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-rw-r--r--tensorflow/docs_src/community/welcome.md4
-rw-r--r--tensorflow/docs_src/install/install_c.md2
-rw-r--r--tensorflow/docs_src/install/install_go.md2
-rw-r--r--tensorflow/docs_src/install/install_java.md22
-rw-r--r--tensorflow/docs_src/install/install_linux.md56
-rw-r--r--tensorflow/docs_src/install/install_mac.md23
-rw-r--r--tensorflow/docs_src/install/install_sources.md4
-rw-r--r--tensorflow/docs_src/install/install_windows.md5
-rw-r--r--tensorflow/docs_src/performance/xla/jit.md2
-rw-r--r--tensorflow/docs_src/programmers_guide/debugger.md3
-rw-r--r--tensorflow/docs_src/programmers_guide/faq.md4
-rw-r--r--tensorflow/docs_src/programmers_guide/summaries_and_tensorboard.md2
-rw-r--r--tensorflow/docs_src/programmers_guide/using_tpu.md7
-rw-r--r--tensorflow/docs_src/tutorials/layers.md12
-rw-r--r--tensorflow/docs_src/tutorials/recurrent_quickdraw.md3
-rw-r--r--tensorflow/docs_src/tutorials/wide.md16
16 files changed, 77 insertions, 90 deletions
diff --git a/tensorflow/docs_src/community/welcome.md b/tensorflow/docs_src/community/welcome.md
index 6d0458e678..9f6fe91b14 100644
--- a/tensorflow/docs_src/community/welcome.md
+++ b/tensorflow/docs_src/community/welcome.md
@@ -51,8 +51,6 @@ Europe:
TensorFlow provides multiple communication paths. To pick the right path,
please read the following list carefully:
- * For new release announcements and security updates, subscribe to
- [announce@tensorflow.org](https://groups.google.com/a/tensorflow.org/forum/#!forum/announce).
* To ask or answer technical questions about TensorFlow, use
[Stack Overflow](https://stackoverflow.com/questions/tagged/tensorflow).
For example, ask or search Stack Overflow about a particular error message
@@ -67,5 +65,5 @@ please read the following list carefully:
on GitHub. For example, use the issue tracker to request a
new operation in TensorFlow.
* To report vulnerabilities, please follow our
- [vulnerability disclosure guidelines](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md).
+ [vulnerability disclosure guidelines](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/SECURITY.md).
diff --git a/tensorflow/docs_src/install/install_c.md b/tensorflow/docs_src/install/install_c.md
index 0481c97885..818798555a 100644
--- a/tensorflow/docs_src/install/install_c.md
+++ b/tensorflow/docs_src/install/install_c.md
@@ -38,7 +38,7 @@ enable TensorFlow for C:
OS="linux" # Change to "darwin" for macOS
TARGET_DIRECTORY="/usr/local"
curl -L \
- "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-${OS}-x86_64-1.6.0.tar.gz" |
+ "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-${OS}-x86_64-1.6.0-rc1.tar.gz" |
sudo tar -C $TARGET_DIRECTORY -xz
The `tar` command extracts the TensorFlow C library into the `lib`
diff --git a/tensorflow/docs_src/install/install_go.md b/tensorflow/docs_src/install/install_go.md
index 8f89898c92..4c6dfa8daf 100644
--- a/tensorflow/docs_src/install/install_go.md
+++ b/tensorflow/docs_src/install/install_go.md
@@ -38,7 +38,7 @@ steps to install this library and enable TensorFlow for Go:
TF_TYPE="cpu" # Change to "gpu" for GPU support
TARGET_DIRECTORY='/usr/local'
curl -L \
- "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-$(go env GOOS)-x86_64-1.6.0.tar.gz" |
+ "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-$(go env GOOS)-x86_64-1.6.0-rc1.tar.gz" |
sudo tar -C $TARGET_DIRECTORY -xz
The `tar` command extracts the TensorFlow C library into the `lib`
diff --git a/tensorflow/docs_src/install/install_java.md b/tensorflow/docs_src/install/install_java.md
index 0ee9c849e1..527884863e 100644
--- a/tensorflow/docs_src/install/install_java.md
+++ b/tensorflow/docs_src/install/install_java.md
@@ -36,7 +36,7 @@ following to the project's `pom.xml` to use the TensorFlow Java APIs:
<dependency>
<groupId>org.tensorflow</groupId>
<artifactId>tensorflow</artifactId>
- <version>1.6.0</version>
+ <version>1.6.0-rc1</version>
</dependency>
```
@@ -65,7 +65,7 @@ As an example, these steps will create a Maven project that uses TensorFlow:
<dependency>
<groupId>org.tensorflow</groupId>
<artifactId>tensorflow</artifactId>
- <version>1.6.0</version>
+ <version>1.6.0-rc1</version>
</dependency>
</dependencies>
</project>
@@ -123,12 +123,12 @@ instead:
<dependency>
<groupId>org.tensorflow</groupId>
<artifactId>libtensorflow</artifactId>
- <version>1.6.0</version>
+ <version>1.6.0-rc1</version>
</dependency>
<dependency>
<groupId>org.tensorflow</groupId>
<artifactId>libtensorflow_jni_gpu</artifactId>
- <version>1.6.0</version>
+ <version>1.6.0-rc1</version>
</dependency>
```
@@ -147,7 +147,7 @@ refer to the simpler instructions above instead.
