Merge pull request tensorflow#10627 from taehoonlee/fix_typos_5

Fix typos

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

@@ -3260,7 +3260,7 @@ def conv2d(x,
       padding: string, `"same"` or `"valid"`.
       data_format: `"channels_last"` or `"channels_first"`.
           Whether to use Theano or TensorFlow data format
-          for inputs/kernels/ouputs.
+          for inputs/kernels/outputs.
       dilation_rate: tuple of 2 integers.
 
   Returns:
@@ -3308,7 +3308,7 @@ def conv2d_transpose(x,
       padding: string, `"same"` or `"valid"`.
       data_format: `"channels_last"` or `"channels_first"`.
           Whether to use Theano or TensorFlow data format
-          for inputs/kernels/ouputs.
+          for inputs/kernels/outputs.
 
   Returns:
       A tensor, result of transposed 2D convolution.
@@ -3394,7 +3394,7 @@ def conv3d(x,
       padding: string, `"same"` or `"valid"`.
       data_format: `"channels_last"` or `"channels_first"`.
           Whether to use Theano or TensorFlow data format
-          for inputs/kernels/ouputs.
+          for inputs/kernels/outputs.
       dilation_rate: tuple of 3 integers.
 
   Returns:

tensorflow/contrib/keras/python/keras/models_test.py

@@ -105,7 +105,7 @@ def custom_loss(y_true, y_pred):
       out2 = model.predict(x)
       self.assertAllClose(out, out2, atol=1e-05)
 
-  def test_fuctional_model_saving(self):
+  def test_functional_model_saving(self):
     if h5py is None:
       return  # Skip test if models cannot be saved.
 

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

@@ -729,7 +729,7 @@ def input_fn():
     self.assertLess(loss, 0.07)
 
   def testSdcaOptimizerRealValuedFeatures(self):
-    """Tests LinearClasssifier with SDCAOptimizer and real valued features."""
+    """Tests LinearClassifier with SDCAOptimizer and real valued features."""
 
     def input_fn():
       return {
@@ -776,7 +776,7 @@ def input_fn():
     self.assertLess(loss, 0.05)
 
   def testSdcaOptimizerBucketizedFeatures(self):
-    """Tests LinearClasssifier with SDCAOptimizer and bucketized features."""
+    """Tests LinearClassifier with SDCAOptimizer and bucketized features."""
 
     def input_fn():
       return {
@@ -802,7 +802,7 @@ def input_fn():
     self.assertGreater(scores['accuracy'], 0.9)
 
   def testSdcaOptimizerSparseFeatures(self):
-    """Tests LinearClasssifier with SDCAOptimizer and sparse features."""
+    """Tests LinearClassifier with SDCAOptimizer and sparse features."""
 
     def input_fn():
       return {
@@ -833,7 +833,7 @@ def input_fn():
     self.assertGreater(scores['accuracy'], 0.9)
 
   def testSdcaOptimizerWeightedSparseFeatures(self):
-    """LinearClasssifier with SDCAOptimizer and weighted sparse features."""
+    """LinearClassifier with SDCAOptimizer and weighted sparse features."""
 
     def input_fn():
       return {
@@ -864,7 +864,7 @@ def input_fn():
     self.assertGreater(scores['accuracy'], 0.9)
 
   def testSdcaOptimizerCrossedFeatures(self):
-    """Tests LinearClasssifier with SDCAOptimizer and crossed features."""
+    """Tests LinearClassifier with SDCAOptimizer and crossed features."""
 
     def input_fn():
       return {
@@ -897,7 +897,7 @@ def input_fn():
     self.assertGreater(scores['accuracy'], 0.9)
 
   def testSdcaOptimizerMixedFeatures(self):
-    """Tests LinearClasssifier with SDCAOptimizer and a mix of features."""
+    """Tests LinearClassifier with SDCAOptimizer and a mix of features."""
 
     def input_fn():
       return {
@@ -1509,7 +1509,7 @@ def input_fn():
     self.assertLess(loss, 0.05)
 
   def testSdcaOptimizerSparseFeaturesWithL1Reg(self):
-    """Tests LinearClasssifier with SDCAOptimizer and sparse features."""
+    """Tests LinearClassifier with SDCAOptimizer and sparse features."""
 
     def input_fn():
       return {
@@ -1581,7 +1581,7 @@ def input_fn():
     self.assertLess(l1_reg_weights_norm, no_l1_reg_weights_norm)
 
   def testSdcaOptimizerBiasOnly(self):
-    """Tests LinearClasssifier with SDCAOptimizer and validates bias weight."""
+    """Tests LinearClassifier with SDCAOptimizer and validates bias weight."""
 
     def input_fn():
       """Testing the bias weight when it's the only feature present.
@@ -1614,7 +1614,7 @@ def input_fn():
         regressor.get_variable_value('linear/bias_weight')[0], 0.25, err=0.1)
 
   def testSdcaOptimizerBiasAndOtherColumns(self):
-    """Tests LinearClasssifier with SDCAOptimizer and validates bias weight."""
+    """Tests LinearClassifier with SDCAOptimizer and validates bias weight."""
 
     def input_fn():
       """Testing the bias weight when there are other features present.
@@ -1676,7 +1676,7 @@ def input_fn():
         regressor.get_variable_value('linear/b/weight')[0], 0.0, err=0.05)
 
   def testSdcaOptimizerBiasAndOtherColumnsFabricatedCentered(self):
-    """Tests LinearClasssifier with SDCAOptimizer and validates bias weight."""
+    """Tests LinearClassifier with SDCAOptimizer and validates bias weight."""
 
     def input_fn():
       """Testing the bias weight when there are other features present.

