Capstone Project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Capstone Project\n",
    "## Image classifier for the SVHN dataset\n",
    "### Instructions\n",
    "\n",
    "In this notebook, you will create a neural network that classifies real-world images digits. You will use concepts from throughout this course in building, training, testing, validating and saving your Tensorflow classifier model.\n",
    "\n",
    "This project is peer-assessed. Within this notebook you will find instructions in each section for how to complete the project. Pay close attention to the instructions as the peer review will be carried out according to a grading rubric that checks key parts of the project instructions. Feel free to add extra cells into the notebook as required.\n",
    "\n",
    "### How to submit\n",
    "\n",
    "When you have completed the Capstone project notebook, you will submit a pdf of the notebook for peer review. First ensure that the notebook has been fully executed from beginning to end, and all of the cell outputs are visible. This is important, as the grading rubric depends on the reviewer being able to view the outputs of your notebook. Save the notebook as a pdf (File -> Download as -> PDF via LaTeX). You should then submit this pdf for review.\n",
    "\n",
    "### Let's get started!\n",
    "\n",
    "We'll start by running some imports, and loading the dataset. For this project you are free to make further imports throughout the notebook as you wish. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from scipy.io import loadmat\n",
    "import cv2\n",
    "from scipy.ndimage.measurements import center_of_mass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def centralize(image):\n",
    "    center_y, center_x = center_of_mass(image)\n",
    "    rows, cols = image.shape\n",
    "    x_shift = np.round(cols / 2.0 - center_x).astype(int)\n",
    "    y_shift = np.round(rows / 2.0 - center_y).astype(int)\n",
    "\n",
    "    matrix = np.float32([[1, 0, x_shift], [0, 1, y_shift]])\n",
    "    dst = cv2.warpAffine(image, matrix, (cols, rows))\n",
    "    return dst"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src='data/svhn_examples.jpg'/>\n",
    "For the capstone project, you will use the [SVHN dataset](http://ufldl.stanford.edu/housenumbers/). This is an  image dataset of over 600,000 digit images in all, and is a harder dataset than MNIST as the numbers appear in the context of natural scene images. SVHN is obtained from house numbers in Google Street View images. \n",
    "\n",
    "* Y. Netzer, T. Wang, A. Coates, A. Bissacco, B. Wu and A. Y. Ng. \"Reading Digits in Natural Images with Unsupervised Feature Learning\". NIPS Workshop on Deep Learning and Unsupervised Feature Learning, 2011.\n",
    "\n",
    "Your goal is to develop an end-to-end workflow for building, training, validating, evaluating and saving a neural network that classifies a real-world image into one of ten classes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Run this cell to load the dataset\n",
    "\n",
    "train = loadmat('data/train_32x32.mat')\n",
    "test = loadmat('data/test_32x32.mat')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Both `train` and `test` are dictionaries with keys `X` and `y` for the input images and labels respectively."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Inspect and preprocess the dataset\n",
    "* Extract the training and testing images and labels separately from the train and test dictionaries loaded for you.\n",
    "* Select a random sample of images and corresponding labels from the dataset (at least 10), and display them in a figure.\n",
    "* Convert the training and test images to grayscale by taking the average across all colour channels for each pixel. _Hint: retain the channel dimension, which will now have size 1._\n",
    "* Select a random sample of the grayscale images and corresponding labels from the dataset (at least 10), and display them in a figure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "import numpy as np\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Conv2D, MaxPooling2D, Flatten, Dropout, BatchNormalization\n",
    "from tensorflow.keras.callbacks import ModelCheckpoint, Callback, EarlyStopping, LearningRateScheduler\n",
