How to build a preprocessing layer with different preprocessing for each feature?

TensorFlow General Discussion
1

Hi Team!!

My autoencoder model has numerical, categorical and text features. I am doing a normalization for numerical, custom encoding for categorical and BERT tokenization for text as preprocessing.

I want to build a preprocessing layer with different custom preprocessing for each feature. All the examples on documentation (e.g. https://www.youtube.com/watch?v=GVShIIh3_yE) I see is for uniform attributes like full text or numerical dataset.

Please help me on this.

3

Hi @saurabh_tripathy,

Sorry for the delay in response.

I recommend going through this feature preprocessing tutorial, as it provides techniques that will be applicable to your use case.

Hope this helps.Thank You.

4

When building an autoencoder that handles numerical, categorical, and text features, it’s essential to preprocess each feature type separately before integrating them into the model. Leveraging TensorFlow’s tf.keras.layers and preprocessing API facilitates the implementation of custom preprocessing for each feature within a unified preprocessing layer.

Text Features:

  • Tokenization: Utilize TensorFlow Text’s BertTokenizer to tokenize and preprocess text data.
  • Pre-trained Embeddings: If enhanced text representation is required, consider incorporating pre-trained BERT embeddings.

Advantages of This Approach:

  • Modularity: Processing each feature type independently ensures a structured and manageable workflow.
  • Customizability: This framework is adaptable, allowing for the integration of more complex preprocessing steps as needed.
  • Compatibility: A well-constructed preprocessing layer integrates seamlessly into your autoencoder, facilitating end-to-end training.

Recommendations:

  1. Tokenizer and Model Selection: Ensure the appropriate BERT tokenizer and embedding model are selected to align with your specific text data requirements.
  2. Efficient Data Handling: For large datasets, employ TensorFlow’s tf.data pipelines to manage batching and shuffling efficiently.
  3. Sequence Management: Adapt preprocessing functions to address specific needs, such as padding text sequences to maintain uniform input lengths.

By following this structured approach, you can enhance the efficiency, flexibility, and scalability of your autoencoder model, leading to improved performance in handling diverse data types. I can send you the sources I use if you still need help.