CODEWITHSUNDEEP
pythonpybrain
emanuele
emanuele

Reputation: 2589

Unable to build the correct ffnn on pybrain

I have trained a ffnn to fit a unknown function with pybrain. I build the ffnn like this

net = buildNetwork(1, 2, 1,hiddenclass=TanhLayer)

I said to pybrain to print the params of the net with the command

print net.params

and pybrain return me the params

(1.76464967 , 0.46764103 , 1.63394395 ,-0.95327762 , 1.19760151, -1.20449402, -1.34050959)

now I want to use this fitted function in another script. I tried 

def netp(Q):
    net = buildNetwork(1, 2, 1,hiddenclass=TanhLayer)
    net._setParameters=(1.76464967 , 0.46764103 , 1.63394395 ,-0.95327762 , 1.19760151, -1.20449402, -1.34050959)
    arg=1.0/float(Q)
    p=float(net.activate([arg]))
    return p

The problem is that the values returned from the nets are completely out of mind. example 

 0.0749046652125 1.0
-2.01920546405 0.5
-1.54408069672 0.333333333333
 1.05895945271 0.25
-1.01314347373 0.2
 1.56555648799 0.166666666667
 0.0824497539453 0.142857142857
 0.531176423655 0.125
 0.504185707604 0.111111111111
 0.841424535805 0.1

where the first column if the output of the net, and the second the input. The output of the net has to be close to the input value. What's the problem? Where I am doing wrong? It's a problem of over fitting or a I am missing something?

Upvotes: 4

Views: 233

Answers (1)

alko
alko

Reputation: 48317

A typo:

net._setParameters=(1.76464967 , 0.46764103 , 1.63394395 ,-0.95327762 , 1.19760151, -1.20449402, -1.34050959)

This line effectively replaces private _setParamethers method with a tuple. Try if replacing this line with

net._setParameters([1.76464967 , 0.46764103 , 1.63394395 ,-0.95327762 , 1.19760151, -1.20449402, -1.34050959])

will help.

Second, don't see reasons for 1/Q operation, so simple 

>>> def netp(Q): return float(net.activate([Q]))
>>> for i in inp:
...   print '{}\t{:.5f}'.format(i, netp(i))

yields

1.0      0.97634
0.5      0.46546
0.33333  0.29013
0.25     0.20762
0.2      0.16058
0.16666  0.13042
0.14285  0.10952
0.125    0.09421
0.11111  0.08254
0.1      0.07335

Upvotes: 2

Related Questions