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# 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`
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