torch.nn.functional.l1_loss(*input*, *target*, *size_average=None*, *reduce=None*, *reduction='mean'*) input = torch.tensor(y_pred) target = torch.tensor(y_true) output = torch.nn.functional.l1_loss(input, target) print(output) # tensor(0.0500, dtype=torch.float64) L2 Loss (Mean...
{ CATCH_RETURN_Tensor( autoopts =torch::nn::functional::MSELossFuncOptions(); ApplyReduction(opts, reduction); res =ResultTensor(torch::nn::functional::mse_loss(*input, *target, opts)); ) } If this is indeed incorrect, I would be happy to submit a fix in a PR, but am still figur...
nn::functional::CrossEntropyFuncOptions options; options.reduction(torch::kSum); std::cout << inputs.min() << '\n'; std::cout << inputs.max() << '\n'; auto loss = torch::nn::functional::cross_entropy(inputs, targets, options); std::cout << loss << '\n'; loss.backward(...
loss = nn.L1Loss() input = torch.randn(3, 5, requires_grad=True) target = torch.randn(3, 5) output = loss(input, target) output.backward() 2 均方误差损失 MSELoss 用于测量输入 x 和目标 y 中每个元素之间的均方误差(平方 L2 范数)。 图1 主要参数: reduction 参数值mean和sum,mean:返回l...
The following are 30 code examples of torch.nn.functional.smooth_l1_loss(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available...
d0)...替换为d0...，然后依次将nn.BCELoss(size_average=True)替换为nn.BCEWithLogitsLoss(size_av...
#定义隐藏单元个数num_input,num_hidden ,num_output =784,256,10#把模型移动到GPU上net = LinearNet(num_input,num_hidden,num_output).to(device ='cuda:0')forparaminnet.state_dict():print(param)loss = nn.CrossEntropyLoss()num_epochs =100net = LinearNet(num_input,num_hidden,num_output)par...
继承torch.nn.Module类写自己的loss。 class MyLoss(torch.nn.Moudle):def __init__(self):super(MyLoss, self).__init__ def forward(self, x, y):loss = torch.mean((x - y) ** 2)return loss 标签平滑(label smoothing) 写一个label_smoothing.py的文件,然后在训练代码里引用,用LSR代替交叉熵...
d0)...替换为d0...，然后依次将nn.BCELoss(size_average=True)替换为nn.BCEWithLogitsLoss(size_av...
loss.backward optimizer.step L1 正则化 l1_regularization = torch.nn.L1Loss(reduction= sum ) loss = ...# Standard cross-entropy loss for param in model.parameters: loss += torch.sum(torch.abs(param)) loss.backward 不对偏置项进行 L2 正则化/权值衰减(weight decay) ...