Deep-Learning-2017-Lecture6RNN.ppt

Lecture 6 Smaller Network: RNN
This is our fully connected network. If x1 .... xn, n is very large and growing,
this network would become too large. We now will input one xi at a time,
and re-use the same edge weights.

How does RNN reduce complexity?
f
h0
h1
y1
x1
f h2
y2
x2
f h3
y3
x3
……
No matter how long the input/output sequence is, we only
need one function f. If f’s are different, then it becomes a
feedforward NN. This may be treated as another compression
from fully connected network.
h and h’ are vectors with
the same dimension
 Given function f: h’,y=f(h,x)

Deep RNN
f1
h
0
h
1
y1
x1
f1
h
2
y2
x2
f1
h
3
y3
x3
……
f2
g
0
g
1
z1
f2
g
2
z2
f2
g
3
z3
……
…
…
…
h’,y = f1(h,x), g’,z = f2(g,y)

f1
h
0
h
1
y1
x1
f1
h
2
y2
x2
f1
h
3
y3
x3
f2
g
0
g
1 f2
g
2 f2
g
3
Bidirectional RNN
x1
x2 x3
z1
z2 z3
f3 f3 f3
p
1
p
2
p
3
p=f3(y,z)
y,h=f1(x,h) z,g = f2(g,x)

Pyramid RNN
 Reducing the number of time steps
W. Chan, N. Jaitly, Q. Le and O. Vinyals, “Listen, attend and spell: A neural
network for large vocabulary conversational speech recognition,” ICASSP, 2016
Bidirectional
RNN
Significantly speed up training

Naïve RNN

f
h h'
y
x
We have ignored the bias
h'
y Wo
Wh
h’
softmax
Wi
x
h
Note, y is computed
from h’

Problems with naive RNN
When dealing with a time series, it tends
to forget old information. When there is a
distant relationship of unknown length, we
wish to have a “memory” to it.
Vanishing gradient problem.

The sigmoid layer outputs numbers between 0-1 determine how much
each component should be let through. Pink X gate is point-wise multiplication.

LSTM
The core idea is this cell
state Ct, it is changed
slowly, with only minor
linear interactions. It is very
easy for information to flow
along it unchanged.
ht-1
Ct-1
This sigmoid gate
determines how much
information goes thru
This decides what info
Is to add to the cell state
Output gate
Controls what
goes into output
Forget input
gate gate
Why sigmoid or tanh:
Sigmoid: 0,1 gating as switch.
Vanishing gradient problem in
LSTM is handled already.
ReLU replaces tanh ok?

it decides what component
is to be updated.
C’t provides change contents
Updating the cell state
Decide what part of the cell
state to output

Peephole LSTM
Allows “peeping into the memory”

Naïve RNN vs LSTM
c changes slowly
h changes faster
ct is ct-1 added by something
ht and ht-1 can be very different
Naïve
RNN
ht
yt
xt
ht-1
LSTM
yt
xt
ct
ht
ht-1
ct-1

xt
z
zi
zf zo
ht-1
ct-1
z
xt
ht-1
W
zi
xt
ht-1
Wi
z
f
xt
ht-1
Wf
zo
xt
ht-1
Wo
= σ( )
= σ( )
= σ( )
Information flow of LSTM
Controls
forget gate
Controls
input gate
Updating
information
Controls
Output gate
These 4 matrix
computation should
be done concurrently.

xt
z
zi
zf zo
ht-1
ct-1
“peephole”
z W
xt
ht-1
ct-1
diagonal
zi
z
f
zo
obtained by the same way
=tanh( )

ht
xt
z
zi
zf zo
yt
ht-1
ct-1 ct
tanh
ct = zf  ct-1 + ziz
ht = zo  tanh(ct)
yt = σ(W’ ht)
Element-wise multiply

LSTM information flow
xt
z
zi
zf zo
yt
ht-1
ct-1 ct
xt+1
z
zi
zf zo
yt+1
ht
ct+1
tanh tanh
ht+1

