Function boolean_mask_v2

tensorflow/python/ops/array_ops.py:1512–1560

Apply boolean mask to tensor. Numpy equivalent is `tensor[mask]`. ```python # 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

(tensor, mask, axis=None, name="boolean_mask")

@tf_export("boolean_mask", v1=[])
@dispatch.add_dispatch_support
def boolean_mask_v2(tensor, mask, axis=None, name="boolean_mask"):
  """Apply boolean mask to tensor.

  Numpy equivalent is `tensor[mask]`.

  ```python
  # 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`&#x27;s shape must match
  the first K dimensions of `tensor`&#x27;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`&#x27;s shape must match
  the first `axis + dim(mask)` dimensions of `tensor`&#x27;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).

  Args:
    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).

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

  Raises:
    ValueError:  If shapes do not conform.

  Examples:

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


@tf_export("sparse.mask", v1=["sparse.mask", "sparse_mask"])