[TOC]
Training and input utilities.
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_statestf.contrib.training.NextQueuedSequenceBatchtf.contrib.training.SequenceQueueingStateSaver
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_sampletf.contrib.training.resample_at_ratetf.contrib.training.stratified_sampletf.contrib.training.weighted_resample
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.buckettf.contrib.training.bucket_by_sequence_length