tensorflow/tensorflow/python/framework/sparse_tensor.py at v2.16.1 · tensorflow/tensorflow · GitHub

History

641 lines (521 loc) · 23.3 KB

Raw

100

101

102

103

104

105

106

107

108

109

110

111

112

113

114

115

116

117

118

119

120

121

122

123

124

125

126

127

128

129

130

131

132

133

134

135

136

137

138

139

140

141

142

143

144

145

146

147

148

149

150

151

152

153

154

155

156

157

158

159

160

161

162

163

164

165

166

167

168

169

170

171

172

173

174

175

176

177

178

179

180

181

182

183

184

185

186

187

188

189

190

191

192

193

194

195

196

197

198

199

200

201

202

203

204

205

206

207

208

209

210

211

212

213

214

215

216

217

218

219

220

221

222

223

224

225

226

227

228

229

230

231

232

233

234

235

236

237

238

239

240

241

242

243

244

245

246

247

248

249

250

251

252

253

254

255

256

257

258

259

260

261

262

263

264

265

266

267

268

269

270

271

272

273

274

275

276

277

278

279

280

281

282

283

284

285

286

287

288

289

290

291

292

293

294

295

296

297

298

299

300

301

302

303

304

305

306

307

308

309

310

311

312

313

314

315

316

317

318

319

320

321

322

323

324

325

326

327

328

329

330

331

332

333

334

335

336

337

338

339

340

341

342

343

344

345

346

347

348

349

350

351

352

353

354

355

356

357

358

359

360

361

362

363

364

365

366

367

368

369

370

371

372

373

374

375

376

377

378

379

380

381

382

383

384

385

386

387

388

389

390

391

392

393

394

395

396

397

398

399

400

401

402

403

404

405

406

407

408

409

410

411

412

413

414

415

416

417

418

419

420

421

422

423

424

425

426

427

428

429

430

431

432

433

434

435

436

437

438

439

440

441

442

443

444

445

446

447

448

449

450

451

452

453

454

455

456

457

458

459

460

461

462

463

464

465

466

467

468

469

470

471

472

473

474

475

476

477

478

479

480

481

482

483

484

485

486

487

488

489

490

491

492

493

494

495

496

497

498

499

500

501

502

503

504

505

506

507

508

509

510

511

512

513

514

515

516

517

518

519

520

521

522

523

524

525

526

527

528

529

530

531

532

533

534

535

536

537

538

539

540

541

542

543

544

545

546

547

548

549

550

551

552

553

554

555

556

557

558

559

560

561

562

563

564

565

566

567

568

569

570

571

572

573

574

575

576

577

578

579

580

581

582

583

584

585

586

587

588

589

590

591

592

593

594

595

596

597

598

599

600

601

602

603

604

605

606

607

608

609

610

611

612

613

614

615

616

617

618

619

620

621

622

623

624

625

626

627

628

629

630

631

632

633

634

635

636

637

638

639

640

641

# Licensed under the Apache License, Version 2.0 (the "License");

# you may not use this file except in compliance with the License.

# You may obtain a copy of the License at

# http://www.apache.org/licenses/LICENSE-2.0

# Unless required by applicable law or agreed to in writing, software

# distributed under the License is distributed on an "AS IS" BASIS,

# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

# See the License for the specific language governing permissions and

# limitations under the License.

# ==============================================================================

"""Sparse tensors."""

# pylint: disable=g-bad-name

import collections

import numpy as np

from tensorflow.core.protobuf import struct_pb2

from tensorflow.python import pywrap_tensorflow # pylint: disable=unused-import

from tensorflow.python import tf2

from tensorflow.python.framework import composite_tensor

from tensorflow.python.framework import constant_op

from tensorflow.python.framework import dtypes

from tensorflow.python.framework import ops

from tensorflow.python.framework import override_binary_operator

from tensorflow.python.framework import tensor

from tensorflow.python.framework import tensor_shape

from tensorflow.python.framework import tensor_spec

from tensorflow.python.framework import tensor_util

from tensorflow.python.framework import type_spec

from tensorflow.python.framework import type_spec_registry

from tensorflow.python.ops import array_ops_stack

from tensorflow.python.ops import gen_math_ops

from tensorflow.python.ops import gen_sparse_ops

from tensorflow.python.saved_model import nested_structure_coder

from tensorflow.python.types import internal

from tensorflow.python.util.tf_export import tf_export

# pylint: disable=protected-access

_eval_using_default_session = tensor._eval_using_default_session

_override_helper = tensor._override_helper

# pylint: enable=protected-access

@tf_export("sparse.SparseTensor", "SparseTensor")

class SparseTensor(internal.NativeObject, composite_tensor.CompositeTensor):

