Dataset for Training

[divamgupta]

I trained the model on various datasets having more then 20k images, but even after several epochs i'm not getting the desired results.

Can I know the dataset on which this model has been trained and tested?

Thank You

[jchen7]

Couple questions, divamgupta. Which version of keras did you try it on? Did you try changing anything to get it to work, or did you run the model as is? I am having some trouble training as well.

Thanks

[kastnerkyle]

You might need to closer match DCGAN's original setup - feeding the samples and data as separate minibatches to the discriminator is important. See the line here and just before https://github.com/Newmu/dcgan_code/blob/master/faces/train_uncond_dcgan.py#L138

[MinaRe]

Dear @jacobgil

Thanks for sharing and nice implementation, I want to generate 5 different type gray scale images (on my dataset) the image input should be hdf5 format?

Thanks in advance!

[jacobgil]

@MinaRe The images should be in any format that can be read with opencv - i.e .jpg, .png, .pgm etc.

[jacobgil]

@MinaRe I'm not sure what is the question. One way to load images would to be just use opencv, and load them into numpy arrays. import cv2 img = cv2.imread("/home/MinaRe/img.png", 0) img = cv2.imresize(img, (32, 32))

And then set X_train to be these images. Since you have many images and the RAM space might be a bottleneck, you will probably want to load a next batch of images from disk in the training loop. Hopefully that helps.