    "from tensorflow.keras.utils import to_categorical\n",
    "from tensorflow.keras import optimizers, activations\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "import pandas as pd\n",
    "\n",
    "%matplotlib inline\n",
    "plt.style.use('seaborn')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of the Training images (32, 32, 3, 73257)\n",
      "Shape of the Training labels (73257, 1)\n",
      "--------------------------------------------------\n",
      "Shape of the Test images (32, 32, 3, 26032)\n",
      "Shape of the Test labels (26032, 1)\n"
     ]
    }
   ],
   "source": [
    "# Extracting the training and testing images and their labels\n",
    "\n",
    "X, y = train['X'] / 255., train['y']\n",
    "X_test, y_test = test['X'] / 255., test['y']\n",
    "\n",
    "assert np.max(X[0]) <= 1\n",
    "assert np.max(X_test[0]) <= 1\n",
    "\n",
    "print(f'Shape of the Training images {X.shape}')\n",
    "print(f'Shape of the Training labels {y.shape}')\n",
    "print('-'*50)\n",
    "print(f'Shape of the Test images {X_test.shape}')\n",
    "print(f'Shape of the Test labels {y_test.shape}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1  2  3  4  5  6  7  8  9 10]\n",
      "As we can see the labels are 1-10 so we change them to 0-9 to match our softmax output\n",
      "Changing the label 10 to 0...\n",
      "[0 1 2 3 4 5 6 7 8 9]\n",
      "After reshaping the shapes are\n",
      "Train : (73257, 32, 32, 3)\n",
      "Test : (26032, 32, 32, 3)\n",
      "\n",
      "After reshaping the shapes are\n",
      "Train-Labels : (73257, 1)\n",
      "Test-Labels : (26032, 1)\n"
     ]
    }
   ],
   "source": [
    "# Reshaping the training and test images\n",
    "\n",
    "X = np.moveaxis(X, -1, 0)\n",
    "X_test = np.moveaxis(X_test, -1, 0)\n",
    "\n",
    "# If we look at the unique labels present in the dataset we find that there is no 0\n",
    "# In the dataset 0 is labelled as 10, so we need to convert it back to 0 to train our model\n",
    "\n",
    "print(f'{np.unique(y)}\\nAs we can see the labels are 1-10 so we change them to 0-9 to match our softmax output')\n",
    "y[np.where(y == 10)] = 0\n",
    "y_test[np.where(y_test == 10)] = 0\n",
    "print(f'Changing the label 10 to 0...\\n{np.unique(y)}')\n",
    "\n",
    "\n",
    "print(f'After reshaping the shapes are\\nTrain : {X.shape}\\nTest : {X_test.shape}')\n",
    "print(f'\\nAfter reshaping the shapes are\\nTrain-Labels : {y.shape}\\nTest-Labels : {y_test.shape}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# A function that plots images, if grayscale plots as binary else plots as RGB\n",
    "\n",
    "def plot_images(random_indexes, nrows, ncols, grayscale=False):\n",
    "    fig, axes = plt.subplots(nrows=num_rows, ncols=num_cols, figsize=(15, 15))\n",
    "\n",
    "    index = 0\n",
    "    for i in range(nrows):\n",
    "        for j in range(ncols):\n",
    "            img = np.squeeze(X[random_indexes[index]])\n",
    "            \n",
    "            if grayscale:\n",
    "                axes[i, j].imshow(img, cmap='binary')\n",
    "            else:\n",
    "                axes[i, j].imshow(img)\n",
    "                \n",
    "            axes[i, j].set_title(str(y[random_indexes[index]]), fontsize=20)\n",
    "                \n",
    "            index += 1\n",
    "\n",
    "    fig.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Displaying 10 random images from the training set\n",
    "# This same 10 images will also be displayed when the images are converted into grayscale (single channel)\n",
    "\n",
    "random_indexes = [random.randint(0, 73256) for _ in range(10)]\n",
    "num_rows = 2\n",
    "num_cols = 5\n",
    "\n",
    "plot_images(random_indexes, num_rows, num_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1080x1080 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of training set : (73257, 32, 32, 1) Labels : (73257, 10, 2)\n",
      "Shape of test set : (26032, 32, 32, 1) Labels : (26032, 10, 2)\n"
     ]
    }
   ],
   "source": [
    "# Grayscaling the images (One-Channel)\n",
    "# Taking the mean of images over the 3rd axis\n",
    "\n",
    "X = np.mean(X, -1, keepdims=True)\n",
    "X_test = np.mean(X_test, -1, keepdims=True)\n",
    "\n",
    "plot_images(random_indexes, num_rows, num_cols, True)\n",
