protected override string ProcessMessageAsLayerOrLoss(Command msgObj, SyftController ctrl)
{
switch (msgObj.functionCall)
{
case "prepare_to_fit":
{
FloatTensor input = ctrl.floatTensorFactory.Get(int.Parse(msgObj.tensorIndexParams[0]));
FloatTensor target = ctrl.floatTensorFactory.Get(int.Parse(msgObj.tensorIndexParams[1]));
Loss.Loss criterion = ctrl.GetLoss(int.Parse(msgObj.tensorIndexParams[2]));
Optimizer optim = ctrl.GetOptimizer(int.Parse(msgObj.tensorIndexParams[3]));
int batch_size = int.Parse(msgObj.tensorIndexParams[4]);
return(PrepareToFit(input, target, criterion, optim, batch_size).ToString());
}
case "fit":
{
int start_batch_id = int.Parse(msgObj.tensorIndexParams[0]);
int end_batch_id = int.Parse(msgObj.tensorIndexParams[1]);
int iters = int.Parse(msgObj.tensorIndexParams[2]);
return(Fit(start_batch_id, end_batch_id, iters));
}
case "evaluate":
{
FloatTensor test_input = ctrl.floatTensorFactory.Get(int.Parse(msgObj.tensorIndexParams[0]));
FloatTensor test_target = ctrl.floatTensorFactory.Get(int.Parse(msgObj.tensorIndexParams[1]));
Loss.Loss criterion = ctrl.GetLoss(int.Parse(msgObj.tensorIndexParams[2]));
int batch_size = int.Parse(msgObj.tensorIndexParams[3]);
return(Evaluate(test_input, test_target, criterion, batch_size));
}
}
return(ProcessMessageAsLayerObject(msgObj, ctrl));
}