Neural Networks F#, XOR classifier and TSP Hopfield solver

It seems that recently thanks to the buzz around Deep Learning, Neural Networks are getting back the attention that they once had. This post contains just a very short introduction to Neural Networks, just enough to understand the F# code which follows. The aim for me was to learn F# and see how easy it can be to write NN code in F#. Two usage of Neural Networks are presented here: XOR classifier (Hello World of NN) and Traveling Salesman Problem solved using Hopfield Neural Network. Both working examples are at GitHub

Neural Networks

Artificial neural networks are computational models with particular properties such as the ability to adapt or learn, to generalize or to cluster and organize data. These models are inspired by the way that the human brain works. The building blocks of Neural Networks are inspired by Neurons - the building blocks of our brain. Neural Network can be seen as interconnected graph of nodes, where each node takes a role of a neuron. Each neuron can receive multiple signals, modify it and send it to other neurons to which it is connected. In this graph some vertexes are used to set the input, some of them are used to perform the computation and other once will hold the output values.

Nodes are connected by edges which have different weights. The weight of the edge specifies how the signals is modified when passing from one node to another.


Perceptron is a basic block of linear neural networks. It is composed of multiple input nodes and a decision node. Perceptron is a single layer network. Input values are linked directly to the output node, connected by edges with certain weights.


The output node takes the incoming values, sums them and based on the result returns an output. The output could be binary or continuous value. For instance a threshold can be used to say whether the output is 1 or 0.

In practice, very often the Sigmoid (logistic) function is used to output value within the interval [0,1].

Linear separability

Single layer perceptron can only learn linerearly separable data.

Imagine we want to separate a set of datapoints into multiple clusters. We visualize the data in 2-dimensional euclidean space. If we can separate the cluster by drawing direct line between the data points, the data is linearly separable. The same can be applied on multi-dimensional data.


In a world of logical functions (we are building XOR classifier), this limitation means that a single layer perceptron is able to learn AND or OR function but it won’t be able to learn XOR function.

One can easily imagine a line that separates all OR positive results from the negatives once. On the other hand there is no straight line that separates the positive XOR results ([0,1] and [1,0]) from the negatives ([0,0] and [1,1])

or_data xor_data

Feed forward multilayer networks

Feed forward network can be thought as composition of perceptrons. Such network has one input layer, multiple hidden layers and one output layer. The feed forward network does not contain cycles, unlike the Hopfield network in the next examples (which is recurrent neural network).

Unlike single layer perceptron, multilayer feed forward network is capable of learning linerably non-separable data such as the results of XOR function.

XOR classifier

This is the first example. This network is called classifier because it learns the XOR function. It can then “classify” the 2 values in the input into single value on the output. Here is how the network looks like:


The NN diagram was drawn in latex, using the tkz-berge package. [Look here if you want to know how] (

The network starts with random weights and continuously updates the weights so that the result in the output node corresponds to the XOR of input values. In order to update the weights Backpropagation (Backwards propagation of errors) technique is used.

The value of the output is compared to the real value (which is known). The error is determined as the differences between the output and the real value. Then the weights of the edges has to be updated to minimalize the error as shown later.


Each layer can be represented as one dimensional array, the weights are stored using 2-dimensional array ([0,1] is the weight between nodes 0 and 1). Here is the function which calculates the sum of values incoming to a single node and passes the value to the activation function. The activation function here can be any function, as it is passed as parameter but here Sigmoid is used.

let pass (input:float[]) (weights:float[,]) activation =
let length = weights |> Array2D.length2
seq {
    for i in 0 .. length-1 do
        let sum = ( weights.[*,i] input) |> Array.sumBy (fun (v,w) -> v * w)
        yield activation sum
} |> Array.ofSeq

By applying the pass multiple times, the whole network can be composed as sequence of pass functions.

The training procedure

The following function is the main loop of the XOR network and we iterate over until we obtain good results and the network is adapted.

let train network rate input target =
  let n1 = completepass input network
  let delta = deltaOutput n1.output target
  let deltaHidden = passDelta n1.hidden delta n1.hiddenToOutput
  let updatedHiddenToOut = updateWeights n1.hidden delta n1.hiddenToOutput rate
  let updatedInToHidden = updateWeights n1.input deltaHidden n1.inputToHidden rate

Function Completepass just calls 2 times the pass function value and gets the input values trough the hidden layer to the output. The output is then compared to desired result and error is estimated. From this error an array of “delta” values per each output node is determined which is then used to update the weights.

