MNIST_in_Keras_for_WCA_Singapore.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/StevenLShafer/Neural-Networks-with-Keras/blob/main/MNIST_in_Keras_for_WCA_Singapore.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Neural Networks with Keras\n",
        "**Steven L. Shafer, MD**\n",
        "\n",
        "*Professor Emeritus, Stanford University*\n",
        "\n",
        "-----\n",
        "\n",
        "Welcome to the Deep Learning with Keras Workshop at the WFSA Congress.\n",
        "\n",
        "This demonstration will be run in **Python**, not in the R programming language.\n",
        "\n",
        "The reason is that the Google Colab Notebooks already have Python installed. That will allow us to use the 90 minutes to focus on the basic concepts of a neural network, rather than spending the entire workshop installing the necessary software on your laptops.\n",
        "\n",
        "The workshop will **not** attempt to teach the Python programming language.\n"
      ],
      "metadata": {
        "id": "kZ3OaQjW6hZx"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Basic terms\n",
        "\n",
        "**Neural Network** refers to simple cells of data that communicate with tens, hundreds, thousands, or even millions of other cells of data, analogous to how nerves communicate with tens, hundreds, thousands, or even millions of other nerves.\n",
        "\n",
        "**Tensors** are collections of these cells. The word **tensor** describes these collections. You already know several kinds of tensors:\n",
        "* **Scalar**: a single number, such as 7. This is a *tensor* of 0 dimensions.\n",
        "* **Vector**: a list of numbers, such as 1,2,3,4,5,6,7. This is a *tensor* of 1 dimension.\n",
        "* **Array**: a two dimensional list, such as an 10 row by 10 column Excel spreadsheet. This is a *tensor* of 2 dimensions.\n",
        "* **Cube**: A three dimensional list, for example ten Excel spreadsheets, each 10 rows and 10 columns. This is a *tensor* of 3 dimensions.\n",
        "* **Hypercube**: A bunch of cubes. This is a *tensor* of 4 dimensions.\n",
        "\n",
        "Our language doesn't have word for tensors of more than 4 dimensions. After 4 dimensions, we just call them *tensors*"
      ],
      "metadata": {
        "id": "Jwx8T_Kb6nxz"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Tensors in Neural Networks\n",
        "\n",
        "As mentioend above, **tensors** are collections of the individual cells that are used in neural networks. We will be building a neural image that recognizes an image. Each image is 28 pixels by 28 pixels. That is a 2 dimensional **tensor**.\n",
        "\n",
        "Our data set consists of 70,000 such images. Thus, our data set is a three dimensional **tensor**, with dimensions of 28 x 28 x 70,000. 60,000 images will be used for training, and 10,000 images will be used to test the model.\n",
        "\n"
      ],
      "metadata": {
        "id": "E8_NXVPs-nwm"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We will create new **tensors** that connect all of these cells. These **tensors** will build on each other, creating more and more connections.\n",
        "\n",
        "However, our final **tensor**, the result, will just be a vector of 10 digits: 0,1,2,3,5,6,7,8,9. That will be a tensor of 1 dimension."
      ],
      "metadata": {
        "id": "f_WwZm0P_sn9"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# TensorFlow\n",
        "You can think of a **neural network** as being a series of **tensors** that flow into each other. That is why Google named the software that manipulated tensors ***TensorFlow***.\n",
        "\n",
        "# Keras\n",
        "The language of ***TensorFlow*** is complex. A program called ***Keras*** is a \"front end\" for ***TensorFlow***. ***Keras*** allows building **neural networks** with a fairly small set of commands that work like Lego blocks, each building upon the prior command."
      ],
      "metadata": {
        "id": "AnqVc2uWAb_5"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uNAsyGCSRi3j"
      },
      "source": [
        "# Introduction to Deep Learning with Keras and TensorFlow\n",
        "\n",
        "**Daniel Moser (UT Southwestern Medical Center)**\n",
        "\n",
        "**Resources: [Xavier Snelgrove](https://github.com/wxs/keras-mnist-tutorial), [Yash Katariya](https://github.com/yashk2810/MNIST-Keras)**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wgJYXISaRi3l"
      },
      "source": [
        "## The Task for the AI\n",
        "\n",
        "Our goal is to construct and train an artificial neural network on thousands of images of handwritten digits so that it may successfully identify others when presented. The data that will be incorporated is the MNIST database which contains 60,000 images for training and 10,000 test images.\n",
        "\n",
        "MNIST stands for the *Modified National Institute of Standards and Technology*, who created this database to test image processing software. It is commonly used for introductions to machine learning and neural networks.\n",
        "\n",
        "We will use Keras with TensorFlow to build a neural network that recognizes the digits in the MNIST database."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v5By3Db8Ri3l"
      },
      "source": [
        "<img src=\"https://github.com/AviatorMoser/keras-mnist-tutorial/blob/master/mnist.png?raw=1\" >"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cSdbwdfbRi3l"
      },
      "source": [
        "## Load the necessary python modules into Google Colab.\n",
        "\n",
        "We will first load some software into the Python environment of Google Colab. You do not need to understand this code. However there are comments below if you are interested."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EKdm6iEoRi3l"
      },
      "outputs": [],
      "source": [
        "%matplotlib inline\n",
        "\n",
        "import numpy as np                   # advanced math library\n",
        "import matplotlib.pyplot as plt      # MATLAB like plotting routines\n",
        "import random                        # for generating random numbers\n",
        "#import np_utils                      # NumPy related tools\n",
        "\n",
        "from keras.datasets import mnist     # MNIST dataset is included in Keras\n",
        "from keras.models import Sequential  # Type of neural network in this example\n",
        "                                     # We will be creating sequential layers of tensors\n",
