public virtual Trainer Configure <T>(Model model, int?epochs, Core.fDataSet dataSet, double?learningRate, double?momentum, int?batchSize, double?beta1, double?beta2)
where T : CNN.Loss, new()
{
this.batchSize = batchSize;
List <Core.fData> dataList = (List <Core.fData>)dataSet.fData;
int index = 0, count;
while (index < dataList.Count())
{
count = (int)(dataList.Count() - index > batchSize ? batchSize : dataList.Count() - index);
Core.fDataSet data = new Core.fDataSet();
data.fData = dataList.GetRange(index, count);
batchImages.Add(data);
index += (int)batchSize;
}
base.Configure(model, epochs, batchImages[0]);
// 0. assert learningRate and momentum
if ((learningRate == null) || (momentum == null) || (beta1 == null) || (beta2 == null))
{
throw new Exception();
}
this.learningRate = learningRate;
this.momentum = momentum;
this.beta1 = beta1;
this.beta2 = beta2;
this.lossfunc = new T();
return(this);
}
public virtual Trainer Configure(Model model, int?epochs, Core.fDataSet dataSet)
{
this.dataSet = dataSet;
this.model = model;
// 0. assert input and output size for all of data set
if (nofSet != 0)
{
throw new Exception();
}
nofSet = dataSet.fData.Count;
//for (int i = 0; i < nofSet; i++)
// if ((System.Math.Sqrt(dataSet.fData[i].Data.Length) != model.Input.Output[0].Rows) || (dataSet.fData[i].Label.Length != model.Output.Output[0].Rows))
// throw new Exception();
// 1. assert epochs
if (epochs == null)
{
throw new Exception();
}
this.epochs = epochs;
return(this);
}
