chapter_linear_NN_classification/1_linear_classification_with_fashionmnist.ipynb

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    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "<a target=\"_blank\" href=\"https://colab.research.google.com/github/ashkan-mokarian/d2l-pytorch/blob/main/chapter_linear_NN_classification/1_linear_classification_with_fashionmnist.ipynb\">\n",
        "  <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/>\n",
        "</a>"
      ],
      "metadata": {
        "id": "rB_fJ9sDcn5B"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install d2l==1.0.0a0"
      ],
      "metadata": {
        "id": "wFlrex-XdL7j"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import time\n",
        "import torch\n",
        "import torchvision\n",
        "from torchvision import transforms\n",
        "from d2l import torch as d2l"
      ],
      "metadata": {
        "id": "xwLovI5Oc0Qe"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "d2l.use_svg_display()"
      ],
      "metadata": {
        "id": "DymUBsWtdcay"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data"
      ],
      "metadata": {
        "id": "0Dq8Iw0lcje_"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "b_VSBBAdcZfD"
      },
      "outputs": [],
      "source": [
        "class FashionMNIST(d2l.DataModule):\n",
        "    \"\"\"The Fashion-MNIST dataset.\"\"\"\n",
        "    def __init__(self, batch_size=64, resize=(28, 28), num_workers=2):\n",
        "        super().__init__(num_workers=num_workers)\n",
        "        self.save_hyperparameters()\n",
        "        trans = transforms.Compose([transforms.Resize(resize),\n",
        "                                    transforms.ToTensor()])\n",
        "        self.train = torchvision.datasets.FashionMNIST(\n",
        "            root=self.root, train=True, transform=trans, download=True)\n",
        "        self.val = torchvision.datasets.FashionMNIST(\n",
        "            root=self.root, train=False, transform=trans, download=True)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = FashionMNIST(resize=(32, 32))\n",
        "print(len(data.train), len(data.val))\n",
        "print(data.train[0][0].shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qCGhsEfUem7E",
        "outputId": "081235bc-9456-452b-97fb-68691dd1bc86"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "60000 10000\n",
            "torch.Size([1, 32, 32])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "@d2l.add_to_class(FashionMNIST)\n",
        "def text_labels(self, indices):\n",
        "    labels = ['t-shirt', 'trouser', 'pullover', 'dress', 'coat',\n",
        "              'sandal', 'shirt', 'sneaker', 'bag', 'ankle boot']\n",
        "    return [labels[int(i)] for i in indices]"
      ],
      "metadata": {
        "id": "l4CVG1vZeu4R"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# for mini-batch data loading\n",
        "@d2l.add_to_class(FashionMNIST)\n",
        "def get_dataloader(self, train):\n",
        "    data = self.train if train else self.val\n",
        "    return torch.utils.data.DataLoader(\n",
        "        data, self.batch_size, shuffle=train, num_workers=self.num_workers)\n",
        "\n",
        "ts = time.time()\n",
        "for X, y in data.train_dataloader():\n",
        "    pass\n",
        "print(\"One pass over train data took:\", time.time()-ts, ' seconds')\n",
        "\n",
        "X.shape, X.dtype, y.shape, y.dtype  # 32 because this is the last batch"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_xUF5glnfPFe",
        "outputId": "df645faf-1191-484b-b7df-27e4288a3914"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "One pass over train data took: 13.411950826644897  seconds\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(torch.Size([32, 1, 32, 32]), torch.float32, torch.Size([32]), torch.int64)"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# d2l for visualization\n",
        "@d2l.add_to_class(FashionMNIST)\n",
        "def visualize(self, batch, nrows=1, ncols=8, labels=[]):\n",
        "    X, y = batch\n",
        "    if not labels:\n",
        "        labels = self.text_labels(y)\n",
        "    d2l.show_images(X.squeeze(1), num_rows=nrows, num_cols=ncols, titles=labels)\n",
        "\n",
        "batch = next(iter(data.train_dataloader()))\n",
        "data.visualize(batch)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 155
        },
        "id": "XeZfP-M5gndZ",
        "outputId": "33d88b08-048f-4499-8117-e10663ee3a3e"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Linear Classification as Regression from Scratch"
      ],
