Package flow

This project implements basic Normalizing Flows in PyTorch and provides functionality for defining your own easily, following the conditioner-transformer architecture.

This is specially useful for lower-dimensional flows and for learning purposes. Nevertheless, work is being done on extending its functionalities to also accomodate for higher dimensional flows.

Supports conditioning flows, meaning, learning probability distributions conditioned by a given conditioning tensor. Specially useful for modelling causal mechanisms.

Expand source code
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from .flow import Flow, Sequential, Transformer, Conditioner, inv_flow

Sub-modules

flow.conditioner

Implementations for Flow-Conditioners …

flow.flow

Abstract classes for the implementation of Flows …

flow.modules

Miscellaneous Flows.

flow.prior

Abstract class for U priors and implementations for common-use priors …

flow.training

Train utilities for flows …

flow.transformer

Implementations for Flow-transformers …

flow.utils

Miscellaneous utility functions.