Activation does it means activating your car with a click ( if it has that ,of course) , well the same concept but in terms of neurons , neuron as in human brain ? , again close enough, neuron but in Artificial Neural Network (ANN).

The activation function decides whether a neuron should be activated or not.

If you have seen an ANN, which I sincerely hope you do you have seen they are linear in nature, so to use non — linearity in them we use activation functions and generate output from input values fed into the network.

Activation functions can be divided into three types

**Linear Activation Function****Binary Step Activation Function****Non — linear Activation Functions**

**Linear Activation Function**

It is proportional to the output values, it just adds the weighted total to the output. It ranges from (-∞ to ∞).

**Mathematically**, the same can be written as

Implementation of the same in Keras is shown below,

### Binary Step Activation Function

It has a specific threshold value that determines whether a neuron should be activated or not.

**Mathematically**, this is the equation of the function

Implementation of the same is not present in Keras so a custom function is made using TensorFlow as follows

### Non — Linear Activation Functions

It allows ANN to adapt according to a variety of data and differentiate between the outputs. It allows the stacking of multiple layers since the output is a combination of input passed through multiple layers of the neural network.

Various non — linear activation functions are discussed below

#### Sigmoid Activation Function

This function accepts the input (number) and returns a number between 0 and 1. It is mainly used in binary classification as the output ranges between 0 and 1 e.g. you train a dog and cat classifier , regardless of how furry that dog is it classifies it as a dog not cat , there is no between , sigmoid is perfect for it.

**Mathematically**, the equation looks like this

Implementation of the same in Keras is shown below,

#### TanH Activation Function

This activation function maps the value into the range [ -1 , 1 ]. The output is zero centered , it helps in mapping the negative input values into strongly negative and zero values to absolute zero.

**Mathematically**, the equation looks like this

Implementation of the same in Keras is shown below

**ReLU ( Rectified Linear Unit)**

It is one of the most commonly used activation functions, it solved the problem of vanishing gradient as the maximum value of the function is one. The range of ReLU is [ 0 , ∞ ].

**Mathematically**, the equation looks like this

Implementation of the same in Keras is shown below,

#### Leaky ReLU

Upgraded version of ReLU like Covid variants .. sensitive topic …ok fine .. getting back to Leaky ReLU , it is upgraded as it solves the dying ReLU problem , as it has small positive slope in negative area.

**Mathematically**, the equation looks like this

Implementation in Keras is coming right below

#### SoftMax Activation Function

Its a combination of lets guess .. is it tanh , hmm not quite , ReLU ? no or its leaky counterpart .. mhh not quite …. ok lets reveal it .. it is a combination of many sigmoid. It determines relative probability.

In Multiclass classification , it is the most commonly used for the last layer of the classifier. It gives the probability of the current class with respect to others.

**Mathematically**, the equation looks like this

Implementation in Keras is given below

The whole notebook containing all codes used above

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