message: Update TFGAN Readme to link to examples.
diff: @@ -8,7 +8,8 @@ explicitly model the distribution and without writing an explicit loss. For
  example, the generator could learn to draw samples from the distribution of
  natural images. For more details on this technique, see
  ['Generative Adversarial Networks'](https://arxiv.org/abs/1406.2661) by
  -Goodfellow et al.
  +Goodfellow et al. See [tensorflow/models](https://github.com/tensorflow/models/tree/master/research/gan/) for examples, and [this tutorial](https://github.com/tensorflow/models/tree/master/research/gan/tutorial.ipynb) for an
  +introduction.
  #### Usage
  ```python
  @@ -23,8 +24,8 @@ mix TFGAN, native TF, and other custom frameworks
  * Use already implemented [GAN losses and penalties](https://www.tensorflow.org/code/tensorflow/contrib/gan/python/losses/python/losses_impl.py) (ex Wasserstein loss, gradient penalty, mutual information penalty, etc)
  * [Monitor and visualize](https://www.tensorflow.org/code/tensorflow/contrib/gan/python/eval/python/summaries_impl.py) GAN progress during training, and [evaluate](https://www.tensorflow.org/code/tensorflow/contrib/gan/python/eval/python/classifier_metrics_impl.py) them
  * Use already-implemented [tricks](https://www.tensorflow.org/code/tensorflow/contrib/gan/python/features/python/) to stabilize and improve training
  -* Develop based on examples of common GAN setups
  -* Use the TFGAN-backed tf.Learn Estimator to easily train a GAN model
  +* Develop based on examples of [common GAN setups](https://github.com/tensorflow/models/tree/master/research/gan/)
  +* Use the TFGAN-backed [GANEstimator](https://www.tensorflow.org/code/tensorflow/contrib/gan/python/estimator/python/gan_estimator_impl.py) to easily train a GAN model
  * Improvements in TFGAN infrastructure will automatically benefit your TFGAN project
  * Stay up-to-date with research as we add more algorithms
  @@ -51,7 +52,7 @@ network to evaluate your unconditional generative model. You can also use
  your own pretrained classifier for more specific performance numbers, or use
  other methods for evaluating conditional generative models.
  -* examples (coming soon):
  +* [examples](https://github.com/tensorflow/models/tree/master/research/gan/) and [tutorial](https://github.com/tensorflow/models/tree/master/research/gan/tutorial.ipynb):
  See examples of how to use TFGAN to make GAN training easier, or use the more complicated examples to jumpstart your
  own project. These include unconditional and conditional GANs, InfoGANs,
  adversarial losses on existing networks, and image-to-image translation.
  