tf.contrib.data.sliding_window_batch(
window_size,
stride=None,
window_shift=None,
window_stride=1
)
Defined in tensorflow/contrib/data/python/ops/sliding.py
.
A sliding window over a dataset. (deprecated) (deprecated arguments)
This transformation passes a sliding window over this dataset. The window size
is window_size
, the stride of the input elements is window_stride
, and the
shift between consecutive windows is window_shift
. If the remaining elements
cannot fill up the sliding window, this transformation will drop the final
smaller element. For example:
# NOTE: The following examples use `{ ... }` to represent the
# contents of a dataset.
a = { [1], [2], [3], [4], [5], [6] }
a.apply(sliding_window_batch(window_size=3)) ==
{ [[1], [2], [3]], [[2], [3], [4]], [[3], [4], [5]], [[4], [5], [6]] }
a.apply(sliding_window_batch(window_size=3, window_shift=2)) ==
{ [[1], [2], [3]], [[3], [4], [5]] }
a.apply(sliding_window_batch(window_size=3, window_stride=2)) ==
{ [[1], [3], [5]], [[2], [4], [6]] }
Args:
window_size
: Atf.int64
scalartf.Tensor
, representing the number of elements in the sliding window. It must be positive.stride
: (Optional.) Atf.int64
scalartf.Tensor
, representing the forward shift of the sliding window in each iteration. The default is1
. It must be positive. Deprecated alias forwindow_shift
.window_shift
: (Optional.) Atf.int64
scalartf.Tensor
, representing the forward shift of the sliding window in each iteration. The default is1
. It must be positive.window_stride
: (Optional.) Atf.int64
scalartf.Tensor
, representing the stride of the input elements in the sliding window. The default is1
. It must be positive.
Returns:
A Dataset
transformation function, which can be passed to
tf.data.Dataset.apply
.
Raises:
ValueError
: if invalid arguments are provided.