In the case of XOR network, there is only one output, so the delta will one dimensional array.

The delta has to be propagated lower so that we can also update the weights between the input and hidden layer.

Backpropagating the error

First the error of each layer has to be calculated. In the example bellow the error of the output layer is the value (t-o) or (Target - Ouput). This value is multiplied by value o*(1-o) which is the derivation of the Sigmoid function. The resulting array contains for each node a delta value, which shall be used to adjust the weights.

let deltaOutput (output:array<float>) (target:array<float>) =
 ( output target) |> (fun (o,t) -> o * (1.0 - o) * (t - o))

Delta propagation

Calculating the delta value for the output layer is not sufficient to correct all the weights in the network, the delta has to be propagated to the lower layers of the network, so that we can update the weights on the input - hidden connections.

let passDelta (outputs:float[]) (delta:float[]) (weights:float[,]) =
    let length = weights |> Array2D.length1
    seq {
        for i in 0 .. length-1 do
            let error = ( weights.[i,*] delta) |> Array.sumBy (fun (v,w) -> v * w)
            yield outputs.[i] * (1.0 - outputs.[i]) * error
    } |> Array.ofSeq

The “error” of the hidden layer is just the delta multiplied by the weight associated to the edge that propagates this delta to the lower value.

The last missing piece is the function that woudl update weights between 2 layers.

let updateWeights (layer:float[]) (delta:float[]) (weights:float[,]) learningRate =
    weights |> Array2D.mapi (fun i j w -> w + learningRate * delta.[j] * layer.[i])

Learning rate is a constant that determines how quickly the edges are updated. If the learning rate is too big, the network might miss the optimal weights. However if it is too small it will take longer time to get to the correct solution.

At the beginning the weight are set to random values between 0 and 1, or slid slightly. From the quick tests that I did it seems that for instance using learning rate of 0.3 it took 20000 iterations to get to a neural network that would XOR the values on it’s inputs.

Hopfield-Tank network and Travelling Salesman Problem

TSP is one of the well known Combinatorial optimization problems and as such, it has been solved in many different ways (Integer Linear Programming, Genetic or Biologically inspired algorithms and other heuristics). Neural Networks are one of the many approaches to provide a solution to this problem.

Even within Neural Networks several different approaches have been developed to solve TSP (eg. elastic nets,self-organizing map). The approach demonstrated here is the oldest one: Hopfield neural network.

Hopfield neural network

Hopfield NN is a recurrent neural network (connections in the network form cycles). The units (nodes) in a Hopfield network can have only 2 values: 0|1 or -1|1 depending on literature, but in either case values that encode a binary state. The network is fully connected (each node is connected with any other node). The connection matrix is symetric, that is the weights on the edges are the same in both directions.

The initial Hopfied network had only binary values (0/1) but to solve TSP and for other problems continuous version of the network is used, where every node has value in range [0,1].

The value of each node depends on the input potential of the node (Ui) and in order to keep it between (0,1) the tanh function is used:


The value of the input potential of each node depends on the values of all nodes that are connected to it and the values of the connections weights. This is actually identical to the way that the nodes in the XOR classifier behaved.


Network energy

Each state of the HNN can be described by a single value called Energy. While iterating and changing the state, the energy of HNN will either stay the same or will get smaller. The energy of the network will eventually convert to a local minimum - the state of the HNN that we target to solve the TSP

Hopfiled approach to TSP

The first and original approach to solve TSP was described in 1985 by Tank and Hopfield in their paper “Neural Computation of Decisions in Optimization Problems”. Since then many more attempts were made. I have found these few papers or surweys available online quite useful and have used them to deduce my implementation:

If I should pick one, I would say that the first one gave me most information about the problem, event though not enough implementation details. The others once then made it (almost) clear how to implement the solution.

Encoding TSP in Hopfield network

The neural network used to encode the TSP and it’s solution is square matrix of nodes having N rows (one for each city) and N columns (one for each position in the tour), where N is the number of cities. When the algorithm finishes and the network converges to it’s final state, each node will have value of either 0 or 1. As said the network in fully inter-connected so there is an edges between each node, which is not shown in the image bellow.



