keras

Keras high-level neural network API. Use for deep learning.

7 stars

byG1Joshi

View on GitHub Installation ↓

Best use case

keras is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Keras high-level neural network API. Use for deep learning.

Teams using keras should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

You only need a quick one-off answer and do not need a reusable workflow.
You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/keras/SKILL.md --create-dirs "https://raw.githubusercontent.com/G1Joshi/Agent-Skills/main/skills/ai-ml/keras/SKILL.md"

Manual Installation

Download SKILL.md from GitHub
Place it in .claude/skills/keras/SKILL.md inside your project
Restart your AI agent — it will auto-discover the skill

How keras Compares

Feature / Agent	keras	Standard Approach
Platform Support	Not specified	Limited / Varies
Context Awareness	High	Baseline
Installation Complexity	Unknown	N/A

Frequently Asked Questions

What does this skill do?

Keras high-level neural network API. Use for deep learning.

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# 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**: Use `keras.ops.*` to remain framework-agnostic.

## References

- [Keras Documentation](https://keras.io/)