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# Sparse Tensors

Note: Functions taking `Tensor` arguments can also take anything accepted by
[`tf.convert_to_tensor`](framework.md#convert_to_tensor).

[TOC]

## Sparse Tensor Representation

Tensorflow supports a `SparseTensor` representation for data that is sparse
in multiple dimensions. Contrast this representation with `IndexedSlices`,
which is efficient for representing tensors that are sparse in their first
dimension, and dense along all other dimensions.

- - -

### `class tf.SparseTensor` {#SparseTensor}

Represents a sparse tensor.

Tensorflow represents a sparse tensor as three separate dense tensors:
`indices`, `values`, and `shape`.  In Python, the three tensors are
collected into a `SparseTensor` class for ease of use.  If you have separate
`indices`, `values`, and `shape` tensors, wrap them in a `SparseTensor`
object before passing to the ops below.

Concretely, the sparse tensor `SparseTensor(indices, values, shape)` is

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

where `N` and `ndims` are the number of values, and number of dimensions in
the `SparseTensor` respectively.

The corresponding dense tensor satisfies

```python
dense.shape = 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

```python
SparseTensor(indices=[[0, 0], [1, 2]], values=[1, 2], shape=[3, 4])
```

represents the dense tensor

```python
[[1, 0, 0, 0]
 [0, 0, 2, 0]
 [0, 0, 0, 0]]
```

- - -

#### `tf.SparseTensor.__init__(indices, values, shape)` {#SparseTensor.__init__}

Creates a `SparseTensor`.

##### Args:


*  <b>`indices`</b>: A 2-D int64 tensor of shape `[N, ndims]`.
*  <b>`values`</b>: A 1-D tensor of any type and shape `[N]`.
*  <b>`shape`</b>: A 1-D int64 tensor of shape `[ndims]`.

##### Returns:

  A `SparseTensor`


- - -

#### `tf.SparseTensor.indices` {#SparseTensor.indices}

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

##### Returns:

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


- - -

#### `tf.SparseTensor.values` {#SparseTensor.values}

The non-zero values in the represented dense tensor.

##### Returns:

  A 1-D Tensor of any data type.


- - -

#### `tf.SparseTensor.dtype` {#SparseTensor.dtype}

The `DType` of elements in this tensor.


- - -

#### `tf.SparseTensor.shape` {#SparseTensor.shape}

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


- - -

#### `tf.SparseTensor.graph` {#SparseTensor.graph}

The `Graph` that contains the index, value, and shape tensors.



#### Other Methods
- - -

#### `tf.SparseTensor.eval(feed_dict=None, session=None)` {#SparseTensor.eval}

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:


*  <b>`feed_dict`</b>: A dictionary that maps `Tensor` objects to feed values.
    See [`Session.run()`](../../api_docs/python/client.md#Session.run) for a
    description of the valid feed values.
*  <b>`session`</b>: (Optional.) The `Session` to be used to evaluate this sparse
    tensor. If none, the default session will be used.

##### Returns:

  A `SparseTensorValue` object.


- - -

#### `tf.SparseTensor.from_value(cls, sparse_tensor_value)` {#SparseTensor.from_value}





- - -

### `class tf.SparseTensorValue` {#SparseTensorValue}

SparseTensorValue(indices, values, shape)
- - -

#### `tf.SparseTensorValue.indices` {#SparseTensorValue.indices}

Alias for field number 0


- - -

#### `tf.SparseTensorValue.shape` {#SparseTensorValue.shape}

Alias for field number 2


- - -

#### `tf.SparseTensorValue.values` {#SparseTensorValue.values}

Alias for field number 1




## Conversion

- - -

### `tf.sparse_to_dense(sparse_indices, output_shape, sparse_values, default_value=0, validate_indices=True, name=None)` {#sparse_to_dense}

Converts a sparse representation into a dense tensor.

Builds an array `dense` with shape `output_shape` such that

```python
# If sparse_indices is scalar
dense[i] = (i == sparse_indices ? sparse_values : default_value)

# If sparse_indices is a vector, then for each i
dense[sparse_indices[i]] = sparse_values[i]

# If sparse_indices is an n by d matrix, then for each i in [0, n)
dense[sparse_indices[i][0], ..., sparse_indices[i][d-1]] = sparse_values[i]
```

All other values in `dense` are set to `default_value`.  If `sparse_values`
is a scalar, all sparse indices are set to this single value.

Indices should be sorted in lexicographic order, and indices must not
contain any repeats. If `validate_indices` is True, these properties
are checked during execution.

##### Args:


*  <b>`sparse_indices`</b>: A 0-D, 1-D, or 2-D `Tensor` of type `int32` or `int64`.
    `sparse_indices[i]` contains the complete index where `sparse_values[i]`
    will be placed.
*  <b>`output_shape`</b>: A 1-D `Tensor` of the same type as `sparse_indices`.  Shape
    of the dense output tensor.
*  <b>`sparse_values`</b>: A 0-D or 1-D `Tensor`.  Values corresponding to each row of
    `sparse_indices`, or a scalar value to be used for all sparse indices.
*  <b>`default_value`</b>: A 0-D `Tensor` of the same type as `sparse_values`.  Value
    to set for indices not specified in `sparse_indices`.  Defaults to zero.
*  <b>`validate_indices`</b>: A boolean value.  If True, indices are checked to make
    sure they are sorted in lexicographic order and that there are no repeats.
*  <b>`name`</b>: A name for the operation (optional).

##### Returns:

  Dense `Tensor` of shape `output_shape`.  Has the same type as
  `sparse_values`.


- - -

### `tf.sparse_tensor_to_dense(sp_input, default_value=0, validate_indices=True, name=None)` {#sparse_tensor_to_dense}

Converts a `SparseTensor` into a dense tensor.

This op is a convenience wrapper around `sparse_to_dense` for `SparseTensor`s.

For example, if `sp_input` has shape `[3, 5]` and non-empty string values:

    [0, 1]: a
    [0, 3]: b
    [2, 0]: c

and `default_value` is `x`, then the output will be a dense `[3, 5]`
string tensor with values:

    [[x a x b x]
     [x x x x x]
     [c x x x x]]

Indices must be without repeats.  This is only
tested if validate_indices is True.

##### Args:


*  <b>`sp_input`</b>: The input `SparseTensor`.
*  <b>`default_value`</b>: Scalar value to set for indices not specified in
    `sp_input`.  Defaults to zero.
*  <b>`validate_indices`</b>: A boolean value.  If `True`, indices are checked to make
    sure they are sorted in lexicographic order and that there are no repeats.
*  <b>`name`</b>: A name prefix for the returned tensors (optional).

##### Returns:

  A dense tensor with shape `sp_input.shape` and values specified by
  the non-empty values in `sp_input`. Indices not in `sp_input` are assigned
  `default_value`.

##### Raises:


*  <b>`TypeError`</b>: If `sp_input` is not a `SparseTensor`.


- - -

### `tf.sparse_to_indicator(sp_input, vocab_size, name=None)` {#sparse_to_indicator}

Converts a `SparseTensor` of ids into a dense bool indicator tensor.

The last dimension of `sp_input.indices` is discarded and replaced with
the values of `sp_input`.  If `sp_input.shape = [D0, D1, ..., Dn, K]`, then
`output.shape = [D0, D1, ..., Dn, vocab_size]`, where

    output[d_0, d_1, ..., d_n, sp_input[d_0, d_1, ..., d_n, k]] = True

and False elsewhere in `output`.

For example, if `sp_input.shape = [2, 3, 4]` with non-empty values:

    [0, 0, 0]: 0
    [0, 1, 0]: 10
    [1, 0, 3]: 103
    [1, 1, 2]: 150
    [1, 1, 3]: 149
    [1, 1, 4]: 150
