Fashion_MNIST_Classifier_PyTorch.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "authorship_tag": "ABX9TyP5v42j91m+RgZCGFbSblg4",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Sara-Esm/Fashion-MNIST-Classifier-PyTorch/blob/main/Fashion_MNIST_Classifier_PyTorch.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sMNwYf3YJWeb",
        "outputId": "403aa33d-ba1e-4a90-9aca-aa8d8b4524fb"
      },
      "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": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 26421880/26421880 [00:00<00:00, 109142458.53it/s]\n"
          ]
        },
        {
          "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": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 29515/29515 [00:00<00:00, 4869788.07it/s]"
          ]
        },
        {
          "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": "stream",
          "name": "stderr",
          "text": [
            "\n",
            "100%|██████████| 4422102/4422102 [00:00<00:00, 62832674.80it/s]\n"
          ]
        },
        {
          "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": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 5148/5148 [00:00<00:00, 7547108.35it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting data/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz to data/FashionMNIST/raw\n",
            "\n"
          ]
        }
      ],
      "source": [
        "#Loading a Dataset\n",
        "import torch\n",
        "from torch.utils.data import Dataset\n",
        "from torchvision import datasets\n",
        "from torchvision.transforms import ToTensor\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "training_data = datasets.FashionMNIST(\n",
        "    root=\"data\",\n",
        "    train=True,\n",
        "    download=True,\n",
        "    transform=ToTensor()\n",
        ")\n",
        "\n",
        "test_data = datasets.FashionMNIST(\n",
        "    root=\"data\",\n",
        "    train=False,\n",
        "    download=True,\n",
        "    transform=ToTensor()\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Iterating and Visualizing the Dataset\n",
        "labels_map = {\n",
        "    0: \"T-Shirt\",\n",
        "    1: \"Trouser\",\n",
        "    2: \"Pullover\",\n",
        "    3: \"Dress\",\n",
        "    4: \"Coat\",\n",
        "    5: \"Sandal\",\n",
        "    6: \"Shirt\",\n",
        "    7: \"Sneaker\",\n",
        "    8: \"Bag\",\n",
        "    9: \"Ankle Boot\",\n",
        "}\n",
        "figure = plt.figure(figsize=(8, 8))\n",
        "cols, rows = 3, 3\n",
        "for i in range(1, cols * rows + 1):\n",
        "    sample_idx = torch.randint(len(training_data), size=(1,)).item()\n",
        "    img, label = training_data[sample_idx]\n",
        "    figure.add_subplot(rows, cols, i)\n",
        "    plt.title(labels_map[label])\n",
        "    plt.axis(\"off\")\n",
        "    plt.imshow(img.squeeze(), cmap=\"gray\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 675
        },
        "id": "XV45kZXKL_mZ",
        "outputId": "6dde984c-46f6-4e1d-c833-5a2237285a4d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x800 with 9 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Preparing data for training with DataLoaders\n",
        "from torch.utils.data import DataLoader\n",
        "\n",
        "train_dataloader = DataLoader(training_data, batch_size=64, shuffle=True)\n",
        "test_dataloader = DataLoader(test_data, batch_size=64, shuffle=True)"
      ],
      "metadata": {
        "id": "kVOnI0GPMgGR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Display image and label.\n",
        "train_features, train_labels = next(iter(train_dataloader))\n",
        "print(f\"Feature batch shape: {train_features.size()}\")\n",
        "print(f\"Labels batch shape: {train_labels.size()}\")\n",
        "img = train_features[0].squeeze()\n",
        "label = train_labels[0]\n",
        "plt.imshow(img, cmap=\"gray\")\n",
        "plt.show()\n",
        "print(f\"Label: {label}\")"
      ],
      "metadata": {
        "id": "814cY7ZoMiDw",
        "outputId": "effa26b2-f0e6-4bfe-df31-4bc665039e5c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 482
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Feature batch shape: torch.Size([64, 1, 28, 28])\n",
            "Labels batch shape: torch.Size([64])\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Label: 9\n"
          ]
        }
      ]
    }
  ]
}