def __init__(self,
diag_bijector=None,
diag_shift=1e-5,
validate_args=False,
name='fill_scale_tril'):
"""Instantiates the `FillScaleTriL` bijector.
Args:
diag_bijector: `Bijector` instance, used to transform the output diagonal
to be positive. Must be an instance of `tf.__internal__.CompositeTensor`
(including `tfb.AutoCompositeTensorBijector`).
Default value: `None` (i.e., `tfb.Softplus()`).
diag_shift: Float value broadcastable and added to all diagonal entries
after applying the `diag_bijector`. Setting a positive
value forces the output diagonal entries to be positive, but
prevents inverting the transformation for matrices with
diagonal entries less than this value.
Default value: `1e-5`.
validate_args: Python `bool` indicating whether arguments should be
checked for correctness.
Default value: `False` (i.e., arguments are not validated).
name: Python `str` name given to ops managed by this object.
Default value: `fill_scale_tril`.
Raises:
TypeError, if `diag_bijector` is not an instance of
`tf.__internal__.CompositeTensor`.
"""
parameters = dict(locals())
with tf.name_scope(name) as name:
if diag_bijector is None:
diag_bijector = softplus.Softplus(validate_args=validate_args)
if not isinstance(diag_bijector, tf.__internal__.CompositeTensor):
raise TypeError('`diag_bijector` must be an instance of '
'`tf.__internal__.CompositeTensor`.')
if diag_shift is not None:
dtype = dtype_util.common_dtype([diag_bijector, diag_shift],
tf.float32)
diag_shift = tensor_util.convert_nonref_to_tensor(
diag_shift, name='diag_shift', dtype=dtype)
diag_bijector = chain.Chain(
[shift.Shift(shift=diag_shift), diag_bijector])
super(FillScaleTriL, self).__init__([
transform_diagonal.TransformDiagonal(
diag_bijector=diag_bijector),
fill_triangular.FillTriangular()
],
validate_args=validate_args,
validate_event_size=False,
parameters=parameters,
name=name)
def _default_event_space_bijector(self):
# TODO(b/145620027) Finalize choice of bijector.
tril_bijector = chain_bijector.Chain([
transform_diagonal_bijector.TransformDiagonal(
diag_bijector=softplus_bijector.Softplus(
validate_args=self.validate_args),
validate_args=self.validate_args),
fill_scale_tril_bijector.FillScaleTriL(
validate_args=self.validate_args)
], validate_args=self.validate_args)
if self.input_output_cholesky:
return tril_bijector
return chain_bijector.Chain([
cholesky_outer_product_bijector.CholeskyOuterProduct(
validate_args=self.validate_args),
tril_bijector
], validate_args=self.validate_args)
def __init__(self,
diag_bijector=None,
diag_shift=1e-5,
validate_args=False,
name="scale_tril"):
"""Instantiates the `ScaleTriL` bijector.
Args:
diag_bijector: `Bijector` instance, used to transform the output diagonal
to be positive.
Default value: `None` (i.e., `tfb.Softplus()`).
diag_shift: Float value broadcastable and added to all diagonal entries
after applying the `diag_bijector`. Setting a positive
value forces the output diagonal entries to be positive, but
prevents inverting the transformation for matrices with
diagonal entries less than this value.
Default value: `1e-5`.
validate_args: Python `bool` indicating whether arguments should be
checked for correctness.
Default value: `False` (i.e., arguments are not validated).
name: Python `str` name given to ops managed by this object.
Default value: `scale_tril`.
"""
with tf.compat.v1.name_scope(name, values=[diag_shift]) as name:
if diag_bijector is None:
diag_bijector = softplus.Softplus(validate_args=validate_args)
if diag_shift is not None:
diag_shift = tf.convert_to_tensor(value=diag_shift,
dtype=diag_bijector.dtype,
name="diag_shift")
diag_bijector = chain.Chain([
affine_scalar.AffineScalar(shift=diag_shift), diag_bijector
])
super(ScaleTriL, self).__init__([
transform_diagonal.TransformDiagonal(
diag_bijector=diag_bijector),
fill_triangular.FillTriangular()
],
validate_args=validate_args,
name=name)
self._use_tf_function = False # So input bijectors cache EagerTensors.
