Keras is a deep learning API designed for human beings, not machines. Keras focuses on debugging speed, code elegance & conciseness, maintainability, and deployability.

Keras Applications are deep learning models that are made available alongside pre-trained weights. These models can be used for prediction, feature extraction, and fine-tuning.

Keras follows the principle of progressive disclosure of complexity: it makes it easy to get started, yet it makes it possible to handle arbitrarily advanced use cases, only requiring incremental learning at …

Keras 3 is a full rewrite of Keras that enables you to run your Keras workflows on top of either JAX, TensorFlow, PyTorch, or OpenVINO (for inference-only), and that unlocks brand new large-scale …

Note that the backbone and activations models are not created with keras.Input objects, but with the tensors that originate from keras.Input objects. Under the hood, the layers and weights will be shared …

Basically, the SELU activation function multiplies scale (> 1) with the output of the keras.activations.elu function to ensure a slope larger than one for positive inputs.

Read our Keras developer guides. Are you looking for tutorials showing Keras in action across a wide range of use cases? See the Keras code examples: over 150 well-explained notebooks …

Apr 12, 2020 · # Define Sequential model with 3 layers model = keras.Sequential( [ layers.Dense(2, activation="relu", name="layer1"), layers.Dense(3, activation="relu", name="layer2"), layers.Dense(4, …

Keras requires that the output of such iterator-likes be unambiguous. The iterator should return a tuple of length 1, 2, or 3, where the optional second and third elements will be used for y and sample_weight …

They're one of the best ways to become a Keras expert. Most of our guides are written as Jupyter notebooks and can be run in one click in Google Colab, a hosted notebook environment that requires …