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Moved support for adding neurons to FCLayer because BlankLayer is pretty much the same as FCLayer and currently useless

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lluni 2022-05-25 00:17:33 +02:00
parent 4766ea0ad9
commit 95501bf4b1
4 changed files with 58 additions and 57 deletions
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52
src/main/java/BlankLayer.java
View file

@ -3,6 +3,7 @@ import org.ejml.simple.SimpleMatrix;
import java.util.Random;
/**
* Goal: initialize layer without any neurons. Not yet implemented.
* Layer initialized with 1 neuron.
* Assumes that each new neuron is fully connected to every previous neuron (this will be changed in the future).
*/
@ -18,57 +19,6 @@ public class BlankLayer extends Layer {
random.doubles(1, -1, 1).toArray());
}
/**
* Updates input size when previous layer has newly added neurons.
* @param n amount of new neurons in previous layer
*/
public void updateInputSize(int n) {
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));
}
}
for (int i = 0; i < newWeights.getNumElements(); i++) {
if (newWeights.get(i) == 0) {
newWeights.set(i, random.nextDouble(-1, 1));
}
}
this.weights = newWeights;
}
/**
* Adds new neurons at the end of the layer
* @param n amount how many new neurons should be added
*/
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;
// 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) {
this.input = inputs;

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 BlankLayer(2));
network.addLayer(new FCLayer(2, 1));
network.addLayer(new ActivationLayer(ActivationFunctions::tanh, ActivationFunctions::tanhPrime));
network.addLayer(new BlankLayer(1));
network.addLayer(new FCLayer(1, 1));
network.addLayer(new ActivationLayer(ActivationFunctions::tanh, ActivationFunctions::tanhPrime));
network.addNeuron(0, 2);

51
src/main/java/FCLayer.java
View file

@ -14,6 +14,57 @@ public class FCLayer extends Layer {
random.doubles(outputSize, -1, 1).toArray());
}
/**
* Updates input size when previous layer has newly added neurons.
* @param n amount of new neurons in previous layer
*/
public void updateInputSize(int n) {
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));
}
}
for (int i = 0; i < newWeights.getNumElements(); i++) {
if (newWeights.get(i) == 0) {
newWeights.set(i, random.nextDouble(-1, 1));
}
}
this.weights = newWeights;
}
/**
* Adds new neurons at the end of the layer
* @param n amount how many new neurons should be added
*/
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;
// 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) {
this.input = inputs;

8
src/main/java/Network.java
View file

@ -23,13 +23,13 @@ public class Network {
* @param n amount how many new neurons should be added
*/
public void addNeuron(int layer, int n) {
if (!(this.layers.get(layer) instanceof BlankLayer)) {
if (!(this.layers.get(layer) instanceof FCLayer)) {
System.out.println("This layer is not a BlankLayer");
} else if (!(this.layers.get(layer + 2) instanceof BlankLayer)) {
} else if (!(this.layers.get(layer + 2) instanceof FCLayer)) {
System.out.println("The next layer is not a BlankLayer");
}
((BlankLayer) this.layers.get(layer)).addNeuron(n);
((BlankLayer) this.layers.get(layer + 2)).updateInputSize(n);
((FCLayer) this.layers.get(layer)).addNeuron(n);
((FCLayer) this.layers.get(layer + 2)).updateInputSize(n);
}
public void use(BiFunction<SimpleMatrix, SimpleMatrix, Double> loss, BiFunction<SimpleMatrix, SimpleMatrix, SimpleMatrix> lossPrime) {