tf.sparse.transpose  |  TensorFlow v2.16.1 (original) (raw)

tf.sparse.transpose

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Transposes a SparseTensor.

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Compat aliases for migration

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tf.compat.v1.sparse.transpose, tf.compat.v1.sparse_transpose

tf.sparse.transpose(
    sp_input, perm=None, name=None
)

Used in the notebooks

Permutes the dimensions according to the value of perm. This is the sparse version of tf.transpose.

The returned tensor's dimension i will correspond to the input dimensionperm[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:

x = tf.SparseTensor(indices=[[0, 1], [0, 3], [2, 3], [3, 1]], values=[1.1, 2.2, 3.3, 4.4], dense_shape=[4, 5]) print('x =', tf.sparse.to_dense(x)) x = tf.Tensor( [[0. 1.1 0. 2.2 0. ] [0. 0. 0. 0. 0. ] [0. 0. 0. 3.3 0. ] [0. 4.4 0. 0. 0. ]], shape=(4, 5), dtype=float32)

x_transpose = tf.sparse.transpose(x) print('x_transpose =', tf.sparse.to_dense(x_transpose)) x_transpose = tf.Tensor( [[0. 0. 0. 0. ] [1.1 0. 0. 4.4] [0. 0. 0. 0. ] [2.2 0. 3.3 0. ] [0. 0. 0. 0. ]], shape=(5, 4), dtype=float32)

Equivalently, you could call tf.sparse.transpose(x, perm=[1, 0]). Theperm argument is more useful for n-dimensional tensors where n > 2.

x = tf.SparseTensor(indices=[[0, 0, 1], [0, 0, 3], [1, 2, 3], [1, 3, 1]], values=[1.1, 2.2, 3.3, 4.4], dense_shape=[2, 4, 5]) print('x =', tf.sparse.to_dense(x)) x = tf.Tensor( [[[0. 1.1 0. 2.2 0. ] [0. 0. 0. 0. 0. ] [0. 0. 0. 0. 0. ] [0. 0. 0. 0. 0. ]] [[0. 0. 0. 0. 0. ] [0. 0. 0. 0. 0. ] [0. 0. 0. 3.3 0. ] [0. 4.4 0. 0. 0. ]]], shape=(2, 4, 5), dtype=float32)

As above, simply calling tf.sparse.transpose will default to perm=[2,1,0].

To take the transpose of a batch of sparse matrices, where 0 is the batch dimension, you would set perm=[0,2,1].

x_transpose = tf.sparse.transpose(x, perm=[0, 2, 1]) print('x_transpose =', tf.sparse.to_dense(x_transpose)) x_transpose = tf.Tensor( [[[0. 0. 0. 0. ] [1.1 0. 0. 0. ] [0. 0. 0. 0. ] [2.2 0. 0. 0. ] [0. 0. 0. 0. ]] [[0. 0. 0. 0. ] [0. 0. 0. 4.4] [0. 0. 0. 0. ] [0. 0. 3.3 0. ] [0. 0. 0. 0. ]]], shape=(2, 5, 4), dtype=float32)