static void Main(string[] args)
{
string path = "D:/2/Train/";
Dataset ds_test = new Dataset(10, ColorModel.Grayscale, 32, 32);
Dataset ds_train = new Dataset(10, ColorModel.Grayscale, 32, 32);
for (int i = 0; i < 10; i++)
{
for (int j = 0; j < 120; j++)//each of 10 directories contains 120 images
{
if (j % 6 != 0)
{
ds_train.AddImage(path + i + "/" + j + ".jpg", i, false, false, false);
}
else
{
ds_test.AddImage(path + i + "/" + j + ".jpg", i, false, false, false);
}
}
}
ds_test.PrepareData();
ds_train.PrepareData();
CNN cnn = new CNN(CNNType.CNN);
cnn.LoadData(ds_train);
cnn.AddLayer(new Layer(15, 5, 5, 1, 1, false, ActivationFcn.ReLU)); //15 kernels 5x5
cnn.AddLayer(new Layer(2, 2, PoolType.Max, 15)); //max pooling 2x2
cnn.AddLayer(new Layer(20, 5, 5, 1, 1, false, ActivationFcn.ReLU)); //20 kernels 5x5
cnn.AddLayer(new Layer(2, 2, PoolType.Max, 20)); //max pooling 2x2
cnn.AddLayer(new Layer(50, LayerType.FullConnected, ActivationFcn.ReLU));
cnn.AddLayer(new Layer(10, LayerType.Hidden, ActivationFcn.Tanh));
cnn.CreateNewCNN();
int Epochs = 10;
int TrainCount = ds_train.Data.Count();
Queue <Out> res = new Queue <Out>();
Out Res;
double cost = 0;
int T = 0;
int F = 0;
cnn.Epohs = 0;
cnn.Iterations = 0;
cnn.LearnRate = 0.01;
for (int i = 0; i < Epochs; i++)//training
{
cnn.MixDataset();
for (int j = 0; j < TrainCount; j++)
{
Res = cnn.BackPropagate(j);
res.Enqueue(Res);
cost += Res.Cost;
if (Res.RecognRes)
{
T++;
}
else
{
F++;
}
cnn.Iterations++;
if (cnn.Epohs > 0)
{
Res = res.Dequeue();
cost -= Res.Cost;
if (Res.RecognRes)
{
T--;
}
else
{
F--;
}
}
if (cnn.Iterations % 100 == 0)
{
if (cnn.Epohs > 0)
{
Console.WriteLine("Epoch: " + cnn.Epohs + " Iteration: " + cnn.Iterations);
Console.WriteLine("Loss: " + String.Format("{0:f4}", cost / TrainCount) + " Accuracy: " + String.Format("{0:f2}", 100.0 * T / (T + F)) + "%");
Console.WriteLine();
}
else
{
Console.WriteLine("Epoch: " + cnn.Epohs + " Iteration: " + cnn.Iterations);
Console.WriteLine("Loss: " + String.Format("{0:f4}", cost / cnn.Iterations) + " Accuracy: " + String.Format("{0:f2}", 100.0 * T / (T + F)) + "%");
Console.WriteLine();
}
}
}
cnn.Epohs++;
cnn.LearnRate *= cnn.LearnRateDecrease;
}
T = 0;
F = 0;
int TestCount = ds_test.Data.Count();
double[] result;
for (int i = 0; i < TestCount; i++)//testing
{
result = cnn.Run(ds_test.Data[i]);
if (result.Max() == result[ds_test.Answers[i]])
{
T++;
}
else
{
F++;
}
Console.WriteLine(" Accuracy: " + String.Format("{0:f2}", 100.0 * T / (T + F)) + "%");
}
Console.ReadKey();
}
