NeuralPy is a Python library for Artificial Neural Networks.
You can run and test different Neural Network algorithms.
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
- 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
Tests
$ pip install tox
$ tox
Download Files
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