Keras: The Python Deep Learning API

About Keras Getting started Developer guides Code examples Keras 3 API documentation Keras 2 API documentation KerasTuner: Hyperparam Tuning KerasHub: Pretrained Models

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KERAS 3.0 RELEASED A superpower for ML developers

Keras is a deep learning API designed for human beings, not machines. Keras focuses on debugging speed, code elegance & conciseness, maintainability, and deployability. When you choose Keras, your codebase is smaller, more readable, easier to iterate on.

API DOCS GUIDES EXAMPLES Copied inputs = keras.Input(shape=(32, 32, 3)) x = layers.Conv2D(32, 3, activation="relu")(inputs) x = layers.Conv2D(64, 3, activation="relu")(x) residual = x = layers.MaxPooling2D(3)(x) x = layers.Conv2D(64, 3, padding="same")(x) x = layers.Activation("relu")(x) x = layers.Conv2D(64, 3, padding="same")(x) x = layers.Activation("relu")(x) x = x + residual x = layers.Conv2D(64, 3, activation="relu")(x) x = layers.GlobalAveragePooling2D()(x) outputs = layers.Dense(10, activation="softmax")(x) model = keras.Model(inputs, outputs, name="mini_resnet") keras.utils.plot_model(model, "mini_resnet.png") model.fit(dataset, epochs=10) Run quickstart Copied causal_lm = keras_hub.models.CausalLM.from_preset( "gemma2_instruct_2b_en", dtype="float16", ) prompt = """<start_of_turn>user Write python code to print the first 100 primes. <end_of_turn> <start_of_turn>model """ text_output = causal_lm.generate(prompt, max_length=512) text_to_image = keras_hub.models.TextToImage.from_preset( "stable_diffusion_3_medium", dtype="float16", ) prompt = "Astronaut in a jungle, detailed" image_output = text_to_image.generate(prompt) Run quickstart

Welcome to multi-framework machine learning

With its multi-backend approach, Keras gives you the freedom to work with JAX, TensorFlow, and PyTorch. Build models that can move seamlessly across these frameworks and leverage the strengths of each ecosystem.

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Developer Guides

VIEW ALL Copied inputs = keras.Input(shape=(28, 28, 1)) x = inputs x = layers.Conv2D(16, 3, activation="relu")(x) x = layers.Conv2D(32, 3, activation="relu")(x) x = layers.MaxPooling2D(3)(x) x = layers.Conv2D(32, 3, activation="relu")(x) x = layers.Conv2D(16, 3, activation="relu")(x) x = layers.GlobalMaxPooling2D()(x) x = layers.Dropout(0.5) outputs = layers.Dense(10) model = keras.Model(inputs, outputs) model.summary()

The Functional API

Starting from the beginning and learn how to build models using the functional building pattern.

VIEW GUIDE Copied model.compile( optimizer="rmsprop", loss="categorical_crossentropy", metrics=["accuracy"], ) history = model.fit( x_train, y_train, batch_size=64, epochs=2, validation_data=(x_val, y_val), )

Training & evaluation with the built-in methods

Train and evaluate your model using model.fit(...).

VIEW GUIDE Copied class MLPBlock(keras.layers.Layer): def __init__(self): super().__init__() self.dense_1 = layers.Dense(32) self.dense_2 = layers.Dense(32) self.dense_3 = layers.Dense(1) def call(self, inputs): x = self.dense_1(inputs) x = keras.activations.relu(x) x = self.dense_2(x) x = keras.activations.relu(x) return self.dense_3(x)

Making new layers and models via subclassing

Learn how to customize your model via subclassing Keras layers.

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KerasHub

The KerasHub library provides Keras 3 implementations of popular model architectures, paired with a collection of pretrained checkpoints available on Kaggle Models. Models can be used for both training and inference, on any of the TensorFlow, JAX, and PyTorch backends.

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GEMMA

Google’s family of lightweight language models built from the same research and technology used to create Gemini.

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LLAMA

Meta’s flagship open text generation models available in a wide range of sizes and precisions.

VIEW DOCUMENTATION KAGGLE DETAILS

STABLE DIFFUSION

Generate image content with this state of the art diffusion model from Stability AI.

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MISTRAL

A generative language from the French company Mistral AI, making frontier models accessible to all.

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Code examples

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Computer vision

Take a look at our examples for doing image classification, object detection, video processing, and more.

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Natural Language Processing

We also have many guides for doing NLP including text classification, machine translation, and language modeling.

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Generative Deep Learning

Get started with generative deep learning with our wealth of guides involving state-of-the-art diffusion models, GANs, and transformer models.

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Trusted for research and production

Keras is used by CERN, NASA, NIH, and many more scientific organizations around the world (and yes, Keras is used at the Large Hadron Collider). Keras is used by Waymo to power self-driving vehicles. Keras partners with Kaggle and HuggingFace to meet ML developers in the tools they use daily.

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Stay in touch

Sign up to our mailing list for regular updates and discussions about the Keras ecosystem. Listen in at our community meetings, and follow us on social media!

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Contributions welcome!

We welcome your code, ideas, and feedback as we continue to grow. Visit our roadmap, contribution guide or GitHub for more information.

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