Fix typos (tensorflow#14516)

tensorflow/contrib/boosted_trees/lib/utils/example.h

@@ -63,7 +63,7 @@ class SparseFloatFeatureColumn {
  public:
   void Reserve(const int32 size) {
     if (!single_dimensional_) {
-      mutlidimensional_values.Reserve(size);
+      multidimensional_values.Reserve(size);
     }
   }
 
@@ -76,15 +76,15 @@ class SparseFloatFeatureColumn {
       DCHECK_EQ(0, feature_idx);
       single_value_ = value;
     } else {
-      mutlidimensional_values.Add(feature_idx, value);
+      multidimensional_values.Add(feature_idx, value);
     }
     initialized_ = true;
   }
 
   void Clear() {
     single_dimensional_ = false;
     initialized_ = false;
-    mutlidimensional_values.Clear();
+    multidimensional_values.Clear();
   }
 
   OptionalValue<T> operator[](int feature_idx) const {
@@ -94,15 +94,15 @@ class SparseFloatFeatureColumn {
     if (single_dimensional_) {
       return OptionalValue<T>(single_value_);
     } else {
-      return mutlidimensional_values[feature_idx];
+      return multidimensional_values[feature_idx];
     }
   }
 
  private:
   bool single_dimensional_;
   bool initialized_;
   T single_value_;
-  SparseMultidimensionalValues<T> mutlidimensional_values;
+  SparseMultidimensionalValues<T> multidimensional_values;
 };
 
 // Holds data for one example and enables lookup by feature column.

tensorflow/contrib/lite/nnapi/NeuralNetworksShim.h

@@ -1454,7 +1454,7 @@ inline int ANeuralNetworksModel_finish(ANeuralNetworksModel* model) {
  * {@link ANeuralNetworksExecution_setOutputFromMemory} and
  * {@link ANeuralNetworksExecution_setOperandValue}.
  *
- * To build a model that can accomodate inputs of various sizes, as you may want
+ * To build a model that can accommodate inputs of various sizes, as you may want
  * to do for a CNN, set the size of the dimensions that will vary at run time to
  * 0. If you do so, provide the full dimensions when calling
  * {@link ANeuralNetworksExecution_setInput} or {@link

tensorflow/contrib/lite/testing/parse_testdata.cc

@@ -232,7 +232,7 @@ TfLiteStatus CheckOutputs(tflite::Interpreter* interpreter,
 //   invoke {
 //     id: xyz
 //     input: 1,2,1,1,1,2,3,4
-//     ouput: 4,5,6
+//     output: 4,5,6
 //   }
 class Invoke : public Message {
  public:

tensorflow/contrib/lite/testing/test_runner.h

@@ -63,7 +63,7 @@ class TestRunner {
   // Run the model.
   virtual void Invoke() = 0;
 
-  // Verify that the contents of all ouputs conform to the existing
+  // Verify that the contents of all outputs conform to the existing
   // expectations. Return true if there are no expectations or they are all
   // satisfied.
   virtual bool CheckResults() = 0;

tensorflow/contrib/lite/toco/model.h

@@ -129,7 +129,7 @@ enum class AxesOrder {
 // The type of the scalars in an array.
 // Note that that does not by itself tell whether the values in the array are
 // real (are literally interpreted as real numbers) or quantized (only acquire
-// a meaning as real numbers in conjuction with QuantizationParams).
+// a meaning as real numbers in conjunction with QuantizationParams).
 //
 // In practice though:
 //   float values are always real

tensorflow/core/grappler/costs/graph_properties.cc

@@ -634,7 +634,7 @@ Status GraphProperties::InferStatically() {
 
   std::unordered_map<const shape_inference::Dimension*, int> dim_ids;
 
-  // Track shapes globally accross the graph.
+  // Track shapes globally across the graph.
   SymbolicShapeManager shape_manager;
   bool found_error = false;
   for (const Node* const node : graph.nodes()) {

tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc

@@ -1146,7 +1146,7 @@ Status ArithmeticOptimizer::SimplifyArithmeticOps(
       if (simplified_node != nullptr) {
         nodes_to_simplify.PushBack(simplified_node);
       }
-      // When `node` is simplifed to another node rather than in-place, the
+      // When `node` is simplified to another node rather than in-place, the
       // consumers of `node` are already redirected to `simplified_tensor`.
       // Re-push the consumers into `nodes_to_simplify` for further
       // optimizations.

tensorflow/core/kernels/dynamic_partition_op_gpu.cu.cc

@@ -153,7 +153,7 @@ class DynamicPartitionOpGPU : public AsyncOpKernel {
                          Tensor* partitions_out, Tensor* indices_out,
                          DoneCallback done) {
     int32 M = std::max(N, num_partitions_);
-    // indices_in will be made slightly larger to accomodate
+    // indices_in will be made slightly larger to accommodate
     // later computations.
     OP_REQUIRES_OK_ASYNC(
         c, c->allocate_temp(DT_INT32, TensorShape({M}), indices_in), done);

tensorflow/core/kernels/slice_op.h

@@ -103,7 +103,7 @@ void SliceUsingEigen(const Device& d, Tensor* out, const Tensor& in,
 namespace functor {
 
 // Template parameter NDIM is not neccesary here. The aim of keeping it
-// is to compile struct slice seperately which minimizes the compiling time.
+// is to compile struct slice separately which minimizes the compiling time.
 template <typename Device, typename T, int NDIM>
 struct Slice {
   void operator()(const Device& d, Tensor* out, const Tensor& in,