Medina_NeuralNet.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Q91KqmCRu64D"
   },
   "source": [
    "# **Project - Artificial Neural Networks: Street View Housing Number Digit Recognition**\n",
    "\n",
    "--------------\n",
    "### **Context:** \n",
    "--------------\n",
    "\n",
    "One of the most interesting tasks in deep learning is to recognize objects in natural scenes. The ability to process visual information using machine learning algorithms can be very useful as demonstrated in various applications.\n",
    "\n",
    "The SVHN dataset contains over 600,000 labeled digits cropped from street level photos. It is one of the most popular image recognition datasets. It has been used in neural networks created by Google to improve map quality by automatically transcribing the address numbers from a patch of pixels. The transcribed number with a known street address helps pinpoint the location of the building it represents. \n",
    "\n",
    "----------------\n",
    "### **Objective:**\n",
    "----------------\n",
    "\n",
    "Build a feed forward neural network model that can identify the digits in the images. \n",
    "\n",
    "-------------\n",
    "### **Dataset**\n",
    "-------------\n",
    "Here, we will use a subset of the original data to save some computation time. The dataset is provided as a .h5 file. The basic preprocessing steps have been done."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8z2Z7-OAs8QG"
   },
   "source": [
    "## **Mount the drive**\n",
    "Let us start by mounting the drive and importing the necessary libraries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "03lDyQUuef7z",
    "outputId": "b7dd8670-513a-4d3e-e166-0003f90844ea"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mounted at /content/drive\n"
     ]
    }
   ],
   "source": [
    "from google.colab import drive\n",
    "drive.mount('/content/drive')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "C8U3DUa3eNsT"
   },
   "source": [
    "## **Importing libraries**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "id": "-dVzeuF3eQx1"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Dropout, Activation, BatchNormalization\n",
    "from tensorflow.keras.utils import to_categorical"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ucnevGLoyKf_"
   },
   "source": [
    "Let us check for the version of TensorFlow."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "W5as47YxyJVk",
    "outputId": "ed4f73fb-9d13-43e1-fb80-f5f610af3e00"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.6.0\n"
     ]
    }
   ],
   "source": [
    "print(tf.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8lsux2ZwyTTR"
   },
   "source": [
    "## **Load the dataset**\n",
    "- Let us now load the dataset that is available as a .h5 file.\n",
    "- Split the data into train and the test dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "id": "BApX9qgNsqV0",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import h5py\n",
    "\n",
    "# Open the file as read only\n",
    "# User can make changes in the path as required\n",
    "h5f = h5py.File('/content/drive/MyDrive/SVHN_single_grey1.h5', 'r')\n",
    "\n",
    "# Load the training and the test set\n",
    "X_train = h5f['X_train'][:]\n",
    "y_train = h5f['y_train'][:]\n",
    "X_test = h5f['X_test'][:]\n",
    "y_test = h5f['y_test'][:]\n",
    "\n",
    "\n",
    "# Close this file\n",
    "h5f.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hVe0CYpUgj7w"
   },
   "source": [
    "Let's check the number of images in the training and testing data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "y3lwKpOefkpA",
    "outputId": "ebb982c8-5648-46fc-b433-866bb2463ee8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(42000, 18000)"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_train), len(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "akTUOfLlgwoM"
   },
   "source": [
    "**Observations**\n",
