GAN/Architectures/models/acgan.py
def build_gan(self):
        """override defining of the combined generator and discriminator model, for updating the generator"""
        # make weights in the discriminator not trainable
        self.d_model.trainable = False
        # connect the outputs of the generator to the inputs of the discriminator
        gan_output = self.d_model(self.g_model.output)
        # define gan model as taking noise and label and outputting real/fake and label outputs
        self.gan_model = Model(self.g_model.input, gan_output)
        # compile model
        opt = Adam(lr=0.0002, beta_1=0.5)
        self.gan_model.compile(loss=['binary_crossentropy', 'sparse_categorical_crossentropy'], optimizer=opt)
override defining of the combined generator and discriminator model, for updating the generator
override defining of the combined generator and discriminator model, for updating the generator
override defining of the combined generator and discriminator model for updating the generator
def build_gan(self):
        self.d_model.trainable = False
        gan_output = self.d_model(self.g_model.output)
        self.gan_model = Model(self.g_model.input, gan_output)
        opt = Adam(lr=0.0002, beta_1=0.5)
        self.gan_model.compile(loss=['binary_crossentropy', 'sparse_categorical_crossentropy'], optimizer=opt)
def build_gan ( self ) : self . d_model . trainable = False gan_output = self . d_model ( self . g_model . output ) self . gan_model = Model ( self . g_model . input , gan_output ) opt = Adam ( lr = 0.0002 , beta_1 = 0.5 ) self . gan_model . compile ( loss = [ 'binary_crossentropy' , 'sparse_categorical_crossentropy' ] , optimizer = opt )
override defining of the combined generator and discriminator model, for updating the generator
override defining of the combined generator and discriminator model for updating the generator
"""override defining of the combined generator and discriminator model, for updating the generator""" # make weights in the discriminator not trainable # connect the outputs of the generator to the inputs of the discriminator # define gan model as taking noise and label and outputting real/fake and label outputs # compile model
self