Copy_of_Part1_MNIST.ipynb

{
  "nbformat": 4,
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  "metadata": {
    "colab": {
      "name": "Copy of Part1_MNIST.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "Xmf_JRJa_N8C"
      ],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/loud1990/courses/blob/main/Copy_of_Part1_MNIST.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Xmf_JRJa_N8C"
      },
      "source": [
        "<table align=\"center\">\n",
        "  <td align=\"center\"><a target=\"_blank\" href=\"http://introtodeeplearning.com\">\n",
        "        <img src=\"https://i.ibb.co/Jr88sn2/mit.png\" style=\"padding-bottom:5px;\" />\n",
        "      Visit MIT Deep Learning</a></td>\n",
        "  <td align=\"center\"><a target=\"_blank\" href=\"https://colab.research.google.com/github/aamini/introtodeeplearning/blob/master/lab2/Part1_MNIST.ipynb\">\n",
        "        <img src=\"https://i.ibb.co/2P3SLwK/colab.png\"  style=\"padding-bottom:5px;\" />Run in Google Colab</a></td>\n",
        "  <td align=\"center\"><a target=\"_blank\" href=\"https://github.com/aamini/introtodeeplearning/blob/master/lab2/Part1_MNIST.ipynb\">\n",
        "        <img src=\"https://i.ibb.co/xfJbPmL/github.png\"  height=\"70px\" style=\"padding-bottom:5px;\"  />View Source on GitHub</a></td>\n",
        "</table>\n",
        "\n",
        "# Copyright Information"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gKA_J7bdP33T"
      },
      "source": [
        "# Copyright 2022 MIT 6.S191 Introduction to Deep Learning. All Rights Reserved.\n",
        "# \n",
        "# Licensed under the MIT License. You may not use this file except in compliance\n",
        "# with the License. Use and/or modification of this code outside of 6.S191 must\n",
        "# reference:\n",
        "#\n",
        "# © MIT 6.S191: Introduction to Deep Learning\n",
        "# http://introtodeeplearning.com\n",
        "#"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Cm1XpLftPi4A"
      },
      "source": [
        "# Laboratory 2: Computer Vision\n",
        "\n",
        "# Part 1: MNIST Digit Classification\n",
        "\n",
        "In the first portion of this lab, we will build and train a convolutional neural network (CNN) for classification of handwritten digits from the famous [MNIST](http://yann.lecun.com/exdb/mnist/) dataset. The MNIST dataset consists of 60,000 training images and 10,000 test images. Our classes are the digits 0-9.\n",
        "\n",
        "First, let's download the course repository, install dependencies, and import the relevant packages we'll need for this lab."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RsGqx_ai_N8F",
        "outputId": "d3b3b0d4-8feb-49bd-80f5-b563ecfbf200",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# Import Tensorflow 2.0\n",
        "%tensorflow_version 2.x\n",
        "import tensorflow as tf \n",
        "\n",
        "!pip install mitdeeplearning\n",
        "import mitdeeplearning as mdl\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import random\n",
        "from tqdm import tqdm\n",
        "\n",
        "# Check that we are using a GPU, if not switch runtimes\n",
        "#   using Runtime > Change Runtime Type > GPU\n",
        "assert len(tf.config.list_physical_devices('GPU')) > 0"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting mitdeeplearning\n",
            "  Downloading mitdeeplearning-0.2.0.tar.gz (2.1 MB)\n",
            "\u001b[K     |████████████████████████████████| 2.1 MB 8.3 MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from mitdeeplearning) (1.21.6)\n",
            "Requirement already satisfied: regex in /usr/local/lib/python3.7/dist-packages (from mitdeeplearning) (2022.6.2)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from mitdeeplearning) (4.64.0)\n",
            "Requirement already satisfied: gym in /usr/local/lib/python3.7/dist-packages (from mitdeeplearning) (0.17.3)\n",
            "Requirement already satisfied: pyglet<=1.5.0,>=1.4.0 in /usr/local/lib/python3.7/dist-packages (from gym->mitdeeplearning) (1.5.0)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from gym->mitdeeplearning) (1.4.1)\n",
            "Requirement already satisfied: cloudpickle<1.7.0,>=1.2.0 in /usr/local/lib/python3.7/dist-packages (from gym->mitdeeplearning) (1.3.0)\n",
            "Requirement already satisfied: future in /usr/local/lib/python3.7/dist-packages (from pyglet<=1.5.0,>=1.4.0->gym->mitdeeplearning) (0.16.0)\n",
            "Building wheels for collected packages: mitdeeplearning\n",
            "  Building wheel for mitdeeplearning (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for mitdeeplearning: filename=mitdeeplearning-0.2.0-py3-none-any.whl size=2115442 sha256=ee49fca5618e2cc71e76bfcde64f843c9b10455cf8c73b374f3f4e3f9bad87bc\n",
