A bit old now, here is some updated code for anyone watching. import random import json import pickle import numpy as np import tensorflow as tf import nltk from nltk.stem import WordNetLemmatizer lemmatizer = WordNetLemmatizer() intents = json.loads(open('intents.json').read()) words = [] classes = [] documents = [] ignoreLetters = ['?', '!', '.', ','] for intent in intents['intents']: for pattern in intent['patterns']: wordList = nltk.word_tokenize(pattern) words.extend(wordList) documents.append((wordList, intent['tag'])) if intent['tag'] not in classes: classes.append(intent['tag']) words = [lemmatizer.lemmatize(word) for word in words if word not in ignoreLetters] words = sorted(set(words)) classes = sorted(set(classes)) pickle.dump(words, open('words.pkl', 'wb')) pickle.dump(classes, open('classes.pkl', 'wb')) training = [] outputEmpty = [0] * len(classes) for document in documents: bag = [] wordPatterns = document[0] wordPatterns = [lemmatizer.lemmatize(word.lower()) for word in wordPatterns] for word in words: bag.append(1) if word in wordPatterns else bag.append(0) outputRow = list(outputEmpty) outputRow[classes.index(document[1])] = 1 training.append(bag + outputRow) random.shuffle(training) training = np.array(training) trainX = training[:, :len(words)] trainY = training[:, len(words):] model = tf.keras.Sequential() model.add(tf.keras.layers.Dense(128, input_shape=(len(trainX[0]),), activation = 'relu')) model.add(tf.keras.layers.Dropout(0.5)) model.add(tf.keras.layers.Dense(64, activation = 'relu')) model.add(tf.keras.layers.Dropout(0.5)) model.add(tf.keras.layers.Dense(len(trainY[0]), activation='softmax')) sgd = tf.keras.optimizers.SGD(learning_rate=0.01, momentum=0.9, nesterov=True) model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy']) model.fit(trainX, trainY, epochs=200, batch_size=5, verbose=1) model.save('chatbot_model.h5') print('Done')
A much-needed updated vision of the chatbot from Tech with Tim. Love this video. From a viewer standpoint and someone who is coding along with you to program this thing. Either don't put a video of yourself on the screen, put it in the upper right-hand corner, or make it smaller. It's hard to code along with you as the camera of yourself blocks some of your code in the video.
Man, you are so underrated. I always search for simple examples of complex topics. After looking at your videos I am able to implement the basics in my projects easily
🎯 Key Takeaways for quick navigation: 00:04 *🤖 Setting up the intense.json file for the chatbot* - Creating an intense.json file to categorize user inputs for the chatbot. - Defining categories such as greetings, questions about prices, and goodbyes. - Specifying patterns and static responses for each category to train the chatbot. 06:12 *💻 Coding the chatbot in Python* - Importing necessary libraries including random, json, pickle, numpy, and nltk. - Lemmatizing words to reduce them to their base form for better performance. - Processing the intense.json file to prepare training data for the chatbot. 19:08 *🧠 Building the neural network model* - Shuffling and converting training data into numpy arrays for features and labels. - Creating a sequential model and adding layers including input, dense, and dropout layers. - Using rectified linear unit (ReLU) activation function and stochastic gradient descent (SGD) optimizer for training. 20:46 *🧠 Building the Neural Network Model* - Building a neural network model for the chatbot. - Adding dense layers with specified neurons and activation functions. - Defining optimizer parameters and compiling the model. 24:12 *💬 Setting up Chatbot Application* - Setting up the chatbot application to use the trained model. - Importing necessary libraries and loading the trained model. - Defining functions for processing user input and generating responses. 27:05 *🧹 Cleaning and Preparing Input Data* - Defining functions for cleaning up sentences and converting them into bags of words. - Preprocessing input data to prepare it for prediction by the neural network. - Ensuring that the input data format aligns with the model's requirements. 29:38 *🤖 Predicting Classes and Generating Responses* - Implementing a function to predict classes based on input sentences using the model. - Establishing a threshold for accepting predictions to manage uncertainty. - Generating responses based on predicted classes and probabilities. Made with HARPA AI
when it comes to trading in the various financial markets the relevance of professional guidance should not be undervalued as it could be the key to any traders success in profit making
@@piricontes2844 I will highly recommend Michael Branagh, he helped me secure a passive source of income, made life quiet easier for me as I no longer had to work round the clock. I'd check if he's taking new investors now, and also ask if I can share his contact information to the public.
@@JacobHKent i have stumbled upon a couple of articles by him, never occured to me to look him up. i would sincerely appreciated if you coild help with his contact info.
@@JacobHKent Mr Branagh is one of the most productive traders out there. i would not say hes the best at what he does but one thing is certain, hes a man of integrity and he certaainly gets results
So...as far as the machine learning goes, it's just a text classification algorithm. I would love to see a version of this with actual text generation as well!
