MNIST 数据加载

import numpy as np
from matplotlib import pyplot as plt
from torchvision import datasets, transforms

def softmax_t(x, t):
    x_exp = np.exp(x /t)
    return x_exp / np.sum(x_exp)

DATA = datasets.MNIST(‘dengyexun‘, train=False, download=False, transform=transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,),(0.3081,))]))
# 生成一个数据data_loader
test_loader_bs1 = torch.utils.data.DataLoader(DATA, batch_size=1, shuffle=True)
print(DATA)
print(test_loader_bs1)
# 封装成一个迭代器
print(iter(test_loader_bs1))
# 取迭代器中的数据
print(next(iter(test_loader_bs1)))