torch.nn.CrossEntropyLoss(*weight=None*, *size_average=None*, *ignore_index=- 100*, *reduce=None*, *reduction='mean'*, *label_smoothing=0.0*) torch.nn.functional.cross_entropy(*input*, *target*, *weight=None*, *size_average=None*, *ignore_index=- 100*, *reduce=None*, *reduction=...
torch.nn.functional.mse_loss(input,target,size_average=None,reduce=None,reduction=mean) → Tensor 参数 size_average: 默认为True, 计算一个batch中所有loss的均值;reduce为 False时,忽略这个参数; reduce: 默认为True, 计算一个batch中所有loss的均值或者和; reduce = False,size_average 参数失效,返回的 l...
reduction 参数值mean和sum,mean:返回loss和的平均值;sum:返回loss的和。默认:mean。 输入和标签: input,target维度相同 import torch import torch.nn as nn loss = nn.MSELoss() input = torch.randn(3, 5, requires_grad=True) target = torch.randn(3, 5) output = loss(input, target) output.back...
torch.nn.functional.binary_cross_entropy(input, target, weight=None, size_average=None, reduce=None, reduction='mean') 计算目标和输出之间二进制交叉熵的函数.请参见 BCELoss.参数:input– 任意形状的tensor target– 与输入形状相同的tensor weight (Tensor, 可选的)– 手动重新调整weight, 如果提供, 它...
CLASStorch.nn.MSELoss(size_average=None, reduce=None, reduction=‘mean’) MSE : Means Square Error Reference:https://pytorch.org/docs/stable/nn.html#torch.nn.MSELoss pytorch自定义网络结构不进行参数初始化会怎样? 答案:不怕不怕,pytorch自己默认有初始化 证据如下: 1)torch.nn.Conv2d的参数初始化...
# 假设x是输入数据,batch_size是批次大小x = torch.randn(batch_size, 784)recon_x, mu, logvar = model(x)loss = nn.functional.binary_cross_entropy(recon_x, x, reduction='sum') \+ 0.5 * torch.sum(torch.exp(logvar) + mu.pow(2) - 1 - logvar) ...
mse_loss¶ torch.nn.functional.mse_loss(input, target, size_average=None, reduce=None, reduction='mean')→ Tensor[source]¶ Measures the element-wise mean squared error. See MSELoss for details. margin_ranking_loss¶ torch.nn.functional.margin_ranking_loss(input1, input2, target, margin...
在pytorch中,经常使用nn.MSELoss作为损失函数,例如 loss=nn.MSELoss() input=torch.randn(3,5,requires_grad=True) target=...torch.randn(3,5) error=loss(input,target) error.backward...
在pytorch中,经常使用nn.MSELoss作为损失函数,例如 loss=nn.MSELoss() input=torch.randn(3,5,requires_grad=True) target=...torch.randn(3,5) error=loss(input,target) error.backward...
avg_factor (int, optional): Average factor that is used to average the loss. Defaults to None. Returns: torch.Tensor: The calculated loss """ loss = self.loss_weight * mse_loss( pred, target, weight, reduction=self.reduction, avg_factor=avg_factor) return loss ...