dm_resnet.py 文件源码

def l1l2_penalty_reg(alpha=1.0, l1_ratio=0.5):
        '''Calculate L1 and L2 penalties for a Keras layer
        This follows the same formulation as in the R package glmnet and Sklearn
        Args:
            alpha ([float]): amount of regularization.
            l1_ratio ([float]): portion of L1 penalty. Setting to 1.0 equals 
                    Lasso.
        '''
        if l1_ratio == .0:
            return l2(alpha)
        elif l1_ratio == 1.:
            return l1(alpha)
        else:
            return l1l2(l1_ratio*alpha, 1./2*(1 - l1_ratio)*alpha)