zoo/dcgan/dcgan_c.py
def gan(self, latent=100, optimizer=Adam(0.0002, 0.5)):
        """ Construct the Combined Generator/Discrimator (GAN)
            latent       : the latent space dimension
            optimizer    : the optimizer
        """
        # The generator takes noise as input and generates fake images
        noise = Input(shape=(latent,))
        fake  = self.g(noise)

        # The discriminator takes generated images as input and determines if real or fake
        valid = self.d(fake)

        # The combined model  (stacked generator and discriminator)
        # Trains the generator to fool the discriminator
        model = Model(noise, valid)
        model.compile(loss='binary_crossentropy', optimizer=optimizer)
        return model
 Construct the Combined Generator/Discrimator (GAN)
            latent       : the latent space dimension
            optimizer    : the optimizer
        
Construct the Combined Generator/Discrimator (GAN)
latent       : the latent space dimension
optimizer    : the optimizer
Construct the Combined Generator / Discrimator ( GAN ) latent : the latent space dimension optimizer : the optimizer
def gan(self, latent=100, optimizer=Adam(0.0002, 0.5)):
        noise = Input(shape=(latent,))
        fake  = self.g(noise)
        valid = self.d(fake)
        model = Model(noise, valid)
        model.compile(loss='binary_crossentropy', optimizer=optimizer)
        return model
def gan ( self , latent = 100 , optimizer = Adam ( 0.0002 , 0.5 ) ) : noise = Input ( shape = ( latent , ) ) fake = self . g ( noise ) valid = self . d ( fake ) model = Model ( noise , valid ) model . compile ( loss = 'binary_crossentropy' , optimizer = optimizer ) return model
Construct the Combined Generator/Discrimator (GAN)
latent       : the latent space dimension
optimizer    : the optimizer
Construct the Combined Generator / Discrimator ( GAN ) latent : the latent space dimension optimizer : the optimizer
""" Construct the Combined Generator/Discrimator (GAN)
            latent       : the latent space dimension
            optimizer    : the optimizer
        """ # The generator takes noise as input and generates fake images # The discriminator takes generated images as input and determines if real or fake # The combined model  (stacked generator and discriminator) # Trains the generator to fool the discriminator
self
latent
optimizer