    "\n",
    "y = to_categorical(y)\n",
    "y_test = to_categorical(y_test)\n",
    "\n",
    "print(f'Shape of training set : {X.shape} Labels : {y.shape}')\n",
    "print(f'Shape of test set : {X_test.shape} Labels : {y_test.shape}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "_, thresh = cv2.threshold(X[0], 255, 255, cv2.THRESH_BINARY)\n",
    "cv2.imshow('Read', thresh)\n",
    "cv2.waitKey(0)\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. MLP neural network classifier\n",
    "* Build an MLP classifier model using the Sequential API. Your model should use only Flatten and Dense layers, with the final layer having a 10-way softmax output. \n",
    "* You should design and build the model yourself. Feel free to experiment with different MLP architectures. _Hint: to achieve a reasonable accuracy you won't need to use more than 4 or 5 layers._\n",
    "* Print out the model summary (using the summary() method)\n",
    "* Compile and train the model (we recommend a maximum of 30 epochs), making use of both training and validation sets during the training run. \n",
    "* Your model should track at least one appropriate metric, and use at least two callbacks during training, one of which should be a ModelCheckpoint callback.\n",
    "* As a guide, you should aim to achieve a final categorical cross entropy training loss of less than 1.0 (the validation loss might be higher).\n",
    "* Plot the learning curves for loss vs epoch and accuracy vs epoch for both training and validation sets.\n",
    "* Compute and display the loss and accuracy of the trained model on the test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential([\n",
    "    Flatten(input_shape=(32, 32, 1)),\n",
    "    Dense(128, activation='relu', name='MLPDense-1'),\n",
    "    Dense(128, activation='relu', name='MLPDense-2'), \n",
    "    Dense(128, activation='relu', name='MLPDense-3'), \n",
    "    Dense(256, activation='relu', name='MLPDense-4'), \n",
    "    Dense(10, activation='softmax', name='MLPOut')\n",
    "])"
   ]
  },
  {
   "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",
      "flatten (Flatten)            (None, 1024)              0         \n",
      "_________________________________________________________________\n",
      "MLPDense-1 (Dense)           (None, 128)               131200    \n",
      "_________________________________________________________________\n",
      "MLPDense-2 (Dense)           (None, 128)               16512     \n",
      "_________________________________________________________________\n",
      "MLPDense-3 (Dense)           (None, 128)               16512     \n",
      "_________________________________________________________________\n",
      "MLPDense-4 (Dense)           (None, 256)               33024     \n",
      "_________________________________________________________________\n",
      "MLPOut (Dense)               (None, 10)                2570      \n",
      "=================================================================\n",
      "Total params: 199,818\n",
      "Trainable params: 199,818\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "class StopTrainingCallback(Callback):\n",
    "    \n",
    "    def on_epoch_end(self, epoch, logs=None):\n",
    "        accuracy = logs.get('accuracy')\n",
    "        val_accuracy = logs.get('val_accuracy')\n",
    "        if accuracy >= 0.95 or val_accuracy >= 0.93:\n",
    "            print(f'Currently in epoch-{epoch}')\n",
    "            print(f'Reached a Training Accuracy of {accuracy} and Validation Accuracy of {val_accuracy}...Training will be stopped to prevent overfitting')\n",
    "            self.model.stop_training = True\n",
    "\n",
    "model_best_val = ModelCheckpoint('model_best_val/checkpoint', monitor='val_loss', \n",
    "                                 save_weights_only=True, save_best_only=True)\n",
    "\n",
    "stop_training = StopTrainingCallback()\n",
    "\n",
    "early_stopping = EarlyStopping(monitor='val_accuracy', min_delta=0.05, patience=10, verbose=1)\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer='adagrad', loss='categorical_crossentropy', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "1054/1054 [==============================] - 4s 4ms/step - loss: 2.1374 - accuracy: 0.2395 - val_loss: 2.0646 - val_accuracy: 0.2624\n",
      "Epoch 2/30\n",
      "1054/1054 [==============================] - 4s 3ms/step - loss: 1.7905 - accuracy: 0.3824 - val_loss: 1.8187 - val_accuracy: 0.3592\n",
      "Epoch 3/30\n",
      "1054/1054 [==============================] - 3s 3ms/step - loss: 1.6083 - accuracy: 0.4568 - val_loss: 1.5906 - val_accuracy: 0.4730\n",
      "Epoch 4/30\n",
      "1054/1054 [==============================] - 4s 4ms/step - loss: 1.5188 - accuracy: 0.4976 - val_loss: 1.5295 - val_accuracy: 0.4897\n",
      "Epoch 5/30\n",
      "1054/1054 [==============================] - 3s 2ms/step - loss: 1.4602 - accuracy: 0.5239 - val_loss: 1.4854 - val_accuracy: 0.5195\n",
      "Epoch 6/30\n",
      "1054/1054 [==============================] - 3s 3ms/step - loss: 1.4114 - accuracy: 0.5445 - val_loss: 1.4900 - val_accuracy: 0.5013\n",
      "Epoch 7/30\n",
      "1054/1054 [==============================] - 4s 4ms/step - loss: 1.3730 - accuracy: 0.5620 - val_loss: 1.3956 - val_accuracy: 0.5566\n",
      "Epoch 8/30\n",
      "1054/1054 [==============================] - 4s 4ms/step - loss: 1.3379 - accuracy: 0.5758 - val_loss: 1.3890 - val_accuracy: 0.5549\n",
      "Epoch 9/30\n",
      "1054/1054 [==============================] - 4s 4ms/step - loss: 1.3075 - accuracy: 0.5866 - val_loss: 1.3430 - val_accuracy: 0.5757\n",
      "Epoch 10/30\n",
      "1054/1054 [==============================] - 3s 2ms/step - loss: 1.2817 - accuracy: 0.5958 - val_loss: 1.3114 - val_accuracy: 0.5920\n",
      "Epoch 11/30\n",
      "1054/1054 [==============================] - 3s 3ms/step - loss: 1.2580 - accuracy: 0.6045 - val_loss: 1.2763 - val_accuracy: 0.5885\n",
      "Epoch 12/30\n",
      "1054/1054 [==============================] - 3s 2ms/step - loss: 1.2389 - accuracy: 0.6109 - val_loss: 1.3418 - val_accuracy: 0.5719\n",
      "Epoch 13/30\n",
      "1054/1054 [==============================] - 3s 3ms/step - loss: 1.2221 - accuracy: 0.6174 - val_loss: 1.2356 - val_accuracy: 0.6159\n",
      "Epoch 14/30\n",
      "1054/1054 [==============================] - 3s 3ms/step - loss: 1.2057 - accuracy: 0.6227 - val_loss: 1.2208 - val_accuracy: 0.6161\n",
      "Epoch 15/30\n",
      "1054/1054 [==============================] - 3s 3ms/step - loss: 1.1926 - accuracy: 0.6276 - val_loss: 1.3377 - val_accuracy: 0.5707\n",
      "Epoch 16/30\n",
      "1054/1054 [==============================] - 4s 4ms/step - loss: 1.1799 - accuracy: 0.6318 - val_loss: 1.1939 - val_accuracy: 0.6292\n",
      "Epoch 17/30\n",
      "1054/1054 [==============================] - 3s 3ms/step - loss: 1.1688 - accuracy: 0.6361 - val_loss: 1.1930 - val_accuracy: 0.6294\n",
      "Epoch 18/30\n",
      "1054/1054 [==============================] - 4s 3ms/step - loss: 1.1586 - accuracy: 0.6401 - val_loss: 1.1733 - val_accuracy: 0.6357\n",
      "Epoch 19/30\n",
      "1054/1054 [==============================] - 3s 3ms/step - loss: 1.1497 - accuracy: 0.6428 - val_loss: 1.1649 - val_accuracy: 0.6426\n",
      "Epoch 20/30\n",
      "1054/1054 [==============================] - 3s 3ms/step - loss: 1.1400 - accuracy: 0.6464 - val_loss: 1.1574 - val_accuracy: 0.6485\n",
      "Epoch 21/30\n",
      "1054/1054 [==============================] - 3s 3ms/step - loss: 1.1315 - accuracy: 0.6488 - val_loss: 1.1608 - val_accuracy: 0.6466\n",
      "Epoch 22/30\n",
      "1054/1054 [==============================] - 5s 4ms/step - loss: 1.1229 - accuracy: 0.6523 - val_loss: 1.1416 - val_accuracy: 0.6465\n",
      "Epoch 23/30\n",
      "1054/1054 [==============================] - 3s 2ms/step - loss: 1.1148 - accuracy: 0.6567 - val_loss: 1.1408 - val_accuracy: 0.6434\n",
      "Epoch 24/30\n",
      "1054/1054 [==============================] - 3s 3ms/step - loss: 1.1086 - accuracy: 0.6591 - val_loss: 1.1309 - val_accuracy: 0.6523\n",
      "Epoch 25/30\n",
      "1054/1054 [==============================] - 3s 3ms/step - loss: 1.1006 - accuracy: 0.6604 - val_loss: 1.1152 - val_accuracy: 0.6589\n",
      "Epoch 26/30\n",
      "1054/1054 [==============================] - 3s 3ms/step - loss: 1.0934 - accuracy: 0.6633 - val_loss: 1.1085 - val_accuracy: 0.6588\n",
      "Epoch 27/30\n",
