TL;DR:请大家尽量使用Transformers的lr_scheduler,特别是已经支持在Transformers已经支持get_cosine_with_min_lr_schedule_with_warmup的情况下,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_...
Describe the bug It's unclear if this is a bug, an intentional design decision, or part of a design trade-off I don't fully understand. Let me explain with an example. I'm using the cosine LR scheduler and my script uses a warm up LR (1e-5), number of warm up epochs (20), ...
scheduler = lr_scheduler.OneCycleLR(optimizer, max_lr=lr, epochs=max_epoch, steps_per_epoch=steps_per_epoch, pct_start=0.1, final_div_factor=10) case'cosineTransformers': scheduler = get_cosine_schedule_with_warmup(optimizer, num_warmup_steps=steps_per_epoch, num_training_steps=max_epoch*...
File "G:\SD_Training\dev_version\sd-scripts\library\train_util.py", line 4513, in get_scheduler_fix return schedule_func( TypeError: get_cosine_schedule_with_warmup() got an unexpected keyword argument 'num_decay_steps' Reinstalling did not solve the problem....
{'total_params': 159498, 'trainable_params': 159498}# 配置模型 from paddle.metric import Accuracy 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, paramete...
>> from cosine_annealing_warmup import CosineAnnealingWarmupRestarts >> >> model = ... >> optimizer = optim.SGD(model.parameters(), lr=0.1, momentum=0.9, weight_decay=1e-5) # lr is min lr >> scheduler = CosineAnnealingWarmupRestarts(optimizer, first_cycle_steps=200, cycle_mult=1.0,...
摘要:CosineWarmup是一种非常实用的训练策略,本次教程将带领大家实现该训练策略。教程将从理论和代码实战两个方面进行。 本文分享自华为云社区《CosineWarmup理论介绍与代码实战》,作者: 李长安。 CosineWarmup是一种非常实用的训练策略,本次教程将带领大家实现该训练策略。教程将从理论和代码实战两个方面进行。 在代码...
'trainable_params': 159498}# 配置模型from paddle.metric import Accuracyscheduler = CosineWarmup(lr=...
warmup_cosine_decay_scheduler.py7.08 KB 一键复制编辑原始数据按行查看历史 wusaifei提交于5年前.first commit importnumpyasnp fromtensorflowimportkeras fromkerasimportbackendasK defcosine_decay_with_warmup(global_step, learning_rate_base, total_steps, ...
WarmupCosine Property Reference Feedback Definition Namespace: Azure.ResourceManager.MachineLearning.Models Assembly: Azure.ResourceManager.MachineLearning.dll Package: Azure.ResourceManager.MachineLearning v1.2.1 Source: LearningRateScheduler.cs Cosine Annealing With Warmup. C# კოპი...