🙄 네이버 영화리뷰 감성분석에 SentencePiece 적용해보기

<NLP기초>

Contexts

1. READY

1-1 오늘의 Exp와 Rubric  
1-2 사용하는 라이브러리

2. GAME

2-1. 데이터 읽어오기  
2-2. 데이터 전처리  
  -1. Tokenize (SentencePiece)
  -2. 학습데이터 전처리
  -3. Split Validation 

2-3. 모델 학습  
2-4. 데이터 평가

3. POTG (best Play Of The Game

3-1. 소감(POTG)  
3-2. 어려웠던 점과 극복방안  
3-3. 추후

1. Ready

1-1. 오늘의 Exp와 Rubric

SentencePiece 는 Google 에서 제공하고 있는 Tokenizer / Detokenizer 이다.

Tokenize 란 NLP 에서 중요한 부분인 ‘단어사전 제작’ 을 의미한다.

직관적으로 생각했을 때, 단어사전은 단어별, 형태소별, 혹은 그 사이 어떤 경계를 나누어 만들 수 있다.

Sentencepiece 는 BPE 와 unigram 이라는 두 가지의 분리 방법을 통해 subword tokenizing model 을 제공하고 있다.

최근 pretrained model 은 대부분 SentencePiece 를 Tokenizer 로 설정하는 추세이기에 NLP 분야 tokenizer 의 표준이라고 표현해도 과언이 아니다.

오늘은 이러한 SentencePiece 를 끌어와 사용하는 것까지를 실습해보기로 한다.

실습에 쓰이는 데이터는
SentencePiece 토크나이저를 학습시킬 한국어 corpus 와
모델을 학습시킬 Naver_Moive txt data 로 한다.

오늘의 rubric

평가문항	상세기준
1. SentencePiece를 이용하여 모델을 만들기까지의 과정이 정상적으로 진행되었는가?	코퍼스 분석, 전처리, SentencePiece 적용, 토크나이저 구현 및 동작이 빠짐없이 진행되었는가?
2. SentencePiece를 통해 만든 Tokenizer가 자연어처리 모델과 결합하여 동작하는가?	SentencePiece 토크나이저가 적용된 Text Classifier 모델이 정상적으로 수렴하여 80% 이상의 test accuracy가 확인되었다.
3. SentencePiece의 성능을 다각도로 비교분석하였는가?	SentencePiece 토크나이저를 활용했을 때의 성능을 다른 토크나이저 혹은 SentencePiece의 다른 옵션의 경우와 비교하여 분석을 체계적으로 진행하였다.

1-2. 사용하는 라이브러리

! python3 --version

Python 3.7.12

#import konlpy 단순 import 는 에러 발생

시작 하기전 Konlpy 라이브러리에 특이사항이 있다.
colab에서는 Konlpy 를 install 할때 별도의 과정을 거쳐야 한다.

!apt-get update
!apt-get install g++ openjdk-8-jdk 
!pip3 install konlpy JPype1-py3
!bash <(curl -s https://raw.githubusercontent.com/konlpy/konlpy/master/scripts/mecab.sh)

import konlpy

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/sbin/ldconfig.real: /usr/local/lib/python3.7/dist-packages/ideep4py/lib/libmkldnn.so.0 is not a symbolic link

