Will Keras 3.0 save TensorFlow’s popularity among developers?

All answers to this question.

Keras 3 is a massive "reset" for the industry. By abstracting the backend, it removes the biggest barrier to entry for TensorFlow: the fear of being "locked in." In my projects this year, I’ve started writing everything in Keras 3. I can prototype in PyTorch because I like its debugging tools, but then I flip the switch to TensorFlow for the final production export because of its superior deployment pipeline. This "Framework Agnostic" approach is the smartest move Google has made in years to keep the TensorFlow ecosystem relevant in a world that was rapidly shifting toward PyTorch.

   Answered 2025-02-05 by Kimberly Dawson

But what about custom layers? If I write a very specific CUDA kernel or a complex custom loss function in PyTorch, will it actually translate perfectly to the JAX or TensorFlow backends? Or is this "universal Keras" only good for standard, "cookie-cutter" neural networks?

   Answered 2025-02-07 by Lawrence Black

• Lawrence, you've found the catch. For standard layers, it’s seamless. But for high-level customization, you still have to use "backend-agnostic" ops provided by Keras. If you go deep into Torch-specific low-level code, you’ll lose that portability. However, Keras 3 includes a huge library of these agnostic ops that cover about 95% of what most developers actually need for daily work.

     Commented 2025-02-08 by Shawn Miller

Interoperability is the winner here. Keras 3 makes the 'Which is better?' question obsolete. The focus is shifting back to model architecture rather than library syntax.

   Answered 2025-02-10 by Diane Warren

• Exactly, Diane. It allows us to stop arguing about syntax and start focusing on solving actual business problems. It’s a huge win for the community as a whole.

     Commented 2025-02-11 by Kimberly Dawson