I got this error: logits and labels must have the same first dimension, got logits shape [3,21] and labels shape [15] when trying to train my chatbot I created using SimpleRNN layers that tensorflow provided. I’ve made sure that I used padding and proper tokenization to make inputs the same size, but I still got this error. My code is below.

import tensorflow as tf
import numpy as np
import json

# Step 1: Prepare the Data
questions = ["What is your name?", "How are you?", "What is the time?"]
answers = ["My name is ChatBot.", "I am fine, thank you.", "I do not have a watch."]

with open('q.txt', 'w') as q_file, open('a.txt', 'w') as a_file:
    for q, a in zip(questions, answers):
        q_file.write(q + 'n')
        a_file.write(a + 'n')

# Step 2: Preprocess the Data
with open('q.txt', 'r') as q_file:
    questions = q_file.readlines()
with open('a.txt', 'r') as a_file:
    answers = a_file.readlines()

tokenizer = tf.keras.preprocessing.text.Tokenizer()
tokenizer.fit_on_texts(questions + answers)

q_sequences = tokenizer.texts_to_sequences(questions)
a_sequences = tokenizer.texts_to_sequences(answers)

q_sequences = tf.keras.preprocessing.sequence.pad_sequences(q_sequences, padding='post')
a_sequences = tf.keras.preprocessing.sequence.pad_sequences(a_sequences, padding='post')

q_sequences = np.array(q_sequences)
a_sequences = np.array(a_sequences)

# Step 3: Build the Model
model = tf.keras.Sequential([
    tf.keras.layers.Embedding(input_dim=len(tokenizer.word_index) + 1, output_dim=64, input_length=q_sequences.shape[1]),
    tf.keras.layers.SimpleRNN(64, return_sequences=True),
    tf.keras.layers.SimpleRNN(64),
    tf.keras.layers.Dense(len(tokenizer.word_index) + 1, activation='softmax')
])

model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
model.summary()

# Step 4: Train the Model
# Adjust labels to match the model's output
# Since we are predicting the next token, slice answers accordingly
model.fit(q_sequences, a_sequences[:, 1:], epochs=10)

# Step 5: Save the Weights and Biases
model.save('chatbot_model.h5')

weights_and_biases = {}
for layer in model.layers:
    if isinstance(layer, tf.keras.layers.SimpleRNN) or isinstance(layer, tf.keras.layers.Dense):
        weights_and_biases[layer.name] = {
            'weights': layer.get_weights()[0].tolist(),
            'biases': layer.get_weights()[1].tolist()
        }

with open('weights_and_biases.json', 'w') as json_file:
    json.dump(weights_and_biases, json_file)

# Step 6: Custom Feedforward Function
def custom_feedforward(input_sequence, weights_and_biases):
    def relu(x):
        return np.maximum(0, x)

    def softmax(x):
        e_x = np.exp(x - np.max(x))
        return e_x / e_x.sum(axis=-1, keepdims=True)

    w_rnn1 = np.array(weights_and_biases['simple_rnn']['weights'])
    b_rnn1 = np.array(weights_and_biases['simple_rnn']['biases'])

    hidden_state = np.zeros((w_rnn1.shape[1],))
    for t in range(input_sequence.shape[0]):
        hidden_state = relu(np.dot(input_sequence[t], w_rnn1) + b_rnn1 + np.dot(hidden_state, w_rnn1.T))

    w_dense = np.array(weights_and_biases['dense']['weights'])
    b_dense = np.array(weights_and_biases['dense']['biases'])

    output = softmax(np.dot(hidden_state, w_dense) + b_dense)
    
    return output

# Example usage of the custom feedforward function
input_sequence = q_sequences[0]
output = custom_feedforward(input_sequence, weights_and_biases)
print(output)

Error happened on model.fit line.

I was expecting the program to train through the 20 epochs properly without any size errors.