    "- There are 42,000 images in the training data and 18,000 images in the testing data. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kxODV6HKykuc"
   },
   "source": [
    "## **Visualizing images**\n",
    "- Use X_train to visualize the first 10 images\n",
    "- Use Y_train to print the first 10 labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 95
    },
    "id": "Bvsc8ytHsqWD",
    "outputId": "72a2875a-eb9d-41ec-b7e4-34c60ab06ee6"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x72 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "label for each of the above image: [2 6 7 4 4 0 3 0 7 3]\n"
     ]
    }
   ],
   "source": [
    "# visualizing the first 10 images in the dataset and their labels\n",
    "plt.figure(figsize=(10, 1))\n",
    "\n",
    "for i in range(10):\n",
    "    plt.subplot(1, 10, i+1)\n",
    "    plt.imshow(X_train[i], cmap=\"gray\")\n",
    "    plt.axis('off')\n",
    "\n",
    "plt.show()\n",
    "print('label for each of the above image: %s' % (y_train[0:10]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kzoyeXHOy80N"
   },
   "source": [
    "## **Data preparation**\n",
    "\n",
    "- Print the first image in the train image and figure out the shape of the images\n",
    "- Reshape the train and the test dataset to flatten them. Figure out the required shape\n",
    "- Normalise the train and the test dataset by dividing by 255\n",
    "- Print the new shapes of the train and the test set\n",
    "- One-hot encode the target variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "NqndzQXng9rL",
    "outputId": "7f325d43-55eb-4cac-a9af-861d20c48cb4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape: (32, 32)\n",
      "\n",
      "First image:\n",
      " [[ 33.0704  30.2601  26.852  ...  71.4471  58.2204  42.9939]\n",
      " [ 25.2283  25.5533  29.9765 ... 113.0209 103.3639  84.2949]\n",
      " [ 26.2775  22.6137  40.4763 ... 113.3028 121.775  115.4228]\n",
      " ...\n",
      " [ 28.5502  36.212   45.0801 ...  24.1359  25.0927  26.0603]\n",
      " [ 38.4352  26.4733  23.2717 ...  28.1094  29.4683  30.0661]\n",
      " [ 50.2984  26.0773  24.0389 ...  49.6682  50.853   53.0377]]\n"
     ]
    }
   ],
   "source": [
    "# Shape of the images and the first image\n",
    "\n",
    "print(\"Shape:\", X_train[0].shape)\n",
    "print()\n",
    "print(\"First image:\\n\", X_train[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "id": "D9YPwf9ysqWU"
   },
   "outputs": [],
   "source": [
    "# Reshaping the dataset to flatten them. Remember that we are trying to reshape the 2D image data into a 1D array\n",
    "\n",
    "X_train = X_train.reshape(X_train.shape[0], 1024)\n",
    "X_test = X_test.reshape(X_test.shape[0], 1024)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "id": "q_yUUTp_mUzB"
   },
   "outputs": [],
   "source": [
    "# Normalize inputs from 0-255 to 0-1\n",
    "X_train = X_train/255\n",
    "X_test = X_test/255"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "t7FSqOpamWkH",
    "outputId": "ffd3c637-249f-44d5-e626-f16127160812"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set: (42000, 1024) (42000,)\n",
      "Test set: (18000, 1024) (18000,)\n"
     ]
    }
   ],
   "source": [
    "# New shape \n",
    "print('Training set:', X_train.shape, y_train.shape)\n",
    "print('Test set:', X_test.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "R3n24cw68Eb1",
    "outputId": "be367ca6-ea48-4569-ee94-239d9f529804"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 1., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 1., 0., 0.],\n",
       "       [0., 0., 1., ..., 0., 0., 0.],\n",
       "       ...,\n",
       "       [0., 0., 0., ..., 1., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 1.],\n",
       "       [0., 0., 1., ..., 0., 0., 0.]], dtype=float32)"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# one hot encode output\n",
    "y_train = to_categorical(y_train)\n",
    "y_test = to_categorical(y_test)\n",
    "\n",
    "# no.of classes\n",
    "y_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ViqPOTquCF76"
   },
   "source": [
