nessai.flows.nsf
Implementation of Neural Spline Flows.
Module Contents
Classes
Implementation of Neural Spline Flow |
- class nessai.flows.nsf.NeuralSplineFlow(features, hidden_features, num_layers, num_blocks_per_layer, num_bins=8, context_features=None, activation=F.relu, dropout_probability=0.0, batch_norm_within_layers=False, batch_norm_between_layers=False, apply_unconditional_transform=False, linear_transform='permutation', tails='linear', tail_bound=5.0, distribution=None, **kwargs)
Bases:
nessai.flows.base.NFlowImplementation of Neural Spline Flow
See: https://arxiv.org/abs/1906.04032
- 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
- num_binsint, optional (8)
Number of bins to use for each spline
- context_featuresint, optional
Number of context (conditional) parameters.
- activationfunction
Activation function implemented in torch
- dropout_probabilityfloat, optional (0.0)
Dropout probability used in each layer of the neural network
- batch_norm_within_layersbool, optional (False)
Enable or disable batch norm within the neural network for each coupling transform
- batch_norm_between_layersbool, optional (False)
Enable or disable batch norm between coupling transforms
- linear_transform{‘permutation’, ‘lu’, ‘svd’}
Linear transform to use between coupling layers. Not recommended when using a custom mask.
- distribution:obj: glasflow.nflows.distributions.Distribution
Distribution for the latent space.
- kwargsdict
Additional kwargs parsed to the spline constructor, e.g. tails or tail_bound. See nflows for details