Activa is a Python package that calculates the activation functions of a set of input numerical values while outputting clear visualization of these values for further analysis.

Features of Activa

• Calculates activation functions (ReLU, Sigmoid, Tanh, Softplus, Gaussian)
• Provides visual displays of each function against it's derivatives
• Outputs a comparison table for each values across all activation functions available

Quick Start

First, install the library:

$ pip install Activa

Example 1 - Terminal

For the example, we will be using the default data in the package, data = [-1, 0, 1]

To import libraries

>> from Activa import ReLU, Sigmoid, Gaussian, Table

1. Implementing the ReLU activation function

>> relu = ReLU()
>> relu.relu_values
array([0, 0, 1])

2. Implementing the Sigmoid activation function

>> sig = Sigmoid()
>> sig.sigmoid_values
array([0.26894142, 0.5       , 0.73105858])

3. Implementing the Gaussian activation function

>> gs = Gaussian()
>> gs.gaussian_values
array([0.36787944, 1.        , 0.36787944])

4. Implementing the comparison table for all activation functions

>> tb = Table()
>> tb.show
   actual_values   sigmoid  relu      tanh  softplus  gaussian
0             -1  0.268941     0 -0.761594  0.313262  0.367879
1              0  0.500000     0  0.000000  0.693147  1.000000
2              1  0.731059     1  0.761594  1.313262  0.367879

Example 2 - Jupyter notebook

We start by importing the necessary libraries

Lets load our data for the example

ReLU

Next, we can now calculate the ReLU values for the data

Also, we can make visualizations for both the ReLU function and its derivatives

Sigmoid

For the Sigmoid activation function, we can now also calculate the Sigmoid values for the data

Also, we can make visualizations for both the Sigmoid function and its derivatives

Comparison Table

We can also view the comparison table for all of the available activation functions across their actual values.

Activa Updates

Kindly checkout the Package for more updates