Neural Networks. Prof. Dr. Rudolf Kruse. Computational Intelligence Group Faculty for Computer Science

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1 Neural Networks Prof. Dr. Rudolf Kruse Computational Intelligence Group Faculty for Computer Science Rudolf Kruse Neural Networks 1

2 Hopfield Networks Rudolf Kruse Neural Networks 2

3 Hopfield Networks A Hopfield network is a neural network with a graph G =(U, C) thatsatisfiesthe following conditions: (i) U hidden =, U in = U out = U, (ii) C = U U {(u, u) u U}. In a Hopfield network all neurons are input as well as output neurons. There are no hidden neurons. Each neuron receives input from all other neurons. Aneuronisnotconnectedtoitself. The connection weights are symmetric, i.e. u, v U, u v : w uv = w vu. Rudolf Kruse Neural Networks 3

4 Hopfield Networks The network input function of each neuron is the weighted sum of the outputs of all other neurons, i.e. u U : f (u) net ( w u, in u )= w u in u = v U {u} w uv out v. The activation function of each neuron is a threshold function, i.e. u U : f (u) act (net u,θ u )= { 1, if netu θ, 1, otherwise. The output function of each neuron is the identity, i.e. u U : f (u) out (act u)=act u. Rudolf Kruse Neural Networks 4

5 Hopfield Networks Alternative activation function u U : f (u) act (net u,θ u, act u )= 1, if net u >θ, 1, if net u <θ, act u, if net u = θ. This activation function has advantages w.r.t. the physical interpretation of a Hopfield network. General weight matrix of a Hopfield network W = 0 w u1 u 2... w u1 u n w u1 u w u2 u n... w u1 u n w u1 u n... 0 Rudolf Kruse Neural Networks 5

6 Hopfield Networks: Examples Very simple Hopfield network u 1 x y 1 W = ( ) x 2 0 y 2 u 2 The behavior of a Hopfield network can depend on the update order. Computations can oscillate if neurons are updated in parallel. Computations always converge if neurons are updated sequentially. Rudolf Kruse Neural Networks 6

7 Hopfield Networks: Examples Parallel update of neuron activations u 1 u 2 input phase 1 1 work phase The computations oscillate, no stable state is reached. Output depends on when the computations are terminated. Rudolf Kruse Neural Networks 7

8 Hopfield Networks: Examples Sequential update of neuron activations u 1 u 2 input phase 1 1 work phase u 1 u 2 input phase 1 1 work phase Regardless of the update order a stable state is reached. Which state is reached depends on the update order. Rudolf Kruse Neural Networks 8

9 Hopfield Networks: Examples Simplified representation of a Hopfield network x 1 0 y 1 x y 2 u u u 2 W = x 3 0 y 3 Symmetric connections between neurons are combined. Inputs and outputs are not explicitely represented. Rudolf Kruse Neural Networks 9

10 Hopfield Networks: State Graph Graph of activation states and transitions +++ u 1 u 2 u 3 u 3 u 2 u 2 u u 3 ++ u 2 u 1 u 3 u 2 u 1 u 3 u 1 u 1 3 u u 1 u 2 u 3 u 1 u 2 u 3 Rudolf Kruse Neural Networks 10

11 Hopfield Networks: Convergence Convergence Theorem: If the activations of the neurons of a Hopfield network are updated sequentially (asynchronously), then a stable state is reached in a finite number of steps. If the neurons are traversed cyclically in an arbitrary, but fixed order, at most n 2 n steps (updates of individual neurons) are needed, where n is the number of neurons of the Hopfield network. The proof is carried out with the help of an energy function. The energy function of a Hopfield network with n neurons u 1,...,u n is E = 1 2 act W act + θ T act = 1 2 u,v U,u v w uv act u act v + u U θ u act u. Rudolf Kruse Neural Networks 11

12 Hopfield Networks: Convergence Consider the energy change resulting from an update that changes an activation: E = E (new) E (old) = ( v U {u} ( = v U {u} ( act (old) u w uv act (new) u w uv act (old) u act (new) u ) ( act v +θ u act (new) u ) act v +θ u act (old) u ) w uv act v v U {u} }{{} =net u net u <θ u :Secondfactorislessthan0. act (new) u = 1 andact (old) u =1,thereforefirstfactorgreaterthan0. Result: E <0. net u θ u :Secondfactorgreaterthanorequalto0. act (new) u =1andact (old) u = 1, therefore first factor less than 0. Result: E 0. θ u ). Rudolf Kruse Neural Networks 12

13 Hopfield Networks: Examples Arrange states in state graph according to their energy E Energy function for example Hopfield network: E = act u1 act u2 2act u1 act u3 act u2 act u3. Rudolf Kruse Neural Networks 13

14 Hopfield Networks: Examples The state graph need not be symmetric 5 E u u u Rudolf Kruse Neural Networks 14

15 Hopfield Networks: Physical Interpretation Physical interpretation: Magnetism AHopfieldnetworkcanbeseenasa(microscopic)modelofmagnetism (so-called Ising model, [Ising 1925]). physical atom magnetic moment (spin) strength of outer magnetic field magnetic coupling of the atoms Hamilton operator of the magnetic field neural neuron activation state threshold value connection weights energy function Rudolf Kruse Neural Networks 15

