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Yusuke Sugomori
committed
dA.java
1 parent eafd9bd commit 6d6b412

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3 files changed

+214
-7
lines changed

3 files changed

+214
-7
lines changed

‎java/DBN/src/RBM.java

+3-3
Original file line numberDiff line numberDiff line change
@@ -4,9 +4,9 @@ public class RBM {
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public int N;
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public int n_visible;
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public int n_hidden;
7-
double[][] W;
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double[] hbias;
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double[] vbias;
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public double[][] W;
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public double[] hbias;
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public double[] vbias;
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public Random rng;
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public double uniform(double min, double max) {

‎java/RBM/src/RBM.java

+4-4
Original file line numberDiff line numberDiff line change
@@ -4,9 +4,9 @@ public class RBM {
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public int N;
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public int n_visible;
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public int n_hidden;
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double[][] W;
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double[] hbias;
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double[] vbias;
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public double[][] W;
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public double[] hbias;
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public double[] vbias;
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public Random rng;
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public double uniform(double min, double max) {
@@ -207,7 +207,7 @@ private static void test_rbm() {
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for(int i=0; i<test_N; i++) {
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rbm.reconstruct(test_X[i], reconstructed_X[i]);
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for(int j=0; j<n_visible; j++) {
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System.out.printf("%.5f", reconstructed_X[i][j]);
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System.out.printf("%.5f ", reconstructed_X[i][j]);
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}
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System.out.println();
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}

‎java/dA/src/dA.java

+207
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,207 @@
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import java.util.Random;
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public class dA {
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public int N;
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public int n_visible;
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public int n_hidden;
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public double[][] W;
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public double[] hbias;
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public double[] vbias;
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public Random rng;
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public double uniform(double min, double max) {
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return rng.nextDouble() * (max - min) + min;
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}
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public int binomial(int n, double p) {
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if(p < 0 || p > 1) return 0;
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int c = 0;
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double r;
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for(int i=0; i<n; i++) {
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r = rng.nextDouble();
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if (r < p) c++;
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}
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return c;
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}
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public static double sigmoid(double x) {
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return 1.0 / (1.0 + Math.pow(Math.E, -x));
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}
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public dA(int N, int n_visible, int n_hidden,
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double[][] W, double[] hbias, double[] vbias, Random rng) {
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this.N = N;
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this.n_visible = n_visible;
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this.n_hidden = n_hidden;
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if(rng == null) this.rng = new Random(1234);
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else this.rng = rng;
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if(W == null) {
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this.W = new double[this.n_hidden][this.n_visible];
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double a = 1.0 / this.n_visible;
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for(int i=0; i<this.n_hidden; i++) {
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for(int j=0; j<this.n_visible; j++) {
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this.W[i][j] = uniform(-a, a);
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}
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}
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} else {
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this.W = W;
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}
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if(hbias == null) {
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this.hbias = new double[this.n_hidden];
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for(int i=0; i<this.n_hidden; i++) this.hbias[i] = 0;
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} else {
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this.hbias = hbias;
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}
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if(vbias == null) {
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this.vbias = new double[this.n_visible];
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for(int i=0; i<this.n_visible; i++) this.vbias[i] = 0;
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} else {
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this.vbias = vbias;
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}
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}
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public void get_corrupted_input(int[] x, int[] tilde_x, double p) {
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for(int i=0; i<n_visible; i++) {
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if(x[i] == 0) {
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tilde_x[i] = 0;
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} else {
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tilde_x[i] = binomial(1, p);
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}
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}
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}
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// Encode
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public void get_hidden_values(int[] x, double[] y) {
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for(int i=0; i<n_hidden; i++) {
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y[i] = 0;
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for(int j=0; j<n_visible; j++) {
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y[i] += W[i][j] * x[j];
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}
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y[i] += hbias[i];
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y[i] = sigmoid(y[i]);
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}
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}
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// Decode
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public void get_reconstructed_input(double[] y, double[] z) {
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for(int i=0; i<n_visible; i++) {
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z[i] = 0;
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for(int j=0; j<n_hidden; j++) {
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z[i] += W[j][i] * y[j];
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}
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z[i] += vbias[i];
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z[i] = sigmoid(z[i]);
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}
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}
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public void train(int[] x, double lr, double corruption_level) {
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int[] tilde_x = new int[n_visible];
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double[] y = new double[n_hidden];
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double[] z = new double[n_visible];
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double[] L_vbias = new double[n_visible];
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double[] L_hbias = new double[n_hidden];
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double p = 1 - corruption_level;
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get_corrupted_input(x, tilde_x, p);
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get_hidden_values(tilde_x, y);
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get_reconstructed_input(y, z);
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// vbias
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for(int i=0; i<n_visible; i++) {
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L_vbias[i] = x[i] - z[i];
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vbias[i] += lr * L_vbias[i] / N;
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}
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// hbias
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for(int i=0; i<n_hidden; i++) {
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L_hbias[i] = 0;
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for(int j=0; j<n_visible; j++) {
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L_hbias[i] += W[i][j] * L_vbias[j];
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}
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L_hbias[i] *= y[i] * (1 - y[i]);
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hbias[i] += lr * L_hbias[i] / N;
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}
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// W
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for(int i=0; i<n_hidden; i++) {
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for(int j=0; j<n_visible; j++) {
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W[i][j] += lr * (L_hbias[i] * tilde_x[j] + L_vbias[j] * y[i]) / N;
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}
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}
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}
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public void reconstruct(int[] x, double[] z) {
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double[] y = new double[n_hidden];
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get_hidden_values(x, y);
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get_reconstructed_input(y, z);
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}
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private static void test_dA() {
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Random rng = new Random(123);
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double learning_rate = 0.1;
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double corruption_level = 0.3;
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int training_epochs = 100;
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int train_N = 10;
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int test_N = 2;
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int n_visible = 20;
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int n_hidden = 5;
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int[][] train_X = {
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{1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0},
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{1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0},
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{1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0},
168+
{1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0},
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{0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0},
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{0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1},
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{0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1},
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{0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1},
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{0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1},
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{0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0}
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};
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dA da = new dA(train_N, n_visible, n_hidden, null, null, null, rng);
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// train
180+
for(int epoch=0; epoch<training_epochs; epoch++) {
181+
for(int i=0; i<train_N; i++) {
182+
da.train(train_X[i], learning_rate, corruption_level);
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}
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}
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// test data
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int[][] test_X = {
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{1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0},
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{0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0}
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};
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double[][] reconstructed_X = new double[test_N][n_visible];
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// test
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for(int i=0; i<test_N; i++) {
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da.reconstruct(test_X[i], reconstructed_X[i]);
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for(int j=0; j<n_visible; j++) {
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System.out.printf("%.5f ", reconstructed_X[i][j]);
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}
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System.out.println();
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}
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}
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public static void main(String[] args) {
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test_dA();
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}
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}

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