Take the following steps to install TensorFlow for Java on Linux or macOS:
1. Download
- [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.6.0.jar),
+ [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.6.0-rc1.jar),
which is the TensorFlow Java Archive (JAR).
2. Decide whether you will run TensorFlow for Java on CPU(s) only or with
@@ -166,7 +166,7 @@ Take the following steps to install TensorFlow for Java on Linux or macOS:
OS=$(uname -s | tr '[:upper:]' '[:lower:]')
mkdir -p ./jni
curl -L \
- "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-${TF_TYPE}-${OS}-x86_64-1.6.0.tar.gz" |
+ "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-${TF_TYPE}-${OS}-x86_64-1.6.0-rc1.tar.gz" |
tar -xz -C ./jni
### Install on Windows
@@ -174,10 +174,10 @@ Take the following steps to install TensorFlow for Java on Linux or macOS:
Take the following steps to install TensorFlow for Java on Windows:
1. Download
- [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.6.0.jar),
+ [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.6.0-rc1.jar),
which is the TensorFlow Java Archive (JAR).
2. Download the following Java Native Interface (JNI) file appropriate for
- [TensorFlow for Java on Windows](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-cpu-windows-x86_64-1.6.0.zip).
+ [TensorFlow for Java on Windows](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-cpu-windows-x86_64-1.6.0-rc1.zip).
3. Extract this .zip file.
@@ -225,7 +225,7 @@ must be part of your `classpath`. For example, you can include the
downloaded `.jar` in your `classpath` by using the `-cp` compilation flag
as follows:
-<pre><b>javac -cp libtensorflow-1.6.0.jar HelloTF.java</b></pre>
+<pre><b>javac -cp libtensorflow-1.6.0-rc1.jar HelloTF.java</b></pre>
### Running
@@ -239,11 +239,11 @@ two files are available to the JVM:
For example, the following command line executes the `HelloTF` program on Linux
and macOS X:
-<pre><b>java -cp libtensorflow-1.6.0.jar:. -Djava.library.path=./jni HelloTF</b></pre>
+<pre><b>java -cp libtensorflow-1.6.0-rc1.jar:. -Djava.library.path=./jni HelloTF</b></pre>
And the following command line executes the `HelloTF` program on Windows:
-<pre><b>java -cp libtensorflow-1.6.0.jar;. -Djava.library.path=jni HelloTF</b></pre>
+<pre><b>java -cp libtensorflow-1.6.0-rc1.jar;. -Djava.library.path=jni HelloTF</b></pre>d
If the program prints <tt>Hello from <i>version</i></tt>, you've successfully
installed TensorFlow for Java and are ready to use the API. If the program
diff --git a/tensorflow/docs_src/install/install_linux.md b/tensorflow/docs_src/install/install_linux.md
index 3e8744bf9d..e3e115d9f6 100644
--- a/tensorflow/docs_src/install/install_linux.md
+++ b/tensorflow/docs_src/install/install_linux.md
@@ -41,8 +41,7 @@ must be installed on your system:
[NVIDIA's documentation](https://developer.nvidia.com/cudnn).
Ensure that you create the `CUDA_HOME` environment variable as
described in the NVIDIA documentation.