        "\n",
        "from keras.layers import Dense, Dropout, Activation # These are different kinds of layers\n",
        "                                                    # that we will use.\n",
        "from keras.utils import to_categorical # This will convert a class into something\n",
        "                                       # called a \"binary class\". I'll explain\n",
        "                                       # this below\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "00b65c_URi3m"
      },
      "source": [
        "## Loading Training Data\n",
        "\n",
        "The MNIST dataset is conveniently bundled within Keras, and we can easily analyze some of its features in Python."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qiYewV4pRi3m",
        "outputId": "5120a3bb-39e8-4e15-ba30-e67553bb5343",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
            "11490434/11490434 [==============================] - 1s 0us/step\n",
            "X_train shape (60000, 28, 28)\n",
            "y_train shape (60000,)\n",
            "X_test shape (10000, 28, 28)\n",
            "y_test shape (10000,)\n"
          ]
        }
      ],
      "source": [
        "# The MNIST data is split between 60,000 28 x 28 pixel training images and 10,000 28 x 28 pixel images\n",
        "(X_train, y_train), (X_test, y_test) = mnist.load_data()\n",
        "\n",
        "print(\"X_train shape\", X_train.shape)\n",
        "print(\"y_train shape\", y_train.shape)\n",
        "print(\"X_test shape\", X_test.shape)\n",
        "print(\"y_test shape\", y_test.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WIoSI09RRi3m"
      },
      "source": [
        "Using matplotlib, we can plot some sample images from the training set directly into this Jupyter Notebook."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ty-uZhctRi3n",
        "outputId": "d3dd0599-b53b-4699-f0e1-df5fea980700",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 907
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 900x900 with 9 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.rcParams['figure.figsize'] = (9,9) # Make the figures a bit bigger\n",
        "\n",
        "for i in range(9):\n",
        "    plt.subplot(3,3,i+1)\n",
        "    num = random.randint(0, len(X_train))\n",
        "    plt.imshow(X_train[num], cmap='gray', interpolation='none')\n",
        "    plt.title(\"Class {}\".format(y_train[num]))\n",
        "\n",
        "plt.tight_layout()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U2h7UP-ERi3n"
      },
      "source": [
        "Let's examine a single digit a little closer. Here is the array (a 28 x 28 **tensor**) representing the last digit."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MOUrS0uQRi3n",
        "outputId": "eb101982-74f8-4cc8-db53-65b69d5fcd6e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0 0 0 0   0   0   0   0   0   0   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   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   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   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   0   0   0   0   0   0   0   0   0   0   0   0   0  0 0 0 0 0 \n",
            "0 0 0 0   0 147 143  38   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0  0 0 0 0 0 \n",
            "0 0 0 0 101 249 252 209   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0  0 0 0 0 0 \n",
            "0 0 0 0  25 199 252 246 197 198 197 197 181  57  57  57  57  57  57  32   0   0   0  0 0 0 0 0 \n",
            "0 0 0 0   0  28  84  99 223 225 223 246 252 252 253 252 252 252 252 216 169  53   0  0 0 0 0 0 \n",
            "0 0 0 0   0   0   0   0   0   0   0  88 205 252 253 252 252 252 252 253 252 227 126  0 0 0 0 0 \n",
            "0 0 0 0   0   0   0   0   0   0   0   0   0   0   0  48  63 140 140 255 253 253 225  0 0 0 0 0 \n",
            "0 0 0 0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0  65 237 252 239 63 0 0 0 0 \n",
            "0 0 0 0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0 113 252 252 223  0 0 0 0 0 \n",
            "0 0 0 0   0   0   0   0   0   0   0   0   0   0   0   0   0   0  16 207 252 252 129  0 0 0 0 0 \n",
            "0 0 0 0   0   0   0   0   0   0   0   0   0   0   0   0   0  16 203 253 252 220  37  0 0 0 0 0 \n",
            "0 0 0 0   0   0   0   0   0   0   0   0   0   0   0  38 144 253 253 255 215  31   0  0 0 0 0 0 \n",
            "0 0 0 0   0   0   0   0   0   0   0   0   0   0 147 234 252 252 252 106  18   0   0  0 0 0 0 0 \n",
            "0 0 0 0   0   0   0   0   0   0   0   0  54 197 253 252 252 198  55   0   0   0   0  0 0 0 0 0 \n",
            "0 0 0 0   0   0   0   0   0   0   0  54 224 252 253 252 198  28   0   0   0   0   0  0 0 0 0 0 \n",
            "0 0 0 0   0   0   0   0   0   0  48 227 252 252 253 127  25   0   0   0   0   0   0  0 0 0 0 0 \n",
            "0 0 0 0   0   0   0   0   0  51 238 253 240 140  79   0   0   0   0   0   0   0   0  0 0 0 0 0 \n",
            "0 0 0 0   0   0   0  10  85 238 252 186  71   0   0   0   0   0   0   0   0   0   0  0 0 0 0 0 \n",
            "0 0 0 0   0   0   0  29 252 253 252 100  57  57 198 197 153   0   0   0   0   0   0  0 0 0 0 0 \n",
            "0 0 0 0   0   0   0  29 252 253 252 252 252 252 240  99  65   0   0   0   0   0   0  0 0 0 0 0 \n",
            "0 0 0 0   0   0   0  13 112 253 252 252 236 112  63   0   0   0   0   0   0   0   0  0 0 0 0 0 \n",
            "0 0 0 0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0  0 0 0 0 0 \n",
            "0 0 0 0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0  0 0 0 0 0 \n",
            "0 0 0 0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0  0 0 0 0 0 \n"
          ]
        }
      ],
      "source": [
        "# just a little function for pretty printing a matrix\n",
        "def matprint(mat, fmt=\"g\"):\n",
        "    col_maxes = [max([len((\"{:\"+fmt+\"}\").format(x)) for x in col]) for col in mat.T]\n",
        "    for x in mat:\n",
        "        for i, y in enumerate(x):\n",
        "            print((\"{:\"+str(col_maxes[i])+fmt+\"}\").format(y), end=\" \")\n",
        "        print(\"\")\n",
        "\n",
        "# now print!\n",
        "matprint(X_train[num])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kOsQ8PMjRi3n"
      },
      "source": [
        "Each pixel is an 8-bit integer from 0-255. 0 is full black, while 255 is full white.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jHB2w06aRi3n"
      },
      "source": [
        "## Formatting the input data layer\n",
