Question

我意识到newff输出固定在[-1,1]范围内，我会做以下操作来测试输出范围外的输出应该如何工作。

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.        ]]

我预计输入0.21052632后，输出大约为-40 但结果不可重复，有时是正确的（约-40）但有时是错误的（变成-70）。

我想知道为什么训练结果不稳定并且有更好的方法来训练产生输出值超出范围的nn [-1,1]

Answer 1

＆＃34; newff＆＃34;有不同的训练方法。根据{{3}}，您可以使用7种不同的列车功能。尝试使用不同的列车功能。 library是关于更改网络属性的示例。这是一个例子。

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

Neurolab newff输出范围和网络的不同结果

1 个答案: