pulsatrix::CrossEntropyLoss Class Reference

loss = -log(softmax(logits)[target_class]), combined for numerical stability (subtract the max logit before exponentiating) rather than computing softmax and log separately. More...

#include <cross_entropy_loss.hpp>

Public Member Functions
	CrossEntropyLoss (DeviceBackend *backend)
	Constructs a cross-entropy loss.
float	forward (const Tensor &logits, int64_t target_class)
	Computes the loss value and caches softmax probabilities/target for backward().
Tensor	backward () const
	Computes the gradient w.r.t. the logits: softmax(logits) - one_hot(target_class).

Detailed Description

loss = -log(softmax(logits)[target_class]), combined for numerical stability (subtract the max logit before exponentiating) rather than computing softmax and log separately.

Note

Not a Module subclass, same as MSELoss – losses are the seed point relevance/ gradient propagation starts from, not something propagate_relevance is defined for.

Takes an integer class index, not a one-hot Tensor – the standard classification- loss convention (mirrors torch.nn.CrossEntropyLoss's (logits, target) signature), and avoids inventing a one-hot Tensor construction step every caller would otherwise need.

Constructor & Destructor Documentation

CrossEntropyLoss()

pulsatrix::CrossEntropyLoss::CrossEntropyLoss ( DeviceBackend *  backend )

explicit

Constructs a cross-entropy loss.

Parameters

backend

Backend to compute through. Not owned; must outlive this loss.

Member Function Documentation

backward()

Tensor pulsatrix::CrossEntropyLoss::backward

(

)

const

Computes the gradient w.r.t. the logits: softmax(logits) - one_hot(target_class).

Returns

Gradient tensor, same shape as the logits passed to forward().

Note

Must be called after forward() – uses the cached softmax probabilities.

forward()

float pulsatrix::CrossEntropyLoss::forward	(	const Tensor &	logits,
		int64_t	target_class
	)

Computes the loss value and caches softmax probabilities/target for backward().

Parameters

logits	Raw (pre-softmax) model output, shape (num_classes,).
target_class	Ground-truth class index, 0-based. Must be in [0, logits.numel()).

Returns

The scalar cross-entropy loss.

Note

Device-generic (GPU-native-kernels Mission 1): only the loss scalar and the target logit cross to the host. target_class range is an PULSATRIX_ASSERT – an internal invariant for this loss's current (non-Python- bound) call sites, not yet a Python-reachable external boundary.

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The documentation for this class was generated from the following file:

• include/pulsatrix/cross_entropy_loss.hpp