# tf.contrib.training.NextQueuedSequenceBatch

## Class NextQueuedSequenceBatch

NextQueuedSequenceBatch stores deferred SequenceQueueingStateSaver data.

This class is instantiated by SequenceQueueingStateSaver and is accessible via its next_batch property.

## Properties

### batch_size

The batch_size of the given batch.

Usually, this is the batch_size requested when initializing the SQSS, but if allow_small_batch=True this will become smaller when inputs are exhausted.

#### Returns:

A scalar integer tensor, the batch_size

### context

A dict mapping keys of input_context to batched context.

#### Returns:

A dict mapping keys of input_context to tensors. If we had at input:

context["name"].get_shape() == [d1, d2, ...]


then for this property:

context["name"].get_shape() == [batch_size, d1, d2, ...]


### insertion_index

The insertion indices of the examples (when they were first added).

These indices start with the value -2**63 and increase with every call to the prefetch op. Each whole example gets its own insertion index, and this is used to prioritize the example so that its truncated segments appear in adjacent iterations, even if new examples are inserted by the prefetch op between iterations.

#### Returns:

An int64 vector of length batch_size, the insertion indices.

### key

The key names of the given truncated unrolled examples.

The format of the key is:

"%05d_of_%05d:%s" % (sequence, sequence_count, original_key)


where original_key is the unique key read in by the prefetcher.

#### Returns:

A string vector of length batch_size, the keys.

### length

The lengths of the given truncated unrolled examples.

For initial iterations, for which sequence * num_unroll < length, this number is num_unroll. For the remainder, this number is between 0 and num_unroll.

#### Returns:

An integer vector of length batch_size, the lengths.

### next_key

The key names of the next (in iteration) truncated unrolled examples.

The format of the key is:

"%05d_of_%05d:%s" % (sequence + 1, sequence_count, original_key)


if sequence + 1 < sequence_count, otherwise:

"STOP:%s" % original_key


where original_key is the unique key read in by the prefetcher.

#### Returns:

A string vector of length batch_size, the keys.

### sequence

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

When an input is split up, the sequence values

0, 1, ..., sequence_count - 1


are assigned to each split.

#### Returns:

An int32 vector Tensor.

### 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.

### sequences

A dict mapping keys of input_sequences to split and rebatched data.

#### Returns:

A dict mapping keys of input_sequences to tensors. If we had at input:

sequences["name"].get_shape() == [None, d1, d2, ...]


where None meant the sequence time was dynamic, then for this property:

sequences["name"].get_shape() == [batch_size, num_unroll, d1, d2, ...].


### total_length

The lengths of the original (non-truncated) unrolled examples.

#### Returns:

An integer vector of length batch_size, the total lengths.

## Methods

### __init__

__init__(state_saver)


### save_state

save_state(
state_name,
value,
name=None
)


Returns an op to save the current batch of state state_name.

#### Args:

• state_name: string, matches a key provided in initial_states.
• value: A Tensor. Its type must match that of initial_states[state_name].dtype. If we had at input:

initial_states[state_name].get_shape() == [d1, d2, ...]


then the shape of value must match:

tf.shape(value) == [batch_size, d1, d2, ...]

• name: string (optional). The name scope for newly created ops.

#### Returns:

A control flow op that stores the new state of each entry into the state saver. This op must be run for every iteration that accesses data from the state saver (otherwise the state saver will never progress through its states and run out of capacity).

#### Raises:

• KeyError: if state_name does not match any of the initial states declared in initial_states.

### state

state(state_name)


Returns batched state tensors.

#### Args:

• state_name: string, matches a key provided in initial_states.

#### Returns:

A Tensor: a batched set of states, either initial states (if this is the first run of the given example), or a value as stored during a previous iteration via save_state control flow. Its type is the same as initial_states["state_name"].dtype. If we had at input:

initial_states[state_name].get_shape() == [d1, d2, ...],


then

state(state_name).get_shape() == [batch_size, d1, d2, ...]


#### Raises:

• KeyError: if state_name does not match any of the initial states declared in initial_states.