标签：iat   sch   ota   ORC   rate   线性   optimizer   loading   阶段   

学习率预热

• 在预热期间，学习率从0线性增加到优化器中的初始lr。

• 在预热阶段之后创建一个schedule，使其学习率从优化器中的初始lr线性降低到0

Parameters

• optimizer (Optimizer) – The optimizer for which to schedule the learning rate.

• num_warmup_steps (int) – The number of steps for the warmup phase.

• num_training_steps (int) – The total number of training steps.

• last_epoch (int, optional, defaults to -1) – The index of the last epoch when resuming training.

Returns

• torch.optim.lr_scheduler.LambdaLR with the appropriate schedule.

# training steps 的数量: [number of batches] x [number of epochs].
total_steps = len(train_dataloader) * epochs

# 设计 learning rate scheduler
scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps = 50, 
                                            num_training_steps = total_steps)

学习率预热(transformers.get_linear_schedule_with_warmup)

标签：iat   sch   ota   ORC   rate   线性   optimizer   loading   阶段   

原文地址：https://www.cnblogs.com/douzujun/p/13868472.html