layers.py 文件源码

def build(self, input_shape):
        super(AttentionLSTM, self).build(input_shape)

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

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

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

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