model.py 文件源码

def apply(self, char_seq, sample_matrix, char_aux):
        # Time as first dimension
        embeddings = self.lookup.apply(char_seq)
        gru_out = self.dgru.apply(
            **merge(self.gru_fork.apply(embeddings, as_dict=True),
                    {'mask': char_aux}))
        if self.dgru_depth > 1:
            gru_out = gru_out[-1]
        sampled_representation = tensor.batched_dot(sample_matrix, gru_out.dimshuffle([1, 0, 2]))
        return sampled_representation.dimshuffle([1, 0, 2])