.. _init:

.. currentmodule:: mlx.nn.init

Initializers
------------

The ``mlx.nn.init`` package contains commonly used initializers for neural
network parameters. Initializers return a function which can be applied to any
input :obj:`mlx.core.array` to produce an initialized output.

For example:

.. code:: python

   import mlx.core as mx
   import mlx.nn as nn

   init_fn = nn.init.uniform()

   # Produces a [2, 2] uniform matrix
   param = init_fn(mx.zeros((2, 2)))

To re-initialize all the parameter in an :obj:`mlx.nn.Module` from say a uniform 
distribution, you can do:

.. code:: python
  
   import mlx.nn as nn
   model = nn.Sequential(nn.Linear(5, 10), nn.ReLU(), nn.Linear(10, 5))
   init_fn = nn.init.uniform(low=-0.1, high=0.1)
   model.apply(init_fn)
   

.. autosummary::
   :toctree: _autosummary

   constant
   normal
   uniform
   identity
   glorot_normal
   glorot_uniform
   he_normal
   he_uniform
   sparse
   orthogonal
