Ty for all your answers so far.
In the first post in this topic I mentioned that my input got a lot of negative values after normalization.

As input I got normalize images in 256x256. The images got negative and positive values after normalizing but that should not be the reason why the net outputs negative values. When they are still negative values after the first convolution layer the ReLU should transform all negative values to zero or Im I completely wrong ?

Do the negative values hinder the training process? Should I try to rescale it into something between[0,1] ?