Fix typos

tensorflow/compiler/xla/service/copy_insertion.cc

@@ -65,7 +65,7 @@ struct SpecialCaseCopyPolicy {
   // output tuple.
   bool copy_root_replicated_buffers = false;
   // If true, insert a copy if a buffer coming from a constant or a parameter
-  // is found wihtin the output tuple.
+  // is found within the output tuple.
   bool copy_parameters_and_constants = false;
 };
 

tensorflow/compiler/xla/service/hlo_evaluator.cc

@@ -1193,7 +1193,7 @@ class HloEvaluator::TypedVisitor : public DfsHloVisitorWithDefault {
           // specifically:
           // - For lhs, the non-contracting dimensions, including the batch
           // dimension have the same index as the `result_index`.
-          // - For rhs, the batch dimension is set seperately from other
+          // - For rhs, the batch dimension is set separately from other
           // non-contracting dimensions, since these other non-contracting
           // dimensions in rhs follow the non-contracting dimensions of lhs in
           // the resulting index.

tensorflow/contrib/kfac/python/ops/optimizer.py

@@ -115,7 +115,7 @@ def __init__(self,
     self._estimation_mode = estimation_mode
     self._colocate_gradients_with_ops = colocate_gradients_with_ops
 
-    # The below paramaters are required only if damping needs to be adapated.
+    # The below parameters are required only if damping needs to be adapated.
     # These parameters can be set by calling
     # set_damping_adaptation_params() explicitly.
     self._damping_adaptation_decay = 0.95

tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h

@@ -2918,7 +2918,7 @@ inline void Concatenation(int concat_dim, const uint8* const* input_data,
                           const int32 output_zeropoint,
                           const float output_scale) {
   // The arguments input_zeropoint and input_scale are expected to be an array
-  // that have the quantization paramaters for all the inputs to the concat
+  // that have the quantization parameters for all the inputs to the concat
   // operator.
   gemmlowp::ScopedProfilingLabel label("Concatenation");
   TFLITE_DCHECK_GT(inputs_count, 1);

tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h

@@ -1464,7 +1464,7 @@ inline void Concatenation(int concat_dim, const uint8* const* input_data,
                           const int32 output_zeropoint,
                           const float output_scale) {
   // The arguments input_zeropoint and input_scale are expected to be an array
-  // that have the quantization paramaters for all the inputs to the concat
+  // that have the quantization parameters for all the inputs to the concat
   // operator.
   TFLITE_DCHECK_GT(inputs_count, 1);
   int64_t concat_size = 0;

tensorflow/contrib/lite/testing/generate_examples.py

@@ -112,7 +112,7 @@
 
 
 class ExtraTocoOptions(object):
-  """Additonal toco options besides input, output, shape."""
+  """Additional toco options besides input, output, shape."""
 
   def __init__(self):
     # Whether to ignore control dependency nodes.

tensorflow/contrib/lite/toco/toco_flags.proto

@@ -121,7 +121,7 @@ message TocoFlags {
   // transformations that are necessary in order to generate inference
   // code for these graphs. Such graphs should be fixed, but as a
   // temporary work-around, setting this reorder_across_fake_quant flag
-  // allows toco to perform necessary graph transformaitons on them,
+  // allows toco to perform necessary graph transformations on them,
   // at the cost of no longer faithfully matching inference and training
   // arithmetic.
   optional bool reorder_across_fake_quant = 8;

tensorflow/contrib/opt/python/training/model_average_optimizer_test.py

@@ -146,7 +146,7 @@ def test1Workers2Period(self):
     self.assertAllEqual(1.0, sessions[0].run(global_var_1))
     self.assertAllEqual(0, sessions[0].run(global_step))
 
-    # iteration 2, global varibale update
+    # iteration 2, global variable update
     thread_0 = self.checkedThread(
         target=self._run, args=(train_ops[0], sessions[0]))
     thread_1 = self.checkedThread(

tensorflow/python/keras/_impl/keras/engine/training_eager.py

@@ -176,7 +176,7 @@ def slice_arrays(arrays, indices, contiguous=True):
   """Slices batches out of provided arrays (workaround for eager tensors).
 
   Unfortunately eager tensors don't have the same slicing behavior as
-  Numpy arrays (they folow  the same slicing behavior as symbolic TF tensors),
+  Numpy arrays (they follow the same slicing behavior as symbolic TF tensors),
   hence we cannot use `generic_utils.slice_arrays` directly
   and we have to implement this workaround based on `concat`. This has a
   performance cost.