renormalization.py 文件源码

def build(self, input_shape):
        self.input_spec = [InputSpec(shape=input_shape)]
        shape = (input_shape[self.axis],)

        self.gamma = self.add_weight(shape,
                                     initializer=self.gamma_init,
                                     regularizer=self.gamma_regularizer,
                                     name='{}_gamma'.format(self.name))
        self.beta = self.add_weight(shape,
                                    initializer=self.beta_init,
                                    regularizer=self.beta_regularizer,
                                    name='{}_beta'.format(self.name))
        self.running_mean = self.add_weight(shape, initializer='zero',
                                            name='{}_running_mean'.format(self.name),
                                            trainable=False)
        # Note: running_std actually holds the running variance, not the running std.
        self.running_std = self.add_weight(shape, initializer='one',
                                           name='{}_running_std'.format(self.name),
                                           trainable=False)

        self.r_max = K.variable(np.ones((1,)), name='{}_r_max'.format(self.name))

        self.d_max = K.variable(np.zeros((1,)), name='{}_d_max'.format(self.name))

        self.t = K.variable(np.zeros((1,)), name='{}_t'.format(self.name))

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