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Keras Review

Keras is a high-level deep learning API that supports rapid experimentation and user-friendliness across multiple backends, including JAX, TensorFlow, and PyTorch.

shipped Jul 23, 2026free
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Keras — product screenshot

Why it matters

1Keras 3 introduced multi-backend support for JAX, TensorFlow, and PyTorch.
2Models can be deployed across smartphones, web browsers, and Java Virtual Machines.
3Keras 3.13.0 (December 2025) requires Python 3.11 or higher.
4Overall user rating of 4.6 out of 5 stars on platforms like Capterra and Softwr.

Specs

GitHub

View Repository →

API Available

Yes, public API

overview

What is Keras?

Keras is a deep learning API tool developed by the Keras team that enables deep learning practitioners and researchers to build, train, and deploy neural networks. It simplifies the complexities associated with deep neural networks, allowing users to create models with minimal lines of code. Keras is an open-source deep learning API written in Python, designed to enable fast experimentation with neural networks. It prioritizes user-friendliness, modularity, and extensibility, making it accessible for both beginners and experienced practitioners in deep learning. The API serves as an interface for various deep learning frameworks, supporting backends such as JAX, TensorFlow, and PyTorch.

features

Key Features of Keras

Keras provides a high-level interface for building, training, and deploying deep learning models, emphasizing modularity and user-friendliness. Its design facilitates efficient debugging, concise code, and maintainability across various deep learning tasks.

  • High-level deep learning API for rapid experimentation.
  • Multi-backend support for JAX, TensorFlow, and PyTorch.
  • Modular and extensible architecture for custom neural network construction.
  • Built-in support for pre-trained models (e.g., VGG, ResNet, MobileNet) for computer vision.
  • New NumPy operations (e.g., unique, fabs, fmax, fmin) in keras.ops (Keras 3.x).
  • Keras-to-Torch export feature for native PyTorch nn.Module format (Keras 3.x).
  • Attention mechanisms with sliding_window parameter for MultiHeadAttention and GroupedQueryAttention (Keras 3.x).
  • Multi-Optimizer support for assigning different optimizers to sub-networks (Keras 3.x).
  • Native Flash Attention support for GPU (cuDNN) and TPU (Pallas kernel) in JAX backend (Keras 3.x).
  • Direct integration with Hugging Face Hub for model saving and loading (Keras 3.x).

use cases

Who Should Use Keras?

Keras is suitable for a broad range of users, from beginners in deep learning to experienced researchers and engineers, due to its emphasis on user-friendliness and modularity. Its capabilities support various applications in both academic and industrial settings.

  • Researchers and Developers: For rapid prototyping and experimentation with deep learning models, including those at CERN, NASA, and NIH.
  • Computer Vision Engineers: For tasks such as image classification, object detection, and facial recognition using pre-trained models like VGG and ResNet.
  • Natural Language Processing (NLP) Practitioners: For building systems like chatbots, translators, and text analyzers.
  • Educators and Students: Its intuitive API and extensive documentation make it ideal for learning and teaching deep learning concepts.
  • Production Engineers: For deploying models across various platforms, including smartphones (iOS and Android), web browsers, and Java Virtual Machines, as seen with Waymo for self-driving vehicles.

how to use

How to Use Keras

Keras provides a high-level API for constructing and training deep learning models with minimal code. Users typically define a model, compile it, and then train it using their data.

  • 1Install Keras: Install the Keras library via pip (pip install keras).
  • 2Choose a Backend: Select a backend (JAX, TensorFlow, or PyTorch) by setting the KERAS_BACKEND environment variable.
  • 3Define a Model: Construct a neural network using the Sequential API or the functional API, adding layers like Dense, Conv2D, or LSTM.
  • 4Compile the Model: Configure the learning process by specifying an optimizer (e.g., Adam), a loss function (e.g., categorical_crossentropy), and metrics (e.g., accuracy).
  • 5Train the Model: Fit the model to training data using the model.fit() method, providing input features and target labels.
  • 6Evaluate and Predict: Assess model performance on test data using model.evaluate() and make predictions on new data using model.predict().

pricing

Keras Pricing & Plans

Keras is an open-source deep learning API and is available for free. There are no subscription fees or usage-based charges associated with using the Keras library itself.

  • Keras: Free

Pros

  • +High-level API simplifies deep learning model construction with minimal code.
  • +Multi-backend support (JAX, TensorFlow, PyTorch) offers flexibility and performance choice.
  • +Strong emphasis on user-friendliness, modularity, and extensibility.
  • +Extensive documentation and active community support.
  • +Supports rapid prototyping and experimentation for deep learning ideas.
  • +Models can be deployed across diverse platforms including mobile, web, and JVMs.

Cons

  • −Mastering advanced features may require additional learning beyond the basic API.
  • −Abstracts away low-level details, which can limit fine-grained control for some users.
  • −Historically, performance could be slower than native backend frameworks for very large datasets, though Keras 3 aims to mitigate this.
  • −Debugging complex backend-specific errors can be challenging due to the abstraction layer.
  • −Requires Python 3.11 or higher starting with Keras 3.13.0 (December 2025).

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Keras vs Competitors

Keras is positioned as a high-level API that simplifies deep learning, offering a balance between ease of use and flexibility. It competes with other frameworks and libraries that aim to streamline deep learning development.

1
PyTorch LightningOn Stork

PyTorch Lightning provides a lightweight wrapper for PyTorch, structuring code to decouple research from engineering and simplifying scalable deep learning experiments.

While Keras offers a high-level API across multiple backends, PyTorch Lightning focuses on organizing native PyTorch code, offering more flexibility for complex research and multi-GPU strategies without sacrificing control. Both are free and open-source, targeting researchers and engineers.

2
FastAI↗

Built on PyTorch, FastAI provides high-level components and intelligent defaults to quickly achieve state-of-the-art results in common deep learning domains with minimal code.

FastAI focuses on providing full workflows for common machine learning tasks and bakes in best practices for faster training and higher accuracy, whereas Keras offers more modular building blocks for custom network construction. Both are free, but FastAI emphasizes ease of use for practitioners and researchers aiming for rapid prototyping.

3
Apache MXNet (Gluon API)↗

Gluon is a high-level API within Apache MXNet that offers a flexible, imperative programming model with dynamic computation graphs, allowing for easy prototyping and efficient training.

Unlike Keras's declarative model and static graphs, Gluon's imperative approach and dynamic graphs provide more flexibility for changing network architecture during runtime. Keras supports multiple backends, while Gluon is natively integrated with MXNet. Both are free and open-source.

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