        "\n",
        "Instead of a 28 x 28 matrix, we rearrange the cells to create a 784-length vector. 28 x 28 = 784, so this doesn't change the number of cells. It just arranges them in a vector, rather than in an array.\n",
        "\n",
        "You may think that this will create a mess. After all, in the array below the 1 in the upper left corner has another 1 to the right, and a 4 below it. However, as we build a neural network, we learn the relationships between all cells, regardless of position. If it is important that the 1 in the upper left corner somehow be connected to the cell to the right, and the cell below, the neural network will figure that out.  \n",
        "\n",
        "We will also normalize the inputs to be in the range [0-1] rather than [0-255]. That just requires dividing each cell by 255. As we build this neural network, all of the cells in all tensors will have values from 0 to 1. That improves processing efficiency."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RUHBnFb4Ri3n"
      },
      "source": [
        "<img src='https://github.com/AviatorMoser/keras-mnist-tutorial/blob/master/flatten.png?raw=1' >"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-jEYiNrIRi3o",
        "outputId": "a2c42c63-50c9-494d-d27c-986d0dee5b7c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training matrix shape (60000, 784)\n",
            "Testing matrix shape (10000, 784)\n"
          ]
        }
      ],
      "source": [
        "# We will first \"flatten\" the individual images, as pictured above\n",
        "X_train = X_train.reshape(60000, 784) # reshape a 60,000 x 28 x 28 tensor into a 60,000 x 784 tensor.\n",
        "X_test = X_test.reshape(10000, 784)   # reshape a 10,000 x 28 x 28 tensor into a 10,000 x 784 tensor.\n",
        "\n",
        "# We will then divide every cell by 255, converting them to numbers from 0 to 1\n",
        "X_train = X_train.astype('float32')/255   # change integers to 32-bit floating point numbers\n",
        "X_test = X_test.astype('float32')/255     # and divide by 255 to change to a 0 1 range\n",
        "\n",
        "print(\"Training matrix shape\", X_train.shape)\n",
        "print(\"Testing matrix shape\", X_test.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nK9IxR4aRi3o"
      },
      "source": [
        "## Define the classifiers\n",
        "We have pictures of the digits from 0 to 9. We know (from a human) which digit appears in each picture. That is the \"class\" of the picture (i.e., the \"class\" is a number from 0 to 9).\n",
        "\n",
        "Our neural network will identify relationships among pixels that best map the pixels into the correct class. We first need to define the classes in a way that permits uncertainty in how well each picture conforms to each classe. That requires defining our classes like this.\n",
        "\n",
        "\n",
        "- 0 -> [__1__, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
        "- 1 -> [0, __1__, 0, 0, 0, 0, 0, 0, 0, 0]\n",
        "- 2 -> [0, 0, __1__, 0, 0, 0, 0, 0, 0, 0]\n",
        "- 3 -> [0, 0, 0, __1__, 0, 0, 0, 0, 0, 0]\n",
        "- 4 -> [0, 0, 0, 0, __1__, 0, 0, 0, 0, 0]\n",
        "- 5 -> [0, 0, 0, 0, 0, __1__, 0, 0, 0, 0]\n",
        "- 6 -> [0, 0, 0, 0, 0, 0, __1__, 0, 0, 0]\n",
        "- 7 -> [0, 0, 0, 0, 0, 0, 0, __1__, 0, 0]\n",
        "- 8 -> [0, 0, 0, 0, 0, 0, 0, 0, __1__, 0]\n",
        "- 9 -> [0, 0, 0, 0, 0, 0, 0, 0, 0, __1__]\n",
        "___\n",
        "\n",
        "If the final output of our network is close to one of these classes, then it is most likely that class. For example, if the final output is:\n",
        "\n",
        "\n",
        "- [0, __0.94__, 0, 0, 0, 0, __0.06__, 0, 0]\n",
        "\n",
        "then this image is probably the digit __1__.\n",
        "\n",
        "___\n",
        "\n",
        "If the output is this:\n",
        "\n",
        "- [0, __0.1__, 0, 0, 0, 0, 0, 0, __0.9__]\n",
        "\n",
        "Then the image is probably the digit __9__.\n",
        "\n",
        "___\n",
        "\n",
        "If the output is this:\n",
        "\n",
        "- [0, __0.60__, __0.40__, 0, 0, 0, 0, 0, 0]\n",
        "\n",
        "Then the image is probably a __1__ (60% chance), but it could be a __2__ (40% chance).\n",
        "\n",
        "___\n",
        "\n",
        "If the output of the network is this:\n",
        "\n",
        "- [0, __0.5__, 0, __0.5__, 0, 0, 0, 0, 0]\n",
        "\n",
        "There is an equal chance that the image is a __1__ or a __3__. The image is ambiguous to the neural network, it could be either.\n",
        "\n",
        "___\n",
        "\n",
        "How about this?\n",
        "\n",
        "- [__0.1__, __0.1__, __0.1__, __0.1__, __0.1__, __0.1__, __0.01__, __0.1__, __0.1__]\n",
        "\n",
        "Each classification is equally probable, so the network has  __no idea__ what digit appears in the image.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "id": "7jrp-03fRi3o"
      },
      "outputs": [],
      "source": [
        "classes = 10 # number of unique digits\n",
        "\n",
        "Y_train = to_categorical(y_train, classes) # This converts the traning classifiers\n",
        "Y_test = to_categorical(y_test, classes)   # This converts the test classifiers"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Let's look at our first y_test and Y_Test"
      ],
      "metadata": {
        "id": "NQeWsu5SGNCv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"This is the first y_test\", y_test[1])\n",
        "print(\"This is the first Y_test\", Y_test[1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "39rD7JkrGhck",
        "outputId": "3170819f-1d45-4752-fe2c-6a8ce05a00a7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "This is the first y_test 2\n",
            "This is the first Y_test [0. 0. 1. 0. 0. 0. 0. 0. 0. 0.]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "y_test[1] simply said the first image was a 2, a class assigned by a human reader.\n",
        "\n",
        "Y_test[1] (with a capital Y) said it there was no chance the first image was a 0, 1, 3, 4, 5, 6, 7, 8, 9, and 100% chance it was a 2.\n",
        "\n",
        "Training and applying a neural network requres that there be a probability of any classification, which is why we created Y_test from y_test.\n"
      ],
      "metadata": {
        "id": "kmziSAhNIT9E"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZrAZyF3HRi3o"
      },
      "source": [
        "# Building a 3-layer fully connected network (FCN)\n",
        "\n",