"""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 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:

```python

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

```python

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

```

represents the dense tensor

```python

[[1, 0, 0, 0]

[0, 0, 2, 0]

[0, 0, 0, 0]]

```

"""

@classmethod

def from_value(cls, sparse_tensor_value):

if not is_sparse(sparse_tensor_value):

raise TypeError(f"Argument sparse_tensor_value={sparse_tensor_value} "

"is neither a SparseTensor nor SparseTensorValue.")

return SparseTensor(

indices=sparse_tensor_value.indices,

values=sparse_tensor_value.values,

dense_shape=sparse_tensor_value.dense_shape)

def __init__(self, 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]`.

Raises:

ValueError: When building an eager SparseTensor if `dense_shape` is

unknown or contains unknown elements (None or -1).

"""

with ops.name_scope(None, "SparseTensor", [indices, values, dense_shape]):

indices = ops.convert_to_tensor(

indices, name="indices", dtype=dtypes.int64)

# TODO(touts): Consider adding mutable_values() when 'values'

# is a VariableOp and updating users of SparseTensor.

values = ops.convert_to_tensor(values, name="values")

dense_shape = ops.convert_to_tensor(

dense_shape, name="dense_shape", dtype=dtypes.int64)

dense_shape_default = tensor_util.constant_value_as_shape(dense_shape)

self._indices = indices

self._values = values

self._dense_shape = dense_shape

self._dense_shape_default = dense_shape_default

indices_shape = indices.shape.with_rank(2)

values_shape = values.shape.with_rank(1)

dense_shape_shape = dense_shape.shape.with_rank(1)

# Assert number of rows in indices match the number of elements in values.

indices_shape.dims[0].assert_is_compatible_with(values_shape.dims[0])

# Assert number of columns in indices matches the number of elements in

# dense_shape.

indices_shape.dims[1].assert_is_compatible_with(dense_shape_shape.dims[0])

def get_shape(self) -> tensor_shape.TensorShape:

"""Get the `TensorShape` representing the shape of the dense tensor.

Returns:

A `TensorShape` object.

"""

return self._dense_shape_default

@property

def indices(self):

"""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.

"""

return self._indices

@property

def values(self):

"""The non-zero values in the represented dense tensor.

Returns:

A 1-D Tensor of any data type.

"""

return self._values

def with_values(self, 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)>

"""

return SparseTensor(self._indices, new_values, self._dense_shape)

@property

def op(self) -> ops.Operation:

"""The `Operation` that produces `values` as an output."""

return self._values.op

@property

def dtype(self):

"""The `DType` of elements in this tensor."""

return self._values.dtype

@property

def dense_shape(self):

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

return self._dense_shape

@property

def shape(self):

"""Get the `TensorShape` representing the shape of the dense tensor.

Returns:

A `TensorShape` object.

"""

return self._dense_shape_default

def set_shape(self, 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.

"""

if not isinstance(shape, tensor_shape.TensorShape):

shape = tensor_shape.TensorShape(shape)

self._dense_shape_default = self._dense_shape_default.merge_with(shape)

@property

def graph(self):

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

return self._indices.graph

def __repr__(self):

return "SparseTensor(indices=%s, values=%s, dense_shape=%s)" % (

self._indices, self._values, self._dense_shape)

def eval(self, 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.

"""

indices, values, dense_shape = _eval_using_default_session(

