A guide to creating a chatbot with Rasa stack and Python.
대화형 AI 시스템은 인간생태계의 필수불가결한 요소가 되었다. 이미 잘 알려져있듯이 Apple의 Siri, Amazon의 Alexa and Microsoft의 Cortana 등이 있다. r머신 러닝 기반의 AI 어시스턴트인 Rasa를 만들어보자.
Objective
본 포스팅에서는 사용자의 기분을 체크해서 힘을 북돋우는 적절한 액션을 취하는 ‘Zoe’ 라고 불리는 챗봇을 만들어 본다. 그리고 다음 포스팅에서는 이를 Slack에 배포해본다. 다음 스크린샷과 같이 구현해볼 것이다.
https://cdn-images-1.medium.com/max/800/1*YCBEujcXGf4MFOT02my5hQ.png
Requirements
이를 구현하기위해 Rasa Stack 과 언어모델을 설치해야한다. 언어모델은 입력되는 텍스트 메시지에서 필요한 정보를 뽑아내기 위해 사용된다. SpaCy 언어모델을 사용할 것이다.
Rasa Stack
Rasa Stack 은 대화 AI를 위한 오픈소스 머신러닝툴이다. 봇은 샘플 대화 기반으로 훈련된 머신러닝모델에 기반한다. 다음과 같이 2개의 프레임워크로 구성된다.
<source> https://cdn-images-1.medium.com/max/800/1*4Wu5EUB9yJ2AUfoPGMSLXw.png
Rasa NLU 의도분류와 엔티티 추출를 가진 자연어 이해를 위한 라이브러리로 챗봇이 사용자가 어떤 말을 하고 있는지 이해하는데 사용된다.
Rasa Core 머신러닝 기반의 대화 관리 챗봇 프레임워크로 NLU의 인풋, 대화 히스토리, 그리고 훈련 데이터에 기반하여 최선의 다음 액션을 예측하는데 사용된다.
Rasa 와 관련된 내용은 다음 공식문서에서 자세히 살펴볼 수 있다.
Installations
주피터 노트북을 이용하여 이하 코드 실행을 진행할 것이다. 이에 앞서 기존에 관련된 라이브러리를 설치한 적이 있다면 해당되는 라이브러리를 완전히 제거하고 새롭게 진행하도록 하자. 본 예제에서는 주요하게 3개의 라이브러리가 사용되는데, 버전이 맞지 않는 경우 제대로 예제 실행을 할 수가 없다. 예를 들어 다음과 같이 말이다. 따라서 반드시 3개의 라이브러리를 제거하고 다시 처음부터 진행하기를 권한다.
import sys
python = sys.executable
# In your environment run:
!{python} -m pip install -U rasa_core==0.9.6 rasa_nlu[spacy];
rasa-core 0.9.6 has requirement rasa-nlu~=0.12.0, but you'll have rasa-nlu 0.14.3 which is incompatible.
rasa-nlu 0.14.3 has requirement scikit-learn=0.20.2, but you'll have scikit-learn 0.19.2 which is incompatible.
1) 기존 설치된 라이브러리 제거
rasa_core 제거
(tfKeras) founder@hilbert:~/rasa$ pip show rasa_core
Name: rasa-core
Version: 0.13.2
Summary: Machine learning based dialogue engine for conversational software.
Home-page: https://rasa.com
Author: Rasa Technologies GmbH
Author-email: hi@rasa.com
License: Apache 2.0
Location: /home/founder/anaconda3/envs/tfKeras/lib/python3.6/site-packages
Requires: flask-jwt-simple, scikit-learn, typing, redis, webexteamssdk, twilio, flask-cors, tqdm, keras-preprocessing, pytz, jsonpickle, gevent, coloredlogs, keras-applications, numpy, tensorflow, slackclient, python-telegram-bot, apscheduler, pika, terminaltables, colorclass, jsonschema, python-dateutil, rasa-core-sdk, ruamel.yaml, pymongo, fakeredis, pydot, questionary, flask, pykwalify, rocketchat-API, packaging, requests, fbmessenger, colorhash, scipy, python-socketio, networkx, rasa-nlu, mattermostwrapper
Required-by:
(tfKeras) founder@hilbert:~/rasa$ pip uninstall rasa_core
Uninstalling rasa-core-0.13.2:
Would remove:
/home/founder/anaconda3/envs/tfKeras/lib/python3.6/site-packages/examples/*
/home/founder/anaconda3/envs/tfKeras/lib/python3.6/site-packages/rasa_core-0.13.2.dist-info/*
/home/founder/anaconda3/envs/tfKeras/lib/python3.6/site-packages/rasa_core/*
Proceed (y/n)? y
Successfully uninstalled rasa-core-0.13.2
(tfKeras) founder@hilbert:~/rasa$ pip show rasa_core
rasa_nlu 제거
(tfKeras) founder@hilbert:~/rasa$ pip show rasa_nlu
Name: rasa-nlu
Version: 0.14.3
Summary: Rasa NLU a natural language parser for bots
Home-page: https://rasa.com
Author: Rasa Technologies GmbH
Author-email: hi@rasa.com
License: Apache 2.0
Location: /home/founder/anaconda3/envs/tfKeras/lib/python3.6/site-packages
Requires: requests, jsonschema, six, boto3, packaging, typing, simplejson, coloredlogs, scikit-learn, matplotlib, tqdm, future, klein, ruamel.yaml, cloudpickle, numpy, gevent
Required-by:
(tfKeras) founder@hilbert:~/rasa$ pip uninstall rasa_nlu
Uninstalling rasa-nlu-0.14.3:
Would remove:
/home/founder/anaconda3/envs/tfKeras/lib/python3.6/site-packages/rasa_nlu-0.14.3.dist-info/*
/home/founder/anaconda3/envs/tfKeras/lib/python3.6/site-packages/rasa_nlu/*
/home/founder/anaconda3/envs/tfKeras/lib/python3.6/site-packages/tests/base/*
/home/founder/anaconda3/envs/tfKeras/lib/python3.6/site-packages/tests/training/*
Proceed (y/n)? y
Successfully uninstalled rasa-nlu-0.14.3
(tfKeras) founder@hilbert:~/rasa$ pip uninstall rasa_nlu
Skipping rasa-nlu as it is not installed.
(tfKeras) founder@hilbert:~/rasa$ pip show rasa_nlu
spacy 제거
(tfKeras) founder@hilbert:~/rasa$ pip show spacy
Name: spacy
Version: 2.0.18
Summary: Industrial-strength Natural Language Processing (NLP) with Python and Cython
Home-page: https://spacy.io
Author: Explosion AI
Author-email: contact@explosion.ai
License: MIT
Location: /home/founder/anaconda3/envs/tfKeras/lib/python3.6/site-packages
Requires: preshed, ujson, dill, numpy, murmurhash, thinc, plac, requests, regex, cymem
Required-by: en-core-web-md
(tfKeras) founder@hilbert:~/rasa$ pip uninstall spacy
Uninstalling spacy-2.0.18:
Would remove:
/home/founder/anaconda3/envs/tfKeras/bin/spacy
/home/founder/anaconda3/envs/tfKeras/lib/python3.6/site-packages/spacy-2.0.18.dist-info/*
/home/founder/anaconda3/envs/tfKeras/lib/python3.6/site-packages/spacy/*
Proceed (y/n)? y
Successfully uninstalled spacy-2.0.18
(tfKeras) founder@hilbert:~/rasa$ pip show spacy
(tfKeras) founder@hilbert:~/rasa$
그리고 노트북 파일과 체크포인트 디렉토리만 남겨두고, rasa 디렉토리 아래 파일 및 하위 디렉토리 삭제한다.
2) Starting Jupyter Notebook with necessary imports
그러면 주피터 노트북을 실행하여 필수 라이브러리를 임포트하자.
