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# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Affine bijector."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import ops
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import check_ops
from tensorflow.python.ops import control_flow_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops.distributions import bijector
__all__ = [
"AffineScalar",
]
class AffineScalar(bijector.Bijector):
"""Compute `Y = g(X; shift, scale) = scale * X + shift`.
Examples:
```python
# Y = X
b = AffineScalar()
# Y = X + shift
b = AffineScalar(shift=[1., 2, 3])
# Y = 2 * X + shift
b = AffineScalar(
shift=[1., 2, 3],
scale=2.)
```
"""
def __init__(self,
shift=None,
scale=None,
validate_args=False,
name="affine_scalar"):
"""Instantiates the `AffineScalar` bijector.
This `Bijector` is initialized with `shift` `Tensor` and `scale` arguments,
giving the forward operation:
```none
Y = g(X) = scale * X + shift
```
if `scale` is not specified, then the bijector has the semantics of
`scale = 1.`. Similarly, if `shift` is not specified, then the bijector
has the semantics of `shift = 0.`.
Args:
shift: Floating-point `Tensor`. If this is set to `None`, no shift is
applied.
scale: Floating-point `Tensor`. If this is set to `None`, no scale is
applied.
validate_args: Python `bool` indicating whether arguments should be
checked for correctness.
name: Python `str` name given to ops managed by this object.
"""
self._graph_parents = []
self._name = name
self._validate_args = validate_args
with self._name_scope("init", values=[scale, shift]):
self._shift = shift
self._scale = scale
if self._shift is not None:
self._shift = ops.convert_to_tensor(shift, name="shift")
if self._scale is not None:
self._scale = ops.convert_to_tensor(self._scale, name="scale")
if validate_args:
self._scale = control_flow_ops.with_dependencies(
[check_ops.assert_none_equal(
self._scale,
array_ops.zeros([], dtype=self._scale.dtype))],
self._scale)
super(AffineScalar, self).__init__(
forward_min_event_ndims=0,
is_constant_jacobian=True,
validate_args=validate_args,
name=name)
@property
def shift(self):
"""The `shift` `Tensor` in `Y = scale @ X + shift`."""
return self._shift
@property
def scale(self):
"""The `scale` `LinearOperator` in `Y = scale @ X + shift`."""
return self._scale
def _forward(self, x):
y = array_ops.identity(x)
if self.scale is not None:
y *= self.scale
if self.shift is not None:
y += self.shift
return y
def _inverse(self, y):
x = array_ops.identity(y)
if self.shift is not None:
x -= self.shift
if self.scale is not None:
x /= self.scale
return x
def _forward_log_det_jacobian(self, x):
# is_constant_jacobian = True for this bijector, hence the
# `log_det_jacobian` need only be specified for a single input, as this will
# be tiled to match `event_ndims`.
if self.scale is None:
return constant_op.constant(0., dtype=x.dtype.base_dtype)
return math_ops.log(math_ops.abs(self.scale))
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