[self.indices, self.values, self.dense_shape], feed_dict, self.graph,

session)

return SparseTensorValue(indices, values, dense_shape)

@staticmethod

def _override_operator(operator, func):

_override_helper(SparseTensor, operator, func)

@property

def _type_spec(self):

return SparseTensorSpec(self.shape, self.dtype)

def _shape_invariant_to_type_spec(self, shape):

# From the tf.while_loop docs: "If a loop variable is a SparseTensor, the

# shape invariant must be TensorShape([r]) where r is the rank of the dense

# tensor represented by the sparse tensor. It means the shapes of the three

# tensors of the SparseTensor are ([None], [None, r], [r]). NOTE: The shape

# invariant here is the shape of the SparseTensor.dense_shape property. It

# must be the shape of a vector.

if shape.ndims is not None and shape.ndims != 1:

raise ValueError(f"Expected a shape with 1 dimension. Obtained: {shape} "

f"which has {shape.ndims} dimensions.")

rank = tensor_shape.dimension_value(shape[0])

return SparseTensorSpec(tensor_shape.unknown_shape(rank), self.dtype)

def consumers(self):

return self._consumers()

def _numpy(self):

"""Returns a numpy `array` with the values for this `SparseTensor`.

Requires that this `SparseTensor` was constructed in eager execution mode.

"""

if not self._is_eager():

raise ValueError("SparseTensor.numpy() is only supported in eager mode.")

arr = np.zeros(self.dense_shape, dtype=self.dtype.as_numpy_dtype())

for i, v in zip(self.indices, self.values):

arr[tuple(i)] = v

return arr

def _is_eager(self):

"""Returns True if this `SparseTensor` was constructed in eager execution.

Requires that each individual component of `SparseTensor`

(`indices`, `values` and `dense_shape`) is an instance of `EagerTensor`.

"""

return all(

isinstance(t, ops.EagerTensor)

for t in (self.indices, self.values, self.dense_shape))

SparseTensorValue = collections.namedtuple("SparseTensorValue",

["indices", "values", "dense_shape"])

tf_export(v1=["SparseTensorValue"])(SparseTensorValue)

@tf_export("SparseTensorSpec")

@type_spec_registry.register("tf.SparseTensorSpec")

class SparseTensorSpec(type_spec.BatchableTypeSpec):

"""Type specification for a `tf.sparse.SparseTensor`."""

__slots__ = ["_shape", "_dtype"]

value_type = property(lambda self: SparseTensor)

def __init__(self, shape=None, dtype=dtypes.float32):

"""Constructs a type specification for a `tf.sparse.SparseTensor`.

Args:

shape: The dense shape of the `SparseTensor`, or `None` to allow any dense

shape.

dtype: `tf.DType` of values in the `SparseTensor`.

"""

self._shape = tensor_shape.as_shape(shape)

self._dtype = dtypes.as_dtype(dtype)

def _serialize(self):

return (self._shape, self._dtype)

@property

def dtype(self):

"""The `tf.dtypes.DType` specified by this type for the SparseTensor."""

return self._dtype

@property

def shape(self):

"""The `tf.TensorShape` specified by this type for the SparseTensor."""

return self._shape

@property

def _component_specs(self):

rank = self._shape.ndims

num_values = None

return [

tensor_spec.TensorSpec([num_values, rank], dtypes.int64),

tensor_spec.TensorSpec([num_values], self._dtype),

tensor_spec.TensorSpec([rank], dtypes.int64)]

def _to_components(self, value):

if isinstance(value, SparseTensorValue):

value = SparseTensor.from_value(value)

return [value.indices, value.values, value.dense_shape]

def _from_components(self, tensor_list):

if (all(isinstance(t, np.ndarray) for t in tensor_list) and

not tf2.enabled()):

return SparseTensorValue(*tensor_list)

else:

result = SparseTensor(*tensor_list)

# Augment the static dense shape with the shape carried by the spec.

result._dense_shape_default = result._dense_shape_default.merge_with( # pylint: disable=protected-access

