event_shape of a Tensor.The semantics of bijector_pad generally follow that of tf$pad()
except that bijector_pad's paddings argument applies to the rightmost
dimensions. Additionally, the new argument axis enables overriding the
dimensions to which paddings is applied. Like paddings, the axis
argument is also relative to the rightmost dimension and must therefore be
negative.
The argument paddings is a vector of integer pairs each representing the
number of left and/or right constant_values to pad to the corresponding
righmost dimensions. That is, unless axis is specified, specifiying kdifferentpaddingsmeans the rightmostkdimensions will be "grown" by the sum of the respectivepaddingsrow. Whenaxisis specified, it indicates the dimension to which the correspondingpaddingselement is applied. By defaultaxisisNULLwhich means it is logically equivalent torange(start=-len(paddings), limit=0)`, i.e., the rightmost dimensions.
tfb_pad(
paddings = list(c(0, 1)),
mode = "CONSTANT",
constant_values = 0,
axis = NULL,
validate_args = FALSE,
name = NULL
)a bijector instance.
A vector-shaped Tensor of integer pairs representing the number
of elements to pad on the left and right, respectively.
Default value: list(reticulate::tuple(0L, 1L)).
One of 'CONSTANT', 'REFLECT', or 'SYMMETRIC'
(case-insensitive). For more details, see tf$pad.
In "CONSTANT" mode, the scalar pad value to use. Must be
same type as tensor. For more details, see tf$pad.
The dimensions for which paddings are applied. Must be 1:1 with
paddings or NULL.
Default value: NULL (i.e., tf$range(start = -length(paddings), limit = 0)).
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 prefixed to Ops created by this class.
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()