Soft computing
This presentation is the property of its rightful owner.
Sponsored Links
1 / 24

Soft Computing PowerPoint PPT Presentation


  • 81 Views
  • Uploaded on
  • Presentation posted in: General

Soft Computing. Lecture 10 Boltzmann machine. 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

Download Presentation

Soft Computing

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Presentation Transcript


Soft computing

Soft Computing

Lecture 10

Boltzmann machine


Definition of wikipedia

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.


Soft computing

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.


Soft computing

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, piof the ithunit being on is given by:

(The scalarT is referred to as the "temperature“

of the system.)


Definition of bm 4

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.


Definition of bm 5

Definition of BM (5)

  • With very low temperatures, e.g. 0.001, 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.


Soft computing

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)


Structure of bm

Structure of BM


Alternative structure of bm

Alternative structure of BM


Training of bm

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.


Training of bm 2

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


Training of bm 3

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.


Training of bm 4

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.


Training of bm 5

Training of BM (5)

Simulated annealing (SA) is a generic probabilisticmeta-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.


Training of bm 6

Training of BM (6)


Similarity and difference of bm and hopfield model

Similarity and difference of BM and Hopfield model


Soft computing

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.


Soft computing

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"); }


Soft computing

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;

} } }


Soft computing

void PropagateUnit(NET* Net, INT i)

{ INT j; REAL Sum, Probability;

Sum = 0;

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; }


Soft computing

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]++;

}

for (i=0; i<Net->Units; i++)

{ Net->Output[i] = Net->On[i] > Net->Off[i];

}

}


Soft computing

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);

}


  • Login