fix: typos using misspell

fix: typos

This PR is part of a campaign to fix a lot of typos on github!
You can see the progress on https://github.com/fixTypos/fix_typos/

https://github.com/client9/misspell

configure

@@ -230,7 +230,7 @@ if [ "$TF_NEED_MKL" == "1" ]; then # TF_NEED_MKL
     if [ -z "$MKL_INSTALL_PATH" ]; then
       MKL_INSTALL_PATH=$default_mkl_path
     fi
-    # Result returned from "read" will be used unexpanded. That make "~" unuseable.
+    # Result returned from "read" will be used unexpanded. That make "~" unusable.
     # Going through one more level of expansion to handle that.
     MKL_INSTALL_PATH=`${PYTHON_BIN_PATH} -c "import os; print(os.path.realpath(os.path.expanduser('${MKL_INSTALL_PATH}')))"`
   fi
@@ -565,7 +565,7 @@ while true; do
     if [ -z "$CUDNN_INSTALL_PATH" ]; then
       CUDNN_INSTALL_PATH=$default_cudnn_path
     fi
-    # Result returned from "read" will be used unexpanded. That make "~" unuseable.
+    # Result returned from "read" will be used unexpanded. That make "~" unusable.
     # Going through one more level of expansion to handle that.
     CUDNN_INSTALL_PATH=`"${PYTHON_BIN_PATH}" -c "import os; print(os.path.realpath(os.path.expanduser('${CUDNN_INSTALL_PATH}')))"`
   fi

tensorflow/compiler/xla/array4d.h

@@ -65,7 +65,7 @@ class Array4D {
     Fill(T());
   }
 
-  // Creates a 4D array, initalized to value.
+  // Creates a 4D array, initialized to value.
   Array4D(int64 planes, int64 depth, int64 height, int64 width, T value)
       : Array4D(planes, depth, height, width) {
     Fill(value);

tensorflow/compiler/xla/client/local_client.h

@@ -56,7 +56,7 @@ class ExecutableBuildOptions {
 
   // If set, this specifies the layout of the result of the computation. If not
   // set, the service will chose the layout of the result. A Shape is used to
-  // store the layout to accomodate tuple result shapes. A value of nullptr
+  // store the layout to accommodate tuple result shapes. A value of nullptr
   // indicates the option has not been set.
   ExecutableBuildOptions& set_result_layout(const Shape& shape_with_layout);
   const Shape* result_layout() const;

tensorflow/compiler/xla/literal_util.h

@@ -128,7 +128,7 @@ class LiteralUtil {
 
   // Creates a new value that has the equivalent value as literal, but conforms
   // to new_layout; e.g. a literal matrix that was in {0, 1} minor-to-major
-  // dimension layout can be re-layed-out as {1, 0} minor-to-major dimension
+  // dimension layout can be re-laid-out as {1, 0} minor-to-major dimension
   // layout and the value in the cell at any given logical index (i0, i1) will
   // be the same.
   //

tensorflow/compiler/xla/service/buffer_liveness_test.cc

@@ -628,7 +628,7 @@ class FusedDynamicUpdateSliceLivenessTest : public BufferLivenessTest {
         BufferLiveness::Run(module.get(),
                             MakeUnique<DependencyHloOrdering>(module.get()))
             .ConsumeValueOrDie();
-    // Return whether or not buffers interfernce is detected between
+    // Return whether or not buffers interference is detected between
     // 'tuple_param0' and 'tuple_root' at shape index '{1}'.
     return TupleElementsMayInterfere(*liveness, tuple_param0, tuple_root, {1});
   }
@@ -740,7 +740,7 @@ class DynamicUpdateSliceLivenessTest : public BufferLivenessTest {
         BufferLiveness::Run(module.get(),
                             MakeUnique<DependencyHloOrdering>(module.get()))
             .ConsumeValueOrDie();
-    // Return whether or not buffers interfernce is detected between
+    // Return whether or not buffers interference is detected between
     // 'tuple_param0' and 'tuple_root' at shape index '{1}'.
     return TupleElementsMayInterfere(*liveness, tuple_param0, tuple_root, {1});
   }

tensorflow/compiler/xla/service/copy_insertion.cc

@@ -141,7 +141,7 @@ class InstructionCopier {
   Status RecordAmbiguousOrNonDistinctIndices(
       const TuplePointsToAnalysis& points_to_analysis);
 
-  // Records instruction buffer indices which have interferring live ranges
+  // Records instruction buffer indices which have interfering live ranges
   // with 'other_instruction' buffers at same index.
   Status RecordIndicesWhichInterfereWithOtherInstruction(
       const BufferLiveness& liveness, const HloInstruction* other_instruction,
@@ -431,7 +431,7 @@ HloInstruction* InstructionCopier::Copy() {
   return copy;
 }
 
-// The 'read_only_indices' are initalized based on points-to analysis on the
+// The 'read_only_indices' are initialized based on points-to analysis on the
 // while body corresponding to 'while_hlo'. If the init buffer corresponding to
 // a read-only index aliases with an entry parameter (or constant), it cannot be
 // considered read-only, and must be copied. This is necessary because some

tensorflow/compiler/xla/service/copy_insertion_test.cc

@@ -972,7 +972,7 @@ TEST_F(WhileCopyInsertionTest, InitPointsToNonDistinct) {
                                   op::Copy(old_init->operand(1)->operand(0)))));
 }
 
-// Tests while init instruction buffer which interfers with while result buffer.
+// Tests while init instruction buffer which interferes with while result buffer.
 //
 //     init_data = Broadcast(...)
 //     add_unrelated = Add(init_data) // takes a reference to cause interference

tensorflow/compiler/xla/service/gpu/convolution_thunk.cc

@@ -125,7 +125,7 @@ tensorflow::Status ConvolutionThunk::ExecuteOnStream(
   CHECK_LE(num_dimensions, 3);
   // cuDNN does not support 1D convolutions. We therefore express 1D
   // convolutions as 2D convolutions where the first spatial dimension is 1.
-  // This matches the behaviour of TF (see definition of conv1d in
+  // This matches the behavior of TF (see definition of conv1d in
   // tensorflow/python/ops/nn_ops.py).
   const int effective_num_dimensions = std::max(2, num_dimensions);
 

tensorflow/compiler/xla/service/gpu/llvm_gpu_backend/gpu_backend_lib.cc

@@ -405,9 +405,9 @@ StatusOr<string> CompileModuleToPtx(llvm::Module* module,
 
   AddOptimizationPasses(flags->opt_level, /*size_level=*/0,
                         target_machine.get(), &module_passes, &function_passes);
-  // Loop unrolling exposes more opportunites for SROA. Therefore, we run SROA
+  // Loop unrolling exposes more opportunities for SROA. Therefore, we run SROA
   // again after the standard optimization passes [http://b/13329423].
-  // TODO(jingyue): SROA may further expose more optimization opportunites, such
+  // TODO(jingyue): SROA may further expose more optimization opportunities, such
   // as more precise alias analysis and more function inlining (SROA may change
   // the inlining cost of a function). For now, running SROA already emits good
   // enough code for the evaluated benchmarks. We may want to run more

tensorflow/compiler/xla/service/gpu/partition_assignment.h

@@ -33,7 +33,7 @@ namespace gpu {
 enum class PartitionStrategy {
   // Optimized for latency by allowing maximum number of registers per thread.
   kLatency,
-  // Optimized for throughtput. This may limit registers per thread and cause
+  // Optimized for throughput. This may limit registers per thread and cause
   // longer latency.
   kThroughput
 };

tensorflow/compiler/xla/service/gpu/while_transformer.cc

@@ -37,7 +37,7 @@ namespace {
 // patterns to match.
 //
 // Each ExprTree node is comprised of an HloOpcode, and a set of operands (each
-// of type ExprTree). Operands can be added by specifing the index and HloOpcode
+// of type ExprTree). Operands can be added by specifying the index and HloOpcode
 // of the operand.
 //
 // For example, the following computation:

tensorflow/compiler/xla/service/hlo_constant_folding.h

@@ -21,7 +21,7 @@ limitations under the License.
 