      "1054/1054 [==============================] - 3s 3ms/step - loss: 1.0877 - accuracy: 0.6653 - val_loss: 1.1101 - val_accuracy: 0.6540\n",
      "Epoch 28/30\n",
      "1054/1054 [==============================] - 3s 3ms/step - loss: 1.0814 - accuracy: 0.6684 - val_loss: 1.1164 - val_accuracy: 0.6519\n",
      "Epoch 29/30\n",
      "1054/1054 [==============================] - 3s 2ms/step - loss: 1.0759 - accuracy: 0.6703 - val_loss: 1.1100 - val_accuracy: 0.6552\n",
      "Epoch 30/30\n",
      "1054/1054 [==============================] - 3s 3ms/step - loss: 1.0694 - accuracy: 0.6718 - val_loss: 1.0901 - val_accuracy: 0.6647\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(X, y, epochs=30, batch_size=64,   \n",
    "                    validation_split=0.08, callbacks=[model_best_val, stop_training])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>val_loss</th>\n",
       "      <th>val_accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.137377</td>\n",
       "      <td>0.239450</td>\n",
       "      <td>2.064631</td>\n",
       "      <td>0.262413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.790484</td>\n",
       "      <td>0.382352</td>\n",
       "      <td>1.818658</td>\n",
       "      <td>0.359154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.608270</td>\n",
       "      <td>0.456822</td>\n",
       "      <td>1.590649</td>\n",
       "      <td>0.472957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.518822</td>\n",
       "      <td>0.497641</td>\n",
       "      <td>1.529516</td>\n",
       "      <td>0.489678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.460203</td>\n",
       "      <td>0.523874</td>\n",
       "      <td>1.485369</td>\n",
       "      <td>0.519536</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       loss  accuracy  val_loss  val_accuracy\n",
       "0  2.137377  0.239450  2.064631      0.262413\n",
       "1  1.790484  0.382352  1.818658      0.359154\n",
       "2  1.608270  0.456822  1.590649      0.472957\n",
       "3  1.518822  0.497641  1.529516      0.489678\n",
       "4  1.460203  0.523874  1.485369      0.519536"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(history.history)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(y=['loss', 'val_loss'], legend=True, title='Loss Vs. Val Loss')\n",
    "df.plot(y=['accuracy', 'val_accuracy'], legend=True, title='Accuracy Vs. Val Acc')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The model scored a loss of 1.1874489784240723 and an accuracy of 0.6391748785972595 on the test set of size 26032\n"
     ]
    }
   ],
   "source": [
    "test_loss, test_acc = model.evaluate(X_test, y_test, verbose=False)\n",
    "print(f'The model scored a loss of {test_loss} and an accuracy of {test_acc} on the test set of size {len(X_test)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. CNN neural network classifier\n",
    "* Build a CNN classifier model using the Sequential API. Your model should use the Conv2D, MaxPool2D, BatchNormalization, Flatten, Dense and Dropout layers. The final layer should again have a 10-way softmax output. \n",
    "* You should design and build the model yourself. Feel free to experiment with different CNN architectures. _Hint: to achieve a reasonable accuracy you won't need to use more than 2 or 3 convolutional layers and 2 fully connected layers.)_\n",
    "* The CNN model should use fewer trainable parameters than your MLP model.\n",
    "* Compile and train the model (we recommend a maximum of 30 epochs), making use of both training and validation sets during the training run.\n",
    "* Your model should track at least one appropriate metric, and use at least two callbacks during training, one of which should be a ModelCheckpoint callback.\n",
    "* You should aim to beat the MLP model performance with fewer parameters!\n",
    "* Plot the learning curves for loss vs epoch and accuracy vs epoch for both training and validation sets.\n",
    "* Compute and display the loss and accuracy of the trained model on the test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "early_stopping_conv = EarlyStopping(monitor='loss', min_delta=0.01, patience=3, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "conv_model = Sequential([\n",
    "    Conv2D(32, (5, 5), activation='relu', input_shape=(32, 32, 1), padding='same'),\n",
