diff --git a/site/en/community/contribute/docs.md b/site/en/community/contribute/docs.md index 29b2b5c9550..34b1619ca5d 100644 --- a/site/en/community/contribute/docs.md +++ b/site/en/community/contribute/docs.md @@ -32,7 +32,7 @@ To participate in the TensorFlow docs community: For details, use the [TensorFlow API docs contributor guide](docs_ref.md). This shows you how to find the -[source file](https://www.tensorflow.org/code/tensorflow/python/) +[source file](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/) and edit the symbol's docstring. Many API reference pages on tensorflow.org include a link to the source file @@ -53,9 +53,9 @@ main tensorflow/tensorflow repo. The reference documentation is generated from code comments and docstrings in the source code for -Python, -C++, and -Java. +Python, +C++, and +Java. Previous versions of the TensorFlow documentation are available as [rX.x branches](https://github.com/tensorflow/docs/branches) in the TensorFlow diff --git a/site/en/community/contribute/docs_ref.md b/site/en/community/contribute/docs_ref.md index fbf207a47f1..41fce4dde40 100644 --- a/site/en/community/contribute/docs_ref.md +++ b/site/en/community/contribute/docs_ref.md @@ -8,7 +8,7 @@ TensorFlow uses [DocTest](https://docs.python.org/3/library/doctest.html) to test code snippets in Python docstrings. The snippet must be executable Python code. To enable testing, prepend the line with `>>>` (three left-angle brackets). For example, here's a excerpt from the `tf.concat` function in the -[array_ops.py](https://www.tensorflow.org/code/tensorflow/python/ops/array_ops.py) +[array_ops.py](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/ops/array_ops.py) source file: ``` @@ -178,7 +178,7 @@ There are two ways to test the code in the docstring locally: * If you are only changing the docstring of a class/function/method, then you can test it by passing that file's path to - [tf_doctest.py](https://www.tensorflow.org/code/tensorflow/tools/docs/tf_doctest.py). + [tf_doctest.py](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tools/docs/tf_doctest.py). For example: 
diff --git a/site/en/guide/create_op.md b/site/en/guide/create_op.md
index 3c84204844c..fa4f573fa32 100644
--- a/site/en/guide/create_op.md
+++ b/site/en/guide/create_op.md
@@ -152,17 +152,17 @@ REGISTER_KERNEL_BUILDER(Name("ZeroOut").Device(DEVICE_CPU), ZeroOutOp);
 >   Important: Instances of your OpKernel may be accessed concurrently.
 >   Your `Compute` method must be thread-safe. Guard any access to class
 >   members with a mutex. Or better yet, don't share state via class members!
->   Consider using a [`ResourceMgr`](https://www.tensorflow.org/code/tensorflow/core/framework/resource_mgr.h)
+>   Consider using a [`ResourceMgr`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/resource_mgr.h)
 >   to keep track of op state.
 
 ### Multi-threaded CPU kernels
 
 To write a multi-threaded CPU kernel, the Shard function in
-[`work_sharder.h`](https://www.tensorflow.org/code/tensorflow/core/util/work_sharder.h)
+[`work_sharder.h`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/util/work_sharder.h)
 can be used. This function shards a computation function across the
 threads configured to be used for intra-op threading (see
 intra_op_parallelism_threads in
-[`config.proto`](https://www.tensorflow.org/code/tensorflow/core/protobuf/config.proto)).
+[`config.proto`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/protobuf/config.proto)).
 
 ### GPU kernels
 
@@ -519,13 +519,13 @@ This asserts that the input is a vector, and returns having set the
 
 *   The `context`, which can either be an `OpKernelContext` or
     `OpKernelConstruction` pointer (see
-    [`tensorflow/core/framework/op_kernel.h`](https://www.tensorflow.org/code/tensorflow/core/framework/op_kernel.h)),
+    [`tensorflow/core/framework/op_kernel.h`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/op_kernel.h)),
     for its `SetStatus()` method.
 *   The condition.  For example, there are functions for validating the shape
     of a tensor in
-    [`tensorflow/core/framework/tensor_shape.h`](https://www.tensorflow.org/code/tensorflow/core/framework/tensor_shape.h)
+    [`tensorflow/core/framework/tensor_shape.h`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/tensor_shape.h)
 *   The error itself, which is represented by a `Status` object, see
-    [`tensorflow/core/platform/status.h`](https://www.tensorflow.org/code/tensorflow/core/platform/status.h). A
+    [`tensorflow/core/platform/status.h`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/platform/status.h). A
     `Status` has both a type (frequently `InvalidArgument`, but see the list of
     types) and a message.  Functions for constructing an error may be found in
     [`tensorflow/core/platform/errors.h`][validation-macros].
@@ -668,7 +668,7 @@ There are shortcuts for common type constraints:
 
 The specific lists of types allowed by these are defined by the functions (like
 `NumberTypes()`) in
-[`tensorflow/core/framework/types.h`](https://www.tensorflow.org/code/tensorflow/core/framework/types.h).
+[`tensorflow/core/framework/types.h`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/types.h).
 In this example the attr `t` must be one of the numeric types:
 
 ```c++
@@ -1226,7 +1226,7 @@ There are several ways to preserve backwards-compatibility.
     type into a list of varying types).
 
 The full list of safe and unsafe changes can be found in
-[`tensorflow/core/framework/op_compatibility_test.cc`](https://www.tensorflow.org/code/tensorflow/core/framework/op_compatibility_test.cc).
+[`tensorflow/core/framework/op_compatibility_test.cc`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/op_compatibility_test.cc).
 If you cannot make your change to an operation backwards compatible, then create
 a new operation with a new name with the new semantics.
 
@@ -1243,16 +1243,16 @@ made when TensorFlow changes major versions, and must conform to the
 You can implement different OpKernels and register one for CPU and another for
 GPU, just like you can [register kernels for different types](#polymorphism).
 There are several examples of kernels with GPU support in
-[`tensorflow/core/kernels/`](https://www.tensorflow.org/code/tensorflow/core/kernels/).
+[`tensorflow/core/kernels/`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/).
 Notice some kernels have a CPU version in a `.cc` file, a GPU version in a file
 ending in `_gpu.cu.cc`, and some code shared in common in a `.h` file.
 
 For example, the `tf.pad` has
 everything but the GPU kernel in [`tensorflow/core/kernels/pad_op.cc`][pad_op].
 The GPU kernel is in
-[`tensorflow/core/kernels/pad_op_gpu.cu.cc`](https://www.tensorflow.org/code/tensorflow/core/kernels/pad_op_gpu.cu.cc),
+[`tensorflow/core/kernels/pad_op_gpu.cu.cc`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/pad_op_gpu.cu.cc),
 and the shared code is a templated class defined in
-[`tensorflow/core/kernels/pad_op.h`](https://www.tensorflow.org/code/tensorflow/core/kernels/pad_op.h).
+[`tensorflow/core/kernels/pad_op.h`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/pad_op.h).
 We organize the code this way for two reasons: it allows you to share common
 code among the CPU and GPU implementations, and it puts the GPU implementation
 into a separate file so that it can be compiled only by the GPU compiler.
@@ -1273,16 +1273,16 @@ kept on the CPU, add a `HostMemory()` call to the kernel registration, e.g.:
 #### Compiling the kernel for the GPU device
 
 Look at
-[cuda_op_kernel.cu.cc](https://www.tensorflow.org/code/tensorflow/examples/adding_an_op/cuda_op_kernel.cu.cc)
+[cuda_op_kernel.cu.cc](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/adding_an_op/cuda_op_kernel.cu.cc)
 for an example that uses a CUDA kernel to implement an op. The
 `tf_custom_op_library` accepts a `gpu_srcs` argument in which the list of source
 files containing the CUDA kernels (`*.cu.cc` files) can be specified. For use
 with a binary installation of TensorFlow, the CUDA kernels have to be compiled
 with NVIDIA's `nvcc` compiler. Here is the sequence of commands you can use to
 compile the
-[cuda_op_kernel.cu.cc](https://www.tensorflow.org/code/tensorflow/examples/adding_an_op/cuda_op_kernel.cu.cc)
+[cuda_op_kernel.cu.cc](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/adding_an_op/cuda_op_kernel.cu.cc)
 and
-[cuda_op_kernel.cc](https://www.tensorflow.org/code/tensorflow/examples/adding_an_op/cuda_op_kernel.cc)
+[cuda_op_kernel.cc](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/adding_an_op/cuda_op_kernel.cc)
 into a single dynamically loadable library:
 
 ```bash
@@ -1412,7 +1412,7 @@ be set to the first input's shape. If the output is selected by its index as in
 
