Keras
Open SourceDeep Learning for humans. .
Published 29 May 2026 · Last updated 27 September 2026
Scores
Popularity4/5
~64K GitHub stars (2026-05-01); ~14M PyPI downloads/month; widely used in industry and education. Score 4 — very established but below PyTorch's research dominance and TensorFlow's enterprise reach.
Learning Curve2/5
Consistently ranked the most beginner-friendly deep learning framework — `model.fit()` and the Sequential API require very little prior knowledge. Score 2 (not 1) because multi-backend switching and subclassing still demand understanding of underlying ML concepts.
Flexibility3/5
Three API styles cover a wide range, but the abstraction layer means very custom low-level operations require dropping down to raw backend code. Score 3 reflects solid configurability within conventions, not full flexibility.
Performance3/5
Some overhead from the high-level abstraction vs raw PyTorch or JAX. JAX backend tends to be fastest; TF backend slightly slower. Keras 3 benchmarks improved over Keras 2 but still below bare-metal backends for highly optimised workloads.
Portability5/5
Keras 3 is the standout story: export to TF SavedModel, PyTorch nn.Module, JAX function, or LiteRT for on-device — the broadest portability of any high-level DL API. Score 5.
About Keras
Keras is a high-level deep learning API built for developer experience: minimal code, clear error messages, and a gradual path from simple models to advanced ones. It offers three ways to build a model: the Sequential API (a stack of layers), the Functional API (a graph of layers for multi-input and multi-output models), and subclassing custom Layer or Model classes for full control. Training runs through the built-in model.fit() loop with callbacks, early stopping, and learning-rate schedules, or through a custom loop when needed.
Keras 3 made the library multi-backend. The same model code runs on TensorFlow, JAX, or PyTorch, selected with one configuration switch, and can be exported as a TensorFlow SavedModel, used as a PyTorch module, or run as a stateless JAX function. That lets a team prototype in Keras and hand the model to a group working in raw PyTorch or JAX without rewriting it. The keras.distribution API covers data and model parallelism on the JAX backend.
Around the core sit two companion libraries. KerasHub provides pre-trained models and presets for text, vision, and generation (Gemma, Llama, Qwen, Mistral, Stable Diffusion), and KerasTuner runs hyperparameter search with random, Bayesian, and Hyperband strategies.
Keras is free and open source under the Apache 2.0 licence, installed with pip alongside the backend of your choice. It is used in research at CERN and NASA and in production at Google, Waymo, and Spotify, and it remains a common first framework in university machine learning courses.
Key Features
- Multi-backend: run the same model on TensorFlow, JAX, or PyTorch with one config switch
- Sequential, Functional, and subclassing APIs for different levels of model complexity
- Built-in training loop (model.fit) with callbacks, early stopping, and learning-rate schedules
- KerasHub: pre-trained models and presets including Gemma, Llama, Qwen, Mistral, and Stable Diffusion
- KerasTuner: hyperparameter search with random, Bayesian, and Hyperband strategies
- Export to TensorFlow SavedModel, a PyTorch module, or a stateless JAX function
- keras.distribution API for data and model parallelism
Pros
- The most beginner-friendly deep learning API, with little boilerplate to train a working model
- Keras 3 removes backend lock-in: switch between TensorFlow, JAX, and PyTorch without rewriting models
- Readable, compact code that suits teaching and fast prototyping
- KerasHub gives ready access to major open-weight models
- Large library of tutorials, books, and course material
Cons
- The high-level abstraction limits fine-grained control for unusual research architectures
- Small performance overhead compared with writing directly in PyTorch or JAX for heavy training
- Most new research papers ship PyTorch code, so Keras ports often lag behind
- Backend-specific behaviour differences can surface when switching backends on complex models
Keras Pricing
Open SourceTech Stacks with Keras
TensorFlow ML Training
ProjectBuild and train ML models using TensorFlow and Keras, from prototyping in Jupyter notebooks to exported models ready for serving. MLflow or Weights & Biases can be added to track experiments and model versions once runs need comparing.
Tools Related to Keras
Works well with Keras(2)
Keras and scikit-learn are complementary: sklearn covers classical ML algorithms and preprocessing pipelines, Keras covers deep learning — a common pattern wraps a Keras model with the sklearn-compatible KerasClassifier/KerasRegressor API.
Jupyter Notebooks are the standard environment for Keras model development — the cell-by-cell workflow matches how practitioners iterate on layers, hyperparameters, and training curves.
Integrates with Keras(2)
MLflow autologging captures Keras training metrics, hyperparameters, and the saved model artifact automatically via mlflow.tensorflow.autolog() — no manual logging code required.
Keras training is commonly tracked with Weights & Biases — WandbCallback is the standard way to add experiment tracking to a Keras training loop with a single line.
Built on (1)
Keras is a Python deep learning API that runs on TensorFlow, JAX, or PyTorch as its backend.
Alternatives to Keras(2)
Keras is a high-level, backend-agnostic API that also runs on JAX and PyTorch; TensorFlow on its own adds lower-level control and its serving and mobile tooling. Pick Keras for portable, readable model code, TensorFlow for its deployment ecosystem.
Keras is a high-level API that can itself run on PyTorch as a backend and suits fast, readable prototyping; raw PyTorch gives full low-level control and is where most research code is published. Pick Keras for simplicity and backend portability, PyTorch for custom architectures.