代码实现 from paddle.optimizer.lr import LinearWarmup from paddle.optimizer.lr import CosineAnnealingDecay class Cosine(CosineAnnealingDecay): """ Cosine learning rate decay lr = 0.05 * (math.cos(epoch * (math.pi / epochs)) + 1) Args: lr(float): initial learning rate ...
使用Deepspeed的lr_scheduler的最后一个理由似乎也已经消失了(Deepspeed仍然有一个优势是资瓷一个额外的参数叫warmup_min_ratio,意思就是说lr先是从从warmup_min_ratio×init_lr值warmup爬到init_lr,然后再用cosine降低到cos_min_ratio×init_lr值,并且额外资瓷一...
nesterov=True)...# Scheduler https://arxiv.org/pdf/1812.01187.pdflf = lambda x: ((1 + math.cos(x * math.pi / epochs)) / 2) * (1 - hyp["lrf"]) + hyp["lrf"] # cosinescheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf)scheduler.last_epoch = start...
scheduler = CosineWarmup( lr=0.5, step_each_epoch=100, epochs=8, warmup_steps=20, start_lr=0, end_lr=0.5, verbose=True) optim = paddle.optimizer.SGD(learning_rate=scheduler, parameters=model2.parameters()) model2.prepare( optim, paddle.nn.CrossEntropyLoss(), Accuracy() ) # 模型训练...
raiseValueError("Unknown scheduler {}".format(scheduler)) 「注意」:当num_warmup_steps参数设置为0时,learning rate没有预热的上升过程,只有从初始设定的learning rate 逐渐衰减到0的过程 图2. warmupcosine 4. 实验 deftrain(trainset, evalset, model, tokenizer, model_dir, lr, epochs, device): ...
classWarmUpCosineDecayScheduler(keras.callbacks.Callback): """Cosine decay with warmup learning rate scheduler """ def__init__(self, learning_rate_base, total_steps, global_step_init=0, warmup_learning_rate=0.0, warmup_steps=0, hold_base_rate_steps=0, ...
'trainable_params': 159498}# 配置模型from paddle.metric import Accuracyscheduler = CosineWarmup(lr=...
classWarmUpCosineDecayScheduler(keras.callbacks.Callback): def__init__(self, learning_rate_base, total_steps, global_step_init=0, warmup_learning_rate=0.0, warmup_steps=0, hold_base_rate_steps=0, verbose=0): super(WarmUpCosineDecayScheduler,self).__init__() ...
warmup_scheduler.step() warm_lr = warmup_scheduler.get_lr() print("warm_lr:%s" % warm_lr) inputs, targets = inputs.to(device), targets.to(device) 4. 总结 在论文中和比赛中一般都会用到warm up技巧,特别是在模型难收敛的任务中。在论文中,MultiStepLR和CosineAnnealingLR两种学习率调节策略用...
case'cosineTorchLambda': warmup_epoch =2warmup_factor =1e-3steps_per_epoch =1deff(current_epoch):""" :current_epoch epoch或者iteration :return 根据step数返回一个学习率倍率因子 注意在训练开始之前,pytorch似乎会提前调用一次lr_scheduler.step()方法 ...