tf.boolean_mask( tensor, mask, name='boolean_mask', axis=None )
Defined in tensorflow/python/ops/array_ops.py
.
See the guide: Tensor Transformations > Slicing and Joining
Apply boolean mask to tensor. Numpy equivalent is tensor[mask]
.
# 1-D example tensor = [0, 1, 2, 3] mask = np.array([True, False, True, False]) boolean_mask(tensor, mask) # [0, 2]
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.
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.(N-K+1)-dimensional tensor populated by entries in tensor
corresponding to True
values in mask
.
ValueError
: If shapes do not conform.Examples:
# 2-D example tensor = [[1, 2], [3, 4], [5, 6]] mask = np.array([True, False, True]) boolean_mask(tensor, mask) # [[1, 2], [5, 6]]
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Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/api_docs/python/tf/boolean_mask