1 | // This file is MACHINE GENERATED! Do not edit. |
2 | |
3 | #ifndef TENSORFLOW_CC_OPS_DATASET_OPS_INTERNAL_H_ |
4 | #define TENSORFLOW_CC_OPS_DATASET_OPS_INTERNAL_H_ |
5 | |
6 | // This file is MACHINE GENERATED! Do not edit. |
7 | |
8 | #include "tensorflow/cc/framework/ops.h" |
9 | #include "tensorflow/cc/framework/scope.h" |
10 | #include "tensorflow/core/framework/tensor.h" |
11 | #include "tensorflow/core/framework/tensor_shape.h" |
12 | #include "tensorflow/core/framework/types.h" |
13 | #include "tensorflow/core/lib/gtl/array_slice.h" |
14 | |
15 | namespace tensorflow { |
16 | namespace ops { |
17 | namespace internal { |
18 | // NOTE: This namespace has internal TensorFlow details that |
19 | // are not part of TensorFlow's public API. |
20 | |
21 | /// @defgroup dataset_ops_internal Dataset Ops Internal |
22 | /// @{ |
23 | |
24 | /// A container for an iterator resource. |
25 | /// |
26 | /// Args: |
27 | /// * scope: A Scope object |
28 | /// |
29 | /// Returns: |
30 | /// * `Output` handle: A handle to the iterator that can be passed to a "MakeIterator" or |
31 | /// "IteratorGetNext" op. In contrast to Iterator, AnonymousIterator prevents |
32 | /// resource sharing by name, and does not keep a reference to the resource |
33 | /// container. |
34 | /// * `Output` deleter: A variant deleter that should be passed into the op that deletes the iterator. |
35 | class AnonymousIteratorV2 { |
36 | public: |
37 | AnonymousIteratorV2(const ::tensorflow::Scope& scope, const DataTypeSlice& |
38 | output_types, const gtl::ArraySlice<PartialTensorShape>& |
39 | output_shapes); |
40 | |
41 | Operation operation; |
42 | ::tensorflow::Output handle; |
43 | ::tensorflow::Output deleter; |
44 | }; |
45 | |
46 | /// A container for an iterator resource. |
47 | /// |
48 | /// Args: |
49 | /// * scope: A Scope object |
50 | /// |
51 | /// Returns: |
52 | /// * `Output`: A handle to the iterator that can be passed to a "MakeIterator" or |
53 | /// "IteratorGetNext" op. In contrast to Iterator, AnonymousIterator prevents |
54 | /// resource sharing by name, and does not keep a reference to the resource |
55 | /// container. |
56 | class AnonymousIteratorV3 { |
57 | public: |
58 | AnonymousIteratorV3(const ::tensorflow::Scope& scope, const DataTypeSlice& |
59 | output_types, const gtl::ArraySlice<PartialTensorShape>& |
60 | output_shapes); |
61 | operator ::tensorflow::Output() const { return handle; } |
62 | operator ::tensorflow::Input() const { return handle; } |
63 | ::tensorflow::Node* node() const { return handle.node(); } |
64 | |
65 | Operation operation; |
66 | ::tensorflow::Output handle; |
67 | }; |
68 | |
69 | /// TODO: add doc. |
70 | /// |
71 | /// Args: |
72 | /// * scope: A Scope object |
73 | /// |
74 | /// Returns: |
75 | /// * `Output` handle |
76 | /// * `Output` deleter |
77 | class AnonymousMemoryCache { |
78 | public: |
79 | AnonymousMemoryCache(const ::tensorflow::Scope& scope); |
80 | |
81 | Operation operation; |
82 | ::tensorflow::Output handle; |
83 | ::tensorflow::Output deleter; |
84 | }; |
85 | |
86 | /// A container for a multi device iterator resource. |
87 | /// |
88 | /// Args: |
89 | /// * scope: A Scope object |
90 | /// |
91 | /// Returns: |
92 | /// * `Output` handle: A handle to a multi device iterator that can be passed to a |
93 | /// "MultiDeviceIteratorGetNextFromShard" op. In contrast to MultiDeviceIterator, |
94 | /// AnonymousIterator prevents resource sharing by name, and does not keep a |
95 | /// reference to the resource container. |
96 | /// * `Output` deleter: A variant deleter that should be passed into the op that deletes the iterator. |
97 | class AnonymousMultiDeviceIterator { |
98 | public: |
99 | AnonymousMultiDeviceIterator(const ::tensorflow::Scope& scope, const |
100 | gtl::ArraySlice<::tensorflow::tstring>& devices, |
101 | const DataTypeSlice& output_types, const |
102 | gtl::ArraySlice<PartialTensorShape>& |
103 | output_shapes); |
104 | |
105 | Operation operation; |
106 | ::tensorflow::Output handle; |
107 | ::tensorflow::Output deleter; |
108 | }; |
109 | |
110 | /// A container for a multi device iterator resource. |
111 | /// |
112 | /// Args: |
113 | /// * scope: A Scope object |
114 | /// |
115 | /// Returns: |
116 | /// * `Output`: A handle to a multi device iterator that can be passed to a |
117 | /// "MultiDeviceIteratorGetNextFromShard" op. In contrast to MultiDeviceIterator, |
118 | /// AnonymousIterator prevents resource sharing by name, and does not keep a |
119 | /// reference to the resource container. |
120 | class AnonymousMultiDeviceIteratorV3 { |
121 | public: |
122 | AnonymousMultiDeviceIteratorV3(const ::tensorflow::Scope& scope, const |
123 | gtl::ArraySlice<::tensorflow::tstring>& devices, |
124 | const DataTypeSlice& output_types, const |
125 | gtl::ArraySlice<PartialTensorShape>& |
126 | output_shapes); |
127 | operator ::tensorflow::Output() const { return handle; } |
128 | operator ::tensorflow::Input() const { return handle; } |
129 | ::tensorflow::Node* node() const { return handle.node(); } |
130 | |
131 | Operation operation; |
132 | ::tensorflow::Output handle; |
133 | }; |
134 | |
135 | /// TODO: add doc. |
136 | /// |
137 | /// Args: |
138 | /// * scope: A Scope object |
139 | /// |
140 | /// Returns: |
141 | /// * `Output` handle |
142 | /// * `Output` deleter |
143 | class AnonymousRandomSeedGenerator { |
144 | public: |
145 | AnonymousRandomSeedGenerator(const ::tensorflow::Scope& scope, |
146 | ::tensorflow::Input seed, ::tensorflow::Input |
147 | seed2); |
148 | |
149 | Operation operation; |
150 | ::tensorflow::Output handle; |
151 | ::tensorflow::Output deleter; |
152 | }; |
153 | |
154 | /// TODO: add doc. |
155 | /// |
156 | /// Args: |
157 | /// * scope: A Scope object |
158 | /// |
159 | /// Returns: |
160 | /// * `Output` handle |
161 | /// * `Output` deleter |
162 | class AnonymousSeedGenerator { |
163 | public: |
164 | AnonymousSeedGenerator(const ::tensorflow::Scope& scope, ::tensorflow::Input |
165 | seed, ::tensorflow::Input seed2, ::tensorflow::Input |
166 | reshuffle); |
167 | |
168 | Operation operation; |
169 | ::tensorflow::Output handle; |
170 | ::tensorflow::Output deleter; |
171 | }; |
172 | |
173 | /// Creates a dataset that batches `batch_size` elements from `input_dataset`. |
174 | /// |
175 | /// Args: |
176 | /// * scope: A Scope object |
177 | /// * batch_size: A scalar representing the number of elements to accumulate in a |
178 | /// batch. |
179 | /// |
180 | /// Returns: |
181 | /// * `Output`: The handle tensor. |
182 | class BatchDataset { |
183 | public: |
184 | /// Optional attribute setters for BatchDataset |
185 | struct Attrs { |
186 | /// Defaults to "" |
187 | TF_MUST_USE_RESULT Attrs Metadata(StringPiece x) { |
188 | Attrs ret = *this; |
189 | ret.metadata_ = x; |
190 | return ret; |
191 | } |
192 | |
193 | StringPiece metadata_ = "" ; |
194 | }; |
195 | BatchDataset(const ::tensorflow::Scope& scope, ::tensorflow::Input |
196 | input_dataset, ::tensorflow::Input batch_size, const |
197 | DataTypeSlice& output_types, const |
198 | gtl::ArraySlice<PartialTensorShape>& output_shapes); |
199 | BatchDataset(const ::tensorflow::Scope& scope, ::tensorflow::Input |
200 | input_dataset, ::tensorflow::Input batch_size, const |
201 | DataTypeSlice& output_types, const |
202 | gtl::ArraySlice<PartialTensorShape>& output_shapes, const |
203 | BatchDataset::Attrs& attrs); |
204 | operator ::tensorflow::Output() const { return handle; } |
205 | operator ::tensorflow::Input() const { return handle; } |
206 | ::tensorflow::Node* node() const { return handle.node(); } |
207 | |
208 | static Attrs Metadata(StringPiece x) { |
209 | return Attrs().Metadata(x); |
210 | } |
211 | |
212 | Operation operation; |
213 | ::tensorflow::Output handle; |
214 | }; |
215 | |
216 | /// Creates a dataset that batches `batch_size` elements from `input_dataset`. |
217 | /// |
218 | /// Args: |
219 | /// * scope: A Scope object |
220 | /// * batch_size: A scalar representing the number of elements to accumulate in a batch. |
221 | /// * drop_remainder: A scalar representing whether the last batch should be dropped in case its size |
222 | /// is smaller than desired. |
223 | /// |
224 | /// Returns: |
225 | /// * `Output`: The handle tensor. |
226 | class BatchDatasetV2 { |
227 | public: |
228 | /// Optional attribute setters for BatchDatasetV2 |
229 | struct Attrs { |
230 | /// Defaults to false |
231 | TF_MUST_USE_RESULT Attrs ParallelCopy(bool x) { |
232 | Attrs ret = *this; |
233 | ret.parallel_copy_ = x; |
234 | return ret; |
235 | } |
236 | |
237 | /// Defaults to "" |
238 | TF_MUST_USE_RESULT Attrs Metadata(StringPiece x) { |
239 | Attrs ret = *this; |
240 | ret.metadata_ = x; |
241 | return ret; |
242 | } |
243 | |
244 | bool parallel_copy_ = false; |
245 | StringPiece metadata_ = "" ; |
246 | }; |
247 | BatchDatasetV2(const ::tensorflow::Scope& scope, ::tensorflow::Input |
248 | input_dataset, ::tensorflow::Input batch_size, |
249 | ::tensorflow::Input drop_remainder, const DataTypeSlice& |
250 | output_types, const gtl::ArraySlice<PartialTensorShape>& |
251 | output_shapes); |
252 | BatchDatasetV2(const ::tensorflow::Scope& scope, ::tensorflow::Input |
253 | input_dataset, ::tensorflow::Input batch_size, |
254 | ::tensorflow::Input drop_remainder, const DataTypeSlice& |
255 | output_types, const gtl::ArraySlice<PartialTensorShape>& |
256 | output_shapes, const BatchDatasetV2::Attrs& attrs); |
257 | operator ::tensorflow::Output() const { return handle; } |
258 | operator ::tensorflow::Input() const { return handle; } |
259 | ::tensorflow::Node* node() const { return handle.node(); } |
260 | |
261 | static Attrs ParallelCopy(bool x) { |
262 | return Attrs().ParallelCopy(x); |
263 | } |
264 | static Attrs Metadata(StringPiece x) { |
265 | return Attrs().Metadata(x); |
266 | } |
267 | |
268 | Operation operation; |
269 | ::tensorflow::Output handle; |
270 | }; |
271 | |
272 | /// Creates a dataset that caches elements from `input_dataset`. |
273 | /// |
274 | /// A CacheDataset will iterate over the input_dataset, and store tensors. If the |
275 | /// cache already exists, the cache will be used. If the cache is inappropriate |
276 | /// (e.g. cannot be opened, contains tensors of the wrong shape / size), an error |
277 | /// will the returned when used. |
278 | /// |
279 | /// Args: |
280 | /// * scope: A Scope object |
281 | /// * filename: A path on the filesystem where we should cache the dataset. Note: this |
282 | /// will be a directory. |
283 | /// |
284 | /// Returns: |
285 | /// * `Output`: The handle tensor. |
286 | class CacheDataset { |
287 | public: |
288 | /// Optional attribute setters for CacheDataset |
289 | struct Attrs { |
290 | /// Defaults to "" |
291 | TF_MUST_USE_RESULT Attrs Metadata(StringPiece x) { |
292 | Attrs ret = *this; |
293 | ret.metadata_ = x; |
294 | return ret; |
295 | } |
296 | |
297 | StringPiece metadata_ = "" ; |
298 | }; |
299 | CacheDataset(const ::tensorflow::Scope& scope, ::tensorflow::Input |
300 | input_dataset, ::tensorflow::Input filename, const DataTypeSlice& |
301 | output_types, const gtl::ArraySlice<PartialTensorShape>& |
302 | output_shapes); |
303 | CacheDataset(const ::tensorflow::Scope& scope, ::tensorflow::Input |
304 | input_dataset, ::tensorflow::Input filename, const DataTypeSlice& |
305 | output_types, const gtl::ArraySlice<PartialTensorShape>& |
306 | output_shapes, const CacheDataset::Attrs& attrs); |
307 | operator ::tensorflow::Output() const { return handle; } |
308 | operator ::tensorflow::Input() const { return handle; } |
309 | ::tensorflow::Node* node() const { return handle.node(); } |
310 | |
311 | static Attrs Metadata(StringPiece x) { |
312 | return Attrs().Metadata(x); |
313 | } |
314 | |
315 | Operation operation; |
316 | ::tensorflow::Output handle; |
317 | }; |
318 | |
319 | /// TODO: add doc. |
320 | /// |
321 | /// Args: |
322 | /// * scope: A Scope object |
323 | /// |
324 | /// Returns: |
325 | /// * `Output`: The handle tensor. |
326 | class CacheDatasetV2 { |
327 | public: |
328 | /// Optional attribute setters for CacheDatasetV2 |
329 | struct Attrs { |
330 | /// Defaults to "" |
331 | TF_MUST_USE_RESULT Attrs Metadata(StringPiece x) { |
332 | Attrs ret = *this; |
333 | ret.metadata_ = x; |
334 | return ret; |
335 | } |
336 | |
337 | StringPiece metadata_ = "" ; |
338 | }; |
339 | CacheDatasetV2(const ::tensorflow::Scope& scope, ::tensorflow::Input |
340 | input_dataset, ::tensorflow::Input filename, ::tensorflow::Input |
341 | cache, const DataTypeSlice& output_types, const |
342 | gtl::ArraySlice<PartialTensorShape>& output_shapes); |
343 | CacheDatasetV2(const ::tensorflow::Scope& scope, ::tensorflow::Input |
344 | input_dataset, ::tensorflow::Input filename, ::tensorflow::Input |
345 | cache, const DataTypeSlice& output_types, const |
346 | gtl::ArraySlice<PartialTensorShape>& output_shapes, const |
347 | CacheDatasetV2::Attrs& attrs); |
348 | operator ::tensorflow::Output() const { return handle; } |
349 | operator ::tensorflow::Input() const { return handle; } |
350 | ::tensorflow::Node* node() const { return handle.node(); } |
351 | |
352 | static Attrs Metadata(StringPiece x) { |
353 | return Attrs().Metadata(x); |
354 | } |
355 | |
356 | Operation operation; |
357 | ::tensorflow::Output handle; |
358 | }; |
359 | |
360 | /// Creates a dataset that concatenates `input_dataset` with `another_dataset`. |
361 | /// |
362 | /// Args: |
363 | /// * scope: A Scope object |
364 | /// |
365 | /// Returns: |
366 | /// * `Output`: The handle tensor. |
367 | class ConcatenateDataset { |
368 | public: |
369 | /// Optional attribute setters for ConcatenateDataset |
370 | struct Attrs { |
371 | /// Defaults to "" |
372 | TF_MUST_USE_RESULT Attrs Metadata(StringPiece x) { |
373 | Attrs ret = *this; |
374 | ret.metadata_ = x; |
375 | return ret; |
376 | } |
377 | |
378 | StringPiece metadata_ = "" ; |
379 | }; |
380 | ConcatenateDataset(const ::tensorflow::Scope& scope, ::tensorflow::Input |
