public static void Kaggle_Test()
{
//DigitImage[] training = CSV_Helper.Read(DataSet.Training);
DigitImage[] testing = CSV_Helper.Read(DataSet.Testing);
int dialogResult;
CNN cnn = CreateCNN(out dialogResult);
DigitImage[] digitImagesDatas = testing;
Stopwatch stopwatch = new Stopwatch();
stopwatch.Start();
int testing_count = digitImagesDatas.Length;
int correct_count = 0;
double timeLimit = 0, error = 0;
Matrix[] input, targets;
Console.WriteLine("System is getting tested. You will see the results when it is done...\n");
if (cursorTopTesting == 0)
{
cursorTopTesting = Console.CursorTop;
}
InitializeInputAndTarget(out input, out targets);
timeLimit = stopwatch.ElapsedMilliseconds;
string submissionCSV = "ImageId,Label" + Environment.NewLine;
for (int i = 0; i < testing_count; i++)
{
for (int j = 0; j < 28; j++)
{
for (int k = 0; k < 28; k++)
{
input[0][j, k] = digitImagesDatas[i].pixels[j][k];
}
}
input[0].Normalize(0f, 255f, 0f, 1f);
Matrix ans = cnn.Predict(input);
submissionCSV += (i + 1).ToString() + "," + ans.GetMaxRowIndex() + Environment.NewLine;
if (stopwatch.ElapsedMilliseconds > timeLimit)
{
// every 0.5 sec update error
timeLimit += 500;
error = cnn.GetError();
}
int val = Map(0, testing_count, 0, 100, i);
ProgressBar(val, i, testing_count, error, stopwatch.ElapsedMilliseconds / 1000.0, cursorTopTesting);
}
CSV_Helper.Write(submissionCSV);
}
public static void CNN_OverfittingTest()
{
CNN cnn = new CNN();
DigitImage[] digitImages = MNIST_Parser.ReadFromFile(DataSet.Testing, 100);
Stopwatch stopwatch = new Stopwatch();
stopwatch.Start();
int iteration_count = 10;
for (int i = 0; i < iteration_count; i++)
{
for (int j = 0; j < digitImages.Length; ++j)
{
Matrix[] input = new Matrix[1];
input[0] = new Matrix(digitImages[j].pixels);
Matrix target = new Matrix(10, 1);
target[(int)digitImages[j].label, 0] = 1f;
cnn.Train(input, target);
double error = cnn.GetError();
int val = (int)((i - 0) / (double)(iteration_count - 1 - 0) * (100 - 0) + 0);
ProgressBar(val, i, iteration_count, error, stopwatch.ElapsedMilliseconds / 1000.0);
}
}
for (int j = 0; j < digitImages.Length; ++j)
{
Matrix[] input = new Matrix[1];
input[0] = new Matrix(digitImages[j].pixels);
Matrix output = cnn.Predict(input);
Console.WriteLine(output.ToString());
Console.WriteLine(digitImages[j].ToString());
Console.ReadLine();
}
}
public static void CNN_Training()
{
int trCount = 60000, tsCount = 10000;
double error = 0f, timeLimit = 0f;
int totalEpoch = 10;
bool predictionIsOn = true;
Random random = new Random();
DigitImage[] trainigDigitImagesDatas = MNIST_Parser.ReadFromFile(DataSet.Training, trCount);
DigitImage[] testingDigitImagesDatas = MNIST_Parser.ReadFromFile(DataSet.Testing, tsCount);
int training_count = trainigDigitImagesDatas.Length;
int dialogResult;
CNN cnn = CreateCNN(out dialogResult);
Matrix[] input, targets;
InitializeInputAndTarget(out input, out targets);
Stopwatch stopwatch = new Stopwatch();
stopwatch.Start();
if (dialogResult == 0)
{
Console.WriteLine("System is getting trained...");
