Apply boolean mask to tensor.

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).


tensor = [0, 1, 2, 3]  # 1-D 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]] # 2-D 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)>

tensor N-D Tensor.
mask K-D boolean Tensor, K <= N and K must be known statically.
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.
name A name for this operation (optional).

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

ValueError If shapes do not conform.


# 2-D example
tensor = [[1, 2], [3, 4], [5, 6]]
mask = np.array([True, False, True])
boolean_mask(tensor, mask)  # [[1, 2], [5, 6]]