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package util;
import java.util.ArrayList;
import java.util.List;
import java.util.Random;
import model.FeedForwardLayer;
import model.GruLayer;
import model.LinearLayer;
import model.LstmLayer;
import model.Model;
import model.NeuralNetwork;
import model.Nonlinearity;
import model.RnnLayer;
public class NeuralNetworkHelper {
public static NeuralNetwork makeLstm(int inputDimension, int hiddenDimension, int hiddenLayers, int outputDimension, Nonlinearity decoderUnit, double initParamsStdDev, Random rng) {
List<Model> layers = new ArrayList<>();
for (int h = 0; h < hiddenLayers; h++) {
if (h == 0) {
layers.add(new LstmLayer(inputDimension, hiddenDimension, initParamsStdDev, rng));
}
else {
layers.add(new LstmLayer(hiddenDimension, hiddenDimension, initParamsStdDev, rng));
}
}
layers.add(new FeedForwardLayer(hiddenDimension, outputDimension, decoderUnit, initParamsStdDev, rng));
return new NeuralNetwork(layers);
}
public static NeuralNetwork makeLstmWithInputBottleneck(int inputDimension, int bottleneckDimension, int hiddenDimension, int hiddenLayers, int outputDimension, Nonlinearity decoderUnit, double initParamsStdDev, Random rng) {
List<Model> layers = new ArrayList<>();
layers.add(new LinearLayer(inputDimension, bottleneckDimension, initParamsStdDev, rng));
for (int h = 0; h < hiddenLayers; h++) {
if (h == 0) {
layers.add(new LstmLayer(bottleneckDimension, hiddenDimension, initParamsStdDev, rng));
}
else {
layers.add(new LstmLayer(hiddenDimension, hiddenDimension, initParamsStdDev, rng));
}
}
layers.add(new FeedForwardLayer(hiddenDimension, outputDimension, decoderUnit, initParamsStdDev, rng));
return new NeuralNetwork(layers);
}
public static NeuralNetwork makeFeedForward(int inputDimension, int hiddenDimension, int hiddenLayers, int outputDimension, Nonlinearity hiddenUnit, Nonlinearity decoderUnit, double initParamsStdDev, Random rng) {
List<Model> layers = new ArrayList<>();
if (hiddenLayers == 0) {
layers.add(new FeedForwardLayer(inputDimension, outputDimension, decoderUnit, initParamsStdDev, rng));
return new NeuralNetwork(layers);
}
else {
for (int h = 0; h < hiddenLayers; h++) {
if (h == 0) {
layers.add(new FeedForwardLayer(inputDimension, hiddenDimension, hiddenUnit, initParamsStdDev, rng));
}
else {
layers.add(new FeedForwardLayer(hiddenDimension, hiddenDimension, hiddenUnit, initParamsStdDev, rng));
}
}
layers.add(new FeedForwardLayer(hiddenDimension, outputDimension, decoderUnit, initParamsStdDev, rng));
return new NeuralNetwork(layers);
}
}
public static NeuralNetwork makeGru(int inputDimension, int hiddenDimension, int hiddenLayers, int outputDimension, Nonlinearity decoderUnit, double initParamsStdDev, Random rng) {
List<Model> layers = new ArrayList<>();
for (int h = 0; h < hiddenLayers; h++) {
if (h == 0) {
layers.add(new GruLayer(inputDimension, hiddenDimension, initParamsStdDev, rng));
}
else {
layers.add(new GruLayer(hiddenDimension, hiddenDimension, initParamsStdDev, rng));
}
}
layers.add(new FeedForwardLayer(hiddenDimension, outputDimension, decoderUnit, initParamsStdDev, rng));
return new NeuralNetwork(layers);
}
public static NeuralNetwork makeRnn(int inputDimension, int hiddenDimension, int hiddenLayers, int outputDimension, Nonlinearity hiddenUnit, Nonlinearity decoderUnit, double initParamsStdDev, Random rng) {
List<Model> layers = new ArrayList<>();
for (int h = 0; h < hiddenLayers; h++) {
if (h == 0) {
layers.add(new RnnLayer(inputDimension, hiddenDimension, hiddenUnit, initParamsStdDev, rng));
}
else {
layers.add(new RnnLayer(hiddenDimension, hiddenDimension, hiddenUnit, initParamsStdDev, rng));
}
}
layers.add(new FeedForwardLayer(hiddenDimension, outputDimension, decoderUnit, initParamsStdDev, rng));
return new NeuralNetwork(layers);
}
}