Login Register

tf.contrib.training.NextQueuedSequenceBatch.sequence_count

tf.contrib.training.NextQueuedSequenceBatch.sequence_count

An int32 vector, length batch_size: the sequence count of each entry.

When an input is split up, the number of splits is equal to: padded_length / num_unroll. This is the sequence_count.

Returns:

An int32 vector Tensor.

Links:

https://www.tensorflow.org/versions/master/api_docs/python/contrib.training.html#NextQueuedSequenceBatch.sequence_count

doc_TensorFlow

2016-10-14 13:07:29

Comments

Leave a Comment

Please login to continue.

Popular Articles

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

tf.contrib.training.NextQueuedSequenceBatch.sequence tf.contrib.training.NextQueuedSequenceBatch.sequence An int32 vector, length batch_size: the sequence index of each entry. When an input is split up

tf.contrib.training.NextQueuedSequenceBatch class tf.contrib.training.NextQueuedSequenceBatch NextQueuedSequenceBatch stores deferred SequenceQueueingStateSaver data. This class is instantiate

tf.contrib.training.NextQueuedSequenceBatch.key tf.contrib.training.NextQueuedSequenceBatch.key The key names of the given truncated unrolled examples. The format of the key is: "%05d_of_%05d:%s"

tf.contrib.training.NextQueuedSequenceBatch.state() tf.contrib.training.NextQueuedSequenceBatch.state(state_name) Returns batched state tensors. Args: state_name: string, matches a key provided in in