    [1, 2, 1]: 121

and `vocab_size = 200`, then the output will be a `[2, 3, 200]` dense bool
tensor with False everywhere except at positions

    (0, 0, 0), (0, 1, 10), (1, 0, 103), (1, 1, 149), (1, 1, 150),
    (1, 2, 121).

Note that repeats are allowed in the input SparseTensor.
This op is useful for converting `SparseTensor`s into dense formats for
compatibility with ops that expect dense tensors.

The input `SparseTensor` must be in row-major order.

##### Args:


*  <b>`sp_input`</b>: A `SparseTensor` with `values` property of type `int32` or
    `int64`.
*  <b>`vocab_size`</b>: A scalar int64 Tensor (or Python int) containing the new size
    of the last dimension, `all(0 <= sp_input.values < vocab_size)`.
*  <b>`name`</b>: A name prefix for the returned tensors (optional)

##### Returns:

  A dense bool indicator tensor representing the indices with specified value.

##### Raises:


*  <b>`TypeError`</b>: If `sp_input` is not a `SparseTensor`.


- - -

### `tf.sparse_merge(sp_ids, sp_values, vocab_size, name=None)` {#sparse_merge}

Combines a batch of feature ids and values into a single `SparseTensor`.

The most common use case for this function occurs when feature ids and
their corresponding values are stored in `Example` protos on disk.
`parse_example` will return a batch of ids and a batch of values, and this
function joins them into a single logical `SparseTensor` for use in
functions such as `sparse_tensor_dense_matmul`, `sparse_to_dense`, etc.

The `SparseTensor` returned by this function has the following properties:

  - `indices` is equivalent to `sp_ids.indices` with the last
    dimension discarded and replaced with `sp_ids.values`.
  - `values` is simply `sp_values.values`.
  - If `sp_ids.shape = [D0, D1, ..., Dn, K]`, then
    `output.shape = [D0, D1, ..., Dn, vocab_size]`.

For example, consider the following feature vectors:

  vector1 = [-3, 0, 0, 0, 0, 0]
  vector2 = [ 0, 1, 0, 4, 1, 0]
  vector3 = [ 5, 0, 0, 9, 0, 0]

These might be stored sparsely in the following Example protos by storing
only the feature ids (column number if the vectors are treated as a matrix)
of the non-zero elements and the corresponding values:

  examples = [Example(features={
                  "ids": Feature(int64_list=Int64List(value=[0])),
                  "values": Feature(float_list=FloatList(value=[-3]))}),
              Example(features={
                  "ids": Feature(int64_list=Int64List(value=[1, 4, 3])),
                  "values": Feature(float_list=FloatList(value=[1, 1, 4]))}),
              Example(features={
                  "ids": Feature(int64_list=Int64List(value=[0, 3])),
                  "values": Feature(float_list=FloatList(value=[5, 9]))})]

The result of calling parse_example on these examples will produce a
dictionary with entries for "ids" and "values". Passing those two objects
to this function along with vocab_size=6, will produce a `SparseTensor` that
sparsely represents all three instances. Namely, the `indices` property will
contain the coordinates of the non-zero entries in the feature matrix (the
first dimension is the row number in the matrix, i.e., the index within the
batch, and the second dimension is the column number, i.e., the feature id);
`values` will contain the actual values. `shape` will be the shape of the
original matrix, i.e., (3, 6). For our example above, the output will be
equal to:

  SparseTensor(indices=[[0, 0], [1, 1], [1, 3], [1, 4], [2, 0], [2, 3]],
               values=[-3, 1, 4, 1, 5, 9],
               shape=[3, 6])

