Py之neurolab：Python庫之neurolab的簡介、安裝、使用方法之詳細攻略

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目錄

neurolab的簡介

neurolab的安裝

neurolab的使用方法

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neurolab的簡介

? ? neurolab是一個簡單而強大的Python神經網絡庫。包含基于神經網絡、訓練算法和靈活的框架來創建和探索其他神經網絡類型。NeuroLab一個具有靈活網絡配置和Python學習算法的基本神經網絡算法庫。為了簡化庫的使用，接口類似于MATLAB（C）的神經網絡工具箱（NNT）的包。該庫基于包NUMPY（http://NoPy.SimP.org），使用一些學習算法。
neurolab

neurolab的安裝

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:Example:

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

>>> 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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