Beispiel #1
0
 static FeedForwardLayer loadFFLayer(string dir)
 {
     try
     {
         //loading noLinearity
         string noLinearity = "";
         using (StreamReader sr = new StreamReader(Path.Combine(dir, "info.csv")))
         {
             noLinearity = sr.ReadLine();
         }
         INonlinearity lin = null;
         if (noLinearity == "LinearUnit")
         {
             lin = new LinearUnit();
         }
         if (noLinearity == "RectifiedLinearUnit")
         {
             lin = new RectifiedLinearUnit();
         }
         if (noLinearity == "SigmoidUnit")
         {
             lin = new SigmoidUnit();
         }
         if (noLinearity == "SineUnit")
         {
             lin = new SineUnit();
         }
         if (noLinearity == "TanhUnit")
         {
             lin = new TanhUnit();
         }
         Matrix w = loadMatrix(Path.Combine(dir, "W.csv"));
         Matrix b = loadMatrix(Path.Combine(dir, "B.csv"));
         return(new FeedForwardLayer(w, b, lin));
     }
     catch (Exception ex)
     {
         Console.WriteLine(ex.Message);
     }
     return(null);
 }
Beispiel #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);
                }
            }
        }