flowjax

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Normalising flow implementations in jax. Training a flow can be done in a few lines of code

from flowjax.flows import BlockNeuralAutoregressiveFlow
from flowjax.train_utils import train_flow
from flowjax.distributions import Normal
from jax import random

data_key, flow_key, train_key = random.split(random.PRNGKey(0), 3)

x = random.uniform(data_key, (10000, 3))  # Toy data
flow = BlockNeuralAutoregressiveFlow(flow_key, Normal(3))
flow, losses = train_flow(train_key, flow, x, learning_rate=0.05)

# We can now evaluate the log-probability of arbitrary points
flow.log_prob(x)

So far the package supports the following:

• Affine coupling flows/RealNVP for conditional/unconditional density estimation and sampling (Dinh et al.)

• Neural spline coupling flows for conditional/unconditional density estimation and sampling (Durkan et al.)

• Block neural autoregressive flows for conditional/unconditional density estimation (De Cao et al.)

For more detailed examples, see examples.

Installation

pip install flowjax

Authors

flowjax was written by Daniel Ward <danielward27@outlook.com>.