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");
}
}