contrib: minor spelling tweaks (#18330)

* contrib: minor spelling tweaks

* Fix lint error

9 files changed, 16 insertions, 16 deletions

diff --git a/tensorflow/contrib/estimator/python/estimator/replicate_model_fn.py b/tensorflow/contrib/estimator/python/estimator/replicate_model_fn.py
index fa2697800e..a8774d6dab 100644
--- a/tensorflow/contrib/estimator/python/estimator/replicate_model_fn.py
+++ b/tensorflow/contrib/estimator/python/estimator/replicate_model_fn.py
@@ -456,7 +456,7 @@ def _get_local_devices(device_type):
 
 
 def _split_batch(features, labels, number_of_shards, device):
-  """Split input features and labes into batches."""
+  """Split input features and labels into batches."""
 
   def ensure_divisible_by_shards(sequence):
     batch_size = ops_lib.convert_to_tensor(sequence).get_shape()[0]
@@ -602,7 +602,7 @@ def _local_device_setter(worker_device, ps_devices, ps_strategy):
 
 
 def _scale_tower_loss(tower_spec, loss_reduction, number_of_towers):
-  """Produce an EstimatorSpec with approproriately scaled loss."""
+  """Produce an EstimatorSpec with appropriately scaled loss."""
   if tower_spec.loss is None:
     return tower_spec
 
diff --git a/tensorflow/contrib/fused_conv/python/ops/fused_conv2d_bias_activation_op.py b/tensorflow/contrib/fused_conv/python/ops/fused_conv2d_bias_activation_op.py
index a97adf622e..983b6dc8e5 100644
--- a/tensorflow/contrib/fused_conv/python/ops/fused_conv2d_bias_activation_op.py
+++ b/tensorflow/contrib/fused_conv/python/ops/fused_conv2d_bias_activation_op.py
@@ -65,7 +65,7 @@ def fused_conv2d_bias_activation(conv_input,
     side_input_scale: A scalar `float32` that will be multiplied by side_input.
         This is optional and defaults to 0.
     side_input: A `Tensor` of the format specified by `data_format`.
-        This is useful for imlementing ResNet blocks.
+        This is useful for implementing ResNet blocks.
     activation_mode: (optional) currently must be the default "Relu".
         Note that in qint8 mode, it also clips to 127, so acts like ReluX.
     data_format: Specifies the data format.
diff --git a/tensorflow/contrib/fused_conv/python/ops/fused_conv2d_bias_activation_op_test.py b/tensorflow/contrib/fused_conv/python/ops/fused_conv2d_bias_activation_op_test.py
index bb155aa249..3d0ed89932 100644
--- a/tensorflow/contrib/fused_conv/python/ops/fused_conv2d_bias_activation_op_test.py
+++ b/tensorflow/contrib/fused_conv/python/ops/fused_conv2d_bias_activation_op_test.py
@@ -566,7 +566,7 @@ def GetInceptionFwdTest(input_size, filter_size, stride, padding,
   return Test
 
 
-def CalculateCovolvedOutputDim(input_dim, filter_dim, stride, padding_type):
+def CalculateConvolvedOutputDim(input_dim, filter_dim, stride, padding_type):
   """Calculates the size of an output dimension of a strided convolution.
 
   Given the sizes of the corresponding dimension of the input and filter shapes,
@@ -827,10 +827,10 @@ class FusedConvInt8Tests(test.TestCase):
             maxval=1.0,
             dtype=dtypes.float32), -1.0, 1.0, dtypes.qint8)
 
-    output_height = CalculateCovolvedOutputDim(input_height, filter_height,
-                                               vertical_stride, padding_type)
-    output_width = CalculateCovolvedOutputDim(input_width, filter_width,
-                                              horizontal_stride, padding_type)
+    output_height = CalculateConvolvedOutputDim(input_height, filter_height,
+                                                vertical_stride, padding_type)
+    output_width = CalculateConvolvedOutputDim(input_width, filter_width,
+                                               horizontal_stride, padding_type)
     print("output_height=", output_height, ", output_width=", output_width)
 
     side_input, _, _ = gen_array_ops.quantize_v2(
diff --git a/tensorflow/contrib/layers/python/kernel_tests/sparse_feature_cross_op_test.py b/tensorflow/contrib/layers/python/kernel_tests/sparse_feature_cross_op_test.py
index f701647c2b..28ddaa69a1 100644
--- a/tensorflow/contrib/layers/python/kernel_tests/sparse_feature_cross_op_test.py
+++ b/tensorflow/contrib/layers/python/kernel_tests/sparse_feature_cross_op_test.py
@@ -200,7 +200,7 @@ class SparseCrossOpTest(test.TestCase):
       self._assert_sparse_tensor_equals(expected_out, sess.run(op))
 
   def test_large_batch(self):
-    """Tests with large batch size to force multithreding.
+    """Tests with large batch size to force multithreading.
     """
     batch_size = 5000
     col1 = []
diff --git a/tensorflow/contrib/layers/python/layers/feature_column.py b/tensorflow/contrib/layers/python/layers/feature_column.py
index 9ccb589d69..3ae07cedab 100644
--- a/tensorflow/contrib/layers/python/layers/feature_column.py
+++ b/tensorflow/contrib/layers/python/layers/feature_column.py
@@ -48,7 +48,7 @@ you should choose depends on (1) the feature type and (2) the model type.
    recommended.
 