 namespace xla {
 
-// A pass which performs constant folding in order to avoid unecessary
+// A pass which performs constant folding in order to avoid unnecessary
 // computation on constants.
 class HloConstantFolding : public HloPassInterface {
  public:

tensorflow/compiler/xla/service/hlo_cost_analysis.h

@@ -133,7 +133,7 @@ class HloCostAnalysis : public DfsHloVisitor {
   int64 bytes_accessed() const { return bytes_accessed_; }
 
  private:
-  // An FMA counts as two floating point operations in these analyses.
+  // An FMA counts as two floating point operations in these analyzes.
   static constexpr int64 kFmaFlops = 2;
 
   // Utility function to handle all element-wise operations.

tensorflow/compiler/xla/service/hlo_evaluator.h

@@ -104,7 +104,7 @@ class HloEvaluator : public DfsHloVisitorWithDefault {
                            std::hash<int>>
       typed_visitors_;
 
-  // Tracks the HLO instruciton and its evaluated literal result.
+  // Tracks the HLO instruction and its evaluated literal result.
   // TODO(b/35950897): have better memory management here to free instructions
   // that are no longer a parent for any other subsequent instruction in
   // post-orderring.

tensorflow/compiler/xla/service/hlo_ordering.cc

@@ -343,7 +343,7 @@ class ListScheduler {
     return freed_bytes;
   }
 
-  // Construct the scheduling priority of the given instruciton.
+  // Construct the scheduling priority of the given instruction.
   Priority GetPriority(const HloInstruction* instruction) {
     return {BytesFreedIfScheduled(instruction), instruction->user_count()};
   }

tensorflow/compiler/xla/service/layout_assignment.h

@@ -273,7 +273,7 @@ class LayoutAssignment : public HloPassInterface {
     return Status::OK();
   }
 
-  // This method can be overriden to mark instructions as requiring the operands
+  // This method can be overridden to mark instructions as requiring the operands
   // to have the same layout as the result, for performance or correctness. This
   // will propagate constraints through the instruction from the result into the
   // operands.

tensorflow/compiler/xla/service/llvm_ir/llvm_util.h

@@ -130,7 +130,7 @@ llvm::AllocaInst* EmitAllocaAtFunctionEntryWithCount(
     llvm::Type* type, llvm::Value* element_count, tensorflow::StringPiece name,
     llvm::IRBuilder<>* ir_builder, int alignment = 0);
 
-// Creates a basic block with the same context and funtion as for the
+// Creates a basic block with the same context and function as for the
 // builder. Inserts at the end of the function if insert_before is
 // null.
 llvm::BasicBlock* CreateBasicBlock(llvm::BasicBlock* insert_before,

tensorflow/compiler/xla/tests/hlo_test_base.h

@@ -65,7 +65,7 @@ class HloTestBase : public ::testing::Test {
   perftools::gputools::DeviceMemoryBase TransferToDevice(
       const Literal& literal);
 
-  // Transfers the array refered to by the given handle from the device and
+  // Transfers the array referred to by the given handle from the device and
   // returns as a Literal.
   std::unique_ptr<Literal> TransferFromDevice(
       const Shape& shape, perftools::gputools::DeviceMemoryBase device_base);

tensorflow/compiler/xla/tests/prng_test.cc

@@ -194,7 +194,7 @@ XLA_TEST_F(PrngTest, MapUsingRng) {
   }
 }
 
-// This tests demonstrates the global seeding behaviour.
+// This tests demonstrates the global seeding behavior.
 // * If a seed is passed in via Execute (ExecuteAndTransfer) then the output is
 //   fixed (i.e., there is a single output for a given seed);
 // * If no seed is passed in then the output of every call can be different;

tensorflow/contrib/bayesflow/python/ops/monte_carlo_impl.py

@@ -177,7 +177,7 @@ def _logspace_mean(log_values):
       `Log[Mean[values]]`.
   """
   # center = Max[Log[values]],  with stop-gradient
-  # The center hopefully keep the exponentiated term small.  It is cancelled
+  # The center hopefully keep the exponentiated term small.  It is canceled
   # from the final result, so putting stop gradient on it will not change the
   # final result.  We put stop gradient on to eliminate unnecessary computation.
   center = array_ops.stop_gradient(_sample_max(log_values))

tensorflow/contrib/boosted_trees/lib/testutil/random_tree_gen.h

@@ -42,7 +42,7 @@ class RandomTreeGen {
   boosted_trees::trees::DecisionTreeConfig Generate(
       const boosted_trees::trees::DecisionTreeConfig& tree);
 
-  // Requried: depth >= 1; tree_count >= 1.
+  // Required: depth >= 1; tree_count >= 1.
   boosted_trees::trees::DecisionTreeEnsembleConfig GenerateEnsemble(
       int dept, int tree_count);
 

tensorflow/contrib/cloud/kernels/bigquery_reader_ops.cc

@@ -46,7 +46,7 @@ Status GetTableAttrs(OpKernelConstruction* context, string* project_id,
 
 }  // namespace
 
-// Note that overriden methods with names ending in "Locked" are called by
+// Note that overridden methods with names ending in "Locked" are called by
 // ReaderBase while a mutex is held.
 // See comments for ReaderBase.
 class BigQueryReader : public ReaderBase {

tensorflow/contrib/cloud/python/ops/bigquery_reader_ops_test.py

@@ -46,7 +46,7 @@ _TABLE = "test-table"
 # The values for rows are generated such that some columns have null values. The
 # general formula here is:
 #   - The int64 column is present in every row.
-#   - The string column is only avaiable in even rows.
+#   - The string column is only available in even rows.
 #   - The float column is only available in every third row.
 _ROWS = [[0, "s_0", 0.1], [1, None, None], [2, "s_2", None], [3, None, 3.1],
          [4, "s_4", None], [5, None, None], [6, "s_6", 6.1], [7, None, None],

tensorflow/contrib/cudnn_rnn/python/ops/cudnn_rnn_ops.py

@@ -141,7 +141,7 @@ _cudnn_rnn_common_doc_string = """
     * Once a while, the user saves the parameter buffer into model checkpoints
         with Saver.save().
     * When restoring, the user creates a RNNParamsSaveable object and uses
-      Saver.restore() to restore the paramter buffer from the canonical format
+      Saver.restore() to restore the parameter buffer from the canonical format
       to a user-defined format, as well as to restore other savable objects
       in the checkpoint file.
 """

tensorflow/contrib/data/python/framework/function.py

@@ -39,7 +39,7 @@ class _ExperimentalFuncGraph(function._FuncGraph):
   _ExperimentalFuncGraph overrides ops.Graph's create_op() so that we can keep
   track of every inputs into every op created inside the function.  If
   any input is from other graphs, we keep track of it in self.capture
-  and substitue the input with a place holder.
+  and substitute the input with a place holder.
 