%matplotlib inline
import logging, io, json, warnings
logging.basicConfig(level="INFO")
warnings.filterwarnings('ignore')
3) Primary Installations
Rasa NLU, Rasa Core, spaCy 언어모델이 필요하다. 설치해보자. 본 튜토리얼에서는 사용하고 있는 Rasa Core 는 최신 버전이 아니다. 최신 버전 사용하여 본 튜토리얼을 진행할 경우 에러를 발생시킬 수 있으므로 반드시 위와 같이 버전을 지정하여 설치하도록 한다.
import sys
python = sys.executable
# In your environment run:
!{python} -m pip install -U rasa_core==0.9.6 rasa_nlu[spacy]==0.12.0;
Collecting rasa_core==0.9.6
Collecting rasa_nlu[spacy]==0.12.0
Requirement already satisfied, skipping upgrade: six~=1.0 in /home/founder/.local/lib/python3.6/site-packages (from rasa_core==0.9.6) (1.12.0)
Requirement already satisfied, skipping upgrade: flask~=1.0 in /home/founder/anaconda3/envs/tfKeras/lib/python3.6/site-packages (from rasa_core==0.9.6) (1.0.2)
Requirement already satisfied, skipping upgrade: h5py~=2.0 in /home/founder/.local/lib/python3.6/site-packages (from rasa_core==0.9.6) (2.8.0)
Requirement already satisfied, skipping upgrade: scikit-learn~=0.19.0 in /home/founder/anaconda3/envs/tfKeras/lib/python3.6/site-packages (from rasa_core==0.9.6) (0.19.2)
Requirement already satisfied, skipping upgrade: flask-cors~=3.0 in /home/founder/anaconda3/envs/tfKeras/lib/python3.6/site-packages (from rasa_core==0.9.6) (3.0.7)
Requirement already satisfied, skipping upgrade: typing~=3.0 in /home/founder/anaconda3/envs/tfKeras/lib/python3.6/site-packages (from rasa_core==0.9.6) (3.6.6)
Requirement already satisfied, skipping upgrade: tensorflow<1.9,>=1.7 in /home/founder/anaconda3/envs/tfKeras/lib/python3.6/site-packages (from rasa_core==0.9.6) (1.8.0)
Requirement already satisfied, skipping upgrade: future~=0.16 in /home/founder/anaconda3/envs/tfKeras/lib/python3.6/site-packages (from rasa_core==0.9.6) (0.17.1)
Requirement already satisfied, skipping upgrade: keras~=2.0 in /home/founder/anaconda3/envs/tfKeras/lib/python3.6/site-packages (from rasa_core==0.9.6) (2.2.4)
Requirement already satisfied, skipping upgrade: numpy~=1.13 in /home/founder/anaconda3/envs/tfKeras/lib/python3.6/site-packages (from rasa_core==0.9.6) (1.16.1)
Requirement already satisfied, skipping upgrade: pykwalify<=1.6.0 in /home/founder/anaconda3/envs/tfKeras/lib/python3.6/site-packages (from rasa_core==0.9.6) (1.6.0)
Requirement already satisfied, skipping upgrade: ruamel.yaml~=0.15.0 in /home/founder/anaconda3/envs/tfKeras/lib/python3.6/site-packages (from rasa_core==0.9.6) (0.15.88)
Requirement already satisfied, skipping upgrade: fakeredis~=0.10.0 in /home/founder/anaconda3/envs/tfKeras/lib/python3.6/site-packages (from rasa_core==0.9.6) (0.10.3)
Requirement already satisfied, skipping upgrade: slackclient~=1.0 in /home/founder/anaconda3/envs/tfKeras/lib/python3.6/site-packages (from rasa_core==0.9.6) (1.3.0)
Requirement already satisfied, skipping upgrade: fbmessenger~=5.0 in /home/founder/anaconda3/envs/tfKeras/lib/python3.6/site-packages (from rasa_core==0.9.6) (5.4.0)
Requirement already satisfied, skipping upgrade: requests~=2.15 in /home/founder/anaconda3/envs/tfKeras/lib/python3.6/site-packages (from rasa_core==0.9.6) (2.21.0)
Requirement already satisfied, skipping upgrade: apscheduler~=3.0 in /home/founder/anaconda3/envs/tfKeras/lib/python3.6/site-packages (from rasa_core==0.9.6) (3.5.3)
Requirement already satisfied, skipping upgrade: networkx~=2.0 in /home/founder/anaconda3/envs/tfKeras/lib/python3.6/site-packages (from rasa_core==0.9.6) (2.2)
Requirement already satisfied, skipping upgrade: graphviz~=0.8.0 in /home/founder/anaconda3/envs/tfKeras/lib/python3.6/site-packages (from rasa_core==0.9.6) (0.8.4)
Requirement already satisfied, skipping upgrade: ConfigArgParse~=0.13.0 in /home/founder/anaconda3/envs/tfKeras/lib/python3.6/site-packages (from rasa_core==0.9.6) (0.13.0)
Requirement already satisfied, skipping upgrade: twilio~=6.0 in /home/founder/anaconda3/envs/tfKeras/lib/python3.6/site-packages (from rasa_core==0.9.6) (6.24.1)
Requirement already satisfied, skipping upgrade: coloredlogs~=10.0 in /home/founder/anaconda3/envs/tfKeras/lib/python3.6/site-packages (from rasa_core==0.9.6) (10.0)
Requirement already satisfied, skipping upgrade: python-telegram-bot~=10.0 in /home/founder/anaconda3/envs/tfKeras/lib/python3.6/site-packages (from rasa_core==0.9.6) (10.1.0)
Requirement already satisfied, skipping upgrade: tqdm~=4.0 in /home/founder/anaconda3/envs/tfKeras/lib/python3.6/site-packages (from rasa_core==0.9.6) (4.31.1)
Requirement already satisfied, skipping upgrade: jsonpickle~=0.9.0 in /home/founder/anaconda3/envs/tfKeras/lib/python3.6/site-packages (from rasa_core==0.9.6) (0.9.6)
Requirement already satisfied, skipping upgrade: colorhash~=1.0 in /home/founder/anaconda3/envs/tfKeras/lib/python3.6/site-packages (from rasa_core==0.9.6) (1.0.2)
Requirement already satisfied, skipping upgrade: mattermostwrapper~=2.0 in /home/founder/anaconda3/envs/tfKeras/lib/python3.6/site-packages (from rasa_core==0.9.6) (2.1)
Requirement already satisfied, skipping upgrade: redis~=2.0 in /home/founder/anaconda3/envs/tfKeras/lib/python3.6/site-packages (from rasa_core==0.9.6) (2.10.6)
Requirement already satisfied, skipping upgrade: pathlib in /home/founder/anaconda3/envs/tfKeras/lib/python3.6/site-packages (from rasa_nlu[spacy]==0.12.0) (1.0.1)
Requirement already satisfied, skipping upgrade: matplotlib in /home/founder/anaconda3/envs/tfKeras/lib/python3.6/site-packages (from rasa_nlu[spacy]==0.12.0) (2.2.3)
Requirement already satisfied, skipping upgrade: packaging in /home/founder/anaconda3/envs/tfKeras/lib/python3.6/site-packages (from rasa_nlu[spacy]==0.12.0) (18.0)
Requirement already satisfied, skipping upgrade: cloudpickle in /home/founder/anaconda3/envs/tfKeras/lib/python3.6/site-packages (from rasa_nlu[spacy]==0.12.0) (0.6.1)
Requirement already satisfied, skipping upgrade: pyyaml in /home/founder/anaconda3/envs/tfKeras/lib/python3.6/site-packages (from rasa_nlu[spacy]==0.12.0) (3.13)
Requirement already satisfied, skipping upgrade: simplejson in /home/founder/anaconda3/envs/tfKeras/lib/python3.6/site-packages (from rasa_nlu[spacy]==0.12.0) (3.16.0)
Requirement already satisfied, skipping upgrade: gevent in /home/founder/anaconda3/envs/tfKeras/lib/python3.6/site-packages (from rasa_nlu[spacy]==0.12.0) (1.4.0)
Requirement already satisfied, skipping upgrade: klein in /home/founder/anaconda3/envs/tfKeras/lib/python3.6/site-packages (from rasa_nlu[spacy]==0.12.0) (17.10.0)
Requirement already satisfied, skipping upgrade: boto3 in /home/founder/anaconda3/envs/tfKeras/lib/python3.6/site-packages (from rasa_nlu[spacy]==0.12.0) (1.9.82)
Requirement already satisfied, skipping upgrade: jsonschema in /home/founder/anaconda3/envs/tfKeras/lib/python3.6/site-packages (from rasa_nlu[spacy]==0.12.0) (2.6.0)
Collecting spacy>2.0; extra == "spacy" (from rasa_nlu[spacy]==0.12.0)
Requirement already satisfied, skipping upgrade: scipy; extra == "spacy" in /home/founder/anaconda3/envs/tfKeras/lib/python3.6/site-packages (from rasa_nlu[spacy]==0.12.0) (1.2.1)
