Learning Rate Scheduler

This repository contains a collection of custom learning rate schedulers. These Scheduler classes can be used to decay the learning rate during training.

Installation

pip install numpy matplotlib

Available Schedulers

• Linear Decay
• Exponential Decay
• Step Decay
• Polynomial Decay

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Explanation

Linear Decay Scheduler:

The learning rate is decayed linearly from the initial learning rate to minimum learning rate over a specified number of epochs. The formula for the learning rate is given below:

lr = lr_min + (lr_max - lr_min) * (1 - epoch / epochs)

Exponential Decay Scheduler:

The learning rate is decayed exponentially over a specified number of epochs. The formula for the learning rate is given below:

lr = lr_min + (lr_max - lr_min) * (decay_rate) ^ (epoch / epochs)

Step Decay Scheduler:

The learning rate is decayed by a factor of gamma every step_size epochs. The formula for the learning rate is given below:

lr = lr_max * gamma ^ (epoch / step_size)

Polynomial Decay Scheduler:

The learning rate is decayed by a factor of gamma every step_size epochs. The formula for the learning rate is given below:

lr = lr_max * (1 - epoch / epochs) ^ (power)

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TODO

• Add more schedulers
• Create examples for each scheduler
• Add documentation

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Contributing

Everyone is welcome to contribute to this repository.