   Each captured input's corresponding place holder is converted into a
   function argument and the caller passes in the captured tensor.

tensorflow/contrib/distributions/python/kernel_tests/vector_student_t_test.py

@@ -38,7 +38,7 @@ class _FakeVectorStudentT(object):
 
   Other `Vector*` implementations need only test new code. That we don't need
   to test every Vector* distribution is good because there aren't SciPy
-  analogues and reimplementing everything in NumPy sort of defeats the point of
+  analogs and reimplementing everything in NumPy sort of defeats the point of
   having the `TransformedDistribution + Affine` API.
   """
 

tensorflow/contrib/distributions/python/ops/binomial.py

@@ -269,7 +269,7 @@ class Binomial(distribution.Distribution):
             message="total_count must be non-negative."),
         distribution_util.assert_integer_form(
             total_count,
-            message="total_count cannot contain fractional componentes."),
+            message="total_count cannot contain fractional components."),
     ], total_count)
 
   def _maybe_assert_valid_sample(self, counts, check_integer=True):

tensorflow/contrib/graph_editor/util.py

@@ -130,7 +130,7 @@ def transform_tree(tree, fn, iterable_type=tuple):
     tree: iterable or not. If iterable, its elements (child) can also be
       iterable or not.
     fn: function to apply to each leaves.
-    iterable_type: type use to construct the resulting tree for unknwon
+    iterable_type: type use to construct the resulting tree for unknown
       iterable, typically `list` or `tuple`.
   Returns:
     A tree whose leaves has been transformed by `fn`.

tensorflow/contrib/hooks/README.md

@@ -5,7 +5,7 @@ of `SessionRunHook` and are to be used with helpers like `MonitoredSession`
 and `learn.Estimator` that wrap `tensorflow.Session`.
 
 The hooks are called between invocations of `Session.run()` to perform custom
-behaviour.
+behavior.
 
 For example the `ProfilerHook` periodically collects `RunMetadata` after
 `Session.run()` and saves profiling information that can be viewed in a

tensorflow/contrib/image/ops/image_ops.cc

@@ -77,7 +77,7 @@ REGISTER_OP("BipartiteMatch")
     .Doc(R"doc(
 Find bipartite matching based on a given distance matrix.
 
-A greedy bi-partite matching alogrithm is used to obtain the matching with the
+A greedy bi-partite matching algorithm is used to obtain the matching with the
 (greedy) minimum distance.
 
 distance_mat: A 2-D float tensor of shape `[num_rows, num_columns]`. It is a

tensorflow/contrib/image/python/ops/image_ops.py

@@ -266,7 +266,7 @@ def bipartite_match(
     top_k=-1):
   """Find bipartite matching based on a given distance matrix.
 
-  A greedy bi-partite matching alogrithm is used to obtain the matching with
+  A greedy bi-partite matching algorithm is used to obtain the matching with
   the (greedy) minimum distance.
 
   Args:

tensorflow/contrib/keras/python/keras/applications/resnet50.py

@@ -59,7 +59,7 @@ def identity_block(input_tensor, kernel_size, filters, stage, block):
 
   Arguments:
       input_tensor: input tensor
-      kernel_size: defualt 3, the kernel size of middle conv layer at main path
+      kernel_size: default 3, the kernel size of middle conv layer at main path
       filters: list of integers, the filterss of 3 conv layer at main path
       stage: integer, current stage label, used for generating layer names
       block: 'a','b'..., current block label, used for generating layer names
@@ -98,7 +98,7 @@ def conv_block(input_tensor, kernel_size, filters, stage, block, strides=(2,
 
   Arguments:
       input_tensor: input tensor
-      kernel_size: defualt 3, the kernel size of middle conv layer at main path
+      kernel_size: default 3, the kernel size of middle conv layer at main path
       filters: list of integers, the filterss of 3 conv layer at main path
       stage: integer, current stage label, used for generating layer names
       block: 'a','b'..., current block label, used for generating layer names

tensorflow/contrib/keras/python/keras/backend.py

@@ -91,7 +91,7 @@ _IMAGE_DATA_FORMAT = 'channels_last'
 def backend():
   """Publicly accessible method for determining the current backend.
 
-  Only exists for API compatibily with multi-backend Keras.
+  Only exists for API compatibility with multi-backend Keras.
 
   Returns:
       The string "tensorflow".
@@ -2617,7 +2617,7 @@ def in_train_phase(x, alt, training=None):
           (tensor or callable that returns a tensor).
       training: Optional scalar tensor
           (or Python boolean, or Python integer)
-          specifing the learning phase.
+          specifying the learning phase.
 
   Returns:
       Either `x` or `alt` based on the `training` flag.
@@ -2660,7 +2660,7 @@ def in_test_phase(x, alt, training=None):
           (tensor or callable that returns a tensor).
       training: Optional scalar tensor
           (or Python boolean, or Python integer)
-          specifing the learning phase.
+          specifying the learning phase.
 
   Returns:
       Either `x` or `alt` based on `K.learning_phase`.

tensorflow/contrib/keras/python/keras/engine/topology.py

@@ -1546,7 +1546,7 @@ class Container(Layer):
     """Retrieve the model's updates.
 
     Will only include updates that are either
-    inconditional, or conditional on inputs to this model
+    unconditional, or conditional on inputs to this model
     (e.g. will not include updates that depend on tensors
     that aren't inputs to this model).
 
@@ -1573,7 +1573,7 @@ class Container(Layer):
     """Retrieve the model's losses.
 
     Will only include losses that are either
-    inconditional, or conditional on inputs to this model
+    unconditional, or conditional on inputs to this model
     (e.g. will not include losses that depend on tensors
     that aren't inputs to this model).
 

tensorflow/contrib/keras/python/keras/wrappers/scikit_learn.py

@@ -109,7 +109,7 @@ class BaseWrapper(object):
     """Gets parameters for this estimator.
 
     Arguments:
-        **params: ignored (exists for API compatiblity).
+        **params: ignored (exists for API compatibility).
 
     Returns:
         Dictionary of parameter names mapped to their values.

tensorflow/contrib/kernel_methods/python/mappers/random_fourier_features_test.py

@@ -85,7 +85,7 @@ class RandomFourierFeatureMapperTest(TensorFlowTestCase):
       mapped_x = rffm.map(x)
       mapped_x_copy = rffm.map(x)
       # Two different evaluations of tensors output by map on the same input
-      # are identical because the same paramaters are used for the mappings.
+      # are identical because the same parameters are used for the mappings.
       self.assertAllClose(mapped_x.eval(), mapped_x_copy.eval(), atol=0.001)
 
   def testTwoMapperObjects(self):

tensorflow/contrib/labeled_tensor/python/ops/core.py

@@ -618,7 +618,7 @@ def identity(labeled_tensor, name=None):
 def slice_function(labeled_tensor, selection, name=None):
   """Slice out a subset of the tensor.
 
