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