task-2-nn/IMT2020080.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "PFvBlb_7enV9"
      },
      "outputs": [],
      "source": [
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6DDMTgoFenWE"
      },
      "source": [
        "\n",
        "[Learn the Basics](intro.html) ||\n",
        "[Quickstart](quickstart_tutorial.html) ||\n",
        "[Tensors](tensorqs_tutorial.html) ||\n",
        "[Datasets & DataLoaders](data_tutorial.html) ||\n",
        "[Transforms](transforms_tutorial.html) ||\n",
        "**Build Model** ||\n",
        "[Autograd](autogradqs_tutorial.html) ||\n",
        "[Optimization](optimization_tutorial.html) ||\n",
        "[Save & Load Model](saveloadrun_tutorial.html)\n",
        "\n",
        "# Build the Neural Network\n",
        "\n",
        "Neural networks comprise of layers/modules that perform operations on data.\n",
        "The [torch.nn](https://pytorch.org/docs/stable/nn.html) namespace provides all the building blocks you need to\n",
        "build your own neural network. Every module in PyTorch subclasses the [nn.Module](https://pytorch.org/docs/stable/generated/torch.nn.Module.html).\n",
        "A neural network is a module itself that consists of other modules (layers). This nested structure allows for\n",
        "building and managing complex architectures easily.\n",
        "\n",
        "In the following sections, we'll build a neural network to classify images in the FashionMNIST dataset.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "6tlgLSkyenWH"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import torch\n",
        "from torch import nn\n",
        "from torch.utils.data import DataLoader\n",
        "from torchvision import datasets, transforms"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uN3Kypp5enWI"
      },
      "source": [
        "## Get Device for Training\n",
        "We want to be able to train our model on a hardware accelerator like the GPU,\n",
        "if it is available. Let's check to see if\n",
        "[torch.cuda](https://pytorch.org/docs/stable/notes/cuda.html) is available, else we\n",
        "continue to use the CPU.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "JBQODHTcenWK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0a2ecf2e-da96-4dda-8149-f04adc205bce"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Using cpu device\n"
          ]
        }
      ],
      "source": [
        "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
        "print(f\"Using {device} device\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GP3G-RAjenWL"
      },
      "source": [
        "## Define the Class\n",
        "We define our neural network by subclassing ``nn.Module``, and\n",
        "initialize the neural network layers in ``__init__``. Every ``nn.Module`` subclass implements\n",
        "the operations on input data in the ``forward`` method.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "QrQ6EonLenWL"
      },
      "outputs": [],
      "source": [
        "class NeuralNetwork(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(NeuralNetwork, self).__init__()\n",
        "        self.flatten = nn.Flatten()\n",
        "        self.linear_relu_stack = nn.Sequential(\n",
        "            nn.Linear(28*28, 512),\n",
        "            nn.ReLU(),\n",
        "            nn.Linear(1024, 512),\n",
        "            nn.ReLU(),\n",
        "            nn.Linear(512, 10),\n",
        "        )\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = self.flatten(x)\n",
        "        logits = self.linear_relu_stack(x)\n",
        "        return logits"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bODhpxruenWN"
      },
      "source": [
        "We create an instance of ``NeuralNetwork``, and move it to the ``device``, and print\n",
        "its structure.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "lkA5mjIVenWO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ab94817e-188c-42cc-bab6-fe8e85be4784"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "NeuralNetwork(\n",
            "  (flatten): Flatten(start_dim=1, end_dim=-1)\n",
            "  (linear_relu_stack): Sequential(\n",
            "    (0): Linear(in_features=784, out_features=512, bias=True)\n",
            "    (1): ReLU()\n",
            "    (2): Linear(in_features=512, out_features=512, bias=True)\n",
            "    (3): ReLU()\n",
            "    (4): Linear(in_features=512, out_features=10, bias=True)\n",
            "  )\n",
            ")\n"
          ]
        }
      ],
      "source": [
        "model = NeuralNetwork().to(device)\n",
        "print(model)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p2iitvuaenWP"
      },
      "source": [
        "To use the model, we pass it the input data. This executes the model's ``forward``,\n",
        "along with some [background operations](https://github.com/pytorch/pytorch/blob/270111b7b611d174967ed204776985cefca9c144/torch/nn/modules/module.py#L866).\n",
        "Do not call ``model.forward()`` directly!\n",
        "\n",
        "Calling the model on the input returns a 2-dimensional tensor with dim=0 corresponding to each output of 10 raw predicted values for each class, and dim=1 corresponding to the individual values of each output.  .\n",
