Rename programmers_guide/ directory to guide/.

Update references in source files and docs in tensorflow and related projects.

PiperOrigin-RevId: 201766994

36 files changed, 73 insertions, 77 deletions

diff --git a/tensorflow/docs_src/api_guides/python/client.md b/tensorflow/docs_src/api_guides/python/client.md
index eef23696db..27fc8610bf 100644
--- a/tensorflow/docs_src/api_guides/python/client.md
+++ b/tensorflow/docs_src/api_guides/python/client.md
@@ -3,7 +3,7 @@
 
 This library contains classes for launching graphs and executing operations.
 
-@{$programmers_guide/low_level_intro$This guide} has examples of how a graph
+@{$guide/low_level_intro$This guide} has examples of how a graph
 is launched in a @{tf.Session}.
 
 ## Session management
diff --git a/tensorflow/docs_src/api_guides/python/input_dataset.md b/tensorflow/docs_src/api_guides/python/input_dataset.md
index a6e2fc48e0..a6612d1bf7 100644
--- a/tensorflow/docs_src/api_guides/python/input_dataset.md
+++ b/tensorflow/docs_src/api_guides/python/input_dataset.md
@@ -2,8 +2,7 @@
 [TOC]
 
 @{tf.data.Dataset} allows you to build complex input pipelines. See the
-@{$datasets$programmer's guide} for an in-depth explanation of how to use this
-API.
+@{$guide/datasets} for an in-depth explanation of how to use this API.
 
 ## Reader classes
 
diff --git a/tensorflow/docs_src/api_guides/python/reading_data.md b/tensorflow/docs_src/api_guides/python/reading_data.md
index 5bbbfd3216..d7d0904ae2 100644
--- a/tensorflow/docs_src/api_guides/python/reading_data.md
+++ b/tensorflow/docs_src/api_guides/python/reading_data.md
@@ -16,8 +16,8 @@ There are four methods of getting data into a TensorFlow program:
 
 ## `tf.data` API
 
-See the @{$datasets$programmer's guide} for an in-depth explanation of
-@{tf.data.Dataset}. The `tf.data` API enables you to extract and preprocess data
+See the @{$guide/datasets} for an in-depth explanation of @{tf.data.Dataset}.
+The `tf.data` API enables you to extract and preprocess data
 from different input/file formats, and apply transformations such as batching,
 shuffling, and mapping functions over the dataset. This is an improved version
 of the old input methods---feeding and `QueueRunner`---which are described
@@ -511,8 +511,8 @@ You can have the train and eval in the same graph in the same process, and share
 their trained variables or layers. See @{$variables$the shared variables tutorial}.
 
 To support the single-graph approach
-@{$programmers_guide/datasets$`tf.data`} also supplies
-@{$programmers_guide/datasets#creating_an_iterator$advanced iterator types} that
+@{$guide/datasets$`tf.data`} also supplies
+@{$guide/datasets#creating_an_iterator$advanced iterator types} that
 that allow the user to change the input pipeline without rebuilding the graph or
 session.
 
diff --git a/tensorflow/docs_src/deploy/distributed.md b/tensorflow/docs_src/deploy/distributed.md
index d7ed6b1deb..8e2c818e39 100644
--- a/tensorflow/docs_src/deploy/distributed.md
+++ b/tensorflow/docs_src/deploy/distributed.md
@@ -2,7 +2,7 @@
 
 This document shows how to create a cluster of TensorFlow servers, and how to
 distribute a computation graph across that cluster. We assume that you are
-familiar with the @{$programmers_guide/low_level_intro$basic concepts} of
+familiar with the @{$guide/low_level_intro$basic concepts} of
 writing low level TensorFlow programs.
 
 ## Hello distributed TensorFlow!
diff --git a/tensorflow/docs_src/extend/architecture.md b/tensorflow/docs_src/extend/architecture.md
index c8f522a03a..84435a57f2 100644
--- a/tensorflow/docs_src/extend/architecture.md
+++ b/tensorflow/docs_src/extend/architecture.md
@@ -7,9 +7,8 @@ learning models and system-level optimizations.
 This document describes the system architecture that makes this
 combination of scale and flexibility possible. It assumes that you have basic familiarity
 with TensorFlow programming concepts such as the computation graph, operations,
-and sessions. See @{$programmers_guide/low_level_intro$this document}
-for an introduction to these topics. Some familiarity
-with @{$distributed$distributed TensorFlow}
+and sessions. See @{$guide/low_level_intro$this document} for an introduction to
+these topics. Some familiarity with @{$distributed$distributed TensorFlow}
 will also be helpful.
 
