contrib: minor spelling tweaks

packages:
  data
  training
  tensor_forest

3 files changed, 6 insertions, 6 deletions

diff --git a/tensorflow/contrib/training/python/training/resample.py b/tensorflow/contrib/training/python/training/resample.py
index b16159bc16..7b8332b1d6 100644
--- a/tensorflow/contrib/training/python/training/resample.py
+++ b/tensorflow/contrib/training/python/training/resample.py
@@ -77,7 +77,7 @@ def resample_at_rate(inputs, rates, scope=None, seed=None, back_prop=False):
 
   Args:
     inputs: A list of tensors, each of which has a shape of `[batch_size, ...]`
-    rates: A tensor of shape `[batch_size]` contiaining the resampling rates
+    rates: A tensor of shape `[batch_size]` containing the resampling rates
        for each input.
     scope: Scope for the op.
     seed: Random seed to use.
diff --git a/tensorflow/contrib/training/python/training/sampling_ops.py b/tensorflow/contrib/training/python/training/sampling_ops.py
index ba888f87dc..7140f2a46d 100644
--- a/tensorflow/contrib/training/python/training/sampling_ops.py
+++ b/tensorflow/contrib/training/python/training/sampling_ops.py
@@ -123,7 +123,7 @@ def rejection_sample(tensors,
         batch_size=batch_size,
         num_threads=queue_threads)
 
-    # Queues return a single tensor if the list of enqued tensors is one. Since
+    # Queues return a single tensor if the list of enqueued tensors is one. Since
     # we want the type to always be the same, always return a list.
     if isinstance(minibatch, ops.Tensor):
       minibatch = [minibatch]
@@ -312,7 +312,7 @@ def _verify_input(tensor_list, labels, probs_list):
   """Verify that batched inputs are well-formed."""
   checked_probs_list = []
   for probs in probs_list:
-    # Since number of classes shouldn't change at runtime, probalities shape
+    # Since number of classes shouldn't change at runtime, probabilities shape
     # should be fully defined.
     probs.get_shape().assert_is_fully_defined()
 
@@ -407,7 +407,7 @@ def _calculate_acceptance_probabilities(init_probs, target_probs):
   ```
 
 
-  A solution for a_i in terms of the other variabes is the following:
+  A solution for a_i in terms of the other variables is the following:
     ```a_i = (t_i / p_i) / max_i[t_i / p_i]```
   """
   # Make list of t_i / p_i.
diff --git a/tensorflow/contrib/training/python/training/sequence_queueing_state_saver.py b/tensorflow/contrib/training/python/training/sequence_queueing_state_saver.py
index 99d486b183..39d75a0806 100644
--- a/tensorflow/contrib/training/python/training/sequence_queueing_state_saver.py
+++ b/tensorflow/contrib/training/python/training/sequence_queueing_state_saver.py
@@ -876,7 +876,7 @@ class SequenceQueueingStateSaver(object):
         ]):
           self._length = array_ops.identity(self._length)
 
-        # Only create barrier; enqueu and dequeue operations happen when you
+        # Only create barrier; enqueue and dequeue operations happen when you
         # access prefetch_op and next_batch.
         self._create_barrier()
         self._scope = scope
@@ -1637,7 +1637,7 @@ def _move_sparse_tensor_out_context(input_context, input_sequences, num_unroll):
 
   For `key, value` pairs in `input_context` with `SparseTensor` `value` removes
   them from `input_context` and transforms the `value` into a sequence and
-  then adding `key`, transformed `value` into `input_seuqences`.
+  then adding `key`, transformed `value` into `input_sequences`.
   The transformation is done by adding a new first dimension of `value_length`
   equal to that of the other values in input_sequences` and tiling the `value`
   every `num_unroll` steps.