Requirement already satisfied, skipping upgrade: sklearn-crfsuite; extra == "spacy" in /home/founder/anaconda3/envs/tfKeras/lib/python3.6/site-packages (from rasa_nlu[spacy]==0.12.0) (0.3.6)
Requirement already satisfied, skipping upgrade: Werkzeug>=0.14 in /home/founder/.local/lib/python3.6/site-packages (from flask~=1.0->rasa_core==0.9.6) (0.14.1)
Requirement already satisfied, skipping upgrade: Jinja2>=2.10 in /home/founder/anaconda3/envs/tfKeras/lib/python3.6/site-packages (from flask~=1.0->rasa_core==0.9.6) (2.10)
Requirement already satisfied, skipping upgrade: click>=5.1 in /home/founder/anaconda3/envs/tfKeras/lib/python3.6/site-packages (from flask~=1.0->rasa_core==0.9.6) (7.0)
Requirement already satisfied, skipping upgrade: itsdangerous>=0.24 in /home/founder/anaconda3/envs/tfKeras/lib/python3.6/site-packages (from flask~=1.0->rasa_core==0.9.6) (1.1.0)
Requirement already satisfied, skipping upgrade: grpcio>=1.8.6 in /home/founder/.local/lib/python3.6/site-packages (from tensorflow<1.9,>=1.7->rasa_core==0.9.6) (1.17.1)
Requirement already satisfied, skipping upgrade: tensorboard<1.9.0,>=1.8.0 in /home/founder/anaconda3/envs/tfKeras/lib/python3.6/site-packages (from tensorflow<1.9,>=1.7->rasa_core==0.9.6) (1.8.0)
Requirement already satisfied, skipping upgrade: absl-py>=0.1.6 in /home/founder/.local/lib/python3.6/site-packages (from tensorflow<1.9,>=1.7->rasa_core==0.9.6) (0.6.1)
Requirement already satisfied, skipping upgrade: protobuf>=3.4.0 in /home/founder/.local/lib/python3.6/site-packages (from tensorflow<1.9,>=1.7->rasa_core==0.9.6) (3.6.1)
Requirement already satisfied, skipping upgrade: wheel>=0.26 in /home/founder/.local/lib/python3.6/site-packages (from tensorflow<1.9,>=1.7->rasa_core==0.9.6) (0.32.3)
Requirement already satisfied, skipping upgrade: termcolor>=1.1.0 in /home/founder/.local/lib/python3.6/site-packages (from tensorflow<1.9,>=1.7->rasa_core==0.9.6) (1.1.0)
Requirement already satisfied, skipping upgrade: astor>=0.6.0 in /home/founder/.local/lib/python3.6/site-packages (from tensorflow<1.9,>=1.7->rasa_core==0.9.6) (0.7.1)
Requirement already satisfied, skipping upgrade: gast>=0.2.0 in /home/founder/.local/lib/python3.6/site-packages (from tensorflow<1.9,>=1.7->rasa_core==0.9.6) (0.2.0)
Requirement already satisfied, skipping upgrade: keras-applications>=1.0.6 in /home/founder/.local/lib/python3.6/site-packages (from keras~=2.0->rasa_core==0.9.6) (1.0.6)
Requirement already satisfied, skipping upgrade: keras-preprocessing>=1.0.5 in /home/founder/.local/lib/python3.6/site-packages (from keras~=2.0->rasa_core==0.9.6) (1.0.5)
Requirement already satisfied, skipping upgrade: docopt>=0.6.2 in /home/founder/anaconda3/envs/tfKeras/lib/python3.6/site-packages (from pykwalify<=1.6.0->rasa_core==0.9.6) (0.6.2)
Requirement already satisfied, skipping upgrade: python-dateutil>=2.4.2 in /home/founder/anaconda3/envs/tfKeras/lib/python3.6/site-packages (from pykwalify<=1.6.0->rasa_core==0.9.6) (2.7.5)
Requirement already satisfied, skipping upgrade: websocket-client<1.0a0,>=0.35 in /home/founder/anaconda3/envs/tfKeras/lib/python3.6/site-packages (from slackclient~=1.0->rasa_core==0.9.6) (0.54.0)
Requirement already satisfied, skipping upgrade: idna<2.9,>=2.5 in /home/founder/anaconda3/envs/tfKeras/lib/python3.6/site-packages (from requests~=2.15->rasa_core==0.9.6) (2.8)
Requirement already satisfied, skipping upgrade: chardet<3.1.0,>=3.0.2 in /home/founder/anaconda3/envs/tfKeras/lib/python3.6/site-packages (from requests~=2.15->rasa_core==0.9.6) (3.0.4)
Requirement already satisfied, skipping upgrade: urllib3<1.25,>=1.21.1 in /home/founder/anaconda3/envs/tfKeras/lib/python3.6/site-packages (from requests~=2.15->rasa_core==0.9.6) (1.24.1)
Requirement already satisfied, skipping upgrade: certifi>=2017.4.17 in /home/founder/anaconda3/envs/tfKeras/lib/python3.6/site-packages (from requests~=2.15->rasa_core==0.9.6) (2018.11.29)
Requirement already satisfied, skipping upgrade: tzlocal>=1.2 in /home/founder/anaconda3/envs/tfKeras/lib/python3.6/site-packages (from apscheduler~=3.0->rasa_core==0.9.6) (1.5.1)
Requirement already satisfied, skipping upgrade: pytz in /home/founder/anaconda3/envs/tfKeras/lib/python3.6/site-packages (from apscheduler~=3.0->rasa_core==0.9.6) (2018.9)
Requirement already satisfied, skipping upgrade: setuptools>=0.7 in /home/founder/.local/lib/python3.6/site-packages (from apscheduler~=3.0->rasa_core==0.9.6) (40.6.3)
Requirement already satisfied, skipping upgrade: decorator>=4.3.0 in /home/founder/anaconda3/envs/tfKeras/lib/python3.6/site-packages (from networkx~=2.0->rasa_core==0.9.6) (4.3.0)
Requirement already satisfied, skipping upgrade: pysocks; python_version >= "3.0" in /home/founder/anaconda3/envs/tfKeras/lib/python3.6/site-packages (from twilio~=6.0->rasa_core==0.9.6) (1.6.8)
Requirement already satisfied, skipping upgrade: PyJWT>=1.4.2 in /home/founder/anaconda3/envs/tfKeras/lib/python3.6/site-packages (from twilio~=6.0->rasa_core==0.9.6) (1.7.1)
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Installing collected packages: cymem, preshed, thinc, regex, spacy, rasa-nlu, rasa-core
Found existing installation: cymem 1.31.2
Uninstalling cymem-1.31.2:
Successfully uninstalled cymem-1.31.2
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!{python} -m spacy download en_core_web_md
Requirement already satisfied: en_core_web_md==2.0.0 from https://github.com/explosion/spacy-models/releases/download/en_core_web_md-2.0.0/en_core_web_md-2.0.0.tar.gz#egg=en_core_web_md==2.0.0 in /home/founder/anaconda3/envs/tfKeras/lib/python3.6/site-packages (2.0.0)
Linking successful
/home/founder/anaconda3/envs/tfKeras/lib/python3.6/site-packages/en_core_web_md
-->
/home/founder/anaconda3/envs/tfKeras/lib/python3.6/site-packages/spacy/data/en_core_web_md
You can now load the model via spacy.load('en_core_web_md')
English Language Model 을 다운로드한다.
!{python} -m spacy link en_core_web_md en --force;
Linking successful
/home/founder/anaconda3/envs/tfKeras/lib/python3.6/site-packages/en_core_web_md
-->
/home/founder/anaconda3/envs/tfKeras/lib/python3.6/site-packages/spacy/data/en
You can now load the model via spacy.load('en')
설치한 라이브러리를 가져온다.
#importing the installations
import rasa_nlu
import rasa_core
import spacy
Teaching the bot to understand user inputs using Rasa NLU
Rasa NLU 를 이용한 사용자 입력을 이해할 수 있도록 봇을 가르친다. NLU 은 봇이 사용자 입력을 어떻게 이해하는지에 대해 가르치는 부분을 담당하고 있다. 아래는 봇으로 구현하고자 하는 대화의 샘플이다.
<source> https://cdn-images-1.medium.com/max/800/1*2ZmeQffwE9JUdSz-dlLKOg.png
이를 달성하기 위해서 Rasa NLU 모델을 빌드하고 훈련용 데이터를 주입할 것이다. 그러면 모델은 그 데이터를 엔티티와 의도로 이루어진 구조화된 포맷으로 변환하게 된다.
1) Preparing the NLU Training Data
훈련용 데이터는 봇으로부터 받기를 기대하는 메시지의 리스트로 이루어져 있다. 데이터에는 Rasa NLU 가 추출하는 법을 학습해야하는 의도와 엔티티에 대해 주석이 달려있다.
그러면 예를 통해 의도와 엔티티에 대한 개념을 이해해보자.
Intent: 의도라는 것은 메시지가 무엇을 말하고 있는가에 대한 것으로, 예를 들면, 날씨 예측 봇의 경우, "What’s the weather like tomorrow?” 은 request_weather 의도를 가지고 있다고 말할 수 있다.
<source> https://cdn-images-1.medium.com/max/800/1*Vxp3-_kh6jS5r5DuxsXLPw.png
Entity: 문장의 구조화된 데이터를 분석하는 방법을 통해 챗봇으로 하여금 사용자가 특별히 무엇을 요구하고 있는지를 이해하는데 도움이 되는 정보들을 말한다. 아래에서 cuisine과 location은 모두 추출된 엔티티들이다.