- * GPU card with CUDA Compute Capability 3.0 or higher for building
- from source and 3.5 or higher for our binaries. See
+ * GPU card with CUDA Compute Capability 3.0 or higher. See
[NVIDIA documentation](https://developer.nvidia.com/cuda-gpus) for
a list of supported GPU cards.
* The libcupti-dev library, which is the NVIDIA CUDA Profile Tools Interface.
@@ -189,7 +188,7 @@ Take the following steps to install TensorFlow with Virtualenv:
Virtualenv environment:
<pre>(tensorflow)$ <b>pip3 install --upgrade \
- https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.6.0-cp34-cp34m-linux_x86_64.whl</b></pre>
+ https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.6.0rc1-cp34-cp34m-linux_x86_64.whl</b></pre>
If you encounter installation problems, see
[Common Installation Problems](#common_installation_problems).
@@ -294,7 +293,7 @@ take the following steps:
<pre>
$ <b>sudo pip3 install --upgrade \
- https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.6.0-cp34-cp34m-linux_x86_64.whl</b>
+ https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.6.0rc1-cp34-cp34m-linux_x86_64.whl</b>
</pre>
If this step fails, see
@@ -357,23 +356,24 @@ where:
to 6006.
* <tt><i>TensorFlowCPUImage</i></tt> is required. It identifies the Docker
container. Specify one of the following values:
- * <tt>tensorflow/tensorflow</tt>, which is the TensorFlow CPU binary image.
- * <tt>tensorflow/tensorflow:latest-devel</tt>, which is the latest
+ * <tt>gcr.io/tensorflow/tensorflow</tt>, which is the TensorFlow CPU binary image.
+ * <tt>gcr.io/tensorflow/tensorflow:latest-devel</tt>, which is the latest
TensorFlow CPU Binary image plus source code.
- * <tt>tensorflow/tensorflow:<i>version</i></tt>, which is the
+ * <tt>gcr.io/tensorflow/tensorflow:<i>version</i></tt>, which is the
specified version (for example, 1.1.0rc1) of TensorFlow CPU binary image.
- * <tt>tensorflow/tensorflow:<i>version</i>-devel</tt>, which is
+ * <tt>gcr.io/tensorflow/tensorflow:<i>version</i>-devel</tt>, which is
the specified version (for example, 1.1.0rc1) of the TensorFlow GPU
binary image plus source code.
- TensorFlow images are available at
+ <tt>gcr.io</tt> is the Google Container Registry. Note that some
+ TensorFlow images are also available at
[dockerhub](https://hub.docker.com/r/tensorflow/tensorflow/).
For example, the following command launches the latest TensorFlow CPU binary image
in a Docker container from which you can run TensorFlow programs in a shell:
<pre>
-$ <b>docker run -it tensorflow/tensorflow bash</b>
+$ <b>docker run -it gcr.io/tensorflow/tensorflow bash</b>
</pre>
The following command also launches the latest TensorFlow CPU binary image in a
@@ -381,7 +381,7 @@ Docker container. However, in this Docker container, you can run TensorFlow
programs in a Jupyter notebook:
<pre>
-$ <b>docker run -it -p 8888:8888 tensorflow/tensorflow</b>
+$ <b>docker run -it -p 8888:8888 gcr.io/tensorflow/tensorflow</b>
</pre>
Docker will download the TensorFlow binary image the first time you launch it.
@@ -405,14 +405,14 @@ where:
<tt><i>hostPort</i></tt> and <code><em>containerPort</em></code> to `8888`.
* <i>TensorFlowGPUImage</i> specifies the Docker container. You must
specify one of the following values:
- * <tt>tensorflow/tensorflow:latest-gpu</tt>, which is the latest
+ * <tt>gcr.io/tensorflow/tensorflow:latest-gpu</tt>, which is the latest
TensorFlow GPU binary image.
- * <tt>tensorflow/tensorflow:latest-devel-gpu</tt>, which is
+ * <tt>gcr.io/tensorflow/tensorflow:latest-devel-gpu</tt>, which is
the latest TensorFlow GPU Binary image plus source code.
- * <tt>tensorflow/tensorflow:<i>version</i>-gpu</tt>, which is the
+ * <tt>gcr.io/tensorflow/tensorflow:<i>version</i>-gpu</tt>, which is the
specified version (for example, 0.12.1) of the TensorFlow GPU
binary image.
