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Apply boolean mask to tensor.
tf.compat.v1.boolean_mask(
tensor, mask, name='boolean_mask', axis=None
)
Numpy equivalent is tensor[mask].
In general, 0 < dim(mask) = K <= dim(tensor), and mask's shape must match
the first K dimensions of tensor's shape. We then have:
boolean_mask(tensor, mask)[i, j1,...,jd] = tensor[i1,...,iK,j1,...,jd]
where (i1,...,iK) is the ith True entry of mask (row-major order).
The axis could be used with mask to indicate the axis to mask from.
In that case, axis + dim(mask) <= dim(tensor) and mask's shape must match
the first axis + dim(mask) dimensions of tensor's shape.
See also: tf.ragged.boolean_mask, which can be applied to both dense and
ragged tensors, and can be used if you need to preserve the masked dimensions
of tensor (rather than flattening them, as tf.boolean_mask does).
Examples:
# 1-D example
tensor = [0, 1, 2, 3]
mask = np.array([True, False, True, False])
tf.boolean_mask(tensor, mask) # [0, 2]
# 2-D example
tensor = [[1, 2], [3, 4], [5, 6]]
mask = np.array([True, False, True])
tf.boolean_mask(tensor, mask) # [[1, 2], [5, 6]]
Args | |
|---|---|
tensor
|
N-D Tensor. |
mask
|
K-D boolean Tensor, K <= N and K must be known statically. |
name
|
A name for this operation (optional). |
axis
|
A 0-D int Tensor representing the axis in tensor to mask from. By
default, axis is 0 which will mask from the first dimension. Otherwise K +
axis <= N.
|
Returns | |
|---|---|
(N-K+1)-dimensional tensor populated by entries in tensor corresponding
to True values in mask.
|
Raises | |
|---|---|
ValueError
|
If shapes do not conform. |