    "**Observations**\n",
    "- Notice that each entry of y_test is a one-hot encoded vector instead of a single label."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yH-gVrzuByNA"
   },
   "source": [
    "## **Model Building**\n",
    "\n",
    "Now, we have done the data preprocessing, let's build an ANN model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "id": "d9ZjN17XGzIA"
   },
   "outputs": [],
   "source": [
    "#Fixing the seed for random number generators\n",
    "np.random.seed(42)\n",
    "import random\n",
    "random.seed(42)\n",
    "tf.random.set_seed(42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UJDUoaEj1d6e"
   },
   "source": [
    "### **Model Architecture**\n",
    "- Write a function that returns a sequential model with the following architecture\n",
    " - First hidden layer with **64 nodes and relu activation** and the input shape which is used above\n",
    " - Second hidden layer with **32 nodes and relu activation**\n",
    " - Output layer with **softmax activation and number of nodes equal to the number of classes**\n",
    " -Compile the model with the **categorical_crossentropy loss, adam optimizer (learning_rate = 0.001), and accuracy metric**. Do not fit the model here, just return the compiled model.\n",
    "- Call the function and store the model in a new variable \n",
    "- Print the summary of the model\n",
    "- Fit on the train data with a **validation split of 0.2, batch size = 128, verbose = 1, and 20 epochs**. Store the model building history to use later for visualization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "id": "Cmi81Gr5sqW-"
   },
   "outputs": [],
   "source": [
    "#Importing losses and optimizers modules\n",
    "from tensorflow.keras import losses\n",
    "from tensorflow.keras import optimizers\n",
    "\n",
    "#Define the function\n",
    "def nn_model_1():\n",
    "    model = Sequential() \n",
    "    #Add layers as per the architecture mentioned above in the same sequence\n",
    "    model.add(Dense(64, activation='relu', input_shape=(1024,)))\n",
    "    model.add(Dense(32, activation='relu'))\n",
    "    model.add(Dense(10, activation = 'softmax'))\n",
    "    \n",
    "    #declare adam optimizer with learning rate of 0.001 \n",
    "    adam = optimizers.Adam(learning_rate=0.001)\n",
    "    \n",
    "    #compile the model\n",
    "    model.compile(adam, loss='categorical_crossentropy',  metrics = ['accuracy'])\n",
    "    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "id": "bGCUI_xsImnH"
   },
   "outputs": [],
   "source": [
    "# Build the model\n",
    "model_1 = nn_model_1()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ckJsLdmdQadZ",
    "outputId": "63cc197a-aa70-416b-fa78-4a9e52e910fa"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_31\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_53 (Dense)             (None, 64)                65600     \n",
      "_________________________________________________________________\n",
      "dense_54 (Dense)             (None, 32)                2080      \n",
      "_________________________________________________________________\n",
      "dense_55 (Dense)             (None, 10)                330       \n",
      "=================================================================\n",
      "Total params: 68,010\n",
      "Trainable params: 68,010\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "#Print the summary\n",
    "model_1.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "3Fy3Dif_zcCk",
    "outputId": "0777d699-01f8-4d65-a0ae-1bd267218b98"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "263/263 [==============================] - 2s 5ms/step - loss: 2.3003 - accuracy: 0.1111 - val_loss: 2.2715 - val_accuracy: 0.1350\n",
      "Epoch 2/20\n",
      "263/263 [==============================] - 1s 4ms/step - loss: 2.1589 - accuracy: 0.2188 - val_loss: 1.9963 - val_accuracy: 0.2890\n",
      "Epoch 3/20\n",
      "263/263 [==============================] - 1s 4ms/step - loss: 1.9012 - accuracy: 0.3310 - val_loss: 1.7857 - val_accuracy: 0.3960\n",