self._shape)

return result

# The SparseTensorSpec tensor_list encoding uses (de)serialize_sparse ops

# to (un)box the component tensors in a way that allows for batching &

# unbatching.

@property

def _flat_tensor_specs(self):

# NOTE(mrry): The default flat shape of a boxed `SparseTensor` is `(3,)`,

# but a `SparseTensorSpec` can also represent a batch of boxed

# `SparseTensor` objects with shape `(..., 3)` (and batches of batches,

# etc.), so the flat shape must be unknown.

return [tensor_spec.TensorSpec(None, dtypes.variant)]

def _to_tensor_list(self, value):

value = SparseTensor.from_value(value)

return [gen_sparse_ops.serialize_sparse(

value.indices, value.values, value.dense_shape,

out_type=dtypes.variant)]

def _to_batched_tensor_list(self, value):

dense_shape = tensor_util.constant_value_as_shape(value.dense_shape)

if self._shape.merge_with(dense_shape).ndims == 0:

raise ValueError(

"Unbatching a sparse tensor is only supported for rank >= 1. "

f"Obtained input: {value}.")

return [gen_sparse_ops.serialize_many_sparse(

value.indices, value.values, value.dense_shape,

out_type=dtypes.variant)]

def _from_compatible_tensor_list(self, tensor_list):

tensor_list = gen_sparse_ops.deserialize_sparse(tensor_list[0], self._dtype)

indices, values, dense_shape = tensor_list

rank = self._shape.ndims

indices.set_shape([None, rank])

# We restore the dense_shape from the SparseTypeSpec. This is necessary

# for shape inference when using placeholder SparseTensors in function

# tracing.

if self._shape.is_fully_defined():

dense_shape = ops.convert_to_tensor(

self._shape, dtype=dtypes.int64, name="shape")

elif (self._shape.rank is not None and

any(dim.value is not None for dim in self._shape.dims)):

pieces = array_ops_stack.unstack(dense_shape, num=self._shape.rank)

for i, dim in enumerate(self._shape.dims):

if dim.value is not None:

pieces[i] = constant_op.constant(dim.value, dense_shape.dtype)

dense_shape = array_ops_stack.stack(pieces)

else:

dense_shape.set_shape([rank])

return SparseTensor(indices, values, dense_shape)

def _batch(self, batch_size):

return SparseTensorSpec(

tensor_shape.TensorShape([batch_size]).concatenate(self._shape),

self._dtype)

def _unbatch(self):

if self._shape.ndims == 0:

raise ValueError("Unbatching a tensor is only supported for rank >= 1")

return SparseTensorSpec(self._shape[1:], self._dtype)

def _to_legacy_output_types(self):

return self._dtype

def _to_legacy_output_shapes(self):

return self._shape

def _to_legacy_output_classes(self):

return SparseTensor

@classmethod

def from_value(cls, value):

if isinstance(value, SparseTensor):

return cls(value.shape, value.dtype)

if isinstance(value, SparseTensorValue):

if isinstance(value.values, np.ndarray):

return cls(value.dense_shape, value.values.dtype)

else:

return cls.from_value(SparseTensor.from_value(value))

else:

raise TypeError("Expected SparseTensor or SparseTensorValue. Received: "

f"{value} of type {type(value).__name__}.")

nested_structure_coder.register_codec(

nested_structure_coder.BuiltInTypeSpecCodec(

SparseTensorSpec, struct_pb2.TypeSpecProto.SPARSE_TENSOR_SPEC

)

# TODO(b/133606651) Delete the SparseTensor registration when CompositeTensor

# is updated to define a _type_spec field (since registration will be

# automatic). Do *not* delete the SparseTensorValue registration.

type_spec.register_type_spec_from_value_converter(

SparseTensor, SparseTensorSpec.from_value)

type_spec.register_type_spec_from_value_converter(

SparseTensorValue, SparseTensorSpec.from_value)

@tf_export(v1=["convert_to_tensor_or_sparse_tensor"])

def convert_to_tensor_or_sparse_tensor(value, dtype=None, name=None):

"""Converts value to a `SparseTensor` or `Tensor`.

