{
"cells": [
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"%reload_ext autoreload\n",
"%autoreload 1\n",
"%aimport d2lzh_pytorch\n",
"import torch\n",
"import torchvision\n",
"import torchvision.transforms as transforms\n",
"import matplotlib.pyplot as plt\n",
"import time\n",
"import sys"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz to D:/workspace/pytorch-test\\FashionMNIST\\raw\\train-images-idx3-ubyte.gz\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100.0%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Extracting D:/workspace/pytorch-test\\FashionMNIST\\raw\\train-images-idx3-ubyte.gz to D:/workspace/pytorch-test\\FashionMNIST\\raw\n",
"Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz to D:/workspace/pytorch-test\\FashionMNIST\\raw\\train-labels-idx1-ubyte.gz\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"111.0%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Extracting D:/workspace/pytorch-test\\FashionMNIST\\raw\\train-labels-idx1-ubyte.gz to D:/workspace/pytorch-test\\FashionMNIST\\raw\n",
"Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz to D:/workspace/pytorch-test\\FashionMNIST\\raw\\t10k-images-idx3-ubyte.gz\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100.0%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Extracting D:/workspace/pytorch-test\\FashionMNIST\\raw\\t10k-images-idx3-ubyte.gz to D:/workspace/pytorch-test\\FashionMNIST\\raw\n",
"Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz to D:/workspace/pytorch-test\\FashionMNIST\\raw\\t10k-labels-idx1-ubyte.gz\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"159.1%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Extracting D:/workspace/pytorch-test\\FashionMNIST\\raw\\t10k-labels-idx1-ubyte.gz to D:/workspace/pytorch-test\\FashionMNIST\\raw\n",
"Processing...\n",
"Done!\n"
]
}
],
"source": [
"mnist_train = torchvision.datasets.FashionMNIST(root='D:/workspace/pytorch-test', train=True, download=True, transform=transforms.ToTensor())\n",
"mnist_test = torchvision.datasets.FashionMNIST(root='D:/workspace/pytorch-test', train=False, download=True, transform=transforms.ToTensor())"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"
"60000 10000\n",
"torch.Size([1, 28, 28]) 9\n"
]
}
],
"source": [
"print(type(mnist_train))\n",
"print(len(mnist_train), len(mnist_test))\n",
"feature, label = mnist_train[0]\n",
"print(feature.shape, label) # Channel x Height x Width"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
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range(10):\n", " X.append(mnist_train[i][0])\n", " y.append(mnist_train[i][1])\n", "d2lzh_pytorch.show_fashion_mnist(X, d2lzh_pytorch.get_fashion_mnist_labels(y))" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "tags": [ "读取小批量" ] }, "outputs": [], "source": [ "batch_size = 256\n", "if sys.platform.startswith('win'):\n", " num_workers = 0 # 0表示不用额外的进程来加速读取数据\n", "else:\n", " num_workers = 4\n", "train_iter = torch.utils.data.DataLoader(mnist_train, batch_size=batch_size, shuffle=True, num_workers=num_workers)\n", "test_iter = torch.utils.data.DataLoader(mnist_test, batch_size=batch_size, shuffle=False, num_workers=num_workers)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "celltoolbar": "Tags", "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.10" } }, "nbformat": 4, "nbformat_minor": 4 }