static void Main(string[] args)
{
try
{
ReadMNIST read = new ReadMNIST("D:\\Source\\C++\\BackpropagationNeuronNetwork\\BackpropagationNeuronNetwork\\bin\\Debug");
DataSet ds = read.LoadDataMNIST();
ReadDatabasePlus readplus = new ReadDatabasePlus();
DataSet ds1 = readplus.Load("data.xml");
ds.CoppyDataset(ds1);
//Backpropagation bnn = new Backpropagation (784, 200, 14);
//printds(ds);
Backpropagation bnn = new Backpropagation("RecogCharacterPlus.xml");
//test(bnn,ds);
//testTextFile(bnn);
double learnRate = 0.1; // learning rate - controls the maginitude of the increase in the change in weights.
double momentum = 0.1; // momentum - to discourage oscillation.
Console.WriteLine("Setting learning rate = " + learnRate.ToString("F2") + " and momentum = " + momentum.ToString("F2"));
int maxEpochs = 500;
int epoch = 0;
double error = double.MaxValue;
Console.WriteLine("\nBeginning training using back-propagation\n");
double[] yValues; // outputs
while (epoch < maxEpochs) // train
{
error = 0.0;
for (int i = 0; i < ds.DataTrain.Count; i++)
{
yValues = bnn.ComputeOutputs(ds.DataTrain[i].input);
error += Backpropagation.Error(ds.DataTrain[i].output, yValues);
bnn.UpdateWeights(ds.DataTrain[i].output, learnRate, momentum);
}
bnn.Save("RecogCharacterPlus.xml");
Console.WriteLine("epoch = " + epoch + " error = " + error);
++epoch;
} // train loop
bnn.Save("RecogCharacter.xml");
Console.ReadLine();
}
catch (Exception ex)
{
Console.WriteLine("Fatal: " + ex.Message);
Console.ReadLine();
}
}
private static void testTextFile(Backpropagation bnn)
{
double[] input = new double[784];
using (TextReader reader = File.OpenText("write.txt"))
{
for (int i = 0; i < 784; i++)
{
input[i] = double.Parse(reader.ReadLine());
}
double[] output = bnn.ComputeOutputs(input);
}
}
private static void test(Backpropagation bnn, DataSet ds)
{
int count = 0;
double[] yValues;
for (int i = 0; i < 10000; i++)
{
yValues = bnn.ComputeOutputs(ds.DataTest[i].input);
bool check = false;
int select = 0;
for (int j = 1; j < 10; j++)
{
if (Math.Abs(1 - yValues[j]) < Math.Abs(1 - yValues[select]))
{
select = j;
}
}
if (ds.DataTest[i].output[select] != 1)
{
int tResult = 0;
Console.WriteLine("test case " + i + " is no ");
for (int k = 0; k < 10; k++)
{
Console.WriteLine(" " + yValues[k]);
if (ds.DataTest[i].output[k] == 1)
{
tResult = k;
}
}
Console.WriteLine(" target result = " + tResult);
}
else
{
count++;
Console.WriteLine("test case " + i + "is yes \n");
for (int k = 0; k < 28; k++)
{
for (int j = 0; j < 28; j++)
{
Console.Write(ds.DataTest[i].input[k * 28 + j] + " ");
}
Console.WriteLine();
}
Console.WriteLine();
}
}
Console.WriteLine("count = " + count);
double result = count / 10000;
Console.WriteLine("rate success = " + result);
}