381 | input_dataset, ::tensorflow::Input another_dataset, const |
382 | DataTypeSlice& output_types, const |
383 | gtl::ArraySlice<PartialTensorShape>& output_shapes); |
384 | ConcatenateDataset(const ::tensorflow::Scope& scope, ::tensorflow::Input |
385 | input_dataset, ::tensorflow::Input another_dataset, const |
386 | DataTypeSlice& output_types, const |
387 | gtl::ArraySlice<PartialTensorShape>& output_shapes, const |
388 | ConcatenateDataset::Attrs& attrs); |
389 | operator ::tensorflow::Output() const { return handle; } |
390 | operator ::tensorflow::Input() const { return handle; } |
391 | ::tensorflow::Node* node() const { return handle.node(); } |
392 | |
393 | static Attrs Metadata(StringPiece x) { |
394 | return Attrs().Metadata(x); |
395 | } |
396 | |
397 | Operation operation; |
398 | ::tensorflow::Output handle; |
399 | }; |
400 | |
401 | /// Returns the cardinality of `input_dataset`. |
402 | /// |
403 | /// Returns the cardinality of `input_dataset`. |
404 | /// |
405 | /// Args: |
406 | /// * scope: A Scope object |
407 | /// * input_dataset: A variant tensor representing the dataset to return cardinality for. |
408 | /// |
409 | /// Returns: |
410 | /// * `Output`: The cardinality of `input_dataset`. Named constants are used to represent |
411 | /// infinite and unknown cardinality. |
412 | class DatasetCardinality { |
413 | public: |
414 | DatasetCardinality(const ::tensorflow::Scope& scope, ::tensorflow::Input |
415 | input_dataset); |
416 | operator ::tensorflow::Output() const { return cardinality; } |
417 | operator ::tensorflow::Input() const { return cardinality; } |
418 | ::tensorflow::Node* node() const { return cardinality.node(); } |
419 | |
420 | Operation operation; |
421 | ::tensorflow::Output cardinality; |
422 | }; |
423 | |
424 | /// Returns a serialized GraphDef representing `input_dataset`. |
425 | /// |
426 | /// Returns a graph representation for `input_dataset`. |
427 | /// |
428 | /// Args: |
429 | /// * scope: A Scope object |
430 | /// * input_dataset: A variant tensor representing the dataset to return the graph representation for. |
431 | /// |
432 | /// Returns: |
433 | /// * `Output`: The graph representation of the dataset (as serialized GraphDef). |
434 | class DatasetToGraph { |
435 | public: |
436 | /// Optional attribute setters for DatasetToGraph |
437 | struct Attrs { |
438 | /// Defaults to [] |
439 | TF_MUST_USE_RESULT Attrs StatefulWhitelist(const gtl::ArraySlice<::tensorflow::tstring>& x) { |
440 | Attrs ret = *this; |
441 | ret.stateful_whitelist_ = x; |
442 | return ret; |
443 | } |
444 | |
445 | /// Defaults to false |
446 | TF_MUST_USE_RESULT Attrs AllowStateful(bool x) { |
447 | Attrs ret = *this; |
448 | ret.allow_stateful_ = x; |
449 | return ret; |
450 | } |
451 | |
452 | /// Defaults to false |
453 | TF_MUST_USE_RESULT Attrs StripDeviceAssignment(bool x) { |
454 | Attrs ret = *this; |
455 | ret.strip_device_assignment_ = x; |
456 | return ret; |
457 | } |
458 | |
459 | gtl::ArraySlice<::tensorflow::tstring> stateful_whitelist_ = {}; |
460 | bool allow_stateful_ = false; |
461 | bool strip_device_assignment_ = false; |
462 | }; |
463 | DatasetToGraph(const ::tensorflow::Scope& scope, ::tensorflow::Input |
464 | input_dataset); |
465 | DatasetToGraph(const ::tensorflow::Scope& scope, ::tensorflow::Input |
466 | input_dataset, const DatasetToGraph::Attrs& attrs); |
467 | operator ::tensorflow::Output() const { return graph; } |
468 | operator ::tensorflow::Input() const { return graph; } |
469 | ::tensorflow::Node* node() const { return graph.node(); } |
470 | |
471 | static Attrs StatefulWhitelist(const gtl::ArraySlice<::tensorflow::tstring>& x) { |
472 | return Attrs().StatefulWhitelist(x); |
473 | } |
474 | static Attrs AllowStateful(bool x) { |
475 | return Attrs().AllowStateful(x); |
476 | } |
477 | static Attrs StripDeviceAssignment(bool x) { |
478 | return Attrs().StripDeviceAssignment(x); |
479 | } |
480 | |
481 | Operation operation; |
482 | ::tensorflow::Output graph; |
483 | }; |
484 | |
485 | /// Returns a serialized GraphDef representing `input_dataset`. |
486 | /// |
487 | /// Returns a graph representation for `input_dataset`. |
488 | /// |
489 | /// Args: |
490 | /// * scope: A Scope object |
491 | /// * input_dataset: A variant tensor representing the dataset to return the graph representation for. |
492 | /// |
493 | /// Returns: |
494 | /// * `Output`: The graph representation of the dataset (as serialized GraphDef). |
495 | class DatasetToGraphV2 { |
496 | public: |
497 | /// Optional attribute setters for DatasetToGraphV2 |
498 | struct Attrs { |
499 | /// Defaults to 0 |
500 | TF_MUST_USE_RESULT Attrs ExternalStatePolicy(int64 x) { |
501 | Attrs ret = *this; |
502 | ret.external_state_policy_ = x; |
503 | return ret; |
504 | } |
505 | |
506 | /// Defaults to false |
507 | TF_MUST_USE_RESULT Attrs StripDeviceAssignment(bool x) { |
508 | Attrs ret = *this; |
509 | ret.strip_device_assignment_ = x; |
510 | return ret; |
511 | } |
512 | |
513 | int64 external_state_policy_ = 0; |
514 | bool strip_device_assignment_ = false; |
515 | }; |
516 | DatasetToGraphV2(const ::tensorflow::Scope& scope, ::tensorflow::Input |
517 | input_dataset); |
518 | DatasetToGraphV2(const ::tensorflow::Scope& scope, ::tensorflow::Input |
519 | input_dataset, const DatasetToGraphV2::Attrs& attrs); |
520 | operator ::tensorflow::Output() const { return graph; } |
521 | operator ::tensorflow::Input() const { return graph; } |
522 | ::tensorflow::Node* node() const { return graph.node(); } |
523 | |
524 | static Attrs ExternalStatePolicy(int64 x) { |
525 | return Attrs().ExternalStatePolicy(x); |
526 | } |
527 | static Attrs StripDeviceAssignment(bool x) { |
528 | return Attrs().StripDeviceAssignment(x); |
529 | } |
530 | |
531 | Operation operation; |
532 | ::tensorflow::Output graph; |
533 | }; |
534 | |
535 | /// Outputs the single element from the given dataset. |
536 | /// |
537 | /// Args: |
538 | /// * scope: A Scope object |
539 | /// * dataset: A handle to a dataset that contains a single element. |
540 | /// |
541 | /// Returns: |
542 | /// * `OutputList`: The components of the single element of `input`. |
543 | class DatasetToSingleElement { |
544 | public: |
545 | /// Optional attribute setters for DatasetToSingleElement |
546 | struct Attrs { |
547 | /// Defaults to "" |
548 | TF_MUST_USE_RESULT Attrs Metadata(StringPiece x) { |
549 | Attrs ret = *this; |
550 | ret.metadata_ = x; |
551 | return ret; |
552 | } |
553 | |
554 | StringPiece metadata_ = "" ; |
555 | }; |
556 | DatasetToSingleElement(const ::tensorflow::Scope& scope, ::tensorflow::Input |
557 | dataset, const DataTypeSlice& output_types, const |
558 | gtl::ArraySlice<PartialTensorShape>& output_shapes); |
559 | DatasetToSingleElement(const ::tensorflow::Scope& scope, ::tensorflow::Input |
560 | dataset, const DataTypeSlice& output_types, const |
561 | gtl::ArraySlice<PartialTensorShape>& output_shapes, |
562 | const DatasetToSingleElement::Attrs& attrs); |
563 | ::tensorflow::Output operator[](size_t index) const { return components[index]; } |
564 | |
565 | |
566 | static Attrs Metadata(StringPiece x) { |
567 | return Attrs().Metadata(x); |
568 | } |
569 | |
570 | Operation operation; |
571 | ::tensorflow::OutputList components; |
572 | }; |
573 | |
574 | /// A container for an iterator resource. |
575 | /// |
576 | /// Args: |
577 | /// * scope: A Scope object |
578 | /// * handle: A handle to the iterator to delete. |
579 | /// * deleter: A variant deleter. |
580 | /// |
581 | /// Returns: |
582 | /// * the created `Operation` |
583 | class DeleteIterator { |
584 | public: |
585 | DeleteIterator(const ::tensorflow::Scope& scope, ::tensorflow::Input handle, |
586 | ::tensorflow::Input deleter); |
587 | operator ::tensorflow::Operation() const { return operation; } |
588 | |
589 | Operation operation; |
590 | }; |
591 | |
592 | /// TODO: add doc. |
593 | /// |
594 | /// Args: |
595 | /// * scope: A Scope object |
596 | /// |
597 | /// Returns: |
598 | /// * the created `Operation` |
599 | class DeleteMemoryCache { |
600 | public: |
601 | DeleteMemoryCache(const ::tensorflow::Scope& scope, ::tensorflow::Input handle, |
602 | ::tensorflow::Input deleter); |
603 | operator ::tensorflow::Operation() const { return operation; } |
604 | |
605 | Operation operation; |
606 | }; |
607 | |
608 | /// A container for an iterator resource. |
609 | /// |
610 | /// Args: |
611 | /// * scope: A Scope object |
612 | /// * multi_device_iterator: A handle to the multi device iterator to delete. |
613 | /// * iterators: A list of iterator handles (unused). This is added so that automatic control dependencies get added during function tracing that ensure this op runs after all the dependent iterators are deleted. |
614 | /// * deleter: A variant deleter. |
615 | /// |
616 | /// Returns: |
617 | /// * the created `Operation` |
618 | class DeleteMultiDeviceIterator { |
619 | public: |
620 | DeleteMultiDeviceIterator(const ::tensorflow::Scope& scope, ::tensorflow::Input |
621 | multi_device_iterator, ::tensorflow::InputList |
622 | iterators, ::tensorflow::Input deleter); |
623 | operator ::tensorflow::Operation() const { return operation; } |
624 | |
625 | Operation operation; |
626 | }; |
627 | |
628 | /// TODO: add doc. |
629 | /// |
630 | /// Args: |
631 | /// * scope: A Scope object |
632 | /// |
633 | /// Returns: |
634 | /// * the created `Operation` |
635 | class DeleteRandomSeedGenerator { |
636 | public: |
637 | DeleteRandomSeedGenerator(const ::tensorflow::Scope& scope, ::tensorflow::Input |
638 | handle, ::tensorflow::Input deleter); |
639 | operator ::tensorflow::Operation() const { return operation; } |
640 | |
641 | Operation operation; |
642 | }; |
643 | |
644 | /// TODO: add doc. |
645 | /// |
646 | /// Args: |
647 | /// * scope: A Scope object |
648 | /// |
649 | /// Returns: |
650 | /// * the created `Operation` |
651 | class DeleteSeedGenerator { |
652 | public: |
653 | DeleteSeedGenerator(const ::tensorflow::Scope& scope, ::tensorflow::Input |
654 | handle, ::tensorflow::Input deleter); |
655 | operator ::tensorflow::Operation() const { return operation; } |
656 | |
657 | Operation operation; |
658 | }; |
659 | |
660 | /// TODO: add doc. |
661 | /// |
662 | /// Args: |
663 | /// * scope: A Scope object |
664 | /// |
665 | /// Returns: |
666 | /// * `Output`: The handle tensor. |
667 | class DummyMemoryCache { |
668 | public: |
669 | DummyMemoryCache(const ::tensorflow::Scope& scope); |
670 | operator ::tensorflow::Output() const { return handle; } |
671 | operator ::tensorflow::Input() const { return handle; } |
672 | ::tensorflow::Node* node() const { return handle.node(); } |
673 | |
674 | Operation operation; |
675 | ::tensorflow::Output handle; |
676 | }; |
677 | |
678 | /// TODO: add doc. |
679 | /// |
680 | /// Args: |
681 | /// * scope: A Scope object |
682 | /// |
683 | /// Returns: |
684 | /// * `Output`: The handle tensor. |
685 | class DummySeedGenerator { |
686 | public: |
687 | DummySeedGenerator(const ::tensorflow::Scope& scope); |
688 | operator ::tensorflow::Output() const { return handle; } |
689 | operator ::tensorflow::Input() const { return handle; } |
690 | ::tensorflow::Node* node() const { return handle.node(); } |
691 | |
692 | Operation operation; |
693 | ::tensorflow::Output handle; |
694 | }; |
695 | |
696 | /// Creates a dataset containing elements of first component of `input_dataset` having true in the last component. |
697 | /// |
698 | /// Args: |
699 | /// * scope: A Scope object |
700 | /// |
701 | /// Returns: |
702 | /// * `Output`: The output tensor. |
703 | class FilterByLastComponentDataset { |
704 | public: |
705 | FilterByLastComponentDataset(const ::tensorflow::Scope& scope, |
706 | ::tensorflow::Input input_dataset, const |
707 | DataTypeSlice& output_types, const |
708 | gtl::ArraySlice<PartialTensorShape>& |
709 | output_shapes); |
710 | operator ::tensorflow::Output() const { return output; } |
711 | operator ::tensorflow::Input() const { return output; } |
712 | ::tensorflow::Node* node() const { return output.node(); } |
713 | |
714 | Operation operation; |
715 | ::tensorflow::Output output; |
716 | }; |
717 | |
718 | /// Creates a dataset containing elements of `input_dataset` matching `predicate`. |
719 | /// |
720 | /// The `predicate` function must return a scalar boolean and accept the |
721 | /// following arguments: |
722 | /// |
723 | /// * One tensor for each component of an element of `input_dataset`. |
724 | /// * One tensor for each value in `other_arguments`. |
725 | /// |
726 | /// Args: |
727 | /// * scope: A Scope object |
728 | /// * other_arguments: A list of tensors, typically values that were captured when |
729 | /// building a closure for `predicate`. |
730 | /// * predicate: A function returning a scalar boolean. |
731 | /// |
732 | /// Returns: |
733 | /// * `Output`: The handle tensor. |
734 | class FilterDataset { |
735 | public: |
736 | /// Optional attribute setters for FilterDataset |
737 | struct Attrs { |
738 | /// Defaults to "" |
739 | TF_MUST_USE_RESULT Attrs Metadata(StringPiece x) { |
740 | Attrs ret = *this; |
741 | ret.metadata_ = x; |
742 | return ret; |
743 | } |
744 | |
745 | StringPiece metadata_ = "" ; |
746 | }; |
747 | FilterDataset(const ::tensorflow::Scope& scope, ::tensorflow::Input |
748 | input_dataset, ::tensorflow::InputList other_arguments, const |
749 | NameAttrList& predicate, const DataTypeSlice& output_types, const |
750 | gtl::ArraySlice<PartialTensorShape>& output_shapes); |
751 | FilterDataset(const ::tensorflow::Scope& scope, ::tensorflow::Input |
752 | input_dataset, ::tensorflow::InputList other_arguments, const |
753 | NameAttrList& predicate, const DataTypeSlice& output_types, const |
754 | gtl::ArraySlice<PartialTensorShape>& output_shapes, const |
755 | FilterDataset::Attrs& attrs); |
756 | operator ::tensorflow::Output() const { return handle; } |