 This document is for developers who want to extend TensorFlow in some way not
diff --git a/tensorflow/docs_src/get_started/_index.yaml b/tensorflow/docs_src/get_started/_index.yaml
index af255a482d..277fc852fb 100644
--- a/tensorflow/docs_src/get_started/_index.yaml
+++ b/tensorflow/docs_src/get_started/_index.yaml
@@ -74,7 +74,7 @@ landing_page:
               The high-level Keras API provides building blocks to create and
               train deep learning models. Start with these beginner-friendly
               notebook examples, then read the
-              <a href="/programmers_guide/keras">TensorFlow Keras guide</a>.
+              <a href="/guide/keras">TensorFlow Keras guide</a>.
             </p>
             <ol style="padding-left:20px;">
               <li><a href="/get_started/basic_classification">Basic classification</a></li>
@@ -85,7 +85,7 @@ landing_page:
             </ol>
           </div>
           <div class="devsite-landing-row-item-buttons" style="margin-top:0;">
-            <a class="button button-primary tfo-button-primary" href="/programmers_guide/keras">Read the Keras guide</a>
+            <a class="button button-primary tfo-button-primary" href="/guide/keras">Read the Keras guide</a>
           </div>
         </div>
     - classname: tfo-landing-row-item-code-block
@@ -123,7 +123,7 @@ landing_page:
           <div class="devsite-landing-row-item-description-content">
             <p>
               Eager execution provides an imperative, define-by-run interface for advanced operations. Write custom layers, forward passes, and training loops with auto‑differentiation. Start with
-              these notebooks, then read the <a href="/programmers_guide/eager">eager execution guide</a>.
+              these notebooks, then read the <a href="/guide/eager">eager execution guide</a>.
             </p>
             <ol style="padding-left:20px;">
               <li>
@@ -165,7 +165,7 @@ landing_page:
             </ol>
           </div>
           <div class="devsite-landing-row-item-buttons">
-            <a class="button button-primary tfo-button-primary" href="/programmers_guide/eager">Read the eager execution guide</a>
+            <a class="button button-primary tfo-button-primary" href="/guide/eager">Read the eager execution guide</a>
           </div>
         </div>
     - custom_html: >
@@ -177,7 +177,7 @@ landing_page:
             <p>
               Estimators can train large models on multiple machines in a
               production environment. Try the examples below and read the
-              <a href="/programmers_guide/estimators">Estimators guide</a>.
+              <a href="/guide/estimators">Estimators guide</a>.
             </p>
             <ol style="padding-left: 20px;">
               <li><a href="/tutorials/text_classification_with_tf_hub">How to build a simple text classifier with TF-Hub</a></li>
@@ -186,7 +186,7 @@ landing_page:
             </ol>
           </div>
           <div class="devsite-landing-row-item-buttons">
-            <a class="button button-primary tfo-button-primary" href="/programmers_guide/estimators">Read the Estimators guide</a>
+            <a class="button button-primary tfo-button-primary" href="/guide/estimators">Read the Estimators guide</a>
           </div>
         </div>
 
diff --git a/tensorflow/docs_src/get_started/next_steps.md b/tensorflow/docs_src/get_started/next_steps.md
index 79c0ef3346..6318a39c6c 100644
--- a/tensorflow/docs_src/get_started/next_steps.md
+++ b/tensorflow/docs_src/get_started/next_steps.md
@@ -2,9 +2,9 @@
 
 ## Learn more about TensorFlow
 
-* The [TensorFlow Guide](/programmers_guide) includes usage guides for the
+* The [TensorFlow Guide](/guide) includes usage guides for the
   high-level APIs, as well as advanced TensorFlow operations.
-* [Premade Estimators](/programmers_guide/premade_estimators) are designed to
+* [Premade Estimators](/guide/premade_estimators) are designed to
   get results out of the box. Use TensorFlow without building your own models.
 * [TensorFlow.js](https://js.tensorflow.org/) allows web developers to train and
   deploy ML models in the browser and using Node.js.
diff --git a/tensorflow/docs_src/programmers_guide/checkpoints.md b/tensorflow/docs_src/guide/checkpoints.md
index 8dfd91e3c8..dfb2626b86 100644
--- a/tensorflow/docs_src/programmers_guide/checkpoints.md
+++ b/tensorflow/docs_src/guide/checkpoints.md
@@ -8,9 +8,8 @@ Estimators. TensorFlow provides two model formats:
 *   SavedModel, which is a format independent of the code that created
     the model.
 
