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『深層学習』第7章「再帰型ニューラルネット」輪読会資料 / Deep Learning Cha...
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Shotaro Ishihara
April 18, 2018
Technology
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310
『深層学習』第7章「再帰型ニューラルネット」輪読会資料 / Deep Learning Chapter 7
http://bookclub.kodansha.co.jp/product?isbn=9784061529021
Shotaro Ishihara
April 18, 2018
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Transcript
7 2016/08/20 1
2 l RNN#' l RNN "
" l RNN & !( $%
3
4 We can get
an idea of the quality of the learned feature vectors by displaying them in a 2-D map.
5 $%"! '(Bag of Words ')N-gram
We can get an idea of the quality " #& or
6 l RNN#' l RNN "
" l RNN & !( $%
7 l RNN#' l RNN "
" l RNN & !( $%
RNN 8
RNN 9 x1 z0
RNN 10 z1 y1
RNN 11 x2 z1
RNN 12 z2 y2
13 l RNN#' l RNN "
" l RNN & !( $%
RNN 14 xt zt-1
y t →
RNN 15 xt zt-1 y
t →
16 l RNN#' l RNN "
" l RNN & !( $%
RNN 17 Back Propagation through time
BPTT 18 % x #!% d $ & y
, ... , y ' % δ ( δ ) * " t t 1 t k out, t j t
BPTT 19 δ k out, 1 δ k out, 2
δ k out, 3 δ k out, t
BPTT 20 t1 t δ
j t
BPTT 21
22 l RNN#' l RNN "
" l RNN & !( $%
23 l RNN#' l RNN "
" l RNN & !( $%
RNN 24 #@10+'<3= 0A; ← &91,?7 &9$)+/" ) 4
*58&90 or :( !.2- ← RNN%>264
LSTM 25 '% (Long Short-Term Memory, LSTM) RNN &# →
&# !$ (+) "*
LSTM 26
LSTM 27
LSTM 28
LSTM 29
30 l RNN#' l RNN "
" l RNN & !( $%
RNN 31 “w n” …… ^
(HMM) 32 %! $ "# $ "#
%!
33 $ .)-+ (Connectionist temporal classification, CTC) HMM#
! RNN &, %*"(, ' &,
CTC 34 X = x , ... ,
x l = l , … , l = p( l | X ) 1 t 1 |l|
CTC 35 l = ‘ab’ t = 6
a, b, , , , a, , , b, , , , , a, , b …
CTC 36 = p( l | X ) a, b,
, , , a, a, , b, , , , , a, , b … p( l1 | X ) = p( l2 | X ) = p( l3 | X ) = = p(a)*p(b)*p( )*p( ) *p( )*p( ) = p(a)*p(a)*p( )*p(b) *p( )*p( ) = p( )*p( )*p( )*p(a)*p( )*p(b)
37 • ;&B(2015):5:#3, .<2 • /%) in $"#3 E?!(2015):
http://www.slideshare.net/shotarosano5/chapter7-50542830, 2016A8*12@C • Recurrent Neural Networks(2014): http://www.slideshare.net/beam2d/pfi-seminar- 20141030rnn?qid=9e5894c7-f162-4da3-b082-a1e4963689e8&v=&b=&from_search=17, 2016A8*12@C • =86 (2013): 7+,4D19+,4D, 2 • LSTM 0(>-'(2016): http://qiita.com/t_Signull/items/21b82be280b46f467d1b, 2016A8*12@C • A. Graves(2008): Supervised sequence labelling with Recurrent Neural Networks, PhD thesis, Technische Universität München, https://www.cs.toronto.edu/~graves/preprint.pdf