@@ -14,21 +14,18 @@ Borrow this photo from the original DC-GAN paper.
## The Conditional-GAN might be a class.
This design we adopt the popular open source design in https://github.com/carpedm20/DCGAN-tensorflow and https://github.com/rajathkmp/DCGAN. It contains following data structure:
### DCGAN(object):
which contains everything required to build a GAN model. It provides following member functions methods as API:
- DCGAN(object): which contains everything required to build a GAN model. It provides following member functions methods as API:
### __init__(...):
Initialize hyper-parameters (like conv dimension and so forth), and declare model parameters of discriminator and generator as well.
- __init__(...): Initialize hyper-parameters (like conv dimension and so forth), and declare model parameters of discriminator and generator as well.
### generator(z, y=None):
Generate a fake image from input noise z. If the label y is provided, the conditional GAN model will be chosen.
- generator(z, y=None): Generate a fake image from input noise z. If the label y is provided, the conditional GAN model will be chosen.
Returns a generated image.
### discriminator(image):
- discriminator(image):
Given an image, decide if it is from a real source or a fake one.
Returns a 0/1 binary label.
### build_model(self):
- build_model(self):
build the whole GAN model, define training loss for both generator and discrimator.
## Discussion on Engine Functions required to build GAN