      "Epoch 4/20\n",
      "263/263 [==============================] - 1s 4ms/step - loss: 1.7175 - accuracy: 0.4176 - val_loss: 1.6407 - val_accuracy: 0.4446\n",
      "Epoch 5/20\n",
      "263/263 [==============================] - 1s 4ms/step - loss: 1.5613 - accuracy: 0.4804 - val_loss: 1.5105 - val_accuracy: 0.4895\n",
      "Epoch 6/20\n",
      "263/263 [==============================] - 1s 4ms/step - loss: 1.4547 - accuracy: 0.5167 - val_loss: 1.4021 - val_accuracy: 0.5435\n",
      "Epoch 7/20\n",
      "263/263 [==============================] - 1s 4ms/step - loss: 1.3823 - accuracy: 0.5434 - val_loss: 1.3564 - val_accuracy: 0.5518\n",
      "Epoch 8/20\n",
      "263/263 [==============================] - 1s 4ms/step - loss: 1.3385 - accuracy: 0.5632 - val_loss: 1.3067 - val_accuracy: 0.5793\n",
      "Epoch 9/20\n",
      "263/263 [==============================] - 1s 4ms/step - loss: 1.3085 - accuracy: 0.5728 - val_loss: 1.2787 - val_accuracy: 0.5888\n",
      "Epoch 10/20\n",
      "263/263 [==============================] - 1s 4ms/step - loss: 1.2805 - accuracy: 0.5841 - val_loss: 1.2593 - val_accuracy: 0.5970\n",
      "Epoch 11/20\n",
      "263/263 [==============================] - 1s 4ms/step - loss: 1.2600 - accuracy: 0.5937 - val_loss: 1.2557 - val_accuracy: 0.5985\n",
      "Epoch 12/20\n",
      "263/263 [==============================] - 1s 4ms/step - loss: 1.2333 - accuracy: 0.6057 - val_loss: 1.2200 - val_accuracy: 0.6180\n",
      "Epoch 13/20\n",
      "263/263 [==============================] - 1s 4ms/step - loss: 1.2110 - accuracy: 0.6184 - val_loss: 1.1967 - val_accuracy: 0.6244\n",
      "Epoch 14/20\n",
      "263/263 [==============================] - 1s 4ms/step - loss: 1.1879 - accuracy: 0.6279 - val_loss: 1.1680 - val_accuracy: 0.6338\n",
      "Epoch 15/20\n",
      "263/263 [==============================] - 1s 4ms/step - loss: 1.1641 - accuracy: 0.6359 - val_loss: 1.2084 - val_accuracy: 0.6132\n",
      "Epoch 16/20\n",
      "263/263 [==============================] - 1s 4ms/step - loss: 1.1520 - accuracy: 0.6422 - val_loss: 1.1583 - val_accuracy: 0.6369\n",
      "Epoch 17/20\n",
      "263/263 [==============================] - 1s 4ms/step - loss: 1.1344 - accuracy: 0.6505 - val_loss: 1.1425 - val_accuracy: 0.6440\n",
      "Epoch 18/20\n",
      "263/263 [==============================] - 1s 4ms/step - loss: 1.1288 - accuracy: 0.6514 - val_loss: 1.1201 - val_accuracy: 0.6558\n",
      "Epoch 19/20\n",
      "263/263 [==============================] - 1s 4ms/step - loss: 1.1117 - accuracy: 0.6588 - val_loss: 1.1237 - val_accuracy: 0.6513\n",
      "Epoch 20/20\n",
      "263/263 [==============================] - 1s 4ms/step - loss: 1.1028 - accuracy: 0.6624 - val_loss: 1.1037 - val_accuracy: 0.6638\n"
     ]
    }
   ],
   "source": [
    "# Fit the model\n",
    "history_model_1 = model_1.fit(X_train, \n",
    "                    y_train,\n",
    "                    validation_split=0.2, \n",
    "                    epochs=20, \n",
    "                    batch_size=128,\n",
    "                    verbose=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tKOckG-KPyLg"
   },
   "source": [
    "### **Plotting the validation and training accuracies**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 497
    },
    "id": "lt77zgGMP4yw",
    "outputId": "0745727d-d5a7-4582-86da-4e742e1fe301"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting the accuracies\n",
    "\n",
    "dict_hist = history_model_1.history\n",
    "list_ep = [i for i in range(1,21)]\n",
    "\n",
    "plt.figure(figsize = (8,8))\n",
    "plt.plot(list_ep,dict_hist['accuracy'],ls = '--', label = 'accuracy')\n",
    "plt.plot(list_ep,dict_hist['val_accuracy'],ls = '--', label = 'val_accuracy')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.xlabel('Epochs')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pGBbQpLONX7k"
   },
   "source": [
    "**Observations:_______**\n",
    "\n",