The video got really good but we don’t really clarify what packages to download. I know you said what at the beginning at Imports but it still gives me error messages with Tensorflow. For example: ' To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags. '
you'd just use discord.py to detect when a message is sent, use that message as the input for the AI, and output the AI's output as the discord message
? - keras in "tensorflow.keras..." is not recognized by 'Pycharm', but it works anyway. Changes: - from tensorflow.keras.optimizers import SGD + from tensorflow.keras.optimizers.legacy import SGD - training = np.array(training) +training = np.array(training, dtype=object) -sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True) +sgd = SGD(learning_rate=0.01, decay=1e-6, momentum=0.9, nesterov=True)
if you get an error with hist = model.fit(np.array(train_x), np.array(train_y), epochs=200, batch_size =5, verbose=1 ), just delete "accuracy in model.compile
for intent in intents['intents']: for pattern in intent['patterns']: word_list = nltk.word_tokenize(pattern) words.append(word_list) documents.append((word_list, intent['tag'])) if intent['tag'] not in classes: classes.append(intent['tag']) This showing me error " For pattern in intent['patterns'] KeyError : 'patterns' Please help me out!
training = np.array(training) throws an error! ValueError: setting an array element with a sequence. The requested array has an inhomogeneous shape after 2 dimensions. The detected shape was (12, 2) + inhomogeneous part.
Sorry if it's too late, but I solved this error with the following: training_array = [] for item in training: part1 = np.array(item[0]) part2 = np.array(item[-1]) list_items = [part1, part2] training_array.append(list_items) Then you can use 'training_array' np.array(training) like.
#Dec 2023- import random import json import numpy as np import pickle import tensorflow as tf 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.keras.preprocessing.sequence import pad_sequences # Download NLTK data nltk.download('punkt') nltk.download('wordnet') 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) documents.append((word_list, intent['tag'])) words.extend(word_list) # Append words to the 'words' list if intent['tag'] not in classes: classes.append(intent['tag']) words = [lemmatizer.lemmatize(word.lower()) 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] # Create a bag of words with 1s and 0s bag = [1 if word in word_patterns else 0 for word in words] # Create the output row output_row = list(output_empty) output_row[classes.index(document[1])] = 1 # Append the bag and output_row as separate lists training.append([bag, output_row]) # Shuffle the training data random.shuffle(training) # Split the training data into X and Y train_x = np.array([item[0] for item in training]) train_y = np.array([item[1] for item in training]) 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=0.01, momentum=0.9, nesterov=True) model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy']) hist = model.fit(train_x, train_y, epochs=200, batch_size=5, verbose=1) model.save('Chatbot_model.h5', hist) print("Done!")
What in the world! Thankfully I found you and now I trained my model correctly. I was getting this error: 78 random.shuffle(training) ---> 79 training = np.array(training) 80 81 train_x = list(training[:, 0]) ValueError: setting an array element with a sequence. The requested array has an inhomogeneous shape after 2 dimensions. The detected shape was (392, 2) + inhomogeneous part. I don't know what to do with this I asked every gpts even got link of source code in github then also getting same error. I just copied your code and hit run and then thankfully it worked.
Nice tutorial on basic Keras NN setup. The truth is there is no need for a NN here. What it does is a simple input text selector and a random generator to output the response text. You could do the same using regexp/similar text/soundex word matching.
NIce video! Can you make an in-depth tutorial on making a friend-like chatbot that uses neural networks and NLP, and adapts it to modern libraries like Keras and TensorFlow? I've been making a project, but I've been having some issues.
Hi NeuralNine, this is a great piece. I would like to ask you if this tutorial would work for another language. Would it be enough to just edit intents, to comply with the language I would like to use? And the second question, do you have some tutorial, on how to implement this bot to streamlabs chatbot for twitch?
When I added training data to the intents.json file, I got this error, anybody who can help? ValueError: Input 0 of layer sequential is incompatible with the layer: expected axis -1 of input shape to have value 38 but received input with shape (None, 84) I figured out that the 84 is just the amount of patterns in the intents.json file, but I haven't figured out why the input shape has to have a value of 38.
Traceback (most recent call last): File "E:/CODEING/lapsever/test.py", line 20, in model = load_model("chatbotmodel.h5") File "C:\Users\Mr.Programmer\PycharmProjects\pythonProject\venv\lib\site-packages\tensorflow_core\python\keras\saving\save.py", line 146, in load_model return hdf5_format.load_model_from_hdf5(filepath, custom_objects, compile) File "C:\Users\Mr.Programmer\PycharmProjects\pythonProject\venv\lib\site-packages\tensorflow_core\python\keras\saving\hdf5_format.py", line 166, in load_model_from_hdf5 model_config = json.loads(model_config.decode('utf-8')) AttributeError: 'str' object has no attribute 'decode' error
Hey NeuralNine, really like the videos. Could you please like zoom in to the code... Or like increase the font. Its kinda difficult to see the code especially when not on full screen. Even when youre showing other stuff like how to install mingw for c++... The text looks really small But i really love your content and learn a lot. So if you could address that i'd be stoked
22:08 Hi, i'm new to DL.. When we use Softmax, if there are (say)two outputs in the network, it will output probabilities like [.4 ,.6] or [.1, .9] or [.5, .5] etc right? (sum always 1).. so even if we enter some gibberish as input, there will always be some output(s) above error threshold of (say) .25. How ill I tweak this so this won't happen? ( i mean if i enter "ahfliehf fsdlihfasdh" to bot, the outputs should be like something like [0.001, 0.00123])
Awesome work bro! I like your videos Kudos to you I suggest you make a tutorial video on show to deploy an AI CHATBOT to back-end server to improve responsiveness and present more details.
