static void Main(string[] args)
{
int siteId = Convert.ToInt32(args[0]);
Console.WriteLine("SiteId = {0}", siteId);
bool advanced_input = (args[1] == "1");
Console.WriteLine("Advanced input: {0}", advanced_input);
int M = Convert.ToInt32(args[2]);
Console.WriteLine("M = {0}", M);
int numEpochs = Convert.ToInt32(args[3]);
Console.WriteLine("numEpochs = {0}", numEpochs);
int inDim = Convert.ToInt32(args[4]);
Console.WriteLine("inDim = {0}", inDim);
int cellDim = Convert.ToInt32(args[5]);
Console.WriteLine("cellDim = {0}", cellDim);
int hiDim = Convert.ToInt32(args[6]);
Console.WriteLine("hidim = {0}", hiDim);
DeviceDescriptor device = DeviceDescriptor.CPUDevice;
Console.WriteLine($"======== running LSTMSequence.Train using {DeviceDescriptor.CPUDevice} ========");
LSTMSequence network = new LSTMSequence(b, siteId, device, advanced_input);
network.Train_predict(M, numEpochs, inDim, cellDim, hiDim);
}
/// <summary>
/// Build and train a RNN model.
/// </summary>
/// <param name="device">CPU or GPU device to train and run the model</param>
public void Train_predict(int M, int numEpochs = 1500, int inDim = 30, int cellDim = 25, int hiDim = 5)
{
string featuresName = "features";
string labelsName = "label";
const int ouDim = 1;
Dictionary <string, Set> dataSet = loadData(inDim, featuresName, labelsName, fun);
var featureSet = dataSet[featuresName];
var labelSet = dataSet[labelsName];
///// Debug data
//int q = 0;
//using (StreamWriter file = new StreamWriter("0.txt"))
//{
// file.WriteLine("Train");
// for (int i = 0; i < featureSet.train.Length; i++)
// {
// file.Write(q + ": ");
// for (int j = 0; j < featureSet.train[i].Length; j++)
// file.Write(featureSet.train[i][j] + " ");
// file.Write(labelSet.train[i][0]);
// file.WriteLine();
// q++;
// }
// file.WriteLine("Valid");
// for (int i = 0; i < featureSet.valid.Length; i++)
// {
// file.Write(q + ": ");
// for (int j = 0; j < featureSet.valid[i].Length; j++)
// file.Write(featureSet.valid[i][j] + " ");
// file.Write(labelSet.valid[i][0]);
// file.WriteLine();
// q++;
// }
// file.WriteLine("Test");
// for (int i = 0; i < featureSet.test.Length; i++)
// {
// file.Write(q + ": ");
// for (int j = 0; j < featureSet.test[i].Length; j++)
// file.Write(featureSet.test[i][j] + " ");
// file.Write(labelSet.test[i][0]);
// file.WriteLine();
// q++;
// }
//}
// build the model
var feature = Variable.InputVariable(new int[] { inDim + (advanced_input ? 2 : 0) }, DataType.Float, featuresName, null, false /*isSparse*/);
var label = Variable.InputVariable(new int[] { ouDim }, DataType.Float, labelsName, new List <CNTK.Axis>()
{
CNTK.Axis.DefaultBatchAxis()
}, false);
var lstmModel = CreateModel(feature, ouDim, hiDim, cellDim, "timeSeriesOutput");
Function trainingLoss = CNTKLib.SquaredError(lstmModel, label, "squarederrorLoss");
Function prediction = CNTKLib.SquaredError(lstmModel, label, "squarederrorEval");
// prepare for training
TrainingParameterScheduleDouble learningRatePerSample = new TrainingParameterScheduleDouble(0.0005, 1);
TrainingParameterScheduleDouble momentumTimeConstant = CNTKLib.MomentumAsTimeConstantSchedule(256);
IList <Learner> parameterLearners = new List <Learner>()
{
Learner.MomentumSGDLearner(lstmModel.Parameters(), learningRatePerSample, momentumTimeConstant, /*unitGainMomentum = */ true)
};
var trainer = Trainer.CreateTrainer(lstmModel, trainingLoss, prediction, parameterLearners);
// train the model
int batchSize = 20;
int outputFrequencyInMinibatches = 50;
int miniBatchCount = 0;
for (int i = 1; i <= numEpochs; i++)
{
//get the next minibatch amount of data
foreach (var miniBatchData in LSTMSequence.nextBatch(featureSet.train, labelSet.train, batchSize))
{
var xValues = Value.CreateBatch <float>(new NDShape(1, inDim + (advanced_input ? 2 : 0)), miniBatchData.X, device);
var yValues = Value.CreateBatch <float>(new NDShape(1, ouDim), miniBatchData.Y, device);
//Combine variables and data in to Dictionary for the training
var batchData = new Dictionary <Variable, Value>();
batchData.Add(feature, xValues);
batchData.Add(label, yValues);
//train minibarch data
trainer.TrainMinibatch(batchData, device);
TestHelper.PrintTrainingProgress(trainer, miniBatchCount++, outputFrequencyInMinibatches);
}
}
predict_test(dataSet, trainer.Model(), inDim, ouDim, batchSize, featuresName, labelsName, M);
predict(dataSet, trainer.Model(), inDim, ouDim, batchSize, featuresName, labelsName, M);
}