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The inputSize of each layer does not need to be specified anymore

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lluni 2022-05-25 00:41:38 +02:00
parent 95501bf4b1
commit 1c66f1b72f
3 changed files with 60 additions and 41 deletions
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4
src/main/java/ExampleXOR.java
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@ -12,9 +12,9 @@ public class ExampleXOR {
new SimpleMatrix(new double[][]{{0}})};
Network network = new Network();
network.addLayer(new FCLayer(2, 3));
network.addLayer(new FCLayer(3));
network.addLayer(new ActivationLayer(ActivationFunctions::tanh, ActivationFunctions::tanhPrime));
network.addLayer(new FCLayer(3, 1));
network.addLayer(new FCLayer(1));
network.addLayer(new ActivationLayer(ActivationFunctions::tanh, ActivationFunctions::tanhPrime));
network.use(LossFunctions::MSE, LossFunctions::MSEPrime);

4
src/main/java/ExampleXORBlankLayers.java
View file

@ -12,9 +12,9 @@ public class ExampleXORBlankLayers {
new SimpleMatrix(new double[][]{{0}})};
Network network = new Network();
network.addLayer(new FCLayer(2, 1));
network.addLayer(new FCLayer(1));
network.addLayer(new ActivationLayer(ActivationFunctions::tanh, ActivationFunctions::tanhPrime));
network.addLayer(new FCLayer(1, 1));
network.addLayer(new FCLayer(1));
network.addLayer(new ActivationLayer(ActivationFunctions::tanh, ActivationFunctions::tanhPrime));
network.addNeuron(0, 2);

93
src/main/java/FCLayer.java
View file

@ -3,15 +3,23 @@ import org.ejml.simple.SimpleMatrix;
import java.util.Random;
public class FCLayer extends Layer {
SimpleMatrix weights;
SimpleMatrix biases;
private SimpleMatrix weights;
private SimpleMatrix biases;
private int numNeurons;
private boolean isInitialized;
public FCLayer(int inputSize, int outputSize) {
public FCLayer(int numNeurons) {
this.numNeurons = numNeurons;
isInitialized = false;
}
private void initialize(int inputSize) {
Random random = new Random();
weights = new SimpleMatrix(inputSize, outputSize, true,
random.doubles((long) inputSize*outputSize, -1, 1).toArray());
biases = new SimpleMatrix(1, outputSize, true,
random.doubles(outputSize, -1, 1).toArray());
this.weights = new SimpleMatrix(inputSize, numNeurons, true,
random.doubles((long) inputSize*numNeurons, -1, 1).toArray());
this.biases = new SimpleMatrix(1, numNeurons, true,
random.doubles(numNeurons, -1, 1).toArray());
this.isInitialized = true;
}
/**
@ -19,21 +27,23 @@ public class FCLayer extends Layer {
* @param n amount of new neurons in previous layer
*/
public void updateInputSize(int n) {
Random random = new Random();
if (isInitialized) {
Random random = new Random();
// add new weights
SimpleMatrix newWeights = new SimpleMatrix(this.weights.numRows() + n, this.weights.numCols());
for (int i = 0; i < this.weights.numRows(); i++) {
for (int j = 0; j < this.weights.numCols(); j++) {
newWeights.set(i, j, this.weights.get(i, j));
// add new weights
SimpleMatrix newWeights = new SimpleMatrix(this.weights.numRows() + n, this.weights.numCols());
for (int i = 0; i < this.weights.numRows(); i++) {
for (int j = 0; j < this.weights.numCols(); j++) {
newWeights.set(i, j, this.weights.get(i, j));
}
}
}
for (int i = 0; i < newWeights.getNumElements(); i++) {
if (newWeights.get(i) == 0) {
newWeights.set(i, random.nextDouble(-1, 1));
for (int i = 0; i < newWeights.getNumElements(); i++) {
if (newWeights.get(i) == 0) {
newWeights.set(i, random.nextDouble(-1, 1));
}
}
this.weights = newWeights;
}
this.weights = newWeights;
}
/**
@ -43,30 +53,39 @@ public class FCLayer extends Layer {
public void addNeuron(int n) {
Random random = new Random();
// add new weights
SimpleMatrix newWeights = new SimpleMatrix(this.weights.numRows(), this.weights.numCols() + n);
for (int i = 0; i < this.weights.numRows(); i++) {
for (int j = 0; j < this.weights.numCols(); j++) {
newWeights.set(i, j, this.weights.get(i, j));
}
}
for (int i = 0; i < newWeights.getNumElements(); i++) {
if (newWeights.get(i) == 0) {
newWeights.set(i, random.nextDouble(-1, 1));
}
}
this.weights = newWeights;
// update neuron count
this.numNeurons += n;
// add new biases
SimpleMatrix newBiases = new SimpleMatrix(1, this.biases.numCols() + n);
double[] newBiasValues = random.doubles(n, -1, 1).toArray();
System.arraycopy(this.biases.getDDRM().data, 0, newBiases.getDDRM().data, 0, this.biases.numCols());
System.arraycopy(newBiasValues, 0, newBiases.getDDRM().data, this.biases.numCols(), n);
this.biases = newBiases;
if (isInitialized) {
// add new weights
SimpleMatrix newWeights = new SimpleMatrix(this.weights.numRows(), this.weights.numCols() + n);
for (int i = 0; i < this.weights.numRows(); i++) {
for (int j = 0; j < this.weights.numCols(); j++) {
newWeights.set(i, j, this.weights.get(i, j));
}
}
for (int i = 0; i < newWeights.getNumElements(); i++) {
if (newWeights.get(i) == 0) {
newWeights.set(i, random.nextDouble(-1, 1));
}
}
this.weights = newWeights;
// add new biases
SimpleMatrix newBiases = new SimpleMatrix(1, this.biases.numCols() + n);
double[] newBiasValues = random.doubles(n, -1, 1).toArray();
System.arraycopy(this.biases.getDDRM().data, 0, newBiases.getDDRM().data, 0, this.biases.numCols());
System.arraycopy(newBiasValues, 0, newBiases.getDDRM().data, this.biases.numCols(), n);
this.biases = newBiases;
}
}
@Override
public SimpleMatrix forwardPropagation(SimpleMatrix inputs) {
if (!isInitialized) {
initialize(inputs.numCols());
}
this.input = inputs;
this.output = this.input.mult(this.weights).plus(this.biases);
return this.output;