220079842_assignment1_solution (5).ipynb

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      "source": [
        "# SIT744 Assignment 1: Image Classification with Deep Feedforward Neural Network\n",
        "\n",
        "<div class=\"alert-info\">\n",
        "    <p>Due: <strong>8:00 pm (AEST) 19 April 2021</strong>  (Monday)</p>\n",
        "\n",
        "\n",
        "This is an <strong>individual</strong> assignment. It contributes <strong>30%</strong> to your final mark. Read the assignment instruction carefully.\n",
        "\n",
        "<h2> What to submit </h2>\n",
        "\n",
        "<p>\n",
        "This assignment is to be completed individually and submitted to CloudDeakin. <strong>By the due date, you are required to submit the following files to the corresponding Assignment (Dropbox) in CloudDeakin</strong>:\n",
        "\n",
        "<ol>\n",
        "<li>\t<strong>[YourID]_assignment1_solution.ipynp</strong>:  This is your Python notebook solution source file. </li>\n",
        "<li>\t<strong>[YourID]_assingment1_output.html</strong>: This is the output of your Python notebook solution <emph>exported</emph> in HTML format.</li>\n",
        "<li>\tExtra files needed to complete your assignment, if any (e.g., images used in your answers).</li>\n",
        "</ol>\n",
        "</p>\n",
        "\n",
        "<p>\n",
        "For example, if your student ID is: 123456, you will then need to submit the following files:\n",
        "<ul>\n",
        "<li> 123456_assignment1_solution.ipynp </li>\n",
        "<li> 123456_assignment1_output.html</li>\n",
        "</ul>\n",
        "</p>\n",
        "\n",
        "<h2> Marking criteria </h2>\n",
        "\n",
        "<p>\n",
        "Your submission will be marked using the following criteria.\n",
        "\n",
        "<ul>\n",
        "<li> Showing good effort through completed tasks.</li>\n",
        "<li> Applying deep learning theory to design suitable deep learning solutions for the tasks.</li>\n",
        "<li> Critically evaluating and reflecting on the pros and cons of various design decisions.</li>\n",
        "<li> Demonstrating creativity and resourcefulness in providing unique individual solutions. (<em>Warning: Highly similar solutions will be investigated for collusion.</em>)</li>\n",
        "<li> Showing attention to details through a good quality assignment report.</li>\n",
        "</ul>\n",
        "</p>\n",
        "\n",
        "<p>\n",
        "Indicative weights of various tasks are provided below, but the assignment will be marked by the overall quality per the above criteria. \n",
        "</p>\n",
        "</div>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JOr2bP8kAzKE"
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      "source": [
        "## Assignment objective\n",
        "\n",
        "\n",
        "\n",
        "This assignment is for you to demonstrate the knowledge in deep learning that you have acquired from the lectures and practical lab materials. Most tasks in this assignment are straightforward applications of the practical materials in weeks 1-5. Going through these materials before attempting this assignment is highly recommended.\n",
        "\n",
        "In this assignment, you are going to work with the Fashion-MNIST dataset for image recognition. The dataset contains 10 classes of 28x28 grayscale images. You will see some examples in the visualization task below. \n",
        "\n",
        "This assignment consists of five tasks.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cDqvFgbRAzKG"
      },
      "source": [
        "## Task 1 Load the data\n",
        "\n",
        "*(weight ~5%)*\n",
        "\n",
        "Load the Fashion MNIST dataset (https://github.com/zalandoresearch/fashion-mnist). You may get the data via Keras (keras.datasets) or Tensorflow Datasets (tfds). "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Krg_ro9M-t_k"
      },
      "source": [
        "import tensorflow as tf\n",
        "import tensorflow_datasets\n",
        "import math\n",
        "import numpy as np\n",
        "\n",
        "# Loading the data using tensorflow\n",
        "fashion_mnist = tf.keras.datasets.fashion_mnist\n",
        "\n",
        "(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()\n",
        "\n",
        "# class names\n",
        "class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',\n",
        "               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']"
      ],
      "execution_count": 143,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nFbty6i5AzKm"
      },
      "source": [
        "## Task 2 Understand the data\n",
        "\n",
        "*(weight ~15%)*\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vg6WnS54Cl_G",
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          "base_uri": "https://localhost:8080/",
          "height": 826
        },
        "outputId": "eabf9c3f-9491-4503-ebb0-67bd932b6237"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# 25 images from the train set in the form of 5x5 matrix\n",
        "print(\"25 images from the train set in the form of 5x5 matrix\")\n",
        "plt.figure(figsize=(5,5))\n",
        "for i in range(25):\n",
        "    plt.subplot(5,5,i+1)\n",
        "    plt.xticks([])\n",
        "    plt.yticks([])\n",
        "    plt.grid(False)\n",
        "    plt.imshow(train_images[i])\n",
        "plt.show()\n",
        "\n",
        "# training data\n",
        "print(\"Data type of training data: \", train_images.dtype)\n",
        "print(\"Shape of training data: \", train_images.shape)\n",
        "print(\"Data type of training labels: \", train_labels.dtype)\n",
        "print(\"Shape of training labels: \", train_labels.shape)\n",
        "print(\"Length of training data: \", len(train_labels))\n",
        "\n",
        "# test data\n",
        "print(\"Data type of test data: \", test_images.dtype)\n",
        "print(\"Shape of test data: \", test_images.shape)\n",
        "print(\"Data type of test labels: \", test_labels.dtype)\n",
        "print(\"Shape of test labels: \", test_labels.shape)\n",
        "print(\"Length of test data: \", len(test_images))\n",
        "\n",
        "# unique labels in the data\n",
        "print(\"Labels  in the data: \", np.unique(test_labels))\n",
        "\n",
        "# size of each image\n",
        "print(\"Size of each image\", train_images[0].shape)\n",
