private static void Validation(string baseName,
LossMulticlassLog net,
IList <Matrix <RgbPixel> > trainingImages,
IList <uint> trainingLabels,
IList <Matrix <RgbPixel> > testingImages,
IList <uint> testingLabels,
bool useConsole,
bool saveToXml,
out double trainAccuracy,
out double testAccuracy)
{
trainAccuracy = 0;
testAccuracy = 0;
using (var predictedLabels = net.Operator(trainingImages))
{
var numRight = 0;
var numWrong = 0;
// And then let's see if it classified them correctly.
for (var i = 0; i < trainingImages.Count; ++i)
{
if (predictedLabels[i] == trainingLabels[i])
{
++numRight;
}
else
{
++numWrong;
}
}
if (useConsole)
{
Console.WriteLine($"training num_right: {numRight}");
Console.WriteLine($"training num_wrong: {numWrong}");
Console.WriteLine($"training accuracy: {numRight / (double)(numRight + numWrong)}");
}
trainAccuracy = numRight / (double)(numRight + numWrong);
using (var predictedLabels2 = net.Operator(testingImages))
{
numRight = 0;
numWrong = 0;
for (var i = 0; i < testingImages.Count; ++i)
{
if (predictedLabels2[i] == testingLabels[i])
{
++numRight;
}
else
{
++numWrong;
}
}
if (useConsole)
{
Console.WriteLine($"testing num_right: {numRight}");
Console.WriteLine($"testing num_wrong: {numWrong}");
Console.WriteLine($"testing accuracy: {numRight / (double)(numRight + numWrong)}");
}
testAccuracy = numRight / (double)(numRight + numWrong);
// Finally, you can also save network parameters to XML files if you want to do
// something with the network in another tool. For example, you could use dlib's
// tools/convert_dlib_nets_to_caffe to convert the network to a caffe model.
if (saveToXml)
{
Dlib.NetToXml(net, $"{baseName}.xml");
}
}
}
}