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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.ops.seq2seq."""
# 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 tensorflow as tf
from tensorflow.contrib import layers
class Seq2SeqTest(tf.test.TestCase):
# test a default call of rnn_decoder
def test_rnn_decoder(self):
pass
# test default call with time_major=True
def test_dynamic_rnn_decoder_time_major(self):
with self.test_session() as sess:
with tf.variable_scope("root", initializer=
tf.constant_initializer(0.5)) as varscope:
# Define inputs/outputs to model
batch_size = 2
encoder_embedding_size = 3
decoder_embedding_size = 4
encoder_hidden_size = 5
decoder_hidden_size = encoder_hidden_size
input_sequence_length = 6
decoder_sequence_length = 7
num_decoder_symbols = 20
start_of_sequence_id = end_of_sequence_id = 1
decoder_embeddings = tf.get_variable('decoder_embeddings',
[num_decoder_symbols, decoder_embedding_size],
initializer=tf.random_normal_initializer(stddev=0.1))
inputs = tf.constant(0.5, shape=[input_sequence_length, batch_size,
encoder_embedding_size])
decoder_inputs = tf.constant(0.4, shape=[decoder_sequence_length,
batch_size,
decoder_embedding_size])
decoder_length = tf.constant(decoder_sequence_length, dtype=tf.int32,
shape=[batch_size,])
with tf.variable_scope("rnn") as scope:
# setting up weights for computing the final output
output_fn = lambda x: layers.linear(x, num_decoder_symbols,
scope=scope)
# Define model
encoder_outputs, encoder_state = tf.nn.dynamic_rnn(
cell=tf.nn.rnn_cell.GRUCell(encoder_hidden_size), inputs=inputs,
dtype=tf.float32, time_major=True, scope=scope)
with tf.variable_scope("decoder") as scope:
# Train decoder
decoder_cell = tf.nn.rnn_cell.GRUCell(decoder_hidden_size)
decoder_fn_train = tf.contrib.seq2seq.simple_decoder_fn_train(
encoder_state=encoder_state)
decoder_outputs_train, decoder_state_train = (
tf.contrib.seq2seq.dynamic_rnn_decoder(
cell=decoder_cell,
decoder_fn=decoder_fn_train,
inputs=decoder_inputs,
sequence_length=decoder_length,
time_major=True,
scope=scope))
decoder_outputs_train = output_fn(decoder_outputs_train)
# Setup variable reuse
scope.reuse_variables()
# Inference decoder
decoder_fn_inference = (
tf.contrib.seq2seq.simple_decoder_fn_inference(
output_fn=output_fn,
encoder_state=encoder_state,
embeddings=decoder_embeddings,
start_of_sequence_id=start_of_sequence_id,
end_of_sequence_id=end_of_sequence_id,
#TODO: find out why it goes to +1
maximum_length=decoder_sequence_length-1,
num_decoder_symbols=num_decoder_symbols,
dtype=tf.int32))
decoder_outputs_inference, decoder_state_inference = (
tf.contrib.seq2seq.dynamic_rnn_decoder(
cell=decoder_cell,
decoder_fn=decoder_fn_inference,
time_major=True,
scope=scope))
# Run model
tf.global_variables_initializer().run()
decoder_outputs_train_res, decoder_state_train_res = sess.run(
[decoder_outputs_train, decoder_state_train])
decoder_outputs_inference_res, decoder_state_inference_res = sess.run(
[decoder_outputs_inference, decoder_state_inference])
# Assert outputs
self.assertEqual((decoder_sequence_length, batch_size,
num_decoder_symbols),
decoder_outputs_train_res.shape)
self.assertEqual((batch_size, num_decoder_symbols),
decoder_outputs_inference_res.shape[1:3])
self.assertEqual((batch_size, decoder_hidden_size),
decoder_state_train_res.shape)
self.assertEqual((batch_size, decoder_hidden_size),
decoder_state_inference_res.shape)
# The dynamic decoder might end earlier than `maximal_length`
# under inference
true_value = (decoder_sequence_length>=
decoder_state_inference_res.shape[0])
self.assertEqual((true_value), True)
if __name__ == '__main__':
tf.test.main()
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