The Scaled Exponential Linear Unit (SELU) is an activation function used in neural networks, particularly in deep learning models. It was introduced to help address issues of vanishing and exploding gradients that can occur during training. The SELU function is defined mathematically as follows:
For an input x, the output f(x) is:
f(x) = λ * (x if x > 0 else α * (exp(x) – 1))
where:
- λ (lambda) is a scaling factor, typically set to approximately 1.0507.
- α (alpha) is a parameter, usually around 1.6733.
SELU has a unique property of self-normalization, meaning that when used appropriately in a network, it helps maintain the mean and variance of the activations close to zero and one, respectively. This property facilitates faster convergence during training and can improve overall model performance.
To effectively use SELU, it is recommended to initialize the weights of the neural network using the LeCun normal initialization method and to avoid dropout layers, as SELU is designed to work best in fully connected architectures without such regularization techniques.
Overall, the SELU activation function is particularly beneficial for deep networks, as it helps stabilize the training process and can lead to better generalization on unseen data.