markdown fixes

PiperOrigin-RevId: 197077588

diff --git a/tensorflow/docs_src/api_guides/python/contrib.framework.md b/tensorflow/docs_src/api_guides/python/contrib.framework.md
index f7f26e5..6b4ce3a 100644 (file)
--- a/tensorflow/docs_src/api_guides/python/contrib.framework.md
+++ b/tensorflow/docs_src/api_guides/python/contrib.framework.md
@@ -18,17 +18,20 @@ Framework utilities.
 *   @{tf.contrib.framework.with_same_shape}
 
 ## Deprecation
+
 *   @{tf.contrib.framework.deprecated}
 *   @{tf.contrib.framework.deprecated_args}
 *   @{tf.contrib.framework.deprecated_arg_values}
 
 ## Arg_Scope
+
 *   @{tf.contrib.framework.arg_scope}
 *   @{tf.contrib.framework.add_arg_scope}
 *   @{tf.contrib.framework.has_arg_scope}
 *   @{tf.contrib.framework.arg_scoped_arguments}
 
 ## Variables
+
 *   @{tf.contrib.framework.add_model_variable}
 *   @{tf.train.assert_global_step}
 *   @{tf.contrib.framework.assert_or_get_global_step}

diff --git a/tensorflow/docs_src/api_guides/python/contrib.learn.md b/tensorflow/docs_src/api_guides/python/contrib.learn.md
index 8b2fffa..03838dc 100644 (file)
--- a/tensorflow/docs_src/api_guides/python/contrib.learn.md
+++ b/tensorflow/docs_src/api_guides/python/contrib.learn.md
@@ -25,6 +25,7 @@ Train and evaluate TensorFlow models.
 *   @{tf.contrib.learn.LogisticRegressor}
 
 ## Distributed training utilities
+
 *   @{tf.contrib.learn.Experiment}
 *   @{tf.contrib.learn.ExportStrategy}
 *   @{tf.contrib.learn.TaskType}

diff --git a/tensorflow/docs_src/api_guides/python/contrib.seq2seq.md b/tensorflow/docs_src/api_guides/python/contrib.seq2seq.md
index 496d43d..143919f 100644 (file)
--- a/tensorflow/docs_src/api_guides/python/contrib.seq2seq.md
+++ b/tensorflow/docs_src/api_guides/python/contrib.seq2seq.md
@@ -21,6 +21,7 @@ wrapper.  An instance of an `AttentionMechanism` is constructed with a
 ### Attention Mechanisms
 
 The two basic attention mechanisms are:
+
 *   @{tf.contrib.seq2seq.BahdanauAttention} (additive attention,
     [ref.](https://arxiv.org/abs/1409.0473))
 *   @{tf.contrib.seq2seq.LuongAttention} (multiplicative attention,
@@ -118,14 +119,17 @@ outputs, _ = tf.contrib.seq2seq.dynamic_decode(
 ```
 
 ### Decoder base class and functions
+
 *   @{tf.contrib.seq2seq.Decoder}
 *   @{tf.contrib.seq2seq.dynamic_decode}
 
 ### Basic Decoder
+
 *   @{tf.contrib.seq2seq.BasicDecoderOutput}
 *   @{tf.contrib.seq2seq.BasicDecoder}
 
 ### Decoder Helpers
+
 *   @{tf.contrib.seq2seq.Helper}
 *   @{tf.contrib.seq2seq.CustomHelper}
 *   @{tf.contrib.seq2seq.GreedyEmbeddingHelper}

diff --git a/tensorflow/docs_src/api_guides/python/meta_graph.md b/tensorflow/docs_src/api_guides/python/meta_graph.md
index 0eff900..f1c3adc 100644 (file)
--- a/tensorflow/docs_src/api_guides/python/meta_graph.md
+++ b/tensorflow/docs_src/api_guides/python/meta_graph.md
@@ -22,14 +22,14 @@ protocol buffer. It contains the following fields:
 * [`GraphDef`](https://www.tensorflow.org/code/tensorflow/core/framework/graph.proto) for describing the graph.
 * [`SaverDef`](https://www.tensorflow.org/code/tensorflow/core/protobuf/saver.proto) for the saver.
 * [`CollectionDef`](https://www.tensorflow.org/code/tensorflow/core/protobuf/meta_graph.proto)
-map that further describes additional components of the model, such as
+map that further describes additional components of the model such as
 @{$python/state_ops$`Variables`},
-@{tf.train.QueueRunner}, etc.  In order for a Python object to be serialized
+@{tf.train.QueueRunner}, etc.
+
+In order for a Python object to be serialized
 to and from `MetaGraphDef`, the Python class must implement `to_proto()` and
 `from_proto()` methods, and register them with the system using
-`register_proto_function`.
-
-  For example,
+`register_proto_function`. For example:
 
   ```Python
   def to_proto(self, export_scope=None):

diff --git a/tensorflow/docs_src/api_guides/python/train.md b/tensorflow/docs_src/api_guides/python/train.md
index 80fe978..cbc5052 100644 (file)
--- a/tensorflow/docs_src/api_guides/python/train.md
+++ b/tensorflow/docs_src/api_guides/python/train.md
@@ -54,6 +54,7 @@ gradients.
 *   @{tf.global_norm}
 
 ## Decaying the learning rate
+
 *   @{tf.train.exponential_decay}
 *   @{tf.train.inverse_time_decay}
 *   @{tf.train.natural_exp_decay}

diff --git a/tensorflow/docs_src/performance/performance_guide.md b/tensorflow/docs_src/performance/performance_guide.md
index b1796cf..cb0f5ca 100644 (file)
--- a/tensorflow/docs_src/performance/performance_guide.md
+++ b/tensorflow/docs_src/performance/performance_guide.md
@@ -78,7 +78,7 @@ training CIFAR-10 illustrates the use of the `tf.data` API along with
 The `tf.data` API utilizes C++ multi-threading and has a much lower overhead
 than the Python-based `queue_runner` that is limited by Python's multi-threading
 performance. A detailed performance guide for the `tf.data` API can be found
-[here](@{$datasets_performance}).
+@{$datasets_performance$here}.
 
 While feeding data using a `feed_dict` offers a high level of flexibility, in
 general `feed_dict` does not provide a scalable solution. If only a single GPU