def __init__(self, img_size=32, stacks=4, skips=4, return_seq=False):
env = gym_super_mario_bros.make('SuperMarioBros-v2')
self.env = JoypadSpace(env, SIMPLE_MOVEMENT)
self.preprocess = Preprocess(img_size, stacks, return_seq)
self.skips = skips
self.action_space = self.env.action_space
self.observation_space = (img_size, img_size, stacks)
class Mario():
def __init__(self, img_size=32, stacks=4, skips=4, return_seq=False):
env = gym_super_mario_bros.make('SuperMarioBros-v2')
self.env = JoypadSpace(env, SIMPLE_MOVEMENT)
self.preprocess = Preprocess(img_size, stacks, return_seq)
self.skips = skips
self.action_space = self.env.action_space
self.observation_space = (img_size, img_size, stacks)
def reset(self):
self.preprocess.reset()
s = self.env.reset()
s = self.preprocess(s)
return s
def step(self, a):
total_r = 0
for i in range(self.skips):
self.env.render()
n_s, r, done, info = self.env.step(a)
n_s = self.preprocess(n_s)
total_r += r
if done: break
return n_s, total_r, done, info
class Pong():
def __init__(self, img_size=32, stacks=4, skips=4, return_seq=False):
self.env = gym.make('Pong-v0')
self.preprocess = Preprocess(img_size, stacks, return_seq)
self.skips = skips
self.pong_action = {0: 0, 1: 2, 2: 3}
self.action_space = self.env.action_space
self.action_space.n = 3
self.observation_space = (img_size, img_size, stacks)
def reset(self):
self.preprocess.reset()
s = self.env.reset()
s = self.preprocess(s)
return s
def step(self, a):
total_r = 0
for i in range(self.skips):
self.env.render()
n_s, r, done, info = self.env.step(self.pong_action[a])
n_s = self.preprocess(n_s)
total_r += r
if done: break
return n_s, total_r, done, info
def __init__(self, img_size=32, stacks=4, skips=4, return_seq=False):
self.env = gym.make('Pong-v0')
self.preprocess = Preprocess(img_size, stacks, return_seq)
self.skips = skips
self.pong_action = {0: 0, 1: 2, 2: 3}
self.action_space = self.env.action_space
self.action_space.n = 3
self.observation_space = (img_size, img_size, stacks)
import os import sys currentdir = os.path.dirname(os.path.realpath(__file__)) parentdir = os.path.dirname(currentdir) #grandparentdir = os.path.dirname(parentdir) sys.path.append(parentdir) print(parentdir) from utils.Preprocess import Preprocess sent = "i love potato, i have a party. so i need your help. i'm happy." p = Preprocess() pos = p.pos(sent) ret = p.get_keywords(pos, without_tag=False) print(ret) ret = p.get_keywords(pos, without_tag=True) print(ret)
sys.path.append(grandparentdir)
from utils.Preprocess import Preprocess
from tensorflow.keras import preprocessing
import pickle
def read_corpus_data(filename):
with open(filename, 'r') as f:
data = [line.split('\t') for line in f.read().splitlines()]
data = data[1:]
return data
corpus_data = read_corpus_data('./corpus.txt')
p = Preprocess()
dict = []
for c in corpus_data:
pos = p.pos(c[1])
for k in pos:
dict.append(k[0])
#사전에 사용될 word2index 생성
#사전의 첫 번째 인덱스에는 OOV 사용
tokenizer = preprocessing.text.Tokenizer(oov_token='OOV')
tokenizer.fit_on_texts(dict)
word_index = tokenizer.word_index
#사전 파일 생성
f = open("chatbot_dict.bin", "wb")
try:
import pandas as pd
import tensorflow as tf
from tensorflow.keras import preprocessing
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Input, Embedding, Dense, Dropout, Conv1D, GlobalMaxPool1D, concatenate
from tensorflow.keras.utils import plot_model
# 데이터 읽어오기
train_file = "drive/MyDrive/deep-chatbot/models/intent/total_train_data.csv"
data = pd.read_csv(train_file, delimiter=',')
queries = data['query'].tolist()
intents = data['intent'].tolist()
from utils.Preprocess import Preprocess
p = Preprocess(word2index_dic=
'drive/MyDrive/deep-chatbot/train_tools/dict/chatbot_dict.bin')
# 단어 시퀀스 생성
sequences = []
for sentence in queries:
pos = p.pos(str(sentence))
keywords = p.get_keywords(pos, without_tag=True)
seq = p.get_wordidx_sequence(keywords)
sequences.append(seq)
# 단어 인덱스 시퀀스 벡터 ○2
# 단어 시퀀스 벡터 크기
from config.GlobalParams import MAX_SEQ_LEN
padded_seqs = preprocessing.sequence.pad_sequences(sequences,
maxlen=MAX_SEQ_LEN,
padding='post')
from utils.Preprocess import Preprocess sent = input() # 전처리 객체 생성 p = Preprocess(userdic='../utils/user_dic.tsv') # 형태소 분석기 실행 pos = p.pos(sent) # 품사 태그와 같이 키워드 출력 ret = p.get_keywords(pos, without_tag=False) print(ret) # 품사 태그 없이 키워드 출력 ret = p.get_keywords(pos, without_tag=True) print(ret)