 There are a number of common shape functions
 that apply to many ops, such as `shape_inference::UnchangedShape` which can be
-found in [common_shape_fns.h](https://www.tensorflow.org/code/tensorflow/core/framework/common_shape_fns.h) and used as follows:
+found in [common_shape_fns.h](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/common_shape_fns.h) and used as follows:
 
 ```c++
 REGISTER_OP("ZeroOut")
@@ -1459,7 +1459,7 @@ provides access to the attributes of the op).
 
 Since shape inference is an optional feature, and the shapes of tensors may vary
 dynamically, shape functions must be robust to incomplete shape information for
-any of the inputs. The `Merge` method in [`InferenceContext`](https://www.tensorflow.org/code/tensorflow/core/framework/shape_inference.h)
+any of the inputs. The `Merge` method in [`InferenceContext`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/shape_inference.h)
 allows the caller to assert that two shapes are the same, even if either
 or both of them do not have complete information. Shape functions are defined
 for all of the core TensorFlow ops and provide many different usage examples.
@@ -1484,7 +1484,7 @@ If you have a complicated shape function, you should consider adding a test for
 validating that various input shape combinations produce the expected output
 shape combinations.  You can see examples of how to write these tests in some
 our
-[core ops tests](https://www.tensorflow.org/code/tensorflow/core/ops/array_ops_test.cc).
+[core ops tests](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/ops/array_ops_test.cc).
 (The syntax of `INFER_OK` and `INFER_ERROR` are a little cryptic, but try to be
 compact in representing input and output shape specifications in tests.  For
 now, see the surrounding comments in those tests to get a sense of the shape
@@ -1497,20 +1497,20 @@ To build a `pip` package for your op, see the
 guide shows how to build custom ops from the TensorFlow pip package instead
 of building TensorFlow from source.
 
-[core-array_ops]:https://www.tensorflow.org/code/tensorflow/core/ops/array_ops.cc
-[python-user_ops]:https://www.tensorflow.org/code/tensorflow/python/user_ops/user_ops.py
-[tf-kernels]:https://www.tensorflow.org/code/tensorflow/core/kernels/
-[user_ops]:https://www.tensorflow.org/code/tensorflow/core/user_ops/
-[pad_op]:https://www.tensorflow.org/code/tensorflow/core/kernels/pad_op.cc
-[standard_ops-py]:https://www.tensorflow.org/code/tensorflow/python/ops/standard_ops.py
-[standard_ops-cc]:https://www.tensorflow.org/code/tensorflow/cc/ops/standard_ops.h
-[python-BUILD]:https://www.tensorflow.org/code/tensorflow/python/BUILD
-[validation-macros]:https://www.tensorflow.org/code/tensorflow/core/platform/errors.h
-[op_def_builder]:https://www.tensorflow.org/code/tensorflow/core/framework/op_def_builder.h
-[register_types]:https://www.tensorflow.org/code/tensorflow/core/framework/register_types.h
-[FinalizeAttr]:https://www.tensorflow.org/code/tensorflow/core/framework/op_def_builder.cc
-[DataTypeString]:https://www.tensorflow.org/code/tensorflow/core/framework/types.cc
-[python-BUILD]:https://www.tensorflow.org/code/tensorflow/python/BUILD
-[types-proto]:https://www.tensorflow.org/code/tensorflow/core/framework/types.proto
-[TensorShapeProto]:https://www.tensorflow.org/code/tensorflow/core/framework/tensor_shape.proto
-[TensorProto]:https://www.tensorflow.org/code/tensorflow/core/framework/tensor.proto
+[core-array_ops]:https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/ops/array_ops.cc
+[python-user_ops]:https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/user_ops/user_ops.py
+[tf-kernels]:https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/
+[user_ops]:https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/user_ops/
+[pad_op]:https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/pad_op.cc
+[standard_ops-py]:https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/ops/standard_ops.py
+[standard_ops-cc]:https://github.com/tensorflow/tensorflow/blob/master/tensorflow/cc/ops/standard_ops.h
+[python-BUILD]:https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/BUILD
+[validation-macros]:https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/platform/errors.h
+[op_def_builder]:https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/op_def_builder.h
+[register_types]:https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/register_types.h
+[FinalizeAttr]:https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/op_def_builder.cc
+[DataTypeString]:https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/types.cc
+[python-BUILD]:https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/BUILD
+[types-proto]:https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/types.proto
+[TensorShapeProto]:https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/tensor_shape.proto
+[TensorProto]:https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/tensor.proto
diff --git a/site/en/guide/versions.md b/site/en/guide/versions.md
index 58f9f3848fb..5e660892b6d 100644
--- a/site/en/guide/versions.md
+++ b/site/en/guide/versions.md
@@ -471,7 +471,7 @@ existing producer scripts will not suddenly use the new functionality.
 1.  Add a new similar op named `SomethingV2` or similar and go through the
     process of adding it and switching existing Python wrappers to use it.
     To ensure forward compatibility use the checks suggested in
-    [compat.py](https://www.tensorflow.org/code/tensorflow/python/compat/compat.py)
+    [compat.py](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/compat/compat.py)
     when changing the Python wrappers.
 2.  Remove the old op (Can only take place with a major version change due to
     backward compatibility).
diff --git a/site/en/r1/guide/autograph.ipynb b/site/en/r1/guide/autograph.ipynb
index f028b33ce9f..6c169066c03 100644
--- a/site/en/r1/guide/autograph.ipynb
+++ b/site/en/r1/guide/autograph.ipynb
@@ -78,7 +78,7 @@
         "id": "CydFK2CL7ZHA"
       },
       "source": [
-        "[AutoGraph](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/autograph/) helps you write complicated graph code using normal Python. Behind the scenes, AutoGraph automatically transforms your code into the equivalent [TensorFlow graph code](https://www.tensorflow.org/r1/guide/graphs). AutoGraph already supports much of the Python language, and that coverage continues to grow. For a list of supported Python language features, see the [Autograph capabilities and limitations](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/autograph/g3doc/reference/limitations.md)."
+        "[AutoGraph](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/python/autograph/) helps you write complicated graph code using normal Python. Behind the scenes, AutoGraph automatically transforms your code into the equivalent [TensorFlow graph code](https://www.tensorflow.org/r1/guide/graphs). AutoGraph already supports much of the Python language, and that coverage continues to grow. For a list of supported Python language features, see the [Autograph capabilities and limitations](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/python/autograph/g3doc/reference/limitations.md)."
       ]
     },
     {
diff --git a/site/en/r1/guide/custom_estimators.md b/site/en/r1/guide/custom_estimators.md
index 87dce26a0dc..7bbf3573909 100644
--- a/site/en/r1/guide/custom_estimators.md
+++ b/site/en/r1/guide/custom_estimators.md
@@ -592,10 +592,10 @@ function for custom Estimators; everything else is the same.
 For more details, be sure to check out:
 
 * The
-  [official TensorFlow implementation of MNIST](https://github.com/tensorflow/models/tree/master/official/r1/mnist),
+  [official TensorFlow implementation of MNIST](https://github.com/tensorflow/models/tree/r1.15/official/r1/mnist),
   which uses a custom estimator.
 * The TensorFlow
-  [official models repository](https://github.com/tensorflow/models/tree/master/official),
+  [official models repository](https://github.com/tensorflow/models/tree/r1.15/official),
   which contains more curated examples using custom estimators.
 * This [TensorBoard video](https://youtu.be/eBbEDRsCmv4), which introduces
   TensorBoard.
diff --git a/site/en/r1/guide/debugger.md b/site/en/r1/guide/debugger.md
index 2b4b6497ec4..963765b97db 100644
--- a/site/en/r1/guide/debugger.md
+++ b/site/en/r1/guide/debugger.md
@@ -10,7 +10,7 @@ due to TensorFlow's computation-graph paradigm.
 This guide focuses on the command-line interface (CLI) of `tfdbg`. For guide on
 how to use the graphical user interface (GUI) of tfdbg, i.e., the
 **TensorBoard Debugger Plugin**, please visit
-[its README](https://github.com/tensorflow/tensorboard/blob/master/tensorboard/plugins/debugger/README.md).
+[its README](https://github.com/tensorflow/tensorboard/blob/r1.15/tensorboard/plugins/debugger/README.md).
 
 Note: The TensorFlow debugger uses a
 [curses](https://en.wikipedia.org/wiki/Curses_\(programming_library\))-based text
@@ -35,7 +35,7 @@ TensorFlow. Later sections of this document describe how to use **tfdbg** with
 higher-level APIs of TensorFlow, including `tf.estimator`, `tf.keras` / `keras`
 and `tf.contrib.slim`. To *observe* such an issue, run the following command
 without the debugger (the source code can be found
-[here](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/debug/examples/v1/debug_mnist.py)):
+[here](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/python/debug/examples/v1/debug_mnist.py)):
 
 
 python -m tensorflow.python.debug.examples.v1.debug_mnist
@@ -64,7 +64,7 @@ numeric problem first surfaced.
 To add support for tfdbg in our example, all that is needed is to add the
 following lines of code and wrap the Session object with a debugger wrapper.
 This code is already added in
-[debug_mnist.py](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/debug/examples/v1/debug_mnist.py),
+[debug_mnist.py](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/python/debug/examples/v1/debug_mnist.py),
 so you can activate tfdbg CLI with the `--debug` flag at the command line.
 
 ```python
@@ -370,7 +370,7 @@ traceback of the node's construction.
 
 From the traceback, you can see that the op is constructed at the following
 line:
-[`debug_mnist.py`](https://www.tensorflow.org/code/tensorflow/python/debug/examples/v1/debug_mnist.py):
+[`debug_mnist.py`](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/python/debug/examples/v1/debug_mnist.py):
 
 ```python
 diff = y_ * tf.log(y)
@@ -457,7 +457,7 @@ accuracy_score = classifier.evaluate(eval_input_fn,
 predict_results = classifier.predict(predict_input_fn, hooks=hooks)
 ```
 
-[debug_tflearn_iris.py](https://www.tensorflow.org/code/tensorflow/python/debug/examples/v1/debug_tflearn_iris.py),
+[debug_tflearn_iris.py](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/python/debug/examples/v1/debug_tflearn_iris.py),
 contains a full example of how to use the tfdbg with `Estimator`s. To run this
 example, do:
 
@@ -501,7 +501,7 @@ TensorFlow backend. You just need to replace `tf.keras.backend` with
 ## Debugging tf-slim with TFDBG
 
 TFDBG supports debugging of training and evaluation with
-[tf-slim](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/slim).
+[tf-slim](https://github.com/tensorflow/tensorflow/tree/r1.15/tensorflow/contrib/slim).
 As detailed below, training and evaluation require slightly different debugging
 workflows.
 
@@ -605,7 +605,7 @@ The `watch_fn` argument accepts a `Callable` that allows you to configure what
 If your model code is written in C++ or other languages, you can also
 modify the `debug_options` field of `RunOptions` to generate debug dumps that
 can be inspected offline. See
-[the proto definition](https://www.tensorflow.org/code/tensorflow/core/protobuf/debug.proto)
+[the proto definition](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/protobuf/debug.proto)
 for more details.
 
 ### Debugging Remotely-Running Estimators
@@ -648,7 +648,7 @@ python -m tensorflow.python.debug.cli.offline_analyzer \
        model, check out
 
    1. The profiling mode of tfdbg: `tfdbg> run -p`.
-   2. [tfprof](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/core/profiler)
+   2. [tfprof](https://github.com/tensorflow/tensorflow/tree/r1.15/tensorflow/core/profiler)
       and other profiling tools for TensorFlow.
 
