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