tfb_reshape: Reshapes the event_shape of a Tensor

In rstudio/tfprobability: Interface to 'TensorFlow Probability'

Reshapes the event_shape of a Tensor

Description

The semantics generally follow that of tf$reshape(), with a few differences:

• The user must provide both the input and output shape, so that the transformation can be inverted. If an input shape is not specified, the default assumes a vector-shaped input, i.e., event_shape_in = list(-1).

• The Reshape bijector automatically broadcasts over the leftmost dimensions of its input (sample_shape and batch_shape); only the rightmost event_ndims_in dimensions are reshaped. The number of dimensions to reshape is inferred from the provided event_shape_in (event_ndims_in = length(event_shape_in)).

Usage

tfb_reshape(
  event_shape_out,
  event_shape_in = c(-1),
  validate_args = FALSE,
  name = NULL
)

Arguments

event_shape_out	An integer-like vector-shaped Tensor representing the event shape of the transformed output.
event_shape_in	An optional integer-like vector-shape Tensor representing the event shape of the input. This is required in order to define inverse operations; the default of list(-1) assumes a vector-shaped input.
validate_args	Logical, default FALSE. Whether to validate input with asserts. If validate_args is FALSE, and the inputs are invalid, correct behavior is not guaranteed.
name	name prefixed to Ops created by this class.

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_chain(), 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_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()

rstudio/tfprobability documentation built on Sept. 11, 2022, 4:32 a.m.