loss_fct = nn.CrossEntropyLoss() # 实例化损失函数 loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) # 注意一下这里的形状 其中, logits张量是模型预测输出,形状为(批大小,序列长度,类别数量) labels张量是实际的标签,形状为(批大小,序列长度) 经过形状调整后 logits.view(-1,...
掩蔽向量中的nan元素,对非nan损失值取平均,倘若向量的元素全为nan,则设loss为0 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(~...
计算的是QA中star_position的loss,同理end_position start_loss = loss_fct(start_logits, start_positions) 其中start_logits.shape=[bs,sl] start_position.shape=[bs],start_position 是起始位置 nn.BCE_loss() 计算的是文本的二分类,非零即一 loss_ = myloss(pred_labels, real_labels) 其中pred_labels...
def forward(self, inputs, labels=None) # outputs = fct(inputs) # loss_fct = ... if labels is not None: loss = loss_fct(outputs, labels) # 在训练模型时直接将labels传入模型,在forward过程中计算loss return loss else: return outputs 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12....
(reduction="mean",weight=weight)loss_fct_logit=nn.BCEWithLogitsLoss(reduction="mean",weight=weight)input_src=torch.Tensor([0.8,0.9,0.3])target=torch.Tensor([1,1,0])print(input_src)print(target)output=m(input_src)loss=loss_fct(output,target)loss_logit=loss_fct_logit(input_src,target)...
importtorchimporttorch.nnasnn# 多分类m=torch.nn.LogSoftmax(dim=1)loss_nll_fct=nn.NLLLoss(reduction="mean")loss_ce_fct=nn.CrossEntropyLoss(reduction="mean")input_src=torch.Tensor([[0.8,0.9,0.3],[0.8,0.9,0.3],[0.8,0.9,0.3],[0.8,0.9,0.3]])target=torch.Tensor([1,1,0,0]).long(...
交叉熵损失函数:使用torch.nn.CrossEntropyLoss计算损失。这里的reduction="sum"表示对所有token的损失求和,而不是取平均。这样可以确保每个token的贡献相同。 loss_fct=torch.nn.CrossEntropyLoss(reduction="sum")loss=loss_fct(shift_logits,shift_labels) ...
(): loss_fct = CrossEntropyLoss(ignore_index=-100, reduction="none") loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1)) non_ignored_mask = (shift_labels != -100).type_as(loss) loss = loss * non_ignored_mask non_ignored_count = non_ignored...
neg_sim_matrix=neg_sim_matrix.squeeze(1)labels=torch.zeros(query_embeddings.shape[0],dtype=torch.long,device=query_embeddings.device)#[batch_size,1+batch_size*neg_nums]pos_neg_score=torch.cat([pos_sim_matrix.unsqueeze(1),neg_sim_matrix],dim=1)/self.temperatureloss=loss_fct(pos_neg_...
val+=loss_valreturnval/output.nelement()beta=1loss_fct=nn.SmoothL1Loss(reduction="mean",beta=beta)input_src=torch.Tensor([[0.8,0.8],[0.9,0.9],[0.3,0.3]])target=torch.Tensor([[0.6,0.6],[0.7,0.8],[0.4,0.5]])print(input_src.size())print(target.size())loss=loss_fct(input_src,targ...