Activation functions in neural networks

An artificial neural network's activation function, which essentially determines whether the neuron should be active or not, is a crucial component. The activation function in artificial neural networks determines the output of a node given an input or group of inputs.

The difference between linear and non-linear activation functions must also be made. Non-linear activation functions use the structure of the neural network to produce greater variance where linear activation functions maintain a constant. To aid in the development of practical models, neural networks frequently employ functions like sigmoid and relu activation function.

The purpose of activation functions in neural networks

It is utilized to determine the neural network's output, such as yes or no. The obtained values are mapped between 0 and 1 or -1 and 1, etc (depending upon the function).

Selecting an activation function

Despite the issues with ReLU that we have discussed, many people have had success using it. It's better to start out by trying out easier things. If your idea involves starting from scratch with coding, ReLUs among all the other strong competitors offer the lowest computational budget and are also incredibly straightforward to implement.

My next option is either a Leaky ReLU or an ELU if ReLU doesn't produce findings that look promising. I've discovered that activations that can result in zero-centered activations are significantly better than those that can't. ELU may have been a very simple alternative, but ELU-based networks take some time to retrain and to make conclusions.

You can evaluated the efficiency of the above activations to that of PReLU and Randomized ReLUs if you have a lot of cognitive effort and resources. If your function indicates overfitting, randomized ReLU may be advantageous.

Conclusion

In this blog, we learned activation function , why we used activation function and how to choose activation function.