anchor.diff:    let validated = List.fold2 (funcNet feedForward) netAcc shuffled_testing_s shuffled_testing_i
     let rms_validated_err = networkDistance validated
   
  -//  if epoch % 100 = 0 then printfn "%f %f" rms_trained_err rms_validated_err
  -  printfn "%f %f" rms_trained_err rms_validated_err
  +  if epoch % 100 = 0 then printfn "%f %f" rms_trained_err rms_validated_err
  +//  printfn "%f %f" rms_trained_err rms_validated_err
     
  -  let best_trained_err = 0.019999 //*** CONSIDER PASSING TO PARAMETER.
  -  let best_test_err = 0.019999
  -  let errTresholdMeet = (rms_trained_err < best_trained_err) && (rms_validated_err < best_test_err)
  -  if errTresholdMeet then trained
  -  else train ((-) epoch 1) trained (training_samples, teaching_inputs) (testing_samples, teaching_testing_inputs)
  +//  let best_trained_err = 0.019999 //*** CONSIDER PASSING TO PARAMETER.
  +//  let best_test_err = 0.019999
  +//  let errTresholdMeet = (rms_trained_err < best_trained_err) && (rms_validated_err < best_test_err)
  +//  if errTresholdMeet then trained
  +//  else train ((-) epoch 1) trained (training_samples, teachingInputs) (testing_samples, testOutputs)
  +  train ((-) epoch 1) trained (training_samples, teachingInputs) (testing_samples, testOutputs)
   
   let inputSize = 7;
   let hiddenSize = 10;
  
positive.code: ﻿
  /// Matrix Transpose.
  let transpose xss =
   let rec f xss acc =
  
negative.code:  TargetOutputs = List.replicate outputSize 0.0
  }
  
  let dataToFloatList separator data = Regex.Split(data, separator) |> Array.Parallel.map float |> Array.toList
  let csvStrToFloatList = dataToFloatList ","
  let data filename = (* replace with your current directory. *)
   File.ReadAllLines(@"D:\Projects\AI\cardiotocography\data and error\"+filename)
   |> Array.toList
   |> List.map csvStrToFloatList
  
  let training_samples = data "training_samples.txt"
  let teaching_inputs = data "teaching_inputs.txt"
  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)
  
  (* val trained : Network = //Error: (Training: 0.016551, Test: 0.017999)
    {LearningRate = 0.001;
  