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, frombufferfrom numpy.linalg import norm, lstsqfrom multiprocessing import Process, Arrayfrom random import samplefrom time import timefrom sys import stdoutfrom ctypes import c_doublefrom h5py import Filedef metrics(a, b): return norm(a - b)def gaussian (x, mu, sigma): return exp(- metrics(mu, x)**2 / (2 * sigma**2))def multiQuadric (x, mu, sigma): return pow(metrics(mu,x)**2 + sigma**2, 0.5)def invMultiQuadric (x, mu, sigma): return pow(metrics(mu,x)**2 + sigma**2, -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,:], mu[j], sigmas[j]) # phi[i, j] = gaussian(x[i,:], mu[j], 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,:], mu[j], sigmas[j]) os = dot(w, transpose(phi)) savetxt('iok3834.txt', os, delimiter='\n') return os

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