public LenetClassifier TestLoadNetwork() { // load network from new format LenetClassifier classifier = new LenetClassifier(); classifier.Load(networkFileName); return classifier; }
public void TestSaveNetwork() { // create lenet LenetClassifier classifier = new LenetClassifier(); classifier.CharClass.TanhSigmoid = false; classifier.CharClass.NetNorm = false; classifier.CharClass.AsciiTarget = true; classifier.JunkClass.TanhSigmoid = false; classifier.Set("junk", 0); // disable junk classifier.SetExtractor("scaledfe"); classifier.Initialize(classesNums); // load char lenet from old file format LenetWrapper.LoadNetwork(classifier.CharClass.HrLenet, oldformatFileName); // save network to new format classifier.Save(networkFileName); }
public void TestRecognize() { LenetClassifier classifier = new LenetClassifier(); classifier.Load(networkFileName); StringBuilder sbout; classifier.GetStdout(out sbout); Console.Write(sbout); DoTestRecognize(classifier); }
private void DoTestRecognize(LenetClassifier classifier) { OutputVector ov = new OutputVector(); Floatarray v = new Floatarray(); Bytearray ba = new Bytearray(1, 1); ImgIo.read_image_gray(ba, testPngFileName); NarrayUtil.Sub(255, ba); v.Copy(ba); v /= 255.0; classifier.XOutputs(ov, v); Console.WriteLine("Featured output class '{0}', score '{1}'", (char)ov.Key(ov.BestIndex), ov.Value(ov.BestIndex)); }
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); }