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Restricted Boltzmann Machine
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import java.util.Random; | |
public class RBM { | |
public int N; | |
public int n_visible; | |
public int n_hidden; | |
public double[][] W; | |
public double[] hbias; | |
public double[] vbias; | |
public Random rng; | |
public double uniform(double min, double max) { | |
return rng.nextDouble() * (max - min) + min; | |
} | |
public 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 = rng.nextDouble(); | |
if (r < p) c++; | |
} | |
return c; | |
} | |
public static double sigmoid(double x) { | |
return 1.0 / (1.0 + Math.pow(Math.E, -x)); | |
} | |
public RBM(int N, int n_visible, int n_hidden, | |
double[][] W, double[] hbias, double[] vbias, Random rng) { | |
this.N = N; | |
this.n_visible = n_visible; | |
this.n_hidden = n_hidden; | |
if(rng == null) this.rng = new Random(1234); | |
else this.rng = rng; | |
if(W == null) { | |
this.W = new double[this.n_hidden][this.n_visible]; | |
double a = 1.0 / this.n_visible; | |
for(int i=0; i<this.n_hidden; i++) { | |
for(int j=0; j<this.n_visible; j++) { | |
this.W[i][j] = uniform(-a, a); | |
} | |
} | |
} else { | |
this.W = W; | |
} | |
if(hbias == null) { | |
this.hbias = new double[this.n_hidden]; | |
for(int i=0; i<this.n_hidden; i++) this.hbias[i] = 0; | |
} else { | |
this.hbias = hbias; | |
} | |
if(vbias == null) { | |
this.vbias = new double[this.n_visible]; | |
for(int i=0; i<this.n_visible; i++) this.vbias[i] = 0; | |
} else { | |
this.vbias = vbias; | |
} | |
} | |
public void 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; | |
W[i][j] += lr *(ph_mean[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; | |
} | |
} | |
public void 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]); | |
} | |
} | |
public void 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]); | |
} | |
} | |
public double 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); | |
} | |
public double 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); | |
} | |
public void 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); | |
} | |
public void 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); | |
} | |
} | |
} |
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