Type RandomWindowInputFn
Namespace tensorflow.contrib.timeseries
Parent TimeSeriesInputFn
Interfaces IRandomWindowInputFn
Wraps a `TimeSeriesReader` to create random batches of windows. Tensors are first collected into sequential windows (in a windowing queue
created by `tf.compat.v1.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
Properties
Public static methods
RandomWindowInputFn NewDyn(object time_series_reader, object window_size, object batch_size, ImplicitContainer<T> queue_capacity_multiplier, ImplicitContainer<T> shuffle_min_after_dequeue_multiplier, ImplicitContainer<T> discard_out_of_order, ImplicitContainer<T> discard_consecutive_batches_limit, ImplicitContainer<T> jitter, ImplicitContainer<T> num_threads, object shuffle_seed)
Configure the RandomWindowInputFn.
Parameters
-
objecttime_series_reader - A TimeSeriesReader object.
-
objectwindow_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).
-
objectbatch_size - The number of windows to place together in a batch. Larger values will lead to more stable gradients during training.
-
ImplicitContainer<T>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`.
-
ImplicitContainer<T>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`.
-
ImplicitContainer<T>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.
-
ImplicitContainer<T>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).
-
ImplicitContainer<T>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.
-
ImplicitContainer<T>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).
-
objectshuffle_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.