| # Copyright 2018 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.softsign import Softsign |
| from tensorflow.python.framework import test_util |
| from tensorflow.python.ops.distributions.bijector_test_util import assert_bijective_and_finite |
| from tensorflow.python.ops.distributions.bijector_test_util import assert_scalar_congruency |
| from tensorflow.python.platform import test |
| |
| |
| class SoftsignBijectorTest(test.TestCase): |
| """Tests the correctness of the Y = g(X) = X / (1 + |X|) transformation.""" |
| |
| def _softsign(self, x): |
| return x / (1. + np.abs(x)) |
| |
| def _softsign_ildj_before_reduction(self, y): |
| """Inverse log det jacobian, before being reduced.""" |
| return -2. * np.log1p(-np.abs(y)) |
| |
| def setUp(self): |
| self._rng = np.random.RandomState(42) |
| |
| @test_util.run_in_graph_and_eager_modes |
| def testBijectorBounds(self): |
| bijector = Softsign(validate_args=True) |
| with self.assertRaisesOpError("greater than -1"): |
| self.evaluate(bijector.inverse(-3.)) |
| with self.assertRaisesOpError("greater than -1"): |
| self.evaluate(bijector.inverse_log_det_jacobian(-3., event_ndims=0)) |
| |
| with self.assertRaisesOpError("less than 1"): |
| self.evaluate(bijector.inverse(3.)) |
| with self.assertRaisesOpError("less than 1"): |
| self.evaluate(bijector.inverse_log_det_jacobian(3., event_ndims=0)) |
| |
| @test_util.run_in_graph_and_eager_modes |
| def testBijectorForwardInverse(self): |
| bijector = Softsign(validate_args=True) |
| self.assertEqual("softsign", bijector.name) |
| x = 2. * self._rng.randn(2, 10) |
| y = self._softsign(x) |
| |
| self.assertAllClose(y, self.evaluate(bijector.forward(x))) |
| self.assertAllClose(x, self.evaluate(bijector.inverse(y))) |
| |
| @test_util.run_in_graph_and_eager_modes |
| def testBijectorLogDetJacobianEventDimsZero(self): |
| bijector = Softsign(validate_args=True) |
| y = self._rng.rand(2, 10) |
| # No reduction needed if event_dims = 0. |
| ildj = self._softsign_ildj_before_reduction(y) |
| |
| self.assertAllClose(ildj, self.evaluate( |
| bijector.inverse_log_det_jacobian(y, event_ndims=0))) |
| |
| @test_util.run_in_graph_and_eager_modes |
| def testBijectorForwardInverseEventDimsOne(self): |
| bijector = Softsign(validate_args=True) |
| self.assertEqual("softsign", bijector.name) |
| x = 2. * self._rng.randn(2, 10) |
| y = self._softsign(x) |
| self.assertAllClose(y, self.evaluate(bijector.forward(x))) |
| self.assertAllClose(x, self.evaluate(bijector.inverse(y))) |
| |
| @test_util.run_in_graph_and_eager_modes |
| def testBijectorLogDetJacobianEventDimsOne(self): |
| bijector = Softsign(validate_args=True) |
| y = self._rng.rand(2, 10) |
| ildj_before = self._softsign_ildj_before_reduction(y) |
| ildj = np.sum(ildj_before, axis=1) |
| self.assertAllClose( |
| ildj, self.evaluate( |
| bijector.inverse_log_det_jacobian(y, event_ndims=1))) |
| |
| def testScalarCongruency(self): |
| with self.test_session(): |
| bijector = Softsign(validate_args=True) |
| assert_scalar_congruency(bijector, lower_x=-20., upper_x=20.) |
| |
| def testBijectiveAndFinite(self): |
| with self.test_session(): |
| bijector = Softsign(validate_args=True) |
| x = np.linspace(-20., 20., 100).astype(np.float32) |
| y = np.linspace(-0.99, 0.99, 100).astype(np.float32) |
| assert_bijective_and_finite( |
| bijector, x, y, event_ndims=0, rtol=1e-3, atol=1e-3) |
| |
| |
| if __name__ == "__main__": |
| test.main() |