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...
inlineimportmathimporttensorflowastffromtensorflow.keras.callbacksimportLearningRateSchedulerfromd2limporttensorflowasd2ldefnet():returntf.keras.models.Sequential([
you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`. Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.
print(f"End of Epoch {epoch + 1}: Current Learning Rate: {current_lr:.6f}") # 绘制学习率曲线 plt.figure(figsize=(10, 6)) plt.plot(range(num_epochs), lr_history, marker='o') plt.xlabel('Epoch') plt.ylabel('Learning Rate') plt.title('Learning Rate Schedule') plt.grid(True) p...
"will result in PyTorch skipping the first value of the learning rate schedule. " "See more details at " "https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate", UserWarning) self._step_count += 1 class _enable_get_lr_call: ...
optimizer.step()lr_schedule.step() ifepoch %10 ==0:print('epoch:',epoch)print('learning rate:',optimizer.state_dict()['param_groups'][0]['lr'])checkpoint = {"net": model.state_dict(),'optimizer': optimizer.state_dict(),"epoch": epoch,'...
plt.plot(range(100),lr_list)plt.xlabel('Epoch')plt.ylabel('Learning Rate')plt.title("Learning rate schedule: CosineAnnealingLR")plt.show() 在这个例子中,初始的学习率是0.1,学习率会按照余弦函数的形式在0.01到0.1之间变化,周期为10个epoch。
Set the learning rate of each parameter group using a cosine annealing schedule. When last_epoch=-1, sets initial lr as lr. Notice that because the schedule is defined recursively, the learning rate can be simultaneously modified outside this scheduler by other operators. If the learning rate ...
step-wise学习率退火,可以看到在warmup阶段学习率是慢慢的上升的,而过了warmup阶段使用相应的学习率schedule fun进行改变 # step-wise learning rate annealing train_step += 1 if args.scheduler in ['cosine', 'constant', 'dev_perf']: # linear warmup stage if train_step < args.warmup_step: curr...
() 方法来更新学习率for epoch in range(100):# 这里省略了模型的训练代码# ...# 更新学习率scheduler.step()lr_list.append(optimizer.param_groups[0]['lr'])# 绘制学习率变化曲线plt.plot(range(100), lr_list)plt.xlabel('Epoch')plt.ylabel('Learning Rate')plt.title("Learning rate schedule: ...