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Creates batches of segments of sequential input.

This method creates a SequenceQueueingStateSaver (SQSS) and adds it to the queuerunners. It returns a NextQueuedSequenceBatch.

It accepts one example at a time identified by a unique input_key. input_sequence is a dict with values that are tensors with time as first dimension. This time dimension must be the same across those tensors of an example. It can vary across examples. Although it always has to be a multiple of num_unroll. Hence, padding may be necessary and it is turned on by default by pad=True.

input_length is a Tensor scalar or an int recording the time dimension prior to padding. It should be between 0 and the time dimension. One reason we want to keep track of it is so that we can take it into consideration when computing the loss. If pad=True then input_length can be None and will be inferred.

This methods segments input_sequence into segments of length num_unroll. It batches input sequences from batch_size many examples. These mini-batches are available through the sequence property of the output. Moreover, for each entry in the batch we can access its original input_key in key and its input length in total_length. length records within this segment how many non-padded time steps there are.

Static features of an example that do not vary across time can be part of the input_context, a dict with Tensor values. This method copies the context for each segment and makes it available in the context of the output.

This method can maintain and update a state for each example. It accepts some initial_states as a dict with Tensor values. The first mini-batch an example is contained has initial_states as entry of the state. If save_state is called then the next segment will have the updated entry of the state. See NextQueuedSequenceBatch for a complete list of properties and methods.

Example usage:

batch_size = 32
num_unroll = 20
num_enqueue_threads = 3
lstm_size = 8
cell = tf.compat.v1.nn.rnn_cell.BasicLSTMCell(num_units=lstm_size)

key, sequences, context = my_parser(raw_data)
initial_state_values = tf.zeros((state_size,), dtype=tf.float32)
initial_states = {"lstm_state": initial_state_values}
batch = tf.batch_sequences_with_states(
    capacity=batch_size * num_enqueue_threads * 2)

inputs = batch.sequences["input"]
context_label = batch.context["label"]

inputs_by_time = tf.split(value=inputs, num_or_size_splits=num_unroll, axis=1)
assert len(inputs_by_time) == num_unroll

lstm_output, _ = tf.contrib.rnn.static_state_saving_rnn(

# Start a prefetcher in the background
sess = tf.compat.v1.Session()


while True:
  # Step through batches, perform training or inference...[lstm_output])

input_key A string scalar Tensor, the unique key for the given input example. This is used to keep track of the split minibatch elements of this input. Batched keys of the current iteration are made accessible via the key property. The shape of input_key (scalar) must be fully specified. Consider setting make_keys_unique to True when iterating over the same input multiple times.

input_sequences A dict mapping string names to Tensor values. The values must all have matching first dimension, called value_length. They may vary from input to input. The remainder of the shape (other than the first dimension) must be fully specified. The SequenceQueueingStateSaver will split these tensors along this first dimension into minibatch elements of dimension num_unrolled. Batched and segmented sequences of the current iteration are made accessible via the sequences property.
input_context A dict mapping string names to Tensor values. The values are treated as "global" across all time splits of the given input example, and will be copied across for all minibatch elements accordingly. Batched and copied context of the current iteration are made accessible via the context property.
input_length None or an int32 scalar Tensor, the length of the sequence prior to padding. If input_length=None and pad=True then the length will be inferred and will be equal to value_length. If pad=False then input_length cannot be None: input_length must be specified. Its shape of input_length (scalar) must be fully specified. Its value may be at most value_length for any given input (see above for the definition of value_length). Batched and total lengths of the current iteration are made accessible via the length and total_length properties.
initial_states A dict mapping string state names to multi-dimensional values (e.g. constants or tensors). This input defines the set of states that will be kept track of during computing iterations, and which can be accessed via the state and save_state methods.
num_unroll Python integer, how many time steps to unroll at a time. The input sequences of length k are then split into k / num_unroll many segments.
batch_size int or int32 scalar Tensor, how large minibatches should be when accessing the state() method and context, sequences, etc, properties.
num_threads The int number of threads enqueuing input examples into a queue.
capacity The max capacity of the queue in number of examples. Needs to be at least batch_size. Defaults to 1000. When iterating over the same input example multiple times reusing their keys the capacity must be smaller than the number of examples.
allow_small_batch If true, the queue will return smaller batches when there aren't enough input examples to fill a whole batch and the end of the input has been reached.
pad If True, input_sequences will be padded to multiple of num_unroll. In that case input_length may be None and is assumed to be the length of first dimension of values in input_sequences (i.e. value_length).
make_keys_unique Whether to append a random integer to the input_key in an effort to make it unique. The seed can be set via make_keys_unique_seed.
make_keys_unique_seed If make_keys_unique=True this fixes the seed with which a random postfix is generated.
name An op name string (optional).

A NextQueuedSequenceBatch with segmented and batched inputs and their states.

TypeError if any of the inputs is not an expected type.
ValueError if any of the input values is inconsistent, e.g. if not enough shape information is available from inputs to build the state saver.