tfb_chain: Bijector which applies a sequence of bijectors

In tfprobability: Interface to 'TensorFlow Probability'

Bijector which applies a sequence of bijectors

Description

Bijector which applies a sequence of bijectors

Usage

tfb_chain(
  bijectors = NULL,
  validate_args = FALSE,
  validate_event_size = TRUE,
  parameters = NULL,
  name = NULL
)

Arguments

bijectors	list of bijector instances. An empty list makes this bijector equivalent to the Identity bijector.
validate_args	Logical indicating whether arguments should be checked for correctness.
validate_event_size	Checks that bijectors are not applied to inputs with incomplete support (that is, inputs where one or more elements are a deterministic transformation of the others). For example, the following LDJ would be incorrect: tfb_chain(list(tfb_scale(), tfb_softmax_centered()))$forward_log_det_jacobian(matrix(1:2, ncol = 2)) The jacobian contribution from tfb_scale() applies to a 2-dimensional input, but the output from tfb_softmax_centered() is a 1-dimensional input embedded in a 2-dimensional space. Setting validate_event_size=TRUE (default) prints warnings in these cases. When validate_args is also TRUE, the warning is promoted to an exception.
parameters	Locals dict captured by subclass constructor, to be used for copy/slice re-instantiation operators.
name	String, name given to ops managed by this object. Default: E.g., tfb_chain(list(tfb_exp(), tfb_softplus()))$name == "chain_of_exp_of_softplus".

Value

a bijector instance.

See Also

For usage examples see tfb_forward(), tfb_inverse(), tfb_inverse_log_det_jacobian().

Other bijectors: tfb_absolute_value(), tfb_affine_linear_operator(), tfb_affine_scalar(), tfb_affine(), tfb_ascending(), tfb_batch_normalization(), tfb_blockwise(), tfb_cholesky_outer_product(), tfb_cholesky_to_inv_cholesky(), tfb_correlation_cholesky(), tfb_cumsum(), tfb_discrete_cosine_transform(), tfb_expm1(), tfb_exp(), tfb_ffjord(), tfb_fill_scale_tri_l(), tfb_fill_triangular(), tfb_glow(), tfb_gompertz_cdf(), tfb_gumbel_cdf(), tfb_gumbel(), tfb_identity(), tfb_inline(), tfb_invert(), tfb_iterated_sigmoid_centered(), tfb_kumaraswamy_cdf(), tfb_kumaraswamy(), tfb_lambert_w_tail(), tfb_masked_autoregressive_default_template(), tfb_masked_autoregressive_flow(), tfb_masked_dense(), tfb_matrix_inverse_tri_l(), tfb_matvec_lu(), tfb_normal_cdf(), tfb_ordered(), tfb_pad(), tfb_permute(), tfb_power_transform(), tfb_rational_quadratic_spline(), tfb_rayleigh_cdf(), tfb_real_nvp_default_template(), tfb_real_nvp(), tfb_reciprocal(), tfb_reshape(), tfb_scale_matvec_diag(), tfb_scale_matvec_linear_operator(), tfb_scale_matvec_lu(), tfb_scale_matvec_tri_l(), tfb_scale_tri_l(), tfb_scale(), tfb_shifted_gompertz_cdf(), tfb_shift(), tfb_sigmoid(), tfb_sinh_arcsinh(), tfb_sinh(), tfb_softmax_centered(), tfb_softplus(), tfb_softsign(), tfb_split(), tfb_square(), tfb_tanh(), tfb_transform_diagonal(), tfb_transpose(), tfb_weibull_cdf(), tfb_weibull()

tfprobability documentation built on Sept. 1, 2022, 5:07 p.m.