<source> https://cdn-images-1.medium.com/max/800/1*84kkw5ddpVzA1IU8e5Sm4w.png
그러면 NLU 훈련용 데이터로 필요를 준비해보자. 훈련 데이터는 nlu,md 파일이라는 이름으로 노트북과 같은 위치에 저장된다. 훈련 데이터는 보통 마크다운 파일로 저장된다.
nlu_md = """
## intent:greet
- hey
- hello there
- hi
- hello there
- good morning
- good evening
- moin
- hey there
- let's go
- hey dude
- goodmorning
- goodevening
- good afternoon
## intent:goodbye
- cu
- good by
- cee you later
- good night
- good afternoon
- bye
- goodbye
- have a nice day
- see you around
- bye bye
- see you later
## intent:mood_affirm
- yes
- indeed
- of course
- that sounds good
- correct
## intent:mood_deny
- no
- never
- I don't think so
- don't like that
- no way
- not really
## intent:mood_great
- perfect
- very good
- great
- amazing
- feeling like a king
- wonderful
- I am feeling very good
- I am great
- I am amazing
- I am going to save the world
- super
- extremely good
- so so perfect
- so good
- so perfect
## intent:mood_unhappy
- my day was horrible
- I am sad
- I don't feel very well
- I am disappointed
- super sad
- I'm so sad
- sad
- very sad
- unhappy
- bad
- very bad
- awful
- terrible
- not so good
- not very good
- extremly sad
- so saad
- Quite bad - can I get a cute picture of a [bird](group:birds), please?
- Really bad and only [doggo](group:shibes) pics and change that.
- Not good. The only thing that could make me fell better is a picture of a cute [kitten](group:cats).
- so sad. Only the picture of a [puppy](group:shibes) could make it better.
- I am very sad. I need a [cat](group:cats) picture.
- Extremely sad. Only the cute [doggo](group:shibes) pics can make me feel better.
- Bad. Please show me a [bird](group:birds) pic!
- Pretty bad to be honest. Can you show me a [puppy](group:shibes) picture to make me fell better?
## intent: inform
- A [dog](group:shibes)
- [dog](group:shibes)
- [bird](group:birds)
- a [cat](group:cats)
- [cat](group:cats)
- a [bird](group:birds)
- of a [dog](group:shibes)
- of a [cat](group:cats)
- a [bird](group:birds), please
- a [dog](group:shibes), please
"""
%store nlu_md > nlu.md
Writing 'nlu_md' (str) to file 'nlu.md'.
2) Defining the NLU Model Configuration
Rasa NLU 는 서로 다른 컴포넌트로 구성된다. 일단 훈련데이터가 준비되면, NLU 모델 파이프라인으로 보낼 수 있다. 파이프라인에 리스팅된 모든 컴포넌트는 하나씩 훈련을 받게 된다. 파이프라인에 대해 더욱 자세한 내용은 여기서 확인할 수 있다.
config = """
language: "en"
pipeline:
- name: "nlp_spacy" # loads the spacy language model
- name: "tokenizer_spacy" # splits the sentence into tokens
- name: "ner_crf" # uses the pretrained spacy NER model
- name: "intent_featurizer_spacy" # transform the sentence into a vector representation
- name: "intent_classifier_sklearn" # uses the vector representation to classify using SVM
- name: "ner_synonyms" # trains the synonyms
"""
%store config > config.yml
Writing 'config' (str) to file 'config.yml'.
이 파일에는 nlu 모델에서 사용할 설정이 포함되어 있다. 이 설정파일은 모델 훈련에 있어 아주 중요한데, 그 이유는 그것이 훈련에 사용되는 몇 가지 중요한 파라메터를 제공하기 때문이다.
3) Training the NLU Model.
I예를 들어 "hello" 같은 메시지를 봇에게 보냈을 때 그러한 사용자의 입력을 이해할 수 있도록 훈련을 시켜보자. 봇은 이 메시지를 환영의 의도가 있는 것으로 파악하게 된다.
from rasa_nlu.training_data import load_data
from rasa_nlu.config import RasaNLUModelConfig
from rasa_nlu.model import Trainer
from rasa_nlu import config
# loading the nlu training samples
training_data = load_data("nlu.md")
# trainer to educate our pipeline
trainer = Trainer(config.load("config.yml"))
# train the model!
interpreter = trainer.train(training_data)
# store it for future use
model_directory = trainer.persist("./models/nlu", fixed_model_name="current")
INFO:rasa_nlu.training_data.loading:Training data format of nlu.md is md
INFO:rasa_nlu.training_data.training_data:Training data stats:
- intent examples: 85 (7 distinct intents)
- Found intents: 'greet', 'mood_unhappy', 'mood_affirm', 'mood_great', 'mood_deny', 'inform', 'goodbye'
- entity examples: 18 (1 distinct entities)
- found entities: 'group'
INFO:rasa_nlu.utils.spacy_utils:Trying to load spacy model with name 'en'
INFO:rasa_nlu.components:Added 'nlp_spacy' to component cache. Key 'nlp_spacy-en'.
INFO:rasa_nlu.model:Starting to train component nlp_spacy
INFO:rasa_nlu.model:Finished training component.
INFO:rasa_nlu.model:Starting to train component tokenizer_spacy
INFO:rasa_nlu.model:Finished training component.
INFO:rasa_nlu.model:Starting to train component ner_crf
INFO:rasa_nlu.model:Finished training component.
INFO:rasa_nlu.model:Starting to train component intent_featurizer_spacy
INFO:rasa_nlu.model:Finished training component.
INFO:rasa_nlu.model:Starting to train component intent_classifier_sklearn
[Parallel(n_jobs=1)]: Done 12 out of 12 | elapsed: 0.1s finished
INFO:rasa_nlu.model:Finished training component.
INFO:rasa_nlu.model:Starting to train component ner_synonyms
INFO:rasa_nlu.model:Finished training component.
INFO:rasa_nlu.model:Successfully saved model into '/home/founder/rasa/models/nlu/default/current'
Fitting 2 folds for each of 6 candidates, totalling 12 fits
이렇게 훈련된 모델은 ‘./models/nlu/current’ 라는 경로에 저장된다.
4) Evaluating the NLU model
이제 모델의 성능을 평가해보자. 랜덤하게 메시지를 전달해보자.
# A helper function for prettier output
def pprint(o):
print(json.dumps(o, indent=2))
pprint(interpreter.parse("I am very sad. Could you send me a cat picture? "))
{
"intent": {
"name": "mood_unhappy",
"confidence": 0.6683543147712094
},
"entities": [
{
"start": 35,
"end": 38,
"value": "cats",
"entity": "group",
"confidence": 0.9107588112250706,
"extractor": "ner_crf",
"processors": [
"ner_synonyms"
]
}
],
"intent_ranking": [
{
"name": "mood_unhappy",
"confidence": 0.6683543147712094
},
{
"name": "goodbye",
"confidence": 0.0920061871439525
},
{
"name": "mood_great",
"confidence": 0.08791280857762719
},
{
"name": "greet",
"confidence": 0.057737993905256246
},
{
"name": "mood_affirm",
"confidence": 0.03508093992810576
},
{
"name": "inform",
"confidence": 0.03343134770242908
},
{
"name": "mood_deny",
"confidence": 0.025476407971419392
}
],
"text": "I am very sad. Could you send me a cat picture? "
}꽤 괜찮게 작동한다. 이제 검증 데이터셋에 이를 적용해보자. 여기서는 nlu.md 데이터를 사용하지만, 사실은 아직까지 다루지 않았던 데이터를 이용하는게 좋다. 아래와 같이 실행하면 다양한 평가결과를 포함하는 Intent Confusion matrix 를 얻게 된다.
from rasa_nlu.evaluate import run_evaluation
run_evaluation("nlu.md", model_directory)
INFO:rasa_nlu.components:Added 'nlp_spacy' to component cache. Key 'nlp_spacy-en'.