##### Args:


*  <b>`sp_ids`</b>: A `SparseTensor` with `values` property of type `int32`
    or `int64`.
*  <b>`sp_values`</b>: A`SparseTensor` of any type.
*  <b>`vocab_size`</b>: A scalar `int64` Tensor (or Python int) containing the new size
    of the last dimension, `all(0 <= sp_ids.values < vocab_size)`.
*  <b>`name`</b>: A name prefix for the returned tensors (optional)

##### Returns:

  A `SparseTensor` compactly representing a batch of feature ids and values,
  useful for passing to functions that expect such a `SparseTensor`.

##### Raises:


*  <b>`TypeError`</b>: If `sp_ids` or `sp_values` are not a `SparseTensor`.



## Manipulation

- - -

### `tf.sparse_concat(concat_dim, sp_inputs, name=None)` {#sparse_concat}

Concatenates a list of `SparseTensor` along the specified dimension.

Concatenation is with respect to the dense versions of each sparse input.
It is assumed that each inputs is a `SparseTensor` whose elements are ordered
along increasing dimension number.

All inputs' shapes must match, except for the concat dimension.  The
`indices`, `values`, and `shapes` lists must have the same length.

The output shape is identical to the inputs', except along the concat
dimension, where it is the sum of the inputs' sizes along that dimension.

The output elements will be resorted to preserve the sort order along
increasing dimension number.

This op runs in `O(M log M)` time, where `M` is the total number of non-empty
values across all inputs. This is due to the need for an internal sort in
order to concatenate efficiently across an arbitrary dimension.

For example, if `concat_dim = 1` and the inputs are

    sp_inputs[0]: shape = [2, 3]
    [0, 2]: "a"
    [1, 0]: "b"
    [1, 1]: "c"

    sp_inputs[1]: shape = [2, 4]
    [0, 1]: "d"
    [0, 2]: "e"

then the output will be

    shape = [2, 7]
    [0, 2]: "a"
    [0, 4]: "d"
    [0, 5]: "e"
    [1, 0]: "b"
    [1, 1]: "c"

Graphically this is equivalent to doing

    [    a] concat [  d e  ] = [    a   d e  ]
    [b c  ]        [       ]   [b c          ]

##### Args:


*  <b>`concat_dim`</b>: Dimension to concatenate along.
*  <b>`sp_inputs`</b>: List of `SparseTensor` to concatenate.
*  <b>`name`</b>: A name prefix for the returned tensors (optional).

##### Returns:

  A `SparseTensor` with the concatenated output.

##### Raises:


*  <b>`TypeError`</b>: If `sp_inputs` is not a list of `SparseTensor`.


- - -

### `tf.sparse_reorder(sp_input, name=None)` {#sparse_reorder}

Reorders a `SparseTensor` into the canonical, row-major ordering.

Note that by convention, all sparse ops preserve the canonical ordering
along increasing dimension number. The only time ordering can be violated
is during manual manipulation of the indices and values to add entries.

Reordering does not affect the shape of the `SparseTensor`.

For example, if `sp_input` has shape `[4, 5]` and `indices` / `values`:

    [0, 3]: b
    [0, 1]: a
    [3, 1]: d
    [2, 0]: c

then the output will be a `SparseTensor` of shape `[4, 5]` and
`indices` / `values`:

    [0, 1]: a
    [0, 3]: b
    [2, 0]: c
    [3, 1]: d

##### Args:


*  <b>`sp_input`</b>: The input `SparseTensor`.
*  <b>`name`</b>: A name prefix for the returned tensors (optional)

##### Returns:

  A `SparseTensor` with the same shape and non-empty values, but in
  canonical ordering.

##### Raises:


*  <b>`TypeError`</b>: If `sp_input` is not a `SparseTensor`.


- - -

### `tf.sparse_split(split_dim, num_split, sp_input, name=None)` {#sparse_split}

Split a `SparseTensor` into `num_split` tensors along `split_dim`.

