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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.
# ==============================================================================
"""Tests for Bijector."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.contrib.distributions.python.ops import bijectors
from tensorflow.contrib.distributions.python.ops import gamma as gamma_lib
from tensorflow.contrib.distributions.python.ops import transformed_distribution as transformed_distribution_lib
from tensorflow.contrib.distributions.python.ops.bijectors.bijector_test_util import assert_scalar_congruency
from tensorflow.python.framework import tensor_shape
from tensorflow.python.ops import array_ops
from tensorflow.python.platform import test
class InvertBijectorTest(test.TestCase):
"""Tests the correctness of the Y = Invert(bij) transformation."""
def testBijector(self):
with self.test_session():
for fwd in [
bijectors.Identity(),
bijectors.Exp(event_ndims=1),
bijectors.Affine(
shift=[0., 1.], scale_diag=[2., 3.], event_ndims=1),
bijectors.Softplus(event_ndims=1),
bijectors.SoftmaxCentered(event_ndims=1),
bijectors.SigmoidCentered(),
]:
rev = bijectors.Invert(fwd)
self.assertEqual("_".join(["invert", fwd.name]), rev.name)
x = [[[1., 2.],
[2., 3.]]]
self.assertAllClose(fwd.inverse(x).eval(), rev.forward(x).eval())
self.assertAllClose(fwd.forward(x).eval(), rev.inverse(x).eval())
self.assertAllClose(
fwd.forward_log_det_jacobian(x).eval(),
rev.inverse_log_det_jacobian(x).eval())
self.assertAllClose(
fwd.inverse_log_det_jacobian(x).eval(),
rev.forward_log_det_jacobian(x).eval())
def testScalarCongruency(self):
with self.test_session():
bijector = bijectors.Invert(bijectors.Exp())
assert_scalar_congruency(
bijector, lower_x=1e-3, upper_x=1.5, rtol=0.05)
def testShapeGetters(self):
with self.test_session():
bijector = bijectors.Invert(bijectors.SigmoidCentered(validate_args=True))
x = tensor_shape.TensorShape([2])
y = tensor_shape.TensorShape([])
self.assertAllEqual(y, bijector.forward_event_shape(x))
self.assertAllEqual(
y.as_list(),
bijector.forward_event_shape_tensor(x.as_list()).eval())
self.assertAllEqual(x, bijector.inverse_event_shape(y))
self.assertAllEqual(
x.as_list(),
bijector.inverse_event_shape_tensor(y.as_list()).eval())
def testDocstringExample(self):
with self.test_session():
exp_gamma_distribution = (
transformed_distribution_lib.TransformedDistribution(
distribution=gamma_lib.Gamma(concentration=1., rate=2.),
bijector=bijectors.Invert(bijectors.Exp())))
self.assertAllEqual(
[], array_ops.shape(exp_gamma_distribution.sample()).eval())
if __name__ == "__main__":
test.main()
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