def maybe_assert_categorical_param_correctness(is_init, validate_args, probs,
logits):
"""Return assertions for `Categorical`-type distributions."""
assertions = []
# In init, we can always build shape and dtype checks because
# we assume shape doesn't change for Variable backed args.
if is_init:
x, name = (probs, 'probs') if logits is None else (logits, 'logits')
if not dtype_util.is_floating(x.dtype):
raise TypeError(
'Argument `{}` must having floating type.'.format(name))
msg = 'Argument `{}` must have rank at least 1.'.format(name)
ndims = tensorshape_util.rank(x.shape)
if ndims is not None:
if ndims < 1:
raise ValueError(msg)
elif validate_args:
x = tf.convert_to_tensor(x)
probs = x if logits is None else None # Retain tensor conversion.
logits = x if probs is None else None
assertions.append(
assert_util.assert_rank_at_least(x, 1, message=msg))
if not validate_args:
assert not assertions # Should never happen.
return []
if logits is not None:
if is_init != tensor_util.is_mutable(logits):
logits = tf.convert_to_tensor(logits)
assertions.extend(
distribution_util.assert_categorical_event_shape(logits))
if probs is not None:
if is_init != tensor_util.is_mutable(probs):
probs = tf.convert_to_tensor(probs)
assertions.extend([
assert_util.assert_non_negative(probs),
assert_util.assert_near(
tf.reduce_sum(probs, axis=-1),
np.array(1, dtype=dtype_util.as_numpy_dtype(probs.dtype)),
message='Argument `probs` must sum to 1.')
])
assertions.extend(
distribution_util.assert_categorical_event_shape(probs))
return assertions
def _parameter_control_dependencies(self, is_init):
assertions = []
if is_init and self.validate_args:
# assert_categorical_event_shape handles both the static and dynamic case.
assertions.extend(
distribution_util.assert_categorical_event_shape(
self._concentration))
if is_init != tensor_util.is_ref(self._total_count):
if self.validate_args:
total_count = tf.convert_to_tensor(self._total_count)
assertions.append(
distribution_util.assert_casting_closed(
total_count,
target_dtype=tf.int32,
message=
'total_count cannot contain fractional components.'))
assertions.append(
assert_util.assert_non_negative(
total_count,
message='total_count must be non-negative'))
if is_init != tensor_util.is_ref(self._concentration):
if self.validate_args:
assertions.append(
assert_util.assert_positive(
self._concentration,
message='Concentration parameter must be positive.'))
return assertions
def _parameter_control_dependencies(self, is_init):
assertions = []
if is_init and self.validate_args:
# assert_categorical_event_shape handles both the static and dynamic case.
assertions.extend(
distribution_util.assert_categorical_event_shape(self._concentration))
if is_init != tensor_util.is_ref(self._total_count):
if self.validate_args:
assertions.extend(
distribution_util.assert_nonnegative_integer_form(
self._total_count))
if is_init != tensor_util.is_ref(self._concentration):
if self.validate_args:
assertions.append(
assert_util.assert_positive(
self._concentration,
message='Concentration parameter must be positive.'))
return assertions