 **Q**: _How do I link tfdbg against my `Session` in Bazel? Why do I see an
@@ -808,4 +808,4 @@ tensor dumps.
        and conditional breakpoints, and tying tensors to their
        graph-construction source code, all in the browser environment.
        To get started, please visit
-       [its README](https://github.com/tensorflow/tensorboard/blob/master/tensorboard/plugins/debugger/README.md).
+       [its README](https://github.com/tensorflow/tensorboard/blob/r1.15/tensorboard/plugins/debugger/README.md).
diff --git a/site/en/r1/guide/distribute_strategy.ipynb b/site/en/r1/guide/distribute_strategy.ipynb
index 3c0b453a278..af50683c845 100644
--- a/site/en/r1/guide/distribute_strategy.ipynb
+++ b/site/en/r1/guide/distribute_strategy.ipynb
@@ -245,7 +245,7 @@
         "\n",
         "`tf.distribute.experimental.MultiWorkerMirroredStrategy` is very similar to `MirroredStrategy`. It implements synchronous distributed training across multiple workers, each with potentially multiple GPUs. Similar to `MirroredStrategy`, it creates copies of all variables in the model on each device across all workers.\n",
         "\n",
-        "It uses [CollectiveOps](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/ops/collective_ops.py) as the multi-worker all-reduce communication method used to keep variables in sync. A collective op is a single op in the TensorFlow graph which can automatically choose an all-reduce algorithm in the TensorFlow runtime according to hardware, network topology and tensor sizes.\n",
+        "It uses [CollectiveOps](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/python/ops/collective_ops.py) as the multi-worker all-reduce communication method used to keep variables in sync. A collective op is a single op in the TensorFlow graph which can automatically choose an all-reduce algorithm in the TensorFlow runtime according to hardware, network topology and tensor sizes.\n",
         "\n",
         "It also implements additional performance optimizations. For example, it includes a static optimization that converts multiple all-reductions on small tensors into fewer all-reductions on larger tensors. In addition, we are designing it to have a plugin architecture - so that in the future, users will be able to plugin algorithms that are better tuned for their hardware. Note that collective ops also implement other collective operations such as broadcast and all-gather.\n",
         "\n",
@@ -490,8 +490,8 @@
         "Here is a list of tutorials and examples that illustrate the above integration end to end with Keras:\n",
         "\n",
         "1. [Tutorial](../tutorials/distribute/keras.ipynb) to train MNIST with `MirroredStrategy`.\n",
-        "2. Official [ResNet50](https://github.com/tensorflow/models/blob/master/official/vision/image_classification/resnet_imagenet_main.py) training with ImageNet data using `MirroredStrategy`.\n",
-        "3. [ResNet50](https://github.com/tensorflow/tpu/blob/master/models/experimental/resnet50_keras/resnet50.py) trained with Imagenet data on Cloud TPus with `TPUStrategy`."
+        "2. Official [ResNet50](https://github.com/tensorflow/models/blob/r1.15/official/vision/image_classification/resnet_imagenet_main.py) training with ImageNet data using `MirroredStrategy`.\n",
+        "3. [ResNet50](https://github.com/tensorflow/tpu/blob/1.15/models/experimental/resnet50_keras/resnet50.py) trained with Imagenet data on Cloud TPus with `TPUStrategy`."
       ]
     },
     {
@@ -595,9 +595,9 @@
         "### Examples and Tutorials\n",
         "Here are some examples that show end to end usage of various strategies with Estimator:\n",
         "\n",
-        "1. [End to end example](https://github.com/tensorflow/ecosystem/tree/master/distribution_strategy) for multi worker training in tensorflow/ecosystem using Kuberentes templates. This example starts with a Keras model and converts it to an Estimator using the `tf.keras.estimator.model_to_estimator` API.\n",
-        "2. Official [ResNet50](https://github.com/tensorflow/models/blob/master/official/r1/resnet/imagenet_main.py) model, which can be trained using either `MirroredStrategy` or `MultiWorkerMirroredStrategy`.\n",
-        "3. [ResNet50](https://github.com/tensorflow/tpu/blob/master/models/experimental/distribution_strategy/resnet_estimator.py) example with TPUStrategy."
+        "1. [End to end example](https://github.com/tensorflow/ecosystem/tree/r1.15/distribution_strategy) for multi worker training in tensorflow/ecosystem using Kuberentes templates. This example starts with a Keras model and converts it to an Estimator using the `tf.keras.estimator.model_to_estimator` API.\n",
+        "2. Official [ResNet50](https://github.com/tensorflow/models/blob/r1.15/official/r1/resnet/imagenet_main.py) model, which can be trained using either `MirroredStrategy` or `MultiWorkerMirroredStrategy`.\n",
+        "3. [ResNet50](https://github.com/tensorflow/tpu/blob/1.15/models/experimental/distribution_strategy/resnet_estimator.py) example with TPUStrategy."
       ]
     },
     {
diff --git a/site/en/r1/guide/eager.ipynb b/site/en/r1/guide/eager.ipynb
index 6a0a78c2443..f76acb4b702 100644
--- a/site/en/r1/guide/eager.ipynb
+++ b/site/en/r1/guide/eager.ipynb
@@ -95,7 +95,7 @@
         "\n",
         "Eager execution supports most TensorFlow operations and GPU acceleration. For a\n",
         "collection of examples running in eager execution, see:\n",
-        "[tensorflow/contrib/eager/python/examples](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/eager/python/examples).\n",
+        "[tensorflow/contrib/eager/python/examples](https://github.com/tensorflow/tensorflow/tree/r1.15/tensorflow/contrib/eager/python/examples).\n",
         "\n",
         "Note: Some models may experience increased overhead with eager execution\n",
         "enabled. Performance improvements are ongoing, but please\n",
@@ -1160,7 +1160,7 @@
         "### Benchmarks\n",
         "\n",
         "For compute-heavy models, such as\n",
-        "[ResNet50](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/eager/python/examples/resnet50)\n",
+        "[ResNet50](https://github.com/tensorflow/tensorflow/tree/r1.15/tensorflow/contrib/eager/python/examples/resnet50)\n",
         "training on a GPU, eager execution performance is comparable to graph execution.\n",
         "But this gap grows larger for models with less computation and there is work to\n",
         "be done for optimizing hot code paths for models with lots of small operations."
@@ -1225,7 +1225,7 @@
         "production deployment. Use `tf.train.Checkpoint` to save and restore model\n",
         "variables, this allows movement between eager and graph execution environments.\n",
         "See the examples in:\n",
-        "[tensorflow/contrib/eager/python/examples](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/eager/python/examples).\n"
+        "[tensorflow/contrib/eager/python/examples](https://github.com/tensorflow/tensorflow/tree/r1.15/tensorflow/contrib/eager/python/examples).\n"
       ]
     },
     {
diff --git a/site/en/r1/guide/extend/architecture.md b/site/en/r1/guide/extend/architecture.md
index 1f2ac53066f..0753824e15e 100644
--- a/site/en/r1/guide/extend/architecture.md
+++ b/site/en/r1/guide/extend/architecture.md
@@ -34,7 +34,7 @@ This document focuses on the following layers:
 *  **Client**:
    *  Defines the computation as a dataflow graph.
    *  Initiates graph execution using a [**session**](
-      https://www.tensorflow.org/code/tensorflow/python/client/session.py).
+      https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/python/client/session.py).
 *  **Distributed Master**
    *  Prunes a specific subgraph from the graph, as defined by the arguments
       to Session.run().
@@ -144,8 +144,8 @@ The distributed master then ships the graph pieces to the distributed tasks.
 
 ### Code
 
-*  [MasterService API definition](https://www.tensorflow.org/code/tensorflow/core/protobuf/master_service.proto)
-*  [Master interface](https://www.tensorflow.org/code/tensorflow/core/distributed_runtime/master_interface.h)
+*  [MasterService API definition](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/protobuf/master_service.proto)
+*  [Master interface](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/distributed_runtime/master_interface.h)
 
 ## Worker Service
 
@@ -178,7 +178,7 @@ For transfers between tasks, TensorFlow uses multiple protocols, including:
 
 We also have preliminary support for NVIDIA's NCCL library for multi-GPU
 communication, see:
-[`tf.contrib.nccl`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/ops/nccl_ops.py).
+[`tf.contrib.nccl`](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/python/ops/nccl_ops.py).
 
 Partitioned Graph
 
@@ -186,9 +186,9 @@ communication, see:
 
 ### Code
 
-*   [WorkerService API definition](https://www.tensorflow.org/code/tensorflow/core/protobuf/worker_service.proto)
-*   [Worker interface](https://www.tensorflow.org/code/tensorflow/core/distributed_runtime/worker_interface.h)
-*   [Remote rendezvous (for Send and Recv implementations)](https://www.tensorflow.org/code/tensorflow/core/distributed_runtime/rpc/rpc_rendezvous_mgr.h)
+*   [WorkerService API definition](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/protobuf/worker_service.proto)
+*   [Worker interface](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/distributed_runtime/worker_interface.h)
+*   [Remote rendezvous (for Send and Recv implementations)](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/distributed_runtime/rpc/rpc_rendezvous_mgr.h)
 
 ## Kernel Implementations
 
@@ -199,7 +199,7 @@ Many of the operation kernels are implemented using Eigen::Tensor, which uses
 C++ templates to generate efficient parallel code for multicore CPUs and GPUs;
 however, we liberally use libraries like cuDNN where a more efficient kernel
 implementation is possible. We have also implemented
-[quantization](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/g3doc/performance/post_training_quantization.md), which enables
+[quantization](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/lite/g3doc/performance/post_training_quantization.md), which enables
 faster inference in environments such as mobile devices and high-throughput
 datacenter applications, and use the
 [gemmlowp](https://github.com/google/gemmlowp) low-precision matrix library to
@@ -215,4 +215,4 @@ experimental implementation of automatic kernel fusion.
 
 ### Code
 
-*   [`OpKernel` interface](https://www.tensorflow.org/code/tensorflow/core/framework/op_kernel.h)
+*   [`OpKernel` interface](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/framework/op_kernel.h)
diff --git a/site/en/r1/guide/extend/bindings.md b/site/en/r1/guide/extend/bindings.md
index 9c10e90840f..7daa2212106 100644
--- a/site/en/r1/guide/extend/bindings.md
+++ b/site/en/r1/guide/extend/bindings.md
@@ -112,11 +112,11 @@ There are a few ways to get a list of the `OpDef`s for the registered ops:
     to interpret the `OpDef` messages.
 -   The C++ function `OpRegistry::Global()->GetRegisteredOps()` returns the same
     list of all registered `OpDef`s (defined in
-    [`tensorflow/core/framework/op.h`](https://www.tensorflow.org/code/tensorflow/core/framework/op.h)). This can be used to write the generator
+    [`tensorflow/core/framework/op.h`](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/framework/op.h)). This can be used to write the generator
     in C++ (particularly useful for languages that do not have protocol buffer
     support).
 -   The ASCII-serialized version of that list is periodically checked in to
-    [`tensorflow/core/ops/ops.pbtxt`](https://www.tensorflow.org/code/tensorflow/core/ops/ops.pbtxt) by an automated process.
+    [`tensorflow/core/ops/ops.pbtxt`](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/ops/ops.pbtxt) by an automated process.
 
 The `OpDef` specifies the following:
 
@@ -159,7 +159,7 @@ between the generated code and the `OpDef`s checked into the repository, but is
 useful for languages where code is expected to be generated ahead of time like
 `go get` for Go and `cargo ops` for Rust. At the other end of the spectrum, for
 some languages the code could be generated dynamically from
-[`tensorflow/core/ops/ops.pbtxt`](https://www.tensorflow.org/code/tensorflow/core/ops/ops.pbtxt).
+[`tensorflow/core/ops/ops.pbtxt`](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/ops/ops.pbtxt).
 
