ggml_cross_entropy_loss: Cross-Entropy Loss (Graph)
In ggmlR: 'GGML' Tensor Operations for Machine Learning
ggml_cross_entropy_loss R Documentation
Cross-Entropy Loss (Graph)
Description
Creates a graph node computing the cross-entropy loss between logits
a and labels b. The op applies its own softmax, so a
must be logits, not probabilities.
Usage
ggml_cross_entropy_loss(ctx, a, b)
Arguments
ctx
GGML context
a
Logits
b
Labels, same shape as a
Details
Differentiable: the backward pass is wired into the graph, so this can be
used as a training loss in a hand-built graph.
Value
A scalar tensor holding the loss
See Also
ggml_cross_entropy_loss_back
ggmlR documentation built on Oct. 23, 2026, 1:07 a.m.
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| ggml_cross_entropy_loss | R Documentation |
Cross-Entropy Loss (Graph)
Description
Creates a graph node computing the cross-entropy loss between logits
a and labels b. The op applies its own softmax, so a
must be logits, not probabilities.
Usage
ggml_cross_entropy_loss(ctx, a, b)
Arguments
ctx |
GGML context |
a |
Logits |
b |
Labels, same shape as |
Details
Differentiable: the backward pass is wired into the graph, so this can be used as a training loss in a hand-built graph.
Value
A scalar tensor holding the loss
See Also
ggml_cross_entropy_loss_back
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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