how to fit a function using PyBrain networks?
pybrain, python
Solution
I suppose your problem is that this network does not fit the function well. The total number of network nodes is too low to properly fit this sin(x) function: the function is too complex. Also, for fitting any function, no more than one hidden layer is required in principle.
For instance, try to remove two hidden layers, and increase the number of hidden nodes (to, say, 20). Your code fits the function just fine then
Problem
there is a function like: y = sin(x) I want to use PyBrain networks to fit the functions, here are what i did: when you run it you will get what i get, the data obtained is far from what it should be. ``` from pybrain.datasets import SupervisedDataSet from pybrain.tools.shortcuts import buildNetwork from pybrain.supervised.trainers import BackpropTrainer import pickle import scipy as sp import numpy as np import pylab as pl x = np.linspace(0, 4*np.pi, 100) ds = SupervisedDataSet(1,1) for i in x: ds.addSample(i,sin(i)) print ds n = buildNetwork(ds.indim,3,3,3,ds.outdim,recurrent=True) t = BackpropTrainer(n,learningrate=0.01,momentum=0.5,verbose=True) t.trainOnDataset(ds,1000) t.testOnData(verbose=True) fileObject = open('trained_net', 'w') pickle.dump(n, fileObject) fileObject.close() fileObject = open('trained_net','r') net = pickle.load(fileObject) y = [] for i in x: y.append(net.activate(i)) pl.plot(x,y) pl.plot(x,np.sin(x)) pl.show() ```