-  This is an analogue of tf.slice.
+  This is an analog of tf.slice.
   For example:
   >>> tensor = tf.reshape(tf.range(0, 6), [3, 2])
   >>> labeled_tensor = lt.LabeledTensor(tensor, ['a', ('b', ['foo', 'bar'])])

tensorflow/contrib/layers/python/layers/target_column_test.py

@@ -28,7 +28,7 @@ from tensorflow.python.platform import test
 
 class RegressionTargetColumnTest(test.TestCase):
 
-  # TODO(zakaria): test multilabel regresssion.
+  # TODO(zakaria): test multilabel regression.
   def testRegression(self):
     target_column = target_column_lib.regression_target()
     with ops.Graph().as_default(), session.Session() as sess:

tensorflow/contrib/learn/python/learn/dataframe/tensorflow_dataframe.py

@@ -97,7 +97,7 @@ class TensorFlowDataFrame(df.DataFrame):
       graph: the `Graph` in which the `DataFrame` should be built.
       session: the `Session` in which to run the columns of the `DataFrame`.
       start_queues: if true, queues will be started before running and halted
-        after producting `n` batches.
+        after producing `n` batches.
       initialize_variables: if true, variables will be initialized.
       **kwargs: Additional keyword arguments e.g. `num_epochs`.
 

tensorflow/contrib/learn/python/learn/estimators/estimator.py

@@ -89,7 +89,7 @@ SCIKIT_DECOUPLE_INSTRUCTIONS = (
 
 
 def _verify_input_args(x, y, input_fn, feed_fn, batch_size):
-  """Verifies validity of co-existance of input arguments."""
+  """Verifies validity of co-existence of input arguments."""
   if input_fn is None:
     if x is None:
       raise ValueError('Either x or input_fn must be provided.')
@@ -358,7 +358,7 @@ class BaseEstimator(
   """
   __metaclass__ = abc.ABCMeta
 
-  # Note that for Google users, this is overriden with
+  # Note that for Google users, this is overridden with
   # learn_runner.EstimatorConfig.
   # TODO(wicke): Remove this once launcher takes over config functionality
   _Config = run_config.RunConfig  # pylint: disable=invalid-name
@@ -703,7 +703,7 @@ class BaseEstimator(
   def _get_eval_ops(self, features, labels, metrics):
     """Method that builds model graph and returns evaluation ops.
 
-    Expected to be overriden by sub-classes that require custom support.
+    Expected to be overridden by sub-classes that require custom support.
 
     Args:
       features: `Tensor` or `dict` of `Tensor` objects.
@@ -1149,7 +1149,7 @@ class Estimator(BaseEstimator):
   def _get_train_ops(self, features, labels):
     """Method that builds model graph and returns trainer ops.
 
-    Expected to be overriden by sub-classes that require custom support.
+    Expected to be overridden by sub-classes that require custom support.
     This implementation uses `model_fn` passed as parameter to constructor to
     build model.
 
@@ -1165,7 +1165,7 @@ class Estimator(BaseEstimator):
   def _get_eval_ops(self, features, labels, metrics):
     """Method that builds model graph and returns evaluation ops.
 
-    Expected to be overriden by sub-classes that require custom support.
+    Expected to be overridden by sub-classes that require custom support.
     This implementation uses `model_fn` passed as parameter to constructor to
     build model.
 
@@ -1204,7 +1204,7 @@ class Estimator(BaseEstimator):
   def _get_predict_ops(self, features):
     """Method that builds model graph and returns prediction ops.
 
-    Expected to be overriden by sub-classes that require custom support.
+    Expected to be overridden by sub-classes that require custom support.
     This implementation uses `model_fn` passed as parameter to constructor to
     build model.
 

tensorflow/contrib/learn/python/learn/estimators/head.py

@@ -620,7 +620,7 @@ def _create_model_fn_ops(features,
     weight_tensor = _weight_tensor(features, weight_column_name)
     loss, weighted_average_loss = loss_fn(labels, logits, weight_tensor)
     # Uses the deprecated API to set the tag explicitly.
-    # Without it, trianing and eval losses will show up in different graphs.
+    # Without it, training and eval losses will show up in different graphs.
     logging_ops.scalar_summary(
         _summary_key(head_name, mkey.LOSS), weighted_average_loss)
 
@@ -1141,7 +1141,7 @@ def _to_labels_tensor(labels, label_name):
   """Returns label as a tensor.
 
   Args:
-    labels: Label `Tensor` or `SparseTensor` or a dict containig labels.
+    labels: Label `Tensor` or `SparseTensor` or a dict containing labels.
     label_name: Label name if labels is a dict.
 
   Returns:
@@ -1575,7 +1575,7 @@ class _MultiHead(Head):
     Args:
       all_model_fn_ops: list of ModelFnOps for the individual heads.
       train_op_fn: Function to create train op. See `create_model_fn_ops`
-          documentaion for more details.
+          documentation for more details.
 
     Returns:
       ModelFnOps that merges all heads for TRAIN.

tensorflow/contrib/learn/python/learn/estimators/rnn_common.py

@@ -119,7 +119,7 @@ def apply_dropout(cells, dropout_keep_probabilities, random_seed=None):
   """
   if len(dropout_keep_probabilities) != len(cells) + 1:
     raise ValueError(
-        'The number of dropout probabilites must be one greater than the '
+        'The number of dropout probabilities must be one greater than the '
         'number of cells. Got {} cells and {} dropout probabilities.'.format(
             len(cells), len(dropout_keep_probabilities)))
   wrapped_cells = [

tensorflow/contrib/learn/python/learn/estimators/run_config.py

@@ -309,7 +309,7 @@ class RunConfig(ClusterConfig, core_run_config.RunConfig):
     Args:
       whitelist: A list of the string names of the properties uid should not
         include. If `None`, defaults to `_DEFAULT_UID_WHITE_LIST`, which
-        includes most properites user allowes to change.
+        includes most properties user allowes to change.
 
     Returns:
       A uid string.

tensorflow/contrib/learn/python/learn/experiment.py

@@ -53,7 +53,7 @@ class Experiment(object):
   """
 
   # TODO(ispir): remove delay_workers_by_global_step and make global step based
-  # waiting as only behaviour.
+  # waiting as only behavior.
   @deprecated_args(
       "2016-10-23",
       "local_eval_frequency is deprecated as local_run will be renamed to "
@@ -550,7 +550,7 @@ class Experiment(object):
     eval_result = None
 
     # Set the default value for train_steps_per_iteration, which will be
-    # overriden by other settings.
+    # overridden by other settings.
     train_steps_per_iteration = 1000
     if self._train_steps_per_iteration is not None:
       train_steps_per_iteration = self._train_steps_per_iteration

tensorflow/contrib/learn/python/learn/learn_runner.py

@@ -155,7 +155,7 @@ def run(experiment_fn, output_dir=None, schedule=None, run_config=None,
       to create the `Estimator` (passed as `model_dir` to its constructor). It
       must return an `Experiment`. For this case, `run_config` and `hparams`
       must be None.
-      2) It accpets two arguments `run_config` and `hparams`, which should be
+      2) It accepts two arguments `run_config` and `hparams`, which should be
       used to create the `Estimator` (`run_config` passed as `config` to its
       constructor; `hparams` used as the hyper-paremeters of the model).
       It must return an `Experiment`. For this case, `output_dir` must be None.

tensorflow/contrib/learn/python/learn/ops/seq2seq_ops.py

@@ -140,7 +140,7 @@ def rnn_seq2seq(encoder_inputs,
     scope: Scope to use, if None new will be produced.
 
   Returns:
-    List of tensors for outputs and states for trianing and sampling sub-graphs.
+    List of tensors for outputs and states for training and sampling sub-graphs.
   """
   with vs.variable_scope(scope or "rnn_seq2seq"):
     _, last_enc_state = rnn.static_rnn(

tensorflow/contrib/learn/python/learn/preprocessing/categorical_vocabulary.py

@@ -128,9 +128,9 @@ class CategoricalVocabulary(object):
       Class name.
 
