Loshchilov 提出了cosine annealing strategy。其简化的版本是将学习率从初始值遵循余弦函数减小到零。假设batchs的总数是 , 那么在batch , 学习率 可以根据以下公式计算出来: v2-6601706c9e819dc047d0dea2adcc0ced_r.jpg 由图所示, cosine decay在开始的时候缓慢的降低学习率,中间的时候几乎是线性的降低学习率,快...
学习率更新曲线 defone_cycle(y1=0.0,y2=1.0,steps=100):returnlambda x:((1-math.cos(x*math.pi/steps))/2)*(y2-y1)+y1
from torch.optim.lr_scheduler import StepLRscheduler = StepLR(optimizer, step_size = 4, # Period of learning rate decay gamma = 0.5) # Multiplicative factor of learning rate decay 2、MultiStepLR MultiStepLR -类似于StepLR -也通过乘法因子降低了学习率，但在可以自定义修改学习率的时间节点。
In this guide, we'll be implementing a learning rate warmup in Keras/TensorFlow as a keras.optimizers.schedules.LearningRateSchedule subclass and keras.callbacks.Callback callback. The learning rate will be increased from 0 to target_lr and apply cosine decay, as this is a very common seconda...
init_decay_epochs(int)- Number of initial decay epochs. min_decay_lr(float or iterable of floats)- Learning rate at the end of decay. restart_interval(int)- Restart interval for fixed cycles. Set to None to disable cycles. Default: None. ...
decay_steps, # 对应\eta_{min}^i / \eta_{max}^i alpha=0.0, name=None ) def decayed_learning_rate(step): step = min(step, decay_steps) cosine_decay = 0.5 * (1 + cos(pi * step / decay_steps)) decayed = (1 - alpha) * cosine_decay + alpha return initial_learning_rate * ...
per_device_eval_batch_size=128,evaluation_strategy="steps",eval_steps=1_000,logging_steps=1_000,gradient_accumulation_steps=8,num_train_epochs=50,weight_decay=0.1,warmup_steps=5_000,lr_scheduler_type="cosine_with_restarts",# that's actually the only relevant linelearning_rate=5e-4,save_...
The weight decay is set to 0.0005. For the case of training on the small dataset, the learning rate is initially 0.1 and divided by 10 at the 16K, 24K, 28k iterations, and we finish the training process at 30k iterations. While the training on the large dataset ter- minates at 240k...
step_size = 4, # Period of learning rate decay gamma = 0.5) # Multiplicative factor of learning rate decay 2、MultiStepLR MultiStepLR -类似于StepLR -也通过乘法因子降低了学习率,但在可以自定义修改学习率的时间节点。 from torch.optim.lr_scheduler import MultiStepLR ...
学习率更新 cosine learning rate decay 在yolov5中,其中的一个学习率更新策略 https://arxiv.org/pdf/1812.01187.pdf 公式 学习率更新曲线 defone_cycle(y1=0.0,y2=1.0,steps=100):returnlambda x:((1-math.cos(x*math.pi/steps))/2)*(y2-y1)+y1...