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Deep generative models implemented with TensorFlow 2.0: eg. Restricted Boltzmann Machine (RBM), Deep Belief Network (DBN), Deep Boltzmann Machine (DBM), Convolutional Variational Auto-Encoder (CVAE), Convolutional Generative Adversarial Network (CGAN)

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Deep Generative Models using TensorFlow 2.0

This repository contains TensorFlow 2.0 python source-code for deep generative models. This repository prioritizes using low-level tensorflow implementations where possible. The intended purpose is to allow for a more in-depth understanding of corresponding algorithms.

Note: Source code in this repository was only tested on the CPU.

Dependencies

This repository's source code was tested with Python versions 3.7.*.

  1. Install python dependencies located in requirements.txt:

    $ pip install -r requirements.txt
  2. Optional: To develop this repository, it is recommended to initialize a pre-commit hook for automatic updates of python dependencies:

    $ ./init.sh

Workflow

Information regarding execution of python scripts can be found in the readme in the /src directory.

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Deep generative models implemented with TensorFlow 2.0: eg. Restricted Boltzmann Machine (RBM), Deep Belief Network (DBN), Deep Boltzmann Machine (DBM), Convolutional Variational Auto-Encoder (CVAE), Convolutional Generative Adversarial Network (CGAN)

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