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https://github.com/ahmedfgad/numpycnn
Building Convolutional Neural Networks From Scratch using NumPy
https://github.com/ahmedfgad/numpycnn
cnn computer-vision conv-layer convnet convolution convolutional-neural-networks data-science filter numpy pygad python relu relu-layer
Last synced: about 3 hours ago
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Building Convolutional Neural Networks From Scratch using NumPy
- Host: GitHub
- URL: https://github.com/ahmedfgad/numpycnn
- Owner: ahmedfgad
- Created: 2018-04-20T12:36:20.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2023-06-19T20:58:06.000Z (over 1 year ago)
- Last Synced: 2024-11-15T02:56:57.215Z (2 days ago)
- Topics: cnn, computer-vision, conv-layer, convnet, convolution, convolutional-neural-networks, data-science, filter, numpy, pygad, python, relu, relu-layer
- Language: Python
- Homepage: https://pygad.readthedocs.io
- Size: 2.55 MB
- Stars: 570
- Watchers: 26
- Forks: 188
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- Funding: .github/FUNDING.yml
Awesome Lists containing this project
README
# NumPyCNN: Implementing Convolutional Neural Networks From Scratch
NumPyCNN is a Python implementation for convolutional neural networks (CNNs) from scratch using NumPy.
**IMPORTANT** *If you are coming for the code of the tutorial titled [Building Convolutional Neural Network using NumPy from Scratch]( [https://www.linkedin.com/pulse/building-convolutional-neural-network-using-numpy-from-ahmed-gad](https://www.linkedin.com/pulse/building-convolutional-neural-network-using-numpy-from-ahmed-gad/)), then it has been moved to the [TutorialProject](https://github.com/ahmedfgad/NumPyCNN/tree/master/TutorialProject) directory on 20 May 2020.*
The project has a single module named `cnn.py` which implements all classes and functions needed to build the CNN.
It is very important to note that the project only implements the **forward pass** of training CNNs and there is **no learning algorithm used**. Just the learning rate is used to make some changes to the weights after each epoch which is better than leaving the weights unchanged.
The project can be used for classification problems where only 1 class per sample is allowed.
The project will be extended to **train CNN using the genetic algorithm** with the help of a library named [PyGAD](https://pypi.org/project/pygad). Check the library's documentation at [Read The Docs](https://pygad.readthedocs.io/): https://pygad.readthedocs.io
# Donation
- [Credit/Debit Card](https://donate.stripe.com/eVa5kO866elKgM0144): https://donate.stripe.com/eVa5kO866elKgM0144
- [Open Collective](https://opencollective.com/pygad): [opencollective.com/pygad](https://opencollective.com/pygad)
- PayPal: Use either this link: [paypal.me/ahmedfgad](https://paypal.me/ahmedfgad) or the e-mail address [email protected]
- Interac e-Transfer: Use e-mail address [email protected]
# Installation
To install [PyGAD](https://pypi.org/project/pygad), simply use pip to download and install the library from [PyPI](https://pypi.org/project/pygad) (Python Package Index). The library is at PyPI at this page https://pypi.org/project/pygad.
Install PyGAD with the following command:
```python
pip install pygad
```
To get started with PyGAD, please read the documentation at [Read The Docs](https://pygad.readthedocs.io/) https://pygad.readthedocs.io.
# PyGAD Source Code
The source code of the PyGAD' modules is found in the following GitHub projects:
- [pygad](https://github.com/ahmedfgad/GeneticAlgorithmPython): (https://github.com/ahmedfgad/GeneticAlgorithmPython)
- [pygad.nn](https://github.com/ahmedfgad/NumPyANN): https://github.com/ahmedfgad/NumPyANN
- [pygad.gann](https://github.com/ahmedfgad/NeuralGenetic): https://github.com/ahmedfgad/NeuralGenetic
- [pygad.cnn](https://github.com/ahmedfgad/NumPyCNN): https://github.com/ahmedfgad/NumPyCNN
- [pygad.gacnn](https://github.com/ahmedfgad/CNNGenetic): https://github.com/ahmedfgad/CNNGenetic
- [pygad.kerasga](https://github.com/ahmedfgad/KerasGA): https://github.com/ahmedfgad/KerasGA
- [pygad.torchga](https://github.com/ahmedfgad/TorchGA): https://github.com/ahmedfgad/TorchGA
The documentation of PyGAD is available at [Read The Docs](https://pygad.readthedocs.io/) https://pygad.readthedocs.io.
