loss_fct = CrossEntropyLoss(reduction='none') att_loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1)) nan_mask = torch.isnan(att_loss) if sum(nan_mask) > 0: if sum(~nan_mask) == 0: print("!"*20,"cross_entopy_loss all is nan","!"*...
y= torch.autograd.grad(p[2], [a], retain_graph=True)print(y) 6.loss - Cross Entropy Loss 分类使用的loss,这里不进行说明。 十一:感知机 1.单层感知机 上标:表示第几层 w:链接左和右 求导过程: loss使用的是MSE 可以明显的看到求导后,和什么有关系。 程序 #单层感知机x = torch.randn(1,10) ...
device)[None, :] < valid_len[:, None] X[~mask] = value return X class MaskedSoftmaxCELoss(nn.CrossEntropyLoss): """带遮蔽的softmax交叉熵损失函数""" # pred的形状:(batch_size,num_steps,vocab_size) # label的形状:(batch_size,num_steps) # valid_len的形状:(batch_size,) def forward...
torch.nn.CrossEntropyLoss调用了函数F.cross_entropy,与tf中不同的是,F.cross_entropy执行包含两部分log_softmax和F.nll_losslog_softmax主要用于解决函数overflow和underflow,加快运算速度,提高数据稳定性。
loss = nn.CrossEntropyLoss(reduction='none') train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size) trainer = torch.optim.SGD(net.parameters(), lr=lr) d2l.train_ch3(net, train_iter, test_iter, loss, num_epochs, trainer) ...
classifier(last_bert_layer) if labels is not None: loss_fct = CrossEntropyLoss() if input_mask is not None: masked_logits = torch.masked_select(logits, input_mask) loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) else: loss = loss_fct(logits.view(-1, self....
num_hiddens2) num_epochs, lr, batch_size = 10, 0.5, 256 loss = nn.CrossEntropyLoss(reduction='none') train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size) trainer = torch.optim.SGD(net.parameters(), lr=lr) train_ch3(net, train_iter, test_iter, loss, num_epochs, train...
It'd be great to have a fused linear and cross-entropy function in PyTorch, for example, torch.nn.functional.linear_cross_entropy. This function acts as a fused linear projection followed by a cross-entropy loss, e.g.def linear_cross_entropy(linear_weights, input, labels): logits = F....
mask = (labels_safe != -100).float() loss = (gathered_logprobs * mask).sum(dim=-1) return -loss def new_method(): token_loss = F.cross_entropy( logits.permute(0, 2, 1), labels, reduction="none", ) loss = token_loss.sum(dim=-1) ...
model = model.to(device)# 定义损失函数和优化器criterion = nn.CrossEntropyLoss()# 对于多分类问题,使用交叉熵损失optimizer = optim.Adam(model.parameters(), lr=0.001) 5. 模型训练 使用train_segmentation_model函数来训练模型。该函数封装了训练循环,并提供了多种选项来配置训练过程。