alpha =float(current_epoch) / (warmup_epoch)# warmup过程中lr倍率因子大小从warmup_factor -> 1returnwarmup_factor * (1- alpha) + alpha# 对于alpha的一个线性变换,alpha是关于x的一个反比例函数变化else:# warmup后lr的倍率因子从1 -> 0# 参考deeplab_
scheduler = get_cosine_schedule_with_warmup(optimizer, num_warmup_steps=len(train_loader),num_training_steps=EPOCHS*len(train_loader)) # AdamW它是 Adam 优化器的一种变体。它的作用是基于梯度更新神经网络的参数,使得损失函数最小化。 # 学习率先线性warmup一个epoch,然后cosine式下降。 # 这里给个小...
train_steps_per_epoch = train_size // config.batch_size scheduler = get_cosine_schedule_with_warmup(optimizer, num_warmup_steps=(config.epoch_num // 10) * train_steps_per_epoch, num_training_steps=config.epoch_num * train_steps_per_epoch) # Train the model logging.info("---Start Tr...
我建议使用torch.save储存模型,而不用transformers的包来做。 6、warm_up def get_cosine_schedule_with_warmup(optimizer, num_warmup_steps, num_training_steps, num_cycles=.5, last_epoch=-1): """ Create a schedule with a learning rate that decreases following the values of the cosine function ...
from transformers import BertModel, BertConfig, BertTokenizer, AdamW, get_cosine_schedule_with_warmup import warnings warnings.filterwarnings('ignore') bert_path = "bert_model/" # 该文件夹下存放三个文件('vocab.txt', 'pytorch_model.bin', 'config.json') ...
from transformers import BertModel, BertConfig, BertTokenizer, AdamW, get_cosine_schedule_with_warmup import warnings warnings.filterwarnings('ignore') bert_path = "bert_model/" # 该文件夹下存放三个文件('vocab.txt', 'pytorch_model.bin', 'config.json') ...
I have the same issue withtorch==1.14from thenvcr.io/nvidia/pytorch:23.01-py3of NGC docker @lantigaYes, the code is from transformers import ( get_polynomial_decay_schedule_with_warmup, get_cosine_schedule_with_warmup, ) scheduler = get_cosine_schedule_with_warmup( optimizer, num_warmup...
parameters(), lr=1e-3) scheduler = get_cosine_schedule_with_warmup(optimizer, num_warmup_steps=10, num_training_steps=100) if load_path is not None: load_checkpoint(load_path, model, optimizer, scheduler) for i in range(total_steps): input = torch.arange(16).reshape(2, 8).to(...
Learning rate schedule - we use cosine LR schedule For bigger batch sizes (512 and up) we use linear warmup of the learning rate during the first couple of epochs according toTraining ImageNet in 1 hour. Warmup length depends on the total training length. ...
support for training with a gradient accumulation base model: linear learning rate warmup for 1,000 iterations, followed by the cosine learning rate schedule, the initial learning rate is set to 0.01, and the final learning rate is set to 0.001 training for 40,000 steps, using a batch size...