Transcript Learning Functions and Neural Networks II

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
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In this lecture
• Multilayer perceptron
(MLP)
– Representation
– Feed forward
– Back-propagation
• Break
• Case studies
• Milestones & forefront
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Perceptron
A 400-26
perceptron
A
B
C
D
⋮
Z
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© Springer
XOR
Exclusive OR
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Root cause
Consider a 2-1 perceptron,
𝑦 = 𝜎 𝑤1 𝑥1 + 𝑤2 𝑥2 + 𝑤0
Let 𝑦 = 0.5,
Wb
Input 0
Input 1
W0
W1
+
fH(x)
Output
𝑤1 𝑥1 + 𝑤2 𝑥2
= 𝜎 −1 0.5 − 𝑤0
= const
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A single perceptron is
limited to learning
linearly separable cases.
Minsky M. L. and Papert S. A. 1969. Perceptrons. Cambridge, MA: MIT Press.
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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).
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How’s that relevant?
Function approximation ∈ Intelligence
The road ahead
Speed
Bearing
Waveform
Wheel turn
Pedal depression
Words
Regression
Recognition
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0
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1
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2
ℎ = tanh(𝑎
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3
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3
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∞
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Matrix representation
𝐷
𝑥∈ℝ
1
𝑀×𝐷
𝑤 ∈ℝ
𝑧 = ℎ(𝑤 1 𝑥 ∈ ℝ𝑀
2
𝐾×𝑀
𝑤 ∈ℝ
𝑦 = 𝜎(𝑤 2 𝑧 ∈ ℝ𝐾
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Knowledge learned by an
MLP is encoded in its
layers of weights.
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What does it learn?
• Decision boundary perspective
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What does it learn?
• Highly non-linear decision boundaries
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What does it learn?
• Real world decision boundaries
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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.
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What does it learn?
• Weight perspective
An 64-M-3 MLP
𝑥 ∈ ℝ𝐷
𝑤 1 ∈ ℝ𝑀×𝐷
𝑧 = ℎ(𝑤 1 𝑥 ∈ ℝ𝑀
𝑤 2 ∈ ℝ𝐾×𝑀
𝑦 = 𝜎(𝑤 2 𝑧 ∈ ℝ𝐾
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How does it learn?
• From examples
0
1
2
3
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5
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8
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Polar bear
Not a polar bear
• By back propagation
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Back propagation
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Gradient descent
“epoch”
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Back propagation
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Back propagation
• Steps
Think about this:
What happens when you train a 10-layer MLP?
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Learning curve
error
Overfitting and cross-validation
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Break
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Design considerations
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Learning task
X - input
Y - output
D
M
K
#layers
Training epochs
Training data
– #
– Source
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Case study 1: digit recognition
An 768-1000-10 MLP
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Case study 1: digit recognition
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Milestones: a race to 100% accuracy on MNIST
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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/
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Milestones: a race to 100% accuracy on MNIST
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Milestones: a race to 100% accuracy on MNIST
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Case study 2: sketch recognition
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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)
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Case study 2: sketch recognition
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Case study 2: sketch recognition
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Case study 3: autonomous driving
Pomerleau, 1995
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Case study 4: sketch beautification
Orbay and Kara, 2011
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Case study 4: sketch beautification
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Case study 4: sketch beautification
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Research forefront
• Deep belief network
– Critique, or classify
– Create, synthesize
Demo at:
http://www.cs.toronto.edu/~hinton/adi/index.htm
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In summary
1.Powerful machinery
2.Feed-forward
3.Back propagation
4.Design considerations
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