Processing triggers for man-db (2.8.3-2ubuntu0.1) ...
Install mecab-ko
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mecab-0.996-ko-0.9.2/
mecab-0.996-ko-0.9.2/example/
mecab-0.996-ko-0.9.2/example/example.cpp
mecab-0.996-ko-0.9.2/example/example_lattice.cpp
mecab-0.996-ko-0.9.2/example/example_lattice.c
mecab-0.996-ko-0.9.2/example/example.c
mecab-0.996-ko-0.9.2/example/thread_test.cpp
mecab-0.996-ko-0.9.2/mecab-config.in
mecab-0.996-ko-0.9.2/man/
mecab-0.996-ko-0.9.2/man/Makefile.am
mecab-0.996-ko-0.9.2/man/mecab.1
mecab-0.996-ko-0.9.2/man/Makefile.in
mecab-0.996-ko-0.9.2/mecab.iss.in
mecab-0.996-ko-0.9.2/config.guess
mecab-0.996-ko-0.9.2/README
mecab-0.996-ko-0.9.2/COPYING
mecab-0.996-ko-0.9.2/CHANGES.md
mecab-0.996-ko-0.9.2/README.md
mecab-0.996-ko-0.9.2/INSTALL
mecab-0.996-ko-0.9.2/config.sub
mecab-0.996-ko-0.9.2/configure.in
mecab-0.996-ko-0.9.2/swig/
mecab-0.996-ko-0.9.2/swig/Makefile
mecab-0.996-ko-0.9.2/swig/version.h.in
mecab-0.996-ko-0.9.2/swig/version.h
mecab-0.996-ko-0.9.2/swig/MeCab.i
mecab-0.996-ko-0.9.2/aclocal.m4
mecab-0.996-ko-0.9.2/LGPL
mecab-0.996-ko-0.9.2/Makefile.am
mecab-0.996-ko-0.9.2/configure
mecab-0.996-ko-0.9.2/tests/
mecab-0.996-ko-0.9.2/tests/autolink/
mecab-0.996-ko-0.9.2/tests/autolink/unk.def
mecab-0.996-ko-0.9.2/tests/autolink/dicrc
mecab-0.996-ko-0.9.2/tests/autolink/dic.csv
mecab-0.996-ko-0.9.2/tests/autolink/test
mecab-0.996-ko-0.9.2/tests/autolink/char.def
mecab-0.996-ko-0.9.2/tests/autolink/matrix.def
mecab-0.996-ko-0.9.2/tests/autolink/test.gld
mecab-0.996-ko-0.9.2/tests/t9/
mecab-0.996-ko-0.9.2/tests/t9/unk.def
mecab-0.996-ko-0.9.2/tests/t9/ipadic.pl
mecab-0.996-ko-0.9.2/tests/t9/dicrc
mecab-0.996-ko-0.9.2/tests/t9/dic.csv
mecab-0.996-ko-0.9.2/tests/t9/test
mecab-0.996-ko-0.9.2/tests/t9/char.def
mecab-0.996-ko-0.9.2/tests/t9/matrix.def
mecab-0.996-ko-0.9.2/tests/t9/mkdic.pl
mecab-0.996-ko-0.9.2/tests/t9/test.gld
mecab-0.996-ko-0.9.2/tests/cost-train/
mecab-0.996-ko-0.9.2/tests/cost-train/ipa.train
mecab-0.996-ko-0.9.2/tests/cost-train/ipa.test
mecab-0.996-ko-0.9.2/tests/cost-train/seed/
mecab-0.996-ko-0.9.2/tests/cost-train/seed/rewrite.def
mecab-0.996-ko-0.9.2/tests/cost-train/seed/feature.def
mecab-0.996-ko-0.9.2/tests/cost-train/seed/unk.def
mecab-0.996-ko-0.9.2/tests/cost-train/seed/dicrc
mecab-0.996-ko-0.9.2/tests/cost-train/seed/dic.csv
mecab-0.996-ko-0.9.2/tests/cost-train/seed/char.def
mecab-0.996-ko-0.9.2/tests/cost-train/seed/matrix.def
mecab-0.996-ko-0.9.2/tests/run-eval.sh
mecab-0.996-ko-0.9.2/tests/run-cost-train.sh
mecab-0.996-ko-0.9.2/tests/Makefile.am
mecab-0.996-ko-0.9.2/tests/katakana/
mecab-0.996-ko-0.9.2/tests/katakana/unk.def
mecab-0.996-ko-0.9.2/tests/katakana/dicrc
mecab-0.996-ko-0.9.2/tests/katakana/dic.csv
mecab-0.996-ko-0.9.2/tests/katakana/test
mecab-0.996-ko-0.9.2/tests/katakana/char.def
mecab-0.996-ko-0.9.2/tests/katakana/matrix.def
mecab-0.996-ko-0.9.2/tests/katakana/test.gld
mecab-0.996-ko-0.9.2/tests/eval/
mecab-0.996-ko-0.9.2/tests/eval/answer
mecab-0.996-ko-0.9.2/tests/eval/system
mecab-0.996-ko-0.9.2/tests/eval/test.gld
mecab-0.996-ko-0.9.2/tests/shiin/
mecab-0.996-ko-0.9.2/tests/shiin/unk.def
mecab-0.996-ko-0.9.2/tests/shiin/dicrc
mecab-0.996-ko-0.9.2/tests/shiin/dic.csv
mecab-0.996-ko-0.9.2/tests/shiin/test
mecab-0.996-ko-0.9.2/tests/shiin/char.def
mecab-0.996-ko-0.9.2/tests/shiin/matrix.def
mecab-0.996-ko-0.9.2/tests/shiin/mkdic.pl
mecab-0.996-ko-0.9.2/tests/shiin/test.gld
mecab-0.996-ko-0.9.2/tests/latin/
mecab-0.996-ko-0.9.2/tests/latin/unk.def
mecab-0.996-ko-0.9.2/tests/latin/dicrc
mecab-0.996-ko-0.9.2/tests/latin/dic.csv
mecab-0.996-ko-0.9.2/tests/latin/test
mecab-0.996-ko-0.9.2/tests/latin/char.def
mecab-0.996-ko-0.9.2/tests/latin/matrix.def
mecab-0.996-ko-0.9.2/tests/latin/test.gld
mecab-0.996-ko-0.9.2/tests/chartype/
mecab-0.996-ko-0.9.2/tests/chartype/unk.def
mecab-0.996-ko-0.9.2/tests/chartype/dicrc
mecab-0.996-ko-0.9.2/tests/chartype/dic.csv
mecab-0.996-ko-0.9.2/tests/chartype/test
mecab-0.996-ko-0.9.2/tests/chartype/char.def
mecab-0.996-ko-0.9.2/tests/chartype/matrix.def
mecab-0.996-ko-0.9.2/tests/chartype/test.gld
mecab-0.996-ko-0.9.2/tests/run-dics.sh
mecab-0.996-ko-0.9.2/tests/ngram/
mecab-0.996-ko-0.9.2/tests/ngram/unk.def
mecab-0.996-ko-0.9.2/tests/ngram/dicrc
mecab-0.996-ko-0.9.2/tests/ngram/dic.csv
mecab-0.996-ko-0.9.2/tests/ngram/test
mecab-0.996-ko-0.9.2/tests/ngram/char.def
mecab-0.996-ko-0.9.2/tests/ngram/matrix.def
mecab-0.996-ko-0.9.2/tests/ngram/test.gld
mecab-0.996-ko-0.9.2/tests/Makefile.in
mecab-0.996-ko-0.9.2/ltmain.sh
mecab-0.996-ko-0.9.2/config.rpath
mecab-0.996-ko-0.9.2/config.h.in
mecab-0.996-ko-0.9.2/mecabrc.in
mecab-0.996-ko-0.9.2/GPL
mecab-0.996-ko-0.9.2/Makefile.train
mecab-0.996-ko-0.9.2/ChangeLog
mecab-0.996-ko-0.9.2/install-sh
mecab-0.996-ko-0.9.2/AUTHORS
mecab-0.996-ko-0.9.2/doc/
mecab-0.996-ko-0.9.2/doc/bindings.html
mecab-0.996-ko-0.9.2/doc/posid.html
mecab-0.996-ko-0.9.2/doc/unk.html
mecab-0.996-ko-0.9.2/doc/learn.html
mecab-0.996-ko-0.9.2/doc/format.html
mecab-0.996-ko-0.9.2/doc/libmecab.html
mecab-0.996-ko-0.9.2/doc/mecab.css
mecab-0.996-ko-0.9.2/doc/feature.html
mecab-0.996-ko-0.9.2/doc/Makefile.am
mecab-0.996-ko-0.9.2/doc/soft.html
mecab-0.996-ko-0.9.2/doc/en/
mecab-0.996-ko-0.9.2/doc/en/bindings.html
mecab-0.996-ko-0.9.2/doc/dic-detail.html
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mecab-0.996-ko-0.9.2/doc/mecab.html
mecab-0.996-ko-0.9.2/doc/index.html
mecab-0.996-ko-0.9.2/doc/result.png
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mecab-0.996-ko-0.9.2/doc/doxygen/globals_eval.html
mecab-0.996-ko-0.9.2/doc/doxygen/classMeCab_1_1Tagger-members.html
mecab-0.996-ko-0.9.2/doc/doxygen/functions_vars.html
mecab-0.996-ko-0.9.2/doc/doxygen/doxygen.css
mecab-0.996-ko-0.9.2/doc/doxygen/tab_r.gif
mecab-0.996-ko-0.9.2/doc/doxygen/classMeCab_1_1Lattice.html
mecab-0.996-ko-0.9.2/doc/doxygen/functions.html
mecab-0.996-ko-0.9.2/doc/doxygen/classMeCab_1_1Tagger.html
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mecab-0.996-ko-0.9.2/doc/doxygen/nav_f.png
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mecab-0.996-ko-0.9.2/doc/doxygen/classMeCab_1_1Model.html
mecab-0.996-ko-0.9.2/doc/doxygen/globals_func.html
mecab-0.996-ko-0.9.2/doc/doxygen/closed.png
mecab-0.996-ko-0.9.2/doc/doxygen/tab_l.gif
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mecab-0.996-ko-0.9.2/doc/doxygen/functions_func.html
mecab-0.996-ko-0.9.2/doc/doxygen/globals_type.html
mecab-0.996-ko-0.9.2/doc/doxygen/classMeCab_1_1Lattice-members.html
mecab-0.996-ko-0.9.2/doc/doxygen/structmecab__node__t.html
mecab-0.996-ko-0.9.2/doc/doxygen/namespacemembers_func.html
mecab-0.996-ko-0.9.2/doc/doxygen/tab_s.png
mecab-0.996-ko-0.9.2/doc/doxygen/structmecab__dictionary__info__t-members.html
mecab-0.996-ko-0.9.2/doc/doxygen/namespacemembers_type.html
mecab-0.996-ko-0.9.2/doc/doxygen/classMeCab_1_1Model-members.html
mecab-0.996-ko-0.9.2/doc/doxygen/structmecab__dictionary__info__t.html
mecab-0.996-ko-0.9.2/doc/doxygen/namespaces.html
mecab-0.996-ko-0.9.2/doc/doxygen/namespacemembers.html
mecab-0.996-ko-0.9.2/doc/doxygen/namespaceMeCab.html
mecab-0.996-ko-0.9.2/doc/doxygen/structmecab__path__t.html
mecab-0.996-ko-0.9.2/doc/doxygen/files.html
mecab-0.996-ko-0.9.2/doc/doxygen/structmecab__node__t-members.html
mecab-0.996-ko-0.9.2/doc/doxygen/index.html
mecab-0.996-ko-0.9.2/doc/doxygen/annotated.html
mecab-0.996-ko-0.9.2/doc/doxygen/globals_defs.html
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mecab-0.996-ko-0.9.2/missing
mecab-0.996-ko-0.9.2/BSD
mecab-0.996-ko-0.9.2/NEWS
mecab-0.996-ko-0.9.2/mkinstalldirs
mecab-0.996-ko-0.9.2/src/
mecab-0.996-ko-0.9.2/src/dictionary.h
mecab-0.996-ko-0.9.2/src/writer.h
mecab-0.996-ko-0.9.2/src/utils.h
mecab-0.996-ko-0.9.2/src/string_buffer.cpp
mecab-0.996-ko-0.9.2/src/tokenizer.cpp
mecab-0.996-ko-0.9.2/src/make.bat
mecab-0.996-ko-0.9.2/src/mecab.h
mecab-0.996-ko-0.9.2/src/freelist.h
mecab-0.996-ko-0.9.2/src/string_buffer.h
mecab-0.996-ko-0.9.2/src/learner_tagger.h
mecab-0.996-ko-0.9.2/src/dictionary_compiler.cpp
mecab-0.996-ko-0.9.2/src/eval.cpp
mecab-0.996-ko-0.9.2/src/mecab-system-eval.cpp
mecab-0.996-ko-0.9.2/src/darts.h
mecab-0.996-ko-0.9.2/src/param.h
mecab-0.996-ko-0.9.2/src/char_property.h
mecab-0.996-ko-0.9.2/src/learner_node.h
mecab-0.996-ko-0.9.2/src/mecab-dict-gen.cpp
mecab-0.996-ko-0.9.2/src/mecab-dict-index.cpp
mecab-0.996-ko-0.9.2/src/winmain.h
mecab-0.996-ko-0.9.2/src/thread.h
mecab-0.996-ko-0.9.2/src/context_id.cpp
mecab-0.996-ko-0.9.2/src/Makefile.am
mecab-0.996-ko-0.9.2/src/connector.h
mecab-0.996-ko-0.9.2/src/common.h
mecab-0.996-ko-0.9.2/src/dictionary_rewriter.cpp
mecab-0.996-ko-0.9.2/src/Makefile.msvc.in
mecab-0.996-ko-0.9.2/src/dictionary_rewriter.h
mecab-0.996-ko-0.9.2/src/feature_index.h
mecab-0.996-ko-0.9.2/src/iconv_utils.cpp
mecab-0.996-ko-0.9.2/src/char_property.cpp
mecab-0.996-ko-0.9.2/src/mecab-test-gen.cpp
mecab-0.996-ko-0.9.2/src/tagger.cpp
mecab-0.996-ko-0.9.2/src/mecab-cost-train.cpp
mecab-0.996-ko-0.9.2/src/learner.cpp
mecab-0.996-ko-0.9.2/src/dictionary.cpp
mecab-0.996-ko-0.9.2/src/lbfgs.cpp
mecab-0.996-ko-0.9.2/src/ucs.h
mecab-0.996-ko-0.9.2/src/writer.cpp
mecab-0.996-ko-0.9.2/src/learner_tagger.cpp
mecab-0.996-ko-0.9.2/src/lbfgs.h
mecab-0.996-ko-0.9.2/src/libmecab.cpp
mecab-0.996-ko-0.9.2/src/tokenizer.h
mecab-0.996-ko-0.9.2/src/mecab.cpp
mecab-0.996-ko-0.9.2/src/utils.cpp
mecab-0.996-ko-0.9.2/src/dictionary_generator.cpp
mecab-0.996-ko-0.9.2/src/param.cpp
mecab-0.996-ko-0.9.2/src/context_id.h
mecab-0.996-ko-0.9.2/src/mmap.h
mecab-0.996-ko-0.9.2/src/viterbi.h
mecab-0.996-ko-0.9.2/src/viterbi.cpp
mecab-0.996-ko-0.9.2/src/stream_wrapper.h
mecab-0.996-ko-0.9.2/src/feature_index.cpp
mecab-0.996-ko-0.9.2/src/nbest_generator.h
mecab-0.996-ko-0.9.2/src/ucstable.h
mecab-0.996-ko-0.9.2/src/nbest_generator.cpp
mecab-0.996-ko-0.9.2/src/iconv_utils.h
mecab-0.996-ko-0.9.2/src/connector.cpp
mecab-0.996-ko-0.9.2/src/Makefile.in
mecab-0.996-ko-0.9.2/src/scoped_ptr.h
mecab-0.996-ko-0.9.2/Makefile.in
checking for a BSD-compatible install... /usr/bin/install -c
checking whether build environment is sane... yes
checking for a thread-safe mkdir -p... /bin/mkdir -p
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checking for mawk... mawk
checking whether make sets $(MAKE)... yes
checking for gcc... gcc
checking whether the C compiler works... yes
checking for C compiler default output file name... a.out
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checking whether gcc accepts -g... yes
checking for gcc option to accept ISO C89... none needed
checking for style of include used by make... GNU
checking dependency style of gcc... none
checking for g++... g++
checking whether we are using the GNU C++ compiler... yes
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checking dependency style of g++... none
checking how to run the C preprocessor... gcc -E
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checking build system type... x86_64-unknown-linux-gnu
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checking for an ANSI C-conforming const... yes
checking whether byte ordering is bigendian... (cached) no
checking for string.h... (cached) yes
checking for stdlib.h... (cached) yes
checking for unistd.h... (cached) yes
checking fcntl.h usability... yes
checking fcntl.h presence... yes
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checking for stdint.h... (cached) yes
checking for sys/stat.h... (cached) yes
checking sys/mman.h usability... yes
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checking sys/times.h usability... yes
checking sys/times.h presence... yes
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checking dirent.h usability... yes
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checking ctype.h usability... yes
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checking for sys/types.h... (cached) yes
checking io.h usability... no
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checking windows.h usability... no
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checking pthread.h usability... yes
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checking size of char... 1
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checking size of size_t... 8
checking for size_t... (cached) yes
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checking for unistd.h... (cached) yes
checking for sys/param.h... yes
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checking for main in -lstdc++... yes
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configure: creating ./config.status
config.status: creating Makefile
config.status: creating src/Makefile
config.status: creating src/Makefile.msvc
config.status: creating man/Makefile
config.status: creating doc/Makefile
config.status: creating tests/Makefile
config.status: creating swig/version.h
config.status: creating mecab.iss
config.status: creating mecab-config
config.status: creating mecabrc
config.status: creating config.h
config.status: executing depfiles commands
config.status: executing libtool commands
config.status: executing default commands
make  all-recursive
make[1]: Entering directory '/tmp/mecab-0.996-ko-0.9.2'
Making all in src
make[2]: Entering directory '/tmp/mecab-0.996-ko-0.9.2/src'
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libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\"/usr/local/etc/mecabrc\" -O3 -Wall -c viterbi.cpp  -fPIC -DPIC -o .libs/viterbi.o
In file included from [01m[Kviterbi.cpp:14:0[m[K:
[01m[Kparam.h:30:13:[m[K [01;35m[Kwarning: [m[K'[01m[KTarget {anonymous}::lexical_cast(Source) [with Target = std::__cxx11::basic_string<char>; Source = std::__cxx11::basic_string<char>][m[K' defined but not used [[01;35m[K-Wunused-function[m[K]
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             [01;35m[K^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~[m[K
libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\"/usr/local/etc/mecabrc\" -O3 -Wall -c viterbi.cpp -o viterbi.o >/dev/null 2>&1
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libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\"/usr/local/etc/mecabrc\" -O3 -Wall -c tagger.cpp  -fPIC -DPIC -o .libs/tagger.o
libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\"/usr/local/etc/mecabrc\" -O3 -Wall -c tagger.cpp -o tagger.o >/dev/null 2>&1
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libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\"/usr/local/etc/mecabrc\" -O3 -Wall -c utils.cpp  -fPIC -DPIC -o .libs/utils.o
libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\"/usr/local/etc/mecabrc\" -O3 -Wall -c utils.cpp -o utils.o >/dev/null 2>&1
