EECS542 Presentation Recurrent Neural Networks Part I Xinchen Yan, Brian Wang Sept. 18, 2014

Outline • Temporal Data Processing • Hidden Markov Model • Recurrent Neural Networks • Long Short-Term Memory

Temporal Data Processing • Speech Recognition • Object Tracking • Activity Recognition • Pose Estimation

Figure credit: Feng

Sequence Labeling • Sequence Classification 1 (“verb”)

• Segment Classification (hamming distance) swim

• Temporal Classification (edit distance) swim

Example: The Dishonest Casino • A casino has two dice: • Fair dice 1 Pr 𝑋 = 𝑘 = , 𝑓𝑜𝑟 1 ≤ 𝑘 ≤ 6 6 • Loaded dice 1 Pr 𝑋 = 𝑘 = , 𝑓𝑜𝑟 1 ≤ 𝑘 ≤ 5 10 1 Pr 𝑋 = 6 = 2 Casino player switches back-&-forth between fair and loaded dice once every 20 turns Slide credit: Eric Xing

Example: The Dishonest Casino • Given: A sequence of rolls by the casino player 124552656214614613613666166466 …. • Questions: • How likely is this sequence, given our model of how the casino works? (Evaluation) • What portion of the sequence was generated with the fair die, and what portion with the loaded die? (Decoding) • How “loaded” is the loaded die? How “fair” is the fair die? How often does the casino player change from fair to loaded, and back? (Learning) Slide credit: Eric Xing

time 

output

output

• Hidden State: 𝐻𝑡 • Output: 𝑂𝑡 • Transition Prob between two states 𝑃(𝐻𝑡 = 𝑘|𝐻𝑡−1 = 𝑗) • Start Prob 𝑃(𝐻1 = 𝑗) • Emission Prob associated with each state 𝑃(𝑂𝑡 = 𝑖|𝐻𝑡 = 𝑗)

output

Hidden Markov Model

Hidden Markov Model (Generative Model)

𝑂𝑡

𝐻𝑡

Slide credit: Eric Xing

Example: dice rolling vs. speech signal • Output sequence: Discrete value

• Output sequence: Continuous data

• Limited number of states

• Syntax, semantics, accent,

rate, volume, and etc. • Temporal Segmentation

Limitations of HMM • Modeling continuous data • Long-term dependencies • Can only remember log(N) bits about what it generated so far.

Slide credit: G. Hinton

Feed-Forward NN vs. Recurrent NN

• “piped” vs. cyclic • Function vs. dynamic system

Definition: Recurrent Neural Networks • • • • •

Observation/Input vector: 𝑥𝑡 Hidden state vector: ℎ𝑡 Output vector: 𝑦𝑡 Weight Matrices Input-Hidden Weights: 𝑊𝐼 Hidden-Hidden Weights: 𝑊𝐻 Hidden-Output Weights: 𝑊𝑂 Updating Rules ℎ𝑡 = 𝜎(𝑊𝐼 𝑥𝑡 + 𝑊𝐻 ℎ𝑡−1 + 𝑏) 𝑦𝑡 = 𝑊𝑂 ℎ𝑡

ℎ𝑡 𝑥𝑡

𝑦𝑡

Unfolded RNN: Shared Weights

Recurrent Neural Networks

time 

output

output

output

hidden

hidden

hidden

input

input

input

• Power of RNN: - Distributed hidden units - Non-linear dynamics ℎ𝑡 = 𝜎(𝑊𝐼 𝑥𝑡 + 𝑊𝐻 ℎ𝑡−1 + 𝑏) • Quote from Hinton

Providing input to recurrent networks

• Specify the initial states of all the units. • Specify the initial states of a subset of the units. • Specify the states of the same subset of the units at every time step.

w1

w3 w4 w2 

• We can specify inputs in several ways:

w1

w3 w4

w2

w1

w3 w4

w2

time

• This is the natural way to model most sequential data.

Slide credit: G. Hinton

Teaching signals for recurrent networks • We can specify targets in several ways:

• Specify desired final activities of all the units • Specify desired activities of all units for the last few steps • Good for learning attractors • It is easy to add in extra error derivatives as we backpropagate.

• Specify the desired activity of a subset of the units.

w1

w3 w4 w2

w1

w3 w4

w2

w1

w3 w4

w2

• The other units are input or hidden units.

