static void Main(string[] args)
{
//TestExp();
int T = 1000;
int nFeature = 784;
int nLabel = 10;
double momentum = 0.0;
IFunction[] type = new IFunction[] { new Sigmoid(1.0, 0.0), new Sigmoid(1.0, 0.0), new Sigmoid(1.0, 0.0) };
IErrorFunction errorFunction = new MediumSquareErrorFunction();
//IFunction type = new Tanh(0.666666, 1.7159, 0.0);
double[] eta = new double[] { 1.0, 1.0, 1.0, 1.0 };
double[] dropoutValue = new double[] { 1.0, 1.0, 1.0 };
int[] nodeLayer = new int[] { nFeature, 400, 20, nLabel };
double[][] dataMatrix = null;
double[][] labelMatrix = null;
readDataset("data.dat", new[] { " " }, ref dataMatrix);
readDataset("tlabel.dat", new[] { " " }, ref labelMatrix);
double[][] testDataMatrix = null;
double[][] testLabelMatrix = null;
readDataset("testmnist.dat", new[] { " " }, ref testDataMatrix);
readDataset("tlabeltest.dat", new[] { " " }, ref testLabelMatrix);
ANN network = new ANN(
T,
nodeLayer,
eta,
momentum,
dropoutValue,
type,
errorFunction);
network.SetThread(6);
#region Start test error
double[][] result = new double[testDataMatrix.Length][];
for (int i = 0; i < testDataMatrix.Length; i++)
{
result[i] = network.GetNetworkOutput(testDataMatrix[i]);
}
result = ResultFinalize(result);
Console.WriteLine("Network error " + GetError(result, testLabelMatrix));
#endregion
Stopwatch stopwatch = new Stopwatch();
stopwatch.Reset();
stopwatch.Start();
double[][] resultTrain = new double[dataMatrix.Length][];
int nTrial = 80;
for (int i = 0; i < nTrial; i++)
{
network.Train(dataMatrix, labelMatrix, 10);
for (int j = 0; j < testDataMatrix.Length; j++)
{
result[j] = network.GetNetworkOutput(testDataMatrix[j]);
}
result = ResultFinalize(result);
Console.WriteLine("Network test error " + GetError(result, testLabelMatrix));
for (int j = 0; j < dataMatrix.Length; j++)
{
resultTrain[j] = network.GetNetworkOutput(dataMatrix[j]);
}
resultTrain = ResultFinalize(resultTrain);
Console.WriteLine("Network train error " + GetError(resultTrain, labelMatrix));
}
stopwatch.Stop();
Console.WriteLine("Train Elapsed={0}", stopwatch.ElapsedMilliseconds * 0.001);
result = new double[testDataMatrix.Length][];
for (int i = 0; i < testDataMatrix.Length; i++)
{
result[i] = network.GetNetworkOutput(testDataMatrix[i]);
}
//Finalizzo il risultato
for (int i = 0; i < testDataMatrix.Length; i++)
{
double max = double.MinValue;
int index = 0;
for (int j = 0; j < result[i].Length; j++)
{
if (result[i][j] > max)
{
max = result[i][j];
index = j;
}
result[i][j] = 0.0;
}
result[i][index] = 1.0;
}
Console.WriteLine("Network error " + GetError(result, testLabelMatrix));
Console.ReadLine();
}
static void RNNExample()
{
int T = 1000;
int nFeature = 6;
int nLabel = 4;
double momentum = 0.0;
//IFunction[] type = new IFunction[] { new Sigmoid(1.0, 0.0), new SoftMax() };
//IFunction[] type = new IFunction[] { new Tanh(0.666666, 1.7159, 0.0), new SoftMax() };
//IFunction[] type = new IFunction[] { new Tanh(0.666666, 1.7159, 0.0), new SoftMax() };
IFunction[] type = new IFunction[] { new Sigmoid(1.0, 0.0), new Sigmoid(1.0, 0.0) };
//IErrorFunction errorFunction = new CrossEntropyErrorFunc();
IErrorFunction errorFunction = new MediumSquareErrorFunction();
double[] eta = new double[] { 0.01, 0.01, 0.01 };
double[] dropoutValue = new double[] { 1.0, 1.0 };
int[] nodeLayer = new int[] { nFeature, 50, nLabel };
int timeLength = 40;
double[][] dataMatrix = null;
double[][] labelMatrix = null;
//Training set
GenerateDataset(timeLength, 12000, ref dataMatrix, ref labelMatrix);
//readDataset("international-airline-passengers.csv", new[] { " " }, ref dataMatrix);
//readDataset("international-airline-passengers.csv", new[] { " " }, ref labelMatrix);
//NormalizeDataColumn(ref dataMatrix);
//NormalizeDataColumn(ref labelMatrix);
RNN network = new RNN(
T,
timeLength,
10,
nodeLayer,
eta,
momentum,
dropoutValue,
type,
errorFunction);
int nTrial = 60;
for (int i = 0; i < nTrial; i++)
{
int batch = 1;// Helper.GetRandom(1, 5);
network.Train(dataMatrix, labelMatrix, batch, true);
Console.WriteLine("Trial " + i);
Console.WriteLine();
}
double[][] testDataMatrix = null;
double[][] testLabelMatrix = null;
//Network test
Console.WriteLine("TEST");
double totErr = 0.0;
for (int i = 0; i < 200; i++)
{
GenerateDataset(timeLength, timeLength, ref testDataMatrix, ref testLabelMatrix);
double[] res = network.GetNetworkOutput(testDataMatrix);
Console.WriteLine("Res " + res[0] + " " + res[1] + " " + res[2] + " " + res[3]);
Console.WriteLine("Expected " + testLabelMatrix[timeLength - 1][0] + " " +
testLabelMatrix[timeLength - 1][1] + " " +
testLabelMatrix[timeLength - 1][2] + " " +
testLabelMatrix[timeLength - 1][3] + " ");
//double diff = (testLabelMatrix[timeLength - 1][0] - res[0]);
//totErr = diff * diff;
//Console.WriteLine("Diff " + diff);
Console.WriteLine();
}
//Console.WriteLine("Test err " + totErr / 100);
Console.ReadLine();
}