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

@@ -123,7 +123,7 @@ def testEstimatorSpec_export_regression_with_probabilities(self):
       self.assertAllEqual(predictions["probabilities"].eval(),
                           regression_output.value.eval())
 
-  def testEstimatorSpec_export_classsification(self):
+  def testEstimatorSpec_export_classification(self):
     predictions = self.create_predictions()
     output_alternatives = {"classification_head": (
         constants.ProblemType.CLASSIFICATION, predictions)}
@@ -143,7 +143,7 @@ def testEstimatorSpec_export_classsification(self):
       self.assertAllEqual(predictions["classes"].eval(),
                           classification_output.classes.eval())
 
-  def testEstimatorSpec_export_classsification_with_missing_scores(self):
+  def testEstimatorSpec_export_classification_with_missing_scores(self):
     predictions = self.create_predictions()
     output_alternatives_predictions = predictions.copy()
     del output_alternatives_predictions["scores"]
@@ -165,7 +165,7 @@ def testEstimatorSpec_export_classsification_with_missing_scores(self):
       self.assertAllEqual(predictions["classes"].eval(),
                           classification_output.classes.eval())
 
-  def testEstimatorSpec_export_classsification_with_missing_scores_proba(self):
+  def testEstimatorSpec_export_classification_with_missing_scores_proba(self):
     predictions = self.create_predictions()
     output_alternatives_predictions = predictions.copy()
     del output_alternatives_predictions["scores"]
@@ -187,7 +187,7 @@ def testEstimatorSpec_export_classsification_with_missing_scores_proba(self):
       self.assertAllEqual(predictions["classes"].eval(),
                           classification_output.classes.eval())
 
-  def testEstimatorSpec_export_classsification_with_missing_classes(self):
+  def testEstimatorSpec_export_classification_with_missing_classes(self):
     predictions = self.create_predictions()
     output_alternatives_predictions = predictions.copy()
     del output_alternatives_predictions["classes"]
@@ -208,7 +208,7 @@ def testEstimatorSpec_export_classsification_with_missing_classes(self):
                           classification_output.scores.eval())
       self.assertIsNone(classification_output.classes)
 
-  def testEstimatorSpec_export_classsification_with_nonstring_classes(self):
+  def testEstimatorSpec_export_classification_with_nonstring_classes(self):
     predictions = self.create_predictions()
     output_alternatives_predictions = predictions.copy()
     output_alternatives_predictions["classes"] = constant_op.constant(

tensorflow/core/kernels/priority_queue.cc

@@ -338,7 +338,7 @@ void PriorityQueue::TryDequeueMany(int num_elements, OpKernelContext* ctx,
             for (; s > 0; --s) {
               if (attempt->tuple.empty()) {
                 // Only allocate tuple when we have something to dequeue
-                // so we don't use exceessive memory when there are many
+                // so we don't use excessive memory when there are many
                 // blocked dequeue attempts waiting.
                 attempt->tuple.reserve(num_components());
                 for (int i = 0; i < num_components(); ++i) {

tensorflow/core/kernels/typed_conditional_accumulator_base.h

@@ -22,7 +22,7 @@ namespace tensorflow {
 
 /*
  * TypedConditionalAccumulatorBase is a templated companion of
- * ConditionalAccumulatorBase which allows for subclassses to use different
+ * ConditionalAccumulatorBase which allows for subclasses to use different
  * types for the input gradients. (See ConditionalAccumulator and
  * SparseConditionalAccumulator.)
  *

tensorflow/python/debug/wrappers/framework_test.py

@@ -215,7 +215,7 @@ def testSessionInit(self):
       wrapper_sess.partial_run_setup(self._p)
 
   def testInteractiveSessionInit(self):
-    """The wrapper should work also on other subclassses of session.Session."""
+    """The wrapper should work also on other subclasses of session.Session."""
 
     TestDebugWrapperSession(
         session.InteractiveSession(), self._dump_root, self._observer)

tensorflow/python/estimator/canned/dnn_linear_combined.py

@@ -374,7 +374,7 @@ def _model_fn(features, labels, mode, config):
 
 
 class DNNLinearCombinedRegressor(estimator.Estimator):
-  """An estimator for TensorFlow Linear and DNN joined models for regresssion.
+  """An estimator for TensorFlow Linear and DNN joined models for regression.
 
   Note: This estimator is also known as wide-n-deep.
 

tensorflow/tensorboard/components/tf_graph_common/graph.ts

@@ -723,7 +723,7 @@ export class MetaedgeImpl implements Metaedge {
       number {
     let opNode = <OpNode> h.node(edge.v);
     if (opNode.outputShapes == null) {
-      // No shape information. Asssume a single number. This gives
+      // No shape information. Assume a single number. This gives
       // a lower bound for the total size.
       return 1;
     }

tensorflow/tools/tfprof/g3doc/command_line.md

@@ -126,7 +126,7 @@ tfprof>
 -show_name_regexes          .*
 -hide_name_regexes          IsVariableInitialized_[0-9]+,save\/.*,^zeros[0-9_]*
 -account_displayed_op_only  false
-# supported select fileds. Availability depends on --[run_meta|checkpoint|op_log]_path.
+# supported select fields. Availability depends on --[run_meta|checkpoint|op_log]_path.
 # [bytes|micros|params|float_ops|occurrence|tensor_value|device|op_types]
 -select                     params
 # format: output_type:key=value,key=value...