    "Jose Medina Observations\n",
    "\n",
    "> - We obtain a training accuracy of `~66.2%` and validation accuracy of `~66.4%`\n",
    "> - We can see that the accuracy improves with each epoch, and though it starts to flatten out towards the end, appears to have further room to improve.\n",
    "> - We can probably improve upon these results, perhaps with hyperparameter tuning, such as by fine-tuning learning rates and experimenting with additional layers.\n",
    ".\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "z0qgLMBZm5-K"
   },
   "source": [
    "\n",
    "Let's build one more model with higher complexity and see if we can improve the performance of the model. \n",
    "\n",
    "First, we need to clear the previous model's history from the keras backend. Also, let's fix the seed again after clearing the backend."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "id": "y98BpLxsok_H"
   },
   "outputs": [],
   "source": [
    "#Clearing backend\n",
    "from tensorflow.keras import backend\n",
    "backend.clear_session()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "id": "HbKi93HTolGW"
   },
   "outputs": [],
   "source": [
    "#Fixing the seed for random number generators\n",
    "np.random.seed(42)\n",
    "import random\n",
    "random.seed(42)\n",
    "tf.random.set_seed(42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lT6o3TIKuCtk"
   },
   "source": [
    "### **Second Model Architecture**\n",
    "- Write a function that returns a sequential model with the following architecture\n",
    " - First hidden layer with **256 nodes and relu activation**\n",
    " - Second hidden layer with **128 nodes and relu activation**\n",
    " - Add the **Dropout layer with rate equal to 0.2**\n",
    " - Third hidden layer with **64 nodes and relu activation**\n",
    " - Fourth hidden layer with **64 nodes and relu activation**\n",
    " - Fifth hidden layer with **32 nodes and relu activation**\n",
    " - Add the **BatchNormalization layer**\n",
    " - Output layer with **softmax activation and number of nodes equal to the number of classes**\n",
    " -Compile the model with the **categorical_crossentropy loss, adam optimizer (learning_rate = 0.0005), and accuracy metric**. Do not fit the model here, just return the compiled model.\n",
    "- Call the function and store the model in a new variable \n",
    "- Print the summary of the model\n",
    "- Fit on the train data with a **validation split of 0.2, batch size = 128, verbose = 1, and 30 epochs**. Store the model building history to use later for visualization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "id": "EEPYLFIPnSDP"
   },
   "outputs": [],
   "source": [
    "#Importing losses and optimizers modules\n",
    "from tensorflow.keras import losses\n",
    "from tensorflow.keras import optimizers\n",
    "\n",
    "#Define the function\n",
    "def nn_model_2():\n",
    "    model = Sequential() \n",
    "    #Add layers as per the architecture mentioned above in the same sequence\n",
    "    model.add(Dense(256, activation='relu', input_shape=(1024,)))\n",
    "    model.add(Dense(128, activation='relu'))\n",
    "    model.add(Dropout(0.2))\n",
    "    model.add(Dense(64, activation='relu'))\n",
    "    model.add(Dense(64, activation='relu'))\n",
    "    model.add(Dense(32, activation='relu'))\n",
    "    model.add(BatchNormalization())\n",
    "    model.add(Dense(10, activation = 'softmax'))\n",
    "\n",
    "    #declare adam optimizer with learning rate of 0.001 \n",
    "    adam = optimizers.Adam(learning_rate=0.0005)\n",
    "    \n",
    "    #compile the model\n",
    "    model.compile(adam, loss='categorical_crossentropy',  metrics = ['accuracy'])\n",
    "    \n",
    "    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "id": "AKjDY4plnSFl"
   },
   "outputs": [],
   "source": [
    "# Build the model\n",
    "model_2 = nn_model_2()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "gz_6SVrU2swZ",
    "outputId": "c9799b86-2d0e-4fa1-ad0f-d2c3330a0c0b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense (Dense)                (None, 256)               262400    \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 128)               32896     \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 64)                8256      \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 64)                4160      \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 32)                2080      \n",
      "_________________________________________________________________\n",
      "batch_normalization (BatchNo (None, 32)                128       \n",
      "_________________________________________________________________\n",
      "dense_5 (Dense)              (None, 10)                330       \n",
      "=================================================================\n",
      "Total params: 310,250\n",
      "Trainable params: 310,186\n",
      "Non-trainable params: 64\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "#Print the model summary\n",
    "model_2.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "IQ0Sc4R-y-uu",
    "outputId": "0a90afa6-e8d8-4746-d347-3bef7486e589"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "263/263 [==============================] - 3s 10ms/step - loss: 2.3475 - accuracy: 0.1018 - val_loss: 2.2903 - val_accuracy: 0.1244\n",
      "Epoch 2/30\n",
      "263/263 [==============================] - 3s 10ms/step - loss: 2.1435 - accuracy: 0.1857 - val_loss: 2.0421 - val_accuracy: 0.2756\n",
      "Epoch 3/30\n",
      "263/263 [==============================] - 2s 9ms/step - loss: 1.7642 - accuracy: 0.3826 - val_loss: 1.5526 - val_accuracy: 0.4861\n",
      "Epoch 4/30\n",
      "263/263 [==============================] - 2s 9ms/step - loss: 1.4718 - accuracy: 0.5024 - val_loss: 1.4331 - val_accuracy: 0.5232\n",
      "Epoch 5/30\n",
      "263/263 [==============================] - 3s 10ms/step - loss: 1.3157 - accuracy: 0.5648 - val_loss: 1.3021 - val_accuracy: 0.5812\n",
      "Epoch 6/30\n",
      "263/263 [==============================] - 3s 10ms/step - loss: 1.2174 - accuracy: 0.6085 - val_loss: 1.1028 - val_accuracy: 0.6511\n",
      "Epoch 7/30\n",
      "263/263 [==============================] - 2s 9ms/step - loss: 1.1441 - accuracy: 0.6333 - val_loss: 1.0375 - val_accuracy: 0.6701\n",
      "Epoch 8/30\n",
      "263/263 [==============================] - 2s 9ms/step - loss: 1.0821 - accuracy: 0.6553 - val_loss: 1.0676 - val_accuracy: 0.6581\n",
      "Epoch 9/30\n",
      "263/263 [==============================] - 2s 9ms/step - loss: 1.0379 - accuracy: 0.6742 - val_loss: 0.9898 - val_accuracy: 0.6836\n",
      "Epoch 10/30\n",
      "263/263 [==============================] - 3s 10ms/step - loss: 1.0003 - accuracy: 0.6836 - val_loss: 1.0037 - val_accuracy: 0.6774\n",
      "Epoch 11/30\n",
      "263/263 [==============================] - 2s 9ms/step - loss: 0.9711 - accuracy: 0.6939 - val_loss: 0.9468 - val_accuracy: 0.7062\n",
      "Epoch 12/30\n",
      "263/263 [==============================] - 3s 10ms/step - loss: 0.9413 - accuracy: 0.7062 - val_loss: 0.9494 - val_accuracy: 0.7027\n",
      "Epoch 13/30\n",
      "263/263 [==============================] - 3s 10ms/step - loss: 0.9287 - accuracy: 0.7051 - val_loss: 0.9364 - val_accuracy: 0.7018\n",
      "Epoch 14/30\n",
      "263/263 [==============================] - 2s 9ms/step - loss: 0.9108 - accuracy: 0.7134 - val_loss: 0.9443 - val_accuracy: 0.6977\n",
      "Epoch 15/30\n",
      "263/263 [==============================] - 3s 10ms/step - loss: 0.8965 - accuracy: 0.7148 - val_loss: 0.8296 - val_accuracy: 0.7398\n",
      "Epoch 16/30\n",
      "263/263 [==============================] - 2s 9ms/step - loss: 0.8549 - accuracy: 0.7317 - val_loss: 0.8132 - val_accuracy: 0.7479\n",
      "Epoch 17/30\n",
      "263/263 [==============================] - 2s 9ms/step - loss: 0.8532 - accuracy: 0.7315 - val_loss: 0.8344 - val_accuracy: 0.7389\n",
      "Epoch 18/30\n",
      "263/263 [==============================] - 2s 9ms/step - loss: 0.8347 - accuracy: 0.7360 - val_loss: 0.8581 - val_accuracy: 0.7302\n",
      "Epoch 19/30\n",
      "263/263 [==============================] - 3s 10ms/step - loss: 0.8245 - accuracy: 0.7389 - val_loss: 0.8748 - val_accuracy: 0.7254\n",
      "Epoch 20/30\n",
      "263/263 [==============================] - 3s 10ms/step - loss: 0.8194 - accuracy: 0.7411 - val_loss: 0.8073 - val_accuracy: 0.7449\n",
      "Epoch 21/30\n",
      "263/263 [==============================] - 2s 9ms/step - loss: 0.8084 - accuracy: 0.7426 - val_loss: 0.7734 - val_accuracy: 0.7567\n",
      "Epoch 22/30\n",
      "263/263 [==============================] - 2s 9ms/step - loss: 0.8060 - accuracy: 0.7422 - val_loss: 0.7760 - val_accuracy: 0.7624\n",
      "Epoch 23/30\n",
      "263/263 [==============================] - 2s 9ms/step - loss: 0.7803 - accuracy: 0.7540 - val_loss: 0.7597 - val_accuracy: 0.7636\n",
      "Epoch 24/30\n",
      "263/263 [==============================] - 2s 9ms/step - loss: 0.7772 - accuracy: 0.7555 - val_loss: 0.7868 - val_accuracy: 0.7585\n",
      "Epoch 25/30\n",
      "263/263 [==============================] - 3s 10ms/step - loss: 0.7711 - accuracy: 0.7583 - val_loss: 0.7348 - val_accuracy: 0.7723\n",
      "Epoch 26/30\n",
      "263/263 [==============================] - 2s 9ms/step - loss: 0.7601 - accuracy: 0.7589 - val_loss: 0.7462 - val_accuracy: 0.7692\n",
      "Epoch 27/30\n",
      "263/263 [==============================] - 2s 9ms/step - loss: 0.7492 - accuracy: 0.7627 - val_loss: 0.7609 - val_accuracy: 0.7673\n",
      "Epoch 28/30\n",
      "263/263 [==============================] - 2s 9ms/step - loss: 0.7546 - accuracy: 0.7587 - val_loss: 0.7336 - val_accuracy: 0.7707\n",
      "Epoch 29/30\n",
      "263/263 [==============================] - 2s 9ms/step - loss: 0.7559 - accuracy: 0.7609 - val_loss: 0.7785 - val_accuracy: 0.7538\n",
      "Epoch 30/30\n",
      "263/263 [==============================] - 2s 9ms/step - loss: 0.7305 - accuracy: 0.7690 - val_loss: 0.7211 - val_accuracy: 0.7775\n"
     ]
    }
   ],
   "source": [
    "# Fit the model\n",
    "history_model_2 = model_2.fit(X_train, \n",
    "                    y_train,\n",
    "                    validation_split=0.2, \n",
    "                    epochs=30, \n",
    "                    batch_size=128,\n",
    "                    verbose=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qtE4DFxdGzIT"
   },
   "source": [
    "### **Plotting the validation and training accuracies**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 497
    },
    "id": "01ig6BrF1KVy",
    "outputId": "5457306f-8c14-4d66-f645-501ea88d0b4f"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting the accuracies\n",
    "\n",
    "dict_hist = history_model_2.history\n",
    "list_ep = [i for i in range(1,31)]\n",
    "\n",
    "plt.figure(figsize = (8,8))\n",
    "plt.plot(list_ep,dict_hist['accuracy'],ls = '--', label = 'accuracy')\n",
    "plt.plot(list_ep,dict_hist['val_accuracy'],ls = '--', label = 'val_accuracy')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.xlabel('Epochs')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VPW1LlD61RDn"
   },
   "source": [
    "**Observations:_______**\n",
    "\n",
    "Jose Medina Observations\n",
    "\n",
    "> - We can see that we have greatly improved the accuracy with the additional complexity of the model \n",
    "with training with validation set accuracy `~77.7%` and training accuracy `~76.9%`\n",
    "> - We see that validation set accuracy and training set accuracy roughly converage which indicates that the model is giving a generalized performance.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8kuXx9Bvu00f"
   },
   "source": [
    "## **Predictions on the test data**\n",
    "\n",
    "- Make predictions on the test set using the second model\n",
    "- Print the obtained results using the classification report and the confusion matrix\n",
    "- Final observations from the obtained results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "id": "xbWMEtTj5Ad0"
