java写的深度学习算法代码

import java.util.Random; public class SdA { public int N; public int n_ins; public int[] hidden_layer_sizes; public int n_outs; public int n_layers; public HiddenLayer[] sigmoid_layers; public dA[] dA_layers; public LogisticRegression log_layer; public Random rng; public static double sigmoid(double x) { return 1.0 / (1.0 + Math.pow(Math.E, -x)); } public SdA(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 HiddenLayer[n_layers]; this.dA_layers = new dA[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 HiddenLayer(this.N, input_size, this.hidden_layer_sizes[i], null, null, rng); // construct dA_layer this.dA_layers[i] = new dA(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 LogisticRegression this.log_layer = new LogisticRegression(this.N, this.hidden_layer_sizes[this.n_layers-1], this.n_outs); } public void pretrain(int[][] train_X, double lr, double corruption_level, 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); } } dA_layers[i].train(layer_input, lr, corruption_level); } } } } 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]; linear_output = 0.0; for(int k=0; k<sigmoid_layers[i].n_out; k++) { 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_sda() { Random rng = new Random(123); double pretrain_lr = 0.1; double corruption_level = 0.3; int pretraining_epochs = 1000; double finetune_lr = 0.1; int finetune_epochs = 500; int train_N = 10; int test_N = 4; int n_ins = 28; int n_outs = 2; int[] hidden_layer_sizes = {15, 15}; int n_layers = hidden_layer_sizes.length; // training data int[][] train_X = { {1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}, {0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}, {1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}, {0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}, {1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}, {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1}, {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1}, {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1}, {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1}, {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1} }; int[][] train_Y = { {1, 0}, {1, 0}, {1, 0}, {1, 0}, {1, 0}, {0, 1}, {0, 1}, {0, 1}, {0, 1}, {0, 1} }; // construct SdA SdA sda = new SdA(train_N, n_ins, hidden_layer_sizes, n_outs, n_layers, rng); // pretrain sda.pretrain(train_X, pretrain_lr, corruption_level, pretraining_epochs); // finetune sda.finetune(train_X, train_Y, finetune_lr, finetune_epochs); // test data int[][] test_X = { {1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}, {1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}, {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1}, {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1} }; double[][] test_Y = new double[test_N][n_outs]; // test for(int i=0; i<test_N; i++) { sda.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_sda(); } }