training = np.array(training) is returning an error, can you help? --------------------------------------------------------------------------- ValueError Traceback (most recent call last) Cell In[24], line 26 24 # shuffle our features and turn into np.array 25 random.shuffle(training) ---> 26 training = np.array(training) 28 # create train and test lists. X - patterns, Y - intents 29 train_x = list(training[:, 0]) ValueError: setting an array element with a sequence. The requested array has an inhomogeneous shape after 2 dimensions. The detected shape was (27, 2) + inhomogeneous part.
Please put more videos like this bro, I needed some sorry more.... And if you are about to launch a video give a gif for that. That thrill will be enough for me to be happy. Don't get faded bro, we need you...
If there’s nothing wrong with the preprocessing you probably have to find a way to properly tune the model with a test set and a training set to prevent under or overfitting.
If you guys keep getting goodbye messages for other things change the error_threshold to 0.5 and if that doesn't work change the epochs in training.py to 800
Hey, any idea how you'd get this to run with tflite? Edit: Okay, I've gotten the model to run, but I realised nltk stemming is really slow. Is there a faster alternative? (actually I'll try figure out exactly what's taking ages) Edit #2: Nevermind, it all works, there's just a delay the first time the process is called for some reason (Which I'll try to trace and fix) Thanks for the great tutorial, can't wait to setup my own personal assistant!
Hello... i have tried to copy your code now in 2023 and apparently there is an issue with training = np.array(training) since the [bag, output_row] columns don't have the same dimensions (maybe I have copied something wrong along the way...). I have came up with a solution that bypass the issue, although its quite a caveman style solution and I do not have the knowledge to understand its consequences, but it does not crash the code and allows me to train the chatbot. solution: when defining the dimensions of: output_empty = [0] * len(classes) instead do: output_empty = [0] * len(words) this way the dimensions of "bag" and "output_row" are the same. (It will fill the space with 0, so it shouldn't change the bot training accuracy... but what do I know :D )
@@Kanishkaran.__ Not really sure, but as I figured lately its quite a lot about version compatibilities with TF and all these days. Try searching if your versions match.
I was recieving TypeError at 14:30 This solve for me " pip install self nltk.download('wordnet') import self words = [lemmatizer.lemmatize(self, word) for word in words if word not in ignore_letters] words = sorted(set(words)) "
Well, I followed along and as I ran it....this guy decided to reply "Hello" with "Talk to you later" LMAOO 😭Even a bot isnt interested in talking with me 😒
Works great! Im having this error when adding more than 3 intents though. ValueError: Input 0 of layer sequential is incompatible with the layer: expected axis -1 of input shape to have value 27 but received input with shape [None, 30]. :/
@@ratto751 I was using 3.8 and I had the same issue as this guy so I looked it up, read an article that told me tensorflow doesn't work beyond 3.6.4 so I switched versions and it completely fixed my issue. So if you really insist that my experience was purely circumstantial
@@maxmayer3551 Would you rather trust an article or the official TensorFlow GitHub and PyPi? I've been able to run TensorFlow on python 3.8. There must be some other problem with your PC since it works with 3.8 on mine.
It's because they optimizer used in 2020 is now considered legacy. I was able to run the pre-processor with the following alterations to my code. # Change the SGD import from tensorflow.keras.optimizers import SGD -> from tensorflow.keras.optimizers.legacy import SGD # Change the numpy training array training = np.array(training,) -> training = np.array(training, dtype=object)
Line 36 'res = model.predict(np.array([bow]))[0]' if i print (res), it shows '[[nan nan nan nan nan nan]]' and nothing works Please help, Im struggling for like 5 days
What is the difference between this and using something like the difflib SequenceMatcher library or the Levenshtein method, as in finding the similarity metric between the given input and a list of sentences and then picking the closest match as a response by looping through all the data one by one? Just tried it, and it works exactly the same but with a few lines of code, which brings me to the question why using AI for this is even necessary?
when i finished this chat i typed in terminal: " hello!" the answer was "sad to see you go :( " hahahahahahah i'm crying !!!! thank you it's working perfectly
@@fernandobarocio2796 He used the intention.py file to answer the users. so you can use your own file or just google it you can find a lot of them. it's not a big problem. good luck.
tenserflow.keras command is not working anymore, it's a naming conflict issue, you can open the __init___.py from tensorflow and edit from near the line 387 as you see: import typing as _typing if _typing.TYPE_CHECKING: from keras.api._v2 import keras else: _keras_module = "keras.api._v2.keras" keras = _LazyLoader("keras", globals(), _keras_module) _module_dir = _module_util.get_parent_dir_for_name(_keras_module) if _module_dir: _current_module.__path__ = [_module_dir] + _current_module.__path__ setattr(_current_module, "keras", keras)
Hi, i followed the tutorial and came across a glitch when it was time to train the bot.. I got the error: Error when checking input: expected dense_14_input to have shape (6,) but got array with shape (1,).. not sure what could be causing it
I'm trying to create a chatbot for customer care, it is going to be a live support agent for industries like Telecom companies, so after I finish with all the knowledge you have shared maybe I will have some questions and everything else.
