From 6553743da6a2a70ecf8a4a65ed6b8281070d47e5 Mon Sep 17 00:00:00 2001 From: Cezary Pukownik Date: Wed, 17 Jun 2020 19:23:54 +0200 Subject: [PATCH] Poprawiono decoder na dekoder --- docs/document.fdb_latexmk | 28 ++++++++++++++-------------- docs/document.log | 4 ++-- docs/document.pdf | Bin 1199946 -> 1199932 bytes docs/document.synctex.gz | Bin 545907 -> 545895 bytes docs/document.tex | 4 ++-- 5 files changed, 18 insertions(+), 18 deletions(-) diff --git a/docs/document.fdb_latexmk b/docs/document.fdb_latexmk index 6646271..4c23d96 100644 --- a/docs/document.fdb_latexmk +++ b/docs/document.fdb_latexmk @@ -1,10 +1,10 @@ # Fdb version 3 -["makeindex document.idx"] 1592412677 "document.idx" "document.ind" "document" 1592414473 - "document.idx" 1592413171 0 d41d8cd98f00b204e9800998ecf8427e "" +["makeindex document.idx"] 1592412677 "document.idx" "document.ind" "document" 1592414613 + "document.idx" 1592414466 0 d41d8cd98f00b204e9800998ecf8427e "" (generated) - "document.ind" "document.ilg" -["pdflatex"] 1592414465 "j:/_MAGISTERKA/praca-magisterska/docs/document.tex" "j:/_MAGISTERKA/praca-magisterska/docs/document.pdf" "document" 1592414473 + "document.ind" +["pdflatex"] 1592414604 "j:/_MAGISTERKA/praca-magisterska/docs/document.tex" "j:/_MAGISTERKA/praca-magisterska/docs/document.pdf" "document" 1592414613 "c:/software/latex/texmf-dist/fonts/enc/dvips/pl/plit.enc" 1550343089 1946 62ba825cda1ff16dbaa60d53ac60525a "" "c:/software/latex/texmf-dist/fonts/enc/dvips/pl/plme.enc" 1550343089 3126 59f33ff3e396436ebc36f4e7b444d5c4 "" "c:/software/latex/texmf-dist/fonts/enc/dvips/pl/plmi.enc" 1550343089 2005 f094775651a1386335c158fb632529f1 "" @@ -193,10 +193,10 @@ "c:/software/latex/texmf-var/web2c/pdftex/pdflatex.fmt" 1550344823 4224189 05b80a2fc98956d1ad757099b6714631 "" "c:/software/latex/texmf.cnf" 1550344763 715 839ef4b14d931ce86420174d3a223d3e "" "chapter-style.sty" 1560952449 376 87871882de090607c810d89fd5ae18d9 "" - "document.aux" 1592414472 12333 f03945da9a0198c6832f0bcc70eb3e8d "" + "document.aux" 1592414612 12333 f03945da9a0198c6832f0bcc70eb3e8d "" "document.ind" 1592412677 0 d41d8cd98f00b204e9800998ecf8427e "makeindex document.idx" - "document.tex" 1592414464 94830 fd19bc0133206b7afb97ef9148e6dce8 "" - "document.toc" 1592414472 5377 99f1fbfeeed311c15641911019d5b141 "" + "document.tex" 1592414602 94830 c9fd5c76ca067f516f1a07b9cc914864 "" + "document.toc" 1592414612 5377 99f1fbfeeed311c15641911019d5b141 "" "images/autoencoder.png" 1592409298 45138 b1cb8e1c5a4277b2e72bf3e77e86d26c "" "images/gradient_descent_1_long.png" 1592412262 47914 5c985451026c8d5e28901587bf5ee835 "" "images/gradient_descent_2_long.png" 1592412282 43574 6b02401960a159a520593d4ccef24fff "" @@ -225,14 +225,14 @@ "images/training_losses.png" 1592382149 57293 6eb12c89458f62456bc8b0860f6df331 "" "images/wartosc_nut.jpg" 1590441554 48213 d311c2cedb2ed53dcf5eff20f70eda80 "" "images/waveform_axis.png" 1592410460 