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")