        "\n",
        "# memory required for training data\n",
        "print(\"Memory required for training data: \", train_images.size * train_images.itemsize, \"bytes\")\n",
        "\n",
        "\n",
        "# plotting the numeric range of input\n",
        "print(\"\\nPlotting the numeric range of input: [0-255]\")\n",
        "plt.figure()\n",
        "plt.imshow(train_images[100])\n",
        "plt.colorbar()\n",
        "plt.grid(False)\n",
        "plt.show()\n",
        "\n",
        "\n",
        "# The numeric range of input data is in the range [0,255], these values need to be normalized to the range [0,1]. \n",
        "# Normalisation ensures that each input parameter (pixel, in this case) has a similar data distribution. \n",
        "# This makes convergence faster while training the network.\n",
        "\n",
        "# normalisation\n",
        "train_images = train_images / 255.0\n",
        "test_images = test_images / 255.0"
      ],
      "execution_count": 144,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "25 images from the train set in the form of 5x5 matrix\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 360x360 with 25 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Data type of training data:  uint8\n",
            "Shape of training data:  (60000, 28, 28)\n",
            "Data type of training labels:  uint8\n",
            "Shape of training labels:  (60000,)\n",
            "Length of training data:  60000\n",
            "Data type of test data:  uint8\n",
            "Shape of test data:  (10000, 28, 28)\n",
            "Data type of test labels:  uint8\n",
            "Shape of test labels:  (10000,)\n",
            "Length of test data:  10000\n",
            "Labels  in the data:  [0 1 2 3 4 5 6 7 8 9]\n",
            "Size of each image (28, 28)\n",
            "Memory required for training data:  47040000 bytes\n",
            "\n",
            "Plotting the numeric range of input: [0-255]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PLYAtx-ZAzKo"
      },
      "source": [
        "Display 25 images from the train set in the form of 5x5 matrix.\n",
        "\n",
        "Answer the following questions:\n",
        "\n",
        "1. What are the data types and shapes of the features and the label? \n",
        "2. What are the unique labels in this dataset?\n",
        "3. How many training images and how many test images?\n",
        "4. What is the size of each image? How much memory is required for holding the whole training data.\n",
        "5. Find out the numeric range of the input. Do we need to rescale the input? Why?\n",
        " "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-tkl-PwsrXip"
      },
      "source": [
        "## Task 3 Construct an input pipeline\n",
        "\n",
        "*(weight ~15%)*\n",
        "\n",
        "Creat train/validate/test data splits and construct tf.data pipelines. Make sure that the training data is batched. \n",
        "\n",
        "- How do you determine the batch size?\n",
        "- Do we need to shuffle the training data? If yes, how do you determine the buffer size?\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hVmNO1PzOU6E"
      },
      "source": [
        "# train, test and validation splits\n",
        "\n",
        "# defining batch size\n",
        "BATCH_SIZE = 512\n",
        "# training dataset\n",
        "fmnist_train_ds = tf.data.Dataset.from_tensor_slices((train_images, train_labels))\n",
        "#fmnist_train_ds = fmnist_train_ds.shuffle(5000).batch(BATCH_SIZE)\n",
        "#fmnist_train_ds = fmnist_train_ds.shuffle(5000)\n",
        "\n",
        "\n",
        "\n",
        "# test dataset\n",
        "fmnist_test_ds = tf.data.Dataset.from_tensor_slices((test_images, test_labels))\n",
        "fmnist_test_ds = fmnist_test_ds.shuffle(5000).batch(BATCH_SIZE)\n",
        "\n",
        "# picking out the validation set from the training dataset\n",
        "fmnist_train = fmnist_train_ds.take(round(len(train_labels)*0.7))\n",
        "fmnist_val = fmnist_train_ds.skip(round(len(train_labels)*0.7))\n",
        "\n",
        "\n",
        "# batch size should be in powers of 2 and the larger the better, provided that the batch fits into the (GPU) memory;\n",
        "# small batches can offer a regularizing effect, perhaps due to the noise they add to the learning process.\n",
        "# larger batches provide a more accurate estimate of the gradient.\n",
        "# the batch size here is 512.\n",
        "# train-data shuffling ensures that the model gets to see all permutauions of input in every batch.\n",
        "\n"
      ],
      "execution_count": 145,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lnE7ZFS_AzLg"
      },
      "source": [
        "## Task 4 Construct a deep forward neural network\n",
        "\n",
        "*(weight ~35%)*"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ChlhkwhkAzLi"
      },
      "source": [
        "### Task 4.1 Setting up a model for training"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yuxhQM6jAzLl"
      },
      "source": [
        "Construct a deep feedforward neural network. You need to decide and report the following configurations:\n",
        "\n",
        "- Output layer: \n",
        "    - How many output nodes?\n",
        "    - Which activation function?\n",
        "- Hidden layers:\n",
        "    - How many hidden layers?\n",
        "    - How many nodes in each layer?\n",
        "    - Which activation function for each layer?\n",
        "- Input layer\n",
        "    - What is the input size?\n",
        "- The loss function\n",
        "- The metrics for model evaluation (which may be different from the loss function)\n",
        "- The optimiser\n",
        "\n",
        "Justify your model design decisions.\n",
        "\n",
        "Plot the model structure `using keras.utils.plot_model` or similar tools.\n",
        "\n",
        "What is the number of trainable parameters in the model. Explain how the total number can be estimated from the model configurations."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dpH3urepb_6v"
      },
      "source": [
        "# Different layers are appropriate for different tensor formats and different types of data processing. \n",
        "# Simple vector data, stored in 2D tensors of shape (samples, features), is often processed by densely connected layers.\n",
