Function dfdx::losses::cross_entropy_with_logits_loss

pub fn cross_entropy_with_logits_loss<T: Reduce1<-1>>(
    logits: T,
    target_probs: &T::NoTape
) -> Tensor0D<T::Tape>

Cross entropy loss. This computes: -(logits.log_softmax() * target_probs).sum(-1).mean()

This will call log_softmax(logits), so make sure logits is not the output from softmax() or log_softmax() already.

Arguments

• logits: The un-normalized output from a model. log_softmax() is called in this function
• target_probs: Target containing probability vectors NOT class indices.

Example

let logits = Tensor1D::new([-1.0, -0.5]);
let target_probs = Tensor1D::new([0.5, 0.5]);
let loss = cross_entropy_with_logits_loss(logits.traced(), &target_probs);