ValueError: Failed to convert a NumPy array to a Tensor (Unsupported object type list)

Question

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

lemmatizer = WordNetLemmatizer()

intents = json.loads(open('intents.json', encoding='utf-8').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(words, 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:
        if word in word_patterns:
            bag.append(1) if word 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(256, input_shape=(len(train_x[0]),), activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(128, 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'])

model.fit(np.array(train_x), np.array(train_y), epochs=500, batch_size=5, verbose=1)
model.save('arthur.model')
print("Done")

I've been making a chatbot folowing this tutorial: https://www.youtube.com/watch?v=1lwddP0KUEg

However, It throws the error: ValueError: Failed to convert a NumPy array to a Tensor (Unsupported object type list).

I'm kind of new to numpy and Tensorflow, so I have no idea what they're about lol.

Answer 1

the code was creating the bag list incorrectly, basically, it was empty. please try the below code:

    for word in words:
        #if word in word_patterns: comment out this line
        bag.append(1) if word in word_patterns else bag.append(0) # and fix its identation

so your train list length will be 40(len(words)) and y of length 3(# no of classes). I hope will make sense.