            "  Stored in directory: /root/.cache/pip/wheels/9a/b9/4f/99b7c8c5c75355550b83e1fcfc02956fb40c35eb01e2262877\n",
            "Successfully built mitdeeplearning\n",
            "Installing collected packages: mitdeeplearning\n",
            "Successfully installed mitdeeplearning-0.2.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HKjrdUtX_N8J"
      },
      "source": [
        "## 1.1 MNIST dataset \n",
        "\n",
        "Let's download and load the dataset and display a few random samples from it:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p2dQsHI3_N8K",
        "outputId": "1f4ab0b9-d226-47e3-c447-a0356483c729",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "mnist = tf.keras.datasets.mnist\n",
        "(train_images, train_labels), (test_images, test_labels) = mnist.load_data()\n",
        "train_images = (np.expand_dims(train_images, axis=-1)/255.).astype(np.float32)\n",
        "train_labels = (train_labels).astype(np.int64)\n",
        "test_images = (np.expand_dims(test_images, axis=-1)/255.).astype(np.float32)\n",
        "test_labels = (test_labels).astype(np.int64)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
            "11493376/11490434 [==============================] - 0s 0us/step\n",
            "11501568/11490434 [==============================] - 0s 0us/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5ZtUqOqePsRD"
      },
      "source": [
        "Our training set is made up of 28x28 grayscale images of handwritten digits. \n",
        "\n",
        "Let's visualize what some of these images and their corresponding training labels look like."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "bDBsR2lP_N8O",
        "outputId": "f4b37867-5614-4ce9-b048-b386a669464d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 589
        }
      },
      "source": [
        "plt.figure(figsize=(10,10))\n",
        "random_inds = np.random.choice(60000,36)\n",
        "for i in range(36):\n",
        "    plt.subplot(6,6,i+1)\n",
        "    plt.xticks([])\n",
        "    plt.yticks([])\n",
        "    plt.grid(False)\n",
        "    image_ind = random_inds[i]\n",
        "    plt.imshow(np.squeeze(train_images[image_ind]), cmap=plt.cm.binary)\n",
        "    plt.xlabel(train_labels[image_ind])"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 36 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V6hd3Nt1_N8q"
      },
      "source": [
        "## 1.2 Neural Network for Handwritten Digit Classification\n",
        "\n",
        "We'll first build a simple neural network consisting of two fully connected layers and apply this to the digit classification task. Our network will ultimately output a probability distribution over the 10 digit classes (0-9). This first architecture we will be building is depicted below:\n",
        "\n",
        "![alt_text](https://raw.githubusercontent.com/aamini/introtodeeplearning/master/lab2/img/mnist_2layers_arch.png \"CNN Architecture for MNIST Classification\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rphS2rMIymyZ"
      },
      "source": [
        "### Fully connected neural network architecture\n",
        "To define the architecture of this first fully connected neural network, we'll once again use the Keras API and define the model using the [`Sequential`](https://www.tensorflow.org/api_docs/python/tf/keras/models/Sequential) class. Note how we first use a [`Flatten`](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Flatten) layer, which flattens the input so that it can be fed into the model. \n",
        "\n",
        "In this next block, you'll define the fully connected layers of this simple work."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MMZsbjAkDKpU"
      },
      "source": [
        "def build_fc_model():\n",
        "  fc_model = tf.keras.Sequential([\n",
        "      # First define a Flatten layer\n",
        "      tf.keras.layers.Flatten(),\n",
        "\n",
        "      # '''TODO: Define the activation function for the first fully connected (Dense) layer.'''\n",
        "      tf.keras.layers.Dense(128, activation=tf.nn.relu),\n",
        "\n",
        "      # '''TODO: Define the second Dense layer to output the classification probabilities'''\n",
        "      tf.keras.layers.Dense(10, activation=tf.nn.softmax)\n",
        "  ])\n",
        "  return fc_model\n",
        "\n",
        "model = build_fc_model()"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VtGZpHVKz5Jt"
      },
      "source": [
        "As we progress through this next portion, you may find that you'll want to make changes to the architecture defined above. **Note that in order to update the model later on, you'll need to re-run the above cell to re-initialize the model.**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mVN1_AeG_N9N"
      },
