layer_autoregressive: Masked Autoencoder for Distribution Estimation

In tfprobability: Interface to 'TensorFlow Probability'

Masked Autoencoder for Distribution Estimation

Description

layer_autoregressive takes as input a Tensor of shape [..., event_size] and returns a Tensor of shape [..., event_size, params]. The output satisfies the autoregressive property. That is, the layer is configured with some permutation ord of {0, ..., event_size-1} (i.e., an ordering of the input dimensions), and the output output[batch_idx, i, ...] for input dimension i depends only on inputs x[batch_idx, j] where ord(j) < ord(i).

Usage

layer_autoregressive(
  object,
  params,
  event_shape = NULL,
  hidden_units = NULL,
  input_order = "left-to-right",
  hidden_degrees = "equal",
  activation = NULL,
  use_bias = TRUE,
  kernel_initializer = "glorot_uniform",
  validate_args = FALSE,
  ...
)

Arguments

object	What to compose the new Layer instance with. Typically a Sequential model or a Tensor (e.g., as returned by layer_input()). The return value depends on object. If object is: missing or NULL, the Layer instance is returned. a Sequential model, the model with an additional layer is returned. a Tensor, the output tensor from layer_instance(object) is returned.
params	integer specifying the number of parameters to output per input.
event_shape	list-like of positive integers (or a single int), specifying the shape of the input to this layer, which is also the event_shape of the distribution parameterized by this layer. Currently only rank-1 shapes are supported. That is, event_shape must be a single integer. If not specified, the event shape is inferred when this layer is first called or built.
hidden_units	list-like of non-negative integers, specifying the number of units in each hidden layer.
input_order	Order of degrees to the input units: 'random', 'left-to-right', 'right-to-left', or an array of an explicit order. For example, 'left-to-right' builds an autoregressive model: p(x) = p(x1) p(x2 | x1) ... p(xD | x<D). Default: 'left-to-right'.
hidden_degrees	Method for assigning degrees to the hidden units: 'equal', 'random'. If 'equal', hidden units in each layer are allocated equally (up to a remainder term) to each degree. Default: 'equal'.
activation	An activation function. See keras::layer_dense. Default: NULL.
use_bias	Whether or not the dense layers constructed in this layer should have a bias term. See keras::layer_dense. Default: TRUE.
kernel_initializer	Initializer for the kernel weights matrix. Default: 'glorot_uniform'.
validate_args	logical, default FALSE. When TRUE, layer parameters are checked for validity despite possibly degrading runtime performance. When FALSE invalid inputs may silently render incorrect outputs.
...	Additional keyword arguments passed to the keras::layer_dense constructed by this layer.

Details

The autoregressive property allows us to use output[batch_idx, i] to parameterize conditional distributions: p(x[batch_idx, i] | x[batch_idx, ] for ord(j) < ord(i)) which give us a tractable distribution over input x[batch_idx]:

p(x[batch_idx]) = prod_i p(x[batch_idx, ord(i)] | x[batch_idx, ord(0:i)])

For example, when params is 2, the output of the layer can parameterize the location and log-scale of an autoregressive Gaussian distribution.

Value

a Keras layer

See Also

Other layers: layer_conv_1d_flipout(), layer_conv_1d_reparameterization(), layer_conv_2d_flipout(), layer_conv_2d_reparameterization(), layer_conv_3d_flipout(), layer_conv_3d_reparameterization(), layer_dense_flipout(), layer_dense_local_reparameterization(), layer_dense_reparameterization(), layer_dense_variational(), layer_variable()

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