D2L_SR.ipynb

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        }
      }
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/rajlm10/D2L-Torch/blob/main/D2L_SR.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "9g-dSsMmUu3y"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "import torchvision\n",
        "from torch.utils import data\n",
        "from torchvision import transforms\n",
        "from torch import nn"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def get_fashion_mnist_labels(labels): \n",
        "  \"\"\"Return text labels for the Fashion-MNIST dataset.\"\"\" \n",
        "  text_labels = ['t-shirt', 'trouser', 'pullover', 'dress', 'coat','sandal', 'shirt', 'sneaker', 'bag', 'ankle boot'] \n",
        "  return [text_labels[int(i)] for i in labels]"
      ],
      "metadata": {
        "id": "OL73EJqfVYUX"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y = torch.tensor([0, 2])\n",
        "y_hat = torch.tensor([[0.1, 0.3, 0.6], [0.3, 0.2, 0.5]]) \n",
        "y_hat[[0, 1], y]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6xp7MBB_V0fd",
        "outputId": "f48c6aea-ed40-46f9-e8a5-298524af5ef1"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([0.1000, 0.5000])"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_hat.shape,len(y_hat)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NEn44YOrWdlh",
        "outputId": "0ffef720-e311-4867-e93c-21dc705ae120"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(torch.Size([2, 3]), 2)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Dummy cross_entropy\n",
        "\n",
        "def cross_entropy(y_hat,y):\n",
        "  return -torch.log(y_hat[range(len(y_hat)),y])\n",
        "cross_entropy(y_hat,y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "crI5qtdtWN6T",
        "outputId": "0f7d8be8-25cb-4ff7-bef2-a6d72675378f"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([2.3026, 0.6931])"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Dummy accuracy\n",
        "\n",
        "def accuracy(y_hat, y):\n",
        "  \"\"\"Compute the number of correct predictions.\"\"\" \n",
        "  if len(y_hat.shape) > 1 and y_hat.shape[1] > 1:\n",
        "    y_hat = y_hat.argmax(axis=1)\n",
        "  cmp = y_hat.type(y.dtype) == y\n",
        "  return float(cmp.type(y.dtype).sum())"
      ],
      "metadata": {
        "id": "Te1_ZFTKWnCk"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def evaluate_accuracy(net, data_iter):\n",
        "  \"\"\"Compute the accuracy for a model on a dataset.\"\"\" \n",
        "  if isinstance(net, torch.nn.Module):\n",
        "    net.eval() # Set the model to evaluation mode\n",
        "  metric = Accumulator(2) # No. of correct predictions, no. of predictions\n",
        "  \n",
        "  with torch.no_grad():\n",
        "    for X, y in data_iter:\n",
        "      metric.add(accuracy(net(X), y), y.numel()) \n",
        "  return metric[0] / metric[1]"
      ],
      "metadata": {
        "id": "t3gIWQ1Wc2NM"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class Accumulator: \n",
        "  \"\"\"For accumulating sums over `n` variables.\"\"\" \n",
        "  def __init__(self, n):\n",
        "    self.data = [0.0] * n \n",
        "    \n",
        "  def add(self, *args):\n",
        "    self.data = [a + float(b) for a, b in zip(self.data, args)] \n",
        "    \n",
        "  def reset(self):\n",
        "    self.data = [0.0] * len(self.data)\n",
        "  \n",
        "  def __getitem__(self, idx): \n",
        "    return self.data[idx]"
      ],
      "metadata": {
        "id": "Txg6drSNW3E2"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def get_workers():\n",
        "  return 4"
      ],
      "metadata": {
        "id": "p2g4aSITYz6i"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def load_fashion_mnist(batch_size,resize=None):\n",
        "  \"\"\"Download the Fashion-MNIST dataset and then load it into memory.\"\"\"\n",
        "  trans=[transforms.ToTensor()] #PIL image to tensor (normalized between 0-1)\n",
        "  if resize:\n",
        "    trans.insert(0,transforms.Resize(resize))\n",
        "\n",
        "  trans=transforms.Compose(trans) #Chains together transforms\n",
        "\n",
        "  mnist_train=torchvision.datasets.FashionMNIST(root=\"../data\", train=True, transform=trans, download=True)\n",
        "  mnist_test=torchvision.datasets.FashionMNIST(root=\"../data\", train=False, transform=trans, download=True)\n",
        "\n",
        "  return data.DataLoader(mnist_train,batch_size,shuffle=True,num_workers=get_workers()),data.DataLoader(mnist_train,batch_size,shuffle=True,num_workers=get_workers())\n"
      ],
      "metadata": {
        "id": "tDsGsqjgXCuw"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_iter, test_iter = load_fashion_mnist(32, resize=64) \n",
        "\n",
        "for X, y in train_iter:\n",
        " print(X.shape, X.dtype, y.shape, y.dtype) \n",
        " break"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        "id": "HdAgndgYY6j6",
        "outputId": "86eca599-0bf4-4212-c261-843241a0242c"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz to ../data/FashionMNIST/raw/train-images-idx3-ubyte.gz\n"
          ]
        },
        {
          "output_type": "display_data",
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            "text/plain": [
              "  0%|          | 0/26421880 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ../data/FashionMNIST/raw/train-images-idx3-ubyte.gz to ../data/FashionMNIST/raw\n",
            "\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz to ../data/FashionMNIST/raw/train-labels-idx1-ubyte.gz\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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              "model_id": "ff98f4a9d64d4ee3af783ccf6608fa78",
