tf.ragged.boolean_mask

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Applies a boolean mask to data without flattening the mask dimensions.

tf.ragged.boolean_mask(
    data, mask, name=None
)

Returns a potentially ragged tensor that is formed by retaining the elements in data where the corresponding value in mask is True.

• output[a1...aA, i, b1...bB] = data[a1...aA, j, b1...bB]

  Where j is the ith True entry of mask[a1...aA].

Note that output preserves the mask dimensions a1...aA; this differs from tf.boolean_mask, which flattens those dimensions.

Args:

• data: A potentially ragged tensor.
• mask: A potentially ragged boolean tensor. mask's shape must be a prefix of data's shape. rank(mask) must be known statically.
• name: A name prefix for the returned tensor (optional).

Returns:

A potentially ragged tensor that is formed by retaining the elements in data where the corresponding value in mask is True.

• rank(output) = rank(data).
• output.ragged_rank = max(data.ragged_rank, rank(mask) - 1).

Raises:

• ValueError: if rank(mask) is not known statically; or if mask.shape is not a prefix of data.shape.

Examples:

>>> # Aliases for True & False so data and mask line up.
>>> T, F = (True, False)

>>> tf.ragged.boolean_mask(  # Mask a 2D Tensor.
...     data=[[1, 2, 3], [4, 5, 6], [7, 8, 9]],
...     mask=[[T, F, T], [F, F, F], [T, F, F]]).to_list()
[[1, 3], [], [7]]

>>> tf.ragged.boolean_mask(  # Mask a 2D RaggedTensor.
...     tf.ragged.constant([[1, 2, 3], [4], [5, 6]]),
...     tf.ragged.constant([[F, F, T], [F], [T, T]])).to_list()
[[3], [], [5, 6]]

>>> tf.ragged.boolean_mask(  # Mask rows of a 2D RaggedTensor.
...     tf.ragged.constant([[1, 2, 3], [4], [5, 6]]),
...     tf.ragged.constant([True, False, True])).to_list()
[[1, 2, 3], [5, 6]]