![[TH] 2024 PMSL SEA W1D5 | Fall | เก็บแต้มของจริง คือวันนี้](http://i.ytimg.com/vi/EciIAlCXg5k/mqdefault.jpg)
A bit old now, here is some updated code for anyone watching.
import random
import json
import pickle
import numpy as np
import tensorflow as tf
import nltk
from nltk.stem import WordNetLemmatizer
lemmatizer = WordNetLemmatizer()
intents = json.loads(open('intents.json').read())
words = []
classes = []
documents = []
ignoreLetters = ['?', '!', '.', ',']
for intent in intents['intents']:
for pattern in intent['patterns']:
wordList = nltk.word_tokenize(pattern)
words.extend(wordList)
documents.append((wordList, intent['tag']))
if intent['tag'] not in classes:
classes.append(intent['tag'])
words = [lemmatizer.lemmatize(word) for word in words if word not in ignoreLetters]
words = sorted(set(words))
classes = sorted(set(classes))
pickle.dump(words, open('words.pkl', 'wb'))
pickle.dump(classes, open('classes.pkl', 'wb'))
training = []
outputEmpty = [0] * len(classes)
for document in documents:
bag = []
wordPatterns = document[0]
wordPatterns = [lemmatizer.lemmatize(word.lower()) for word in wordPatterns]
for word in words:
bag.append(1) if word in wordPatterns else bag.append(0)
outputRow = list(outputEmpty)
outputRow[classes.index(document[1])] = 1
training.append(bag + outputRow)
random.shuffle(training)
training = np.array(training)
trainX = training[:, :len(words)]
trainY = training[:, len(words):]
model = tf.keras.Sequential()
model.add(tf.keras.layers.Dense(128, input_shape=(len(trainX[0]),), activation = 'relu'))
model.add(tf.keras.layers.Dropout(0.5))
model.add(tf.keras.layers.Dense(64, activation = 'relu'))
model.add(tf.keras.layers.Dropout(0.5))
model.add(tf.keras.layers.Dense(len(trainY[0]), activation='softmax'))
sgd = tf.keras.optimizers.SGD(learning_rate=0.01, momentum=0.9, nesterov=True)
model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])
model.fit(trainX, trainY, epochs=200, batch_size=5, verbose=1)
model.save('chatbot_model.h5')
print('Done')
man you just saved my code by changing words.append to words.extend
tyvm bro
Thank you
thaks bro
trainX = training[:, :len(words)]
trainY = training[:, len(words):]
oh my god thank you so much now it works
sike bro
Instructions unclear, made Ultron. 😂
😂 this had me laughing way more than I expected lol
same😅
😂😂😂😂
Awesome content! when are we getting the biceps tutorial tho?
Some day maybe😂😂
@@NeuralNine i am am i am iam
the chat bot said that idk what im doing
@@fortniteawesomeparadys6242 It's becoming self aware. "I AM!"
Oh God. I need that tutorial ❤️
Man, you are THE ONLY ONE explaining this intent type of neural net-based chatbot. Thanks a lot
A much-needed updated vision of the chatbot from Tech with Tim. Love this video. From a viewer standpoint and someone who is coding along with you to program this thing. Either don't put a video of yourself on the screen, put it in the upper right-hand corner, or make it smaller. It's hard to code along with you as the camera of yourself blocks some of your code in the video.
This video is more explanatory and far better than the tech tim video, and the tech tim guy was just rushing,
question, what kind of neural network architecture used here? Is it RNN?
@@tomcat9761 No. It's a simple ANN
hey. where can i get source code? any idea?
try watching the video
Videos on neural network theory would be fantastic! Thanks again for another great video!
question, what kind of neural network architecture used here? Is it RNN?
@@tomcat9761 i don't know
@@tomcat9761 its ANN
Man, you are so underrated. I always search for simple examples of complex topics. After looking at your videos I am able to implement the basics in my projects easily
th-cam.com/channels/0Bn9e36XqiiNZp9ClkPUww.html
hey. where can i get source code? any idea?
type it, lmao. @@mahnoorhome
Ah! Was waiting for this only!🔥
enjoy it! :)
Me too🤠
@@NeuralNine Hello, this error is returning: *lemmatize() missing 1 required positional argument: 'word'*
I never opened a TH-cam notification so fast
hahahaha thank you :)
Same
same
th-cam.com/channels/0Bn9e36XqiiNZp9ClkPUww.html
@@vipulkumar9858 why this comment??
🎯 Key Takeaways for quick navigation:
00:04 *🤖 Setting up the intense.json file for the chatbot*
- Creating an intense.json file to categorize user inputs for the chatbot.
- Defining categories such as greetings, questions about prices, and goodbyes.
- Specifying patterns and static responses for each category to train the chatbot.