        "\n",
        "# single-label, multiclass classification problem\n",
        "\n",
        "# Layer-1: Flattening: Input Image: 28*28 = 784 pixels, gets converted into a 1D array;\n",
        "# Layer-2: Dense layer: 128 units, relu allows the model to solve nonlinear problems; number of hidden layers = 128\n",
        "# Layer-3: Output layer: 10 units, softmax provides probabilities for each possible output class, as this is a case of single-label, multiclass classification problem;\n",
        "\n",
        "\n",
        "model = tf.keras.Sequential([\n",
        "    tf.keras.layers.Flatten(input_shape=(28, 28, 1)),\n",
        "    tf.keras.layers.Dense(128, activation=tf.nn.relu),\n",
        "    tf.keras.layers.Dense(256, activation=tf.nn.relu),\n",
        "    tf.keras.layers.Dense(10, activation=tf.nn.softmax)\n",
        "])"
      ],
      "execution_count": 146,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jVZawM4BcKJr"
      },
      "source": [
        "# Categorical crossentropy loss function is almost always suited for multiclass classification.\n",
        "# It minimizes the distance between the probability distributions output by the network and the true distribution of the targets.\n",
        "# Since, the labels here are encoded as integers, SparseCategoricalCrossentropy() is used.\n",
        "\n",
        "# Accuracy works well to evaluate a classification model as the outputs are discrete labels.\n",
        "\n",
        "model.compile(optimizer='adam',\n",
        "              loss=tf.keras.losses.SparseCategoricalCrossentropy(),\n",
        "              metrics=['accuracy'])"
      ],
      "execution_count": 147,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2yELI3vhBLTq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 533
        },
        "outputId": "7f393c50-c578-4d4c-b391-74116912a8b0"
      },
      "source": [
        "from tensorflow.keras.utils import plot_model\n",
        "plot_model(model, show_shapes=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MEaLJ9bZE446",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "83960022-c3db-45e9-8add-6c6df9a118e2"
      },
      "source": [
        "# saving the initial weights for later experiments\n",
        "init_weights = model.get_weights()\n",
        "\n",
        "names = [weight.name for layer in model.layers for weight in layer.weights]\n",
        "\n",
        "for name, weight in zip(names, init_weights):\n",
        "    print(name, weight.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "dense/kernel:0 (784, 128)\n",
            "dense/bias:0 (128,)\n",
            "dense_1/kernel:0 (128, 256)\n",
            "dense_1/bias:0 (256,)\n",
            "dense_2/kernel:0 (256, 10)\n",
            "dense_2/bias:0 (10,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MZ2BVK5tAzMM"
      },
      "source": [
        "### Task 4.2 Fitting the model\n",
        "\n",
        "Before you fit the model. Think about what initialisation method have you chosen? If you did not specify the initialisation method, find out what is the default one. Choose a layer and visualise its initial weights. (Hint: You may use UMAP to visualise a collection of high-dimension vectors.)\n",
        "\n",
        "Decide and report the following training setting:\n",
        "\n",
        "1. The training batch size\n",
        "2. The number of training epochs (at least 1,000 epochs recommended)\n",
        "3. The learning rate. If you used momentum or a learning rate schedule, please report the configuration as well.\n",
        "\n",
        "Now fit the model. Show how the training loss changes. How did you decide when to stop training?\n",
        "\n",
        "After fitting the model, visualise the model weights again. How did the weights change? Why?\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-F2RvaPdcRdz"
      },
      "source": [
        "tf.config.run_functions_eagerly(True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EsBPCoXMin7k",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7589d250-c781-4dbc-eaab-d22266f95964"
      },
      "source": [
        "\n",
        "history = model.fit(\n",
        "    train_images, train_labels,\n",
        "    epochs=12, batch_size=128, validation_split=0.33) "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/tensorflow/python/data/ops/dataset_ops.py:3504: UserWarning: Even though the tf.config.experimental_run_functions_eagerly option is set, this option does not apply to tf.data functions. tf.data functions are still traced and executed as graphs.\n",
            "  \"Even though the tf.config.experimental_run_functions_eagerly \"\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/12\n",
            "315/315 [==============================] - 8s 24ms/step - loss: 0.8185 - accuracy: 0.7204 - val_loss: 0.4446 - val_accuracy: 0.8393\n",
            "Epoch 2/12\n",
            "315/315 [==============================] - 7s 23ms/step - loss: 0.4101 - accuracy: 0.8533 - val_loss: 0.4050 - val_accuracy: 0.8561\n",
            "Epoch 3/12\n",
            "315/315 [==============================] - 7s 23ms/step - loss: 0.3710 - accuracy: 0.8657 - val_loss: 0.3525 - val_accuracy: 0.8743\n",
            "Epoch 4/12\n",
            "315/315 [==============================] - 7s 23ms/step - loss: 0.3325 - accuracy: 0.8793 - val_loss: 0.3569 - val_accuracy: 0.8685\n",
            "Epoch 5/12\n",
            "315/315 [==============================] - 7s 22ms/step - loss: 0.3113 - accuracy: 0.8852 - val_loss: 0.3470 - val_accuracy: 0.8756\n",
            "Epoch 6/12\n",
            "315/315 [==============================] - 7s 23ms/step - loss: 0.2871 - accuracy: 0.8938 - val_loss: 0.3488 - val_accuracy: 0.8737\n",
            "Epoch 7/12\n",
            "315/315 [==============================] - 7s 23ms/step - loss: 0.2804 - accuracy: 0.8966 - val_loss: 0.3334 - val_accuracy: 0.8816\n",
            "Epoch 8/12\n",
            "315/315 [==============================] - 7s 22ms/step - loss: 0.2785 - accuracy: 0.8971 - val_loss: 0.3239 - val_accuracy: 0.8834\n",
            "Epoch 9/12\n",
            "315/315 [==============================] - 7s 22ms/step - loss: 0.2431 - accuracy: 0.9078 - val_loss: 0.3085 - val_accuracy: 0.8903\n",
            "Epoch 10/12\n",
            "315/315 [==============================] - 7s 22ms/step - loss: 0.2364 - accuracy: 0.9105 - val_loss: 0.3378 - val_accuracy: 0.8784\n",
