Activation Function in Neural Network

# Activation Function in Neural Network

Activation Function

It’s just a thing function that you use to get the output of node. It is also known as Transfer Function. It could be basically divided into 2 types:

Linear Activation Function
Non-linear Activation Function
1. The main terminologies needed to understand for nonlinear functions are:
2. Derivative or Differential: Change in y-axis w.r.t. change in x-axis.It is also known as slope.
  
  Monotonic function: A function which is either entirely non-increasing or non-decreasing.

# Why we use activation functions with Neural Networks?

It is used to determine the output of neural network like yes or no. It maps the resulting values in between 0 to 1 or -1 to 1 etc. (depending upon the function).

# Common Activation Functions

# 1. Sigmoid

The Sigmoid Function curve looks like a S-shape.

The main reason why we use sigmoid function is because it exists between (0 to 1). Therefore, it is especially used for models where we have to predict the probability as an output. Since probability of anything exists only between the range of 0 and 1, sigmoid is the right choice.

The function is differentiable. That means, we can find the slope of the sigmoid curve at any two points.

The function is monotonic but function’s derivative is not.

The softmax function is a more generalized logistic activation function which is used for multiclass classification. The logistic sigmoid function can cause a neural network to get stuck at the training time.

# 2.Tanh or hyperbolic tangent Activation Function

tanh is also like logistic sigmoid but better. The range of the tanh function is from (-1 to 1). tanh is also sigmoidal (s - shaped).

The advantage is that the negative inputs will be mapped strongly negative and the zero inputs will be mapped near zero in the tanh graph.

The function is differentiable.

The function is monotonic while its derivative is not monotonic.

The tanh function is mainly used classification between two classes.

Both tanh and logistic sigmoid activation functions are used in feed-forward nets.

# 3. ReLU (Rectified Linear Unit) Activation Function

The ReLU is the most used activation function in the world right now. Since, it is used in almost all the convolutional neural networks or deep learning.

The function and its derivative both are monotonic.

But the issue is that all the negative values become zero immediately which decreases the ability of the model to fit or train from the data properly. That means any negative input given to the ReLU activation function turns the value into zero immediately in the graph, which in turns affects the resulting graph by not mapping the negative values appropriately.