        "We get the prediction probabilities by passing it through an instance of the ``nn.Softmax`` module.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "1K_v7erZenWQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "153e0617-01f8-41c6-ea0a-4f725d6880e3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicted class: tensor([7])\n"
          ]
        }
      ],
      "source": [
        "X = torch.rand(1, 28, 28, device=device)\n",
        "logits = model(X)\n",
        "pred_probab = nn.Softmax(dim=1)(logits)\n",
        "y_pred = pred_probab.argmax(1)\n",
        "print(f\"Predicted class: {y_pred}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lJQ4U-SjenWR"
      },
      "source": [
        "--------------\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0nsy_P1senWR"
      },
      "source": [
        "## Model Layers\n",
        "\n",
        "Let's break down the layers in the FashionMNIST model. To illustrate it, we\n",
        "will take a sample minibatch of 3 images of size 28x28 and see what happens to it as\n",
        "we pass it through the network.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "w_-J3rpzenWS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3c34b35b-d013-4a4a-8b85-a8b10a6138df"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([3, 28, 28])\n"
          ]
        }
      ],
      "source": [
        "input_image = torch.rand(3,28,28)\n",
        "print(input_image.size())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zQFQ0bIDenWS"
      },
      "source": [
        "### nn.Flatten\n",
        "We initialize the [nn.Flatten](https://pytorch.org/docs/stable/generated/torch.nn.Flatten.html)\n",
        "layer to convert each 2D 28x28 image into a contiguous array of 784 pixel values (\n",
        "the minibatch dimension (at dim=0) is maintained).\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "JvZYMYc8enWT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ede4db92-d980-4a21-db3c-9483942eabad"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([3, 784])\n"
          ]
        }
      ],
      "source": [
        "flatten = nn.Flatten()\n",
        "flat_image = flatten(input_image)\n",
        "print(flat_image.size())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RgERh2fnenWT"
      },
      "source": [
        "### nn.Linear\n",
        "The [linear layer](https://pytorch.org/docs/stable/generated/torch.nn.Linear.html)\n",
        "is a module that applies a linear transformation on the input using its stored weights and biases.\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "vcnfsNwbenWT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0c119009-8b05-440d-a08a-b96727b7fe04"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([3, 20])\n"
          ]
        }
      ],
      "source": [
        "layer1 = nn.Linear(in_features=28*28, out_features=20)\n",
        "hidden1 = layer1(flat_image)\n",
        "print(hidden1.size())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "msNk4n_eenWU"
      },
      "source": [
        "### nn.ReLU\n",
        "Non-linear activations are what create the complex mappings between the model's inputs and outputs.\n",
        "They are applied after linear transformations to introduce *nonlinearity*, helping neural networks\n",
        "learn a wide variety of phenomena.\n",
        "\n",
        "In this model, we use [nn.ReLU](https://pytorch.org/docs/stable/generated/torch.nn.ReLU.html) between our\n",
        "linear layers, but there's other activations to introduce non-linearity in your model.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "-6K3fKA9enWU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c17d6a5a-d291-4e9d-8af3-ce3000bf7f7f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Before ReLU: tensor([[ 0.7210, -0.3010, -0.1459,  0.0172,  0.0240, -0.0220, -0.7960, -0.1611,\n",
            "         -0.1615, -0.0387,  0.3698, -0.3994, -0.0815, -0.2248, -0.0395,  0.5085,\n",
            "         -0.6069,  0.0868,  0.1932, -0.2477],\n",
            "        [ 0.5804, -0.2024, -0.1334,  0.0671,  0.2918, -0.2627, -0.6421,  0.0056,\n",
            "         -0.5336,  0.0089,  0.1533, -0.1974,  0.0403, -0.5343, -0.0034,  0.4866,\n",
            "         -0.2967,  0.1894,  0.2431, -0.4405],\n",
            "        [ 0.5606, -0.2770,  0.1515,  0.2455,  0.6308, -0.1042, -0.6837,  0.0689,\n",
            "         -0.6841,  0.4043,  0.1064, -0.0737, -0.1920, -0.3831, -0.1199,  0.4274,\n",
            "         -0.7854,  0.3748,  0.2905, -0.3670]], grad_fn=<AddmmBackward0>)\n",
            "\n",
            "\n",
            "After ReLU: tensor([[0.7210, 0.0000, 0.0000, 0.0172, 0.0240, 0.0000, 0.0000, 0.0000, 0.0000,\n",
            "         0.0000, 0.3698, 0.0000, 0.0000, 0.0000, 0.0000, 0.5085, 0.0000, 0.0868,\n",
            "         0.1932, 0.0000],\n",
            "        [0.5804, 0.0000, 0.0000, 0.0671, 0.2918, 0.0000, 0.0000, 0.0056, 0.0000,\n",
            "         0.0089, 0.1533, 0.0000, 0.0403, 0.0000, 0.0000, 0.4866, 0.0000, 0.1894,\n",
            "         0.2431, 0.0000],\n",
            "        [0.5606, 0.0000, 0.1515, 0.2455, 0.6308, 0.0000, 0.0000, 0.0689, 0.0000,\n",
            "         0.4043, 0.1064, 0.0000, 0.0000, 0.0000, 0.0000, 0.4274, 0.0000, 0.3748,\n",