16 Hopfield Networks: Associative Memory Idea: Use stable states to store patterns First: Store only one pattern x =(act (l) u 1,...,act (l) u n ) { 1, 1} n, n 2, i.e., find weights, so that pattern is a stable state. Necessary and sufficient condition: S(W x θ )= x, where with S :IR n { 1, 1} n, x y i {1,...,n} : y i = { 1, if xi 0, 1, otherwise. Rudolf Kruse Neural Networks 16

17 Hopfield Networks: Associative Memory If θ = 0 anappropriatematrixw can easily be found. It suffices W x = c x with c IR +. Algebraically: Find a matrix W that has a positive eigenvalue w.r.t. x. Choose W = x x T E where x x T is the so-called outer product. With this matrix we have W x = ( x x T ) x }{{} E x = x = n x x = (n 1) x. ( ) = x ( x T x ) x }{{} = x 2 =n Rudolf Kruse Neural Networks 17

18 Hopfield Networks: Associative Memory Hebbian learning rule [Hebb 1949] Written in individual weights the computation of the weight matrix reads: w uv = 0, if u = v, 1, if u v, act (p) u 1, otherwise. Originally derived from a biological analogy. =act (v) u, Strengthen connection between neurons that are active at the same time. Note that this learning rule also stores the complement of the pattern: With W x =(n 1) x it is also W( x )=(n 1)( x ). Rudolf Kruse Neural Networks 18

19 Hopfield Networks: Associative Memory Storing several patterns Choose W x j = m i=1 If patterns are orthogonal, we have and therefore W i x j = x T i x j = = m i=1 m i=1 ( x i x i T ) x j { 0, if i j, n, if i = j, W x j =(n m) x j. m E x j }{{} = x j x i ( x i T x j) m x j Rudolf Kruse Neural Networks 19

20 Hopfield Networks: Associative Memory Storing several patterns Result: As long as m<n, x is a stable state of the Hopfield network. Note that the complements of the patterns are also stored. With W x j =(n m) x j it is also W( x j )=(n m)( x j ). But: Capacity is very small compared to the number of possible states (2 n ). Non-orthogonal patterns: W x j =(n m) x j + m i=1 i j x i ( x T i x j) }{{} disturbance term. Rudolf Kruse Neural Networks 20

21 Associative Memory: Example Example: Store patterns x 1 =(+1, +1, 1, 1) and x 2 =( 1, +1, 1, +1). where W 1 = W = W 1 + W 2 = x 1 x 1 T + x 2 x 2 T 2E , W 2 = The full weight matrix is: Therefore it is W = W x 1 =(+2, +2, 2, 2) and W x 1 =( 2, +2, 2, +2). Rudolf Kruse Neural Networks 21

22 Associative Memory: Examples Example: Storing bit maps of numbers Left: Bit maps stored in a Hopfield network. Right: Reconstruction of a pattern from a random input. Rudolf Kruse Neural Networks 22

23 Hopfield Networks: Associative Memory Training a Hopfield network with the Delta rule Necessary condition for pattern x being a stable state: s(0 + w u1 u 2 act (p) u w u1 u n act (p) u n θ u1 )=act (p) u 1, s(w u2 u 1 act (p) u w u2 u n act (p) u n θ u2 )=act (p) u 2,..... s(w un u 1 act (p) u 1 + w un u 2 act (p) u with the standard threshold function θ un )=act (p) u n. s(x) = { 1, if x 0, 1, otherwise. Rudolf Kruse Neural Networks 23

24 Hopfield Networks: Associative Memory Training a Hopfield network with the Delta rule Turn weight matrix into a weight vector: w =( w u1 u 2, w u1 u 3,..., w u1 u n, w u2 u 3,..., w u2 u n,.... w un 1 u n, θ u1, θ u2,..., θ un ). Construct input vectors for a threshold logic unit z 2 =(act (p) u 1, 0,...,0, }{{} n 2zeros Apply Delta rule training until convergence. act (p) u 3,...,act (p) u n,...0, 1, 0,...,0 ). }{{} n 2zeros Rudolf Kruse Neural Networks 24

25 Demonstration Software: xhfn/whfn Demonstration of Hopfield networks as associative memory: Visualization of the association/recognition process Two-dimensional networks of arbitrary size Rudolf Kruse Neural Networks 25

26 Hopfield Networks: Solving Optimization Problems Use energy minimization to solve optimization problems General procedure: Transform function to optimize into a function to minimize. Transform function into the form of an energy function of a Hopfield network. Read the weights and threshold values from the energy function. Construct the corresponding Hopfield network. Initialize Hopfield network randomly and update until convergence. Read solution from the stable state reached. Repeat several times and use best solution found. Rudolf Kruse Neural Networks 26