06:12 *💻 Coding the chatbot in Python*
- Importing necessary libraries including random, json, pickle, numpy, and nltk.
- Lemmatizing words to reduce them to their base form for better performance.
- Processing the intense.json file to prepare training data for the chatbot.
19:08 *🧠 Building the neural network model*
- Shuffling and converting training data into numpy arrays for features and labels.
- Creating a sequential model and adding layers including input, dense, and dropout layers.
- Using rectified linear unit (ReLU) activation function and stochastic gradient descent (SGD) optimizer for training.
20:46 *🧠 Building the Neural Network Model*
- Building a neural network model for the chatbot.
- Adding dense layers with specified neurons and activation functions.
- Defining optimizer parameters and compiling the model.
24:12 *💬 Setting up Chatbot Application*
- Setting up the chatbot application to use the trained model.
- Importing necessary libraries and loading the trained model.
- Defining functions for processing user input and generating responses.
27:05 *🧹 Cleaning and Preparing Input Data*
- Defining functions for cleaning up sentences and converting them into bags of words.
- Preprocessing input data to prepare it for prediction by the neural network.
- Ensuring that the input data format aligns with the model's requirements.
29:38 *🤖 Predicting Classes and Generating Responses*
- Implementing a function to predict classes based on input sentences using the model.
- Establishing a threshold for accepting predictions to manage uncertainty.
- Generating responses based on predicted classes and probabilities.
Made with HARPA AI
when it comes to trading in the various financial markets the relevance of professional guidance should not be undervalued as it could be the key to any traders success in profit making
spot on! but the usual problem is discovering well grounded professionals who can be trusted. have you got any reconmendations?
i could not have said it any better.
@@piricontes2844 I will highly recommend Michael Branagh, he helped me secure a passive source of income, made life quiet easier for me as I no longer had to work round the clock. I'd check if he's taking new investors now, and also ask if I can share his contact information to the public.
@@JacobHKent i have stumbled upon a couple of articles by him, never occured to me to look him up. i would sincerely appreciated if you coild help with his contact info.
@@JacobHKent Mr Branagh is one of the most productive traders out there. i would not say hes the best at what he does but one thing is certain, hes a man of integrity and he certaainly gets results
So...as far as the machine learning goes, it's just a text classification algorithm. I would love to see a version of this with actual text generation as well!
I mean it would be possible to combine it with the text generation tutorial I made some time ago ^^
@@NeuralNine Can't seem to find it!
@@SimonTiger I was hoping to find a comment like yours here. This may be the video he is referring to. th-cam.com/video/QM5XDc4NQJo/w-d-xo.html
hey. where can i get source code? any idea?
@@NeuralNine YOU should really make that happen as a video!!!
The video got really good but we don’t really clarify what packages to download. I know you said what at the beginning at Imports but it still gives me error messages with Tensorflow.
For example: '
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags. '
did you get it right??
Hey, did you find the solution?
PLEASE help
@@ibaadahmed5925 Hey, did you find the solution?
PLEASE help
it would be cool if you made a video where you combined a chatbot like this with/into a discord bot!
you'd just use discord.py to detect when a message is sent, use that message as the input for the AI, and output the AI's output as the discord message
@@owenknowles3796 didn't think about that thanks! Will try when computer switches on
@@pw5687 How'd it go?
I would love to see a video about the theory, great video, thank you:)
? - keras in "tensorflow.keras..." is not recognized by 'Pycharm', but it works anyway.
Changes:
- from tensorflow.keras.optimizers import SGD
+ from tensorflow.keras.optimizers.legacy import SGD
- training = np.array(training)
+training = np.array(training, dtype=object)
-sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
+sgd = SGD(learning_rate=0.01, decay=1e-6, momentum=0.9, nesterov=True)
For anyone having issues with the model not running. change train_x and train_y into a tensor and adjust the input shape of the model to be 15, 5
what do you mean by change them into a tensor
can you share the code?
Hey bro, your programming style is sophistecated 🧑💻
Bro You are one of the most Underrated YT channel I've ever seen..... Keep up the good work : - )
I love your videos! They're so useful :D I've just purchased your 7 in 1 book from amazon
brother please do not stop! you are awesome
if you get an error with hist = model.fit(np.array(train_x), np.array(train_y), epochs=200, batch_size =5, verbose=1 ), just delete "accuracy in model.compile
for intent in intents['intents']:
for pattern in intent['patterns']:
word_list = nltk.word_tokenize(pattern)
words.append(word_list)
documents.append((word_list, intent['tag']))
if intent['tag'] not in classes:
classes.append(intent['tag'])
This showing me error
" For pattern in intent['patterns']
KeyError : 'patterns'
Please help me out!
sooo interested to the neural network theory!!! Love this channel so much, i've learned so much, thank you!!
training = np.array(training) throws an error!
ValueError: setting an array element with a sequence. The requested array has an inhomogeneous shape after 2 dimensions. The detected shape was (12, 2) + inhomogeneous part.