//if we never assigned this assign only once
if (cursorTopTraining == -1)
{
cursorTopTraining = Console.CursorTop;
}
for (int epoch = 0; epoch < totalEpoch; epoch++)
{
double lossSum = 0.0;
DigitImage[] digitImages = trainigDigitImagesDatas.OrderBy(image => random.Next(training_count)).ToArray();
for (int i = 0; i < training_count; i++)
{
for (int j = 0; j < 28; j++)
{
for (int k = 0; k < 28; k++)
{
input[0][j, k] = digitImages[i].pixels[j][k];
}
}
input[0].Normalize(0f, 255f, 0f, 1f);
cnn.Train(input, targets[digitImages[i].label]);
//if (stopwatch.ElapsedMilliseconds > timeLimit)
// {
// every 0.5 sec update error
//timeLimit += 500;
error = cnn.GetError();
lossSum += error;
//}
int val = Map(0, training_count * totalEpoch, 0, 100, training_count * epoch + i);
ProgressBar(val, training_count * epoch + i, training_count * totalEpoch, lossSum / (i + 1), stopwatch.ElapsedMilliseconds / 1000.0, cursorTopTraining);
}
CNN_Testing(testingDigitImagesDatas, predictionIsOn, cnn, epoch + 1);
}
Console.WriteLine("\nSystem has been trained.");
}
else
{
CNN_Testing(testingDigitImagesDatas, predictionIsOn, cnn, -1);
}
}
public static void CNN_Testing(DigitImage[] digitImagesDatas, bool predictionIsOn, CNN cnn, int iterationCount)
{
Stopwatch stopwatch = new Stopwatch();
stopwatch.Start();
int testing_count = digitImagesDatas.Length;
int correct_count = 0;
double timeLimit = 0, error = 0;
Matrix[] input, targets;
Console.WriteLine("System is getting tested. You will see the results when it is done...\n");
if (cursorTopTesting == 0)
{
cursorTopTesting = Console.CursorTop;
}
InitializeInputAndTarget(out input, out targets);
timeLimit = stopwatch.ElapsedMilliseconds;
for (int i = 0; i < testing_count; i++)
{
for (int j = 0; j < 28; j++)
{
for (int k = 0; k < 28; k++)
{
input[0][j, k] = digitImagesDatas[i].pixels[j][k];
}
}
input[0].Normalize(0f, 255f, 0f, 1f);
Matrix ans = null;
if (predictionIsOn)
{
ans = cnn.Predict(input);
}
else
{
cnn.Train(input, targets[digitImagesDatas[i].label]);
ans = cnn.Layers[cnn.Layers.Length - 1].Output[0];
}
if (ans.GetMaxRowIndex() == digitImagesDatas[i].label)
{
correct_count++;
}
if (stopwatch.ElapsedMilliseconds > timeLimit)
{
// every 0.5 sec update error
timeLimit += 500;
error = cnn.GetError();
}
int val = Map(0, testing_count, 0, 100, i);
ProgressBar(val, i, testing_count, error, stopwatch.ElapsedMilliseconds / 1000.0, cursorTopTesting);
}
double accuracy = (correct_count * 1f / testing_count) * 100.0;
Console.WriteLine("\nIteration Count:" + iterationCount);
Console.WriteLine("\nTime :" + (stopwatch.ElapsedMilliseconds / 1000.0).ToString("F4"));
Console.WriteLine("\nAccuracy: %{0:F2}\n", accuracy);
Console.WriteLine("Correct/All: {0}/{1}", correct_count, testing_count);
cursorTopTesting = Console.CursorTop;
if (accuracy >= 95 && iterationCount != -1)
{
string name = accuracy.ToString("F2") + "__" + iterationCount + "__";
Random random = new Random();
int length = 6;
for (int i = 0; i < length; i++)
{
char c = (char)random.Next('A', 'F' + 1);
name += c;
}
cnn.SaveData(name + ".json");
}
}