之前一直提到这个CosineWarmup，但是一直没有实现过，这次也算是填了一个很早之前就挖的坑。同样，这里也不再设置对比实验，因为这个东西确实很管用。小模型和小数据集可能不太能够体现该训练策略的有效性。大家如果有兴趣可以使用更大的模型、更大的数据集测试一下。#人工智能# ...
nesterov=True)...# Scheduler https://arxiv.org/pdf/1812.01187.pdflf = lambda x: ((1 + math.cos(x * math.pi / epochs)) / 2) * (1 - hyp["lrf"]) + hyp["lrf"] # cosinescheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf)scheduler.last_epoch = start...
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, parameters=model2.parameters()) model2.prepare( optim, paddle.nn.CrossEntropyLoss(), Accuracy() ) # 模型训练...
# lr = scheduler.get_lr()[0] # print(f'Epoch {epoch+1}, Learning Rate: {lr:.6f}, Loss Value: {loss.item()}') print(f'Epoch {epoch+1}, Learning Rate: {batch_lr:.6f}, Loss Value: {loss.item()}') 争对第一种方式选择的优化器有: 1、cosine decay schedule progress = (epoch...
def cosine_scheduler(self, max_lr, min_lr, epochs, niter_per_ep, warmup_epochs=5, start_warmup_value=0, warmup_steps=-1, times=2): warmup_schedule = np.array([]) warmup_iters = warmup_epochs * niter…
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, ...
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...
'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, ...
_warmup_steps=warmup_steps, num_training_steps=t_total) elif scheduler == 'warmupcosinewithhardrestarts': return transformers.get_cosine_with_hard_restarts_schedule_with_warmup(optimizer, num_warmup_steps=warmup_steps, num_training_steps=t_total) else: raise ValueError("Unknown scheduler {}"...