Learning Rate Scheduling: Full Guide

Learning Rate Scheduling

Adaptive adjustment of the learning rate is crucial for optimal training.

Learning Rate Scheduling – a technique that modifies the learning rate during training to improve convergence, fine-tune at the end, and avoid local minima.

ReduceLROnPlateau, OneCycle, testing

Selecting an optimal schedule requires careful documentation.

5. Tools and Libraries

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13. Theoretical Foundations

Learning Rate Scheduling relies on:

Optimization Theory: Large steps initially, small steps at the end.

Frequently asked questions

What is OneCycle: warmup to high LR, retention?

OneCycle: warmup to a high learning rate, maintain that high rate, and then decay. It’s commonly used in the fastai library and can lead to ‘super-convergence’.

Is Cosine annealing popular and well-performing?

Cosine annealing is a popular choice and generally works well for most tasks. OneCycle is designed for rapid learning, while step decay offers simplicity.

Should you experiment with the initial learning rate?

It’s beneficial to experiment with different learning rates, starting with a learning rate finder or standard values like 0.001 for Adam and 0.01 for SGD.

What are typical learning rate values (1e-6 to 1e-4) and how do they depend on the initial value?

Typical learning rates range from 1e-6 to 1e-4, depending on the initial learning rate. Ensure you don’t choose a rate that's too small, as this can stall the training process.

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