If the `sp_input.shape[split_dim]` is not an integer multiple of `num_split`
each slice starting from 0:`shape[split_dim] % num_split` gets extra one
dimension. For example, if `split_dim = 1` and `num_split = 2` and the
input is:

    input_tensor = shape = [2, 7]
    [    a   d e  ]
    [b c          ]

Graphically the output tensors are:

    output_tensor[0] =
    [    a ]
    [b c   ]

    output_tensor[1] =
    [ d e  ]
    [      ]

##### Args:


*  <b>`split_dim`</b>: A 0-D `int32` `Tensor`. The dimension along which to split.
*  <b>`num_split`</b>: A Python integer. The number of ways to split.
*  <b>`sp_input`</b>: The `SparseTensor` to split.
*  <b>`name`</b>: A name for the operation (optional).

##### Returns:

  `num_split` `SparseTensor` objects resulting from splitting `value`.

##### Raises:


*  <b>`TypeError`</b>: If `sp_input` is not a `SparseTensor`.


- - -

### `tf.sparse_retain(sp_input, to_retain)` {#sparse_retain}

Retains specified non-empty values within a `SparseTensor`.

For example, if `sp_input` has shape `[4, 5]` and 4 non-empty string values:

    [0, 1]: a
    [0, 3]: b
    [2, 0]: c
    [3, 1]: d

and `to_retain = [True, False, False, True]`, then the output will
be a `SparseTensor` of shape `[4, 5]` with 2 non-empty values:

    [0, 1]: a
    [3, 1]: d

##### Args:


*  <b>`sp_input`</b>: The input `SparseTensor` with `N` non-empty elements.
*  <b>`to_retain`</b>: A bool vector of length `N` with `M` true values.

##### Returns:

  A `SparseTensor` with the same shape as the input and `M` non-empty
  elements corresponding to the true positions in `to_retain`.

##### Raises:


*  <b>`TypeError`</b>: If `sp_input` is not a `SparseTensor`.


- - -

### `tf.sparse_fill_empty_rows(sp_input, default_value, name=None)` {#sparse_fill_empty_rows}

Fills empty rows in the input 2-D `SparseTensor` with a default value.

This op adds entries with the specified `default_value` at index
`[row, 0]` for any row in the input that does not already have a value.

For example, suppose `sp_input` has shape `[5, 6]` and non-empty values:

    [0, 1]: a
    [0, 3]: b
    [2, 0]: c
    [3, 1]: d

Rows 1 and 4 are empty, so the output will be of shape `[5, 6]` with values:

    [0, 1]: a
    [0, 3]: b
    [1, 0]: default_value
    [2, 0]: c
    [3, 1]: d
    [4, 0]: default_value

Note that the input may have empty columns at the end, with no effect on
this op.

The output `SparseTensor` will be in row-major order and will have the
same shape as the input.

This op also returns an indicator vector such that

    empty_row_indicator[i] = True iff row i was an empty row.

##### Args:


*  <b>`sp_input`</b>: A `SparseTensor` with shape `[N, M]`.
*  <b>`default_value`</b>: The value to fill for empty rows, with the same type as
    `sp_input.`
*  <b>`name`</b>: A name prefix for the returned tensors (optional)

##### Returns:


*  <b>`sp_ordered_output`</b>: A `SparseTensor` with shape `[N, M]`, and with all empty
    rows filled in with `default_value`.
*  <b>`empty_row_indicator`</b>: A bool vector of length `N` indicating whether each
    input row was empty.

##### Raises:


*  <b>`TypeError`</b>: If `sp_input` is not a `SparseTensor`.



## Math Operations
- - -

### `tf.sparse_add(a, b, thresh=0)` {#sparse_add}

Adds two tensors, at least one of each is a `SparseTensor`.

If one `SparseTensor` and one `Tensor` are passed in, returns a `Tensor`.  If
both arguments are `SparseTensor`s, this returns a `SparseTensor`.  The order
