training.py

import random
import json
import pickle
from re import VERBOSE
from nltk import metrics
import numpy as np
from tensorflow.python import keras
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.loads(open('intents.json').read())

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 = [list.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.pkl', "wb"))
pickle.dump(classes, open('classes.pkl', "wb"))

training = []
output_empty = [0] * len(classes)
for document in documents:
    bag = []
    word_patterns = document[0]
    word_patterns = [Lemmatizer.lemmatize(word.lower()) for word in word_patterns]
    for word in words:
        bag.append(1) if words in word_patterns else bag.append(0)
    output_row = list(output_empty)
    output_row[classes.index(document[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='catagorical crossentropy', optimizer=sgd, metrics=['accuracy'])

model.fit(np.array(train_x), np.array(train_y), epachs=200, batch_size = 5, verbose = 1)
model.save('chatbot_model.model')
print("Done!")