nn_bce_loss function

Binary cross entropy loss

Creates a criterion that measures the Binary Cross Entropy between the target and the output:

nn_bce_loss(weight = NULL, reduction = "mean")

Arguments

• weight: (Tensor, optional): a manual rescaling weight given to the loss of each batch element. If given, has to be a Tensor of size nbatch.
• reduction: (string, 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 of elements in the output, 'sum': the output will be summed.

Details

The unreduced (i.e. with reduction set to 'none') loss can be described as:

where $N$ is the batch size. If reduction is not 'none'

(default 'mean'), then

This is used for measuring the error of a reconstruction in for example an auto-encoder. Note that the targets $y$ should be numbers between 0 and 1.

Notice that if $x_n$ is either 0 or 1, one of the log terms would be mathematically undefined in the above loss equation. PyTorch chooses to set $\log (0) = -\infty$, since $\lim_{x\to 0} \log (x) = -\infty$.

However, an infinite term in the loss equation is not desirable for several reasons. For one, if either $y_n = 0$ or $(1 - y_n) = 0$, then we would be multiplying 0 with infinity. Secondly, if we have an infinite loss value, then we would also have an infinite term in our gradient, since $\lim_{x\to 0} \frac{d}{dx} \log (x) = \infty$.

This would make BCELoss's backward method nonlinear with respect to $x_n$, and using it for things like linear regression would not be straight-forward. Our solution is that BCELoss clamps its log function outputs to be greater than or equal to -100. This way, we can always have a finite loss value and a linear backward method.

Shape

• Input: $(N, *)$ where $*$ means, any number of additional dimensions
• Target: $(N, *)$, same shape as the input
• Output: scalar. If reduction is 'none', then $(N, *)$, same shape as input.

Examples

if (torch_is_installed()) {
m <- nn_sigmoid()
loss <- nn_bce_loss()
input <- torch_randn(3, requires_grad = TRUE)
target <- torch_rand(3)
output <- loss(m(input), target)
output$backward()
}