val-iisc / deligan Goto Github PK

if I want to apply deli-GAN's spirits to some other application like conditional GAN , so the batch size have to fixed? I think this is not convenient.

Hi,
I noticed the paper said the trained latent space is a mixed Gaussian distribution with trainable variance and expectation:

In particular, we propose a reparameterization of the latent space as a Mixture- of-Gaussians model.

However, it seems that in the script the latent space applied here is a uniform distribution with trainable variance and expectation:
display_z = np.random.uniform(-1.0, 1.0, [batchsize, z_dim]).astype(np.float32)
I don't quite understand this inconsistency.

Hello,I am sorry to disturb you!I want to train deli_gan by cpu ,but there a lot of issues about it. should I modify the code ?

Hi,

I was running your script in toy directory to reproduce results shown in paper, however ending up with mode collapse for both unimodal and multimodal data.

To deduce the reflection on initialization, I've run this test for 5 times on both datasets, results are pretty similar.

Any comments?

Hi, when I utilize the trained model to generate the samples, which the corresponding code is

#Uncomment this block for generating GAN samples from given model ''' f = np.load(args.results_dir + '/disc_params1180.npz') param_values = [f['arr_%d' % i] for i in range(len(f.files))] for i,p in enumerate(disc_params): p.set_value(param_values[i]) print("disc_params fed") f = np.load(args.results_dir + '/gen_params1180.npz') param_values = [f['arr_%d' % i] for i in range(len(f.files))] for i,p in enumerate(gen_params): p.set_value(param_values[i]) print("gen_params fed") samples=[] for i in range(50): sample_x = samplefun() samples.append(sample_x) samples = np.concatenate(samples,0) print(samples) #sys.exit() np.save(args.results_dir + '/samples50k.npy', samples) print("samples saved") sys.exit() '''

but for each iteration i, the image in the sample_x are diverse, but every sample_x is the same. what' wrong with it?

I got this error when I run the code in the folder sketches,but I can't find the solution to this error.

Two questions:
1.Do you find performance reduction if you increase the size of the generated image? Such as the dataset of the sketch? Can you give some suggestions if I want to increase the size of the results?
2.How to modify the network if I want to generate specific kind of images, just like the conditional GAN? Any suggestions?
Thanks!

I've tried the mnist model. However, I only got some noises.....any comments?