     Raises:
-      ValueError: if this vocabulary wasn't initalized with support_reverse.
+      ValueError: if this vocabulary wasn't initialized with support_reverse.
     """
     if not self._support_reverse:
-      raise ValueError("This vocabulary wasn't initalized with "
+      raise ValueError("This vocabulary wasn't initialized with "
                        "support_reverse to support reverse() function.")
     return self._reverse_mapping[class_id]

tensorflow/contrib/learn/python/learn/trainable.py

@@ -49,7 +49,7 @@ class Trainable(object):
       steps: Number of steps for which to train model. If `None`, train forever.
         'steps' works incrementally. If you call two times fit(steps=10) then
         training occurs in total 20 steps. If you don't want to have incremental
-        behaviour please set `max_steps` instead. If set, `max_steps` must be
+        behavior please set `max_steps` instead. If set, `max_steps` must be
         `None`.
       batch_size: minibatch size to use on the input, defaults to first
         dimension of `x`. Must be `None` if `input_fn` is provided.

tensorflow/contrib/learn/python/learn/utils/export.py

@@ -89,7 +89,7 @@ def _export_graph(graph, saver, checkpoint_path, export_dir,
 def generic_signature_fn(examples, unused_features, predictions):
   """Creates generic signature from given examples and predictions.
 
-  This is needed for backward compatibility with default behaviour of
+  This is needed for backward compatibility with default behavior of
   export_estimator.
 
   Args:

tensorflow/contrib/learn/python/learn/utils/saved_model_export_utils.py

@@ -309,7 +309,7 @@ def get_most_recent_export(export_dir_base):
                      directories.
 
   Returns:
-    A gc.Path, whith is just a namedtuple of (path, export_version).
+    A gc.Path, with is just a namedtuple of (path, export_version).
   """
   select_filter = gc.largest_export_versions(1)
   results = select_filter(gc.get_paths(export_dir_base,

tensorflow/contrib/learn/python/learn/utils/saved_model_export_utils_test.py

@@ -109,7 +109,7 @@ class SavedModelExportUtilsTest(test.TestCase):
     self.assertEqual(actual_signature_def, expected_signature_def)
 
   def test_build_standardized_signature_def_classification2(self):
-    """Tests multiple output tensors that include classes and probabilites."""
+    """Tests multiple output tensors that include classes and probabilities."""
     input_tensors = {
         "input-1":
             array_ops.placeholder(

tensorflow/contrib/legacy_seq2seq/python/kernel_tests/seq2seq_test.py

@@ -837,7 +837,7 @@ class Seq2SeqTest(test.TestCase):
   #     with variable_scope.variable_scope("new"):
   #       _, losses2 = SampleGRUSeq2Seq
   #           inp, out, weights, per_example_loss=True)
-  #       # First loss is scalar, the second one is a 1-dimensinal tensor.
+  #       # First loss is scalar, the second one is a 1-dimensional tensor.
   #       self.assertEqual([], losses1[0].get_shape().as_list())
   #       self.assertEqual([None], losses2[0].get_shape().as_list())
 

tensorflow/contrib/memory_stats/python/kernel_tests/memory_stats_ops_test.py

@@ -49,7 +49,7 @@ class MemoryStatsOpsTest(test_util.TensorFlowTestCase):
   # The memory for matrix "a" can be reused for matrix "d". Therefore, this
   # computation needs space for only three matrix plus some small overhead.
   def testChainOfMatmul(self):
-    # MaxBytesInUse is registerd on GPU only. See kernels/memory_stats_ops.cc.
+    # MaxBytesInUse is registered on GPU only. See kernels/memory_stats_ops.cc.
     if not test.is_gpu_available():
       return
 

tensorflow/contrib/metrics/python/ops/metric_ops_test.py

@@ -1507,7 +1507,7 @@ class StreamingAUCTest(test.TestCase):
       self.assertAlmostEqual(1, auc.eval(), 6)
 
   def np_auc(self, predictions, labels, weights):
-    """Computes the AUC explicitely using Numpy.
+    """Computes the AUC explicitly using Numpy.
 
     Args:
       predictions: an ndarray with shape [N].

tensorflow/contrib/rnn/python/ops/rnn_cell.py

@@ -466,7 +466,7 @@ class GridLSTMCell(core_rnn_cell.RNNCell):
         state is clipped by this value prior to the cell output activation.
       initializer: (optional) The initializer to use for the weight and
         projection matrices, default None.
-      num_unit_shards: (optional) int, defualt 1, How to split the weight
+      num_unit_shards: (optional) int, default 1, How to split the weight
         matrix. If > 1,the weight matrix is stored across num_unit_shards.
       forget_bias: (optional) float, default 1.0, The initial bias of the
         forget gates, used to reduce the scale of forgetting at the beginning
@@ -1809,12 +1809,12 @@ class PhasedLSTMCell(core_rnn_cell.RNNCell):
           period during which the gates are open.
       trainable_ratio_on: bool, weather ratio_on is trainable.
       period_init_min: float or scalar float Tensor. With value > 0.
-          Minimum value of the initalized period.
+          Minimum value of the initialized period.
           The period values are initialized by drawing from the distribution:
           e^U(log(period_init_min), log(period_init_max))
           Where U(.,.) is the uniform distribution.
       period_init_max: float or scalar float Tensor.
-          With value > period_init_min. Maximum value of the initalized period.
+          With value > period_init_min. Maximum value of the initialized period.
       reuse: (optional) Python boolean describing whether to reuse variables
         in an existing scope. If not `True`, and the existing scope already has
         the given variables, an error is raised.

tensorflow/contrib/seq2seq/python/ops/attention_wrapper.py

@@ -474,7 +474,7 @@ class AttentionWrapperState(
 
     Returns:
       A new `AttentionWrapperState` whose properties are the same as
-      this one, except any overriden properties as provided in `kwargs`.
+      this one, except any overridden properties as provided in `kwargs`.
     """
     return super(AttentionWrapperState, self)._replace(**kwargs)
 

