tfb_masked_dense: Autoregressively masked dense layer

In tfprobability: Interface to 'TensorFlow Probability'

Autoregressively masked dense layer

Description

Analogous to tf$layers$dense.

Usage

tfb_masked_dense(
  inputs,
  units,
  num_blocks = NULL,
  exclusive = FALSE,
  kernel_initializer = NULL,
  reuse = NULL,
  name = NULL,
  ...
)

Arguments

inputs	Tensor input.
units	integer scalar representing the dimensionality of the output space.
num_blocks	integer scalar representing the number of blocks for the MADE masks.
exclusive	logical scalar representing whether to zero the diagonal of the mask, used for the first layer of a MADE.
kernel_initializer	Initializer function for the weight matrix. If NULL (default), weights are initialized using the tf$glorot_random_initializer
reuse	logical scalar representing whether to reuse the weights of a previous layer by the same name.
name	string used to describe ops managed by this function.
...	tf$layers$dense arguments

Details

See Germain et al. (2015)for detailed explanation.

Value

tensor

References

• Mathieu Germain, Karol Gregor, Iain Murray, and Hugo Larochelle. MADE: Masked Autoencoder for Distribution Estimation. In International Conference on Machine Learning, 2015.

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_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()

tfprobability documentation built on Sept. 1, 2022, 5:07 p.m.