diff --git a/tensorflow/compiler/xla/service/compile_only_service.h b/tensorflow/compiler/xla/service/compile_only_service.h
index b19f4bd5921..3358305c03c 100644
--- a/tensorflow/compiler/xla/service/compile_only_service.h
+++ b/tensorflow/compiler/xla/service/compile_only_service.h
@@ -55,7 +55,7 @@ class CompileOnlyService : public Service {
 
   // Override Service methods that require or imply the existence of an
   // execute backend.  Note that this does not include TransferToClient, as
-  // computing contants produces global data that we may wish to transfer.
+  // computing constants produces global data that we may wish to transfer.
   tensorflow::Status Execute(const ExecuteRequest* arg,
                              ExecuteResponse* result) override {
     return Unimplemented("CompileOnlyService does not support execution.");
diff --git a/tensorflow/contrib/distributions/python/ops/vector_laplace_linear_operator.py b/tensorflow/contrib/distributions/python/ops/vector_laplace_linear_operator.py
index fd2c46d94de..fdee57695e4 100644
--- a/tensorflow/contrib/distributions/python/ops/vector_laplace_linear_operator.py
+++ b/tensorflow/contrib/distributions/python/ops/vector_laplace_linear_operator.py
@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Vectorized Laplace distribution class, directly using LinearOpeartor."""
+"""Vectorized Laplace distribution class, directly using LinearOperator."""
 
 from __future__ import absolute_import
 from __future__ import division
diff --git a/tensorflow/contrib/seq2seq/python/ops/attention_wrapper.py b/tensorflow/contrib/seq2seq/python/ops/attention_wrapper.py
index b659988a560..fbbfb25aea5 100644
--- a/tensorflow/contrib/seq2seq/python/ops/attention_wrapper.py
+++ b/tensorflow/contrib/seq2seq/python/ops/attention_wrapper.py
@@ -344,7 +344,7 @@ class LuongAttention(_BaseAttentionMechanism):
       #   [batch_size, 1, depth] . [batch_size, depth, max_time]
       # resulting in an output shape of:
       #   [batch_time, 1, max_time].
-      # we then squeee out the center singleton dimension.
+      # we then squeeze out the center singleton dimension.
       score = math_ops.matmul(query, self.keys, transpose_b=True)
       score = array_ops.squeeze(score, [1])
 
diff --git a/tensorflow/core/framework/graph_def_util.h b/tensorflow/core/framework/graph_def_util.h
index 56355eaf367..950737c39aa 100644
--- a/tensorflow/core/framework/graph_def_util.h
+++ b/tensorflow/core/framework/graph_def_util.h
@@ -62,7 +62,7 @@ Status AddDefaultAttrsToGraphDef(GraphDef* graph_def,
 // attr with a default was added). Note that this will not affect
 // attrs with non-default values, so you must run a
 // ValidateGraphDef...() function to see if the result is in fact
-// compatible. If not nulllptr, the op/attr pairs that were removed
+// compatible. If not nullptr, the op/attr pairs that were removed
 // are added to '*op_attr_removed'.
 //
 // Expected usage, for a producer that wants to prepare a graph for
diff --git a/tensorflow/core/grappler/optimizers/constant_folding.h b/tensorflow/core/grappler/optimizers/constant_folding.h
index dca8f174682..36c9ad2c649 100644
--- a/tensorflow/core/grappler/optimizers/constant_folding.h
+++ b/tensorflow/core/grappler/optimizers/constant_folding.h
@@ -28,7 +28,7 @@ namespace grappler {
 const char kConstantFoldingConst[] = "ConstantFolding";
 const char kConstantFoldingCtrl[] = "ConstantFoldingCtrl";
 
-// Contant folding optimization for a graph.
+// Constant folding optimization for a graph.
 class ConstantFolding : public GraphOptimizer {
  public:
   ConstantFolding();
diff --git a/tensorflow/core/grappler/optimizers/layout_optimizer.cc b/tensorflow/core/grappler/optimizers/layout_optimizer.cc
index 0a802fa0f5a..5ac42c2abab 100644
--- a/tensorflow/core/grappler/optimizers/layout_optimizer.cc
+++ b/tensorflow/core/grappler/optimizers/layout_optimizer.cc
@@ -927,7 +927,7 @@ struct TuningConfig {
   // Conv2DBackpropFilter will use a specialized GEMM implementation, which is
   // usually faster than the NCHW implementation. The downside is that this
   // might result in more non-cancellable layout conversion nodes (implemented
-  // by the Tranpose op).
+  // by the Transpose op).
   bool no_gemm;
 };
 
diff --git a/tensorflow/python/ops/lookup_ops.py b/tensorflow/python/ops/lookup_ops.py
index 43af95e2784..3ae9316fee2 100644
--- a/tensorflow/python/ops/lookup_ops.py
+++ b/tensorflow/python/ops/lookup_ops.py
@@ -988,9 +988,9 @@ def index_table_from_tensor(vocabulary_list,
   Sample Usages:
 
   ```python
-  vocabulary_list = t.constant(["emerson", "lake", "palmer")
+  vocabulary_list = tf.constant(["emerson", "lake", "palmer"])
   table = tf.contrib.lookup.index_table_from_tensor(
-      vocabulary_list=vocabulary_list, num_oov_buckets=1, default_value=-1)
+      mapping=vocabulary_list, num_oov_buckets=1, default_value=-1)
   features = tf.constant(["emerson", "lake", "and", "palmer"])
   ids = table.lookup(features)
   ...
@@ -1160,7 +1160,7 @@ def index_to_string_table_from_tensor(vocabulary_list,
   Sample Usages:
 
   ```python
-  vocabulary_list = t.constant(["emerson", "lake", "palmer")
+  vocabulary_list = tf.constant(["emerson", "lake", "palmer"])
   indices = tf.constant([1, 5], tf.int64)
   table = tf.contrib.lookup.index_to_string_table_from_tensor(
       vocabulary_list, default_value="UNKNOWN")