neurolab的简介
neurolab是一个简单而强大的Python神经网络库。包含基于神经网络、训练算法和灵活的框架来创建和探索其他神经网络类型。NeuroLab一个具有灵活网络配置和Python学习算法的基本神经网络算法库。为了简化库的使用，接口类似于MATLAB（C）的神经网络工具箱（NNT）的包。该库基于包NUMPY（http://NoPy.SimP.org），使用一些学习算法。
neurolab
neurolab的安装
pip install neurolab
neurolab的使用方法
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
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]])
