Type SparseTensor
Namespace tensorflow
Parent PythonObjectContainer
Interfaces _TensorLike, CompositeTensor, ISparseTensor
Represents a sparse tensor. TensorFlow represents a sparse tensor as three separate dense tensors:
`indices`, `values`, and `dense_shape`. In Python, the three tensors are
collected into a `SparseTensor` class for ease of use. If you have separate
`indices`, `values`, and `dense_shape` tensors, wrap them in a `SparseTensor`
object before passing to the ops below. Concretely, the sparse tensor `SparseTensor(indices, values, dense_shape)`
comprises the following components, where `N` and `ndims` are the number
of values and number of dimensions in the `SparseTensor`, respectively: * `indices`: A 2-D int64 tensor of dense_shape `[N, ndims]`, which specifies
the indices of the elements in the sparse tensor that contain nonzero
values (elements are zero-indexed). For example, `indices=[[1,3], [2,4]]`
specifies that the elements with indexes of [1,3] and [2,4] have
nonzero values. * `values`: A 1-D tensor of any type and dense_shape `[N]`, which supplies the
values for each element in `indices`. For example, given
`indices=[[1,3], [2,4]]`, the parameter `values=[18, 3.6]` specifies
that element [1,3] of the sparse tensor has a value of 18, and element
[2,4] of the tensor has a value of 3.6. * `dense_shape`: A 1-D int64 tensor of dense_shape `[ndims]`, which specifies
the dense_shape of the sparse tensor. Takes a list indicating the number of
elements in each dimension. For example, `dense_shape=[3,6]` specifies a
two-dimensional 3x6 tensor, `dense_shape=[2,3,4]` specifies a
three-dimensional 2x3x4 tensor, and `dense_shape=[9]` specifies a
one-dimensional tensor with 9 elements. The corresponding dense tensor satisfies:
By convention, `indices` should be sorted in row-major order (or equivalently
lexicographic order on the tuples `indices[i]`). This is not enforced when
`SparseTensor` objects are constructed, but most ops assume correct ordering.
If the ordering of sparse tensor `st` is wrong, a fixed version can be
obtained by calling `tf.sparse.reorder(st)`. Example: The sparse tensor
represents the dense tensor
Show Example
dense.shape = dense_shape
dense[tuple(indices[i])] = values[i]