Sorry if it's too late, but I solved this error with the following:
training_array = []
for item in training:
part1 = np.array(item[0])
part2 = np.array(item[-1])
list_items = [part1, part2]
training_array.append(list_items)
Then you can use 'training_array' np.array(training) like.
#Dec 2023-
import random
import json
import numpy as np
import pickle
import tensorflow as tf
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.keras.preprocessing.sequence import pad_sequences
# Download NLTK data
nltk.download('punkt')
nltk.download('wordnet')
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)
documents.append((word_list, intent['tag']))
words.extend(word_list) # Append words to the 'words' list
if intent['tag'] not in classes:
classes.append(intent['tag'])
words = [lemmatizer.lemmatize(word.lower()) 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]
# Create a bag of words with 1s and 0s
bag = [1 if word in word_patterns else 0 for word in words]
# Create the output row
output_row = list(output_empty)
output_row[classes.index(document[1])] = 1
# Append the bag and output_row as separate lists
training.append([bag, output_row])
# Shuffle the training data
random.shuffle(training)
# Split the training data into X and Y
train_x = np.array([item[0] for item in training])
train_y = np.array([item[1] for item in training])
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=0.01, momentum=0.9, nesterov=True)
model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])
hist = model.fit(train_x, train_y, epochs=200, batch_size=5, verbose=1)
model.save('Chatbot_model.h5', hist)
print("Done!")
What in the world! Thankfully I found you and now I trained my model correctly. I was getting this error:
78 random.shuffle(training)
---> 79 training = np.array(training)
80
81 train_x = list(training[:, 0])
ValueError: setting an array element with a sequence. The requested array has an inhomogeneous shape after 2 dimensions. The detected shape was (392, 2) + inhomogeneous part.
I don't know what to do with this I asked every gpts even got link of source code in github then also getting same error. I just copied your code and hit run and then thankfully it worked.
THANKS A LOT.
Thank you. Great commentary and clear explanations. Easy to follow how you built up the application. Perfect.
I have to make one for school so this is perfect timing ;^)
Our school only do pascal bruh
When will you open a Discord Server?
Bro was years ahead
the thing about this intelligent ai is just that its not context based cuz it don't have access in the conversation history
Never seen this type of content before, really amazing
yeah, his tutorials are very applicable. i wonder what would happen to my knowledge if i carry one of that out each day for 1 month staright
Man do i love that intro 🔥
and this is how my creator came to life.
Nice tutorial on basic Keras NN setup. The truth is there is no need for a NN here. What it does is a simple input text selector and a random generator to output the response text. You could do the same using regexp/similar text/soundex word matching.
I was hoping i wouldn't be the only one realizing this. Using a NN for this is such overkill.
btw if you struggle with translation to vscode on the importation of tensorflow instead type "from tensorflow.python.keras.layers ...." and so on
Thank you so much!! I was going crazy trying to find a solution.
Ah yes, now I can make a very own best friend who can understand me :DDDD
yeah, i am also making it for myself. 😀😉
same :D
@@blue_is_cool16 encouter any error ????
@@selwyntayong7286 yeah I needed to download the modules.
Which I did :D
The code is not working.
output_row[classes.index(document[1])] = 1
IndexError: list index out of range
20:42 very interested in the theory, I like how you explain and would be cool to hear it from you
NIce video! Can you make an in-depth tutorial on making a friend-like chatbot that uses neural networks and NLP, and adapts it to modern libraries like Keras and TensorFlow? I've been making a project, but I've been having some issues.
Hey ...have u made the project?
Any progress?
Hi, great content! Although, is it possible for you to upload the code in your videos to a github repo? Thanks
That will be great!
Hi NeuralNine, this is a great piece. I would like to ask you if this tutorial would work for another language. Would it be enough to just edit intents, to comply with the language I would like to use? And the second question, do you have some tutorial, on how to implement this bot to streamlabs chatbot for twitch?
I tried it with german words and all I had to do is define the language in the word_tokenize function:
nltk.word_tokenize(pattern,language='german')
hey. where can i get source code? any idea?
Well done. As I am new to chatbot() and python. Your video helped to fill in all the gaps in Tim video. Thanks brother.
this tutorial is very useful! can I have the project source code for self-learning?
This is an amazing tutorial brother, thanks for all the knowledge you've shared! You earned a subscriber!
hell yeah, my first AI. Thanks, man
bro your intro is fire
Can you make a series on neural network theory? This video was very helpful.Thanks
there is correction on line 51 in chatbot.py ."responses" should be replaced with "response"
I code exactly same but couldn't able to to write any msg after "GO! Bot is running". can you help me? how to sort it out
one thing i hated is that tensorflow couldnt download on my pc
Thanks 👍 I was actually waiting for this.
When I added training data to the intents.json file, I got this error, anybody who can help?
