AI News, Advanced Topics in Neural Networks

Advanced Topics in Neural Networks

As you have likely come to realize from your own adventures with neural networks, and possibly from other articles and research literature, the learning rate is a very important part of neural network training.

A smaller learning rate results in slower learning, and whilst convergence is possible, it may only occur after an inordinate number of epochs, which is computationally inefficient.

Clearly, there is a sweet spot in between that is optimal for a given neural architecture — this is unique and is a function of the neural loss surface.

In addition to this temporal dependence, the sweet spot is also spatially dependent — since certain locations on the loss surface may have extremely sharp or shallow gradients — which further complicates matters.

The only way to achieve better results is to use a dynamic learning rate that tries to leverage the spatial and temporal variations in the optimal learning rate.

The main use of cyclical learning rates is to escape local extreme points, especially sharp local minima (overfitting).

After 30 iterations, the learning rate scheduler resets the learning rate to the same value as epoch 1, and then the learning rate scheduler repeats the same exponentially decay.

This idea is similar to the cyclical learning rate except for the learning rate graph typically looks more like a sawtooth wave rather than something symmetric and cyclic.

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