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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 contrib.seq2seq.python.seq2seq.loss_ops."""
# pylint: disable=unused-import,g-bad-import-order
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
# pylint: enable=unused-import
import numpy as np
import tensorflow as tf
class LossTest(tf.test.TestCase):
def testSequenceLoss(self):
with self.test_session() as sess:
with tf.variable_scope("root",
initializer=tf.constant_initializer(0.5)) as varscope:
batch_size = 2
sequence_length = 3
number_of_classes = 5
logits = [tf.constant(i + 0.5, shape=[batch_size, number_of_classes])
for i in range(sequence_length)]
logits = tf.stack(logits, axis=1)
targets = [tf.constant(i, tf.int32, shape=[batch_size]) for i in
range(sequence_length)]
targets = tf.stack(targets, axis=1)
weights = [tf.constant(1.0, shape=[batch_size]) for i in
range(sequence_length)]
weights = tf.stack(weights, axis=1)
average_loss_per_example = tf.contrib.seq2seq.sequence_loss(
logits, targets, weights,
average_across_timesteps=True,
average_across_batch=True)
res = sess.run(average_loss_per_example)
self.assertAllClose(1.60944, res)
average_loss_per_sequence = tf.contrib.seq2seq.sequence_loss(
logits, targets, weights,
average_across_timesteps=False,
average_across_batch=True)
res = sess.run(average_loss_per_sequence)
compare_per_sequence = np.ones((sequence_length)) * 1.60944
self.assertAllClose(compare_per_sequence, res)
average_loss_per_batch = tf.contrib.seq2seq.sequence_loss(
logits, targets, weights,
average_across_timesteps=True,
average_across_batch=False)
res = sess.run(average_loss_per_batch)
compare_per_batch = np.ones((batch_size)) * 1.60944
self.assertAllClose(compare_per_batch, res)
total_loss = tf.contrib.seq2seq.sequence_loss(
logits, targets, weights,
average_across_timesteps=False,
average_across_batch=False)
res = sess.run(total_loss)
compare_total = np.ones((batch_size, sequence_length)) * 1.60944
self.assertAllClose(compare_total, res)
if __name__ == '__main__':
tf.test.main()
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