 #### Handling Constants
 
@@ -228,4 +228,4 @@ At this time, support for gradients, functions and control flow operations ("if"
 and "while") is not available in languages other than Python. This will be
 updated when the [C API] provides necessary support.
 
-[C API]: https://www.tensorflow.org/code/tensorflow/c/c_api.h
+[C API]: https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/c/c_api.h
diff --git a/site/en/r1/guide/extend/filesystem.md b/site/en/r1/guide/extend/filesystem.md
index 4d34c07102e..2d6ea0c4645 100644
--- a/site/en/r1/guide/extend/filesystem.md
+++ b/site/en/r1/guide/extend/filesystem.md
@@ -54,7 +54,7 @@ To implement a custom filesystem plugin, you must do the following:
 ### The FileSystem interface
 
 The `FileSystem` interface is an abstract C++ interface defined in
-[file_system.h](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/platform/file_system.h).
+[file_system.h](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/platform/file_system.h).
 An implementation of the `FileSystem` interface should implement all relevant
 the methods defined by the interface. Implementing the interface requires
 defining operations such as creating `RandomAccessFile`, `WritableFile`, and
@@ -70,26 +70,26 @@ involves calling `stat()` on the file and then returns the filesize as reported
 by the return of the stat object. Similarly, for the `HDFSFileSystem`
 implementation, these calls simply delegate to the `libHDFS` implementation of
 similar functionality, such as `hdfsDelete` for
-[DeleteFile](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/platform/hadoop/hadoop_file_system.cc#L386).
+[DeleteFile](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/platform/hadoop/hadoop_file_system.cc#L386).
 
 We suggest looking through these code examples to get an idea of how different
 filesystem implementations call their existing libraries. Examples include:
 
 *   [POSIX
-    plugin](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/platform/posix/posix_file_system.h)
+    plugin](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/platform/posix/posix_file_system.h)
 *   [HDFS
-    plugin](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/platform/hadoop/hadoop_file_system.h)
+    plugin](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/platform/hadoop/hadoop_file_system.h)
 *   [GCS
-    plugin](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/platform/cloud/gcs_file_system.h)
+    plugin](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/platform/cloud/gcs_file_system.h)
 *   [S3
-    plugin](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/platform/s3/s3_file_system.h)
+    plugin](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/platform/s3/s3_file_system.h)
 
 #### The File interfaces
 
 Beyond operations that allow you to query and manipulate files and directories
 in a filesystem, the `FileSystem` interface requires you to implement factories
 that return implementations of abstract objects such as the
-[RandomAccessFile](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/platform/file_system.h#L223),
+[RandomAccessFile](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/platform/file_system.h#L223),
 the `WritableFile`, so that TensorFlow code and read and write to files in that
 `FileSystem` implementation.
 
@@ -224,7 +224,7 @@ it will use the `FooBarFileSystem` implementation.
 
 Next, you must build a shared object containing this implementation. An example
 of doing so using bazel's `cc_binary` rule can be found
-[here](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/BUILD#L244),
+[here](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/python/BUILD#L244),
 but you may use any build system to do so. See the section on [building the op library](../extend/op.md#build_the_op_library) for similar
 instructions.
 
@@ -236,7 +236,7 @@ passing the path to the shared object. Calling this in your client program loads
 the shared object in the process, thus registering your implementation as
 available for any file operations going through the `FileSystem` interface. You
 can see
-[test_file_system.py](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/framework/file_system_test.py)
+[test_file_system.py](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/python/framework/file_system_test.py)
 for an example.
 
 ## What goes through this interface?
diff --git a/site/en/r1/guide/extend/formats.md b/site/en/r1/guide/extend/formats.md
index 3b7b4aafbd6..bdebee5487d 100644
--- a/site/en/r1/guide/extend/formats.md
+++ b/site/en/r1/guide/extend/formats.md
@@ -28,11 +28,11 @@ individual records in a file. There are several examples of "reader" datasets
 that are already built into TensorFlow:
 
 *   `tf.data.TFRecordDataset`
-    ([source in `kernels/data/reader_dataset_ops.cc`](https://www.tensorflow.org/code/tensorflow/core/kernels/data/reader_dataset_ops.cc))
+    ([source in `kernels/data/reader_dataset_ops.cc`](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/kernels/data/reader_dataset_ops.cc))
 *   `tf.data.FixedLengthRecordDataset`
-    ([source in `kernels/data/reader_dataset_ops.cc`](https://www.tensorflow.org/code/tensorflow/core/kernels/data/reader_dataset_ops.cc))
+    ([source in `kernels/data/reader_dataset_ops.cc`](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/kernels/data/reader_dataset_ops.cc))
 *   `tf.data.TextLineDataset`
-    ([source in `kernels/data/reader_dataset_ops.cc`](https://www.tensorflow.org/code/tensorflow/core/kernels/data/reader_dataset_ops.cc))
+    ([source in `kernels/data/reader_dataset_ops.cc`](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/kernels/data/reader_dataset_ops.cc))
 
 Each of these implementations comprises three related classes:
 
@@ -279,7 +279,7 @@ if __name__ == "__main__":
 ```
 
 You can see some examples of `Dataset` wrapper classes in
-[`tensorflow/python/data/ops/dataset_ops.py`](https://www.tensorflow.org/code/tensorflow/python/data/ops/dataset_ops.py).
+[`tensorflow/python/data/ops/dataset_ops.py`](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/python/data/ops/dataset_ops.py).
 
 ## Writing an Op for a record format
 
@@ -297,7 +297,7 @@ Examples of Ops useful for decoding records:
 
 Note that it can be useful to use multiple Ops to decode a particular record
 format.  For example, you may have an image saved as a string in
-[a `tf.train.Example` protocol buffer](https://www.tensorflow.org/code/tensorflow/core/example/example.proto).
+[a `tf.train.Example` protocol buffer](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/example/example.proto).
 Depending on the format of that image, you might take the corresponding output
 from a `tf.parse_single_example` op and call `tf.image.decode_jpeg`,
 `tf.image.decode_png`, or `tf.decode_raw`.  It is common to take the output
diff --git a/site/en/r1/guide/extend/model_files.md b/site/en/r1/guide/extend/model_files.md
index 30e73a5169e..e590fcf1f27 100644
--- a/site/en/r1/guide/extend/model_files.md
+++ b/site/en/r1/guide/extend/model_files.md
@@ -28,7 +28,7 @@ by calling `as_graph_def()`, which returns a `GraphDef` object.
 
 The GraphDef class is an object created by the ProtoBuf library from the
 definition in
-[tensorflow/core/framework/graph.proto](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/graph.proto). The protobuf tools parse
+[tensorflow/core/framework/graph.proto](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/framework/graph.proto). The protobuf tools parse
 this text file, and generate the code to load, store, and manipulate graph
 definitions. If you see a standalone TensorFlow file representing a model, it's
 likely to contain a serialized version of one of these `GraphDef` objects
@@ -87,7 +87,7 @@ for node in graph_def.node
 ```
 
 Each node is a `NodeDef` object, defined in
-[tensorflow/core/framework/node_def.proto](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/node_def.proto). These
+[tensorflow/core/framework/node_def.proto](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/framework/node_def.proto). These
 are the fundamental building blocks of TensorFlow graphs, with each one defining
 a single operation along with its input connections. Here are the members of a
 `NodeDef`, and what they mean.
@@ -107,7 +107,7 @@ This defines what operation to run, for example `"Add"`, `"MatMul"`, or
 `"Conv2D"`. When a graph is run, this op name is looked up in a registry to
 find an implementation. The registry is populated by calls to the
 `REGISTER_OP()` macro, like those in
-[tensorflow/core/ops/nn_ops.cc](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/ops/nn_ops.cc).
+[tensorflow/core/ops/nn_ops.cc](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/ops/nn_ops.cc).
 
 ### `input`
 
@@ -133,7 +133,7 @@ size of filters for convolutions, or the values of constant ops. Because there
 can be so many different types of attribute values, from strings, to ints, to
 arrays of tensor values, there's a separate protobuf file defining the data
 structure that holds them, in
-[tensorflow/core/framework/attr_value.proto](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/attr_value.proto).
+[tensorflow/core/framework/attr_value.proto](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/framework/attr_value.proto).
 
 Each attribute has a unique name string, and the expected attributes are listed
 when the operation is defined. If an attribute isn't present in a node, but it
@@ -151,7 +151,7 @@ the file format during training. Instead, they're held in separate checkpoint
 files, and there are `Variable` ops in the graph that load the latest values
 when they're initialized. It's often not very convenient to have separate files
 when you're deploying to production, so there's the
-[freeze_graph.py](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/tools/freeze_graph.py) script that takes a graph definition and a set
+[freeze_graph.py](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/python/tools/freeze_graph.py) script that takes a graph definition and a set
 of checkpoints and freezes them together into a single file.
 
 What this does is load the `GraphDef`, pull in the values for all the variables
@@ -167,7 +167,7 @@ the most common problems is extracting and interpreting the weight values. A
 common way to store them, for example in graphs created by the freeze_graph
 script, is as `Const` ops containing the weights as `Tensors`. These are
 defined in
-[tensorflow/core/framework/tensor.proto](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/tensor.proto), and contain information
+[tensorflow/core/framework/tensor.proto](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/framework/tensor.proto), and contain information
 about the size and type of the data, as well as the values themselves. In
 Python, you get a `TensorProto` object from a `NodeDef` representing a `Const`
 op by calling something like `some_node_def.attr['value'].tensor`.
diff --git a/site/en/r1/guide/extend/op.md b/site/en/r1/guide/extend/op.md
index dc2d9fbe678..186d9c28c04 100644
--- a/site/en/r1/guide/extend/op.md
+++ b/site/en/r1/guide/extend/op.md
@@ -47,7 +47,7 @@ To incorporate your custom op you'll need to:
     test the op in C++. If you define gradients, you can verify them with the
     Python `tf.test.compute_gradient_error`.
     See
-    [`relu_op_test.py`](https://www.tensorflow.org/code/tensorflow/python/kernel_tests/relu_op_test.py) as
+    [`relu_op_test.py`](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/python/kernel_tests/relu_op_test.py) as
     an example that tests the forward functions of Relu-like operators and
     their gradients.
 
@@ -155,17 +155,17 @@ REGISTER_KERNEL_BUILDER(Name("ZeroOut").Device(DEVICE_CPU), ZeroOutOp);
 >   Important: Instances of your OpKernel may be accessed concurrently.
 >   Your `Compute` method must be thread-safe. Guard any access to class
 >   members with a mutex. Or better yet, don't share state via class members!
->   Consider using a [`ResourceMgr`](https://www.tensorflow.org/code/tensorflow/core/framework/resource_mgr.h)
+>   Consider using a [`ResourceMgr`](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/framework/resource_mgr.h)
 >   to keep track of op state.
 
