anchor.diff:          mb.SetSize nInput (fullDataset.[0].InputS |> ArrayND.nElems)
           mb.SetSize nClass (fullDataset.[0].TargetS |> ArrayND.nElems)
           mb.SetSize nHidden 10
  -        let mi = mb.Instantiate DevCuda
  +        let mi = mb.Instantiate dev
           
           // loss expression
           let loss = MLP.loss mlp input.T target.T
   
           // optimizer
  -        let opt = Adam (loss, mi.ParameterVector, DevCuda)
  +        let opt = Adam (loss, mi.ParameterVector, dev)
           let optCfg = opt.DefaultCfg
   
           let smplVarEnv (smpl: singleSample) =
  
positive.code:         mb.SetSize nHidden 10
          mb.SetSize nTrn 20
  
          let mi = mb.Instantiate DevCuda
  
          // loss expression
          let loss = MLGPT.loss mlmgp input.T target.T
  
          // optimizer
          let opt = Adam (loss, mi.ParameterVector, DevCuda)
          let optCfg = opt.DefaultCfg
  
          let smplVarEnv (smpl: singleSample) =
  
negative.code: 
  module Program =
      
      let classificationMLMGP dev=
          printfn "Training 2Layer MLMGP on abalone dataset gender classification"
  
          printfn "Training 2 Layer MLP on abalone dataset gender classification"
          ///Load the ablone dataset, classify gender from data
          let fullDataset = (DataParser.loadSingleDataset "abalone.data.txt" [0] ',')
          
          let data = TrnValTst.Of(fullDataset)
          if dev = DevCuda then
              let data = (TrnValTst.Of(fullDataset)).ToCuda()
              ()
          ///classified the dataset using a MLP with one hidden layer
          ///(analogous to Lern Mnist Project)
          let mb = ModelBuilder<single> "MultiGPModel"
  