torch.nn.functional.binary_cross_entropy#

torch.nn.functional.binary_cross_entropy(input,target,weight=None,size_average=None,reduce=None,reduction='mean')[source]#

Compute Binary Cross Entropy between the target and input probabilities.

SeeBCELoss for details.

Parameters

• input (Tensor) – Tensor of arbitrary shape as probabilities.

• target (Tensor) – Tensor of the same shape as input with values between 0 and 1.

• weight (Tensor,optional) – a manual rescaling weightif provided it’s repeated to match input tensor shape

• size_average (bool,optional) – Deprecated (seereduction).

• reduce (bool,optional) – Deprecated (seereduction).

• reduction (str,optional) – Specifies the reduction to apply to the output:'none' |'mean' |'sum'.'none': no reduction will be applied,'mean': the sum of the output will be divided by the number ofelements in the output,'sum': the output will be summed. Note:size_averageandreduce are in the process of being deprecated, and in the meantime,specifying either of those two args will overridereduction. Default:'mean'

Return type

Tensor

Examples:

>>>input=torch.randn(3,2,requires_grad=True)>>>target=torch.rand(3,2,requires_grad=False)>>>loss=F.binary_cross_entropy(torch.sigmoid(input),target)>>>loss.backward()