      "source": [
        "Let's take a step back and think about the network we've just created. The first layer in this network, `tf.keras.layers.Flatten`, transforms the format of the images from a 2d-array (28 x 28 pixels), to a 1d-array of 28 * 28 = 784 pixels. You can think of this layer as unstacking rows of pixels in the image and lining them up. There are no learned parameters in this layer; it only reformats the data.\n",
        "\n",
        "After the pixels are flattened, the network consists of a sequence of two `tf.keras.layers.Dense` layers. These are fully-connected neural layers. The first `Dense` layer has 128 nodes (or neurons). The second (and last) layer (which you've defined!) should return an array of probability scores that sum to 1. Each node contains a score that indicates the probability that the current image belongs to one of the handwritten digit classes.\n",
        "\n",
        "That defines our fully connected model! "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gut8A_7rCaW6"
      },
      "source": [
        "\n",
        "\n",
        "### Compile the model\n",
        "\n",
        "Before training the model, we need to define a few more settings. These are added during the model's [`compile`](https://www.tensorflow.org/api_docs/python/tf/keras/models/Sequential#compile) step:\n",
        "\n",
        "* *Loss function* — This defines how we measure how accurate the model is during training. As was covered in lecture, during training we want to minimize this function, which will \"steer\" the model in the right direction.\n",
        "* *Optimizer* — This defines how the model is updated based on the data it sees and its loss function.\n",
        "* *Metrics* — Here we can define metrics used to monitor the training and testing steps. In this example, we'll look at the *accuracy*, the fraction of the images that are correctly classified.\n",
        "\n",
        "We'll start out by using a stochastic gradient descent (SGD) optimizer initialized with a learning rate of 0.1. Since we are performing a categorical classification task, we'll want to use the [cross entropy loss](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/sparse_categorical_crossentropy).\n",
        "\n",
        "You'll want to experiment with both the choice of optimizer and learning rate and evaluate how these affect the accuracy of the trained model. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lhan11blCaW7"
      },
      "source": [
        "'''TODO: Experiment with different optimizers and learning rates. How do these affect\n",
        "    the accuracy of the trained model? Which optimizers and/or learning rates yield\n",
        "    the best performance?'''\n",
        "model.compile(optimizer=tf.keras.optimizers.SGD(learning_rate=1e-1), \n",
        "              loss='sparse_categorical_crossentropy',\n",
        "              metrics=['accuracy'])"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qKF6uW-BCaW-"
      },
      "source": [
        "### Train the model\n",
        "\n",
        "We're now ready to train our model, which will involve feeding the training data (`train_images` and `train_labels`) into the model, and then asking it to learn the associations between images and labels. We'll also need to define the batch size and the number of epochs, or iterations over the MNIST dataset, to use during training. \n",
        "\n",
        "In Lab 1, we saw how we can use `GradientTape` to optimize losses and train models with stochastic gradient descent. After defining the model settings in the `compile` step, we can also accomplish training by calling the [`fit`](https://www.tensorflow.org/api_docs/python/tf/keras/models/Sequential#fit) method on an instance of the `Model` class. We will use this to train our fully connected model\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EFMbIqIvQ2X0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bb3ac159-ac50-463a-98ee-d2d22b9d119e"
      },
      "source": [
        "# Define the batch size and the number of epochs to use during training\n",
        "BATCH_SIZE = 64\n",
        "EPOCHS = 5\n",
        "\n",
        "model.fit(train_images, train_labels, batch_size=BATCH_SIZE, epochs=EPOCHS)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "938/938 [==============================] - 5s 2ms/step - loss: 0.3670 - accuracy: 0.8985\n",
            "Epoch 2/5\n",
            "938/938 [==============================] - 2s 2ms/step - loss: 0.1953 - accuracy: 0.9441\n",
            "Epoch 3/5\n",
            "938/938 [==============================] - 2s 2ms/step - loss: 0.1469 - accuracy: 0.9586\n",
            "Epoch 4/5\n",
            "938/938 [==============================] - 2s 2ms/step - loss: 0.1189 - accuracy: 0.9663\n",
            "Epoch 5/5\n",