tensorflow/contrib/slim/README.md

@@ -352,7 +352,7 @@ we can both ensure that each layer uses the same values and simplify the code:
 ```
 
 As the example illustrates, the use of arg_scope makes the code cleaner,
-simpler and easier to maintain. Notice that while argument values are specifed
+simpler and easier to maintain. Notice that while argument values are specified
 in the arg_scope, they can be overwritten locally. In particular, while
 the padding argument has been set to 'SAME', the second convolution overrides
 it with the value of 'VALID'.

tensorflow/contrib/slim/python/slim/data/dataset_data_provider.py

@@ -33,7 +33,7 @@ To read data using multiple readers simultaneous with shuffling:
       shuffle=True)
   images, labels = pascal_voc_data_provider.get(['images', 'labels'])
 
-Equivalently, one may request different fields of the same sample seperately:
+Equivalently, one may request different fields of the same sample separately:
 
   [images] = pascal_voc_data_provider.get(['images'])
   [labels] = pascal_voc_data_provider.get(['labels'])

tensorflow/contrib/tensorboard/plugins/projector/__init__.py

@@ -40,7 +40,7 @@ def visualize_embeddings(summary_writer, config):
   """Stores a config file used by the embedding projector.
 
   Args:
-    summary_writer: The summary writer used for writting events.
+    summary_writer: The summary writer used for writing events.
     config: `tf.contrib.tensorboard.plugins.projector.ProjectorConfig`
       proto that holds the configuration for the projector such as paths to
       checkpoint files and metadata files for the embeddings. If

tensorflow/contrib/tensorboard/plugins/projector/projector_api_test.py

@@ -46,7 +46,7 @@ class ProjectorApiTest(test.TestCase):
     writer = writer_lib.FileWriter(temp_dir)
     projector.visualize_embeddings(writer, config)
 
-    # Read the configuratin from disk and make sure it matches the original.
+    # Read the configurations from disk and make sure it matches the original.
     with gfile.GFile(os.path.join(temp_dir, 'projector_config.pbtxt')) as f:
       config2 = projector_config_pb2.ProjectorConfig()
       text_format.Parse(f.read(), config2)

tensorflow/contrib/training/python/training/evaluation.py

@@ -370,7 +370,7 @@ def evaluate_repeatedly(checkpoint_dir,
 
   One may also consider using a `tf.contrib.training.SummaryAtEndHook` to record
   summaries after the `eval_ops` have run. If `eval_ops` is `None`, the
-  summaries run immedietly after the model checkpoint has been restored.
+  summaries run immediately after the model checkpoint has been restored.
 
   Note that `evaluate_once` creates a local variable used to track the number of
   evaluations run via `tf.contrib.training.get_or_create_eval_step`.

tensorflow/contrib/training/python/training/hparam.py

@@ -422,7 +422,7 @@ class HParams(object):
     elif issubclass(param_type, float):
       typename = 'float'
     else:
-      raise ValueError('Unsupported paramter type: %s' % str(param_type))
+      raise ValueError('Unsupported parameter type: %s' % str(param_type))
 
     suffix = 'list' if is_list else 'value'
     return '_'.join([typename, suffix])

tensorflow/contrib/training/python/training/sequence_queueing_state_saver.py

@@ -344,7 +344,7 @@ def _prepare_sequence_inputs(inputs, states):
   key = _check_rank(inputs.key, 0)
 
   if length.dtype != dtypes.int32:
-    raise TypeError("length dtype must be int32, but recieved: %s" %
+    raise TypeError("length dtype must be int32, but received: %s" %
                     length.dtype)
   if key.dtype != dtypes.string:
     raise TypeError("key dtype must be string, but received: %s" % key.dtype)
@@ -1673,7 +1673,7 @@ def _move_sparse_tensor_out_context(input_context, input_sequences, num_unroll):
     shape = array_ops.concat(
         [array_ops.expand_dims(value_length, 0), sp_tensor.dense_shape], 0)
 
-    # Construct new indices by mutliplying old ones and prepending [0, n).
+    # Construct new indices by multiplying old ones and prepending [0, n).
     # First multiply indices n times along a newly created 0-dimension.
     multiplied_indices = array_ops.tile(
         array_ops.expand_dims(sp_tensor.indices, 0),

tensorflow/core/common_runtime/constant_folding.cc

@@ -83,7 +83,7 @@ bool IsConstantFoldable(const Node* n,
 }
 
 // Returns the constant foldable nodes in `nodes` in topological order.
-// Populates `constant_control_deps` with the non-constant control depedencies
+// Populates `constant_control_deps` with the non-constant control dependencies
 // of each constant node.
 void FindConstantFoldableNodes(
     const Graph* graph, ConstantFoldingOptions opts, std::vector<Node*>* nodes,

tensorflow/core/common_runtime/executor.h

@@ -74,8 +74,8 @@ class Executor {
   //
   // RunAsync() uses "cancellation_manager", if not nullptr, to
   // register callbacks that should be called if the graph computation
-  // is cancelled. Note that the callbacks merely unblock any
-  // long-running computation, and a cancelled step will terminate by
+  // is canceled. Note that the callbacks merely unblock any
+  // long-running computation, and a canceled step will terminate by
   // returning/calling the DoneCallback as usual.
   //
   // RunAsync() dispatches closures to "runner". Typically, "runner"

tensorflow/core/common_runtime/session_factory.h

@@ -47,7 +47,7 @@ class SessionFactory {
   // Old sessions may continue to have side-effects on resources not in
   // containers listed in "containers", and thus may affect future
   // sessions' results in ways that are hard to predict.  Thus, if well-defined
-  // behaviour is desired, is it recommended that all containers be listed in
+  // behavior is desired, is it recommended that all containers be listed in
   // "containers".
   //
   // If the "containers" vector is empty, the default container is assumed.

tensorflow/core/common_runtime/simple_graph_execution_state.cc

@@ -243,7 +243,7 @@ Status SimpleGraphExecutionState::InitBaseGraph(
       session_options_->config.graph_options().rewrite_options();
 
   if (grappler::MetaOptimizerEnabled(rewrite_options)) {
-    // Adding this functionalty in steps. The first step is to make sure
+    // Adding this functionality in steps. The first step is to make sure
     // we don't break dependencies. The second step will be to turn the
     // functionality on by default.
     grappler::GrapplerItem item;

tensorflow/core/common_runtime/simple_placer.cc

@@ -660,7 +660,7 @@ Status SimplePlacer::Run() {
       if (!edge->IsControlEdge() &&
           (IsRefType(node->input_type(edge->dst_input())) ||
            node->input_type(edge->dst_input()) == DT_RESOURCE)) {
-        // If both the source node and this node have paritally
+        // If both the source node and this node have partially
         // specified a device, then 'node's device should be
         // cleared: the reference edge forces 'node' to be on the
         // same device as the source node.

tensorflow/core/debug/debug_service.proto

@@ -20,7 +20,7 @@ package tensorflow;
 import "tensorflow/core/util/event.proto";
 
 // Reply message from EventListener to the client, i.e., to the source of the
-// Event protocal buffers, e.g., debug ops inserted by a debugged runtime to a
+// Event protocol buffers, e.g., debug ops inserted by a debugged runtime to a
 // TensorFlow graph being executed.
 message EventReply {
   message DebugOpStateChange {

tensorflow/core/distributed_runtime/graph_mgr.h

@@ -108,9 +108,9 @@ class GraphMgr {
   };
 
   struct Item : public core::RefCounted {
-    // TOOD(zhifengc): Keeps a copy of the original graph if the need arises.
-    // TOOD(zhifengc): Stats, updated by multiple runs potentially.
-    // TOOD(zhifengc): Dup-detection. Ensure step_id only run once.
+    // TODO(zhifengc): Keeps a copy of the original graph if the need arises.
+    // TODO(zhifengc): Stats, updated by multiple runs potentially.
+    // TODO(zhifengc): Dup-detection. Ensure step_id only run once.
     ~Item() override;
 
     // Session handle.
@@ -126,7 +126,7 @@ class GraphMgr {
     // has a root executor which may call into the runtime library.
     std::vector<ExecutionUnit> units;
 
-    // Used to deresgister a cost model when cost model is requried in graph
+    // Used to deresgister a cost model when cost model is required in graph
     // manager.
     GraphMgr* graph_mgr;
   };
@@ -157,7 +157,7 @@ class GraphMgr {
