R: Computes Kullback-Leibler divergence loss between 'y

loss_kl_divergence {keras3}

R Documentation

Computes Kullback-Leibler divergence loss between `y_true` & `y_pred`.

Formula:

loss <- y_true * log(y_true / y_pred)

loss_kl_divergence(
  y_true,
  y_pred,
  ...,
  reduction = "sum_over_batch_size",
  name = "kl_divergence"
)

`y_true`	Tensor of true targets.
`y_pred`	Tensor of predicted targets.
`...`	For forward/backward compatability.
`reduction`	Type of reduction to apply to the loss. In almost all cases this should be `"sum_over_batch_size"`. Supported options are `"sum"`, `"sum_over_batch_size"` or `NULL`.
`name`	Optional name for the loss instance.

KL Divergence loss values with shape = ⁠[batch_size, d0, .. dN-1]⁠.

y_true <- random_uniform(c(2, 3), 0, 2)
y_pred <- random_uniform(c(2,3))
loss <- loss_kl_divergence(y_true, y_pred)
loss

## tf.Tensor([3.5312676 0.2128672], shape=(2), dtype=float32)

[Package keras3 version 0.2.0 Index]