            "         0.2905, 0.0000]], grad_fn=<ReluBackward0>)\n"
          ]
        }
      ],
      "source": [
        "print(f\"Before ReLU: {hidden1}\\n\\n\")\n",
        "hidden1 = nn.ReLU()(hidden1)\n",
        "print(f\"After ReLU: {hidden1}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xd9cIl7HenWV"
      },
      "source": [
        "### nn.Sequential\n",
        "[nn.Sequential](https://pytorch.org/docs/stable/generated/torch.nn.Sequential.html) is an ordered\n",
        "container of modules. The data is passed through all the modules in the same order as defined. You can use\n",
        "sequential containers to put together a quick network like ``seq_modules``.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "VdiThgzQenWW"
      },
      "outputs": [],
      "source": [
        "seq_modules = nn.Sequential(\n",
        "    flatten,\n",
        "    layer1,\n",
        "    nn.ReLU(),\n",
        "    nn.Linear(20, 10)\n",
        ")\n",
        "input_image = torch.rand(3,28,28)\n",
        "logits = seq_modules(input_image)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZVroiEobenWW"
      },
      "source": [
        "### nn.Softmax\n",
        "The last linear layer of the neural network returns `logits` - raw values in [-\\infty, \\infty] - which are passed to the\n",
        "[nn.Softmax](https://pytorch.org/docs/stable/generated/torch.nn.Softmax.html) module. The logits are scaled to values\n",
        "[0, 1] representing the model's predicted probabilities for each class. ``dim`` parameter indicates the dimension along\n",
        "which the values must sum to 1.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "PCjGb23JenWX"
      },
      "outputs": [],
      "source": [
        "softmax = nn.Softmax(dim=1)\n",
        "pred_probab = softmax(logits)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tmWDUM3wenWX"
      },
      "source": [
        "## Model Parameters\n",
        "Many layers inside a neural network are *parameterized*, i.e. have associated weights\n",
        "and biases that are optimized during training. Subclassing ``nn.Module`` automatically\n",
        "tracks all fields defined inside your model object, and makes all parameters\n",
        "accessible using your model's ``parameters()`` or ``named_parameters()`` methods.\n",
        "\n",
        "In this example, we iterate over each parameter, and print its size and a preview of its values.\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "3VTXD3QBenWX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8a258c7d-2ac1-4eed-91b0-8c731da98d9c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model structure: NeuralNetwork(\n",
            "  (flatten): Flatten(start_dim=1, end_dim=-1)\n",
            "  (linear_relu_stack): Sequential(\n",
            "    (0): Linear(in_features=784, out_features=512, bias=True)\n",
            "    (1): ReLU()\n",
            "    (2): Linear(in_features=512, out_features=512, bias=True)\n",
            "    (3): ReLU()\n",
            "    (4): Linear(in_features=512, out_features=10, bias=True)\n",
            "  )\n",
            ")\n",
            "\n",
            "\n",
            "Layer: linear_relu_stack.0.weight | Size: torch.Size([512, 784]) | Values : tensor([[-0.0092,  0.0149, -0.0268,  ..., -0.0057,  0.0184, -0.0011],\n",
            "        [-0.0234,  0.0063, -0.0246,  ..., -0.0035, -0.0014, -0.0025]],\n",
            "       grad_fn=<SliceBackward0>) \n",
            "\n",
            "Layer: linear_relu_stack.0.bias | Size: torch.Size([512]) | Values : tensor([-0.0200, -0.0078], grad_fn=<SliceBackward0>) \n",
            "\n",
            "Layer: linear_relu_stack.2.weight | Size: torch.Size([512, 512]) | Values : tensor([[ 0.0065,  0.0024, -0.0098,  ...,  0.0244, -0.0283,  0.0269],\n",
            "        [-0.0232, -0.0148, -0.0303,  ..., -0.0203, -0.0206,  0.0238]],\n",
            "       grad_fn=<SliceBackward0>) \n",
            "\n",
            "Layer: linear_relu_stack.2.bias | Size: torch.Size([512]) | Values : tensor([-0.0010,  0.0171], grad_fn=<SliceBackward0>) \n",
            "\n",
            "Layer: linear_relu_stack.4.weight | Size: torch.Size([10, 512]) | Values : tensor([[ 0.0220,  0.0367, -0.0385,  ...,  0.0227,  0.0084,  0.0218],\n",
            "        [ 0.0312, -0.0297,  0.0116,  ..., -0.0078, -0.0128, -0.0031]],\n",
            "       grad_fn=<SliceBackward0>) \n",
            "\n",
            "Layer: linear_relu_stack.4.bias | Size: torch.Size([10]) | Values : tensor([-0.0150, -0.0097], grad_fn=<SliceBackward0>) \n",
            "\n"
          ]
        }
      ],
      "source": [
        "print(f\"Model structure: {model}\\n\\n\")\n",
        "\n",
        "for name, param in model.named_parameters():\n",
        "    print(f\"Layer: {name} | Size: {param.size()} | Values : {param[:2]} \\n\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wEN66SoxenWY"
      },
      "source": [
        "--------------\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JvJjwTdpenWY"
      },
      "source": [
        "## Further Reading\n",
        "- [torch.nn API](https://pytorch.org/docs/stable/nn.html)\n",
        "\n"
      ]
    }
  ],
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