# Training 128x128 autoencoders on 512x512 images, produces strange gridline after recombining

So I'm training an autoencoder that can recreate 128x128 images, so it can recreate any images by splitting them into 128x128 patches first, running it through the autoencoder, and having them combined with each other to form the original image.

I should be using other dimensions too but right now I'm testing this with 512x512 images. So :

   x = next_train_batch(25)

for xx in range(0,4):
x_index = xx*128
for y in range(0,4):
y_index = y*128

this_image = x[:, x_index:x_index+128, y_index:y_index+128, :]
sess.run(training, feed_dict={x_in: this_image, step_iter_global:step_iter})


This is how I'm doing it now. But this keeps happening:

True, this is just in the beginning of the training process. But I guess I wonder why the autoencoder is having a hard time learning the edges of the patches. These gridlines actually are still present in visualizations of certain layers in the autoencoder (the reason I'm doing this is because I'm trying new layers that could potentially improve autoencoders) even if the output image doesn't have it.