def predict(self, encoder_outputs, encoder_decoder_attention_bias):
"""Return predicted sequence."""
batch_size = tf.shape(encoder_outputs)[0]
input_length = tf.shape(encoder_outputs)[1]
max_decode_length = input_length + self.params["extra_decode_length"]
symbols_to_logits_fn = self._get_symbols_to_logits_fn(
max_decode_length)
# Create initial set of IDs that will be passed into symbols_to_logits_fn.
initial_ids = tf.zeros([batch_size], dtype=tf.int32)
# Create cache storing decoder attention values for each layer.
cache = {
"layer_%d" % layer: {
"k":
tf.zeros([batch_size, 0, self.params["hidden_size"]],
dtype=encoder_outputs.dtype),
"v":
tf.zeros([batch_size, 0, self.params["hidden_size"]],
dtype=encoder_outputs.dtype),
}
for layer in range(self.params["num_hidden_layers"])
}
# Add encoder output and attention bias to the cache.
cache["encoder_outputs"] = encoder_outputs
cache[
"encoder_decoder_attention_bias"] = encoder_decoder_attention_bias
# Use beam search to find the top beam_size sequences and scores.
decoded_ids, scores = beam_search.sequence_beam_search(
symbols_to_logits_fn=symbols_to_logits_fn,
initial_ids=initial_ids,
initial_cache=cache,
vocab_size=self.params["tgt_vocab_size"],
beam_size=self.params["beam_size"],
alpha=self.params["alpha"],
max_decode_length=max_decode_length,
eos_id=self.params["EOS_ID"])
# Get the top sequence for each batch element
top_decoded_ids = decoded_ids[:, 0, 1:]
top_scores = scores[:, 0]
# this isn't particularly efficient
logits = self.decode_pass(top_decoded_ids, encoder_outputs,
encoder_decoder_attention_bias)
return { #"logits": tf.ones(shape=[tf.shape(top_decoded_ids)[0],
# tf.shape(top_decoded_ids)[1],
# self.params["tgt_vocab_size"]]),
"logits": logits,
"samples": [top_decoded_ids],
"final_state": None,
"final_sequence_lengths": None
}
def predict(self, encoder_outputs, encoder_outputs_b,
inputs_attention_bias):
"""Return predicted sequence."""
batch_size = tf.shape(encoder_outputs)[0]
input_length = tf.shape(encoder_outputs)[1]
max_decode_length = input_length + self.params["extra_decode_length"]
symbols_to_logits_fn = self._get_symbols_to_logits_fn()
# Create initial set of IDs that will be passed into symbols_to_logits_fn.
initial_ids = tf.zeros([batch_size],
dtype=tf.int32) + self.params["GO_SYMBOL"]
cache = {}
# Add encoder outputs and attention bias to the cache.
cache["encoder_outputs"] = encoder_outputs
cache["encoder_outputs_b"] = encoder_outputs_b
if inputs_attention_bias is not None:
cache["inputs_attention_bias"] = inputs_attention_bias
# Use beam search to find the top beam_size sequences and scores.
decoded_ids, scores = beam_search.sequence_beam_search(
symbols_to_logits_fn=symbols_to_logits_fn,
initial_ids=initial_ids,
initial_cache=cache,
vocab_size=self.params["tgt_vocab_size"],
beam_size=self.params["beam_size"],
alpha=self.params["alpha"],
max_decode_length=max_decode_length,
eos_id=self.params["EOS_ID"])
# Get the top sequence for each batch element
top_decoded_ids = decoded_ids[:, 0, :]
top_scores = scores[:, 0]
# this isn't particularly efficient
logits = self.decode_pass(top_decoded_ids, encoder_outputs,
encoder_outputs_b, inputs_attention_bias)
return {
"logits": logits,
"outputs": [top_decoded_ids],
"final_state": None,
"final_sequence_lengths": None
}