ag_cross_entropy_loss: Categorical Cross-Entropy loss
In ggmlR: 'GGML' Tensor Operations for Machine Learning
ag_cross_entropy_loss R Documentation
Categorical Cross-Entropy loss
Description
Generic CE: -sum(target * log(pred)) / batch_size.
The gradient w.r.t. pred is -target / pred / n.
Use ag_softmax_cross_entropy_loss() for the numerically stable
combined softmax + CE (fused gradient (p - y) / n).
Usage
ag_cross_entropy_loss(pred, target)
Arguments
pred
ag_tensor [classes, batch_size] probabilities (any, not just softmax)
target
matrix [classes, batch_size] one-hot (or soft) labels
Value
scalar ag_tensor
ggmlR documentation built on July 14, 2026, 1:08 a.m.
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| ag_cross_entropy_loss | R Documentation |
Categorical Cross-Entropy loss
Description
Generic CE: -sum(target * log(pred)) / batch_size.
The gradient w.r.t. pred is -target / pred / n.
Use ag_softmax_cross_entropy_loss() for the numerically stable
combined softmax + CE (fused gradient (p - y) / n).
Usage
ag_cross_entropy_loss(pred, target)
Arguments
pred |
ag_tensor [classes, batch_size] probabilities (any, not just softmax) |
target |
matrix [classes, batch_size] one-hot (or soft) labels |
Value
scalar ag_tensor
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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