Merge changes from github.

END_PUBLIC

---
Commit 1e1b3d902 authored by Pete Warden<pete@petewarden.com>
Committed by gunan<gunan@google.com>:
Changed output directory for Pi CI build to fix permissions problem with nightlies (#13257)

* Fix for RTLD_GLOBAL breakage of Pi builds, and removed Eigen version change for Pi that's no longer needed

* Fixed Pi Zero OpenBLAS build problems and tidied up directories used

* More robust checks in Pi build script

* Changed output directory for Pi CI build to fix permissions problem

---
Commit fe3a2e65c authored by Yan Facai (???)<facai.yan@gmail.com>
Committed by drpngx<drpngx@users.noreply.github.com>:
check invalid string type for dest_nodes in extract_sub_graph (#13057)

* BUG: check str type

* TST: add unit test

* CLN: remove list check

* CLN: use warning

* CLN: 2 indent

* CLN: raise TypeError if not list

* CLN: check string only

---
Commit 225ab7629 authored by Jean Wanka<jm.wanka@gmail.com>
Committed by Jean Wanka<jm.wanka@gmail.com>:
Fix polynomial decay with cycle for global step=0

For polynomial decay with cycle=True the learning rate at
step 0 becomes NaN, because in the process of calculating it we
devide by 0. This change should fix it, by setting the multiplier
for the decay steps to one for global_step=0.

---
Commit 286f57061 authored by Bjarke Hammersholt Roune<broune@google.com>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Make Service::TransferToClient not attempt to manipulate the literal when the transfer failed, preventing a crash and allowing the caller to see the reason for the failed transfer.

PiperOrigin-RevId: 169770126

---
Commit e0501bc4d authored by Yong Tang<yong.tang.github@outlook.com>
Committed by Shanqing Cai<cais@google.com>:
Fix GRUBlockCell parameter naming inconsistency (#13153)

* Fix GRUBlockCell parameter naming inconsistency

This fix tries to fix the issue in 13137 where
parameter `cell_size` is used instead of `num_units`.
This is inconsistent with other RNN cells.

This fix adds support of `num_units` while at the same
time maintains backward compatiblility for `cell_size`.

This fix fixes 13137.

Signed-off-by: Yong Tang <yong.tang.github@outlook.com>

* Add `@deprecated_args` for 'cell_size' in `GRUBlockCell`

This commit adds `@deprecated_args` for 'cell_size' in `GRUBlockCell`

Signed-off-by: Yong Tang <yong.tang.github@outlook.com>

* Address review comment

Signed-off-by: Yong Tang <yong.tang.github@outlook.com>

---
Commit 02a2eba05 authored by Pete Warden<pete@petewarden.com>
Committed by gunan<gunan@google.com>:
Fix for RTLD_GLOBAL breakage of Pi builds, and removed Eigen version change that's no longer needed (#13251)

* Fix for RTLD_GLOBAL breakage of Pi builds, and removed Eigen version change for Pi that's no longer needed

* Fixed Pi Zero OpenBLAS build problems and tidied up directories used

* More robust checks in Pi build script

---
Commit 8ef722253 authored by Sanjoy Das<sanjoy@google.com>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Remove a redundant setName.

The EmitComputation should have emitted a function with the right name, so use a
CHECK instead.

PiperOrigin-RevId: 169764856

---
Commit 1b94147dc authored by Neal Wu<wun@google.com>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Fix broken GitHub links in tensorflow and tensorflow_models resulting from The Great Models Move (a.k.a. the research subfolder)

PiperOrigin-RevId: 169763373

---
Commit b1ada5f0c authored by Justine Tunney<jart@google.com>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Fix TensorBoard python -m invoke in docs

PiperOrigin-RevId: 169758752

---
Commit 2957cd894 authored by Mustafa Ispir<ispir@google.com>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Local run option of estimator training.

PiperOrigin-RevId: 169756384

---
Commit 1dc2fe7ac authored by Gunhan Gulsoy<gunan@google.com>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
BEGIN_PUBLIC
Automated g4 rollback of changelist 166264198

PiperOrigin-RevId: 169998124

1 files changed, 12 insertions, 18 deletions

diff --git a/tensorflow/contrib/crf/README.md b/tensorflow/contrib/crf/README.md
index a184e321bb..b58cc2dd04 100644
--- a/tensorflow/contrib/crf/README.md
+++ b/tensorflow/contrib/crf/README.md
@@ -46,31 +46,25 @@ with tf.Graph().as_default():
     log_likelihood, transition_params = tf.contrib.crf.crf_log_likelihood(
         unary_scores, y_t, sequence_lengths_t)
 
+    # Compute the viterbi sequence and score.
+    viterbi_sequence, viterbi_score = tf.contrib.crf.crf_decode(
+        unary_scores, transition_params, sequence_lengths_t)
+
     # Add a training op to tune the parameters.
     loss = tf.reduce_mean(-log_likelihood)
     train_op = tf.train.GradientDescentOptimizer(0.01).minimize(loss)
 
-    # Train for a fixed number of iterations.
     session.run(tf.global_variables_initializer())
+
+    mask = (np.expand_dims(np.arange(num_words), axis=0) <
+            np.expand_dims(sequence_lengths, axis=1))
+    total_labels = np.sum(sequence_lengths)
+
+    # Train for a fixed number of iterations.
     for i in range(1000):
-      tf_unary_scores, tf_transition_params, _ = session.run(
-          [unary_scores, transition_params, train_op])
+      tf_viterbi_sequence, _ = session.run([viterbi_sequence, train_op])
       if i % 100 == 0:
-        correct_labels = 0
-        total_labels = 0
-        for tf_unary_scores_, y_, sequence_length_ in zip(tf_unary_scores, y,
-                                                          sequence_lengths):
-          # Remove padding from the scores and tag sequence.
-          tf_unary_scores_ = tf_unary_scores_[:sequence_length_]
-          y_ = y_[:sequence_length_]
-
-          # Compute the highest scoring sequence.
-          viterbi_sequence, _ = tf.contrib.crf.viterbi_decode(
-              tf_unary_scores_, tf_transition_params)
-
-          # Evaluate word-level accuracy.
-          correct_labels += np.sum(np.equal(viterbi_sequence, y_))
-          total_labels += sequence_length_
+        correct_labels = np.sum((y == tf_viterbi_sequence) * mask)
         accuracy = 100.0 * correct_labels / float(total_labels)
         print("Accuracy: %.2f%%" % accuracy)
 ```