ValueError: Input 0 of layer sequential is incompatible with the layer: expected axis -1 of input shape to have value 38 but received input with shape (None, 84)
I figured out that the 84 is just the amount of patterns in the intents.json file, but I haven't figured out why the input shape has to have a value of 38.
man I was just building a hard-coded chatbot, this vid came in the right time for me. thanks bro
@Hotdog_man2 Minecraft lol yea
Traceback (most recent call last):
File "E:/CODEING/lapsever/test.py", line 20, in
model = load_model("chatbotmodel.h5")
File "C:\Users\Mr.Programmer\PycharmProjects\pythonProject\venv\lib\site-packages\tensorflow_core\python\keras\saving\save.py", line 146, in load_model
return hdf5_format.load_model_from_hdf5(filepath, custom_objects, compile)
File "C:\Users\Mr.Programmer\PycharmProjects\pythonProject\venv\lib\site-packages\tensorflow_core\python\keras\saving\hdf5_format.py", line 166, in load_model_from_hdf5
model_config = json.loads(model_config.decode('utf-8'))
AttributeError: 'str' object has no attribute 'decode'
error
This was amazing! Thank you so much I have learned a lot :D
Hey NeuralNine, really like the videos.
Could you please like zoom in to the code... Or like increase the font. Its kinda difficult to see the code especially when not on full screen.
Even when youre showing other stuff like how to install mingw for c++... The text looks really small
But i really love your content and learn a lot. So if you could address that i'd be stoked
on mobile just reverse pinch the video to zoom
Error: No name ‘models’ in module ‘LazyLoader’
i have'nt watched your video before this video but now ican't stop watching your video
22:08 Hi, i'm new to DL.. When we use Softmax, if there are (say)two outputs in the network, it will output probabilities like [.4 ,.6] or [.1, .9] or [.5, .5] etc right? (sum always 1).. so even if we enter some gibberish as input, there will always be some output(s) above error threshold of (say) .25.
How ill I tweak this so this won't happen? ( i mean if i enter "ahfliehf fsdlihfasdh" to bot, the outputs should be like something like [0.001, 0.00123])
Awesome work bro!
I like your videos
Kudos to you
I suggest you make a tutorial video on show to deploy an AI CHATBOT to back-end server to improve responsiveness and present more details.
a series about NN/ ai in general would be dope
Joey to the bot: how u doin...😎
Your videos are always amazing, always i learn a lot from your videos 🤩🤩
appreciate it :D
@@NeuralNine Hey man for some reason whenever I say Hello or one of the greetings it says a goodbye response. Any idea why?
@@NeuralNine can i get the source code please
training = np.array(training) is returning an error, can you help?
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
Cell In[24], line 26
24 # shuffle our features and turn into np.array
25 random.shuffle(training)
---> 26 training = np.array(training)
28 # create train and test lists. X - patterns, Y - intents
29 train_x = list(training[:, 0])
ValueError: setting an array element with a sequence. The requested array has an inhomogeneous shape after 2 dimensions. The detected shape was (27, 2) + inhomogeneous part.
Please put more videos like this bro, I needed some sorry more....
And if you are about to launch a video give a gif for that. That thrill will be enough for me to be happy. Don't get faded bro, we need you...
UNDERRATED , u deserve a million sub
Can you please provide the files of the bot
(because mine didn't worked)
my bot keeps saying the intent is goodbye even if I say "how are you?" it says hey or goodbye or talk to you later
I have similar problem.
my bot keeps saying "hi", ""hello", "what can i do for you", even if i ask "what is your name"
please help
If there’s nothing wrong with the preprocessing you probably have to find a way to properly tune the model with a test set and a training set to prevent under or overfitting.
Hey, I have a Problem after training , type error in last 2 functions, While true, get_response
If you guys keep getting goodbye messages for other things change the error_threshold to 0.5 and if that doesn't work change the epochs in training.py to 800
Are you ever going to make a tutorial on a chatbot that generates its own responses?
Hey manx nice content, always excited when you post a new video
Hey, any idea how you'd get this to run with tflite?
Edit: Okay, I've gotten the model to run, but I realised nltk stemming is really slow. Is there a faster alternative?
(actually I'll try figure out exactly what's taking ages)
Edit #2: Nevermind, it all works, there's just a delay the first time the process is called for some reason (Which I'll try to trace and fix)
Thanks for the great tutorial, can't wait to setup my own personal assistant!
Hello... i have tried to copy your code now in 2023 and apparently there is an issue with training = np.array(training) since the [bag, output_row] columns don't have the same dimensions (maybe I have copied something wrong along the way...).
I have came up with a solution that bypass the issue, although its quite a caveman style solution and I do not have the knowledge to understand its consequences, but it does not crash the code and allows me to train the chatbot.
solution:
when defining the dimensions of:
output_empty = [0] * len(classes)
instead do:
output_empty = [0] * len(words)
this way the dimensions of "bag" and "output_row" are the same. (It will fill the space with 0, so it shouldn't change the bot training accuracy... but what do I know :D )
Hey what about importing tensorflow, I tried copying too but mines showing error like 'rebuild tensorflow with appropriate compiler flags'
@@Kanishkaran.__ Not really sure, but as I figured lately its quite a lot about version compatibilities with TF and all these days. Try searching if your versions match.
@@bloodang6793 What's the version used here?
@@Kanishkaran.__ Hey, did you get a solution?
