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assets
experiments
models
.gitignore
README.md
config.py
data_loader.py
download.py
evaluations.py
folder.py
layers.py
main.py
netgen.py
netgenrunner.py
preprocessing.py
runner.py
tessellater.py
trainer.py
trainer_config.py
utils.py

README.md

Tensorflow ConnectorGraph Framework

The ConnectorGraph Framework is an extension of Tensorflow that is designed to handle large-scale deep neural networks that are composed of multiple, reusable subnetworks (called SubGraphs).

ConnectorGraph adds a number of useful features to stock Tensorflow:

  • a simplified interface for constructing large-scale, deep neural networks out of reusable components (SubGraphs)
  • a simplified interface for building reusable SubGraphs that are made of multiple Tensorflow Ops (e.g., an autoencoder)
  • a simpler framework for sharing and reusing pretrained Tensorflow neural networks with and without freezing
  • a simpler framework for incremental training routines where different parts of the network are trained and fixed before other parts of the network are trained
  • a hierarchically symmetric structure where ConnectorGraphs can be used as the SubGraphs of yet larger ConnectorGraphs

The models folder comes with 5 SubGraphs that construct the BEGAN Tensorflow model and an extension called the Scaled BEGAN GMSM as well as the associated ConnectorGraph composition file. Below are the results of the Scaled BEGAN GMSM after training for ~200k epochs on 64X64px images from the CelebA dataset.

Raw output

Generator output

Interpolations

Generator interpolations

This code was originally forked and built off of the BEGAN Tensorflow model. The ConnectorGraph framework was inspired by my own work building on Taehoon Kim's original code.

Requirements

  • Python 2.7
  • Pillow
  • tqdm
  • TensorFlow 1.1.0 (Need nightly build which can be found in here, if not you'll see ValueError: 'image' must be three-dimensional.)

Usage (to train)

First download CelebA datasets with:

$ apt-get install p7zip-full # ubuntu
$ brew install p7zip # Mac
$ python download.py

or you can use your own dataset by placing images like:

data
└── YOUR_DATASET_NAME
    ├── xxx.jpg (name doesn't matter)
    ├── yyy.jpg
    └── ...

For the original BEGAN model, enter the following in a python interpreter in the ConnectorGraph folder:

from main import *
t = main(model='cqs_cg', type_='began_b16_z128_sz64_h128_g0.5')

For multiple GPU setups, manually select which GPU you would like to use using the following Bash command:

export CUDA_VISIBLE_DEVICES=#

Set '#' to the index of the desired device.

...More details to come...

Related works

Author

Michael O. Vertolli / @MichaelOVertolli