Keras implementation of Deep Convolutional Generative Adversarial Networks
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README.md

KERAS-DCGAN

Implementation of http://arxiv.org/abs/1511.06434 with the (awesome) keras library, for generating artificial images with deep learning.

This trains two adversarial deep learning models on real images, in order to produce artificial images that look real.

The generator model tries to produce images that look real and get a high score from the discriminator.

The discriminator model tries to tell apart between real images and artificial images from the generator.


This assumes theano ordering. You can still use this with tensorflow, by setting "image_dim_ordering": "th" in ~/.keras/keras.json (although this will be slower).


Usage

Training:

python dcgan.py --mode train --batch_size <batch_size>

python dcgan.py --mode train --path ~/images --batch_size 128

Image generation:

python dcgan.py --mode generate --batch_size <batch_size>

python dcgan.py --mode generate --batch_size <batch_size> --nice : top 5% images according to discriminator

python dcgan.py --mode generate --batch_size 128


Result

generated images :

generated_image.png

nice_generated_image.png

train process :

training_process.gif