static int Sample(CharRNNSamplingParameters args)
{
string saveFile = Path.Combine(args.saveDir, ConfigFileName);
if (!File.Exists(saveFile))
{
Console.Error.WriteLine($"Save file {saveFile} does not exist. Perhaps you've forgottent to train?");
return(1);
}
var savedArgs = JsonConvert.DeserializeObject <CharRNNModelParameters>(File.ReadAllText(saveFile));
var(chars, vocabulary) = LoadCharsVocabulary(Path.Combine(args.saveDir, CharsVocabularyFileName));
string prime = string.IsNullOrEmpty(args.prime) ? chars[0].ToString() : args.prime;
var model = new CharRNNModel(savedArgs, training: false);
new Session().UseSelf(session => {
tf.global_variables_initializer().run();
var saver = new Saver(tf.global_variables());
var checkpoint = tf.train.get_checkpoint_state(args.saveDir);
if (checkpoint?.model_checkpoint_path != null)
{
saver.restore(session, checkpoint.model_checkpoint_path);
Console.WriteLine(model.Sample(session, chars, vocabulary, prime: prime, num: args.count));
}
});
return(0);
}
static int Train(CharRNNTrainingParameters args)
{
var dataLoader = new TextLoader(args.dataDir, args.BatchSize, args.SeqLength);
args.VocabularySize = dataLoader.vocabularySize;
dynamic checkpoint = null;
if (!string.IsNullOrEmpty(args.initFrom))
{
checkpoint = tf.train.latest_checkpoint(args.initFrom);
var savedArgs =
JsonConvert.DeserializeObject <CharRNNModelParameters>(
File.ReadAllText(Path.Combine(args.initFrom, ConfigFileName)));
Trace.Assert(savedArgs.ModelType == args.ModelType);
Trace.Assert(savedArgs.RNNSize == args.RNNSize);
Trace.Assert(savedArgs.LayerCount == args.LayerCount);
Trace.Assert(savedArgs.SeqLength == args.SeqLength);
var(chars, vocabulary) = LoadCharsVocabulary(Path.Combine(args.saveDir, CharsVocabularyFileName));
Trace.Assert(dataLoader.chars.SequenceEqual(chars));
Trace.Assert(dataLoader.vocabulary.SequenceEqual(vocabulary));
}
Directory.CreateDirectory(args.saveDir);
File.WriteAllText(Path.Combine(args.saveDir, ConfigFileName), JsonConvert.SerializeObject(args));
File.WriteAllText(Path.Combine(args.saveDir, CharsVocabularyFileName), JsonConvert.SerializeObject((dataLoader.chars, dataLoader.vocabulary)));
var model = new CharRNNModel(args, training: true);
new Session().UseSelf(session => {
var summaries = tf.summary.merge_all();
var writer = new FileWriter(Path.Combine(args.logDir, DateTime.Now.ToString("s").Replace(':', '-')));
writer.add_graph(session.graph);
session.run(new dynamic[] { tf.global_variables_initializer() });
var globals = tf.global_variables();
var saver = new Saver(globals);
if (checkpoint != null)
{
saver.restore(session, checkpoint);
}
int totalNumberOfBatches = args.epochs * dataLoader.batchCount;
for (int epoch = 0; epoch < args.epochs; epoch++)
{
session.run(new[] { tf.assign(
model.learningRate,
tf.constant(args.learningRate * Math.Pow(args.decayRate, epoch))) });
dataLoader.ResetBatchPointer();
var state = session.run(model.initialState.Items().Cast <object>());
var stopwatch = Stopwatch.StartNew();
for (int batch = 0; batch < dataLoader.batchCount; batch++)
{
stopwatch.Restart();
var(input, targets) = dataLoader.NextBatch();
var feed = new PythonDict <dynamic, dynamic> {
[model.inputData] = input,
[model.targets] = targets,
};
foreach (var(i, tuple) in model.initialState.Items().Enumerate())
{
feed[tuple.c] = state[i].c;
feed[tuple.h] = state[i].h;
}
var step = session.run(new dynamic[] { summaries, model.cost, model.finalState, model.trainOp }, feed);
int currentBatch = (epoch * dataLoader.batchCount) + batch;
writer.add_summary(step[0], currentBatch);
var time = stopwatch.Elapsed;
Console.WriteLine(
$"{currentBatch}/{totalNumberOfBatches} " +
$"(epoch {epoch}), " +
$"train loss = {step[1]}, time/batch = {time}");
if ((currentBatch % args.saveEvery == 0) ||
(epoch == args.epochs - 1 && batch == dataLoader.batchCount - 1))
{
string checkpointPath = Path.Combine(args.saveDir, "model.ckpt");
saver.save(session, checkpointPath, global_step: currentBatch);
Console.WriteLine("model saved to " + checkpointPath);
}
}
}
});
return(0);
}