代码

import numpy as np
import torch
import torchvision
import torchvision.transforms as transforms
from torch.utils import data# H,W,C -> C,H,W
mnist_train = torchvision.datasets.FashionMNIST(root="./data", train=True, download=True,transform=transforms.ToTensor())
mnist_test = torchvision.datasets.FashionMNIST(root="./data", train=False, download=True,transform=transforms.ToTensor())
batch_size = 256
# 随机读取⼩批量
train_loader = data.DataLoader(mnist_train, batch_size, shuffle=True)
test_loader = data.DataLoader(mnist_test, batch_size, shuffle=True)# feature, label = mnist_train[0]
# print(feature.shape, label) # torch.Size([1, 28, 28]) 9num_inputs = 784
num_outputs = 10def softmax(X):X_exp = X.exp()partition = X_exp.sum(dim=1, keepdim=True)  # 按行return X_exp / partition  # 这⾥应⽤了⼴播机制def net(X):return softmax(torch.mm(X.view((-1, num_inputs)), W) + b)def cross_entropy(y_hat, y):return - torch.log(y_hat.gather(1, y.view(-1, 1)))def sgd(params, lr, batch_size):for param in params:param.data -= lr * param.grad / batch_size  # 注意这⾥更改param时⽤的param.datadef accuracy(y_hat, y):return (y_hat.argmax(dim=1) == y).float().mean().item()W = torch.tensor(np.random.normal(0, 0.01, (num_inputs, num_outputs)), dtype=torch.float)
b = torch.zeros(num_outputs, dtype=torch.float)
W.requires_grad_()
b.requires_grad_()num_epochs, lr = 10, 0.1
loss = cross_entropy
optimizer = sgd
for epoch in range(1, 1 + num_epochs):total_loss = 0.0train_sample = 0.0train_acc_sum = 0for x, y in train_loader:y_hat = net(x)l = loss(y_hat, y) # 256,1# 梯度清零l.sum().backward()sgd([W, b], lr, batch_size)  # 使用参数的梯度更新参数W.grad.data.zero_()b.grad.data.zero_()total_loss += l.sum().item()train_sample += y.shape[0]train_acc_sum += (y_hat.argmax(dim=1) == y).sum().item()print('epoch %d, loss %.4f, train acc %.3f' % (epoch, total_loss / train_sample, train_acc_sum / train_sample,))with torch.no_grad():total_loss = 0.0test_sample = 0.0test_acc_sum = 0for x, y in test_loader:y_hat = net(x)l = loss(y_hat, y)  # 256,1total_loss += l.sum().item()test_sample += y.shape[0]test_acc_sum += (y_hat.argmax(dim=1) == y).sum().item()print('loss %.4f, test acc %.3f' % (total_loss / test_sample, test_acc_sum / test_sample,))

结果