static void Main(string[] args)
{
System.Random r = new Random(5);
Preprocess();
S2S = new AttentionSeq2Seq(32, 16, 1, input, output, true);
try { S2S.Load(); } catch (Exception) { }
int c = 0;
S2S.IterationDone += (a1, a2) =>
{
CostEventArg ep = a2 as CostEventArg;
if (c % 100 == 0)
{
Console.WriteLine($"Cost {ep.Cost} Iteration {ep.Iteration} k {c}");
S2S.Save();
}
c++;
};
MainThread = new Thread(new ThreadStart(Train));
MainThread.Start();
ReadThread = new Thread(new ThreadStart(ReadingConsole));
ReadThread.Start();
}
static void Main(string[] args)
{
Logger.LogFile = $"{nameof(Seq2SeqConsole)}_{GetTimeStamp(DateTime.Now)}.log";
Options options = new Options();
ArgParser argParser = new ArgParser(args, options);
AttentionSeq2Seq ss = null;
if (String.Equals(options.TaskName, "train", StringComparison.InvariantCultureIgnoreCase))
{
Corpus trainCorpus = new Corpus(options.TrainCorpusPath, options.SrcLang, options.TgtLang, options.ShuffleBlockSize);
if (File.Exists(options.ModelFilePath) == false)
{
ss = new AttentionSeq2Seq(options.WordVectorSize, options.HiddenSize, options.Depth, trainCorpus, options.SrcVocab, options.TgtVocab, options.SrcEmbeddingModelFilePath, options.TgtEmbeddingModelFilePath,
options.SparseFeature, true, options.ModelFilePath);
}
else
{
Logger.WriteLine($"Loading model from '{options.ModelFilePath}'...");
ss = new AttentionSeq2Seq();
ss.Load(options.ModelFilePath);
ss.TrainCorpus = trainCorpus;
}
Logger.WriteLine($"Source Language = '{options.SrcLang}'");
Logger.WriteLine($"Target Language = '{options.TgtLang}'");
Logger.WriteLine($"SSE Enable = '{System.Numerics.Vector.IsHardwareAccelerated}'");
Logger.WriteLine($"SSE Size = '{System.Numerics.Vector<float>.Count * 32}'");
Logger.WriteLine($"Processor counter = '{Environment.ProcessorCount}'");
Logger.WriteLine($"Hidden Size = '{ss.HiddenSize}'");
Logger.WriteLine($"Word Vector Size = '{ss.WordVectorSize}'");
Logger.WriteLine($"Learning Rate = '{options.LearningRate}'");
Logger.WriteLine($"Network Layer = '{ss.Depth}'");
Logger.WriteLine($"Use Sparse Feature = '{options.SparseFeature}'");
ss.IterationDone += ss_IterationDone;
ss.Train(300, options.LearningRate);
}
else if (String.Equals(options.TaskName, "test", StringComparison.InvariantCultureIgnoreCase))
{
ss = new AttentionSeq2Seq();
ss.Load(options.ModelFilePath);
List <string> outputLines = new List <string>();
var data_sents_raw1 = File.ReadAllLines(options.InputTestFile);
foreach (string line in data_sents_raw1)
{
List <string> outputWords = ss.Predict(line.ToLower().Trim().Split(' ').ToList());
outputLines.Add(String.Join(" ", outputWords));
}
File.WriteAllLines(options.OutputTestFile, outputLines);
}
else
{
argParser.Usage();
}
}
private void button5_Click(object sender, EventArgs e)
{
this.TrainButton.Enabled = true;
ss.Load();
this.PredictButton.Enabled = true;
ResultTxtBox.Enabled = true;
SrcTxtBox.Enabled = true;
}
