Figure 2.

Conditional generative adversarial network (cGAN) architecture. Generators were U-Nets with eight convolution and eight deconvolution layers, each with rectified linear unit (ReLU) activation. Discriminators consisted of five convolutional layers with ReLU activation. Square matrix size and number of filters (initially 64 for phase networks) are indicated beneath the layers. Convolution stride was 2 except where specified. Skip connections are shown with arrows. Dropout was applied at indicated layers (dark blue). Batch normalization (red) was used for phase networks, but not for magnitude networks. Filters for phase networks were 8 × 8. Magnitude networks used double the number of filters, with filter size = 4 × 4.