Question 1

How do I use Tensorflow-Project-Template with my own dataset?

Accepted Answer

Extend the DataGenerator class in data_loader/data_generator.py to handle your data format and preprocessing, then pass it to the trainer instance in main.py, following the example structure.

Question 2

Tensorflow-Project-Template or Keras for quick prototyping?

Accepted Answer

Keras offers higher-level APIs for faster model building and training, while this template provides more structure and control, making it better for larger, maintainable projects but slower for initial experiments.

Question 3

Does this template work with TensorFlow 2.x?

Accepted Answer

It uses TensorFlow 1.x-style sessions and savers, so you may need to enable compatibility modes or refactor code for TF 2.x's eager execution; check the GitHub issues for community updates.

Question 4

How to add custom metrics to the logger?

Accepted Answer

In your trainer's train_step or train_epoch methods, create a dictionary of TensorFlow tensors you want to track and pass it to logger.summarize(), as outlined in the logger.py documentation.

Question 5

Is Comet.ml integration mandatory?

Accepted Answer

No, Comet.ml is optional; you can disable it by leaving the comet_api_key empty in the JSON config file, and rely solely on TensorBoard for logging.

Question 6

Can I use this for production deployments?

Accepted Answer

It provides a solid foundation for structuring training pipelines, but you'll need to add custom code for model serving, monitoring, and integration with deployment platforms like TensorFlow Serving.

Tensorflow-Project-Template

What is Tensorflow-Project-Template?

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