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TensorFlow 1 version View source on GitHub

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.

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: 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]]