tfprobability: layer_kl_divergence_add_loss – R documentation

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tfprobability

layer_kl_divergence_add_loss

Pass-through layer that adds a KL divergence penalty to the model loss

Description

Pass-through layer that adds a KL divergence penalty to the model loss

Usage

layer_kl_divergence_add_loss(
  object,
  distribution_b,
  use_exact_kl = FALSE,
  test_points_reduce_axis = NULL,
  test_points_fn = tf$convert_to_tensor,
  weight = NULL,
  ...
)

Arguments

`object`	Model or layer object
`distribution_b`	Distribution instance corresponding to b as in `KL[a, b]`. The previous layer's output is presumed to be a Distribution instance and is a.
`use_exact_kl`	Logical indicating if KL divergence should be calculated exactly via `tfp$distributions$kl_divergence` or via Monte Carlo approximation. Default value: FALSE.
`test_points_reduce_axis`	Integer vector or scalar representing dimensions over which to reduce_mean while calculating the Monte Carlo approximation of the KL divergence. As is with all tf$reduce_* ops, NULL means reduce over all dimensions; () means reduce over none of them. Default value: () (i.e., no reduction).
`test_points_fn`	A callable taking a `tfp$distributions$Distribution` instance and returning a tensor used for random test points to approximate the KL divergence. Default value: tf$convert_to_tensor.
`weight`	Multiplier applied to the calculated KL divergence for each Keras batch member. Default value: NULL (i.e., do not weight each batch member).
`...`	Additional arguments passed to `args` of `keras::create_layer`.

Value

a Keras layer