   },
   "outputs": [],
   "source": [
    "test_pred = model_2.predict(X_test)\n",
    "\n",
    "test_pred = np.argmax(test_pred, axis=-1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "i3li8Ib08yts"
   },
   "source": [
    "**Note:** Earlier, we noticed that each entry of the test data is a one-hot encoded vector but to print the classification report and confusion matrix, we must convert each entry of y_test to a single label."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "id": "NByu7uAQ8x9P"
   },
   "outputs": [],
   "source": [
    "#Converting each entry to single label from one-hot encoded vector\n",
    "y_test = np.argmax(y_test, axis=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 623
    },
    "id": "xRddeJ-3EHT1",
    "outputId": "bac5e6a1-9a67-453c-c4d6-dda008f82ef4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.87      0.76      0.81      1814\n",
      "           1       0.78      0.80      0.79      1828\n",
      "           2       0.77      0.80      0.78      1803\n",
      "           3       0.69      0.77      0.73      1719\n",
      "           4       0.76      0.85      0.81      1812\n",
      "           5       0.78      0.71      0.74      1768\n",
      "           6       0.84      0.71      0.77      1832\n",
      "           7       0.78      0.83      0.80      1808\n",
      "           8       0.71      0.75      0.73      1812\n",
      "           9       0.78      0.75      0.77      1804\n",
      "\n",
      "    accuracy                           0.77     18000\n",
      "   macro avg       0.78      0.77      0.77     18000\n",
      "weighted avg       0.78      0.77      0.77     18000\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#importing required functions\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "#Printing the classification report\n",
    "print(classification_report(y_test, test_pred))\n",
    "\n",
    "#Plotting the heatmap using confusion matrix\n",
    "cm = confusion_matrix(y_test, test_pred) #Write the code for creating confusion matrix using actual labels and predicted labels\n",
    "\n",
    "plt.figure(figsize=(8,5))\n",
    "sns.heatmap(cm, annot=True,  fmt='.0f')\n",
    "plt.ylabel('Actual')\n",
    "plt.xlabel('Predicted')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DjErl4GA2u9s"
   },
   "source": [
    "#### **Observations:__________**\n",
    "\n",
    "Jose Medina Observations\n",
    "\n",
    "> - The classification report reveals the precision, recall, and f1-score by  class. \n",
    "> - We observe that, generally, the model does a decent job - recall results for all classes are `>70%` with several `>=80%`.\n",
    "> - We observe that class 0 and class 4 have the highest f1-score of with `81%` each.\n",
    "> - We observe also observe that class 3 and class 8 have the lowest f1-score of with `73%` each.\n",
    "> - We also note that precision for lcass 3 is lowest with `69%`.\n",
    "> - We observe that weighted avg precision of `78%` and weighted avg recall of `77%` suggests the model is well balanced in weight false/true positives and false/true negatives.\n",
    "\n",
    "\n",
    "## Conclusion:\n",
    "\n",
    "In this exercise \n",
    "- we ingested the SVHN data. \n",
    "- flattened the input images (vectorizing them) effectively making them suitable for neural network training\n",
    "- we constructed a preliminary ANN with 3 layers\n",
    "- we then constructed a more complex ANN with 8 layers (which included a drop out layer and a batchnormalization layer)\n",
    "- After training the final model for 30 epochs, we achieved a weighted average F1-score of 77%.\n",
    "- **Possible improvements include, potentially, exploring over-sampling techniques and synthetic training data generation techniques for classes that porformed most poorly in precision/recall (for example: class 3, 5, 8) in order to raise the accuracy for those classes.** "
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