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diff --git a/tensorflow/docs_src/api_guides/python/contrib.training.md b/tensorflow/docs_src/api_guides/python/contrib.training.md deleted file mode 100644 index 068efdc829..0000000000 --- a/tensorflow/docs_src/api_guides/python/contrib.training.md +++ /dev/null @@ -1,50 +0,0 @@ -# Training (contrib) -[TOC] - -Training and input utilities. - -## Splitting sequence inputs into minibatches with state saving - -Use `tf.contrib.training.SequenceQueueingStateSaver` or -its wrapper `tf.contrib.training.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. - -* `tf.contrib.training.batch_sequences_with_states` -* `tf.contrib.training.NextQueuedSequenceBatch` -* `tf.contrib.training.SequenceQueueingStateSaver` - - -## Online data resampling - -To resample data with replacement on a per-example basis, use -`tf.contrib.training.rejection_sample` or -`tf.contrib.training.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 `tf.contrib.training.weighted_resample` -(which also returns the actual resampling rate used for each output example). - -Use `tf.contrib.training.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. - -* `tf.contrib.training.rejection_sample` -* `tf.contrib.training.resample_at_rate` -* `tf.contrib.training.stratified_sample` -* `tf.contrib.training.weighted_resample` - -## Bucketing - -Use `tf.contrib.training.bucket` or -`tf.contrib.training.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`. - -* `tf.contrib.training.bucket` -* `tf.contrib.training.bucket_by_sequence_length` |