        "<img src= \"https://github.com/StevenLShafer/Neural-Networks-with-Keras/blob/main/StackedNN.png?raw=1\">\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "id": "jlLwQACfRi3o"
      },
      "outputs": [],
      "source": [
        "# The Sequential model is a linear stack of layers and is very common.\n",
        "\n",
        "model = Sequential() # This is the Keras command to start creating a sequential model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pgetdp7pRi3o"
      },
      "source": [
        "## The first hidden layer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "id": "DqKPIo81Ri3o"
      },
      "outputs": [],
      "source": [
        "# The first hidden layer is a set of 512 nodes (artificial neurons).\n",
        "# Each node will receive an element from each input vector and apply some weight and bias to it.\n",
        "\n",
        "model.add(Dense(512, input_shape=(784,))) #(784,) is not a typo -- that represents a 784 length vector!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "id": "xLxMLEnsRi3o"
      },
      "outputs": [],
      "source": [
        "# An \"activation\" is a non-linear function applied to the output of the layer above.\n",
        "# It checks the new value of the node, and decides whether that \"neuron\" has fired.\n",
        "# The Rectified Linear Unit (ReLU) converts all negative inputs to nodes in the next layer to be zero.\n",
        "# Those inputs are then not considered to be fired.\n",
        "# Positive values of a node are unchanged.\n",
        "\n",
        "model.add(Activation('relu'))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2n7PSmXWRi3p"
      },
      "source": [
        "$$f(x) = max (0,x)$$\n",
        "<img src = \"https://github.com/AviatorMoser/keras-mnist-tutorial/blob/master/relu.jpg?raw=1\">"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "id": "zIipzVTKRi3p"
      },
      "outputs": [],
      "source": [
        "# Dropout zeroes a selection of random outputs (i.e., disables their activation)\n",
        "# Dropout helps protect the model from memorizing or \"overfitting\" the training data.\n",
        "model.add(Dropout(0.2))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GUDYOPmTRi3p"
      },
      "source": [
        "## Adding the second hidden layer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "id": "puGTfKHwRi3p"
      },
      "outputs": [],
      "source": [
        "# The second hidden layer appears identical to our first layer.\n",
        "# However, instead of each of the 512-node receiving 784-inputs from the input image data,\n",
        "# they receive 512 inputs from the output of the first 512-node layer.\n",
        "\n",
        "model.add(Dense(512))\n",
        "model.add(Activation('relu'))\n",
        "model.add(Dropout(0.2))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zFD0AMZKRi3p"
      },
      "source": [
        "## The Final Output Layer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "id": "cv_jsd7VRi3p"
      },
      "outputs": [],
      "source": [
        "# The final layer of 10 neurons in fully-connected to the previous 512-node layer.\n",
        "# The final layer of a FCN should be equal to the number of desired classes (10 in this case).\n",
        "model.add(Dense(10))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "id": "3m_b8-5gRi3p"
      },
      "outputs": [],
      "source": [
        "# The \"softmax\" activation represents a probability distribution over K different possible outcomes.\n",
        "# Its values are all non-negative and sum to 1.\n",
        "\n",
        "model.add(Activation('softmax'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "c0J66zIGRi3p",
        "outputId": "ec4518b1-52ff-4db4-f392-ab246bc8b797",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense (Dense)               (None, 512)               401920    \n",
            "                                                                 \n",
            " activation (Activation)     (None, 512)               0         \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 512)               0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 512)               262656    \n",
            "                                                                 \n",
            " activation_1 (Activation)   (None, 512)               0         \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 512)               0         \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 10)                5130      \n",
            "                                                                 \n",
            " activation_2 (Activation)   (None, 10)                0         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 669706 (2.55 MB)\n",
            "Trainable params: 669706 (2.55 MB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "# Summarize the built model\n",
        "\n",
        "model.summary()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The first dense layer represents a mapping of **each** of the 784 cells (a single image) + 1 additional cell (an annotation) into an array of 512 elements. This dense layer has 512 cells. However, each of the 785 cells in the image has a unique parameter that maps it into the 512 cell dense layer. The neural network has to thus figure out 401920 parameters, which is the coefficient to go from the initial image to this dense layer.\n",
        "\n",
        "The layer marked dense 1 is the second layer. The 512 cells in the first layer, plus an annotation layer, are mapped to 512 cells in the second layer. To create this map, the system will estimate an additional 262656 parameters (512 * 513).\n",
        "\n",
        "The last layer is output layer. Each of the 512 cells in the dense 1 laywer, plus an annotation cell, is mapped into just ten cells, which are the probabilities of digits 0, 1, 2, 3, 4, 5, 6, 7, 8, and 9, respectively. The system will estimate 5130 parameters to create this map.\n",
        "\n",
        "The net result is that the system will estimate 669706 parameters. These parameters provide a path from the original MNIST image to the probabilities of each digit.\n",
        "\n",
        "The second dense layer"
      ],
      "metadata": {
        "id": "JXzE8b7OexgZ"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bzdy8fG9Ri3p"
      },
      "source": [
        "## Compiling the model\n",
