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GeezerVison is showing github.com — DeepLearning/java/src/DeepLearning/DBN.java at master · cdingding/DeepLearning

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package DeepLearning;
import java.util.Random;
import static DeepLearning.utils.*;
public class DBN {
public int N;
public int n_ins;
public int[] hidden_layer_sizes;
public int n_outs;
public int n_layers;
public HiddenLayerDiscrete[] sigmoid_layers;
public RBM[] rbm_layers;
public LogisticRegressionDiscrete log_layer;
public Random rng;
public DBN(int N, int n_ins, int[] hidden_layer_sizes, int n_outs, int n_layers, Random rng) {
int input_size;
this.N = N;
this.n_ins = n_ins;
this.hidden_layer_sizes = hidden_layer_sizes;
this.n_outs = n_outs;
this.n_layers = n_layers;
this.sigmoid_layers = new HiddenLayerDiscrete[n_layers];
this.rbm_layers = new RBM[n_layers];
if(rng == null) this.rng = new Random(1234);
else this.rng = rng;
// construct multi-layer
for(int i=0; i<this.n_layers; i++) {
if(i == 0) {
input_size = this.n_ins;
} else {
input_size = this.hidden_layer_sizes[i-1];
}
// construct sigmoid_layer
this.sigmoid_layers[i] = new HiddenLayerDiscrete(this.N, input_size, this.hidden_layer_sizes[i], null, null, rng);
// construct rbm_layer
this.rbm_layers[i] = new RBM(this.N, input_size, this.hidden_layer_sizes[i], this.sigmoid_layers[i].W, this.sigmoid_layers[i].b, null, rng);
}
// layer for output using Logistic Regression
this.log_layer = new LogisticRegressionDiscrete(this.N, this.hidden_layer_sizes[this.n_layers-1], this.n_outs);
}
public void pretrain(int[][] train_X, double lr, int k, int epochs) {
int[] layer_input = new int[0];
int prev_layer_input_size;
int[] prev_layer_input;
for(int i=0; i<n_layers; i++) { // layer-wise
for(int epoch=0; epoch<epochs; epoch++) { // training epochs
for(int n=0; n<N; n++) { // input x1...xN
// layer input
for(int l=0; l<=i; l++) {
if(l == 0) {
layer_input = new int[n_ins];
for(int j=0; j<n_ins; j++) layer_input[j] = train_X[n][j];
} else {
if(l == 1) prev_layer_input_size = n_ins;
else prev_layer_input_size = hidden_layer_sizes[l-2];
prev_layer_input = new int[prev_layer_input_size];
for(int j=0; j<prev_layer_input_size; j++) prev_layer_input[j] = layer_input[j];
layer_input = new int[hidden_layer_sizes[l-1]];
sigmoid_layers[l-1].sample_h_given_v(prev_layer_input, layer_input);
}
}
rbm_layers[i].contrastive_divergence(layer_input, lr, k);
}
}
}
}
public void finetune(int[][] train_X, int[][] train_Y, double lr, int epochs) {
int[] layer_input = new int[0];
// int prev_layer_input_size;
int[] prev_layer_input = new int[0];
for(int epoch=0; epoch<epochs; epoch++) {
for(int n=0; n<N; n++) {
// layer input
for(int i=0; i<n_layers; i++) {
if(i == 0) {
prev_layer_input = new int[n_ins];
for(int j=0; j<n_ins; j++) prev_layer_input[j] = train_X[n][j];
} else {
prev_layer_input = new int[hidden_layer_sizes[i-1]];
for(int j=0; j<hidden_layer_sizes[i-1]; j++) prev_layer_input[j] = layer_input[j];
}
layer_input = new int[hidden_layer_sizes[i]];
sigmoid_layers[i].sample_h_given_v(prev_layer_input, layer_input);
}
log_layer.train(layer_input, train_Y[n], lr);
}
// lr *= 0.95;
}
}
public void predict(int[] x, double[] y) {
double[] layer_input = new double[0];
// int prev_layer_input_size;
double[] prev_layer_input = new double[n_ins];
for(int j=0; j<n_ins; j++) prev_layer_input[j] = x[j];
double linear_output;
// layer activation
for(int i=0; i<n_layers; i++) {
layer_input = new double[sigmoid_layers[i].n_out];
for(int k=0; k<sigmoid_layers[i].n_out; k++) {
linear_output = 0.0;
for(int j=0; j<sigmoid_layers[i].n_in; j++) {
linear_output += sigmoid_layers[i].W[k][j] * prev_layer_input[j];
}
linear_output += sigmoid_layers[i].b[k];
layer_input[k] = sigmoid(linear_output);
}
if(i < n_layers-1) {
prev_layer_input = new double[sigmoid_layers[i].n_out];
for(int j=0; j<sigmoid_layers[i].n_out; j++) prev_layer_input[j] = layer_input[j];
}
}
for(int i=0; i<log_layer.n_out; i++) {
y[i] = 0;
for(int j=0; j<log_layer.n_in; j++) {
y[i] += log_layer.W[i][j] * layer_input[j];
}
y[i] += log_layer.b[i];
}
log_layer.softmax(y);
}
private static void test_dbn() {
Random rng = new Random(123);
double pretrain_lr = 0.1;
int pretraining_epochs = 1000;
int k = 1;
double finetune_lr = 0.1;
int finetune_epochs = 500;
int train_N = 6;
int test_N = 4;
int n_ins = 6;
int n_outs = 2;
int[] hidden_layer_sizes = {3, 3};
int n_layers = hidden_layer_sizes.length;
// training data
int[][] train_X = {
{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}
};
int[][] train_Y = {
{1, 0},
{1, 0},
{1, 0},
{0, 1},
{0, 1},
{0, 1},
};
// construct DNN.DBN
DBN dbn = new DBN(train_N, n_ins, hidden_layer_sizes, n_outs, n_layers, rng);
// pretrain
dbn.pretrain(train_X, pretrain_lr, k, pretraining_epochs);
// finetune
dbn.finetune(train_X, train_Y, finetune_lr, finetune_epochs);
// test data
int[][] test_X = {
{1, 1, 0, 0, 0, 0},
{1, 1, 1, 1, 0, 0},
{0, 0, 0, 1, 1, 0},
{0, 0, 1, 1, 1, 0},
};
double[][] test_Y = new double[test_N][n_outs];
// test
for(int i=0; i<test_N; i++) {
dbn.predict(test_X[i], test_Y[i]);
for(int j=0; j<n_outs; j++) {
System.out.print(test_Y[i][j] + " ");
}
System.out.println();
}
}
public static void main(String[] args) {
test_dbn();
}
}
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