Example #1
0
        public LenetClassifier TestLoadNetwork()
        {
            // load network from new format
            LenetClassifier classifier = new LenetClassifier();
            classifier.Load(networkFileName);

            return classifier;
        }
Example #2
0
        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);
        }
Example #3
0
        public void TestRecognize()
        {
            LenetClassifier classifier = new LenetClassifier();
            classifier.Load(networkFileName);

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

            DoTestRecognize(classifier);
        }
Example #4
0
 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));
 }
Example #5
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);
        }