Merge changes from github.

PiperOrigin-RevId: 189945839

5 files changed, 10 insertions, 10 deletions

diff --git a/tensorflow/contrib/gan/python/eval/python/classifier_metrics_impl.py b/tensorflow/contrib/gan/python/eval/python/classifier_metrics_impl.py
index 7e86d10b64..47e51415fd 100644
--- a/tensorflow/contrib/gan/python/eval/python/classifier_metrics_impl.py
+++ b/tensorflow/contrib/gan/python/eval/python/classifier_metrics_impl.py
@@ -321,7 +321,7 @@ def classifier_score(images, classifier_fn, num_batches=1):
 
   NOTE: This function consumes images, computes their logits, and then
   computes the classifier score. If you would like to precompute many logits for
-  large batches, use clasifier_score_from_logits(), which this method also
+  large batches, use classifier_score_from_logits(), which this method also
   uses.
 
   Args:
@@ -454,7 +454,7 @@ def frechet_classifier_distance(real_images,
 
   This technique is described in detail in https://arxiv.org/abs/1706.08500.
   Given two Gaussian distribution with means m and m_w and covariance matrices
-  C and C_w, this function calcuates
+  C and C_w, this function calculates
 
               |m - m_w|^2 + Tr(C + C_w - 2(C * C_w)^(1/2))
 
@@ -467,7 +467,7 @@ def frechet_classifier_distance(real_images,
   Frechet distance is biased. It is more biased for small sample sizes. (e.g.
   even if the two distributions are the same, for a small sample size, the
   expected Frechet distance is large). It is important to use the same
-  sample size to compute frechet classifier distance when comparing two
+  sample size to compute Frechet classifier distance when comparing two
   generative models.
 
   NOTE: This function consumes images, computes their activations, and then
@@ -659,7 +659,7 @@ def frechet_classifier_distance_from_activations(real_activations,
 
   This technique is described in detail in https://arxiv.org/abs/1706.08500.
   Given two Gaussian distribution with means m and m_w and covariance matrices
-  C and C_w, this function calcuates
+  C and C_w, this function calculates
 
                 |m - m_w|^2 + Tr(C + C_w - 2(C * C_w)^(1/2))
 
diff --git a/tensorflow/contrib/gan/python/eval/python/sliced_wasserstein_impl.py b/tensorflow/contrib/gan/python/eval/python/sliced_wasserstein_impl.py
index 9bebcacbe4..4b10bc0f8e 100644
--- a/tensorflow/contrib/gan/python/eval/python/sliced_wasserstein_impl.py
+++ b/tensorflow/contrib/gan/python/eval/python/sliced_wasserstein_impl.py
@@ -212,7 +212,7 @@ def sliced_wasserstein_distance(real_images,
   Args:
       real_images: (tensor) Real images (batch, height, width, channels).
       fake_images: (tensor) Fake images (batch, height, width, channels).
-      resolution_min: (int) Minimum resolution for the Laplacion pyramid.
+      resolution_min: (int) Minimum resolution for the Laplacian pyramid.
       patches_per_image: (int) Number of patches to extract per image per
         Laplacian level.
       patch_size: (int) Width of a square patch.
@@ -221,7 +221,7 @@ def sliced_wasserstein_distance(real_images,
       use_svd: experimental method to compute a more accurate distance.
   Returns:
       List of tuples (distance_real, distance_fake) for each level of the
-      Laplacian pyramid from the highest resoluion to the lowest.
+      Laplacian pyramid from the highest resolution to the lowest.
         distance_real is the Wasserstein distance between real images
         distance_fake is the Wasserstein distance between real and fake images.
   Raises:
diff --git a/tensorflow/contrib/gan/python/features/python/conditioning_utils_impl.py b/tensorflow/contrib/gan/python/features/python/conditioning_utils_impl.py
index cd31c62667..e2594faf85 100644
--- a/tensorflow/contrib/gan/python/features/python/conditioning_utils_impl.py
+++ b/tensorflow/contrib/gan/python/features/python/conditioning_utils_impl.py
@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Miscellanous utilities for TFGAN code and examples.
+"""Miscellaneous utilities for TFGAN code and examples.
 
 Includes:
 1) Conditioning the value of a Tensor, based on techniques from
diff --git a/tensorflow/contrib/gan/python/features/python/random_tensor_pool_impl.py b/tensorflow/contrib/gan/python/features/python/random_tensor_pool_impl.py
index 4cfae0de44..9e4ec59e70 100644
--- a/tensorflow/contrib/gan/python/features/python/random_tensor_pool_impl.py
+++ b/tensorflow/contrib/gan/python/features/python/random_tensor_pool_impl.py
@@ -17,7 +17,7 @@
 We use this to keep a history of values created by a generator, such that
 a discriminator can randomly be trained on some older samples, not just the
 current one. This can help to not let the discriminator get too far ahead of the
-generator and also to keep the system from oscilating, if the discriminator
+generator and also to keep the system from oscillating, if the discriminator
 forgets too fast what past samples from the generator looked like.
 
 See the following papers for more details.
@@ -97,7 +97,7 @@ def tensor_pool(input_values,
         dtypes=[v.dtype for v in input_values],
         shapes=None)
 
-    # In pseudeo code this code does the following:
+    # In pseudo code this code does the following:
     # if not pool_full:
     #   enqueue(input_values)
     #   return input_values
diff --git a/tensorflow/contrib/gan/python/features/python/virtual_batchnorm_test.py b/tensorflow/contrib/gan/python/features/python/virtual_batchnorm_test.py
index 845f89827b..2fe06a2872 100644
--- a/tensorflow/contrib/gan/python/features/python/virtual_batchnorm_test.py
+++ b/tensorflow/contrib/gan/python/features/python/virtual_batchnorm_test.py
@@ -148,7 +148,7 @@ class VirtualBatchnormTest(test.TestCase):
       self.assertAllClose(bn_np[i, ...], vb_np)
 
   def test_minibatch_independent(self):
-    """Test that virtual batch normalized exampels are independent.
+    """Test that virtual batch normalized examples are independent.
 
     Unlike batch normalization, virtual batch normalization has the property
     that the virtual batch normalized value of an example is independent of the