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Simple and powerfull neural network library for python

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  • License: GNU Library or Lesser General Public License (LGPL) (LGPL-3)
  • Author: Zuev Evgenij
  • Tags neural network, neural networks, neural nets, backpropagation, python, matlab, numpy, machine learning
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Project description

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 neural network types.

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, initialization and activation functions

  • Unlimited number of neural layers and number of neurons in layers

  • 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]])
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