This Bijector is initialized with shift Tensor and scale arguments,
giving the forward operation: Y = g(X) = scale @ X + shift
where the scale term is logically equivalent to:
scale = scale_identity_multiplier * tf.diag(tf.ones(d)) + tf.diag(scale_diag) + scale_tril + scale_perturb_factor @ diag(scale_perturb_diag) @ tf.transpose([scale_perturb_factor]))
tfb_affine(
shift = NULL,
scale_identity_multiplier = NULL,
scale_diag = NULL,
scale_tril = NULL,
scale_perturb_factor = NULL,
scale_perturb_diag = NULL,
adjoint = FALSE,
validate_args = FALSE,
name = "affine",
dtype = NULL
)a bijector instance.
Floating-point Tensor. If this is set to NULL, no shift is applied.
floating point rank 0 Tensor representing a scaling done
to the identity matrix. When scale_identity_multiplier = scale_diag = scale_tril = NULL then
scale += IdentityMatrix. Otherwise no scaled-identity-matrix is added to scale.
Floating-point Tensor representing the diagonal matrix.
scale_diag has shape [N1, N2, ... k], which represents a k x k diagonal matrix.
When NULL no diagonal term is added to scale.
Floating-point Tensor representing the lower triangular matrix.
scale_tril has shape [N1, N2, ... k, k], which represents a k x k lower triangular matrix.
When NULL no scale_tril term is added to scale. The upper triangular elements above the diagonal are ignored.
Floating-point Tensor representing factor matrix with last
two dimensions of shape (k, r) When NULL, no rank-r update is added to scale.
Floating-point Tensor representing the diagonal matrix.
scale_perturb_diag has shape [N1, N2, ... r], which represents an r x r diagonal matrix.
When NULL low rank updates will take the form scale_perturb_factor * scale_perturb_factor.T.
Logical indicating whether to use the scale matrix as specified or its adjoint. Default value: FALSE.
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.
tf$DType to prefer when converting args to Tensors. Else, we fall back to a
common dtype inferred from the args, finally falling back to float32.
If NULL of scale_identity_multiplier, scale_diag, or scale_tril are specified then
scale += IdentityMatrix Otherwise specifying a scale argument has the semantics of
scale += Expand(arg), i.e., scale_diag != NULL means scale += tf$diag(scale_diag).
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_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_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()