GAN/Architectures/models/conditional_gan.py
def build_gan(self):
        """define the combined generator and discriminator model, for updating the generator"""
        # make weights in the discriminator not trainable
        self.d_model.trainable = False
        # get noise and label inputs from generator model
        gen_noise, gen_label = self.g_model.input
        # get image output from the generator model
        gen_output = self.g_model.output
        # connect image output and label input from generator as inputs to discriminator
        gan_output = self.d_model([gen_output, gen_label])
        # define gan model as taking noise and label and outputting a classification
        self.gan_model = Model([gen_noise, gen_label], gan_output)
        # compile model
        opt = Adam(lr=0.0002, beta_1=0.5)
        self.gan_model.compile(loss='binary_crossentropy', optimizer=opt)
define the combined generator and discriminator model, for updating the generator
define the combined generator and discriminator model, for updating the generator
define the combined generator and discriminator model for updating the generator
def build_gan(self):
        self.d_model.trainable = False
        gen_noise, gen_label = self.g_model.input
        gen_output = self.g_model.output
        gan_output = self.d_model([gen_output, gen_label])
        self.gan_model = Model([gen_noise, gen_label], gan_output)
        opt = Adam(lr=0.0002, beta_1=0.5)
        self.gan_model.compile(loss='binary_crossentropy', optimizer=opt)
def build_gan ( self ) : self . d_model . trainable = False gen_noise , gen_label = self . g_model . input gen_output = self . g_model . output gan_output = self . d_model ( [ gen_output , gen_label ] ) self . gan_model = Model ( [ gen_noise , gen_label ] , gan_output ) opt = Adam ( lr = 0.0002 , beta_1 = 0.5 ) self . gan_model . compile ( loss = 'binary_crossentropy' , optimizer = opt )
define the combined generator and discriminator model, for updating the generator
define the combined generator and discriminator model for updating the generator
"""define the combined generator and discriminator model, for updating the generator""" # make weights in the discriminator not trainable # get noise and label inputs from generator model # get image output from the generator model # connect image output and label input from generator as inputs to discriminator # define gan model as taking noise and label and outputting a classification # compile model
self