anchor.diff:  let testing_samples = data "testing_samples.txt"
   let test_outputs = data "test_outputs.txt"
   
  -let epoch = 1
  -
  +let epoch = 1000
  +
  +printfn "Training..."
   let trained = train epoch network (training_samples, teaching_inputs) (testing_samples, test_outputs) 
positive.code:    (netAcc.SecondHiddenLayer.Weights |> matrixToString) + "," +
     (netAcc.SecondHiddenLayer.Bias |> vectorToString) + "," +
     (netAcc.OutputLayer.Weights |> matrixToString) + "," +
     (netAcc.OutputLayer.Bias |> vectorToString)
    logToDataFile "weightsAndBiases.txt" <| logNetworkParameters
  
    if epoch % 100 = 0 then printfn "%f %f" rms_trained_err rms_validated_err
  
negative.code:    log fullfilepath
  
    (* write error *)
    let errors trained_err validated_err = trained_err + "," + validated_err + "\n"
    logToDataFile "errors.txt" <| errors (rms_trained_err.ToString()) (rms_validated_err.ToString())
  
    (* write appropriate parameters.
  