Section 1.2: Changing the Model
GAN Playground/Model Builder:
here

The Generator needs the second FC layer to transform the shape [256] output of the first FC layer because [256] cannot be reshaped to [28,28,1]. The Generator needs a final output of [28,28,1], therefore the generator reshapes the array into [256] via 1st FC Layer, [784] via 2nd FC Layer, and [28,28,1] via Reshape Layer. It is also not possible to reshape [600] to [20,20,2], but it IS possible to reshape [800] to [20,10,4].

I experimented with the default fullyconnected and convolutional models by training both models with ~250000 training examples. At ~250000 the default fullyconnected model was unable to train the generator to generate images that looked vaguely like digits. The generated images stayed pixelated and somewhat random. In contrast, the default convolutional model was able to generate images that started taking the shape of images digits after ~250000 training examples.
Default FullyConnected Model
Default Convolutional Model

The performance of the system's default fully connected model was worse than the performance of the default convolutional model, as shown in the observations and screenshots provided in the question above.