TensorFlow: conv improvements, label_image example, and

a few other changes.

Changes:
- Some improvements to convolution by using 32-bit indices by
  @benoitsteiner. Not all calls converted yet.  Also some
  improvements to pooling as well by @benoitsteiner.

- Improvements to sparse matmul CPU implementation by Ashish

- Some fixes to warnings by @vrv

- Doc fixes to padding by @Yangqing

- Some improvements to Tensor wrappers by Eider

- Speed up of matrix inverse on CPU by Rasmus

- Add an example of doing image inference from a pre-trained model
  by @petewarden.

- fixed formula in mnist example by nodir

- Updates to event accumulator by Cassandra

- Slight changes to tensor c api by @mrry

- Handling of strings in listdiff by Phil

- Fix negative fraction-of-queue-full stats by Frank

- Type-checking improvement to importer by Yaroslav

- logdir recursive search for Tensorboard by @danmane

- Session.run() checks for empty graph by Manoj

Base CL: 108013706

1 files changed, 27 insertions, 29 deletions

diff --git a/tensorflow/python/kernel_tests/listdiff_op_test.py b/tensorflow/python/kernel_tests/listdiff_op_test.py
index 14d657c805..040d9eb3bd 100644
--- a/tensorflow/python/kernel_tests/listdiff_op_test.py
+++ b/tensorflow/python/kernel_tests/listdiff_op_test.py
@@ -10,57 +10,56 @@ import numpy as np
 from six.moves import xrange  # pylint: disable=redefined-builtin
 import tensorflow as tf
 
+_TYPES = [tf.int32, tf.int64, tf.float32, tf.float64, tf.string]
+
 
 class ListDiffTest(tf.test.TestCase):
 
-  def _testListDiff(self, x, y, out, idx, dtype=np.int32):
-    x = np.array(x, dtype=dtype)
-    y = np.array(y, dtype=dtype)
-    out = np.array(out, dtype=dtype)
-    idx = np.array(idx, dtype=dtype)
+  def _testListDiff(self, x, y, out, idx):
+    for dtype in _TYPES:
+      if dtype == tf.string:
+        x = [str(a) for a in x]
+        y = [str(a) for a in y]
+        out = [str(a) for a in out]
 
-    with self.test_session() as sess:
-      x_tensor = tf.convert_to_tensor(x)
-      y_tensor = tf.convert_to_tensor(y)
-      out_tensor, idx_tensor = tf.listdiff(x_tensor, y_tensor)
-      tf_out, tf_idx = sess.run([out_tensor, idx_tensor])
+      with self.test_session() as sess:
+        x_tensor = tf.convert_to_tensor(x, dtype=dtype)
+        y_tensor = tf.convert_to_tensor(y, dtype=dtype)
+        out_tensor, idx_tensor = tf.listdiff(x_tensor, y_tensor)
+        tf_out, tf_idx = sess.run([out_tensor, idx_tensor])
 
-    self.assertAllEqual(tf_out, out)
-    self.assertAllEqual(tf_idx, idx)
-    self.assertEqual(1, out_tensor.get_shape().ndims)
-    self.assertEqual(1, idx_tensor.get_shape().ndims)
+      self.assertAllEqual(tf_out, out)
+      self.assertAllEqual(tf_idx, idx)
+      self.assertEqual(1, out_tensor.get_shape().ndims)
+      self.assertEqual(1, idx_tensor.get_shape().ndims)
 
   def testBasic1(self):
     x = [1, 2, 3, 4]
     y = [1, 2]
     out = [3, 4]
     idx = [2, 3]
-    for t in [np.int32, np.int64, np.float, np.double]:
-      self._testListDiff(x, y, out, idx, dtype=t)
+    self._testListDiff(x, y, out, idx)
 
   def testBasic2(self):
     x = [1, 2, 3, 4]
     y = [2]
     out = [1, 3, 4]
     idx = [0, 2, 3]
-    for t in [np.int32, np.int64, np.float, np.double]:
-      self._testListDiff(x, y, out, idx, dtype=t)
+    self._testListDiff(x, y, out, idx)
 
   def testBasic3(self):
     x = [1, 4, 3, 2]
     y = [4, 2]
     out = [1, 3]
     idx = [0, 2]
-    for t in [np.int32, np.int64, np.float, np.double]:
-      self._testListDiff(x, y, out, idx, dtype=t)
+    self._testListDiff(x, y, out, idx)
 
   def testDuplicates(self):
     x = [1, 2, 4, 3, 2, 3, 3, 1]
     y = [4, 2]
     out = [1, 3, 3, 3, 1]
     idx = [0, 3, 5, 6, 7]
-    for t in [np.int32, np.int64, np.float, np.double]:
-      self._testListDiff(x, y, out, idx, dtype=t)
+    self._testListDiff(x, y, out, idx)
 
   def testRandom(self):
     num_random_tests = 10
@@ -78,38 +77,37 @@ class ListDiffTest(tf.test.TestCase):
       else:
         out = []
         idx = []
-      for t in [np.int32, np.int64, np.float, np.double]:
-        self._testListDiff(x, y, out, idx, dtype=t)
+      self._testListDiff(list(x), list(y), out, idx)
 
-  def testInt32FullyOverlapping(self):
+  def testFullyOverlapping(self):
     x = [1, 2, 3, 4]
     y = [1, 2, 3, 4]
     out = []
     idx = []
     self._testListDiff(x, y, out, idx)
 
-  def testInt32NonOverlapping(self):
+  def testNonOverlapping(self):
     x = [1, 2, 3, 4]
     y = [5, 6]
     out = x
     idx = np.arange(len(x))
     self._testListDiff(x, y, out, idx)
 
-  def testInt32EmptyX(self):
+  def testEmptyX(self):
     x = []
     y = [1, 2]
     out = []
     idx = []
     self._testListDiff(x, y, out, idx)
 
-  def testInt32EmptyY(self):
+  def testEmptyY(self):
     x = [1, 2, 3, 4]
     y = []
     out = x
     idx = np.arange(len(x))
     self._testListDiff(x, y, out, idx)
 
-  def testInt32EmptyXY(self):
+  def testEmptyXY(self):
     x = []
     y = []
     out = []