Slide credit: G. Hinton

Next Q: Training Recurrent Neural Networks w1 w3

w2 w4

Backprop through time (BPTT)

time=3

w1

w3 w4 w2

w1

w3 w4

w2

w1

w3 w4

w2

time=2

time=1

time=0

Slide credit: G. Hinton

Recall: Training a FFNN • The algorithm: - Provide: 𝑥, 𝑦 - Learn: 𝑊 (1) , ⋯ , 𝑊 𝐿 - Forward Pass: 𝑧 (𝑖+1) = 𝑊 (𝑖) 𝑎(𝑖) , 𝑎(𝑖+1) = 𝑓(𝑧 (𝑖+1) ) - Backprop: 𝛿 (𝑖)

=

𝑇 (𝑖+1) (𝑖) 𝑊 𝛿

𝛻W i J W, b; x, y = 𝛿

⋅ 𝑓′(𝑧 (𝑖) )

𝑖+1

𝑎

(𝑖) 𝑇

About function 𝑓 • Example: Sigmoid Function •𝑓 𝑧 =

1 1+𝑒 −𝑡

∈ (0,1)

• 𝑓 ′ 𝑧 = 𝑓 𝑧 1 − 𝑓 𝑧 ≤ 0.25 • Backprop Analysis (RNN): Magnitude of gradients 𝑞 (𝑖) 𝜕𝛿 𝑇 𝑖+𝑚−1 (𝑖+𝑚−1) ) = 𝑊 𝑓′(𝑧 𝜕𝛿 (𝐿) 𝑚=1 (𝑖) 𝜕𝛿 ′ 𝑁𝑒𝑡 𝑞 ≤ 𝑊 max 𝑓 𝜕𝛿 (𝐿)

Exploding/Vanishing gradients • Better Initialization • Long-range dependencies

Long Short-Term Memory • Memory Block: Basic Unit • Store and Access information Xt-1

Ht-1

Yt-1

Block Xt-1

Ht-1

Yt-1

Ht

Yt

Ct-1

Xt

Ht

Yt

Xt

Ct

Memory Cell and Gates • Input Gate: 𝑖𝑡 • Forget Gate: 𝑓𝑡 • Output Gate: 𝑜𝑡 • Cell Activation Vector: 𝑐𝑡

Example: LSTM network • 4 input units • 5 output units • 1 block - 2 LSTM cells

LSTM: Forward Pass

• Input Gate:

𝑖𝑡 = 𝜎(𝑊𝑥𝑖 𝑥𝑡 + 𝑊ℎ𝑖 ℎ𝑡−1 + 𝑊𝑐𝑖 𝑐𝑡−1 + 𝑏𝑖 )

• Forget Gate:

𝑓𝑡 = 𝜎(𝑊𝑥𝑓 𝑥𝑡 + 𝑊ℎ𝑓 ℎ𝑡−1 + 𝑊𝑐𝑓 𝑐𝑡−1 + 𝑏𝑓 )

• Output Gate:

𝑜𝑡 = 𝜎 𝑊𝑥𝑜 𝑥𝑡 + 𝑊ℎ𝑜 ℎ𝑡−1 + 𝑊𝑐𝑜 𝑐𝑡 + 𝑏𝑜

Preservation of gradient information • LSTM: 1 input unit, 1 hidden unit, & 1 output unit • Node: black (activated) • Gate: “-” (closed), “o” (open)

LSTM: Forward Pass

• Memory cell:

𝑐𝑡 = 𝑓𝑡 𝑐𝑡−1 + 𝑖𝑡 tanh(𝑊𝑥𝑐 𝑥𝑡 + 𝑊ℎ𝑐 ℎ𝑡−1 + 𝑏𝑐 )

• Hidden Vector: ℎ𝑡 = 𝑜𝑡 tanh(𝑐𝑡 )

How LSTM deals with V/E Gradients? • RNN hidden unit ℎ𝑡 = 𝜎 𝑊𝐼 𝑥𝑡 + 𝑊𝐻 ℎ𝑡−1 + 𝑏ℎ • Memory cell (Linear Unit) 𝑐𝑡 = 𝑓𝑡 𝑐𝑡−1 + 𝑖𝑡 tanh(𝑊𝑥𝑐 𝑥𝑡 + 𝑊ℎ𝑐 ℎ𝑡−1 + 𝑏𝑐 )

Summary • HMM: discrete data • RNN: continuous domain • LSTM: long-range dependencies

The End Thank you!

Recurrent Neural Networks

Sep 18, 2014 - Memory Cell and Gates. • Input Gate: ... How LSTM deals with V/E Gradients? • RNN hidden ... Memory cell (Linear Unit). . =  ...

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