GAN/Architectures/models/conditional_gan.py
def generate_generator_prediction_samples(self, n_samples):
        """use the generator to generate n fake examples"""
        # generate points in latent space
        latent_points, labels = self.generate_latent_points(n_samples)
        # predict outputs
        X = self.generator_prediction(latent_points, labels)
        # create class labels
        y = np.zeros((n_samples, 1))
        return [X, labels], y
use the generator to generate n fake examples
use the generator to generate n fake examples
use the generator to generate n fake examples
def generate_generator_prediction_samples(self, n_samples):
        latent_points, labels = self.generate_latent_points(n_samples)
        X = self.generator_prediction(latent_points, labels)
        y = np.zeros((n_samples, 1))
        return [X, labels], y
def generate_generator_prediction_samples ( self , n_samples ) : latent_points , labels = self . generate_latent_points ( n_samples ) X = self . generator_prediction ( latent_points , labels ) y = np . zeros ( ( n_samples , 1 ) ) return [ X , labels ] , y
use the generator to generate n fake examples
use the generator to generate n fake examples
"""use the generator to generate n fake examples""" # generate points in latent space # predict outputs # create class labels
self
n_samples