//Find the category and confidence chain of an image
public List <Result> Run(List <CategoryNetwork> networks, Category root, string imageFile)
{
var output = new List <Result>();
var currentCategory = root;
//keep deeping until you hit a terminal node
while (currentCategory.Children.Count > 0)
{
var net = GetNetwork(networks, currentCategory);
var netout = net.Network.Compute(ImageExtractor.ExtractImageFeatures(imageFile));
var max = netout.Max();
var maxI = Array.IndexOf(netout, max);
var result = new Result(net.Category.Children[maxI], max);
output.Add(result);
currentCategory = result.Category;
}
return(output);
}
//train the classifier networks
public double Train(List <CategoryNetwork> networks, List <TrainingSet> trainingSetList, List <TrainingSet> validationSetList = null)
{
var ng = new NetworkGraph();
//open a new training process display in a new thread
new Thread(() =>
{
ng.Load += (sender, e) => (sender as NetworkGraph).Visible = true;
ng.ShowDialog();
}).Start();
//start fresh, reset all networks
foreach (var net in networks)
{
net.ClearData();
}
//create a list of categoryNetworks and add the images to them to generate epochs
for (int i = 0; i < trainingSetList.Count; i++)
{
var cat = trainingSetList[i].Category;
var imageData = ImageExtractor.ExtractImageFeatures(trainingSetList[i].ImageFile);
while (cat.Parent != null)
{
var n = GetNetwork(networks, cat.Parent);
n.AddData(cat, ref imageData);
cat = cat.Parent;
}
}
//for each network begin training with paralell rprop
foreach (var net in networks)
{
var teacher = new ParallelResilientBackpropagationLearning(net.Network);
//display the category being trained
ng.AddTitle(net.Category.Name);
ng.ResetData();
// teacher.UpdateUpperBound = 500;
var inputs = net.ImageData.ToArray();
var outputs = new double[net.Catetgories.Count][];
//dtermine which documents belong to the calidation set for the network currently being trained
var thisValSet = new List <TrainingSet>();
if (validationSetList != null)
{
foreach (var td in validationSetList)
{
var cat = td.Category;
while (cat != null)
{
if (cat.Parent == net.Category)
{
thisValSet.Add(new TrainingSet(td.ImageFile, cat));
break;
}
cat = cat.Parent;
}
}
}
//vectorize these images and their respective categorical classification
var valOuts = thisValSet.Select(x => CategoryExtractor.ExtractCategoryFeature(x.Category)).ToArray();
var valIns = thisValSet.Select(x => ImageExtractor.ExtractImageFeatures(x.ImageFile)).ToArray();
for (int i = 0; i < net.Catetgories.Count; i++)
{
outputs[i] = CategoryExtractor.ExtractCategoryFeature(net.Catetgories[i]);
}
int k = 0;
//find the current batch error during training
double localError = teacher.ComputeError(inputs, outputs) / inputs.Length;
double validationError = 1;
DateTime start = DateTime.Now;
ng.AddPoint(localError, localError, DateTime.Now - start);
//find the validation error by checking the validation set on the network
while (validationError > .05 && localError > .04)
{
localError = teacher.RunEpoch(inputs, outputs) / inputs.Length;
if (valIns.Length > 0)
{
validationError = teacher.ComputeError(valIns, valOuts) / valIns.Length;
}
ng.AddPoint(localError, validationError, DateTime.Now - start);
k++;
}
}
return(0);
}
