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
)
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) .
|