private static DataSetItem[] LoadNativeSet(string filePath)
{
FileStream fs = new FileStream(filePath, FileMode.Open, FileAccess.Read, FileShare.Read);
BinaryReader br = new BinaryReader(fs);
int count = br.ReadInt32();
int inputCount = br.ReadInt32();
DataSetItem[] result = new DataSetItem[count];
for (int i = 0; i < result.Length; i++)
{
char character = br.ReadChar();
double[] inputs = new double[inputCount];
for (int j = 0; j < inputCount; j++)
{
byte b = br.ReadByte();
inputs[j] = (((double)b) / 255.0) * 1.275 - 0.1;
}
DataSetItem item = new DataSetItem();
item.Character = character;
item.Inputs = inputs;
result[i] = item;
}
br.Close();
return(result);
}
public TrainingResults Train(DataSetItem inputs)
{
PropogateForward(inputs);
Marking.CorrectClass = Array.IndexOf(configuration.Characters, inputs.Character);
Array.ForEach(reverseSteps, step => step.PropogateBackwards());
int correctOutputIndex = Array.IndexOf(configuration.Characters, inputs.Character);
return(new TrainingResults(Marking.Output, correctOutputIndex));
}
public void Initialise()
{
Console.WriteLine("Loading Training Data Set...");
TrainingDataSet = DataSetItem.GetTrainingSet().Randomise(0);
Console.WriteLine("Loading Generalisation Data Set...");
GeneralisationDataSet = DataSetItem.GetGeneralisationSet().Randomise(1);
Console.WriteLine("Creating LeNet...");
Network = new LeNetNetwork('0', '1', '2', '3', '4', '5', '6', '7', '8', '9');
Snapshot = new LeNetSnapshot(Network);
}
private static IList <DataSetItem> LoadGeneralisationDataset()
{
if (!File.Exists(GeneralisationDatasetCache))
{
DataSetItem[] set = DataSetItem.LoadLeCunSet(GeneralisationDatasetImages, GeneralisationDatasetLabels);
DataSetItem.SaveNativeSet(GeneralisationDatasetCache, set);
return(set);
}
else
{
return(DataSetItem.LoadNativeSet(GeneralisationDatasetCache));
}
}
private static IList <DataSetItem> LoadTrainingDataset()
{
if (!File.Exists(TrainingDatasetCache))
{
DataSetItem[] set = DataSetItem.LoadLeCunSet(TrainingDatasetImages, TrainingDatasetLabels);
DataSetItem.SaveNativeSet(TrainingDatasetCache, set);
return(set);
}
else
{
return(DataSetItem.LoadNativeSet(TrainingDatasetCache));
}
}
private static DataSetItem[] LoadLeCunSet(string imagePath, string labelPath)
{
FileStream ImageStream = new FileStream(imagePath, FileMode.Open);
FileStream LabelStream = new FileStream(labelPath, FileMode.Open);
BinaryReader brImage = new BinaryReader(ImageStream);
BinaryReader brLabel = new BinaryReader(LabelStream);
if (ReadBigEndianInteger(brImage) != 2051)
throw new InvalidDataException("Invalid magic in specified image file.");
if (ReadBigEndianInteger(brLabel) != 2049)
throw new InvalidDataException("Invalid magic in specified label file.");
int ItemCount = ReadBigEndianInteger(brImage);
if (ReadBigEndianInteger(brLabel) != ItemCount)
throw new InvalidDataException("Number of images and labels do not match.");
int rows = ReadBigEndianInteger(brImage);
int columns = ReadBigEndianInteger(brImage);
DataSetItem[] items = new DataSetItem[ItemCount];
for (int i = 0; i < ItemCount; i++)
{
char character = (char)(brLabel.ReadByte().ToString()[0]);
// Read image with border of 2 on all sides.
double[] inputs = new double[(rows + 4) * (columns + 4)];
for (int x = 0; x < (rows + 4); x++)
for (int y = 0; y < (columns + 4); y++)
{
inputs[x + y * (rows + 4)] = -0.1;
}
int inputIndex = 2;
for (int y = 2; y < (columns + 2); y++)
{
for (int x = 2; x < (rows + 2); x++)
{
// Background pixel is -0.1 (black) and Foreground is 1.175.
