plt.xlabel('Step')plt.ylabel('Learning Rate')plt.title('Learning Rate Schedules')plt.legend()plt.show() 得到的图像如下: WarmupCosineLR 并且注意到,虽然后面的decay阶段从图像上看似乎差异不大,但是实际上我手动算了一下,仍然会有0.4%左右的误差。 注意到Deepspeed的框架实现的WarmupCosineLR似乎有两个...
decay_type == 'step': scheduler = lrs.StepLR( my_optimizer, step_size=args.lr_decay, gamma=args.gamma ) elif args.decay_type.find('step') >= 0: milestones = args.decay_type.split('_') milestones.pop(0) milestones = list(map(lambda x: int(x), milestones)) scheduler = lrs....
[self.sparse_feature_number + 1, 1], padding_idx=0, param_attr=fluid.ParamAttr( initializer=fluid.initializer.TruncatedNormalInitializer( loc=0.0, scale=init_value_), regularizer=fluid.regularizer.L1DecayRegularizer(self.reg)) ) reshape_emb = fluid.layers.reshape(emb, shape=[-1, 1]) return...
default=[2,5,7],help="For MultiFactorScheduler step")parser.add_argument('--lr_decay_factor',type=float,default=0.1)args,_=parser.parse_known_args()defget_lr_scheduler(args):lr_scheduler=MultiStepLR(args=args)returnlr_schedulerclassMultiStepLR(Callback):"""Learning rate scheduler....
因此才会有CosineDecayLR出现1e-6数量级的负数出现,考虑到是硬件平台的差异,目前可以用以下方式规避 importmindspore.opsasPimportmindspore.common.dtypeasmstypefrommindsporeimportcontextfrommindspore.nn.learning_rate_scheduleimportLearningRateScheduleclassCosineDecayLR(LearningRateSchedule):def__init__(self, min_lr...
learning_rate 5e-5 \ --weight_decay 0.1 \ --adam_beta2 0.95 \ --warmup_ratio 0.01 \ --lr_scheduler_type "cosine" \ --logging_steps 1 \ --report_to "none" \ --model_max_length 4096 \ --gradient_checkpointing True \ --lazy_preprocess True \ --deepspeed ds_config_zero3.json...
step_size (int): Period of learning rate decay.gamma (float): Multiplicative factor of learning rate decay.Default: 0.1.last_epoch (int): The index of last epoch. Default: -1.verbose (bool): If ``True``, prints a message to stdout for ...
type: "BatchNorm" bottom: "conv2_em" top: "conv2_em" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } } 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11.
--weight-decay: 1e-2 --clip-grad: 1.0 --lr-warmup-fraction: .01 --log-interval: 1 --save-interval: 10000 --eval-interval: 1000 --eval-iters: 10 --transformer-impl: transformer_engine --tensor-model-parallel-size: 2 --pipeline-model-parallel-size: 2 --sequence-parallel: true --...
caffe 中base_lr、weight_decay、lr_mult、decay_mult代表什么意思? 在机器学习或者模式识别中,会出现overfitting,而当网络逐渐overfitting时网络权值逐渐变大,因此,为了避免出现overfitting,会给误差函数添加一个惩罚项,常用的惩罚项是所有权重的平方乘以一个衰减常量之和。其用来惩罚大的权值。