            "Epoch 11/12\n",
            "315/315 [==============================] - 7s 23ms/step - loss: 0.2372 - accuracy: 0.9114 - val_loss: 0.3197 - val_accuracy: 0.8904\n",
            "Epoch 12/12\n",
            "315/315 [==============================] - 7s 22ms/step - loss: 0.2255 - accuracy: 0.9155 - val_loss: 0.3064 - val_accuracy: 0.8922\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZydRZpuI9zp9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "bb0902a8-6dd5-4cce-8565-cdc35c188c61"
      },
      "source": [
        "acc = history.history['accuracy']\n",
        "val_acc = history.history['val_accuracy']\n",
        "loss = history.history['loss']\n",
        "val_loss = history.history['val_loss']\n",
        "epochs = range(1, len(acc) + 1)\n",
        "\n",
        "plt.figure(figsize=(15,4))\n",
        "\n",
        "ax1 = plt.subplot(1, 2, 1)\n",
        "plt.plot(epochs, loss, label='Training loss')\n",
        "plt.plot(epochs, val_loss, label='Validation loss')\n",
        "plt.fill_between(epochs, loss,val_loss,color='g',alpha=.1)\n",
        "\n",
        "plt.title('Training and validation loss')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "\n",
        "ax2 = plt.subplot(1, 2, 2)\n",
        "plt.plot(epochs, acc, label='Training accuracy')\n",
        "plt.plot(epochs, val_acc, label='Validation accuracy')\n",
        "plt.fill_between(epochs, acc,val_acc,color='g',alpha=.1)\n",
        "plt.title('Training and validation accuracy')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gzOFN785WjWO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2d3b0904-8b3e-4c39-fc97-a0846bb5b754"
      },
      "source": [
        "# weights after training\n",
        "\n",
        "f_weights = model.get_weights()\n",
        "\n",
        "names = [weight.name for layer in model.layers for weight in layer.weights]\n",
        "\n",
        "for name, weight in zip(names, f_weights):\n",
        "    print(name, weight.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "dense_3/kernel:0 (784, 128)\n",
            "dense_3/bias:0 (128,)\n",
            "dense_4/kernel:0 (128, 256)\n",
            "dense_4/bias:0 (256,)\n",
            "dense_5/kernel:0 (256, 10)\n",
            "dense_5/bias:0 (10,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E45zJVjSdNpI",
        "outputId": "159f44e1-3f42-4fe2-ee5b-04f7a2c220b8"
      },
      "source": [
        "test_loss, test_accuracy = model.evaluate(fmnist_test_ds, steps=math.ceil(len(test_labels)/32))\n",
        "print('Accuracy on test dataset after initial training: ', test_accuracy)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            " 17/313 [>.............................] - ETA: 4s - loss: 0.8828 - accuracy: 0.8843WARNING:tensorflow:Your input ran out of data; interrupting training. Make sure that your dataset or generator can generate at least `steps_per_epoch * epochs` batches (in this case, 313 batches). You may need to use the repeat() function when building your dataset.\n",
            "313/313 [==============================] - 0s 849us/step - loss: 0.8788 - accuracy: 0.8835\n",
            "Accuracy on test dataset after initial training:  0.8834999799728394\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9C2VXCM-4Nw9"
      },
      "source": [
        "### Task 4.3 Check the training using TensorBoard\n",
        "\n",
        "Use TensorBoard to visualise the training process. Show screenshots of your TensorBoard output.\n",
        "\n",
        "**Optional task:** Record the gradients during training and use TensorBoard to visualise the gradients."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gHQVKiwwFYSi",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "55cd499b-f441-42ff-d6ea-dd744ad9bc2f"
      },
      "source": [
        "!rm -rf ./logs/\n",
        "#tf.compat.v1.disable_v2_behavior()\n",
        "\n",
        "from datetime import datetime\n",
        "import os\n",
        "\n",
        "root_logdir = \"logs\"\n",
        "run_id = datetime.now().strftime(\"%Y%m%d-%H%M%S\")\n",
        "logdir = os.path.join(root_logdir, run_id)\n",
        "\n",
        "callbacks = [\n",
        "    tf.keras.callbacks.TensorBoard(\n",
        "        log_dir=logdir,\n",
        "        histogram_freq=1,\n",
        "         write_graph=True, \n",
        "         write_images=True,\n",
        "         write_grads=True\n",
        "    )\n",
        "]\n",
        "\n",
        "# tensorboard_callback = tf.compat.v1.keras.callbacks.TensorBoard(log_dir=logdir, histogram_freq=1, write_grads =True)\n",
        "# tbCallBack = tf.keras.callbacks.TensorBoard(log_dir='./Graph', histogram_freq=0, write_graph=True, write_images=True)\n",
        "\n",
        "# def create_model():\n",
        "#   model = tf.keras.Sequential([\n",
        "#     tf.keras.layers.Flatten(input_shape=(28, 28, 1)),\n",
        "#     tf.keras.layers.Dense(128, activation=tf.nn.relu),\n",
        "#     tf.keras.layers.Dense(10, activation=tf.nn.softmax)\n",
        "# ])\n",
        "#   return model\n",
        "# model = create_model()\n",
        "\n",
        "# model.compile(optimizer='adam',\n",
        "#               loss=tf.keras.losses.SparseCategoricalCrossentropy(),\n",
        "#               metrics=['accuracy'])\n",
        "\n",
        "# reset the training\n",
        "model.set_weights(init_weights)\n",
        "\n",
        "history = model.fit(train_images, \n",
        "                      train_labels, \n",
        "                      epochs=12, \n",
        "                      batch_size=128,\n",
        "                      callbacks=callbacks, validation_split=0.33)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:`write_grads` will be ignored in TensorFlow 2.0 for the `TensorBoard` Callback.\n",
            "Epoch 1/12\n",
            "  1/315 [..............................] - ETA: 6s - loss: 2.3026 - accuracy: 0.1328"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/tensorflow/python/data/ops/dataset_ops.py:3504: UserWarning: Even though the tf.config.experimental_run_functions_eagerly option is set, this option does not apply to tf.data functions. tf.data functions are still traced and executed as graphs.\n",
