PyTorch: %matplotlib inline
import torch
import torchvision
from torch.utils import data
from torchvision import transforms
from d2l import torch as d2l
d2l.use_svg_display()
trans = transforms.ToTensor()
mnist_train = torchvision.datasets.FashionMNIST(
    root="../data", train=True, transform=trans, download=True)
mnist_test = torchvision.datasets.FashionMNIST(
    root="../data", train=False, transform=trans, download=True)
def show_images(imgs, num_rows, num_cols, titles=None, scale=1.5):
    figsize = (num_cols * scale, num_rows * scale)
    _, axes = d2l.plt.subplots(num_rows, num_cols, figsize=figsize)
    axes = axes.flatten()
    for i, (ax, img) in enumerate(zip(axes, imgs)):
        if torch.is_tensor(img):
            ax.imshow(img.numpy())
        else:
            ax.imshow(img)
        ax.axes.get_xaxis().set_visible(False)
        ax.axes.get_yaxis().set_visible(False)
        if titles:
            ax.set_title(titles[i])
    return axes
X, y = next(iter(data.DataLoader(mnist_train, batch_size=18)))
show_images(X.reshape(18, 28, 28), 2, 9, titles=get_fashion_mnist_labels(y));
batch_size = 256
    return 4
train_iter = data.DataLoader(mnist_train, batch_size, shuffle=True, num_workers=get_dataloader_workers())
def load_data_fashion_mnist(batch_size, resize=None):
    trans = [transforms.ToTensor()]
    if resize:
        trans.insert(0, transforms.Resize(resize))
    trans = transforms.Compose(trans)
    mnist_train = torchvision.datasets.FashionMNIST(root="../data", train=True, transform=trans, download=True)
    mnist_test = torchvision.datasets.FashionMNIST(root="../data", train=False, transform=trans, download=True)
    return (data.DataLoader(mnist_train, batch_size, shuffle=True, num_workers=get_dataloader_workers()),
            data.DataLoader(mnist_test, batch_size, shuffle=False, num_workers=get_dataloader_workers()))


Paddle: %matplotlib inline
import warnings
from d2l import paddle as d2l
warnings.filterwarnings("ignore")
import sys
import paddle
from paddle.vision import transforms
d2l.use_svg_display()
trans = transforms.ToTensor()
mnist_train = paddle.vision.datasets.FashionMNIST(mode="train", transform=trans)
mnist_test = paddle.vision.datasets.FashionMNIST(mode="test", transform=trans)
def show_images(imgs, num_rows, num_cols, titles=None, scale=1.5):
    figsize = (num_cols * scale, num_rows * scale)
    _, axes = d2l.plt.subplots(num_rows, num_cols, figsize=figsize)
    axes = axes.flatten()
    for i, (ax, img) in enumerate(zip(axes, imgs)):
        if paddle.is_tensor(img):
            ax.imshow(img.numpy())
        else:
            ax.imshow(img)
        ax.axes.get_xaxis().set_visible(False)
        ax.axes.get_yaxis().set_visible(False)
        if titles:
            ax.set_title(titles[i])
    return axes
X, y = next(iter(paddle.io.DataLoader(mnist_train, batch_size=18)))
show_images(X.reshape([18, 28, 28]), 2, 9, titles=get_fashion_mnist_labels(y));
batch_size = 256
    return 4
train_iter = paddle.io.DataLoader(dataset=mnist_train, batch_size=batch_size,  shuffle=True,  return_list=True, num_workers=get_dataloader_workers())
    trans = [transforms.ToTensor()]
    if resize:
        trans.insert(0, transforms.Resize(resize))
    trans = transforms.Compose(trans)
    mnist_train = paddle.vision.datasets.FashionMNIST(mode="train", transform=trans)
    mnist_test = paddle.vision.datasets.FashionMNIST(mode="test", transform=trans)
    return (paddle.io.DataLoader(dataset=mnist_train, batch_size=batch_size, shuffle=True, return_list=True, num_workers=get_dataloader_workers()),
            paddle.io.DataLoader(dataset=mnist_test, batch_size=batch_size, return_list=True, shuffle=True, num_workers=get_dataloader_workers()))