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| # 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 the metrics module.""" | |
| import contextlib | |
| import numpy as np | |
| import tensorflow as tf | |
| import metrics | |
| class AccuracyTest(tf.test.TestCase): | |
| def setUp(self): | |
| tf.test.TestCase.setUp(self) | |
| self.rng = np.random.RandomState([11, 23, 50]) | |
| self.num_char_classes = 3 | |
| self.batch_size = 4 | |
| self.seq_length = 5 | |
| self.rej_char = 42 | |
| def initialized_session(self): | |
| """Wrapper for test session context manager with required initialization. | |
| Yields: | |
| A session object that should be used as a context manager. | |
| """ | |
| with self.cached_session() as sess: | |
| sess.run(tf.global_variables_initializer()) | |
| sess.run(tf.local_variables_initializer()) | |
| yield sess | |
| def _fake_labels(self): | |
| return self.rng.randint( | |
| low=0, | |
| high=self.num_char_classes, | |
| size=(self.batch_size, self.seq_length), | |
| dtype='int32') | |
| def _incorrect_copy(self, values, bad_indexes): | |
| incorrect = np.copy(values) | |
| incorrect[bad_indexes] = values[bad_indexes] + 1 | |
| return incorrect | |
| def test_sequence_accuracy_identical_samples(self): | |
| labels_tf = tf.convert_to_tensor(self._fake_labels()) | |
| accuracy_tf = metrics.sequence_accuracy(labels_tf, labels_tf, | |
| self.rej_char) | |
| with self.initialized_session() as sess: | |
| accuracy_np = sess.run(accuracy_tf) | |
| self.assertAlmostEqual(accuracy_np, 1.0) | |
| def test_sequence_accuracy_one_char_difference(self): | |
| ground_truth_np = self._fake_labels() | |
| ground_truth_tf = tf.convert_to_tensor(ground_truth_np) | |
| prediction_tf = tf.convert_to_tensor( | |
| self._incorrect_copy(ground_truth_np, bad_indexes=((0, 0)))) | |
| accuracy_tf = metrics.sequence_accuracy(prediction_tf, ground_truth_tf, | |
| self.rej_char) | |
| with self.initialized_session() as sess: | |
| accuracy_np = sess.run(accuracy_tf) | |
| # 1 of 4 sequences is incorrect. | |
| self.assertAlmostEqual(accuracy_np, 1.0 - 1.0 / self.batch_size) | |
| def test_char_accuracy_one_char_difference_with_padding(self): | |
| ground_truth_np = self._fake_labels() | |
| ground_truth_tf = tf.convert_to_tensor(ground_truth_np) | |
| prediction_tf = tf.convert_to_tensor( | |
| self._incorrect_copy(ground_truth_np, bad_indexes=((0, 0)))) | |
| accuracy_tf = metrics.char_accuracy(prediction_tf, ground_truth_tf, | |
| self.rej_char) | |
| with self.initialized_session() as sess: | |
| accuracy_np = sess.run(accuracy_tf) | |
| chars_count = self.seq_length * self.batch_size | |
| self.assertAlmostEqual(accuracy_np, 1.0 - 1.0 / chars_count) | |
| if __name__ == '__main__': | |
| tf.test.main() | |