            "  \"Even though the tf.config.experimental_run_functions_eagerly \"\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "315/315 [==============================] - 8s 24ms/step - loss: 0.5599 - accuracy: 0.8010 - val_loss: 0.4268 - val_accuracy: 0.8444\n",
            "Epoch 2/12\n",
            "315/315 [==============================] - 7s 23ms/step - loss: 0.3811 - accuracy: 0.8636 - val_loss: 0.3775 - val_accuracy: 0.8668\n",
            "Epoch 3/12\n",
            "315/315 [==============================] - 7s 23ms/step - loss: 0.3502 - accuracy: 0.8719 - val_loss: 0.3477 - val_accuracy: 0.8734\n",
            "Epoch 4/12\n",
            "315/315 [==============================] - 7s 23ms/step - loss: 0.3210 - accuracy: 0.8834 - val_loss: 0.3696 - val_accuracy: 0.8647\n",
            "Epoch 5/12\n",
            "315/315 [==============================] - 7s 23ms/step - loss: 0.3019 - accuracy: 0.8893 - val_loss: 0.3392 - val_accuracy: 0.8797\n",
            "Epoch 6/12\n",
            "315/315 [==============================] - 7s 23ms/step - loss: 0.2934 - accuracy: 0.8920 - val_loss: 0.3204 - val_accuracy: 0.8826\n",
            "Epoch 7/12\n",
            "315/315 [==============================] - 7s 23ms/step - loss: 0.2740 - accuracy: 0.8985 - val_loss: 0.3221 - val_accuracy: 0.8802\n",
            "Epoch 8/12\n",
            "315/315 [==============================] - 7s 23ms/step - loss: 0.2565 - accuracy: 0.9051 - val_loss: 0.3288 - val_accuracy: 0.8826\n",
            "Epoch 9/12\n",
            "315/315 [==============================] - 7s 23ms/step - loss: 0.2555 - accuracy: 0.9047 - val_loss: 0.3255 - val_accuracy: 0.8851\n",
            "Epoch 10/12\n",
            "315/315 [==============================] - 7s 23ms/step - loss: 0.2371 - accuracy: 0.9107 - val_loss: 0.3327 - val_accuracy: 0.8815\n",
            "Epoch 11/12\n",
            "315/315 [==============================] - 7s 23ms/step - loss: 0.2360 - accuracy: 0.9112 - val_loss: 0.3251 - val_accuracy: 0.8860\n",
            "Epoch 12/12\n",
            "315/315 [==============================] - 7s 23ms/step - loss: 0.2182 - accuracy: 0.9185 - val_loss: 0.3309 - val_accuracy: 0.8834\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X5L0t5dKFfMT"
      },
      "source": [
        "%reload_ext tensorboard\n",
        "%tensorboard --logdir=logs"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oxltxSp5jt5T",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "65e90534-d96d-4f18-bbff-7c0a67819f48"
      },
      "source": [
        "test_loss, test_accuracy = model.evaluate(fmnist_test_ds, steps=math.ceil(len(test_labels)/32))\n",
        "print('Accuracy on test dataset:', test_accuracy)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            " 17/313 [>.............................] - ETA: 4s - loss: 0.3540 - accuracy: 0.8767WARNING:tensorflow:Your input ran out of data; interrupting training. Make sure that your dataset or generator can generate at least `steps_per_epoch * epochs` batches (in this case, 313 batches). You may need to use the repeat() function when building your dataset.\n",
            "313/313 [==============================] - 0s 844us/step - loss: 0.3606 - accuracy: 0.8740\n",
            "Accuracy on test dataset: 0.8740000128746033\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4EgXZCBNAzMs"
      },
      "source": [
        "## Task 5 Overfitting and regularisation\n",
        "\n",
        "*(weight ~30%)*\n",
        "\n",
        "Go back to the previous task. Plot the training and validation loss and accuracy. Answer the following questions:\n",
        "\n",
        "1. Do you see overfitting or underfitting? Why?\n",
        "2. If you see overfitting, at which epoch did it happen?\n",
        "\n",
        "Now retrain the model with only 200 training examples. (Make sure that you  reinitialise the weights before retraining.) Do you see overfitting? How did the validation loss and accuracy change?\n",
        "\n",
        "Neural networks are overparametrised models, meaning there can be more parameters than the training examples. Some form of regularisation is almost always necessary to obtain a useful model. Below are some options:\n",
        "\n",
        "1. Add dropout\n",
        "2. Add Batch Normalisation\n",
        "3. Add layer-specific weight regularizations\n",
        "4. Change the learning rate\n",
        "\n",
        "In addition, you may also try changing the weight initialisation method.\n",
        "\n",
        "Apply different regularisation techniques to the model training. You may also try other techniques for improving training such as learning rate scheduling (see https://www.tensorflow.org/guide/keras/train_and_evaluate#using_learning_rate_schedules).\n",
        "\n",
        "Run **five or more** experiments of different training configurations and record the validation accuracy achieved in the Markdown table below. You may modify the table heading to match your experiment design.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 808
        },
        "id": "zPPqsZsQpY7o",
        "outputId": "1200033b-9c3d-4df3-b7dd-310915fc03d2"
      },
      "source": [
        "# The model is observed to be overfitting as the accuracy reaches its peak and then starts to decrease and increase again, after 80 epochs. \n",
        "# When the model is trained on only 200 examples, it completely memorises the data resulting in 100% accuracy on the training dataset but a low accuracy on the test dataset.\n",
        "\n",
        "# training the model only on 200 images\n",
        "\n",
        "train_images_200 = train_images[:200]\n",
        "train_labels_200 = train_labels[:200]\n",
        "model.set_weights(init_weights)\n",
        "\n",
        "history = model.fit(train_images_200, \n",
        "                      train_labels_200, \n",
        "                      epochs=12, \n",
        "                      batch_size=32,\n",
        "                      callbacks=callbacks, validation_split=0.33)\n",
        "\n",
        "acc = history.history['accuracy']\n",
        "val_acc = history.history['val_accuracy']\n",
        "loss = history.history['loss']\n",
        "val_loss = history.history['val_loss']\n",
        "epochs = range(1, len(acc) + 1)\n",
        "\n",
        "plt.figure(figsize=(15,4))\n",
        "\n",
        "ax1 = plt.subplot(1, 2, 1)\n",
        "plt.plot(epochs, loss, label='Training loss')\n",
        "plt.plot(epochs, val_loss, label='Validation loss')\n",