 ### Multi-threaded CPU kernels
 
 To write a multi-threaded CPU kernel, the Shard function in
-[`work_sharder.h`](https://www.tensorflow.org/code/tensorflow/core/util/work_sharder.h)
+[`work_sharder.h`](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/util/work_sharder.h)
 can be used. This function shards a computation function across the
 threads configured to be used for intra-op threading (see
 intra_op_parallelism_threads in
-[`config.proto`](https://www.tensorflow.org/code/tensorflow/core/protobuf/config.proto)).
+[`config.proto`](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/protobuf/config.proto)).
 
 ### GPU kernels
 
@@ -486,13 +486,13 @@ This asserts that the input is a vector, and returns having set the
 
 *   The `context`, which can either be an `OpKernelContext` or
     `OpKernelConstruction` pointer (see
-    [`tensorflow/core/framework/op_kernel.h`](https://www.tensorflow.org/code/tensorflow/core/framework/op_kernel.h)),
+    [`tensorflow/core/framework/op_kernel.h`](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/framework/op_kernel.h)),
     for its `SetStatus()` method.
 *   The condition.  For example, there are functions for validating the shape
     of a tensor in
-    [`tensorflow/core/framework/tensor_shape.h`](https://www.tensorflow.org/code/tensorflow/core/framework/tensor_shape.h)
+    [`tensorflow/core/framework/tensor_shape.h`](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/framework/tensor_shape.h)
 *   The error itself, which is represented by a `Status` object, see
-    [`tensorflow/core/lib/core/status.h`](https://www.tensorflow.org/code/tensorflow/core/lib/core/status.h). A
+    [`tensorflow/core/lib/core/status.h`](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/lib/core/status.h). A
     `Status` has both a type (frequently `InvalidArgument`, but see the list of
     types) and a message.  Functions for constructing an error may be found in
     [`tensorflow/core/lib/core/errors.h`][validation-macros].
@@ -633,7 +633,7 @@ define an attr with constraints, you can use the following ``s:
 
     The specific lists of types allowed by these are defined by the functions
     (like `NumberTypes()`) in
-    [`tensorflow/core/framework/types.h`](https://www.tensorflow.org/code/tensorflow/core/framework/types.h).
+    [`tensorflow/core/framework/types.h`](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/framework/types.h).
     In this example the attr `t` must be one of the numeric types:
 
     ```c++
@@ -1180,7 +1180,7 @@ There are several ways to preserve backwards-compatibility.
    type into a list of varying types).
 
 The full list of safe and unsafe changes can be found in
-[`tensorflow/core/framework/op_compatibility_test.cc`](https://www.tensorflow.org/code/tensorflow/core/framework/op_compatibility_test.cc).
+[`tensorflow/core/framework/op_compatibility_test.cc`](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/framework/op_compatibility_test.cc).
 If you cannot make your change to an operation backwards compatible, then create
 a new operation with a new name with the new semantics.
 
@@ -1197,16 +1197,16 @@ made when TensorFlow changes major versions, and must conform to the
 You can implement different OpKernels and register one for CPU and another for
 GPU, just like you can [register kernels for different types](#polymorphism).
 There are several examples of kernels with GPU support in
-[`tensorflow/core/kernels/`](https://www.tensorflow.org/code/tensorflow/core/kernels/).
+[`tensorflow/core/kernels/`](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/kernels/).
 Notice some kernels have a CPU version in a `.cc` file, a GPU version in a file
 ending in `_gpu.cu.cc`, and some code shared in common in a `.h` file.
 
 For example, the `tf.pad` has
 everything but the GPU kernel in [`tensorflow/core/kernels/pad_op.cc`][pad_op].
 The GPU kernel is in
-[`tensorflow/core/kernels/pad_op_gpu.cu.cc`](https://www.tensorflow.org/code/tensorflow/core/kernels/pad_op_gpu.cu.cc),
+[`tensorflow/core/kernels/pad_op_gpu.cu.cc`](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/kernels/pad_op_gpu.cu.cc),
 and the shared code is a templated class defined in
-[`tensorflow/core/kernels/pad_op.h`](https://www.tensorflow.org/code/tensorflow/core/kernels/pad_op.h).
+[`tensorflow/core/kernels/pad_op.h`](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/kernels/pad_op.h).
 We organize the code this way for two reasons: it allows you to share common
 code among the CPU and GPU implementations, and it puts the GPU implementation
 into a separate file so that it can be compiled only by the GPU compiler.
@@ -1227,16 +1227,16 @@ kept on the CPU, add a `HostMemory()` call to the kernel registration, e.g.:
 #### Compiling the kernel for the GPU device
 
 Look at
-[cuda_op_kernel.cu.cc](https://www.tensorflow.org/code/tensorflow/examples/adding_an_op/cuda_op_kernel.cu.cc)
+[cuda_op_kernel.cu.cc](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/examples/adding_an_op/cuda_op_kernel.cu.cc)
 for an example that uses a CUDA kernel to implement an op. The
 `tf_custom_op_library` accepts a `gpu_srcs` argument in which the list of source
 files containing the CUDA kernels (`*.cu.cc` files) can be specified. For use
 with a binary installation of TensorFlow, the CUDA kernels have to be compiled
 with NVIDIA's `nvcc` compiler. Here is the sequence of commands you can use to
 compile the
-[cuda_op_kernel.cu.cc](https://www.tensorflow.org/code/tensorflow/examples/adding_an_op/cuda_op_kernel.cu.cc)
+[cuda_op_kernel.cu.cc](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/examples/adding_an_op/cuda_op_kernel.cu.cc)
 and
-[cuda_op_kernel.cc](https://www.tensorflow.org/code/tensorflow/examples/adding_an_op/cuda_op_kernel.cc)
+[cuda_op_kernel.cc](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/examples/adding_an_op/cuda_op_kernel.cc)
 into a single dynamically loadable library:
 
 ```bash
@@ -1361,7 +1361,7 @@ be set to the first input's shape. If the output is selected by its index as in
 
 There are a number of common shape functions
 that apply to many ops, such as `shape_inference::UnchangedShape` which can be
-found in [common_shape_fns.h](https://www.tensorflow.org/code/tensorflow/core/framework/common_shape_fns.h) and used as follows:
+found in [common_shape_fns.h](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/framework/common_shape_fns.h) and used as follows:
 
 ```c++
 REGISTER_OP("ZeroOut")
@@ -1408,7 +1408,7 @@ provides access to the attributes of the op).
 
 Since shape inference is an optional feature, and the shapes of tensors may vary
 dynamically, shape functions must be robust to incomplete shape information for
-any of the inputs. The `Merge` method in [`InferenceContext`](https://www.tensorflow.org/code/tensorflow/core/framework/shape_inference.h)
+any of the inputs. The `Merge` method in [`InferenceContext`](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/framework/shape_inference.h)
 allows the caller to assert that two shapes are the same, even if either
 or both of them do not have complete information. Shape functions are defined
 for all of the core TensorFlow ops and provide many different usage examples.
@@ -1433,7 +1433,7 @@ If you have a complicated shape function, you should consider adding a test for
 validating that various input shape combinations produce the expected output
 shape combinations.  You can see examples of how to write these tests in some
 our
-[core ops tests](https://www.tensorflow.org/code/tensorflow/core/ops/array_ops_test.cc).
+[core ops tests](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/ops/array_ops_test.cc).
 (The syntax of `INFER_OK` and `INFER_ERROR` are a little cryptic, but try to be
 compact in representing input and output shape specifications in tests.  For
 now, see the surrounding comments in those tests to get a sense of the shape
@@ -1446,20 +1446,20 @@ To build a `pip` package for your op, see the
 guide shows how to build custom ops from the TensorFlow pip package instead
 of building TensorFlow from source.
 
-[core-array_ops]:https://www.tensorflow.org/code/tensorflow/core/ops/array_ops.cc
-[python-user_ops]:https://www.tensorflow.org/code/tensorflow/python/user_ops/user_ops.py
-[tf-kernels]:https://www.tensorflow.org/code/tensorflow/core/kernels/
-[user_ops]:https://www.tensorflow.org/code/tensorflow/core/user_ops/
-[pad_op]:https://www.tensorflow.org/code/tensorflow/core/kernels/pad_op.cc
-[standard_ops-py]:https://www.tensorflow.org/code/tensorflow/python/ops/standard_ops.py
-[standard_ops-cc]:https://www.tensorflow.org/code/tensorflow/cc/ops/standard_ops.h
-[python-BUILD]:https://www.tensorflow.org/code/tensorflow/python/BUILD
-[validation-macros]:https://www.tensorflow.org/code/tensorflow/core/lib/core/errors.h
-[op_def_builder]:https://www.tensorflow.org/code/tensorflow/core/framework/op_def_builder.h
-[register_types]:https://www.tensorflow.org/code/tensorflow/core/framework/register_types.h
-[FinalizeAttr]:https://www.tensorflow.org/code/tensorflow/core/framework/op_def_builder.cc
-[DataTypeString]:https://www.tensorflow.org/code/tensorflow/core/framework/types.cc
-[python-BUILD]:https://www.tensorflow.org/code/tensorflow/python/BUILD
-[types-proto]:https://www.tensorflow.org/code/tensorflow/core/framework/types.proto
-[TensorShapeProto]:https://www.tensorflow.org/code/tensorflow/core/framework/tensor_shape.proto
-[TensorProto]:https://www.tensorflow.org/code/tensorflow/core/framework/tensor.proto
+[core-array_ops]:https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/ops/array_ops.cc
+[python-user_ops]:https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/python/user_ops/user_ops.py
+[tf-kernels]:https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/kernels/
+[user_ops]:https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/user_ops/
+[pad_op]:https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/kernels/pad_op.cc
+[standard_ops-py]:https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/python/ops/standard_ops.py
+[standard_ops-cc]:https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/cc/ops/standard_ops.h
+[python-BUILD]:https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/python/BUILD
+[validation-macros]:https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/lib/core/errors.h
+[op_def_builder]:https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/framework/op_def_builder.h
+[register_types]:https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/framework/register_types.h
+[FinalizeAttr]:https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/framework/op_def_builder.cc
+[DataTypeString]:https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/framework/types.cc
+[python-BUILD]:https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/python/BUILD
+[types-proto]:https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/framework/types.proto
+[TensorShapeProto]:https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/framework/tensor_shape.proto
+[TensorProto]:https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/framework/tensor.proto
diff --git a/site/en/r1/guide/feature_columns.md b/site/en/r1/guide/feature_columns.md
index 5a4dfbbf46d..e4259f85e9f 100644
--- a/site/en/r1/guide/feature_columns.md
+++ b/site/en/r1/guide/feature_columns.md
@@ -562,7 +562,7 @@ For more examples on feature columns, view the following:
 
 * The [Low Level Introduction](../guide/low_level_intro.md#feature_columns) demonstrates how
   experiment directly with `feature_columns` using TensorFlow's low level APIs.
-* The [Estimator wide and deep learning tutorial](https://github.com/tensorflow/models/tree/master/official/r1/wide_deep)
+* The [Estimator wide and deep learning tutorial](https://github.com/tensorflow/models/tree/r1.15/official/r1/wide_deep)
   solves a binary classification problem using `feature_columns` on a variety of
   input data types.
 
diff --git a/site/en/r1/guide/performance/benchmarks.md b/site/en/r1/guide/performance/benchmarks.md
index 8998c0723db..a56959ea416 100644
--- a/site/en/r1/guide/performance/benchmarks.md
+++ b/site/en/r1/guide/performance/benchmarks.md
@@ -401,7 +401,7 @@ GPUs | InceptionV3 (batch size 32) | ResNet-50 (batch size 32)
 ## Methodology
 
 This
-[script](https://github.com/tensorflow/benchmarks/tree/master/scripts/tf_cnn_benchmarks)
+[script](https://github.com/tensorflow/benchmarks/tree/r1.15/scripts/tf_cnn_benchmarks)
 was run on the various platforms to generate the above results.
 
 In order to create results that are as repeatable as possible, each test was run
diff --git a/site/en/r1/guide/performance/overview.md b/site/en/r1/guide/performance/overview.md
index 461fa4feb58..be7217f4b99 100644
--- a/site/en/r1/guide/performance/overview.md
+++ b/site/en/r1/guide/performance/overview.md
@@ -19,9 +19,9 @@ Reading large numbers of small files significantly impacts I/O performance.
 One approach to get maximum I/O throughput is to preprocess input data into
 larger (~100MB) `TFRecord` files. For smaller data sets (200MB-1GB), the best
 approach is often to load the entire data set into memory. The document
-[Downloading and converting to TFRecord format](https://github.com/tensorflow/models/tree/master/research/slim#downloading-and-converting-to-tfrecord-format)
+[Downloading and converting to TFRecord format](https://github.com/tensorflow/models/tree/r1.15/research/slim#downloading-and-converting-to-tfrecord-format)
 includes information and scripts for creating `TFRecord`s, and this
-[script](https://github.com/tensorflow/models/tree/master/research/tutorials/image/cifar10_estimator/generate_cifar10_tfrecords.py)
+[script](https://github.com/tensorflow/models/tree/r1.15/research/tutorials/image/cifar10_estimator/generate_cifar10_tfrecords.py)
 converts the CIFAR-10 dataset into `TFRecord`s.
 
 While feeding data using a `feed_dict` offers a high level of flexibility, in
diff --git a/site/en/r1/guide/saved_model.md b/site/en/r1/guide/saved_model.md
index 623863a9df9..34447ffe861 100644
--- a/site/en/r1/guide/saved_model.md
+++ b/site/en/r1/guide/saved_model.md
@@ -23,7 +23,7 @@ TensorFlow saves variables in binary *checkpoint files* that map variable
 names to tensor values.
 
 Caution: TensorFlow model files are code. Be careful with untrusted code.
-See [Using TensorFlow Securely](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md)
+See [Using TensorFlow Securely](https://github.com/tensorflow/tensorflow/blob/r1.15/SECURITY.md)
 for details.
 
 ### Save variables
@@ -148,7 +148,7 @@ Notes:
    `tf.variables_initializer` for more information.
 
 *  To inspect the variables in a checkpoint, you can use the
-   [`inspect_checkpoint`](https://www.tensorflow.org/code/tensorflow/python/tools/inspect_checkpoint.py)
+   [`inspect_checkpoint`](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/python/tools/inspect_checkpoint.py)
    library, particularly the `print_tensors_in_checkpoint_file` function.
 
 *  By default, `Saver` uses the value of the `tf.Variable.name` property
@@ -159,7 +159,7 @@ Notes:
 ### Inspect variables in a checkpoint
 
 We can quickly inspect variables in a checkpoint with the
-[`inspect_checkpoint`](https://www.tensorflow.org/code/tensorflow/python/tools/inspect_checkpoint.py) library.
+[`inspect_checkpoint`](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/python/tools/inspect_checkpoint.py) library.
 
 Continuing from the save/restore examples shown earlier:
 
@@ -216,7 +216,7 @@ simple_save(session,
 
 This configures the `SavedModel` so it can be loaded by
 [TensorFlow serving](https://www.tensorflow.org/tfx/tutorials/serving/rest_simple) and supports the
-[Predict API](https://github.com/tensorflow/serving/blob/master/tensorflow_serving/apis/predict.proto).
+[Predict API](https://github.com/tensorflow/serving/blob/r1.15/tensorflow_serving/apis/predict.proto).
 To access the classify, regress, or multi-inference APIs, use the manual
 `SavedModel` builder APIs or an `tf.estimator.Estimator`.
 
@@ -328,7 +328,7 @@ with tf.Session(graph=tf.Graph()) as sess:
 ### Load a SavedModel in C++
 
 The C++ version of the SavedModel
-[loader](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/cc/saved_model/loader.h)
+[loader](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/cc/saved_model/loader.h)
 provides an API to load a SavedModel from a path, while allowing
 `SessionOptions` and `RunOptions`.
 You have to specify the tags associated with the graph to be loaded.
@@ -383,20 +383,20 @@ reuse and share across tools consistently.
 You may use sets of tags to uniquely identify a `MetaGraphDef` saved in a
 SavedModel. A subset of commonly used tags is specified in:
 
-* [Python](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/saved_model/tag_constants.py)
-* [C++](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/cc/saved_model/tag_constants.h)
+* [Python](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/python/saved_model/tag_constants.py)
+* [C++](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/cc/saved_model/tag_constants.h)
 
 
 #### Standard SignatureDef constants
 
-A [**SignatureDef**](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/protobuf/meta_graph.proto)
+A [**SignatureDef**](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/protobuf/meta_graph.proto)
 is a protocol buffer that defines the signature of a computation
 supported by a graph.
 Commonly used input keys, output keys, and method names are
 defined in:
 
-* [Python](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/saved_model/signature_constants.py)
-* [C++](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/cc/saved_model/signature_constants.h)
+* [Python](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/python/saved_model/signature_constants.py)
+* [C++](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/cc/saved_model/signature_constants.h)
 
 ## Using SavedModel with Estimators
 
@@ -408,7 +408,7 @@ To prepare a trained Estimator for serving, you must export it in the standard
 SavedModel format. This section explains how to:
 
 * Specify the output nodes and the corresponding
-  [APIs](https://github.com/tensorflow/serving/blob/master/tensorflow_serving/apis/prediction_service.proto)
+  [APIs](https://github.com/tensorflow/serving/blob/r1.15/tensorflow_serving/apis/prediction_service.proto)
   that can be served (Classify, Regress, or Predict).
 * Export your model to the SavedModel format.
 * Serve the model from a local server and request predictions.
@@ -506,7 +506,7 @@ Each `output` value must be an `ExportOutput` object  such as
 `tf.estimator.export.PredictOutput`.
 
 These output types map straightforwardly to the
-[TensorFlow Serving APIs](https://github.com/tensorflow/serving/blob/master/tensorflow_serving/apis/prediction_service.proto),
+[TensorFlow Serving APIs](https://github.com/tensorflow/serving/blob/r1.15/tensorflow_serving/apis/prediction_service.proto),
 and so determine which request types will be honored.
 
 Note: In the multi-headed case, a `SignatureDef` will be generated for each
@@ -515,7 +515,7 @@ the same keys.  These `SignatureDef`s differ only in their outputs, as
 provided by the corresponding `ExportOutput` entry.  The inputs are always
 those provided by the `serving_input_receiver_fn`.
 An inference request may specify the head by name.  One head must be named
-using [`signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY`](https://www.tensorflow.org/code/tensorflow/python/saved_model/signature_constants.py)
+using [`signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY`](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/python/saved_model/signature_constants.py)
 indicating which `SignatureDef` will be served when an inference request
 does not specify one.
 
@@ -566,9 +566,9 @@ Now you have a server listening for inference requests via gRPC on port 9000!
 ### Request predictions from a local server
 
 The server responds to gRPC requests according to the
-[PredictionService](https://github.com/tensorflow/serving/blob/master/tensorflow_serving/apis/prediction_service.proto#L15)
+[PredictionService](https://github.com/tensorflow/serving/blob/r1.15/tensorflow_serving/apis/prediction_service.proto#L15)
 gRPC API service definition.  (The nested protocol buffers are defined in
-various [neighboring files](https://github.com/tensorflow/serving/blob/master/tensorflow_serving/apis)).
+various [neighboring files](https://github.com/tensorflow/serving/blob/r1.15/tensorflow_serving/apis)).
 
 From the API service definition, the gRPC framework generates client libraries
 in various languages providing remote access to the API.  In a project using the
@@ -620,7 +620,7 @@ The returned result in this example is a `ClassificationResponse` protocol
 buffer.
 
 This is a skeletal example; please see the [Tensorflow Serving](../deploy/index.md)
-documentation and [examples](https://github.com/tensorflow/serving/tree/master/tensorflow_serving/example)
+documentation and [examples](https://github.com/tensorflow/serving/tree/r1.15/tensorflow_serving/example)
 for more details.
 
 > Note: `ClassificationRequest` and `RegressionRequest` contain a
diff --git a/site/en/r1/guide/using_tpu.md b/site/en/r1/guide/using_tpu.md
index 74169092189..e3e338adf49 100644
--- a/site/en/r1/guide/using_tpu.md
+++ b/site/en/r1/guide/using_tpu.md
@@ -7,8 +7,8 @@ changing the *hardware accelerator* in your notebook settings:
 TPU-enabled Colab notebooks are available to test:
 
   1. [A quick test, just to measure FLOPS](https://colab.research.google.com/notebooks/tpu.ipynb).
-  2. [A CNN image classifier with `tf.keras`](https://colab.research.google.com/github/tensorflow/tpu/blob/master/tools/colab/fashion_mnist.ipynb).
-  3. [An LSTM markov chain text generator with `tf.keras`](https://colab.research.google.com/github/tensorflow/tpu/blob/master/tools/colab/shakespeare_with_tpu_and_keras.ipynb)
+  2. [A CNN image classifier with `tf.keras`](https://colab.research.google.com/github/tensorflow/tpu/blob/r1.15/tools/colab/fashion_mnist.ipynb).
+  3. [An LSTM markov chain text generator with `tf.keras`](https://colab.research.google.com/github/tensorflow/tpu/blob/r1.15/tools/colab/shakespeare_with_tpu_and_keras.ipynb)
 
 ## TPUEstimator
 
@@ -25,7 +25,7 @@ Cloud TPU is to define the model's inference phase (from inputs to predictions)
 outside of the `model_fn`. Then maintain separate implementations of the
 `Estimator` setup and `model_fn`, both wrapping this inference step. For an
 example of this pattern compare the `mnist.py` and `mnist_tpu.py` implementation in
-[tensorflow/models](https://github.com/tensorflow/models/tree/master/official/r1/mnist).
+[tensorflow/models](https://github.com/tensorflow/models/tree/r1.15/official/r1/mnist).
 
