RankOrderedAutoencoder.py 文件源码

def update_weights_final(self):
        # clip the gradient norm
        norm = np.sqrt(np.sum(self.gradient ** 2, axis=0))
        norm_check = norm > self.norm_limit
        self.gradient[:, norm_check] = ((self.gradient[:, norm_check]) / norm[norm_check]) * self.norm_limit
        # update weights
        self.weights += self.gradient * (self.learning_rate)
        # update output average for sorting weights
        self.output_average *= 0.99999
        self.output_average += self.output.ravel() * 0.00001