teras v0.3.1

• Add hidden layer to Classification and Regression heads to make training easy out of the box as currently using some backbones directly with Classification or Regression head was resulting in weird behavior.
• Update SAINTEmbedding layer to make it serializable
• Some bugs/typos fixes

teras v0.3.0

• Re-written from scratch to fully base it on Keras 3
• Now supports all popular ML backends, namely TensorFlow, JAX and PyTorch
• Docs re-written from scratch (A complete new look, totally didn't yoink from JAX)
• Removed almost all of janky/hacky code
• Removed LayerFlow API
• Add task independent Backbone models (idea taken from KerasCV)
• Loads of cleaning and much more!

Teras v0.2.0

• Redesign the whole library
• All the models for classification and regression are now made up of Keras Functional API
• LayerFlow API now serves as the parent to the default parametric API
• LayerFlow API now requires all the sub-layers needed for a model or non-atomic layer during instantiation
• This point above get rids of any discrepancies and assumptions made by the LayerFlow API by trying to plug customized and default layers together, often resulting in errors
• Make models saving and reloading compatible with the Keras V3 or .keras format
• This redesign is also a preparation step to make this library fully compatible with Keras Core and hence backend agnostic

Teras v0.1.1

Bugfixes with some LayerFlow models instantiations
Update dataframe_to_tf_dataset utility function to handle multi-label datasets

Teras v0.1.0

Hello world! It's the first Teras release!! Lets goooo!!