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carpedm20 / DCGAN-tensorflow

  • четверг, 24 марта 2016 г. в 02:13:03
https://github.com/carpedm20/DCGAN-tensorflow

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A tensorflow implementation of Deep Convolutional Generative Adversarial Networks



DCGAN in Tensorflow

Tensorflow implementation of Deep Convolutional Generative Adversarial Networks which is a stabilize Generative Adversarial Networks. The referenced torch code can be found here.

alt tag

To avoid the fast convergence of D (discriminator) network, G (generatior) network is updatesd twice for each D network update which is a different from original paper.

Online Demo

link

Prerequisites

Usage

First, download dataset with:

$ mkdir data
$ python download.py --datasets celebA

To train a model with celebA dataset:

$ python main.py --dataset celebA --is_train True --is_crop True

To test with an existing model:

$ python main.py --dataset celebA --is_crop True

Or, you can use your own dataset (without central crop) by:

$ mkdir data/DATASET_NAME
... add images to data/DATASET_NAME ...
$ python main.py --dataset DATASET_NAME --is_train True
$ python main.py --dataset DATASET_NAME

Results

result

After 6th epoch:

result3

After 10th epoch:

result4

With asian face dataset (with high noises):

custom_result1

custom_result1

custom_result2

More results can be found here and here.

Training details

Details of the loss of Discriminator and Generator (with custom dataset not celebA).

d_loss

g_loss

Details of the histogram of true and fake result of discriminator (with custom dataset not celebA).

d_hist

d__hist

Author

Taehoon Kim / @carpedm20