So to some extent it depends how complicated your model is. Tribuo's next release exposes TF models but wraps up all the fitting, evaluation and prediction in it's interface to make it a lot simpler. It's not the same as Keras, it's a little bit more like scikit-learn as we don't have callbacks in Tribuo.
However TF-Java will have this in the future, it's just a lot of stuff to build with a much smaller team than the Keras team.
int32. Other parts are
float64. Any ideas?
Okay, thank you.
Another question: My dataset is dynamically generated from a database. All online tutorials I saw focus on pulling in an existing dataset (e.g. MNIST) or from CSV files on disk. Neither seems like a good fit for my case. Should I be "streaming" data from the database to the model for training somehow? Or am I expected to construct a fixed-sized tensor and populate it column by column based on the database resultset?
jpypeto pass a Java
Streamover to the python API but then I have no way to convert it to a dataset.