- * <tt>tensorflow/tensorflow:<i>version</i>-devel-gpu</tt>, which is
+ * <tt>gcr.io/tensorflow/tensorflow:<i>version</i>-devel-gpu</tt>, which is
the specified version (for example, 0.12.1) of the TensorFlow GPU
binary image plus source code.
@@ -421,7 +421,7 @@ following command launches the latest TensorFlow GPU binary image in a
Docker container from which you can run TensorFlow programs in a shell:
<pre>
-$ <b>nvidia-docker run -it tensorflow/tensorflow:latest-gpu bash</b>
+$ <b>nvidia-docker run -it gcr.io/tensorflow/tensorflow:latest-gpu bash</b>
</pre>
The following command also launches the latest TensorFlow GPU binary image
@@ -429,13 +429,13 @@ in a Docker container. In this Docker container, you can run TensorFlow
programs in a Jupyter notebook:
<pre>
-$ <b>nvidia-docker run -it -p 8888:8888 tensorflow/tensorflow:latest-gpu</b>
+$ <b>nvidia-docker run -it -p 8888:8888 gcr.io/tensorflow/tensorflow:latest-gpu</b>
</pre>
The following command installs an older TensorFlow version (0.12.1):
<pre>
-$ <b>nvidia-docker run -it -p 8888:8888 tensorflow/tensorflow:0.12.1-gpu</b>
+$ <b>nvidia-docker run -it -p 8888:8888 gcr.io/tensorflow/tensorflow:0.12.1-gpu</b>
</pre>
Docker will download the TensorFlow binary image the first time you launch it.
@@ -480,7 +480,7 @@ Take the following steps to install TensorFlow in an Anaconda environment:
<pre>
(tensorflow)$ <b>pip install --ignore-installed --upgrade \
- https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.6.0-cp34-cp34m-linux_x86_64.whl</b></pre>
+ https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.6.0rc1-cp34-cp34m-linux_x86_64.whl</b></pre>
<a name="ValidateYourInstallation"></a>
## Validate your installation
@@ -505,7 +505,7 @@ If you installed through Docker, start a Docker container
from which you can run bash. For example:
<pre>
-$ <b>docker run -it tensorflow/tensorflow bash</b>
+$ <b>docker run -it gcr.io/tensorflow/tensorflow bash</b>
</pre>
@@ -647,14 +647,14 @@ This section documents the relevant values for Linux installations.
CPU only:
<pre>
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.6.0-cp27-none-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.6.0rc1-cp27-none-linux_x86_64.whl
</pre>
GPU support:
<pre>
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.6.0-cp27-none-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.6.0rc1-cp27-none-linux_x86_64.whl
</pre>
Note that GPU support requires the NVIDIA hardware and software described in
@@ -666,14 +666,14 @@ Note that GPU support requires the NVIDIA hardware and software described in
CPU only:
<pre>
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.6.0-cp34-cp34m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.6.0rc1-cp34-cp34m-linux_x86_64.whl
</pre>
GPU support:
<pre>
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.6.0-cp34-cp34m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.6.0rc1-cp34-cp34m-linux_x86_64.whl
</pre>
Note that GPU support requires the NVIDIA hardware and software described in
@@ -685,14 +685,14 @@ Note that GPU support requires the NVIDIA hardware and software described in
CPU only:
<pre>
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.6.0-cp35-cp35m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.6.0rc1-cp35-cp35m-linux_x86_64.whl
</pre>
GPU support:
<pre>
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.6.0-cp35-cp35m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.6.0rc1-cp35-cp35m-linux_x86_64.whl
</pre>
@@ -704,14 +704,14 @@ Note that GPU support requires the NVIDIA hardware and software described in
CPU only:
<pre>
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.6.0-cp36-cp36m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.6.0rc1-cp36-cp36m-linux_x86_64.whl
</pre>
GPU support:
<pre>
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.6.0-cp36-cp36m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.6.0rc1-cp36-cp36m-linux_x86_64.whl
</pre>
diff --git a/tensorflow/docs_src/install/install_mac.md b/tensorflow/docs_src/install/install_mac.md
index 205db8e6bd..623ca6bb79 100644
--- a/tensorflow/docs_src/install/install_mac.md
+++ b/tensorflow/docs_src/install/install_mac.md
@@ -119,7 +119,7 @@ Take the following steps to install TensorFlow with Virtualenv:
TensorFlow in the active Virtualenv is as follows:
<pre> $ <b>pip install --upgrade \
- https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.6.0-py2-none-any.whl</b></pre>
+ https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.6.0rc1-py2-none-any.whl</b></pre>
If you encounter installation problems, see
[Common Installation Problems](#common-installation-problems).
