DMN.py 文件源码

def get_output_for(self, inputs, **kwargs):
        input          = inputs[0]
        input_word     = T.flatten(inputs[1])
        word_dropout   = inputs[2]        

        # Apply word embedding
        sentence_rep = self.SemMem.get_output_for([input, word_dropout])

        # Apply GRU Layer
        gru_outs = self.GRU.get_output_for([sentence_rep])

        # Extract candidate fact from GRU's output by input_word variable
        # resolving input with adtional word
        # e.g. John when to the hallway nil nil nil -> [GRU1, ... ,GRU8] -> GRU5
        candidate_facts = T.reshape(
            gru_outs[T.arange(gru_outs.shape[0],dtype='int32'), input_word-1], 
            (-1, input.shape[1], self.hid_state_size))
        return candidate_facts