Learning with Neural Networks

Learning with Neural Networks Artificial Intelligence CMSC 25000 February 19, 2002

Agenda • Neural Networks: • Biological analogy • Review: single-layer perceptrons • Perceptron: Pros & Cons • Neural Networks: Multilayer perceptrons • Neural net training: Backpropagation • Strengths & Limitations • Conclusions

Neurons: The Concept Dendrites Axon Nucleus Cell Body Neurons: Receive inputs from other neurons (via synapses) When input exceeds threshold, “fires” Sends output along axon to other neurons Brain: 10^11 neurons, 10^16 synapses

Perceptron Structure Single neuron-like element -Binary inputs &output -Weighted sum of inputs > threshold y w0 wn w1 w3 w2 x0=-1 x1 x2 x3 xn . . . • Until perceptron correct output for all • If the perceptron is correct, do nothing • If the percepton is wrong, • If it incorrectly says “yes”, • Subtract input vector from weight vector • Otherwise, add input vector to it compensates for threshold x0 w0

x2 0 0 0 0 + +++ + + 0 0 0 x1 Perceptron Learning • Perceptrons learn linear decision boundaries • E.g. • Guaranteed to converge, if linearly separable • Many simple functions NOT learnable x2 + 0 But not 0 + x1 xor

Neural Nets • Multi-layer perceptrons • Inputs: real-valued • Intermediate “hidden” nodes • Output(s): one (or more) discrete-valued X1 Y1 Y2 X2 X3 X4 Inputs Hidden Hidden Outputs

Neural Nets • Pro: More general than perceptrons • Not restricted to linear discriminants • Multiple outputs: one classification each • Con: No simple, guaranteed training procedure • Use greedy, hill-climbing procedure to train • “Gradient descent”, “Backpropagation”

Solving the XOR Problem o1 w11 Network Topology: 2 hidden nodes 1 output w13 x1 w01 w21 y -1 w23 w12 w03 w22 x2 -1 w02 o2 Desired behavior: x1 x2 o1 o2 y 0 0 0 0 0 1 0 0 1 1 0 1 0 1 1 1 1 1 1 0 -1 Weights: w11= w12=1 w21=w22 = 1 w01=3/2; w02=1/2; w03=1/2 w13=-1; w23=1

Backpropagation • Greedy, Hill-climbing procedure • Weights are parameters to change • Original hill-climb changes one parameter/step • Slow • If smooth function, change all parameters/step • Gradient descent • Backpropagation: Computes current output, works backward to correct error

Producing a Smooth Function • Key problem: • Pure step threshold is discontinuous • Not differentiable • Solution: • Sigmoid (squashed ‘s’ function): Logistic fn

Neural Net Training • Goal: • Determine how to change weights to get correct output • Large change in weight to produce large reduction in error • Approach: • Compute actual output: o • Compare to desired output: d • Determine effect of each weight w on error = d-o • Adjust weights

z1 z2 z3 y3 z3 w03 -1 w23 w13 y1 y2 z2 z1 w21 w01 w22 w02 w11 -1 w12 -1 x2 x1 Neural Net Example xi : ith sample input vector w : weight vector yi*: desired output for ith sample - Sum of squares error over training samples From 6.034 notes lozano-perez Full expression of output in terms of input and weights

Gradient Descent • Error: Sum of squares error of inputs with current weights • Compute rate of change of error wrt each weight • Which weights have greatest effect on error? • Effectively, partial derivatives of error wrt weights • In turn, depend on other weights => chain rule

E = G(w) Error as function of weights Find rate of change of error Follow steepest rate of change Change weights s.t. error is minimized Gradient Descent dG dw E G(w) w0w1 w Local minima

z1 z2 z3 y3 z3 w03 -1 w23 w13 y1 y2 z2 z1 w21 w01 w22 w02 w11 -1 w12 -1 x2 x1 Gradient of Error - Note: Derivative of sigmoid: ds(z1) = s(z1)(1-s(z1)) dz1 From 6.034 notes lozano-perez MIT AI lecture notes, Lozano-Perez 2000

From Effect to Update • Gradient computation: • How each weight contributes to performance • To train: • Need to determine how to CHANGE weight based on contribution to performance • Need to determine how MUCH change to make per iteration • Rate parameter ‘r’ • Large enough to learn quickly • Small enough reach but not overshoot target values

Backpropagation Procedure i j k • Pick rate parameter ‘r’ • Until performance is good enough, • Do forward computation to calculate output • Compute Beta in output node with • Compute Beta in all other nodes with • Compute change for all weights with

y3 z3 w03 -1 w13 y1 w23 y2 z2 z1 w21 w01 w22 w02 -1 w11 w12 -1 x2 x1 Backprop Example Forward prop: Compute zi and yi given xk, wl From 6.034 notes lozano-perez

Backpropagation Observations • Procedure is (relatively) efficient • All computations are local • Use inputs and outputs of current node • What is “good enough”? • Rarely reach target (0 or 1) outputs • Typically, train until within 0.1 of target

Neural Net Summary • Training: • Backpropagation procedure • Gradient descent strategy (usual problems) • Prediction: • Compute outputs based on input vector & weights • Pros: Very general, Fast prediction • Cons: Training can be VERY slow (1000’s of epochs), Overfitting

Training Strategies • Online training: • Update weights after each sample • Offline (batch training): • Compute error over all samples • Then update weights • Online training “noisy” • Sensitive to individual instances • However, may escape local minima

Training Strategy • To avoid overfitting: • Split data into: training, validation, & test • Also, avoid excess weights (less than # samples) • Initialize with small random weights • Small changes have noticeable effect • Use offline training • Until validation set minimum • Evaluate on test set • No more weight changes

Classification • Neural networks best for classification task • Single output -> Binary classifier • Multiple outputs -> Multiway classification • Applied successfully to learning pronunciation • Sigmoid pushes to binary classification • Not good for regression

Neural Net Conclusions • Simulation based on neurons in brain • Perceptrons (single neuron) • Guaranteed to find linear discriminant • IF one exists -> problem XOR • Neural nets (Multi-layer perceptrons) • Very general • Backpropagation training procedure • Gradient descent - local min, overfitting issues

Learning with Neural Networks