        "When compiing a model you need to specify a **loss function** and an **optimizer**. The loss function we'll use here is called *categorical cross-entropy*. This loss function is well-suited to comparing two probability distributions.\n",
        "\n",
        "Our predictions areb based on the predictions of the probability of each digits (e.g. \"we're 80% confident this image is a 3, 10% sure it's an 8, 5% it's a 2, etc.\").\n",
        "\n",
        "The cross-entropy function is a measure of how different your predicted distribution is from the target distribution. [More detail at Wikipedia](https://en.wikipedia.org/wiki/Cross_entropy)\n",
        "\n",
        "All forms of iterative estimation involve minimizing the error between measured and predicted values. In meachine learning this is called the \"optimizer.\" The optimizer helps determine how quickly the model learns through **gradient descent**. The rate at which descends a gradient is called the **learning rate**.\n",
        "\n",
        "In 2014 a new method was proposed called **Adaptive Moment Estimation**, usually just called **ADAM**. This has become particularly popular for estimating the parameters in neural networks, and will be used in this example."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XIkoJ7M8Ri3p"
      },
      "source": [
        "<img src = \"https://github.com/AviatorMoser/keras-mnist-tutorial/blob/master/gradient_descent.png?raw=1\" >"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CtHecAsQRi3q"
      },
      "source": [
        "<img src = \"https://github.com/AviatorMoser/keras-mnist-tutorial/blob/master/learning_rate.png?raw=1\" >"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pFnpY0JIRi3q"
      },
      "source": [
        "So are smaller learning rates better? Not quite! It's important for an optimizer not to get stuck in local minima while neglecting the global minimum of the loss function. Sometimes that means trying a larger learning rate to jump out of a local minimum."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7-mPbyKHRi3q"
      },
      "source": [
        "<img src = 'https://github.com/AviatorMoser/keras-mnist-tutorial/blob/master/complicated_loss_function.png?raw=1' >"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "id": "tG2YLuBVRi3q"
      },
      "outputs": [],
      "source": [
        "# Let's use the Adam optimizer for learning\n",
        "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xUi0-gNxRi3q"
      },
      "source": [
        "## Train the model!\n",
        "This is the fun part!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vgHwldrsRi3t"
      },
      "source": [
        "The batch size determines over how much data per step is used to compute the loss function, gradients, and back propagation. Large batch sizes allow the network to complete it's training faster; however, there are other factors beyond training speed to consider.\n",
        "\n",
        "Too large of a batch size smoothes the local minima of the loss function, causing the optimizer to settle in one because it thinks it found the global minimum.\n",
        "\n",
        "Too small of a batch size creates a very noisy loss function, and the optimizer may never find the global minimum.\n",
        "\n",
        "So a good batch size may take some trial and error to find!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-FyGcTVwRi3t",
        "outputId": "8e1ad034-a8d8-4bf4-c8c4-12da8929a40b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "469/469 [==============================] - 5s 4ms/step - loss: 0.2503 - accuracy: 0.9242\n",
            "Epoch 2/5\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0980 - accuracy: 0.9688\n",
            "Epoch 3/5\n",
            "469/469 [==============================] - 3s 6ms/step - loss: 0.0710 - accuracy: 0.9773\n",
            "Epoch 4/5\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0551 - accuracy: 0.9827\n",
            "Epoch 5/5\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0448 - accuracy: 0.9856\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x7ae03c923580>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "model.fit(\n",
        "    X_train, # The images\n",
        "    Y_train, # The classifiers (0 through 9, each as the probablities as shown above)\n",
        "    batch_size=128,\n",
        "    epochs=5,\n",
        "    verbose=1\n",
        "    )"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1kMI4NHeRi3t"
      },
      "source": [
        "The two numbers, in order, represent the value of the loss function of the network on the training set, and the overall accuracy of the network on the training data. But how does it do on data it did not train on?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b0ca-d2RRi3t"
      },
      "source": [
        "## Evaluate Model's Accuracy on Test Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iFpS_qGfRi3u",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5e2981ca-387b-43d7-80b1-96c2d1626862"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 1s 2ms/step - loss: 0.0648 - accuracy: 0.9817\n",
            "Test score: 0.06478189677000046\n",
            "Test accuracy: 0.9817000031471252\n"
          ]
        }
      ],
      "source": [
        "score = model.evaluate(X_test, Y_test)\n",
        "print('Test score:', score[0])\n",
        "print('Test accuracy:', score[1])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2f58jd0_Ri3u"
      },
      "source": [
        "### Inspecting the output\n",
        "\n",
        "It's always a good idea to inspect the output and make sure everything looks sane. Here we'll look at some examples it gets right, and some examples it gets wrong."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "id": "R2VKli14Ri3u",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f870ba4b-fa21-42fe-a0bf-4579c679c932"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 1s 2ms/step\n"
          ]
        }
      ],
      "source": [
        "# The predict_classes function outputs the highest probability class\n",
        "# according to the trained classifier for each input example.\n",
        "predicted_classes = np.argmax(model.predict(X_test), axis = 1) #,axis=1)model.predict_classes(X_test)\n",
        "\n",
        "# Check which items we got right / wrong\n",
        "correct_indices = np.nonzero(predicted_classes == y_test)[0]\n",
        "\n",
        "incorrect_indices = np.nonzero(predicted_classes != y_test)[0]\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Hj18bnm1Ri3u",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 907
        },
