Applies a boolean mask to data
without flattening the mask dimensions.
tf.ragged.boolean_mask(
data, mask, name=None
)
Used in the notebooks
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 i
th 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]]