static void Main(string[] args)
{
float learningStep = 0.0025f;
int epochCount = 200;
int batchCount = 32;
ILabeledData trainingSet = MNISTDataset.LoadDataset("dataset/train-images.idx3-ubyte", "dataset/train-labels.idx1-ubyte");
ILabeledData testingSet = MNISTDataset.LoadDataset("dataset/t10k-images.idx3-ubyte", "dataset/t10k-labels.idx1-ubyte");
Console.WriteLine(string.Format("Training Dataset : \n{0}", (MNISTDataset)trainingSet));
Console.WriteLine(string.Format("\nTesting Dataset : \n{0}", (MNISTDataset)testingSet));
Network network = new Network(trainingSet);
network.AddLayer(new Layer(16, Activation.Relu));
network.AddLayer(new Layer(16, Activation.Relu));
network.Build();
Console.WriteLine(string.Format("Starting training\n\nepoch count : {0}\nbatch size : {1}\nlearning step : {2,4:F2}", epochCount, batchCount, learningStep));
for (int epoch = 0; epoch < epochCount; epoch++)
{
Console.WriteLine(string.Format("Starting epoch {0}", epoch));
network.Train(trainingSet, batchCount, learningStep, true);
}
Console.WriteLine("Finished training. Evaluating performance");
int total = testingSet.GetDataCount();
int correctPrediction = 0;
float cost = 0;
for (int i = 0; i < testingSet.GetDataCount(); i++)
{
float[] output = network.Predict(testingSet, i);
cost += network.CalculateCost(testingSet.GetTarget(i));
if (((MNISTDataset)testingSet).CheckPrediction(output, i))
{
correctPrediction++;
}
}
Console.WriteLine(string.Format("Cost : {0,4:F2}\nCorrect percentage : {1,4:F2}", cost / total, ((float)correctPrediction / total) * 100f));
Console.WriteLine(string.Format("Press any key to continue.."));
Console.ReadKey(true);
Random rand = new Random();
while (true)
{
int id = rand.Next(0, trainingSet.GetDataCount());
float[] input = trainingSet.GetInput(id);
Console.WriteLine(((MNISTDataset)trainingSet).GetImage(id));
Console.WriteLine("Number is : " + ((MNISTDataset)trainingSet).GetLabel(id));
float[] prediction = network.Predict(trainingSet, id);
for (int i = 0; i < prediction.Length; i++)
{
Console.Write(string.Format("{0} - {1,4:F2}%; ", i, prediction[i] * 100));
}
Console.WriteLine(string.Format("\nPredicted number is : {0}\n", ((MNISTDataset)trainingSet).PredictedNumber(prediction)));
Console.Write("\nTest again? (y/n)");
ConsoleKeyInfo key = Console.ReadKey();
Console.WriteLine("");
while (key.KeyChar != 'y' && key.KeyChar != 'n')
{
Console.Write(string.Format("Test again? (y/n)"));
key = Console.ReadKey();
Console.WriteLine("");
}
if (key.KeyChar == 'n')
{
break;
}
}
}
public static MNISTDataset LoadDataset(string imageFile, string labelFile)
{
MNISTDataset dataset = new MNISTDataset();
Int32 dataCount = 0;
using (FileStream fs = File.OpenRead(imageFile))
{
byte[] infoBuffer = new byte[4];
fs.Read(infoBuffer, 0, 4);
fs.Read(infoBuffer, 0, 4);
if (BitConverter.IsLittleEndian)
{
Array.Reverse(infoBuffer);
}
dataCount = BitConverter.ToInt32(infoBuffer);
fs.Read(infoBuffer, 0, 4);
if (BitConverter.IsLittleEndian)
{
Array.Reverse(infoBuffer);
}
Int32 rowCount = BitConverter.ToInt32(infoBuffer);
fs.Read(infoBuffer, 0, 4);
if (BitConverter.IsLittleEndian)
{
Array.Reverse(infoBuffer);
}
Int32 colCount = BitConverter.ToInt32(infoBuffer);
dataset.count = dataCount;
dataset.rowCount = rowCount;
dataset.columnCount = colCount;
byte[] dataBuffer = new byte[dataCount * rowCount * colCount];
fs.Read(dataBuffer, 0, dataBuffer.Length);
dataset.data = dataBuffer;
}
Int32 labelCount = 0;
using (FileStream fs = File.OpenRead(labelFile))
{
byte[] infoBuffer = new byte[4];
fs.Read(infoBuffer, 0, 4);
fs.Read(infoBuffer, 0, 4);
if (BitConverter.IsLittleEndian)
{
Array.Reverse(infoBuffer);
}
labelCount = BitConverter.ToInt32(infoBuffer);
byte[] labelBuffer = new byte[dataCount];
fs.Read(labelBuffer, 0, dataCount);
dataset.labels = labelBuffer;
}
if (dataCount != labelCount)
{
throw new Exception("Data count is not equal to label count!");
}
dataset.byteCount = dataset.columnCount * dataset.rowCount;
return(dataset);
}
