tfp.substrates.numpy.sts.sample_uniform_initial_state

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

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

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.

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).