使用Fashion-MNIST数据集完成Softmax回归模型搭建

import matplotlib.pyplot as plt
import torch
import torchvision
from d2l import torch as d2l
from IPython import display


d2l.use_svg_display()

"""
# 1, 读取数据集
# 1.1,通过ToTensor实例将图像数据从PIL类型变换为32位浮点数格式。
# 并除以255使得所有像素数值均在0~1之间
trans = torchvision.transforms.ToTensor()
mnist_train_dataset = torchvision.datasets.FashionMNIST(root="../data", train=True, transform=trans, download=True)
mnist_test_dataset = torchvision.datasets.FashionMNIST(root="../data", train=False, transform=trans, download=True)

print(mnist_train_dataset[0][0].shape)

def get_fashion_mnist_labels(labels):
    
    text_labels = ['t-shrit', 'trouser', 'pullover', 'dress', 'coat', 'sandal', 'shirt', 'sneaker', 'bag', 'ankle boot']
    return [text_labels[int(i)] for i in labels]

# 1.2, 创建一个函数来可视化这些样本
def show_dataset_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):
            # 图片张量
            #ax.imshow(img.numpy())
            plt.imshow(img.numpy())
        else:
            # PIL图片
            #ax.imshow(img)
            plt.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(torch.utils.data.DataLoader(mnist_train_dataset, batch_size=18)))
show_dataset_images(X.reshape(18, 28, 28), 2, 9, titles=get_fashion_mnist_labels(y));

batch_size1 = 256
"""

# 2, 读取小批量
def get_dataloader_workers():
    """使用4个进程来读取数据"""
    return 4

# 2.1, 为了使我们在读取训练集和测试集时更容易，我们使用内置的数据迭代器，
# train_iter = torch.utils.data.DataLoader(mnist_train_dataset, batch_size1, shuffle=True, num_workers=get_dataloader_workers())

def load_data_fashion_mnist(batch_size, resize=None): #@save
    
    trans = [torchvision.transforms.ToTensor()]
    if resize:
        trans.insert(0, torchvision.transforms.Resize(resize))
    trans = torchvision.transforms.Compose(trans)
    mnist_train_dataset = torchvision.datasets.FashionMNIST(root="../data", train=True, transform=trans, download=True)
    mnist_test_dataset = torchvision.datasets.FashionMNIST(root="../data", train=False, transform=trans, download=True)
    return (torch.utils.data.DataLoader(mnist_train_dataset, batch_size, shuffle=True, num_workers=get_dataloader_workers()),
            torch.utils.data.DataLoader(mnist_test_dataset, batch_size, shuffle=False, num_workers=get_dataloader_workers()))

# 2.2 测试加载batch_size=32的样本。
train_iter1, test_iter1 = load_data_fashion_mnist(32, resize=64)
for X1, Y1 in train_iter1:
    print(X1.shape, X1.dtype, Y1.shape, Y1.dtype)
    break

# 3, Softmax回归从零开始实现
# 3.1 使用Fashion-MNIST数据集，并设置数据迭代器的批量大小为256.
batch_size2 = 256
train_dataset_iterator, test_dataset_iterator = d2l.load_data_fashion_mnist(batch_size2, resize=None)

# 4, 初始化模型参数
# 4.1 数据集中每个样本是28x28的图像，将该图像展平为一维向量为784长度，
#     由于有10个类别，那么权重将是784x10的矩阵，偏置将构成一个1x10的行向量，
#     我们仍然使用正态分布初始化权重Weight,偏置bais初始化为0.
num_inputs = 784
num_outputs = 10

weight = torch.normal(0, 0.01, size=(num_in