Deeplearning Principle and Implementation (2)

The code for rbm c ++ is introduced below. Some of the code written by Daniel can be used in conjunction with the previous blog to deepen your understanding of RBM theory...

RBM class definition declaration:

class RBM {public:  int N;  int n_visible;  int n_hidden;  double **W;  double *hbias;  double *vbias;  RBM(int, int, int, double**, double*, double*);  ~RBM();  void contrastive_divergence(int*, double, int);  void sample_h_given_v(int*, double*, int*);  void sample_v_given_h(int*, double*, int*);  double propup(int*, double*, double);  double propdown(int*, int, double);  void gibbs_hvh(int*, double*, int*, double*, int*);  void reconstruct(int*, double*);};

From the above statement, we can intuitively see that it exactly corresponds to the formula symbol of the previous article. The following is the code implementation part:

#include <iostream>#include <math.h>#include "RBM.h"using namespace std;double uniform(double min, double max) {  return rand() / (RAND_MAX + 1.0) * (max - min) + min;}int binomial(int n, double p) {  if(p < 0 || p > 1) return 0;    int c = 0;  double r;    for(int i=0; i<n; i++) {    r = rand() / (RAND_MAX + 1.0);    if (r < p) c++;  }  return c;}double sigmoid(double x) {  return 1.0 / (1.0 + exp(-x));}RBM::RBM(int size, int n_v, int n_h, double **w, double *hb, double *vb) {  N = size;  n_visible = n_v;  n_hidden = n_h;  if(w == NULL) {    W = new double*[n_hidden];    for(int i=0; i<n_hidden; i++) W[i] = new double[n_visible];    double a = 1.0 / n_visible;    for(int i=0; i<n_hidden; i++) {      for(int j=0; j<n_visible; j++) {        W[i][j] = uniform(-a, a);      }    }  } else {    W = w;  }  if(hb == NULL) {    hbias = new double[n_hidden];    for(int i=0; i<n_hidden; i++) hbias[i] = 0;  } else {    hbias = hb;  }  if(vb == NULL) {    vbias = new double[n_visible];    for(int i=0; i<n_visible; i++) vbias[i] = 0;  } else {    vbias = vb;  }}RBM::~RBM() {  for(int i=0; i<n_hidden; i++) delete[] W[i];  delete[] W;  delete[] hbias;  delete[] vbias;}void RBM::contrastive_divergence(int *input, double lr, int k) {  double *ph_mean = new double[n_hidden];  int *ph_sample = new int[n_hidden];  double *nv_means = new double[n_visible];  int *nv_samples = new int[n_visible];  double *nh_means = new double[n_hidden];  int *nh_samples = new int[n_hidden];  /* CD-k */  sample_h_given_v(input, ph_mean, ph_sample);  for(int step=0; step<k; step++) {    if(step == 0) {      gibbs_hvh(ph_sample, nv_means, nv_samples, nh_means, nh_samples);    } else {      gibbs_hvh(nh_samples, nv_means, nv_samples, nh_means, nh_samples);    }  }  for(int i=0; i<n_hidden; i++) {    for(int j=0; j<n_visible; j++) {      W[i][j] += lr * (ph_sample[i] * input[j] - nh_means[i] * nv_samples[j]) / N;    }    hbias[i] += lr * (ph_sample[i] - nh_means[i]) / N;  }  for(int i=0; i<n_visible; i++) {    vbias[i] += lr * (input[i] - nv_samples[i]) / N;  }  delete[] ph_mean;  delete[] ph_sample;  delete[] nv_means;  delete[] nv_samples;  delete[] nh_means;  delete[] nh_samples;}void RBM::sample_h_given_v(int *v0_sample, double *mean, int *sample) {  for(int i=0; i<n_hidden; i++) {    mean[i] = propup(v0_sample, W[i], hbias[i]);    sample[i] = binomial(1, mean[i]);  }}void RBM::sample_v_given_h(int *h0_sample, double *mean, int *sample) {  for(int i=0; i<n_visible; i++) {    mean[i] = propdown(h0_sample, i, vbias[i]);    sample[i] = binomial(1, mean[i]);  }}double RBM::propup(int *v, double *w, double b) {  double pre_sigmoid_activation = 0.0;  for(int j=0; j<n_visible; j++) {    pre_sigmoid_activation += w[j] * v[j];  }  pre_sigmoid_activation += b;  return sigmoid(pre_sigmoid_activation);}double RBM::propdown(int *h, int i, double b) {  double pre_sigmoid_activation = 0.0;  for(int j=0; j<n_hidden; j++) {    pre_sigmoid_activation += W[j][i] * h[j];  }  pre_sigmoid_activation += b;  return sigmoid(pre_sigmoid_activation);}void RBM::gibbs_hvh(int *h0_sample, double *nv_means, int *nv_samples, \                    double *nh_means, int *nh_samples) {  sample_v_given_h(h0_sample, nv_means, nv_samples);  sample_h_given_v(nv_samples, nh_means, nh_samples);}void RBM::reconstruct(int *v, double *reconstructed_v) {  double *h = new double[n_hidden];  double pre_sigmoid_activation;  for(int i=0; i<n_hidden; i++) {    h[i] = propup(v, W[i], hbias[i]);  }  for(int i=0; i<n_visible; i++) {    pre_sigmoid_activation = 0.0;    for(int j=0; j<n_hidden; j++) {      pre_sigmoid_activation += W[j][i] * h[j];    }    pre_sigmoid_activation += vbias[i];    reconstructed_v[i] = sigmoid(pre_sigmoid_activation);  }  delete[] h;}void test_rbm() {  srand(0);  double learning_rate = 0.1;  int training_epochs = 1000;  int k = 1;    int train_N = 6;  int test_N = 2;  int n_visible = 6;  int n_hidden = 3;  // training data  int train_X[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}  };  // construct RBM  RBM rbm(train_N, n_visible, n_hidden, NULL, NULL, NULL);  // train  for(int epoch=0; epoch<training_epochs; epoch++) {    for(int i=0; i<train_N; i++) {      rbm.contrastive_divergence(train_X[i], learning_rate, k);    }  }  // test data  int test_X[2][6] = {    {1, 1, 0, 0, 0, 0},    {0, 0, 0, 1, 1, 0}  };  double reconstructed_X[2][6];  // test  for(int i=0; i<test_N; i++) {    rbm.reconstruct(test_X[i], reconstructed_X[i]);    for(int j=0; j<n_visible; j++) {      printf("%.5f ", reconstructed_X[i][j]);    }    cout << endl;  }}int main() {  test_rbm();  return 0;}

Simply paste the running results to provide convenience for those who are the ultimate thinker

0.98472 0.67248 0.99120 0.01000 0.01311 0.01020
0.01021 0.00720 0.99525 0.65553 0.98403 0.00497

Reprinted please indicate the source: http://blog.csdn.net/cuoqu/article/details/8887882