# binary_cross_entropy_with_logits#

composer.loss.binary_cross_entropy_with_logits(input, target, weight=None, size_average=None, reduce=None, reduction='sum', pos_weight=None, scale_by_batch_size=True)[source]#

Replacement for binary_cross_entropy_with_logits that handles class indices or one-hot labels.

binary_cross_entropy_with_logits with reduction = 'mean' will typically result in very small loss values (on the order of 1e-3), which necessitates scaling the learning rate by 1e3 to allow the model to learn. This implementation avoids this by using reduction = sum and scaling the loss inversely proportionally to the batch size.

Parameters
• input (Tensor) โ $$(N, C)$$ where C = number of classes or $$(N, C, H, W)$$ in case of 2D Loss, or $$(N, C, d_1, d_2, ..., d_K)$$ where $$K \geq 1$$ in the case of K-dimensional loss. input is expected to contain unnormalized scores (often referred to as logits).

• target (Tensor) โ If containing class indices, shape $$(N)$$ where each value is $$0 \leq \text{targets}[i] \leq C-1$$, or $$(N, d_1, d_2, ..., d_K)$$ with $$K \geq 1$$ in the case of K-dimensional loss. If containing class probabilities, same shape as the input.

• weight (Tensor, optional) โ a manual rescaling weight given to each class. If given, has to be a Tensor of size C. Default: None.

• size_average (bool, optional) โ Deprecated (see reduction). By default, the losses are averaged over each loss element in the batch. Note that for some losses, there multiple elements per sample. If the field size_average is set to False, the losses are instead summed for each minibatch. Ignored when reduce is False. Default: True

• reduce (bool, optional) โ Deprecated (see reduction). By default, the losses are averaged or summed over observations for each minibatch depending on size_average. When reduce is False, returns a loss per batch element instead and ignores size_average. Default: True

• reduction (str, optional) โ 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 of elements in the output, 'sum': the output will be summed. Note: size_average and reduce are in the process of being deprecated, and in the meantime, specifying either of those two args will override reduction. Default: 'sum'

• pos_weight (Tensor, optional) โ a weight of positive examples. Must be a vector with length equal to the number of classes.

• scale_by_batch_size (bool, optional) โ Whether to scale the loss by the batch size (i.e. input.shape[0]). Default: True`.