Args:

value: A `SparseTensor`, `SparseTensorValue`, or an object whose type has a

registered `Tensor` conversion function.

dtype: Optional element type for the returned tensor. If missing, the type

is inferred from the type of `value`.

name: Optional name to use if a new `Tensor` is created.

Returns:

A `SparseTensor` or `Tensor` based on `value`.

Raises:

RuntimeError: If result type is incompatible with `dtype`.

"""

if dtype is not None:

dtype = dtypes.as_dtype(dtype)

if isinstance(value, SparseTensorValue):

value = SparseTensor.from_value(value)

if isinstance(value, SparseTensor):

if dtype and not dtype.is_compatible_with(value.dtype):

raise RuntimeError(f"Sparse dtype mismatch. Requested: {dtype.name}, "

f" Actual: {value.dtype.name}")

return value

return ops.convert_to_tensor(value, dtype=dtype, name=name)

def is_sparse(x):

"""Check whether `x` is sparse.

Check whether an object is a `tf.sparse.SparseTensor` or

`tf.compat.v1.SparseTensorValue`.

Args:

x: A python object to check.

Returns:

`True` iff `x` is a `tf.sparse.SparseTensor` or

`tf.compat.v1.SparseTensorValue`.

"""

return isinstance(x, (SparseTensor, SparseTensorValue))

# Conversion table for __truediv__. None entries mean no conversion required.

_TRUEDIV_TABLE = {

dtypes.uint8: dtypes.float32,

dtypes.int8: dtypes.float32,

dtypes.uint16: dtypes.float32,

dtypes.int16: dtypes.float32,

dtypes.uint32: dtypes.float64,

dtypes.int32: dtypes.float64,

dtypes.uint64: dtypes.float64,

dtypes.int64: dtypes.float64,

dtypes.bfloat16: None,

dtypes.float16: None,

dtypes.float32: None,

dtypes.float64: None,

dtypes.complex64: None,

dtypes.complex128: None,

}

# NOTE: the support of "sparse (true)div dense" is currently not baked in into

# "tf.(true_)div()". Until such an API decision is made, the supported usage is

# to explicitly use the "/" operator to invoke either truediv or div.

def _sparse_dense_truediv(sp_indices, sp_values, sp_shape, y, name=None):

"""Internal helper function for 'sp_t / dense_t'."""

with ops.name_scope(

name, "truediv", [sp_indices, sp_values, sp_shape, y]

) as name:

sp_values = ops.convert_to_tensor(sp_values, name="sp_values")

y = ops.convert_to_tensor(y, name="y")

x_dtype = sp_values.dtype.base_dtype

y_dtype = y.dtype.base_dtype

if x_dtype != y_dtype:

raise TypeError(

"`x` and `y` must have the same dtype, "

f"got {x_dtype!r} != {y_dtype!r}."

)

try:

dtype = _TRUEDIV_TABLE[x_dtype]

except KeyError as exc:

raise TypeError(

f"Invalid dtype {x_dtype!r} in __truediv__. Expected one "

f"of {{{', '.join([repr(x) for x in _TRUEDIV_TABLE.keys()])}}}."

) from exc

if dtype is not None:

sp_values = gen_math_ops.cast(sp_values, dtype)

y = gen_math_ops.cast(y, dtype)

return gen_sparse_ops.sparse_dense_cwise_div(

sp_indices, sp_values, sp_shape, y, name=name

)

# NOTE(aselle): When integer division is added for sparse_dense_cwise,

# div, truediv, and floordiv should be delegated appropriately for

# Python semantics, analogous to dense cwise tensor operations.

override_binary_operator.override_binary_operator_helper(

gen_sparse_ops.sparse_dense_cwise_div, "div", SparseTensor

) # pylint: disable=protected-access

override_binary_operator.override_binary_operator_helper(

_sparse_dense_truediv, "truediv", SparseTensor

) # pylint: disable=protected-access

override_binary_operator.override_binary_operator_helper(

gen_sparse_ops.sparse_dense_cwise_mul, "mul", SparseTensor

) # pylint: disable=protected-access

tensorflow/tensorflow/python/framework/sparse_tensor.py at v2.16.1 · tensorflow/tensorflow · GitHub

Uh oh!

Expand file tree

Breadcrumbs

sparse_tensor.py

Latest commit

History

Breadcrumbs

sparse_tensor.py

File metadata and controls