我发现了几个错误-首先你需要扁平化图像，你需要一个热编码BCE损失或你应该使用交叉熵损失，你需要激活函数，例如Sigmoid，请参阅下面的代码

mld = nn.Sequential(nn.Flatten(), nn.Linear(784,10), nn.Sigmoid())
loss_list=[]
epochs=100
loss_fn = nn.BCELoss()
optimizer = optim.SGD(mld.parameters(), lr=0.1)
mld.train()
for epoch in range(epochs):
  for X_train, y_train in train_loader:
    pred = mld(X_train.float())
    print(pred.shape)
    print(torch.nn.functional.one_hot(y_train).shape)
    loss = loss_fn(pred, torch.nn.functional.one_hot(y_train, num_classes=10).float())
    optimizer.zero_grad()
    loss.backward()
    print(loss.item())
    loss_list.append(loss.item())
    optimizer.step()

mld.eval()
with torch.no_grad():
    y_pred = mld(X_train)
accuracy = (y_pred.round() == y).float().mean()
print("Accuracy {:.2f}".format(accuracy * 100))

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