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libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\"/usr/local/etc/mecabrc\" -O3 -Wall -c eval.cpp  -fPIC -DPIC -o .libs/eval.o
libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\"/usr/local/etc/mecabrc\" -O3 -Wall -c eval.cpp -o eval.o >/dev/null 2>&1
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libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\"/usr/local/etc/mecabrc\" -O3 -Wall -c iconv_utils.cpp  -fPIC -DPIC -o .libs/iconv_utils.o
libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\"/usr/local/etc/mecabrc\" -O3 -Wall -c iconv_utils.cpp -o iconv_utils.o >/dev/null 2>&1
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libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\"/usr/local/etc/mecabrc\" -O3 -Wall -c dictionary_rewriter.cpp  -fPIC -DPIC -o .libs/dictionary_rewriter.o
libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\"/usr/local/etc/mecabrc\" -O3 -Wall -c dictionary_rewriter.cpp -o dictionary_rewriter.o >/dev/null 2>&1
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libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\"/usr/local/etc/mecabrc\" -O3 -Wall -c dictionary_generator.cpp  -fPIC -DPIC -o .libs/dictionary_generator.o
libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\"/usr/local/etc/mecabrc\" -O3 -Wall -c dictionary_generator.cpp -o dictionary_generator.o >/dev/null 2>&1
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libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\"/usr/local/etc/mecabrc\" -O3 -Wall -c dictionary_compiler.cpp  -fPIC -DPIC -o .libs/dictionary_compiler.o
libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\"/usr/local/etc/mecabrc\" -O3 -Wall -c dictionary_compiler.cpp -o dictionary_compiler.o >/dev/null 2>&1
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libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\"/usr/local/etc/mecabrc\" -O3 -Wall -c context_id.cpp  -fPIC -DPIC -o .libs/context_id.o
libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\"/usr/local/etc/mecabrc\" -O3 -Wall -c context_id.cpp -o context_id.o >/dev/null 2>&1
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libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\"/usr/local/etc/mecabrc\" -O3 -Wall -c connector.cpp  -fPIC -DPIC -o .libs/connector.o
libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\"/usr/local/etc/mecabrc\" -O3 -Wall -c connector.cpp -o connector.o >/dev/null 2>&1
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libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\"/usr/local/etc/mecabrc\" -O3 -Wall -c nbest_generator.cpp  -fPIC -DPIC -o .libs/nbest_generator.o
libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\"/usr/local/etc/mecabrc\" -O3 -Wall -c nbest_generator.cpp -o nbest_generator.o >/dev/null 2>&1
/bin/bash ../libtool --tag=CXX   --mode=compile g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102   -DMECAB_DEFAULT_RC="\"/usr/local/etc/mecabrc\""    -O3 -Wall  -c -o writer.lo writer.cpp
libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\"/usr/local/etc/mecabrc\" -O3 -Wall -c writer.cpp  -fPIC -DPIC -o .libs/writer.o
libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\"/usr/local/etc/mecabrc\" -O3 -Wall -c writer.cpp -o writer.o >/dev/null 2>&1
/bin/bash ../libtool --tag=CXX   --mode=compile g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102   -DMECAB_DEFAULT_RC="\"/usr/local/etc/mecabrc\""    -O3 -Wall  -c -o string_buffer.lo string_buffer.cpp
libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\"/usr/local/etc/mecabrc\" -O3 -Wall -c string_buffer.cpp  -fPIC -DPIC -o .libs/string_buffer.o
libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\"/usr/local/etc/mecabrc\" -O3 -Wall -c string_buffer.cpp -o string_buffer.o >/dev/null 2>&1
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libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\"/usr/local/etc/mecabrc\" -O3 -Wall -c param.cpp  -fPIC -DPIC -o .libs/param.o
libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\"/usr/local/etc/mecabrc\" -O3 -Wall -c param.cpp -o param.o >/dev/null 2>&1
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libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\"/usr/local/etc/mecabrc\" -O3 -Wall -c tokenizer.cpp  -fPIC -DPIC -o .libs/tokenizer.o
libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\"/usr/local/etc/mecabrc\" -O3 -Wall -c tokenizer.cpp -o tokenizer.o >/dev/null 2>&1
/bin/bash ../libtool --tag=CXX   --mode=compile g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102   -DMECAB_DEFAULT_RC="\"/usr/local/etc/mecabrc\""    -O3 -Wall  -c -o char_property.lo char_property.cpp
libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\"/usr/local/etc/mecabrc\" -O3 -Wall -c char_property.cpp  -fPIC -DPIC -o .libs/char_property.o
libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\"/usr/local/etc/mecabrc\" -O3 -Wall -c char_property.cpp -o char_property.o >/dev/null 2>&1
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libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\"/usr/local/etc/mecabrc\" -O3 -Wall -c dictionary.cpp  -fPIC -DPIC -o .libs/dictionary.o
libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\"/usr/local/etc/mecabrc\" -O3 -Wall -c dictionary.cpp -o dictionary.o >/dev/null 2>&1
/bin/bash ../libtool --tag=CXX   --mode=compile g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102   -DMECAB_DEFAULT_RC="\"/usr/local/etc/mecabrc\""    -O3 -Wall  -c -o feature_index.lo feature_index.cpp
libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\"/usr/local/etc/mecabrc\" -O3 -Wall -c feature_index.cpp  -fPIC -DPIC -o .libs/feature_index.o
libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\"/usr/local/etc/mecabrc\" -O3 -Wall -c feature_index.cpp -o feature_index.o >/dev/null 2>&1
/bin/bash ../libtool --tag=CXX   --mode=compile g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102   -DMECAB_DEFAULT_RC="\"/usr/local/etc/mecabrc\""    -O3 -Wall  -c -o lbfgs.lo lbfgs.cpp
libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\"/usr/local/etc/mecabrc\" -O3 -Wall -c lbfgs.cpp  -fPIC -DPIC -o .libs/lbfgs.o
libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\"/usr/local/etc/mecabrc\" -O3 -Wall -c lbfgs.cpp -o lbfgs.o >/dev/null 2>&1
/bin/bash ../libtool --tag=CXX   --mode=compile g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102   -DMECAB_DEFAULT_RC="\"/usr/local/etc/mecabrc\""    -O3 -Wall  -c -o learner_tagger.lo learner_tagger.cpp
libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\"/usr/local/etc/mecabrc\" -O3 -Wall -c learner_tagger.cpp  -fPIC -DPIC -o .libs/learner_tagger.o
[01m[Klearner_tagger.cpp:25:7:[m[K [01;35m[Kwarning: [m[K'[01m[Kchar* MeCab::{anonymous}::mystrdup(const string&)[m[K' defined but not used [[01;35m[K-Wunused-function[m[K]
 char *[01;35m[Kmystrdup[m[K(const std::string &str) {
       [01;35m[K^~~~~~~~[m[K
libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\"/usr/local/etc/mecabrc\" -O3 -Wall -c learner_tagger.cpp -o learner_tagger.o >/dev/null 2>&1
/bin/bash ../libtool --tag=CXX   --mode=compile g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102   -DMECAB_DEFAULT_RC="\"/usr/local/etc/mecabrc\""    -O3 -Wall  -c -o learner.lo learner.cpp
libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\"/usr/local/etc/mecabrc\" -O3 -Wall -c learner.cpp  -fPIC -DPIC -o .libs/learner.o
libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\"/usr/local/etc/mecabrc\" -O3 -Wall -c learner.cpp -o learner.o >/dev/null 2>&1
/bin/bash ../libtool --tag=CXX   --mode=compile g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102   -DMECAB_DEFAULT_RC="\"/usr/local/etc/mecabrc\""    -O3 -Wall  -c -o libmecab.lo libmecab.cpp
libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\"/usr/local/etc/mecabrc\" -O3 -Wall -c libmecab.cpp  -fPIC -DPIC -o .libs/libmecab.o
libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\"/usr/local/etc/mecabrc\" -O3 -Wall -c libmecab.cpp -o libmecab.o >/dev/null 2>&1
/bin/bash ../libtool --tag=CXX   --mode=link g++  -O3 -Wall  -no-undefined -version-info 2:0:0  -o libmecab.la -rpath /usr/local/lib viterbi.lo tagger.lo utils.lo eval.lo iconv_utils.lo dictionary_rewriter.lo dictionary_generator.lo dictionary_compiler.lo context_id.lo connector.lo nbest_generator.lo writer.lo string_buffer.lo param.lo tokenizer.lo char_property.lo dictionary.lo feature_index.lo lbfgs.lo learner_tagger.lo learner.lo libmecab.lo  -lpthread -lpthread  -lstdc++ 
libtool: link: g++  -fPIC -DPIC -shared -nostdlib /usr/lib/gcc/x86_64-linux-gnu/7/../../../x86_64-linux-gnu/crti.o /usr/lib/gcc/x86_64-linux-gnu/7/crtbeginS.o  .libs/viterbi.o .libs/tagger.o .libs/utils.o .libs/eval.o .libs/iconv_utils.o .libs/dictionary_rewriter.o .libs/dictionary_generator.o .libs/dictionary_compiler.o .libs/context_id.o .libs/connector.o .libs/nbest_generator.o .libs/writer.o .libs/string_buffer.o .libs/param.o .libs/tokenizer.o .libs/char_property.o .libs/dictionary.o .libs/feature_index.o .libs/lbfgs.o .libs/learner_tagger.o .libs/learner.o .libs/libmecab.o   -lpthread -L/usr/lib/gcc/x86_64-linux-gnu/7 -L/usr/lib/gcc/x86_64-linux-gnu/7/../../../x86_64-linux-gnu -L/usr/lib/gcc/x86_64-linux-gnu/7/../../../../lib -L/lib/x86_64-linux-gnu -L/lib/../lib -L/usr/lib/x86_64-linux-gnu -L/usr/lib/../lib -L/usr/local/cuda/lib64/stubs -L/usr/lib/gcc/x86_64-linux-gnu/7/../../.. -lstdc++ -lm -lc -lgcc_s /usr/lib/gcc/x86_64-linux-gnu/7/crtendS.o /usr/lib/gcc/x86_64-linux-gnu/7/../../../x86_64-linux-gnu/crtn.o  -O3   -Wl,-soname -Wl,libmecab.so.2 -o .libs/libmecab.so.2.0.0
libtool: link: (cd ".libs" && rm -f "libmecab.so.2" && ln -s "libmecab.so.2.0.0" "libmecab.so.2")
libtool: link: (cd ".libs" && rm -f "libmecab.so" && ln -s "libmecab.so.2.0.0" "libmecab.so")
libtool: link: ar cru .libs/libmecab.a  viterbi.o tagger.o utils.o eval.o iconv_utils.o dictionary_rewriter.o dictionary_generator.o dictionary_compiler.o context_id.o connector.o nbest_generator.o writer.o string_buffer.o param.o tokenizer.o char_property.o dictionary.o feature_index.o lbfgs.o learner_tagger.o learner.o libmecab.o
ar: `u' modifier ignored since `D' is the default (see `U')
libtool: link: ranlib .libs/libmecab.a
libtool: link: ( cd ".libs" && rm -f "libmecab.la" && ln -s "../libmecab.la" "libmecab.la" )
g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102   -DMECAB_DEFAULT_RC="\"/usr/local/etc/mecabrc\""    -O3 -Wall  -c -o mecab.o mecab.cpp
/bin/bash ../libtool --tag=CXX   --mode=link g++  -O3 -Wall    -o mecab mecab.o libmecab.la -lpthread -lpthread  -lstdc++ 
libtool: link: g++ -O3 -Wall -o .libs/mecab mecab.o  ./.libs/libmecab.so -lpthread -lstdc++
g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102   -DMECAB_DEFAULT_RC="\"/usr/local/etc/mecabrc\""    -O3 -Wall  -c -o mecab-dict-index.o mecab-dict-index.cpp
/bin/bash ../libtool --tag=CXX   --mode=link g++  -O3 -Wall    -o mecab-dict-index mecab-dict-index.o libmecab.la -lpthread -lpthread  -lstdc++ 
libtool: link: g++ -O3 -Wall -o .libs/mecab-dict-index mecab-dict-index.o  ./.libs/libmecab.so -lpthread -lstdc++
g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102   -DMECAB_DEFAULT_RC="\"/usr/local/etc/mecabrc\""    -O3 -Wall  -c -o mecab-dict-gen.o mecab-dict-gen.cpp
/bin/bash ../libtool --tag=CXX   --mode=link g++  -O3 -Wall    -o mecab-dict-gen mecab-dict-gen.o libmecab.la -lpthread -lpthread  -lstdc++ 
libtool: link: g++ -O3 -Wall -o .libs/mecab-dict-gen mecab-dict-gen.o  ./.libs/libmecab.so -lpthread -lstdc++
g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102   -DMECAB_DEFAULT_RC="\"/usr/local/etc/mecabrc\""    -O3 -Wall  -c -o mecab-cost-train.o mecab-cost-train.cpp
/bin/bash ../libtool --tag=CXX   --mode=link g++  -O3 -Wall    -o mecab-cost-train mecab-cost-train.o libmecab.la -lpthread -lpthread  -lstdc++ 
libtool: link: g++ -O3 -Wall -o .libs/mecab-cost-train mecab-cost-train.o  ./.libs/libmecab.so -lpthread -lstdc++
g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102   -DMECAB_DEFAULT_RC="\"/usr/local/etc/mecabrc\""    -O3 -Wall  -c -o mecab-system-eval.o mecab-system-eval.cpp
/bin/bash ../libtool --tag=CXX   --mode=link g++  -O3 -Wall    -o mecab-system-eval mecab-system-eval.o libmecab.la -lpthread -lpthread  -lstdc++ 
libtool: link: g++ -O3 -Wall -o .libs/mecab-system-eval mecab-system-eval.o  ./.libs/libmecab.so -lpthread -lstdc++
g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102   -DMECAB_DEFAULT_RC="\"/usr/local/etc/mecabrc\""    -O3 -Wall  -c -o mecab-test-gen.o mecab-test-gen.cpp
/bin/bash ../libtool --tag=CXX   --mode=link g++  -O3 -Wall    -o mecab-test-gen mecab-test-gen.o libmecab.la -lpthread -lpthread  -lstdc++ 
libtool: link: g++ -O3 -Wall -o .libs/mecab-test-gen mecab-test-gen.o  ./.libs/libmecab.so -lpthread -lstdc++
make[2]: Leaving directory '/tmp/mecab-0.996-ko-0.9.2/src'
Making all in man
make[2]: Entering directory '/tmp/mecab-0.996-ko-0.9.2/man'
make[2]: Nothing to be done for 'all'.
make[2]: Leaving directory '/tmp/mecab-0.996-ko-0.9.2/man'
Making all in doc
make[2]: Entering directory '/tmp/mecab-0.996-ko-0.9.2/doc'
make[2]: Nothing to be done for 'all'.
make[2]: Leaving directory '/tmp/mecab-0.996-ko-0.9.2/doc'
Making all in tests
make[2]: Entering directory '/tmp/mecab-0.996-ko-0.9.2/tests'
make[2]: Nothing to be done for 'all'.
make[2]: Leaving directory '/tmp/mecab-0.996-ko-0.9.2/tests'
make[2]: Entering directory '/tmp/mecab-0.996-ko-0.9.2'
make[2]: Leaving directory '/tmp/mecab-0.996-ko-0.9.2'
make[1]: Leaving directory '/tmp/mecab-0.996-ko-0.9.2'
Making check in src
make[1]: Entering directory '/tmp/mecab-0.996-ko-0.9.2/src'
make[1]: Nothing to be done for 'check'.
make[1]: Leaving directory '/tmp/mecab-0.996-ko-0.9.2/src'
Making check in man
make[1]: Entering directory '/tmp/mecab-0.996-ko-0.9.2/man'
make[1]: Nothing to be done for 'check'.
make[1]: Leaving directory '/tmp/mecab-0.996-ko-0.9.2/man'
Making check in doc
make[1]: Entering directory '/tmp/mecab-0.996-ko-0.9.2/doc'
make[1]: Nothing to be done for 'check'.
make[1]: Leaving directory '/tmp/mecab-0.996-ko-0.9.2/doc'
Making check in tests
make[1]: Entering directory '/tmp/mecab-0.996-ko-0.9.2/tests'
make  check-TESTS
make[2]: Entering directory '/tmp/mecab-0.996-ko-0.9.2/tests'
./pos-id.def is not found. minimum setting is used
reading ./unk.def ... 2
emitting double-array: 100% |###########################################| 
./model.def is not found. skipped.
./pos-id.def is not found. minimum setting is used
reading ./dic.csv ... 177
emitting double-array: 100% |###########################################| 
reading ./matrix.def ... 178x178
emitting matrix      : 100% |###########################################| 

