Generative Adversarial Networks Projects

The architecture of the discriminator network

The discriminator network is also a deep convolutional network. It contains eight convolution blocks followed by two dense (fully connected) layers. Each convolution block is followed by a batch normalization layer. There are two dense layers at the end of the network, which work as a classification block. The last layer predicts the probability of an image belonging to the real dataset or to the fake dataset. The detailed configuration of the discriminator network is shown in the following table:

Layer name	Hyperparameters	Input shape	Output shape
Input layer	None	(256, 256, 3)	(256, 256, 3)
2D convolution layer	filters=64, kernel_size=3, strides=1, padding='same', activation='leakyrelu' ...