FastAi What does the slice(lr) do in fit_one_cycle()

FastAi: Understanding slice(lr) in fit_one_cycle()

The fit_one_cycle() function in FastAi is a powerful training method that implements the “1cycle” learning rate policy. This policy involves gradually increasing the learning rate from a small value to a larger value, then decreasing it back down to a small value, all within a single training epoch. The slice(lr) argument in fit_one_cycle() plays a crucial role in defining this learning rate schedule.

Understanding slice(lr)

slice(lr) is a FastAi-specific construct that provides a way to specify the learning rate schedule within fit_one_cycle(). Here’s a breakdown:

How slice(lr) Works

The slice(lr) argument essentially dictates how the learning rate will be adjusted throughout the training epoch. It can be used to create a variety of learning rate schedules, each with its unique behavior:

• Constant Learning Rate: If you pass a single value to slice(lr), the learning rate will remain constant throughout the entire epoch.
• Linear Ramp-up and Ramp-down: By passing a slice object to slice(lr), you can define a linear ramp-up and ramp-down schedule. For example, slice(lr/10, lr, lr/10) will linearly increase the learning rate from lr/10 to lr during the first part of the epoch, then linearly decrease it back to lr/10 in the second part of the epoch.
• Custom Schedules: slice(lr) allows you to use custom learning rate schedules by providing a list of learning rates or a custom function that defines the learning rate at each training step.

Example

Let’s illustrate how slice(lr) is used within the context of fit_one_cycle().

Assume you want to train a model using the 1cycle policy with a maximum learning rate of 0.01. You can achieve this with the following code:

 learn.fit_one_cycle(10, slice(0.001, 0.01, 0.001)) 

In this example, the learning rate will linearly increase from 0.001 to 0.01 during the first half of the epoch and then decrease back to 0.001 during the second half of the epoch.

Key Points

• The slice(lr) argument provides a flexible way to control the learning rate schedule within fit_one_cycle().
• You can use slice(lr) to implement both basic and custom learning rate schedules.
• The specific values in the slice(lr) object will determine how the learning rate changes throughout the training process.

Conclusion

Understanding the slice(lr) argument within FastAi’s fit_one_cycle() is crucial for fine-tuning your model training process. It allows you to precisely define the learning rate schedule, which can have a significant impact on the convergence of your model and its overall performance.