LostTech.TensorFlow : API Documentation

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] 



Public instance methods

object get_shape_dyn_()

Public static methods

object from_value_dyn<TClass>(object sparse_tensor_value)

TClass from_value<TClass>(ndarray sparse_tensor_value)

TClass from_value<TClass>(CompositeTensor sparse_tensor_value)

TClass from_value<TClass>(object sparse_tensor_value)

Public properties

object dense_shape get;

object dense_shape_dyn get;

object dtype get;

object dtype_dyn get;

object graph get;

object graph_dyn get;

object indices get;

object indices_dyn get;

object op get;

object op_dyn get;

object PythonObject get;

TensorShape shape get;

object shape_dyn get;

object values get;

object values_dyn get;