# 필요한 모듈 임포트
import pandas as pd
import tensorflow as tf
from tensorflow.keras import preprocessing
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Input, Embedding, Dense, Dropout, Conv1D, GlobalMaxPool1D, concatenate
# 데이터 읽어오기
train_file = "total_train_data.csv"
data = pd.read_csv(train_file, delimiter=',', encoding='cp949')
queries = data['query'].tolist()
intents = data['intent'].tolist()
from utils.Preprocess import Preprocess
p = Preprocess(word2index_dic='../../train_tools/dict/chatbot_dict.bin',
userdic='../../utils/user_dic.tsv')
# 단어 시퀀스 생성
sequences = []
for sentence in queries:
pos = p.pos(sentence)
keywords = p.get_keywords(pos, without_tag=True)
seq = p.get_wordidx_sequence(keywords)
sequences.append(seq)
# 단어 인덱스 시퀀스 벡터 ○2
# 단어 시퀀스 벡터 크기
padded_seqs = preprocessing.sequence.pad_sequences(sequences,
maxlen=15,
padding='post')
from tensorflow.keras import preprocessing
import pickle
# 말뭉치 데이터 읽어오기
def read_corpus_data(filename):
with open(filename, 'r') as f:
data = [line.split('\t') for line in f.read().splitlines()]
return data
# 말뭉치 데이터 가져오기
corpus_data = read_corpus_data('./corpus.txt')
# 망뭉치 데이터에서 키워드만 추출해서 사전 리스트 생성
p = Preprocess(word2index_dic='chatbot_dict.bin',
userdic = '../../utils/user_dic.tsv')
dict = []
for c in corpus_data:
pos = p.pos(c[1])
for k in pos:
dict.append(k[0])
# keywords = p.get_keywords(pos, without_tag=True)
# for k in keywords:
# dict.append(k)
# 사전에 사용될 word2index 생성
# 사전의 첫번 째 인덱스에는 OOV 사용
tokenizer = preprocessing.text.Tokenizer(oov_token='OOV')
tokenizer.fit_on_texts(dict)
word_index = tokenizer.word_index
def read_file(file_name):
sents = []
with open(file_name, 'r', encoding='utf-8') as f:
lines = f.readlines()
for idx, l in enumerate(lines):
if l[0] == ';' and lines[idx + 1][0] == '$':
this_sent = []
elif l[0] == '$' and lines[idx - 1][0] == ';':
continue
elif l[0] == '\n':
sents.append(this_sent)
else:
this_sent.append(tuple(l.split()))
return sents
p = Preprocess(word2index_dic='drive/MyDrive/deep-chatbot/train_tools/dict/chatbot_dict.bin')
# 학습용 말뭉치 데이터를 불러옴
corpus = read_file('drive/MyDrive/deep-chatbot/models/ner/ner_train.txt')
# 말뭉치 데이터에서 단어와 BIO 태그만 불러와 학습용 데이터셋 생성
sentences, tags = [], []
for t in corpus:
tagged_sentence = []
sentence, bio_tag = [], []
for w in t:
tagged_sentence.append((w[1], w[3]))
sentence.append(w[1])
bio_tag.append(w[3])
sentences.append(sentence)
import sys
sys.path.append('/content/drive/MyDrive/deep-chatbot/')
import tensorflow as tf
from tensorflow.keras.models import Model, load_model
from tensorflow.keras import preprocessing
intent_labels = {0: "인사", 1: "질문"}
# 의도 분류 모델 불러오기
model = load_model('drive/MyDrive/deep-chatbot/models/intent/intent_model.h5')
query = "데이터베이스"
from utils.Preprocess import Preprocess
p = Preprocess(word2index_dic='drive/MyDrive/deep-chatbot/train_tools/dict/chatbot_dict.bin',
userdic='drive/MyDrive/deep-chatbot/utils/user_dic.tsv')
pos = p.pos(query)
keywords = p.get_keywords(pos, without_tag=True)
seq = p.get_wordidx_sequence(keywords)
sequences = [seq]
# 단어 시퀀스 벡터 크기
from config.GlobalParams import MAX_SEQ_LEN
padded_seqs = preprocessing.sequence.pad_sequences(sequences, maxlen=MAX_SEQ_LEN, padding='post')
predict = model.predict(padded_seqs)
predict_class = tf.math.argmax(predict, axis=1)
print(query)
print("의도 예측 점수 : ", predict)
print("의도 예측 클래스 : ", predict_class.numpy())
print("의도 : ", intent_labels[predict_class.numpy()[0]])
from utils.Preprocess import Preprocess sent = "김포시에 있는 고려병원 위치 알려줘" p = Preprocess(userdic='../utils/user_dic.txt') # 사용자 사전 pos = p.pos(sent) # 제외시킬 품사 // 형태소단위로 끊은 형태 (품사가 아직 제외가 안됨)! # print(pos[0]) # print(pos[1]) # print(pos[2]) # print(pos[3]) # print(pos[4]) # print(pos[5]) # print(pos[6]) # print(pos[7]) # print(pos[8]) # print(pos[9]) # print(pos) # ret = p.get_keywords(pos, without_tag= False) # print(pos) # print(ret) # # # 태그 없이 단어만 추출 ret = p.get_keywords(pos, without_tag=True) print(ret)
from models.intent.IntentModel import IntentModel
from utils.Preprocess import Preprocess
p = Preprocess(word2index_dic='train_tools/dict/chatbot_dict.bin',
userdic='../Tokenizing/user_dic.txt')
intent = IntentModel(model_name='models/intent/intent_model.h5', proprocess=p)
query = "안녕하세요."
