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Learning Rate Scheduling

Learning Rate Scheduling is an important concept in the domain of Deep Learning, especially when we are using Gradient Descent-based optimization algorithms. In this article, we will delve into the concept and understand its practical implementation using the PyTorch library.

What is the Learning Rate?​

Before discussing Learning Rate Scheduling, let's understand what a Learning Rate is. The learning rate determines the step size at each iteration while moving towards a minimum of a loss function. In simpler terms, it controls how much we are adjusting the weights of our network with respect to the loss gradient.

optimizer = torch.optim.SGD(model.parameters(), lr=0.1)

In the code snippet above, lr=0.1 denotes the learning rate for Stochastic Gradient Descent (SGD) optimizer.

Why do we need Learning Rate Scheduling?​

A constant learning rate might not be efficient during the training of our network. At the beginning of training, we might afford to make larger changes to our weights, but as we come closer to the optimal solution, we'd want our updates to be smaller, so we don't overshoot the minimum.

This is where Learning Rate Scheduling comes in. It allows for the learning rate to decrease after a certain number of epochs or after a particular condition is met.

Types of Learning Rate Schedules​

PyTorch provides several methods to adjust the learning rate during training. Let's discuss some of them.

1. StepLR​

StepLR decays the learning rate of each parameter group by gamma every step_size epochs.

scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=30, gamma=0.1)

2. ExponentialLR​

ExponentialLR decays the learning rate of each parameter group by gamma every epoch.

scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer, gamma=0.1)

3. ReduceLROnPlateau​

Reduce learning rate when a metric has stopped improving. This scheduler reads a metrics quantity and if no improvement is seen for a 'patience' number of epochs, the learning rate is reduced.

scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, 'min')

Applying Learning Rate Scheduler​

Here is a simple example of how to use the learning rate scheduler in a training loop.

optimizer = torch.optim.SGD(model.parameters(), lr=0.1)
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=30, gamma=0.1)

for epoch in range(100):
    # Training loop code here
    # ...

    # Step the scheduler
    scheduler.step()

In the above example, the learning rate will be reduced by a factor of 0.1 every 30 epochs.

Conclusion​

Learning Rate Scheduling is a powerful technique for training deep neural networks. It can speed up the training process and can lead to better performance and quicker convergence. PyTorch provides a wide range of learning rate schedules and the flexibility to define your own, if needed. Remember, choosing the right learning rate schedule and its parameters can be critical, and it might require some experimentation.

I hope this article helps you understand the concept of Learning Rate Scheduling and how to use it in PyTorch. Happy learning!