NumPyANN: Building Neural Networks using NumPy

NumPyANN is a Python project for building artificial neural networks using NumPy.

NumPyANN is part of PyGAD which is an open-source Python 3 library for implementing the genetic algorithm and optimizing machine learning algorithms. Both regression and classification neural networks are supported starting from PyGAD 2.7.0.

Check documentation of the NeuralGenetic project in the PyGAD's documentation: https://pygad.readthedocs.io/en/latest/README_pygad_nn_ReadTheDocs.html

The library is under active development and more features are added regularly. If you want a feature to be supported, please check the Contact Us section to send a request.

Before using NumPyANN, install PyGAD.

Donation

You can donate via Open Collective: opencollective.com/pygad.

To donate using PayPal, use either this link: paypal.me/ahmedfgad or the e-mail address [email protected].

Tutorial Project

IMPORTANT If you are coming for the code of the tutorial titled Artificial Neural Network Implementation using NumPy and Classification of the Fruits360 Image Dataset, then it has been moved to the TutorialProject directory on 10 May 2020.

Installation

To install PyGAD, simply use pip to download and install the library from PyPI (Python Package Index). The library lives a PyPI at this page https://pypi.org/project/pygad.

For Windows, issue the following command:

pip3 install pygad

PyGAD is developed in Python 3.7.3 and depends on NumPy for creating and manipulating arrays and Matplotlib for creating figures. The exact NumPy version used in developing PyGAD is 1.16.4. For Matplotlib, the version is 3.1.0.

To get started with PyGAD, please read the documentation at Read The Docs https://pygad.readthedocs.io.

PyGAD Source Code

The source code of the PyGAD' modules is found in the following GitHub projects:

The documentation of PyGAD is available at Read The Docs https://pygad.readthedocs.io.

PyGAD Documentation

The documentation of the PyGAD library is available at Read The Docs at this link: https://pygad.readthedocs.io. It discusses the modules supported by PyGAD, all its classes, methods, attribute, and functions. For each module, a number of examples are given.

If there is an issue using PyGAD, feel free to post at issue in this GitHub repository https://github.com/ahmedfgad/GeneticAlgorithmPython or by sending an e-mail to [email protected].

If you built a project that uses PyGAD, then please drop an e-mail to [email protected] with the following information so that your project is included in the documentation.

Project title
Brief description
Preferably, a link that directs the readers to your project

Please check the Contact Us section for more contact details.

Life Cycle of PyGAD

The next figure lists the different stages in the lifecycle of an instance of the pygad.GA class. Note that PyGAD stops when either all generations are completed or when the function passed to the on_generation parameter returns the string stop.

The next code implements all the callback functions to trace the execution of the genetic algorithm. Each callback function prints its name.

import pygad
import numpy

function_inputs = [4,-2,3.5,5,-11,-4.7]
desired_output = 44

def fitness_func(solution, solution_idx):
    output = numpy.sum(solution*function_inputs)
    fitness = 1.0 / (numpy.abs(output - desired_output) + 0.000001)
    return fitness

fitness_function = fitness_func

def on_start(ga_instance):
    print("on_start()")

def on_fitness(ga_instance, population_fitness):
    print("on_fitness()")

def on_parents(ga_instance, selected_parents):
    print("on_parents()")

def on_crossover(ga_instance, offspring_crossover):
    print("on_crossover()")

def on_mutation(ga_instance, offspring_mutation):
    print("on_mutation()")

def on_generation(ga_instance):
    print("on_generation()")

def on_stop(ga_instance, last_population_fitness):
    print("on_stop()")

ga_instance = pygad.GA(num_generations=3,
                       num_parents_mating=5,
                       fitness_func=fitness_function,
                       sol_per_pop=10,
                       num_genes=len(function_inputs),
                       on_start=on_start,
                       on_fitness=on_fitness,
                       on_parents=on_parents,
                       on_crossover=on_crossover,
                       on_mutation=on_mutation,
                       on_generation=on_generation,
                       on_stop=on_stop)

ga_instance.run()

Based on the used 3 generations as assigned to the num_generations argument, here is the output.

