Educational python for Neural Networks, written in pure Python/NumPy.

Related tags

Machine Learningpythoneducationdeep-learning
Overview

EpyNN

EpyNN is written in pure Python/NumPy.

If you use EpyNN in academia, please cite:

Malard F., Danner L., Rouzies E., Meyer J. G., Lescop E., Olivier-Van Stichelen S. EpyNN: Educational python for Neural Networks, 2021, Submitted.

Documentation

Please visit https://epynn.net/ for extensive documentation.

Purpose

EpyNN is intended for teachers, students, scientists, or more generally anyone with minimal skills in Python programming who wish to understand and build from basic implementations of Neural Network architectures.

Although EpyNN can be used for production, it is meant to be a library of homogeneous architecture templates and practical examples which is expected to save an important amount of time for people who wish to learn, teach or develop from scratch.

Content

EpyNN features scalable, minimalistic and homogeneous implementations of major Neural Network architectures in pure Python/Numpy including:

Model and function rules and definition:

While not enhancing, extending or replacing EpyNN's documentation, series of live examples in Python and Jupyter notebook formats are offered online and within the archive, including:

Reliability

EpyNN has been cross-validated against TensorFlow/Keras API and provides identical results for identical configurations in the limit of float64 precision.

Please see Is EpyNN reliable? for details and executable codes.

Recommended install

  • Linux/MacOS
# Use bash shell
bash

# Clone git repository
git clone https://github.com/Synthaze/EpyNN

# Alternatively, not recommended
# pip3 install EpyNN
# epynn

# Change directory to EpyNN
cd EpyNN

# Install EpyNN dependencies
pip3 install -r requirements.txt

# Export EpyNN path in $PYTHONPATH for current session
export PYTHONPATH=$PYTHONPATH:$PWD

Linux: Permanent export of EpyNN directory path in $PYTHONPATH.

> ~/.bashrc # Source .bashrc to refresh $PYTHONPATH source ~/.bashrc "> 
# Append export instruction to the end of .bashrc file
echo "export PYTHONPATH=$PYTHONPATH:$PWD" >> ~/.bashrc

# Source .bashrc to refresh $PYTHONPATH
source ~/.bashrc

MacOS: Permanent export of EpyNN directory path in $PYTHONPATH.

> ~/.bash_profile # Source .bash_profile to refresh $PYTHONPATH source ~/.bash_profile "> 
# Append export instruction to the end of .bash_profile file
echo "export PYTHONPATH=$PYTHONPATH:$PWD" >> ~/.bash_profile

# Source .bash_profile to refresh $PYTHONPATH
source ~/.bash_profile
  • Windows
# Clone git repository
git clone https://github.com/Synthaze/EpyNN

# Alternatively, not recommended
# pip3 install EpyNN
# epynn

# Change directory to EpyNN
chdir EpyNN

# Install EpyNN dependencies
pip3 install -r requirements.txt

# Show full path of EpyNN directory
echo %cd%

Copy the full path of EpyNN directory, then go to: Control Panel > System > Advanced > Environment variable

If you already have PYTHONPATH in the User variables section, select it and click Edit, otherwise click New to add it.

Paste the full path of EpyNN directory in the input field, keep in mind that paths in PYTHONPATH should be comma-separated.

ANSI coloring schemes do work on native Windows10 and later. For prior Windows versions, users should configure their environment to work with ANSI coloring schemes for optimal experience.

Current release

1.0 - Initial release

  • nnlibs contains API sources.
  • nnlive contains live examples in Python and Jupyter notebook formats.
  • https://epynn.net/ contains extensive documentation.

See CHANGELOG.md for past releases.

Project tree

nnlibs

nnlive

You might also like...
A concept I came up which ditches the idea of
A concept I came up which ditches the idea of "layers" in a neural network.

Dynet A concept I came up which ditches the idea of "layers" in a neural network. Install Copy Dynet.py to your project. Run the example Install matpl

Anik Patel 4 Dec 5, 2021
Python library which makes it possible to dynamically mask/anonymize data using JSON string or python dict rules in a PySpark environment.

pyspark-anonymizer Python library which makes it possible to dynamically mask/anonymize data using JSON string or python dict rules in a PySpark envir

null 6 Jun 30, 2022
learn python in 100 days, a simple step could be follow from beginner to master of every aspect of python programming and project also include side project which you can use as demo project for your personal portfolio
learn python in 100 days, a simple step could be follow from beginner to master of every aspect of python programming and project also include side project which you can use as demo project for your personal portfolio

learn python in 100 days, a simple step could be follow from beginner to master of every aspect of python programming and project also include side project which you can use as demo project for your personal portfolio

BDFD 6 Nov 5, 2022
High performance, easy-to-use, and scalable machine learning (ML) package, including linear model (LR), factorization machines (FM), and field-aware factorization machines (FFM) for Python and CLI interface.
High performance, easy-to-use, and scalable machine learning (ML) package, including linear model (LR), factorization machines (FM), and field-aware factorization machines (FFM) for Python and CLI interface.

What is xLearn? xLearn is a high performance, easy-to-use, and scalable machine learning package that contains linear model (LR), factorization machin

Chao Ma 3k Jan 8, 2023
A modular active learning framework for Python
A modular active learning framework for Python

Modular Active Learning framework for Python3 Page contents Introduction Active learning from bird's-eye view modAL in action From zero to one in a fe

modAL 1.9k Dec 31, 2022
A library of extension and helper modules for Python's data analysis and machine learning libraries.
A library of extension and helper modules for Python's data analysis and machine learning libraries.

