tf.sparse_fill_empty_rows(sp_input, default_value, name=None)

tf.sparse_fill_empty_rows(sp_input, default_value, name=None)

See the guide: Sparse Tensors > Manipulation

Fills empty rows in the input 2-D SparseTensor with a default value.

This op adds entries with the specified default_value at index [row, 0] for any row in the input that does not already have a value.

For example, suppose sp_input has shape [5, 6] and non-empty values:

[0, 1]: a
[0, 3]: b
[2, 0]: c
[3, 1]: d

Rows 1 and 4 are empty, so the output will be of shape [5, 6] with values:

[0, 1]: a
[0, 3]: b
[1, 0]: default_value
[2, 0]: c
[3, 1]: d
[4, 0]: default_value

Note that the input may have empty columns at the end, with no effect on this op.

The output SparseTensor will be in row-major order and will have the same shape as the input.

This op also returns an indicator vector such that

empty_row_indicator[i] = True iff row i was an empty row.

Args:

• sp_input: A SparseTensor with shape [N, M].
• default_value: The value to fill for empty rows, with the same type as sp_input.
• name: A name prefix for the returned tensors (optional)

Returns:

• sp_ordered_output: A SparseTensor with shape [N, M], and with all empty rows filled in with default_value.
• empty_row_indicator: A bool vector of length N indicating whether each input row was empty.

Raises:

• TypeError: If sp_input is not a SparseTensor.

Defined in tensorflow/python/ops/sparse_ops.py.