SELU Overview: The Self-Normalizing Activation Function
If you've ever heard of neural networks, you might have come across the term "activation function". Activation functions are mathematical formulas that decide on whether a neuron in a neural network should fire or not given the inputs it receives. They are a crucial part of modern machine learning algorithms that allow artificial intelligence to learn from data.
One of the newest activation functions that have been developed is the Scaled Exponential Linear Unit or SELU. It was introduced by Klambauer et al. in 2017 and has since become a popular activation function due to its self-normalizing properties. In the following paragraphs, we'll dive deeper into what SELU is and how it works.
What is SELU?
SELU is a piece-wise function that maps inputs to outputs. It stands for Scaled Exponential Linear Unit, which describes its mathematical formula. It is similar to a ReLU (Rectified Linear Unit) function but with a few key differences that make it self-normalizing.
The SELU activation function is defined by Klambauer et al. as follows:
"The SELU activation function is given by"
$$f\left(x\right) = \lambda{x} \text{ if } x \geq{0}$$
$$f\left(x\right) = \lambda{\alpha\left(\exp\left(x\right) -1 \right)} \text{ if } x < 0 $$
In the above equations, $\lambda$ is a scaling constant, and $\alpha$ is the ELU (Exponential Linear Unit) function constant, which is approximately 1.6733. The authors have shown that if the weights in each layer of the neural network are initialized according to a specific rule, and if each layer applies the SELU activation function, then the output from each layer will have zero mean and unit variance. This property ensures that the signal passing through the neural network does not explode nor vanish, which is often an issue in deep neural networks.
Why Use SELU?
SELU is a valuable activation function due to its self-normalizing properties, which allow for faster and more stable convergence of deep neural networks. By ensuring that signals through the network have zero mean and unit variance, we can hope for better performance in our machine learning models. This is important because deep neural networks are notorious for being difficult to optimize, and SELU represents an innovation in this area.
The benefits of SELU do not only come from its self-normalizing properties. In fact, SELU has been found to outperform other activation functions in certain tasks, such as image recognition and natural language processing. These results suggest that SELU could be a valuable tool in a wide range of applications.
How to Use SELU?
Using SELU is quite straightforward; all you need to do is implement the above formula in your neural network architecture. You must also initialize the weights of your neural network according to a specific algorithm, which the authors of SELU recommend. The initialization scheme is essential in ensuring that the self-normalizing properties of SELU function correctly.
One thing to note is that SELU does not always outperform other activation functions. In some cases, ReLU or other activation functions might be more appropriate. It is best to experiment with different activation functions and architectures to see which one works best for your particular problem.
The Scaled Exponential Linear Unit or SELU is a recent innovation in the field of deep learning that has shown promising results. It is an activation function that ensures signals through the neural network have zero mean and unit variance, which helps to improve performance and reduce computational resources. SELU has been found to outperform other activation functions in certain tasks, making it a valuable tool to experiment with in deep learning applications.
Remember, however, that not all problems require SELU, and experimenting with different activation functions and neural network architectures is essential to finding the best solution.
