NeuroLab

Neurolab is a simple and powerful Neural Network Library for Python. Contains based neural networks, train algorithms and flexible framework to create and explore other networks

Develop

Develop process migrate to GitHub:

• Source code
• Issues tracker
• Releases

Features

• Pure python + numpy
• API like Neural Network Toolbox (NNT) from MATLAB
• Interface to use train algorithms form scipy.optimize
• Flexible network configurations and learning algorithms. You may change: train, error, initializetion and activation functions
• Variety of supported types of Artificial Neural Network and learning algorithms
• Python 3 support https://www.paypal.com/cgi-bin/webscr?hosted_button_id=HRF5T66LM4L7G&cmd=_s-xclick https://www.paypalobjects.com/en_US/i/btn/btn_donate_LG.gif] '>

Example

```

import numpy as np import neurolab as nl

Create train samples

input = np.random.uniform(-0.5, 0.5, (10, 2)) target = (input[:, 0] + input[:, 1]).reshape(10, 1)

Create network with 2 inputs, 5 neurons in input layer and 1 in output layer

net = nl.net.newff([[-0.5, 0.5], [-0.5, 0.5]], [5, 1])

Train process

err = net.train(input, target, show=15) Epoch: 15; Error: 0.150308402918; Epoch: 30; Error: 0.072265865089; Epoch: 45; Error: 0.016931355131; The goal of learning is reached

Test

net.sim([[0.2, 0.1]]) # 0.2 + 0.1 array([[ 0.28757596]]) ```

Install

Install neurolab using setuptools/distribute:

easy_install neurolab

Or pip:

pip install neurolab

Or, if you don't have setuptools/distribute installed, use the download link at right to download the source package, and install it in the normal fashion: Unzip the source package, cd to the new directory, and:

python setup.py install

Support neural networks types

• Single layer perceptron

  • create function: neurolab.net.newp()
  • example of use: newp
  • default train function: neurolab.train.train_delta()
  • support train functions: train_gd, train_gda, train_gdm, train_gdx, train_rprop, train_bfgs, train_cg

• Multilayer feed forward perceptron

  • create function: neurolab.net.newff()
  • example of use: newff
  • default train function: neurolab.train.train_gdx()
  • support train functions: train_gd, train_gda, train_gdm, train_rprop, train_bfgs, train_cg

• Competing layer (Kohonen Layer)

  • create function: neurolab.net.newc()
  • example of use: newc
  • default train function: neurolab.train.train_cwta()
  • support train functions: train_wta

• Learning Vector Quantization (LVQ)

  • create function: neurolab.net.newlvq()
  • example of use: newlvq
  • default train function: neurolab.train.train_lvq()

• Elman Recurrent network

  • create function: neurolab.net.newelm()
  • example of use: newelm
  • default train function: neurolab.train.train_gdx()
  • support train functions: train_gd, train_gda, train_gdm, train_rprop, train_bfgs, train_cg

• Hopfield Recurrent network

  • create function: neurolab.net.newhop()
  • example of use: newhop

• Hemming Recurrent network

  • create function: neurolab.net.newhem()
  • example of use: newhem