Neurolab newff输出范围和来自网络的不同结果

Question

我意识到newff输出固定在范围[-1,1]，我做了以下测试，应该如何输出范围外的工作。

import neurolab as nl 
import numpy as np 

# Create train samples 
x = np.linspace(-7, 7, 20) 
y = x * 10 

size = len(x) 

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

norm_inp = nl.tool.Norm(inp) 
inp = norm_inp(inp) 

norm_tar = nl.tool.Norm(tar) 
tar = norm_tar(tar) 

# Create network with 2 layers and random initialized 
# as I normalized the inp, the input range is set to [0, 1] (BTW, I don't know how 
#to norm it to [-1, 1]) 
net = nl.net.newff([[0, 1]],[5, 1]) 

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

# Simulate network 
out = norm_tar.renorm(net.sim([[ 0.21052632 ]])) 

print "final output:-----------------" 
print out 

inp before norm 
[[-7.  ] 
[-6.26315789] 
[-5.52631579] 
[-4.78947368] 
[-4.05263158] 
[-3.31578947] 
[-2.57894737] 
[-1.84210526] 
[-1.10526316] 
[-0.36842105] 
[ 0.36842105] 
[ 1.10526316] 
[ 1.84210526] 
[ 2.57894737] 
[ 3.31578947] 
[ 4.05263158] 
[ 4.78947368] 
[ 5.52631579] 
[ 6.26315789] 
[ 7.  ]] 

tar before norm 
[[-70.  ] 
[-62.63157895] 
[-55.26315789] 
[-47.89473684] 
[-40.52631579] 
[-33.15789474] 
[-25.78947368] 
[-18.42105263] 
[-11.05263158] 
[ -3.68421053] 
[ 3.68421053] 
[ 11.05263158] 
[ 18.42105263] 
[ 25.78947368] 
[ 33.15789474] 
[ 40.52631579] 
[ 47.89473684] 
[ 55.26315789] 
[ 62.63157895] 
[ 70.  ]] 

我想到了要重归一化后，各地-40为输入0.21052632 但结果是不可重复的，有时是正确的（约-40），但有时是错误的（成为-70）。

我很奇怪，为什么训练结果并不稳定，是否有更好的方式来训练产生的输出值超出范围[-1,1]

Answer 1

没有关于“newff”不同的训练方法的神经网络。根据library，您可以使用7种不同的火车功能。尝试使用不同的火车功能。 Here是关于更改网络属性的示例。这里是例子。

import neurolab as nl 
# Create 
net = nl.net.newff([[-1, 1]], [5, 1]) 
# Default train function (train_gdx) 
print net.trainf # Trainer(TrainGDX) 
# Change train function 
net.trainf = nl.train.train_bfgs