keras
Keras high-level neural network API. Use for deep learning.
Install
npx skillscat add g1joshi/agent-skills/keras Install via the SkillsCat registry.
We need to produce a 2-3 sentence plain-text summary, objective, factual, no marketing language, no superlatives, no calls to action. Must be at most 60 words. Summarize: Keras high-level neural network API, multi-backend (JAX, PyTorch, TensorFlow), allows writing once run anywhere, simplifies model building with model.fit, functional API, keras.ops for framework-agnostic ops. Problem solved: need for portable deep learning code across frameworks, reduces lock-in, simplifies usage.
Keras
Keras 3 is a game changer: it is now multi-backend. You can write Keras code and run it on top of JAX, PyTorch, or TensorFlow.
When to Use
- Portability: Write once, run on any framework.
- Simplicity:
model.fit()is still the cleanest API in the industry. - XLA: Keras 3 enables XLA compilation on all backends by default.
Core Concepts
Backend Agnostic
The Model is just a blueprint. You choose the engine at runtime.os.environ["KERAS_BACKEND"] = "jax"
Functional API
Defining models as a graph of layers: x = Dense()(inputs).
Keras Core (keras.ops)
A numpy-like API that works across all frameworks (differentiable numpy).
Best Practices (2025)
Do:
- Use Keras 3: Migrate from
tf.keras. - Use JAX backend: For fastest training on TPUs/GPUs.
- Use PyTorch backend: If you need to integrate into a larger PyTorch codebase.
Don't:
- Don't mix
tf.*ops: Usekeras.ops.*to remain framework-agnostic.
References
Install
npx skillscat add g1joshi/agent-skills/keras Install via the SkillsCat registry.