GAN/Architectures/models/conditional_gan.py
def generate_latent_points(self, n_samples):
        """generate points in latent space as input for the generator"""
        # generate points in the latent space
        latent_points = super().generate_latent_points(n_samples)
        # generate labels
        labels = np.random.randint(0, self.n_classes, n_samples)
        return [latent_points, labels]
generate points in latent space as input for the generator
generate points in latent space as input for the generator
generate points in latent space as input for the generator
def generate_latent_points(self, n_samples):
        latent_points = super().generate_latent_points(n_samples)
        labels = np.random.randint(0, self.n_classes, n_samples)
        return [latent_points, labels]
def generate_latent_points ( self , n_samples ) : latent_points = super ( ) . generate_latent_points ( n_samples ) labels = np . random . randint ( 0 , self . n_classes , n_samples ) return [ latent_points , labels ]
generate points in latent space as input for the generator
generate points in latent space as input for the generator
"""generate points in latent space as input for the generator""" # generate points in the latent space # generate labels
self
n_samples