Torchflows is a library for generative modeling and density estimation using normalizing flows. It implements many normalizing flow architectures and their building blocks for:
- easy use of normalizing flows as trainable distributions;
- easy implementation of new normalizing flows.
Example use:
import torch
from torchflows.flows import Flow
from torchflows.architectures import RealNVP
torch.manual_seed(0)
n_data = 1000
n_dim = 3
x = torch.randn(n_data, n_dim) # Generate some training data
bijection = RealNVP(n_dim) # Create the bijection
flow = Flow(bijection) # Create the normalizing flow
flow.fit(x) # Fit the normalizing flow to training data
log_prob = flow.log_prob(x) # Compute the log probability of training data
x_new = flow.sample(50) # Sample 50 new data points
print(log_prob.shape) # (100,)
print(x_new.shape) # (50, 3)Check examples and documentation, including the list of supported architectures here. We also provide examples here.
We support Python versions 3.7 and upwards.
Install Torchflows via pip:
pip install torchflows
Install Torchflows directly from Github:
pip install git+https://github.com/davidnabergoj/torchflows.git
Setup for development:
git clone https://github.com/davidnabergoj/torchflows.git
cd torchflows
pip install -r requirements.txt