    "    MaxPooling2D((2, 2)),\n",
    "    Conv2D(32, (3, 3), activation='relu', padding='same'),\n",
    "    MaxPooling2D((2, 2)),\n",
    "    Flatten(),\n",
    "    Dropout(0.25),\n",
    "    Dense(64, activation='relu'),\n",
    "    Dropout(0.25),\n",
    "    Dense(128, activation='relu'),\n",
    "    Dropout(0.25),\n",
    "    Dense(128, activation='relu'),\n",
    "    Dropout(0.25),\n",
    "    Dense(10, activation='softmax')\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d (Conv2D)              (None, 32, 32, 32)        832       \n",
      "_________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D) (None, 16, 16, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 16, 16, 32)        9248      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 8, 8, 32)          0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 2048)              0         \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 2048)              0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 64)                131136    \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 128)               8320      \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 128)               16512     \n",
      "_________________________________________________________________\n",
      "dropout_3 (Dropout)          (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 10)                1290      \n",
      "=================================================================\n",
      "Total params: 167,338\n",
      "Trainable params: 167,338\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "conv_model.summary()\n",
    "conv_model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "conv_model_best_val = ModelCheckpoint('conv_model_best_val/checkpoint', monitor='val_loss', \n",
    "                                 save_weights_only=True, save_best_only=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "2107/2107 [==============================] - 59s 28ms/step - loss: 1.2104 - accuracy: 0.5916 - val_loss: 0.5341 - val_accuracy: 0.8340\n",
      "Epoch 2/30\n",
      "2107/2107 [==============================] - 60s 28ms/step - loss: 0.7018 - accuracy: 0.7832 - val_loss: 0.4788 - val_accuracy: 0.8562\n",
      "Epoch 3/30\n",
      "2107/2107 [==============================] - 58s 28ms/step - loss: 0.6295 - accuracy: 0.8055 - val_loss: 0.4572 - val_accuracy: 0.8587\n",
      "Epoch 4/30\n",
      "2107/2107 [==============================] - 59s 28ms/step - loss: 0.5938 - accuracy: 0.8196 - val_loss: 0.4306 - val_accuracy: 0.8669\n",
      "Epoch 5/30\n",
      "2107/2107 [==============================] - 59s 28ms/step - loss: 0.5668 - accuracy: 0.8280 - val_loss: 0.3937 - val_accuracy: 0.8773\n",
      "Epoch 6/30\n",
      "2107/2107 [==============================] - 62s 29ms/step - loss: 0.5529 - accuracy: 0.8322 - val_loss: 0.3911 - val_accuracy: 0.8814\n",
      "Epoch 7/30\n",
      "2107/2107 [==============================] - 59s 28ms/step - loss: 0.5351 - accuracy: 0.8381 - val_loss: 0.4235 - val_accuracy: 0.8696\n",
      "Epoch 8/30\n",
      "2107/2107 [==============================] - 59s 28ms/step - loss: 0.5242 - accuracy: 0.8415 - val_loss: 0.3821 - val_accuracy: 0.8852\n",
      "Epoch 9/30\n",
      "2107/2107 [==============================] - 60s 29ms/step - loss: 0.5119 - accuracy: 0.8465 - val_loss: 0.3703 - val_accuracy: 0.8900\n",
      "Epoch 10/30\n",
      "2107/2107 [==============================] - 59s 28ms/step - loss: 0.5046 - accuracy: 0.8485 - val_loss: 0.3860 - val_accuracy: 0.8804\n",
      "Epoch 11/30\n",
      "2107/2107 [==============================] - 59s 28ms/step - loss: 0.4935 - accuracy: 0.8507 - val_loss: 0.3826 - val_accuracy: 0.8838\n",
      "Epoch 12/30\n",
      "2107/2107 [==============================] - 59s 28ms/step - loss: 0.4933 - accuracy: 0.8501 - val_loss: 0.3698 - val_accuracy: 0.8922\n",
      "Epoch 13/30\n",
      "2107/2107 [==============================] - 60s 28ms/step - loss: 0.4795 - accuracy: 0.8554 - val_loss: 0.3481 - val_accuracy: 0.8959\n",
      "Epoch 14/30\n",
      "2107/2107 [==============================] - 60s 28ms/step - loss: 0.4727 - accuracy: 0.8583 - val_loss: 0.3624 - val_accuracy: 0.8911\n",
      "Epoch 15/30\n",
