task-2-nn/IMT2020103.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "AxNxSVxbfAuW"
      },
      "outputs": [],
      "source": [
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qsxQgJ_DfAua"
      },
      "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": 35,
      "metadata": {
        "id": "bwCvShFlfAuc"
      },
      "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": "0ktyO9BHfAud"
      },
      "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": 36,
      "metadata": {
        "id": "Vg-vaPQQfAud",
        "outputId": "d4f36fbd-8d2f-4ada-ffda-282e4344a558",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "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": "fm1rvA-UfAue"
      },
      "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": 37,
      "metadata": {
        "id": "PuZiiM9dfAuf"
      },
      "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(512, 512),\n",
        "            nn.ReLU(),\n",
        "            nn.Linear(512, 100),\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": "e2ohrpLAfAug"
      },
      "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": 38,
      "metadata": {
        "id": "OCI3Ok5tfAug",
        "outputId": "b365fc31-6425-45f2-dacc-df4637e50993",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "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=100, bias=True)\n",
            "  )\n",
            ")\n"
          ]
        }
      ],
      "source": [
        "model = NeuralNetwork().to(device)\n",
        "print(model)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cBrAj-CjfAuh"
      },
      "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": 39,
      "metadata": {
        "id": "tMhPZh2JfAuh",
        "outputId": "a5acbe20-bf17-4204-ec36-5efca045bf77",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicted class: tensor([30])\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": "-lbzl2uufAui"
      },
      "source": [
        "--------------\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wwmztHGvfAui"
      },
      "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": 40,
      "metadata": {
        "id": "ndGF9NK3fAuj",
        "outputId": "1b6fa1e6-e3b0-4322-ab49-9fb457536d42",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "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": "RKlH0psIfAuj"
      },
      "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": 41,
      "metadata": {
        "id": "-ZoapAvjfAuj",
        "outputId": "97fe5f37-bf1f-490c-8bac-8e356ae79da3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "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": "ZkLfMAhMfAuk"
      },
      "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": 42,
      "metadata": {
        "id": "ljYukXgOfAuk",
        "outputId": "707627c9-1d71-435e-ad82-a2fdf6ca9538",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "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": "H3bUMoMXfAuk"
      },
      "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": 43,
      "metadata": {
        "id": "UXC6fVN0fAuk",
        "outputId": "8c388783-48c1-4cf4-8a84-92d25fcc2e92",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Before ReLU: tensor([[-0.2351, -0.4614,  0.3215, -0.5926,  0.4342, -0.2528,  0.3768,  0.1966,\n",
            "         -0.1050, -0.0588,  0.3054,  0.0962, -0.1508, -0.4216,  0.1248, -0.1530,\n",
            "          0.1084, -0.8020, -0.4118,  0.1163],\n",
            "        [-0.3540, -0.6387,  0.2492, -0.4191,  0.6060, -0.0477,  0.1351,  0.2171,\n",
            "         -0.1949,  0.2263, -0.0861,  0.1480, -0.0166, -0.2859,  0.3671, -0.2075,\n",
            "         -0.0536, -0.7117, -0.0325, -0.1530],\n",
            "        [-0.1040,  0.0088,  0.3799, -0.8336,  0.1725,  0.1940,  0.4035, -0.2050,\n",
            "         -0.2919, -0.0169,  0.2829,  0.3285, -0.0131, -0.6093,  0.3734,  0.0074,\n",
            "          0.1324, -0.4626, -0.0356,  0.0316]], grad_fn=<AddmmBackward0>)\n",
            "\n",
            "\n",
            "After ReLU: tensor([[0.0000, 0.0000, 0.3215, 0.0000, 0.4342, 0.0000, 0.3768, 0.1966, 0.0000,\n",
            "         0.0000, 0.3054, 0.0962, 0.0000, 0.0000, 0.1248, 0.0000, 0.1084, 0.0000,\n",
            "         0.0000, 0.1163],\n",
            "        [0.0000, 0.0000, 0.2492, 0.0000, 0.6060, 0.0000, 0.1351, 0.2171, 0.0000,\n",
            "         0.2263, 0.0000, 0.1480, 0.0000, 0.0000, 0.3671, 0.0000, 0.0000, 0.0000,\n",
            "         0.0000, 0.0000],\n",
            "        [0.0000, 0.0088, 0.3799, 0.0000, 0.1725, 0.1940, 0.4035, 0.0000, 0.0000,\n",
            "         0.0000, 0.2829, 0.3285, 0.0000, 0.0000, 0.3734, 0.0074, 0.1324, 0.0000,\n",
            "         0.0000, 0.0316]], 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": "_t67dKtifAul"
      },
      "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": 44,
      "metadata": {
        "id": "js-yir3mfAul"
      },
      "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": "3JwZCgJRfAul"
      },
      "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": 45,
      "metadata": {
        "id": "vu2TjwcRfAum"
      },
      "outputs": [],
      "source": [
        "softmax = nn.Softmax(dim=1)\n",
        "pred_probab = softmax(logits)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TCs2Udr2fAum"
      },
      "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": 46,
      "metadata": {
        "id": "jqZQYHzUfAum",
        "outputId": "2e6cd5dd-6165-4fe6-d2f3-983ad02caa83",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "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=100, bias=True)\n",
            "  )\n",
            ")\n",
            "\n",
            "\n",
            "Layer: linear_relu_stack.0.weight | Size: torch.Size([512, 784]) | Values : tensor([[ 0.0242, -0.0127, -0.0051,  ...,  0.0146,  0.0055,  0.0297],\n",
            "        [ 0.0075,  0.0013,  0.0216,  ..., -0.0227, -0.0113,  0.0354]],\n",
            "       grad_fn=<SliceBackward0>) \n",
            "\n",
            "Layer: linear_relu_stack.0.bias | Size: torch.Size([512]) | Values : tensor([-0.0116,  0.0079], grad_fn=<SliceBackward0>) \n",
            "\n",
            "Layer: linear_relu_stack.2.weight | Size: torch.Size([512, 512]) | Values : tensor([[-2.3847e-03,  3.2733e-02,  5.0150e-03,  ...,  9.5076e-03,\n",
            "         -3.4433e-03, -2.3907e-02],\n",
            "        [-7.0717e-03,  1.7836e-02,  3.7821e-03,  ..., -3.9846e-02,\n",
            "          2.2889e-02, -6.7914e-05]], grad_fn=<SliceBackward0>) \n",
            "\n",
            "Layer: linear_relu_stack.2.bias | Size: torch.Size([512]) | Values : tensor([0.0126, 0.0201], grad_fn=<SliceBackward0>) \n",
            "\n",
            "Layer: linear_relu_stack.4.weight | Size: torch.Size([100, 512]) | Values : tensor([[-0.0026, -0.0156, -0.0205,  ..., -0.0037, -0.0261, -0.0371],\n",
            "        [-0.0153,  0.0230,  0.0088,  ..., -0.0019, -0.0431, -0.0262]],\n",
            "       grad_fn=<SliceBackward0>) \n",
            "\n",
            "Layer: linear_relu_stack.4.bias | Size: torch.Size([100]) | Values : tensor([0.0316, 0.0371], 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": "0q7gk6zBfAum"
      },
      "source": [
        "--------------\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gVm-1kGGfAum"
      },
      "source": [
        "## Further Reading\n",
        "- [torch.nn API](https://pytorch.org/docs/stable/nn.html)\n",
        "\n"
      ]
    }
  ],
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