private static void Main(string[] args)
{
try
{
// This example is going to run on the MNIST dataset.
if (args.Length != 1)
{
Console.WriteLine("This example needs the MNIST dataset to run!");
Console.WriteLine("You can get MNIST from http://yann.lecun.com/exdb/mnist/");
Console.WriteLine("Download the 4 files that comprise the dataset, decompress them, and");
Console.WriteLine("put them in a folder. Then give that folder as input to this program.");
return;
}
Dlib.LoadMNISTDataset(args[0],
out var trainingImages,
out var trainingLabels,
out var testingImages,
out var testingLabels);
// Make an instance of our inception network.
using (var net = new LossMulticlassLog())
{
Console.WriteLine($"The net has {net.NumLayers} layers in it.");
Console.WriteLine(net);
Console.WriteLine("Training NN...");
using (var trainer = new DnnTrainer <LossMulticlassLog>(net))
{
trainer.SetLearningRate(0.01);
trainer.SetMinLearningRate(0.00001);
trainer.SetMiniBatchSize(128);
trainer.BeVerbose();
trainer.SetSynchronizationFile("inception_sync", 20);
// Train the network. This might take a few minutes...
LossMulticlassLog.Train(trainer, trainingImages, trainingLabels);
// At this point our net object should have learned how to classify MNIST images. But
// before we try it out let's save it to disk. Note that, since the trainer has been
// running images through the network, net will have a bunch of state in it related to
// the last batch of images it processed (e.g. outputs from each layer). Since we
// don't care about saving that kind of stuff to disk we can tell the network to forget
// about that kind of transient data so that our file will be smaller. We do this by
// "cleaning" the network before saving it.
net.Clean();
LossMulticlassLog.Serialize(net, "mnist_network_inception.dat");
// Now if we later wanted to recall the network from disk we can simply say:
// deserialize("mnist_network_inception.dat") >> net;
// Now let's run the training images through the network. This statement runs all the
// images through it and asks the loss layer to convert the network's raw output into
// labels. In our case, these labels are the numbers between 0 and 9.
using (var predictedLabels = net.Operator(trainingImages))
{
var numRight = 0;
var numWrong = 0;
// And then let's see if it classified them correctly.
for (var i = 0; i < trainingImages.Count; ++i)
{
if (predictedLabels[i] == trainingLabels[i])
{
++numRight;
}
else
{
++numWrong;
}
}
Console.WriteLine($"training num_right: {numRight}");
Console.WriteLine($"training num_wrong: {numWrong}");
Console.WriteLine($"training accuracy: {numRight / (double)(numRight + numWrong)}");
// Let's also see if the network can correctly classify the testing images.
// Since MNIST is an easy dataset, we should see 99% accuracy.
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;
}
}
Console.WriteLine($"testing num_right: {numRight}");
Console.WriteLine($"testing num_wrong: {numWrong}");
Console.WriteLine($"testing accuracy: {numRight / (double)(numRight + numWrong)}");
}
}
}
}
}
catch (Exception e)
{
Console.WriteLine(e);
}
}