      "2107/2107 [==============================] - 59s 28ms/step - loss: 0.4712 - accuracy: 0.8571 - val_loss: 0.3667 - val_accuracy: 0.8877\n",
      "Epoch 16/30\n",
      "2107/2107 [==============================] - 60s 29ms/step - loss: 0.4665 - accuracy: 0.8594 - val_loss: 0.3332 - val_accuracy: 0.8995\n",
      "Epoch 17/30\n",
      "2107/2107 [==============================] - 59s 28ms/step - loss: 0.4625 - accuracy: 0.8617 - val_loss: 0.3668 - val_accuracy: 0.8935\n",
      "Epoch 18/30\n",
      "2107/2107 [==============================] - 60s 28ms/step - loss: 0.4575 - accuracy: 0.8620 - val_loss: 0.3450 - val_accuracy: 0.8971\n",
      "Epoch 19/30\n",
      "2107/2107 [==============================] - 60s 28ms/step - loss: 0.4578 - accuracy: 0.8634 - val_loss: 0.3633 - val_accuracy: 0.8908\n",
      "Epoch 20/30\n",
      "2107/2107 [==============================] - 60s 28ms/step - loss: 0.4487 - accuracy: 0.8653 - val_loss: 0.3560 - val_accuracy: 0.8932\n",
      "Epoch 21/30\n",
      "2107/2107 [==============================] - 72s 34ms/step - loss: 0.4399 - accuracy: 0.8684 - val_loss: 0.3339 - val_accuracy: 0.8978\n",
      "Epoch 22/30\n",
      "2107/2107 [==============================] - 65s 31ms/step - loss: 0.4488 - accuracy: 0.8661 - val_loss: 0.3535 - val_accuracy: 0.8946\n",
      "Epoch 23/30\n",
      "2107/2107 [==============================] - 60s 29ms/step - loss: 0.4480 - accuracy: 0.8657 - val_loss: 0.3444 - val_accuracy: 0.8983\n",
      "Epoch 24/30\n",
      "2107/2107 [==============================] - 58s 28ms/step - loss: 0.4389 - accuracy: 0.8677 - val_loss: 0.3370 - val_accuracy: 0.9014\n",
      "Epoch 25/30\n",
      "2107/2107 [==============================] - 60s 28ms/step - loss: 0.4399 - accuracy: 0.8687 - val_loss: 0.3559 - val_accuracy: 0.8920\n",
      "Epoch 26/30\n",
      "2107/2107 [==============================] - 63s 30ms/step - loss: 0.4340 - accuracy: 0.8685 - val_loss: 0.3375 - val_accuracy: 0.9000\n",
      "Epoch 27/30\n",
      "2107/2107 [==============================] - 63s 30ms/step - loss: 0.4324 - accuracy: 0.8698 - val_loss: 0.3359 - val_accuracy: 0.8981\n",
      "Epoch 28/30\n",
      "2107/2107 [==============================] - 71s 34ms/step - loss: 0.4274 - accuracy: 0.8711 - val_loss: 0.3303 - val_accuracy: 0.9007\n",
      "Epoch 29/30\n",
      "2107/2107 [==============================] - 70s 33ms/step - loss: 0.4277 - accuracy: 0.8720 - val_loss: 0.3464 - val_accuracy: 0.8993\n",
      "Epoch 30/30\n",
      "2107/2107 [==============================] - 63s 30ms/step - loss: 0.4227 - accuracy: 0.8733 - val_loss: 0.3248 - val_accuracy: 0.9041\n"
     ]
    }
   ],
   "source": [
    "conv_history = conv_model.fit(X, y, epochs=30,  \n",
    "                    validation_split=0.08,\n",
    "                    callbacks=[conv_model_best_val, stop_training])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>val_loss</th>\n",
       "      <th>val_accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.210446</td>\n",
       "      <td>0.591563</td>\n",
       "      <td>0.534120</td>\n",
       "      <td>0.833987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.701846</td>\n",
       "      <td>0.783207</td>\n",
       "      <td>0.478764</td>\n",
       "      <td>0.856168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.629514</td>\n",
       "      <td>0.805493</td>\n",
       "      <td>0.457236</td>\n",
       "      <td>0.858727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.593787</td>\n",
       "      <td>0.819589</td>\n",
       "      <td>0.430626</td>\n",
       "      <td>0.866917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.566829</td>\n",
       "      <td>0.828046</td>\n",
       "      <td>0.393708</td>\n",
       "      <td>0.877325</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       loss  accuracy  val_loss  val_accuracy\n",
       "0  1.210446  0.591563  0.534120      0.833987\n",
       "1  0.701846  0.783207  0.478764      0.856168\n",
       "2  0.629514  0.805493  0.457236      0.858727\n",
       "3  0.593787  0.819589  0.430626      0.866917\n",
       "4  0.566829  0.828046  0.393708      0.877325"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conv_df = pd.DataFrame(conv_history.history)\n",
