本文实例讲述了Python实现的径向基(RBF)神经网络。分享给大家供大家参考,具体如下:
from numpy import array,append,vstack,transpose,reshape,\ dot,true_divide,mean,exp,sqrt,log,\ loadtxt,savetxt,zeros,frombuffer from numpy.linalg import norm,lstsq from multiprocessing import Process,Array from random import sample from time import time from sys import stdout from ctypes import c_double from h5py import File def metrics(a,b): return norm(a - b) def gaussian (x,mu,sigma): return exp(- metrics(mu,x)**2 / (2 * sigma**2)) def multiQuadric (x,sigma): return pow(metrics(mu,x)**2 + sigma**2,0.5) def invMultiQuadric (x,-0.5) def plateSpine (x,mu): r = metrics(mu,x) return (r**2) * log(r) class Rbf: def __init__(self,prefix = 'rbf',workers = 4,extra_neurons = 0,from_files = None): self.prefix = prefix self.workers = workers self.extra_neurons = extra_neurons # Import partial model if from_files is not None: w_handle = self.w_handle = File(from_files['w'],'r') mu_handle = self.mu_handle = File(from_files['mu'],'r') sigma_handle = self.sigma_handle = File(from_files['sigma'],'r') self.w = w_handle['w'] self.mu = mu_handle['mu'] self.sigmas = sigma_handle['sigmas'] self.neurons = self.sigmas.shape[0] def _calculate_error(self,y): self.error = mean(abs(self.os - y)) self.relative_error = true_divide(self.error,mean(y)) def _generate_mu(self,x): n = self.n extra_neurons = self.extra_neurons # Todo: Make reusable mu_clusters = loadtxt('clusters100.txt',delimiter='\t') mu_indices = sample(range(n),extra_neurons) mu_new = x[mu_indices,:] mu = vstack((mu_clusters,mu_new)) return mu def _calculate_sigmas(self): neurons = self.neurons mu = self.mu sigmas = zeros((neurons,)) for i in xrange(neurons): dists = [0 for _ in xrange(neurons)] for j in xrange(neurons): if i != j: dists[j] = metrics(mu[i],mu[j]) sigmas[i] = mean(dists)* 2 # max(dists) / sqrt(neurons * 2)) return sigmas def _calculate_phi(self,x): C = self.workers neurons = self.neurons mu = self.mu sigmas = self.sigmas phi = self.phi = None n = self.n def heavy_lifting(c,phi): s = jobs[c][1] - jobs[c][0] for k,i in enumerate(xrange(jobs[c][0],jobs[c][1])): for j in xrange(neurons): # phi[i,j] = metrics(x[i,:],mu[j])**3) # phi[i,j] = plateSpine(x[i,mu[j])) # phi[i,j] = invMultiQuadric(x[i,mu[j],sigmas[j])) phi[i,j] = multiQuadric(x[i,sigmas[j]) # phi[i,j] = gaussian(x[i,sigmas[j])) if k % 1000 == 0: percent = true_divide(k,s)*100 print(c,': {:2.2f}%'.format(percent)) print(c,': Done') # distributing the work between 4 workers shared_array = Array(c_double,n * neurons) phi = frombuffer(shared_array.get_obj()) phi = phi.reshape((n,neurons)) jobs = [] workers = [] p = n / C m = n % C for c in range(C): jobs.append((c*p,(c+1)*p + (m if c == C-1 else 0))) worker = Process(target = heavy_lifting,args = (c,phi)) workers.append(worker) worker.start() for worker in workers: worker.join() return phi def _do_algebra(self,y): phi = self.phi w = lstsq(phi,y)[0] os = dot(w,transpose(phi)) return w,os # Saving to HDF5 os_h5 = os_handle.create_dataset('os',data = os) def train(self,x,y): self.n = x.shape[0] ## Initialize HDF5 caches prefix = self.prefix postfix = str(self.n) + '-' + str(self.extra_neurons) + '.hdf5' name_template = prefix + '-{}-' + postfix phi_handle = self.phi_handle = File(name_template.format('phi'),'w') os_handle = self.w_handle = File(name_template.format('os'),'w') w_handle = self.w_handle = File(name_template.format('w'),'w') mu_handle = self.mu_handle = File(name_template.format('mu'),'w') sigma_handle = self.sigma_handle = File(name_template.format('sigma'),'w') ## Mu generation mu = self.mu = self._generate_mu(x) self.neurons = mu.shape[0] print('({} neurons)'.format(self.neurons)) # Save to HDF5 mu_h5 = mu_handle.create_dataset('mu',data = mu) ## Sigma calculation print('Calculating Sigma...') sigmas = self.sigmas = self._calculate_sigmas() # Save to HDF5 sigmas_h5 = sigma_handle.create_dataset('sigmas',data = sigmas) print('Done') ## Phi calculation print('Calculating Phi...') phi = self.phi = self._calculate_phi(x) print('Done') # Saving to HDF5 print('Serializing...') phi_h5 = phi_handle.create_dataset('phi',data = phi) del phi self.phi = phi_h5 print('Done') ## Algebra print('Doing final algebra...') w,os = self.w,_ = self._do_algebra(y) # Saving to HDF5 w_h5 = w_handle.create_dataset('w',data = w) os_h5 = os_handle.create_dataset('os',data = os) ## Calculate error self._calculate_error(y) print('Done') def predict(self,test_data): mu = self.mu = self.mu.value sigmas = self.sigmas = self.sigmas.value w = self.w = self.w.value print('Calculating phi for test data...') phi = self._calculate_phi(test_data) os = dot(w,transpose(phi)) savetxt('iok3834.txt',os,delimiter='\n') return os @property def summary(self): return '\n'.join( \ ['-----------------','Training set size: {}'.format(self.n),'Hidden layer size: {}'.format(self.neurons),'-----------------','Absolute error : {:02.2f}'.format(self.error),'Relative error : {:02.2f}%'.format(self.relative_error * 100)]) def predict(test_data): mu = File('rbf-mu-212243-2400.hdf5','r')['mu'].value sigmas = File('rbf-sigma-212243-2400.hdf5','r')['sigmas'].value w = File('rbf-w-212243-2400.hdf5','r')['w'].value n = test_data.shape[0] neur = mu.shape[0] mu = transpose(mu) mu.reshape((n,neur)) phi = zeros((n,neur)) for i in range(n): for j in range(neur): phi[i,j] = multiQuadric(test_data[i,sigmas[j]) os = dot(w,transpose(phi)) savetxt('iok3834.txt',delimiter='\n') return os
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