lr = paddle.optimizer.lr.CosineAnnealingDecay(learning_rate=get('LEARNING_RATE.params.lr'), T_max=step_each_epoch * EPOCHS) return paddle.optimizer.Momentum(learning_rate=lr, parameters=parameters, weight_decay=paddle.regularizer.L2Decay(get('OPTIMIZER.regularizer.factor'))) # 模型训练配置 model....
We combine general loss {\mathcal {V}} with the regularizer induced by the boosting kernel from the linear case to define a new class of kernel-based boosting algorithms. More specifically, given a kernel K, let VDV^T be the SVD of UKU^T. First, assume P_{\lambda ,\nu } invertible...