Learning Rate

What is a Learning Rate?

When it comes to machine learning and neural networks, the learning rate is a hyperparameter that determines the step size for the model as it updates weights during training. The learning rate controls how many adjustments to the model in response to the estimated error each time the model weights are updated.

We can explain the overall concept in a much more relatable metaphor. Imagine you’re in a mountainous region and blindfolded, then given a goal to find the lowest point in the valley. You can feel the slope of the ground under your feet and decide which direction to step. In this metaphor, the learning rate is the size of your steps.

  • If you take small steps (a small learning rate), you’ll ensure you don’t overshoot the lowest point, but it might take a long time to get to the end goal.
  • If you take large steps (a high learning rate), you might get to the vicinity of the lowest point quickly, but you could overshoot it and keep walking around, possibly missing it entirely.
  • In extreme cases, you might even climb out of the valley and end up somewhere higher.

You can likely see how this relates to training a machine learning model. Finding the right learning rate for the task significantly affects the result.

Understanding Learning Rates in Machine Learning

The learning rate in machine learning influences how a model updates its internal parameters, such as weights in neural networks, during the training process.

This rate represents the magnitude of the adjustments made to the model in response to the error observed in the training data.Β  Your chosen rate depends on how you wish to balance training efficiency and model accuracy. Too high, and the model might fail to converge. Too low, and training might be too slow or get stuck entirely.

Techniques like learning rate annealing, which gradually reduces the learning rate, or an adaptive learning rate, which adjusts the learning rate based on training progress, are often employed in practice. You can also explore a tuning learning rate, which may be necessary.

Let’s take a closer look at the two general types of learning rates to help demonstrate why choosing the right option or a different technique altogether is so crucial.

Low Learning Rate

This method takes smaller, more cautious steps. While this approach can provide a more refined movement towards the ideal solution, it can become too slow, taking more iterations to converge. Additionally, there’s a risk of getting stuck in local minima, which are points in the loss surface that are lower than neighboring points but are not necessarily the globally optimal solution.

High Learning Rate

This route takes larger steps towards minimizing the loss. A higher learning rate can speed up the training process, but there’s a risk: large steps might overshoot the optimal solution, causing the model to oscillate and possibly diverge, never settling on a good solution.

Learning Rate Calculation

The learning rate calculation describes a range of methods used to adjust or determine the learning rate value during the training of machine learning models, also known as adaptive learning rates.

Rather than setting a static learning rate that may be too high or too low, adaptive techniques modify the learning rate based on various factors, typically the progress of training or the recent history of gradients. Let’s dive into some standard learning rate calculation techniques:

Decaying Learning Rate

This method gradually reduces the learning rate over time or as training progresses. The basic premise is that taking smaller steps to avoid overshooting is beneficial as you approach the minimum loss function, while larger steps are worthwhile early on in the training process. There are a few ways to implement decay:

  • Exponential decay: The learning rate decreases at an exponential rate throughout training, which makes the rate become shorter more rapidly.
  • Scheduled drop learning rate: This approach reduces the learning rate at predetermined epochs or after a set number of iterations. A scheduled drop learning rate is a good choice for datasets that have been used before or a use case the team is familiar with.
  • Step decay: The learning rate is decreased by a particular factor after specific epochs. For instance, you might reduce the learning rate by half every ten epochs. The increment remains the same throughout the training process.

Adaptive Learning Rate

Adaptive methods adjust the learning rate based on the recent history of the training process, often adapting it per parameter:

  • Adagrad: This technique scales the learning rate in relation to the square root of the sum of all previous squared gradients. The result is that parameters with large gradients have their learning rates reduced, while those with small gradients have their learning rates increased.
  • RMSprop: Inspired by and based on Adagrad, RMSprop divides the learning rate by an exponentially decaying average of gradients. This decay helps address Adagrad’s rapid decrease in learning rate, which may be necessary for some datasets.
  • Adam (Adaptive Moment Estimation): Combining momentum and RMSprop ideas, Adam maintains an exponentially decaying average based on past gradients. The learning rate is then adjusted for each parameter based on these averages.

Each of these methods is more computationally intensive than other options, but the tradeoff may be worth it depending on the dataset and use case.

Cyclical Learning Rate

This low-intensity technique periodically varies the learning rate between two boundaries set by the engineers: a lower and an upper limit for the learning rate. The result is oscillating the learning rate. There are two ways this methodology can be enacted:

  1. Triangular Policy: A simple way to cycle the learning rate between the lower and upper bound.
  2. Annealing Policy: The learning rate gradually decreases from the upper to the lower bound over a cycle.

Ultimately, each of the above methods optimizes the training process by adaptively adjusting the learning rate. The best approach often depends on the specific problem, dataset, and model architecture, so experimenting with a combination of these strategies can often produce the best results.