tfb_pad {tfprobability}

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()