tfp.experimental.bijectors.build_trainable_highway_flow

Builds a HighwayFlow parameterized by trainable variables.

tfp.experimental.bijectors.build_trainable_highway_flow(
    width,
    residual_fraction_initial_value=0.5,
    activation_fn=None,
    gate_first_n=None,
    seed=None,
    validate_args=False
)

The variables are transformed to enforce the following parameter constraints:

• residual_fraction is bounded between 0 and 1.
• upper_diagonal_weights_matrix is a randomly initialized (lower) diagonal matrix with positive diagonal of size width x width.
• lower_diagonal_weights_matrix is a randomly initialized lower diagonal matrix with ones on the diagonal of size width x width;
• bias is a randomly initialized vector of size width.

Args
width	Input dimension of the bijector.
residual_fraction_initial_value	Initial value for gating parameter, must be between 0 and 1.
activation_fn	Callable invertible activation function (e.g., tf.nn.softplus), or None.
gate_first_n	Decides which part of the input should be gated (useful for example when using auxiliary variables).
seed	Seed for random initialization of the weights.
validate_args	Python bool. Whether to validate input with runtime assertions. Default value: False.

Returns
trainable_highway_flow	The initialized bijector.