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-rw-r--r--tensorflow/contrib/tpu/python/tpu/tpu_estimator.py12
1 files changed, 6 insertions, 6 deletions
diff --git a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py
index f61f6bb52e..4354735744 100644
--- a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py
+++ b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py
@@ -137,7 +137,7 @@ def _increase_eval_step_op(iterations_per_loop):
"""Returns an op to increase the eval step for TPU evaluation.
Args:
- iterations_per_loop: Tensor. The number of eval steps runnining in TPU
+ iterations_per_loop: Tensor. The number of eval steps running in TPU
system before returning to CPU host for each `Session.run`.
Returns:
@@ -609,17 +609,17 @@ class _StoppingPredictHook(session_run_hook.SessionRunHook):
# batch. And we append one more batch to signal the system it should stop.
# The data flow might look like
#
- # batch 0: images, labels, stop = 0 (user provideded)
- # batch 1: images, labels, stop = 0 (user provideded)
+ # batch 0: images, labels, stop = 0 (user provided)
+ # batch 1: images, labels, stop = 0 (user provided)
# ...
- # batch 99: images, labels, stop = 0 (user provideded)
+ # batch 99: images, labels, stop = 0 (user provided)
# batch 100: images, labels, stop = 1 (TPUEstimator appended)
#
# where the final batch (id = 100) is appended by TPUEstimator, so we
# should drop it before returning the predictions to user.
# To achieve that, we throw the OutOfRangeError in after_run. Once
# Monitored Session sees this error in SessionRunHook.after_run, the
- # "current" prediciton, i.e., batch with id=100, will be discarded
+ # "current" prediction, i.e., batch with id=100, will be discarded
# immediately
raise errors.OutOfRangeError(None, None, 'Stopped by stopping signal.')
@@ -758,7 +758,7 @@ class _InputPipeline(object):
2. (features, labels)
Internally, form 1 is reformed to `(features, None)` as features and labels
- are passed separatedly to underlying methods. For TPU training, TPUEstimator
+ are passed separately to underlying methods. For TPU training, TPUEstimator
may expect multiple `features` and `labels` tuples one for each core.
TPUEstimator allows various different structures for inputs (namely `features`