model.parameters()返回模型的全部参数,并将它们传入Adam函数构造出一个Adam优化器,并设置 learning rate=0.1。 因此该 Adam 优化器的 param_groups 维护的就是模型 model 的全部参数,并且学习率为0.1,这样在调用optimizer_Adam.step()时,就会对model的全部参数进行更新。 4. param_groups Optimizer的param_groups是...
fromtorch.optim.lr_schedulerimportStepLR# torch.optim.lr_scheduler.StepLR(optimizer, step_size, gamma=0.1, last_epoch=- 1, verbose=False)deftrain_step():passbegin_epoch=0max_epoch=200# 从 0 到 200 epoch# 绘制 200 轮的学习率曲线defshow_lr(begin_epoch,max_epoch,scheduler):lr=[]...
import torchfrom torch.optim.lr_scheduler import StepLR # Import your choice of scheduler hereimport matplotlib.pyplot as pltfrom matplotlib.ticker import MultipleLocatorLEARNING_RATE = 1e-3EPOCHS = 4STEPS_IN_EPOCH = 8# Set model and optimizermodel = torch.nn.Linear(2, 1)optimizer = torch.op...
learning_rates.append(optimizer.param_groups[0]["lr"]) scheduler.step() # Visualize learinig rate scheduler fig, ax = plt.subplots(1,1, figsize=(10,5)) ax.plot(range(EPOCHS*STEPS_IN_EPOCH), learning_rates, marker='o', color='black') ax.set_xlim([0, EPOCHS*STEPS_IN_EPOCH]) ax...
scheduler = StepLR(optimizer, step_size = 4, # Period of learning rate decay gamma = 0.5) # Multiplicative factor of learning rate decay 2、MultiStepLR MultiStepLR -类似于StepLR -也通过乘法因子降低了学习率,但在可以自定义修改学习率的时间节点。
optimizer = optim.Adam(net.parameters(), lr=0.0001) # 定义优化器 # train best_acc = 0.0 for epoch in range(EPOCHS): net.train() # 开启dropout running_loss = 0.0 for images,labels in tqdm(train_loader): images,labels = images.to(DEVICE),labels.to(DEVICE) ...
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') ...
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...
set_seed(1) # 设置随机种子#构建可学习参数weight = torch.randn((2, 2), requires_grad=True)weight.grad = torch.ones((2, 2))#传入可学习参数,学习率设置为1optimizer = optim.SGD([weight], lr=0.1) (2)step(): 一次梯度下降更新参数 ...
如下设置将optimizer的可更新参数分为不同的三组，每组使⽤不同的策略 optimizer = torch.optim.SGD([{'params': other_params},{'params': first_params, 'lr': 0.01*args.learning_rate},{'params': second_params, 'weight_decay': args.weight_decay}],lr=args.learning_rate,momentum=args.momentum...