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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
import numpy as np
from tensorflow.contrib.distributions.python.ops.bijectors.bijector_test_util import assert_bijective_and_finite
from tensorflow.contrib.distributions.python.ops.bijectors.softmax_centered import SoftmaxCentered
from tensorflow.python.framework import tensor_shape
from tensorflow.python.platform import test
rng = np.random.RandomState(42)
class SoftmaxCenteredBijectorTest(test.TestCase):
"""Tests correctness of the Y = g(X) = exp(X) / sum(exp(X)) transformation."""
def testBijectorScalar(self):
with self.test_session():
softmax = SoftmaxCentered() # scalar by default
self.assertEqual("softmax_centered", softmax.name)
x = np.log([[2., 3, 4],
[4., 8, 12]])
y = [[[2. / 3, 1. / 3],
[3. / 4, 1. / 4],
[4. / 5, 1. / 5]],
[[4. / 5, 1. / 5],
[8. / 9, 1. / 9],
[12. / 13, 1. / 13]]]
self.assertAllClose(y, softmax.forward(x).eval())
self.assertAllClose(x, softmax.inverse(y).eval())
self.assertAllClose(
-np.sum(np.log(y), axis=2),
softmax.inverse_log_det_jacobian(y).eval(),
atol=0.,
rtol=1e-7)
self.assertAllClose(
-softmax.inverse_log_det_jacobian(y).eval(),
softmax.forward_log_det_jacobian(x).eval(),
atol=0.,
rtol=1e-7)
def testBijectorVector(self):
with self.test_session():
softmax = SoftmaxCentered(event_ndims=1)
self.assertEqual("softmax_centered", softmax.name)
x = np.log([[2., 3, 4], [4., 8, 12]])
y = [[0.2, 0.3, 0.4, 0.1], [0.16, 0.32, 0.48, 0.04]]
self.assertAllClose(y, softmax.forward(x).eval())
self.assertAllClose(x, softmax.inverse(y).eval())
self.assertAllClose(
-np.sum(np.log(y), axis=1),
softmax.inverse_log_det_jacobian(y).eval(),
atol=0.,
rtol=1e-7)
self.assertAllClose(
-softmax.inverse_log_det_jacobian(y).eval(),
softmax.forward_log_det_jacobian(x).eval(),
atol=0.,
rtol=1e-7)
def testShapeGetters(self):
with self.test_session():
for x, y, b in ((tensor_shape.TensorShape([]),
tensor_shape.TensorShape([2]),
SoftmaxCentered(
event_ndims=0, validate_args=True)),
(tensor_shape.TensorShape([4]),
tensor_shape.TensorShape([5]),
SoftmaxCentered(
event_ndims=1, validate_args=True))):
self.assertAllEqual(y, b.forward_event_shape(x))
self.assertAllEqual(y.as_list(),
b.forward_event_shape_tensor(x.as_list()).eval())
self.assertAllEqual(x, b.inverse_event_shape(y))
self.assertAllEqual(x.as_list(),
b.inverse_event_shape_tensor(y.as_list()).eval())
def testBijectiveAndFinite(self):
with self.test_session():
softmax = SoftmaxCentered(event_ndims=1)
x = np.linspace(-50, 50, num=10).reshape(5, 2).astype(np.float32)
# Make y values on the simplex with a wide range.
y_0 = np.ones(5).astype(np.float32)
y_1 = (1e-5 * rng.rand(5)).astype(np.float32)
y_2 = (1e1 * rng.rand(5)).astype(np.float32)
y = np.array([y_0, y_1, y_2])
y /= y.sum(axis=0)
y = y.T # y.shape = [5, 3]
assert_bijective_and_finite(softmax, x, y)
if __name__ == "__main__":
test.main()
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