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")
weight |
(Tensor, optional): a manual rescaling weight given to the loss
of each batch element. If given, has to be a Tensor of size |
reduction |
(string, optional): Specifies the reduction to apply to the output:
|
The unreduced (i.e. with reduction set to 'none') loss can be described as:
\ell(x, y) = L = \{l_1,…,l_N\}^\top, \quad l_n = - w_n ≤ft[ y_n \cdot \log x_n + (1 - y_n) \cdot \log (1 - x_n) \right]
where N is the batch size. If reduction is not 'none'
(default 'mean'), then
\ell(x, y) = ≤ft\{ \begin{array}{ll} \mbox{mean}(L), & \mbox{if reduction} = \mbox{'mean';}\\ \mbox{sum}(L), & \mbox{if reduction} = \mbox{'sum'.} \end{array} \right.
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) = -∞, since \lim_{x\to 0} \log (x) = -∞.
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) = ∞.
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.
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.
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()
}Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.