              "version_minor": 0,
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            "text/plain": [
              "  0%|          | 0/29515 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ../data/FashionMNIST/raw/train-labels-idx1-ubyte.gz to ../data/FashionMNIST/raw\n",
            "\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz to ../data/FashionMNIST/raw/t10k-images-idx3-ubyte.gz\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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              "model_id": "f2fc92c209cf4278a4bbc3a9cdb5afea",
              "version_minor": 0,
              "version_major": 2
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            "text/plain": [
              "  0%|          | 0/4422102 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ../data/FashionMNIST/raw/t10k-images-idx3-ubyte.gz to ../data/FashionMNIST/raw\n",
            "\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz to ../data/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6a124f3615584729bb760ceb2d4ff522",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/5148 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ../data/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz to ../data/FashionMNIST/raw\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py:481: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  cpuset_checked))\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([32, 1, 64, 64]) torch.float32 torch.Size([32]) torch.int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "batch_size = 256\n",
        "train_iter, test_iter = load_fashion_mnist(batch_size)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EMEKe1JrZH1j",
        "outputId": "b74699ba-2dab-460f-d79a-6555e42c65de"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py:481: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  cpuset_checked))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "net=nn.Sequential(nn.Flatten(),nn.Linear(784,10))\n",
        "\n",
        "def init_weights(layer):\n",
        "  if type(layer) == nn.Linear:\n",
        "    nn.init.normal_(layer.weight, std=0.01)\n",
        "\n",
        "net.apply(init_weights)\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Uv9I54ymZV3f",
        "outputId": "fd1aeaff-7b60-4f3c-d5f5-ff6b570e8a0a"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Sequential(\n",
              "  (0): Flatten(start_dim=1, end_dim=-1)\n",
              "  (1): Linear(in_features=784, out_features=10, bias=True)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "net[1].weight.size(),net[1].bias.size()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "djNh29XWZ9JA",
        "outputId": "bf521907-c2ae-4543-b489-c367fddbc92d"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(torch.Size([10, 784]), torch.Size([10]))"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "loss = nn.CrossEntropyLoss(reduction='none')\n",
        "trainer = torch.optim.SGD(net.parameters(), lr=0.1)"
      ],
      "metadata": {
        "id": "wxPPCdCQaHSh"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def train_epoch(net,training_set,loss,optimizer):\n",
        "  #Note training set is an iterator\n",
        "  if isinstance(net,torch.nn.Module):\n",
        "    net.train()\n",
        "\n",
        "  metric=Accumulator(3) #stores sum of training loss, sum of training accuracy, no. of examples\n",
        "\n",
        "  for X,y in training_set:\n",
        "    y_hat=net(X) # n X 10\n",
        "    l=loss(y_hat,y) # nX10, nX1 -> nX1\n",
        "\n",
        "    if isinstance(optimizer,torch.optim.Optimizer):\n",
        "      optimizer.zero_grad()\n",
        "      l.mean().backward()\n",
        "      optimizer.step()\n",
        "\n",
        "    metric.add(float(l.sum()),accuracy(y_hat,y),y.shape[0])\n",
        "\n",
        "  return metric[0]/metric[2], metric[1]/metric[2]\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "U9Q9njJhaNNp"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def train(net,training_set,test_set,loss,optimizer,num_epochs):\n",
        "  for epoch in range(num_epochs):\n",
        "    train_metrics=train_epoch(net,training_set,loss,optimizer)\n",
        "    test_acc = evaluate_accuracy(net, test_set)\n",
        "    \n",
        "    print(f'''epoch {epoch}: {train_metrics},{test_acc}''')\n",
        "\n"
      ],
      "metadata": {
        "id": "PJ_QH3eycU2a"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train(net,train_iter,test_iter,loss,trainer,10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ML-qQWdrdpoA",
        "outputId": "a1e957fd-d7ee-4cc7-995d-d57991b68228"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py:481: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  cpuset_checked))\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 0: (0.787415256690979, 0.7483833333333333),0.7823833333333333\n",
            "epoch 1: (0.5705126907348633, 0.8134),0.8203666666666667\n",
            "epoch 2: (0.5246704514821371, 0.8264833333333333),0.8280166666666666\n",
            "epoch 3: (0.5017361728668213, 0.8326333333333333),0.8306333333333333\n",
            "epoch 4: (0.485469190343221, 0.8365166666666667),0.83725\n",
            "epoch 5: (0.4740749600728353, 0.8404833333333334),0.8304166666666667\n",
            "epoch 6: (0.46519881744384767, 0.8426333333333333),0.8284666666666667\n",
            "epoch 7: (0.4582118424097697, 0.8457333333333333),0.8485833333333334\n",
            "epoch 8: (0.4531071772257487, 0.84645),0.8452\n",
            "epoch 9: (0.4472584587097168, 0.8480166666666666),0.8478166666666667\n"
          ]
        }
      ]
    }
  ]
}