eager: Update READMEs and links.

- guide.md: Update links now that documentation of the latest release (1.4)
  includes what we want.
- model READMEs: The example models are included in the TensorFlow pip package,
  so you do not need to build from source to run the benchmarks.

PiperOrigin-RevId: 174426563

3 files changed, 31 insertions, 9 deletions

diff --git a/tensorflow/contrib/eager/python/examples/resnet50/README.md b/tensorflow/contrib/eager/python/examples/resnet50/README.md
index f6c1defa42..db023e6c97 100644
--- a/tensorflow/contrib/eager/python/examples/resnet50/README.md
+++ b/tensorflow/contrib/eager/python/examples/resnet50/README.md
@@ -11,7 +11,18 @@ Contents:
 
 # Benchmarks
 
-Using a synthetic data.
+Using a synthetic data, run:
+
+```
+# Using eager execution
+python resnet50_test.py --benchmarks=.
+
+# Using graph execution
+python resnet50_graph_test.py --benchmarks=.
+```
+
+The above uses the model definition included with the TensorFlow pip
+package. To build (and run benchmarks) from source:
 
 ```
 # Using eager execution
diff --git a/tensorflow/contrib/eager/python/examples/rnn_ptb/README.md b/tensorflow/contrib/eager/python/examples/rnn_ptb/README.md
index ea92d59e58..743ebb68ee 100644
--- a/tensorflow/contrib/eager/python/examples/rnn_ptb/README.md
+++ b/tensorflow/contrib/eager/python/examples/rnn_ptb/README.md
@@ -20,6 +20,18 @@ Benchmarks (using synthetic data):
 
 ```
 # Using eager execution
+python rnn_ptb_test.py --benchmarks=.
+
+# Using graph execution
+python rnn_ptb_graph_test.py --benchmarks=.
+```
+
+The above uses the model definition included with the TensorFlow pip
+package. To build (and run benchmarks) from source:
+
+
+```
+# Using eager execution
 bazel run -c opt --config=cuda :rnn_ptb_test -- --benchmarks=.
 
 # Using graph execution
diff --git a/tensorflow/contrib/eager/python/g3doc/guide.md b/tensorflow/contrib/eager/python/g3doc/guide.md
index 230fc893bf..147b7047f4 100644
--- a/tensorflow/contrib/eager/python/g3doc/guide.md
+++ b/tensorflow/contrib/eager/python/g3doc/guide.md
@@ -388,7 +388,7 @@ many arguments.
 
 In fact, eager execution encourages use of the [Keras](https://keras.io)-style
 "Layer" classes in the
-[`tf.layers`](https://www.tensorflow.org/versions/master/api_docs/python/tf/layers)
+[`tf.layers`](https://www.tensorflow.org/api_docs/python/tf/layers)
 module.
 
 Furthermore, you may want to apply more sophisticated techniques to compute
@@ -488,10 +488,10 @@ parameters of the model as arguments to the `loss` function.
 ### Using Keras and the Layers API
 
 [Keras](https://keras.io) is a popular API for defining model structures. The
-[`tf.keras.layers`](https://www.tensorflow.org/versions/master/api_docs/python/tf/keras/layers)
+[`tf.keras.layers`](https://www.tensorflow.org/api_docs/python/tf/keras/layers)
 module provides a set of building blocks for models and is implemented using the
 `tf.layers.Layer` subclasses in the
-[`tf.layers`](https://www.tensorflow.org/versions/master/api_docs/python/tf/layers)
+[`tf.layers`](https://www.tensorflow.org/api_docs/python/tf/layers)
 module. We encourage the use of these same building blocks when using
 TensorFlow's eager execution feature. For example, the very same linear
 regression model can be built using `tf.layers.Dense`:
@@ -608,9 +608,9 @@ it provides conveniences like keeping track of all model variables and methods
 to save and restore from checkpoints.
 
 Sub-classes of `tfe.Network` may register `Layer`s (like classes in
-[`tf.layers`](https://www.tensorflow.org/versions/master/api_docs/python/tf/layers),
+[`tf.layers`](https://www.tensorflow.org/api_docs/python/tf/layers),
 or [Keras
-layers](https://www.tensorflow.org/versions/master/api_docs/python/tf/keras/layers))
+layers](https://www.tensorflow.org/api_docs/python/tf/keras/layers))
 using a call to `self.track_layer()` and define the computation in an
 implementation of `call()`.
 
@@ -800,7 +800,7 @@ example in
 
 The discussion above has been centered around the computation executed by your
 model. The
-[`tf.data`](https://www.tensorflow.org/versions/master/api_docs/python/tf/data)
+[`tf.data`](https://www.tensorflow.org/api_docs/python/tf/data)
 module provides APIs to build complex input pipelines from simple, reusable
 pieces.
 
@@ -810,8 +810,7 @@ However, the process of iterating over elements of the dataset differs between
 eager execution and graph construction. When eager execution is enabled, the
 discussion on iterator creation using `make_one_shot_iterator()` and
 `get_next()` in the
-[Programmer's
-Guide](https://www.tensorflow.org/versions/master/programmers_guide/datasets) is
+[Programmer's Guide](https://www.tensorflow.org/programmers_guide/datasets) is
 *not* applicable. Instead, a more Pythonic `Iterator` class is available.
 
 For example: