Python实现的径向基（RBF）神经网络示例
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, 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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