# Transposed convolution as upsampling in DCGAN

I read several papers and articles where it is suggested that transposed convolution with 2 strides is better than upsampling then convolution.

However implementing such model with the transposed convolution resulted in heavy checkboard effect, where the whole generated image is just a pattern of squares and no learning takes place. How to properly implement it without totally messing up the generation? With the upsampling+convolution I got okay result but I want to improve my model. I am trying to generate images based on the CelebA dataset.

I use keras with tf and I used the following code:

model.add(Conv2DTranspose(256, 5, 2, padding='same'))