花了好多天去推導RBM公式，只能說數學是硬傷，推導過程在后面給出大概，看了下yusugomori的java版源碼，又花了一天時間來寫C++版本，其主要思路參照yusugomori。發現java和C++好多地方差不多，呵呵。本人乃初學小娃，錯誤難免，多多指教。

出處：http://www.cnblogs.com/wn19910213/p/3441707.html

RBM.h

1 #include <iostream> 2 3 using namespace std; 4 5 class RBM 6 { 7 public: 8 size_t N; 9 size_t n_visible; 10 size_t n_hidden; 11 double **W; 12 double *hbias; 13 double *vbias; 14 15 RBM(size_t,size_t,size_t,double**,double*,double*); 16 ~RBM(); 17 18 void contrastive_divergence(int *,double,int); 19 void sample_h_given_v(int*,double*,int*); 20 double sigmoid(double); 21 double Vtoh_sigm(int* ,double* ,int ); 22 void gibbs_hvh(int* ,double* ,int* ,double* ,int* ); 23 double HtoV_sigm(int* ,int ,int ); 24 void sample_v_given_h(int* ,double* ,int* ); 25 void reconstruct(int* ,double*); 26 27 private: 28 };

RBM.cpp

#include <iostream> #include <stdlib.h> #include <stdio.h> #include <cmath> #include <cstring> #include "RBM.h"using namespace std;void test_rbm(); double uniform(double ,double); double binomial(double );int main() {test_rbm();return 0; }//start void test_rbm() {srand(0);size_t train_N = 6;size_t test_N = 2;size_t n_visible = 6;size_t n_hidden = 3;double learning_rate = 0.1;int training_num = 1000;int k = 1;int train_data[6][6] = {{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, 0, 1, 0},{0, 0, 1, 1, 1, 0}};RBM rbm(train_N,n_visible,n_hidden,NULL,NULL,NULL); //第一步、構造for(size_t j=0;j<training_num;j++){for(size_t i=0;i<train_N;i++){rbm.contrastive_divergence(train_data[i],learning_rate,k); //第二步、訓練數據集 }}//測試數據int test_data[2][6] = {{1,1,0,0,0,0},{0,0,0,1,1,0}};double reconstructed_data[2][6];for(size_t i=0;i<test_N;i++){rbm.reconstruct(test_data[i],reconstructed_data[i]); //第三步、重構數據，其主要過程就是說把訓練出來的權重，偏移量拿出來對測試數據先轉換到隱含層，在轉換回來。for(size_t j=0;j<n_visible;j++){cout << reconstructed_data[i][j] << " ";}cout << endl;} }void RBM::reconstruct(int* test_data,double* reconstructed_data) {double* h = new double[n_hidden];double temp;for(size_t i=0;i<n_hidden;i++){h[i] = Vtoh_sigm(test_data,W[i],hbias[i]);}for(size_t i=0;i<n_visible;i++){temp = 0.0;for(size_t j=0;j<n_hidden;j++){temp += W[j][i] * h[j];}temp += vbias[i];reconstructed_data[i] = sigmoid(temp);}delete[] h; }//第二步1、CD-K void RBM::contrastive_divergence(int *train_data,double learning_rate,int k) {double* ph_sigm_out = new double[n_hidden];int* ph_sample = new int[n_hidden];double* nv_sigm_outs = new double[n_visible];int* nv_samples = new int[n_visible];double* nh_sigm_outs = new double[n_hidden];int* nh_samples = new int[n_hidden];sample_h_given_v(train_data,ph_sigm_out,ph_sample); //獲得h0for(size_t i=0;i<k;i++) //根據hinton教授指出只需要抽樣到V1即可有足夠好的近似，所以k=1 {if(i == 0){gibbs_hvh(ph_sample,nv_sigm_outs,nv_samples,nh_sigm_outs,nh_samples); //獲得V1,h1 }else{gibbs_hvh(nh_samples,nv_sigm_outs,nv_samples,nh_sigm_outs,nh_samples);}}//更新權值，雙向偏移量。