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","!"*...
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 ...
I am using a dataset with 59 classes. My predicted mask has shape [4, 59, 256, 256] (float) and Ground Truth Mask is of shape [4, 256, 256] (float). I passed these two tensors to the CrossEntropyLoss() function. loss = criterion(pred, target) It returns the following error on...
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) ...
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) ...
loss = torch.nn.CrossEntropyLoss() # 交叉熵损失函数 optimizer = torch.optim.SGD(net.parameters(), lr) 评估函数 # 评估函数 def evaluate(data_iter, net): right_sum, n, loss_sum = 0.0, 0, 0.0 for x, y in data_iter: y_ = net(x) ...
ghm (acc_sum=[1...] + momentum=1 ) + pytorch F.binary_cross_entropy_with_logits => 异常bce; 这个意思?? 还有一开始说错了,我复现用的softmax loss,不是bce loss,但含义一样的。代码里其实主要就是: cross_entropy=-y_truth*keras.backend.log(y_pred)loss=cross_entropy/grad_density ...
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....
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, trainer) ...