TensorFlow: upstream changes to git.

Change 109321497
	Move all images to images directory to make docs versioning easier
	- adjust all paths in the docs to point to the new locations
	- remove some now redundant section-order tags added for the old website
Change 109317807
	Added a kernel op to compute the eigendecomposition of a self-adjoint matrix.

	Added a new kernel op called self_adjoint_eig (and a batch_self_adjoint_eig) that
	computes the eigendecomposition of a self-adjoint matrix. The return value is
	the concatenation of the eigenvalues as a row vector, and the eigenvectors.
Change 109310773
	Change `_read32()` in the MNIST input example to return an int.

	Currently we return a 1-D numpy array with 1 element. Numpy has
	recently deprecated the ability to treat this as a scalar, and as a
	result this tutorial fails. The fix returns the 0th element of the
	array instead.
Change 109301269
	Re-arrange TensorBoard demo files.
Change 109273589
	add ci_build for ci.tensorflow.org
Change 109260293
	Speed up NodeDef -> OpKernel process by not spending time generating
	an error message for missing "_kernel" attr that will be thrown away.
Change 109257179
	TensorFlow:make event_file_loader_test hermetic by using tempfile
	instead of fixed filenames.  Without this change, running
	event_file_loader_test twice in the same client (locally)
	causes it to fail, because it writes into the same file and appends
	another event, instead of starting from scratch.
Change 109256464
	Minor cleanup in TensorBoard server code
Change 109255382
	Change to reduce critical section times in gpu_event_mgr.h:
	(1) Call stream->ThenRecordEvent outside the EventMgr critical section
	(2) Do memory deallocation outside the critical section

	Speeds up one configuration of ptb_word_lm from 2924 words per
	second (wps) to 3278 wps on my desktop machine with a Titan X.
Change 109254843
	Fix use of uninitialized memory in test.
Change 109250995
	python_config.sh needs a license header

	Otherwise the license test fails.
Change 109249914
	add ci_build for ci.tensorflow.org
Change 109249397
	Fixes reduce_sum (complex) on GPU segfaults.

	Fixes #357

Change 109245652
	add ci_build for ci.tensorflow.org

Base CL: 109321563

1 files changed, 8 insertions, 8 deletions

diff --git a/tensorflow/g3doc/tutorials/deep_cnn/index.md b/tensorflow/g3doc/tutorials/deep_cnn/index.md
index 59d106680e..66614d402f 100644
--- a/tensorflow/g3doc/tutorials/deep_cnn/index.md
+++ b/tensorflow/g3doc/tutorials/deep_cnn/index.md
@@ -9,7 +9,7 @@ CIFAR-10 classification is a common benchmark problem in machine learning.  The
 problem is to classify RGB 32x32 pixel images across 10 categories:
 ```airplane, automobile, bird, cat, deer, dog, frog, horse, ship, and truck.```
 
-![CIFAR-10 Samples](./cifar_samples.png "CIFAR-10 Samples, from http://www.cs.toronto.edu/~kriz/cifar.html")
+![CIFAR-10 Samples](../../images/cifar_samples.png "CIFAR-10 Samples, from http://www.cs.toronto.edu/~kriz/cifar.html")
 
 For more details refer to the [CIFAR-10 page](http://www.cs.toronto.edu/~kriz/cifar.html)
 and a [Tech Report](http://www.cs.toronto.edu/~kriz/learning-features-2009-TR.pdf)
@@ -135,7 +135,7 @@ so that we may visualize them in TensorBoard.  This is a good practice to verify
 that inputs are built correctly.
 
 <div style="width:50%; margin:auto; margin-bottom:10px; margin-top:20px;">
-  <img style="width:70%" src="./cifar_image_summary.png">
+  <img style="width:70%" src="../../images/cifar_image_summary.png">
 </div>
 
 Reading images from disk and distorting them can use a non-trivial amount of
@@ -164,7 +164,7 @@ Layer Name | Description
 Here is a graph generated from TensorBoard describing the inference operation:
 
 <div style="width:15%; margin:auto; margin-bottom:10px; margin-top:20px;">
-  <img style="width:100%" src="./cifar_graph.png">
+  <img style="width:100%" src="../../images/cifar_graph.png">
 </div>
 
 > **EXERCISE**: The output of `inference` are un-normalized logits. Try editing
@@ -199,7 +199,7 @@ loss and all these weight decay terms, as returned by the `loss()` function.
 
 We visualize it in TensorBoard with a [`scalar_summary`](../../api_docs/python/train.md#scalar_summary):
 
-![CIFAR-10 Loss](./cifar_loss.png "CIFAR-10 Total Loss")
+![CIFAR-10 Loss](../../images/cifar_loss.png "CIFAR-10 Total Loss")
 
 We train the model using standard
 [gradient descent](https://en.wikipedia.org/wiki/Gradient_descent)
@@ -208,7 +208,7 @@ with a learning rate that
 [exponentially decays](../../api_docs/python/train.md#exponential_decay)
 over time.
 
-![CIFAR-10 Learning Rate Decay](./cifar_lr_decay.png "CIFAR-10 Learning Rate Decay")
+![CIFAR-10 Learning Rate Decay](../../images/cifar_lr_decay.png "CIFAR-10 Learning Rate Decay")
 
 The `train()` function adds the operations needed to minimize the objective by
 calculating the gradient and updating the learned variables (see
@@ -289,8 +289,8 @@ For instance, we can watch how the distribution of activations and degree of
 sparsity in `local3` features evolve during training:
 
 <div style="width:100%; margin:auto; margin-bottom:10px; margin-top:20px; display: flex; flex-direction: row">
-  <img style="flex-grow:1; flex-shrink:1;" src="./cifar_sparsity.png">
-  <img style="flex-grow:1; flex-shrink:1;" src="./cifar_activations.png">
+  <img style="flex-grow:1; flex-shrink:1;" src="../../images/cifar_sparsity.png">
+  <img style="flex-grow:1; flex-shrink:1;" src="../../images/cifar_activations.png">
 </div>
 
 Individual loss functions, as well as the total loss, are particularly
@@ -372,7 +372,7 @@ processing a batch of data.
 Here is a diagram of this model:
 
 <div style="width:40%; margin:auto; margin-bottom:10px; margin-top:20px;">
-  <img style="width:100%" src="./Parallelism.png">
+  <img style="width:100%" src="../../images/Parallelism.png">
 </div>
 
 Note that each GPU computes inference as well as the gradients for a unique