 ### Run a TPUEstimator locally
 
@@ -350,10 +350,10 @@ in bytes. A minimum of a few MB (`buffer_size=8*1024*1024`) is recommended so
 that data is available when needed.
 
 The TPU-demos repo includes
-[a script](https://github.com/tensorflow/tpu/blob/master/tools/datasets/imagenet_to_gcs.py)
+[a script](https://github.com/tensorflow/tpu/blob/1.15/tools/datasets/imagenet_to_gcs.py)
 for downloading the imagenet dataset and converting it to an appropriate format.
 This together with the imagenet
-[models](https://github.com/tensorflow/tpu/tree/master/models)
+[models](https://github.com/tensorflow/tpu/tree/r1.15/models)
 included in the repo demonstrate all of these best-practices.
 
 ## Next steps
diff --git a/site/en/r1/guide/version_compat.md b/site/en/r1/guide/version_compat.md
index 6702f6e0819..a765620518d 100644
--- a/site/en/r1/guide/version_compat.md
+++ b/site/en/r1/guide/version_compat.md
@@ -49,19 +49,19 @@ patch versions.  The public APIs consist of
   submodules, but is not documented, then it is **not** considered part of the
   public API.
 
-* The [C API](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/c/c_api.h).
+* The [C API](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/c/c_api.h).
 
 * The following protocol buffer files:
-    * [`attr_value`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/attr_value.proto)
-    * [`config`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/protobuf/config.proto)
-    * [`event`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/util/event.proto)
-    * [`graph`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/graph.proto)
-    * [`op_def`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/op_def.proto)
-    * [`reader_base`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/reader_base.proto)
-    * [`summary`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/summary.proto)
-    * [`tensor`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/tensor.proto)
-    * [`tensor_shape`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/tensor_shape.proto)
-    * [`types`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/types.proto)
+    * [`attr_value`](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/framework/attr_value.proto)
+    * [`config`](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/protobuf/config.proto)
+    * [`event`](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/util/event.proto)
+    * [`graph`](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/framework/graph.proto)
+    * [`op_def`](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/framework/op_def.proto)
+    * [`reader_base`](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/framework/reader_base.proto)
+    * [`summary`](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/framework/summary.proto)
+    * [`tensor`](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/framework/tensor.proto)
+    * [`tensor_shape`](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/framework/tensor_shape.proto)
+    * [`types`](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/framework/types.proto)
 
 
 ## What is *not* covered
@@ -79,7 +79,7 @@ backward incompatible ways between minor releases. These include:
     such as:
 
   - [C++](./extend/cc.md) (exposed through header files in
-    [`tensorflow/cc`](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/cc)).
+    [`tensorflow/cc`](https://github.com/tensorflow/tensorflow/tree/r1.15/tensorflow/cc)).
   - [Java](../api_docs/java/reference/org/tensorflow/package-summary),
   - [Go](https://pkg.go.dev/github.com/tensorflow/tensorflow/tensorflow/go)
   - [JavaScript](https://js.tensorflow.org)
@@ -209,7 +209,7 @@ guidelines for evolving `GraphDef` versions.
 There are different data versions for graphs and checkpoints. The two data
 formats evolve at different rates from each other and also at different rates
 from TensorFlow. Both versioning systems are defined in
-[`core/public/version.h`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/public/version.h).
+[`core/public/version.h`](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/public/version.h).
 Whenever a new version is added, a note is added to the header detailing what
 changed and the date.
 
@@ -224,7 +224,7 @@ We distinguish between the following kinds of data version information:
   (`min_producer`).
 
 Each piece of versioned data has a [`VersionDef
-versions`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/versions.proto)
+versions`](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/framework/versions.proto)
 field which records the `producer` that made the data, the `min_consumer`
 that it is compatible with, and a list of `bad_consumers` versions that are
 disallowed.
@@ -239,7 +239,7 @@ accept a piece of data if the following are all true:
 *   `consumer` not in data's `bad_consumers`
 
 Since both producers and consumers come from the same TensorFlow code base,
-[`core/public/version.h`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/public/version.h)
+[`core/public/version.h`](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/public/version.h)
 contains a main data version which is treated as either `producer` or
 `consumer` depending on context and both `min_consumer` and `min_producer`
 (needed by producers and consumers, respectively). Specifically,
@@ -309,7 +309,7 @@ existing producer scripts will not suddenly use the new functionality.
 1.  Add a new similar op named `SomethingV2` or similar and go through the
     process of adding it and switching existing Python wrappers to use it.
     To ensure forward compatibility use the checks suggested in
-    [compat.py](https://www.tensorflow.org/code/tensorflow/python/compat/compat.py)
+    [compat.py](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/python/compat/compat.py)
     when changing the Python wrappers.
 2.  Remove the old op (Can only take place with a major version change due to
     backward compatibility).
diff --git a/site/en/r1/tutorials/README.md b/site/en/r1/tutorials/README.md
index 5094e645e6e..9ff164ad77c 100644
--- a/site/en/r1/tutorials/README.md
+++ b/site/en/r1/tutorials/README.md
@@ -68,4 +68,4 @@ implement common ML algorithms. See the
 * [Boosted trees](./estimators/boosted_trees.ipynb)
 * [Gradient Boosted Trees: Model understanding](./estimators/boosted_trees_model_understanding.ipynb)
 * [Build a Convolutional Neural Network using Estimators](./estimators/cnn.ipynb)
-* [Wide and deep learning with Estimators](https://github.com/tensorflow/models/tree/master/official/r1/wide_deep)
+* [Wide and deep learning with Estimators](https://github.com/tensorflow/models/tree/r1.15/official/r1/wide_deep)
diff --git a/site/en/r1/tutorials/images/deep_cnn.md b/site/en/r1/tutorials/images/deep_cnn.md
index ef259516952..885f3907aa7 100644
--- a/site/en/r1/tutorials/images/deep_cnn.md
+++ b/site/en/r1/tutorials/images/deep_cnn.md
@@ -80,15 +80,15 @@ for details.  It consists of 1,068,298 learnable parameters and requires about
 ## Code Organization
 
 The code for this tutorial resides in
-[`models/tutorials/image/cifar10/`](https://github.com/tensorflow/models/tree/master/research/tutorials/image/cifar10/).
+[`models/tutorials/image/cifar10/`](https://github.com/tensorflow/models/tree/r1.15/research/tutorials/image/cifar10/).
 
 File | Purpose
 --- | ---
-[`cifar10_input.py`](https://github.com/tensorflow/models/tree/master/research/tutorials/image/cifar10/cifar10_input.py) | Loads CIFAR-10 dataset using [tensorflow-datasets library](https://github.com/tensorflow/datasets).
-[`cifar10.py`](https://github.com/tensorflow/models/tree/master/research/tutorials/image/cifar10/cifar10.py) | Builds the CIFAR-10 model.
-[`cifar10_train.py`](https://github.com/tensorflow/models/tree/master/research/tutorials/image/cifar10/cifar10_train.py) | Trains a CIFAR-10 model on a CPU or GPU.
-[`cifar10_multi_gpu_train.py`](https://github.com/tensorflow/models/tree/master/research/tutorials/image/cifar10/cifar10_multi_gpu_train.py) | Trains a CIFAR-10 model on multiple GPUs.
-[`cifar10_eval.py`](https://github.com/tensorflow/models/tree/master/research/tutorials/image/cifar10/cifar10_eval.py) | Evaluates the predictive performance of a CIFAR-10 model.
+[`cifar10_input.py`](https://github.com/tensorflow/models/tree/r1.15/research/tutorials/image/cifar10/cifar10_input.py) | Loads CIFAR-10 dataset using [tensorflow-datasets library](https://github.com/tensorflow/datasets).
+[`cifar10.py`](https://github.com/tensorflow/models/tree/r1.15/research/tutorials/image/cifar10/cifar10.py) | Builds the CIFAR-10 model.
+[`cifar10_train.py`](https://github.com/tensorflow/models/tree/r1.15/research/tutorials/image/cifar10/cifar10_train.py) | Trains a CIFAR-10 model on a CPU or GPU.
+[`cifar10_multi_gpu_train.py`](https://github.com/tensorflow/models/tree/r1.15/research/tutorials/image/cifar10/cifar10_multi_gpu_train.py) | Trains a CIFAR-10 model on multiple GPUs.
+[`cifar10_eval.py`](https://github.com/tensorflow/models/tree/r1.15/research/tutorials/image/cifar10/cifar10_eval.py) | Evaluates the predictive performance of a CIFAR-10 model.
 
 To run this tutorial, you will need to:
 
@@ -99,7 +99,7 @@ pip install tensorflow-datasets
 ## CIFAR-10 Model
 
 The CIFAR-10 network is largely contained in
-[`cifar10.py`](https://github.com/tensorflow/models/tree/master/research/tutorials/image/cifar10/cifar10.py).
+[`cifar10.py`](https://github.com/tensorflow/models/tree/r1.15/research/tutorials/image/cifar10/cifar10.py).
 The complete training
 graph contains roughly 765 operations. We find that we can make the code most
 reusable by constructing the graph with the following modules:
diff --git a/site/en/r1/tutorials/images/image_recognition.md b/site/en/r1/tutorials/images/image_recognition.md
index 2cbf9eee378..cb66e594629 100644
--- a/site/en/r1/tutorials/images/image_recognition.md
+++ b/site/en/r1/tutorials/images/image_recognition.md
@@ -146,7 +146,7 @@ Next, try it out on your own images by supplying the --image= argument, e.g.,
 bazel-bin/tensorflow/examples/label_image/label_image --image=my_image.png
 ```
 
-If you look inside the [`tensorflow/examples/label_image/main.cc`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/label_image/main.cc)
+If you look inside the [`tensorflow/examples/label_image/main.cc`](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/examples/label_image/main.cc)
 file, you can find out
 how it works. We hope this code will help you integrate TensorFlow into
 your own applications, so we will walk step by step through the main functions:
@@ -164,7 +164,7 @@ training. If you have a graph that you've trained yourself, you'll just need
 to adjust the values to match whatever you used during your training process.
 
 You can see how they're applied to an image in the
-[`ReadTensorFromImageFile()`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/label_image/main.cc#L88)
+[`ReadTensorFromImageFile()`](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/examples/label_image/main.cc#L88)
 function.
 
