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) layerself.n_hidden = n_hidden    # num of units in hidden layerif rng is None:rng = numpy.random.RandomState(1234)if W is None:a = 1. / n_visibleinitial_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_Wif hbias is None:hbias = numpy.zeros(n_hidden)  # initialize h bias 0if vbias is None:vbias = numpy.zeros(n_visible)  # initialize v bias 0self.rng = rngself.input = inputself.W = Wself.hbias = hbiasself.vbias = vbiasdef 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_samplesself.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: binomialn=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: binomialn=1,p=v1_mean)return [v1_mean, v1_sample]def propup(self, v):pre_sigmoid_activation = numpy.dot(v, self.W) + self.hbiasreturn sigmoid(pre_sigmoid_activation)def propdown(self, h):pre_sigmoid_activation = numpy.dot(h, self.W.T) + self.vbiasreturn 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.hbiassigmoid_activation_h = sigmoid(pre_sigmoid_activation_h)pre_sigmoid_activation_v = numpy.dot(sigmoid_activation_h, self.W.T) + self.vbiassigmoid_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_entropydef 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_vdef 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)# trainfor 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)

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