-This document focuses on checkpoints. For details on SavedModel, see the
-@{$saved_model$Saving and Restoring} chapter of the
-*TensorFlow Programmer's Guide*.
+This document focuses on checkpoints. For details on `SavedModel`, see the
+@{$saved_model$Saving and Restoring} guide.
 
 
 ## Sample code
@@ -232,8 +231,7 @@ This separation will keep your checkpoints recoverable.
 Checkpoints provide an easy automatic mechanism for saving and restoring
 models created by Estimators.
 
-See the @{$saved_model$Saving and Restoring}
-chapter of the *TensorFlow Programmer's Guide* for details on:
+See the @{$saved_model$Saving and Restoring} guide for details about:
 
 *   Saving and restoring models using low-level TensorFlow APIs.
 *   Exporting and importing models in the SavedModel format, which is a
diff --git a/tensorflow/docs_src/programmers_guide/custom_estimators.md b/tensorflow/docs_src/guide/custom_estimators.md
index fb20b35c12..fb20b35c12 100644
--- a/tensorflow/docs_src/programmers_guide/custom_estimators.md
+++ b/tensorflow/docs_src/guide/custom_estimators.md
diff --git a/tensorflow/docs_src/programmers_guide/datasets.md b/tensorflow/docs_src/guide/datasets.md
index 8b69860a68..8b69860a68 100644
--- a/tensorflow/docs_src/programmers_guide/datasets.md
+++ b/tensorflow/docs_src/guide/datasets.md
diff --git a/tensorflow/docs_src/programmers_guide/datasets_for_estimators.md b/tensorflow/docs_src/guide/datasets_for_estimators.md
index 345a31b985..b04af78cd8 100644
--- a/tensorflow/docs_src/programmers_guide/datasets_for_estimators.md
+++ b/tensorflow/docs_src/guide/datasets_for_estimators.md
@@ -91,8 +91,8 @@ print(mnist_ds)
 ```
 
 This will print the following line, showing the
-@{$programmers_guide/tensors#shapes$shapes} and
-@{$programmers_guide/tensors#data_types$types} of the items in
+@{$guide/tensors#shapes$shapes} and
+@{$guide/tensors#data_types$types} of the items in
 the dataset. Note that a `Dataset` does not know how many items it contains.
 
 ``` None
@@ -128,7 +128,7 @@ print(dataset)
 
 Here we see that when a `Dataset` contains structured elements, the `shapes`
 and `types` of the `Dataset` take on the same structure. This dataset contains
-dictionaries of @{$programmers_guide/tensors#rank$scalars}, all of type
+dictionaries of @{$guide/tensors#rank$scalars}, all of type
 `tf.float64`.
 
 The first line of the iris `train_input_fn` uses the same functionality, but
@@ -382,6 +382,6 @@ Estimator. Consider the following documents next:
 * The @{$low_level_intro#datasets$Low Level Introduction}, which demonstrates
   how to experiment directly with `tf.data.Datasets` using TensorFlow's low
   level APIs.
-* @{$programmers_guide/datasets} which goes into great detail about additional
+* @{$guide/datasets} which goes into great detail about additional
   functionality of `Datasets`.
 
diff --git a/tensorflow/docs_src/programmers_guide/debugger.md b/tensorflow/docs_src/guide/debugger.md
index 5cf9af904a..5cf9af904a 100644
--- a/tensorflow/docs_src/programmers_guide/debugger.md
+++ b/tensorflow/docs_src/guide/debugger.md
diff --git a/tensorflow/docs_src/programmers_guide/eager.md b/tensorflow/docs_src/guide/eager.md
index b2bc3273b4..b2bc3273b4 100644
--- a/tensorflow/docs_src/programmers_guide/eager.md
+++ b/tensorflow/docs_src/guide/eager.md
diff --git a/tensorflow/docs_src/programmers_guide/embedding.md b/tensorflow/docs_src/guide/embedding.md
index 8a98367dfb..8a98367dfb 100644
--- a/tensorflow/docs_src/programmers_guide/embedding.md
+++ b/tensorflow/docs_src/guide/embedding.md
diff --git a/tensorflow/docs_src/programmers_guide/estimators.md b/tensorflow/docs_src/guide/estimators.md
index b13b47184d..78b30c3040 100644
--- a/tensorflow/docs_src/programmers_guide/estimators.md
+++ b/tensorflow/docs_src/guide/estimators.md
@@ -81,7 +81,7 @@ of the following four steps:
            ...  # manipulate dataset, extracting the feature dict and the label
            return feature_dict, label
 
-    (See @{$programmers_guide/datasets} for full details.)
+    (See @{$guide/datasets} for full details.)
 
