Lecture 11: Multi-layer Perceptron Forward Pass Backpropagation - - PowerPoint PPT Presentation

Lecture 11:

−Multi-layer Perceptron −Forward Pass −Backpropagation

Aykut Erdem

November 2016 Hacettepe University

Administrative

• Assignment 2 due Nov. 10, 2016!

• Midterm exam on Monday, Nov. 14, 2016

− You are responsible from the beginning till the end

• f this week

− You can prepare and bring a full-page copy sheet

(A4-paper, both sides) to the exam.


• Assignment 3 will be out soon!

− It is due December 1, 2016 − You will implement a 2-layer Neural Network

2

Last time… Linear classification

3

slide by Fei-Fei Li & Andrej Karpathy & Justin Johnson

4

slide by Fei-Fei Li & Andrej Karpathy & Justin Johnson

Last time… Linear classification

5

Last time… Linear classification

slide by Fei-Fei Li & Andrej Karpathy & Justin Johnson

Interactive web demo time….

6

http://vision.stanford.edu/teaching/cs231n/linear-classify-demo/

slide by Fei-Fei Li & Andrej Karpathy & Justin Johnson

Last time… Perceptron

7

f(x) = X

i

wixi = hw, xi x1 x2 x3 xn . . .

• utput

w1 wn

synaptic weights

slide by Alex Smola

This Week

• Multi-layer perceptron

• Forward Pass
• Backward Pass


8

Introduction

9

A brief history of computers

10

1970s 1980s 1990s 2000s 2010s Data

102 103 105 108 1011

RAM

? 1MB 100MB 10GB 1TB

CPU

? 10MF 1GF 100GF 1PF GPU

• Data grows


at higher exponent

• Moore’s law (silicon) vs. Kryder’s law (disks)
• Early algorithms data bound, now CPU/RAM bound

deep nets kernel 
 methods deep nets

slide by Alex Smola

Not linearly separable data

• Some datasets are not linearly separable!
• e.g. XOR problem

• Nonlinear separation is trivial

11

slide by Alex Smola

Addressing non-linearly separable data

• Two options:
• Option 1: Non-linear functions
• Option 2: Non-linear classifiers

12

slide by Dhruv Batra

Option 1 — Non-linear features

13

• Choose non-linear features, e.g.,
• Typical linear features: w0 + Σi wi xi
• Example of non-linear features:
• Degree 2 polynomials, w0 + Σi wi xi + Σij wij xi xj
• Classifier hw(x) still linear in parameters w
• As easy to learn
• Data is linearly separable in higher dimensional

spaces

• Express via kernels

slide by Dhruv Batra

Option 2 — Non-linear classifiers

14

• Choose a classifier hw(x) that is non-linear in

parameters w, e.g.,

• Decision trees, neural networks,…
• More general than linear classifiers
• But, can often be harder to learn (non-convex
• ptimization required)
• Often very useful (outperforms linear classifiers)
• In a way, both ideas are related

slide by Dhruv Batra

Biological Neurons

• Soma (CPU)


Cell body - combines signals


• Dendrite (input bus)


Combines the inputs from 
 several other nerve cells


• Synapse (interface)


Interface and parameter store between neurons


• Axon (cable)


May be up to 1m long and will transport the activation signal to neurons at different locations

15

slide by Alex Smola

Recall: The Neuron Metaphor

• Neurons
• accept information from multiple inputs,
• transmit information to other neurons.
• Multiply inputs by weights along edges
• Apply some function to the set of inputs at each

node

16

slide by Dhruv Batra

Types of Neuron

• Potentially more. Requires a convex

loss function for gradient descent training.

17

Linear Neuron Logistic Neuron

θ1 θ2 θD θ0 1 f(~ x, ✓) θ1 θ2 θD θ0 1 f(~ x, ✓) θ1 θ2 θD θ0 1 f(~ x, ✓)

Perceptron

y = θ0 + X

i

xiθi

y = ⇢ 1 if z ≥ 0

• therwise

z = θ0 + X

i

xiθi

z = θ0 + X

i

xiθi y = 1 1 + e−z

slide by Dhruv Batra

Limitation

• A single “neuron” is still a linear decision

boundary

• What to do?
• Idea: Stack a bunch of them together!

18

slide by Dhruv Batra

Nonlinearities via Layers

• Cascade neurons together
• The output from one layer is the input to the next
• Each layer has its own sets of weights

19

y1i(x) = σ(hw1i, xi) y2(x) = σ(hw2, y1i) y1i = k(xi, x) Kernels

Deep Nets

• ptimize

all weights

slide by Alex Smola

Nonlinearities via Layers

20

y1i(x) = σ(hw1i, xi) y2i(x) = σ(hw2i, y1i) y3(x) = σ(hw3, y2i)

slide by Alex Smola

Representational Power

• Neural network with at least one hidden layer is a universal

approximator (can represent any function). 


Proof in: Approximation by Superpositions of Sigmoidal Function, Cybenko, paper
 
 


• The capacity of the network increases with more hidden

units and more hidden layers

21

slide by Raquel Urtasun, Richard Zemel, Sanja Fidler

A simple example

• Consider a neural network

with two layers of neurons.

• neurons in the top layer

represent known shapes.

• neurons in the bottom layer

represent pixel intensities.


• A pixel gets to vote if it has

ink on it.

• Each inked pixel can vote

for several different shapes. 


• The shape that gets the

most votes wins.

22

0 1 2 3 4 5 6 7 8 9

• ¡ ¡𝑔(∑𝑥𝑦)

𝑦 𝑦 𝑦 𝑦 … …

slide by Geoffrey Hinton

How to display the weights

23

Give each output unit its own “map” of the input image and display the weight coming from each pixel in the location of that pixel in the map. Use a black or white blob with the area representing the magnitude of the weight and the color representing the sign.

The input image

1 2 3 4 5 6 7 8 9 0

slide by Geoffrey Hinton

How to learn the weights

24

Show the network an image and increment the weights from active pixels to the correct class. Then decrement the weights from active pixels to whatever class the network guesses.

The image

1 2 3 4 5 6 7 8 9 0

slide by Geoffrey Hinton

25

1 2 3 4 5 6 7 8 9 0

The image

slide by Geoffrey Hinton

26

1 2 3 4 5 6 7 8 9 0

The image

slide by Geoffrey Hinton

27

1 2 3 4 5 6 7 8 9 0

The image

slide by Geoffrey Hinton

28

1 2 3 4 5 6 7 8 9 0

The image

slide by Geoffrey Hinton

29

1 2 3 4 5 6 7 8 9 0

The image

slide by Geoffrey Hinton

The learned weights

30

1 2 3 4 5 6 7 8 9 0

The details of the learning algorithm will be explained later.

The image

slide by Geoffrey Hinton

Why insufficient

• A two layer network with a single winner in the top

layer is equivalent to having a rigid template for each shape.

• The winner is the template that has the biggest
• verlap with the ink.

• The ways in which hand-written digits vary are

much too complicated to be captured by simple template matches of whole shapes.

• To capture all the allowable variations of a digit we

need to learn the features that it is composed of.

31

slide by Geoffrey Hinton

Multilayer Perceptron

32

• Layer Representation
• (typically) iterate between


linear mapping Wx and 
 nonlinear function

• Loss function


to measure quality of
 estimate so far

yi = Wixi xi+1 = σ(yi)

x1 x2 x3 x4 y

W1 W2 W3 W4

l(y, yi)

slide by Alex Smola

Forward Pass

33

Forward Pass: What does the Network Compute?

• Output of the network can be written as:

(j indexing hidden units, k indexing the output units, D number of inputs)

• Activation functions f , g : sigmoid/logistic, tanh, or rectified linear (ReLU)

34

slide by Raquel Urtasun, Richard Zemel, Sanja Fidler

X

• k(x)

= g(wk0 +

J

X

j=1

hj(x)wkj)

σ(z) = 1 1 + exp(−z), tanh(z) = exp(z) − exp(−z) exp(z) + exp(−z), ReLU(z) = max(0, z) hj(x) = f (vj0 +

D

X

i=1

xivji)

Forward Pass in Python

• Example code for a forward pass for a 3-layer network in Python: 


• Can be implemented efficiently using matrix operations
• Example above: W1 is matrix of size 4 × 3, W2 is 4 × 4. What about

biases and W3?

35

slide by Raquel Urtasun, Richard Zemel, Sanja Fidler

[http://cs231n.github.io/neural-networks-1/]

Special Case

• What is a single layer (no hiddens) network with a sigmoid act.

function?

• Network:
• Logistic regression!

36

slide by Raquel Urtasun, Richard Zemel, Sanja Fidler

• k(x)

= 1 1 + exp(−zk) zk = wk0 +

J

X

j=1

xjwkj

Example

• Classify image of handwritten digit (32x32 pixels): 4 vs non-4
• How would you build your network?
• For example, use one hidden layer and the sigmoid activation function:
• How can we train the network, that is, adjust all the parameters w?

