Learning Functions and Neural Networks II

Report
24-787 Lecture 9
Learning Functions and
Neural Networks II
Luoting Fu
Spring 2012
Previous lecture
Physiological basis
Perceptron
Input 0
Wb
Input 1
X0
X1
W0
W1
+
fH
fH(x)
Applications
x
Demos
Output
Y
Y = u(W0X0 + W1X1 + Wb)
Δ Wi = η (Y0-Y) Xi
2
In this lecture
• Multilayer perceptron
(MLP)
– Representation
– Feed forward
– Back-propagation
• Break
• Case studies
• Milestones & forefront
2
3
Perceptron
A 400-26
perceptron
A
B
C
D
⋮
Z
4
© Springer
XOR
Exclusive OR
5
Root cause
Consider a 2-1 perceptron,
 =  1 1 + 2 2 + 0
Let  = 0.5,
Wb
Input 0
Input 1
W0
W1
+
fH(x)
Output
1 1 + 2 2
=  −1 0.5 − 0
= const
6
A single perceptron is
limited to learning
linearly separable cases.
Minsky M. L. and Papert S. A. 1969. Perceptrons. Cambridge, MA: MIT Press.
7
8
An MLP can learn any
continuous function.
Cybenko., G. (1989) "Approximations by superpositions of sigmoidal functions",
Mathematics of Control, Signals, and Systems, 2 (4), 303-314
A single perceptron is limited to learning linearly
separable cases (linear function).
9
How’s that relevant?
Function approximation ∈ Intelligence
The road ahead
Speed
Bearing
Waveform
Wheel turn
Pedal depression
Words
Regression
Recognition
10
11
0
12
1
13
2
ℎ = tanh(
14
3
15
3
16
∞
17
18
Matrix representation

∈ℝ
1
×
 ∈ℝ
 = ℎ( 1  ∈ ℝ
2
×
 ∈ℝ
 = ( 2  ∈ ℝ
19
Knowledge learned by an
MLP is encoded in its
layers of weights.
20
What does it learn?
• Decision boundary perspective
21
What does it learn?
• Highly non-linear decision boundaries
22
What does it learn?
• Real world decision boundaries
23
An MLP can learn any
continuous function.
Cybenko., G. (1989) "Approximations by superpositions of sigmoidal functions",
Mathematics of Control, Signals, and Systems, 2 (4), 303-314
Think Fourier.
24
What does it learn?
• Weight perspective
An 64-M-3 MLP
 ∈ ℝ
 1 ∈ ℝ×
 = ℎ( 1  ∈ ℝ
 2 ∈ ℝ×
 = ( 2  ∈ ℝ
25
How does it learn?
• From examples
0
1
2
3
4
5
6
7
8
9
Polar bear
Not a polar bear
• By back propagation
26
Back propagation
27
Gradient descent
“epoch”
28
29
Back propagation
30
Back propagation
• Steps
Think about this:
What happens when you train a 10-layer MLP?
31
Learning curve
error
Overfitting and cross-validation
32
Break
33
Design considerations
•
•
•
•
•
•
•
•
•
Learning task
X - input
Y - output
D
M
K
#layers
Training epochs
Training data
– #
– Source
34
Case study 1: digit recognition
An 768-1000-10 MLP
28
28
35
Case study 1: digit recognition
36
Milestones: a race to 100% accuracy on MNIST
37
Milestones: a race to 100% accuracy on MNIST
CLASSIFIER
ERROR
RATE (%)
Perceptron
12.0
LeCun et al. 1998
2-layer NN, 1000 hidden units
4.5
LeCun et al. 1998
5-layer Convolutional net
0.95
LeCun et al. 1998
5-layer Convolutional net
0.4
Simard et al. 2003
6-layer NN 784-2500-2000-15001000-500-10 (on GPU)
0.35
Ciresan et al. 2010
Reported by
See full list at http://yann.lecun.com/exdb/mnist/
38
Milestones: a race to 100% accuracy on MNIST
39
Milestones: a race to 100% accuracy on MNIST
40
Case study 2: sketch recognition
41
Case study 2: sketch recognition
• Convolutional neural network
Scope
Transf. Fun.
Gain
Sum
Sine wave
…
Or
Convolution
Sub-sampling
Product
Matrices
Element
of a vector
(LeCun, 1998)
42
Case study 2: sketch recognition
43
Case study 2: sketch recognition
44
Case study 3: autonomous driving
Pomerleau, 1995
45
Case study 4: sketch beautification
Orbay and Kara, 2011
46
Case study 4: sketch beautification
47
Case study 4: sketch beautification
48
Research forefront
• Deep belief network
– Critique, or classify
– Create, synthesize
Demo at:
http://www.cs.toronto.edu/~hinton/adi/index.htm
49
In summary
1.Powerful machinery
2.Feed-forward
3.Back propagation
4.Design considerations
50

similar documents