torch.nn.functional.binary_cross_entropy_with_logits#

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

Compute Binary Cross Entropy between target and input logits.

SeeBCEWithLogitsLoss for details.

Parameters

• input (Tensor) – Tensor of arbitrary shape as unnormalized scores (often referred to as logits).

• 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'

• pos_weight (Tensor,optional) – a weight of positive examples to be broadcasted with target.Must be a tensor with equal size along the class dimension to the number of classes.Pay close attention to PyTorch’s broadcasting semantics in order to achieve the desiredoperations. For a target of size [B, C, H, W] (where B is batch size) pos_weight ofsize [B, C, H, W] will apply different pos_weights to each element of the batch or[C, H, W] the same pos_weights across the batch. To apply the same positive weightalong all spatial dimensions for a 2D multi-class target [C, H, W] use: [C, 1, 1].Default:None

Return type

Tensor

Examples:

>>>input=torch.randn(3,requires_grad=True)>>>target=torch.empty(3).random_(2)>>>loss=F.binary_cross_entropy_with_logits(input,target)>>>loss.backward()