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HiddenLayer.java
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106 lines (84 loc) · 2.98 KB
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package DeepLearning;
import java.util.Random;
import java.util.function.DoubleFunction;
import static DeepLearning.utils.*;
public class HiddenLayer {
public int N;
public int n_in;
public int n_out;
public double[][] W;
public double[] b;
public Random rng;
public DoubleFunction<Double> activation;
public DoubleFunction<Double> dactivation;
public HiddenLayer(int N, int n_in, int n_out, double[][] W, double[] b, Random rng, String activation) {
this.N = N;
this.n_in = n_in;
this.n_out = n_out;
if (rng == null) this.rng = new Random(1234);
else this.rng = rng;
if (W == null) {
this.W = new double[n_out][n_in];
double a = 1.0 / this.n_in;
for(int i=0; i<n_out; i++) {
for(int j=0; j<n_in; j++) {
this.W[i][j] = uniform(-a, a, rng);
}
}
} else {
this.W = W;
}
if (b == null) this.b = new double[n_out];
else this.b = b;
if (activation == "sigmoid" || activation == null) {
this.activation = (double x) -> sigmoid(x);
this.dactivation = (double x) -> dsigmoid(x);
} else if (activation == "tanh") {
this.activation = (double x) -> tanh(x);
this.dactivation = (double x) -> dtanh(x);
} else if (activation == "ReLU") {
this.activation = (double x) -> ReLU(x);
this.dactivation = (double x) -> dReLU(x);
} else {
throw new IllegalArgumentException("activation function not supported");
}
}
public double output(double[] input, double[] w, double b) {
double linear_output = 0.0;
for(int j=0; j<n_in; j++) {
linear_output += w[j] * input[j];
}
linear_output += b;
return activation.apply(linear_output);
}
public void forward(double[] input, double[] output) {
for(int i=0; i<n_out; i++) {
output[i] = this.output(input, W[i], b[i]);
}
}
public void backward(double[] input, double[] dy, double[] prev_layer_input, double[] prev_layer_dy, double[][] prev_layer_W, double lr) {
if(dy == null) dy = new double[n_out];
int prev_n_in = n_out;
int prev_n_out = prev_layer_dy.length;
for(int i=0; i<prev_n_in; i++) {
dy[i] = 0;
for(int j=0; j<prev_n_out; j++) {
dy[i] += prev_layer_dy[j] * prev_layer_W[j][i];
}
dy[i] *= dactivation.apply(prev_layer_input[i]);
}
for(int i=0; i<n_out; i++) {
for(int j=0; j<n_in; j++) {
W[i][j] += lr * dy[i] * input[j] / N;
}
b[i] += lr * dy[i] / N;
}
}
public int[] dropout(int size, double p, Random rng) {
int[] mask = new int[size];
for(int i=0; i<size; i++) {
mask[i] = binomial(1, p, rng);
}
return mask;
}
}