on_start()

on_fitness()
on_parents()
on_crossover()
on_mutation()
on_generation()

on_fitness()
on_parents()
on_crossover()
on_mutation()
on_generation()

on_fitness()
on_parents()
on_crossover()
on_mutation()
on_generation()

on_stop()

Example

Check the PyGAD's documentation for information about the implementation of this example.

import numpy
import pygad.nn

# Preparing the NumPy array of the inputs.
data_inputs = numpy.array([[1, 1],
                           [1, 0],
                           [0, 1],
                           [0, 0]])

# Preparing the NumPy array of the outputs.
data_outputs = numpy.array([0, 
                            1, 
                            1, 
                            0])

# The number of inputs (i.e. feature vector length) per sample
num_inputs = data_inputs.shape[1]
# Number of outputs per sample
num_outputs = 2

HL1_neurons = 2

# Building the network architecture.
input_layer = pygad.nn.InputLayer(num_inputs)
hidden_layer1 = pygad.nn.DenseLayer(num_neurons=HL1_neurons, previous_layer=input_layer, activation_function="relu")
output_layer = pygad.nn.DenseLayer(num_neurons=num_outputs, previous_layer=hidden_layer1, activation_function="softmax")

# Training the network.
pygad.nn.train(num_epochs=10,
               last_layer=output_layer,
               data_inputs=data_inputs,
               data_outputs=data_outputs,
               learning_rate=0.01)

# Using the trained network for predictions.
predictions = pygad.nn.predict(last_layer=output_layer, data_inputs=data_inputs)

# Calculating some statistics
num_wrong = numpy.where(predictions != data_outputs)[0]
num_correct = data_outputs.size - num_wrong.size
accuracy = 100 * (num_correct/data_outputs.size)
print("Number of correct classifications : {num_correct}.".format(num_correct=num_correct))
print("Number of wrong classifications : {num_wrong}.".format(num_wrong=num_wrong.size))
print("Classification accuracy : {accuracy}.".format(accuracy=accuracy))

For More Information

There are different resources that can be used to get started with the genetic algorithm and building it in Python.

Tutorial: Implementing Genetic Algorithm in Python

To start with coding the genetic algorithm, you can check the tutorial titled Genetic Algorithm Implementation in Python available at these links:

This tutorial is prepared based on a previous version of the project but it still a good resource to start with coding the genetic algorithm.

Tutorial: Introduction to Genetic Algorithm

Get started with the genetic algorithm by reading the tutorial titled Introduction to Optimization with Genetic Algorithm which is available at these links:

Tutorial: Build Neural Networks in Python

Read about building neural networks in Python through the tutorial titled Artificial Neural Network Implementation using NumPy and Classification of the Fruits360 Image Dataset available at these links:

Tutorial: Optimize Neural Networks with Genetic Algorithm

Read about training neural networks using the genetic algorithm through the tutorial titled Artificial Neural Networks Optimization using Genetic Algorithm with Python available at these links:

Tutorial: Building CNN in Python

To start with coding the genetic algorithm, you can check the tutorial titled Building Convolutional Neural Network using NumPy from Scratch available at these links:

This tutorial) is prepared based on a previous version of the project but it still a good resource to start with coding CNNs.

Tutorial: Derivation of CNN from FCNN

Get started with the genetic algorithm by reading the tutorial titled Derivation of Convolutional Neural Network from Fully Connected Network Step-By-Step which is available at these links:

Book: Practical Computer Vision Applications Using Deep Learning with CNNs

You can also check my book cited as Ahmed Fawzy Gad 'Practical Computer Vision Applications Using Deep Learning with CNNs'. Dec. 2018, Apress, 978-1-4842-4167-7 which discusses neural networks, convolutional neural networks, deep learning, genetic algorithm, and more.

Find the book at these links:

Citing PyGAD - Bibtex Formatted Citation

If you used PyGAD, please consider adding a citation to the following paper about PyGAD:

@misc{gad2021pygad,
      title={PyGAD: An Intuitive Genetic Algorithm Python Library}, 
      author={Ahmed Fawzy Gad},
      year={2021},
      eprint={2106.06158},
      archivePrefix={arXiv},
      primaryClass={cs.NE}
}

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