# PyGAD Documentation
The documentation of the PyGAD library is available at [Read The Docs](https://pygad.readthedocs.io) 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) 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.
```python
import pygad
import numpy
function_inputs = [4,-2,3.5,5,-11,-4.7]
desired_output = 44
def fitness_func(ga_instance, 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](https://pygad.readthedocs.io/en/latest/cnn.html) for information about the implementation of this example.
```python
import numpy
import pygad.cnn
train_inputs = numpy.load("dataset_inputs.npy")
train_outputs = numpy.load("dataset_outputs.npy")
sample_shape = train_inputs.shape[1:]
num_classes = 4
input_layer = pygad.cnn.Input2D(input_shape=sample_shape)
conv_layer1 = pygad.cnn.Conv2D(num_filters=2,
kernel_size=3,
previous_layer=input_layer,
activation_function=None)
relu_layer1 = pygad.cnn.Sigmoid(previous_layer=conv_layer1)
average_pooling_layer = pygad.cnn.AveragePooling2D(pool_size=2,
previous_layer=relu_layer1,
stride=2)
conv_layer2 = pygad.cnn.Conv2D(num_filters=3,
kernel_size=3,
previous_layer=average_pooling_layer,
activation_function=None)
relu_layer2 = pygad.cnn.ReLU(previous_layer=conv_layer2)
max_pooling_layer = pygad.cnn.MaxPooling2D(pool_size=2,
previous_layer=relu_layer2,
stride=2)
conv_layer3 = pygad.cnn.Conv2D(num_filters=1,
kernel_size=3,
previous_layer=max_pooling_layer,
activation_function=None)
relu_layer3 = pygad.cnn.ReLU(previous_layer=conv_layer3)
pooling_layer = pygad.cnn.AveragePooling2D(pool_size=2,
previous_layer=relu_layer3,
stride=2)
flatten_layer = pygad.cnn.Flatten(previous_layer=pooling_layer)
dense_layer1 = pygad.cnn.Dense(num_neurons=100,
previous_layer=flatten_layer,
activation_function="relu")
dense_layer2 = pygad.cnn.Dense(num_neurons=num_classes,
previous_layer=dense_layer1,
activation_function="softmax")
model = pygad.cnn.Model(last_layer=dense_layer2,
epochs=1,
learning_rate=0.01)
model.summary()
model.train(train_inputs=train_inputs,
train_outputs=train_outputs)
predictions = model.predict(data_inputs=train_inputs)
print(predictions)
num_wrong = numpy.where(predictions != train_outputs)[0]
num_correct = train_outputs.size - num_wrong.size
accuracy = 100 * (num_correct/train_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 building CNN and its Python implementation.
## Tutorial: Implementing Genetic Algorithm in Python
To start with coding the genetic algorithm, you can check the tutorial titled [**Genetic Algorithm Implementation in Python**](https://www.linkedin.com/pulse/genetic-algorithm-implementation-python-ahmed-gad) available at these links:
- [LinkedIn](https://www.linkedin.com/pulse/genetic-algorithm-implementation-python-ahmed-gad)
- [Towards Data Science](https://towardsdatascience.com/genetic-algorithm-implementation-in-python-5ab67bb124a6)
- [KDnuggets](https://www.kdnuggets.com/2018/07/genetic-algorithm-implementation-python.html)
[This tutorial](https://www.linkedin.com/pulse/genetic-algorithm-implementation-python-ahmed-gad) is prepared based on a previous version of the project but it still a good resource to start with coding the genetic algorithm.
