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NeuralPy is the Artificial Neural Network library implemented in Python.

Project description

NeuralPy is a Python library for Artificial Neural Networks. You can run and test different Neural Network algorithms.

Travis

Installation

$ pip install neural-python

Dependence

  • Python 2.7, 3.3, 3.4

  • NumPy >= 1.9.0

  • SciPy >= 0.14.0

  • Matplotlib >= 1.4.0

Next steps

  • Bug fixing and version stabilization

  • Speeding up algorithms

  • Adding more algorithms

Library support

  • Radial Basis Functions Networks (RBFN)

  • Backpropagation and different optimization for it

  • Neural Network Ensembles

  • Associative and Autoasociative Memory

  • Competitive Networks

  • Step update algorithms for backpropagation

  • Weight control algorithms for backpropagation

  • Basic Linear Networks

Algorithms

  • Backpropagation

    • Classic Gradient Descent

    • Mini-batch Gradient Descent

    • Conjugate Gradient

      • Fletcher-Reeves

      • Polak-Ribiere

      • Hestenes-Stiefel

      • Conjugate Descent

      • Liu-Storey

      • Dai-Yuan

    • quasi-Newton

      • BFGS

      • DFP

      • PSB

      • SR1

    • Levenberg-Marquardt

    • Hessian diagonal

    • Momentum

    • RPROP

    • iRPROP+

    • QuickProp

  • Weight update rules

    • Weight Decay

    • Weight Elimination

  • Learning rate update rules

    • Adaptive Learning Rate

    • Error difference Update

    • Linear search by Golden Search or Brent

    • Wolfe line search

    • Search than converge

    • Simple Step Minimization

  • Ensembles

    • Mixture of Experts

    • Dynamically Averaged Network (DAN)

  • Radial Basis Functions Networks (RBFN)

    • Generalized Regression Neural Network (GRNN)

    • Probabilistic Neural Network (PNN)

    • Radial basis function K-means

  • Autoasociative Memory

    • Discrete BAM Network

    • CMAC Network

    • Discrete Hopfield Network

  • Competitive Networks

    • Adaptive Resonance Theory (ART1) Network

    • Self-Organizing Feature Map (SOFM or SOM)

  • Linear networks

    • Perceptron

    • LMS Network

    • Modified Relaxation Network

  • Associative

    • OJA

    • Kohonen

    • Instar

    • Hebb

Tests

$ pip install tox
$ tox

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