Merge changes from github.

PiperOrigin-RevId: 194031845

2 files changed, 8 insertions, 8 deletions

diff --git a/tensorflow/contrib/factorization/python/ops/gmm_ops.py b/tensorflow/contrib/factorization/python/ops/gmm_ops.py
index 5d77bc77e1..ccdd679d6a 100644
--- a/tensorflow/contrib/factorization/python/ops/gmm_ops.py
+++ b/tensorflow/contrib/factorization/python/ops/gmm_ops.py
@@ -54,10 +54,10 @@ def _covariance(x, diag):
   diagonal matrix just the diagonal is returned.
   """
   num_points = math_ops.to_float(array_ops.shape(x)[0])
-  x -= math_ops.reduce_mean(x, 0, keep_dims=True)
+  x -= math_ops.reduce_mean(x, 0, keepdims=True)
   if diag:
     cov = math_ops.reduce_sum(
-        math_ops.square(x), 0, keep_dims=True) / (num_points - 1)
+        math_ops.square(x), 0, keepdims=True) / (num_points - 1)
   else:
     cov = math_ops.matmul(x, x, transpose_a=True) / (num_points - 1)
   return cov
@@ -313,7 +313,7 @@ class GmmAlgorithm(object):
     # TODO(xavigonzalvo): look into alternatives to log for
     # reparametrization of variance parameters.
     det_expanded = math_ops.reduce_sum(
-        math_ops.log(self._covs + 1e-3), 1, keep_dims=True)
+        math_ops.log(self._covs + 1e-3), 1, keepdims=True)
     diff = shard - self._means
     x2 = math_ops.square(diff)
     cov_expanded = array_ops.expand_dims(1.0 / (self._covs + 1e-3), 2)
@@ -351,7 +351,7 @@ class GmmAlgorithm(object):
       shard_id: id of current shard_id.
     """
     self._prior_probs[shard_id] = math_ops.reduce_logsumexp(
-        self._probs[shard_id], axis=1, keep_dims=True)
+        self._probs[shard_id], axis=1, keepdims=True)
 
   def _define_expectation_operation(self, shard_id):
     # Shape broadcasting.
@@ -375,7 +375,7 @@ class GmmAlgorithm(object):
     """
     # Soft assignment of each data point to each of the two clusters.
     self._points_in_k[shard_id] = math_ops.reduce_sum(
-        self._w[shard_id], 0, keep_dims=True)
+        self._w[shard_id], 0, keepdims=True)
     # Partial means.
     w_mul_x = array_ops.expand_dims(
         math_ops.matmul(
@@ -454,7 +454,7 @@ class GmmAlgorithm(object):
     for shard_id, prior_probs in enumerate(self._prior_probs):
       op.append(prior_probs + math_ops.log(self._w[shard_id]))
     self._scores = array_ops.squeeze(
-        math_ops.reduce_logsumexp(op, axis=2, keep_dims=True), axis=0)
+        math_ops.reduce_logsumexp(op, axis=2, keepdims=True), axis=0)
 
 
 def gmm(inp,
diff --git a/tensorflow/contrib/factorization/python/ops/kmeans.py b/tensorflow/contrib/factorization/python/ops/kmeans.py
index bfe338c9f9..9ffdd3ba5e 100644
--- a/tensorflow/contrib/factorization/python/ops/kmeans.py
+++ b/tensorflow/contrib/factorization/python/ops/kmeans.py
@@ -374,11 +374,11 @@ class KMeansClustering(estimator.Estimator):
               than `num_clusters`, a TensorFlow runtime error occurs.
       distance_metric: The distance metric used for clustering. One of:
         * `KMeansClustering.SQUARED_EUCLIDEAN_DISTANCE`: Euclidean distance
-             between vectors `u` and `v` is defined as `\\(||u - v||_2\\)`
+             between vectors `u` and `v` is defined as \\(||u - v||_2\\)
              which is the square root of the sum of the absolute squares of
              the elements' difference.
         * `KMeansClustering.COSINE_DISTANCE`: Cosine distance between vectors
-             `u` and `v` is defined as `\\(1 - (u . v) / (||u||_2 ||v||_2)\\)`.
+             `u` and `v` is defined as \\(1 - (u . v) / (||u||_2 ||v||_2)\\).
       random_seed: Python integer. Seed for PRNG used to initialize centers.
       use_mini_batch: A boolean specifying whether to use the mini-batch k-means
         algorithm. See explanation above.