tf.raw_ops.WindowDataset

Combines (nests of) input elements into a dataset of (nests of) windows.

tf.raw_ops.WindowDataset(
    input_dataset,
    size,
    shift,
    stride,
    drop_remainder,
    output_types,
    output_shapes,
    metadata='',
    name=None
)

A "window" is a finite dataset of flat elements of size size (or possibly fewer if there are not enough input elements to fill the window and drop_remainder evaluates to false).

The shift argument determines the number of input elements by which the window moves on each iteration. The first element in the kth window will be element

1 + (k-1) * shift

of the input dataset. In particular, the first element of the first window will always be the first element of the input dataset.

If the stride parameter is greater than 1, then each window will skip (stride - 1) input elements between each element that appears in the window. Output windows will still contain size elements regardless of the value of stride.

The stride argument determines the stride of the input elements, and the shift argument determines the shift of the window.

For example, letting {...} to represent a Dataset:

• tf.data.Dataset.range(7).window(2) produces { {0, 1}, {2, 3}, {4, 5}, {6} }
• tf.data.Dataset.range(7).window(3, 2, 1, True) produces { {0, 1, 2}, {2, 3, 4}, {4, 5, 6} }
• tf.data.Dataset.range(7).window(3, 1, 2, True) produces { {0, 2, 4}, {1, 3, 5}, {2, 4, 6} }

Note that when the window transformation is applied to a dataset of nested elements, it produces a dataset of nested windows.

For example:

• tf.data.Dataset.from_tensor_slices((range(4), range(4))).window(2) produces {({0, 1}, {0, 1}), ({2, 3}, {2, 3})}
• tf.data.Dataset.from_tensor_slices({"a": range(4)}).window(2) produces { {"a": {0, 1} }, {"a": {2, 3} } }