            "938/938 [==============================] - 2s 2ms/step - loss: 0.1002 - accuracy: 0.9716\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fd42d6efc50>"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W3ZVOhugCaXA"
      },
      "source": [
        "As the model trains, the loss and accuracy metrics are displayed. With five epochs and a learning rate of 0.01, this fully connected model should achieve an accuracy of approximatley 0.97 (or 97%) on the training data."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oEw4bZgGCaXB"
      },
      "source": [
        "### Evaluate accuracy on the test dataset\n",
        "\n",
        "Now that we've trained the model, we can ask it to make predictions about a test set that it hasn't seen before. In this example, the `test_images` array comprises our test dataset. To evaluate accuracy, we can check to see if the model's predictions match the labels from the `test_labels` array. \n",
        "\n",
        "Use the [`evaluate`](https://www.tensorflow.org/api_docs/python/tf/keras/models/Sequential#evaluate) method to evaluate the model on the test dataset!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VflXLEeECaXC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fd34a1f0-392d-43c4-b738-b3497be95a37"
      },
      "source": [
        "'''TODO: Use the evaluate method to test the model!'''\n",
        "test_loss, test_acc = model.evaluate(test_images, test_labels) # TODO\n",
        "\n",
        "print('Test accuracy:', test_acc)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 1s 2ms/step - loss: 0.1010 - accuracy: 0.9710\n",
            "Test accuracy: 0.9710000157356262\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yWfgsmVXCaXG"
      },
      "source": [
        "You may observe that the accuracy on the test dataset is a little lower than the accuracy on the training dataset. This gap between training accuracy and test accuracy is an example of *overfitting*, when a machine learning model performs worse on new data than on its training data. \n",
        "\n",
        "What is the highest accuracy you can achieve with this first fully connected model? Since the handwritten digit classification task is pretty straightforward, you may be wondering how we can do better...\n",
        "\n",
        "![Deeper...](https://i.kym-cdn.com/photos/images/newsfeed/000/534/153/f87.jpg)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "baIw9bDf8v6Z"
      },
      "source": [
        "## 1.3 Convolutional Neural Network (CNN) for handwritten digit classification"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_J72Yt1o_fY7"
      },
      "source": [
        "As we saw in lecture, convolutional neural networks (CNNs) are particularly well-suited for a variety of tasks in computer vision, and have achieved near-perfect accuracies on the MNIST dataset. We will now build a CNN composed of two convolutional layers and pooling layers, followed by two fully connected layers, and ultimately output a probability distribution over the 10 digit classes (0-9). The CNN we will be building is depicted below:\n",
        "\n",
        "![alt_text](https://raw.githubusercontent.com/aamini/introtodeeplearning/master/lab2/img/convnet_fig.png \"CNN Architecture for MNIST Classification\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EEHqzbJJAEoR"
      },
      "source": [
        "### Define the CNN model\n",
        "\n",
        "We'll use the same training and test datasets as before, and proceed similarly as our fully connected network to define and train our new CNN model. To do this we will explore two layers we have not encountered before: you can use  [`keras.layers.Conv2D` ](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Conv2D) to define convolutional layers and [`keras.layers.MaxPool2D`](https://www.tensorflow.org/api_docs/python/tf/keras/layers/MaxPool2D) to define the pooling layers. Use the parameters shown in the network architecture above to define these layers and build the CNN model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vec9qcJs-9W5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fa720d36-f0d6-49a2-9e5d-c05f9b541da3"
      },
      "source": [
        "def build_cnn_model():\n",
        "    cnn_model = tf.keras.Sequential([\n",
        "\n",
        "        # TODO: Define the first convolutional layer\n",
        "        tf.keras.layers.Conv2D(filters=24, kernel_size=(3,3), activation=tf.nn.relu),\n",
        "        #tf.keras.layers.Conv2D('''TODO'''), \n",
        "\n",
        "        # TODO: Define the first max pooling layer\n",
        "        tf.keras.layers.MaxPool2D(pool_size=(2,2)),\n",
        "        #tf.keras.layers.MaxPool2D('''TODO'''),\n",
        "\n",
        "        # TODO: Define the second convolutional layer\n",
