public void ProcessWhatIs()
{
String filename = GetArg("image");
try
{
var img = new Bitmap(filename);
var input = new ImageMLData(img);
input.Downsample(downsample, false, downsampleHeight,
downsampleWidth, 1, -1);
int winner = network.Winner(input);
app.WriteLine("What is: " + filename + ", it seems to be: "
+ neuron2identity[winner]);
}
catch (Exception e)
{
app.WriteLine("Error loading: " + filename + ", " + e.Message);
}
}
private void ProcessNetwork()
{
app.WriteLine("Downsampling images...");
foreach (ImagePair pair in imageList)
{
var ideal = new BasicMLData(outputCount);
int idx = pair.Identity;
for (int i = 0; i < outputCount; i++)
{
if (i == idx)
{
ideal[i] = 1;
}
else
{
ideal[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);
}
