1、StepLR 在每个预定义的训练步骤数之后，StepLR通过乘法因子降低学习率。from torch.optim.lr_scheduler import StepLRscheduler = StepLR(optimizer, step_size = 4, # Period of learning rate decay gamma = 0.5) # Multiplicative factor of learning rate decay 2、MultiStepLR MultiStepLR -类似...
optimizer.param_groups:列表,每个元素都是一个字典,每个元素包含的关键字有:'params', 'lr', 'betas', 'eps', 'weight_decay', 'amsgrad',params类是各个网络的参数放在了一起。这个属性也继承自torch.optim.Optimizer父类。 由于上述两个属性都继承自所有优化器共同的基类,所以是所有优化器类都有的属性,并且...
momentum, weight_decay=args.weight_decay) exp_lr_scheduler = lr_scheduler.MultiStepLR(optimizer, milestones=args.milestones, gamma=args.gamma) for epoch in range(args.epochs): loss_record = AverageMeter() model.train() exp_lr_scheduler.step() for batch_idx, ((x, _), label, idx) in ...
"""Decays the learning rate of each parameter group by gamma every epoch.When last_epoch=-1, sets initial lr as lr.Args: optimizer (Optimizer): Wrapped optimizer.gamma (float): Multiplicative factor of learning rate decay.last_epoch (int): The index of last epoch. Default: -1.verbose...
weight_decay=0, amsgrad=False) ExpLR = torch.optim.lr_scheduler.ExponentialLR(optimizer, gamma=0.9999) for epoch in range(epoch_num): for step, (batch_x, batch_y) in enumerate(loader): y_pred = model(batch_x) loss = loss_func(y_pred, batch_y) ...
weight_decay(float)- 权值衰减系数,也就是L2正则项的系数 nesterov(bool)- 通常默认false,是否使用NAG(Nesterov accelerated gradient) 前面的方法对学习率都是全局的进行操作,并且所有参数学习率的调整都相同。我能不能思考对每个参数采用不同的学习率调整方式呢?
sgd_config = {'params': net.parameters(),'lr':1e-7,'weight_decay':5e-4,'momentum':0.9} optimizer = SGD(**sgd_config) optimizer.load_state_dict(torch.load('your_save_optimizer_params.pt')) scheduler = lr_scheduler.StepLR(optimizer, step_size=30, gamma=0.1, last...
// The learning rate decay policy. The currently implemented learning rate // policies are as follows: // - fixed: always return base_lr. // - step: return base_lr * gamma ^ (floor(iter / step)) // - exp: return base_lr * gamma ^ iter // - inv: return base_lr * ...
对于class torch.optim.Adam(params, lr=0.001, betas=(0.9, 0.999), eps=1e-08, weight_decay=0, amsgrad=False) params (iterable):需要优化的网络参数,传进来的网络参数必须是Iterable。 优化一个网络,网络的每一层看做一个parameter group,一整个网络就是parameter groups(一般给赋值为net.parameters()——...
gamma: 1.5 beta: 1.0 loss_weight: 1.0 CascadeTwoFCHead: mlp_dim: 1024 LearningRate: base_lr: 0.0000125 schedulers: !PiecewiseDecay gamma: 0.1 milestones: [24000, 26000] !LinearWarmup start_factor: 0.1 steps: 1000 OptimizerBuilder: