Impact of learning rate on a model

In Machine Learning, there are two types of parameters: machine learnable parameters and hyper-parameters. Machine-learnable parameters are estimated by the algorithm during training, while hyper-parameters are set by the data scientist or ML engineer to regulate how the algorithm learns and modifies the model’s performance. One such hyper-parameter is the learning rate, denoted by α, which controls the pace at which an algorithm updates the values of parameter estimates. In this article, we will learn about the impact of learning rate on a model.

Effect of Learning Rate

Learning Rate :The learning rate is a hyperparameter in machine learning that controls the step size at which the weights of a neural network are updated during training. It specifies the amount by which the model’s parameters are altered in the direction opposite to the gradient of the loss function.

While training a machine learning models the learning rate is used to adjust the model’s parameters to a small positive value typically which is in the range between 0.0 to 1.0. The learning rate is crucial in determining the speed at which a model learns. A well-calibrated learning rate will enable the model to learn the function given the available resources, such as the number of layers and nodes per layer in a specific number of training epochs. The learning rate determines the weights of the neural network based on loss gradient, indicating how often the network changes the parameters it has learned. An efficient learning rate is one that is low enough for the network to converge at some point of time while also being high enough to train in a reasonable amount of time. Smaller learning rates necessitate more training epochs, while larger learning rates result in faster changes but can lead to suboptimal final weights.

To determine the weights of a neural network, stochastic gradient descent is used, which estimates the error gradient for the current model state using instances from the training dataset and then updates the model’s weights using backpropagation. We have to avoid using a learning rate which is too high or too low and instead set up the model to determine a decent enough set of weights to approximate the mapping issue as represented by the training dataset.

Example:

Imagine you are learning to play a video game that involves jumping over obstacles. If you always jump too early or too late you will keep failing and have to restart the game. But if you try to adjust your timing by a small amount each time we can eventually find the sweet spot where you can consistently get over the obstacles.

Similarily in machine learning, low learning rate will result in longer training times and increased costs. On the other hand high learning rate can cause the model to overshoot or fail to converge. Therefore, finding the right learning rate is crucial to achieving best results without wasting time and resources. 

Discovering the ideal learning rate for a particular problem can be a challenging task, but there are several well-established techniques available that can help. Adaptive learning rate techniques involve dynamically adjusting the learning rate instead of using a constant value.

Various Learning Rate Approaches

There are several different techniques for adjusting the learning rate in machine learning models:

1. Fixed Learning Rate: This is a widely-used optimization approach for estimating model parameters in machine learning. In this approach, a constant learning rate is chosen and used throughout the training process. Initially, each parameter is assumed or assigned random values. A cost function is generated using the initial values, and the parameter estimations are improved over time to minimize the cost function.
2. Learning Rate Schedules: Learning rate schedules adjust the learning rate based on predefined rules or functions. Learning rate schedules help to adapt the learning rate according to the progress of training and can improve convergence and performance. Some of the common learning rate scheduling methods are decay, exponential decay, and polynomial decay.
   • Step Decay: The learning rate is decreases by a certain factor at specific epochs or after a fixed number of iterations.
   • Exponential Decay: The learning rate is exponentially decreased over time.
   • Polynomial Decay: The learning rate is decreased polynomially over time.
3. Adaptive learning rate: Adaptive learning rates dynamically adjust the learning rate to achieve optimal results by keeping track of the model’s performance. The learning rate increases or decreases depending on the cost function’s gradient value, slowing down and speeding up at steeper and shallower regions of the cost function curve, respectively. 
   • AdaGrad: Adjusts the learning rate individually for each parameter based on their historical gradient information. It reduces the learning rate for frequently updated parameters.
   • RMSprop: A variation of AdaGrad that addresses its overly aggressive learning rate decay. It maintains a moving average of squared gradients to adapt the learning rate.
   • Adam: Combines the concepts of AdaGrad and RMSprop. It incorporates adaptive learning rates and momentum to achieve fast convergence.
4. Scheduled Drop Learning Rate: This method lowers the learning rate by a specified proportion at a specified frequency, as opposed to the decay technique where the learning rate decreases repetitively.
5. Cycling Learning Rate: The learning rate in this technique cyclically varies within a predefined range during training. The learning rate fluctuates in a triangular shape between the minimum and maximum rates, maintaining a constant frequency. One prominent strategy is the triangular learning rate policy, in which the learning rate is linearly increased and then decreased within a cycle. It aim to explore different learning rates during training to escape from poor local minima and accelerate convergence.
6. Decaying Learning Rate: In this approach, the learning rate decreases as the number of epochs/iterations increases.

Conclusion:

The learning rate is a crucial hyperparameter in machine learning that controls the pace at which an algorithm updates parameter estimates. A well-calibrated learning rate is necessary for a model to learn the function given the available resources. If the learning rate is too low, it results in longer training times, while a high learning rate can cause the model to overshoot or fail to converge. Various techniques, such as decaying, scheduled drop, and cycling, can be used to optimize the learning rate. Finding the right learning rate is essential to achieving accurate predictions without wasting time and resources.