Metadata-Version: 2.1
Name: TFGENZOO
Version: 1.2.4.post8
Summary: helper of building generative model with Tensorflow 2.x
Home-page: https://github.com/MokkeMeguru/TFGENZOO
Author: MokkeMeguru
Author-email: meguru.mokke@gmail.com
License: MIT
Description: # TFGENZOO (Generative Model x Tensorflow 2.x)
        
        ![img](https://github.com/MokkeMeguru/TFGENZOO/workflows/tensorflow%20test/badge.svg?branch=master)
        ![img](https://img.shields.io/badge/License-MIT-yellow.svg)
        ![img](https://img.shields.io/badge/python-3.7-blue.svg)
        ![img](https://img.shields.io/badge/tensorflow-%3E%3D2.2.0-brightgreen.svg)
        ![img](https://badge.fury.io/py/TFGENZOO.svg)
        
        # What&rsquo;s this repository?
        
        This is a repository for some researcher to build some Generative models using Tensorflow 2.x.
        
        I NEED YOUR HELP(please let me know about formula, implementation and anything you worried)
        
        ![img](https://raw.githubusercontent.com/MokkeMeguru/TFGENZOO/master/docs/tfgenzoo_header.png)
        
        # Zen of this repository
        
            We don't want to need flexible architectures.
            We need strict definitions for shapes, parameters, and formulas.
            We should Implement correct codes with well-documented(tested).
        
        # How to use?
        
        ## By Install
        
        - pipenv
        
              pipenv install TFGENZOO==1.2.4.post7
        
        - pip
        
              pip install TFGENZOO==1.2.4.post7
        
        ## Source build for development
        
        1.  clone this repository (If you want to do it, I will push this repository to PYPI)
        2.  build this repository `docker-compose build`
        3.  run the environment `sh run_script.sh`
        4.  connect it via VSCode or Emacs or vi or anything.
        
        # Examples
        
        - [TFGENZOO_EXAMPLE](https://github.com/MokkeMeguru/TFGENZOO_EXAMPLE)
        
        # Roadmap
        
        - [x] Flow-based Model Architecture (RealNVP, Glow)
        - [ ] i-ResNet Model Architecture (i-ResNet, i-RevNet)
        - [ ] GANs Model Architecture (GANs)
        
        # Remarkable Backlog
        
        Whole backlog is [here](https://github.com/MokkeMeguru/TFGENZOO/wiki/Backlog)
        
        ## News [2020/6/16]
        
        New training results [Oxford-flower102](https://www.tensorflow.org/datasets/catalog/oxford_flowers102) with only 8 hours! (Quadro P6000 x 1)
        
        <table border="2" cellspacing="0" cellpadding="6" rules="groups" frame="hsides">
        
        <colgroup>
        <col  class="org-left" />
        
        <col  class="org-right" />
        
        <col  class="org-right" />
        
        <col  class="org-left" />
        </colgroup>
        <thead>
        <tr>
        <th scope="col" class="org-left">data</th>
        <th scope="col" class="org-right">NLL(test)</th>
        <th scope="col" class="org-right">epoch</th>
        <th scope="col" class="org-left">pretrained</th>
        </tr>
        </thead>
        
        <tbody>
        <tr>
        <td class="org-left">Oxford-flower102</td>
        <td class="org-right">4.590211391448975</td>
        <td class="org-right">1024</td>
        <td class="org-left">---</td>
        </tr>
        </tbody>
        </table>
        
        ![img](https://raw.githubusercontent.com/MokkeMeguru/TFGENZOO/master/docs/oxford.png)
        
        see more detail, you can see [my internship&rsquo;s report](https://docs.google.com/presentation/d/12z6MZizIsytLxUb2ly7vYorFiKruIGZ2ckQ0-By4b6s/edit?usp=sharing) (Japanese only, if you need translated version, please contact me.)
        
        # Contact
        
        MokkeMeguru ([@MokkeMeguru](https://twitter.com/MeguruMokke)): DM or Mention Please (in Any language).
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.6
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: POSIX :: Linux
Classifier: Operating System :: Microsoft :: Windows
Classifier: Environment :: GPU :: NVIDIA CUDA :: 10.0
Classifier: Environment :: GPU :: NVIDIA CUDA :: 10.1
Description-Content-Type: text/markdown