                               CancellationManager* cancellation_manager,
                               StatusCallback done);
 
-  // Don't attempt to process cost models unless explicitely requested for at
+  // Don't attempt to process cost models unless explicitly requested for at
   // least one of the items.
   bool skip_cost_models_ = true;
 

tensorflow/core/distributed_runtime/master.cc

@@ -25,7 +25,7 @@ limitations under the License.
 // A Master discovers remote devices on-demand and keeps track of
 // statistics of those remote devices.
 //
-// Each session analyses the graph, places nodes across available
+// Each session analyzes the graph, places nodes across available
 // devices, and ultimately drives the graph computation by initiating
 // RunGraph on the workers.
 

tensorflow/core/distributed_runtime/rpc/grpc_call.h

@@ -89,7 +89,7 @@ class UntypedCall : public core::RefCounted {
   virtual void RequestReceived(Service* service, bool ok) = 0;
 
   // This method will be called either (i) when the server is notified
-  // that the request has been cancelled, or (ii) when the request completes
+  // that the request has been canceled, or (ii) when the request completes
   // normally. The implementation should distinguish these cases by querying
   // the `grpc::ServerContext` associated with the request.
   virtual void RequestCancelled(Service* service, bool ok) = 0;
@@ -175,7 +175,7 @@ class Call : public UntypedCall<Service> {
   }
 
   // Registers `callback` as the function that should be called if and when this
-  // call is cancelled by the client.
+  // call is canceled by the client.
   void SetCancelCallback(std::function<void()> callback) {
     mutex_lock l(mu_);
     cancel_callback_ = std::move(callback);

tensorflow/core/distributed_runtime/rpc/grpc_master_service.cc

@@ -25,7 +25,7 @@ limitations under the License.
 // A GrpcMasterService discovers remote devices in the background and
 // keeps track of statistics of those remote devices.
 //
-// Each session analyses the graph, places nodes across available
+// Each session analyzes the graph, places nodes across available
 // devices, and ultimately drives the graph computation by initiating
 // RunGraph on workers.
 #include "tensorflow/core/distributed_runtime/rpc/grpc_master_service.h"

tensorflow/core/distributed_runtime/rpc/grpc_session_test.cc

@@ -517,7 +517,7 @@ TEST(GrpcSessionTest, Error) {
     //
     // Subgraph for "b" sleeps at the node "b_delay". When the sleep
     // finishes, the subgraph "b" will continue execution till it
-    // notices that it is cancelled. Meanwhile, subgraph's executor
+    // notices that it is canceled. Meanwhile, subgraph's executor
     // and its related state (registered ops) should still be alive.
     auto b = test::graph::Constant(&g, Tensor());
     b->set_assigned_device_name(dev_b);

tensorflow/core/distributed_runtime/worker_cache_logger.cc

@@ -35,7 +35,7 @@ void WorkerCacheLogger::SetLogging(bool v) {
     ++want_logging_count_;
   } else {
     --want_logging_count_;
-    // If RPCs get cancelled, it may be possible for the count
+    // If RPCs get canceled, it may be possible for the count
     // to go negative.  This should not be a fatal error, since
     // logging is non-critical.
     if (want_logging_count_ < 0) want_logging_count_ = 0;

tensorflow/core/framework/cancellation.h

@@ -36,7 +36,7 @@ namespace tensorflow {
 // CancellationManager::get_cancellation_token.
 typedef int64 CancellationToken;
 
-// A callback that is invoked when a step is cancelled.
+// A callback that is invoked when a step is canceled.
 //
 // NOTE(mrry): See caveats about CancelCallback implementations in the
 // comment for CancellationManager::RegisterCallback.

tensorflow/core/framework/function_test.cc

@@ -163,7 +163,7 @@ REGISTER_OP("HasDefaultType")
 
 // This verifies that a function using an op before a type attr (with
 // a default) is added, still works.  This is important for backwards
-// compatibilty.
+// compatibility.
 TEST(TFunc, MissingTypeAttr) {
   auto fdef = FDH::Create(
       // Name
@@ -1021,7 +1021,7 @@ TEST(FunctionLibraryDefinitionTest, AddLibrary) {
   EXPECT_EQ(s.error_message(),
             "Gradient for function 'XTimesTwo' already exists.");
 
-  // No conflicing functions or gradients OK
+  // No conflicting functions or gradients OK
   proto.Clear();
   *proto.add_function() = test::function::XTimesFour();
   grad.set_function_name(test::function::XTimes16().signature().name());

tensorflow/core/graph/graph_constructor.h

@@ -51,7 +51,7 @@ extern Status ConvertGraphDefToGraph(const GraphConstructorOptions& opts,
 // On error, returns non-OK and leaves *g unmodified.
 //
 // "shape_refiner" can be null. It should be non-null if the caller
-// intends to add additonal nodes to the graph after the import. This
+// intends to add additional nodes to the graph after the import. This
 // allows the caller to validate shapes of those nodes (since
 // ShapeRefiner::AddNode must be called in topological order).
 //

tensorflow/core/grappler/costs/analytical_cost_estimator.h

@@ -40,7 +40,7 @@ class AnalyticalCostEstimator : public CostEstimator {
   explicit AnalyticalCostEstimator(Cluster* cluster, bool use_static_shapes);
   ~AnalyticalCostEstimator() override {}
 
-  // Initalizes the estimator for the specified grappler item.
+  // Initializes the estimator for the specified grappler item.
   // This implementation always returns OK.
   Status Initialize(const GrapplerItem& item) override;
 

tensorflow/core/grappler/costs/cost_estimator.h

@@ -130,7 +130,7 @@ class CostEstimator {
  public:
   virtual ~CostEstimator() {}
 
-  // Initalizes the estimator for the specified grappler item.
+  // Initializes the estimator for the specified grappler item.
   // The estimator shouldn't be used if this function returns any status other
   // that OK.
   virtual Status Initialize(const GrapplerItem& item) = 0;

tensorflow/core/grappler/costs/measuring_cost_estimator.h

@@ -50,7 +50,7 @@ class MeasuringCostEstimator : public CostEstimator {
                                   int measurement_threads);
   ~MeasuringCostEstimator() override {}
 
-  // Initalizes the estimator for the specified grappler item.
+  // Initializes the estimator for the specified grappler item.
   // This implementation always returns OK.
   Status Initialize(const GrapplerItem& item) override;
 

tensorflow/core/grappler/costs/op_level_cost_estimator.h

@@ -36,7 +36,7 @@ class OpLevelCostEstimator {
 
  protected:
   // Returns an estimate of device performance (in billions of operations
-  // executed per second) and memory bandwith (in GigaBytes/second) for the
+  // executed per second) and memory bandwidth (in GigaBytes/second) for the
   // specified device.
   virtual std::pair<double, double> GetDeviceInfo(
       const DeviceProperties& device) const;

tensorflow/core/grappler/optimizers/model_pruner.cc

@@ -46,7 +46,7 @@ Status ModelPruner::Optimize(Cluster* cluster, const GrapplerItem& item,
     if (nodes_to_preserve.find(node.name()) != nodes_to_preserve.end()) {
       continue;
     }
-    // Don't remove nodes that are explicitely placed.
+    // Don't remove nodes that are explicitly placed.
     if (!node.device().empty()) {
       continue;
     }

tensorflow/core/grappler/optimizers/model_pruner.h

@@ -22,7 +22,7 @@ namespace tensorflow {
 namespace grappler {
 
 // Prune a model to make it more efficient:
-// * Remove unecessary operations.
+// * Remove unnecessary operations.
 // * Optimize gradient computations.
 class ModelPruner : public GraphOptimizer {
  public:

tensorflow/core/grappler/utils.h

@@ -34,7 +34,7 @@ class NodeMap {
   NodeDef* GetNode(const string& name);
   std::set<NodeDef*> GetOutputs(const string& node_name);
   // This method doesn't record the outputs of the added node; the outputs need
-  // to be explictly added by the AddOutput method.
+  // to be explicitly added by the AddOutput method.
   void AddNode(const string& name, NodeDef* node);
   void AddOutput(const string& node, const string& output);
   void UpdateOutput(const string& node, const string& old_output,

tensorflow/core/kernels/cast_op.h

@@ -50,7 +50,7 @@ template <typename From, typename To>
 struct scalar_cast_op<std::complex<From>, To> {
   EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE To
   operator()(const std::complex<From>& a) const {
-    // Replicate numpy behaviour of returning just the real part
