您必须
clone使用参数，否则只需复制引用即可。
weights = []for param in model.parameters(): weights.append(param.clone())criterion = nn.BCELoss() # criterion and optimizer setupoptimizer = optim.Adam(model.parameters(), lr=0.001)foo = torch.randn(3, 10) # fake inputtarget = torch.randn(3, 5) # fake targetresult = model(foo) # predictions and comparison and backproploss = criterion(result, target)optimizer.zero_grad()loss.backward()optimizer.step()weights_after_backprop = [] # weights after backpropfor param in model.parameters(): weights_after_backprop.append(param.clone()) # only layer1's weight should update, layer2 is not usedfor i in zip(weights, weights_after_backprop): print(torch.equal(i[0], i[1]))
这使
FalseFalseTrueTrue
