The discriminator network is inspired by the architecture of PatchGAN. It contains eight convolutional blocks, a dense layer, and a flatten layer. The discriminator network takes a set of patches extracted from an image of a dimension of (256, 256, 1) and predicts the probability of the given patches. Let's implement the discriminator in Keras.
- Start by initializing the hyperparameters required for the generator network:
kernel_size = 4strides = 2leakyrelu_alpha = 0.2padding = 'same'num_filters_start = 64 # Number of filters to start withnum_kernels = 100kernel_dim = 5patchgan_output_dim = (256, 256, 1)patchgan_patch_dim = (256, 256, 1)# Calculate number of patchesnumber_patches = int((patchgan_output_dim[0] / ...
