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| author |  CQY <qychen@pku.edu.cn> | 2017-12-15 15:43:55 +0800 |
|---|---|---|
| committer | CQY <qychen@pku.edu.cn> | 2017-12-15 15:43:55 +0800 |
| commit | 7d2a601fb5c5cb06173ec4fa3737a363fce58f5b (patch) | |
| tree | bf9876ea03c95975aa2bfb70ed077cad6af58159 | |
| parent | 798fa36d11119e6fdc13b90a14abfe1805e7de90 (diff) | |
Fix api usage in examples of gan
| -rw-r--r-- | tensorflow/contrib/gan/README.md | 16 |
1 files changed, 8 insertions, 8 deletions
diff --git a/tensorflow/contrib/gan/README.md b/tensorflow/contrib/gan/README.md index 4bca0a1d62..4ead66ca13 100644 --- a/tensorflow/contrib/gan/README.md +++ b/tensorflow/contrib/gan/README.md @@ -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) |