        "        tf.keras.layers.Conv2D(filters=36, kernel_size=(3,3), activation=tf.nn.relu),\n",
        "        #tf.keras.layers.Conv2D('''TODO'''),\n",
        "\n",
        "        # TODO: Define the second max pooling layer\n",
        "        tf.keras.layers.MaxPool2D(pool_size=(2,2)),\n",
        "        #tf.keras.layers.MaxPool2D('''TODO'''),\n",
        "\n",
        "        tf.keras.layers.Flatten(),\n",
        "        tf.keras.layers.Dense(128, activation=tf.nn.relu),\n",
        "\n",
        "        # TODO: Define the last Dense layer to output the classification \n",
        "        # probabilities. Pay attention to the activation needed a probability\n",
        "        # output\n",
        "        tf.keras.layers.Dense(10, activation=tf.nn.softmax)\n",
        "        #'''TODO: Dense layer to output classification probabilities'''\n",
        "    ])\n",
        "    \n",
        "    return cnn_model\n",
        "  \n",
        "cnn_model = build_cnn_model()\n",
        "# Initialize the model by passing some data through\n",
        "cnn_model.predict(train_images[[0]])\n",
        "# Print the summary of the layers in the model.\n",
        "print(cnn_model.summary())"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d (Conv2D)             (None, 26, 26, 24)        240       \n",
            "                                                                 \n",
            " max_pooling2d (MaxPooling2D  (None, 13, 13, 24)       0         \n",
            " )                                                               \n",
            "                                                                 \n",
            " conv2d_1 (Conv2D)           (None, 11, 11, 36)        7812      \n",
            "                                                                 \n",
            " max_pooling2d_1 (MaxPooling  (None, 5, 5, 36)         0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " flatten_1 (Flatten)         (None, 900)               0         \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 128)               115328    \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 10)                1290      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 124,670\n",
            "Trainable params: 124,670\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kUAXIBynCih2"
      },
      "source": [
        "### Train and test the CNN model\n",
        "\n",
        "Now, as before, we can define the loss function, optimizer, and metrics through the `compile` method. Compile the CNN model with an optimizer and learning rate of choice:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vheyanDkCg6a"
      },
      "source": [
        "#'''TODO: Define the compile operation with your optimizer and learning rate of choice'''\n",
        "cnn_model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=1e-3), loss='sparse_categorical_crossentropy', metrics=['accuracy']) # TODO"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U19bpRddC7H_"
      },
      "source": [
        "As was the case with the fully connected model, we can train our CNN using the `fit` method via the Keras API."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YdrGZVmWDK4p",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5f799b51-b5d9-42b1-a9b4-c9bb1f2f4f66"
      },
      "source": [
        "#'''TODO: Use model.fit to train the CNN model, with the same batch_size and number of epochs previously used.'''\n",
        "cnn_model.fit(train_images, train_labels, batch_size=BATCH_SIZE, epochs=EPOCHS)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "938/938 [==============================] - 5s 4ms/step - loss: 0.1840 - accuracy: 0.9455\n",
            "Epoch 2/5\n",
            "938/938 [==============================] - 3s 3ms/step - loss: 0.0547 - accuracy: 0.9828\n",
            "Epoch 3/5\n",
            "938/938 [==============================] - 3s 3ms/step - loss: 0.0387 - accuracy: 0.9882\n",
            "Epoch 4/5\n",
            "938/938 [==============================] - 3s 3ms/step - loss: 0.0283 - accuracy: 0.9909\n",
            "Epoch 5/5\n",
            "938/938 [==============================] - 3s 3ms/step - loss: 0.0216 - accuracy: 0.9932\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fd4a0014a90>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pEszYWzgDeIc"
      },
      "source": [
        "Great! Now that we've trained the model, let's evaluate it on the test dataset using the [`evaluate`](https://www.tensorflow.org/api_docs/python/tf/keras/models/Sequential#evaluate) method:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JDm4znZcDtNl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "78ab2eba-1840-4982-a271-f8a7e080354c"
      },
      "source": [
        "#'''TODO: Use the evaluate method to test the model!'''\n",