 2.  **Define the feature columns.** Each @{tf.feature_column}
     identifies a feature name, its type, and any input pre-processing.
diff --git a/tensorflow/docs_src/programmers_guide/faq.md b/tensorflow/docs_src/guide/faq.md
index b6291a9ffa..b6291a9ffa 100644
--- a/tensorflow/docs_src/programmers_guide/faq.md
+++ b/tensorflow/docs_src/guide/faq.md
diff --git a/tensorflow/docs_src/programmers_guide/feature_columns.md b/tensorflow/docs_src/guide/feature_columns.md
index 90f5c53a17..1013ec910c 100644
--- a/tensorflow/docs_src/programmers_guide/feature_columns.md
+++ b/tensorflow/docs_src/guide/feature_columns.md
@@ -534,7 +534,7 @@ embedding_column = tf.feature_column.embedding_column(
     dimension=embedding_dimensions)
 ```
 
-@{$programmers_guide/embedding$Embeddings} is a significant topic within machine
+@{$guide/embedding$Embeddings} is a significant topic within machine
 learning. This information was just to get you started using them as feature
 columns.
 
diff --git a/tensorflow/docs_src/programmers_guide/graph_viz.md b/tensorflow/docs_src/guide/graph_viz.md
index f581ae56da..f581ae56da 100644
--- a/tensorflow/docs_src/programmers_guide/graph_viz.md
+++ b/tensorflow/docs_src/guide/graph_viz.md
diff --git a/tensorflow/docs_src/programmers_guide/graphs.md b/tensorflow/docs_src/guide/graphs.md
index f0dd8def17..e6246ef148 100644
--- a/tensorflow/docs_src/programmers_guide/graphs.md
+++ b/tensorflow/docs_src/guide/graphs.md
@@ -93,7 +93,7 @@ to all API functions in the same context.  For example:
   stored value. The @{tf.Variable} object also has methods such as
   @{tf.Variable.assign$`assign`} and @{tf.Variable.assign_add$`assign_add`} that
   create @{tf.Operation} objects that, when executed, update the stored value.
-  (See @{$programmers_guide/variables} for more information about variables.)
+  (See @{$guide/variables} for more information about variables.)
 
 * Calling @{tf.train.Optimizer.minimize} will add operations and tensors to the
   default graph that calculates gradients, and return a @{tf.Operation} that,
diff --git a/tensorflow/docs_src/programmers_guide/index.md b/tensorflow/docs_src/guide/index.md
index 9c58a3b45e..eefdb9ceae 100644
--- a/tensorflow/docs_src/programmers_guide/index.md
+++ b/tensorflow/docs_src/guide/index.md
@@ -1,17 +1,17 @@
-# Programmer's Guide
+# TensorFlow Guide
 
 The documents in this unit dive into the details of how TensorFlow
 works. The units are as follows:
 
 ## High Level APIs
 
-  * @{$programmers_guide/keras}, TensorFlow's high-level API for building and
+  * @{$guide/keras}, TensorFlow's high-level API for building and
     training deep learning models.
-  * @{$programmers_guide/eager}, an API for writing TensorFlow code
+  * @{$guide/eager}, an API for writing TensorFlow code
     imperatively, like you would use Numpy.
-  * @{$programmers_guide/estimators}, a high-level API that provides
+  * @{$guide/estimators}, a high-level API that provides
     fully-packaged models ready for large-scale training and production.
-  * @{$programmers_guide/datasets}, easy input pipelines to bring your data into
+  * @{$guide/datasets}, easy input pipelines to bring your data into
     your TensorFlow program.
 
 ## Estimators
@@ -34,13 +34,13 @@ works. The units are as follows:
 
 ## Low Level APIs
 
-  * @{$programmers_guide/low_level_intro}, which introduces the
+  * @{$guide/low_level_intro}, which introduces the
     basics of how you can use TensorFlow outside of the high Level APIs.
-  * @{$programmers_guide/tensors}, which explains how to create,
+  * @{$guide/tensors}, which explains how to create,
     manipulate, and access Tensors--the fundamental object in TensorFlow.
-  * @{$programmers_guide/variables}, which details how
+  * @{$guide/variables}, which details how
     to represent shared, persistent state in your program.
-  * @{$programmers_guide/graphs}, which explains:
+  * @{$guide/graphs}, which explains:
       * dataflow graphs, which are TensorFlow's representation of computations
         as dependencies between operations.
       * sessions, which are TensorFlow's mechanism for running dataflow graphs
@@ -50,19 +50,19 @@ works. The units are as follows:
     such as Estimators or Keras, the high-level API creates and manages
     graphs and sessions for you, but understanding graphs and sessions
     can still be helpful.
-  * @{$programmers_guide/saved_model}, which
+  * @{$guide/saved_model}, which
     explains how to save and restore variables and models.
 
 ## ML Concepts
 
-  * @{$programmers_guide/embedding}, which introduces the concept
+  * @{$guide/embedding}, which introduces the concept
     of embeddings, provides a simple example of training an embedding in
     TensorFlow, and explains how to view embeddings with the TensorBoard
     Embedding Projector.
 
 ## Debugging
 
-  * @{$programmers_guide/debugger}, which
+  * @{$guide/debugger}, which
     explains how to use the TensorFlow debugger (tfdbg).
 
 ## TensorBoard
@@ -70,17 +70,17 @@ works. The units are as follows:
 TensorBoard is a utility to visualize different aspects of machine learning.
 The following guides explain how to use TensorBoard:
 
-  * @{$programmers_guide/summaries_and_tensorboard},
+  * @{$guide/summaries_and_tensorboard},
     which introduces TensorBoard.
-  * @{$programmers_guide/graph_viz}, which
+  * @{$guide/graph_viz}, which
     explains how to visualize the computational graph.
-  * @{$programmers_guide/tensorboard_histograms} which demonstrates the how to
+  * @{$guide/tensorboard_histograms} which demonstrates the how to
     use TensorBoard's histogram dashboard.
 
 
 ## Misc
 
-  * @{$programmers_guide/version_compat},
+  * @{$guide/version_compat},
     which explains backward compatibility guarantees and non-guarantees.
-  * @{$programmers_guide/faq}, which contains frequently asked
+  * @{$guide/faq}, which contains frequently asked
     questions about TensorFlow.
diff --git a/tensorflow/docs_src/programmers_guide/keras.md b/tensorflow/docs_src/guide/keras.md
index c6aca7ebf4..83172dab7f 100644
--- a/tensorflow/docs_src/programmers_guide/keras.md
+++ b/tensorflow/docs_src/guide/keras.md
@@ -19,7 +19,7 @@ fast prototyping, advanced research, and production, with three key advantages:
 [Keras API specification](https://keras.io){:.external}. This is a high-level
 API to build and train models that includes first-class support for
 TensorFlow-specific functionality, such as [eager execution](#eager_execution),
-`tf.data` pipelines, and [Estimators](/programmers_guide/estimators).
+`tf.data` pipelines, and [Estimators](./estimators.md).
 `tf.keras` makes TensorFlow easier to use without sacrificing flexibility and
 performance.
 
@@ -35,8 +35,8 @@ from tensorflow import keras
 * The `tf.keras` version in the latest TensorFlow release might not be the same
   as the latest `keras` version from PyPI. Check `tf.keras.__version__`.
 * When [saving a model's weights](#weights_only), `tf.keras` defaults to the
-  [checkpoint format](/get_started/checkpoints). Pass `save_format='h5'` to use
-  HDF5.
+  [checkpoint format](../get_started/checkpoints.md). Pass `save_format='h5'` to
+  use HDF5.
 
 ## Build a simple model
 
@@ -179,7 +179,7 @@ model.fit(data, labels, epochs=10, batch_size=32,
 
 ### Input tf.data datasets
 
-Use the [Datasets API](/programmers_guide/datasets) to scale to large datasets
+Use the [Datasets API](./datasets.md) to scale to large datasets
 or multi-device training. Pass a `tf.data.Dataset` instance to the `fit`
 method:
 
@@ -285,7 +285,7 @@ your own forward pass. Create layers in the `__init__` method and set them as
 attributes of the class instance. Define the forward pass in the `call` method.
 
 Model subclassing is particularly useful when
-[eager execution](/programmers_guide/eager) is enabled since the forward pass
+[eager execution](./eager.md) is enabled since the forward pass
 can be written imperatively.
 
 Key Point: Use the right API for the job. While model subclassing offers
@@ -410,7 +410,7 @@ during training. You can write your own custom callback, or use the built-in
 * `tf.keras.callbacks.EarlyStopping`: Interrupt training when validation
   performance has stopped improving.
 * `tf.keras.callbacks.TensorBoard`: Monitor the model's behavior using
-  [TensorBoard](/programmers_guide/summaries_and_tensorboard).
+  [TensorBoard](./summaries_and_tensorboard.md).
 
 To use a `tf.keras.callbacks.Callback`, pass it to the model's `fit` method:
 
@@ -442,8 +442,8 @@ model.load_weights('my_model')
 ```
 