    "conv_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "conv_df.plot(y=['loss', 'val_loss'], legend=True, title='CNN || Loss Vs. Val Loss')\n",
    "conv_df.plot(y=['accuracy', 'val_accuracy'], legend=True, title='CNN || Accuracy Vs. Val Acc')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Convolutional model scored a loss of 0.37199723720550537 and accuracy of 0.8925937414169312\n"
     ]
    }
   ],
   "source": [
    "conv_loss, conv_accuracy = conv_model.evaluate(X_test, y_test, verbose=False)\n",
    "print(f'The Convolutional model scored a loss of {conv_loss} and accuracy of {conv_accuracy}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Get model predictions\n",
    "* Load the best weights for the MLP and CNN models that you saved during the training run.\n",
    "* Randomly select 5 images and corresponding labels from the test set and display the images with their labels.\n",
    "* Alongside the image and label, show each model’s predictive distribution as a bar chart, and the final model prediction given by the label with maximum probability."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best weights loaded for MLP model and Conv Model!\n"
     ]
    }
   ],
   "source": [
    "model.load_weights('model_best_val/checkpoint')\n",
    "conv_model.load_weights('conv_model_best_val/checkpoint')\n",
    "\n",
    "print('Best weights loaded for MLP model and Conv Model!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Loss of MLP is 1.1874489784240723 and the Accuracy of MLP is 0.6391748785972595\n",
      "The Loss of CNN is 0.37199723720550537 and the Accuracy of CNN is 0.8925937414169312\n"
     ]
    }
   ],
   "source": [
    "mlp_loss, mlp_acc = model.evaluate(X_test, y_test, verbose=False)\n",
    "convolution_loss, convolution_acc = conv_model.evaluate(X_test, y_test, verbose=False)\n",
    "\n",
    "print(f'The Loss of MLP is {mlp_loss} and the Accuracy of MLP is {mlp_acc}')\n",
    "print(f'The Loss of CNN is {convolution_loss} and the Accuracy of CNN is {convolution_acc}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rand_idxs = [random.randint(0, 26031) for _ in range(5)]\n",
    "\n",
    "imgs = [X_test[i] for i in rand_idxs]\n",
    "labels = [y_test[i] for i in rand_idxs]\n",
    "mlp_predictions = model.predict(np.array(imgs))\n",
    "conv_predictions = conv_model.predict(np.array(imgs))\n",
    "\n",
    "fig, axes = plt.subplots(nrows=1, ncols=5, figsize=(12, 12))\n",
    "\n",
    "for i in range(5):\n",
    "    axes[i].imshow(np.squeeze(imgs[i]), cmap='binary')\n",
    "    axes[i].grid(False)\n",
    "    axes[i].set_xticks([])\n",
    "    axes[i].set_yticks([])\n",
    "    display_str = f\"Original label : {np.argmax(labels[i])}\\nMLP Predicted : {np.argmax(mlp_predictions[i])}\\nCNN Predicted : {np.argmax(conv_predictions[i])}\"\n",
    "    axes[i].text(0.5, -0.4, display_str, size=13, ha=\"center\", \n",
    "         transform=axes[i].transAxes)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x1800 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The above distributions correspond to the 5 images shown earlier, the correct label is shown in red color\n"
     ]
    }
   ],
   "source": [
    "# Predictions distribution\n",
    "fig, axes = plt.subplots(nrows=5, ncols=2, figsize=(25, 25))\n",
    "\n",
    "fig.tight_layout()\n",
    "\n",
    "axes[0][0].set_title('MLP Output Probabilities', fontsize=35)\n",
    "axes[0][1].set_title('CNN Output Probabilities', fontsize=35)\n",
    "\n",
    "for i in range(5):\n",
    "        mlp_bars = axes[i, 0].bar(list(range(10)), mlp_predictions[i], edgecolor='black')\n",
    "        conv_bars = axes[i, 1].bar(list(range(10)), conv_predictions[i], edgecolor='black')\n",
    "        \n",
    "        mlp_bars[np.argmax(labels[i])].set_color('#bb3f3f')\n",
    "        conv_bars[np.argmax(labels[i])].set_color('#bb3f3f')\n",
    "        \n",
    "plt.show()\n",
    "\n",
    "print('The above distributions correspond to the 5 images shown earlier, the correct label is shown in red color')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## I loved the course!\n",
    "## Thanks in advance for reviewing my capstone project\n",
    "\n",
    "## Any kind of suggestions will be appreciated : )"
   ]
  }
 ],
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