        "test_loss, test_acc = cnn_model.evaluate(test_images, test_labels) # TODO\n",
        "\n",
        "print('Test accuracy:', test_acc)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0344 - accuracy: 0.9883\n",
            "Test accuracy: 0.9883000254631042\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2rvEgK82Glv9"
      },
      "source": [
        "What is the highest accuracy you're able to achieve using the CNN model, and how does the accuracy of the CNN model compare to the accuracy of the simple fully connected network? What optimizers and learning rates seem to be optimal for training the CNN model? "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xsoS7CPDCaXH"
      },
      "source": [
        "### Make predictions with the CNN model\n",
        "\n",
        "With the model trained, we can use it to make predictions about some images. The [`predict`](https://www.tensorflow.org/api_docs/python/tf/keras/models/Sequential#predict) function call generates the output predictions given a set of input samples.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gl91RPhdCaXI"
      },
      "source": [
        "predictions = cnn_model.predict(test_images)"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x9Kk1voUCaXJ"
      },
      "source": [
        "With this function call, the model has predicted the label for each image in the testing set. Let's take a look at the prediction for the first image in the test dataset:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3DmJEUinCaXK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bf2001b1-305b-4320-e27b-2a48b1ce57da"
      },
      "source": [
        "predictions[0]"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1.40973810e-08, 3.30999200e-07, 1.15644106e-07, 9.54639674e-08,\n",
              "       1.01134645e-09, 1.09284179e-10, 3.07618037e-12, 9.99999404e-01,\n",
              "       2.47886263e-08, 1.71164967e-08], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-hw1hgeSCaXN"
      },
      "source": [
        "As you can see, a prediction is an array of 10 numbers. Recall that the output of our model is a probability distribution over the 10 digit classes. Thus, these numbers describe the model's \"confidence\" that the image corresponds to each of the 10 different digits. \n",
        "\n",
        "Let's look at the digit that has the highest confidence for the first image in the test dataset:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qsqenuPnCaXO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cdd227e7-0f51-456e-b1ff-f09e35299249"
      },
      "source": [
        "'''TODO: identify the digit with the highest confidence prediction for the first\n",
        "    image in the test dataset. '''\n",
        "prediction = np.argmax(predictions[0]) # TODO\n",
        "\n",
        "print(prediction)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "7\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E51yS7iCCaXO"
      },
      "source": [
        "So, the model is most confident that this image is a \"???\". We can check the test label (remember, this is the true identity of the digit) to see if this prediction is correct:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Sd7Pgsu6CaXP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "c9ffb2b4-e0ab-4299-818e-9ffc8c709588"
      },
      "source": [
        "print(\"Label of this digit is:\", test_labels[0])\n",
        "plt.imshow(test_images[0,:,:,0], cmap=plt.cm.binary)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Label of this digit is: 7\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fd42d890bd0>"
            ]
          },
          "metadata": {},
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPsAAAD4CAYAAAAq5pAIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAANPUlEQVR4nO3df6hc9ZnH8c9n3TSCqZq7ucRo46abiBLETcsQVivVVTckQYj9RxKkZEE2BRVbKLriolX8J6w2paBUE5WmS9dSTCVBgls3VDR/WDKaqDGy668bm3DNnRihKQjZpM/+cU/KNd45M86ZX8nzfsFlZs4z55zHg5+cued75n4dEQJw5vurQTcAoD8IO5AEYQeSIOxAEoQdSOKv+7mzOXPmxIIFC/q5SyCVsbExHT582NPVKoXd9nJJP5V0lqQnI2J92fsXLFiger1eZZcAStRqtaa1jj/G2z5L0mOSVkhaLGmN7cWdbg9Ab1X5nX2ppPci4oOIOCbpV5JWdactAN1WJewXSfrDlNcHimWfY3ud7brteqPRqLA7AFX0/Gp8RGyMiFpE1EZHR3u9OwBNVAn7QUnzp7z+WrEMwBCqEvZdki6x/XXbX5G0WtK27rQFoNs6HnqLiOO275D0X5ocens6It7uWmcAuqrSOHtEbJe0vUu9AOghbpcFkiDsQBKEHUiCsANJEHYgCcIOJEHYgSQIO5AEYQeSIOxAEoQdSIKwA0kQdiAJwg4kQdiBJAg7kARhB5Ig7EAShB1IgrADSRB2IAnCDiRB2IEkCDuQBGEHkiDsQBKEHUiCsANJEHYgCcIOJFFpymbbY5KOSjoh6XhE1LrRFIDuqxT2wj9GxOEubAdAD/ExHkiiathD0m9tv2Z73XRvsL3Odt12vdFoVNwdgE5VDfvVEfFNSSsk3W7726e+ISI2RkQtImqjo6MVdwegU5XCHhEHi8cJSc9JWtqNpgB0X8dht32O7a+efC5pmaS93WoMQHdVuRo/V9Jztk9u5z8j4oWudAWg6zoOe0R8IOnvu9gLgB5i6A1IgrADSRB2IAnCDiRB2IEkuvFFmBSeffbZprVNmzaVrnvhhReW1s8+++zS+i233FJav+CCC5rWFi1aVLou8uDMDiRB2IEkCDuQBGEHkiDsQBKEHUiCsANJMM7eprvuuqtpbWxsrKf7fvzxx0vr5557btPa4sWLu93OaWP+/PlNa3fffXfpurXamfeHkjmzA0kQdiAJwg4kQdiBJAg7kARhB5Ig7EASjLO36cknn2xae+ONN0rXbTXWvW/fvtL67t27S+svvfRS09qrr75auu7FF19cWv/oo49K61XMmDGjtD5nzpzS+vj4eGm97L+9bAxeYpwdwGmMsANJEHYgCcIOJEHYgSQIO5AEYQeSYJy9Tddff31HtXYsX7680vqffvpp01qrMfpW48m7du3qqKd2zJw5s7R+6aWXltYvu+yy0vqRI0ea1hYuXFi67pmo5Znd9tO2J2zvnbJsxPaLtt8tHmf3tk0AVbXzMf7nkk499dwjaUdEXCJpR/EawBBrGfaIeFnSqZ+HVknaXDzfLOmmLvcFoMs6vUA3NyJO3pj8saS5zd5oe53tuu16o9HocHcAqqp8NT4iQlKU1DdGRC0iaqOjo1V3B6BDnYb9kO15klQ8TnSvJQC90GnYt0laWzxfK2lrd9oB0Cstx9ltPyPpWklzbB+Q9CNJ6yX92vatkvZLurmXTaLc7NnNRz6vu+66Stuueg9BFVu2bCmtl91fIElXXHFF09rq1as76ul01jLsEbGmSWlw/xcA+NK4XRZIgrADSRB2IAnCDiRB2IEk+IorBmZiovxerNtuu620PnnzZnP3339/09rIyEjpumcizuxAEoQdSIKwA0kQdiAJwg4kQdiBJAg7kATj7BiYxx57rLTeahz+/PPPL623+lPU2XBmB5Ig7EAShB1IgrADSRB2IAnCDiRB2IEkGGdHT+3cubNpbf369ZW2vXVr+XQFl19+eaXtn2k4swNJEHYgCcIOJEHYgSQIO5AEYQeSIOxAEoyzo6e2b9/etHbs2LHSdW+44YbS+pVXXtlRT1m1PLPbftr2hO29U5Y9YPug7T3Fz8retgmgqnY+xv9c0vJplv8kIpYUP83/+QYwFFqGPSJelnSkD70A6KEqF+jusP1m8TF/drM32V5nu2673mg0KuwOQBWdhv1nkhZKWiJpXNKPm70xIjZGRC0iaqOjox3uDkBVHYU9Ig5FxImI+LOkTZKWdrctAN3WUdhtz5vy8juS9jZ7L4Dh0HKc3fYzkq6VNMf2AUk/knSt7SWSQtKYpO/1sEcMsc8++6y0/sILLzStzZw5s3TdBx98sLQ+Y8aM0jo+r2XYI2LNNIuf6kEvAHqI22WBJAg7kARhB5Ig7EAShB1Igq+4opKHH364tL579+6mtRUrVpSue9VVV3XUE6bHmR1IgrADSRB2IAnCDiRB2IEkCDuQBGEHkmCcHaWef/750vpDDz1UWj/vvPOa1u67776OekJnOLMDSRB2IAnCDiRB2IEkCDuQBGEHkiDsQBKMsyf3ySeflNbvvPPO0vrx48dL6ytXNp/glymX+4szO5AEYQeSIOxAEoQdSIKwA0kQdiAJwg4kwTj7Ge7EiROl9eXLl5fWP/zww9L6okWLSuutvu+O/ml5Zrc93/bvbO+z/bbt7xfLR2y/aPvd4nF279sF0Kl2PsYfl/TDiFgs6R8k3W57saR7JO2IiEsk7SheAxhSLcMeEeMR8Xrx/KikdyRdJGmVpM3F2zZLuqlXTQKo7ktdoLO9QNI3JP1e0tyIGC9KH0ua22SddbbrtuuNRqNCqwCqaDvstmdJ2iLpBxHxx6m1iAhJMd16EbExImoRURsdHa3ULIDOtRV22zM0GfRfRsRvisWHbM8r6vMkTfSmRQDd0HLozbYlPSXpnYjYMKW0TdJaSeuLx6096RCVvP/++6X1er1eafsbNmworS9cuLDS9tE97Yyzf0vSdyW9ZXtPsexeTYb817ZvlbRf0s29aRFAN7QMe0TslOQm5eu72w6AXuF2WSAJwg4kQdiBJAg7kARhB5LgK65ngP379zetLVu2rNK2H3nkkdL6jTfeWGn76B/O7EAShB1IgrADSRB2IAnCDiRB2IEkCDuQBOPsZ4Annniiaa1sDL4d11xzTWl98s8d4HTAmR1IgrADSRB2IAnCDiRB2IEkCDuQBGEHkmCc/TTwyiuvlNYfffTRPnWC0xlndiAJwg4kQdiBJAg7kARhB5Ig7EAShB1Iop352edL+oWkuZJC0saI+KntByT9i6RG8dZ7I2J7rxrNbOfOnaX1o0ePdrztRYsWldZnzZrV8bYxXNq5qea4pB9GxOu2vyrpNdsvFrWfRET5LAIAhkI787OPSxovnh+1/Y6ki3rdGIDu+lK/s9teIOkbkn5fLLrD9pu2n7Y9u8k662zXbdcbjcZ0bwHQB22H3fYsSVsk/SAi/ijpZ5IWSlqiyTP/j6dbLyI2RkQtImqjo6NdaBlAJ9oKu+0Zmgz6LyPiN5IUEYci4kRE/FnSJklLe9cmgKpaht2Tfz70KUnvRMSGKcvnTXnbdyTt7X57ALqlnavx35L0XUlv2d5TLLtX0hrbSzQ5HDcm6Xs96RCVLFmypLS+Y8eO0vrIyEg328EAtXM1fqek6f44OGPqwGmEO+iAJAg7kARhB5Ig7EAShB1IgrADSTgi+razWq0W9Xq9b/sDsqnVaqrX69POo82ZHUiCsANJEHYgCcIOJEHYgSQIO5AEYQeS6Os4u+2GpP1TFs2RdLhvDXw5w9rbsPYl0Vunutnb30bEtH//ra9h/8LO7XpE1AbWQIlh7W1Y+5LorVP96o2P8UAShB1IYtBh3zjg/ZcZ1t6GtS+J3jrVl94G+js7gP4Z9JkdQJ8QdiCJgYTd9nLb/2P7Pdv3DKKHZmyP2X7L9h7bA/3yfTGH3oTtvVOWjdh+0fa7xeO0c+wNqLcHbB8sjt0e2ysH1Nt827+zvc/227a/Xywf6LEr6asvx63vv7PbPkvS/0r6J0kHJO2StCYi9vW1kSZsj0mqRcTAb8Cw/W1Jf5L0i4i4vFj275KORMT64h/K2RHxr0PS2wOS/jToabyL2YrmTZ1mXNJNkv5ZAzx2JX3drD4ct0Gc2ZdKei8iPoiIY5J+JWnVAPoYehHxsqQjpyxeJWlz8XyzJv9n6bsmvQ2FiBiPiNeL50clnZxmfKDHrqSvvhhE2C+S9Icprw9ouOZ7D0m/tf2a7XWDbmYacyNivHj+saS5g2xmGi2n8e6nU6YZH5pj18n051Vxge6Lro6Ib0paIen24uPqUIrJ38GGaey0rWm8+2Waacb/YpDHrtPpz6saRNgPSpo/5fXXimVDISIOFo8Tkp7T8E1FfejkDLrF48SA+/mLYZrGe7ppxjUEx26Q058PIuy7JF1i++u2vyJptaRtA+jjC2yfU1w4ke1zJC3T8E1FvU3S2uL5WklbB9jL5wzLNN7NphnXgI/dwKc/j4i+/0haqckr8u9L+rdB9NCkr7+T9Ebx8/age5P0jCY/1v2fJq9t3CrpbyTtkPSupP+WNDJEvf2HpLckvanJYM0bUG9Xa/Ij+puS9hQ/Kwd97Er66stx43ZZIAku0AFJEHYgCcIOJEHYgSQIO5AEYQeSIOxAEv8Pvvby5fbVYvAAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ygh2yYC972ne"
      },
      "source": [
        "It is! Let's visualize the classification results on the MNIST dataset. We will plot images from the test dataset along with their predicted label, as well as a histogram that provides the prediction probabilities for each of the digits:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HV5jw-5HwSmO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 252
        },
        "outputId": "9f4e1159-62e4-4b8d-e18a-8a546cf55b9d"
      },
      "source": [
        "#@title Change the slider to look at the model's predictions! { run: \"auto\" }\n",
        "\n",
        "image_index = 100 #@param {type:\"slider\", min:0, max:100, step:1}\n",
        "plt.subplot(1,2,1)\n",
        "mdl.lab2.plot_image_prediction(image_index, predictions, test_labels, test_images)\n",
        "plt.subplot(1,2,2)\n",
        "mdl.lab2.plot_value_prediction(image_index, predictions,  test_labels)"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kgdvGD52CaXR"
      },
      "source": [
        "We can also plot several images along with their predictions, where correct prediction labels are blue and incorrect prediction labels are grey. The number gives the percent confidence (out of 100) for the predicted label. Note the model can be very confident in an incorrect prediction!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hQlnbqaw2Qu_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "1c9c2373-d3db-4cb8-d8be-ae02bc7afb6e"