[](https://www.linkedin.com/pulse/genetic-algorithm-implementation-python-ahmed-gad)
## Tutorial: Introduction to Genetic Algorithm
Get started with the genetic algorithm by reading the tutorial titled [**Introduction to Optimization with Genetic Algorithm**](https://www.linkedin.com/pulse/introduction-optimization-genetic-algorithm-ahmed-gad) which is available at these links:
* [LinkedIn](https://www.linkedin.com/pulse/introduction-optimization-genetic-algorithm-ahmed-gad)
* [Towards Data Science](https://www.kdnuggets.com/2018/03/introduction-optimization-with-genetic-algorithm.html)
* [KDnuggets](https://towardsdatascience.com/introduction-to-optimization-with-genetic-algorithm-2f5001d9964b)
[](https://www.linkedin.com/pulse/introduction-optimization-genetic-algorithm-ahmed-gad)
## 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**](https://www.linkedin.com/pulse/artificial-neural-network-implementation-using-numpy-fruits360-gad) available at these links:
* [LinkedIn](https://www.linkedin.com/pulse/artificial-neural-network-implementation-using-numpy-fruits360-gad)
* [Towards Data Science](https://towardsdatascience.com/artificial-neural-network-implementation-using-numpy-and-classification-of-the-fruits360-image-3c56affa4491)
* [KDnuggets](https://www.kdnuggets.com/2019/02/artificial-neural-network-implementation-using-numpy-and-image-classification.html)
[](https://www.linkedin.com/pulse/artificial-neural-network-implementation-using-numpy-fruits360-gad)
## 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**](https://www.linkedin.com/pulse/artificial-neural-networks-optimization-using-genetic-ahmed-gad) available at these links:
- [LinkedIn](https://www.linkedin.com/pulse/artificial-neural-networks-optimization-using-genetic-ahmed-gad)
- [Towards Data Science](https://towardsdatascience.com/artificial-neural-networks-optimization-using-genetic-algorithm-with-python-1fe8ed17733e)
- [KDnuggets](https://www.kdnuggets.com/2019/03/artificial-neural-networks-optimization-genetic-algorithm-python.html)
[](https://www.linkedin.com/pulse/artificial-neural-networks-optimization-using-genetic-ahmed-gad)
## 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**](https://www.linkedin.com/pulse/building-convolutional-neural-network-using-numpy-from-ahmed-gad) available at these links:
- [LinkedIn](https://www.linkedin.com/pulse/building-convolutional-neural-network-using-numpy-from-ahmed-gad)
- [Towards Data Science](https://towardsdatascience.com/building-convolutional-neural-network-using-numpy-from-scratch-b30aac50e50a)
- [KDnuggets](https://www.kdnuggets.com/2018/04/building-convolutional-neural-network-numpy-scratch.html)
- [Chinese Translation](http://m.aliyun.com/yunqi/articles/585741)
[This tutorial](https://www.linkedin.com/pulse/building-convolutional-neural-network-using-numpy-from-ahmed-gad)) is prepared based on a previous version of the project but it still a good resource to start with coding CNNs.
[](https://www.linkedin.com/pulse/building-convolutional-neural-network-using-numpy-from-ahmed-gad)
## 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**](https://www.linkedin.com/pulse/derivation-convolutional-neural-network-from-fully-connected-gad) which is available at these links:
* [LinkedIn](https://www.linkedin.com/pulse/derivation-convolutional-neural-network-from-fully-connected-gad)
* [Towards Data Science](https://towardsdatascience.com/derivation-of-convolutional-neural-network-from-fully-connected-network-step-by-step-b42ebafa5275)
* [KDnuggets](https://www.kdnuggets.com/2018/04/derivation-convolutional-neural-network-fully-connected-step-by-step.html)
[](https://www.linkedin.com/pulse/derivation-convolutional-neural-network-from-fully-connected-gad)
## 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**](https://www.amazon.com/Practical-Computer-Vision-Applications-Learning/dp/1484241665) which discusses neural networks, convolutional neural networks, deep learning, genetic algorithm, and more.
Find the book at these links:
- [Amazon](https://www.amazon.com/Practical-Computer-Vision-Applications-Learning/dp/1484241665)
- [Springer](https://link.springer.com/book/10.1007/978-1-4842-4167-7)
- [Apress](https://www.apress.com/gp/book/9781484241660)
- [O'Reilly](https://www.oreilly.com/library/view/practical-computer-vision/9781484241677)
- [Google Books](https://books.google.com.eg/books?id=xLd9DwAAQBAJ)
# 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}
}
```
# Contact Us
* E-mail: [email protected]
* [LinkedIn](https://www.linkedin.com/in/ahmedfgad)
* [Amazon Author Page](https://amazon.com/author/ahmedgad)
* [Heartbeat](https://heartbeat.fritz.ai/@ahmedfgad)
* [Paperspace](https://blog.paperspace.com/author/ahmed)
* [KDnuggets](https://kdnuggets.com/author/ahmed-gad)
* [TowardsDataScience](https://towardsdatascience.com/@ahmedfgad)
* [GitHub](https://github.com/ahmedfgad)