 By default, this saves the model's weights in the
-[TensorFlow checkpoint](/get_started/checkpoints) file format. Weights can also
-be saved to the Keras HDF5 format (the default for the multi-backend
+[TensorFlow checkpoint](../get_started/checkpoints.md) file format. Weights can
+also be saved to the Keras HDF5 format (the default for the multi-backend
 implementation of Keras):
 
 ```python
@@ -509,7 +509,7 @@ model = keras.models.load_model('my_model.h5')
 
 ## Eager execution
 
-[Eager execution](/programmers_guide/eager) is an imperative programming
+[Eager execution](./eager.md) is an imperative programming
 environment that evaluates operations immediately. This is not required for
 Keras, but is supported by `tf.keras` and useful for inspecting your program and
 debugging.
@@ -520,7 +520,7 @@ especially benefits *model subclassing* and building *custom layers*—the APIs
 that require you to write the forward pass as code (instead of the APIs that
 create models by assembling existing layers).
 
-See the [eager execution guide](/programmers_guide/eager#build_a_model) for
+See the [eager execution guide](./eager.md#build_a_model) for
 examples of using Keras models with custom training loops and `tf.GradientTape`.
 
 
@@ -528,14 +528,14 @@ examples of using Keras models with custom training loops and `tf.GradientTape`.
 
 ### Estimators
 
-The [Estimators](/programmers_guide/estimators) API is used for training models
+The [Estimators](./estimators.md) API is used for training models
 for distributed environments. This targets industry use cases such as
 distributed training on large datasets that can export a model for production.
 
 A `tf.keras.Model` can be trained with the `tf.estimator` API by converting the
 model to an `tf.estimator.Estimator` object with
 `tf.keras.estimator.model_to_estimator`. See
-[Creating Estimators from Keras models](/programmers_guide/estimators#creating_estimators_from_keras_models).
+[Creating Estimators from Keras models](./estimators.md#creating_estimators_from_keras_models).
 
