Combine GAN Generator output before feeding to Discriminator

I’m fairly new to the area of machine learning, but I figured I’d give this a try. Right now I’m trying to build a training pipeline for a CPPN that generates patterns. Since I want to be able to produce literally any resolution I’m outputting a single value per pixel. This works totally fine when using Model.predict(), since I can just aggregate the output based on my input Tensor configuration.

Now, for the GAN part, I need to somehow propagate the output from the Generator (which as mentioned is a dense layer with one unit, activated by a sigmoid function) to the Discriminator, which has an Input layer with the shape of the image provided, e.g. (width=100, height=100, channels=3).

Maybe what I’m trying to do is just not possible or the convention, but I’m stuck at the point where I need to properly align input and output, since when trying to build the GAN model right now it’s ranting about the shapes not being equal. Using Model.predict() doesn’t work either, since it’s not returning a Tensor and is being executed in batches.

Any tips for a fellow noob just starting out?