# 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 # 这里没使用预热,直接从初始学习率开始下降 scheduler = get_linear_schedule_with_warmup( optimizer, num_warmup_steps=0, # The number of steps for the warmup phase. num_training
1 optimizer = AdamW(model.parameters(), lr=LR, correct_bias=False)scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=WARMUP_STEPS, num_training_steps=t_total)forepochinrange(EPOCHS): loss.backward() optimizer.step() scheduler.step() 1 2 3 4 5 6 7 8 当保存和加...
scheduler = get_cosine_schedule_with_warmup(optimizer, num_warmup_steps=steps_per_epoch, num_training_steps=max_epoch*steps_per_epoch) case'WSD': scheduler = warmup_stable_decay(optimizer, total_steps) case'warmup_drop': scheduler = linear_warmup_drop(optimizer, steps_per_epoch,0.5, [2,...
2. get_linear_schedule_with_warmup from transformers import BertConfig, AdamW, get_linear_schedule_with_warmup optimizer = AdamW(optimizer_grouped_parameters, lr=self.args.learning_rate, eps=self.args.adam_epsilon) scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=self.arg...
scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=num_warmup_steps, num_training_steps=num_training_steps) # PyTorch调度程序用法如下: for batch in train_data: model.train() loss = model(batch) loss.backward()
import torch from datasets import load_dataset, load_metric from torch.utils.data import DataLoader from transformers import ( AdamW, AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, BertForSequenceClassification, ) tokenizer = AutoTokenizer.from_pretrained("bert-base-ca...
13 warm up 14 ChainedScheduler 15 SequentialLR 1 LambdaLR 以自定义一个函数作为乘法因子控制衰减。 公式: 函数: 代码语言:javascript 代码运行次数:0 运行 AI代码解释 """ 将每个参数组的学习率设置为初始 lr 乘以给定函数.当 last_epoch=-1时,设置 lr 为 初始 lr.""" ...
args.warmup_steps = int(t_total * args.warmup_proportion) optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate, eps=args.adam_epsilon) scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=args.warmup_steps, ...
epochs scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=0, num_training_steps=total_steps) for epoch in range(self.epochs): self.model.train() total_loss, total_val_loss = 0, 0 total_eval_accuracy = 0 print('epoch:' , epoch , ', step_number:' , len(train...
(optimizer_grouped_parameters, lr=self.hparams.learning_rate, eps=self.hparams.adam_epsilon) scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=self.hparams.warmup_steps, num_training_steps=self.total_steps) scheduler = {"scheduler": scheduler, "interval": "step", "...