LRs = [optimizer.param_groups[0]['lr']]
时间: 2023-10-08 12:08:48 浏览: 45
这行代码将当前优化器的学习率(lr)添加到列表`LRs`中。
`optimizer.param_groups`是一个列表,包含了优化器的所有参数组。每个参数组是一个字典,其中包含了该参数组的相关信息,例如学习率、权重衰减等。
在这段代码中,`optimizer.param_groups[0]`表示获取第一个参数组(通常是唯一的一个参数组),然后通过`['lr']`获取该参数组中学习率的值。
将学习率添加到`LRs`列表中的目的可能是为了记录每个epoch结束时的学习率变化,方便后续分析和可视化。每次更新学习率后,你可以调用这行代码将学习率添加到列表中。
相关问题
将代码转化为paddlepaddle框架可以使用的代码:class CosineAnnealingWarmbootingLR: # cawb learning rate scheduler: given the warm booting steps, calculate the learning rate automatically def __init__(self, optimizer, epochs=0, eta_min=0.05, steps=[], step_scale=0.8, lf=None, batchs=0, warmup_epoch=0, epoch_scale=1.0): self.warmup_iters = batchs * warmup_epoch self.optimizer = optimizer self.eta_min = eta_min self.iters = -1 self.iters_batch = -1 self.base_lr = [group['lr'] for group in optimizer.param_groups] self.step_scale = step_scale steps.sort() self.steps = [warmup_epoch] + [i for i in steps if (i < epochs and i > warmup_epoch)] + [epochs] self.gap = 0 self.last_epoch = 0 self.lf = lf self.epoch_scale = epoch_scale # Initialize epochs and base learning rates for group in optimizer.param_groups: group.setdefault('initial_lr', group['lr']) def step(self, external_iter = None): self.iters += 1 if external_iter is not None: self.iters = external_iter # cos warm boot policy iters = self.iters + self.last_epoch scale = 1.0 for i in range(len(self.steps)-1): if (iters <= self.steps[i+1]): self.gap = self.steps[i+1] - self.steps[i] iters = iters - self.steps[i] if i != len(self.steps)-2: self.gap += self.epoch_scale break scale *= self.step_scale if self.lf is None: for group, lr in zip(self.optimizer.param_groups, self.base_lr): group['lr'] = scale * lr * ((((1 + math.cos(iters * math.pi / self.gap)) / 2) ** 1.0) * (1.0 - self.eta_min) + self.eta_min) else: for group, lr in zip(self.optimizer.param_groups, self.base_lr): group['lr'] = scale * lr * self.lf(iters, self.gap) return self.optimizer.param_groups[0]['lr'] def step_batch(self): self.iters_batch += 1 if self.iters_batch < self.warmup_iters: rate = self.iters_batch / self.warmup_iters for group, lr in zip(self.optimizer.param_groups, self.base_lr): group['lr'] = lr * rate return self.optimizer.param_groups[0]['lr'] else: return None
将代码转化为PaddlePaddle框架可以使用的代码:
class CosineAnnealingWarmbootingLR(paddle.optimizer.lr.CosineAnnealingDecay):
def __init__(self, T_max, T_warmup, eta_min=0, last_epoch=-1, verbose=False):
super(CosineAnnealingWarmbootingLR, self).__init__(T_max=T_max, eta_min=eta_min, last_epoch=last_epoch, verbose=verbose)
self.T_warmup = T_warmup
def get_lr(self):
if self.last_epoch < self.T_warmup:
return [(self.base_lr * self.last_epoch) / self.T_warmup for _ in self.base_lrs]
else:
return super(CosineAnnealingWarmbootingLR, self).get_lr()
val_acc_history = [] train_acc_history = [] train_losses = [] valid_losses = [] LRs = [optimizer.param_groups[0]['lr']]
这段代码是用来记录训练过程中的一些指标,其中:
- `train_acc_history` 存储每个 epoch 训练集的准确率;
- `val_acc_history` 存储每个 epoch 验证集的准确率;
- `train_losses` 存储每个 epoch 训练集的损失值;
- `valid_losses` 存储每个 epoch 验证集的损失值;
- `LRs` 存储每个 epoch 的学习率。
在训练过程中,每个 epoch 结束后,会将上述指标记录下来,以便后续分析和可视化。其中,学习率(LR)的变化也很重要,可以帮助我们调整模型的训练策略,以达到更好的效果。