def _parameter_properties(cls, dtype, num_classes=None):
return dict(cutpoints=parameter_properties.ParameterProperties(
event_ndims=1,
shape_fn=parameter_properties.SHAPE_FN_NOT_IMPLEMENTED,
default_constraining_bijector_fn=(
lambda: invert_bijector.Invert(ordered_bijector.Ordered()))),
loc=parameter_properties.ParameterProperties())
def _parameter_properties(cls, dtype, num_classes=None):
# pylint: disable=g-long-lambda
return dict(
outcomes=parameter_properties.ParameterProperties(
event_ndims=None,
shape_fn=lambda sample_shape: [num_classes],
default_constraining_bijector_fn=invert_bijector.Invert(
ordered_bijector.Ordered())),
logits=parameter_properties.ParameterProperties(
event_ndims=1,
shape_fn=lambda sample_shape: ps.concat(
[sample_shape, [num_classes]], axis=0)),
probs=parameter_properties.ParameterProperties(
event_ndims=1,
shape_fn=lambda sample_shape: ps.concat(
[sample_shape, [num_classes]], axis=0),
default_constraining_bijector_fn=softmax_centered_bijector.
SoftmaxCentered,
is_preferred=False),
rtol=parameter_properties.ParameterProperties(
event_ndims=
None, # TODO(b/187469130): standardize batch semantics.
default_constraining_bijector_fn=(
lambda: softplus_bijector.Softplus(low=dtype_util.eps(dtype
))),
is_preferred=False),
atol=parameter_properties.ParameterProperties(
event_ndims=
None, # TODO(b/187469130): standardize batch semantics.
default_constraining_bijector_fn=(
lambda: softplus_bijector.Softplus(low=dtype_util.eps(dtype
))),
is_preferred=False))
def _parameter_properties(cls, dtype, num_classes=None):
# pylint: disable=g-long-lambda
return dict(cutpoints=parameter_properties.ParameterProperties(
event_ndims=1,
shape_fn=lambda sample_shape: ps.concat(
[sample_shape, [num_classes]], axis=0),
default_constraining_bijector_fn=lambda: invert_bijector.Invert(
ordered_bijector.Ordered())),
loc=parameter_properties.ParameterProperties())