757 | operator ::tensorflow::Input() const { return handle; } |
758 | ::tensorflow::Node* node() const { return handle.node(); } |
759 | |
760 | static Attrs Metadata(StringPiece x) { |
761 | return Attrs().Metadata(x); |
762 | } |
763 | |
764 | Operation operation; |
765 | ::tensorflow::Output handle; |
766 | }; |
767 | |
768 | /// Creates a dataset by applying `tf.data.Options` to `input_dataset`. |
769 | /// |
770 | /// Args: |
771 | /// * scope: A Scope object |
772 | /// * input_dataset: A variant tensor representing the input dataset. |
773 | /// |
774 | /// Returns: |
775 | /// * `Output`: The handle tensor. |
776 | class FinalizeDataset { |
777 | public: |
778 | /// Optional attribute setters for FinalizeDataset |
779 | struct Attrs { |
780 | /// Defaults to false |
781 | TF_MUST_USE_RESULT Attrs HasCapturedRef(bool x) { |
782 | Attrs ret = *this; |
783 | ret.has_captured_ref_ = x; |
784 | return ret; |
785 | } |
786 | |
787 | bool has_captured_ref_ = false; |
788 | }; |
789 | FinalizeDataset(const ::tensorflow::Scope& scope, ::tensorflow::Input |
790 | input_dataset, const DataTypeSlice& output_types, const |
791 | gtl::ArraySlice<PartialTensorShape>& output_shapes); |
792 | FinalizeDataset(const ::tensorflow::Scope& scope, ::tensorflow::Input |
793 | input_dataset, const DataTypeSlice& output_types, const |
794 | gtl::ArraySlice<PartialTensorShape>& output_shapes, const |
795 | FinalizeDataset::Attrs& attrs); |
796 | operator ::tensorflow::Output() const { return handle; } |
797 | operator ::tensorflow::Input() const { return handle; } |
798 | ::tensorflow::Node* node() const { return handle.node(); } |
799 | |
800 | static Attrs HasCapturedRef(bool x) { |
801 | return Attrs().HasCapturedRef(x); |
802 | } |
803 | |
804 | Operation operation; |
805 | ::tensorflow::Output handle; |
806 | }; |
807 | |
808 | /// Creates a dataset that emits the records from one or more binary files. |
809 | /// |
810 | /// Args: |
811 | /// * scope: A Scope object |
812 | /// * filenames: A scalar or a vector containing the name(s) of the file(s) to be |
813 | /// read. |
814 | /// * header_bytes: A scalar representing the number of bytes to skip at the |
815 | /// beginning of a file. |
816 | /// * record_bytes: A scalar representing the number of bytes in each record. |
817 | /// * footer_bytes: A scalar representing the number of bytes to skip at the end |
818 | /// of a file. |
819 | /// * buffer_size: A scalar representing the number of bytes to buffer. Must be > 0. |
820 | /// |
821 | /// Returns: |
822 | /// * `Output`: The handle tensor. |
823 | class FixedLengthRecordDataset { |
824 | public: |
825 | /// Optional attribute setters for FixedLengthRecordDataset |
826 | struct Attrs { |
827 | /// Defaults to "" |
828 | TF_MUST_USE_RESULT Attrs Metadata(StringPiece x) { |
829 | Attrs ret = *this; |
830 | ret.metadata_ = x; |
831 | return ret; |
832 | } |
833 | |
834 | StringPiece metadata_ = "" ; |
835 | }; |
836 | FixedLengthRecordDataset(const ::tensorflow::Scope& scope, ::tensorflow::Input |
837 | filenames, ::tensorflow::Input , |
838 | ::tensorflow::Input record_bytes, ::tensorflow::Input |
839 | , ::tensorflow::Input buffer_size); |
840 | FixedLengthRecordDataset(const ::tensorflow::Scope& scope, ::tensorflow::Input |
841 | filenames, ::tensorflow::Input , |
842 | ::tensorflow::Input record_bytes, ::tensorflow::Input |
843 | , ::tensorflow::Input buffer_size, const |
844 | FixedLengthRecordDataset::Attrs& attrs); |
845 | operator ::tensorflow::Output() const { return handle; } |
846 | operator ::tensorflow::Input() const { return handle; } |
847 | ::tensorflow::Node* node() const { return handle.node(); } |
848 | |
849 | static Attrs Metadata(StringPiece x) { |
850 | return Attrs().Metadata(x); |
851 | } |
852 | |
853 | Operation operation; |
854 | ::tensorflow::Output handle; |
855 | }; |
856 | |
857 | /// TODO: add doc. |
858 | /// |
859 | /// Args: |
860 | /// * scope: A Scope object |
861 | /// |
862 | /// Returns: |
863 | /// * `Output`: The handle tensor. |
864 | class FixedLengthRecordDatasetV2 { |
865 | public: |
866 | /// Optional attribute setters for FixedLengthRecordDatasetV2 |
867 | struct Attrs { |
868 | /// Defaults to "" |
869 | TF_MUST_USE_RESULT Attrs Metadata(StringPiece x) { |
870 | Attrs ret = *this; |
871 | ret.metadata_ = x; |
872 | return ret; |
873 | } |
874 | |
875 | StringPiece metadata_ = "" ; |
876 | }; |
877 | FixedLengthRecordDatasetV2(const ::tensorflow::Scope& scope, |
878 | ::tensorflow::Input filenames, ::tensorflow::Input |
879 | , ::tensorflow::Input record_bytes, |
880 | ::tensorflow::Input , |
881 | ::tensorflow::Input buffer_size, ::tensorflow::Input |
882 | compression_type); |
883 | FixedLengthRecordDatasetV2(const ::tensorflow::Scope& scope, |
884 | ::tensorflow::Input filenames, ::tensorflow::Input |
885 | , ::tensorflow::Input record_bytes, |
886 | ::tensorflow::Input , |
887 | ::tensorflow::Input buffer_size, ::tensorflow::Input |
888 | compression_type, const |
889 | FixedLengthRecordDatasetV2::Attrs& attrs); |
890 | operator ::tensorflow::Output() const { return handle; } |
891 | operator ::tensorflow::Input() const { return handle; } |
892 | ::tensorflow::Node* node() const { return handle.node(); } |
893 | |
894 | static Attrs Metadata(StringPiece x) { |
895 | return Attrs().Metadata(x); |
896 | } |
897 | |
898 | Operation operation; |
899 | ::tensorflow::Output handle; |
900 | }; |
901 | |
902 | /// Creates a dataset that applies `f` to the outputs of `input_dataset`. |
903 | /// |
904 | /// Unlike MapDataset, the `f` in FlatMapDataset is expected to return a |
905 | /// Dataset variant, and FlatMapDataset will flatten successive results |
906 | /// into a single Dataset. |
907 | /// |
908 | /// Args: |
909 | /// * scope: A Scope object |
910 | /// * f: A function mapping elements of `input_dataset`, concatenated with |
911 | /// `other_arguments`, to a Dataset variant that contains elements matching |
912 | /// `output_types` and `output_shapes`. |
913 | /// |
914 | /// Returns: |
915 | /// * `Output`: The handle tensor. |
916 | class FlatMapDataset { |
917 | public: |
918 | /// Optional attribute setters for FlatMapDataset |
919 | struct Attrs { |
920 | /// Defaults to "" |
921 | TF_MUST_USE_RESULT Attrs Metadata(StringPiece x) { |
922 | Attrs ret = *this; |
923 | ret.metadata_ = x; |
924 | return ret; |
925 | } |
926 | |
927 | StringPiece metadata_ = "" ; |
928 | }; |
929 | FlatMapDataset(const ::tensorflow::Scope& scope, ::tensorflow::Input |
930 | input_dataset, ::tensorflow::InputList other_arguments, const |
931 | NameAttrList& f, const DataTypeSlice& output_types, const |
932 | gtl::ArraySlice<PartialTensorShape>& output_shapes); |
933 | FlatMapDataset(const ::tensorflow::Scope& scope, ::tensorflow::Input |
934 | input_dataset, ::tensorflow::InputList other_arguments, const |
935 | NameAttrList& f, const DataTypeSlice& output_types, const |
936 | gtl::ArraySlice<PartialTensorShape>& output_shapes, const |
937 | FlatMapDataset::Attrs& attrs); |
938 | operator ::tensorflow::Output() const { return handle; } |
939 | operator ::tensorflow::Input() const { return handle; } |
940 | ::tensorflow::Node* node() const { return handle.node(); } |
941 | |
942 | static Attrs Metadata(StringPiece x) { |
943 | return Attrs().Metadata(x); |
944 | } |
945 | |
946 | Operation operation; |
947 | ::tensorflow::Output handle; |
948 | }; |
949 | |
950 | /// Creates a dataset that invokes a function to generate elements. |
951 | /// |
952 | /// Args: |
953 | /// * scope: A Scope object |
954 | /// |
955 | /// Returns: |
956 | /// * `Output`: The handle tensor. |
957 | class GeneratorDataset { |
958 | public: |
959 | /// Optional attribute setters for GeneratorDataset |
960 | struct Attrs { |
961 | /// Defaults to "" |
962 | TF_MUST_USE_RESULT Attrs Metadata(StringPiece x) { |
963 | Attrs ret = *this; |
964 | ret.metadata_ = x; |
965 | return ret; |
966 | } |
967 | |
968 | StringPiece metadata_ = "" ; |
969 | }; |
970 | GeneratorDataset(const ::tensorflow::Scope& scope, ::tensorflow::InputList |
971 | init_func_other_args, ::tensorflow::InputList |
972 | next_func_other_args, ::tensorflow::InputList |
973 | finalize_func_other_args, const NameAttrList& init_func, const |
974 | NameAttrList& next_func, const NameAttrList& finalize_func, |
975 | const DataTypeSlice& output_types, const |
976 | gtl::ArraySlice<PartialTensorShape>& output_shapes); |
977 | GeneratorDataset(const ::tensorflow::Scope& scope, ::tensorflow::InputList |
978 | init_func_other_args, ::tensorflow::InputList |
979 | next_func_other_args, ::tensorflow::InputList |
980 | finalize_func_other_args, const NameAttrList& init_func, const |
981 | NameAttrList& next_func, const NameAttrList& finalize_func, |
982 | const DataTypeSlice& output_types, const |
983 | gtl::ArraySlice<PartialTensorShape>& output_shapes, const |
984 | GeneratorDataset::Attrs& attrs); |
985 | operator ::tensorflow::Output() const { return handle; } |
986 | operator ::tensorflow::Input() const { return handle; } |
987 | ::tensorflow::Node* node() const { return handle.node(); } |
988 | |
989 | static Attrs Metadata(StringPiece x) { |
990 | return Attrs().Metadata(x); |
991 | } |
992 | |
993 | Operation operation; |
994 | ::tensorflow::Output handle; |
995 | }; |
996 | |
997 | /// Returns the `tf.data.Options` attached to `input_dataset`. |
998 | /// |
999 | /// Args: |
1000 | /// * scope: A Scope object |
1001 | /// * input_dataset: A variant tensor representing the input dataset. |
1002 | /// |
1003 | /// Returns: |
1004 | /// * `Output`: The serialized_options tensor. |
1005 | class GetOptions { |
1006 | public: |
1007 | GetOptions(const ::tensorflow::Scope& scope, ::tensorflow::Input input_dataset); |
1008 | operator ::tensorflow::Output() const { return serialized_options; } |
1009 | operator ::tensorflow::Input() const { return serialized_options; } |
1010 | ::tensorflow::Node* node() const { return serialized_options.node(); } |
1011 | |
1012 | Operation operation; |
1013 | ::tensorflow::Output serialized_options; |
1014 | }; |
1015 | |
1016 | /// Creates a dataset that applies `f` to the outputs of `input_dataset`. |
1017 | /// |
1018 | /// Unlike MapDataset, the `f` in InterleaveDataset is expected to return |
1019 | /// a Dataset variant, and InterleaveDataset will flatten successive |
1020 | /// results into a single Dataset. Unlike FlatMapDataset, |
1021 | /// InterleaveDataset will interleave sequences of up to `block_length` |
1022 | /// consecutive elements from `cycle_length` input elements. |
1023 | /// |
1024 | /// Args: |
1025 | /// * scope: A Scope object |
1026 | /// * f: A function mapping elements of `input_dataset`, concatenated with |
1027 | /// `other_arguments`, to a Dataset variant that contains elements matching |
1028 | /// `output_types` and `output_shapes`. |
1029 | /// |
1030 | /// Returns: |
1031 | /// * `Output`: The handle tensor. |
1032 | class InterleaveDataset { |
1033 | public: |
1034 | /// Optional attribute setters for InterleaveDataset |
1035 | struct Attrs { |
1036 | /// Defaults to "" |
1037 | TF_MUST_USE_RESULT Attrs Metadata(StringPiece x) { |
1038 | Attrs ret = *this; |
1039 | ret.metadata_ = x; |
1040 | return ret; |
1041 | } |
1042 | |
1043 | StringPiece metadata_ = "" ; |
1044 | }; |
1045 | InterleaveDataset(const ::tensorflow::Scope& scope, ::tensorflow::Input |
1046 | input_dataset, ::tensorflow::InputList other_arguments, |
1047 | ::tensorflow::Input cycle_length, ::tensorflow::Input |
1048 | block_length, const NameAttrList& f, const DataTypeSlice& |
1049 | output_types, const gtl::ArraySlice<PartialTensorShape>& |
1050 | output_shapes); |
1051 | InterleaveDataset(const ::tensorflow::Scope& scope, ::tensorflow::Input |
1052 | input_dataset, ::tensorflow::InputList other_arguments, |
1053 | ::tensorflow::Input cycle_length, ::tensorflow::Input |
1054 | block_length, const NameAttrList& f, const DataTypeSlice& |
1055 | output_types, const gtl::ArraySlice<PartialTensorShape>& |
1056 | output_shapes, const InterleaveDataset::Attrs& attrs); |
1057 | operator ::tensorflow::Output() const { return handle; } |
1058 | operator ::tensorflow::Input() const { return handle; } |
1059 | ::tensorflow::Node* node() const { return handle.node(); } |
1060 | |
1061 | static Attrs Metadata(StringPiece x) { |
1062 | return Attrs().Metadata(x); |
1063 | } |
1064 | |
1065 | Operation operation; |
1066 | ::tensorflow::Output handle; |
1067 | }; |
1068 | |
1069 | /// TODO: add doc. |
1070 | /// |
1071 | /// Args: |
1072 | /// * scope: A Scope object |
1073 | /// |
1074 | /// Returns: |
1075 | /// * `Output`: The resource_handle tensor. |
1076 | class IteratorFromStringHandleV2 { |
1077 | public: |
1078 | /// Optional attribute setters for IteratorFromStringHandleV2 |
1079 | struct Attrs { |
1080 | /// Defaults to [] |
1081 | TF_MUST_USE_RESULT Attrs OutputTypes(const DataTypeSlice& x) { |
1082 | Attrs ret = *this; |
1083 | ret.output_types_ = x; |
1084 | return ret; |
1085 | } |
1086 | |
1087 | /// Defaults to [] |
1088 | TF_MUST_USE_RESULT Attrs OutputShapes(const gtl::ArraySlice<PartialTensorShape>& x) { |
1089 | Attrs ret = *this; |
1090 | ret.output_shapes_ = x; |
1091 | return ret; |
1092 | } |
1093 | |
1094 | DataTypeSlice output_types_ = {}; |
1095 | gtl::ArraySlice<PartialTensorShape> output_shapes_ = {}; |
1096 | }; |
1097 | IteratorFromStringHandleV2(const ::tensorflow::Scope& scope, |
1098 | ::tensorflow::Input string_handle); |
1099 | IteratorFromStringHandleV2(const ::tensorflow::Scope& scope, |
1100 | ::tensorflow::Input string_handle, const |
1101 | IteratorFromStringHandleV2::Attrs& attrs); |
1102 | operator ::tensorflow::Output() const { return resource_handle; } |
1103 | operator ::tensorflow::Input() const { return resource_handle; } |
1104 | ::tensorflow::Node* node() const { return resource_handle.node(); } |
1105 | |
1106 | static Attrs OutputTypes(const DataTypeSlice& x) { |
1107 | return Attrs().OutputTypes(x); |
1108 | } |
