Mnistのデータセット

MnistDataSet.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "MnistDataSet.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "1xksVLnlKtGY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 316
        },
        "outputId": "4af7e81c-dad7-415f-9d0b-dfbd63e9b98d"
      },
      "source": [
        "import torch\n",
        "import torchvision\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "#Tensor型に変換して正規化する前処理\n",
        "transform = torchvision.transforms.Compose([torchvision.transforms.ToTensor(),torchvision.transforms.Normalize((0.5,), (0.5,))])\n",
        "\n",
        "# Mnistのデータセットクラス\n",
        "trainset = torchvision.datasets.MNIST(root = \"Root\", train = True, download = True, transform = transform)\n",
        "\n",
        "# 最初の画像を表示\n",
        "plt.imshow(trainset[0][0].numpy().reshape(28, 28), cmap='gray')\n",
        "\n",
        "# 最初のラベルをプリント\n",
        "print(trainset[0][1])\n",
        "\n",
        "#trainsetをシャッフルしたものを取り出すデータローダーを作成\n",
        "#num_workersは指定した数のプロセスでデータをロードする\n",
        "trainloader = torch.utils.data.DataLoader(trainset, batch_size = 100, shuffle = True, num_workers = 2)\n",
        "\n",
        "#batch size 100 1チャンネル 28x28の画像のshapeとラベルのshape\n",
        "print(iter(trainloader).next()[0].shape)\n",
        "print(iter(trainloader).next()[1].shape)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "5\n",
            "torch.Size([100, 1, 28, 28])\n",
            "torch.Size([100])\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}