DCGAN

Keras implementation of the following paper on MNIST database.

Radford, Alec, Luke Metz, and Soumith Chintala. "Unsupervised representation learning with deep convolutional generative adversarial networks." arXiv preprint arXiv:1511.06434 (2015).
link to paper

Dependencies

• Keras
• Numpy
• matplotlib
• sklearn ( used only for shuffling the data )

Usage

• dcgan.py, main file.

• generateRandom.py, uses the saved trained model generator_200.h5 inside the models folder to generate images.

• metrics folder contains the discriminator loss and generator loss after every epoch saved in numpy's npy format.

Results

• Generated images after the final epoch

• GIF of the network learning the handwritten digits after every 5 epoch

Note

• Using batch normalization as suggested in the paper did not work as expected. Do let me know if I have erred.

• The data is normalized before being fed into the network

• I have concatenated both the train and val data for the train dataset thus 70000 samples of 28*28 each.

• While runnning generateRandom.py you might get an error initNormal not a valid initializations or something like that. Keras does not save the user initialized functions in the model, to resolve this error, add the following in python/site-packages/keras/initializations.py. This ensures that all the weights are initialized from a zero centered normal distribution with standard deviation 0.02.

    def initNormal(shape, name=None):
      return normal(shape, scale=0.02, name=name)