static void Main(string[] args)
{
Console.WriteLine("MNIST Test");
int seed;
using (var rng = new RNGCryptoServiceProvider())
{
var buffer = new byte[sizeof(int)];
rng.GetBytes(buffer);
seed = BitConverter.ToInt32(buffer, 0);
}
RandomProvider.SetSeed(seed);
var assembly = Assembly.GetExecutingAssembly();
var filename = "CNN.xml";
var serializer = new DataContractSerializer(typeof(IEnumerable <Layer>), new Type[] { typeof(Convolution), typeof(BatchNormalization), typeof(Activation), typeof(ReLU), typeof(MaxPooling), typeof(FullyConnected), typeof(Softmax) });
var random = RandomProvider.GetRandom();
var trainingList = new List <Tuple <double[], double[]> >();
var testList = new List <Tuple <double[], double[]> >();
var accuracyList = new List <double>();
var lossList = new List <double>();
var logPath = "Log.csv";
var channels = 1;
var imageWidth = 28;
var imageHeight = 28;
var filters = 30;
var filterWidth = 5;
var filterHeight = 5;
var poolWidth = 2;
var poolHeight = 2;
var activationMapWidth = Convolution.GetActivationMapLength(imageWidth, filterWidth);
var activationMapHeight = Convolution.GetActivationMapLength(imageHeight, filterHeight);
var outputWidth = MaxPooling.GetOutputLength(activationMapWidth, poolWidth);
var outputHeight = MaxPooling.GetOutputLength(activationMapHeight, poolHeight);
Model model;
using (Stream
imagesStream = assembly.GetManifestResourceStream("MNISTTest.train-images.idx3-ubyte"),
labelsStream = assembly.GetManifestResourceStream("MNISTTest.train-labels.idx1-ubyte"))
{
foreach (var image in MnistImage.Load(imagesStream, labelsStream).Take(1000))
{
var t = new double[10];
for (int i = 0; i < 10; i++)
{
if (i == image.Label)
{
t[i] = 1.0;
}
else
{
t[i] = 0.0;
}
}
trainingList.Add(Tuple.Create <double[], double[]>(image.Normalize(), t));
}
}
using (Stream
imagesStream = assembly.GetManifestResourceStream("MNISTTest.t10k-images.idx3-ubyte"),
labelsStream = assembly.GetManifestResourceStream("MNISTTest.t10k-labels.idx1-ubyte"))
{
foreach (var image in MnistImage.Load(imagesStream, labelsStream).Take(1000))
{
var t = new double[10];
for (int i = 0; i < 10; i++)
{
if (i == image.Label)
{
t[i] = 1.0;
}
else
{
t[i] = 0.0;
}
}
testList.Add(Tuple.Create <double[], double[]>(image.Normalize(), t));
}
}
if (File.Exists(filename))
{
using (XmlReader xmlReader = XmlReader.Create(filename))
{
model = new Model((IEnumerable <Layer>)serializer.ReadObject(xmlReader), new Adam(), new SoftmaxCrossEntropy());
}
}
else
{
/*model = new Model(new Layer[] {
* new Convolutional(channels, imageWidth, imageHeight, filters, filterWidth, filterHeight, (fanIn, fanOut) => Initializers.HeNormal(fanIn)),
* new Activation(filters * activationMapWidth * activationMapHeight, new ReLU()),
* new MaxPooling(filters, activationMapWidth, activationMapHeight, poolWidth, poolHeight),
* new FullyConnected(filters * outputWidth * outputHeight, 100, (fanIn, fanOut) => Initializers.HeNormal(fanIn)),
* new Activation(100, new ReLU()),
* new Softmax(100, 10, (fanIn, fanOut) => Initializers.GlorotNormal(fanIn, fanOut))
* }, new Adam(), new SoftmaxCrossEntropy());*/
/*var inputLayer = new Convolutional(channels, imageWidth, imageHeight, filters, filterWidth, filterHeight, (fanIn, fanOut) => Initializers.HeNormal(fanIn));
*
* new Softmax(
* new Activation(
* new FullyConnected(
* new MaxPooling(
* new Activation(inputLayer, new ReLU()),
* filters, inputLayer.ActivationMapWidth, inputLayer.ActivationMapHeight, poolWidth, poolHeight),
* 100, (fanIn, fanOut) => Initializers.HeNormal(fanIn)),
* new ReLU()),
* 10, (fanIn, fanOut) => Initializers.GlorotNormal(fanIn, fanOut));
*
* model = new Model(inputLayer, new Adam(), new SoftmaxCrossEntropy());*/
model = new Model(
new Convolution(channels, imageWidth, imageHeight, filters, filterWidth, filterHeight, (fanIn, fanOut) => Initializers.HeNormal(fanIn),
new Activation(new ReLU(),
new MaxPooling(filters, activationMapWidth, activationMapHeight, poolWidth, poolHeight,
new FullyConnected(filters * outputWidth * outputHeight, (fanIn, fanOut) => Initializers.HeNormal(fanIn),
new Activation(new ReLU(),
new Softmax(100, 10, (fanIn, fanOut) => Initializers.GlorotNormal(fanIn, fanOut))))))),
new Adam(), new SoftmaxCrossEntropy());
int epochs = 50;
int iterations = 1;
model.Stepped += (sender, e) =>
{
double tptn = 0.0;
trainingList.ForEach(x =>
{
var vector = model.Predicate(x.Item1);
var i = ArgMax(vector);
var j = ArgMax(x.Item2);
if (i == j && Math.Round(vector[i]) == x.Item2[j])
{
tptn += 1.0;
}
});
var accuracy = tptn / trainingList.Count;
accuracyList.Add(accuracy);
lossList.Add(model.Loss);
