RBM代码Python

# -*- coding: utf-8 -*- ''' Created on 2016年4月1日 @author: LIU ''' import sys import numpy import matplotlib.pylab as plt import numpy as np import random from scipy.linalg import norm import PIL.Image from utils import * class RBM(object): def __init__(self, input=None, n_visible=2, n_hidden=3, \ W=None, hbias=None, vbias=None, rng=None): self.n_visible = n_visible # num of units in visible (input) layer self.n_hidden = n_hidden # num of units in hidden layer if rng is None: rng = numpy.random.RandomState(1234) if W is None: a = 1. / n_visible initial_W = numpy.array(rng.uniform( # initialize W uniformly(随机生成实数在-a-a之间) low=-a, high=a, size=(n_visible, n_hidden))) W = initial_W if hbias is None: hbias = numpy.zeros(n_hidden) # initialize h bias 0 if vbias is None: vbias = numpy.zeros(n_visible) # initialize v bias 0 self.rng = rng self.input = input self.W = W self.hbias = hbias self.vbias = vbias def contrastive_divergence(self, lr=0.1, k=1, input=None): if input is not None: self.input = input ''' CD-ks算法 ''' ph_mean, ph_sample = self.sample_h_given_v(self.input) chain_start = ph_sample #实现一步吉布斯采样通过给隐层采样 for step in xrange(k): if step == 0: nv_means, nv_samples,\ nh_means, nh_samples = self.gibbs_hvh(chain_start) else: nv_means, nv_samples,\ nh_means, nh_samples = self.gibbs_hvh(nh_samples) # chain_end = nv_samples self.W += lr * (numpy.dot(self.input.T, ph_mean) - numpy.dot(nv_samples.T, nh_means)) self.vbias += lr * numpy.mean(self.input - nv_samples, axis=0) self.hbias += lr * numpy.mean(ph_mean - nh_means, axis=0) # cost = self.get_reconstruction_cross_entropy() # return cost # 通过给出显层单元推断出隐层单元的  #计算隐层单元的激活率通过给出显层，得到一个采样通过给他们的 def sample_h_given_v(self, v0_sample): h1_mean = self.propup(v0_sample) h1_sample = self.rng.binomial(size=h1_mean.shape, # discrete: binomial n=1, p=h1_mean) return [h1_mean, h1_sample] #一一步吉布斯采样通过从隐层率开始 def sample_v_given_h(self, h0_sample): v1_mean = self.propdown(h0_sample) v1_sample = self.rng.binomial(size=v1_mean.shape, # discrete: binomial n=1, p=v1_mean) return [v1_mean, v1_sample] def propup(self, v): pre_sigmoid_activation = numpy.dot(v, self.W) + self.hbias return sigmoid(pre_sigmoid_activation) def propdown(self, h): pre_sigmoid_activation = numpy.dot(h, self.W.T) + self.vbias return sigmoid(pre_sigmoid_activation) #转换函数主要功能是通过给定的隐层采样来执行cd更新 def gibbs_hvh(self, h0_sample): v1_mean, v1_sample = self.sample_v_given_h(h0_sample) h1_mean, h1_sample = self.sample_h_given_v(v1_sample) return [v1_mean, v1_sample, h1_mean, h1_sample] #计算重构误差  def get_reconstruction_cross_entropy(self): pre_sigmoid_activation_h = numpy.dot(self.input, self.W) + self.hbias sigmoid_activation_h = sigmoid(pre_sigmoid_activation_h) pre_sigmoid_activation_v = numpy.dot(sigmoid_activation_h, self.W.T) + self.vbias sigmoid_activation_v = sigmoid(pre_sigmoid_activation_v) cross_entropy = - numpy.mean( numpy.sum(self.input * numpy.log(sigmoid_activation_v) + (1 - self.input) * numpy.log(1 - sigmoid_activation_v), axis=1)) return cross_entropy def reconstruct(self, v): h = sigmoid(numpy.dot(v, self.W) + self.hbias) reconstructed_v = sigmoid(numpy.dot(h, self.W.T) + self.vbias) return reconstructed_v def readData(path): data = [] for line in open(path, 'r'): ele = line.split(' ') tmp = [] for e in ele: if e != '': tmp.append(float(e.strip(' '))) data.append(tmp) return data def test_rbm(learning_rate=0.1, k=1, training_epochs=50): # data = numpy.array([[1,1,1,0,0,0], # [1,0,1,0,0,0], # [1,1,1,0,0,0], # [0,0,1,1,1,0], # [0,0,1,1,0,0], # [0,0,1,1,1,0]]) data = readData('data.txt') data = np.array(data) data = data.transpose() rng = numpy.random.RandomState(123) # construct RBM # rbm = RBM(input=data, n_visible=6, n_hidden=2, rng=rng) rbm = RBM(input=data, n_visible=784, n_hidden=2, rng=rng) # train for epoch in xrange(training_epochs): rbm.contrastive_divergence(lr=learning_rate, k=k) cost = rbm.get_reconstruction_cross_entropy() print >> sys.stderr, 'Training epoch %d, cost is ' % epoch, cost # test # v = numpy.array([[1, 1, 0, 0, 0, 0], # [0, 0, 0, 1, 1, 0]]) v=data[1,:] print rbm.reconstruct(v) if __name__ == "__main__": test_rbm()

# -*- coding: utf-8 -*- ''' Created on 2016年4月1日 @author: LIU ''' import numpy numpy.seterr(all='ignore') def sigmoid(x): return 1. / (1 + numpy.exp(-x)) def dsigmoid(x): return x * (1. - x) # def tanh(x): # return numpy.tanh(x) #  # def dtanh(x): # return 1. - x * x #  # def softmax(x): # e = numpy.exp(x - numpy.max(x)) # prevent overflow # if e.ndim == 1: # return e / numpy.sum(e, axis=0) # else:  # return e / numpy.array([numpy.sum(e, axis=1)]).T # ndim = 2 #  #  # def ReLU(x): # return x * (x > 0) #  # def dReLU(x): # return 1. * (x > 0)