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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
@contextlib.contextmanager
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()
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