anchor.diff:  let add xs ys = List.map2 (+) xs ys
   
   /// Logistic Sigmoid.
  -let logSigmoid x        = (/) 1.0 ((+) 1.0 (exp -x))
  +let logSigmoid x = (/) 1.0 ((+) 1.0 (exp -x))
   
   /// Derivative of Logistic Sigmoid.
  -let deltaLogSigmoid x    = (*) x ((-) 1.0 x)
  +let deltaLogSigmoid x = (*) x ((-) 1.0 x)
   
   /// Derivative of TanH i.e. sec^2h.
   let deltaTanH x = (/) 1.0 <| (*) (cosh x) (cosh x)
  
positive.code: let outputSize = 13;
  
  let network = {
   N = 0.001
   M = 0.9
   Inputs = List.replicate inputSize 0.0
   InputToHiddenWeights = List.chunkBySize inputSize (listRandElems ((*) inputSize hiddenSize))
   HiddenBias = listRandElems hiddenSize
   HiddenToOutputWeights = List.chunkBySize hiddenSize (listRandElems ((*) hiddenSize outputSize))
   HiddenNetOutputs = List.empty
   HiddenPrevDeltas = List.replicate hiddenSize (List.replicate inputSize 0.0)
   HiddenBiasPrevDeltas = List.replicate hiddenSize 0.0
   OutputBias = listRandElems outputSize
   OutputPrevDeltas = List.replicate outputSize (List.replicate hiddenSize 0.0)
   OutputBiasPrevDeltas = List.replicate outputSize 0.0
   Outputs = List.empty
   TargetOutputs = List.replicate outputSize 0.0
  }
  
  let training_samples = [| [ 0.500; 0.000; 0.000; 0.000; 0.000; 0.000; 0.000;  ] |]
  let teaching_inputs = [| [ 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 1.0; 0.0; 0.0; 1.0; 0.0;  ] |]
  let testing_samples = [| [ 0.692; 0.000; 0.000; 0.174; 0.000; 0.000; 0.000; ] |]
  let teaching_samples_inputs = [| [ 1.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 1.0; 0.0; 0.0;  ] |]
  
  let epoch = 20
  
  
negative.code:  =
   let trainOnce = feedForward >> backPropagate
  
   let networkDistance network = distance network.TargetOutputs network.OutputLayer.NetOutputs
  
   match epoch with
   | 0 -> netAcc
  