由于hinton提出的CD-K，可以知道其v0代表的是原始數據x//h0即ph_sigm_out，h0近似等于對v0下h的概率//v1即代表的是經過一次轉換后的x，近似等于對h0下v的概率。//h1同理。CD-K主要就是求出這個三個數據，便能夠很好的近似計算梯度。至于為什么我也不知道。for(size_t i=0;i<n_hidden;i++){for(size_t j=0;j<n_visible;j++){//可以根據權重公式發現，其實P（hi=1|v）代表的就是h0，p(hi=1|Vyk)和Vyk代表的就是h1和V1.W[i][j] += learning_rate * (ph_sigm_out[i] * train_data[j] - nh_sigm_outs[i] * nv_samples[j]) / N;}hbias[i] += learning_rate * (ph_sample[i] - nh_sigm_outs[i]) / N;}for(size_t i=0;i<n_visible;i++){vbias[i] += learning_rate * (train_data[i] - nv_samples[i]) / N;}delete[] ph_sigm_out;delete[] ph_sample;delete[] nv_sigm_outs;delete[] nv_samples;delete[] nh_sigm_outs;delete[] nh_samples; }void RBM::gibbs_hvh(int* ph_sample,double* nv_sigm_outs,int* nv_samples,double* nh_sigm_outs,int* nh_samples) {sample_v_given_h(ph_sample,nv_sigm_outs,nv_samples);sample_h_given_v(nv_samples,nh_sigm_outs,nh_samples); }void RBM::sample_v_given_h(int* h0_sample,double* nv_sigm_outs,int* nv_samples) {for(size_t i=0;i<n_visible;i++){nv_sigm_outs[i] = HtoV_sigm(h0_sample,i,vbias[i]);nv_samples[i] = binomial(nv_sigm_outs[i]);} }double RBM::HtoV_sigm(int* h0_sample,int i,int vbias) {double temp = 0.0;for(size_t j=0;j<n_hidden;j++){temp += W[j][i] * h0_sample[j];}temp += vbias;return sigmoid(temp); }void RBM::sample_h_given_v(int* train_data,double* ph_sigm_out,int* ph_sample) {for(size_t i=0;i<n_hidden;i++){ph_sigm_out[i] = Vtoh_sigm(train_data,W[i],hbias[i]);ph_sample[i] = binomial(ph_sigm_out[i]);} }double binomial(double p) {if(p<0 || p>1){return 0;}double r = rand()/(RAND_MAX + 1.0);if(r < p){return 1;}else{return 0;} }double RBM::Vtoh_sigm(int* train_data,double* W,int hbias) {double temp = 0.0;for(size_t i=0;i<n_visible;i++){temp += W[i] * train_data[i];}temp += hbias;return sigmoid(temp); }double RBM::sigmoid(double x) {return 1.0/(1.0 + exp(-x)); }RBM::RBM(size_t train_N,size_t n_v,size_t n_h,double **w,double *hb,double *vb) {N = train_N;n_visible = n_v;n_hidden = n_h;if(w == NULL){W = new double*[n_hidden];double a = 1.0/n_visible;for(size_t i=0;i<n_hidden;i++){W[i] = new double[n_visible];}for(size_t i=0;i<n_hidden;i++){for(size_t j=0;j<n_visible;j++){W[i][j] = uniform(-a,a);}}}else{W = w;}if(hb == NULL){hbias = new double[n_hidden];for(size_t i=0;i<n_hidden;i++){hbias[i] = 0.0;}}else{hbias = hb;}if(vb == NULL){vbias = new double[n_visible];for(size_t i=0;i<n_visible;i++){vbias[i] = 0.0;}}else{vbias = vb;} }RBM::~RBM() {for(size_t i=0;i<n_hidden;i++){delete[] W[i];}delete[] W;delete[] hbias;delete[] vbias; }double uniform(double min,double max) {return rand() / (RAND_MAX + 1.0) * (max - min) + min; }

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轉載于:https://www.cnblogs.com/wn19910213/p/3441707.html

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