tf.contrib.timeseries.RandomWindowInputFn

Class RandomWindowInputFn

Wraps a TimeSeriesReader to create random batches of windows.

Tensors are first collected into sequential windows (in a windowing queue created by tf.train.batch, based on the order returned from time_series_reader), then these windows are randomly batched (in a RandomShuffleQueue), the Tensors returned by create_batch having shapes prefixed by [batch_size, window_size].

This TimeSeriesInputFn is useful for both training and quantitative evaluation (but be sure to run several epochs for sequential models such as StructuralEnsembleRegressor to completely flush stale state left over from training). For qualitative evaluation or when preparing for predictions, use WholeDatasetInputFn.

Methods

__init__

__init__(
window_size,
batch_size,
queue_capacity_multiplier=1000,
shuffle_min_after_dequeue_multiplier=2,
jitter=True,
shuffle_seed=None
)


Configure the RandomWindowInputFn.

Args:

• time_series_reader: A TimeSeriesReader object.
• window_size: The number of examples to keep together sequentially. This controls the length of truncated backpropagation: smaller values mean less sequential computation, which can lead to faster training, but create a coarser approximation to the gradient (which would ideally be computed by a forward pass over the entire sequence in order).
• batch_size: The number of windows to place together in a batch. Larger values will lead to more stable gradients during training.
• queue_capacity_multiplier: The capacity for the queues used to create batches, specified as a multiple of batch_size (for RandomShuffleQueue) and batch_size * window_size (for the FIFOQueue). Controls the maximum number of windows stored. Should be greater than shuffle_min_after_dequeue_multiplier.
• shuffle_min_after_dequeue_multiplier: The minimum number of windows in the RandomShuffleQueue after a dequeue, which controls the amount of entropy introduced during batching. Specified as a multiple of batch_size.
• discard_out_of_order: If True, windows of data which have times which decrease (a higher time followed by a lower time) are discarded. If False, the window and associated features are instead sorted so that times are non-decreasing. Discarding is typically faster, as models do not have to deal with artificial gaps in the data. However, discarding does create a bias where the beginnings and endings of files are under-sampled.
• discard_consecutive_batches_limit: Raise an OutOfRangeError if more than this number of batches are discarded without a single non-discarded window (prevents infinite looping when the dataset is too small).
• jitter: If True, randomly discards examples between some windows in order to avoid deterministic chunking patterns. This is important for models like AR which may otherwise overfit a fixed chunking.
• num_threads: Use this number of threads for queues. Setting a value of 1 removes one source of non-determinism (and in combination with shuffle_seed should provide deterministic windowing).
• shuffle_seed: A seed for window shuffling. The default value of None provides random behavior. With shuffle_seed set and num_threads=1, provides deterministic behavior.

__call__

__call__()


create_batch

create_batch()


Create queues to window and batch time series data.

Returns:

A dictionary of Tensors corresponding to the output of self._reader (from the time_series_reader constructor argument), each with shapes prefixed by [batch_size, window_size].