MachineLearningProject.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyOASfM7UV3Dl3uyo+7SqyQ0",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/awaistahir29/MachineLearingIIProject/blob/main/MachineLearningProject.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o2Tl-LSmILyZ",
        "outputId": "873ad2e9-d025-4d9e-8359-d7c89d72f498"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Colab only includes TensorFlow 2.x; %tensorflow_version has no effect.\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: mitdeeplearning in /usr/local/lib/python3.10/dist-packages (0.3.0)\n",
            "Requirement already satisfied: gym in /usr/local/lib/python3.10/dist-packages (from mitdeeplearning) (0.25.2)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from mitdeeplearning) (1.22.4)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from mitdeeplearning) (4.65.0)\n",
            "Requirement already satisfied: regex in /usr/local/lib/python3.10/dist-packages (from mitdeeplearning) (2022.10.31)\n",
            "Requirement already satisfied: cloudpickle>=1.2.0 in /usr/local/lib/python3.10/dist-packages (from gym->mitdeeplearning) (2.2.1)\n",
            "Requirement already satisfied: gym-notices>=0.0.4 in /usr/local/lib/python3.10/dist-packages (from gym->mitdeeplearning) (0.0.8)\n"
          ]
        }
      ],
      "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"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**1.1 MNIST dataset**\n",
        "\n",
        "Let's download and load the dataset and display a few random samples from it:"
      ],
      "metadata": {
        "id": "7egyI5liJiq1"
      }
    },
    {
      "cell_type": "code",
      "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)"
      ],
      "metadata": {
        "id": "78fcUy9hJKnI"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "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."
      ],
      "metadata": {
        "id": "GBzb9b73JXym"
      }
    },
    {
      "cell_type": "code",
      "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])"
      ],
      "metadata": {
        "id": "HynZCqQ9JUHK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 826
        },
        "outputId": "3e90ed38-15e0-47b4-f10f-79e7611d9b9b"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 36 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "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\")"
      ],
      "metadata": {
        "id": "8ePzeeokJ1yD"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "crsz9hPFIdaJ"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "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."
      ],
      "metadata": {
        "id": "ac52B0j3J-3c"
      }
    },
    {
      "cell_type": "code",
      "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",
        "      # tf.keras.layers.Dense(128, activation= '''TODO'''),\n",
        "\n",
        "      # '''TODO: Define the second Dense layer to output the classification probabilities'''\n",
        "      tf.keras.layers.Dense(10, activation=tf.nn.softmax) \n",
        "      # [TODO Dense layer to output classification probabilities]\n",
        "      \n",
        "  ])\n",
        "  return fc_model\n",
        "\n",
        "model = build_fc_model()"
      ],
      "metadata": {
        "id": "xT1GB6DFKBiS"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "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.**"
      ],
      "metadata": {
        "id": "JTuTrUAxLka0"
      }
    },
    {
      "cell_type": "markdown",
      "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! "
      ],
      "metadata": {
        "id": "_yeXZn1LLrue"
      }
    },
    {
      "cell_type": "markdown",
      "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. "
      ],
      "metadata": {
        "id": "fnaBoPc4MJ4l"
      }
    },
    {
      "cell_type": "code",
      "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'])"
      ],
      "metadata": {
        "id": "wdPQIf1LMO9b"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "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"
      ],
      "metadata": {
        "id": "yIzDfo8_MUd-"
      }
    },
    {
      "cell_type": "code",
      "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)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oe8yu3VlMfv-",
        "outputId": "570ac4b3-4485-49eb-c60b-d5ad9a8827c2"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "938/938 [==============================] - 5s 4ms/step - loss: 0.3710 - accuracy: 0.8957\n",
            "Epoch 2/5\n",
            "938/938 [==============================] - 4s 4ms/step - loss: 0.1994 - accuracy: 0.9427\n",
            "Epoch 3/5\n",
            "938/938 [==============================] - 4s 5ms/step - loss: 0.1493 - accuracy: 0.9573\n",
            "Epoch 4/5\n",
            "938/938 [==============================] - 5s 5ms/step - loss: 0.1211 - accuracy: 0.9653\n",
            "Epoch 5/5\n",
            "938/938 [==============================] - 4s 4ms/step - loss: 0.1018 - accuracy: 0.9710\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f7317384fa0>"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "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."