done!
./pos-id.def is not found. minimum setting is used
reading ./unk.def ... 2
emitting double-array: 100% |###########################################| 
./model.def is not found. skipped.
./pos-id.def is not found. minimum setting is used
reading ./dic.csv ... 83
emitting double-array: 100% |###########################################| 
reading ./matrix.def ... 84x84
emitting matrix      : 100% |###########################################| 

done!
./pos-id.def is not found. minimum setting is used
reading ./unk.def ... 2
emitting double-array: 100% |###########################################| 
./model.def is not found. skipped.
./pos-id.def is not found. minimum setting is used
reading ./dic.csv ... 450
emitting double-array: 100% |###########################################| 
reading ./matrix.def ... 1x1

done!
./pos-id.def is not found. minimum setting is used
reading ./unk.def ... 2
emitting double-array: 100% |###########################################| 
./model.def is not found. skipped.
./pos-id.def is not found. minimum setting is used
reading ./dic.csv ... 162
emitting double-array: 100% |###########################################| 
reading ./matrix.def ... 3x3
emitting matrix      : 100% |###########################################| 

done!
./pos-id.def is not found. minimum setting is used
reading ./unk.def ... 2
emitting double-array: 100% |###########################################| 
./model.def is not found. skipped.
./pos-id.def is not found. minimum setting is used
reading ./dic.csv ... 4
emitting double-array: 100% |###########################################| 
reading ./matrix.def ... 1x1

done!
./pos-id.def is not found. minimum setting is used
reading ./unk.def ... 11
emitting double-array: 100% |###########################################| 
./model.def is not found. skipped.
./pos-id.def is not found. minimum setting is used
reading ./dic.csv ... 1
reading ./matrix.def ... 1x1

done!
./pos-id.def is not found. minimum setting is used
reading ./unk.def ... 2
emitting double-array: 100% |###########################################| 
./model.def is not found. skipped.
./pos-id.def is not found. minimum setting is used
reading ./dic.csv ... 1
reading ./matrix.def ... 1x1

done!
PASS: run-dics.sh
PASS: run-eval.sh
seed/pos-id.def is not found. minimum setting is used
reading seed/unk.def ... 40
emitting double-array: 100% |###########################################| 
seed/model.def is not found. skipped.
seed/pos-id.def is not found. minimum setting is used
reading seed/dic.csv ... 4335
emitting double-array: 100% |###########################################| 
reading seed/matrix.def ... 1x1

done!
reading corpus ...
Number of sentences: 34
Number of features:  64108
eta:                 0.00005
freq:                1
eval-size:           6
unk-eval-size:       4
threads:             1
charset:             EUC-JP
C(sigma^2):          1.00000

iter=0 err=1.00000 F=0.35771 target=2406.28355 diff=1.00000
iter=1 err=0.97059 F=0.65652 target=1484.25231 diff=0.38318
iter=2 err=0.91176 F=0.79331 target=863.32765 diff=0.41834
iter=3 err=0.85294 F=0.89213 target=596.72480 diff=0.30881
iter=4 err=0.61765 F=0.95467 target=336.30744 diff=0.43641
iter=5 err=0.50000 F=0.96702 target=246.53039 diff=0.26695
iter=6 err=0.35294 F=0.95472 target=188.93963 diff=0.23361
iter=7 err=0.20588 F=0.99106 target=168.62665 diff=0.10751
iter=8 err=0.05882 F=0.99777 target=158.64865 diff=0.05917
iter=9 err=0.08824 F=0.99665 target=154.14530 diff=0.02839
iter=10 err=0.08824 F=0.99665 target=151.94257 diff=0.01429
iter=11 err=0.02941 F=0.99888 target=147.20825 diff=0.03116
iter=12 err=0.00000 F=1.00000 target=147.34956 diff=0.00096
iter=13 err=0.02941 F=0.99888 target=146.32592 diff=0.00695
iter=14 err=0.00000 F=1.00000 target=145.77299 diff=0.00378
iter=15 err=0.02941 F=0.99888 target=145.24641 diff=0.00361
iter=16 err=0.00000 F=1.00000 target=144.96490 diff=0.00194
iter=17 err=0.02941 F=0.99888 target=144.90246 diff=0.00043
iter=18 err=0.00000 F=1.00000 target=144.75959 diff=0.00099
iter=19 err=0.00000 F=1.00000 target=144.71727 diff=0.00029
iter=20 err=0.00000 F=1.00000 target=144.66337 diff=0.00037
iter=21 err=0.00000 F=1.00000 target=144.61349 diff=0.00034
iter=22 err=0.00000 F=1.00000 target=144.62987 diff=0.00011
iter=23 err=0.00000 F=1.00000 target=144.60060 diff=0.00020
iter=24 err=0.00000 F=1.00000 target=144.59125 diff=0.00006
iter=25 err=0.00000 F=1.00000 target=144.58619 diff=0.00004
iter=26 err=0.00000 F=1.00000 target=144.58219 diff=0.00003
iter=27 err=0.00000 F=1.00000 target=144.58059 diff=0.00001

Done! writing model file ... 
model-ipadic.c1.0.f1.model is not a binary model. reopen it as text mode...
reading seed/unk.def ... 40
reading seed/dic.csv ... 4335
emitting model-ipadic.c1.0.f1.dic/left-id.def/ model-ipadic.c1.0.f1.dic/right-id.def
emitting model-ipadic.c1.0.f1.dic/unk.def ... 40
emitting model-ipadic.c1.0.f1.dic/dic.csv ... 4335
emitting matrix      : 100% |###########################################| 
copying seed/char.def to model-ipadic.c1.0.f1.dic/char.def
copying seed/rewrite.def to model-ipadic.c1.0.f1.dic/rewrite.def
copying seed/dicrc to model-ipadic.c1.0.f1.dic/dicrc
copying seed/feature.def to model-ipadic.c1.0.f1.dic/feature.def
copying model-ipadic.c1.0.f1.model to model-ipadic.c1.0.f1.dic/model.def

done!
model-ipadic.c1.0.f1.dic/pos-id.def is not found. minimum setting is used
reading model-ipadic.c1.0.f1.dic/unk.def ... 40
emitting double-array: 100% |###########################################| 
model-ipadic.c1.0.f1.dic/pos-id.def is not found. minimum setting is used
reading model-ipadic.c1.0.f1.dic/dic.csv ... 4335
emitting double-array: 100% |###########################################| 
reading model-ipadic.c1.0.f1.dic/matrix.def ... 346x346
emitting matrix      : 100% |###########################################| 

done!
              precision          recall         F
LEVEL 0:    12.8959(57/442) 11.8998(57/479) 12.3779
LEVEL 1:    12.2172(54/442) 11.2735(54/479) 11.7264
LEVEL 2:    11.7647(52/442) 10.8559(52/479) 11.2921
LEVEL 4:    11.7647(52/442) 10.8559(52/479) 11.2921
PASS: run-cost-train.sh
==================
All 3 tests passed
==================
make[2]: Leaving directory '/tmp/mecab-0.996-ko-0.9.2/tests'
make[1]: Leaving directory '/tmp/mecab-0.996-ko-0.9.2/tests'
make[1]: Entering directory '/tmp/mecab-0.996-ko-0.9.2'
make[1]: Leaving directory '/tmp/mecab-0.996-ko-0.9.2'
Making install in src
make[1]: Entering directory '/tmp/mecab-0.996-ko-0.9.2/src'
make[2]: Entering directory '/tmp/mecab-0.996-ko-0.9.2/src'
test -z "/usr/local/lib" || /bin/mkdir -p "/usr/local/lib"
 /bin/bash ../libtool   --mode=install /usr/bin/install -c   libmecab.la '/usr/local/lib'
libtool: install: /usr/bin/install -c .libs/libmecab.so.2.0.0 /usr/local/lib/libmecab.so.2.0.0
libtool: install: (cd /usr/local/lib && { ln -s -f libmecab.so.2.0.0 libmecab.so.2 || { rm -f libmecab.so.2 && ln -s libmecab.so.2.0.0 libmecab.so.2; }; })
libtool: install: (cd /usr/local/lib && { ln -s -f libmecab.so.2.0.0 libmecab.so || { rm -f libmecab.so && ln -s libmecab.so.2.0.0 libmecab.so; }; })
libtool: install: /usr/bin/install -c .libs/libmecab.lai /usr/local/lib/libmecab.la
libtool: install: /usr/bin/install -c .libs/libmecab.a /usr/local/lib/libmecab.a
libtool: install: chmod 644 /usr/local/lib/libmecab.a
libtool: install: ranlib /usr/local/lib/libmecab.a
libtool: finish: PATH="/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/snap/bin:/sbin" ldconfig -n /usr/local/lib
----------------------------------------------------------------------
Libraries have been installed in:
   /usr/local/lib

If you ever happen to want to link against installed libraries
in a given directory, LIBDIR, you must either use libtool, and
specify the full pathname of the library, or use the `-LLIBDIR'
flag during linking and do at least one of the following:
   - add LIBDIR to the `LD_LIBRARY_PATH' environment variable
     during execution
   - add LIBDIR to the `LD_RUN_PATH' environment variable
     during linking
   - use the `-Wl,-rpath -Wl,LIBDIR' linker flag
   - have your system administrator add LIBDIR to `/etc/ld.so.conf'

