Keras is a high-level deep learning API designed for human-centered, intuitive model building. Originally created as an independent library and later integrated into TensorFlow, Keras enables fast experimentation with neural networks through its Sequential, Functional, and Subclassing APIs. It abstracts the complexity of backend tensor operations while providing full control for advanced use cases, making it the go-to framework for both beginners and production environments. The key insight: Keras treats models as composable graphs of layers—once you understand this mental model, building anything from simple classifiers to multi-input transformer architectures becomes remarkably straightforward.
What This Cheat Sheet Covers
This topic spans 27 focused tables and 151 indexed concepts. Below is a complete table-by-table outline of this topic, spanning foundational concepts through advanced details.
Table 1: Model APIs
Keras gives you three ways to define a model, and which one you reach for is mostly a question of how much flexibility you need. Sequential is the quick path for a simple stack of layers, the Functional API draws an explicit graph for anything with branches or shared layers, and Subclassing hands you a Python call method for fully dynamic architectures.
| API | Example | Description |
|---|---|---|
model = Sequential([Dense(64), Dense(10)]) | • Linear stack of layers • simplest API for single-input, single-output models with no branching or sharing | |
x = Input(shape=(784,))y = Dense(10)(x)model = Model(x, y) | • Explicit layer graph definition • enables multi-input/output, shared layers, and residual connections • Most flexible for production |