 ```C++
@@ -334,7 +334,7 @@ The `PrintTopLabels()` function takes those sorted results, and prints them out
 friendly way. The `CheckTopLabel()` function is very similar, but just makes sure that
 the top label is the one we expect, for debugging purposes.
 
-At the end, [`main()`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/label_image/main.cc#L252)
+At the end, [`main()`](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/examples/label_image/main.cc#L252)
 ties together all of these calls.
 
 ```C++
diff --git a/site/en/r1/tutorials/keras/save_and_restore_models.ipynb b/site/en/r1/tutorials/keras/save_and_restore_models.ipynb
index e9d112bd3f3..04cc94417a9 100644
--- a/site/en/r1/tutorials/keras/save_and_restore_models.ipynb
+++ b/site/en/r1/tutorials/keras/save_and_restore_models.ipynb
@@ -115,7 +115,7 @@
         "\n",
         "Sharing this data helps others understand how the model works and try it themselves with new data.\n",
         "\n",
-        "Caution: Be careful with untrusted code—TensorFlow models are code. See [Using TensorFlow Securely](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for details.\n",
+        "Caution: Be careful with untrusted code—TensorFlow models are code. See [Using TensorFlow Securely](https://github.com/tensorflow/tensorflow/blob/r1.15/SECURITY.md) for details.\n",
         "\n",
         "### Options\n",
         "\n",
diff --git a/site/en/r1/tutorials/load_data/tf_records.ipynb b/site/en/r1/tutorials/load_data/tf_records.ipynb
index fa7bf83c8bb..45635034c69 100644
--- a/site/en/r1/tutorials/load_data/tf_records.ipynb
+++ b/site/en/r1/tutorials/load_data/tf_records.ipynb
@@ -141,7 +141,7 @@
       "source": [
         "Fundamentally a `tf.Example` is a `{\"string\": tf.train.Feature}` mapping.\n",
         "\n",
-        "The `tf.train.Feature` message type can accept one of the following three types (See the [`.proto` file](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/example/feature.proto) for reference). Most other generic types can be coerced into one of these.\n",
+        "The `tf.train.Feature` message type can accept one of the following three types (See the [`.proto` file](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/example/feature.proto) for reference). Most other generic types can be coerced into one of these.\n",
         "\n",
         "1. `tf.train.BytesList` (the following types can be coerced)\n",
         "\n",
@@ -276,7 +276,7 @@
         "\n",
         "1. We create a map (dictionary) from the feature name string to the encoded feature value produced in #1.\n",
         "\n",
-        "1. The map produced in #2 is converted to a [`Features` message](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/example/feature.proto#L85)."
+        "1. The map produced in #2 is converted to a [`Features` message](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/example/feature.proto#L85)."
       ]
     },
     {
@@ -365,7 +365,7 @@
         "id": "XftzX9CN_uGT"
       },
       "source": [
-        "For example, suppose we have a single observation from the dataset, `[False, 4, bytes('goat'), 0.9876]`. We can create and print the `tf.Example` message for this observation using `create_message()`. Each single observation will be written as a `Features` message as per the above. Note that the `tf.Example` [message](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/example/example.proto#L88) is just a wrapper around the `Features` message."
+        "For example, suppose we have a single observation from the dataset, `[False, 4, bytes('goat'), 0.9876]`. We can create and print the `tf.Example` message for this observation using `create_message()`. Each single observation will be written as a `Features` message as per the above. Note that the `tf.Example` [message](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/example/example.proto#L88) is just a wrapper around the `Features` message."
       ]
     },
     {
diff --git a/site/en/r1/tutorials/representation/kernel_methods.md b/site/en/r1/tutorials/representation/kernel_methods.md
index 67adc4951c6..227fe81d515 100644
--- a/site/en/r1/tutorials/representation/kernel_methods.md
+++ b/site/en/r1/tutorials/representation/kernel_methods.md
@@ -24,7 +24,7 @@ following sources for an introduction:
 Currently, TensorFlow supports explicit kernel mappings for dense features only;
 TensorFlow will provide support for sparse features at a later release.
 
-This tutorial uses [tf.contrib.learn](https://www.tensorflow.org/code/tensorflow/contrib/learn/python/learn)
+This tutorial uses [tf.contrib.learn](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/contrib/learn/python/learn)
 (TensorFlow's high-level Machine Learning API) Estimators for our ML models.
 If you are not familiar with this API, The [Estimator guide](../../guide/estimators.md)
 is a good place to start. We will use the MNIST dataset. The tutorial consists
@@ -131,7 +131,7 @@ In addition to experimenting with the (training) batch size and the number of
 training steps, there are a couple other parameters that can be tuned as well.
 For instance, you can change the optimization method used to minimize the loss
 by explicitly selecting another optimizer from the collection of
-[available optimizers](https://www.tensorflow.org/code/tensorflow/python/training).
+[available optimizers](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/python/training).
 As an example, the following code constructs a LinearClassifier estimator that
 uses the Follow-The-Regularized-Leader (FTRL) optimization strategy with a
 specific learning rate and L2-regularization.
diff --git a/site/en/r1/tutorials/representation/linear.md b/site/en/r1/tutorials/representation/linear.md
index 5516672b34a..d996a13bc1f 100644
--- a/site/en/r1/tutorials/representation/linear.md
+++ b/site/en/r1/tutorials/representation/linear.md
@@ -12,7 +12,7 @@ those tools. It explains:
 
 Read this overview to decide whether the Estimator's linear model tools  might
 be useful to you. Then work through the
-[Estimator wide and deep learning tutorial](https://github.com/tensorflow/models/tree/master/official/r1/wide_deep)
+[Estimator wide and deep learning tutorial](https://github.com/tensorflow/models/tree/r1.15/official/r1/wide_deep)
 to give it a try. This overview uses code samples from the tutorial, but the
 tutorial walks through the code in greater detail.
 
@@ -177,7 +177,7 @@ the name of a `FeatureColumn`. Each key's value is a tensor containing the
 values of that feature for all data instances. See
 [Premade Estimators](../../guide/premade_estimators.md#input_fn) for a
 more comprehensive look at input functions, and `input_fn` in the
-[wide and deep learning tutorial](https://github.com/tensorflow/models/tree/master/official/r1/wide_deep)
+[wide and deep learning tutorial](https://github.com/tensorflow/models/tree/r1.15/official/r1/wide_deep)
 for an example implementation of an input function.
 
 The input function is passed to the `train()` and `evaluate()` calls that
@@ -236,4 +236,4 @@ e = tf.estimator.DNNLinearCombinedClassifier(
     dnn_hidden_units=[100, 50])
 ```
 For more information, see the
-[wide and deep learning tutorial](https://github.com/tensorflow/models/tree/master/official/r1/wide_deep).
+[wide and deep learning tutorial](https://github.com/tensorflow/models/tree/r1.15/official/r1/wide_deep).
diff --git a/site/en/r1/tutorials/representation/word2vec.md b/site/en/r1/tutorials/representation/word2vec.md
index c76db7ab108..517a5dbc5c5 100644
--- a/site/en/r1/tutorials/representation/word2vec.md
+++ b/site/en/r1/tutorials/representation/word2vec.md
@@ -327,7 +327,7 @@ for inputs, labels in generate_batch(...):
 ```
 
 See the full example code in
-[tensorflow/examples/tutorials/word2vec/word2vec_basic.py](https://www.tensorflow.org/code/tensorflow/examples/tutorials/word2vec/word2vec_basic.py).
+[tensorflow/examples/tutorials/word2vec/word2vec_basic.py](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/examples/tutorials/word2vec/word2vec_basic.py).
 
 ## Visualizing the learned embeddings
 
@@ -341,7 +341,7 @@ t-SNE.
 Et voila! As expected, words that are similar end up clustering nearby each
 other. For a more heavyweight implementation of word2vec that showcases more of
 the advanced features of TensorFlow, see the implementation in
-[models/tutorials/embedding/word2vec.py](https://github.com/tensorflow/models/tree/master/research/tutorials/embedding/word2vec.py).
+[models/tutorials/embedding/word2vec.py](https://github.com/tensorflow/models/tree/r1.15/research/tutorials/embedding/word2vec.py).
 
 ## Evaluating embeddings: analogical reasoning
 
@@ -357,7 +357,7 @@ Download the dataset for this task from
 
 To see how we do this evaluation, have a look at the `build_eval_graph()` and
 `eval()` functions in
-[models/tutorials/embedding/word2vec.py](https://github.com/tensorflow/models/tree/master/research/tutorials/embedding/word2vec.py).
+[models/tutorials/embedding/word2vec.py](https://github.com/tensorflow/models/tree/r1.15/research/tutorials/embedding/word2vec.py).
 
 The choice of hyperparameters can strongly influence the accuracy on this task.
 To achieve state-of-the-art performance on this task requires training over a
diff --git a/site/en/r1/tutorials/sequences/recurrent_quickdraw.md b/site/en/r1/tutorials/sequences/recurrent_quickdraw.md
index 435076f629c..d6a85377d17 100644
--- a/site/en/r1/tutorials/sequences/recurrent_quickdraw.md
+++ b/site/en/r1/tutorials/sequences/recurrent_quickdraw.md
@@ -109,7 +109,7 @@ This download will take a while and download a bit more than 23GB of data.
 
 To convert the `ndjson` files to
 [TFRecord](../../api_guides/python/python_io.md#TFRecords_Format_Details) files containing
-[`tf.train.Example`](https://www.tensorflow.org/code/tensorflow/core/example/example.proto)
+[`tf.train.Example`](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/example/example.proto)
 protos run the following command.
 
 ```shell
@@ -213,7 +213,7 @@ screen coordinates and normalize the size such that the drawing has unit height.
 
 Finally, we compute the differences between consecutive points and store these
 as a `VarLenFeature` in a
-[tensorflow.Example](https://www.tensorflow.org/code/tensorflow/core/example/example.proto)
+[tensorflow.Example](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/example/example.proto)
 under the key `ink`. In addition we store the `class_index` as a single entry
 `FixedLengthFeature` and the `shape` of the `ink` as a `FixedLengthFeature` of
 length 2.