Introduction to the TLearn Simulator

CS/PY 399 Lab Presentation # 5 February 8, 2001 Mount Union College

TLearn Software

Developed by Cognitive Psychologists to study properties of connectionist models and learning– Kim Plunkett, Oxford

• Experimental Psychologist

– Jeffrey Elman, U.C. San Diego• Cognitive Psychologist

Simulates massively-parallel networks on serial computer platforms

Notational Conventions

TLearn uses a slightly different notation than that which we have been using

Input signals are treated as nodes in the network, and displayed on screen as squares

Other nodes (representing neurons) are displayed as circles

Input and output values can be any real numbers (decimals allowed)

Weight Adjustments: Learning

TLearn uses a more sophisticated rule than the simple one seen last week

Let tkp be the target (desired) output for node k on pattern p

Let okp be the actual (obtained) output for node k on pattern p

Weight Adjustments: Learning

Error for node k on pattern p (kp ) is the difference between target output and observed output, times the derivative of the activation function for node k– why? Don’t ask! (actually, this value

simulates actual observed learning)

kp = (tkp - okp) · [okp · (1 - okp) ]

Weight Adjustments: Learning

This is used to calculate adjustments to weights

Let wkj be the weight on the connection from node j to node k (backwards notation is what the authors use)

Let wkj be the change required for wkj due to training

wkj is determined by: error for node k, input from node j, learning rate ()

Weight Adjustments: Learning

wkj = · kp · ojp

is small (< 1, usually 0.05 to 0.5), to keep weights from making wild swings that overshoot goals for all patterns

This actually makes sense . . .– a larger error (kp) should make wkj larger

– if ojp is large, it contributed a great deal to the error, so it should contribute a large value to the weight adjustment

Weight Adjustments: Learning

The preceding is called the delta rule Used in Backpropagation Training

– error adjustments are propagated backwards from output layer to previous layers when weight changes are calculated

Luckily, the simulator will perform these calculations for you!

Read more in Ch. 1 of Plunkett & Elman

TLearn Simulation Basics

For each problem on which you will work, the simulator maintains a PROJECT description file

Each project consists of three text files:– .CF file: configuration information about the

network’s architecture– .DATA file: input for each of the network’s

training cases– .TEACH file: output for each training case

TLearn Simulation Basics

Each file must contain information in EXACTLY the format TLearn expects, or else the simulation won’t work

Example: AND project from Chapter 3 folder– 2 inputs, one outupt, output = 1 only if both

inputs = 1

.DATA and .TEACH Files

.DATA File format

first line: distributed or localist– to start, we’ll always use distributed

second line: n = # of training cases next n lines: inputs for each training

case– a list of v values, separated by spaces,

where v = # of inputs in network

.TEACH File format

first line: distributed or localist– must match mode used in .DATA file

second line: n = # of training cases next n lines: outputs for each training case

– a list of w values, separated by spaces, where w = # of outputs in network

– a value may be *, meaning output is ignored during training for this pattern

.CF File

.CF File format

Three sections NODES: section

– nodes = # of non-input units in network– inputs = # of inputs to network– outputs = # of output units– output node is ___ <== which node is the

output node?• > 1 output node ==> syntax changes to “output

nodes are”

.CF File format

CONNECTIONS: section– groups = 0 ( explained later )– 1 from i1-i2 (says that node # 1 gets values

from input nodes i1 and i2)– 1 from 0 (says that node # 1 gets values

from the bias node -- explained below) input nodes always start with i1, i2, etc. non-input nodes start with 1, 2, etc.

.CF File format

SPECIAL: section– selected = 1 (special simulator results

reporting)– weight-limit = 1.00 (range of random

weight values to use in initial network creation)

Bias node

TLearn units all have same threshold– defined by logistic function

values are represented by a bias node– connected to all non-input nodes– signal always = 1– weight of the connection is -– same as a perceptron with a threshold

• example on board

Network Arch. with Bias Node

.CF File Example (Draw it!)

– NODES:• nodes = 5

• inputs = 3

• outputs = 2

• output nodes are 4-5

– CONNECTIONS:• groups = 0

• 1-3 from i1-i3

• 4-5 from 1-3

• 1-5 from 0

Learning to use TLearn

Chapter 3 of the Plunkett and Elman text is a step-by-step description of several TLearn Training sessions.

Best way to learn: Hands-on! Try Lab Exercise # 5

Introduction to the TLearn Simulator

CS/PY 399 Lab Presentation # 5 February 8, 2001 Mount Union College