      ],
      "metadata": {
        "id": "MBVWXkdrMkYZ"
      }
    },
    {
      "cell_type": "markdown",
      "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!"
      ],
      "metadata": {
        "id": "lIQWU2cGM_dh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "'''TODO: Use the evaluate method to test the model!'''\n",
        "test_loss, test_acc = model.evaluate(test_images, test_labels) # TODO\n",
        "# test_loss, test_acc = # TODO\n",
        "\n",
        "print('Test accuracy:', test_acc)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Li_vsF1INOLp",
        "outputId": "a9b2bb2d-0ac4-4b4b-bded-97b1ae3d50bb"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 1s 4ms/step - loss: 0.1110 - accuracy: 0.9664\n",
            "Test accuracy: 0.9664000272750854\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "It can be observed 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. "
      ],
      "metadata": {
        "id": "zwxB6Ng4RRoT"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1.3 Convolutional Neural Network (CNN) for handwritten digit classification"
      ],
      "metadata": {
        "id": "5ozHJutbSYSX"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "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\")"
      ],
      "metadata": {
        "id": "JSgD-4rjSbF0"
      }
    },
    {
      "cell_type": "markdown",
      "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."
      ],
      "metadata": {
        "id": "nGP4mwOkSpQm"
      }
    },
    {
      "cell_type": "code",
      "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())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8VtrgXNNStML",
        "outputId": "2668ffa3-43b3-4720-cc9c-f4d3e970397b"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 278ms/step\n",
            "Model: \"sequential_3\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d_2 (Conv2D)           (None, 26, 26, 24)        240       \n",
            "                                                                 \n",
            " max_pooling2d_2 (MaxPooling  (None, 13, 13, 24)       0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " conv2d_3 (Conv2D)           (None, 11, 11, 36)        7812      \n",
            "                                                                 \n",
            " max_pooling2d_3 (MaxPooling  (None, 5, 5, 36)         0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " flatten_3 (Flatten)         (None, 900)               0         \n",
            "                                                                 \n",
            " dense_6 (Dense)             (None, 128)               115328    \n",
            "                                                                 \n",
            " dense_7 (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",
      "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:"
      ],
      "metadata": {
        "id": "FnHZg72TSxcW"
      }
    },
    {
      "cell_type": "code",
      "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),\n",
        "              loss='sparse_categorical_crossentropy',\n",
        "              metrics=['accuracy'])\n",
        "# cnn_model.compile(optimizer='''TODO''', loss='''TODO''', metrics=['accuracy']) # TODO"
      ],
      "metadata": {
        "id": "sae7U1VvSzmi"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "As was the case with the fully connected model, we can train our CNN using the `fit` method via the Keras API."
