/**
* Give a prediction based on some input.
*
* @param input The input to the neural network which is equal in size to the number of input
* neurons.
* @return The output of the neural network.
*/
public Matrix predict(Matrix input) {
if (input.getNumRows() != layers.get(0).getLayerSize().getInputSize()) {
throw new InvalidParameterException(
"Input size did not match the input size of the first layer");
}
Matrix modInput = (Matrix) input.clone();
for (Layer l : layers) {
modInput = l.activate(modInput);
}
return modInput;
}
/**
* Applies the activation function to the processed input.
*
* @param input The input to the activation function.
* @return The output of the activation function.
*/
private Matrix applyFunction(Matrix input) {
Matrix activated = (Matrix) input.clone();
for (int row = 0; row < input.getNumRows(); row++)
for (int col = 0; col < input.getNumCols(); col++)
activated.set(row, col, function.activate(input.get(row, col)));
if (function instanceof Softmax) {
double sum = activated.sum();
if (sum != 0) activated = activated.multiply(1 / sum);
}
return activated;
}
private Matrix applyFunctionDerivative(Matrix input) {
Matrix activated = (Matrix) input.clone();
if (function instanceof Softmax)
activated =
activated.map(
new Matrix.Function() {
@Override
public double function(double x) {
return Math.exp(x);
}
});
else
activated =
activated.map(
new Matrix.Function() {
@Override
public double function(double x) {
return function.derivative(x);
}
});
if (function instanceof Softmax) {
double sum = activated.sum();
if (sum != 0) activated = activated.multiply(1 / sum);
activated = activated.subtract(input);
activated =
activated.map(
new Matrix.Function() {
@Override
public double function(double x) {
return function.activate(x);
}
});
}
return activated;
}
/**
* Set the weight matrix of a layer of the neural network.
*
* @param layer The layer number of the neural network.
* @param weights The new weight matrix for the layer.
*/
public void setWeights(int layer, Matrix weights) {
layers.get(layer).weightMatrix = (Matrix) weights.clone();
}