NeuPy is the Artificial Neural Network library implemented in Python.
Project description
NeuPy is a Python library for Artificial Neural Networks. You can run and test different Neural Network algorithms.
Installation
$ pip install neupy
Links
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
Project details
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