tfp.substrates.numpy.sts.sample_uniform_initial_state

Initialize from a uniform [-2, 2] distribution in unconstrained space.

Main aliases

tfp.substrates.numpy.sts.sample_uniform_initial_state(
    parameter, return_constrained=True, init_sample_shape=(), seed=None
)

Args
`parameter`	`sts.Parameter` named tuple instance.
`return_constrained`	if `True`, re-applies the constraining bijector to return initializations in the original domain. Otherwise, returns initializations in the unconstrained space. Default value: `True`.
`init_sample_shape`	`sample_shape` of the sampled initializations. Default value: `[]`.
`seed`	PRNG seed; see `tfp.random.sanitize_seed` for details.

Returns
`uniform_initializer`	`Tensor` of shape `concat([init_sample_shape, parameter.prior.batch_shape, transformed_event_shape])`, where `transformed_event_shape` is `parameter.prior.event_shape`, if `return_constrained=True`, and otherwise it is `parameter.bijector.inverse_event_shape(parameteter.prior.event_shape)`.

Args