        "outputId": "db73e0bb-00ea-4c75-cdf8-9ee59ddd3d39"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 900x900 with 9 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure()\n",
        "for i, correct in enumerate(correct_indices[:9]):\n",
        "    plt.subplot(3,3,i+1)\n",
        "    plt.imshow(X_test[correct].reshape(28,28), cmap='gray', interpolation='none')\n",
        "    plt.title(\"Predicted {}, Class {}\".format(predicted_classes[correct], y_test[correct]))\n",
        "\n",
        "plt.tight_layout()\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure()\n",
        "for i, incorrect in enumerate(incorrect_indices[:9]):\n",
        "    plt.subplot(3,3,i+1)\n",
        "    plt.imshow(X_test[incorrect].reshape(28,28), cmap='gray', interpolation='none')\n",
        "    plt.title(\"Predicted {}, Class {}\".format(predicted_classes[incorrect], y_test[incorrect]))\n",
        "\n",
        "plt.tight_layout()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 907
        },
        "id": "qdvBV3yFn7-c",
        "outputId": "3b4578d0-4ef7-4b77-968f-714f804fe590"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 900x900 with 9 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from keras import backend as K\n",
        "get_layer_output = K.function([model.layers[0].input],\n",
        "                                  [model.layers[5].output])\n",
        "nb = 234\n",
        "img = [X_test[nb,].reshape(1,784)]\n",
        "layer_output = get_layer_output(img)[0]\n",
        "plt.rcParams['figure.figsize'] = (10,10)\n",
        "plt.subplot(1,3,1)\n",
        "plt.imshow(X_test[nb,].reshape(28,28), cmap='gray', interpolation='none')\n",
        "plt.title(\"Image\")\n",
        "plt.subplot(1,3,3)\n",
        "plt.imshow(layer_output.reshape(32,16), cmap='gray', interpolation='none')\n",
        "plt.title(\"Last hidden layer representation\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 556
        },
        "id": "Fj5xHszADYj4",
        "outputId": "3e5c33c9-86f2-434d-ab9d-ef0269d268ef"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Last hidden layer representation')"
            ]
          },
          "metadata": {},
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U98gXYGPRi3u"
      },
      "source": [
        "# Introducing Convolution! What is it?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cs0tH9PxRi3u"
      },
      "source": [
        "Before, we built a network that accepts the normalized pixel values of each value and operates soley on those values. What if we could instead feed different features (e.g. **curvature, edges**) of each image into a network, and have the network learn which features are important for classifying an image?\n",
        "\n",
        "This possible through convolution! Convolution applies **kernels** (filters) that traverse through each image and generate **feature maps**."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dV3FPLZjRi3u"
      },
      "source": [
        "<img src = 'https://github.com/AviatorMoser/keras-mnist-tutorial/blob/master/convolution.gif?raw=1' >"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nL2AUbuhRi3u"
      },
      "source": [
        "In the above example, the image is a 5 x 5 matrix and the kernel going over it is a 3 x 3 matrix. A dot product operation takes place between the image and the kernel and the convolved feature is generated. Each kernel in a CNN learns a different characteristic of an image.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k4Z_OpL4Ri3v"
      },
      "source": [
        "Kernels in deep learning networks are used in similar ways, i.e. highlighting some feature. Combined with a system called **max pooling**, the non-highlighted elements are discarded from each feature map, leaving only the features of interest, reducing the number of learned parameters, and decreasing the computational cost (e.g. system memory)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FHrmIDDqRi3v"
      },
      "source": [
        "<img src = 'https://github.com/AviatorMoser/keras-mnist-tutorial/blob/master/max_pooling.png?raw=1' >"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lHE9A0mURi3v"
      },
      "source": [
        "We can also take convolutions of convolutions -- we can stack as many convolutions as we want, as long as there are enough pixels to fit a kernel.\n",
        "\n",
        "*Warning: What you may find down there in those deep convolutions may not appear recognizable to you.*"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iSlyYrfpRi3v"
      },
      "source": [
        "## Building a \"Deep\" Convolutional Neural Network"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "id": "BkW1bLjCRi3v"
      },
      "outputs": [],
      "source": [
        "# import some additional tools\n",
        "\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "from keras.layers import Conv2D, MaxPooling2D, ZeroPadding2D, GlobalAveragePooling2D, Flatten\n",
        "from keras.layers import BatchNormalization"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "id": "AwbIeEcNRi3v"
      },
      "outputs": [],
      "source": [
        "# Reload the MNIST data\n",
        "(X_train, y_train), (X_test, y_test) = mnist.load_data()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LCIMKLxsRi3v",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "59592ee0-6fc1-42a2-f7c6-c24455758630"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training matrix shape (60000, 28, 28, 1)\n",
            "Testing matrix shape (10000, 28, 28, 1)\n"
          ]
        }
      ],
      "source": [
        "# Again, do some formatting\n",
        "# Except we do not flatten each image into a 784-length vector because we want to perform convolutions first\n",
        "\n",
        "X_train = X_train.reshape(60000, 28, 28, 1) #add an additional dimension to represent the single-channel\n",
        "X_test = X_test.reshape(10000, 28, 28, 1)\n",
        "\n",
        "X_train = X_train.astype('float32')         # change integers to 32-bit floating point numbers\n",
        "X_test = X_test.astype('float32')\n",
        "\n",
        "X_train /= 255                              # normalize each value for each pixel for the entire vector for each input\n",
        "X_test /= 255\n",
        "\n",
        "print(\"Training matrix shape\", X_train.shape)\n",
        "print(\"Testing matrix shape\", X_test.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "id": "FGJwVFLMRi3w"
      },
      "outputs": [],
      "source": [