@@ -242,7 +242,7 @@ take the following steps:
issue the following command:
<pre> $ <b>sudo pip install --upgrade \
- https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.6.0-py2-none-any.whl</b> </pre>
+ https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.6.0rc1-py2-none-any.whl</b> </pre>
If the preceding command fails, see
[installation problems](#common-installation-problems).
@@ -292,23 +292,24 @@ where:
to 6006.
* <i>TensorFlowImage</i> is required. It identifies the Docker container.
You must specify one of the following values:
- * <code>tensorflow/tensorflow</code>: TensorFlow binary image.
- * <code>tensorflow/tensorflow:latest-devel</code>: TensorFlow
+ * <code>gcr.io/tensorflow/tensorflow</code>: TensorFlow binary image.
+ * <code>gcr.io/tensorflow/tensorflow:latest-devel</code>: TensorFlow
Binary image plus source code.
-The TensorFlow images are available at
+<code>gcr.io</code> is the Google Container Registry. Note that some
+TensorFlow images are also available at
[dockerhub](https://hub.docker.com/r/tensorflow/tensorflow/).
For example, the following command launches a TensorFlow CPU binary image
in a Docker container from which you can run TensorFlow programs in a shell:
-<pre>$ <b>docker run -it tensorflow/tensorflow bash</b></pre>
+<pre>$ <b>docker run -it gcr.io/tensorflow/tensorflow bash</b></pre>
The following command also launches a TensorFlow CPU binary image in a
Docker container. However, in this Docker container, you can run
TensorFlow programs in a Jupyter notebook:
-<pre>$ <b>docker run -it -p 8888:8888 tensorflow/tensorflow</b></pre>
+<pre>$ <b>docker run -it -p 8888:8888 gcr.io/tensorflow/tensorflow</b></pre>
Docker will download the TensorFlow binary image the first time you launch it.
@@ -350,7 +351,7 @@ Take the following steps to install TensorFlow in an Anaconda environment:
TensorFlow for Python 2.7:
<pre> (<i>targetDirectory</i>)$ <b>pip install --ignore-installed --upgrade \
- https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.6.0-py2-none-any.whl</b></pre>
+ https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.6.0rc1-py2-none-any.whl</b></pre>
<a name="ValidateYourInstallation"></a>
@@ -375,7 +376,7 @@ do the following:
If you installed through Docker, start a Docker container that runs bash.
For example:
-<pre>$ <b>docker run -it tensorflow/tensorflow bash</b></pre>
+<pre>$ <b>docker run -it gcr.io/tensorflow/tensorflow bash</b></pre>
@@ -523,7 +524,7 @@ This section documents the relevant values for Mac OS installations.
<pre>
-https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.6.0-py2-none-any.whl
+https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.6.0rc1-py2-none-any.whl
</pre>
@@ -531,5 +532,5 @@ https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.6.0-py2-none-any.
<pre>
-https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.6.0-py3-none-any.whl
+https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.6.0rc1-py3-none-any.whl
</pre>
diff --git a/tensorflow/docs_src/install/install_sources.md b/tensorflow/docs_src/install/install_sources.md
index c09c9c2c0c..acf0af0d9d 100644
--- a/tensorflow/docs_src/install/install_sources.md
+++ b/tensorflow/docs_src/install/install_sources.md
@@ -359,10 +359,10 @@ Invoke `pip install` to install that pip package.
The filename of the `.whl` file depends on your platform.
