anchor.diff:                  Inputs = List.empty
                   Weights = ((*) hiddenSize outputSize) |> listRandElems |> List.chunkBySize hiddenSize
                   Bias = listRandElems outputSize
  +                Gradients = List.empty
                   PrevDeltas = List.replicate outputSize <| List.replicate hiddenSize 0.0
                   BiasPrevDeltas = List.replicate outputSize 0.0
                   NetOutputs = List.empty
  
positive.code:  |> 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 = 4000
  
  printfn "Training..."
  let trained = train epoch network (training_samples, teaching_inputs) (testing_samples, test_outputs) 
negative.code:                       Inputs = List.empty
                        Weights = ((*) hiddenSize hiddenSize) |> listRandElems |> List.chunkBySize hiddenSize
                        Bias = listRandElems hiddenSize
                        Gradients = List.empty
                        PrevDeltas = List.replicate hiddenSize <| List.replicate hiddenSize 0.0
                        BiasPrevDeltas = List.replicate hiddenSize 0.0
                        NetOutputs = List.empty
  