I need help making my Neural Network make itself smarter - numpy

Question

Recently i followed a video from a channel from NeuralNine on creating a AI Chatbot, i Followed the video and vaguely understand the code, i'm a beginner to coding and didnt understand all of it however i managed to resolve the multiple problems that arose on my own, However the training code doesnt make the bot smarter i essentially want a way to load the previous training model and then run the code again and improve the accuracy each time. However at the moment it seems to just be overwriting the previous training model and not improving itself (i think please correct me if im wrong), i just cant see any code that is overwriting and making the model "chatbotmodel.h5" "smarter".
Here is "my" (NeuralNine's) Code:
|Training.py|
import random
import json
import pickle
import 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
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 = [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 word in word_patterns 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=300, batch_size=8, verbose=1)
model.save('chatbotmodel.h5', hist)
print("Done")

Answer 1

Your problem is not the code. One fundamental principle to understand in machine learning is that training more your model doesn't necessary make it more efficient.
I suggest that you take a look at overfitting problem. The data distribution is also very important.
Moreover, if NeuralNine didn't train more the model, maybe there are some reasons.