tf.contrib.training.NextQueuedSequenceBatch.context

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

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tf.contrib.training.bucket()

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

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tf.contrib.training.NextQueuedSequenceBatch.sequences

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

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tf.contrib.training.SequenceQueueingStateSaver.barrier

tf.contrib.training.SequenceQueueingStateSaver.barrier

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tf.contrib.training.SequenceQueueingStateSaver.next_batch

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

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tf.contrib.training.NextQueuedSequenceBatch.

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

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tf.contrib.training.NextQueuedSequenceBatch.save_state()

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

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tf.contrib.training.SequenceQueueingStateSaver.batch_size

tf.contrib.training.SequenceQueueingStateSaver.batch_size

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tf.contrib.training.SequenceQueueingStateSaver.

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

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tf.contrib.training.NextQueuedSequenceBatch.sequence

tf.contrib.training.NextQueuedSequenceBatch.sequence An int32 vector, length batch_size: the sequence index of each

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