@@mysterious4496 no
I was recieving TypeError at 14:30
This solve for me
"
pip install self
nltk.download('wordnet')
import self
words = [lemmatizer.lemmatize(self, word) for word in words if word not in ignore_letters]
words = sorted(set(words))
"
Yes I’m very interested in neural network theory! 🙋♂️🙋♂️🙋♂️🙋♂️🙋♂️
Well, I followed along and as I ran it....this guy decided to reply "Hello" with "Talk to you later" LMAOO 😭Even a bot isnt interested in talking with me 😒
Works great! Im having this error when adding more than 3 intents though.
ValueError: Input 0 of layer sequential is incompatible with the layer: expected axis -1 of input shape to have value 27 but received input with shape [None, 30]. :/
Me too! did you find the solution??
I found the error. I forgot to change the model name in the chatbot.py , it was .model but the correct is .h5
@@netofis omg thank you so much I've been searching for the solution to this error for an hour 😭
@@netofis Thank you so much, was struggling for 2 hours
I keep getting hit by errors everywhere despite following the code a-z
thank you ! needed this in my school project
Hello, i am unable to install tensorflow!!please help
Use this video its very good and well we th-cam.com/video/zRY5lx-So-c/w-d-xo.html
tensorflow does not support python versions after 3.6.4, so you'll have to change your version
@@maxmayer3551 That's pure BS. It's available till Python 3.8
@@ratto751 I was using 3.8 and I had the same issue as this guy so I looked it up, read an article that told me tensorflow doesn't work beyond 3.6.4 so I switched versions and it completely fixed my issue. So if you really insist that my experience was purely circumstantial
@@maxmayer3551 Would you rather trust an article or the official TensorFlow GitHub and PyPi? I've been able to run TensorFlow on python 3.8. There must be some other problem with your PC since it works with 3.8 on mine.
can i have the source code?
Give us the source code plsss
No
@@Vincenzo_Cassano Yes?
No
@@Vincenzo_Cassano Yes??
Awesome!
thanks :)
Thanks for the Video Florian! 👏👏
Sir, statement training= np.array(training) is showing error of array being inhomogeneous shape after 2 dimension. What would be the solution?
It's because they optimizer used in 2020 is now considered legacy. I was able to run the pre-processor with the following alterations to my code.
# Change the SGD import
from tensorflow.keras.optimizers import SGD -> from tensorflow.keras.optimizers.legacy import SGD
# Change the numpy training array
training = np.array(training,) -> training = np.array(training, dtype=object)
@@dominickblue6006 thank you
This helped me learn a lot about neural networks thanks!
Line 36 'res = model.predict(np.array([bow]))[0]' if i print (res), it shows '[[nan nan nan nan nan nan]]' and nothing works
Please help, Im struggling for like 5 days
Don't waste your time, this code doesn't work
@@gowthamselvam2499 Fr? Thank you. But I need to make chatbot. Do u have any sites where I could find tutorial?
Its not letting my respond to my chatbot? How can I fix this?
I gonna be the first man to "Get Penny in trouble" get my chatbot up the duff!
What is the difference between this and using something like the difflib SequenceMatcher library or the Levenshtein method, as in finding the similarity metric between the given input and a list of sentences and then picking the closest match as a response by looping through all the data one by one? Just tried it, and it works exactly the same but with a few lines of code, which brings me to the question why using AI for this is even necessary?
when i finished this chat i typed in terminal: " hello!"
the answer was "sad to see you go :( "
hahahahahahah i'm crying !!!!
thank you it's working perfectly
I have that exact error... how did you fix it?
@@fernandobarocio2796
He used the intention.py file to answer the users.
so you can use your own file or just google it you can find a lot of them.
it's not a big problem.
good luck.
Can you please zoom a bit more on the code? Would be better to understand 🙂.
As usual great video, excelent channel
You're so underrated bro
This tutorial is fantastic! Thank you for sharing.
th-cam.com/channels/0Bn9e36XqiiNZp9ClkPUww.html
tenserflow.keras command is not working anymore, it's a naming conflict issue, you can open the __init___.py from tensorflow and edit from near the line 387 as you see: import typing as _typing
if _typing.TYPE_CHECKING:
from keras.api._v2 import keras
else:
_keras_module = "keras.api._v2.keras"
keras = _LazyLoader("keras", globals(), _keras_module)
_module_dir = _module_util.get_parent_dir_for_name(_keras_module)
if _module_dir:
_current_module.__path__ = [_module_dir] + _current_module.__path__
setattr(_current_module, "keras", keras)
Hi, i followed the tutorial and came across a glitch when it was time to train the bot.. I got the error: Error when checking input: expected dense_14_input to have shape (6,) but got array with shape (1,).. not sure what could be causing it
Great video, I learned so much, thank you!
I am getting this error please help!
"UnboundLocalError: local variable 'result' referenced before assignment"
received the same error. Have u found a solution to this? If so, please help me with it
Btw Nesterov is to implement an accelerating learning rate ,I think
Can you post a video on how to extract entities from intents and deploy the chatbot on a website or something. thanks
I'm trying to create a chatbot for customer care, it is going to be a live support agent for industries like Telecom companies, so after I finish with all the knowledge you have shared maybe I will have some questions and everything else.