________________________________________________________________\n",
      "conv2d_17 (Conv2D)           (None, 9, 9, 64)          36928     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_12 (MaxPooling (None, 4, 4, 64)          0         \n",
      "_________________________________________________________________\n",
      "conv2d_18 (Conv2D)           (None, 2, 2, 128)         73856     \n",
      "_________________________________________________________________\n",
      "flatten_7 (Flatten)          (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "dense_7 (Dense)              (None, 10)                5130      \n",
      "=================================================================\n",
      "Total params: 134,730\n",
      "Trainable params: 134,730\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Components in training step \n",
    "### Optimizer, Loss function, accuracy metrics "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(\n",
    "    optimizer = 'adam',\n",
    "    loss = 'sparse_categorical_crossentropy',\n",
    "    metrics = ['accuracy']\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training Step \n",
    "## Training for 5 epochs. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 60000 samples\n",
      "Epoch 1/5\n",
      "60000/60000 [==============================] - 94s 2ms/sample - loss: 0.1670 - accuracy: 0.9578\n",
      "Epoch 2/5\n",
      "60000/60000 [==============================] - 88s 1ms/sample - loss: 0.0537 - accuracy: 0.9829\n",
      "Epoch 3/5\n",
      "60000/60000 [==============================] - 90s 1ms/sample - loss: 0.0420 - accuracy: 0.9871\n",
      "Epoch 4/5\n",
      "60000/60000 [==============================] - 91s 2ms/sample - loss: 0.0373 - accuracy: 0.9884\n",
      "Epoch 5/5\n",
      "60000/60000 [==============================] - 92s 2ms/sample - loss: 0.0325 - accuracy: 0.9902\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x1f4280d63c8>"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(train_images, train_labels,  epochs = 5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test Step \n",
    "## Perform Test with Test data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000/1 - 4s - loss: 0.0191 - accuracy: 0.9889\n",
      "\n",
      "Test loss :  0.036512752435775474\n",
      "Test accuracy : 0.9889\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": "markdown",
   "metadata": {},
   "source": [
    "## Before prediction, change test image's type to float 32. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "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": 84,
   "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": "markdown",
   "metadata": {},
   "source": [
    "## Functions for plot images, probability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "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": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "(train_images, train_labels), (test_images, test_labels) = mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "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",
    "  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')\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": 88,
   "metadata": {
    "scrolled": true
   },
   "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": "markdown",
   "metadata": {},
   "source": [
    "## Plot images and probability that model predicted wrong"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "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": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "def correct_mnist(prediction_array, true_label):\n",
    "    correct_index = []\n",
    "    \n",
    "    for i in range(true_label.shape[0]):\n",
    "        if np.argmax(prediction_array[i]) == true_label[i]:\n",
    "            correct_index.append(i)\n",
    "    return correct_index\n",
    "\n",
    "\n",
    "def plot_correct(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": "markdown",
   "metadata": {},
   "source": [
    "## Find index of wrong prediction\n",
    "## Plot first 10 wrong predicted images and probability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[8, 415, 447, 625, 646, 659, 716, 726, 947, 1006]\n"
     ]
    }
   ],
   "source": [
    "index = error_mnist(pred, test_labels)\n",
    "index_slice = index[:10]\n",
    "print(index[:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "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)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Find index of right prediction\n",
    "## Plot first 10 right predicted images and probability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 1, 2, 3, 4, 5, 6, 7, 9, 10]\n"
     ]
    }
   ],
   "source": [
    "index = correct_mnist(pred, test_labels)\n",
    "index_slice = index[:10]\n",
    "print(index[:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x720 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_correct(index_slice, pred, test_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "DONE"
   ]
  }
 ],
 "metadata": {
  "file_extension": ".py",
  "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"
  },
  "mimetype": "text/x-python",
  "name": "python",
  "npconvert_exporter": "python",
  "pygments_lexer": "ipython3",
  "version": 3
 },
 "nbformat": 4,
 "nbformat_minor": 2
}