37

!vs.!all)?! ust!all!the!parameters! ,!to! ut!this!is!a!complicated!

• k(x)

= 1 1 + exp(−zk) zk = wk0 +

J

X

j=1

hj(x)wkj

slide by Raquel Urtasun, Richard Zemel, Sanja Fidler

s: P

k 1 2(o(n) k

− t(n)

k )2

P

(n)

Training Neural Networks

• Find weights: 



 
 
 where o = f(x;w) is the output of a neural network

• Define a loss function, e.g.:
• Squared loss:
• Cross-entropy loss:
• Gradient descent: 



 
 
 where η is the learning rate (and E is error/loss)

38

w∗ = argmin

w N

X

n=1

loss(o(n), t(n))

2 k

−

k

s: − P

k t(n) k

log o(n)

k

P wt+1 = wt − η ∂E ∂wt

slide by Raquel Urtasun, Richard Zemel, Sanja Fidler

Useful derivatives

39

name function derivative Sigmoid σ(z) =

1 1+exp(−z)

σ(z) · (1 − σ(z)) Tanh tanh(z) = exp(z)−exp(−z)

exp(z)+exp(−z)

1/ cosh2(z) ReLU ReLU(z) = max(0, z) ( 1, if z > 0 0, if z ≤ 0

slide by Raquel Urtasun, Richard Zemel, Sanja Fidler

Backpropagation and Neural Networks

40

Recap: Loss function/Optimization

41

• 3.45
• 8.87

0.09 2.9 4.48 8.02 3.78 1.06

• 0.36
• 0.72
• 0.51

6.04 5.31

• 4.22
• 4.19

3.58 4.49

• 4.37
• 2.09
• 2.93

3.42 4.64 2.65 5.1 2.64 5.55

• 4.34
• 1.5
• 4.79

6.14

slide by Fei-Fei Li & Andrej Karpathy & Justin Johnson

• 1. Define a loss function that

quantifies our unhappiness with the scores across the training data.


• 2. Come up with a way of

efficiently finding the parameters that minimize the loss function. (optimization)

TODO:

We defined a (linear) score function:

42

slide by Fei-Fei Li & Andrej Karpathy & Justin Johnson

Softmax Classifier (Multinomial Logistic Regression)

43

slide by Fei-Fei Li & Andrej Karpathy & Justin Johnson

Softmax Classifier (Multinomial Logistic Regression)

44

slide by Fei-Fei Li & Andrej Karpathy & Justin Johnson

Softmax Classifier (Multinomial Logistic Regression)

45

slide by Fei-Fei Li & Andrej Karpathy & Justin Johnson

Softmax Classifier (Multinomial Logistic Regression)

46

slide by Fei-Fei Li & Andrej Karpathy & Justin Johnson

Softmax Classifier (Multinomial Logistic Regression)

47

slide by Fei-Fei Li & Andrej Karpathy & Justin Johnson

Softmax Classifier (Multinomial Logistic Regression)

48

slide by Fei-Fei Li & Andrej Karpathy & Justin Johnson

Softmax Classifier (Multinomial Logistic Regression)

49

slide by Fei-Fei Li & Andrej Karpathy & Justin Johnson

Softmax Classifier (Multinomial Logistic Regression)

50

slide by Fei-Fei Li & Andrej Karpathy & Justin Johnson

Softmax Classifier (Multinomial Logistic Regression)

Softmax Classifier (Multinomial Logistic Regression)

51

slide by Fei-Fei Li & Andrej Karpathy & Justin Johnson

Optimization

52

Gradient Descent

53

slide by Fei-Fei Li & Andrej Karpathy & Justin Johnson

Mini-batch Gradient Descent

• only use a small portion of the training set

to compute the gradient

54

slide by Fei-Fei Li & Andrej Karpathy & Justin Johnson

Mini-batch Gradient Descent

• only use a small portion of the training set

to compute the gradient

55 there are also more fancy update formulas (momentum, Adagrad, RMSProp, Adam, …)

slide by Fei-Fei Li & Andrej Karpathy & Justin Johnson

The effects of different update form formulas

56

(image credits to Alec Radford)

slide by Fei-Fei Li & Andrej Karpathy & Justin Johnson

57

slide by Fei-Fei Li & Andrej Karpathy & Justin Johnson

58

slide by Fei-Fei Li & Andrej Karpathy & Justin Johnson