56303 20c29ea310c3d0f7bef501c488c7de5a "" - "j:/_MAGISTERKA/praca-magisterska/docs/document.aux" 1592414472 12333 f03945da9a0198c6832f0bcc70eb3e8d "" - "j:/_MAGISTERKA/praca-magisterska/docs/document.tex" 1592414464 94830 fd19bc0133206b7afb97ef9148e6dce8 "" + "j:/_MAGISTERKA/praca-magisterska/docs/document.aux" 1592414612 12333 f03945da9a0198c6832f0bcc70eb3e8d "" + "j:/_MAGISTERKA/praca-magisterska/docs/document.tex" 1592414602 94830 c9fd5c76ca067f516f1a07b9cc914864 "" "pythonhighlight.sty" 1590660734 4822 44a39a68d852c9742af161f7166b2a03 "" (generated) - "document.log" - "j:/_MAGISTERKA/praca-magisterska/docs/document.log" - "document.idx" - "document.pdf" "document.toc" - "j:/_MAGISTERKA/praca-magisterska/docs/document.pdf" "document.aux" + "document.idx" + "j:/_MAGISTERKA/praca-magisterska/docs/document.log" + "document.pdf" + "document.log" + "j:/_MAGISTERKA/praca-magisterska/docs/document.pdf" diff --git a/docs/document.log b/docs/document.log index c26e9c6..51cd090 100644 --- a/docs/document.log +++ b/docs/document.log @@ -1,4 +1,4 @@ -This is pdfTeX, Version 3.14159265-2.6-1.40.19 (TeX Live 2018/W32TeX) (preloaded format=pdflatex 2019.2.16) 17 JUN 2020 19:21 +This is pdfTeX, Version 3.14159265-2.6-1.40.19 (TeX Live 2018/W32TeX) (preloaded format=pdflatex 2019.2.16) 17 JUN 2020 19:23 entering extended mode restricted \write18 enabled. file:line:error style messages enabled. @@ -803,7 +803,7 @@ Here is how much of TeX's memory you used: 1141 hyphenation exceptions out of 8191 55i,12n,50p,1575b,1971s stack positions out of 5000i,500n,10000p,200000b,80000s {c:/software/latex/texmf-dist/fonts/enc/dvips/pl/plit.enc}{c:/software/latex/texmf-dist/fonts/enc/dvips/pl/pltt.enc}{c:/software/latex/texmf-dist/fonts/enc/dvips/pl/plrm.enc}{c:/software/latex/texmf-dist/fonts/enc/dvips/pl/plms.enc}{c:/software/latex/texmf-dist/fonts/enc/dvips/pl/plmi.enc}{c:/software/latex/texmf-dist/fonts/enc/dvips/pl/plme.enc}{c:/software/latex/texmf-dist/fonts/enc/dvips/pl/plsc.enc} -Output written on j:/_MAGISTERKA/praca-magisterska/docs/document.pdf (61 pages, 1199946 bytes). +Output written on j:/_MAGISTERKA/praca-magisterska/docs/document.pdf (61 pages, 1199932 bytes). 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zhZTqE`@q9TquZ55IDF7BZxq zZX*(Z;fb_hH$Fl|8+pbUsa`OpZL!Nc(-8`Pk@6wZ=PsBsV&n))VUW%UWKr~K#Ga$? z9doa4b0EHx-jTIa+_e*yCMJS<`wD>aok23W{eG6;W@!r8%;$xCod~U3y`*8%NX{`s z!&_tj@M*o#@XjM9CukBaT3Zn`Wq6M&*-B%0loKTzBmR%X6&LdD*#)~3<)hC$<}4Px z=WBy1vW(B)x)La(|MyNa^xOa8PsLA93;(^I7Qg=E|NY^wYWU&5{>$(G@ay0HumAH; vKmG6@{^gIq`vbi=F-iU^)B*jw-~YqU|NO6i_~UPX`r-crPOIa*%5*LOCeum* diff --git a/docs/document.tex b/docs/document.tex index 02296f7..9e720b9 100644 --- a/docs/document.tex +++ b/docs/document.tex @@ -453,7 +453,7 @@ The rest of this chapter discusses an example of using the proposed model to gen Podczas uczenia autoenkodera przedstawia się dane wejściowe jako cel. W ten sposób ta architektura