See any operating system documentation about shared libraries for
more information, such as the ld(1) and ld.so(8) manual pages.
----------------------------------------------------------------------
test -z "/usr/local/bin" || /bin/mkdir -p "/usr/local/bin"
  /bin/bash ../libtool   --mode=install /usr/bin/install -c mecab '/usr/local/bin'
libtool: install: /usr/bin/install -c .libs/mecab /usr/local/bin/mecab
test -z "/usr/local/libexec/mecab" || /bin/mkdir -p "/usr/local/libexec/mecab"
  /bin/bash ../libtool   --mode=install /usr/bin/install -c mecab-dict-index mecab-dict-gen mecab-cost-train mecab-system-eval mecab-test-gen '/usr/local/libexec/mecab'
libtool: install: /usr/bin/install -c .libs/mecab-dict-index /usr/local/libexec/mecab/mecab-dict-index
libtool: install: /usr/bin/install -c .libs/mecab-dict-gen /usr/local/libexec/mecab/mecab-dict-gen
libtool: install: /usr/bin/install -c .libs/mecab-cost-train /usr/local/libexec/mecab/mecab-cost-train
libtool: install: /usr/bin/install -c .libs/mecab-system-eval /usr/local/libexec/mecab/mecab-system-eval
libtool: install: /usr/bin/install -c .libs/mecab-test-gen /usr/local/libexec/mecab/mecab-test-gen
test -z "/usr/local/include" || /bin/mkdir -p "/usr/local/include"
 /usr/bin/install -c -m 644 mecab.h '/usr/local/include'
make[2]: Leaving directory '/tmp/mecab-0.996-ko-0.9.2/src'
make[1]: Leaving directory '/tmp/mecab-0.996-ko-0.9.2/src'
Making install in man
make[1]: Entering directory '/tmp/mecab-0.996-ko-0.9.2/man'
make[2]: Entering directory '/tmp/mecab-0.996-ko-0.9.2/man'
make[2]: Nothing to be done for 'install-exec-am'.
test -z "/usr/local/share/man/man1" || /bin/mkdir -p "/usr/local/share/man/man1"
 /usr/bin/install -c -m 644 mecab.1 '/usr/local/share/man/man1'
make[2]: Leaving directory '/tmp/mecab-0.996-ko-0.9.2/man'
make[1]: Leaving directory '/tmp/mecab-0.996-ko-0.9.2/man'
Making install in doc
make[1]: Entering directory '/tmp/mecab-0.996-ko-0.9.2/doc'
make[2]: Entering directory '/tmp/mecab-0.996-ko-0.9.2/doc'
make[2]: Nothing to be done for 'install-exec-am'.
make[2]: Nothing to be done for 'install-data-am'.
make[2]: Leaving directory '/tmp/mecab-0.996-ko-0.9.2/doc'
make[1]: Leaving directory '/tmp/mecab-0.996-ko-0.9.2/doc'
Making install in tests
make[1]: Entering directory '/tmp/mecab-0.996-ko-0.9.2/tests'
make[2]: Entering directory '/tmp/mecab-0.996-ko-0.9.2/tests'
make[2]: Nothing to be done for 'install-exec-am'.
make[2]: Nothing to be done for 'install-data-am'.
make[2]: Leaving directory '/tmp/mecab-0.996-ko-0.9.2/tests'
make[1]: Leaving directory '/tmp/mecab-0.996-ko-0.9.2/tests'
make[1]: Entering directory '/tmp/mecab-0.996-ko-0.9.2'
make[2]: Entering directory '/tmp/mecab-0.996-ko-0.9.2'
test -z "/usr/local/bin" || /bin/mkdir -p "/usr/local/bin"
 /usr/bin/install -c mecab-config '/usr/local/bin'
test -z "/usr/local/etc" || /bin/mkdir -p "/usr/local/etc"
 /usr/bin/install -c -m 644 mecabrc '/usr/local/etc'
make[2]: Leaving directory '/tmp/mecab-0.996-ko-0.9.2'
make[1]: Leaving directory '/tmp/mecab-0.996-ko-0.9.2'
Install mecab-ko-dic
Install mecab-ko-dic
  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
                                 Dload  Upload   Total   Spent    Left  Speed
  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0
100 47.4M  100 47.4M    0     0  7757k      0  0:00:06  0:00:06 --:--:-- 10.1M
mecab-ko-dic-2.1.1-20180720/
mecab-ko-dic-2.1.1-20180720/configure
mecab-ko-dic-2.1.1-20180720/COPYING
mecab-ko-dic-2.1.1-20180720/autogen.sh
mecab-ko-dic-2.1.1-20180720/Place-station.csv
mecab-ko-dic-2.1.1-20180720/NNG.csv
mecab-ko-dic-2.1.1-20180720/README
mecab-ko-dic-2.1.1-20180720/EF.csv
mecab-ko-dic-2.1.1-20180720/MAG.csv
mecab-ko-dic-2.1.1-20180720/Preanalysis.csv
mecab-ko-dic-2.1.1-20180720/NNB.csv
mecab-ko-dic-2.1.1-20180720/Person-actor.csv
mecab-ko-dic-2.1.1-20180720/VV.csv
mecab-ko-dic-2.1.1-20180720/Makefile.in
mecab-ko-dic-2.1.1-20180720/matrix.def
mecab-ko-dic-2.1.1-20180720/EC.csv
mecab-ko-dic-2.1.1-20180720/NNBC.csv
mecab-ko-dic-2.1.1-20180720/clean
mecab-ko-dic-2.1.1-20180720/ChangeLog
mecab-ko-dic-2.1.1-20180720/J.csv
mecab-ko-dic-2.1.1-20180720/.keep
mecab-ko-dic-2.1.1-20180720/feature.def
mecab-ko-dic-2.1.1-20180720/Foreign.csv
mecab-ko-dic-2.1.1-20180720/XPN.csv
mecab-ko-dic-2.1.1-20180720/EP.csv
mecab-ko-dic-2.1.1-20180720/NR.csv
mecab-ko-dic-2.1.1-20180720/left-id.def
mecab-ko-dic-2.1.1-20180720/Place.csv
mecab-ko-dic-2.1.1-20180720/Symbol.csv
mecab-ko-dic-2.1.1-20180720/dicrc
mecab-ko-dic-2.1.1-20180720/NP.csv
mecab-ko-dic-2.1.1-20180720/ETM.csv
mecab-ko-dic-2.1.1-20180720/IC.csv
mecab-ko-dic-2.1.1-20180720/Place-address.csv
mecab-ko-dic-2.1.1-20180720/Group.csv
mecab-ko-dic-2.1.1-20180720/model.def
mecab-ko-dic-2.1.1-20180720/XSN.csv
mecab-ko-dic-2.1.1-20180720/INSTALL
mecab-ko-dic-2.1.1-20180720/rewrite.def
mecab-ko-dic-2.1.1-20180720/Inflect.csv
mecab-ko-dic-2.1.1-20180720/configure.ac
mecab-ko-dic-2.1.1-20180720/NNP.csv
mecab-ko-dic-2.1.1-20180720/CoinedWord.csv
mecab-ko-dic-2.1.1-20180720/XSV.csv
mecab-ko-dic-2.1.1-20180720/pos-id.def
mecab-ko-dic-2.1.1-20180720/Makefile.am
mecab-ko-dic-2.1.1-20180720/unk.def
mecab-ko-dic-2.1.1-20180720/missing
mecab-ko-dic-2.1.1-20180720/VCP.csv
mecab-ko-dic-2.1.1-20180720/install-sh
mecab-ko-dic-2.1.1-20180720/Hanja.csv
mecab-ko-dic-2.1.1-20180720/MAJ.csv
mecab-ko-dic-2.1.1-20180720/XSA.csv
mecab-ko-dic-2.1.1-20180720/Wikipedia.csv
mecab-ko-dic-2.1.1-20180720/tools/
mecab-ko-dic-2.1.1-20180720/tools/add-userdic.sh
mecab-ko-dic-2.1.1-20180720/tools/mecab-bestn.sh
mecab-ko-dic-2.1.1-20180720/tools/convert_for_using_store.sh
mecab-ko-dic-2.1.1-20180720/user-dic/
mecab-ko-dic-2.1.1-20180720/user-dic/nnp.csv
mecab-ko-dic-2.1.1-20180720/user-dic/place.csv
mecab-ko-dic-2.1.1-20180720/user-dic/person.csv
mecab-ko-dic-2.1.1-20180720/user-dic/README.md
mecab-ko-dic-2.1.1-20180720/NorthKorea.csv
mecab-ko-dic-2.1.1-20180720/VX.csv
mecab-ko-dic-2.1.1-20180720/right-id.def
mecab-ko-dic-2.1.1-20180720/VA.csv
mecab-ko-dic-2.1.1-20180720/char.def
mecab-ko-dic-2.1.1-20180720/NEWS
mecab-ko-dic-2.1.1-20180720/MM.csv
mecab-ko-dic-2.1.1-20180720/ETN.csv
mecab-ko-dic-2.1.1-20180720/AUTHORS
mecab-ko-dic-2.1.1-20180720/Person.csv
mecab-ko-dic-2.1.1-20180720/XR.csv
mecab-ko-dic-2.1.1-20180720/VCN.csv
Looking in current directory for macros.
configure.ac:2: warning: AM_INIT_AUTOMAKE: two- and three-arguments forms are deprecated.  For more info, see:
configure.ac:2: http://www.gnu.org/software/automake/manual/automake.html#Modernize-AM_005fINIT_005fAUTOMAKE-invocation
checking for a BSD-compatible install... /usr/bin/install -c
checking whether build environment is sane... yes
/tmp/mecab-ko-dic-2.1.1-20180720/missing: Unknown `--is-lightweight' option
Try `/tmp/mecab-ko-dic-2.1.1-20180720/missing --help' for more information
configure: WARNING: 'missing' script is too old or missing
checking for a thread-safe mkdir -p... /bin/mkdir -p
checking for gawk... no
checking for mawk... mawk
checking whether make sets $(MAKE)... yes
checking whether make supports nested variables... yes
checking for mecab-config... /usr/local/bin/mecab-config
checking that generated files are newer than configure... done
configure: creating ./config.status
config.status: creating Makefile
/usr/local/lib
/sbin/ldconfig.real: /usr/local/lib/python3.7/dist-packages/ideep4py/lib/libmkldnn.so.0 is not a symbolic link

/usr/local/libexec/mecab/mecab-dict-index -d . -o . -f UTF-8 -t UTF-8
reading ./unk.def ... 13
emitting double-array: 100% |###########################################| 
reading ./Inflect.csv ... 44820
reading ./XSN.csv ... 124
reading ./ETM.csv ... 133
reading ./Hanja.csv ... 125750
reading ./Place-address.csv ... 19301
reading ./VX.csv ... 125
reading ./Place.csv ... 30303
reading ./Preanalysis.csv ... 5
reading ./EC.csv ... 2547
reading ./NR.csv ... 482
reading ./MAJ.csv ... 240
reading ./EF.csv ... 1820
reading ./NNP.csv ... 2371
reading ./Symbol.csv ... 16
reading ./J.csv ... 416
reading ./XPN.csv ... 83
reading ./NNB.csv ... 140
reading ./Place-station.csv ... 1145
reading ./IC.csv ... 1305
reading ./MAG.csv ... 14242
reading ./Person-actor.csv ... 99230
reading ./XSA.csv ... 19
reading ./Wikipedia.csv ... 36762
reading ./VV.csv ... 7331
reading ./CoinedWord.csv ... 148
reading ./Person.csv ... 196459
reading ./Foreign.csv ... 11690
reading ./NorthKorea.csv ... 3
reading ./NNG.csv ... 208524
reading ./XR.csv ... 3637
reading ./MM.csv ... 453
reading ./XSV.csv ... 23
reading ./VCN.csv ... 7
reading ./VA.csv ... 2360
reading ./VCP.csv ... 9
reading ./ETN.csv ... 14
reading ./EP.csv ... 51
reading ./NP.csv ... 342
reading ./NNBC.csv ... 677
reading ./Group.csv ... 3176
emitting double-array: 100% |###########################################| 
reading ./matrix.def ... 3822x2693
emitting matrix      : 100% |###########################################| 

done!
echo To enable dictionary, rewrite /usr/local/etc/mecabrc as \"dicdir = /usr/local/lib/mecab/dic/mecab-ko-dic\"
To enable dictionary, rewrite /usr/local/etc/mecabrc as "dicdir = /usr/local/lib/mecab/dic/mecab-ko-dic"
make[1]: Entering directory '/tmp/mecab-ko-dic-2.1.1-20180720'
make[1]: Nothing to be done for 'install-exec-am'.
 /bin/mkdir -p '/usr/local/lib/mecab/dic/mecab-ko-dic'
 /usr/bin/install -c -m 644 model.bin matrix.bin char.bin sys.dic unk.dic left-id.def right-id.def rewrite.def pos-id.def dicrc '/usr/local/lib/mecab/dic/mecab-ko-dic'
make[1]: Leaving directory '/tmp/mecab-ko-dic-2.1.1-20180720'
Install mecab-python
/tmp /tmp/mecab-ko-dic-2.1.1-20180720
Cloning into 'mecab-python-0.996'...
Unpacking objects: 100% (17/17), done.
/tmp/mecab-ko-dic-2.1.1-20180720
Processing /tmp/mecab-python-0.996
[33m  DEPRECATION: A future pip version will change local packages to be built in-place without first copying to a temporary directory. We recommend you use --use-feature=in-tree-build to test your packages with this new behavior before it becomes the default.
   pip 21.3 will remove support for this functionality. You can find discussion regarding this at https://github.com/pypa/pip/issues/7555.[0m
Building wheels for collected packages: mecab-python
  Building wheel for mecab-python (setup.py) ... [?25l[?25hdone
  Created wheel for mecab-python: filename=mecab_python-0.996_ko_0.9.2-cp37-cp37m-linux_x86_64.whl size=141818 sha256=f8224281c456f08d8c3637aca638579c5ffafda3456d907e82f6ed0598c31843
  Stored in directory: /root/.cache/pip/wheels/40/7b/9f/2922869bef86c3354ae7034f7a3647c573ee1997c2dad0290a
[33m  WARNING: Built wheel for mecab-python is invalid: Metadata 1.2 mandates PEP 440 version, but '0.996-ko-0.9.2' is not[0m
Failed to build mecab-python
Installing collected packages: mecab-python
    Running setup.py install for mecab-python ... [?25l[?25hdone
[33m  DEPRECATION: mecab-python was installed using the legacy 'setup.py install' method, because a wheel could not be built for it. A possible replacement is to fix the wheel build issue reported above. You can find discussion regarding this at https://github.com/pypa/pip/issues/8368.[0m
Successfully installed mecab-python-0.996-ko-0.9.2
Done.

#주요 라이브러리 버전 확인

import tensorflow as tf #NLP 모델 생성
import numpy as np #데이터 배열 처리
import matplotlib as plt #시각화
import konlpy

print(tf.__version__)
print(np.__version__)
print(plt.__version__)
print(konlpy.__version__)

2.8.0
1.21.5
3.2.2
0.6.0

!pip install sentencepiece

Collecting sentencepiece
  Downloading sentencepiece-0.1.96-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.2 MB)
[K     |████████████████████████████████| 1.2 MB 4.3 MB/s 
[?25hInstalling collected packages: sentencepiece
Successfully installed sentencepiece-0.1.96

#이 외 라이브러리

import matplotlib.pyplot as plt #시각화 라이브러리 pyplot
import pandas as pd #데이터 배열
import sentencepiece as spm #우리가 사용할 Tokenizer

from google.colab import files 
import io # kolab 데이터 경로 라이브러리

# LSTM 라이브러리

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Embedding, Dense, LSTM

2. GAME

2-1. 데이터 읽어오기

깃허브에서는 NLP 학습을 위해 일반 이용자들이 다양한 말뭉치를 제공해주고 있다.

그 중 여기 에서는 네이버의 영화 리뷰에 대한 말뭉치를 제시한다.

from google.colab import drive
drive.mount('/content/drive')

Mounted at /content/drive

from google.colab import files

uploaded = files.upload()

Upload widget is only available when the cell has been executed in the current browser session. Please rerun this cell to enable.

Saving ratings_train.txt to ratings_train.txt
Saving ratings_test.txt to ratings_test.txt

train_data= pd.read_table(io.StringIO(uploaded['ratings_train.txt'].decode('utf-8')))

train_data.head()

	id	document	label
0	9976970	아 더빙.. 진짜 짜증나네요 목소리	0
1	3819312	흠...포스터보고 초딩영화줄....오버연기조차 가볍지 않구나	1
2	10265843	너무재밓었다그래서보는것을추천한다	0
3	9045019	교도소 이야기구먼 ..솔직히 재미는 없다..평점 조정	0
4	6483659	사이몬페그의 익살스런 연기가 돋보였던 영화!스파이더맨에서 늙어보이기만 했던 커스틴 ...	1

  <script>
    const buttonEl =
      document.querySelector('#df-f70194ae-0c56-4494-bfd2-e4f316891ca1 button.colab-df-convert');
    buttonEl.style.display =
      google.colab.kernel.accessAllowed ? 'block' : 'none';

    async function convertToInteractive(key) {
      const element = document.querySelector('#df-f70194ae-0c56-4494-bfd2-e4f316891ca1');
      const dataTable =
        await google.colab.kernel.invokeFunction('convertToInteractive',
                                                 [key], {});
      if (!dataTable) return;

      const docLinkHtml = 'Like what you see? Visit the ' +
        '<a target="_blank" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'
        + ' to learn more about interactive tables.';
      element.innerHTML = '';
      dataTable['output_type'] = 'display_data';
      await google.colab.output.renderOutput(dataTable, element);
      const docLink = document.createElement('div');
      docLink.innerHTML = docLinkHtml;
      element.appendChild(docLink);
    }
  </script>
</div>

test_data = pd.read_table(io.StringIO(uploaded['ratings_test.txt'].decode('utf-8')))

test_data.head()

	id	document	label
0	6270596	굳 ㅋ	1
1	9274899	GDNTOPCLASSINTHECLUB	0
2	8544678	뭐야 이 평점들은.... 나쁘진 않지만 10점 짜리는 더더욱 아니잖아	0
3	6825595	지루하지는 않은데 완전 막장임... 돈주고 보기에는....	0
4	6723715	3D만 아니었어도 별 다섯 개 줬을텐데.. 왜 3D로 나와서 제 심기를 불편하게 하죠??	0

  <script>
    const buttonEl =
      document.querySelector('#df-a9a4f778-8733-4490-b8d0-1d8b358002c8 button.colab-df-convert');
    buttonEl.style.display =
      google.colab.kernel.accessAllowed ? 'block' : 'none';

    async function convertToInteractive(key) {
      const element = document.querySelector('#df-a9a4f778-8733-4490-b8d0-1d8b358002c8');
      const dataTable =
        await google.colab.kernel.invokeFunction('convertToInteractive',
                                                 [key], {});
      if (!dataTable) return;

      const docLinkHtml = 'Like what you see? Visit the ' +
        '<a target="_blank" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'
        + ' to learn more about interactive tables.';
      element.innerHTML = '';
      dataTable['output_type'] = 'display_data';
      await google.colab.output.renderOutput(dataTable, element);
      const docLink = document.createElement('div');
      docLink.innerHTML = docLinkHtml;
      element.appendChild(docLink);
    }
  </script>
</div>

2-2.데이터 전처리

2-2-1. Tokenizer 생성

우리가 단어사전을 만들기 위해 사용할
데이터는 따로 있다. 더 크고 방대한
한국어 자료를 사용해 더 명료하고 명확한
한국어 단어 사전을 만들 것이다.

corpus_path = '/content/drive/MyDrive/Colab_Notebooks/Aiffel/data/korean-english-park.train.ko'

with open(corpus_path, "r") as f:
    raw = f.read().splitlines()

print("Data Size:", len(raw))

print("Example:")
for sen in raw[0:100][::20]: print(">>", sen)

Data Size: 94123
Example:
>> 개인용 컴퓨터 사용의 상당 부분은 "이것보다 뛰어날 수 있느냐?"
>> 북한의 핵무기 계획을 포기하도록 하려는 압력이 거세지고 있는 가운데, 일본과 북한의 외교관들이 외교 관계를 정상화하려는 회담을 재개했다.
>> "경호 로보트가 침입자나 화재를 탐지하기 위해서 개인적으로, 그리고 전문적으로 사용되고 있습니다."
>> 수자원부 당국은 논란이 되고 있고, 막대한 비용이 드는 이 사업에 대해 내년에 건설을 시작할 계획이다.
>> 또한 근력 운동은 활발하게 걷는 것이나 최소한 20분 동안 뛰는 것과 같은 유산소 활동에서 얻는 운동 효과를 심장과 폐에 주지 않기 때문에, 연구학자들은 근력 운동이 심장에 큰 영향을 미치는지 여부에 대해 논쟁을 해왔다.

이전에 이 자료를 탐색 및 분석했기 때문에 EDA 과정은 생략하고 바로 전처리에 들어가도록 하겠다.

전처리 과정은

중복데이터 삭제

max_len = 150
min_len = 10
padding 처리

cleaned_corpus = list(set(raw)) # 1.중복제거

max_len = 150 # 2번
min_len = 10 #3번

# 길이 조건에 맞는 문장 선택
filtered_corpus = [s for s in cleaned_corpus if (len(s) < max_len) & (len(s) >= min_len)]

# 분포도로 시각화.
sentence_length = np.zeros((max_len), dtype=np.int)

for sen in filtered_corpus:
    sentence_length[len(sen)-1] += 1

plt.bar(range(max_len), sentence_length, width=1.0)
plt.title("Sentence Length Distribution")
plt.show()

/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:10: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
  # Remove the CWD from sys.path while we load stuff.

png

해당 데이터를 SentencePiece 라이브러리를 통해

Tokenize 시킨다.

temp_file = '/content/drive/MyDrive/Colab_Notebooks/Aiffel/data/korean-english-park.train.ko.temp'

vocab_size = 8000

model_type = 'bpe'
with open(temp_file, 'w') as f:
    for row in filtered_corpus:   # 이전 스텝에서 정제했던 corpus를 활용합니다.
        f.write(str(row) + '\n')

spm.SentencePieceTrainer.Train(
    f'--input={temp_file} --model_prefix=korean_spm --vocab_size={vocab_size} --model_type={model_type}'    #korean_spm 에 저장
)
#위 Train에서  --model_type = 'unigram'이 디폴트 적용되어 있습니다. --model_type = 'bpe' 로 옵션을 주어 변경할 수 있습니다.


!ls -l korean_spm*

-rw-r--r-- 1 root root 371954 Mar 23 03:11 korean_spm.model
-rw-r--r-- 1 root root 117142 Mar 23 03:11 korean_spm.vocab

위 코드를 실행하면 정상적으로 SentencePiece 모델 학습이 완료된다. 이후에는

korean_spm.model 파일과
korean_spm.vocab vocabulary 파일이 root에 생성된다.

다음은 이렇게 학습한 model 데이터의 활용이다.

s = spm.SentencePieceProcessor()
s.Load('korean_spm.model')

# SentencePiece를 활용한 sentence -> encoding
tokensIDs = s.EncodeAsIds('아버지가방에들어가신다.')
print(tokensIDs)

# SentencePiece를 활용한 sentence -> encoded pieces
print(s.SampleEncodeAsPieces('아버지가방에들어가신다.',1, 0.0))

# SentencePiece를 활용한 encoding -> sentence 복원
print(s.DecodeIds(tokensIDs))

[1243, 11, 302, 7, 3608, 11, 287, 38, 3]
['▁아버지', '가', '방', '에', '들어', '가', '신', '다', '.']
아버지가방에들어가신다.

EncodeAsIds = 글자를 벡터화 리스트로 반환

SampleEncodeAsPieces = 글자를 나눈 방법을 리스트로 반환

DecodeIds = 벡터화 리스트를 글자로 변환

해당 SentencePiece 를 영화 리뷰 데이터로 Tokenize 하기 위한 코드는 다음과 같다.

# s = spm.SentencePieceProcessor()
def sp_tokenize(s, corpus):

    tensor = []

    for sen in corpus:
        tensor.append(s.EncodeAsIds(sen)) # 문장이 벡터화된 리스트로 변환되어 tensor 에 입력

    with open("./korean_spm.vocab", 'r') as f:
        vocab = f.readlines()

    word_index = {}
    index_word = {}

    for idx, line in enumerate(vocab):
        word = line.split("\t")[0]

        word_index.update({idx:word}) #word_to_index 저장
        index_word.update({word:idx}) #index_to)word 저장

    tensor = tf.keras.preprocessing.sequence.pad_sequences(tensor, padding='post') # tensor 문장 중 가장 긴 문장을 기준으로 패딩

    return tensor, word_index, index_word

2-2-2. 학습 데이터 전처리

이제는 학습시키기 위해 영화리뷰 데이터를 전처리하도록 한다.

