CLR can help the network to escape from local minima and reach the global minimum. (Which can occur when using fixed learning rates). In addition by exploring a larger range of parameter values, it can help speed up convergence.

I would refer you to the original paper, if you haven't read it already:

https://arxiv.org/abs/1506.01186

thanks Christian Schmidt

Hey Titas De

Cyclical learning rates are an important technique for training deep learning neural nets. This technique involves gradually increasing and decreasing the learning rate during the training process, rather than using a fixed learning rate throughout the entire training process.

As suggested by Christian Schmidt , using a cyclical learning rate can help a neural network converge faster during the training process. This is because it allows the learning rate to be increased during periods of rapid learning and decreased during periods of slower learning. This helps prevent the learning rate from being too high or too low, which can result in slow convergence or instability.

It can help networks converge faster, improve generalization, increase robustness to noise, and improve accuracy on a wide range of tasks.

Thank you.

Cool. Rahul Pandya and Christian Schmidt - any idea if CLR can be used to deal with vanishing / exploding gradients also ?

Hey Titas De ,

Such gradients lead to instable training of data models.

CLR can be implemented in a way to handle this issue by shifting, that is, increasing or decreasing the learning rate of the data model while training it.

Either of the cases, If the gradient are vanishing or limiting to smaller values or if its exploding, just decrease the learning rate to prevent instable training. If the performance is good and progressing well you can increase it.

Though CLRs are easy to implement, if CLR is not giving a good performance with such gradients, you can implement weight initialization, batch normalization, gradient clipping( ADAM or RMSprop).

Thank you.

Rahul Pandya : Thanks - those 3 approaches are decent enough.

Titas De For sure, trying to avoid vanishing/exploding gradients is one of the main purposes of a CLR. Btw, these are easily implemented in TF/PyTorch if you want to explore it further.

https://pytorch.org/docs/stable/generated/torch.optim.lr_scheduler.CyclicLR.html

Rahul Pandya Don't you think it's a bit scummy to copy paste whole answers from ChatGPT without citing it? On a RESEARCH site? ;) I've seen it on basically every question in this forum and it's quite frustrating.

Hey Christian Schmidt ,

My main motive here was to help. I found a few blogs, and sites but they were not giving precise info and showing several wayward methodologies for CLR.

I put it on ChatGpt and it gave an easy approach based on learning rates. I put it here.

I am not here claiming any of the credits of work anyways. Just trying to help. :)

Thanks Christian Schmidt

CLR gives an approach for setting the global learning rates for training neural networks that eliminate the need to perform tons of experiments to find the best values with no additional computation

Thanks Abdelouafi Boukhris

The use of cyclical pace of learning makes the speed of learning dynamic. When using cyclical learning rates, the learning rate increases gradually to a certain maximum, and then decreases to a minimum. After that, the cycle repeats.

Cyclical learning rates involve changing a neural network's learning rate while it is being trained. The learning rate is modified across a cycle of multiple epochs as opposed to being fixed.

Cyclical learning rate is helpful during training deep learning neural networks for reasons of faster training time, robustness to hyperparameters to select the best model, improved generalization of the model, and improved accuracy of the model.

Thanks Betelhem Zewdu Wubineh