cite us using bibtex

Train invertible generative models using the simplicity of Keras (aka normalizing flows).

Example: Invertible Residual Networks.

from invtf import Generator, InvResNet, faces

gen = Generator()

for _ in range(10): 
    gen.add(InvResNet())

gen.compile()
gen.fit(faces())

Most recent invertible generative model [1,2,3,4] have been reproduced in InvTF. Pretrained models are automatically downloaded when needed.

Example: Use pretrained model.

from invtf import Glow

glow.interpolate(faces()[0], faces()[1])

glow.generate(10)

Please see our tutorial and documentation for more information.

TLDR: Easily train reversible generative models with Keras.

"invtf/" the package directory.

"articles/": notes / ipython notebook explaining previous articles reproducing their experiments in ipython notebooks.

"examples": implementation of a lot of examples, naming conforms with "dataset" x "model".py examples: "syntheticNormal_feedforward.py" "cifar10_glow.py" Besides show-casing how the framework can be used, these files are also used for test casing, just run 'run_all.py' (might take itme).

Developed using tensorflow-gpu 2.0beta, ubuntu 16.04 LTS, cuda 10, RTX 2080 8 GB