diff options
Diffstat (limited to 'tensorflow/docs_src/api_guides/python/contrib.training.md')
| -rw-r--r-- | tensorflow/docs_src/api_guides/python/contrib.training.md | 34 |
1 files changed, 17 insertions, 17 deletions
diff --git a/tensorflow/docs_src/api_guides/python/contrib.training.md b/tensorflow/docs_src/api_guides/python/contrib.training.md index 87395d930b..068efdc829 100644 --- a/tensorflow/docs_src/api_guides/python/contrib.training.md +++ b/tensorflow/docs_src/api_guides/python/contrib.training.md @@ -5,46 +5,46 @@ 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 +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} +* `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 +`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} +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 +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} +* `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 +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} +* `tf.contrib.training.bucket` +* `tf.contrib.training.bucket_by_sequence_length` |