requires_grad=True) optimizer = optim.Adam([W, b]) for sample, target in zip(data, targets): # clear out the gradients of all Variables # in this optimizer (i.e. W, b) optimizer.zero_grad() output = linear_model(sample, W, b) loss = (output - target) ** 2 loss.backward(...
zero_grad() output, hidden = model(data, hidden) loss = criterion(output.view(-1, ntokens), targets) loss.backward() # `clip_grad_norm` helps prevent the exploding gradient problem in RNNs / LSTMs. torch.nn.utils.clip_grad_norm(model.parameters(), args.clip) for p in model....
(2条消息)python zero_grad()_Florence_Janie的博客-CSDN博客_zero_grad http://t.cn/A6USi0lh
【python zero_grad()】 https://blog.csdn.net/qq_34690929/article/details/79934843 关于为什么要将参数梯度置零: 根据pytorch中的backward()函数的计算,当网络参量进行反馈时,梯度是被积累的而不是被替换掉;但是在每一个batch时毫无疑问并不需要将两个batch的梯度混合起来累积,因此这里就需要每个batch设置一遍zer...