private void ProcessNetwork()
        {
            app.WriteLine("Downsampling images...");

            foreach (ImagePair pair in imageList)
            {
                IMLData ideal = new BasicMLData(outputCount);
                int     idx   = pair.Identity;
                for (int i = 0; i < outputCount; i++)
                {
                    if (i == idx)
                    {
                        ideal.Data[i] = 1;
                    }
                    else
                    {
                        ideal.Data[i] = -1;
                    }
                }

                try
                {
                    var img  = new Bitmap(pair.File);
                    var data = new ImageMLData(img);
                    training.Add(data, ideal);
                }
                catch (Exception e)
                {
                    app.WriteLine("Error loading: " + pair.File
                                  + ": " + e.Message);
                }
            }

            String strHidden1 = GetArg("hidden1");
            String strHidden2 = GetArg("hidden2");

            if (training.Count == 0)
            {
                app.WriteLine("No images to create network for.");
                return;
            }

            training.Downsample(downsampleHeight, downsampleWidth);

            int hidden1 = int.Parse(strHidden1);
            int hidden2 = int.Parse(strHidden2);

            network = EncogUtility.SimpleFeedForward(training
                                                     .InputSize, hidden1, hidden2,
                                                     training.IdealSize, true);
            app.WriteLine("Created network: " + network);
        }
Exemplo n.º 2
0
        private void Learn_Click(object sender, RoutedEventArgs e)
        {
            var downsample = new Downsampler();
            var training   = new ImageMLDataSet(downsample, true, 1, -1);

            for (var i = 0; i < Images.Count; ++i)
            {
                var ideal = new BasicMLData(DIGITS_COUNT);
                for (int j = 0; j < DIGITS_COUNT; ++j)
                {
                    if (j == i)
                    {
                        ideal[j] = 1;
                    }
                    else
                    {
                        ideal[j] = -1;
                    }
                }
                foreach (var img in Images[i])
                {
                    MemoryStream  stream  = new MemoryStream();
                    BitmapEncoder encoder = new BmpBitmapEncoder();
                    encoder.Frames.Add(BitmapFrame.Create(img));
                    encoder.Save(stream);

                    var bitmap = new Drawing.Bitmap(stream);
                    var data   = new ImageMLData(bitmap);
                    training.Add(data, ideal);
                }
            }

            training.Downsample(DIGIT_HEIGHT, DIGIT_WIDTH);

            network = EncogUtility.SimpleFeedForward(training.InputSize, 35, 0, training.IdealSize, true);

            double strategyError  = 0.01;
            int    strategyCycles = 2000;

            var train = new ResilientPropagation(network, training);

            //train.AddStrategy(new ResetStrategy(strategyError, strategyCycles));
            EncogUtility.TrainDialog(train, network, training);

            EncogDirectoryPersistence.SaveObject(new FileInfo("network.eg"), network);
        }