The Swish activation function is a relatively new activation function that has gained popularity in deep learning neural networks. It was introduced by researchers at Google in 2017.

The Swish activation function is defined as follows:

`f(x) = x * sigmoid(beta * x)`

where `sigmoid`

is the sigmoid function, `x`

is the input to the activation function, and `beta`

is a learnable parameter.

The behavior of the Swish activation function is similar to that of the ReLU activation function. However, the S**wish activation function has a smooth curve, whereas the ReLU activation function has a sharp kink at the origin.**

The Swish activation function has some advantages over the ReLU activation function. In particular, it is continuously differentiable and has a non-monotonic behavior, which can improve the learning process in some cases.

Recent research has suggested that the Swish activation function can outperform the ReLU activation function in some deep learning neural networks. However, it is important to note that the performance of the Swish activation function can depend on the specific task and dataset, and further research is needed to fully understand its effectiveness.