        "plt.fill_between(epochs, loss,val_loss,color='g',alpha=.1)\n",
        "\n",
        "plt.title('Training and validation loss')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "\n",
        "ax2 = plt.subplot(1, 2, 2)\n",
        "plt.plot(epochs, acc, label='Training accuracy')\n",
        "plt.plot(epochs, val_acc, label='Validation accuracy')\n",
        "plt.fill_between(epochs, acc,val_acc,color='g',alpha=.1)\n",
        "plt.title('Training and validation accuracy')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.legend()\n",
        "plt.show()\n",
        "\n",
        "test_loss, test_accuracy = model.evaluate(fmnist_test_ds, steps=math.ceil(len(test_labels)/32))\n",
        "print('Accuracy on test dataset after training on only 200 images: ', test_accuracy)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/12\n",
            "\r1/5 [=====>........................] - ETA: 0s - loss: 2.2706 - accuracy: 0.1250"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/tensorflow/python/data/ops/dataset_ops.py:3504: UserWarning: Even though the tf.config.experimental_run_functions_eagerly option is set, this option does not apply to tf.data functions. tf.data functions are still traced and executed as graphs.\n",
            "  \"Even though the tf.config.experimental_run_functions_eagerly \"\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "5/5 [==============================] - 0s 119ms/step - loss: 2.2012 - accuracy: 0.1940 - val_loss: 1.7969 - val_accuracy: 0.4697\n",
            "Epoch 2/12\n",
            "5/5 [==============================] - 0s 33ms/step - loss: 1.5117 - accuracy: 0.5373 - val_loss: 1.2357 - val_accuracy: 0.5303\n",
            "Epoch 3/12\n",
            "5/5 [==============================] - 0s 32ms/step - loss: 0.9001 - accuracy: 0.7090 - val_loss: 1.3109 - val_accuracy: 0.5758\n",
            "Epoch 4/12\n",
            "5/5 [==============================] - 0s 34ms/step - loss: 0.7038 - accuracy: 0.7612 - val_loss: 0.9751 - val_accuracy: 0.6667\n",
            "Epoch 5/12\n",
            "5/5 [==============================] - 0s 33ms/step - loss: 0.5627 - accuracy: 0.7985 - val_loss: 1.1302 - val_accuracy: 0.6818\n",
            "Epoch 6/12\n",
            "5/5 [==============================] - 0s 37ms/step - loss: 0.3669 - accuracy: 0.8657 - val_loss: 1.1285 - val_accuracy: 0.6364\n",
            "Epoch 7/12\n",
            "5/5 [==============================] - 0s 36ms/step - loss: 0.3374 - accuracy: 0.8955 - val_loss: 1.2198 - val_accuracy: 0.6364\n",
            "Epoch 8/12\n",
            "5/5 [==============================] - 0s 33ms/step - loss: 0.3073 - accuracy: 0.8731 - val_loss: 1.0367 - val_accuracy: 0.7273\n",
            "Epoch 9/12\n",
            "5/5 [==============================] - 0s 43ms/step - loss: 0.2448 - accuracy: 0.9104 - val_loss: 0.9582 - val_accuracy: 0.7727\n",
            "Epoch 10/12\n",
            "5/5 [==============================] - 0s 35ms/step - loss: 0.1975 - accuracy: 0.9104 - val_loss: 1.3398 - val_accuracy: 0.6667\n",
            "Epoch 11/12\n",
            "5/5 [==============================] - 0s 34ms/step - loss: 0.2105 - accuracy: 0.9403 - val_loss: 1.0502 - val_accuracy: 0.7273\n",
            "Epoch 12/12\n",
            "5/5 [==============================] - 0s 35ms/step - loss: 0.1550 - accuracy: 0.9328 - val_loss: 1.1934 - val_accuracy: 0.7424\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            " 18/313 [>.............................] - ETA: 3s - loss: 1.1364 - accuracy: 0.6953WARNING:tensorflow:Your input ran out of data; interrupting training. Make sure that your dataset or generator can generate at least `steps_per_epoch * epochs` batches (in this case, 313 batches). You may need to use the repeat() function when building your dataset.\n",
            "313/313 [==============================] - 0s 875us/step - loss: 1.1480 - accuracy: 0.6942\n",
            "Accuracy on test dataset after training on only 200 images:  0.6941999793052673\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ojtjPPs7Imk1"
      },
      "source": [
        "# adding dropout, batch normalisation, changing the learning rate\n",
        "\n",
        "from keras.layers import Dropout\n",
        "from keras.layers import Activation\n",
        "from keras.regularizers import l2\n",
        "from keras.optimizers import SGD\n",
        "from keras.optimizers import RMSprop\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "\n",
        "def optimiser_sgd(learning_rate):\n",
        "  learning_rate = learning_rate\n",
        "  decay_rate = learning_rate / 50\n",
        "  momentum = 0.8\n",
        "\n",
        "  sgd = SGD(lr=learning_rate, momentum=momentum, decay=decay_rate, nesterov=False)\n",
        "  return sgd"
      ],
      "execution_count": 161,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TuRPVveRJv5e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3b7e9c30-a8e9-4d45-934d-e72a94a3d1bc"
      },
      "source": [
        "learning_rates = [0.1, 0.01, 0.001, 0.002, 0.02]\n",
        "dropouts = [0.1, 0.2, 0.25, 0.3, 0.15]\n",
        "\n",
        "for i in range(0, 5):\n",
        "  for j in range(0, 5):\n",
        "    \n",
        "    model_s = tf.keras.Sequential([\n",
        "      tf.keras.layers.Flatten(input_shape=(28, 28, 1)),\n",
        "      tf.keras.layers.Dense(128, activation=tf.nn.relu),\n",
        "      tf.keras.layers.Dropout(dropouts[i]),\n",
        "      tf.keras.layers.Dense(256, activation=tf.nn.relu),\n",
        "      tf.keras.layers.Dense(10, activation=tf.nn.softmax)\n",
        "    ])\n",
        "    model_s.compile(optimizer=optimiser_sgd(learning_rates[j]),\n",
        "                loss=tf.keras.losses.SparseCategoricalCrossentropy(),\n",
        "                metrics=['accuracy'])\n",
        "    history = model_s.fit(train_images, \n",
        "                      train_labels,\n",
        "                        epochs=50, \n",
        "                        batch_size=128, validation_split=0.33)\n",
        "    acc = history.history['accuracy']\n",
        "    val_acc = history.history['val_accuracy']\n",
        "    loss = history.history['loss']\n",
        "    val_loss = history.history['val_loss']\n",
        "    epochs = range(1, len(acc) + 1)\n",
        "\n",
        "    plt.figure(figsize=(15,4))\n",
