What is Polynomial Rate Decay?
Polynomial Rate Decay is a technique used in machine learning to adjust the learning rate of neural networks in a polynomial manner. It is a popular technique used to improve the performance of deep learning models.
When training a neural network model, it is essential to adjust its learning rate. The learning rate determines how fast or slow a model learns from the data. If the learning rate is too high, the model may not converge and overshoot the optimal solution. On the other hand, if the learning rate is too low, the model may take a long time to converge and may get stuck in the local optima.
Polynomial Rate Decay helps finding a balance. It gradually decreases the learning rate over time, which helps the model to converge faster and get closer to the optimal solution without overshooting.
How does Polynomial Rate Decay work?
Polynomial Rate Decay works by decreasing the learning rate according to a polynomial function of the current training step or epoch. The polynomial function can be a simple linear function or a higher degree polynomial function.
The most popular polynomial function used for Polynomial Rate Decay is the Polynomial Decay Function. The Polynomial Decay Function is represented by the following formula.
learning_rate = (initial_learning_rate - end_learning_rate) * (1 - (step/total_steps))^power + end_learning_rate
In this formula, initial_learning_rate is the initial learning rate set at the beginning of training. end_learning_rate is the desired minimum learning rate at the end of training. total_steps is the total number of steps (or epochs) for training. step is the current training step or epoch. Finally, power is the degree of the polynomial function, which determines how fast or slow the learning rate decreases over time.
As the training step (or epoch) increases, the value of (1 - (step/total_steps))^power decreases, thereby decreasing the learning rate gradually over time.
Advantages of Polynomial Rate Decay
Polynomial Rate Decay offers several advantages over other learning rate scheduling techniques. Some of the benefits are:
Better Convergence
With Polynomial Rate Decay, the model's learning rate decreases over time, which helps it converge better and faster. This leads to better performance in terms of accuracy and the ability to generalize well to new data.
Stable Learning
Polynomial Rate Decay helps maintain stable learning during training. The gradual decrease in learning rate prevents the model from making abrupt changes, which can cause instability and degrade the model's performance.
Less Manual Tuning
Polynomial Rate Decay requires minimal manual tuning of the hyperparameters. The initial and end learning rates, total steps and power are the only parameters that need to be set. This makes the technique easy to use and less prone to error.
When to use Polynomial Rate Decay?
Polynomial Rate Decay is suitable for a wide range of deep learning applications, especially in cases where a high degree of accuracy is desired. It is particularly effective in applications that require long training times and large datasets, where a slow and stable learning rate is crucial to avoid overfitting.
Polynomial Rate Decay is also useful in deep reinforcement learning applications, where the network is trained using an iterative process and must adapt to changing environments over a long period.
Polynomial Rate Decay is an effective technique for improving the learning rate of deep neural networks. It helps to decrease the learning rate gradually over time, leading to stable learning and better convergence. Its benefits include faster convergence, better generalization, stability, and reduced manual tuning. Polynomial Rate Decay is suitable for a wide range of deep learning applications, especially those that require high accuracy and long training times.
