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Apply boolean mask to tensor.
tf.boolean_mask(
tensor, mask, axis=None, name='boolean_mask'
)
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
(rowmajor 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:
tensor = [0, 1, 2, 3] # 1D example
mask = np.array([True, False, True, False])
tf.boolean_mask(tensor, mask)
<tf.Tensor: shape=(2,), dtype=int32, numpy=array([0, 2], dtype=int32)>
tensor = [[1, 2], [3, 4], [5, 6]] # 2D example
mask = np.array([True, False, True])
tf.boolean_mask(tensor, mask)
<tf.Tensor: shape=(2, 2), dtype=int32, numpy=
array([[1, 2],
[5, 6]], dtype=int32)>
Returns  

(NK+1)dimensional tensor populated by entries in tensor corresponding
to True values in mask .

Raises  

ValueError

If shapes do not conform. 
Examples:
# 2D example
tensor = [[1, 2], [3, 4], [5, 6]]
mask = np.array([True, False, True])
boolean_mask(tensor, mask) # [[1, 2], [5, 6]]