tf.sparse_to_indicator()

tf.sparse_to_indicator(sp_input, vocab_size, name=None)

Converts a SparseTensor of ids into a dense bool indicator tensor.

The last dimension of sp_input.indices is discarded and replaced with the values of sp_input. If sp_input.shape = [D0, D1, ..., Dn, K], then output.shape = [D0, D1, ..., Dn, vocab_size], where

output[d_0, d_1, ..., d_n, sp_input[d_0, d_1, ..., d_n, k]] = True

and False elsewhere in output.

For example, if sp_input.shape = [2, 3, 4] with non-empty values:

[0, 0, 0]: 0
[0, 1, 0]: 10
[1, 0, 3]: 103
[1, 1, 2]: 150
[1, 1, 3]: 149
[1, 1, 4]: 150
[1, 2, 1]: 121

and vocab_size = 200, then the output will be a [2, 3, 200] dense bool tensor with False everywhere except at positions

(0, 0, 0), (0, 1, 10), (1, 0, 103), (1, 1, 149), (1, 1, 150),
(1, 2, 121).

Note that repeats are allowed in the input SparseTensor. This op is useful for converting SparseTensors into dense formats for compatibility with ops that expect dense tensors.

The input SparseTensor must be in row-major order.

Args:

• sp_input: A SparseTensor with values property of type int32 or int64.
• vocab_size: A scalar int64 Tensor (or Python int) containing the new size of the last dimension, all(0 <= sp_input.values < vocab_size).
• name: A name prefix for the returned tensors (optional)

Returns:

A dense bool indicator tensor representing the indices with specified value.

Raises:

• TypeError: If sp_input is not a SparseTensor.