tf.sparse_transpose(sp_input, perm=None, name=None)

tf.sparse_transpose(sp_input, perm=None, name=None)

See the guide: Sparse Tensors > Manipulation

Transposes a SparseTensor

The returned tensor's dimension i will correspond to the input dimension perm[i]. If perm is not given, it is set to (n-1...0), where n is the rank of the input tensor. Hence by default, this operation performs a regular matrix transpose on 2-D input Tensors.

For example, if sp_input has shape [4, 5] and indices / values:

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

then the output will be a SparseTensor of shape [5, 4] and indices / values:

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

Args:

• sp_input: The input SparseTensor.
• perm: A permutation of the dimensions of sp_input.
• name: A name prefix for the returned tensors (optional) Returns: A transposed SparseTensor.

Raises:

• TypeError: If sp_input is not a SparseTensor.

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