The discriminator in our GAN is a feed-forward neural network with five layers, including an input and an output layer, and three dense layers. The discriminator network is a classifier and is slightly different from the generator network. It processes an image and outputs a probability of the image belonging to a particular class.

The following diagram shows the flow of tensors and the input and output shapes of the tensors for each layer in the discriminator network:

Let's discuss how the discriminator processes data in forward propagation during the training of the network:

1. Initially, it receives an input of a shape of 28x28. 
2. The input layer takes the input tensor, which is a tensor with a shape of (batch_sizex28x28), and passes it to the first hidden layer without any modifications.
3. Next, the flattening layer flattens the tensor to a 784-dimensional vector, which gets passed to the first hidden (dense) layer. The first and second hidden layers modify this to a 500-dimensional vector.
4. The last layer is the output layer, which is again a dense layer, with one unit (a neuron) and sigmoid as the activation function. It outputs a single value, either a 0 or a 1. A value of 0 indicates that the provided image is fake, while a value of 1 indicates that the provided image is real.