오타 등 필요없는 문자 제거
중복값 제거
결측값 제거


def preprocessing(train_data, test_data):
    train_data['document'] = train_data['document'].str.replace("[^ㄱ-ㅎㅏ-ㅣ가-힣 ]","")
    test_data['document'] = test_data['document'].str.replace("[^ㄱ-ㅎㅏ-ㅣ가-힣 ]","") #한글이 아닌 문자를 공백으로 변환

    train_data['document'] = train_data['document'].str.replace('^ +', "")
    train_data['document'].replace('', np.nan, inplace=True)

    test_data['document'] = test_data['document'].str.replace('^ +', "") #긴 공백을 공백으로 변경
    test_data['document'].replace('', np.nan, inplace=True)  # 공백을 NaN 으로 변경

    train_data.drop_duplicates(subset=['document'], inplace=True) 
    test_data.drop_duplicates(subset=['document'], inplace=True) #중복값 제거

    train_data = train_data.dropna(how = 'any') 
    test_data = test_data.dropna(how = 'any') #결측치 제거

    return train_data, test_data
  
train, test = preprocessing(train_data, test_data)

train.head()

/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:3: FutureWarning: The default value of regex will change from True to False in a future version.
  This is separate from the ipykernel package so we can avoid doing imports until
/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:4: FutureWarning: The default value of regex will change from True to False in a future version.
  after removing the cwd from sys.path.
/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: FutureWarning: The default value of regex will change from True to False in a future version.
  
/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:9: FutureWarning: The default value of regex will change from True to False in a future version.
  if __name__ == '__main__':

	id	document	label
0	9976970	아 더빙 진짜 짜증나네요 목소리	0
1	3819312	흠 포스터보고 초딩영화줄 오버연기조차 가볍지 않구나	1
2	10265843	너무재밓었다그래서보는것을추천한다	0
3	9045019	교도소 이야기구먼 솔직히 재미는 없다 평점 조정	0
4	6483659	사이몬페그의 익살스런 연기가 돋보였던 영화 스파이더맨에서 늙어보이기만 했던 커스틴 ...	1

  <script>
    const buttonEl =
      document.querySelector('#df-c3f4c367-1db1-48cb-8ade-ac1bb06e3012 button.colab-df-convert');
    buttonEl.style.display =
      google.colab.kernel.accessAllowed ? 'block' : 'none';

    async function convertToInteractive(key) {
      const element = document.querySelector('#df-c3f4c367-1db1-48cb-8ade-ac1bb06e3012');
      const dataTable =
        await google.colab.kernel.invokeFunction('convertToInteractive',
                                                 [key], {});
      if (!dataTable) return;

      const docLinkHtml = 'Like what you see? Visit the ' +
        '<a target="_blank" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'
        + ' to learn more about interactive tables.';
      element.innerHTML = '';
      dataTable['output_type'] = 'display_data';
      await google.colab.output.renderOutput(dataTable, element);
      const docLink = document.createElement('div');
      docLink.innerHTML = docLinkHtml;
      element.appendChild(docLink);
    }
  </script>
</div>

print(train.isnull().values.any())
print(test.isnull().values.any())

False
False

print(len(train), len(test))

144975 48751

길이 분포를 확인하고 적절한 크기로 padding 한다.

# 데이터 길이 분포 확인하기

min_len = 999
max_len = 0
sum_len = 0

for sen in train['document']:
    length = len(sen)
    
    # 문장 최소 길이 찾기
    if min_len > length: 
        min_len = length
    
    # 문장 최대 길이 찾기
    if max_len < length: 
        max_len = length
        
    # 전체 문장총 길이
    sum_len += length

print("문장의 최단 길이:", min_len)
print("문장의 최장 길이:", max_len)
print("문장의 평균 길이:", sum_len // len(train_data))

# 전체 길이만큼 0벡터 ==> 길이에 따른 문장의 수를 저장하기 위해 먼저 0으로 이루어진 리스트를 만든다!!
sentence_length = np.zeros((max_len), dtype=np.int)
print('')


for sen in train['document']:
    sentence_length[len(sen)-1] += 1 # 0으로 이루어진 벡터에 문장 count를 더한 뒤 넣는다.

plt.bar(range(max_len), sentence_length, width=1.0) # 너비는 1.0씩 늘어나도록 설정
plt.title("문장 길이별 분포")
plt.show()

문장의 최단 길이: 1
문장의 최장 길이: 145
문장의 평균 길이: 36



/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:26: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 47928 missing from current font.
  font.set_text(s, 0.0, flags=flags)
/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 51109 missing from current font.
  font.set_text(s, 0.0, flags=flags)
/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 44600 missing from current font.
  font.set_text(s, 0.0, flags=flags)
/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 51060 missing from current font.
  font.set_text(s, 0.0, flags=flags)
/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 48324 missing from current font.
  font.set_text(s, 0.0, flags=flags)
/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 48516 missing from current font.
  font.set_text(s, 0.0, flags=flags)
/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 54252 missing from current font.
  font.set_text(s, 0.0, flags=flags)
/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 47928 missing from current font.
  font.set_text(s, 0, flags=flags)
/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 51109 missing from current font.
  font.set_text(s, 0, flags=flags)
/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 44600 missing from current font.
  font.set_text(s, 0, flags=flags)
/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 51060 missing from current font.
  font.set_text(s, 0, flags=flags)
/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 48324 missing from current font.
  font.set_text(s, 0, flags=flags)
/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 48516 missing from current font.
  font.set_text(s, 0, flags=flags)
/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 54252 missing from current font.
  font.set_text(s, 0, flags=flags)

png

min_list = [s for s in train['document'] if (len(s) <= 2)]

min_list[:10]

['최고', '졸작', '대박', '아', '점', '버려', '망함', '굳굳', '안습', '잼']

데이터 분포를 고려하여 45이하 데이터를 사용하도록 하겠다.

원래는 2글자 이하 데이터도 제거하려 하였으나,
위 데이터를 보고 충분히 유의미하다고 느꼈다.

train_list = [s for s in train['document'] if (len(s) <= 35)]
test_list = [s for s in test['document'] if (len(s) <= 35)]

train_df = pd.DataFrame(train_list)
test_df = pd.DataFrame(test_list)

# 길이 40이하인 데이터를 기존 데이터와 병합.

new_train_df = pd.merge(train_data, train_df, how='inner', left_on='document', right_on=0)
new_test_df = pd.merge(test_data, test_df, how='inner', left_on='document', right_on=0)

train_data = new_train_df[['id', 'document', 'label']]
test_data = new_test_df[['id', 'document', 'label']]

train_data

	id	document	label
0	9976970	아 더빙 진짜 짜증나네요 목소리	0
1	3819312	흠 포스터보고 초딩영화줄 오버연기조차 가볍지 않구나	1
2	10265843	너무재밓었다그래서보는것을추천한다	0
3	9045019	교도소 이야기구먼 솔직히 재미는 없다 평점 조정	0
4	7797314	원작의 긴장감을 제대로 살려내지못했다	0
...	...	...	...
93794	6222902	인간이 문제지 소는 뭔죄인가	0
93795	8549745	평점이 너무 낮아서	1
93796	9311800	이게 뭐요 한국인은 거들먹거리고 필리핀 혼혈은 착하다	0
93797	2376369	청춘 영화의 최고봉 방황과 우울했던 날들의 자화상	1
93798	9619869	한국 영화 최초로 수간하는 내용이 담긴 영화	0

93799 rows × 3 columns

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    async function convertToInteractive(key) {
      const element = document.querySelector('#df-6b82ed66-34b6-44e2-bfa5-f1150a3e85f3');
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                                                 [key], {});
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      const docLinkHtml = 'Like what you see? Visit the ' +
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      const docLink = document.createElement('div');
      docLink.innerHTML = docLinkHtml;
      element.appendChild(docLink);
    }
  </script>
</div>

print("훈련데이터 : ",len(train_data))
print("테스트데이터 : ",len(test_data))

훈련데이터 :  93799
테스트데이터 :  31631

텍스트 정제 후엔 훈련된 tokenizer 를 이용해
문장을 vector 화 시킨다.

# tensor 화 시킨다
X_train,X_train_word_index, X_train_index_word = sp_tokenize(s, train_data['document'])
X_test,X_test_word_index, X_test_index_word = sp_tokenize(s, test_data['document'])

# label 데이터 분리

y_train = np.array(list(train_data['label']))
y_test = np.array(list(test_data['label']))

for i in range(3):
  print(list(train['document'])[i])
  print(X_train[i])
  print()

아 더빙   진짜 짜증나네요 목소리
[ 141  106 2611  912 4856    4 4856  752   69  554  514 2648    0    0
    0    0    0    0    0    0    0    0    0    0    0    0    0    0
    0    0    0    0    0    0    0]

흠   포스터보고 초딩영화줄    오버연기조차 가볍지 않구나
[   4 7663  490 1756  146   14  439 3174 2766 1791  222  408  381   41
 4189    4   11 7570   29 1311  230   69    0    0    0    0    0    0
    0    0    0    0    0    0    0]

너무재밓었다그래서보는것을추천한다
[1328  437    0  266  254  591   95  146   10 1960    5 1011  703  249
    0    0    0    0    0    0    0    0    0    0    0    0    0    0
    0    0    0    0    0    0    0]

print("학습데이터 :",len(X_train))
print("타겟데이터 :",len(y_train))

학습데이터 : 93799
타겟데이터 : 93799

93799 개의 텍스트가 35개의 숫자 데이터로 정의되었다.
padding 까지 씌어졌음을 알 수 있다.

2-2-3. val 분리

from sklearn.model_selection import train_test_split
train_input, val_input, train_target, val_target = train_test_split(X_train, y_train, test_size=0.2, random_state=64)

print(len(train_input))
print(len(val_input))

75039
18760

결과적으로, 총 데이터는 다음과 같다.

train_input : 학습시킬 문장의 tensor
train_target :학습시킬 문장의 label

val_input : 검증할 문장의 tensor
val_target : 검증할 문장의 label

X_test : 측정할 문장의 tensor
y_test : 측정할 문장의 label

2-3.모델 학습

훈련된 데이터를 통해 다양한 학습을 시킬 것이다.

RNN 모델 사용

vocab_size = 8000  # 어휘 사전의 크기입니다
word_vector_dim = 100  # 단어 하나를 표현하는 임베딩 벡터의 차원수입니다. 

# RNN 방식

model = tf.keras.Sequential()
model.add(tf.keras.layers.Embedding(vocab_size, word_vector_dim, input_shape=(None,)))
model.add(tf.keras.layers.LSTM(8))   
model.add(tf.keras.layers.Dense(8, activation='relu'))
model.add(tf.keras.layers.Dense(1, activation='sigmoid'))  # 최종 출력은 긍정/부정을 나타내는 1dim 입니다.

model.summary()

Model: "sequential"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 embedding (Embedding)       (None, None, 100)         800000    
                                                                 
 lstm (LSTM)                 (None, 8)                 3488      
                                                                 
 dense (Dense)               (None, 8)                 72        
                                                                 
 dense_1 (Dense)             (None, 1)                 9         
                                                                 
=================================================================
Total params: 803,569
Trainable params: 803,569
Non-trainable params: 0
_________________________________________________________________

model.compile(optimizer='adam',
              loss='binary_crossentropy',
              metrics=['accuracy'])
              
epochs=20  # 몇 epoch를 훈련하면 좋을지 결과를 보면서 바꾸어 봅시다. 

history = model.fit(train_input,
                    train_target,
                    epochs=epochs,
                    batch_size=64,
                    validation_data=(val_input, val_target),
                    verbose=1)

