tf.contrib.training.NextQueuedSequenceBatch.save_state(state_name, value, name=None) Returns an op to save the current batch of

tf.contrib.training.SequenceQueueingStateSaver.barrier

tf.contrib.training.NextQueuedSequenceBatch.sequences A dict mapping keys of input_sequences to split and rebatched

tf.contrib.training.SequenceQueueingStateSaver.next_batch The NextQueuedSequenceBatch providing access to batched

tf.contrib.training.NextQueuedSequenceBatch.context A dict mapping keys of input_context to batched context.

tf.contrib.training.bucket(tensors, which_bucket, batch_size, num_buckets, num_threads=1, capacity=32, shapes=None, dynamic_pad=False, allow_smaller_final_batch=False

tf.contrib.training.NextQueuedSequenceBatch.__init__(state_saver)

tf.contrib.training.NextQueuedSequenceBatch.total_length The lengths of the original (non-truncated) unrolled examples.

tf.contrib.training.NextQueuedSequenceBatch.sequence_count An int32 vector, length batch_size: the sequence count

tf.contrib.training.SequenceQueueingStateSaver.__init__(batch_size, num_unroll, input_length, input_key, input_sequences, input_context, initial_states, capacity=None