Relu2

Relu2, also known as the Swish-1 activation function, is a variant of the Rectified Linear Unit (ReLU) activation function used in artificial neural networks. It was introduced as part of the Swish family of activation functions, which aim to improve the performance of neural networks by providing a smooth and non-monotonic curve. The Swish-1 function is defined as:

f(x) = x * sigmoid(βx)

where β is a learnable parameter that controls the shape of the function. When β is set to 1, the function simplifies to:

f(x) = x * sigmoid(x)

This variant is often referred to as Relu2 due to its similarity to the standard ReLU function, which can be seen as a special case of Swish-1 when β approaches infinity. The Swish-1 function has been shown to outperform ReLU in various tasks, including image classification and natural language processing, due to its ability to model more complex relationships in the data. However, it also introduces additional computational overhead due to the need to compute the sigmoid function and the learnable parameter. As with other activation functions, the choice of using Swish-1 or ReLU in a neural network depends on the specific task and dataset, and may require empirical evaluation to determine the best performance.