      "metadata": {
        "id": "_VdI8HrPiMjN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Just for testing.\n",
        "y = torch.tensor([0, 2])\n",
        "y_hat = torch.tensor([[0.6, 0.3, 0.1], [0.3, 0.4, 0.3]])\n",
        "\n",
        "acc = y_hat.argmax(dim=1) == y\n",
        "acc = acc.sum() / acc.numel()\n",
        "acc"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eOcTitY37YBm",
        "outputId": "c402bd31-50cc-43bb-90e2-c792d8a4e94a"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor(0.5000)"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Softmax\n",
        "def softmax(o):\n",
        "    o_exp = torch.exp(o)\n",
        "    return o_exp / o_exp.sum(dim=1, keepdim=True)\n",
        "o = torch.randn((2,3))\n",
        "y_hat = softmax(o)\n",
        "print(y_hat.sum(dim=1))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dQpGoGtB8Z5d",
        "outputId": "7fd055ad-398a-4d0d-f4d9-e9a4e719bd10"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([1.0000, 1.0000])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Model"
      ],
      "metadata": {
        "id": "KV51pkra_IXh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class SoftmaxRegressionScratch(d2l.Classifier):\n",
        "    def __init__(self, num_inputs, num_outputs, lr, sigma=0.01):\n",
        "        super().__init__()\n",
        "        self.save_hyperparameters()\n",
        "        self.W = torch.normal(0, sigma, size=(num_inputs, num_outputs), requires_grad=True)\n",
        "        self.b = torch.zeros(size=(num_outputs,), requires_grad=True)\n",
        "    \n",
        "    def parameters(self):\n",
        "        return [self.W, self.b]\n",
        "\n",
        "    def forward(self, X):\n",
        "        X = X.reshape(-1, self.W.shape[0])\n",
        "        return softmax(torch.matmul(X, self.W) + self.b )"
      ],
      "metadata": {
        "id": "6DdIqF6d9a9z"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Cross-entropy loss"
      ],
      "metadata": {
        "id": "b5HPU0Gf_J-Y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def cross_entropy(y_hat, y):\n",
        "    return -torch.log(\n",
        "        y_hat[list(range(len(y))), y]\n",
        "    ).mean()\n",
        "\n",
        "@d2l.add_to_class(SoftmaxRegressionScratch)\n",
        "def loss(self, y_hat, y):\n",
        "    return cross_entropy(y_hat, y)"
      ],
      "metadata": {
        "id": "ydp44n0p_MEW"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Training"
      ],
      "metadata": {
        "id": "Kk9kioNcACnH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data = FashionMNIST(batch_size=256)\n",
        "model = SoftmaxRegressionScratch(num_inputs=784, num_outputs=10, lr=0.1)\n",
        "trainer = d2l.Trainer(max_epochs=10)\n",
        "trainer.fit(model, data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "xfvIg7PB_0Gk",
        "outputId": "abc20f89-3c6c-426f-edb0-03e6092bc23a"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## prediction (and showing misclasified samples)"
      ],
      "metadata": {
        "id": "zlOucujFChlY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X, y = next(iter(data.val_dataloader()))\n",
        "preds = model(X).argmax(dim=1)\n",
        "\n",
        "wrong = preds.type(y.dtype) != y\n",
        "X, y, preds = X[wrong], y[wrong], preds[wrong]\n",
        "\n",
        "labels = [a+'\\n'+b for a,b in zip(data.text_labels(y), data.text_labels(preds))]\n",
        "data.visualize([X, y], labels=labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "id": "JXX3hjJF_h6j",
        "outputId": "480af94e-332e-4433-9183-fb48e02a9a17"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x150 with 8 Axes>"
            ],
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          "metadata": {}
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Concise implementation"
      ],
      "metadata": {
        "id": "s1BEyjtSESBt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from torch import nn\n",
        "from torch import functional as F"
      ],
      "metadata": {
        "id": "bkaj7hqBEU4Q"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class SoftmaxRegression(d2l.Module):\n",
        "    def __init__(self):\n",
        "        super().__init__(lr)\n",
        "        self.save_hyperparameters()\n",
        "        self.net = nn.Sequential([nn.Flatten(), nn.LazyLinear(nun_outputs)])\n",
        "\n",
        "    def forward(self, X):\n",
        "        return self.net(X)"
      ],
      "metadata": {
        "id": "MV0kI2VjEeig"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}