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Chapter 5
NEURAL NETWORKS
by S. Betul Ceran
Outline
 Introduction
 Feed-forward Network Functions
 Network Training
 Error Backpropagation
 Regularization
Introduction
Multi-Layer Perceptron (1)
 Layered perceptron networks can realize any logical function, however
there is no simple way to estimate the parameters/generalize the (single
layer) Perceptron convergence procedure
 Multi-layer perceptron (MLP) networks are a class of models that are
formed from layered sigmoidal nodes, which can be used for
regression or classification purposes.
 They are commonly trained using gradient descent on a mean
squared error performance function, using a technique known as
error back propagation in order to calculate the gradients.
 Widely applied to many prediction and classification problems over
the past 15 years.
Multi-Layer Perceptron (2)
 XOR (exclusive OR)
problem
 0+0=0
 1+1=2=0 mod 2
 1+0=1
 0+1=1
 Perceptron does not
work here!
Single layer generates a
linear decision boundary
Universal Approximation
1st layer
2nd layer
3rd layer
Universal Approximation: Three-layer network can in principle
approximate any function with any accuracy!
Feed-forward Network Functions
 M


y (x, w )  f   wjj ( x) 
 j 1

 f: nonlinear activation function
 Extensions to previous linear models by hidden units:
(1)
– Make basis function Φ depend on the parameters
– Adjust these parameters during training
 Construct linear combinations of the input variables x1, …, xD.
D
a j   w(ji1) xi w(j10)
(2)
i 1
 Transform each of them using a nonlinear activation function
zj  h(aj )
(3)
Cont’d
 Linearly combine them to give output unit activations
M
ak   wkj( 2) z j  wk( 20)
(4)
j 1
 Key difference with perceptron is the continuous sigmoidal
nonlinearities in the hidden units i.e. neural network
function is differentiable w.r.t network parameters
 Whereas perceptron uses step-functions
Weight-space symmetry
 Network function is unchanged by certain permutations and
the sign flips in the weight space.
 E.g. tanh(– a) = –tanh(a) ………flip the sign of all weights out of that hidden unit
Two-layer neural network
zj: hidden unit
 M ( 2)  D (1)

(1) 
( 2)
yk ( X, W)  f   wkj h  w ji xi  w j 0   wk 0 
 i 1

 j 1

A multi-layer perceptron fitting into different functions
f(x)=x2
f(x)=sin(x)
f(x)=|x|
f(x)=H(x)
Network Training
 Problem of assigning ‘credit’ or ‘blame’ to individual
elements involved in forming overall response of a
learning system (hidden units)
 In neural networks, problem relates to deciding which
weights should be altered, by how much and in which
direction.
 Analogous to deciding how much a weight in the early
layer contributes to the output and thus the error
 We therefore want to find out how weight wij affects the
error ie we want:
E (t )
wij (t )
Error Backpropagation
Two phases of back-propagation
Activation and Error back-propagation
Weight updates
Other minimization procedures
Two schemes of training
 There are two schemes of updating weights
– Batch: Update weights after all patterns have been
presented (epoch).
– Online: Update weights after each pattern is presented.
 Although the batch update scheme implements the
true gradient descent, the second scheme is often
preferred since
– it requires less storage,
– it has more noise, hence is less likely to get stuck in a
local minima (which is a problem with nonlinear
activation functions). In the online update scheme,
order of presentation matters!
Problems of back-propagation
 It is extremely slow, if it does converge.
 It may get stuck in a local minima.
 It is sensitive to initial conditions.
 It may start oscillating.
Regularization (1)
 How to adjust the number of hidden units to
get the best performance while avoiding
over-fitting
 Add a penalty term to the error function
 The simplest regularizer is the weight
decay:
 T
~
E (w )  E (w )  w w
2
Changing number of hidden units
Over-fitting
Sinusoidal data set
Regularization (2)
 One approach is to
choose the specific
solution having the
smallest validation set
error
Error vs. Number of hidden units
Consistent Gaussian Priors
 One disadvantage of weight decay is its
inconsistency with certain scaling properties of
network mappings
w
 A linear transformation
in the input would be
reflected to the weights such that the overall
mapping unchanged
1
~
~
w 
 w  w
x 
 x  ax  b
j0
i
i
i
ji
ji
a
ji
b
~
w j0 
 w j 0  w j   w ji
a i
Cont’d
 A similar transformation can be achieved in the
output by changing the 2nd layer weights
accordingly
yk 
 ~yk  cyk  d
~  cw
wkj 
 w
kj
kj
~  cw  d
wk 0 
 w
k0
k0
 Then a regularizer of the following form would be
invariant under the linear transformations:
1
 W1: set of weights in 1st layer
 W2: set of weights in 2nd layer
2
w 
2
wW1
2
2
2
w

wW2
Effect of consistent gaussian priors
Early Stopping
 A method to
– obtain good generalization performance and
– control the effective complexity of the network
 Instead of iteratively reducing the error until
a minimum of the training data set has been
reached
 Stop at the point of smallest error w.r.t. the
validation data set
Effect of early stopping
Training Set
Error vs. Number of iterations
Validation Set
A slight increase in
the validation set error
Invariances
 Alternative approaches for encouraging an
adaptive model to exhibit the required invariances
 E.g. position within the image, size
Various approaches
1. Augment the training set using
transformed replicas according to the
desired invariances
2. Add a regularization term to the error
function; tangent propagation
3. Extract the invariant features in the preprocessing for later use.
4. Build the invariance properteis into the
network structure; convolutional networks
Tangent Propagation (Simard et al., 1992)
 A continuous
transformation on a
particular input vextor
xn can be approximated
by the tangent vector τn
 A regularization
function can be derived
by differentiating the
output function y w.r.t.
the transformation
parameter, ξ
Tangent vector implementation
Original image
x
Tangent vector
corresponding to
a clockwise rotation
Adding a small
contribution from
the tangent vector
x+ετ
True image rotated
References
 Neurocomputing course slides by Erol Sahin. METU,




Turkey.
Backpropagation of a Multi-Layer Perceptron by
Alexander Samborskiy. University of Missouri, Columbia.
Neural Networks - A Systematic Introduction by Raul
Rojas. Springer.
Introduction to Machine Learning by Ethem Alpaydin.
MIT Press.
Neural Networks course slides by Andrew Philippides.
University of Sussex, UK.