def dynamic_decode(self,
embedding,
start_tokens,
end_token,
vocab_size=None,
initial_state=None,
output_layer=None,
maximum_iterations=250,
mode=tf.estimator.ModeKeys.PREDICT,
memory=None,
memory_sequence_length=None,
dtype=None,
return_alignment_history=False):
batch_size = tf.shape(start_tokens)[0]
helper = tf.contrib.seq2seq.GreedyEmbeddingHelper(
embedding,
start_tokens,
end_token)
cell, initial_state = self._build_cell(
mode,
batch_size,
initial_state=initial_state,
memory=memory,
memory_sequence_length=memory_sequence_length,
dtype=dtype,
alignment_history=return_alignment_history)
if output_layer is None:
output_layer = build_output_layer(self.num_units, vocab_size, dtype=dtype or memory.dtype)
decoder = tf.contrib.seq2seq.BasicDecoder(
cell,
helper,
initial_state,
output_layer=output_layer)
outputs, state, length = tf.contrib.seq2seq.dynamic_decode(
decoder, maximum_iterations=maximum_iterations)
predicted_ids = outputs.sample_id
log_probs = logits_to_cum_log_probs(outputs.rnn_output, length)
# Make shape consistent with beam search.
predicted_ids = tf.expand_dims(predicted_ids, 1)
length = tf.expand_dims(length, 1)
log_probs = tf.expand_dims(log_probs, 1)
if return_alignment_history:
alignment_history = _get_alignment_history(state)
if alignment_history is not None:
alignment_history = tf.expand_dims(alignment_history, 1)
return (predicted_ids, state, length, log_probs, alignment_history)
return (predicted_ids, state, length, log_probs)
def dynamic_decode(self,
embedding,
start_tokens,
end_token,
vocab_size,
initial_state=None,
maximum_iterations=250,
mode=tf.estimator.ModeKeys.PREDICT,
memory=None,
memory_sequence_length=None,
dtype=None):
batch_size = tf.shape(start_tokens)[0]
helper = tf.contrib.seq2seq.GreedyEmbeddingHelper(
embedding,
start_tokens,
end_token)
cell, initial_state = self._build_cell(
mode,
batch_size,
initial_state=initial_state,
memory=memory,
memory_sequence_length=memory_sequence_length,
dtype=dtype)
output_layer = self._build_output_layer(vocab_size, dtype=dtype or memory.dtype)
decoder = tf.contrib.seq2seq.BasicDecoder(
cell,
helper,
initial_state,
output_layer=output_layer)
outputs, state, length = tf.contrib.seq2seq.dynamic_decode(
decoder, maximum_iterations=maximum_iterations)
predicted_ids = outputs.sample_id
log_probs = logits_to_cum_log_probs(outputs.rnn_output, length)
# Make shape consistent with beam search.
predicted_ids = tf.expand_dims(predicted_ids, 1)
length = tf.expand_dims(length, 1)
log_probs = tf.expand_dims(log_probs, 1)
return (predicted_ids, state, length, log_probs)
def dynamic_decode(self,
embedding,
start_tokens,
end_token,
vocab_size,
initial_state=None,
maximum_iterations=250,
mode=tf.estimator.ModeKeys.PREDICT,
memory=None,
memory_sequence_length=None):
batch_size = tf.shape(start_tokens)[0]
helper = tf.contrib.seq2seq.GreedyEmbeddingHelper(
embedding, start_tokens, end_token)
cell, initial_state = self._build_cell(
mode,
batch_size,
initial_state=initial_state,
memory=memory,
memory_sequence_length=memory_sequence_length)
output_layer = self._build_output_layer(vocab_size)
decoder = tf.contrib.seq2seq.BasicDecoder(cell,
helper,
initial_state,
output_layer=output_layer)
outputs, state, length = tf.contrib.seq2seq.dynamic_decode(
decoder, maximum_iterations=maximum_iterations)
predicted_ids = outputs.sample_id
log_probs = logits_to_cum_log_probs(outputs.rnn_output, length)
# Make shape consistent with beam search.
predicted_ids = tf.expand_dims(predicted_ids, 1)
log_probs = tf.expand_dims(log_probs, 1)
return (predicted_ids, state, length, log_probs)