intent_predict = intent.predict_class(query)
print(intent_predict)
import sys
sys.path.append('/content/drive/MyDrive/deep-chatbot/')
import pickle
from utils.Preprocess import Preprocess
# 단어 사전 불러오기
f = open("drive/MyDrive/deep-chatbot/train_tools/dict/chatbot_dict.bin", "rb")
word_index = pickle.load(f)
f.close()
sent = "데이터베이스가 뭔지 알려줘"
# 전처리 객체 생성
p = Preprocess(userdic='drive/MyDrive/deep-chatbot/utils/user_dic.tsv')
# 형태소분석기 실행
pos = p.pos(sent)
# 품사 태그 없이 키워드 출력
keywords = p.get_keywords(pos, without_tag=True)
for word in keywords:
try:
print(word, word_index[word])
except KeyError:
# 해당 단어가 사전에 없는 경우, OOV 처리
print(word, word_index['OOV'])
from utils.Preprocess import Preprocess
from tensorflow.keras import preprocessing
#sent = "내일 오전 10시에 짬뽕 주문하고 싶어ㅋㅋ"
sent = "내일 오전 10시에 탕수육 주문하고 싶어"
p = Preprocess(word2index_dic='../train_tools/dict/chatbot_dict.bin',
userdic='../utils/user_dic.tsv')
pos = p.pos(sent)
keywords = p.get_keywords(pos, without_tag=False)
print(keywords)
# w2i = p.get_wordidx_sequence(keywords)
# sequences = [w2i]
#
# MAX_SEQ_LEN = 15 # 임베딩 벡터 크기
# padded_seqs = preprocessing.sequence.pad_sequences(sequences, maxlen=MAX_SEQ_LEN, padding='post')
#
# print(keywords)
# print(sequences)
# print(padded_seqs)
from utils.Preprocess import Preprocess
from models.intent.IntentModel import IntentModel
p = Preprocess(word2index_dic='../train_tools/dict/chatbot_dict.bin',
userdic='../utils/user_dic.tsv')
intent = IntentModel(model_name='../models/intent/intent_model.h5',
proprocess=p)
query = "오늘 탕수육 주문 가능한가요?"
predict = intent.predict_class(query)
predict_label = intent.labels[predict]
print(query)
print("의도 예측 클래스 : ", predict)
print("의도 예측 레이블 : ", predict_label)
with open(file_name, 'r', encoding='utf-8') as f:
lines = f.readlines()
for idx, l in enumerate(lines):
if l[0] == ';' and lines[idx + 1][0] == '$':
this_sent = []
elif l[0] == '$' and lines[idx - 1][0] == ';':
continue
elif l[0] == '\n':
sents.append(this_sent)
else:
this_sent.append(tuple(l.split()))
return sents
# 전처리 객체 생성
p = Preprocess(word2index_dic='../../train_tools/dict/chatbot_dict.bin',
userdic='../../utils/user_dic.tsv')
# 학습용 말뭉치 데이터를 불러옴
corpus = read_file('ner_train.txt')
# 말뭉치 데이터에서 단어와 BIO 태그만 불러와 학습용 데이터셋 생성
sentences, tags = [], []
for t in corpus:
tagged_sentence = []
sentence, bio_tag = [], []
for w in t:
tagged_sentence.append((w[1], w[3]))
sentence.append(w[1])
bio_tag.append(w[3])
sentences.append(sentence)