of arguments does not matter.  Use vanilla `tf.add()` for adding two dense
`Tensor`s.

The indices of any input `SparseTensor` are assumed ordered in standard
lexicographic order.  If this is not the case, before this step run
`SparseReorder` to restore index ordering.

If both arguments are sparse, we perform "clipping" as follows.  By default,
if two values sum to zero at some index, the output `SparseTensor` would still
include that particular location in its index, storing a zero in the
corresponding value slot.  To override this, callers can specify `thresh`,
indicating that if the sum has a magnitude strictly smaller than `thresh`, its
corresponding value and index would then not be included.  In particular,
`thresh == 0.0` (default) means everything is kept and actual thresholding
happens only for a positive value.

For example, suppose the logical sum of two sparse operands is (densified):

    [       2]
    [.1     0]
    [ 6   -.2]

Then,

    - thresh == 0 (the default): all 5 index/value pairs will be returned.
    - thresh == 0.11: only .1 and 0  will vanish, and the remaining three
        index/value pairs will be returned.
    - thresh == 0.21: .1, 0, and -.2 will vanish.

##### Args:


*  <b>`a`</b>: The first operand; `SparseTensor` or `Tensor`.
*  <b>`b`</b>: The second operand; `SparseTensor` or `Tensor`.  At least one operand
    must be sparse.
*  <b>`thresh`</b>: A 0-D `Tensor`.  The magnitude threshold that determines if an
  output value/index pair takes space.  Its dtype should match that of the
  values if they are real; if the latter are complex64/complex128, then the
  dtype should be float32/float64, correspondingly.

##### Returns:

  A `SparseTensor` or a `Tensor`, representing the sum.

##### Raises:


*  <b>`TypeError`</b>: If both `a` and `b` are `Tensor`s.  Use `tf.add()` instead.


- - -

### `tf.sparse_tensor_dense_matmul(sp_a, b, adjoint_a=False, adjoint_b=False, name=None)` {#sparse_tensor_dense_matmul}

Multiply SparseTensor (of rank 2) "A" by dense matrix "B".

No validity checking is performed on the indices of A.  However, the following
input format is recommended for optimal behavior:

if adjoint_a == false:
  A should be sorted in lexicographically increasing order.  Use
  sparse_reorder if you're not sure.
if adjoint_a == true:
  A should be sorted in order of increasing dimension 1 (i.e., "column major"
  order instead of "row major" order).

Deciding when to use sparse_tensor_dense_matmul vs. matmul(sp_a=True):

There are a number of questions to ask in the decision process, including:

* Will the SparseTensor A fit in memory if densified?
* Is the column count of the product large (>> 1)?
* Is the density of A larger than approximately 15%?

If the answer to several of these questions is yes, consider
converting the SparseTensor to a dense one and using tf.matmul with sp_a=True.

This operation tends to perform well when A is more sparse, if the column size
of the product is small (e.g. matrix-vector multiplication), if sp_a.shape
takes on large values.

Below is a rough speed comparison between sparse_tensor_dense_matmul,
labelled 'sparse', and matmul(sp_a=True), labelled 'dense'.  For purposes of
the comparison, the time spent converting from a SparseTensor to a dense
Tensor is not included, so it is overly conservative with respect to
the time ratio.

Benchmark system:
CPU: Intel Ivybridge with HyperThreading (6 cores) dL1:32KB dL2:256KB dL3:12MB
GPU: NVidia Tesla k40c

Compiled with:
-c opt --config=cuda --copt=-mavx