If the node in i-th row and j-th column has value 1, the city (i) will be in the tour on position (j).

As described above the network evolves and each node changes it’s value over time. The secret of the algorithm is thus to provide such update rule that at the end the matrix of nodes will follow these criteria:

  • Only one node in each row will have value 1
  • Only one node in each column will have value 1
  • There will be exactly N nodes having value 1
  • The distances of the tour will be minimal

To come up with such update rule, Energy function has to be determine which will attain it’s minimum for optimal solution of TSP. The energy function has to take into account the above specified rules. The following definition will satisfy the rules. Note that the following equations were taken from the J.Y Potvin’s -paper


A,B,C and D are constants and the 4 summations here correspond to the 4 above mentioned points.

Describing the dynamic of the network

There are two ways to describe the behavior and the dynamic of the network:

  • From the TSP energy function and the standard Hopfield network energy function we determine the weights between the nodes. Then we can let the network evolve over time. Since we have the weights of the connections between the nodes, we could iterate over the nodes, sum all the weighted inputs and set the output of the node. That would be an approach similar to the one taken on the XOR. This has one disadvantage, that is that the connection weights have to be determined from the equality between the standard Hopfield model energy function and the TSP Hopfield model function. There is an easier way without determining the connections weights.
  • The easier way is to describe the change of the input potential without determining the weights of the edges. Then we can just iterate or randomly choose nodes, determine the new value of the potential and update the values of the connected nodes. This seems maybe ackward, because the network will evolve without having specific weighed connections between nodes. This options is described further.

The change in the node’s input potential

One of the definition of the input potential of a node (i) can be described by:


What we want to determine, is how this value will change over time, when the network evolves. The change of the potential over time is it’s partial derivation with respect to the time.


When doing a computer simulation we will described the value of Ui in time T+1, or here T+ delta t. Where delta T is some small time interval.


This equation is at the hearth of this algorithm. We now have an exact specification of what the value of potential Ui will be in time T+1. That is our iterating algorithm has to perform exactly this updated.

Implementation details

  • Compute the distance matrix, containing the distance between each 2 cities
  • Initialize the network with random values
  • Pick randomly a node, compute the change in the input potential
  • Update the value of the node from the input potential change
  • Every n-iterations calculate the energy of the network
  • Once the energy of the network stabilizes or you have reached fixed number of iterations - stop the process
  • Translate the network into a city path

The network can be stored in 2-dimensional matrix. One can choose between storing the input potential of each node or the value of the each node, because the value of the node can be calculated from the potential at any time. I have chosen the second option and I store the input potential two dimensional array u.

The following code is the calculation of the input potential change of single node at coordinates (city,position). This is just a F# translation of the of the equation above.

let singlePass (distances:float[,]) (u:float[,]) pms city position =
  let n = Array2D.length2 u

  let values = u |> toValues pms

  let aSum = sumAllBut position (values |> rowi city)

  let bSum = sumAllBut city (values |> coli position)

  let cSum = (values |> Seq.cast<float> |> Seq.sum) - float(n+1)

  let dSum = dSumCalc distances city position values

  let dudt = -pms.A*aSum - pms.B*bSum - pms.C*cSum - pms.D*dSum
  //value of input potential in t+ delta_t
  let r = u.[city,position] + pms.dTime*(-u.[city,position] + dudt)
  //Alternatively according to the paper by Jacek Mandziuk one can just use the update value
  //let r = dudt

There are few things to note. This code takes the distances matrix, the current state of the network (the value of input potential of each node), parameters of the network (constants A,B,C,D from equations above) and row (city) and the column (position) of the node that we are updating. The toValues method takes the current value of each node potential and returns a matrix of node values. The rowi and coli method return respecively one row or one column from 2 dimensional array.

