def seq2seq_f(encoder_inputs, decoder_inputs, do_decode): return rl_seq2seq.embedding_attention_seq2seq( encoder_inputs, decoder_inputs, cell, num_encoder_symbols=source_vocab_size, num_decoder_symbols=target_vocab_size, embedding_size=size, output_projection=output_projection, feed_previous=do_decode, dtype=dtype)
def seq2seq_f(encoder_inputs, decoder_inputs, do_decode): return rl_seq2seq.embedding_attention_seq2seq( encoder_inputs, decoder_inputs, cell, num_encoder_symbols=self.source_vocab_size, num_decoder_symbols=self.target_vocab_size, embedding_size=self.emb_dim, output_projection=output_projection, feed_previous=do_decode, # 是否把上一轮的预测作为这一轮的输入 || 是否在测试 mc_search=self.mc_search, # TODO(Zhu) 文件位置:seq2seq._argmax_or_mcsearch 什么意思? dtype=self.dtype)
def seq2seq_f(encoder_inputs, decoder_inputs, do_decode): return rl_seq2seq.embedding_attention_seq2seq( encoder_inputs, decoder_inputs, cell, num_encoder_symbols=source_vocab_size, num_decoder_symbols=target_vocab_size, embedding_size=emb_dim, output_projection=output_projection, feed_previous=do_decode, mc_search=self.mc_search, dtype=tf.float32)