Implementation of various Generative Adversarial Networks (GANs) in Keras.
The code is written in Python3 using the Keras library with a tensorflow backend. It uses the following libraries
- Keras
- Matplotlib
- Numpy
- Scikit-learn
pip3 install -U keras matplotlib numpy scikit-learnThe repository can be cloned using
git clone https://github.com/Thijsvanede/GANs.gitThis repository is organised as follows:
GANs
├── LICENSE
├── README.md
├── saved
└── srcThe src directory contains the implementation of the different GANs discussed in the report.
The saved directory contains trained models for reproduction of the results.
For the implementation of the different GAN techniques see the file README.md file in the src directory here.
All GAN implementations are based on the Extensible GAN framework explained here.
The GAN class is used for training and predicting of GANs. This class also acts as a superclass for all other GAN implementations. The class is easily extendible by overriding several key methods. This is described in the section GAN extensions
In order to extend the GAN with custom implementations, one can implement the build_generator() and build_discriminator() methods of the GAN subclass. These classes should return a keras.model of the desired generator and discriminator object respectively.
__init__(self, dim_input_g=100,
dim_input_d=(28, 28),
optimizer=Adam(beta_1=0.5)):
Generative Adversarial Network.
Parameters
----------
dim_input_g : int, default=100
Dimension of generator input.
dim_input_d : tuple, default=(28, 28)
Dimension of discriminator input.
optimizer : keras.optimizer, default=Adam(beta_1=0.5)
Optimiser to use for training.
train(self, X_train, iterations=1000,
batch_size=64,
k=1,
sample_interval=100,
verbose=True):
Train the Generative Adversarial Network with given samples.
Parameters
----------
X_train : np.array of shape=(n_samples, dim_input_d)
Real samples to train with.
iterations : int, default=1000
Number of iterations to use for training.
batch_size : int, default=64
Number of samples in each batch, 1 batch is used per iteration.
k : int, default=1
Number of discriminator updates per generator update.
sample_interval : int, default=100
Iteration interval at which to output randomly generated
results of generator.
verbose : boolean, default=True
If verbose is set, print current status.
generate(self, noise=None, amount=5):
Generate output from given noise.
Parameters
----------
noise : np.array of shape=(n_samples, dim_input_generator), optional
If given, generate output from given noise.
amount : int, default=5
If no noise is given, generate the amount of output data given
by this integer.
Returns
-------
result : np.array of shape=(n_samples, dim_output)
Generated data.
save(self, out_gen, out_dis, out_com):
Saves weights of GAN to outfile.
Parameters
----------
out_gen : string
Path to output file for generator.
out_dis : string
Path to output file for discriminator.
out_com : string
Path to output file for combined model.
load(self, in_gen, in_dis, in_com):
Loads weights of GAN from infile.
Parameters
----------
in_gen : string
Path to input file for generator.
in_dis : string
Path to input file for discriminator.
in_com : string
Path to input file for combined model.
sample_images(self, outfile, data=None, width=5, height=5):
Generate width x height images and write them to outfile.
Parameters
----------
outfile : string
Path to outfile to write image to.
width : int, default=5
Number of generated images in width of output figure.
height : int, default=5
Number of generated images in height of output figure.