def test_with_dynamic_inputs(self):
embeddings = tf.get_variable("W_embed",
[self.vocab_size, self.input_depth])
helper = decode_helper.GreedyEmbeddingHelper(embedding=embeddings,
start_tokens=[0] *
self.batch_size,
end_token=-1)
decoder_fn = self.create_decoder(helper=helper,
mode=tf.contrib.learn.ModeKeys.INFER)
initial_state = decoder_fn.cell.zero_state(self.batch_size,
dtype=tf.float32)
decoder_output, _ = decoder_fn(initial_state, helper)
#pylint: disable=E1101
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
decoder_output_ = sess.run(decoder_output)
np.testing.assert_array_equal(
decoder_output_.logits.shape,
[self.max_decode_length, self.batch_size, self.vocab_size])
np.testing.assert_array_equal(
decoder_output_.predicted_ids.shape,
[self.max_decode_length, self.batch_size])
def _decode_infer(self, decoder, bridge, _encoder_output, features,
labels):
"""Runs decoding in inference mode"""
batch_size = self.batch_size(features, labels)
if self.use_beam_search:
batch_size = self.params["inference.beam_search.beam_width"]
target_start_id = self.target_vocab_info.special_vocab.SEQUENCE_START
helper_infer = tf_decode_helper.GreedyEmbeddingHelper(
embedding=self.target_embedding,
start_tokens=tf.fill([batch_size], target_start_id),
end_token=self.target_vocab_info.special_vocab.SEQUENCE_END)
decoder_initial_state = bridge()
return decoder(decoder_initial_state, helper_infer)
def test_with_beam_search(self):
self.batch_size = 1
# Batch size for beam search must be 1.
config = beam_search.BeamSearchConfig(
beam_width=10,
vocab_size=self.vocab_size,
eos_token=self.vocab_size - 2,
length_penalty_weight=0.6,
choose_successors_fn=beam_search.choose_top_k)
embeddings = tf.get_variable("W_embed",
[self.vocab_size, self.input_depth])
helper = decode_helper.GreedyEmbeddingHelper(embedding=embeddings,
start_tokens=[0] *
config.beam_width,
end_token=-1)
decoder_fn = self.create_decoder(helper=helper,
mode=tf.contrib.learn.ModeKeys.INFER)
decoder_fn = beam_search_decoder.BeamSearchDecoder(decoder=decoder_fn,
config=config)
initial_state = decoder_fn.cell.zero_state(self.batch_size,
dtype=tf.float32)
decoder_output, _ = decoder_fn(initial_state, helper)
#pylint: disable=E1101
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
decoder_output_ = sess.run(decoder_output)
np.testing.assert_array_equal(
decoder_output_.predicted_ids.shape,
[self.max_decode_length, 1, config.beam_width])
np.testing.assert_array_equal(
decoder_output_.beam_search_output.beam_parent_ids.shape,
[self.max_decode_length, 1, config.beam_width])
np.testing.assert_array_equal(
decoder_output_.beam_search_output.scores.shape,
[self.max_decode_length, 1, config.beam_width])
np.testing.assert_array_equal(
decoder_output_.beam_search_output.original_outputs.predicted_ids.
shape, [self.max_decode_length, 1, config.beam_width])
np.testing.assert_array_equal(
decoder_output_.beam_search_output.original_outputs.logits.shape,
[self.max_decode_length, 1, config.beam_width, self.vocab_size])
return decoder_output
