public DBNLearning(DBNNetwork dbn)
{
this.dbn = dbn;
rbm_learnings = new RBMLearning[dbn.LayersCount];
}
public DBNLearning(DBNNetwork dbn)
{
this.dbn = dbn;
rbm_learnings = new RBMLearning[dbn.LayersCount];
}
static void Main()
{
int numImages = 100;
double[][] result = new double[numImages][];
double[][] labels = new double[numImages][];
FileStream ifsPixels = new FileStream("F:/data/train-images.idx3-ubyte", FileMode.Open);
FileStream ifsLabels = new FileStream("F:/data/train-labels.idx1-ubyte", FileMode.Open);
BinaryReader brImages = new BinaryReader(ifsPixels);
BinaryReader brLabels = new BinaryReader(ifsLabels);
int magic1 = brImages.ReadInt32(); // stored as Big Endian
magic1 = ReverseBytes(magic1); // convert to Intel format
int imageCount = brImages.ReadInt32();
imageCount = ReverseBytes(imageCount);
int numRows = brImages.ReadInt32();
numRows = ReverseBytes(numRows);
int numCols = brImages.ReadInt32();
numCols = ReverseBytes(numCols);
int magic2 = brLabels.ReadInt32();
magic2 = ReverseBytes(magic2);
int numLabels = brLabels.ReadInt32();
numLabels = ReverseBytes(numLabels);
// each image
for (int di = 0; di < numImages; ++di)
{
Console.WriteLine(di);
result[di] = new double[784];
labels[di] = new double[10];
for (int i = 0; i < 784; ++i) // get 28x28 pixel values
{
byte b = brImages.ReadByte();
result[di][i] = (double)b / 255.0;
}
byte lbl = brLabels.ReadByte(); // get the label
labels[di][lbl] = 1;
} // each image
SigmoidFunction function = new SigmoidFunction(0.01);
DBNNetwork dbn = new DBNNetwork(function, 784, 100, 100);
DBNLearning dbn_learn = new DBNLearning(dbn);
dbn_learn.Epoch = 1;
dbn_learn.RunEpoch(result);
ActivationNetwork nn = new ActivationNetwork(function, 100, 10);
BackPropagationLearning bp = new BackPropagationLearning(nn);
for (int i = 0; i < numImages; i++)
{
Console.WriteLine(i);
result[i] = dbn.Compute(result[i]);
}
double error = 1;
bp.LearningRate = 1;
for (int i = 0; i < 100 && error > 0.1; i++)
{
Console.WriteLine(error);
error = bp.RunEpoch(result, labels);
}
int count = 0;
for (int i = 0; i < numImages; i++)
{
double[] test = nn.Compute(result[i]);
double max = 0;
int ind = 0;
int ans = 0;
for (int j = 0; j < 10; j++)
{
if (max < test[j])
{
ind = j;
max = test[j];
}
if (labels[i][j] == 1)
{
ans = j;
}
}
if (ind == ans)
{
count++;
}
}
Console.WriteLine(count);
Console.ReadLine();
}
static void Main()
{
int numImages = 100;
double[][] result = new double[numImages][];
double[][] labels = new double[numImages][];
FileStream ifsPixels = new FileStream("F:/data/train-images.idx3-ubyte", FileMode.Open);
FileStream ifsLabels = new FileStream("F:/data/train-labels.idx1-ubyte", FileMode.Open);
BinaryReader brImages = new BinaryReader(ifsPixels);
BinaryReader brLabels = new BinaryReader(ifsLabels);
int magic1 = brImages.ReadInt32(); // stored as Big Endian
magic1 = ReverseBytes(magic1); // convert to Intel format
int imageCount = brImages.ReadInt32();
imageCount = ReverseBytes(imageCount);
int numRows = brImages.ReadInt32();
numRows = ReverseBytes(numRows);
int numCols = brImages.ReadInt32();
numCols = ReverseBytes(numCols);
int magic2 = brLabels.ReadInt32();
magic2 = ReverseBytes(magic2);
int numLabels = brLabels.ReadInt32();
numLabels = ReverseBytes(numLabels);
// each image
for (int di = 0; di < numImages; ++di)
{
Console.WriteLine(di);
result[di] = new double[784];
labels[di] = new double[10];
for (int i = 0; i < 784; ++i) // get 28x28 pixel values
{
byte b = brImages.ReadByte();
result[di][i] = (double)b/255.0;
}
byte lbl = brLabels.ReadByte(); // get the label
labels[di][lbl] = 1;
} // each image
SigmoidFunction function = new SigmoidFunction(0.01);
DBNNetwork dbn = new DBNNetwork(function, 784, 100, 100);
DBNLearning dbn_learn = new DBNLearning(dbn);
dbn_learn.Epoch = 1;
dbn_learn.RunEpoch(result);
ActivationNetwork nn = new ActivationNetwork(function, 100, 10);
BackPropagationLearning bp = new BackPropagationLearning(nn);
for(int i = 0; i < numImages; i++)
{
Console.WriteLine(i);
result[i] = dbn.Compute(result[i]);
}
double error = 1;
bp.LearningRate = 1;
for(int i = 0; i < 100 && error > 0.1; i++)
{
Console.WriteLine(error);
error = bp.RunEpoch(result, labels);
}
int count = 0;
for (int i = 0; i < numImages; i++)
{
double[] test = nn.Compute(result[i]);
double max = 0;
int ind = 0;
int ans = 0;
for(int j = 0; j < 10; j++)
{
if(max < test[j])
{
ind = j;
max = test[j];
}
if(labels[i][j] == 1)
{
ans = j;
}
}
if(ind == ans)
count++;
}
Console.WriteLine(count);
Console.ReadLine();
}
