lr_scheduler.CosineAnnealingWarmRestartsSet the learning rate of each parameter group using a cosine ...
self.__dict__.update(state_dict)def get_last_lr(self):""" Return last computed learning rate by current scheduler."""returnself._last_lr def get_lr(self):# Compute learning rate using chainable form of the schedulerraise NotImplementedError def print_lr(self, is_verbose, group, lr,epoch...
LEARNING_RATE + 0.0001])ax.set_xlabel('Steps')ax.set_ylabel('Learning Rate')ax.spines['top'].set_visible(False)ax.spines['right'].set_visible(False)ax.xaxis.set_major_locator(MultipleLocator(STEPS_IN_EPOCH))
ax.set_xlim([0, EPOCHS*STEPS_IN_EPOCH]) ax.set_ylim([0, LEARNING_RATE + 0.0001]) ax.set_xlabel('Steps') ax.set_ylabel('Learning Rate') ax.spines['top'].set_visible(False) ax.spines['right'].set_visible(False) ax.xaxis.set_major_locator(MultipleLocator(STEPS_IN_EPOCH)) ax.xax...
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。 from torch.optim.lr_scheduler import MultiStepLR scheduler = MultiStepLR(optimizer, ...
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 ...
LEARNING_RATE = 1e-3 EPOCHS = 4 STEPS_IN_EPOCH = 8 # Set model and optimizer model = torch.nn.Linear(2, 1) optimizer = torch.optim.SGD(model.parameters(), lr=LEARNING_RATE) # Define your scheduler here as described above # ... ...
ax.set_title('learning rate curve') ax.scatter([iforiinrange(len(lr_list))], lr_list) plt.show()if__name__=='__main__': test_scheduler() 首先测试了学习率变化,可以看出每10个epoch为余弦函数的半个周期变化, 若将scheduler的step内参数设置为1,则学习率固定为此时的第1轮数值。
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step_size =4,# Period of learning rate decaygamma =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。 fromtorch.optim.lr_scheduler import MultiStepLRscheduler= MultiStepLR(optimizer,mileston...