INFO:rasa_nlu.training_data.loading:Training data format of nlu.md is md
INFO:rasa_nlu.training_data.training_data:Training data stats:
- intent examples: 85 (7 distinct intents)
- Found intents: 'greet', 'mood_unhappy', 'mood_affirm', 'mood_great', 'mood_deny', 'inform', 'goodbye'
- entity examples: 18 (1 distinct entities)
- found entities: 'group'
INFO:rasa_nlu.evaluate:Intent evaluation results:
INFO:rasa_nlu.evaluate:Intent Evaluation: Only considering those 85 examples that have a defined intent out of 85 examples
INFO:rasa_nlu.evaluate:F1-Score: 0.9881730469965763
INFO:rasa_nlu.evaluate:Precision: 0.9890756302521008
INFO:rasa_nlu.evaluate:Accuracy: 0.9882352941176471
INFO:rasa_nlu.evaluate:Classification report:
precision recall f1-score support
goodbye 1.00 0.91 0.95 11
greet 0.93 1.00 0.96 13
inform 1.00 1.00 1.00 10
mood_affirm 1.00 1.00 1.00 5
mood_deny 1.00 1.00 1.00 6
mood_great 1.00 1.00 1.00 15
mood_unhappy 1.00 1.00 1.00 25
avg / total 0.99 0.99 0.99 85
INFO:rasa_nlu.evaluate:Confusion matrix, without normalization:
[[10 1 0 0 0 0 0]
[ 0 13 0 0 0 0 0]
[ 0 0 10 0 0 0 0]
[ 0 0 0 5 0 0 0]
[ 0 0 0 0 6 0 0]
[ 0 0 0 0 0 15 0]
[ 0 0 0 0 0 0 25]]
INFO:rasa_nlu.evaluate:Entity evaluation results:
INFO:rasa_nlu.evaluate:Evaluation for entity extractor: ner_crf
INFO:rasa_nlu.evaluate:F1-Score: 0.9775529042760154
INFO:rasa_nlu.evaluate:Precision: 0.9780517143345049
INFO:rasa_nlu.evaluate:Accuracy: 0.9787985865724381
INFO:rasa_nlu.evaluate:Classification report:
precision recall f1-score support
group 0.93 0.72 0.81 18
no_entity 0.98 1.00 0.99 265
avg / total 0.98 0.98 0.98 283
Teaching the bot to respond using Rasa Core
이제 봇은 사용자가 말하는게 무엇인지를 이해할 수 있게 되었다. 이제 다음 단계는 봇으로 하여금 응답 메시지를 보내도록 하는 것이다. 우리의 경우에는 사용자의 기분을 업시키기 위해 개, 고양이 또는 사용자의 선택에 따라 새와 같은 이미지를 보내는 것이다. Rasa Core 를 이용한 대화 관리 모델을 훈련시켜 봇으로 하여금 응답을 하도록 해보자.
1) Writing Stories
대화 관리 모델의 훈련 데이터를 스토리라고 부른다. 스토리는 사용자와 봇 사이에 발생할 수 있는 실제 대화의 부분을 담고 있다. 사용자의 입력은 의도와 엔티티로, 그리고 챗봇의 반응은 액션으로 표현된다. 그러면 전형적인 스토리의 형태를 살펴보자.
stories_md = """
## happy path <!-- name of the story - just for debugging -->
* greet
- utter_greet
* mood_great <!-- user utterance, in format intent[entities] -->
- utter_happy
* mood_affirm
- utter_happy
* mood_affirm
- utter_goodbye
## sad path 1 <!-- this is already the start of the next story -->
* greet
- utter_greet <!-- action the bot should execute -->
* mood_unhappy
- utter_ask_picture
* inform{"animal":"dog"}
- action_retrieve_image
- utter_did_that_help
* mood_affirm
- utter_happy
## sad path 2
* greet
- utter_greet
* mood_unhappy
- utter_ask_picture
* inform{"group":"cat"}
- action_retrieve_image
- utter_did_that_help
* mood_deny
- utter_goodbye
## sad path 3
* greet
- utter_greet
* mood_unhappy{"group":"puppy"}
- action_retrieve_image
- utter_did_that_help
* mood_affirm
- utter_happy
## strange user
* mood_affirm
- utter_happy
* mood_affirm
- utter_unclear
## say goodbye
* goodbye
- utter_goodbye
## fallback
- utter_unclear
"""
%store stories_md > stories.md
Writing 'stories_md' (str) to file 'stories.md'
전형적인 스토리의 형태는 다음과 같다.
- ## - 스토리의 시작
- * 는 엔티티의 형태로 사용자에 의해 전송된 메시지
- - 는 봇에 의해 일어나는 액션
2) Defining a Domain
도메인은 작동중인 봇이 살아가는 우주라고 말할 수 있다. 여기에는 사용자 입력이 얻고자 하는 것, 예측가능한 액션에는 무엇이 있는지, 그리고 어떻게 반응하고, 무슨 정보를 저장하는지에 대한 것들이 모두 포함된다. 도메인은 주요한 5개의 부분 - intents, slots, entities, actions, templates - 으로 구성된다. 앞의 2개에 대해서는 이미 살펴보았고, 나머지에 대해 알아보자.
- slots: 봇이 대화를 추적하는게 가능하도록 하는 값에 대한 일종의 플레이스홀더
- actions: 봇의 말 또는 행위에 대한 것
- templates: 봇이 말하고자하는 것에 대한 템플릿 문자열
도메인은 domain.yml 파일 형태로 도메인을 정의할 수 있다. 다음을 보자.
domain_yml = """
intents:
- greet
- goodbye
- mood_affirm
- mood_deny
- mood_great
- mood_unhappy
- inform
slots:
group:
type: text
entities:
- group
actions:
- utter_greet
- utter_did_that_help
- utter_happy
- utter_goodbye
- utter_unclear
- utter_ask_picture
- __main__.ApiAction
templates:
utter_greet:
- text: "Hey! How are you?"
utter_did_that_help:
- text: "Did that help you?"
utter_unclear:
- text: "I am not sure what you are aiming for."
utter_happy:
- text: "Great carry on!"
utter_goodbye:
- text: "Bye"
utter_ask_picture:
- text: "To cheer you up, I can show you a cute picture of a dog, cat or a bird. Which one do you choose?"
"""
%store domain_yml > domain.yml
Writing 'domain_yml' (str) to file 'domain.yml'.
3) Custom Actions
사용자의 선택에 따라 봇이 개나 고양이, 새의 사진을 가져오는 API 콜을 만들도록 해야하기 때문에, 커스톰 액션을 만들 필요가 있다. 봇 슬롯 그룹의 값을 가져옴으로써 어떤 그림을 보여줘야하는지 봇이 알 수 있게 된다.
from rasa_core.actions import Action
from rasa_core.events import SlotSet
from IPython.core.display import Image, display
import requests
class ApiAction(Action):
def name(self):
return "action_retrieve_image"
def run(self, dispatcher, tracker, domain):
group = tracker.get_slot('group')
r = requests.get('http://shibe.online/api/{}?count=1&urls=true&httpsUrls=true'.format(group))
response = r.content.decode()
response = response.replace('["',"")
response = response.replace('"]',"")
#display(Image(response[0], height=550, width=520))
dispatcher.utter_message("Here is something to cheer you up: {}".format(response))
4) Visualising the Training Data
!sudo apt-get -qq install -y graphviz libgraphviz-dev pkg-config;
!breq install graphviz
!{python} -m pip install pygraphviz;
Selecting previously unselected package libxdot4.
(Reading database ... 196425 files and directories currently installed.)
Preparing to unpack .../libxdot4_2.40.1-2_amd64.deb ...
Unpacking libxdot4 (2.40.1-2) ...
Selecting previously unselected package libgvc6-plugins-gtk.
Preparing to unpack .../libgvc6-plugins-gtk_2.40.1-2_amd64.deb ...
Unpacking libgvc6-plugins-gtk (2.40.1-2) ...
Selecting previously unselected package libgraphviz-dev.
Preparing to unpack .../libgraphviz-dev_2.40.1-2_amd64.deb ...
Unpacking libgraphviz-dev (2.40.1-2) ...
Setting up libxdot4 (2.40.1-2) ...
Setting up libgvc6-plugins-gtk (2.40.1-2) ...
Setting up libgraphviz-dev (2.40.1-2) ...
Processing triggers for libc-bin (2.27-3ubuntu1) ...
Processing triggers for man-db (2.8.3-2) ...
/bin/sh: 1: breq: not found
Collecting pygraphviz
Using cached https://files.pythonhosted.org/packages/7e/b1/d6d849ddaf6f11036f9980d433f383d4c13d1ebcfc3cd09bc845bda7e433/pygraphviz-1.5.zip
Building wheels for collected packages: pygraphviz
Running setup.py bdist_wheel for pygraphviz ... done
Stored in directory: /home/founder/.cache/pip/wheels/65/54/69/1aee9e66ab19916293208d4c9de0d3898adebe6b2eeff6476b
Successfully built pygraphviz
Installing collected packages: pygraphviz
Successfully installed pygraphviz-1.5
from IPython.display import Image
from rasa_core.agent import Agent
agent = Agent('domain.yml')
agent.visualize("stories.md", "story_graph.png", max_history=2)
Image(filename="story_graph.png")
INFO:apscheduler.scheduler:Scheduler started
Processed Story Blocks: 100%|██████████| 7/7 [00:00<00:00, 657.90it/s, # trackers=1]
5) Training a Dialogue Model
마지막으로 정책을 포함하고 있는 대화 관리 모델을 훈련시켜보자. 본 튜토리얼에서는 케라스로 구현을 하게 된다. 모델의 주요 컴포넌트는 recurrent neural network (an LSTM)이다.
from rasa_core.policies import FallbackPolicy, KerasPolicy, MemoizationPolicy
from rasa_core.agent import Agent
# this will catch predictions the model isn't very certain about
# there is a threshold for the NLU predictions as well as the action predictions
fallback = FallbackPolicy(fallback_action_name="utter_unclear",
core_threshold=0.2,
nlu_threshold=0.1)
agent = Agent('domain.yml', policies=[MemoizationPolicy(), KerasPolicy(), fallback])
# loading our neatly defined training dialogues
training_data = agent.load_data('stories.md')
agent.train(
training_data,
validation_split=0.0,
epochs=200
)
agent.persist('models/dialogue')
Using TensorFlow backend.