1109 | static Attrs OutputShapes(const gtl::ArraySlice<PartialTensorShape>& x) { |
1110 | return Attrs().OutputShapes(x); |
1111 | } |
1112 | |
1113 | Operation operation; |
1114 | ::tensorflow::Output resource_handle; |
1115 | }; |
1116 | |
1117 | /// TODO: add doc. |
1118 | /// |
1119 | /// Args: |
1120 | /// * scope: A Scope object |
1121 | /// |
1122 | /// Returns: |
1123 | /// * `Output`: The handle tensor. |
1124 | class IteratorV2 { |
1125 | public: |
1126 | IteratorV2(const ::tensorflow::Scope& scope, StringPiece shared_name, |
1127 | StringPiece container, const DataTypeSlice& output_types, const |
1128 | gtl::ArraySlice<PartialTensorShape>& output_shapes); |
1129 | operator ::tensorflow::Output() const { return handle; } |
1130 | operator ::tensorflow::Input() const { return handle; } |
1131 | ::tensorflow::Node* node() const { return handle.node(); } |
1132 | |
1133 | Operation operation; |
1134 | ::tensorflow::Output handle; |
1135 | }; |
1136 | |
1137 | /// Creates a dataset that applies `f` to the outputs of `input_dataset`. |
1138 | /// |
1139 | /// Args: |
1140 | /// * scope: A Scope object |
1141 | /// |
1142 | /// Returns: |
1143 | /// * `Output`: The handle tensor. |
1144 | class MapDataset { |
1145 | public: |
1146 | /// Optional attribute setters for MapDataset |
1147 | struct Attrs { |
1148 | /// Defaults to true |
1149 | TF_MUST_USE_RESULT Attrs UseInterOpParallelism(bool x) { |
1150 | Attrs ret = *this; |
1151 | ret.use_inter_op_parallelism_ = x; |
1152 | return ret; |
1153 | } |
1154 | |
1155 | /// Defaults to false |
1156 | TF_MUST_USE_RESULT Attrs PreserveCardinality(bool x) { |
1157 | Attrs ret = *this; |
1158 | ret.preserve_cardinality_ = x; |
1159 | return ret; |
1160 | } |
1161 | |
1162 | /// Defaults to "" |
1163 | TF_MUST_USE_RESULT Attrs Metadata(StringPiece x) { |
1164 | Attrs ret = *this; |
1165 | ret.metadata_ = x; |
1166 | return ret; |
1167 | } |
1168 | |
1169 | bool use_inter_op_parallelism_ = true; |
1170 | bool preserve_cardinality_ = false; |
1171 | StringPiece metadata_ = "" ; |
1172 | }; |
1173 | MapDataset(const ::tensorflow::Scope& scope, ::tensorflow::Input input_dataset, |
1174 | ::tensorflow::InputList other_arguments, const NameAttrList& f, |
1175 | const DataTypeSlice& output_types, const |
1176 | gtl::ArraySlice<PartialTensorShape>& output_shapes); |
1177 | MapDataset(const ::tensorflow::Scope& scope, ::tensorflow::Input input_dataset, |
1178 | ::tensorflow::InputList other_arguments, const NameAttrList& f, |
1179 | const DataTypeSlice& output_types, const |
1180 | gtl::ArraySlice<PartialTensorShape>& output_shapes, const |
1181 | MapDataset::Attrs& attrs); |
1182 | operator ::tensorflow::Output() const { return handle; } |
1183 | operator ::tensorflow::Input() const { return handle; } |
1184 | ::tensorflow::Node* node() const { return handle.node(); } |
1185 | |
1186 | static Attrs UseInterOpParallelism(bool x) { |
1187 | return Attrs().UseInterOpParallelism(x); |
1188 | } |
1189 | static Attrs PreserveCardinality(bool x) { |
1190 | return Attrs().PreserveCardinality(x); |
1191 | } |
1192 | static Attrs Metadata(StringPiece x) { |
1193 | return Attrs().Metadata(x); |
1194 | } |
1195 | |
1196 | Operation operation; |
1197 | ::tensorflow::Output handle; |
1198 | }; |
1199 | |
1200 | /// Maps a function on the list of tensors unpacked from arguments on dimension 0. |
1201 | /// The function given by `f` is assumed to be stateless, and is executed |
1202 | /// concurrently on all the slices; up to batch_size (i.e. the size of the 0th |
1203 | /// dimension of each argument) functions will be scheduled at once. |
1204 | /// |
1205 | /// The `max_intra_op_parallelism` attr, which defaults to 1, can be used to |
1206 | /// limit the intra op parallelism. To limit inter-op parallelism, a user can |
1207 | /// set a private threadpool on the dataset using `tf.data.Options`'s |
1208 | /// `ThreadingOptions`. |
1209 | /// |
1210 | /// Note that this op is not exposed to users directly, but is invoked in tf.data |
1211 | /// rewrites. |
1212 | /// |
1213 | /// Args: |
1214 | /// * scope: A Scope object |
1215 | /// * arguments: A list of tensors whose types are `Targuments`, corresponding to the inputs |
1216 | /// the function should be mapped over. |
1217 | /// * captured_inputs: A list of tensors whose types are `Tcaptured`, corresponding to the captured |
1218 | /// inputs of the defun. |
1219 | /// * output_types: A list of types. |
1220 | /// * output_shapes: A list of shapes. |
1221 | /// |
1222 | /// Returns: |
1223 | /// * `OutputList`: A list of output tensors whose types are `output_types` and whose dimensions |
1224 | /// 0 are the same as the dimensions 0 of the tensors in `arguments`, and whose |
1225 | /// remaining dimensions correspond to those in `output_shapes`. |
1226 | class MapDefun { |
1227 | public: |
1228 | /// Optional attribute setters for MapDefun |
1229 | struct Attrs { |
1230 | /// Defaults to 1 |
1231 | TF_MUST_USE_RESULT Attrs MaxIntraOpParallelism(int64 x) { |
1232 | Attrs ret = *this; |
1233 | ret.max_intra_op_parallelism_ = x; |
1234 | return ret; |
1235 | } |
1236 | |
1237 | int64 max_intra_op_parallelism_ = 1; |
1238 | }; |
1239 | MapDefun(const ::tensorflow::Scope& scope, ::tensorflow::InputList arguments, |
1240 | ::tensorflow::InputList captured_inputs, const DataTypeSlice& |
1241 | output_types, const gtl::ArraySlice<PartialTensorShape>& |
1242 | output_shapes, const NameAttrList& f); |
1243 | MapDefun(const ::tensorflow::Scope& scope, ::tensorflow::InputList arguments, |
1244 | ::tensorflow::InputList captured_inputs, const DataTypeSlice& |
1245 | output_types, const gtl::ArraySlice<PartialTensorShape>& |
1246 | output_shapes, const NameAttrList& f, const MapDefun::Attrs& attrs); |
1247 | ::tensorflow::Output operator[](size_t index) const { return output[index]; } |
1248 | |
1249 | |
1250 | static Attrs MaxIntraOpParallelism(int64 x) { |
1251 | return Attrs().MaxIntraOpParallelism(x); |
1252 | } |
1253 | |
1254 | Operation operation; |
1255 | ::tensorflow::OutputList output; |
1256 | }; |
1257 | |
1258 | /// Identity transformation that models performance. |
1259 | /// |
1260 | /// Identity transformation that models performance. |
1261 | /// |
1262 | /// Args: |
1263 | /// * scope: A Scope object |
1264 | /// * input_dataset: A variant tensor representing the input dataset. |
1265 | /// |
1266 | /// Returns: |
1267 | /// * `Output`: The handle tensor. |
1268 | class ModelDataset { |
1269 | public: |
1270 | /// Optional attribute setters for ModelDataset |
1271 | struct Attrs { |
1272 | /// Defaults to 0 |
1273 | TF_MUST_USE_RESULT Attrs Algorithm(int64 x) { |
1274 | Attrs ret = *this; |
1275 | ret.algorithm_ = x; |
1276 | return ret; |
1277 | } |
1278 | |
1279 | /// Defaults to 0 |
1280 | TF_MUST_USE_RESULT Attrs CpuBudget(int64 x) { |
1281 | Attrs ret = *this; |
1282 | ret.cpu_budget_ = x; |
1283 | return ret; |
1284 | } |
1285 | |
1286 | /// Defaults to 0 |
1287 | TF_MUST_USE_RESULT Attrs RamBudget(int64 x) { |
1288 | Attrs ret = *this; |
1289 | ret.ram_budget_ = x; |
1290 | return ret; |
1291 | } |
1292 | |
1293 | int64 algorithm_ = 0; |
1294 | int64 cpu_budget_ = 0; |
1295 | int64 ram_budget_ = 0; |
1296 | }; |
1297 | ModelDataset(const ::tensorflow::Scope& scope, ::tensorflow::Input |
1298 | input_dataset, const DataTypeSlice& output_types, const |
1299 | gtl::ArraySlice<PartialTensorShape>& output_shapes); |
1300 | ModelDataset(const ::tensorflow::Scope& scope, ::tensorflow::Input |
1301 | input_dataset, const DataTypeSlice& output_types, const |
1302 | gtl::ArraySlice<PartialTensorShape>& output_shapes, const |
1303 | ModelDataset::Attrs& attrs); |
1304 | operator ::tensorflow::Output() const { return handle; } |
1305 | operator ::tensorflow::Input() const { return handle; } |
1306 | ::tensorflow::Node* node() const { return handle.node(); } |
1307 | |
1308 | static Attrs Algorithm(int64 x) { |
1309 | return Attrs().Algorithm(x); |
1310 | } |
1311 | static Attrs CpuBudget(int64 x) { |
1312 | return Attrs().CpuBudget(x); |
1313 | } |
1314 | static Attrs RamBudget(int64 x) { |
1315 | return Attrs().RamBudget(x); |
1316 | } |
1317 | |
1318 | Operation operation; |
1319 | ::tensorflow::Output handle; |
1320 | }; |
1321 | |
1322 | /// Creates a MultiDeviceIterator resource. |
1323 | /// |
1324 | /// Args: |
1325 | /// * scope: A Scope object |
1326 | /// * devices: A list of devices the iterator works across. |
1327 | /// * shared_name: If non-empty, this resource will be shared under the given name |
1328 | /// across multiple sessions. |
1329 | /// * container: If non-empty, this resource is placed in the given container. |
1330 | /// Otherwise, a default container is used. |
1331 | /// * output_types: The type list for the return values. |
1332 | /// * output_shapes: The list of shapes being produced. |
1333 | /// |
1334 | /// Returns: |
1335 | /// * `Output`: Handle to the resource created. |
1336 | class MultiDeviceIterator { |
1337 | public: |
1338 | MultiDeviceIterator(const ::tensorflow::Scope& scope, const |
1339 | gtl::ArraySlice<::tensorflow::tstring>& devices, |
1340 | StringPiece shared_name, StringPiece container, const |
1341 | DataTypeSlice& output_types, const |
1342 | gtl::ArraySlice<PartialTensorShape>& output_shapes); |
1343 | operator ::tensorflow::Output() const { return handle; } |
1344 | operator ::tensorflow::Input() const { return handle; } |
1345 | ::tensorflow::Node* node() const { return handle.node(); } |
1346 | |
1347 | Operation operation; |
1348 | ::tensorflow::Output handle; |
1349 | }; |
1350 | |
1351 | /// Generates a MultiDeviceIterator resource from its provided string handle. |
1352 | /// |
1353 | /// Args: |
1354 | /// * scope: A Scope object |
1355 | /// * string_handle: String representing the resource. |
1356 | /// |
1357 | /// Optional attributes (see `Attrs`): |
1358 | /// * output_types: The type list for the return values. |
1359 | /// * output_shapes: The list of shapes being produced. |
1360 | /// |
1361 | /// Returns: |
1362 | /// * `Output`: A MultiDeviceIterator resource. |
1363 | class MultiDeviceIteratorFromStringHandle { |
1364 | public: |
1365 | /// Optional attribute setters for MultiDeviceIteratorFromStringHandle |
1366 | struct Attrs { |
1367 | /// The type list for the return values. |
1368 | /// |
1369 | /// Defaults to [] |
1370 | TF_MUST_USE_RESULT Attrs OutputTypes(const DataTypeSlice& x) { |
1371 | Attrs ret = *this; |
1372 | ret.output_types_ = x; |
1373 | return ret; |
1374 | } |
1375 | |
1376 | /// The list of shapes being produced. |
1377 | /// |
1378 | /// Defaults to [] |
1379 | TF_MUST_USE_RESULT Attrs OutputShapes(const gtl::ArraySlice<PartialTensorShape>& x) { |
1380 | Attrs ret = *this; |
1381 | ret.output_shapes_ = x; |
1382 | return ret; |
1383 | } |
1384 | |
1385 | DataTypeSlice output_types_ = {}; |
1386 | gtl::ArraySlice<PartialTensorShape> output_shapes_ = {}; |
1387 | }; |
1388 | MultiDeviceIteratorFromStringHandle(const ::tensorflow::Scope& scope, |
1389 | ::tensorflow::Input string_handle); |
1390 | MultiDeviceIteratorFromStringHandle(const ::tensorflow::Scope& scope, |
1391 | ::tensorflow::Input string_handle, const |
1392 | MultiDeviceIteratorFromStringHandle::Attrs& |
1393 | attrs); |
1394 | operator ::tensorflow::Output() const { return multi_device_iterator; } |
1395 | operator ::tensorflow::Input() const { return multi_device_iterator; } |
1396 | ::tensorflow::Node* node() const { return multi_device_iterator.node(); } |
1397 | |
1398 | static Attrs OutputTypes(const DataTypeSlice& x) { |
1399 | return Attrs().OutputTypes(x); |
1400 | } |
1401 | static Attrs OutputShapes(const gtl::ArraySlice<PartialTensorShape>& x) { |
1402 | return Attrs().OutputShapes(x); |
1403 | } |
1404 | |
1405 | Operation operation; |
1406 | ::tensorflow::Output multi_device_iterator; |
1407 | }; |
1408 | |
1409 | /// Gets next element for the provided shard number. |
1410 | /// |
1411 | /// Args: |
1412 | /// * scope: A Scope object |
1413 | /// * multi_device_iterator: A MultiDeviceIterator resource. |
1414 | /// * shard_num: Integer representing which shard to fetch data for. |
1415 | /// * incarnation_id: Which incarnation of the MultiDeviceIterator is running. |
1416 | /// * output_types: The type list for the return values. |
1417 | /// * output_shapes: The list of shapes being produced. |
1418 | /// |
1419 | /// Returns: |
1420 | /// * `OutputList`: Result of the get_next on the dataset. |
1421 | class MultiDeviceIteratorGetNextFromShard { |
1422 | public: |
1423 | MultiDeviceIteratorGetNextFromShard(const ::tensorflow::Scope& scope, |
1424 | ::tensorflow::Input multi_device_iterator, |
1425 | ::tensorflow::Input shard_num, |
1426 | ::tensorflow::Input incarnation_id, const |
1427 | DataTypeSlice& output_types, const |
1428 | gtl::ArraySlice<PartialTensorShape>& |
1429 | output_shapes); |
1430 | ::tensorflow::Output operator[](size_t index) const { return components[index]; } |
1431 | |
1432 | |
1433 | Operation operation; |
1434 | ::tensorflow::OutputList components; |
1435 | }; |
1436 | |
1437 | /// Initializes the multi device iterator with the given dataset. |
1438 | /// |
1439 | /// Args: |
1440 | /// * scope: A Scope object |
1441 | /// * dataset: Dataset to be iterated upon. |
1442 | /// * multi_device_iterator: A MultiDeviceIteratorResource. |
1443 | /// * max_buffer_size: The maximum size of the host side per device buffer to keep. |
1444 | /// |
1445 | /// Returns: |
1446 | /// * `Output`: An int64 indicating which incarnation of the MultiDeviceIterator |
1447 | /// is running. |
1448 | class MultiDeviceIteratorInit { |
1449 | public: |
1450 | MultiDeviceIteratorInit(const ::tensorflow::Scope& scope, ::tensorflow::Input |
1451 | dataset, ::tensorflow::Input multi_device_iterator, |
1452 | ::tensorflow::Input max_buffer_size); |
1453 | operator ::tensorflow::Output() const { return incarnation_id; } |
1454 | operator ::tensorflow::Input() const { return incarnation_id; } |
