training.py

import random
import numpy as np
import json
import pickle

import nltk
nltk.download('wordnet')
nltk.download('punkt')

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

# lemmatizer is initialized and content of intents.json is loaded into dict called intents
lemmatizer = WordNetLemmatizer()
intents = json.loads(open('intents.json').read())

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

# access each element of each intent in json file that we put into the dictionary
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.pkl', 'wb'))
pickle.dump(classes, open('classes.pkl', 'wb'))

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

for doc in documents:
    # binary format
    bag = []
    word_patterns = doc[0]
    word_patterns = [lemmatizer.lemmatize(word.lower()) for word in word_patterns]
    for word in words:
        if word in word_patterns:
            bag.append(int(1))  # Append 1 if word is in word_patterns
        else:
            bag.append(0)  # Append 0 if word is not in word_patterns

    output_row = list(output_empty)
    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])

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'))

# stochastic gradient descent
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('NNchatbot.model', hist)
print("done")