private static void Load()
{
// Load data
Console.WriteLine("Loading the dataset...");
Bitmap bmp = new Bitmap(Test_SimpleNumbers.file);
for (int numberIdx = 0; numberIdx < 10; numberIdx++)
{
byte[] data = new byte[3 * 5];
for (int y = 0; y < 5; y++)
{
for (int x = 0; x < 3; x++)
{
Color color = bmp.GetPixel(numberIdx * 3 + x, y);
data[x + y * 3] = color.R;
}
}
MnistEntry entry = new MnistEntry(data, numberIdx);
Test_SimpleNumbers.testing.Add(entry);
}
if (Test_SimpleNumbers.testing.Count == 0)
{
Console.WriteLine("Missing file.");
Console.ReadKey();
return;
}
}
private static void Test(NeuralNetwork nn, int tests = -1)
{
tests = tests == -1 ? testing.Count : tests;
tests = Math.Min(testing.Count, tests);
int score = 0;
for (int i = 0; i < tests; i++)
{
Console.WriteLine($"Starting Test {i}/{tests}");
MnistEntry entry = testing[i];
NNFeedData inputData = new NNFeedData(3, 5, ConvertArray(entry.Image));
NNFeedData outputData = nn.FeedForward(inputData);
(int label, float value)guess = ArrayToLabel(outputData.CopyData());
if (guess.label == entry.Label)
{
score++;
}
Console.WriteLine($"{entry.Label} | {guess.label} ({guess.value:F2})");
}
Console.WriteLine($"{score}/{tests}");
}
private static void Train(NeuralNetwork nn, float learningRate, int runs)
{
Console.WriteLine("Preparing Training...");
NNFeedData[] trainingInputData = new NNFeedData[training.Count];
NNFeedData[] trainingTargetData = new NNFeedData[training.Count];
for (int i = 0; i < training.Count; i++)
{
MnistEntry entry = training[i];
trainingInputData[i] = new NNFeedData(28, 28, 1, ConvertArray(entry.Image));
trainingTargetData[i] = new NNFeedData(10, 1, 1, LabelToArray(entry.Label));
}
NNTrainingData trainingData = new NNPreloadedTrainingData(trainingInputData, trainingTargetData);
NNBackpropagationData backpropagationData = new NNBackpropagationData(trainingData, learningRate, (o, t) => o - t);
double totalTime = 0;
Console.WriteLine("Starting Training...");
for (int trainingRuns = 0; trainingRuns < runs; trainingRuns++)
{
DateTime start = DateTime.UtcNow;
backpropagationData.BatchTrainingStartingCallback = (trainingDataIndex, trainingSets) => {
start = DateTime.UtcNow;
};
backpropagationData.BatchTrainingFinishedCallback = (trainingDataIndex, trainingSets) => {
totalTime += (DateTime.UtcNow - start).TotalMilliseconds;
double avgTime = totalTime / (trainingDataIndex + 1);
double remainingTime = avgTime * (trainingSets - trainingDataIndex);
TimeSpan avgTSpan = TimeSpan.FromMilliseconds(avgTime);
TimeSpan remTSpan = TimeSpan.FromMilliseconds(remainingTime);
TimeSpan totTSpan = TimeSpan.FromMilliseconds(totalTime);
string avgTS = $"{avgTSpan:ss\\.ffff}";
string remTS = $"{remTSpan:hh\\:mm\\:ss}";
string totTS = $"{totTSpan:hh\\:mm\\:ss}";
Save(nn, $"primary_{trainingDataIndex}");
Console.WriteLine($"Finished Training {trainingDataIndex}/{trainingSets} Passed:{totTS} Remaining:{remTS} Avg:{avgTS}");
};
nn.PropagateBackward(backpropagationData);
}
}