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...
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...
Shape: BCELoss class torch.nn.BCELoss(weight=None, size_average=None, reduce=None, reduction='mean')[source] Creates a criterion that measures the Binary Cross Entropy between the target and the output: The unreduced (i.e. with reduction set to 'none') loss can be described as: ℓ(...
**二分类交叉熵损失(Binary Cross Entropy Loss, BCE Loss)** - **类名**: `torch.nn.BCELoss` - **用途**: 用于二分类问题,计算预测概率与目标标签之间的二进制交叉熵。 - **公式**: $L = -\frac{1}{N} \sum_{i=1}^{N} (t_i \cdot \log(p_i) + (1 - t_i) \cdot \log(1 ...
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。
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....
nn.CrossEntropyLoss()交叉熵损失函数 在pytorch中nn.CrossEntropyLoss()为交叉熵损失函数,用于解决多分类问题,也可用于解决二分类问题。 BCELoss是Binary CrossEntropyLoss的缩写,nn.BCELoss()为二元交叉熵损失函数,只能解决二分类问题。在使用nn.BCELoss()作为损失函数时,需要在该层前面加上Sigmoid函数,一般使用nn...
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,...
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...