For example, the following command will install the pip package
-for TensorFlow 1.6.0 on Linux:
+for TensorFlow 1.6.0rc1 on Linux:
<pre>
-$ <b>sudo pip install /tmp/tensorflow_pkg/tensorflow-1.6.0-py2-none-any.whl</b>
+$ <b>sudo pip install /tmp/tensorflow_pkg/tensorflow-1.6.0rc1-py2-none-any.whl</b>
</pre>
## Validate your installation
diff --git a/tensorflow/docs_src/install/install_windows.md b/tensorflow/docs_src/install/install_windows.md
index 2413bc9cfb..f0a30ee394 100644
--- a/tensorflow/docs_src/install/install_windows.md
+++ b/tensorflow/docs_src/install/install_windows.md
@@ -17,7 +17,7 @@ You must choose one of the following types of TensorFlow to install:
NVIDIA® GPU, you must install this version. Note that this version of
TensorFlow is typically much easier to install (typically,
in 5 or 10 minutes), so even if you have an NVIDIA GPU, we recommend
- installing this version first. Prebuilt binaries will use AVX instructions.
+ installing this version first.
* **TensorFlow with GPU support**. TensorFlow programs typically run
significantly faster on a GPU than on a CPU. Therefore, if your
system has a NVIDIA® GPU meeting the prerequisites shown below
@@ -41,8 +41,7 @@ installed on your system:
Note that cuDNN is typically installed in a different location from the
other CUDA DLLs. Ensure that you add the directory where you installed
the cuDNN DLL to your `%PATH%` environment variable.
- * GPU card with CUDA Compute Capability 3.0 or higher for building
- from source and 3.5 or higher for our binaries. See
+ * GPU card with CUDA Compute Capability 3.0 or higher. See
[NVIDIA documentation](https://developer.nvidia.com/cuda-gpus) for a
list of supported GPU cards.
diff --git a/tensorflow/docs_src/performance/xla/jit.md b/tensorflow/docs_src/performance/xla/jit.md
index d9a979ccbd..d4dc3e57c8 100644
--- a/tensorflow/docs_src/performance/xla/jit.md
+++ b/tensorflow/docs_src/performance/xla/jit.md
@@ -157,7 +157,7 @@ to fuse Ops is visible by starting at `hlo_graph_0.dot` and viewing each diagram
in succession.
To Render the .dot file into a png, install
-[GraphViz](https://www.graphviz.org/download/) and run:
+[GraphViz](http://www.graphviz.org/Download..php) and run:
```shell
dot -Tpng hlo_graph_80.dot -o hlo_graph_80.png
diff --git a/tensorflow/docs_src/programmers_guide/debugger.md b/tensorflow/docs_src/programmers_guide/debugger.md
index d1399814ee..5fb1c2da88 100644
--- a/tensorflow/docs_src/programmers_guide/debugger.md
+++ b/tensorflow/docs_src/programmers_guide/debugger.md
@@ -459,7 +459,7 @@ accuracy_score = classifier.evaluate(x=test_set.data,
[debug_tflearn_iris.py](https://www.tensorflow.org/code/tensorflow/python/debug/examples/debug_tflearn_iris.py),
-based on [tf-learn's iris tutorial](https://www.tensorflow.org/versions/r1.2/get_started/tflearn), contains a full example of how to
+based on {$tflearn$tf-learn's iris tutorial}, contains a full example of how to
use the tfdbg with `Estimator`s. To run this example, do:
```none
@@ -753,7 +753,6 @@ There are three possible workarounds or solutions:
# For LocalCLIDebugHook
hooks = [tf_debug.LocalCLIDebugHook(dump_root="/with/lots/of/space")]
```
-
Make sure that the directory pointed to by dump_root is empty or nonexistent.
tfdbg cleans up the dump directories before exiting.
* Reduce the batch size used during the runs.
diff --git a/tensorflow/docs_src/programmers_guide/faq.md b/tensorflow/docs_src/programmers_guide/faq.md
index 392ac6f7f1..1548d43877 100644
--- a/tensorflow/docs_src/programmers_guide/faq.md
+++ b/tensorflow/docs_src/programmers_guide/faq.md
@@ -159,7 +159,7 @@ available. These operations allow you to build sophisticated
@{$reading_data$input pipelines}, at the cost of making the
TensorFlow computation somewhat more complicated. See the how-to documentation
for
-@{$reading_data#creating_threads_to_prefetch_using_queuerunner_objects$using `QueueRunner` objects to drive queues and readers}
+@{$reading_data#creating-threads-to-prefetch-using-queuerunner-objects$using `QueueRunner` objects to drive queues and readers}
for more information on how to use them.
