QnA.py 文件源码

def build(self, input_shape):
        assert len(input_shape) >= 3
        self.input_spec = [InputSpec(shape=input_shape)]

        if not self.layer.built:
            self.layer.build(input_shape)
            self.layer.built = True

        super(AttentionLSTMWrapper, self).build()

        if hasattr(self.attention_vec, '_keras_shape'):
            attention_dim = self.attention_vec._keras_shape[1]
        else:
            raise Exception('Layer could not be build: No information about expected input shape.')

        self.U_a = self.layer.inner_init((self.layer.output_dim, self.layer.output_dim), name='{}_U_a'.format(self.name))
        self.b_a = K.zeros((self.layer.output_dim,), name='{}_b_a'.format(self.name))

        self.U_m = self.layer.inner_init((attention_dim, self.layer.output_dim), name='{}_U_m'.format(self.name))
        self.b_m = K.zeros((self.layer.output_dim,), name='{}_b_m'.format(self.name))

        if self.single_attention_param:
            self.U_s = self.layer.inner_init((self.layer.output_dim, 1), name='{}_U_s'.format(self.name))
            self.b_s = K.zeros((1,), name='{}_b_s'.format(self.name))
        else:
            self.U_s = self.layer.inner_init((self.layer.output_dim, self.layer.output_dim), name='{}_U_s'.format(self.name))
            self.b_s = K.zeros((self.layer.output_dim,), name='{}_b_s'.format(self.name))

        self.trainable_weights = [self.U_a, self.U_m, self.U_s, self.b_a, self.b_m, self.b_s]