 ```python
 model = keras.Sequential([layers.Dense(10,activation='softmax'),
@@ -548,8 +548,8 @@ model.compile(optimizer=tf.train.RMSPropOptimizer(0.001),
 estimator = keras.estimator.model_to_estimator(model)
 ```
 
-Note: Enable [eager execution](/programmers_guide/eager) for debugging
-[Estimator input functions](/programmers_guide/premade_estimators#create_input_functions)
+Note: Enable [eager execution](./eager.md) for debugging
+[Estimator input functions](./premade_estimators.md#create_input_functions)
 and inspecting data.
 
 ### Multiple GPUs
diff --git a/tensorflow/docs_src/programmers_guide/leftnav_files b/tensorflow/docs_src/guide/leftnav_files
index 357a2a1cb9..357a2a1cb9 100644
--- a/tensorflow/docs_src/programmers_guide/leftnav_files
+++ b/tensorflow/docs_src/guide/leftnav_files
diff --git a/tensorflow/docs_src/programmers_guide/low_level_intro.md b/tensorflow/docs_src/guide/low_level_intro.md
index 478e2bb70b..665a5568b4 100644
--- a/tensorflow/docs_src/programmers_guide/low_level_intro.md
+++ b/tensorflow/docs_src/guide/low_level_intro.md
@@ -303,7 +303,7 @@ while True:
     break
 ```
 
-For more details on Datasets and Iterators see: @{$programmers_guide/datasets}.
+For more details on Datasets and Iterators see: @{$guide/datasets}.
 
 ## Layers
 
diff --git a/tensorflow/docs_src/programmers_guide/premade_estimators.md b/tensorflow/docs_src/guide/premade_estimators.md
index 02e2caf64b..3e910c1fe2 100644
--- a/tensorflow/docs_src/programmers_guide/premade_estimators.md
+++ b/tensorflow/docs_src/guide/premade_estimators.md
@@ -78,10 +78,10 @@ provides a programming stack consisting of multiple API layers:
 
 We strongly recommend writing TensorFlow programs with the following APIs:
 
-* @{$programmers_guide/estimators$Estimators}, which represent a complete model.
+* @{$guide/estimators$Estimators}, which represent a complete model.
   The Estimator API provides methods to train the model, to judge the model's
   accuracy, and to generate predictions.
-* @{$programmers_guide/datasets_for_estimators}, which build a data input
+* @{$guide/datasets_for_estimators}, which build a data input
   pipeline. The Dataset API has methods to load and manipulate data, and feed
   it into your model. The Dataset API meshes well with the Estimators API.
 