1455 | ::tensorflow::Node* node() const { return incarnation_id.node(); } |
1456 | |
1457 | Operation operation; |
1458 | ::tensorflow::Output incarnation_id; |
1459 | }; |
1460 | |
1461 | /// Produces a string handle for the given MultiDeviceIterator. |
1462 | /// |
1463 | /// Args: |
1464 | /// * scope: A Scope object |
1465 | /// * multi_device_iterator: A MultiDeviceIterator resource. |
1466 | /// |
1467 | /// Returns: |
1468 | /// * `Output`: A string representing the resource. |
1469 | class MultiDeviceIteratorToStringHandle { |
1470 | public: |
1471 | MultiDeviceIteratorToStringHandle(const ::tensorflow::Scope& scope, |
1472 | ::tensorflow::Input multi_device_iterator); |
1473 | operator ::tensorflow::Output() const { return string_handle; } |
1474 | operator ::tensorflow::Input() const { return string_handle; } |
1475 | ::tensorflow::Node* node() const { return string_handle.node(); } |
1476 | |
1477 | Operation operation; |
1478 | ::tensorflow::Output string_handle; |
1479 | }; |
1480 | |
1481 | /// Creates a dataset by applying optimizations to `input_dataset`. |
1482 | /// |
1483 | /// Creates a dataset by applying optimizations to `input_dataset`. |
1484 | /// |
1485 | /// Args: |
1486 | /// * scope: A Scope object |
1487 | /// * input_dataset: A variant tensor representing the input dataset. |
1488 | /// * optimizations: A `tf.string` vector `tf.Tensor` identifying optimizations to use. |
1489 | /// |
1490 | /// Returns: |
1491 | /// * `Output`: The handle tensor. |
1492 | class OptimizeDataset { |
1493 | public: |
1494 | /// Optional attribute setters for OptimizeDataset |
1495 | struct Attrs { |
1496 | /// Defaults to [] |
1497 | TF_MUST_USE_RESULT Attrs OptimizationConfigs(const gtl::ArraySlice<::tensorflow::tstring>& x) { |
1498 | Attrs ret = *this; |
1499 | ret.optimization_configs_ = x; |
1500 | return ret; |
1501 | } |
1502 | |
1503 | gtl::ArraySlice<::tensorflow::tstring> optimization_configs_ = {}; |
1504 | }; |
1505 | OptimizeDataset(const ::tensorflow::Scope& scope, ::tensorflow::Input |
1506 | input_dataset, ::tensorflow::Input optimizations, const |
1507 | DataTypeSlice& output_types, const |
1508 | gtl::ArraySlice<PartialTensorShape>& output_shapes); |
1509 | OptimizeDataset(const ::tensorflow::Scope& scope, ::tensorflow::Input |
1510 | input_dataset, ::tensorflow::Input optimizations, const |
1511 | DataTypeSlice& output_types, const |
1512 | gtl::ArraySlice<PartialTensorShape>& output_shapes, const |
1513 | OptimizeDataset::Attrs& attrs); |
1514 | operator ::tensorflow::Output() const { return handle; } |
1515 | operator ::tensorflow::Input() const { return handle; } |
1516 | ::tensorflow::Node* node() const { return handle.node(); } |
1517 | |
1518 | static Attrs OptimizationConfigs(const gtl::ArraySlice<::tensorflow::tstring>& x) { |
1519 | return Attrs().OptimizationConfigs(x); |
1520 | } |
1521 | |
1522 | Operation operation; |
1523 | ::tensorflow::Output handle; |
1524 | }; |
1525 | |
1526 | /// Creates a dataset by applying related optimizations to `input_dataset`. |
1527 | /// |
1528 | /// Creates a dataset by applying related optimizations to `input_dataset`. |
1529 | /// |
1530 | /// Args: |
1531 | /// * scope: A Scope object |
1532 | /// * input_dataset: A variant tensor representing the input dataset. |
1533 | /// * optimizations_enabled: A `tf.string` vector `tf.Tensor` identifying user enabled optimizations. |
1534 | /// * optimizations_disabled: A `tf.string` vector `tf.Tensor` identifying user disabled optimizations. |
1535 | /// * optimizations_default: A `tf.string` vector `tf.Tensor` identifying optimizations by default. |
1536 | /// |
1537 | /// Returns: |
1538 | /// * `Output`: The handle tensor. |
1539 | class OptimizeDatasetV2 { |
1540 | public: |
1541 | /// Optional attribute setters for OptimizeDatasetV2 |
1542 | struct Attrs { |
1543 | /// Defaults to [] |
1544 | TF_MUST_USE_RESULT Attrs OptimizationConfigs(const gtl::ArraySlice<::tensorflow::tstring>& x) { |
1545 | Attrs ret = *this; |
1546 | ret.optimization_configs_ = x; |
1547 | return ret; |
1548 | } |
1549 | |
1550 | gtl::ArraySlice<::tensorflow::tstring> optimization_configs_ = {}; |
1551 | }; |
1552 | OptimizeDatasetV2(const ::tensorflow::Scope& scope, ::tensorflow::Input |
1553 | input_dataset, ::tensorflow::Input optimizations_enabled, |
1554 | ::tensorflow::Input optimizations_disabled, |
1555 | ::tensorflow::Input optimizations_default, const |
1556 | DataTypeSlice& output_types, const |
1557 | gtl::ArraySlice<PartialTensorShape>& output_shapes); |
1558 | OptimizeDatasetV2(const ::tensorflow::Scope& scope, ::tensorflow::Input |
1559 | input_dataset, ::tensorflow::Input optimizations_enabled, |
1560 | ::tensorflow::Input optimizations_disabled, |
1561 | ::tensorflow::Input optimizations_default, const |
1562 | DataTypeSlice& output_types, const |
1563 | gtl::ArraySlice<PartialTensorShape>& output_shapes, const |
1564 | OptimizeDatasetV2::Attrs& attrs); |
1565 | operator ::tensorflow::Output() const { return handle; } |
1566 | operator ::tensorflow::Input() const { return handle; } |
1567 | ::tensorflow::Node* node() const { return handle.node(); } |
1568 | |
1569 | static Attrs OptimizationConfigs(const gtl::ArraySlice<::tensorflow::tstring>& x) { |
1570 | return Attrs().OptimizationConfigs(x); |
1571 | } |
1572 | |
1573 | Operation operation; |
1574 | ::tensorflow::Output handle; |
1575 | }; |
1576 | |
1577 | /// Creates a dataset by attaching tf.data.Options to `input_dataset`. |
1578 | /// |
1579 | /// Args: |
1580 | /// * scope: A Scope object |
1581 | /// * input_dataset: A variant tensor representing the input dataset. |
1582 | /// * serialized_options: A `tf.string` scalar `tf.Tensor` of serialized `tf.data.Options` protocol buffer. |
1583 | /// |
1584 | /// Returns: |
1585 | /// * `Output`: The handle tensor. |
1586 | class OptionsDataset { |
1587 | public: |
1588 | /// Optional attribute setters for OptionsDataset |
1589 | struct Attrs { |
1590 | /// Defaults to "" |
1591 | TF_MUST_USE_RESULT Attrs Metadata(StringPiece x) { |
1592 | Attrs ret = *this; |
1593 | ret.metadata_ = x; |
1594 | return ret; |
1595 | } |
1596 | |
1597 | StringPiece metadata_ = "" ; |
1598 | }; |
1599 | OptionsDataset(const ::tensorflow::Scope& scope, ::tensorflow::Input |
1600 | input_dataset, StringPiece serialized_options, const |
1601 | DataTypeSlice& output_types, const |
1602 | gtl::ArraySlice<PartialTensorShape>& output_shapes); |
1603 | OptionsDataset(const ::tensorflow::Scope& scope, ::tensorflow::Input |
1604 | input_dataset, StringPiece serialized_options, const |
1605 | DataTypeSlice& output_types, const |
1606 | gtl::ArraySlice<PartialTensorShape>& output_shapes, const |
1607 | OptionsDataset::Attrs& attrs); |
1608 | operator ::tensorflow::Output() const { return handle; } |
1609 | operator ::tensorflow::Input() const { return handle; } |
1610 | ::tensorflow::Node* node() const { return handle.node(); } |
1611 | |
1612 | static Attrs Metadata(StringPiece x) { |
1613 | return Attrs().Metadata(x); |
1614 | } |
1615 | |
1616 | Operation operation; |
1617 | ::tensorflow::Output handle; |
1618 | }; |
1619 | |
1620 | /// Creates a dataset that batches and pads `batch_size` elements from the input. |
1621 | /// |
1622 | /// Args: |
1623 | /// * scope: A Scope object |
1624 | /// * batch_size: A scalar representing the number of elements to accumulate in a |
1625 | /// batch. |
1626 | /// * padded_shapes: A list of int64 tensors representing the desired padded shapes |
1627 | /// of the corresponding output components. These shapes may be partially |
1628 | /// specified, using `-1` to indicate that a particular dimension should be |
1629 | /// padded to the maximum size of all batch elements. |
1630 | /// * padding_values: A list of scalars containing the padding value to use for |
1631 | /// each of the outputs. |
1632 | /// |
1633 | /// Returns: |
1634 | /// * `Output`: The handle tensor. |
1635 | class PaddedBatchDataset { |
1636 | public: |
1637 | /// Optional attribute setters for PaddedBatchDataset |
1638 | struct Attrs { |
1639 | /// Defaults to "" |
1640 | TF_MUST_USE_RESULT Attrs Metadata(StringPiece x) { |
1641 | Attrs ret = *this; |
1642 | ret.metadata_ = x; |
1643 | return ret; |
1644 | } |
1645 | |
1646 | StringPiece metadata_ = "" ; |
1647 | }; |
1648 | PaddedBatchDataset(const ::tensorflow::Scope& scope, ::tensorflow::Input |
1649 | input_dataset, ::tensorflow::Input batch_size, |
1650 | ::tensorflow::InputList padded_shapes, |
1651 | ::tensorflow::InputList padding_values, const |
1652 | gtl::ArraySlice<PartialTensorShape>& output_shapes); |
1653 | PaddedBatchDataset(const ::tensorflow::Scope& scope, ::tensorflow::Input |
1654 | input_dataset, ::tensorflow::Input batch_size, |
1655 | ::tensorflow::InputList padded_shapes, |
1656 | ::tensorflow::InputList padding_values, const |
1657 | gtl::ArraySlice<PartialTensorShape>& output_shapes, const |
1658 | PaddedBatchDataset::Attrs& attrs); |
1659 | operator ::tensorflow::Output() const { return handle; } |
1660 | operator ::tensorflow::Input() const { return handle; } |
1661 | ::tensorflow::Node* node() const { return handle.node(); } |
1662 | |
1663 | static Attrs Metadata(StringPiece x) { |
1664 | return Attrs().Metadata(x); |
1665 | } |
1666 | |
1667 | Operation operation; |
1668 | ::tensorflow::Output handle; |
1669 | }; |
1670 | |
1671 | /// Creates a dataset that batches and pads `batch_size` elements from the input. |
1672 | /// |
1673 | /// Args: |
1674 | /// * scope: A Scope object |
1675 | /// * batch_size: A scalar representing the number of elements to accumulate in a |
1676 | /// batch. |
1677 | /// * padded_shapes: A list of int64 tensors representing the desired padded shapes |
1678 | /// of the corresponding output components. These shapes may be partially |
1679 | /// specified, using `-1` to indicate that a particular dimension should be |
1680 | /// padded to the maximum size of all batch elements. |
1681 | /// * padding_values: A list of scalars containing the padding value to use for |
1682 | /// each of the outputs. |
1683 | /// * drop_remainder: A scalar representing whether the last batch should be dropped in case its size |
1684 | /// is smaller than desired. |
1685 | /// |
1686 | /// Returns: |
1687 | /// * `Output`: The handle tensor. |
1688 | class PaddedBatchDatasetV2 { |
1689 | public: |
1690 | /// Optional attribute setters for PaddedBatchDatasetV2 |
1691 | struct Attrs { |
1692 | /// Defaults to false |
1693 | TF_MUST_USE_RESULT Attrs ParallelCopy(bool x) { |
1694 | Attrs ret = *this; |
1695 | ret.parallel_copy_ = x; |
1696 | return ret; |
1697 | } |
1698 | |
1699 | /// Defaults to "" |
1700 | TF_MUST_USE_RESULT Attrs Metadata(StringPiece x) { |
1701 | Attrs ret = *this; |
1702 | ret.metadata_ = x; |
1703 | return ret; |
1704 | } |
1705 | |
1706 | bool parallel_copy_ = false; |
1707 | StringPiece metadata_ = "" ; |
1708 | }; |
1709 | PaddedBatchDatasetV2(const ::tensorflow::Scope& scope, ::tensorflow::Input |
1710 | input_dataset, ::tensorflow::Input batch_size, |
1711 | ::tensorflow::InputList padded_shapes, |
1712 | ::tensorflow::InputList padding_values, |
1713 | ::tensorflow::Input drop_remainder, const |
1714 | gtl::ArraySlice<PartialTensorShape>& output_shapes); |
1715 | PaddedBatchDatasetV2(const ::tensorflow::Scope& scope, ::tensorflow::Input |
1716 | input_dataset, ::tensorflow::Input batch_size, |
1717 | ::tensorflow::InputList padded_shapes, |
1718 | ::tensorflow::InputList padding_values, |
1719 | ::tensorflow::Input drop_remainder, const |
1720 | gtl::ArraySlice<PartialTensorShape>& output_shapes, const |
1721 | PaddedBatchDatasetV2::Attrs& attrs); |
1722 | operator ::tensorflow::Output() const { return handle; } |
1723 | operator ::tensorflow::Input() const { return handle; } |
1724 | ::tensorflow::Node* node() const { return handle.node(); } |
1725 | |
1726 | static Attrs ParallelCopy(bool x) { |
1727 | return Attrs().ParallelCopy(x); |
1728 | } |
1729 | static Attrs Metadata(StringPiece x) { |
1730 | return Attrs().Metadata(x); |
1731 | } |
1732 | |
1733 | Operation operation; |
1734 | ::tensorflow::Output handle; |
1735 | }; |
1736 | |
1737 | /// TODO: add doc. |
1738 | /// |
1739 | /// Args: |
1740 | /// * scope: A Scope object |
1741 | /// |
1742 | /// Returns: |
1743 | /// * `Output`: The handle tensor. |
1744 | class ParallelBatchDataset { |
1745 | public: |
1746 | /// Optional attribute setters for ParallelBatchDataset |
1747 | struct Attrs { |
1748 | /// Defaults to false |
1749 | TF_MUST_USE_RESULT Attrs ParallelCopy(bool x) { |
1750 | Attrs ret = *this; |
1751 | ret.parallel_copy_ = x; |
1752 | return ret; |
1753 | } |
1754 | |
1755 | /// Defaults to "default" |
1756 | TF_MUST_USE_RESULT Attrs Deterministic(StringPiece x) { |
1757 | Attrs ret = *this; |
1758 | ret.deterministic_ = x; |
1759 | return ret; |
1760 | } |
1761 | |
1762 | /// Defaults to "" |
1763 | TF_MUST_USE_RESULT Attrs Metadata(StringPiece x) { |
1764 | Attrs ret = *this; |
1765 | ret.metadata_ = x; |
1766 | return ret; |
1767 | } |
1768 | |
1769 | bool parallel_copy_ = false; |
1770 | StringPiece deterministic_ = "default" ; |
1771 | StringPiece metadata_ = "" ; |
1772 | }; |
1773 | ParallelBatchDataset(const ::tensorflow::Scope& scope, ::tensorflow::Input |
1774 | input_dataset, ::tensorflow::Input batch_size, |
1775 | ::tensorflow::Input num_parallel_calls, |
1776 | ::tensorflow::Input drop_remainder, const DataTypeSlice& |
1777 | output_types, const gtl::ArraySlice<PartialTensorShape>& |
1778 | output_shapes); |
1779 | ParallelBatchDataset(const ::tensorflow::Scope& scope, ::tensorflow::Input |
1780 | input_dataset, ::tensorflow::Input batch_size, |
1781 | ::tensorflow::Input num_parallel_calls, |
1782 | ::tensorflow::Input drop_remainder, const DataTypeSlice& |
1783 | output_types, const gtl::ArraySlice<PartialTensorShape>& |
1784 | output_shapes, const ParallelBatchDataset::Attrs& attrs); |
1785 | operator ::tensorflow::Output() const { return handle; } |
