| # Copyright 2017 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 Clip Operations.""" |
| |
| import numpy as np |
| |
| from tensorflow.python.framework import constant_op |
| from tensorflow.python.framework import indexed_slices as indexed_slices_lib |
| from tensorflow.python.framework import ops |
| from tensorflow.python.framework import test_util |
| from tensorflow.python.ops import clip_ops |
| from tensorflow.python.ops import numerics |
| from tensorflow.python.platform import test |
| |
| |
| class ClipOpsTest(test.TestCase): |
| |
| def __init__(self, method_name="runTest"): |
| super(ClipOpsTest, self).__init__(method_name) |
| |
| def _testClipTensorByNorm(self, inputs, max_norm, expected): |
| input_op = constant_op.constant(inputs) |
| clipped = clip_ops.clip_by_norm(input_op, max_norm) |
| check_op = numerics.add_check_numerics_ops() |
| result, _ = self.evaluate([clipped, check_op]) |
| self.assertAllClose(result, expected) |
| |
| def _testClipTensorByGlobalNorm(self, inputs, max_norm, expected): |
| clipped = clip_ops.clip_by_global_norm(inputs, max_norm) |
| result, _ = self.evaluate(clipped) |
| self.assertAllClose(result, expected) |
| |
| def _testNonFiniteClippingByGlobalNorm(self, inputs, max_norm): |
| clipped = clip_ops.clip_by_global_norm(inputs, max_norm) |
| result, _ = self.evaluate(clipped) |
| self.assertTrue(np.all(np.isnan(result))) |
| |
| def _testClipIndexedSlicesByNorm(self, values, indices, shape, max_norm, |
| axes): |
| values = constant_op.constant(values) |
| indices = constant_op.constant(indices) |
| shape = constant_op.constant(shape) |
| # IndexedSlices mode |
| indexed_slices = indexed_slices_lib.IndexedSlices(values, indices, shape) |
| clipped = clip_ops.clip_by_norm(indexed_slices, max_norm, axes) |
| # clipped should be IndexedSlices |
| self.assertIsInstance(clipped, indexed_slices_lib.IndexedSlices) |
| clipped = ops.convert_to_tensor(clipped) |
| |
| # Tensor mode |
| dense_tensor = ops.convert_to_tensor(indexed_slices) |
| dense_clipped = clip_ops.clip_by_norm(dense_tensor, max_norm, axes) |
| result, expected = self.evaluate([clipped, dense_clipped]) |
| self.assertAllClose(result, expected) |
| |
| @test_util.run_deprecated_v1 |
| def testClipTensorByNorm(self): |
| # Simple example |
| self._testClipTensorByNorm([[-3.0, 0.0, 0.0], [4.0, 0.0, 0.0]], 4.0, |
| [[-2.4, 0.0, 0.0], [3.2, 0.0, 0.0]]) |
| # No clipping. |
| self._testClipTensorByNorm([[1.0, 0.0, 0.0], [1.0, 0.0, 0.0]], 4.0, |
| [[1.0, 0.0, 0.0], [1.0, 0.0, 0.0]]) |
| # Zero norm |
| self._testClipTensorByNorm([[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]], 4.0, |
| [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]) |
| |
| @test_util.run_deprecated_v1 |
| def testClipTensorByGlobalNorm(self): |
| # Simple example |
| self._testClipTensorByGlobalNorm([[-3.0, 0.0, 0.0], [4.0, 0.0, 0.0]], 4.0, |
| [[-2.4, 0.0, 0.0], [3.2, 0.0, 0.0]]) |
| # No clipping. |
| self._testClipTensorByGlobalNorm([[1.0, 0.0, 0.0], [1.0, 0.0, 0.0]], 4.0, |
| [[1.0, 0.0, 0.0], [1.0, 0.0, 0.0]]) |
| # Zero norm. |
| self._testClipTensorByGlobalNorm([[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]], 4.0, |
| [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]) |
| |
| @test_util.run_deprecated_v1 |
| def testGlobalClipWithNonfinite(self): |
| self._testNonFiniteClippingByGlobalNorm( |
| [[-3.0, 0.0, 0.0], [float("inf"), 0.0, 0.0]], 4.0) |
| self._testNonFiniteClippingByGlobalNorm( |
| [[-3.0, 0.0, 0.0], [float("-inf"), 0.0, 0.0]], 4.0) |
| self._testNonFiniteClippingByGlobalNorm( |
| [[-3.0, 0.0, 0.0], [float("nan"), 0.0, 0.0]], 4.0) |
| |
| def testClipIndexedSlicesByNorm(self): |
| values = [[[-3.0, 0.0, 0.0], [4.0, 0.0, 0.0]], |
| [[0.0, 2.0, 0.0], [0.0, 0.0, -1.0]]] |
| indices = [2, 6] |
| shape = [10, 2, 3] |
| # Axes == None |
| self._testClipIndexedSlicesByNorm(values, indices, shape, 4.0, None) |
| |
| # Axes == 0 |
| self._testClipIndexedSlicesByNorm(values, indices, shape, 4.0, 0) |
| |
| # Axes == 1 |
| self._testClipIndexedSlicesByNorm(values, indices, shape, 4.0, 1) |
| |
| # Axes == 2 |
| self._testClipIndexedSlicesByNorm(values, indices, shape, 4.0, 1) |
| |
| # Axes == [0, 1] |
| self._testClipIndexedSlicesByNorm(values, indices, shape, 4.0, [0, 1]) |
| |
| # Axes == [0, 1] |
| self._testClipIndexedSlicesByNorm(values, indices, shape, 4.0, [0, 2]) |
| |
| # Axes == [0, 1] |
| self._testClipIndexedSlicesByNorm(values, indices, shape, 4.0, [1, 2]) |
| |
| # Axes == [0, 1] |
| self._testClipIndexedSlicesByNorm(values, indices, shape, 4.0, [0, 1, 2]) |
| |
| |
| if __name__ == "__main__": |
| test.main() |