Fix trivial typos in source codes / comments

tensorflow/contrib/framework/python/framework/tensor_util_test.py

@@ -241,7 +241,7 @@ def test_empty_tensor_doesnt_raise(self):
       out.eval()
 
 
-class AssertPostiveTest(tf.test.TestCase):
+class AssertPositiveTest(tf.test.TestCase):
 
   def test_raises_when_negative(self):
     with self.test_session():

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

@@ -404,7 +404,7 @@ def save(self, path):
         # Save graph definition.
         _write_with_backup(os.path.join(path, 'graph.pbtxt'), str(self._graph.as_graph_def()))
 
-        # Save saver defintion.
+        # Save saver definition.
         _write_with_backup(os.path.join(path, 'saver.pbtxt'), str(self._saver.as_saver_def()))
 
         # Save checkpoints.
@@ -441,7 +441,7 @@ def _restore(self, path):
                      graph_def, name='', return_elements=endpoints)
             saver_filename = os.path.join(path, 'saver.pbtxt')
             if not os.path.exists(saver_filename):
-                raise ValueError("Restore folder doesn't contain saver defintion.")
+                raise ValueError("Restore folder doesn't contain saver definition.")
             with gfile.Open(saver_filename) as fsaver:
                 saver_def = train.SaverDef()
                 text_format.Merge(fsaver.read(), saver_def)

tensorflow/contrib/learn/python/learn/io/data_feeder.py

@@ -39,7 +39,7 @@ def _get_in_out_shape(x_shape, y_shape, n_classes, batch_size):
     if y_shape is None:
         return input_shape, None
     y_shape = list(y_shape[1:]) if len(y_shape) > 1 else []
-    # Skip first dimention if it is 1.
+    # Skip first dimension if it is 1.
     if y_shape and y_shape[0] == 1:
         y_shape = y_shape[1:]
     if n_classes > 1:

tensorflow/examples/skflow/text_classification.py

@@ -80,7 +80,7 @@ def rnn_model(X, y):
 classifier = skflow.TensorFlowEstimator(model_fn=rnn_model, n_classes=15,
     steps=1000, optimizer='Adam', learning_rate=0.01, continue_training=True)
 
-# Continously train for 1000 steps & predict on test set.
+# Continuously train for 1000 steps & predict on test set.
 while True:
     classifier.fit(X_train, y_train, logdir='/tmp/tf_examples/word_rnn')
     score = metrics.accuracy_score(y_test, classifier.predict(X_test))

tensorflow/python/summary/event_accumulator.py

@@ -519,11 +519,11 @@ def _Purge(self, event, by_tags):
 
     If by_tags is True, purge all events that occurred after the given
     event.step, but only for the tags that the event has. Non-sequential
-    event.steps suggest that a Tensorflow restart occured, and we discard
+    event.steps suggest that a Tensorflow restart occurred, and we discard
     the out-of-order events to display a consistent view in TensorBoard.
 
     Discarding by tags is the safer method, when we are unsure whether a restart
-    has occured, given that threading in supervisor can cause events of
+    has occurred, given that threading in supervisor can cause events of
     different tags to arrive with unsynchronized step values.
 
     If by_tags is False, then purge all events with event.step greater than the

tensorflow/python/training/sync_replicas_optimizer.py

@@ -503,8 +503,8 @@ def get_clean_up_op(self):
   def get_init_tokens_op(self, num_tokens=-1):
     """Returns the op to fill the sync_token_queue with the tokens.
 
-    This is supposed to be executed in the begining of the chief/sync thread
-    so that even if the total_num_replicas is less than replicas_to_agregate,
+    This is supposed to be executed in the beginning of the chief/sync thread
+    so that even if the total_num_replicas is less than replicas_to_aggregate,
     the model can still proceed as the replicas can compute multiple steps per
     variable update. Make sure:
     `num_tokens >= replicas_to_aggregate - total_num_replicas`.