tf.sparse.SparseTensor

Represents a sparse tensor.

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Main aliases

tf.SparseTensor

Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.SparseTensor, tf.compat.v1.sparse.SparseTensor

tf.sparse.SparseTensor(
    indices, values, dense_shape
)

Used in the notebooks

Used in the guide	Used in the tutorials
Working with sparse tensors Migrate from TPU embedding_columns to TPUEmbedding layer tf.data: Build TensorFlow input pipelines Ragged tensors Introduction to Tensors	Client-efficient large-model federated learning via `federated_select` and sparse aggregation Text generation with an RNN TFX Estimator Component Tutorial TFX Keras Component Tutorial Graph-based Neural Structured Learning in TFX

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 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 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 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]]

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].

Raises
ValueError	When building an eager SparseTensor if dense_shape is unknown or contains unknown elements (None or -1).

Attributes
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.
op	The Operation that produces values as an output.
shape	Get the TensorShape representing the shape of the dense tensor.
values	The non-zero values in the represented dense tensor.

Methods

consumers

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consumers()

eval

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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.

Note: 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

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@classmethod
from_value(
    sparse_tensor_value
)

get_shape

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get_shape() -> tf.TensorShape

Get the TensorShape representing the shape of the dense tensor.

Returns
A TensorShape object.

set_shape

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set_shape(
    shape
)

Updates the TensorShape representing the shape of the dense tensor.

With eager execution this operates as a shape assertion. Here the shapes match:

st = tf.SparseTensor(
  indices=[[0, 0], [1, 2]], values=[1, 2], dense_shape=[3, 4])
st.set_shape([3, 4])

Passing a None in the new shape allows any value for that axis:

st.set_shape([3, None])

An error is raised if an incompatible shape is passed.

st.set_shape([1, 4])
Traceback (most recent call last):

ValueError: Tensor's shape (3, 4) is not compatible with supplied
shape [1, 4]

When executing in a tf.function, or building a model using tf.keras.Input, SparseTensor.set_shape will merge the given shape with the current shape of this tensor, and set the tensor's shape to the merged value (see tf.TensorShape.merge_with for details):

st = tf.keras.Input(shape=[None, None, 3], sparse=True)
print(st.shape)
(None, None, None, 3)

Dimensions set to None are not updated:

st.set_shape([None, 224, 224, None])
print(st.shape)
(None, 224, 224, 3)

The main use case for this is to provide additional shape information that cannot be inferred from the graph alone.

Caution: set_shape ensures that the applied shape is compatible with the existing shape, but it does not check at runtime. Setting incorrect shapes can result in inconsistencies between the statically-known graph and the runtime value of tensors.

Args
shape	A TensorShape representing the shape of this tensor, a TensorShapeProto, a list, a tuple, or None.

Raises
ValueError	If shape is not compatible with the current shape of this tensor.

with_values

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with_values(
    new_values
)

Returns a copy of self with values replaced by new_values.

This method produces a new SparseTensor that has the same nonzero indices and same dense_shape, but updated values.

Args
new_values	The values of the new SparseTensor. Needs to have the same shape as the current .values Tensor. May have a different type than the current values.

Returns
A SparseTensor with identical indices and shape but updated values.

Example usage:

st = tf.sparse.from_dense([[1, 0, 2, 0], [3, 0, 0, 4]])
tf.sparse.to_dense(st.with_values([10, 20, 30, 40]))  # 4 nonzero values
<tf.Tensor: shape=(2, 4), dtype=int32, numpy=
array([[10,  0, 20,  0],
       [30,  0,  0, 40]], dtype=int32)>

__div__

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__div__(
    y
)

Component-wise divides a SparseTensor by a dense Tensor.

Limitation: this Op only broadcasts the dense side to the sparse side, but not the other direction.

Args
sp_indices	A Tensor of type int64. 2-D. N x R matrix with the indices of non-empty values in a SparseTensor, possibly not in canonical ordering.
sp_values	A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, complex64, int64, qint8, quint8, qint32, bfloat16, qint16, quint16, uint16, complex128, half, uint32, uint64. 1-D. N non-empty values corresponding to sp_indices.
sp_shape	A Tensor of type int64. 1-D. Shape of the input SparseTensor.
dense	A Tensor. Must have the same type as sp_values. R-D. The dense Tensor operand.
name	A name for the operation (optional).

Returns
A Tensor. Has the same type as sp_values.

__mul__

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__mul__(
    y
)

Component-wise multiplies a SparseTensor by a dense Tensor.

The output locations corresponding to the implicitly zero elements in the sparse tensor will be zero (i.e., will not take up storage space), regardless of the contents of the dense tensor (even if it's +/-INF and that INF*0 == NaN).

Limitation: this Op only broadcasts the dense side to the sparse side, but not the other direction.

Args
sp_indices	A Tensor of type int64. 2-D. N x R matrix with the indices of non-empty values in a SparseTensor, possibly not in canonical ordering.
sp_values	A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, complex64, int64, qint8, quint8, qint32, bfloat16, qint16, quint16, uint16, complex128, half, uint32, uint64. 1-D. N non-empty values corresponding to sp_indices.
sp_shape	A Tensor of type int64. 1-D. Shape of the input SparseTensor.
dense	A Tensor. Must have the same type as sp_values. R-D. The dense Tensor operand.
name	A name for the operation (optional).

Returns
A Tensor. Has the same type as sp_values.

__truediv__

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__truediv__(
    y
)

Internal helper function for 'sp_t / dense_t'.