      },
      "source": [
        "# Plots the first X test images, their predicted label, and the true label\n",
        "# Color correct predictions in blue, incorrect predictions in red\n",
        "num_rows = 5\n",
        "num_cols = 4\n",
        "num_images = num_rows*num_cols\n",
        "plt.figure(figsize=(2*2*num_cols, 2*num_rows))\n",
        "for i in range(num_images):\n",
        "  plt.subplot(num_rows, 2*num_cols, 2*i+1)\n",
        "  mdl.lab2.plot_image_prediction(i, predictions, test_labels, test_images)\n",
        "  plt.subplot(num_rows, 2*num_cols, 2*i+2)\n",
        "  mdl.lab2.plot_value_prediction(i, predictions, test_labels)\n"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x720 with 40 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k-2glsRiMdqa"
      },
      "source": [
        "## 1.4 Training the model 2.0\n",
        "\n",
        "Earlier in the lab, we used the [`fit`](https://www.tensorflow.org/api_docs/python/tf/keras/models/Sequential#fit) function call to train the model. This function is quite high-level and intuitive, which is really useful for simpler models. As you may be able to tell, this function abstracts away many details in the training call, and we have less control over training model, which could be useful in other contexts. \n",
        "\n",
        "As an alternative to this, we can use the [`tf.GradientTape`](https://www.tensorflow.org/api_docs/python/tf/GradientTape) class to record differentiation operations during training, and then call the [`tf.GradientTape.gradient`](https://www.tensorflow.org/api_docs/python/tf/GradientTape#gradient) function to actually compute the gradients. You may recall seeing this in Lab 1 Part 1, but let's take another look at this here.\n",
        "\n",
        "We'll use this framework to train our `cnn_model` using stochastic gradient descent."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wq34id-iN1Ml",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 558
        },
        "outputId": "84d7c600-e336-418d-b5ed-8528ecaf9c61"
      },
      "source": [
        "# Rebuild the CNN model\n",
        "cnn_model = build_cnn_model()\n",
        "\n",
        "batch_size = 12\n",
        "loss_history = mdl.util.LossHistory(smoothing_factor=0.95) # to record the evolution of the loss\n",
        "plotter = mdl.util.PeriodicPlotter(sec=2, xlabel='Iterations', ylabel='Loss', scale='semilogy')\n",
        "optimizer = tf.keras.optimizers.SGD(learning_rate=1e-2) # define our optimizer\n",
        "\n",
        "if hasattr(tqdm, '_instances'): tqdm._instances.clear() # clear if it exists\n",
        "\n",
        "for idx in tqdm(range(0, train_images.shape[0], batch_size)):\n",
        "  # First grab a batch of training data and convert the input images to tensors\n",
        "  (images, labels) = (train_images[idx:idx+batch_size], train_labels[idx:idx+batch_size])\n",
        "  images = tf.convert_to_tensor(images, dtype=tf.float32)\n",
        "\n",
        "  # GradientTape to record differentiation operations\n",
        "  with tf.GradientTape() as tape:\n",
        "    #'''TODO: feed the images into the model and obtain the predictions'''\n",
        "    logits = cnn_model(images) # TODO\n",
        "\n",
        "    #'''TODO: compute the categorical cross entropy loss\n",
        "    loss_value = tf.keras.backend.sparse_categorical_crossentropy(labels, logits) # TODO\n",
        "\n",
        "  loss_history.append(loss_value.numpy().mean()) # append the loss to the loss_history record\n",
        "  plotter.plot(loss_history.get())\n",
        "\n",
        "  # Backpropagation\n",
        "  #'''TODO: Use the tape to compute the gradient against all parameters in the CNN model.\n",
        "   #   Use cnn_model.trainable_variables to access these parameters.''' \n",
        "  grads = tape.gradient(loss_value, cnn_model.trainable_variables) # TODO\n",
        "  optimizer.apply_gradients(zip(grads, cnn_model.trainable_variables))\n"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 5000/5000 [00:50<00:00, 99.24it/s]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3cNtDhVaqEdR"
      },
      "source": [
        "## 1.5 Conclusion\n",
        "In this part of the lab, you had the chance to play with different MNIST classifiers with different architectures (fully-connected layers only, CNN), and experiment with how different hyperparameters affect accuracy (learning rate, etc.). The next part of the lab explores another application of CNNs, facial detection, and some drawbacks of AI systems in real world applications, like issues of bias. "
      ]
    }
  ]
}