a8_dynamic_memory_network.py 文件源码

def answer_module(self):
        """ Answer Module:generate an answer from the final memory vector.
        Input:
            hidden state from episodic memory module:[batch_size,hidden_size]
            question:[batch_size, embedding_size]
        """
        steps=self.sequence_length if self.decode_with_sequences else 1 #decoder for a list of tokens with sequence. e.g."x1 x2 x3 x4..."
        a=self.m_T #init hidden state
        y_pred=tf.zeros((self.batch_size,self.hidden_size)) #TODO usually we will init this as a special token '<GO>', you can change this line by pass embedding of '<GO>' from outside.
        logits_list=[]
        logits_return=None
        for i in range(steps):
            cell = rnn.GRUCell(self.hidden_size)
            y_previous_q=tf.concat([y_pred,self.query_embedding],axis=1) #[batch_hidden_size*2]
            _, a = cell( y_previous_q,a)
            logits=tf.layers.dense(a,units=self.num_classes) #[batch_size,vocab_size]
            logits_list.append(logits)
        if self.decode_with_sequences:#need to get sequences.
            logits_return = tf.stack(logits_list, axis=1)  # [batch_size,sequence_length,num_classes]
        else:#only need to get an answer, not sequences
            logits_return = logits_list[0]  #[batcj_size,num_classes]

        return logits_return