.. _initialisers: Initialisers ============ Weight initialisation is crucial for training neural networks effectively. Proper initialisation helps prevent vanishing or exploding gradients and can significantly impact convergence speed. The athena library provides various initialisation strategies suited for different activation functions and network architectures. .. toctree:: :maxdepth: 1 :caption: Available Initialisers glorot_uniform glorot_normal he_uniform he_normal lecun_uniform lecun_normal zeros ones identity gaussian Creating custom initialisers ---------------------------- The athena library is designed with extensibility in mind, allowing users to create custom initialisers by extending the ``base_initialiser_type``. See the tutorial: :ref:`Creating Custom Initialisers `