tfb_real_nvp_default_template: Build a scale-and-shift function using a multi-layer neural...

In rstudio/tfprobability: Interface to 'TensorFlow Probability'

Build a scale-and-shift function using a multi-layer neural network

Description

This will be wrapped in a make_template to ensure the variables are only created once. It takes the d-dimensional input x[0:d] and returns the D-d dimensional outputs loc ("mu") and log_scale ("alpha").

Usage

tfb_real_nvp_default_template(
  hidden_layers,
  shift_only = FALSE,
  activation = tf$nn$relu,
  name = NULL,
  ...
)

Arguments

hidden_layers	list-like of non-negative integer, scalars indicating the number of units in each hidden layer. Default: list(512, 512).
shift_only	logical indicating if only the shift term shall be computed (i.e. NICE bijector). Default: FALSE.
activation	Activation function (callable). Explicitly setting to NULL implies a linear activation.
name	A name for ops managed by this function. Default: "tfb_real_nvp_default_template".
...	tf$layers$dense arguments

Details

The default template does not support conditioning and will raise an exception if condition_kwargs are passed to it. To use conditioning in real nvp bijector, implement a conditioned shift/scale template that handles the condition_kwargs.

Value

list of:

• shift: Float-like Tensor of shift terms

• log_scale: Float-like Tensor of log(scale) terms

References

• George Papamakarios, Theo Pavlakou, and Iain Murray. Masked Autoregressive Flow for Density Estimation. In Neural Information Processing Systems, 2017.

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_pad(), tfb_permute(), tfb_power_transform(), tfb_rational_quadratic_spline(), tfb_rayleigh_cdf(), 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()

rstudio/tfprobability documentation built on Sept. 11, 2022, 4:32 a.m.