nessai.flows.maf
================

.. py:module:: nessai.flows.maf

.. autoapi-nested-parse::

   Implementation of MaskedAutoregressiveFlow.

   ..
       !! processed by numpydoc !!


Classes
-------

.. autoapisummary::

   nessai.flows.maf.MaskedAutoregressiveFlow


Module Contents
---------------

.. py:class:: MaskedAutoregressiveFlow(features, hidden_features, num_layers, num_blocks_per_layer, context_features=None, use_residual_blocks=True, use_random_masks=False, use_random_permutations=False, activation=F.relu, dropout_probability=0.0, batch_norm_within_layers=False, batch_norm_between_layers=False)

   Bases: :py:obj:`nessai.flows.base.NFlow`


   
   Autoregressive flow with masked coupling transforms.

   Based on the implementation from nflows:         https://github.com/bayesiains/nflows/blob/master/nflows/flows/autoregressive.py
       but also included context features.

   :Parameters:

       **features** : int
           Number of features (dimensions) in the data space

       **hidden_features** : int
           Number of neurons per layer in each neural network

       **num_layers** : int
           Number of coupling transformations

       **num_blocks_per_layer** : int
           Number of layers (or blocks for resnet) per neural network for
           each coupling transform

       **context_features** : int, optional
           Number of context (conditional) parameters.

       **use_residual_blocks** : bool, optional
           Use residual blocks in the MADE network.

       **use_random_masks** : bool, optional
           Use random masks in the MADE network.

       **use_random_permutation** : bool, optional
           Use a random permutation instead of the default reverse permutation.

       **activation** : function, optional
           Activation function implemented in torch.

       **dropout_probability** : float, optional
           Dropout probability used in each layer of the neural network

       **batch_norm_within_layers** : bool, optional
           Enable or disable batch norm within the neural network for each coupling
           transform

       **batch_norm_between_layers** : bool, optional
           Enable or disable batch norm between coupling transforms














   ..
       !! processed by numpydoc !!