package recunn.examples;
import java.util.Random;

import recunn.datasets.TextGenerationUnbroken;
import recunn.datastructs.DataSet;
import recunn.model.Model;
import recunn.trainer.Trainer;
import recunn.util.NeuralNetworkHelper;

public class ExampleLilDicky {
	public static void main(String[] args) throws Exception {
		
		Random rng = new Random();
		int totalSequences = 2000;
		int sequenceMinLength = 10;
		int sequenceMaxLength = 100; 
		String textSource = "LilDicky";
		DataSet data = new TextGenerationUnbroken("datasets/text/"+textSource+".txt", totalSequences, sequenceMinLength, sequenceMaxLength, rng);
		String savePath = "saved_models/"+textSource+".ser";
		boolean initFromSaved = true; //set this to false to start with a fresh model
		boolean overwriteSaved = true;
		
		TextGenerationUnbroken.reportSequenceLength = 500;
		
		int bottleneckSize = 10; //one-hot input is squeezed through this
		int hiddenDimension = 200;
		int hiddenLayers = 1;
		double learningRate = 0.001;
		double initParamsStdDev = 0.08;
		
		Model lstm = NeuralNetworkHelper.makeLstmWithInputBottleneck( 
				data.inputDimension, bottleneckSize, 
				hiddenDimension, hiddenLayers, 
				data.outputDimension, data.getModelOutputUnitToUse(), 
				initParamsStdDev, rng);
		
		int reportEveryNthEpoch = 10;
		int trainingEpochs = 1000;
		
		Trainer.train(trainingEpochs, learningRate, lstm, data, reportEveryNthEpoch, initFromSaved, overwriteSaved, savePath, rng);
		
		System.out.println("done.");
	}
}