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Lecture 10 Boltzmann machine

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1 Lecture 10 Boltzmann machine
Soft Computing Lecture 10 Boltzmann machine

2 Definition of wikipedia
A Boltzmann machine is a type of stochastic recurrent neural network originally invented by Geoffrey Hinton and Terry Sejnowski. Boltzmann machines can be seen as the stochastic, generative counterpart of Hopfield nets. They were an early example of neural networks capable of forming internal representations. Because they are very slow to simulate they are not very useful for most practical purposes. However, they are theoretically intriguing due to the biological plausibility of their training algorithm.

3 Definition of BM (2) Boltzmann machine, like a Hopfield net is a network of binary units with an "energy" defined for the network. Unlike Hopfield nets though, Boltzmann machines only ever have units that take values of 1 or 0. The global energy, E, in a Boltzmann machine is identical to that of a Hopfield network, that is:                                     Where: wij is the connection weight from unit j to unit i. si is the state (1 or 0) of unit i. θi is the threshold of unit i.

4 Definition of BM (3) Thus, the difference in the global energy that results from a single unit i being 0 or 1, written ΔEi, is given by: A Boltzmann machine is made up of stochastic units. The probability, pi of the ith unit being on is given by: (The scalar T is referred to as the "temperature“ of the system.)

5 Definition of BM (4) Notice that temperature T plays a crucial role in the equation, and that in the course of running the network, the value of T will start high, and gradually 'cool down' to a lower value. This is a continuous function that transforms any inputs - from –infinity to +infinity - into real numbers in the interval [0, 1]. This is the logistic function, & has a characteristic sigmoid shape: So when Net = 0, e-Net = 1, because any number raised to the power 0 is 1. This true for all temperatures. So always prob(A = 1) = 1/2. I.e. if the netinput is 0, it's as likely to fire as not.

6 Definition of BM (5) With very low temperatures, e.g , if you get a little bit of positive activation, the probability it will fire goes to 1. conversely, with if it goes negative, i.e. at very low temperatures - as it approaches 0 - the Boltzmann machine becomes deterministic. Otherwise, the higher the temperature, the more it diverges from this.

7 Definition of BM (6) Units are divided into "visible" units, V, and "hidden" units, H. The visible units are those which receive information from the "environment", i.e. those units that receive binary state vectors for training. The connections in a Boltzmann machine have three restrictions on them: (No unit has a connection with itself) (All connections are symmetric) (Visible units have no connections between them)

8 Structure of BM

9 Alternative structure of BM

10 Training of BM Boltzmann machines can be viewed as a type of maximum likelihood model, i.e. training involves modifying the parameters (weights) in the network to maximize the probability of the network producing the data as it was seen in the training set. In other words, the network must successfully model the probabilities of the data in the environment. There are two phases to Boltzmann machine training. One is the "positive“ phase where the visible units' states are clamped to a particular binary state vector from the training set. The other is the "negative" phase where the network is allowed to run freely, i.e. no units have their state determined by external data. A vector over the visible units is denoted Vα and a vector over the hidden units is denoted as Hβ. The probabilities P+(S) and P−(S) represent the probability for a given state, S, in the positive and negative phases respectively. Note that this means that P+ (Vα) is determined by the environment for every Vα, because the visible units are set by the environment in the positive phase.

11 Training of BM (2) Boltzmann machines are trained using a gradient descent algorithm, so a given weight, wij is changed by subtracting the partial derivative of a cost function with respect to the weight. The cost function used for Boltzmann machines, G, is given as: This means that the cost function is lowest when the probability of a vector in the negative phase is equivalent to the probability of the same vector in the positive phase. As well, it ensures that the most probable vectors in the data have the greatest effect on the cost

12 Training of BM (3) This cost function would seem to be complicated to perform gradient descent with. Suprisingly though, the gradient with respect to a given weight, wij, at thermal equilibrium is given by the very simple equation: Where:      is the probability of units i and j both being on in the positive phase.      is the probability of units i and j both being on in the negative phase.

13 Training of BM (4) This result follows from the fact that at thermal equilibrium the probability of any state when the network is free-running is given by the Boltzmann distribution (hence the name "Boltzmann machine"). A state of thermal equilibrium is crucial for this though, hence the network must be brought to thermal equilibrium before the probabilities of two units both being on are calculated. Thermal equilibrium is achieved with simulated annealing in a Boltzmann machine. It is the necessity of simulated annealing which can make training a Boltzmann machine on a digital computer a very slow process. However, this learning rule is fairly biologically plausible because the only information needed to change the weights is provided by "local" information. That is, the connection (or synapse biologically speaking) does not need information about anything other than the two neurons it connects. This is far more biologically realistic than the information needed by a connection in many other neural network training algorithms, such as backpropagation.

