Load() public method

public Load ( BinaryReader reader ) : void
reader System.IO.BinaryReader
return void
RowDataset8 Class Documentation
Exemplo n.º 1
0
        public void TestRowDataset()
        {
            DRandomizer.Default.init_drand(DateTime.Now.Millisecond);

            // load Mnist datasource
            MnistDatasource mds = new MnistDatasource();
            mds.LoadFromFile(mnistFileNamePrefix);

            // convert mnist to RowDataset8
            RowDataset8 ds8 = MnistDatasetConvert.GetRowDataset8(mds, classes);

            // show random sample to console
            Floatarray fa = new Floatarray();
            int isample = (int)DRandomizer.Default.drand(ds8.nSamples(), 0);
            ds8.Input(fa, isample);
            Console.WriteLine("Char is '{0}'", (char)ds8.Cls(isample));
            NarrayShow.ShowConsole(fa);

            // compare random float sample and original mnist
            StdInput inp1 = new StdInput(mds.ImagesData[isample], mds.ImgHeight, mds.ImgWidth);
            StdInput inp2 = new StdInput(fa);
            Console.WriteLine("Arrays is identical? {0}", Equals(inp1.GetDataBuffer(), inp2.GetDataBuffer()));

            // save RowDataset8 to file
            Console.WriteLine("Saving {0} samples..", ds8.nSamples());
            ds8.Save(mnistFileNamePrefix + dsExt);

            // load RowDataset8 from file
            RowDataset8 ds = new RowDataset8();
            ds.Load(mnistFileNamePrefix + dsExt);
            Console.WriteLine("Loaded {0} samples", ds.nSamples());
        }
Exemplo n.º 2
0
        public void TestTrainSimple()
        {
            // create lenet
            LenetClassifier classifier = new LenetClassifier();
            classifier.Set("junk", 0);      // disable junk
            classifier.SetExtractor("scaledfe");
            classifier.Initialize(classesNums);

            StringBuilder sbout;
            classifier.GetStdout(out sbout);
            Console.Write(sbout);

            // load RowDataset8 from file
            RowDataset8 ds = new RowDataset8();
            ds.Load(trainDatasetFileName);

            // do train
            classifier.Set("epochs", 3);
            classifier.XTrain(ds);

            // save classifier to file
            classifier.Save(trainNetworkFileName);

            // test recognize
            DoTestRecognize(classifier);
        }