Keras
Keras 3 is a multi-backend deep learning framework, with support for JAX, TensorFlow, and PyTorch. Effortlessly build and train models for computer vision, natural language processing, audio processing, timeseries forecasting, recommender systems, etc.
- Accelerated model development: Ship deep learning solutions faster thanks to the high-level UX of Keras and the availability of easy-to-debug runtimes like PyTorch or JAX eager execution.
- State-of-the-art performance: By picking the backend that is the fastest for your model architecture (often JAX!), leverage speedups ranging from 20% to 350% compared to other frameworks. Benchmark here.
- Datacenter-scale training: Scale confidently from your laptop to large clusters of GPUs or TPUs.
pip install keras
Docs
Optimizers
Loss Functions
The purpose of loss functions is to compute the quantity that a model should seek to minimize during training.
Note that all losses are available both via a class handle and via a function handle. The class handles enable you to pass configuration arguments to the constructor (e.g. loss_fn = CategoricalCrossentropy(from_logits=True)), and they perform reduction by default when used in a standalone way (see details below).
Probabilistic losses
- BinaryCrossentropy class
- BinaryFocalCrossentropy class
- CategoricalCrossentropy class
- CategoricalFocalCrossentropy class
- SparseCategoricalCrossentropy class
- Poisson class
- binary_crossentropy function
- categorical_crossentropy function
- sparse_categorical_crossentropy function
- poisson function
- KLDivergence class
- kl_divergence function
- CTC class
Regression losses
- MeanSquaredError class
- MeanAbsoluteError class
- MeanAbsolutePercentageError class
- MeanSquaredLogarithmicError class
- CosineSimilarity class
- mean_squared_error function
- mean_absolute_error function
- mean_absolute_percentage_error function
- mean_squared_logarithmic_error function
- cosine_similarity function
- Huber class
- huber function
- LogCosh class
- log_cosh function
Hinge losses for "maximum-margin" classification
- Hinge class
- SquaredHinge class
- CategoricalHinge class
- hinge function
- squared_hinge function
- categorical_hinge function