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

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