```tensorflow/python/sparse_tensor_dense_matmul_op_test --benchmarks
A sparse [m, k] with % nonzero values between 1% and 80%
B dense [k, n]

% nnz    n       gpu     m       k       dt(dense)       dt(sparse)      dt(sparse)/dt(dense)
0.01     1       True    100     100     0.000221166     0.00010154      0.459112
0.01     1       True    100     1000    0.00033858      0.000109275     0.322745
0.01     1       True    1000    100     0.000310557     9.85661e-05     0.317385
0.01     1       True    1000    1000    0.0008721       0.000100875     0.115669
0.01     1       False   100     100     0.000208085     0.000107603     0.51711
0.01     1       False   100     1000    0.000327112     9.51118e-05     0.290762
0.01     1       False   1000    100     0.000308222     0.00010345      0.335635
0.01     1       False   1000    1000    0.000865721     0.000101397     0.117124
0.01     10      True    100     100     0.000218522     0.000105537     0.482958
0.01     10      True    100     1000    0.000340882     0.000111641     0.327506
0.01     10      True    1000    100     0.000315472     0.000117376     0.372064
0.01     10      True    1000    1000    0.000905493     0.000123263     0.136128
0.01     10      False   100     100     0.000221529     9.82571e-05     0.44354
0.01     10      False   100     1000    0.000330552     0.000112615     0.340687
0.01     10      False   1000    100     0.000341277     0.000114097     0.334324
0.01     10      False   1000    1000    0.000819944     0.000120982     0.147549
0.01     25      True    100     100     0.000207806     0.000105977     0.509981
0.01     25      True    100     1000    0.000322879     0.00012921      0.400181
0.01     25      True    1000    100     0.00038262      0.000141583     0.370035
0.01     25      True    1000    1000    0.000865438     0.000202083     0.233504
0.01     25      False   100     100     0.000209401     0.000104696     0.499979
0.01     25      False   100     1000    0.000321161     0.000130737     0.407076
0.01     25      False   1000    100     0.000377012     0.000136801     0.362856
0.01     25      False   1000    1000    0.000861125     0.00020272      0.235413
0.2      1       True    100     100     0.000206952     9.69219e-05     0.46833
0.2      1       True    100     1000    0.000348674     0.000147475     0.422959
0.2      1       True    1000    100     0.000336908     0.00010122      0.300439
0.2      1       True    1000    1000    0.001022        0.000203274     0.198898
0.2      1       False   100     100     0.000207532     9.5412e-05      0.459746
0.2      1       False   100     1000    0.000356127     0.000146824     0.41228
0.2      1       False   1000    100     0.000322664     0.000100918     0.312764
0.2      1       False   1000    1000    0.000998987     0.000203442     0.203648
0.2      10      True    100     100     0.000211692     0.000109903     0.519165
0.2      10      True    100     1000    0.000372819     0.000164321     0.440753
0.2      10      True    1000    100     0.000338651     0.000144806     0.427596
0.2      10      True    1000    1000    0.00108312      0.000758876     0.70064
0.2      10      False   100     100     0.000215727     0.000110502     0.512231
0.2      10      False   100     1000    0.000375419     0.0001613       0.429653
0.2      10      False   1000    100     0.000336999     0.000145628     0.432132
0.2      10      False   1000    1000    0.00110502      0.000762043     0.689618
0.2      25      True    100     100     0.000218705     0.000129913     0.594009
0.2      25      True    100     1000    0.000394794     0.00029428      0.745402
0.2      25      True    1000    100     0.000404483     0.0002693       0.665788
0.2      25      True    1000    1000    0.0012002       0.00194494      1.62052
0.2      25      False   100     100     0.000221494     0.0001306       0.589632
0.2      25      False   100     1000    0.000396436     0.000297204     0.74969
0.2      25      False   1000    100     0.000409346     0.000270068     0.659754
0.2      25      False   1000    1000    0.00121051      0.00193737      1.60046
0.5      1       True    100     100     0.000214981     9.82111e-05     0.456836
0.5      1       True    100     1000    0.000415328     0.000223073     0.537101
