tfb_pad: Pads a value to the 'event_shape' of a 'Tensor'.

In tfprobability: Interface to 'TensorFlow Probability'

Pads a value to the event_shape of a Tensor.

Description

The semantics of bijector_pad generally follow that of tf$pad() except that bijector_pad's paddings argument applies to the rightmost dimensions. Additionally, the new argument axis enables overriding the dimensions to which paddings is applied. Like paddings, the axis argument is also relative to the rightmost dimension and must therefore be negative. The argument paddings is a vector of integer pairs each representing the number of left and/or right constant_values to pad to the corresponding righmost dimensions. That is, unless axis is specified, specifiying kdifferentpaddingsmeans the rightmostkdimensions will be "grown" by the sum of the respectivepaddingsrow. Whenaxisis specified, it indicates the dimension to which the correspondingpaddingselement is applied. By defaultaxisisNULLwhich means it is logically equivalent torange(start=-len(paddings), limit=0)', i.e., the rightmost dimensions.

Usage

tfb_pad(
  paddings = list(c(0, 1)),
  mode = "CONSTANT",
  constant_values = 0,
  axis = NULL,
  validate_args = FALSE,
  name = NULL
)

Arguments

paddings	A vector-shaped Tensor of integer pairs representing the number of elements to pad on the left and right, respectively. Default value: list(reticulate::tuple(0L, 1L)).
mode	One of 'CONSTANT', 'REFLECT', or 'SYMMETRIC' (case-insensitive). For more details, see tf$pad.
constant_values	In "CONSTANT" mode, the scalar pad value to use. Must be same type as tensor. For more details, see tf$pad.
axis	The dimensions for which paddings are applied. Must be 1:1 with paddings or NULL. Default value: NULL (i.e., tf$range(start = -length(paddings), limit = 0)).
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_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.