Processed Story Blocks: 100%|██████████| 7/7 [00:00<00:00, 597.92it/s, # trackers=1]
Processed Story Blocks: 100%|██████████| 7/7 [00:00<00:00, 364.70it/s, # trackers=7]
Processed Story Blocks: 100%|██████████| 7/7 [00:00<00:00, 257.86it/s, # trackers=13]
Processed Story Blocks: 100%|██████████| 7/7 [00:00<00:00, 251.92it/s, # trackers=10]
INFO:rasa_core.featurizers:Creating states and action examples from collected trackers (by MaxHistoryTrackerFeaturizer)...
Processed trackers: 100%|██████████| 206/206 [00:03<00:00, 60.83it/s, # actions=248]
INFO:rasa_core.featurizers:Created 248 action examples.
Processed actions: 248it [00:00, 759.24it/s, # examples=248]
INFO:rasa_core.policies.memoization:Memorized 248 unique action examples.
INFO:rasa_core.featurizers:Creating states and action examples from collected trackers (by MaxHistoryTrackerFeaturizer)...
Processed trackers: 100%|██████████| 206/206 [00:02<00:00, 61.71it/s, # actions=248]
INFO:rasa_core.featurizers:Created 248 action examples.
INFO:rasa_core.policies.keras_policy:Fitting model with 248 total samples and a validation split of 0.0
그리고 “Sorry, I didn’t understand that” 과 같은 대비책도 마련해놔야 한다. 이를 위해서는 정책 앙상블에 FallbackPolicy 를 추가한다.
최적화된 케라스 정책 모델은 다음과 같다.
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
masking_1 (Masking) (None, 5, 18) 0
_________________________________________________________________
lstm_1 (LSTM) (None, 32) 6528
_________________________________________________________________
dense_1 (Dense) (None, 9) 297
_________________________________________________________________
activation_1 (Activation) (None, 9) 0
=================================================================
Total params: 6,825
Trainable params: 6,825
Non-trainable params: 0
_________________________________________________________________
모델은 ‘models/dialogue’ 경로에 저장된다.
Epoch 1/200
248/248 [==============================] - 1s 3ms/step - loss: 2.1964 - acc: 0.1573
Epoch 2/200
248/248 [==============================] - 0s 191us/step - loss: 2.1178 - acc: 0.3992
Epoch 3/200
248/248 [==============================] - 0s 179us/step - loss: 2.0545 - acc: 0.4677
Epoch 4/200
248/248 [==============================] - 0s 184us/step - loss: 1.9949 - acc: 0.4718
Epoch 5/200
248/248 [==============================] - 0s 211us/step - loss: 1.9206 - acc: 0.4718
Epoch 6/200
248/248 [==============================] - 0s 199us/step - loss: 1.8613 - acc: 0.4677
Epoch 7/200
248/248 [==============================] - 0s 204us/step - loss: 1.7997 - acc: 0.4677
Epoch 8/200
248/248 [==============================] - 0s 193us/step - loss: 1.7418 - acc: 0.4677
Epoch 9/200
248/248 [==============================] - 0s 179us/step - loss: 1.7043 - acc: 0.4677
Epoch 10/200
248/248 [==============================] - 0s 182us/step - loss: 1.6838 - acc: 0.4677
Epoch 11/200
248/248 [==============================] - 0s 182us/step - loss: 1.6445 - acc: 0.4677
Epoch 12/200
248/248 [==============================] - 0s 184us/step - loss: 1.6238 - acc: 0.4677
Epoch 13/200
248/248 [==============================] - 0s 203us/step - loss: 1.5891 - acc: 0.4677
Epoch 14/200
248/248 [==============================] - 0s 179us/step - loss: 1.5670 - acc: 0.4677
Epoch 15/200
248/248 [==============================] - 0s 194us/step - loss: 1.5419 - acc: 0.4677
Epoch 16/200
248/248 [==============================] - 0s 207us/step - loss: 1.5013 - acc: 0.4677
Epoch 17/200
248/248 [==============================] - 0s 194us/step - loss: 1.4888 - acc: 0.4677
Epoch 18/200
248/248 [==============================] - 0s 197us/step - loss: 1.4496 - acc: 0.4677
Epoch 19/200
248/248 [==============================] - 0s 195us/step - loss: 1.4264 - acc: 0.4677
Epoch 20/200
248/248 [==============================] - 0s 179us/step - loss: 1.3835 - acc: 0.4798
Epoch 21/200
248/248 [==============================] - 0s 193us/step - loss: 1.3665 - acc: 0.4677
Epoch 22/200
248/248 [==============================] - 0s 219us/step - loss: 1.3229 - acc: 0.4758
Epoch 23/200
248/248 [==============================] - 0s 184us/step - loss: 1.2707 - acc: 0.5363
Epoch 24/200
248/248 [==============================] - 0s 186us/step - loss: 1.2414 - acc: 0.5161
Epoch 25/200
248/248 [==============================] - 0s 213us/step - loss: 1.1796 - acc: 0.5565
Epoch 26/200
248/248 [==============================] - 0s 191us/step - loss: 1.1602 - acc: 0.5766
Epoch 27/200
248/248 [==============================] - 0s 190us/step - loss: 1.0962 - acc: 0.6411
Epoch 28/200
248/248 [==============================] - 0s 189us/step - loss: 1.0891 - acc: 0.6250
Epoch 29/200
248/248 [==============================] - 0s 179us/step - loss: 1.0696 - acc: 0.6411
Epoch 30/200
248/248 [==============================] - 0s 186us/step - loss: 0.9878 - acc: 0.6774
Epoch 31/200
248/248 [==============================] - 0s 183us/step - loss: 0.9294 - acc: 0.6855
Epoch 32/200
248/248 [==============================] - 0s 188us/step - loss: 0.8899 - acc: 0.7137
Epoch 33/200
248/248 [==============================] - 0s 191us/step - loss: 0.8980 - acc: 0.6774
Epoch 34/200
248/248 [==============================] - 0s 179us/step - loss: 0.8649 - acc: 0.7258
Epoch 35/200
248/248 [==============================] - 0s 186us/step - loss: 0.8284 - acc: 0.7097
Epoch 36/200
248/248 [==============================] - 0s 182us/step - loss: 0.8008 - acc: 0.7460
Epoch 37/200
248/248 [==============================] - 0s 182us/step - loss: 0.7445 - acc: 0.7581
Epoch 38/200
248/248 [==============================] - 0s 191us/step - loss: 0.7340 - acc: 0.7661
Epoch 39/200
248/248 [==============================] - 0s 188us/step - loss: 0.7528 - acc: 0.7500
Epoch 40/200
248/248 [==============================] - 0s 190us/step - loss: 0.7061 - acc: 0.7581
Epoch 41/200
248/248 [==============================] - 0s 193us/step - loss: 0.6745 - acc: 0.7984
Epoch 42/200
248/248 [==============================] - 0s 182us/step - loss: 0.6599 - acc: 0.8065
Epoch 43/200
248/248 [==============================] - 0s 181us/step - loss: 0.6584 - acc: 0.7984
Epoch 44/200
248/248 [==============================] - 0s 186us/step - loss: 0.6315 - acc: 0.8306
Epoch 45/200
248/248 [==============================] - 0s 186us/step - loss: 0.6455 - acc: 0.7782
Epoch 46/200
248/248 [==============================] - 0s 239us/step - loss: 0.6177 - acc: 0.8387
Epoch 47/200
248/248 [==============================] - 0s 197us/step - loss: 0.6025 - acc: 0.8387
Epoch 48/200
248/248 [==============================] - 0s 246us/step - loss: 0.5874 - acc: 0.8629
Epoch 49/200
248/248 [==============================] - 0s 190us/step - loss: 0.5522 - acc: 0.8871
Epoch 50/200
248/248 [==============================] - 0s 192us/step - loss: 0.5401 - acc: 0.8710
Epoch 51/200
248/248 [==============================] - 0s 178us/step - loss: 0.5599 - acc: 0.8548
Epoch 52/200
248/248 [==============================] - 0s 183us/step - loss: 0.4985 - acc: 0.8911
Epoch 53/200
248/248 [==============================] - 0s 194us/step - loss: 0.5098 - acc: 0.8790
Epoch 54/200
248/248 [==============================] - 0s 183us/step - loss: 0.5042 - acc: 0.8911
Epoch 55/200
248/248 [==============================] - 0s 182us/step - loss: 0.4659 - acc: 0.8911
Epoch 56/200
248/248 [==============================] - 0s 182us/step - loss: 0.4699 - acc: 0.8831
Epoch 57/200
248/248 [==============================] - 0s 190us/step - loss: 0.4587 - acc: 0.9153
Epoch 58/200
248/248 [==============================] - 0s 183us/step - loss: 0.4658 - acc: 0.9032
Epoch 59/200
