Exemple #1
0
        public static string generateOutput(NeuralNetwork network, char start, int length)
        {
            Matrix input  = new Matrix(FieldLetterTranslator.traslateToField(start));
            Graph  g      = new Graph(false);
            Random rnd    = new Random();
            string result = "";

            for (int i = 0; i < length; i++)
            {
                Matrix output = network.Activate(input, g);
                //char act = FieldLetterTranslator.traslateToLetter(output.W);
                char act = 'a';
                try
                {
                    act = FieldLetterTranslator.Letters[Util.PickIndexFromRandomVector(output, rnd)];
                }
                catch (Exception ex)
                {
                    Console.WriteLine(ex.Message);
                }
                input   = new Matrix(FieldLetterTranslator.traslateToField(act));
                g       = new Graph(false);
                result += act;
            }
            return(result.Replace('$', '\n'));
        }
Exemple #2
0
        public static void Run()
        {
            Random rnd = new Random();

            FieldLetterTranslator.addChar((char)10);
            Console.WriteLine("Generate started");
            DataSet data = new TextDataSetGenerator(@"C:\Users\Kubik.HOME-PC\Dropbox\shakespear.txt");

            Console.WriteLine("Generate comlpeat");
            int           inputDimension  = FieldLetterTranslator.Letters.Length;
            int           hiddenDimension = 128;
            int           outputDimension = FieldLetterTranslator.Letters.Length;
            int           hiddenLayers    = 2;
            double        learningRate    = 0.0012;
            double        initPatStdDev   = 0.08;
            INonlinearity lin             = new SigmoidUnit();
            NeuralNetwork network         = NetworkBuilder.MakeLstm(inputDimension, hiddenDimension, hiddenLayers, outputDimension, lin, initPatStdDev, rnd);

            string output;


            int reportEveryNthEpoch = 50;
            int trainingEpochs      = 50;

            for (int i = 0; i < trainingEpochs; i++)
            {
                Trainer.train <NeuralNetwork>(1, learningRate, network, data, reportEveryNthEpoch, rnd);
                if (Directory.Exists(@"C:\Users\Kubik.HOME-PC\Documents\NeuralsTraing4\step" + i.ToString()))
                {
                    Directory.Delete(@"C:\Users\Kubik.HOME-PC\Documents\NeuralsTraing4\step" + i.ToString(), true);
                }
                learningRate *= 0.85;
                Directory.CreateDirectory(@"C:\Users\Kubik.HOME-PC\Documents\NeuralsTraing4\step" + i.ToString());
                NetworkBuilder.SaveLSTM(network, @"C:\Users\Kubik.HOME-PC\Documents\NeuralsTraing4\step" + i.ToString());
                output = generateOutput(network, 'a', 1000);
                using (StreamWriter sw = new StreamWriter(Path.Combine(@"C:\Users\Kubik.HOME-PC\Documents\NeuralsTraing4\outputs", "output" + i.ToString() + ".txt")))
                {
                    sw.WriteLine(output);
                }
            }
        }