      embedded_dept_column = embedding_column(
-       sparse_column_with_keys("department", ["math", "philosphy", ...]),
+       sparse_column_with_keys("department", ["math", "philosophy", ...]),
        dimension=10)
 
 * Wide (aka linear) models (`LinearClassifier`, `LinearRegressor`).
diff --git a/tensorflow/contrib/layers/python/layers/feature_column_ops.py b/tensorflow/contrib/layers/python/layers/feature_column_ops.py
index 78affea44c..06060b99e7 100644
--- a/tensorflow/contrib/layers/python/layers/feature_column_ops.py
+++ b/tensorflow/contrib/layers/python/layers/feature_column_ops.py
@@ -815,7 +815,7 @@ class _Transformer(object):
   """
 
   def __init__(self, columns_to_tensors):
-    """Initializes transfomer.
+    """Initializes transformer.
 
     Args:
       columns_to_tensors: A mapping from feature columns to tensors. 'string'
@@ -908,7 +908,7 @@ def _gather_feature_columns(feature_columns):
 
 
 def _check_forbidden_sequence_columns(feature_columns):
-  """Recursively cecks `feature_columns` for `_FORBIDDEN_SEQUENCE_COLUMNS`."""
+  """Recursively checks `feature_columns` for `_FORBIDDEN_SEQUENCE_COLUMNS`."""
   all_feature_columns = _gather_feature_columns(feature_columns)
   for feature_column in all_feature_columns:
     if isinstance(feature_column, _FORBIDDEN_SEQUENCE_COLUMNS):
diff --git a/tensorflow/contrib/layers/python/layers/layers.py b/tensorflow/contrib/layers/python/layers/layers.py
index 949e73deff..151fc7a0d7 100644
--- a/tensorflow/contrib/layers/python/layers/layers.py
+++ b/tensorflow/contrib/layers/python/layers/layers.py
@@ -1542,7 +1542,7 @@ def dense_to_sparse(tensor, eos_token=0, outputs_collections=None, scope=None):
   Args:
      tensor: An `int` `Tensor` to be converted to a `Sparse`.
      eos_token: An integer.
-       It is part of the target label that signfies the end of a sentence.
+       It is part of the target label that signifies the end of a sentence.
      outputs_collections: Collection to add the outputs.
      scope: Optional scope for name_scope.
   """
@@ -1686,7 +1686,7 @@ def _inner_flatten(inputs, new_rank, output_collections=None, scope=None):
     output_collections: Collection to which the outputs will be added.
     scope: Optional scope for `name_scope`.
   Returns:
-    A `Tensor` or `SparseTensor` conataining the same values as `inputs`, but
+    A `Tensor` or `SparseTensor` containing the same values as `inputs`, but
     with innermost dimensions flattened to obtain rank `new_rank`.
 
   Raises:
diff --git a/tensorflow/contrib/meta_graph_transform/meta_graph_transform.py b/tensorflow/contrib/meta_graph_transform/meta_graph_transform.py
index ff88b4fa84..4fe4e8d044 100644
--- a/tensorflow/contrib/meta_graph_transform/meta_graph_transform.py
+++ b/tensorflow/contrib/meta_graph_transform/meta_graph_transform.py
@@ -348,7 +348,7 @@ def _freeze_graph_with_def_protos(input_graph_def, output_node_names,
                                   input_saver_def, input_checkpoint):
   """Converts all variables in a graph and checkpoint into constants.
 
-  During this process, we need to retain certain initialzer nodes (e.g. table
+  During this process, we need to retain certain initializer nodes (e.g. table
   initializer nodes). Instead of determining which dependencies
   of the shared initializer node (e.g. group_deps) to keep, we
   reconstruct the connections between the individual initializer nodes and
diff --git a/tensorflow/contrib/optimizer_v2/optimizer_v2.py b/tensorflow/contrib/optimizer_v2/optimizer_v2.py
index 25d19578ea..ce15db6f1e 100644
--- a/tensorflow/contrib/optimizer_v2/optimizer_v2.py
+++ b/tensorflow/contrib/optimizer_v2/optimizer_v2.py
@@ -579,7 +579,7 @@ class OptimizerV2(optimizer_v1.Optimizer):
 
   ### State
 
-  Internal methods apre passed a `state` argument with the correct
+  Internal methods are passed a `state` argument with the correct
   values to use for the slot and non-slot variables, and the hyper
   parameters.
   """