        "# convert to 10 categorical classes (as before)\n",
        "\n",
        "nb_classes = 10 # number of unique digits\n",
        "\n",
        "Y_train = to_categorical(y_train, nb_classes)\n",
        "Y_test = to_categorical(y_test, nb_classes)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "id": "WBXjsRAoRi3w"
      },
      "outputs": [],
      "source": [
        "model = Sequential()                                 # Linear stacking of layers\n",
        "\n",
        "# Convolution Layer 1\n",
        "model.add(Conv2D(32, (3, 3), input_shape=(28,28,1))) # 32 different 3x3 kernels -- so 32 feature maps\n",
        "model.add(BatchNormalization(axis=-1))               # normalize each feature map before activation\n",
        "convLayer01 = Activation('relu')                     # activation\n",
        "model.add(convLayer01)\n",
        "\n",
        "# Convolution Layer 2\n",
        "model.add(Conv2D(32, (3, 3)))                        # 32 different 3x3 kernels -- so 32 feature maps\n",
        "model.add(BatchNormalization(axis=-1))               # normalize each feature map before activation\n",
        "model.add(Activation('relu'))                        # activation\n",
        "convLayer02 = MaxPooling2D(pool_size=(2,2))          # Pool the max values over a 2x2 kernel\n",
        "model.add(convLayer02)\n",
        "\n",
        "# Convolution Layer 3\n",
        "model.add(Conv2D(64,(3, 3)))                         # 64 different 3x3 kernels -- so 64 feature maps\n",
        "model.add(BatchNormalization(axis=-1))               # normalize each feature map before activation\n",
        "convLayer03 = Activation('relu')                     # activation\n",
        "model.add(convLayer03)\n",
        "\n",
        "# Convolution Layer 4\n",
        "model.add(Conv2D(64, (3, 3)))                        # 64 different 3x3 kernels -- so 64 feature maps\n",
        "model.add(BatchNormalization(axis=-1))               # normalize each feature map before activation\n",
        "model.add(Activation('relu'))                        # activation\n",
        "convLayer04 = MaxPooling2D(pool_size=(2,2))          # Pool the max values over a 2x2 kernel\n",
        "model.add(convLayer04)\n",
        "model.add(Flatten())                                 # Flatten final 4x4x64 output matrix into a 1024-length vector\n",
        "\n",
        "# Fully Connected Layer 5\n",
        "model.add(Dense(512))                                # 512 FCN nodes\n",
        "model.add(BatchNormalization())                      # normalization\n",
        "model.add(Activation('relu'))                        # activation\n",
        "\n",
        "# Fully Connected Layer 6\n",
        "model.add(Dropout(0.2))                              # 20% dropout of randomly selected nodes\n",
        "model.add(Dense(10))                                 # final 10 FCN nodes\n",
        "model.add(Activation('softmax'))                     # softmax activation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rOu-oEGfRi3w",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1c97dd73-642e-4293-9e6e-aa8fe01925c3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d (Conv2D)             (None, 26, 26, 32)        320       \n",
            "                                                                 \n",
            " batch_normalization (Batch  (None, 26, 26, 32)        128       \n",
            " Normalization)                                                  \n",
            "                                                                 \n",
            " activation_3 (Activation)   (None, 26, 26, 32)        0         \n",
            "                                                                 \n",
            " conv2d_1 (Conv2D)           (None, 24, 24, 32)        9248      \n",
            "                                                                 \n",
            " batch_normalization_1 (Bat  (None, 24, 24, 32)        128       \n",
            " chNormalization)                                                \n",
            "                                                                 \n",
            " activation_4 (Activation)   (None, 24, 24, 32)        0         \n",
            "                                                                 \n",
            " max_pooling2d (MaxPooling2  (None, 12, 12, 32)        0         \n",
            " D)                                                              \n",
            "                                                                 \n",
            " conv2d_2 (Conv2D)           (None, 10, 10, 64)        18496     \n",
            "                                                                 \n",
            " batch_normalization_2 (Bat  (None, 10, 10, 64)        256       \n",
            " chNormalization)                                                \n",
            "                                                                 \n",
            " activation_5 (Activation)   (None, 10, 10, 64)        0         \n",
            "                                                                 \n",
            " conv2d_3 (Conv2D)           (None, 8, 8, 64)          36928     \n",
            "                                                                 \n",
            " batch_normalization_3 (Bat  (None, 8, 8, 64)          256       \n",
            " chNormalization)                                                \n",
            "                                                                 \n",
            " activation_6 (Activation)   (None, 8, 8, 64)          0         \n",
            "                                                                 \n",
            " max_pooling2d_1 (MaxPoolin  (None, 4, 4, 64)          0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 1024)              0         \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 512)               524800    \n",
            "                                                                 \n",
            " batch_normalization_4 (Bat  (None, 512)               2048      \n",
            " chNormalization)                                                \n",
            "                                                                 \n",
            " activation_7 (Activation)   (None, 512)               0         \n",
            "                                                                 \n",
            " dropout_2 (Dropout)         (None, 512)               0         \n",
