Merge pull request #6463 from gunan/cp

Cherrypick doc updates to r0.12 branch.

tensorflow/contrib/learn/python/learn/monitors.py

@@ -68,6 +68,8 @@ Example:
     x, y, steps=2, batch_size=1, monitors=[example_monitor])
 ```
 
+## Ops
+
 @@get_default_monitors
 @@BaseMonitor
 @@CaptureVariable

tensorflow/contrib/slim/python/slim/data/tfexample_decoder.py

@@ -273,9 +273,10 @@ class Image(ItemHandler):
         is stored.
       format_key: the name of the TF-Example feature in which the image format
         is stored.
-      shape: the output shape of the image. If provided, the image is reshaped
-        accordingly. If left as None, no reshaping is done. A shape should be
-        supplied only if all the stored images have the same shape.
+      shape: the output shape of the image as 1-D `Tensor`
+        [height, width, channels]. If provided, the image is reshaped
+        accordingly. If left as None, no reshaping is done. A shape should
+        be supplied only if all the stored images have the same shape.
       channels: the number of channels in the image.
     """
     if not image_key:
@@ -300,11 +301,12 @@ class Image(ItemHandler):
     """Decodes the image buffer.
 
     Args:
-      image_buffer: T tensor representing the encoded image tensor.
+      image_buffer: The tensor representing the encoded image tensor.
       image_format: The image format for the image in `image_buffer`.
 
     Returns:
-      A decoder image.
+      A tensor that represents decoded image of self._shape, or
+      (?, ?, self._channels) if self._shape is not specified.
     """
     def decode_png():
       return image_ops.decode_png(image_buffer, self._channels)
@@ -329,7 +331,8 @@ class Image(ItemHandler):
       }
       default_decoder = decode_jpg
 
-    image = control_flow_ops.case(pred_fn_pairs, default=default_decoder, exclusive=True)
+    image = control_flow_ops.case(
+        pred_fn_pairs, default=default_decoder, exclusive=True)
 
     image.set_shape([None, None, self._channels])
     if self._shape is not None:

tensorflow/g3doc/api_docs/python/contrib.learn.monitors.md

@@ -58,6 +58,8 @@ Example:
     x, y, steps=2, batch_size=1, monitors=[example_monitor])
 ```
 
+## Ops
+
 - - -
 
 ### `tf.contrib.learn.monitors.get_default_monitors(loss_op=None, summary_op=None, save_summary_steps=100, output_dir=None, summary_writer=None)` {#get_default_monitors}

tensorflow/g3doc/get_started/os_setup.md

@@ -8,11 +8,11 @@ github source.
 The TensorFlow Python API supports Python 2.7 and Python 3.3+.
 
 The GPU version works best with Cuda Toolkit 8.0 and
-cuDNN v5.  Other versions are supported (Cuda toolkit >= 7.0 and
+cuDNN v5.1.  Other versions are supported (Cuda toolkit >= 7.0 and
 cuDNN >= v3) only when installing from sources.
 Please see [Cuda installation](#optional-install-cuda-gpus-on-linux) for
-details. For Mac OS X, please see [Setup GPU for
-Mac](#optional-setup-gpu-for-mac).
+details. For Mac OS X, please see 
+[Setup GPU for Mac](#optional-setup-gpu-for-mac).
 
 ## Overview
 
@@ -43,8 +43,8 @@ If you encounter installation errors, see
 [Pip](https://en.wikipedia.org/wiki/Pip_(package_manager)) is a package
 management system used to install and manage software packages written in
 Python. We provide pip packages for TensorFlow on Linux, Mac OS X, and
-Windows. For Windows instructions, please see [Pip installation on
-Windows](#pip-installation-on-windows).
+Windows. For Windows instructions, please see 
+[Pip installation on Windows](#pip-installation-on-windows).
 
 The packages that will be installed or upgraded during the pip install are
 listed in the [REQUIRED_PACKAGES section of
@@ -507,7 +507,7 @@ the Docker container.
 ### (Optional, Linux) Enable GPU Support
 
 If you installed the GPU version of TensorFlow, you must also install the Cuda
-Toolkit 8.0 and cuDNN v5.  Please see [Cuda
+Toolkit 8.0 and cuDNN v5.1.  Please see [Cuda
 installation](#optional-install-cuda-gpus-on-linux).
 
 You also need to set the `LD_LIBRARY_PATH` and `CUDA_HOME` environment
@@ -661,7 +661,7 @@ Install the toolkit into e.g. `/usr/local/cuda`.
 
 [https://developer.nvidia.com/cudnn](https://developer.nvidia.com/cudnn)
 
-Download cuDNN v5.
+Download cuDNN v5.1.
 
 Uncompress and copy the cuDNN files into the toolkit directory. Assuming the
 toolkit is installed in `/usr/local/cuda`, run the following commands (edited
@@ -937,20 +937,20 @@ package:
 
 ```bash
 # Ubuntu/Linux 64-bit:
-$ pip install --upgrade https://storage.googleapis.com/tensorflow/linux/cpu/protobuf-3.0.0-cp27-none-linux_x86_64.whl
+$ pip install --upgrade https://storage.googleapis.com/tensorflow/linux/cpu/protobuf-3.1.0-cp27-none-linux_x86_64.whl
 
 # Mac OS X:
-$ pip install --upgrade https://storage.googleapis.com/tensorflow/mac/cpu/protobuf-3.0.0-cp27-cp27m-macosx_10_11_x86_64.whl
+$ pip install --upgrade https://storage.googleapis.com/tensorflow/mac/cpu/protobuf-3.1.0-cp27-none-macosx_10_11_x86_64.whl
 ```
 
 And for Python 3.5:
 
 ```bash
 # Ubuntu/Linux 64-bit:
-$ pip3 install --upgrade https://storage.googleapis.com/tensorflow/linux/cpu/protobuf-3.0.0-cp35-cp35m-linux_x86_64.whl
+$ pip3 install --upgrade https://storage.googleapis.com/tensorflow/linux/cpu/protobuf-3.1.0-cp35-none-linux_x86_64.whl
 
 # Mac OS X:
-$ pip3 install --upgrade https://storage.googleapis.com/tensorflow/mac/cpu/protobuf-3.0.0-cp35-cp35m-macosx_10_11_x86_64.whl
+$ pip3 install --upgrade https://storage.googleapis.com/tensorflow/mac/cpu/protobuf-3.1.0-cp35-none-macosx_10_11_x86_64.whl
 ```
 
 If your system/configuration is not listed above, you can use the following
@@ -1126,7 +1126,7 @@ Traceback (most recent call last):
 TypeError: __init__() got an unexpected keyword argument 'syntax'
 ```
 
-This is due to a conflict between protobuf versions (we require protobuf 3.0.0).
+This is due to a conflict between protobuf versions (we require protobuf 3.1.0).
 The best current solution is to make sure older versions of protobuf are not
 installed, such as: