return torch._C._nn.binary_cross_entropy(input, target, weight, reduction_enum) def binary_cross_entropy_with_logits( input: Tensor, target: Tensor, weight: Optional[Tensor] = None, size_average: Optional[bool] = None, reduce: Optional[bool] = None, reduction: str = "mean", pos_weight...
5. 带Logits 的二分类交叉熵损失(Binary Cross Entropy with Logits Loss, BCEWithLogitsLoss) 类名: torch.nn.BCEWithLogitsLoss 用途: 结合了 Sigmoid 和 BCELoss,通常用于二分类问题。 优点: 数值上更稳定,计算效率更高。 示例代码: criterion = nn.BCEWithLogitsLoss() inputs = torch.tensor([[0.8], ...
print(f"Gradient function for z = {z.grad_fn}")print(f"Gradient function for loss = {loss.grad_fn}")---Gradientfunctionforz=<AddBackward0objectat0x7e7ffe3f6140>Gradientfunctionforloss=<BinaryCrossEntropyWithLogitsBackward0objectat0x7e7ffe3f6710> 计算梯度Gradients 为了优化神经网络中参数的权重,...
print(f"Gradient function for z ={z.grad_fn}")print(f"Gradient function for loss ={loss.grad_fn}") 1. 2. 输出如下: Gradient function for z = <AddBackward0 object at 0x7ff369b0d310> Gradient function for loss = <BinaryCrossEntropyWithLogitsBackward0 object at 0x7ff3772319d0> 计算梯...
Gradient function for z = <AddBackward0 object at 0x7fafdf903048> Gradient function for loss = <BinaryCrossEntropyWithLogitsBackward object at 0x7fafdf903048> 计算梯度 为了优化神经网络的权重参数,我们需要计算损失函数对于参数的导数,也就是说我们需要在x与y固定的情况下计算\frac{\partial loss}{\part...
loss = torch.nn.functional.binary_cross_entropy_with_logits(z, y) Tensors、Functions and Computational graph 上述代码定义了下面的computational graph: 在该网络中,w和b是parameters,是我们需要优化的。因此,我们需要能够计算损失函数关于这些变量的梯度。因此,我们设置了这些tensor的requires_grad属性。
.nn.functional.alpha_dropout()torch.nn.functional.avg_pool1d()torch.nn.functional.avg_pool2d()torch.nn.functional.avg_pool3d()torch.nn.functional.batch_norm()torch.nn.functional.bilinear()torch.nn.functional.binary_cross_entropy()torch.nn.functional.binary_cross_entropy_with_logits()torch.nn....
loss = torch.nn.functional.binary_cross_entropy_with_logits(z, y) 张量、函数与计算图 以上代码定义了以下计算图: 在这个网络中,w和b是需要优化的参数。因此,我们需要能够计算损失函数相对于这些变量的梯度。为了做到这一点,我们设置了这些张量的requires_grad属性为True。
Function that measures the Binary Cross Entropy between the target and the output. See BCELoss for details. Parameters input –Tensor of arbitrary shape target –Tensor of the same shape as input weight (Tensor, optional)– a manual rescaling weight if provided it’s repeated to match input...
binary_cross_entropy_with_logits(x_recon, x, size_average=False).div(batch_size) elif distribution == 'gaussian': x_recon = F.sigmoid(x_recon) recon_loss = F.mse_loss(x_recon, x, size_average=False).div(batch_size) else: recon_loss = None return recon_loss ...