GAN/Architectures/models/infogan.py
def generate_generator_prediction_samples(self, n_samples):
        """use the generator to generate n fake examples"""
        # generate points in latent space and control codes
        z_input, _ = self.generate_latent_points(n_samples)
        # predict outputs
        images = self.generator_prediction(z_input)
        # create class labels
        y = np.zeros((n_samples, 1))
        return images, 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):
        z_input, _ = self.generate_latent_points(n_samples)
        images = self.generator_prediction(z_input)
        y = np.zeros((n_samples, 1))
        return images, y
def generate_generator_prediction_samples ( self , n_samples ) : z_input , _ = self . generate_latent_points ( n_samples ) images = self . generator_prediction ( z_input ) y = np . zeros ( ( n_samples , 1 ) ) return images , 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 and control codes # predict outputs # create class labels
self
n_samples