stara się odtworzyć funkcje identycznościowe. Zadanie nie jest trywialne jak mogłoby się zdawać, ponieważ zazwyczaj ukryta warstwa jest mniejszego rozmiaru niż dane wejściowe. Z tego względu autoenkoder jest zmuszony do wydobycia istotnych cech danych wejściowych, skompresowania, a następnie jak najwierniejszego ich odtworzenia. Część kompresująca dane nazywana jest enkoderem, natomiast część dekompresująca dekoderem. Cechy, które zostały odkryte przez autoenkoder nazywane są zmiennymi utajnionymi (\textit{ang. latent variables}). Zarówno encoder jak i dekoder można wyodrębnić z autoenkodera i wykorzystywać go jako osobną sieć neuronową. - Ciekawą cechą dekodera jest jego generatywny charakter, ponieważ dostarczając zupełnie nowe informacje jako zmienne wejściowe, decoder otworzy je na podobieństwo danych, na których został nauczony. + Ciekawą cechą dekodera jest jego generatywny charakter, ponieważ dostarczając zupełnie nowe informacje jako zmienne wejściowe, dekoder otworzy je na podobieństwo danych, na których został nauczony. \subsection{Rekurencyjne sieci neuronowe} \label{section:rnn} @@ -534,7 +534,7 @@ The rest of this chapter discusses an example of using the proposed model to gen Model w architekturze Sequence-to-sequence (\textit{ang. seq2seq}) został wynaleziony z myślą o tłumaczeniu maszynowym języków, ale zastosowanie dla niego znaleziono również w rozpoznawaniu mowy, opisywaniu wideo, czy tworzeniu chatbotów. Jego główną zaletą jest przetwarzanie sekwencji elementów o różnych długościach. Jest to naturalne, ponieważ tłumacząc z języka na język często tą samą sentencję można wyrazić różną liczbę słów. Dla przykładu zdanie po Polsku ,,Co dzisiaj robisz?'' zawiera trzy słowa, natomiast przetłumaczone na Angielski ,,What are you doing today?'' zawiera pięć słów. Nie można tego osiągnąć zwykłą siecią LSTM, dlatego model seq2seq został zaprojektowany, aby móc go zastosować do tego typu problemów \cite{seq2seq}. %\footnote{https://towardsdatascience.com/understanding-encoder-decoder-sequence-to-sequence %-model-679e04af4346 26.05.2020 14:58} - Model sequence-to-sequence ma dwie części, encoder i decoder. Obie części są w zasadzie dwiema zupełnie osobnymi modelami, połączonymi ze sobą w jedną sieć. Schemat modelu sequence-to-sequence przedstawiono na rysunku~\ref{fig:seq2seq}. + Model sequence-to-sequence ma dwie części, enkoder i dekoder. Obie części są w zasadzie dwiema zupełnie osobnymi modelami, połączonymi ze sobą w jedną sieć. Schemat modelu sequence-to-sequence przedstawiono na rysunku~\ref{fig:seq2seq}. Zadaniem enkodera, podobnie jak zostało to opisane w rozdziale~\ref{section:autoencoder} o autoenkoderze, jest wydobycie z wektora wejściowego najistotniejszych informacji i skompresowanie ich. Następnie wektor stanu enkodera jest przekazywany do dekodera, który na jego podstawie rekonstruuje sekwencję.

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