10 files changed, 18 insertions, 18 deletions
diff --git a/tensorflow/contrib/lite/kernels/conv.cc b/tensorflow/contrib/lite/kernels/conv.cc
index e0cd12f1b4..b91ba1a03d 100644
--- a/tensorflow/contrib/lite/kernels/conv.cc
+++ b/tensorflow/contrib/lite/kernels/conv.cc
@@ -64,7 +64,7 @@ struct OpData {
 
   TfLitePaddingValues padding;
   // The scaling factor from input to output (aka the 'real multiplier') can
-  // be represented as a fixed point multiplier plus a left shift.
+  // be represented as a fixed point multipler plus a left shift.
   int32_t output_multiplier;
   int output_shift;
   // The range of the fused activation layer. For example for kNone and
diff --git a/tensorflow/contrib/lite/kernels/depthwise_conv.cc b/tensorflow/contrib/lite/kernels/depthwise_conv.cc
index cad9ce114c..15dbfe08c8 100644
--- a/tensorflow/contrib/lite/kernels/depthwise_conv.cc
+++ b/tensorflow/contrib/lite/kernels/depthwise_conv.cc
@@ -52,7 +52,7 @@ enum KernelType {
 struct OpData {
   TfLitePaddingValues padding;
   // The scaling factor from input to output (aka the 'real multiplier') can
-  // be represented as a fixed point multiplier plus a left shift.
+  // be represented as a fixed point multipler plus a left shift.
   int32_t output_multiplier;
   int output_shift;
   // The range of the fused activation layer. For example for kNone and
diff --git a/tensorflow/contrib/lite/kernels/fully_connected.cc b/tensorflow/contrib/lite/kernels/fully_connected.cc
index 888e67966c..a77fe94e49 100644
--- a/tensorflow/contrib/lite/kernels/fully_connected.cc
+++ b/tensorflow/contrib/lite/kernels/fully_connected.cc
@@ -48,7 +48,7 @@ enum KernelType {
 
 struct OpData {
   // The scaling factor from input to output (aka the 'real multiplier') can
-  // be represented as a fixed point multiplier plus a left shift.
+  // be represented as a fixed point multipler plus a left shift.
   int32_t output_multiplier;
   int output_shift;
   // The range of the fused activation layer. For example for kNone and
diff --git a/tensorflow/contrib/lite/kernels/kernel_util.h b/tensorflow/contrib/lite/kernels/kernel_util.h
index 21da1daff7..28f53b9fbb 100644
--- a/tensorflow/contrib/lite/kernels/kernel_util.h
+++ b/tensorflow/contrib/lite/kernels/kernel_util.h
@@ -58,7 +58,7 @@ inline bool IsConstantTensor(TfLiteTensor* tensor) {
 }
 
 // Determines whether tensor is dynamic. Note that a tensor can be non-const and
-// not dynamic. This function specifically checks for a dynamic tensor.
+// not dynamic. This function specificially checks for a dynamic tensor.
 inline bool IsDynamicTensor(TfLiteTensor* tensor) {
   return tensor->allocation_type == kTfLiteDynamic;
 }
diff --git a/tensorflow/contrib/lite/kernels/lsh_projection.cc b/tensorflow/contrib/lite/kernels/lsh_projection.cc
index 0ee35775d5..5f73b56ed9 100644
--- a/tensorflow/contrib/lite/kernels/lsh_projection.cc
+++ b/tensorflow/contrib/lite/kernels/lsh_projection.cc
@@ -13,7 +13,7 @@ See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
 
-// LSH Projection projects an input to a bit vector via locality sensitive
+// LSH Projection projects an input to a bit vector via locality senstive
 // hashing.
 //
 // Options:
diff --git a/tensorflow/contrib/lite/kernels/lstm.cc b/tensorflow/contrib/lite/kernels/lstm.cc
index 8cf1165135..b9255b23a5 100644
--- a/tensorflow/contrib/lite/kernels/lstm.cc
+++ b/tensorflow/contrib/lite/kernels/lstm.cc
@@ -213,9 +213,9 @@ TfLiteStatus CheckInputTensorDimensions(TfLiteContext* context,
   // present.
   // 2) If projection weight is present, then projection bias is optional.
   // TODO(ghodrat): make sure this is correct.
-  const bool projection_tensors_consistent =
+  const bool projecton_tensors_consistent =
       ((projection_weights != nullptr) || (projection_bias == nullptr));
-  TF_LITE_ENSURE(context, projection_tensors_consistent == true);
+  TF_LITE_ENSURE(context, projecton_tensors_consistent == true);
 
   return kTfLiteOk;
 }
@@ -357,7 +357,7 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
   const int n_output = recurrent_to_output_weights->dims->data[1];
 