        "\n",
        "    ax1 = plt.subplot(1, 2, 1)\n",
        "    plt.plot(epochs, loss, label='Training loss')\n",
        "    plt.plot(epochs, val_loss, label='Validation loss')\n",
        "    plt.fill_between(epochs, loss,val_loss,color='g',alpha=.1)\n",
        "\n",
        "    plt.title('Training and validation loss')\n",
        "    plt.xlabel('Epochs')\n",
        "    plt.ylabel('Loss')\n",
        "    plt.legend()\n",
        "\n",
        "    ax2 = plt.subplot(1, 2, 2)\n",
        "    plt.plot(epochs, acc, label='Training accuracy')\n",
        "    plt.plot(epochs, val_acc, label='Validation accuracy')\n",
        "    plt.fill_between(epochs, acc,val_acc,color='g',alpha=.1)\n",
        "    plt.title('Training and validation accuracy')\n",
        "    plt.xlabel('Epochs')\n",
        "    plt.ylabel('Accuracy')\n",
        "    plt.legend()\n",
        "    plt.show()\n",
        "    test_loss, test_accuracy = model_s.evaluate(fmnist_test_ds)\n",
        "    print('Accuracy on test dataset for:', test_accuracy, \"Learning rate: \"+str(learning_rates[j])+\" Dropout: \"+str(dropouts[i])+\"\\n\\n\")\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/50\n",
            "315/315 [==============================] - 3s 8ms/step - loss: 0.8351 - accuracy: 0.6976 - val_loss: 0.5120 - val_accuracy: 0.8193\n",
            "Epoch 2/50\n",
            "315/315 [==============================] - 2s 7ms/step - loss: 0.4377 - accuracy: 0.8405 - val_loss: 0.3816 - val_accuracy: 0.8630\n",
            "Epoch 3/50\n",
            "315/315 [==============================] - 2s 6ms/step - loss: 0.3776 - accuracy: 0.8605 - val_loss: 0.3729 - val_accuracy: 0.8636\n",
            "Epoch 4/50\n",
            "315/315 [==============================] - 2s 6ms/step - loss: 0.3518 - accuracy: 0.8727 - val_loss: 0.3502 - val_accuracy: 0.8731\n",
            "Epoch 5/50\n",
            "315/315 [==============================] - 2s 6ms/step - loss: 0.3284 - accuracy: 0.8780 - val_loss: 0.3514 - val_accuracy: 0.8708\n",
            "Epoch 6/50\n",
            "315/315 [==============================] - 2s 6ms/step - loss: 0.3300 - accuracy: 0.8786 - val_loss: 0.3459 - val_accuracy: 0.8724\n",
            "Epoch 7/50\n",
            "315/315 [==============================] - 2s 6ms/step - loss: 0.3108 - accuracy: 0.8842 - val_loss: 0.3447 - val_accuracy: 0.8755\n",
            "Epoch 8/50\n",
            "315/315 [==============================] - 2s 6ms/step - loss: 0.3107 - accuracy: 0.8856 - val_loss: 0.3265 - val_accuracy: 0.8791\n",
            "Epoch 9/50\n",
            "202/315 [==================>...........] - ETA: 0s - loss: 0.2908 - accuracy: 0.8905"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d1LRIBrEgu92"
      },
      "source": [
        "\n",
        "|Dropout (rate)   | Batch Normalisation (Y/N)  | Optimiser  | Learning Rate  | Number of Epochs   |  Validation Accuracy |\n",
        "|---|---|---|---|---|---|\n",
        "| 0.1  |  Y |  SGD | 0.1  | 35  |  0.8921 |\n",
        "| 0.1  |  Y |  SGD |  0.01 |  35 | 0.8836  |\n",
        "|  0.1 | Y  |  SGD |  0.001 |  35 | 0.8478  |\n",
        "| 0.1  |  Y |  SGD | 0.002  | 35  |  0.8630 |\n",
        "| 0.1  |  Y |  SGD | 0.02 | 35  |  0.9083 |\n",
        "| 0.2  |  Y |  SGD | 0.1 | 35  |  0.8931 |\n",
        "| 0.2  |  Y |  SGD | 0.01 | 35  |  0.8916 |\n",
        "| 0.2  |  Y |  SGD | 0.001 | 35  |  0.8458 |\n",
        "| 0.2  |  Y |  SGD | 0.002 | 35  |  0.8616 |\n",
        "| 0.2  |  Y |  SGD | 0.02 | 35  |  0.8333 |\n",
        "| 0.25  |  Y |  SGD | 0.1 | 35  |  0.8911 |\n",
        "| 0.25  |  Y |  SGD | 0.01 | 35  |  0.8816 |\n",
        "| 0.25  |  Y |  SGD | 0.001 | 35  |  0.8446 |\n",
        "| 0.25  |  Y |  SGD | 0.002 | 35  |  0.8602 |\n",
        "| 0.25  |  Y |  SGD | 0.02 | 35  |  0.8838 |\n",
        "| 0.3  |  Y |  SGD | 0.1 | 35  |  0.8914 |\n",
        "| 0.3  |  Y |  SGD | 0.01 | 35  |  0.8834 |\n",
        "| 0.3  |  Y |  SGD | 0.001 | 35  |  0.8401 |\n",
        "| 0.3  |  Y |  SGD | 0.002 | 35  |  0.8604 |\n",
        "| 0.3  |  Y |  SGD | 0.02 | 35  |  0.8856 |\n",
        "| 0.15  |  Y |  SGD | 0.1 | 35  |  0.8890 |\n",
        "| 0.15  |  Y |  SGD | 0.01 | 35  |  0.8851 |\n",
        "| 0.15  |  Y |  SGD | 0.001 | 35  |  0.8472 |\n",
        "| 0.15  |  Y |  SGD | 0.002 | 35  |  0.8637 |\n",
        "| 0.15  |  Y |  SGD | 0.02 | 35  |  0.8882 |\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cQ66UDpg6q9P"
      },
      "source": [
        "# Layer specific weight regularisation. This structure was applied using the best case of learning rate and dropout combination from the previous experiment.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HnkmXXbglGqU"
      },
      "source": [
        "from keras.callbacks import LearningRateScheduler\n",
        "from keras.callbacks import History\n",
        "\n",
        "\n",
        "decay_rate = 0.001\n",
        "learning_rate = 0.001\n",
        "\n",
        "def exp_decay(epoch):\n",
        "    lrate = learning_rate * np.exp(-decay_rate*epoch)\n",
        "    return lrate\n",
        "    \n",
        "# learning schedule callback\n",
        "loss_history = History()\n",
        "lr_rate = LearningRateScheduler(exp_decay)\n",
        "callbacks_list = [loss_history, lr_rate]"
      ],
      "execution_count": 181,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nujfQsaIllMj"
      },
      "source": [
        "from keras.layers.normalization import BatchNormalization\n",
        "from keras.regularizers import l2\n",
        "\n",
        "model_d = tf.keras.Sequential([\n",
        "    tf.keras.layers.Flatten(input_shape=(28, 28, 1)),\n",
        "    tf.keras.layers.Dense(128, kernel_regularizer=l2(0.01), bias_regularizer=l2(0.01), activation=tf.nn.relu),\n",
        "    tf.keras.layers.BatchNormalization(),\n",
        "    tf.keras.layers.Dropout(0.1),\n",
        "    tf.keras.layers.Dense(256, kernel_regularizer=l2(0.01), bias_regularizer=l2(0.01), activation=tf.nn.relu),\n",
        "    tf.keras.layers.Dense(10, activation=tf.nn.softmax)\n",
        "])"
      ],
      "execution_count": 163,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 754
        },
        "id": "Q2Wbul8hlVk_",
        "outputId": "e1a3a689-8d04-49eb-cd51-f967e240b3c2"
      },
      "source": [
        "from tensorflow.keras.utils import plot_model\n",
