LEARNING_RATE_value = sess.run(LEARNING_RATE) x_value = sess.run(x) print ("After %s iteration(s): x%s is %f, learning rate is %f." \ % (i+1, i+1, x_value, LEARNING_RATE_value)) 运行结果: 这里写图片描述 After 1 iteration(s): x1 is 4.000000, learning rate is 0.096000. Af...
def get_learning_rate(optimizer): lr=[] for param_group in optimizer.param_groups: lr +=[ param_group['lr'] ] return lr 也可以直接使用optimizer.param_groups[0]['lr']来查看当前的学习率。 设置learning rate的两种方式 self.optimizer = optim.Adam(self.model.parameters(), lr= self.lr) se...
# 记录当前学习率(用于监控) current_lr=scheduler.get_last_lr()[0] print(f"Epoch{epoch+1}/{num_epochs}, Learning Rate:{current_lr:.6f}") 在这个示例中,我们执行以下关键步骤: 定义模型和优化器。 使用之前实现的CosineWarmupScheduler初始化学习率调度器。 在每个训练epoch中: 执行标准的前向传播、...
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))
1、查看learning rate https://discuss.pytorch.org/t/current-learning-rate-and-cosine-annealing/8952 是从pytorch官方社区看到的解决方案。 def get_learning_rate(optimizer): lr=[] for param_group in optimizer.param_groups: lr +=[ param_group['lr'] ] ...
optimizer = torch.optim.SGD(model.parameters(), lr=LEARNING_RATE) # Define your scheduler here as described above # ... # Get learning rates as each training step learning_rates = [] for i in range(EPOCHS*STEPS_IN_EPOCH): optimizer.step() ...
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 ...
深度学习Pytorch-学习率Learning Rate调整策略 0. 往期内容 1. 学习率调整 2. class _LRScheduler 3. 6种学习率调整策略 3.1 optim.lr_scheduler.StepLR(optimizer, step_size, gamma=0.1, last_epoch=- 1) 3.2 optim.lr_scheduler.MultiStepLR(optimizer, milestones, gamma=0.1, last_epoch=- 1) 3.3 opt...
scheduler=CosineAnnealingLR(optimizer,T_max=32,# Maximum numberofiterations.eta_min=1e-4)# Minimum learning rate. 两位Kaggle大赛大师Philipp Singer和Yauhen Babakhin建议使用余弦衰减作为深度迁移学习[2]的学习率调度器。 8、CosineAnnealingWarmRestartsLR ...
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...