message: Fix api usage in examples of gan
diff: @@ -99,8 +99,8 @@ gan_model = tfgan.gan_model(
  # Build the GAN loss.
  gan_loss = tfgan.gan_loss(
  gan_model,
  - generator_loss_fn=tfgan_losses.wasserstein_generator_loss,
  - discriminator_loss_fn=tfgan_losses.wasserstein_discriminator_loss)
  + generator_loss_fn=tfgan.losses.wasserstein_generator_loss,
  + discriminator_loss_fn=tfgan.losses.wasserstein_discriminator_loss)
  # Create the train ops, which calculate gradients and apply updates to weights.
  train_ops = tfgan.gan_train_ops(
  @@ -161,8 +161,8 @@ gan_model = tfgan.gan_model(
  # Build the GAN loss and standard pixel loss.
  gan_loss = tfgan.gan_loss(
  gan_model,
  - generator_loss_fn=tfgan_losses.wasserstein_generator_loss,
  - discriminator_loss_fn=tfgan_losses.wasserstein_discriminator_loss,
  + generator_loss_fn=tfgan.losses.wasserstein_generator_loss,
  + discriminator_loss_fn=tfgan.losses.wasserstein_discriminator_loss,
  gradient_penalty=1.0)
  l1_pixel_loss = tf.norm(gan_model.real_data - gan_model.generated_data, ord=1)
  @@ -193,8 +193,8 @@ gan_model = tfgan.gan_model(
  # Build the GAN loss and standard pixel loss.
  gan_loss = tfgan.gan_loss(
  gan_model,
  - generator_loss_fn=tfgan_losses.least_squares_generator_loss,
  - discriminator_loss_fn=tfgan_losses.least_squares_discriminator_loss)
  + generator_loss_fn=tfgan.losses.least_squares_generator_loss,
  + discriminator_loss_fn=tfgan.losses.least_squares_discriminator_loss)
  l1_pixel_loss = tf.norm(gan_model.real_data - gan_model.generated_data, ord=1)
  # Modify the loss tuple to include the pixel loss.
  @@ -223,8 +223,8 @@ gan_model = tfgan.infogan_model(
  # Build the GAN loss with mutual information penalty.
  gan_loss = tfgan.gan_loss(
  gan_model,
  - generator_loss_fn=tfgan_losses.wasserstein_generator_loss,
  - discriminator_loss_fn=tfgan_losses.wasserstein_discriminator_loss,
  + generator_loss_fn=tfgan.losses.wasserstein_generator_loss,
  + discriminator_loss_fn=tfgan.losses.wasserstein_discriminator_loss,
  gradient_penalty=1.0,
  mutual_information_penalty_weight=1.0)
  