The sumAllBut method adds all elements of one-dimensional array except an element at position which is passed to the method. The dSumCalc method is the only one with a bit more complexity and it calculates the D value of the equation above (the one that assures the minimization of the TSP circuit)

let rowi row (network:float[,]) =
  network.[row,*] |> Array.mapi (fun j e -> (e,j))

let coli col (network:float[,]) =
  network.[*,col] |> Array.mapi (fun j e -> (e,j))

let sumAllBut (i:int) (values:(float*int)[]) =
  Array.fold (fun acc (e,j) -> if i<>j then acc + e else acc) 0.0 values

let dSumCalc distances city position (v:float[,]) =
  let n = v |> Array2D.length1
  (distances |> rowi city) |> Array.sumBy (fun (e,i) ->
      let index1 = (n+position+1) % n
      let index2  = (n+position-1) % n
      e*(v.[i,index1] + v.[i,index2])

let toValues (pms:HopfieldTspParams) u =
  u|> (fun (ui) -> v ui pms)

//calculates the value of node from input potential
let v (ui:float) (parameters:HopfieldTspParams) = (1.0 + tanh(ui*parameters.alfa))/2.0

The method which updates the input potential of single node can be called in 2 different ways. Either we pick the nodes randomly multiple times or we loop over all the nodes serially. If the update is serial then the only random element of the algorithm is the initialization of the network.

let serialIteration u pms distances =
  u |> Array2D.mapi (fun i j x -> singlePass distances u pms i j)

let randomIteration u pms distances =
  let r = new Random(DateTime.Now.Millisecond)
  let n = Array2D.length1 u
  for i in 0 .. 1000*n do
      let city = r.Next(n)
      let position = r.Next(n)
      u.[city, position] <- singlePass distances u pms city position

The random iteration here is repeated 1000*n times where n is the number of cities, which is probably more than enough since the network seems to converge much sooner. Just for the sake of completeness, here is the method that runs 10 times either serial or random iteration.

let initAndRunUntilStable cities pms distances =
  let u = initialize cities pms
  {1 .. 10} |> Seq.fold (fun uNext i ->
          match pms.Update with
              | Random -> randomIteration uNext pms distances
              | Serial -> serialIteration uNext pms distances
  ) u

And here high level method, that generates a random example of TSP problem, calculates distances between all cities and runs the algorithm until a stable and correct solution is found. That is until the network returns feasible solution.

let sampleRun (pms:HopfieldTspParams ) (n:int) =
  let cities = generateRandomCities n
  let distances = calculateDistances cities
  let networks = Seq.initInfinite (fun i -> initAndRunUntilStable cities pms distances)
  let paths = networks |> (fun v-> currentPath v)
  let validPath = paths |> Seq.find (fun path -> isFeasable path)
  cities, validPath


The results are not overwhelming, on the other hand I have only implemented the most simple version of the algorithm. In the original paper the authors stated that the convergence rate to feasable solutions was about 50%. But depending on the parameters one can get better results. See bellow one of the runs of the algorithm on 6 random nodes.


Here is the complete list of parameters for Hopfield network and the values that I ended up using:

  • Alpha: the parameters which shapes the Sigmoid decision function applied on each node (500)
  • A: input potential change function, sum of rows (500)
  • B: input potential change function, sum of columns (500)
  • C: input potential change function, max N cities (200)
  • D: input potential change function, minimization of tour length (300)
  • dTime: represents the delta T, the small update in time for each update of the input potential value (0.00001)

I have also used the standard update rule to obtain new value of the input potential which takes into account the current input potential.

   let dudt = -pms.A*aSum - pms.B*bSum - pms.C*cSum - pms.D*dSum
  //value of input potential in t+ delta_t
  u.[city,position] = u.[city,position] + pms.dTime*(-u.[city,position] + dudt)

According to the paper by Jacek Mandziuk one can just use the updated values as the new input potential, so that the update rule would become only:

  let dudt = -pms.A*aSum - pms.B*bSum - pms.C*cSum - pms.D*dSum
  u.[city,position] = dudt;

This rule didn’t work for me. The convergence rate wasn’t better neither were the tours lengths. Of course for such version, different values for network parameters have to be used.

Note that GitHub repository and specifically the Hopfield module contains more code:

  • Method to determine the correct parameters. The performance of the algorithm greatly depends on the parameters of the Energy function and on the parameter alfa, which amplifies the effect of the input potential on the value of given node.
  • Few lines are also available to draw the solution using Windows Forms Charts, through F#
Written on March 19, 2015