248/248 [==============================] - 0s 187us/step - loss: 0.4079 - acc: 0.9234
Epoch 60/200
248/248 [==============================] - 0s 193us/step - loss: 0.4340 - acc: 0.9113
Epoch 61/200
248/248 [==============================] - 0s 195us/step - loss: 0.4236 - acc: 0.9113
Epoch 62/200
248/248 [==============================] - 0s 200us/step - loss: 0.4023 - acc: 0.9194
Epoch 63/200
248/248 [==============================] - 0s 223us/step - loss: 0.3834 - acc: 0.9315
Epoch 64/200
248/248 [==============================] - 0s 181us/step - loss: 0.3777 - acc: 0.9355
Epoch 65/200
248/248 [==============================] - 0s 190us/step - loss: 0.3531 - acc: 0.9435
Epoch 66/200
248/248 [==============================] - 0s 232us/step - loss: 0.3770 - acc: 0.9395
Epoch 67/200
248/248 [==============================] - 0s 200us/step - loss: 0.3417 - acc: 0.9355
Epoch 68/200
248/248 [==============================] - 0s 190us/step - loss: 0.3284 - acc: 0.9516
Epoch 69/200
248/248 [==============================] - 0s 184us/step - loss: 0.3285 - acc: 0.9476
Epoch 70/200
248/248 [==============================] - 0s 192us/step - loss: 0.3546 - acc: 0.9194
Epoch 71/200
248/248 [==============================] - 0s 185us/step - loss: 0.3384 - acc: 0.9274
Epoch 72/200
248/248 [==============================] - 0s 181us/step - loss: 0.2976 - acc: 0.9556
Epoch 73/200
248/248 [==============================] - 0s 197us/step - loss: 0.3297 - acc: 0.9315
Epoch 74/200
248/248 [==============================] - 0s 187us/step - loss: 0.3007 - acc: 0.9435
Epoch 75/200
248/248 [==============================] - 0s 189us/step - loss: 0.3187 - acc: 0.9194
Epoch 76/200
248/248 [==============================] - 0s 192us/step - loss: 0.2989 - acc: 0.9395
Epoch 77/200
248/248 [==============================] - 0s 181us/step - loss: 0.3354 - acc: 0.9153
Epoch 78/200
248/248 [==============================] - 0s 173us/step - loss: 0.2916 - acc: 0.9435
Epoch 79/200
248/248 [==============================] - 0s 191us/step - loss: 0.2907 - acc: 0.9315
Epoch 80/200
248/248 [==============================] - 0s 220us/step - loss: 0.2697 - acc: 0.9355
Epoch 81/200
248/248 [==============================] - 0s 182us/step - loss: 0.2429 - acc: 0.9637
Epoch 82/200
248/248 [==============================] - 0s 198us/step - loss: 0.2485 - acc: 0.9556
Epoch 83/200
248/248 [==============================] - 0s 196us/step - loss: 0.2425 - acc: 0.9516
Epoch 84/200
248/248 [==============================] - 0s 197us/step - loss: 0.2533 - acc: 0.9476
Epoch 85/200
248/248 [==============================] - 0s 193us/step - loss: 0.2639 - acc: 0.9355
Epoch 86/200
248/248 [==============================] - 0s 201us/step - loss: 0.2543 - acc: 0.9355
Epoch 87/200
248/248 [==============================] - 0s 213us/step - loss: 0.2274 - acc: 0.9476
Epoch 88/200
248/248 [==============================] - 0s 190us/step - loss: 0.1907 - acc: 0.9758
Epoch 89/200
248/248 [==============================] - 0s 199us/step - loss: 0.2290 - acc: 0.9435
Epoch 90/200
248/248 [==============================] - 0s 220us/step - loss: 0.2408 - acc: 0.9597
Epoch 91/200
248/248 [==============================] - 0s 185us/step - loss: 0.1973 - acc: 0.9637
Epoch 92/200
248/248 [==============================] - 0s 199us/step - loss: 0.1618 - acc: 0.9879
Epoch 93/200
248/248 [==============================] - 0s 196us/step - loss: 0.2006 - acc: 0.9556
Epoch 94/200
248/248 [==============================] - 0s 197us/step - loss: 0.2229 - acc: 0.9435
Epoch 95/200
248/248 [==============================] - 0s 188us/step - loss: 0.1920 - acc: 0.9597
Epoch 96/200
248/248 [==============================] - 0s 197us/step - loss: 0.1915 - acc: 0.9556
Epoch 97/200
248/248 [==============================] - 0s 191us/step - loss: 0.1850 - acc: 0.9556
Epoch 98/200
248/248 [==============================] - 0s 198us/step - loss: 0.1952 - acc: 0.9637
Epoch 99/200
248/248 [==============================] - 0s 197us/step - loss: 0.1769 - acc: 0.9476
Epoch 100/200
248/248 [==============================] - 0s 185us/step - loss: 0.1715 - acc: 0.9637
Epoch 101/200
248/248 [==============================] - 0s 195us/step - loss: 0.1794 - acc: 0.9677
Epoch 102/200
248/248 [==============================] - 0s 184us/step - loss: 0.1975 - acc: 0.9556
Epoch 103/200
248/248 [==============================] - 0s 242us/step - loss: 0.1823 - acc: 0.9597
Epoch 104/200
248/248 [==============================] - 0s 194us/step - loss: 0.1587 - acc: 0.9677
Epoch 105/200
248/248 [==============================] - 0s 186us/step - loss: 0.1517 - acc: 0.9758
Epoch 106/200
248/248 [==============================] - 0s 199us/step - loss: 0.1768 - acc: 0.9476
Epoch 107/200
248/248 [==============================] - 0s 210us/step - loss: 0.1812 - acc: 0.9476
Epoch 108/200
248/248 [==============================] - 0s 173us/step - loss: 0.1626 - acc: 0.9597
Epoch 109/200
248/248 [==============================] - 0s 195us/step - loss: 0.1519 - acc: 0.9758
Epoch 110/200
248/248 [==============================] - 0s 202us/step - loss: 0.1378 - acc: 0.9677
Epoch 111/200
248/248 [==============================] - 0s 187us/step - loss: 0.1638 - acc: 0.9556
Epoch 112/200
248/248 [==============================] - 0s 210us/step - loss: 0.1680 - acc: 0.9597
Epoch 113/200
248/248 [==============================] - 0s 198us/step - loss: 0.1362 - acc: 0.9677
Epoch 114/200
248/248 [==============================] - 0s 201us/step - loss: 0.1515 - acc: 0.9556
Epoch 115/200
248/248 [==============================] - 0s 189us/step - loss: 0.1467 - acc: 0.9597
Epoch 116/200
248/248 [==============================] - 0s 196us/step - loss: 0.1282 - acc: 0.9758
Epoch 117/200
248/248 [==============================] - 0s 199us/step - loss: 0.1530 - acc: 0.9556
Epoch 118/200
248/248 [==============================] - 0s 188us/step - loss: 0.1533 - acc: 0.9677
Epoch 119/200
248/248 [==============================] - 0s 192us/step - loss: 0.1202 - acc: 0.9677
Epoch 120/200
248/248 [==============================] - 0s 253us/step - loss: 0.1785 - acc: 0.9516
Epoch 121/200
248/248 [==============================] - 0s 192us/step - loss: 0.1232 - acc: 0.9798
Epoch 122/200
248/248 [==============================] - 0s 186us/step - loss: 0.1221 - acc: 0.9839
Epoch 123/200
248/248 [==============================] - 0s 186us/step - loss: 0.1598 - acc: 0.9516
Epoch 124/200
248/248 [==============================] - 0s 193us/step - loss: 0.1398 - acc: 0.9637
Epoch 125/200
248/248 [==============================] - 0s 201us/step - loss: 0.1502 - acc: 0.9556
Epoch 126/200
248/248 [==============================] - 0s 209us/step - loss: 0.1334 - acc: 0.9839
Epoch 127/200
248/248 [==============================] - 0s 204us/step - loss: 0.1354 - acc: 0.9556
Epoch 128/200
248/248 [==============================] - 0s 197us/step - loss: 0.1261 - acc: 0.9758
Epoch 129/200
248/248 [==============================] - 0s 201us/step - loss: 0.1005 - acc: 0.9798
Epoch 130/200
248/248 [==============================] - 0s 193us/step - loss: 0.0984 - acc: 0.9839
Epoch 131/200
248/248 [==============================] - 0s 190us/step - loss: 0.1070 - acc: 0.9758
Epoch 132/200
248/248 [==============================] - 0s 233us/step - loss: 0.1385 - acc: 0.9718
Epoch 133/200
248/248 [==============================] - 0s 197us/step - loss: 0.0894 - acc: 0.9879
Epoch 134/200
248/248 [==============================] - 0s 205us/step - loss: 0.0867 - acc: 0.9919
Epoch 135/200
248/248 [==============================] - 0s 187us/step - loss: 0.1365 - acc: 0.9556