+    // Replicate numpy behavior of returning just the real part
     return static_cast<To>(a.real());
   }
 };
@@ -59,7 +59,7 @@ template <typename From, typename To>
 struct scalar_cast_op<From, std::complex<To>> {
   EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::complex<To> operator()(
       const From& a) const {
-    // Replicate numpy behaviour of setting the imaginary part to 0
+    // Replicate numpy behavior of setting the imaginary part to 0
     return std::complex<To>(static_cast<To>(a), To(0));
   }
 };

tensorflow/core/kernels/conv_ops_fused.cc

@@ -713,7 +713,7 @@ class FusedResizeConv2DUsingGemmOp : public OpKernel {
       const int32 before =
           paddings_matrix(d, 0);  // Pad before existing elements.
       const int32 after =
-          paddings_matrix(d, 1);  // Pad after exisitng elements.
+          paddings_matrix(d, 1);  // Pad after existing elements.
       OP_REQUIRES(context, before >= 0 && after >= 0,
                   errors::InvalidArgument("paddings must be non-negative: ",
                                           before, " ", after));

tensorflow/core/kernels/cuda_solvers.h

@@ -116,7 +116,7 @@ class CudaSolver {
   // Launches a memcpy of solver status data specified by dev_lapack_info from
   // device to the host, and asynchronously invokes the given callback when the
   // copy is complete. The first Status argument to the callback will be
-  // Status::OK if all lapack infos retrived are zero, otherwise an error status
+  // Status::OK if all lapack infos retrieved are zero, otherwise an error status
   // is given. The second argument contains a host-side copy of the entire set
   // of infos retrieved, and can be used for generating detailed error messages.
   Status CopyLapackInfoToHostAsync(

tensorflow/core/kernels/deep_conv2d.cc

@@ -26,7 +26,7 @@ limitations under the License.
 
 namespace tensorflow {
 
-// DeepConv2D is a Conv2D implementation specialzied for deep convolutions (i.e
+// DeepConv2D is a Conv2D implementation specialized for deep convolutions (i.e
 // large 'in_depth' and 'out_depth' product. See cost models below for details).
 //
 // DeepConv2D is implemented by computing the following equation:

tensorflow/core/kernels/deep_conv2d.h

@@ -22,7 +22,7 @@ namespace tensorflow {
 
 class OpKernelContext;
 
-// DeepConv2D is a Conv2D implementation specialzied for deep (i.e. large
+// DeepConv2D is a Conv2D implementation specialized for deep (i.e. large
 // in_depth * out_depth product) convolutions (see deep_conv2d.cc for details).
 
 // DeepConv2DTransform is an interface for implementing transforms for

tensorflow/core/kernels/hinge-loss.h

@@ -44,7 +44,7 @@ class HingeLossUpdater : public DualLossUpdater {
                             const double current_dual, const double wx,
                             const double weighted_example_norm) const final {
     // Intutitvely there are 3 cases:
-    // a. new optimal value of the dual variable falls withing the admissible
+    // a. new optimal value of the dual variable falls within the admissible
     // range [0, 1]. In this case we set new dual to this value.
     // b. new optimal value is < 0. Then, because of convexity, the optimal
     // valid value for new dual = 0

tensorflow/core/kernels/image_resizer_state.h

@@ -13,7 +13,7 @@ See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
 
-// This is a helper struct to package up the input and ouput
+// This is a helper struct to package up the input and output
 // parameters of an image resizer (the height, widths, etc.).  To
 // reduce code duplication and ensure consistency across the different
 // resizers, it performs the input validation.

tensorflow/core/kernels/map_stage_op.cc

@@ -238,7 +238,7 @@ private:
     {
       IncompleteTuple empty(dtypes_.size());
 
-      // Initialise empty tuple with given dta
+      // Initialize empty tuple with given dta
       for(std::size_t i = 0; i < findices.dimension(0); ++i)
       {
         std::size_t index = findices(i);

tensorflow/core/kernels/meta_support.h

@@ -64,7 +64,7 @@ bool IsSupportedAndEnabled();
 //     sum((a_data[i, l] + offset_a) * (b_data[l, j] + offset_b)) : l in [0, k)
 //
 // If transpose_a is false the lhs operand has row major layout, otherwise
-// column major. Similarily transpose_b describes the layout of the rhs operand.
+// column major. Similarly transpose_b describes the layout of the rhs operand.
 // lda, ldb, and ldc are the strides of the lhs operand, rhs operand and the
 // result arrays.
 void QuantizedGemm(OpKernelContext* context, bool transpose_a, bool transpose_b,

tensorflow/core/kernels/mkl_conv_grad_input_ops.cc

@@ -295,7 +295,7 @@ class MklConv2DCustomBackpropInputOp : public OpKernel {
         dnnDelete_F32(mkl_convert_filter);
       } else {
         // If we do not need any layout conversion for filter, then
-        // we direclty assign input filter to resources[].
+        // we directly assign input filter to resources[].
         conv_res[dnnResourceFilter] =
             static_cast<void*>(const_cast<T*>(filter.flat<T>().data()));
       }

tensorflow/core/kernels/padded_batch_dataset_op.cc

@@ -306,7 +306,7 @@ class PaddedBatchDatasetOp : public OpKernel {
             const TensorShape& element_shape =
                 batch_elements[i][component_index].shape();
             // TODO(mrry): Perform this check in the shape function if
-            // enough static information is avaiable to do so.
+            // enough static information is available to do so.
             if (element_shape.dims() != padded_shape.dims()) {
               return errors::InvalidArgument(
                   "All elements in a batch must have the same rank as the "

tensorflow/core/kernels/quantization_utils.h

@@ -80,7 +80,7 @@ float QuantizedToFloat(T input, float range_min, float range_max) {
       static_cast<int64>(Eigen::NumTraits<T>::lowest());
   const double offset_input = static_cast<double>(input) - lowest_quantized;
   // For compatibility with DEQUANTIZE_WITH_EIGEN, we should convert
-  // range_scale to a float, otherwise range_min_rounded might be slighly
+  // range_scale to a float, otherwise range_min_rounded might be slightly
   // different.
   const double range_min_rounded =
       round(range_min / static_cast<float>(range_scale)) *

tensorflow/core/kernels/smooth-hinge-loss.h

@@ -35,7 +35,7 @@ class SmoothHingeLossUpdater : public DualLossUpdater {
                             const double current_dual, const double wx,
                             const double weighted_example_norm) const final {
     // Intutitvely there are 3 cases:
-    // a. new optimal value of the dual variable falls withing the admissible
+    // a. new optimal value of the dual variable falls within the admissible
     // range [0, 1]. In this case we set new dual to this value.
     // b. new optimal value is < 0. Then, because of convexity, the optimal
     // valid value for new dual = 0

tensorflow/core/kernels/sparse_tensor_dense_add_op.h

@@ -24,7 +24,7 @@ limitations under the License.
 namespace tensorflow {
 namespace functor {
 
-// TOOD(zongheng): this should be a general functor that powers SparseAdd and
+// TODO(zongheng): this should be a general functor that powers SparseAdd and
 // ScatterNd ops.  It should be moved to its own head file, once the other ops
 // are implemented.
 template <typename Device, typename T, typename Index, int NDIMS,

tensorflow/core/lib/gtl/optional.h

@@ -541,7 +541,7 @@ class optional : private internal_optional::optional_data<T>,
   // opt.emplace(arg1,arg2,arg3);  (Constructs Foo(arg1,arg2,arg3))
   //
   // If the optional is non-empty, and the `args` refer to subobjects of the
-  // current object, then behaviour is undefined.  This is because the current
+  // current object, then behavior is undefined.  This is because the current
   // object will be destructed before the new object is constructed with `args`.
   //
   template <typename... Args,
@@ -586,7 +586,7 @@ class optional : private internal_optional::optional_data<T>,
 
   // [optional.observe], observers
   // You may use `*opt`, and `opt->m`, to access the underlying T value and T's
-  // member `m`, respectively.  If the optional is empty, behaviour is
+  // member `m`, respectively.  If the optional is empty, behavior is
   // undefined.
   constexpr const T* operator->() const { return this->pointer(); }
   T* operator->() {