Example #1
0
        static void Main(string[] args)
        {
            //MLPWithBiasTest test = new MLPWithBiasTest();
            //MLPTest test = new MLPTest();
            //test.TestXOR();
            //test.TestDigit();

            //return;

            /*
             * CsvMgrTest test = new CsvMgrTest();
             * test.LoadTest("test.csv");
             */

            string filename = "MLP-";

            filename += DateTime.Now.Ticks;
            filename += ".txt";
            StreamWriter sw = new StreamWriter(filename);

            Console.SetOut(sw);
            sw.AutoFlush = true;

            MnistDataMgr trainDataMgr = new MnistDataMgr("train.csv");

            trainDataMgr.Load();

            Console.WriteLine("Train data loaded");

            MnistDataMgr testDataMgr = new MnistDataMgr("test.csv");

            testDataMgr.Load();

            Console.WriteLine("Test data loaded");

            MnistMLPTrainer trainer = new MnistMLPTrainer(trainDataMgr);

            //int[] layerStruct = { trainDataMgr.inputNum, 100, 50, 20, 10 };
            //int[] layerStruct = { trainDataMgr.inputNum, 150, 50, 10 };
            //int[] layerStruct = { trainDataMgr.inputNum, 100, 50, 10 };
            int[] layerStruct = { trainDataMgr.inputNum, 80, 30, 10 };

            trainer.InitMLP(layerStruct, 0.8);
            //trainer.InitMLP(200, 0.8);
            trainer.OnlineTrain();
            //trainer.BatchTrain();

            //dataMgr.Dump("test2.csv");

            MnistMLPTester tester = new MnistMLPTester(trainer.GetNetwork(), testDataMgr);

            tester.Test();

            trainer.GetNetwork().DumpToFile();

            sw.Flush();
            sw.Close();

            return;
        }
Example #2
0
        static void Main(string[] args)
        {
            string filename = "DBN-";

            filename += DateTime.Now.Ticks;
            filename += ".txt";
            StreamWriter sw = new StreamWriter(filename);

            Console.SetOut(sw);
            sw.AutoFlush = true;

            MnistDataMgr trainDataMgr = new MnistDataMgr("train.csv");

            trainDataMgr.Load();

            Console.WriteLine("Train data loaded");

            MnistDataMgr testDataMgr = new MnistDataMgr("test.csv");

            testDataMgr.Load();

            Console.WriteLine("Test data loaded");

            //int [] netStruct = {trainDataMgr.inputNum, 100, 50, 20};
            //int[] netStruct = { trainDataMgr.inputNum, 200 };
            int[] netStruct = { trainDataMgr.inputNum, 200 };


            LRBM lrbm = new LRBM(1, netStruct);

            LRBMTrainer lrbmTrainer = new LRBMTrainer(trainDataMgr, lrbm);

            lrbmTrainer.BatchTrain();
            //lrbmTrainer.OnlineTrain();

            lrbm.DumpToFile();

            //DeepBeliefNet dbn = new DeepBeliefNet(0.8, lrbm, 10);
            DeepBeliefNet dbn = new DeepBeliefNet(0.8, lrbm, new int[] { 200, 10 });

            dbn.DumpToFile();
            dbn.GetMLP().DumpToFile();
            //dbn.GetMLP().ClearBias();

            //dbn.GetMLP()._trainType = MLP.TrainType.TRAIN_ONLY_OUTPUT_LAYER;
            dbn.GetMLP()._trainType    = MLP.TrainType.NORMAL_BP;
            dbn.GetMLP().learningDepth = 2;

            MnistMLPTrainer trainer = new MnistMLPTrainer(trainDataMgr, dbn.GetMLP());

            trainer.OnlineTrain();

            MnistMLPTester tester = new MnistMLPTester(dbn.GetMLP(), testDataMgr);

            tester.Test();

            sw.Flush();
            sw.Close();
        }