onto_attention.py 文件源码

def build(self, input_shape):
        self.input_spec = [InputSpec(shape=input_shape)]
        input_dim = input_shape[4] - 1  # ignore sense prior parameter
        self.input_dim = input_dim
        # Saving onto-lstm weights to set them later. This way, LSTM's build method won't 
        # delete them.
        initial_ontolstm_weights = self.initial_weights
        self.initial_weights = None
        lstm_input_shape = input_shape[:2] + (input_dim,) # removing senses and hyps
        # Now calling LSTM's build to initialize the LSTM weights
        super(OntoAttentionLSTM, self).build(lstm_input_shape)
        # This would have changed the input shape and ndim. Reset it again.
        self.input_spec = [InputSpec(shape=input_shape)]

        if self.use_attention:
            # Following are the attention parameters
            self.input_hyp_projector = self.inner_init((input_dim, self.output_dim),
                name='{}_input_hyp_projector'.format(self.name)) # Projection operator for synsets
            self.context_hyp_projector = self.inner_init((self.output_dim, self.output_dim),
                name='{}_context_hyp_projector'.format(self.name)) # Projection operator for hidden state (context)
            self.hyp_projector2 = self.inner_init((self.output_dim, self.output_dim),
                name='{}_hyp_projector2'.format(self.name)) # Projection operator for hidden state (context)
            self.hyp_scorer = self.init((self.output_dim,), name='{}_hyp_scorer'.format(self.name))

            # LSTM's build method would have initialized trainable_weights. Add to it.
            self.trainable_weights.extend([self.input_hyp_projector, self.context_hyp_projector,
                                           self.hyp_projector2, self.hyp_scorer])


        if initial_ontolstm_weights is not None:
            self.set_weights(initial_ontolstm_weights)
            del initial_ontolstm_weights