Console.WriteLine("Epoch {0}/{1}", iterations, epochs);
Console.WriteLine("Accuracy: {0}, Loss: {1}", accuracy, model.Loss);
iterations++;
};
Console.WriteLine("Training...");
var stopwatch = Stopwatch.StartNew();
model.Fit(trainingList, epochs, 100);
stopwatch.Stop();
Console.WriteLine("Done ({0}).", stopwatch.Elapsed.ToString());
}
double testTptn = 0.0;
testList.ForEach(x =>
{
var vector = model.Predicate(x.Item1);
var i = ArgMax(vector);
var j = ArgMax(x.Item2);
if (i == j && Math.Round(vector[i]) == x.Item2[j])
{
testTptn += 1.0;
}
});
Console.WriteLine("Accuracy: {0}", testTptn / testList.Count);
if (accuracyList.Count > 0)
{
var logDictionary = new Dictionary <string, IEnumerable <double> >();
logDictionary.Add("Accuracy", accuracyList);
logDictionary.Add("Loss", lossList);
ToCsv(logPath, logDictionary);
Console.WriteLine("Saved log to {0}...", logPath);
}
XmlWriterSettings settings = new XmlWriterSettings();
settings.Indent = true;
settings.Encoding = new System.Text.UTF8Encoding(false);
using (XmlWriter xmlWriter = XmlWriter.Create(filename, settings))
{
serializer.WriteObject(xmlWriter, model.Layers);
xmlWriter.Flush();
}
}
static void Main(string[] args)
{
Console.WriteLine("MNIST Test");
int seed;
using (var rng = new RNGCryptoServiceProvider())
{
var buffer = new byte[sizeof(int)];
rng.GetBytes(buffer);
seed = BitConverter.ToInt32(buffer, 0);
}
RandomProvider.SetSeed(seed);
var assembly = Assembly.GetExecutingAssembly();
var random = RandomProvider.GetRandom();
var trainingList = new List <Tuple <double[], double[]> >();
var testList = new List <Tuple <double[], double[]> >();
var accuracyList = new List <double>();
var lossList = new List <double>();
var logDictionary = new Dictionary <string, IEnumerable <double> >();
var logPath = "Log.csv";
var channels = 1;
var imageWidth = 28;
var imageHeight = 28;
var filters = 30;
var filterWidth = 5;
var filterHeight = 5;
var poolWidth = 2;
var poolHeight = 2;
using (Stream
imagesStream = assembly.GetManifestResourceStream("MNISTTest.train-images.idx3-ubyte"),
labelsStream = assembly.GetManifestResourceStream("MNISTTest.train-labels.idx1-ubyte"))
{
foreach (var image in MnistImage.Load(imagesStream, labelsStream).Take(1000))
{
var t = new double[10];
for (int i = 0; i < 10; i++)
{
if (i == image.Label)
{
t[i] = 1.0;
}
else
{
t[i] = 0.0;
}
}
trainingList.Add(Tuple.Create <double[], double[]>(image.Normalize(), t));
}
}
using (Stream
imagesStream = assembly.GetManifestResourceStream("MNISTTest.t10k-images.idx3-ubyte"),
labelsStream = assembly.GetManifestResourceStream("MNISTTest.t10k-labels.idx1-ubyte"))
{
foreach (var image in MnistImage.Load(imagesStream, labelsStream).Take(1000))
{
var t = new double[10];
for (int i = 0; i < 10; i++)
{
if (i == image.Label)
{
t[i] = 1.0;
}
else
{
t[i] = 0.0;
}
}
testList.Add(Tuple.Create <double[], double[]>(image.Normalize(), t));
}
}
var inputLayer = new ConvolutionalPoolingLayer(channels, imageWidth, imageHeight, filters, filterWidth, filterHeight, poolWidth, poolHeight, new ReLU(), (index, fanIn, fanOut) => Initializers.HeNormal(fanIn));
var hiddenLayer = new FullyConnectedLayer(inputLayer, 100, new ReLU(), (index, fanIn, fanOut) => Initializers.HeNormal(fanIn));
var outputLayer = new SoftmaxLayer(hiddenLayer, 10, (index, fanIn, fanOut) => Initializers.GlorotNormal(fanIn, fanOut));
var network = new Network(inputLayer, outputLayer, new Adam(), new SoftmaxCrossEntropy());
int epochs = 50;
int iterations = 1;
network.Stepped += (sender, e) =>
{
double tptn = 0;
trainingList.ForEach(x =>
{
var vector = network.Predicate(x.Item1);
var i = ArgMax(vector);
var j = ArgMax(x.Item2);
if (i == j && Math.Round(vector[i]) == x.Item2[j])
{
tptn += 1.0;
}
});
var accuracy = tptn / trainingList.Count;
accuracyList.Add(accuracy);
lossList.Add(network.Loss);
Console.WriteLine("Epoch {0}/{1}", iterations, epochs);
Console.WriteLine("Accuracy: {0}, Loss: {1}", accuracy, network.Loss);
iterations++;
};
Console.WriteLine("Training...");
var stopwatch = Stopwatch.StartNew();
network.Train(trainingList, epochs, 100);
stopwatch.Stop();
Console.WriteLine("Done ({0}).", stopwatch.Elapsed.ToString());
double testTptn = 0;
testList.ForEach(x =>
{
var vector = network.Predicate(x.Item1);
var i = ArgMax(vector);
var j = ArgMax(x.Item2);
if (i == j && Math.Round(vector[i]) == x.Item2[j])
{
testTptn += 1.0;
}
});
Console.WriteLine("Accuracy: {0}", testTptn / testList.Count);
logDictionary.Add("Accuracy", accuracyList);
logDictionary.Add("Loss", lossList);
ToCsv(logPath, logDictionary);
Console.Write("Saved log to {0}...", logPath);
}