@@ -173,7 +173,7 @@ example is an Iris Versicolor.
 An Estimator is TensorFlow's high-level representation of a complete model. It
 handles the details of initialization, logging, saving and restoring, and many
 other features so you can concentrate on your model. For more details see
-@{$programmers_guide/estimators}.
+@{$guide/estimators}.
 
 An Estimator is any class derived from @{tf.estimator.Estimator}. TensorFlow
 provides a collection of
@@ -424,7 +424,7 @@ Now that you've gotten started writing TensorFlow programs, consider the
 following material:
 
 * @{$checkpoints$Checkpoints} to learn how to save and restore models.
-* @{$programmers_guide/datasets_for_estimators} to learn more about importing
+* @{$guide/datasets_for_estimators} to learn more about importing
   data into your model.
 * @{$custom_estimators$Creating Custom Estimators} to learn how to
   write your own Estimator, customized for a particular problem.
diff --git a/tensorflow/docs_src/programmers_guide/saved_model.md b/tensorflow/docs_src/guide/saved_model.md
index c6ef87c54a..27ef7bb0da 100644
--- a/tensorflow/docs_src/programmers_guide/saved_model.md
+++ b/tensorflow/docs_src/guide/saved_model.md
@@ -3,7 +3,7 @@
 The @{tf.train.Saver} class provides methods to save and restore models. The
 @{tf.saved_model.simple_save} function is an easy way to build a
 @{tf.saved_model$saved model} suitable for serving.
-[Estimators](@{$programmers_guide/estimators}) automatically save and restore
+[Estimators](@{$guide/estimators}) automatically save and restore
 variables in the `model_dir`.
 
 ## Save and restore variables
@@ -299,7 +299,7 @@ following:
    added attributes with defaults don't cause older model consumers to fail
    loading models regenerated with newer training binaries.
 
-See [compatibility guidance](https://www.tensorflow.org/programmers_guide/version_compat)
+See [compatibility guidance](./version_compat.md)
 for more information.
 
 ### Loading a SavedModel in Python
diff --git a/tensorflow/docs_src/programmers_guide/summaries_and_tensorboard.md b/tensorflow/docs_src/guide/summaries_and_tensorboard.md
index fadfa03e78..fadfa03e78 100644
--- a/tensorflow/docs_src/programmers_guide/summaries_and_tensorboard.md
+++ b/tensorflow/docs_src/guide/summaries_and_tensorboard.md
diff --git a/tensorflow/docs_src/programmers_guide/tensorboard_histograms.md b/tensorflow/docs_src/guide/tensorboard_histograms.md
index 918deda190..918deda190 100644
--- a/tensorflow/docs_src/programmers_guide/tensorboard_histograms.md
+++ b/tensorflow/docs_src/guide/tensorboard_histograms.md
diff --git a/tensorflow/docs_src/programmers_guide/tensors.md b/tensorflow/docs_src/guide/tensors.md
index 1248c3cabe..7227260f1a 100644
--- a/tensorflow/docs_src/programmers_guide/tensors.md
+++ b/tensorflow/docs_src/guide/tensors.md
@@ -26,7 +26,7 @@ some cases it's only possible to find the shape of a tensor at graph execution
 time.
 
 Some types of tensors are special, and these will be covered in other
-units of the Programmer's guide. The main ones are:
+units of the TensorFlow guide. The main ones are:
 
   * `tf.Variable`
   * `tf.constant`
@@ -230,7 +230,7 @@ yet_another = tf.reshape(matrixAlt, [13, 2, -1])  # ERROR!
 ## Data types
 
 In addition to dimensionality, Tensors have a data type. Refer to the
-`tf.DataType` page in the programmer's guide for a full list of the data types.
+`tf.DType` page for a complete list of the data types.
 
 It is not possible to have a `tf.Tensor` with more than one data type. It is
 possible, however, to serialize arbitrary data structures as `string`s and store
diff --git a/tensorflow/docs_src/programmers_guide/using_gpu.md b/tensorflow/docs_src/guide/using_gpu.md
index c429ca4750..c429ca4750 100644
--- a/tensorflow/docs_src/programmers_guide/using_gpu.md
+++ b/tensorflow/docs_src/guide/using_gpu.md
diff --git a/tensorflow/docs_src/programmers_guide/using_tpu.md b/tensorflow/docs_src/guide/using_tpu.md
index 44aabf0557..41d80d9d60 100644
--- a/tensorflow/docs_src/programmers_guide/using_tpu.md
+++ b/tensorflow/docs_src/guide/using_tpu.md
@@ -171,7 +171,7 @@ This section details the changes you must make to the model function
 During regular usage TensorFlow attempts to determine the shapes of each
 `tf.Tensor` during graph construction. During execution any unknown shape
 dimensions are determined dynamically,
-see @{$programmers_guide/tensors#shape$Tensor Shapes} for more details.
+see @{$guide/tensors#shape$Tensor Shapes} for more details.
 
