nessai.flows.maf
Implementation of MaskedAutoregressiveFlow.
Module Contents
Classes
Autoregressive flow with masked coupling transforms. |
- class nessai.flows.maf.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:
nessai.flows.base.NFlowAutoregressive 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:
- featuresint
Number of features (dimensions) in the data space
- hidden_featuresint
Number of neurons per layer in each neural network
- num_layersint
Number of coupling transformations
- num_blocks_per_layerint
Number of layers (or blocks for resnet) per neural network for each coupling transform
- context_featuresint, optional
Number of context (conditional) parameters.
- use_residual_blocksbool, optional
Use residual blocks in the MADE network.
- use_random_masksbool, optional
Use random masks in the MADE network.
- use_random_permutationbool, optional
Use a random permutation instead of the default reverse permutation.
- activationfunction, optional
Activation function implemented in torch.
- dropout_probabilityfloat, optional
Dropout probability used in each layer of the neural network
- batch_norm_within_layersbool, optional
Enable or disable batch norm within the neural network for each coupling transform
- batch_norm_between_layersbool, optional
Enable or disable batch norm between coupling transforms