autoencoders.py

from tensorflow.keras.models import Model
from tensorflow.keras.layers import Input, Embedding, LSTM, Dense

# Define input shape
input_dim = # Define your vocabulary size or embedding input size
latent_dim = 256  # Dimension of the latent space representation

# Encoder
inputs = Input(shape=(max_sequence_length,))
embedding = Embedding(input_dim, 100)(inputs)  # Adjust embedding size as needed
lstm = LSTM(latent_dim)(embedding)
encoder_output = Dense(latent_dim, activation='relu')(lstm)

# Decoder
decoder_input = Dense(max_sequence_length, activation='relu')(encoder_output)
decoder_output = LSTM(input_dim, return_sequences=True)(decoder_input)

# Autoencoder model
autoencoder = Model(inputs, decoder_output)
autoencoder.compile(optimizer='adam', loss='categorical_crossentropy')

# Train the autoencoder
autoencoder.fit(train_data, train_data, epochs=10, batch_size=32, validation_data=(val_data, val_data))