public async Task DeserializeNet()
{
try
{
var net = await MRSerializer.FromFile <NeuralNet <NeuralLayer, NeuralCell, NeuralBias, SigmoidFunction, NeuralLayer, NeuralCell, SigmoidFunction> >(FILE_PATH);
}
catch (Exception ex)
{
var e = ex;
}
}
public async Task TestSimple1100Task()
{
var net = NetSimple11000;
var trainer = new NeuralNetTrainer(net, Consts.XorSet, 50, 0.001, 0.0001, 1, Consts.TraceLog);
var result = trainer.SimpleTrain();
foreach (var e in Consts.XorSet)
{
var response = net.Activate(e.Item1);
Trace.WriteLine($"{e.Item1[0]} - {e.Item1[1]}\tResponse: {response[0]}\tCorrect: {e.Item2[0]}");
}
if (result.ResultError < 0.2)
{
await MRSerializer.ToFile(FILE_PATH, result.TargetNet, true);
}
}
public async Task NeuralWordsTest()
{
// get GAME OF THRONES
string regexText = string.Empty;
try
{
var fullText = await File.ReadAllTextAsync(Consts.GAME_OF_THRONES_PATH);
regexText = new Regex("Page [0-9]+").Replace(fullText, string.Empty);
}
catch (Exception ex)
{
var e = ex;
}
var vReader = new VReader(Consts.VOCAB_PATH);
vReader.UploadBinary();
var bag = MRWordBag.CreateToWords(regexText, 4);
// create traine vectors
var allSet = new List <Tuple <double[], double[]> >();
foreach (var step in bag.Read())
{
bool isValid = true;
foreach (var v in step)
{
if (!vReader.Vocab.ContainsWord(v) || !vReader.Vocab.ContainsWord(v))
{
isValid = false;
break;
}
}
if (!isValid)
{
continue;
}
var forInput = step.Take(3);
List <double> input = new List <double>();
foreach (var i in forInput)
{
input.AddRange(vReader.Vocab.GetRepresentationFor(i).NumericVector.Select(x => (double)x).ToList());
}
var forOut = step.Last();
double[] output = vReader.Vocab.GetRepresentationFor(forOut).NumericVector.Select(x => (double)x).ToArray();
allSet.Add(new Tuple <double[], double[]>(input.ToArray(), output));
}
var trainSet = allSet.Take(allSet.Count - 10).ToArray();
var checkSet = allSet.TakeLast(10).ToArray();
var trainRates = new double[] { 0.00005d, 0.00001d };
foreach (var rate in trainRates)
{
foreach (var net in NetsWordTest)
{
Trace.WriteLine($"Train net: layers: {net.HiddenLayersCount} | neurons: {net.Hidden.First().NeuronsCount}\tRate: {rate}");
var trainer = new NeuralNetTrainer(net, trainSet, 500, 1, rate, 1, Consts.TraceLog);
var trainResult = trainer.SimpleTrain();
Trace.WriteLine("-- check net --");
foreach (var s in checkSet)
{
var response = net.Activate(s.Item1);
var responseR = new Representation(response.Select(x => (float)x).ToArray());
var responseWord = vReader.Vocab.Distance(responseR, 1)?.FirstOrDefault()?.Representation;
var correct = vReader.Vocab.Distance(new Representation(s.Item2.Select(x => (float)x).ToArray()), 1)?.FirstOrDefault()?.Representation;
Trace.WriteLine($"Correct: {correct.WordOrNull}\tResponse: {responseWord.WordOrNull}");
}
var name = $"Neural net ({net.HiddenLayersCount}-{net.Hidden.First().NeuronsCount}-epochs-{trainResult.EpochFinished}-error-{trainResult.ResultError}-time-{trainResult.TotalTimeMs})";
await MRSerializer.ToFile($"d://{name}.txt", net, true);
}
}
}
