from torch.optim.lr_scheduler import PolynomialLRscheduler = PolynomialLR(optimizer, total_iters = 8, # The number of steps that the scheduler decays the learning rate. power = 1) # The power of the polynomial.下图为power= 1时的学习率衰减结果。power= 1时，学习率衰减如下所示。7、Cos...
scheduler = ConstantLR(optimizer, factor = 0.5, # The number we multiply learning rate until the milestone. total_iters = 8) # The number of steps that the scheduler decays the learning rate 如果起始因子小于1,那么学习率调度器在训练过程中会提高学习率,而不是降低学习率。 4、LinearLR LinearLR...
scheduler = CyclicLR(optimizer, base_lr = 0.0001, # Initial learning rate which is the lower boundary in the cycle for each parameter group max_lr = 1e-3, # Upper learning rate boundaries in the cycle for each parameter group step_size_up = 4, # Number of training iterations in the ...
optim.Optimizer): def __init__(self, params, lr=1e-2, noise_scale=1e-2): defaults = dict(lr=lr, noise_scale=noise_scale) super(SGLD, self).__init__(params, defaults) def step(self): for group in self.param_groups: for p in group['params']: if p.grad is None: continue ...
ylabel("Learning rate") plt.legend() plt.show() 结果 图3 等间隔调整学习率StepLR 2、MultiStepLR 功能:按给定间隔调整学习率 lr_scheduler.MultiStepLR(optimizer,milestones,gamma,last_epoch=-1) 主要参数:milestones设定调整时刻数 gamma调整系数 如构建个list设置milestones=[50,125,180],在第50次、...
scheduler = CosineAnnealingWarmRestarts(optimizer, T_0 = 8,# Number of iterations for the first restart T_mult = 1, # A factor increases TiTi after a restart eta_min = 1e-4) # Minimum learning rate 这个计划调度于2017年[1]推出。虽然增加LR会导致模型发散但是这种有意的分歧使模型能够逃避局...
5. **学习率查找器(Learning Rate Finder)**: 在训练开始前,快速地遍历不同的学习率,以找到最佳的学习率范围。 ```python from torch.optim.lr_schedulerimport_LRScheduler class LRFinder(_LRScheduler): def __init__(self, optimizer,end_lr=10,num_it=100,last_epoch=-1): ...
ValueError: Optimizer should be an instance of pyro.optim.PyroOptim class. 1. 2. 3. 4. 5. 6. 正确的方法是 optimizer = torch.optim.SGD # 指数下降学习率 pyro_scheduler = pyro.optim.ExponentialLR({'optimizer': optimizer, 'optim_args': {'lr': learn_rate}, 'gamma': 0.1}) ...
1.3 optimizer的方法 基本方法 •zero_grad():清空所管理参数的梯度, 这里注意Pytorch有一个特性就是张量梯度不自动清零 •step():执行一步更新 class Optimizer(object):def zero_grad(self):for group in self.param_groups:for p in group['params']:if p.grad is not None:p.grad.detach_()p.grad...
scheduler= optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=10) epochs= 30lr_list=list()foriinrange(epochs):print(scheduler.get_last_lr()) lr_list.append(scheduler.get_last_lr()) scheduler.step() _, ax=plt.subplots() ax.set_title('learning rate curve') ...