1786 | operator ::tensorflow::Input() const { return handle; } |
1787 | ::tensorflow::Node* node() const { return handle.node(); } |
1788 | |
1789 | static Attrs ParallelCopy(bool x) { |
1790 | return Attrs().ParallelCopy(x); |
1791 | } |
1792 | static Attrs Deterministic(StringPiece x) { |
1793 | return Attrs().Deterministic(x); |
1794 | } |
1795 | static Attrs Metadata(StringPiece x) { |
1796 | return Attrs().Metadata(x); |
1797 | } |
1798 | |
1799 | Operation operation; |
1800 | ::tensorflow::Output handle; |
1801 | }; |
1802 | |
1803 | /// Creates a dataset containing elements of `input_dataset` matching `predicate`. |
1804 | /// |
1805 | /// The `predicate` function must return a scalar boolean and accept the |
1806 | /// following arguments: |
1807 | /// |
1808 | /// * One tensor for each component of an element of `input_dataset`. |
1809 | /// * One tensor for each value in `other_arguments`. |
1810 | /// |
1811 | /// Unlike a "FilterDataset", which applies `predicate` sequentially, this dataset |
1812 | /// invokes up to `num_parallel_calls` copies of `predicate` in parallel. |
1813 | /// |
1814 | /// |
1815 | /// Args: |
1816 | /// * scope: A Scope object |
1817 | /// * other_arguments: A list of tensors, typically values that were captured when |
1818 | /// building a closure for `predicate`. |
1819 | /// * num_parallel_calls: The number of concurrent invocations of `predicate` that process |
1820 | /// elements from `input_dataset` in parallel. |
1821 | /// * predicate: A function returning a scalar boolean. |
1822 | /// |
1823 | /// Optional attributes (see `Attrs`): |
1824 | /// * deterministic: A string indicating the op-level determinism to use. Deterministic controls |
1825 | /// whether the interleave is allowed to return elements out of order if the next |
1826 | /// element to be returned isn't available, but a later element is. Options are |
1827 | /// "true", "false", and "default". "default" indicates that determinism should be |
1828 | /// decided by the `experimental_deterministic` parameter of `tf.data.Options`. |
1829 | /// |
1830 | /// Returns: |
1831 | /// * `Output`: The handle tensor. |
1832 | class ParallelFilterDataset { |
1833 | public: |
1834 | /// Optional attribute setters for ParallelFilterDataset |
1835 | struct Attrs { |
1836 | /// A string indicating the op-level determinism to use. Deterministic controls |
1837 | /// whether the interleave is allowed to return elements out of order if the next |
1838 | /// element to be returned isn't available, but a later element is. Options are |
1839 | /// "true", "false", and "default". "default" indicates that determinism should be |
1840 | /// decided by the `experimental_deterministic` parameter of `tf.data.Options`. |
1841 | /// |
1842 | /// Defaults to "default" |
1843 | TF_MUST_USE_RESULT Attrs Deterministic(StringPiece x) { |
1844 | Attrs ret = *this; |
1845 | ret.deterministic_ = x; |
1846 | return ret; |
1847 | } |
1848 | |
1849 | /// Defaults to "" |
1850 | TF_MUST_USE_RESULT Attrs Metadata(StringPiece x) { |
1851 | Attrs ret = *this; |
1852 | ret.metadata_ = x; |
1853 | return ret; |
1854 | } |
1855 | |
1856 | StringPiece deterministic_ = "default" ; |
1857 | StringPiece metadata_ = "" ; |
1858 | }; |
1859 | ParallelFilterDataset(const ::tensorflow::Scope& scope, ::tensorflow::Input |
1860 | input_dataset, ::tensorflow::InputList other_arguments, |
1861 | ::tensorflow::Input num_parallel_calls, const |
1862 | NameAttrList& predicate, const DataTypeSlice& |
1863 | output_types, const gtl::ArraySlice<PartialTensorShape>& |
1864 | output_shapes); |
1865 | ParallelFilterDataset(const ::tensorflow::Scope& scope, ::tensorflow::Input |
1866 | input_dataset, ::tensorflow::InputList other_arguments, |
1867 | ::tensorflow::Input num_parallel_calls, const |
1868 | NameAttrList& predicate, const DataTypeSlice& |
1869 | output_types, const gtl::ArraySlice<PartialTensorShape>& |
1870 | output_shapes, const ParallelFilterDataset::Attrs& attrs); |
1871 | operator ::tensorflow::Output() const { return handle; } |
1872 | operator ::tensorflow::Input() const { return handle; } |
1873 | ::tensorflow::Node* node() const { return handle.node(); } |
1874 | |
1875 | static Attrs Deterministic(StringPiece x) { |
1876 | return Attrs().Deterministic(x); |
1877 | } |
1878 | static Attrs Metadata(StringPiece x) { |
1879 | return Attrs().Metadata(x); |
1880 | } |
1881 | |
1882 | Operation operation; |
1883 | ::tensorflow::Output handle; |
1884 | }; |
1885 | |
1886 | /// Creates a dataset that applies `f` to the outputs of `input_dataset`. |
1887 | /// |
1888 | /// The resulting dataset is similar to the `InterleaveDataset`, except that the |
1889 | /// dataset will fetch records from the interleaved datasets in parallel. |
1890 | /// |
1891 | /// The `tf.data` Python API creates instances of this op from |
1892 | /// `Dataset.interleave()` when the `num_parallel_calls` parameter of that method |
1893 | /// is set to any value other than `None`. |
1894 | /// |
1895 | /// By default, the output of this dataset will be deterministic, which may result |
1896 | /// in the dataset blocking if the next data item to be returned isn't available. |
1897 | /// In order to avoid head-of-line blocking, one can set the |
1898 | /// `experimental_deterministic` parameter of `tf.data.Options` to `False`, |
1899 | /// which can improve performance at the expense of non-determinism. |
1900 | /// |
1901 | /// Args: |
1902 | /// * scope: A Scope object |
1903 | /// * input_dataset: Dataset that produces a stream of arguments for the function `f`. |
1904 | /// * other_arguments: Additional arguments to pass to `f` beyond those produced by `input_dataset`. |
1905 | /// Evaluated once when the dataset is instantiated. |
1906 | /// * cycle_length: Number of datasets (each created by applying `f` to the elements of |
1907 | /// `input_dataset`) among which the `ParallelInterleaveDatasetV2` will cycle in a |
1908 | /// round-robin fashion. |
1909 | /// * block_length: Number of elements at a time to produce from each interleaved invocation of a |
1910 | /// dataset returned by `f`. |
1911 | /// * num_parallel_calls: Determines the number of threads that should be used for fetching data from |
1912 | /// input datasets in parallel. The Python API `tf.data.experimental.AUTOTUNE` |
1913 | /// constant can be used to indicate that the level of parallelism should be autotuned. |
1914 | /// * f: A function mapping elements of `input_dataset`, concatenated with |
1915 | /// `other_arguments`, to a Dataset variant that contains elements matching |
1916 | /// `output_types` and `output_shapes`. |
1917 | /// |
1918 | /// Returns: |
1919 | /// * `Output`: The handle tensor. |
1920 | class ParallelInterleaveDatasetV2 { |
1921 | public: |
1922 | /// Optional attribute setters for ParallelInterleaveDatasetV2 |
1923 | struct Attrs { |
1924 | /// Defaults to false |
1925 | TF_MUST_USE_RESULT Attrs Sloppy(bool x) { |
1926 | Attrs ret = *this; |
1927 | ret.sloppy_ = x; |
1928 | return ret; |
1929 | } |
1930 | |
1931 | /// Defaults to "" |
1932 | TF_MUST_USE_RESULT Attrs Metadata(StringPiece x) { |
1933 | Attrs ret = *this; |
1934 | ret.metadata_ = x; |
1935 | return ret; |
1936 | } |
1937 | |
1938 | bool sloppy_ = false; |
1939 | StringPiece metadata_ = "" ; |
1940 | }; |
1941 | ParallelInterleaveDatasetV2(const ::tensorflow::Scope& scope, |
1942 | ::tensorflow::Input input_dataset, |
1943 | ::tensorflow::InputList other_arguments, |
1944 | ::tensorflow::Input cycle_length, |
1945 | ::tensorflow::Input block_length, |
1946 | ::tensorflow::Input num_parallel_calls, const |
1947 | NameAttrList& f, const DataTypeSlice& output_types, |
1948 | const gtl::ArraySlice<PartialTensorShape>& |
1949 | output_shapes); |
1950 | ParallelInterleaveDatasetV2(const ::tensorflow::Scope& scope, |
1951 | ::tensorflow::Input input_dataset, |
1952 | ::tensorflow::InputList other_arguments, |
1953 | ::tensorflow::Input cycle_length, |
1954 | ::tensorflow::Input block_length, |
1955 | ::tensorflow::Input num_parallel_calls, const |
1956 | NameAttrList& f, const DataTypeSlice& output_types, |
1957 | const gtl::ArraySlice<PartialTensorShape>& |
1958 | output_shapes, const |
1959 | ParallelInterleaveDatasetV2::Attrs& attrs); |
1960 | operator ::tensorflow::Output() const { return handle; } |
1961 | operator ::tensorflow::Input() const { return handle; } |
1962 | ::tensorflow::Node* node() const { return handle.node(); } |
1963 | |
1964 | static Attrs Sloppy(bool x) { |
1965 | return Attrs().Sloppy(x); |
1966 | } |
1967 | static Attrs Metadata(StringPiece x) { |
1968 | return Attrs().Metadata(x); |
1969 | } |
1970 | |
1971 | Operation operation; |
1972 | ::tensorflow::Output handle; |
1973 | }; |
1974 | |
1975 | /// Creates a dataset that applies `f` to the outputs of `input_dataset`. |
1976 | /// |
1977 | /// The resulting dataset is similar to the `InterleaveDataset`, except that the |
1978 | /// dataset will fetch records from the interleaved datasets in parallel. |
1979 | /// |
1980 | /// The `tf.data` Python API creates instances of this op from |
1981 | /// `Dataset.interleave()` when the `num_parallel_calls` parameter of that method |
1982 | /// is set to any value other than `None`. |
1983 | /// |
1984 | /// By default, the output of this dataset will be deterministic, which may result |
1985 | /// in the dataset blocking if the next data item to be returned isn't available. |
1986 | /// In order to avoid head-of-line blocking, one can either set the `deterministic` |
1987 | /// attribute to "false", or leave it as "default" and set the |
1988 | /// `experimental_deterministic` parameter of `tf.data.Options` to `False`. |
1989 | /// This can improve performance at the expense of non-determinism. |
1990 | /// |
1991 | /// Args: |
1992 | /// * scope: A Scope object |
1993 | /// * input_dataset: Dataset that produces a stream of arguments for the function `f`. |
1994 | /// * other_arguments: Additional arguments to pass to `f` beyond those produced by `input_dataset`. |
1995 | /// Evaluated once when the dataset is instantiated. |
1996 | /// * cycle_length: Number of datasets (each created by applying `f` to the elements of |
1997 | /// `input_dataset`) among which the `ParallelInterleaveDatasetV2` will cycle in a |
1998 | /// round-robin fashion. |
1999 | /// * block_length: Number of elements at a time to produce from each interleaved invocation of a |
2000 | /// dataset returned by `f`. |
2001 | /// * num_parallel_calls: Determines the number of threads that should be used for fetching data from |
2002 | /// input datasets in parallel. The Python API `tf.data.experimental.AUTOTUNE` |
2003 | /// constant can be used to indicate that the level of parallelism should be autotuned. |
2004 | /// * f: A function mapping elements of `input_dataset`, concatenated with |
2005 | /// `other_arguments`, to a Dataset variant that contains elements matching |
2006 | /// `output_types` and `output_shapes`. |
2007 | /// |
2008 | /// Optional attributes (see `Attrs`): |
2009 | /// * deterministic: A string indicating the op-level determinism to use. Deterministic controls |
2010 | /// whether the interleave is allowed to return elements out of order if the next |
2011 | /// element to be returned isn't available, but a later element is. Options are |
2012 | /// "true", "false", and "default". "default" indicates that determinism should be |
2013 | /// decided by the `experimental_deterministic` parameter of `tf.data.Options`. |
2014 | /// |
2015 | /// Returns: |
2016 | /// * `Output`: The handle tensor. |
2017 | class ParallelInterleaveDatasetV3 { |
2018 | public: |
2019 | /// Optional attribute setters for ParallelInterleaveDatasetV3 |
2020 | struct Attrs { |
2021 | /// A string indicating the op-level determinism to use. Deterministic controls |
2022 | /// whether the interleave is allowed to return elements out of order if the next |
2023 | /// element to be returned isn't available, but a later element is. Options are |
2024 | /// "true", "false", and "default". "default" indicates that determinism should be |
2025 | /// decided by the `experimental_deterministic` parameter of `tf.data.Options`. |
2026 | /// |
2027 | /// Defaults to "default" |
2028 | TF_MUST_USE_RESULT Attrs Deterministic(StringPiece x) { |
2029 | Attrs ret = *this; |
2030 | ret.deterministic_ = x; |
2031 | return ret; |
2032 | } |
2033 | |
2034 | /// Defaults to "" |
2035 | TF_MUST_USE_RESULT Attrs Metadata(StringPiece x) { |
2036 | Attrs ret = *this; |
2037 | ret.metadata_ = x; |
2038 | return ret; |
2039 | } |
2040 | |
2041 | StringPiece deterministic_ = "default" ; |
2042 | StringPiece metadata_ = "" ; |
2043 | }; |
2044 | ParallelInterleaveDatasetV3(const ::tensorflow::Scope& scope, |
2045 | ::tensorflow::Input input_dataset, |
2046 | ::tensorflow::InputList other_arguments, |
2047 | ::tensorflow::Input cycle_length, |
2048 | ::tensorflow::Input block_length, |
2049 | ::tensorflow::Input num_parallel_calls, const |
2050 | NameAttrList& f, const DataTypeSlice& output_types, |
2051 | const gtl::ArraySlice<PartialTensorShape>& |
2052 | output_shapes); |
2053 | ParallelInterleaveDatasetV3(const ::tensorflow::Scope& scope, |
2054 | ::tensorflow::Input input_dataset, |
2055 | ::tensorflow::InputList other_arguments, |
2056 | ::tensorflow::Input cycle_length, |
2057 | ::tensorflow::Input block_length, |
2058 | ::tensorflow::Input num_parallel_calls, const |
2059 | NameAttrList& f, const DataTypeSlice& output_types, |
2060 | const gtl::ArraySlice<PartialTensorShape>& |
2061 | output_shapes, const |
2062 | ParallelInterleaveDatasetV3::Attrs& attrs); |
2063 | operator ::tensorflow::Output() const { return handle; } |
2064 | operator ::tensorflow::Input() const { return handle; } |
2065 | ::tensorflow::Node* node() const { return handle.node(); } |
2066 | |
2067 | static Attrs Deterministic(StringPiece x) { |
2068 | return Attrs().Deterministic(x); |
2069 | } |
2070 | static Attrs Metadata(StringPiece x) { |
2071 | return Attrs().Metadata(x); |
2072 | } |
2073 | |
2074 | Operation operation; |
2075 | ::tensorflow::Output handle; |
2076 | }; |
2077 | |
2078 | /// Creates a dataset that applies `f` to the outputs of `input_dataset`. |
2079 | /// |
2080 | /// The resulting dataset is similar to the `InterleaveDataset`, except that the |
2081 | /// dataset will fetch records from the interleaved datasets in parallel. |
2082 | /// |
2083 | /// The `tf.data` Python API creates instances of this op from |
2084 | /// `Dataset.interleave()` when the `num_parallel_calls` parameter of that method |
2085 | /// is set to any value other than `None`. |
2086 | /// |
2087 | /// By default, the output of this dataset will be deterministic, which may result |
2088 | /// in the dataset blocking if the next data item to be returned isn't available. |
2089 | /// In order to avoid head-of-line blocking, one can either set the `deterministic` |
2090 | /// attribute to "false", or leave it as "default" and set the |
2091 | /// `experimental_deterministic` parameter of `tf.data.Options` to `False`. |
2092 | /// This can improve performance at the expense of non-determinism. |
2093 | /// |
2094 | /// Args: |
2095 | /// * scope: A Scope object |
2096 | /// * input_dataset: Dataset that produces a stream of arguments for the function `f`. |
2097 | /// * other_arguments: Additional arguments to pass to `f` beyond those produced by `input_dataset`. |
2098 | /// Evaluated once when the dataset is instantiated. |
2099 | /// * cycle_length: Number of datasets (each created by applying `f` to the elements of |
2100 | /// `input_dataset`) among which the `ParallelInterleaveDatasetV2` will cycle in a |
2101 | /// round-robin fashion. |
2102 | /// * block_length: Number of elements at a time to produce from each interleaved invocation of a |
2103 | /// dataset returned by `f`. |
2104 | /// * buffer_output_elements: The number of elements each iterator being interleaved should buffer (similar |
2105 | /// to the `.prefetch()` transformation for each interleaved iterator). |
2106 | /// * prefetch_input_elements: Determines the number of iterators to prefetch, allowing buffers to warm up and |
2107 | /// data to be pre-fetched without blocking the main thread. |
2108 | /// * num_parallel_calls: Determines the number of threads that should be used for fetching data from |
2109 | /// input datasets in parallel. The Python API `tf.data.experimental.AUTOTUNE` |
2110 | /// constant can be used to indicate that the level of parallelism should be autotuned. |
2111 | /// * f: A function mapping elements of `input_dataset`, concatenated with |
2112 | /// `other_arguments`, to a Dataset variant that contains elements matching |
2113 | /// `output_types` and `output_shapes`. |
2114 | /// |
2115 | /// Optional attributes (see `Attrs`): |
2116 | /// * deterministic: A string indicating the op-level determinism to use. Deterministic controls |
2117 | /// whether the interleave is allowed to return elements out of order if the next |
2118 | /// element to be returned isn't available, but a later element is. Options are |
2119 | /// "true", "false", and "default". "default" indicates that determinism should be |
2120 | /// decided by the `experimental_deterministic` parameter of `tf.data.Options`. |
2121 | /// |
2122 | /// Returns: |
2123 | /// * `Output`: The handle tensor. |
2124 | class ParallelInterleaveDatasetV4 { |
2125 | public: |
2126 | /// Optional attribute setters for ParallelInterleaveDatasetV4 |
2127 | struct Attrs { |
2128 | /// A string indicating the op-level determinism to use. Deterministic controls |
2129 | /// whether the interleave is allowed to return elements out of order if the next |
2130 | /// element to be returned isn't available, but a later element is. Options are |
2131 | /// "true", "false", and "default". "default" indicates that determinism should be |
2132 | /// decided by the `experimental_deterministic` parameter of `tf.data.Options`. |
2133 | /// |
2134 | /// Defaults to "default" |
2135 | TF_MUST_USE_RESULT Attrs Deterministic(StringPiece x) { |
2136 | Attrs ret = *this; |
2137 | ret.deterministic_ = x; |
2138 | return ret; |
2139 | } |
2140 | |
2141 | /// Defaults to "" |
2142 | TF_MUST_USE_RESULT Attrs Metadata(StringPiece x) { |
2143 | Attrs ret = *this; |
2144 | ret.metadata_ = x; |
2145 | return ret; |
2146 | } |
2147 | |
2148 | StringPiece deterministic_ = "default" ; |
2149 | StringPiece metadata_ = "" ; |
2150 | }; |
2151 | ParallelInterleaveDatasetV4(const ::tensorflow::Scope& scope, |
2152 | ::tensorflow::Input input_dataset, |
2153 | ::tensorflow::InputList other_arguments, |
2154 | ::tensorflow::Input cycle_length, |
2155 | ::tensorflow::Input block_length, |
2156 | ::tensorflow::Input buffer_output_elements, |
2157 | ::tensorflow::Input prefetch_input_elements, |
2158 | ::tensorflow::Input num_parallel_calls, const |
2159 | NameAttrList& f, const DataTypeSlice& output_types, |
2160 | const gtl::ArraySlice<PartialTensorShape>& |
2161 | output_shapes); |
2162 | ParallelInterleaveDatasetV4(const ::tensorflow::Scope& scope, |
2163 | ::tensorflow::Input input_dataset, |
2164 | ::tensorflow::InputList other_arguments, |
2165 | ::tensorflow::Input cycle_length, |
2166 | ::tensorflow::Input block_length, |
2167 | ::tensorflow::Input buffer_output_elements, |
2168 | ::tensorflow::Input prefetch_input_elements, |
2169 | ::tensorflow::Input num_parallel_calls, const |
2170 | NameAttrList& f, const DataTypeSlice& output_types, |
2171 | const gtl::ArraySlice<PartialTensorShape>& |
2172 | output_shapes, const |
2173 | ParallelInterleaveDatasetV4::Attrs& attrs); |
2174 | operator ::tensorflow::Output() const { return handle; } |
2175 | operator ::tensorflow::Input() const { return handle; } |
2176 | ::tensorflow::Node* node() const { return handle.node(); } |
2177 | |
2178 | static Attrs Deterministic(StringPiece x) { |
2179 | return Attrs().Deterministic(x); |
2180 | } |
2181 | static Attrs Metadata(StringPiece x) { |
2182 | return Attrs().Metadata(x); |
2183 | } |
2184 | |
2185 | Operation operation; |
2186 | ::tensorflow::Output handle; |
2187 | }; |
2188 | |
2189 | /// Creates a dataset that applies `f` to the outputs of `input_dataset`. |
2190 | /// |
2191 | /// Unlike a "MapDataset", which applies `f` sequentially, this dataset invokes up |
2192 | /// to `num_parallel_calls` copies of `f` in parallel. |
2193 | /// |
2194 | /// Args: |
2195 | /// * scope: A Scope object |
2196 | /// * num_parallel_calls: The number of concurrent invocations of `f` that process |
2197 | /// elements from `input_dataset` in parallel. |
2198 | /// |
2199 | /// Returns: |
2200 | /// * `Output`: The handle tensor. |
2201 | class ParallelMapDataset { |
2202 | public: |
2203 | /// Optional attribute setters for ParallelMapDataset |
2204 | struct Attrs { |
2205 | /// Defaults to true |
2206 | TF_MUST_USE_RESULT Attrs UseInterOpParallelism(bool x) { |
2207 | Attrs ret = *this; |
2208 | ret.use_inter_op_parallelism_ = x; |
2209 | return ret; |
2210 | } |
2211 | |
2212 | /// Defaults to false |
2213 | TF_MUST_USE_RESULT Attrs Sloppy(bool x) { |
2214 | Attrs ret = *this; |
2215 | ret.sloppy_ = x; |
2216 | return ret; |
2217 | } |
2218 | |
2219 | /// Defaults to false |
2220 | TF_MUST_USE_RESULT Attrs PreserveCardinality(bool x) { |
2221 | Attrs ret = *this; |
2222 | ret.preserve_cardinality_ = x; |
2223 | return ret; |
2224 | } |
2225 | |
2226 | /// Defaults to "" |
2227 | TF_MUST_USE_RESULT Attrs Metadata(StringPiece x) { |
2228 | Attrs ret = *this; |
2229 | ret.metadata_ = x; |
2230 | return ret; |
2231 | } |
2232 | |
2233 | bool use_inter_op_parallelism_ = true; |
2234 | bool sloppy_ = false; |
2235 | bool preserve_cardinality_ = false; |
2236 | StringPiece metadata_ = "" ; |
2237 | }; |
2238 | ParallelMapDataset(const ::tensorflow::Scope& scope, ::tensorflow::Input |
2239 | input_dataset, ::tensorflow::InputList other_arguments, |
2240 | ::tensorflow::Input num_parallel_calls, const NameAttrList& |
2241 | f, const DataTypeSlice& output_types, const |
2242 | gtl::ArraySlice<PartialTensorShape>& output_shapes); |
2243 | ParallelMapDataset(const ::tensorflow::Scope& scope, ::tensorflow::Input |
2244 | input_dataset, ::tensorflow::InputList other_arguments, |
2245 | ::tensorflow::Input num_parallel_calls, const NameAttrList& |
2246 | f, const DataTypeSlice& output_types, const |
2247 | gtl::ArraySlice<PartialTensorShape>& output_shapes, const |
2248 | ParallelMapDataset::Attrs& attrs); |
2249 | operator ::tensorflow::Output() const { return handle; } |
2250 | operator ::tensorflow::Input() const { return handle; } |
2251 | ::tensorflow::Node* node() const { return handle.node(); } |
2252 | |
2253 | static Attrs UseInterOpParallelism(bool x) { |
2254 | return Attrs().UseInterOpParallelism(x); |
2255 | } |
2256 | static Attrs Sloppy(bool x) { |
2257 | return Attrs().Sloppy(x); |
2258 | } |
2259 | static Attrs PreserveCardinality(bool x) { |
2260 | return Attrs().PreserveCardinality(x); |
2261 | } |
2262 | static Attrs Metadata(StringPiece x) { |
2263 | return Attrs().Metadata(x); |
2264 | } |
2265 | |
2266 | Operation operation; |
2267 | ::tensorflow::Output handle; |
2268 | }; |
2269 | |
2270 | /// Creates a dataset that applies `f` to the outputs of `input_dataset`. |
2271 | /// |
2272 | /// Unlike a "MapDataset", which applies `f` sequentially, this dataset invokes up |
2273 | /// to `num_parallel_calls` copies of `f` in parallel. |
2274 | /// |
2275 | /// Args: |
2276 | /// * scope: A Scope object |
2277 | /// * num_parallel_calls: The number of concurrent invocations of `f` that process |
2278 | /// elements from `input_dataset` in parallel. |
2279 | /// |
2280 | /// Returns: |
2281 | /// * `Output`: The handle tensor. |
2282 | class ParallelMapDatasetV2 { |
2283 | public: |
2284 | /// Optional attribute setters for ParallelMapDatasetV2 |
2285 | struct Attrs { |
2286 | /// Defaults to true |
2287 | TF_MUST_USE_RESULT Attrs UseInterOpParallelism(bool x) { |
2288 | Attrs ret = *this; |
2289 | ret.use_inter_op_parallelism_ = x; |
2290 | return ret; |
2291 | } |
2292 | |
2293 | /// Defaults to "default" |
2294 | TF_MUST_USE_RESULT Attrs Deterministic(StringPiece x) { |
2295 | Attrs ret = *this; |
2296 | ret.deterministic_ = x; |
2297 | return ret; |
2298 | } |
2299 | |
2300 | /// Defaults to false |
2301 | TF_MUST_USE_RESULT Attrs PreserveCardinality(bool x) { |
2302 | Attrs ret = *this; |
2303 | ret.preserve_cardinality_ = x; |
2304 | return ret; |
2305 | } |
2306 | |
2307 | /// Defaults to "" |
2308 | TF_MUST_USE_RESULT Attrs Metadata(StringPiece x) { |
2309 | Attrs ret = *this; |
2310 | ret.metadata_ = x; |
2311 | return ret; |
2312 | } |
2313 | |
2314 | bool use_inter_op_parallelism_ = true; |
2315 | StringPiece deterministic_ = "default" ; |
2316 | bool preserve_cardinality_ = false; |
2317 | StringPiece metadata_ = "" ; |
2318 | }; |
2319 | ParallelMapDatasetV2(const ::tensorflow::Scope& scope, ::tensorflow::Input |
2320 | input_dataset, ::tensorflow::InputList other_arguments, |
2321 | ::tensorflow::Input num_parallel_calls, const |
2322 | NameAttrList& f, const DataTypeSlice& output_types, const |
2323 | gtl::ArraySlice<PartialTensorShape>& output_shapes); |
2324 | ParallelMapDatasetV2(const ::tensorflow::Scope& scope, ::tensorflow::Input |
2325 | input_dataset, ::tensorflow::InputList other_arguments, |
2326 | ::tensorflow::Input num_parallel_calls, const |
2327 | NameAttrList& f, const DataTypeSlice& output_types, const |
2328 | gtl::ArraySlice<PartialTensorShape>& output_shapes, const |
2329 | ParallelMapDatasetV2::Attrs& attrs); |
2330 | operator ::tensorflow::Output() const { return handle; } |
2331 | operator ::tensorflow::Input() const { return handle; } |
2332 | ::tensorflow::Node* node() const { return handle.node(); } |
2333 | |
2334 | static Attrs UseInterOpParallelism(bool x) { |
2335 | return Attrs().UseInterOpParallelism(x); |
2336 | } |
2337 | static Attrs Deterministic(StringPiece x) { |
2338 | return Attrs().Deterministic(x); |
2339 | } |
2340 | static Attrs PreserveCardinality(bool x) { |
2341 | return Attrs().PreserveCardinality(x); |
2342 | } |
2343 | static Attrs Metadata(StringPiece x) { |
2344 | return Attrs().Metadata(x); |
2345 | } |
2346 | |
2347 | Operation operation; |
2348 | ::tensorflow::Output handle; |
2349 | }; |
2350 | |
2351 | /// Creates a dataset that asynchronously prefetches elements from `input_dataset`. |
2352 | /// |
2353 | /// Args: |
2354 | /// * scope: A Scope object |
2355 | /// * buffer_size: The maximum number of elements to buffer in an iterator over |
2356 | /// this dataset. |
2357 | /// |
2358 | /// Returns: |
2359 | /// * `Output`: The handle tensor. |
2360 | class PrefetchDataset { |
2361 | public: |
2362 | /// Optional attribute setters for PrefetchDataset |
2363 | struct Attrs { |
2364 | /// Defaults to 0 |
2365 | TF_MUST_USE_RESULT Attrs SlackPeriod(int64 x) { |
2366 | Attrs ret = *this; |
2367 | ret.slack_period_ = x; |
2368 | return ret; |
2369 | } |
2370 | |
2371 | /// Defaults to true |
2372 | TF_MUST_USE_RESULT Attrs LegacyAutotune(bool x) { |
2373 | Attrs ret = *this; |
2374 | ret.legacy_autotune_ = x; |
2375 | return ret; |
2376 | } |
2377 | |
2378 | /// Defaults to 0 |
2379 | TF_MUST_USE_RESULT Attrs BufferSizeMin(int64 x) { |
2380 | Attrs ret = *this; |
2381 | ret.buffer_size_min_ = x; |
2382 | return ret; |
2383 | } |
2384 | |
2385 | /// Defaults to "" |
2386 | TF_MUST_USE_RESULT Attrs Metadata(StringPiece x) { |
2387 | Attrs ret = *this; |
2388 | ret.metadata_ = x; |
2389 | return ret; |
2390 | } |
2391 | |
2392 | int64 slack_period_ = 0; |
2393 | bool legacy_autotune_ = true; |
2394 | int64 buffer_size_min_ = 0; |
2395 | StringPiece metadata_ = "" ; |
2396 | }; |
2397 | PrefetchDataset(const ::tensorflow::Scope& scope, ::tensorflow::Input |
2398 | input_dataset, ::tensorflow::Input buffer_size, const |
2399 | DataTypeSlice& output_types, const |
2400 | gtl::ArraySlice<PartialTensorShape>& output_shapes); |