0.5      1       True    1000    100     0.000358324     0.00011269      0.314492
0.5      1       True    1000    1000    0.00137612      0.000437401     0.317851
0.5      1       False   100     100     0.000224196     0.000101423     0.452386
0.5      1       False   100     1000    0.000400987     0.000223286     0.556841
0.5      1       False   1000    100     0.000368825     0.00011224      0.304318
0.5      1       False   1000    1000    0.00136036      0.000429369     0.31563
0.5      10      True    100     100     0.000222125     0.000112308     0.505608
0.5      10      True    100     1000    0.000461088     0.00032357      0.701753
0.5      10      True    1000    100     0.000394624     0.000225497     0.571422
0.5      10      True    1000    1000    0.00158027      0.00190898      1.20801
0.5      10      False   100     100     0.000232083     0.000114978     0.495418
0.5      10      False   100     1000    0.000454574     0.000324632     0.714146
0.5      10      False   1000    100     0.000379097     0.000227768     0.600817
0.5      10      False   1000    1000    0.00160292      0.00190168      1.18638
0.5      25      True    100     100     0.00023429      0.000151703     0.647501
0.5      25      True    100     1000    0.000497462     0.000598873     1.20386
0.5      25      True    1000    100     0.000460778     0.000557038     1.20891
0.5      25      True    1000    1000    0.00170036      0.00467336      2.74845
0.5      25      False   100     100     0.000228981     0.000155334     0.678371
0.5      25      False   100     1000    0.000496139     0.000620789     1.25124
0.5      25      False   1000    100     0.00045473      0.000551528     1.21287
0.5      25      False   1000    1000    0.00171793      0.00467152      2.71927
0.8      1       True    100     100     0.000222037     0.000105301     0.47425
0.8      1       True    100     1000    0.000410804     0.000329327     0.801664
0.8      1       True    1000    100     0.000349735     0.000131225     0.375212
0.8      1       True    1000    1000    0.00139219      0.000677065     0.48633
0.8      1       False   100     100     0.000214079     0.000107486     0.502085
0.8      1       False   100     1000    0.000413746     0.000323244     0.781261
0.8      1       False   1000    100     0.000348983     0.000131983     0.378193
0.8      1       False   1000    1000    0.00136296      0.000685325     0.50282
0.8      10      True    100     100     0.000229159     0.00011825      0.516017
0.8      10      True    100     1000    0.000498845     0.000532618     1.0677
0.8      10      True    1000    100     0.000383126     0.00029935      0.781336
0.8      10      True    1000    1000    0.00162866      0.00307312      1.88689
0.8      10      False   100     100     0.000230783     0.000124958     0.541452
0.8      10      False   100     1000    0.000493393     0.000550654     1.11606
0.8      10      False   1000    100     0.000377167     0.000298581     0.791642
0.8      10      False   1000    1000    0.00165795      0.00305103      1.84024
0.8      25      True    100     100     0.000233496     0.000175241     0.75051
0.8      25      True    100     1000    0.00055654      0.00102658      1.84458
0.8      25      True    1000    100     0.000463814     0.000783267     1.68875
0.8      25      True    1000    1000    0.00186905      0.00755344      4.04132
0.8      25      False   100     100     0.000240243     0.000175047     0.728625
0.8      25      False   100     1000    0.000578102     0.00104499      1.80763
0.8      25      False   1000    100     0.000485113     0.000776849     1.60138
0.8      25      False   1000    1000    0.00211448      0.00752736      3.55992
```

##### Args:


*  <b>`sp_a`</b>: SparseTensor A, of rank 2.
*  <b>`b`</b>: A dense Matrix with the same dtype as sp_a.
*  <b>`adjoint_a`</b>: Use the adjoint of A in the matrix multiply.  If A is complex,
    this is transpose(conj(A)).  Otherwise it's transpose(A).
*  <b>`adjoint_b`</b>: Use the adjoint of B in the matrix multiply.  If B is complex,
    this is transpose(conj(B)).  Otherwise it's transpose(B).
*  <b>`name`</b>: A name prefix for the returned tensors (optional)

##### Returns:

  A dense matrix (pseudo-code in dense np.matrix notation):
    A = A.H if adjoint_a else A
    B = B.H if adjoint_b else B
    return A*B


