Link docstrings to module guides and remove redundant text.

Change: 147402322

1 files changed, 1 insertions, 37 deletions

diff --git a/tensorflow/contrib/training/__init__.py b/tensorflow/contrib/training/__init__.py
index ed975b2913..245f36c2fe 100644
--- a/tensorflow/contrib/training/__init__.py
+++ b/tensorflow/contrib/training/__init__.py
@@ -12,51 +12,15 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Training and input utilities.
-
-## Splitting sequence inputs into minibatches with state saving
-
-Use [`SequenceQueueingStateSaver`](#SequenceQueueingStateSaver) or
-its wrapper [`batch_sequences_with_states`](#batch_sequences_with_states) if
-you have input data with a dynamic primary time / frame count axis which
-you'd like to convert into fixed size segments during minibatching, and would
-like to store state in the forward direction across segments of an example.
+"""Training and input utilities. See @{$python/contrib.training} guide.
 
 @@batch_sequences_with_states
 @@NextQueuedSequenceBatch
 @@SequenceQueueingStateSaver
-
-
-## Online data resampling
-
-To resample data with replacement on a per-example basis, use
-['rejection_sample'](#rejection_sample) or
-['resample_at_rate'](#resample_at_rate). For `rejection_sample`, provide
-a boolean Tensor describing whether to accept or reject. Resulting batch sizes
-are always the same. For `resample_at_rate`, provide the desired rate for each
-example. Resulting batch sizes may vary. If you wish to specify relative
-rates, rather than absolute ones, use ['weighted_resample'](#weighted_resample)
-(which also returns the actual resampling rate used for each output example).
-
-Use ['stratified_sample'](#stratified_sample) to resample without replacement
-from the data to achieve a desired mix of class proportions that the Tensorflow
-graph sees. For instance, if you have a binary classification dataset that is
-99.9% class 1, a common approach is to resample from the data so that the data
-is more balanced.
-
 @@rejection_sample
 @@resample_at_rate
 @@stratified_sample
 @@weighted_resample
-
-## Bucketing
-
-Use ['bucket'](#bucket) or
-['bucket_by_sequence_length'](#bucket_by_sequence_length) to stratify
-minibatches into groups ("buckets").  Use `bucket_by_sequence_length`
-with the argument `dynamic_pad=True` to receive minibatches of similarly
-sized sequences for efficient training via `dynamic_rnn`.
-
 @@bucket
 @@bucket_by_sequence_length
 """