Epoch 136/200
248/248 [==============================] - 0s 193us/step - loss: 0.1151 - acc: 0.9597
Epoch 137/200
248/248 [==============================] - 0s 199us/step - loss: 0.0994 - acc: 0.9879
Epoch 138/200
248/248 [==============================] - 0s 185us/step - loss: 0.1113 - acc: 0.9677
Epoch 139/200
248/248 [==============================] - 0s 194us/step - loss: 0.0904 - acc: 0.9879
Epoch 140/200
248/248 [==============================] - 0s 189us/step - loss: 0.0883 - acc: 0.9839
Epoch 141/200
248/248 [==============================] - 0s 187us/step - loss: 0.0936 - acc: 0.9839
Epoch 142/200
248/248 [==============================] - 0s 180us/step - loss: 0.1322 - acc: 0.9597
Epoch 143/200
248/248 [==============================] - 0s 221us/step - loss: 0.0907 - acc: 0.9839
Epoch 144/200
248/248 [==============================] - 0s 198us/step - loss: 0.1022 - acc: 0.9758
Epoch 145/200
248/248 [==============================] - 0s 209us/step - loss: 0.0993 - acc: 0.9758
Epoch 146/200
248/248 [==============================] - 0s 196us/step - loss: 0.0886 - acc: 0.9758
Epoch 147/200
248/248 [==============================] - 0s 214us/step - loss: 0.0925 - acc: 0.9919
Epoch 148/200
248/248 [==============================] - 0s 194us/step - loss: 0.0955 - acc: 0.9718
Epoch 149/200
248/248 [==============================] - 0s 186us/step - loss: 0.0880 - acc: 0.9839
Epoch 150/200
248/248 [==============================] - 0s 188us/step - loss: 0.0973 - acc: 0.9758
Epoch 151/200
248/248 [==============================] - 0s 173us/step - loss: 0.0870 - acc: 0.9798
Epoch 152/200
248/248 [==============================] - 0s 170us/step - loss: 0.0849 - acc: 0.9919
Epoch 153/200
248/248 [==============================] - 0s 187us/step - loss: 0.0801 - acc: 0.9758
Epoch 154/200
248/248 [==============================] - 0s 182us/step - loss: 0.0866 - acc: 0.9798
Epoch 155/200
248/248 [==============================] - 0s 194us/step - loss: 0.1014 - acc: 0.9758
Epoch 156/200
248/248 [==============================] - 0s 204us/step - loss: 0.0687 - acc: 0.9839
Epoch 157/200
248/248 [==============================] - 0s 214us/step - loss: 0.1113 - acc: 0.9637
Epoch 158/200
248/248 [==============================] - 0s 208us/step - loss: 0.0777 - acc: 0.9718
Epoch 159/200
248/248 [==============================] - 0s 221us/step - loss: 0.0838 - acc: 0.9919
Epoch 160/200
248/248 [==============================] - 0s 187us/step - loss: 0.0893 - acc: 0.9798
Epoch 161/200
248/248 [==============================] - 0s 202us/step - loss: 0.0813 - acc: 0.9758
Epoch 162/200
248/248 [==============================] - 0s 201us/step - loss: 0.0812 - acc: 0.9879
Epoch 163/200
248/248 [==============================] - 0s 195us/step - loss: 0.0950 - acc: 0.9677
Epoch 164/200
248/248 [==============================] - 0s 198us/step - loss: 0.0636 - acc: 0.9960
Epoch 165/200
248/248 [==============================] - 0s 197us/step - loss: 0.0671 - acc: 0.9919
Epoch 166/200
248/248 [==============================] - 0s 196us/step - loss: 0.0726 - acc: 0.9879
Epoch 167/200
248/248 [==============================] - 0s 221us/step - loss: 0.0601 - acc: 0.9919
Epoch 168/200
248/248 [==============================] - 0s 196us/step - loss: 0.0594 - acc: 0.9919
Epoch 169/200
248/248 [==============================] - 0s 191us/step - loss: 0.0357 - acc: 0.9960
Epoch 170/200
248/248 [==============================] - 0s 185us/step - loss: 0.0671 - acc: 0.9879
Epoch 171/200
248/248 [==============================] - 0s 193us/step - loss: 0.0724 - acc: 0.9879
Epoch 172/200
248/248 [==============================] - 0s 189us/step - loss: 0.0749 - acc: 0.9758
Epoch 173/200
248/248 [==============================] - 0s 174us/step - loss: 0.1039 - acc: 0.9758
Epoch 174/200
248/248 [==============================] - 0s 183us/step - loss: 0.0533 - acc: 0.9919
Epoch 175/200
248/248 [==============================] - 0s 198us/step - loss: 0.0779 - acc: 0.9839
Epoch 176/200
248/248 [==============================] - 0s 188us/step - loss: 0.0625 - acc: 0.9798
Epoch 177/200
248/248 [==============================] - 0s 196us/step - loss: 0.0596 - acc: 0.9919
Epoch 178/200
248/248 [==============================] - 0s 199us/step - loss: 0.0544 - acc: 0.9960
Epoch 179/200
248/248 [==============================] - 0s 203us/step - loss: 0.0669 - acc: 0.9758
Epoch 180/200
248/248 [==============================] - 0s 193us/step - loss: 0.0570 - acc: 0.9919
Epoch 181/200
248/248 [==============================] - 0s 195us/step - loss: 0.0577 - acc: 0.9919
Epoch 182/200
248/248 [==============================] - 0s 189us/step - loss: 0.0572 - acc: 0.9798
Epoch 183/200
248/248 [==============================] - 0s 227us/step - loss: 0.0516 - acc: 0.9919
Epoch 184/200
248/248 [==============================] - 0s 183us/step - loss: 0.0727 - acc: 0.9839
Epoch 185/200
248/248 [==============================] - 0s 211us/step - loss: 0.0529 - acc: 0.9960
Epoch 186/200
248/248 [==============================] - 0s 201us/step - loss: 0.0665 - acc: 0.9798
Epoch 187/200
248/248 [==============================] - 0s 199us/step - loss: 0.0929 - acc: 0.9758
Epoch 188/200
248/248 [==============================] - 0s 212us/step - loss: 0.0659 - acc: 0.9919
Epoch 189/200
248/248 [==============================] - 0s 187us/step - loss: 0.0578 - acc: 0.9839
Epoch 190/200
248/248 [==============================] - 0s 190us/step - loss: 0.0577 - acc: 0.9879
Epoch 191/200
248/248 [==============================] - 0s 175us/step - loss: 0.0674 - acc: 0.9798
Epoch 192/200
248/248 [==============================] - 0s 203us/step - loss: 0.0654 - acc: 0.9839
Epoch 193/200
248/248 [==============================] - 0s 197us/step - loss: 0.0459 - acc: 0.9960
Epoch 194/200
248/248 [==============================] - 0s 186us/step - loss: 0.0693 - acc: 0.9798
Epoch 195/200
248/248 [==============================] - 0s 192us/step - loss: 0.0459 - acc: 0.9879
Epoch 196/200
248/248 [==============================] - 0s 195us/step - loss: 0.0617 - acc: 0.9839
Epoch 197/200
248/248 [==============================] - 0s 204us/step - loss: 0.0650 - acc: 0.9879
Epoch 198/200
248/248 [==============================] - 0s 198us/step - loss: 0.0723 - acc: 0.9798
Epoch 199/200
248/248 [==============================] - 0s 209us/step - loss: 0.0526 - acc: 0.9879
Epoch 200/200
248/248 [==============================] - 0s 189us/step - loss: 0.0593 - acc: 0.9919
INFO:rasa_core.policies.keras_policy:Done fitting keras policy model
INFO:rasa_core.agent:Persisted model to '/home/founder/rasa/models/dialogue'
5) Time to chat
다음 코드를 실행하여 봇과 채팅을 진행해보자.
#Starting the Bot
from rasa_core.agent import Agent
agent = Agent.load('models/dialogue', interpreter=model_directory)
INFO:rasa_nlu.components:Added 'nlp_spacy' to component cache. Key 'nlp_spacy-en'.
print("Your bot is ready to talk! Type your messages here or send 'stop'")
while True:
a = input()
if a == 'stop':
break
responses = agent.handle_message(a)
for response in responses:
print(response["text"])
Your bot is ready to talk! Type your messages here or send 'stop'
hello
Hey! How are you?
fine
To cheer you up, I can show you a cute picture of a dog, cat or a bird. Which one do you choose?
dog
Here is something to cheer you up: Invalid endpoint.
Did that help you?
yes
Great carry on!
thank you
Bye
stop
이것으로 사용자의 입력을 받아 문맥적으로 적절한 반응을 내놓는 채팅 봇을 만들어보았다. 다음 포스팅에서는 Slack 을 통해 이를 구현해본다.
[원문] https://towardsdatascience.com/building-a-conversational-chatbot-for-slack-using-rasa-and-python-part-1-bca5cc75d32f
