def infer(self,
features=None,
decode_length=1,
beam_size=1,
top_beams=1,
alpha=0.0,
use_tpu=False):
"""Predict."""
features["targets"] = tf.identity(features["inputs"])
logits, _ = self(features)
log_probs = common_layers.log_prob_from_logits(logits)
predictions, scores = common_layers.argmax_with_score(log_probs)
return {
"outputs": predictions,
"scores": scores,
}
def infer(self,
features=None,
decode_length=1,
beam_size=1,
top_beams=1,
alpha=0.0,
use_tpu=False):
"""Predict."""
del decode_length, beam_size, top_beams, alpha, use_tpu
assert features is not None
logits, _ = self(features)
assert len(logits.get_shape()) == 5
logits = tf.squeeze(logits, [1, 2, 3])
log_probs = common_layers.log_prob_from_logits(logits)
predictions, scores = common_layers.argmax_with_score(log_probs)
return {
"outputs": predictions,
"scores": scores,
}
def infer(self,
features=None,
decode_length=50,
beam_size=1,
top_beams=1,
alpha=0.0,
use_tpu=False):
"""Predict."""
del decode_length, beam_size, top_beams, alpha, use_tpu
assert features is not None
logits, _ = self(features) # pylint: disable=not-callable
assert len(logits.get_shape()) == 5
logits = tf.squeeze(logits, [1, 2, 3])
log_probs = common_layers.log_prob_from_logits(logits)
predictions, scores = common_layers.argmax_with_score(log_probs)
return {
"outputs": predictions,
"scores": scores,
}
