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AdaNet is a TensorFlow framework for fast and flexible AutoML with learning guarantees. AdaNet is a lightweight TensorFlow-based framework for automatically learning high-quality models with minimal expert intervention. AdaNet builds on recent AutoML efforts to be fast and flexible while providing learning guarantees. Importantly, AdaNet provides a general framework for not only learning a neural network architecture but also for learning to the ensemble to obtain even better models. At each iteration, it measures the ensemble loss for each candidate, and selects the best one to move onto the next iteration. Adaptive neural architecture search and ensemble learning in a single train call. Regression, binary and multi-class classification, and multi-head task support. A tf.estimator.Estimator API for training, evaluation, prediction, and serving models.
Features
- Provide familiar APIs (e.g. Keras, Estimator) for training, evaluating, and serving models
- Scale with available compute and quickly produce high quality models
- Allow researchers and practitioners to extend AdaNet to novel subnetwork architectures, search spaces, and tasks
- Optimize an objective that offers theoretical learning guarantees
- Learning guarantees
- Ease of use
Project Samples
