def test_require_non_negative(self):
v = TensorArgValidator('xyz')
# test static values
for o in [0, 0., 1e-7, 1, 1.]:
self.assertEqual(v.require_non_negative(o), o)
for o in [-1., -1, -1e-7]:
with pytest.raises(ValueError, match='xyz must be non-negative'):
_ = v.require_non_negative(o)
# test dynamic values
with self.test_session():
for o, dtype in zip(
[0, 0., 1e-7, 1, 1.],
[tf.int32, tf.float32, tf.float32, tf.int32, tf.float32]):
self.assertAllClose(
v.require_non_negative(tf.constant(o, dtype=dtype)).eval(),
o)
for o, dtype in zip([-1., -1, -1e-7],
[tf.float32, tf.int32, tf.float32]):
with pytest.raises(Exception,
match='xyz must be non-negative'):
_ = v.require_non_negative(tf.constant(
o, dtype=dtype)).eval()
def validate_group_ndims(group_ndims, name=None):
"""
Validate the specified value for `group_ndims` argument.
If the specified `group_ndims` is a dynamic :class:`~tf.Tensor`,
additional assertion will be added to the graph node of `group_ndims`.
Args:
group_ndims: Object to be validated.
name: TensorFlow name scope of the graph nodes. (default
"validate_group_ndims")
Returns:
tf.Tensor or int: The validated `group_ndims`.
Raises:
ValueError: If the specified `group_ndims` cannot pass validation.
"""
@contextlib.contextmanager
def gen_name_scope():
if is_tensor_object(group_ndims):
with tf.name_scope(name, default_name='validate_group_ndims'):
yield
else:
yield
with gen_name_scope():
validator = TensorArgValidator('group_ndims')
group_ndims = validator.require_non_negative(
validator.require_int32(group_ndims)
)
return group_ndims
def __init__(self,
distribution,
tensor,
n_samples=None,
group_ndims=0,
is_reparameterized=None,
flow_origin=None,
log_prob=None):
"""
Construct the :class:`StochasticTensor`.
Args:
distribution (tfsnippet.distributions.Distribution): The
distribution of this :class:`StochasticTensor`.
tensor (tf.Tensor or TensorWrapper): The samples or observations
of this :class:`StochasticTensor`.
n_samples (tf.Tensor or int): The number of samples taken in
:class:`Distribution.sample`. If not :obj:`None`, the first
dimension of `tensor` should be the sampling dimension.
group_ndims (int or tf.Tensor): The number of dimensions to be
considered as events group in samples. (default 0)
is_reparameterized (bool): Whether or not the samples are
re-parameterized? If not specified, will inherit from
:attr:`tfsnippet.distributions.Distribution.is_reparameterized`.
log_prob (Tensor or None): Pre-computed log-density of `tensor`,
given `group_ndims`.
flow_origin (StochasticTensor): The original stochastic tensor
from the base distribution of a
:class:`tfsnippet.FlowDistribution`.
"""
from tfsnippet.utils import TensorArgValidator, validate_group_ndims_arg
if is_reparameterized is None:
is_reparameterized = distribution.is_reparameterized
if log_prob is not None and not is_tensor_object(log_prob):
log_prob = tf.convert_to_tensor(log_prob)
n_samples = validate_n_samples_arg(n_samples, 'n_samples')
if n_samples is not None:
with tf.name_scope('validate_n_samples'):
validator = TensorArgValidator('n_samples')
n_samples = validator.require_non_negative(
validator.require_int32(n_samples))
group_ndims = validate_group_ndims_arg(group_ndims)
super(StochasticTensor, self).__init__()
self._self_distribution = distribution
self._self_tensor = tf.convert_to_tensor(tensor)
self._self_n_samples = n_samples
self._self_group_ndims = group_ndims
self._self_is_reparameterized = is_reparameterized
self._self_flow_origin = flow_origin
self._self_log_prob = log_prob
self._self_prob = None
def __init__(self,
distribution,
tensor,
n_samples=None,
group_ndims=0,
is_reparameterized=None):
"""
Construct the :class:`StochasticTensor`.
Args:
distribution (tfsnippet.distributions.Distribution): The
distribution of this :class:`StochasticTensor`.
tensor (tf.Tensor or TensorWrapper): The samples or observations
of this :class:`StochasticTensor`.
n_samples (tf.Tensor or int): The number of samples taken in
:class:`Distribution.sample`. If not :obj:`None`, the first
dimension of `tensor` should be the sampling dimension.
group_ndims (int or tf.Tensor): The number of dimensions to be
considered as events group in samples. (default 0)
is_reparameterized (bool): Whether or not the samples are
re-parameterized? If not specified, will inherit from
:attr:`tfsnippet.distributions.Distribution.is_reparameterized`.
"""
from tfsnippet.distributions import validate_group_ndims
if is_reparameterized is None:
is_reparameterized = distribution.is_reparameterized
n_samples = validate_n_samples(n_samples, 'n_samples')
if n_samples is not None:
with tf.name_scope('validate_n_samples'):
validator = TensorArgValidator('n_samples')
n_samples = validator.require_non_negative(
validator.require_int32(n_samples))
group_ndims = validate_group_ndims(group_ndims)
super(StochasticTensor, self).__init__()
self._self_distribution = distribution
self._self_tensor = tf.convert_to_tensor(tensor)
self._self_n_samples = n_samples
self._self_group_ndims = group_ndims
self._self_is_reparameterized = is_reparameterized
self._self_log_prob = None
self._self_prob = None