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

希望本文所述对大家Python程序设计有所帮助。