public static NNsTestResults <int> test(List <NeuralNetwork> nns, int iterations, ExperimentLog log = null)
{
FileStream testImages = new FileStream("data/test_imgs", FileMode.Open);
FileStream testLabels = new FileStream("data/test_lbls", FileMode.Open);
testImages.Read(new byte[4 * 4], 0, 4 * 4);
testLabels.Read(new byte[4 * 2], 0, 4 * 2);
List <double> input = new List <double>();
for (int i = 0; i < 784; i++)
{
input.Add(0);
}
List <int> correctAnswersAmount = nns.Select(nn => 0).ToList();
List <int> correctAnswers = new List <int>(iterations * 1000);
List <List <int> > answers = nns.Select(nn => new List <int>()).ToList();
log?.startPhase("Testing",
$"MNIST dataset",
iterations * 1000);
for (int h = 0; h < iterations; h++)
{
for (int i = 0; i < 1000; i++)
{
int digit = Utils.getNextByte(testLabels);
correctAnswers.Add(digit);
byte[] byteInput = new byte[784];
testImages.Read(byteInput, 0, 784);
for (int j = 0; j < 784; j++)
{
input[j] = byteInput[j] / 255d;
}
for (int nnIndex = 0; nnIndex < nns.Count; nnIndex++)
{
NeuralNetwork nn = nns[nnIndex];
nn.putData(input);
nn.run();
int answer = nn.getMaxIndexInOutput();
if (answer == digit)
{
correctAnswersAmount[nnIndex]++;
}
answers[nnIndex].Add(answer);
}
}
Console.Write(".");
}
log?.endPhase();
return(new NNsTestResults <int>(correctAnswersAmount, correctAnswers, answers));
}
public static void train(List <NeuralNetwork> nns, int iterations, ExperimentLog log = null)
{
FileStream trainImages = new FileStream("data/train_imgs", FileMode.Open);
FileStream trainLabels = new FileStream("data/train_lbls", FileMode.Open);
trainImages.Read(new byte[4 * 4], 0, 4 * 4);
trainLabels.Read(new byte[4 * 2], 0, 4 * 2);
List <double> answer = new List <double>();
for (int i = 0; i < 10; i++)
{
answer.Add(0);
}
List <double> input = new List <double>();
for (int i = 0; i < 784; i++)
{
input.Add(0);
}
log?.startPhase("Training",
$"MNIST dataset, learning step {learningStep}, correct answer as 1, wrong answer as 0",
iterations * 1000);
for (int h = 0; h < iterations; h++)
{
for (int i = 0; i < 1000; i++)
{
int digit = Utils.getNextByte(trainLabels);
byte[] byteInput = new byte[784];
trainImages.Read(byteInput, 0, 784);
for (int j = 0; j < 784; j++)
{
input[j] = byteInput[j] / 255d;
}
foreach (NeuralNetwork nn in nns)
{
nn.putData(input);
nn.run();
}
answer[digit] = 1;
foreach (NeuralNetwork nn in nns)
{
nn.backpropagate(answer, learningStep);
}
answer[digit] = 0;
}
Console.Write(".");
}
log?.endPhase();
}
private static NeuralNetwork deserialize(string path, ExperimentLog?log = null)
{
log?.startPhase("Deserialization", $"source file {path}", 0);
string json = File.ReadAllText(path);
NeuralNetwork nn = NeuralNetwork.deserialize(json);
log?.endPhase();
log?.recordTopologyFromNetwork(nn);
return(nn);
}
