neurolab 0.0.9

Simple and powerfull neural network library for python

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.

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

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]])

Now 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()