Mlxtend (machine learning extensions) is a Python library of useful tools for the day-to-day data science tasks. Sebastian Raschka 2014-2021 Links Doc

Sebastian Raschka 4.2k Dec 29, 2022
Sequence learning toolkit for Python

seqlearn seqlearn is a sequence classification toolkit for Python. It is designed to extend scikit-learn and offer as similar as possible an API. Comp

Lars 653 Dec 27, 2022
Simple structured learning framework for python

PyStruct PyStruct aims at being an easy-to-use structured learning and prediction library. Currently it implements only max-margin methods and a perce

pystruct 666 Jan 3, 2023
Python implementation of the rulefit algorithm

RuleFit Implementation of a rule based prediction algorithm based on the rulefit algorithm from Friedman and Popescu (PDF) The algorithm can be used f

Christoph Molnar 326 Jan 2, 2023
Comments
  • update train for images

    update train for images

    better to pick first label of each class programmatically otherwise it can change when then set of images changes. In my nb the indexes you had hardcoded were both class 0

    opened by jgmeyerucsd 1
Releases(v1.2)
Owner
GitHub Repository https://epynn.net
MegFlow - Efficient ML solutions for long-tailed demands.

Efficient ML solutions for long-tailed demands.

旷视天元 MegEngine 371 Dec 21, 2022
moDel Agnostic Language for Exploration and eXplanation

moDel Agnostic Language for Exploration and eXplanation Overview Unverified black box model is the path to the failure. Opaqueness leads to distrust.

Model Oriented 1.2k Jan 04, 2023
Programming assignments and quizzes from all courses within the Machine Learning Engineering for Production (MLOps) specialization offered by deeplearning.ai

Machine Learning Engineering for Production (MLOps) Specialization on Coursera (offered by deeplearning.ai) Programming assignments from all courses i

Aman Chadha 173 Jan 05, 2023
Built on python (Mathematical straight fit line coordinates error predictor machine learning foundational model)

Sum-Square_Error-Business-Analytical-Tool- Built on python (Mathematical straight fit line coordinates error predictor machine learning foundational m

om Podey 1 Dec 03, 2021
Stacked Generalization (Ensemble Learning)

Stacking (stacked generalization) Overview ikki407/stacking - Simple and useful stacking library, written in Python. User can use models of scikit-lea

Ikki Tanaka 192 Dec 23, 2022
Hypernets: A General Automated Machine Learning framework to simplify the development of End-to-end AutoML toolkits in specific domains.

A General Automated Machine Learning framework to simplify the development of End-to-end AutoML toolkits in specific domains.

DataCanvas 216 Dec 23, 2022
Bottleneck a collection of fast, NaN-aware NumPy array functions written in C.

Bottleneck Bottleneck is a collection of fast, NaN-aware NumPy array functions written in C. As one example, to check if a np.array has any NaNs using

Python for Data 835 Dec 27, 2022
ELI5 is a Python package which helps to debug machine learning classifiers and explain their predictions

A library for debugging/inspecting machine learning classifiers and explaining their predictions

154 Dec 17, 2022
This jupyter notebook project was completed by me and my friend using the dataset from Kaggle

ARM This jupyter notebook project was completed by me and my friend using the dataset from Kaggle. The world Happiness 2017, which ranks 155 countries

1 Jan 23, 2022
A toolbox to iNNvestigate neural networks' predictions!

iNNvestigate neural networks! Table of contents Introduction Installation Usage and Examples More documentation Contributing Releases Introduction In

Maximilian Alber 1.1k Jan 05, 2023
Quantum Machine Learning

The Machine Learning package simply contains sample datasets at present. It has some classification algorithms such as QSVM and VQC (Variational Quantum Classifier), where this data can be used for e

Qiskit 364 Jan 08, 2023
This is a curated list of medical data for machine learning

Medical Data for Machine Learning This is a curated list of medical data for machine learning. This list is provided for informational purposes only,

Andrew L. Beam 5.4k Dec 26, 2022
AP1 Transcription Factor Binding Site Prediction

A machine learning project that predicted binding sites of AP1 transcription factor, using ChIP-Seq data and local DNA shape information.

1 Jan 21, 2022
MasTrade is a trading bot in baselines3,pytorch,gym

mastrade MasTrade is a trading bot in baselines3,pytorch,gym idea we have for example 1 btc and we buy a crypto with it with market option to trade in

Masoud Azizi 18 May 24, 2022
Machine Learning Algorithms

Machine-Learning-Algorithms In this project, the dataset was created through a survey opened on Google forms. The purpose of the form is to find the p

Göktuğ Ayar 3 Aug 10, 2022
This is a Machine Learning model which predicts the presence of Diabetes in Patients

Diabetes Disease Prediction This is a machine Learning mode which tries to determine if a person has a diabetes or not. Data The dataset is in comma s

Edem Gold 4 Mar 16, 2022
Generate music from midi files using BPE and markov model

Generate music from midi files using BPE and markov model

Aditya Khadilkar 37 Oct 24, 2022
🎛 Distributed machine learning made simple.

🎛 lazycluster Distributed machine learning made simple. Use your preferred distributed ML framework like a lazy engineer. Getting Started • Highlight

Machine Learning Tooling 44 Nov 27, 2022
healthy and lesion models for learning based on the joint estimation of stochasticity and volatility

health-lesion-stovol healthy and lesion models for learning based on the joint estimation of stochasticity and volatility Reference please cite this p

5 Nov 01, 2022
Library of Stan Models for Survival Analysis

survivalstan: Survival Models in Stan author: Jacki Novik Overview Library of Stan Models for Survival Analysis Features: Variety of standard survival

Hammer Lab 122 Jan 06, 2023