python 神经网络包 NeuroLab

neurolab模块相当于Matlab的神经网络工具箱(NNT)

neurolab模块支持的网络类型：

• 单层感知机(single layer perceptron)
• 多层前馈感知机(Multilayer feed forward perceptron)
• 竞争层(Kohonen Layer)
• 学习向量量化(Learning Vector Quantization)
• Elman循环网络(Elman recurrent network)
• Hopfield循环网络(Hopfield recurrent network)
• 卷边循环网络(Hemming recurrent network)

这里以多层前馈网络为例：neurolab.net.newff(minmax, size, transf=None)

Parameters:

minmax: list of list, the outer list is the number of input neurons, inner lists must contain 2 elements: min and max Range of input value size: the length of list equal to the number of layers except input layer, the element of the list is the neuron number for corresponding layer Contains the number of neurons for each layer transf: list (default TanSig) List of activation function for each layer

minmax:列表的列表，外层列表表示输入层的神经元个数，内层列表必须包含两个元素：max和min

size:列表的长度等于出去输入层的网络的层数，列表的元素对应于各层的神经元个数

transf：激活函数，默认为TanSig。

举例2：

perceptron = nl.net.newp([[0, 2],[0, 2]], 1)
第一个参数列表的长度表示输出的节点的个数，列表中得每一个元素包含两个值：最大值和最小值。
第二个参数：The value “1” indicates that there is a single neuron in this network.
error = perceptron.train(input_data, output, epochs=50, show=15, lr=0.01)
epochs：表示迭代训练的次数，show：表示终端输出的频率，lr：表示学习率

举例3：

import numpy as np
import neurolab as nl

input = np.random.uniform(0, 0.1, (1000, 225))
output = input[:,:10] + input[:,10:20]
# 2 layers with 225 inputs 50 neurons in hidden\input layer and 10 in output
# for 3 layers use some thet: nl.net.newff([[0, .1]]*225, [50, 40, 10])
net = nl.net.newff([[0, .1]]*225, [50, 10])
net.trainf = nl.train.train_bfgs

e = net.train(input, output, show=1, epochs=100, goal=0.0001)

举例4：

import neurolab as nl
import numpy as np
# Create train samples
x = np.linspace(-7, 7, 20)
y = np.sin(x) * 0.5

size = len(x)

inp = x.reshape(size,1)
tar = y.reshape(size,1)

# Create network with 2 layers and random initialized
net = nl.net.newff([[-7, 7]],[5, 1])

# Train network
error = net.train(inp, tar, epochs=500, show=100, goal=0.02)

# Simulate network
out = net.sim(inp)

# Plot result
import pylab as pl
pl.subplot(211)
pl.plot(error)
pl.xlabel('Epoch number')
pl.ylabel('error (default SSE)')

x2 = np.linspace(-6.0,6.0,150)
y2 = net.sim(x2.reshape(x2.size,1)).reshape(x2.size)
print(len(y2))
y3 = out.reshape(size)
pl.subplot(212)
pl.plot(x2, y2, '-',x , y, '.', x, y3, 'p')
pl.legend(['train target', 'net output'])
pl.show()

资料还有很多，以后继续补充

重点参考：官网

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