training.py

import random, json, pickle, numpy as np
import nltk
from nltk.stem import WordNetLemmatizer
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Activation, Dropout
from tensorflow.keras.optimizers import SGD
from tensorflow.python.keras import activations

lemmatizer = WordNetLemmatizer()

intents = json.load(open('intents.json','r'))

words = []
classes = []
documents = []
ignore_letters = ["?","!",".",","]

for intent in intents['intents']:
    for pattern in intent['patterns']:
        word_list = nltk.word_tokenize(pattern)
        words.extend(word_list)
        documents.append((word_list, intent['tag'])) 
        if intent['tag'] not in classes:
            classes.append(intent['tag'])

words= [lemmatizer.lemmatize(word) for word in words if word not in ignore_letters]
words= sorted(set(words))

classes = sorted(set(classes))

pickle.dump(words, open('words.pk1','wb'))
pickle.dump(classes, open('classes.pk1','wb'))

training = []
output_empty = [0] * len(classes)

for document in documents:
    bag=[]
    word_patters=document[0]
    word_patters=[lemmatizer.lemmatize(word.lower()) for word in word_patters]
    for word in words:
        bag.append(1) if word in word_patters else bag.append(0)

    output_row = list(output_empty)
    output_row[classes.index(document[1])] = 1
    training.append([bag, output_row])

random.shuffle(training)
training = np.array(training)

train_x = list(training[:, 0])
train_y = list(training[:, 1])

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(lr=0.01, 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('chatbotmodel.h5',hist)
print('Done')