14 Training of BM (5) Simulated annealing (SA) is a generic probabilistic meta-algorithm for the global optimization problem, namely locating a good approximation to the global optimum of a given function in a large search space. It was independently invented by S. Kirkpatrick, C. D. Gelatt and M. P. Vecchi in 1983, and by V. Cerny in 1985. The name and inspiration come from annealing in metallurgy, a technique involving heating and controlled cooling of a material to increase the size of its crystals and reduce their defects. The heat causes the atoms to become unstuck from their initial positions (a local minimum of the internal energy) and wander randomly through states of higher energy; the slow cooling gives them more chances of finding configurations with lower internal energy than the initial one.

15 Training of BM (6)


17 Similarity and difference of BM and Hopfield model

18 Sometimes machine boltzmann are used in combination
with Hopfield model and perceptron as device for find global minimum of energy function or error function correspondingly. In first case during of working (recall) state of any neuron is changed according of T (temperature) and if energy function decreases then this changing is accepted and process continues. In perceptron during of learning any weight is changed and this changing is accepted or not in according with estimation of error function. This process of changing may be executed in mixture with usual process of working or learning or after it to improve result.


20 Boltzmann Machine Simulator
/****************************************************************************** ========================================== Network: Boltzmann Machine with Simulated Annealing Application: Optimization Traveling Salesman Problem void InitializeApplication(NET* Net) { INT n1,n2; REAL x1,x2,y1,y2; REAL Alpha1, Alpha2; Gamma = 7; for (n1=0; n1<NUM_CITIES; n1++) { for (n2=0; n2<NUM_CITIES; n2++) { Alpha1 = ((REAL) n1 / NUM_CITIES) * 2 * PI; Alpha2 = ((REAL) n2 / NUM_CITIES) * 2 * PI; x1 = cos(Alpha1); y1 = sin(Alpha1); x2 = cos(Alpha2); y2 = sin(Alpha2); Distance[n1][n2] = sqrt(sqr(x1-x2) + sqr(y1-y2)); } } f = fopen("BOLTZMAN.txt", "w"); fprintf(f, "Temperature Valid Length Tour\n\n"); }

21 void CalculateWeights(NET* Net) { INT n1,n2,n3,n4; INT i,j;
INT Pred_n3, Succ_n3; REAL Weight; for (n1=0; n1<NUM_CITIES; n1++) { for (n2=0; n2<NUM_CITIES; n2++) { i = n1*NUM_CITIES+n2; for (n3=0; n3<NUM_CITIES; n3++) { for (n4=0; n4<NUM_CITIES; n4++) { j = n3*NUM_CITIES+n4; Weight = 0; if (i!=j) { Pred_n3 = (n3==0 ? NUM_CITIES-1 : n3-1); Succ_n3 = (n3==NUM_CITIES-1 ? 0 : n3+1); if ((n1==n3) OR (n2==n4)) Weight = -Gamma; else if ((n1 == Pred_n3) OR (n1 == Succ_n3)) Weight = -Distance[n2][n4]; } Net->Weight[i][j] = Weight; Net->Threshold[i] = -Gamma/2; } } }

22 void PropagateUnit(NET* Net, INT i) { INT j; REAL Sum, Probability;
for (j=0; j<Net->Units; j++) { Sum += Net->Weight[i][j] * Net->Output[j]; } Sum -= Net->Threshold[i]; Probability = 1 / (1 + exp(-Sum / Net->Temperature)); if (RandomEqualREAL(0, 1) <= Probability) Net->Output[i] = TRUE; else Net->Output[i] = FALSE; }

23 void BringToThermalEquilibrium(NET* Net) { INT n,i;
for (i=0; i<Net->Units; i++) { Net->On[i] = 0; Net->Off[i] = 0; } for (n=0; n<1000*Net->Units; n++) { PropagateUnit(Net, i = RandomEqualINT(0, Net->Units-1)); for (n=0; n<100*Net->Units; n++) { PropagateUnit(Net, i = RandomEqualINT(0, Net->Units-1)); if (Net->Output[i]) Net->On[i]++; else Net->Off[i]++; { Net->Output[i] = Net->On[i] > Net->Off[i];

24 { Net->Temperature = 100; do { BringToThermalEquilibrium(Net);
void Anneal(NET* Net) { Net->Temperature = 100; do { BringToThermalEquilibrium(Net); WriteTour(Net); Net->Temperature *= 0.99; } while (NOT ValidTour(Net)); void main() { NET Net; InitializeRandoms(); GenerateNetwork(&Net); InitializeApplication(&Net); CalculateWeights(&Net); SetRandom(&Net); Anneal(&Net); FinalizeApplication(&Net); }

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