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); } } }