   // Since we have already checked that weights are all there or none, we can
-  // check the existence of only one to get the condition.
+  // check the existense of only one to the get the condition.
   const bool use_cifg = (input_to_input_weights == nullptr);
   const bool use_peephole = (cell_to_output_weights != nullptr);
 
diff --git a/tensorflow/contrib/lite/kernels/reshape.cc b/tensorflow/contrib/lite/kernels/reshape.cc
index 438f70d311..f3e6ddc9f4 100644
--- a/tensorflow/contrib/lite/kernels/reshape.cc
+++ b/tensorflow/contrib/lite/kernels/reshape.cc
@@ -49,20 +49,20 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
 
   TfLiteIntArray* output_size = TfLiteIntArrayCreate(params->num_dimensions);
   int num_output_elements = 1;
-  int stretch_dim = -1;
+  int strech_dim = -1;
   for (int i = 0; i < params->num_dimensions; ++i) {
     int value = params->shape[i];
     if (value == -1) {
-      TF_LITE_ENSURE_EQ(context, stretch_dim, -1);
-      stretch_dim = i;
+      TF_LITE_ENSURE_EQ(context, strech_dim, -1);
+      strech_dim = i;
     } else {
       num_output_elements *= value;
       output_size->data[i] = value;
     }
   }
-  if (stretch_dim != -1) {
-    output_size->data[stretch_dim] = num_input_elements / num_output_elements;
-    num_output_elements *= output_size->data[stretch_dim];
+  if (strech_dim != -1) {
+    output_size->data[strech_dim] = num_input_elements / num_output_elements;
+    num_output_elements *= output_size->data[strech_dim];
   }
 
   TF_LITE_ENSURE_EQ(context, num_input_elements, num_output_elements);
diff --git a/tensorflow/contrib/lite/kernels/reshape_test.cc b/tensorflow/contrib/lite/kernels/reshape_test.cc
index aecbd0399f..0fbcf6e6aa 100644
--- a/tensorflow/contrib/lite/kernels/reshape_test.cc
+++ b/tensorflow/contrib/lite/kernels/reshape_test.cc
@@ -60,7 +60,7 @@ TEST(ReshapeOpTest, TooManyDimensions) {
 
 TEST(ReshapeOpTest, TooManySpecialDimensions) {
   EXPECT_DEATH(ReshapeOpModel({1, 2, 4, 1}, {-1, -1, 2, 4}),
-               "stretch_dim != -1");
+               "strech_dim != -1");
 }
 
 TEST(ReshapeOpTest, SimpleTest) {
diff --git a/tensorflow/contrib/lite/kernels/test_util.cc b/tensorflow/contrib/lite/kernels/test_util.cc
index 0bb28b50b2..373310bd87 100644
--- a/tensorflow/contrib/lite/kernels/test_util.cc
+++ b/tensorflow/contrib/lite/kernels/test_util.cc
@@ -141,8 +141,8 @@ void SingleOpModel::SetBuiltinOp(BuiltinOperator type,
 
 void SingleOpModel::SetCustomOp(
     const string& name, const std::vector<uint8_t>& custom_option,
-    const std::function<TfLiteRegistration*()>& registration) {
-  custom_registrations_[name] = registration;
+    const std::function<TfLiteRegistration*()>& registeration) {
+  custom_registrations_[name] = registeration;
   opcodes_.push_back(
       CreateOperatorCodeDirect(builder_, BuiltinOperator_CUSTOM, name.data()));
   operators_.push_back(CreateOperator(
diff --git a/tensorflow/contrib/lite/kernels/unidirectional_sequence_lstm.cc b/tensorflow/contrib/lite/kernels/unidirectional_sequence_lstm.cc
index 42941a97db..508a570e2e 100644
--- a/tensorflow/contrib/lite/kernels/unidirectional_sequence_lstm.cc
+++ b/tensorflow/contrib/lite/kernels/unidirectional_sequence_lstm.cc
@@ -360,7 +360,7 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
   const int n_output = recurrent_to_output_weights->dims->data[1];
 
   // Since we have already checked that weights are all there or none, we can
-  // check the existence of only one to get the condition.
+  // check the existense of only one to the get the condition.
   const bool use_cifg = (input_to_input_weights == nullptr);
   const bool use_peephole = (cell_to_output_weights != nullptr);