## Variables
@@ -272,7 +272,7 @@ Prefer predefined TensorFlow operations such as @{tf.decode_raw},
If your data is not easily parsable with the built-in TensorFlow operations,
consider converting it, offline, to a format that is easily parsable, such
-as @{tf.python_io.TFRecordWriter$`TFRecord`} format.
+as ${tf.python_io.TFRecordWriter$`TFRecord`} format.
The more efficient method to customize the parsing behavior is to
@{$adding_an_op$add a new op written in C++} that parses your
diff --git a/tensorflow/docs_src/programmers_guide/summaries_and_tensorboard.md b/tensorflow/docs_src/programmers_guide/summaries_and_tensorboard.md
index fadfa03e78..79280d246a 100644
--- a/tensorflow/docs_src/programmers_guide/summaries_and_tensorboard.md
+++ b/tensorflow/docs_src/programmers_guide/summaries_and_tensorboard.md
@@ -83,7 +83,7 @@ data than you need, though. Instead, consider running the merged summary op
every `n` steps.
The code example below is a modification of the
-[simple MNIST tutorial](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/tutorials/mnist/mnist.py),
+@{$layers$simple MNIST tutorial},
in which we have added some summary ops, and run them every ten steps. If you
run this and then launch `tensorboard --logdir=/tmp/tensorflow/mnist`, you'll be able
to visualize statistics, such as how the weights or accuracy varied during
diff --git a/tensorflow/docs_src/programmers_guide/using_tpu.md b/tensorflow/docs_src/programmers_guide/using_tpu.md
index a9c2cb3e33..d74d7f3181 100644
--- a/tensorflow/docs_src/programmers_guide/using_tpu.md
+++ b/tensorflow/docs_src/programmers_guide/using_tpu.md
@@ -129,9 +129,10 @@ my_tpu_estimator = tf.contrib.tpu.TPUEstimator(
Typically the `FLAGS` would be set by command line arguments. To switch from
training locally to training on a cloud TPU you would need to:
-* Set `FLAGS.use_tpu` to `True`
-* Set `FLAGS.tpu_name` so the `tf.contrib.cluster_resolver.TPUClusterResolver` can find it
-* Set `FLAGS.model_dir` to a Google Cloud Storage bucket url (`gs://`).
+ 1) Set `FLAGS.use_tpu` to `True`
+ 1) Set `FLAGS.tpu_name` so the
+ `tf.contrib.cluster_resolver.TPUClusterResolver` can find it
+ 1) Set `FLAGS.model_dir` to a Google Cloud Storage bucket url (`gs://`).
## Optimizer
diff --git a/tensorflow/docs_src/tutorials/layers.md b/tensorflow/docs_src/tutorials/layers.md
index 9b17d0d4d5..ee03f440c9 100644
--- a/tensorflow/docs_src/tutorials/layers.md
+++ b/tensorflow/docs_src/tutorials/layers.md
@@ -193,7 +193,7 @@ to calculate loss, configure the training op, and generate predictions. If
you're already experienced with CNNs and @{$get_started/custom_estimators$TensorFlow `Estimator`s},
and find the above code intuitive, you may want to skim these sections or just
skip ahead to ["Training and Evaluating the CNN MNIST
-Classifier"](#training_and_evaluating_the_cnn_mnist_classifier).
+Classifier"](#training-and-evaluating-the-cnn-mnist-classifier).
### Input Layer
@@ -446,7 +446,7 @@ tf.nn.softmax(logits, name="softmax_tensor")
> Note: We use the `name` argument to explicitly name this operation
> `softmax_tensor`, so we can reference it later. (We'll set up logging for the
-> softmax values in ["Set Up a Logging Hook"](#set-up-a-logging-hook)).
+> softmax values in ["Set Up a Logging Hook"](#set-up-a-logging-hook).
We compile our predictions in a dict, and return an `EstimatorSpec` object:
@@ -534,8 +534,9 @@ if mode == tf.estimator.ModeKeys.TRAIN:
```
> Note: For a more in-depth look at configuring training ops for Estimator model
-> functions, see @{$get_started/custom_estimators#defining_the_training_op_for_the_model$"Defining the training op for the model"}
-> in the @{$get_started/custom_estimators$"Creating Estimators in tf.estimator."} tutorial.