        "plot_model(model_d, show_shapes=True)"
      ],
      "execution_count": 164,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 164
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TPGHTdQ3lq19"
      },
      "source": [
        "model_d.compile(optimizer=optimiser_sgd(0.001),\n",
        "              loss=tf.keras.losses.SparseCategoricalCrossentropy(),\n",
        "              metrics=['accuracy'])"
      ],
      "execution_count": 165,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qtFIaSl0lMY7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 635
        },
        "outputId": "12fad8f7-7717-42fd-bb61-089d7e709bde"
      },
      "source": [
        "regularised_layers_model_history = model_d.fit(train_images, \n",
        "                                     train_labels,\n",
        "                                     validation_split=0.3,\n",
        "                                     batch_size=512,\n",
        "                                     epochs=10,\n",
        "                                    callbacks = callbacks_list)\n",
        "\n",
        "acc = regularised_layers_model_history.history['accuracy']\n",
        "val_acc = regularised_layers_model_history.history['val_accuracy']\n",
        "loss = regularised_layers_model_history.history['loss']\n",
        "val_loss = regularised_layers_model_history.history['val_loss']\n",
        "epochs = range(1, len(acc) + 1)\n",
        "\n",
        "plt.figure(figsize=(15,4))\n",
        "\n",
        "ax1 = plt.subplot(1, 2, 1)\n",
        "plt.plot(epochs, loss, label='Training loss')\n",
        "plt.plot(epochs, val_loss, label='Validation loss')\n",
        "plt.fill_between(epochs, loss,val_loss,color='g',alpha=.1)\n",
        "\n",
        "plt.title('Training and validation loss')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "\n",
        "ax2 = plt.subplot(1, 2, 2)\n",
        "plt.plot(epochs, acc, label='Training accuracy')\n",
        "plt.plot(epochs, val_acc, label='Validation accuracy')\n",
        "plt.fill_between(epochs, acc,val_acc,color='g',alpha=.1)\n",
        "plt.title('Training and validation accuracy')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 187,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "83/83 [==============================] - 1s 17ms/step - loss: 0.2294 - accuracy: 0.9479 - val_loss: 0.3699 - val_accuracy: 0.8949\n",
            "Epoch 2/10\n",
            "83/83 [==============================] - 1s 16ms/step - loss: 0.2252 - accuracy: 0.9492 - val_loss: 0.3668 - val_accuracy: 0.8976\n",
            "Epoch 3/10\n",
            "83/83 [==============================] - 1s 16ms/step - loss: 0.2240 - accuracy: 0.9496 - val_loss: 0.3633 - val_accuracy: 0.8976\n",
            "Epoch 4/10\n",
            "83/83 [==============================] - 1s 15ms/step - loss: 0.2217 - accuracy: 0.9507 - val_loss: 0.3632 - val_accuracy: 0.8995\n",
            "Epoch 5/10\n",
            "83/83 [==============================] - 1s 16ms/step - loss: 0.2223 - accuracy: 0.9510 - val_loss: 0.3663 - val_accuracy: 0.8967\n",
            "Epoch 6/10\n",
            "83/83 [==============================] - 1s 16ms/step - loss: 0.2216 - accuracy: 0.9509 - val_loss: 0.3641 - val_accuracy: 0.8979\n",
            "Epoch 7/10\n",
            "83/83 [==============================] - 1s 17ms/step - loss: 0.2212 - accuracy: 0.9527 - val_loss: 0.3636 - val_accuracy: 0.8977\n",
            "Epoch 8/10\n",
            "83/83 [==============================] - 1s 16ms/step - loss: 0.2180 - accuracy: 0.9526 - val_loss: 0.3684 - val_accuracy: 0.8964\n",
            "Epoch 9/10\n",
            "83/83 [==============================] - 1s 16ms/step - loss: 0.2188 - accuracy: 0.9530 - val_loss: 0.3634 - val_accuracy: 0.8997\n",
            "Epoch 10/10\n",
            "83/83 [==============================] - 1s 16ms/step - loss: 0.2203 - accuracy: 0.9532 - val_loss: 0.3684 - val_accuracy: 0.8974\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uVPDQes4Xo5k",
        "outputId": "7c693287-5e76-4525-a990-fea10d96f27a"
      },
      "source": [
        "test_loss, test_accuracy = model_d.evaluate(test_images, test_labels, steps=math.ceil(len(test_labels)/32))\n",
        "print('Accuracy on test dataset after layer specific weight regularisation:', test_accuracy)"
      ],
      "execution_count": 189,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "313/313 [==============================] - 0s 2ms/step - loss: 0.3889 - accuracy: 0.8905\n",
            "Accuracy on test dataset after layer specific weight regularisation: 0.890500009059906\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dLjJA98Qgxg5"
      },
      "source": [
        "\n",
        "Answer the following questions:\n",
        "\n",
        "1. Which configuration achieved the best validation accuracy? Report the test accuracy of your final model.\n",
        "2. Which setting had the most impact and which one had the least impact?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DKnAsWhyZ9iG"
      },
      "source": [
        "# The batch size, learning rate decay and layer-specific weight regularisations made the most impact on improving the accuracy whereas, batch normalisation has the least effect for the model in this case. After fine-tuning the model, the accuracy on the test set was slightly improved as compared to the initial training accuracy."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UgzZh0jN5ULb"
      },
      "source": [
        "## Task 6 Generate HTML output\n",
        "\n",
        "Use *nbconvert* to convert your completed notebook into an HTML file and name it **[YourID]_assingment1_output.html**."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qn6n1hIcyMVG"
      },
      "source": [
        "!apt-get update\n",
        "!apt-get install texlive texlive-xetex texlive-latex-extra pandoc --fix-missing\n",
        "!pip install pypandoc"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XYnrANKFyMYm"
      },
      "source": [
        "!cp drive/MyDrive/Colab_Notebooks/Maze.ipynb ./"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D2tAsyR2yMcQ"
      },
      "source": [
        "!jupyter nbconvert --to PDF \"Maze.ipynb\""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cWbr7jjEAzNb"
      },
      "source": [
        "---\n",
        "**END OF ASSIGNMENT ONE**\n",
        "\n"
      ]
    }
  ]
}