[人工智能]3.28图像分类数据集

# %matplotlib inline  #jupyter notebook中使用的
import torch
import torchvision
from torch.utils import data
from torchvision import transforms
from d2l import torch as d2l
from matplotlib import pyplot as plt

d2l.use_svg_display() #用svg显示图片
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)

# print(len(mnist_train), len(mnist_test))
# print(mnist_train[0][0].shape)

#可视化数据集
def get_fashion_mnist_labels(labels):
    """返回Fashion-MNIST数据集的文本标签。"""
    text_labels = [
        't-shirt', 'trouser', 'pullover', 'dress', 'coat', 'sandal', 'shirt',
        'sneaker', 'bag', 'ankle boot']
    return [text_labels[int(i)] for i in labels]

def show_images(imgs, num_rows, num_cols, titles=None, scale=1.5):
    """Plot a list of images."""
    figsize = (num_cols * scale, num_rows * scale)
    _, axes = d2l.plt.subplots(num_rows, num_cols, figsize=figsize)
    axes = axes.flatten() #降维
    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)#不显示坐标轴
        ax.axes.get_yaxis().set_visible(False)
        if titles:
            ax.set_title(titles[i])
    return axes
# 几个样本的图像及其相应的标签
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))
plt.show()