Epoch 1/20
1173/1173 [==============================] - 10s 7ms/step - loss: 0.3819 - accuracy: 0.8278 - val_loss: 0.4099 - val_accuracy: 0.8082
Epoch 2/20
1173/1173 [==============================] - 7s 6ms/step - loss: 0.3558 - accuracy: 0.8400 - val_loss: 0.4398 - val_accuracy: 0.8047
Epoch 3/20
1173/1173 [==============================] - 7s 6ms/step - loss: 0.3288 - accuracy: 0.8556 - val_loss: 0.4236 - val_accuracy: 0.8152
Epoch 4/20
1173/1173 [==============================] - 7s 6ms/step - loss: 0.3030 - accuracy: 0.8692 - val_loss: 0.4304 - val_accuracy: 0.8148
Epoch 5/20
1173/1173 [==============================] - 7s 6ms/step - loss: 0.2802 - accuracy: 0.8822 - val_loss: 0.4366 - val_accuracy: 0.8165
Epoch 6/20
1173/1173 [==============================] - 7s 6ms/step - loss: 0.2577 - accuracy: 0.8941 - val_loss: 0.4608 - val_accuracy: 0.8129
Epoch 7/20
1173/1173 [==============================] - 7s 6ms/step - loss: 0.2365 - accuracy: 0.9049 - val_loss: 0.4742 - val_accuracy: 0.8113
Epoch 8/20
1173/1173 [==============================] - 7s 6ms/step - loss: 0.2172 - accuracy: 0.9141 - val_loss: 0.4833 - val_accuracy: 0.8073
Epoch 9/20
1173/1173 [==============================] - 7s 6ms/step - loss: 0.2026 - accuracy: 0.9217 - val_loss: 0.5415 - val_accuracy: 0.8120
Epoch 10/20
1173/1173 [==============================] - 7s 6ms/step - loss: 0.1873 - accuracy: 0.9290 - val_loss: 0.5531 - val_accuracy: 0.8073
Epoch 11/20
1173/1173 [==============================] - 7s 6ms/step - loss: 0.1758 - accuracy: 0.9345 - val_loss: 0.5657 - val_accuracy: 0.8078
Epoch 12/20
1173/1173 [==============================] - 7s 6ms/step - loss: 0.1642 - accuracy: 0.9397 - val_loss: 0.5720 - val_accuracy: 0.8062
Epoch 13/20
1173/1173 [==============================] - 7s 6ms/step - loss: 0.1560 - accuracy: 0.9425 - val_loss: 0.5678 - val_accuracy: 0.8077
Epoch 14/20
1173/1173 [==============================] - 7s 6ms/step - loss: 0.1477 - accuracy: 0.9469 - val_loss: 0.6246 - val_accuracy: 0.8075
Epoch 15/20
1173/1173 [==============================] - 8s 6ms/step - loss: 0.1410 - accuracy: 0.9496 - val_loss: 0.6327 - val_accuracy: 0.8041
Epoch 16/20
1173/1173 [==============================] - 8s 7ms/step - loss: 0.1337 - accuracy: 0.9528 - val_loss: 0.6378 - val_accuracy: 0.8033
Epoch 17/20
1173/1173 [==============================] - 7s 6ms/step - loss: 0.1293 - accuracy: 0.9552 - val_loss: 0.6658 - val_accuracy: 0.8033
Epoch 18/20
1173/1173 [==============================] - 7s 6ms/step - loss: 0.1234 - accuracy: 0.9578 - val_loss: 0.6399 - val_accuracy: 0.8010
Epoch 19/20
1173/1173 [==============================] - 7s 6ms/step - loss: 0.1176 - accuracy: 0.9605 - val_loss: 0.7190 - val_accuracy: 0.8000
Epoch 20/20
1173/1173 [==============================] - 7s 6ms/step - loss: 0.1143 - accuracy: 0.9617 - val_loss: 0.6888 - val_accuracy: 0.8025

LSTM

다음으로는 LSTM 모델을 이용해 학습을 진행했다.
손실함수와 최적화함수는 tensor flow 권장 사항을 따랐다.

# LSTM

model = Sequential()
model.add(Embedding(vocab_size, 100)) # 임베딩 레이어
model.add(LSTM(128)) # LSTM 레이어
model.add(Dense(1, activation='sigmoid')) # 출력 레이어

model.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=['acc'])

epochs=20  # 몇 epoch를 훈련하면 좋을지 결과를 보면서 바꾸어 봅시다. 

history = model.fit(train_input,
                    train_target,
                    epochs=epochs,
                    batch_size=64,
                    validation_data=(val_input, val_target),
                    verbose=1)

Epoch 1/20
1173/1173 [==============================] - 11s 8ms/step - loss: 0.4747 - acc: 0.7743 - val_loss: 0.4453 - val_acc: 0.8026
Epoch 2/20
1173/1173 [==============================] - 8s 7ms/step - loss: 0.4123 - acc: 0.8123 - val_loss: 0.4038 - val_acc: 0.8118
Epoch 3/20
1173/1173 [==============================] - 8s 7ms/step - loss: 0.3873 - acc: 0.8236 - val_loss: 0.4068 - val_acc: 0.8171
Epoch 4/20
1173/1173 [==============================] - 8s 7ms/step - loss: 0.3695 - acc: 0.8326 - val_loss: 0.3877 - val_acc: 0.8201
Epoch 5/20
1173/1173 [==============================] - 8s 7ms/step - loss: 0.3545 - acc: 0.8420 - val_loss: 0.3878 - val_acc: 0.8239
Epoch 6/20
1173/1173 [==============================] - 8s 7ms/step - loss: 0.3404 - acc: 0.8503 - val_loss: 0.3892 - val_acc: 0.8233
Epoch 7/20
1173/1173 [==============================] - 8s 7ms/step - loss: 0.3272 - acc: 0.8582 - val_loss: 0.4038 - val_acc: 0.8201
Epoch 8/20
1173/1173 [==============================] - 8s 7ms/step - loss: 0.3160 - acc: 0.8649 - val_loss: 0.3799 - val_acc: 0.8292
Epoch 9/20
1173/1173 [==============================] - 8s 7ms/step - loss: 0.3042 - acc: 0.8701 - val_loss: 0.3780 - val_acc: 0.8266
Epoch 10/20
1173/1173 [==============================] - 8s 7ms/step - loss: 0.2929 - acc: 0.8774 - val_loss: 0.3885 - val_acc: 0.8285
Epoch 11/20
1173/1173 [==============================] - 8s 7ms/step - loss: 0.2812 - acc: 0.8827 - val_loss: 0.4003 - val_acc: 0.8289
Epoch 12/20
1173/1173 [==============================] - 8s 7ms/step - loss: 0.2688 - acc: 0.8889 - val_loss: 0.4134 - val_acc: 0.8292
Epoch 13/20
1173/1173 [==============================] - 8s 7ms/step - loss: 0.2533 - acc: 0.8970 - val_loss: 0.4477 - val_acc: 0.8150
Epoch 14/20
1173/1173 [==============================] - 8s 7ms/step - loss: 0.2401 - acc: 0.9041 - val_loss: 0.4366 - val_acc: 0.8217
Epoch 15/20
1173/1173 [==============================] - 8s 7ms/step - loss: 0.2265 - acc: 0.9101 - val_loss: 0.4455 - val_acc: 0.8181
Epoch 16/20
1173/1173 [==============================] - 8s 7ms/step - loss: 0.2133 - acc: 0.9178 - val_loss: 0.4690 - val_acc: 0.8197
Epoch 17/20
1173/1173 [==============================] - 9s 7ms/step - loss: 0.2002 - acc: 0.9237 - val_loss: 0.4750 - val_acc: 0.8135
Epoch 18/20
1173/1173 [==============================] - 8s 7ms/step - loss: 0.1866 - acc: 0.9295 - val_loss: 0.5083 - val_acc: 0.8131
Epoch 19/20
1173/1173 [==============================] - 8s 7ms/step - loss: 0.1741 - acc: 0.9356 - val_loss: 0.5606 - val_acc: 0.8115
Epoch 20/20
1173/1173 [==============================] - 8s 7ms/step - loss: 0.1614 - acc: 0.9406 - val_loss: 0.5704 - val_acc: 0.8082

2-4.데이터 평가

종합적으로 5번의 시도를 했으며,
batch size = 64
epochs = 20 으로 통일하고

tokenizer 와 학습 모델에 변화를 주었다.

영화리뷰 데이터로 Sentencepiece Tokenize : RNN

한국어 corpus 데이터로 Sentencepiece Tokenize : RNN (임베딩 차원 20)
임베딩 차원 100
LSTM 모델 설정 (임베딩 차원 100)
SentencePiece 모델 타입 BPE 로 변경

# tokenizer 를 영화 리뷰 데이터로 제작

result = model.evaluate(X_test,  y_test, verbose=2)
print(result)

1524/1524 - 6s - loss: 0.6932 - accuracy: 0.4982 - 6s/epoch - 4ms/step
[0.6932055950164795, 0.4981846511363983]

# 한국어 corpus 사용

result = model.evaluate(X_test,  y_test, verbose=2)
print(result)

1524/1524 - 6s - loss: 0.6933 - acc: 0.4982 - 6s/epoch - 4ms/step
[0.6933001279830933, 0.4981846511363983]

# 임베딩 차원 100

results = model.evaluate(X_test,  y_test, verbose=2)

print(results)

989/989 - 3s - loss: 0.6922 - accuracy: 0.8013 - 3s/epoch - 3ms/step
[0.6921951770782471, 0.8012709021568298]

# LSTM 모델 사용
results = model.evaluate(X_test,  y_test, verbose=2)

print(results)

989/989 - 4s - loss: 0.5414 - acc: 0.8038 - 4s/epoch - 4ms/step
[0.5413879156112671, 0.8038316965103149]

# SentencePiece 모델 타입 변경 BPE

results = model.evaluate(X_test,  y_test, verbose=2)

print(results)

989/989 - 5s - loss: 0.5565 - acc: 0.8088 - 5s/epoch - 5ms/step
[0.5565109848976135, 0.8087635636329651]

결과적으로

한국어 corpus 를 이용해 임베딩 차원 100 으로 두고 LSTM 모델을 사용한 BPE 방식 Tokenizer 가 가장 큰 결과를 얻었다

Accuracy = 0.808%

다양한 시도를 통해 알 수 있었던 점

임베딩차원의 크기가 정확도에 큰 영향을 미친다.
RNN 보다 LSTM 이 Loss 를 대폭 줄인다.

그 외의 차이는 미미한 것으로 보인다.
하지만 모델의 특성상 batch_size 나 임베딩차원, 전처리 방식에
변화를 주었을 때 또 다른 영향을 끼칠 수도 있음이 나의 결론이다.

3. POTG

3-1. 소감

”👍 NLP 뿌시기 1단계 통과!”

드디어 Tokenizer 가 무엇인지 감을 잡은 것 같습니다.
Tokenize 를 하기 위해선 tokenizer 에 들어갈 데이터도 전처리를 해주어야 합니다. 그리고 tokenizer 에 저장되어 있는 word_to_index 데이터로

저희가 학습할 데이터를 tensor 화 해줍니다.

tensor 라 함은 우리의 문장 데이터가 숫자로 변환된 리스트 데이터를 의미합니다.

tensor 는 tokenizer 텍스트 데이터를 토대로 어떤 기준을 통해 글을 나눠야 할지 판별해주는 역할을 합니다.

3-2. 어려웠던 점과 극복방안

-1. 불용어 제거

morphs 에서는 불용어를 제거하는데 (왜냐하면 그냥 단어를 분리만 해서 리턴해주니까) Sentencepiece 는 모든 단어를 이미 tensor 화 해서 주기 때문에
불용어를 제거할 수가 없다.

그렇다면 불용어를 제거하지 않아도 되는가?

어차피 SentencePiece Train 을 하기 위해서 문장을 전처리 후에 넣게 되는데, 그러므로 불용어를 처리하고 넣을 수 잇다.

-2 전처리 ” 데이터
결측치를 제거해도 min_len 이 0 이 나오는 상황이 생겼는데, ” 데이터가 남아있기 때문이다. 때문에 판다스 메서드를 통해 이러한 부분을 제거해 주었다.

-3. 오류
Another metric with the same name already exists. 라는 오류문이 tp_tokenize 과정에서 자꾸 났는데, stack over flow 검색 결과 keras 가 두 개 설치되어 있어 나타나는 현상이라고 한다. tensorflow 버전 문제라고 판단해 keras 버전을 2.6에서 2.8로 변경하고서는 오류가 나타나지 않았다.

-4. 차이가 나는 indexing

어떤 부분에선가 train 데이터와 test 데이터가 동일하게 전처리되지 않았다. 다시 한번 꼼꼼히 진행하면서(한 코드씩 비교해가면서) 문제를 해결할 수 있었다.

-5. 결과가 나오지 않는 모델

다양한 모델 변경을 해가면서 이전 test 타입과 이후 test 타입에 차이가 있어 나타난 현상이었다. 새로운 변수명을 주어 해결할 수 있었다.

3-3. 추후

NLP 학습의 전체적인 큰 그림을 그려보고 싶다.

tokenize 를 사용함과 사용하지 않을 때 padding 이나 임베딩에 차이가 있다.
tokenizer 를 통해 그런 부분이 해소되기 때문이다. 그래서 처음에
왜 padding 을 두번 해주는지 등 이해가 안되는 부분이 많았다.

전체적인 그림을 그릴 수 있다면 이런 문제가 해소될 것이다.

Contexts
1-1. 오늘의 Exp와 Rubric
1-2. 사용하는 라이브러리
2-1. 데이터 읽어오기
2-2.데이터 전처리
2-3.모델 학습
2-4.데이터 평가
3-1. 소감
- - ”👍 NLP 뿌시기 1단계 통과!”
3-2. 어려웠던 점과 극복방안
3-3. 추후

NLP 에서 데이터 전처리 및 토큰화란

NLP 전처리 필수 Vecorize