def get_initial_feedables(self) -> DecoderFeedables: feedables = AutoregressiveDecoder.get_initial_feedables(self) rnn_feedables = RNNFeedables( prev_contexts=[tf.zeros([self.batch_size, a.context_vector_size]) for a in self.attentions], prev_rnn_state=self.initial_state, prev_rnn_output=self.initial_state) return feedables._replace(other=rnn_feedables)
def get_initial_feedables(self) -> DecoderFeedables: feedables = AutoregressiveDecoder.get_initial_feedables(self) rnn_feedables = RNNFeedables(prev_contexts=[ tf.zeros([self.batch_size, a.context_vector_size]) for a in self.attentions ], prev_rnn_state=self.initial_state, prev_rnn_output=self.initial_state) return feedables._replace(other=rnn_feedables)
def get_initial_feedables(self) -> DecoderFeedables: feedables = AutoregressiveDecoder.get_initial_feedables(self) tr_feedables = TransformerFeedables( input_sequence=tf.zeros( shape=[self.batch_size, 0, self.dimension], dtype=tf.float32, name="input_sequence"), input_mask=tf.zeros( shape=[self.batch_size, 0, 1], dtype=tf.float32, name="input_mask")) return feedables._replace(other=tr_feedables)