GRU – gated recurrent unit
(more compression)
It combines the forget and input into a single update gate.
It also merges the cell state and hidden state. This is simpler
than LSTM. There are many other variants too.
reset gate
X,*: element-wise multiply
LSTM
Update gate

GRUs also takes xt and ht-1 as inputs. They perform some
calculations and then pass along ht. What makes them different
from LSTMs is that GRUs don't need the cell layer to pass values
along. The calculations within each iteration insure that the ht
values being passed along either retain a high amount of old
information or are jump-started with a high amount of new
information.

x f1 a1
f2 a2
f3 a3
f4 y
x1
h0
f h1
x2
f
x3
h2
f
x4
h3 f g y4
t is layer
t is time step
We will turn the recurrent network 90 degrees.
Feed-forward vs Recurrent Network
1. Feedforward network does not have input at each step
2. Feedforward network has different parameters for each layer
at = ft(at-1) = σ(Wtat-1 + bt)
at= f(at-1, xt) = σ(Wh at-1 + Wixt + bi)

ht-1
r z
yt
xt
ht-1
h'
xt
1-
ht
reset update
No input xt at
each step
at-1 is the output of
the (t-1)-th layer
at is the output of
the t-th layer
No output yt at
each step
No reset gate
at-1 at
at-1

Highway Network
• Residual Network
• Highway Network
Deep Residual Learning for Image
Recognition
http://arxiv.org/abs/1512.03385
Training Very Deep Networks
https://arxiv.org/pdf/1507.06228v
2.pdf
+
copy
copy
Gate
controlle
r
at-1
at-1
at at
at-1
h’
h’
z controls red arrow
h’=σ(Wat-1)
z=σ(W’at-1)
at = z  at-1 + (1-z)  h

Input layer
output layer
Input layer
output layer
Input layer
output layer
Highway Network automatically
determines the layers needed!

Grid LSTM
LSTM
y
x
c’
h’
h
c
Grid
LSTM
c’
h’
h
c
Memory for both
time and depth
b
a
b’
a’
time
depth

Grid LSTM
Grid
LSTM
c’
h’
h
c
b
a
b’
a’
h'
z
zi
zf zo
h
c
tanh
c'
a
b
a'
b'
You can generalize this to 3D, and more.

Neural machine translation
LSTM

Sequence to sequence chat model

Chat with context
U: Hi
U: Hi
M: Hi
M: Hello
M: Hi
M: Hello
Serban, Iulian V., Alessandro Sordoni, Yoshua Bengio, Aaron Courville, and Joelle
Pineau, 2015 "Building End-To-End Dialogue Systems Using Generative Hierarchical

Baidu’s speech recognition using RNN

Image caption generation using attention
(From CY Lee lecture)
filter filter filter
match 0.7
CNN
z0
A vector for
each region
z0 is initial parameter, it is also learned

Image Caption Generation
CNN
A vector for
each region
0.7 0.1 0.1
0.1 0.0 0.0
weighted
sum
z1
Word 1
z0
Attention to
a region

CNN
A vector for
each region
z0
0.0 0.8 0.2
0.0 0.0 0.0
weighted
sum
z1
Word 1
z2
Word 2

Kelvin Xu, Jimmy Ba, Ryan Kiros, Kyunghyun Cho, Aaron
Courville, Ruslan Salakhutdinov, Richard Zemel, Yoshua Bengio, “Show,
Attend and Tell: Neural Image Caption Generation with Visual Attention”,
ICML, 2015

Li Yao, Atousa Torabi, Kyunghyun Cho, Nicolas Ballas, Christopher Pal, Hugo
Larochelle, Aaron Courville, “Describing Videos by Exploiting Temporal Structure”, ICCV,
2015
* Possible project?

Deep-Learning-2017-Lecture6RNN.ppt

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