tf.contrib.training.SequenceQueueingStateSaver.num_unroll

tf.contrib.training.SequenceQueueingStateSaver.close(cancel_pending_enqueues=False, name=None) Closes the barrier and the FIFOQueue

tf.contrib.training.stratified_sample(tensors, labels, target_probs, batch_size, init_probs=None, enqueue_many=False, queue_capacity=16, threads_per_queue=1, name=None)

tf.contrib.training.weighted_resample(inputs, weights, overall_rate, scope=None, mean_decay=0.999, warmup=10, seed=None) Performs

class tf.contrib.training.SequenceQueueingStateSaver SequenceQueueingStateSaver provides access to stateful values from input

tf.contrib.training.SequenceQueueingStateSaver.prefetch_op The op used to prefetch new data into the state saver.

tf.contrib.training.bucket_by_sequence_length(input_length, tensors, batch_size, bucket_boundaries, num_threads=1, capacity=32, shapes=None, dynamic_pad=False, allo

tf.contrib.training.NextQueuedSequenceBatch.state(state_name) Returns batched state tensors. Args:

tf.contrib.training.NextQueuedSequenceBatch.key The key names of the given truncated unrolled examples. The

tf.contrib.training.resample_at_rate(inputs, rates, scope=None, seed=None, back_prop=False) Given inputs tensors