boolean_mask.py 文件源码

def sparse_boolean_mask(sparse_tensor, mask, name="sparse_boolean_mask"):
  """Boolean mask for `SparseTensor`s.

  Args:
    sparse_tensor: a `SparseTensor`.
    mask: a 1D boolean dense`Tensor` whose length is equal to the 0th dimension
      of `sparse_tensor`.
    name: optional name for this operation.
  Returns:
    A `SparseTensor` that contains row `k` of `sparse_tensor` iff `mask[k]` is
    `True`.
  """
  # TODO(jamieas): consider mask dimension > 1 for symmetry with `boolean_mask`.
  with ops.name_scope(name, values=[sparse_tensor, mask]):
    mask = ops.convert_to_tensor(mask)
    mask_rows = array_ops.where(mask)
    first_indices = array_ops.squeeze(array_ops.slice(sparse_tensor.indices,
                                                      [0, 0], [-1, 1]))

    # Identify indices corresponding to the rows identified by mask_rows.
    sparse_entry_matches = functional_ops.map_fn(
        lambda x: math_ops.equal(first_indices, x),
        mask_rows,
        dtype=dtypes.bool)
    # Combine the rows of index_matches to form a mask for the sparse indices
    # and values.
    to_retain = array_ops.reshape(
        functional_ops.foldl(math_ops.logical_or, sparse_entry_matches), [-1])

    return sparse_ops.sparse_retain(sparse_tensor, to_retain)