Standardizes backend-agnostic tensor math for BayesFlow extensions using the Keras 3 functional API.
This skill ensures that all tensor operations within the BayesFlow ecosystem—including custom loss functions, neural network layers, and approximator overrides—adhere to Keras 3 multi-backend standards. By enforcing the use of keras.ops instead of framework-specific libraries like PyTorch, JAX, or NumPy, it guarantees that your models and extensions remain portable across different computational backends while implementing advanced patterns like Straight-Through Estimators (STE) and memory-efficient chunked log-probability calculations.
主要功能
01Enforces keras.ops for backend-agnostic tensor mathematics
02Prevents direct dependencies on PyTorch, JAX, or NumPy in compute paths
030 GitHub stars
04Implements memory-saving patterns like detached sampling and chunked processing
05Automates proper casting and shape handling for multi-backend compatibility
