def build_tensorboard(self):
    from logger import Logger
    if os.path.exists(self.log_path):
        shutil.rmtree(self.log_path)
    os.makedirs(self.log_path)
    self.logger = Logger(self.log_path)

            # (1) Log values of the losses (scalars)
            info = {
                'd_loss_real': d_loss_real.item(),
                'd_loss_fake': d_loss_fake.item(),
                'd_loss': d_loss.item(),
                'g_loss_fake': g_loss_fake.item(),
                'ave_gamma_l3': self.G.attn1.gamma.mean().item(),
                'ave_gamma_l4': self.G.attn2.gamma.mean().item(),
            }


            for tag, value in info.items():
                self.logger.scalar_summary(tag, value, step + 1)


        # Sample images / Save and log
        if (step + 1) % self.sample_step == 0:

            # (2) Log values and gradients of the parameters (histogram)
            for net, name in zip([self.G, self.D], ['G_', 'D_']):
                for tag, value in net.named_parameters():
                    tag = name + tag.replace('.', '/')
                    self.logger.histo_summary(tag, self.to_np(value), step + 1)

            # (3) Log the images
            info = {

                'fake_images': self.to_np(fake_images.view(*display_vars)[:10, :, :, :]),
                'real_images': self.to_np(real_images.view(*display_vars)[:10, :, :, :]),
            }

            fake_images, _, _ = self.G(fixed_z)
            save_image(denorm(fake_images.data),
                       os.path.join(self.sample_path, '{}_fake.png'.format(step + 1)))

            info['fixed_fake_images'] = self.to_np(denorm(real_images.data).view(*display_vars)[:10, :, :, :])

            for tag, image in info.items():
                self.logger.image_summary(tag, image, step + 1)