 To run on Cloud TPUs TensorFlow models are compiled using @{$xla$XLA}.
 XLA uses a similar system for determining shapes at compile time. XLA requires
@@ -195,7 +195,7 @@ TPU.
 
 Build your evaluation metrics dictionary in a stand-alone `metric_fn`.
 
-<!-- TODO(markdaoust) link to programmers_guide/metrics when it exists -->
+<!-- TODO(markdaoust) link to guide/metrics when it exists -->
 
 Evaluation metrics are an essential part of training a model. These are fully
 supported on Cloud TPUs, but with a slightly different syntax.
diff --git a/tensorflow/docs_src/programmers_guide/variables.md b/tensorflow/docs_src/guide/variables.md
index cd8c4b5b9a..cd8c4b5b9a 100644
--- a/tensorflow/docs_src/programmers_guide/variables.md
+++ b/tensorflow/docs_src/guide/variables.md
diff --git a/tensorflow/docs_src/programmers_guide/version_compat.md b/tensorflow/docs_src/guide/version_compat.md
index 72e427c5f8..72e427c5f8 100644
--- a/tensorflow/docs_src/programmers_guide/version_compat.md
+++ b/tensorflow/docs_src/guide/version_compat.md
diff --git a/tensorflow/docs_src/install/install_go.md b/tensorflow/docs_src/install/install_go.md
index 55bc0f64e7..2c126df5aa 100644
--- a/tensorflow/docs_src/install/install_go.md
+++ b/tensorflow/docs_src/install/install_go.md
@@ -6,7 +6,7 @@ a Go application. This guide explains how to install and set up the
 [TensorFlow Go package](https://godoc.org/github.com/tensorflow/tensorflow/tensorflow/go).
 
 Warning: The TensorFlow Go API is *not* covered by the TensorFlow
-[API stability guarantees](https://www.tensorflow.org/programmers_guide/version_semantics).
+[API stability guarantees](../guide/version_semantics.md).
 
 
 ## Supported Platforms
diff --git a/tensorflow/docs_src/install/install_java.md b/tensorflow/docs_src/install/install_java.md
index 637231da12..692dfc9cef 100644
--- a/tensorflow/docs_src/install/install_java.md
+++ b/tensorflow/docs_src/install/install_java.md
@@ -7,7 +7,7 @@ Java application. This guide explains how to install
 and use it in a Java application.
 
 Warning: The TensorFlow Java API is *not* covered by the TensorFlow
-[API stability guarantees](https://www.tensorflow.org/programmers_guide/version_semantics).
+[API stability guarantees](../guide/version_semantics.md).
 
 
 ## Supported Platforms
diff --git a/tensorflow/docs_src/tutorials/deep_cnn.md b/tensorflow/docs_src/tutorials/deep_cnn.md
index 6361079671..3b9c0f57a1 100644
--- a/tensorflow/docs_src/tutorials/deep_cnn.md
+++ b/tensorflow/docs_src/tutorials/deep_cnn.md
@@ -268,7 +268,7 @@ in `cifar10_input.py`.
 
 `cifar10_train.py` periodically @{tf.train.Saver$saves}
 all model parameters in
-@{$programmers_guide/saved_model$checkpoint files}
+@{$guide/saved_model$checkpoint files}
 but it does *not* evaluate the model. The checkpoint file
 will be used by `cifar10_eval.py` to measure the predictive
 performance (see [Evaluating a Model](#evaluating-a-model) below).
diff --git a/tensorflow/docs_src/tutorials/layers.md b/tensorflow/docs_src/tutorials/layers.md
index 0f17899dae..212e337637 100644
--- a/tensorflow/docs_src/tutorials/layers.md
+++ b/tensorflow/docs_src/tutorials/layers.md
@@ -627,7 +627,7 @@ operation earlier when we generated the probabilities in `cnn_model_fn`.
 > argument, TensorFlow will assign a default name. A couple easy ways to
 > discover the names applied to operations are to visualize your graph on
 > @{$graph_viz$TensorBoard}) or to enable the
-> @{$programmers_guide/debugger$TensorFlow Debugger (tfdbg)}.
+> @{$guide/debugger$TensorFlow Debugger (tfdbg)}.
 
 Next, we create the `LoggingTensorHook`, passing `tensors_to_log` to the
 `tensors` argument. We set `every_n_iter=50`, which specifies that probabilities