27 Hopfield Networks: Activation Transformation AHopfieldnetworkmaybedefinedeitherwithactivations 1and1orwithactivations 0and1.Thenetworkscanbetransformedintoeachother. From act u { 1, 1} to act u {0, 1}: From act u {0, 1} to act u { 1, 1}: wuv 0 = 2wuv and θu 0 = θu + wuv v U {u} w uv = 1 2 w0 uv and θ u = θ 0 u 1 2 wuv. 0 v U {u} Rudolf Kruse Neural Networks 27

28 Hopfield Networks: Solving Optimization Problems Combination lemma: Let two Hopfield networks on the same set U of neurons with weights w uv, (i) thresholdvaluesθ u (i) and energy functions E i = 1 2 u U v U {u} w (i) uv act u act v + u U θ (i) u act u, i =1, 2, be given. Furthermore let a, b IR. Then E = ae 1 +be 2 is the energy function of the Hopfield network on the neurons in U that has the weights w uv = aw uv (1) + bw uv (2) and the threshold values θ u = aθ u (1) + bθ u (2). Proof: Just do the computations. Idea: Additional conditions can be formalized separately and incorporated later. Rudolf Kruse Neural Networks 28

29 Hopfield Networks: Solving Optimization Problems Example: Traveling salesman problem Idea: Represent tour by a matrix city step An element a ij of the matrix is 1 if the i-th city is visited in the j-th step and 0 otherwise. Each matrix element will be represented by a neuron. Rudolf Kruse Neural Networks 29

30 Hopfield Networks: Solving Optimization Problems Minimization of the tour length E 1 = n n n j 1 =1 j 2 =1 i=1 Double summation over steps (index i) needed: where E 1 = (i 1,j 1 ) {1,...,n} 2 d j1 j 2 m ij1 m (i mod n)+1,j2. (i 2,j 2 ) {1,...,n} 2 d j1 j 2 δ (i1 mod n)+1,i 2 m i1 j 1 m i2 j 2, δ ab = Symmetric version of the energy function: E 1 = 1 2 (i 1,j 1 ) {1,...,n} 2 (i 2,j 2 ) {1,...,n} 2 { 1, if a = b, 0, otherwise. d j1 j 2 (δ (i1 mod n)+1,i 2 + δ i1,(i 2 mod n)+1 ) m i 1 j 1 m i2 j 2. Rudolf Kruse Neural Networks 30

31 Hopfield Networks: Solving Optimization Problems Additional conditions that have to be satisfied: Each city is visited on exactly one step of the tour: j {1,...,n} : n i=1 m ij =1, i.e., each column of the matrix contains exactly one 1. On each step of the tour exactly one city is visited: i {1,...,n} : n j=1 m ij =1, i.e., each row of the matrix contains exactly one 1. These conditions are incorporated by finding additional functions to optimize. Rudolf Kruse Neural Networks 31

32 Hopfield Networks: Solving Optimization Problems Formalization of first condition as a minimization problem: E2 = = = 2 n n n m ij 2 m ij +1 j=1 i=1 i=1 n n n m i1 j m i2 j j=1 i 1 =1 i 2 =1 n n n n n j=1 i 1 =1 i 2 =1 m i1 jm i2 j 2 2 j=1 i=1 n i=1 m ij + n. m ij +1 Double summation over cities (index i) needed: E 2 = (i 1,j 1 ) {1,...,n} 2 δ j1 j 2 m i1 j 1 m i2 j 2 2 m ij. (i 2,j 2 ) {1,...,n} 2 (i,j) {1,...,n} 2 Rudolf Kruse Neural Networks 32

33 Hopfield Networks: Solving Optimization Problems Resulting energy function: E 2 = 1 2 (i 1,j 1 ) {1,...,n} 2 (i 2,j 2 ) {1,...,n} 2 2δ j1 j 2 m i1 j 1 m i2 j 2 + (i,j) {1,...,n} 2 2m ij Second additional condition is handled in a completely analogous way: E 3 = 1 2 (i 1,j 1 ) {1,...,n} 2 (i 2,j 2 ) {1,...,n} 2 2δ i1 i 2 m i1 j 1 m i2 j 2 + (i,j) {1,...,n} 2 2m ij. Combining the energy functions: E = ae 1 + be 2 + ce 3 where b a = c a > 2 max d (j 1,j 2 ) {1,...,n} 2 j 1 j 2. Rudolf Kruse Neural Networks 33

34 Hopfield Networks: Solving Optimization Problems From the resulting energy function we can read the weights w (i1,j 1 )(i 2,j 2 ) = ad j 1 j 2 (δ (i1 mod n)+1,i 2 + δ i1,(i 2 mod n)+1 ) 2bδ j1 j }{{} 2 2cδ i1 i }{{}}{{ 2 } from E 1 from E 2 from E 3 and the threshold values: θ (i,j) = 0a }{{} from E 1 2b }{{} from E 2 2c }{{} = 2(b + c). from E 3 Problem: Random initialization and update until convergence not always leads to a matrix that represents a tour, leave alone an optimal one. Rudolf Kruse Neural Networks 34

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