tf.SparseTensor

class tf.SparseTensor

See the guide: Sparse Tensors > Sparse Tensor Representation

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:

dense.shape = dense_shape
dense[tuple(indices[i])] = values[i]

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

SparseTensor(indices=[[0, 0], [1, 2]], values=[1, 2], dense_shape=[3, 4])

represents the dense tensor

[[1, 0, 0, 0]
 [0, 0, 2, 0]
 [0, 0, 0, 0]]

Properties

dense_shape

A 1-D Tensor of int64 representing the shape of the dense tensor.

dtype

The DType of elements in this tensor.

graph

The Graph that contains the index, value, and dense_shape tensors.

indices

The indices of non-zero values in the represented dense tensor.

Returns:

A 2-D Tensor of int64 with dense_shape [N, ndims], where N is the number of non-zero values in the tensor, and ndims is the rank.

op

The Operation that produces values as an output.

values

The non-zero values in the represented dense tensor.

Returns:

A 1-D Tensor of any data type.

Methods

__init__(indices, values, dense_shape)

Creates a SparseTensor.

Args:

• indices: A 2-D int64 tensor of shape [N, ndims].
• values: A 1-D tensor of any type and shape [N].
• dense_shape: A 1-D int64 tensor of shape [ndims].

Returns:

A SparseTensor.

eval(feed_dict=None, session=None)

Evaluates this sparse tensor in a Session.

Calling this method will execute all preceding operations that produce the inputs needed for the operation that produces this tensor.

N.B. Before invoking SparseTensor.eval(), its graph must have been launched in a session, and either a default session must be available, or session must be specified explicitly.

Args:

• feed_dict: A dictionary that maps Tensor objects to feed values. See tf.Session.run for a description of the valid feed values.
• session: (Optional.) The Session to be used to evaluate this sparse tensor. If none, the default session will be used.

Returns:

A SparseTensorValue object.

from_value(cls, sparse_tensor_value)

get_shape()

Get the TensorShape representing the shape of the dense tensor.

Returns:

A TensorShape object.

Defined in tensorflow/python/framework/sparse_tensor.py.