// Refer to page 7 of LeCun's document on Gradient-based learning applied to document recognition.
inputs[inputIndex] = (((double)brImage.ReadByte()) / 255.0) * 1.275 - 0.1;
inputIndex += 1;
}
inputIndex += 4;
}
items[i] = new DataSetItem() { Inputs = inputs, Character = character };
}
return items;
}
private static DataSetItem[] LoadLeCunSet(string imagePath, string labelPath)
{
FileStream ImageStream = new FileStream(imagePath, FileMode.Open);
FileStream LabelStream = new FileStream(labelPath, FileMode.Open);
BinaryReader brImage = new BinaryReader(ImageStream);
BinaryReader brLabel = new BinaryReader(LabelStream);
if (ReadBigEndianInteger(brImage) != 2051)
{
throw new InvalidDataException("Invalid magic in specified image file.");
}
if (ReadBigEndianInteger(brLabel) != 2049)
{
throw new InvalidDataException("Invalid magic in specified label file.");
}
int ItemCount = ReadBigEndianInteger(brImage);
if (ReadBigEndianInteger(brLabel) != ItemCount)
{
throw new InvalidDataException("Number of images and labels do not match.");
}
int rows = ReadBigEndianInteger(brImage);
int columns = ReadBigEndianInteger(brImage);
DataSetItem[] items = new DataSetItem[ItemCount];
for (int i = 0; i < ItemCount; i++)
{
char character = (char)(brLabel.ReadByte().ToString()[0]);
// Read image with border of 2 on all sides.
double[] inputs = new double[(rows + 4) * (columns + 4)];
for (int x = 0; x < (rows + 4); x++)
{
for (int y = 0; y < (columns + 4); y++)
{
inputs[x + y * (rows + 4)] = -0.1;
}
}
int inputIndex = 2;
for (int y = 2; y < (columns + 2); y++)
{
for (int x = 2; x < (rows + 2); x++)
{
// Background pixel is -0.1 (black) and Foreground is 1.175.
// Refer to page 7 of LeCun's document on Gradient-based learning applied to document recognition.
inputs[inputIndex] = (((double)brImage.ReadByte()) / 255.0) * 1.275 - 0.1;
inputIndex += 1;
}
inputIndex += 4;
}
items[i] = new DataSetItem()
{
Inputs = inputs, Character = character
};
}
return(items);
}
public void PropogateForward(DataSetItem inputs)
{
inputLayer.SetInputs(inputs.Inputs);
Array.ForEach(forwardSteps, step => step.PropogateForward());
}
public TrainingResults Train(DataSetItem inputs)
{
PropogateForward(inputs);
Marking.CorrectClass = Array.IndexOf(configuration.Characters, inputs.Character);
Array.ForEach(reverseSteps, step => step.PropogateBackwards());
int correctOutputIndex = Array.IndexOf(configuration.Characters, inputs.Character);
return new TrainingResults(Marking.Output, correctOutputIndex);
}
public void PropogateForward(DataSetItem inputs)
{
inputLayer.SetInputs(inputs.Inputs);
Array.ForEach(forwardSteps, step => step.PropogateForward());
}
private static void SaveNativeSet(string filePath, DataSetItem[] set)
{
FileStream fs = new FileStream(filePath, FileMode.OpenOrCreate, FileAccess.Write, FileShare.Read);
BinaryWriter bw = new BinaryWriter(fs);
bw.Write(set.Length);
bw.Write(set[0].Inputs.Length);
for (int i = 0; i < set.Length; i++)
{
bw.Write(set[i].Character);
for (int j = 0; j < set[i].Inputs.Length; j++)
{
double value = Math.Round(((set[i].Inputs[j] + 0.1) / 1.275) * 255.0);
byte byteValue = (byte)value;
bw.Write(byteValue);
}
}
bw.Flush();
fs.SetLength(fs.Position);
fs.Close();
}
private static DataSetItem[] LoadNativeSet(string filePath)
{
FileStream fs = new FileStream(filePath, FileMode.Open, FileAccess.Read, FileShare.Read);
BinaryReader br = new BinaryReader(fs);
int count = br.ReadInt32();
int inputCount = br.ReadInt32();
DataSetItem[] result = new DataSetItem[count];
for (int i = 0; i < result.Length; i++)
{
char character = br.ReadChar();
double[] inputs = new double[inputCount];
for (int j = 0; j < inputCount; j++)
{
byte b = br.ReadByte();
inputs[j] = (((double)b) / 255.0) * 1.275 - 0.1;
}
DataSetItem item = new DataSetItem();
item.Character = character;
item.Inputs = inputs;
result[i] =item;
}
br.Close();
return result;
}