+> functions, see @{$get_started/custom_estimators#defining-the-training-op-for-the-model$"Defining
+> the training op for the model"} in the @{$get_started/custom_estimators$"Creating Estimations in
+> tf.estimator"} tutorial.
### Add evaluation metrics
@@ -624,8 +625,7 @@ operation earlier when we generated the probabilities in `cnn_model_fn`.
> Note: If you don't explicitly assign a name to an operation via the `name`
> argument, TensorFlow will assign a default name. A couple easy ways to
> discover the names applied to operations are to visualize your graph on
-> @{$graph_viz$TensorBoard}) or to enable the
-> @{$programmers_guide/debugger$TensorFlow Debugger (tfdbg)}.
+> @{$graph_viz$TensorBoard}) or to enable the @{$debugger$TensorFlow Debugger (tfdbg)}.
Next, we create the `LoggingTensorHook`, passing `tensors_to_log` to the
`tensors` argument. We set `every_n_iter=50`, which specifies that probabilities
diff --git a/tensorflow/docs_src/tutorials/recurrent_quickdraw.md b/tensorflow/docs_src/tutorials/recurrent_quickdraw.md
index 7584a76ba5..e22536adb6 100644
--- a/tensorflow/docs_src/tutorials/recurrent_quickdraw.md
+++ b/tensorflow/docs_src/tutorials/recurrent_quickdraw.md
@@ -109,8 +109,7 @@ This download will take a while and download a bit more than 23GB of data.
To convert the `ndjson` files to
@{$python/python_io#tfrecords_format_details$TFRecord} files containing
-[`tf.train.Example`](https://www.tensorflow.org/code/tensorflow/core/example/example.proto)
-protos run the following command.
+${tf.train.Example} protos run the following command.
```shell
python create_dataset.py --ndjson_path rnn_tutorial_data \
diff --git a/tensorflow/docs_src/tutorials/wide.md b/tensorflow/docs_src/tutorials/wide.md
index 27ce75a30d..005dc020f9 100644
--- a/tensorflow/docs_src/tutorials/wide.md
+++ b/tensorflow/docs_src/tutorials/wide.md
@@ -74,8 +74,8 @@ Here's a list of columns available in the Census Income dataset:
| relationship | Categorical | Wife, Own-child, Husband, |
: : : Not-in-family, Other-relative, :
: : : Unmarried. :
-| race | Categorical | Amer-Indian-Eskimo, Asian-Pac- |
-: : : Islander, Black, White, Other. :
+| race | Categorical | White, Asian-Pac-Islander, |
+: : : Amer-Indian-Eskimo, Other, Black. :
| gender | Categorical | Female, Male. |
| capital_gain | Continuous | Capital gains recorded. |
| capital_loss | Continuous | Capital Losses recorded. |
@@ -247,7 +247,7 @@ hours_per_week = tf.feature_column.numeric_column('hours_per_week')
### Making Continuous Features Categorical through Bucketization
Sometimes the relationship between a continuous feature and the label is not
-linear. As a hypothetical example, a person's income may grow with age in the
+linear. As an hypothetical example, a person's income may grow with age in the
early stage of one's career, then the growth may slow at some point, and finally
the income decreases after retirement. In this scenario, using the raw `age` as
a real-valued feature column might not be a good choice because the model can
@@ -361,16 +361,6 @@ The first line of the final output should be something like
`accuracy: 0.83557522`, which means the accuracy is 83.6%. Feel free to try more
features and transformations and see if you can do even better!
-After the model is evaluated, we can use the model to predict whether an individual has an annual income of over
-50,000 dollars given an individual's information input.
-```python
- pred_iter = model.predict(input_fn=lambda: input_fn(FLAGS.test_data, 1, False, 1))
- for pred in pred_iter:
- print(pred['classes'])
-```
-
-The model prediction output would be like `[b'1']` or `[b'0']` which means whether corresponding individual has an annual income of over 50,000 dollars or not.
-
If you'd like to see a working end-to-end example, you can download our
[example code](https://github.com/tensorflow/models/tree/master/official/wide_deep/wide_deep.py)
and set the `model_type` flag to `wide`.
generated by cgit v1.2.3 (git 2.39.1) at 2024-06-25 22:51:36 +0000