def body(self, features):
if self._hparams.initializer == "orthogonal":
raise ValueError("LSTM models fail with orthogonal initializer.")
train = self._hparams.mode == tf.estimator.ModeKeys.TRAIN
return lstm_seq2seq_internal_dynamic(
features.get("inputs"), features["targets"],
seq2seq_hparams.chatbot_lstm_hparams(), train)[0]
def body(self, features):
if self._hparams.initializer == "orthogonal":
raise ValueError("LSTM models fail with orthogonal initializer.")
train = self._hparams.mode == tf.estimator.ModeKeys.TRAIN
""" Modified """
# using the custom lstm_seq2seq_internal_dynamic
return gradient_checkpointed_seq2seq.lstm_seq2seq_internal_dynamic(
features.get("inputs"), features["targets"],
seq2seq_hparams.chatbot_lstm_hparams(), train)