      ],
      "metadata": {
        "id": "WkkuTigwS39O"
      }
    },
    {
      "cell_type": "code",
      "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)\n",
        "# cnn_model.fit('''TODO''')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c8I23MWSS7s6",
        "outputId": "32355b81-8d3a-4697-ec3a-61d6b443f21e"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "938/938 [==============================] - 6s 4ms/step - loss: 0.1884 - accuracy: 0.9442\n",
            "Epoch 2/5\n",
            "938/938 [==============================] - 3s 4ms/step - loss: 0.0553 - accuracy: 0.9836\n",
            "Epoch 3/5\n",
            "938/938 [==============================] - 3s 4ms/step - loss: 0.0381 - accuracy: 0.9879\n",
            "Epoch 4/5\n",
            "938/938 [==============================] - 4s 4ms/step - loss: 0.0281 - accuracy: 0.9915\n",
            "Epoch 5/5\n",
            "938/938 [==============================] - 3s 4ms/step - loss: 0.0215 - accuracy: 0.9930\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f73172892a0>"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "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:"
      ],
      "metadata": {
        "id": "sqTq6oiMS-Vk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "'''TODO: Use the evaluate method to test the model!'''\n",
        "test_loss, test_acc = cnn_model.evaluate(test_images, test_labels)\n",
        "# test_loss, test_acc = # TODO\n",
        "\n",
        "print('Test accuracy:', test_acc)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UjAPhSKTTClG",
        "outputId": "0bb20dbe-039d-4e46-e057-0a30a1aa297c"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 1s 2ms/step - loss: 0.0324 - accuracy: 0.9895\n",
            "Test accuracy: 0.9894999861717224\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "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"
      ],
      "metadata": {
        "id": "lpRQKxYMTFVW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "predictions = cnn_model.predict(test_images)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yFtH20XEZMsl",
        "outputId": "c0c8316b-b0c5-441d-d709-f3f645934741"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 1s 2ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "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:"
      ],
      "metadata": {
        "id": "lyVCPw44TJxc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "predictions[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2C9J1a6OZPUt",
        "outputId": "9bd930d8-c798-4010-84b6-6d2d9ae6eab2"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1.4658844e-08, 7.8190226e-08, 4.5531824e-06, 3.2846644e-06,\n",
              "       3.2393019e-10, 2.4285616e-09, 7.0115802e-14, 9.9999177e-01,\n",
              "       1.6464480e-08, 2.5428326e-07], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "It can be seen, 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:"
      ],
      "metadata": {
        "id": "p49Fi9mMTNGu"
      }
    },
    {
      "cell_type": "code",
      "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]) \n",
        "# prediction = # TODO\n",
        "\n",
        "print(prediction)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CqN1gRqWTVqG",
        "outputId": "7948b4d0-9723-49ab-bf39-7b0bcb6a59a4"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "7\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "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:"
      ],
      "metadata": {
        "id": "apjxs1L7UCEW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Label of this digit is:\", test_labels[0])\n",
        "plt.imshow(test_images[0,:,:,0], cmap=plt.cm.binary)"
      ],
      "metadata": {
        "id": "wHE8vs36UHzI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        },
        "outputId": "22786975-de33-4e3d-bce0-1f359d0db6c1"
      },
      "execution_count": 33,
      "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 0x7f731728bd00>"
            ]
          },
          "metadata": {},
          "execution_count": 33
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "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:"
      ],
      "metadata": {
        "id": "Ow07GlchULjY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Change the slider to look at the model's predictions! { run: \"auto\" }\n",
        "\n",
        "image_index = 46 #@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)"
      ],
      "metadata": {
        "id": "uSlDUPOvUS96",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "outputId": "fe8e1775-226a-4dce-8dcc-ddaee07c273e"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "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!"
      ],
      "metadata": {
        "id": "bP7pIxdrUXvG"
      }
    },
    {
      "cell_type": "code",
      "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)"
      ],
      "metadata": {
        "id": "LESGGHwuUZtP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 826
        },
        "outputId": "a3f35205-dde9-4086-e75a-6d31fa98d2e5"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x1000 with 40 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "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."
      ],
      "metadata": {
        "id": "cWmZ1BqaUeO0"
      }
    },
    {
      "cell_type": "code",
      "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)\n",
        "    # logits = # TODO\n",
        "\n",
        "    #'''TODO: compute the categorical cross entropy loss\n",
        "    loss_value = tf.keras.backend.sparse_categorical_crossentropy(labels, logits)\n",
        "    # loss_value = tf.keras.backend.sparse_categorical_crossentropy('''TODO''', '''TODO''') # 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)\n",
        "  # grads = # TODO\n",
        "  optimizer.apply_gradients(zip(grads, cnn_model.trainable_variables))"
      ],
      "metadata": {
        "id": "tICEzgU7Uh6n",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 898
        },
        "outputId": "b121a2c4-f408-4b9c-fa75-4805f48fb234"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 5000/5000 [01:49<00:00, 45.61it/s]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "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. "
      ],
      "metadata": {
        "id": "01VCsHV-UlyR"
      }
    }
  ]
}