empatia-projekt/train_chatbot.py

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2023-06-11 00:23:39 +02:00
import json
import pickle
import random
import nltk
import numpy as np
from keras.layers import Dense, Dropout
from keras.models import Sequential
from keras.optimizers import SGD
from nltk.stem import WordNetLemmatizer
lemmatizer = WordNetLemmatizer()
words = []
classes = []
documents = []
ignore_words = ['?', '!']
data_file = open('intents.json', encoding='utf-8').read()
intents = json.loads(data_file)
for intent in intents:
for pattern in intent['patterns']:
w = nltk.word_tokenize(pattern)
words.extend(w)
documents.append((w, intent['tag']))
if intent['tag'] not in classes:
classes.append(intent['tag'])
words = [lemmatizer.lemmatize(w.lower()) for w in words if w not in ignore_words]
words = sorted(list(set(words)))
classes = sorted(list(set(classes)))
# documents = combination between patterns and intents
print(len(documents), "documents")
# classes = intents
print(len(classes), "classes", classes)
# words = all words, vocabulary
print(len(words), "unique lemmatized words", words)
pickle.dump(words, open('words.pkl', 'wb'))
pickle.dump(classes, open('classes.pkl', 'wb'))
training = []
for doc in documents:
pattern_words = [lemmatizer.lemmatize(word.lower()) for word in doc[0]]
bag = [1 if w in pattern_words else 0 for w in words]
output_row = [0 for _ in range(len(classes))]
output_row[classes.index(doc[1])] = 1
training.append([bag, output_row])
random.shuffle(training)
training = np.array(training)
train_x = list(training[:, 0])
train_y = list(training[:, 1])
print("Training data created")
model = Sequential()
model.add(Dense(128, input_shape=(len(train_x[0]),), activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(64, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(len(train_y[0]), activation='softmax'))
sgd = SGD(learning_rate=0.005, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])
hist = model.fit(np.array(train_x), np.array(train_y), epochs=200, batch_size=5, verbose=1)
model.save('chatbot_model.h5', hist)
print("model created")