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A Tensorflow implementation of CycleGAN using Eager Execution, tf.keras.layers, and tf.data.

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CycleGAN-Tensorflow

A Tensorflow implementation of Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks using Eager Execution, tf.keras.layers, and tf.data.

Requirements:

  • Tensorflow 1.11.0
  • Python 3.6

Project Organization

├── LICENSE
├── Makefile           <- Makefile with commands like `make data` or `make train`
├── README.md
├── datasets           <- All datasets are stored here
│
├── saved_models       <- Checkpointed models and tensorboard summaries
│
├── setup.py           <- makes project pip installable (pip install -e .) so src can be imported
└── src                <- Source code for use in this project
    ├── __init__.py    <- Makes src a Python module
    │
    ├── train.py       <- Run this to train
    │
    ├── test.py        <- Run this to test
    │
    ├── data       <- Code for downloading or loading data  
    │   ├── data.py
    │   └── download_data.py
    │
    ├── models         <- Code for defining the network structure and loss functions
    │   ├── __init__.py
    │   ├── network.py
    │   ├── cyclegan.py     <- CycleGAN model class
    │   └── losses.py
    │
    └── utils               <- Utility files
        ├── options.py                 <- File for command line options
        └── image_history_buffer.py

Project based on the cookiecutter data science project template. #cookiecutterdatascience

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A Tensorflow implementation of CycleGAN using Eager Execution, tf.keras.layers, and tf.data.

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