            "                                                                 \n",
            " dense_4 (Dense)             (None, 10)                5130      \n",
            "                                                                 \n",
            " activation_8 (Activation)   (None, 10)                0         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 597738 (2.28 MB)\n",
            "Trainable params: 596330 (2.27 MB)\n",
            "Non-trainable params: 1408 (5.50 KB)\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "id": "rzBfpQcoRi3w"
      },
      "outputs": [],
      "source": [
        "# we'll use the same optimizer\n",
        "\n",
        "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "id": "PlGeTaCXRi3w"
      },
      "outputs": [],
      "source": [
        "# data augmentation prevents overfitting by slightly changing the data randomly\n",
        "# Keras has a great built-in feature to do automatic augmentation\n",
        "\n",
        "gen = ImageDataGenerator(rotation_range=8, width_shift_range=0.08, shear_range=0.3,\n",
        "                         height_shift_range=0.08, zoom_range=0.08)\n",
        "\n",
        "test_gen = ImageDataGenerator()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "id": "IuaGOrzzRi3w"
      },
      "outputs": [],
      "source": [
        "# We can then feed our augmented data in batches\n",
        "# Besides loss function considerations as before, this method actually results in significant memory savings\n",
        "# because we are actually LOADING the data into the network in batches before processing each batch\n",
        "\n",
        "# Before the data was all loaded into memory, but then processed in batches.\n",
        "\n",
        "train_generator = gen.flow(X_train, Y_train, batch_size=128)\n",
        "test_generator = test_gen.flow(X_test, Y_test, batch_size=128)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "e4AuNUTURi3w",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1c610af3-aa49-49e6-dc76-f2522d09c587"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "468/468 [==============================] - 22s 48ms/step - loss: 0.0213 - accuracy: 0.9930 - val_loss: 0.0306 - val_accuracy: 0.9905\n",
            "Epoch 2/5\n",
            "468/468 [==============================] - 22s 48ms/step - loss: 0.0206 - accuracy: 0.9934 - val_loss: 0.0183 - val_accuracy: 0.9945\n",
            "Epoch 3/5\n",
            "468/468 [==============================] - 22s 48ms/step - loss: 0.0201 - accuracy: 0.9938 - val_loss: 0.0185 - val_accuracy: 0.9950\n",
            "Epoch 4/5\n",
            "468/468 [==============================] - 21s 45ms/step - loss: 0.0190 - accuracy: 0.9940 - val_loss: 0.0239 - val_accuracy: 0.9928\n",
            "Epoch 5/5\n",
            "468/468 [==============================] - 22s 48ms/step - loss: 0.0170 - accuracy: 0.9947 - val_loss: 0.0221 - val_accuracy: 0.9932\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x7adfa23bfdc0>"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ],
      "source": [
        "# We can now train our model which is fed data by our batch loader\n",
        "# Steps per epoch should always be total size of the set divided by the batch size\n",
        "\n",
        "# SIGNIFICANT MEMORY SAVINGS (important for larger, deeper networks)\n",
        "\n",
        "#model.fit_generator(train_generator, steps_per_epoch=60000//128, epochs=5, verbose=1,\n",
        "#                    validation_data=test_generator, validation_steps=10000//128)\n",
        "model.fit(train_generator, steps_per_epoch=60000//128, epochs=5, verbose=1,\n",
        "         validation_data=test_generator, validation_steps=10000//128)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Notice how the accuracy did not improve much after the second run (\"epoch\"). That shows that the convolution neural network on the first pass, epoch 1, did about as well as possible with this approach. Nothing was learned on subsequent epochs."
      ],
      "metadata": {
        "id": "G1UHkoL92cZH"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "64Gwx6CNRi3w",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "96f07abc-a38d-4bbb-c1b2-00055f887613"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 2s 5ms/step - loss: 0.0220 - accuracy: 0.9932\n",
            "Test score: 0.02202834002673626\n",
            "Test accuracy: 0.9932000041007996\n"
          ]
        }
      ],
      "source": [
        "score = model.evaluate(X_test, Y_test)\n",
        "print('Test score:', score[0])\n",
        "print('Test accuracy:', score[1])"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "An accuracy of 0.9932 means that out of 10,000 validation images, only 68 were incorrectly recognized."
      ],
      "metadata": {
        "id": "DsBmlF5D2-yC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "predicted_classes = np.argmax(model.predict(X_test), axis = 1) #,axis=1)model.predict_classes(X_test)\n",
        "correct_indices = np.nonzero(predicted_classes == y_test)[0]\n",
        "incorrect_indices = np.nonzero(predicted_classes != y_test)[0]\n",
        "plt.figure()\n",
        "for i, incorrect in enumerate(incorrect_indices[:9]):\n",
        "    plt.subplot(3,3,i+1)\n",
        "    plt.imshow(X_test[incorrect].reshape(28,28), cmap='gray', interpolation='none')\n",
        "    plt.title(\"Predicted {}, Class {}\".format(predicted_classes[incorrect], y_test[incorrect]))\n",
        "\n",
        "plt.tight_layout()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "wa__p8MC9vK-",
        "outputId": "768f1795-f3a8-4f1d-82c6-1b9fc51e2bac"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 1s 3ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 9 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "che-0_lQRi3x"
      },
      "source": [
        "## Great results!\n",
        "\n",
        "In the few places where the neural network misread the image, it isn't clear that a human would have done any better."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Additional Training\n",
        "\n",
        "For more training and MANY MANY EXAMPLES of Neural Networks visit\n",
        "\n",
        "#https://keras.io/\n"
      ],
      "metadata": {
        "id": "0qHWFv4P_-2h"
      }
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    },
    "colab": {
      "provenance": [],
      "gpuType": "V100",
      "include_colab_link": true
    },
    "accelerator": "GPU"
  },
  "nbformat": 4,
  "nbformat_minor": 0
}