2401 | PrefetchDataset(const ::tensorflow::Scope& scope, ::tensorflow::Input |
2402 | input_dataset, ::tensorflow::Input buffer_size, const |
2403 | DataTypeSlice& output_types, const |
2404 | gtl::ArraySlice<PartialTensorShape>& output_shapes, const |
2405 | PrefetchDataset::Attrs& attrs); |
2406 | operator ::tensorflow::Output() const { return handle; } |
2407 | operator ::tensorflow::Input() const { return handle; } |
2408 | ::tensorflow::Node* node() const { return handle.node(); } |
2409 | |
2410 | static Attrs SlackPeriod(int64 x) { |
2411 | return Attrs().SlackPeriod(x); |
2412 | } |
2413 | static Attrs LegacyAutotune(bool x) { |
2414 | return Attrs().LegacyAutotune(x); |
2415 | } |
2416 | static Attrs BufferSizeMin(int64 x) { |
2417 | return Attrs().BufferSizeMin(x); |
2418 | } |
2419 | static Attrs Metadata(StringPiece x) { |
2420 | return Attrs().Metadata(x); |
2421 | } |
2422 | |
2423 | Operation operation; |
2424 | ::tensorflow::Output handle; |
2425 | }; |
2426 | |
2427 | /// Creates a dataset with a range of values. Corresponds to python's xrange. |
2428 | /// |
2429 | /// Args: |
2430 | /// * scope: A Scope object |
2431 | /// * start: corresponds to start in python's xrange(). |
2432 | /// * stop: corresponds to stop in python's xrange(). |
2433 | /// * step: corresponds to step in python's xrange(). |
2434 | /// |
2435 | /// Returns: |
2436 | /// * `Output`: The handle tensor. |
2437 | class RangeDataset { |
2438 | public: |
2439 | /// Optional attribute setters for RangeDataset |
2440 | struct Attrs { |
2441 | /// Defaults to "" |
2442 | TF_MUST_USE_RESULT Attrs Metadata(StringPiece x) { |
2443 | Attrs ret = *this; |
2444 | ret.metadata_ = x; |
2445 | return ret; |
2446 | } |
2447 | |
2448 | /// Defaults to false |
2449 | TF_MUST_USE_RESULT Attrs ReplicateOnSplit(bool x) { |
2450 | Attrs ret = *this; |
2451 | ret.replicate_on_split_ = x; |
2452 | return ret; |
2453 | } |
2454 | |
2455 | StringPiece metadata_ = "" ; |
2456 | bool replicate_on_split_ = false; |
2457 | }; |
2458 | RangeDataset(const ::tensorflow::Scope& scope, ::tensorflow::Input start, |
2459 | ::tensorflow::Input stop, ::tensorflow::Input step, const |
2460 | DataTypeSlice& output_types, const |
2461 | gtl::ArraySlice<PartialTensorShape>& output_shapes); |
2462 | RangeDataset(const ::tensorflow::Scope& scope, ::tensorflow::Input start, |
2463 | ::tensorflow::Input stop, ::tensorflow::Input step, const |
2464 | DataTypeSlice& output_types, const |
2465 | gtl::ArraySlice<PartialTensorShape>& output_shapes, const |
2466 | RangeDataset::Attrs& attrs); |
2467 | operator ::tensorflow::Output() const { return handle; } |
2468 | operator ::tensorflow::Input() const { return handle; } |
2469 | ::tensorflow::Node* node() const { return handle.node(); } |
2470 | |
2471 | static Attrs Metadata(StringPiece x) { |
2472 | return Attrs().Metadata(x); |
2473 | } |
2474 | static Attrs ReplicateOnSplit(bool x) { |
2475 | return Attrs().ReplicateOnSplit(x); |
2476 | } |
2477 | |
2478 | Operation operation; |
2479 | ::tensorflow::Output handle; |
2480 | }; |
2481 | |
2482 | /// Reduces the input dataset to a singleton using a reduce function. |
2483 | /// |
2484 | /// Args: |
2485 | /// * scope: A Scope object |
2486 | /// * input_dataset: A variant tensor representing the input dataset. |
2487 | /// * initial_state: A nested structure of tensors, representing the initial state of the |
2488 | /// transformation. |
2489 | /// * f: A function that maps `(old_state, input_element)` to `new_state`. It must take |
2490 | /// two arguments and return a nested structures of tensors. The structure of |
2491 | /// `new_state` must match the structure of `initial_state`. |
2492 | /// |
2493 | /// Returns: |
2494 | /// * `OutputList`: The components tensor. |
2495 | class ReduceDataset { |
2496 | public: |
2497 | /// Optional attribute setters for ReduceDataset |
2498 | struct Attrs { |
2499 | /// Defaults to true |
2500 | TF_MUST_USE_RESULT Attrs UseInterOpParallelism(bool x) { |
2501 | Attrs ret = *this; |
2502 | ret.use_inter_op_parallelism_ = x; |
2503 | return ret; |
2504 | } |
2505 | |
2506 | /// Defaults to "" |
2507 | TF_MUST_USE_RESULT Attrs Metadata(StringPiece x) { |
2508 | Attrs ret = *this; |
2509 | ret.metadata_ = x; |
2510 | return ret; |
2511 | } |
2512 | |
2513 | bool use_inter_op_parallelism_ = true; |
2514 | StringPiece metadata_ = "" ; |
2515 | }; |
2516 | ReduceDataset(const ::tensorflow::Scope& scope, ::tensorflow::Input |
2517 | input_dataset, ::tensorflow::InputList initial_state, |
2518 | ::tensorflow::InputList other_arguments, const NameAttrList& f, |
2519 | const DataTypeSlice& output_types, const |
2520 | gtl::ArraySlice<PartialTensorShape>& output_shapes); |
2521 | ReduceDataset(const ::tensorflow::Scope& scope, ::tensorflow::Input |
2522 | input_dataset, ::tensorflow::InputList initial_state, |
2523 | ::tensorflow::InputList other_arguments, const NameAttrList& f, |
2524 | const DataTypeSlice& output_types, const |
2525 | gtl::ArraySlice<PartialTensorShape>& output_shapes, const |
2526 | ReduceDataset::Attrs& attrs); |
2527 | ::tensorflow::Output operator[](size_t index) const { return components[index]; } |
2528 | |
2529 | |
2530 | static Attrs UseInterOpParallelism(bool x) { |
2531 | return Attrs().UseInterOpParallelism(x); |
2532 | } |
2533 | static Attrs Metadata(StringPiece x) { |
2534 | return Attrs().Metadata(x); |
2535 | } |
2536 | |
2537 | Operation operation; |
2538 | ::tensorflow::OutputList components; |
2539 | }; |
2540 | |
2541 | /// Creates a dataset that emits the outputs of `input_dataset` `count` times. |
2542 | /// |
2543 | /// Args: |
2544 | /// * scope: A Scope object |
2545 | /// * count: A scalar representing the number of times that `input_dataset` should |
2546 | /// be repeated. A value of `-1` indicates that it should be repeated infinitely. |
2547 | /// |
2548 | /// Returns: |
2549 | /// * `Output`: The handle tensor. |
2550 | class RepeatDataset { |
2551 | public: |
2552 | /// Optional attribute setters for RepeatDataset |
2553 | struct Attrs { |
2554 | /// Defaults to "" |
2555 | TF_MUST_USE_RESULT Attrs Metadata(StringPiece x) { |
2556 | Attrs ret = *this; |
2557 | ret.metadata_ = x; |
2558 | return ret; |
2559 | } |
2560 | |
2561 | StringPiece metadata_ = "" ; |
2562 | }; |
2563 | RepeatDataset(const ::tensorflow::Scope& scope, ::tensorflow::Input |
2564 | input_dataset, ::tensorflow::Input count, const DataTypeSlice& |
2565 | output_types, const gtl::ArraySlice<PartialTensorShape>& |
2566 | output_shapes); |
2567 | RepeatDataset(const ::tensorflow::Scope& scope, ::tensorflow::Input |
2568 | input_dataset, ::tensorflow::Input count, const DataTypeSlice& |
2569 | output_types, const gtl::ArraySlice<PartialTensorShape>& |
2570 | output_shapes, const RepeatDataset::Attrs& attrs); |
2571 | operator ::tensorflow::Output() const { return handle; } |
2572 | operator ::tensorflow::Input() const { return handle; } |
2573 | ::tensorflow::Node* node() const { return handle.node(); } |
2574 | |
2575 | static Attrs Metadata(StringPiece x) { |
2576 | return Attrs().Metadata(x); |
2577 | } |
2578 | |
2579 | Operation operation; |
2580 | ::tensorflow::Output handle; |
2581 | }; |
2582 | |
2583 | /// TODO: add doc. |
2584 | /// |
2585 | /// Args: |
2586 | /// * scope: A Scope object |
2587 | /// |
2588 | /// Returns: |
2589 | /// * `Output`: The handle tensor. |
2590 | class RewriteDataset { |
2591 | public: |
2592 | RewriteDataset(const ::tensorflow::Scope& scope, ::tensorflow::Input |
2593 | input_dataset, ::tensorflow::Input rewrite_name, const |
2594 | DataTypeSlice& output_types, const |
2595 | gtl::ArraySlice<PartialTensorShape>& output_shapes); |
2596 | operator ::tensorflow::Output() const { return handle; } |
2597 | operator ::tensorflow::Input() const { return handle; } |
2598 | ::tensorflow::Node* node() const { return handle.node(); } |
2599 | |
2600 | Operation operation; |
2601 | ::tensorflow::Output handle; |
2602 | }; |
2603 | |
2604 | /// Creates a `Dataset` that includes only 1/`num_shards` of this dataset. |
2605 | /// |
2606 | /// Args: |
2607 | /// * scope: A Scope object |
2608 | /// * num_shards: An integer representing the number of shards operating in parallel. |
2609 | /// * index: An integer representing the current worker index. |
2610 | /// |
2611 | /// Returns: |
2612 | /// * `Output`: The handle tensor. |
2613 | class ShardDataset { |
2614 | public: |
2615 | /// Optional attribute setters for ShardDataset |
2616 | struct Attrs { |
2617 | /// Defaults to false |
2618 | TF_MUST_USE_RESULT Attrs RequireNonEmpty(bool x) { |
2619 | Attrs ret = *this; |
2620 | ret.require_non_empty_ = x; |
2621 | return ret; |
2622 | } |
2623 | |
2624 | /// Defaults to "" |
2625 | TF_MUST_USE_RESULT Attrs Metadata(StringPiece x) { |
2626 | Attrs ret = *this; |
2627 | ret.metadata_ = x; |
2628 | return ret; |
2629 | } |
2630 | |
2631 | bool require_non_empty_ = false; |
2632 | StringPiece metadata_ = "" ; |
2633 | }; |
2634 | ShardDataset(const ::tensorflow::Scope& scope, ::tensorflow::Input |
2635 | input_dataset, ::tensorflow::Input num_shards, ::tensorflow::Input |
2636 | index, const DataTypeSlice& output_types, const |
2637 | gtl::ArraySlice<PartialTensorShape>& output_shapes); |
2638 | ShardDataset(const ::tensorflow::Scope& scope, ::tensorflow::Input |
2639 | input_dataset, ::tensorflow::Input num_shards, ::tensorflow::Input |
2640 | index, const DataTypeSlice& output_types, const |
2641 | gtl::ArraySlice<PartialTensorShape>& output_shapes, const |
2642 | ShardDataset::Attrs& attrs); |
2643 | operator ::tensorflow::Output() const { return handle; } |
2644 | operator ::tensorflow::Input() const { return handle; } |
2645 | ::tensorflow::Node* node() const { return handle.node(); } |
2646 | |
2647 | static Attrs RequireNonEmpty(bool x) { |
2648 | return Attrs().RequireNonEmpty(x); |
2649 | } |
2650 | static Attrs Metadata(StringPiece x) { |
2651 | return Attrs().Metadata(x); |
2652 | } |
2653 | |
2654 | Operation operation; |
2655 | ::tensorflow::Output handle; |
2656 | }; |
2657 | |
2658 | /// Creates a dataset that shuffles and repeats elements from `input_dataset` |
2659 | /// |
2660 | /// pseudorandomly. |
2661 | /// |
2662 | /// Args: |
2663 | /// * scope: A Scope object |
2664 | /// * buffer_size: The number of output elements to buffer in an iterator over |
2665 | /// this dataset. Compare with the `min_after_dequeue` attr when creating a |
2666 | /// `RandomShuffleQueue`. |
2667 | /// * seed: A scalar seed for the random number generator. If either `seed` or |
2668 | /// `seed2` is set to be non-zero, the random number generator is seeded |
2669 | /// by the given seed. Otherwise, a random seed is used. |
2670 | /// * seed2: A second scalar seed to avoid seed collision. |
2671 | /// * count: A scalar representing the number of times the underlying dataset |
2672 | /// should be repeated. The default is `-1`, which results in infinite repetition. |
2673 | /// |
2674 | /// Returns: |
2675 | /// * `Output`: The handle tensor. |
2676 | class ShuffleAndRepeatDataset { |
2677 | public: |
2678 | /// Optional attribute setters for ShuffleAndRepeatDataset |
2679 | struct Attrs { |
2680 | /// Defaults to true |
2681 | TF_MUST_USE_RESULT Attrs ReshuffleEachIteration(bool x) { |
2682 | Attrs ret = *this; |
2683 | ret.reshuffle_each_iteration_ = x; |
2684 | return ret; |
2685 | } |
2686 | |
2687 | /// Defaults to "" |
2688 | TF_MUST_USE_RESULT Attrs Metadata(StringPiece x) { |
2689 | Attrs ret = *this; |
2690 | ret.metadata_ = x; |
2691 | return ret; |
2692 | } |
2693 | |
2694 | bool reshuffle_each_iteration_ = true; |
2695 | StringPiece metadata_ = "" ; |
2696 | }; |
2697 | ShuffleAndRepeatDataset(const ::tensorflow::Scope& scope, ::tensorflow::Input |
2698 | input_dataset, ::tensorflow::Input buffer_size, |
2699 | ::tensorflow::Input seed, ::tensorflow::Input seed2, |
2700 | ::tensorflow::Input count, const DataTypeSlice& |
2701 | output_types, const |
2702 | gtl::ArraySlice<PartialTensorShape>& output_shapes); |
2703 | ShuffleAndRepeatDataset(const ::tensorflow::Scope& scope, ::tensorflow::Input |
2704 | input_dataset, ::tensorflow::Input buffer_size, |
2705 | ::tensorflow::Input seed, ::tensorflow::Input seed2, |
2706 | ::tensorflow::Input count, const DataTypeSlice& |
2707 | output_types, const |
2708 | gtl::ArraySlice<PartialTensorShape>& output_shapes, |
2709 | const ShuffleAndRepeatDataset::Attrs& attrs); |
2710 | operator ::tensorflow::Output() const { return handle; } |
2711 | operator ::tensorflow::Input() const { return handle; } |
2712 | ::tensorflow::Node* node() const { return handle.node(); } |
2713 | |
2714 | static Attrs ReshuffleEachIteration(bool x) { |
2715 | return Attrs().ReshuffleEachIteration(x); |
2716 | } |
2717 | static Attrs Metadata(StringPiece x) { |
2718 | return Attrs().Metadata(x); |
2719 | } |
2720 | |
2721 | Operation operation; |
2722 | ::tensorflow::Output handle; |
2723 | }; |
2724 | |
2725 | /// TODO: add doc. |
2726 | /// |
2727 | /// Args: |
2728 | /// * scope: A Scope object |
2729 | /// |
2730 | /// Returns: |
2731 | /// * `Output`: The handle tensor. |
2732 | class ShuffleAndRepeatDatasetV2 { |
2733 | public: |
2734 | /// Optional attribute setters for ShuffleAndRepeatDatasetV2 |
2735 | struct Attrs { |
2736 | /// Defaults to true |
2737 | TF_MUST_USE_RESULT Attrs ReshuffleEachIteration(bool x) { |
2738 | Attrs ret = *this; |
2739 | ret.reshuffle_each_iteration_ = x; |
2740 | return ret; |
2741 | } |
2742 | |
2743 | /// Defaults to "" |
2744 | TF_MUST_USE_RESULT Attrs Metadata(StringPiece x) { |
2745 | Attrs ret = *this; |
2746 | ret.metadata_ = x; |
2747 | return ret; |
2748 | } |
2749 | |
2750 | bool reshuffle_each_iteration_ = true; |
2751 | StringPiece metadata_ = "" ; |
2752 | }; |
2753 | ShuffleAndRepeatDatasetV2(const ::tensorflow::Scope& scope, ::tensorflow::Input |
2754 | input_dataset, ::tensorflow::Input buffer_size, |
2755 | ::tensorflow::Input seed, ::tensorflow::Input seed2, |
2756 | ::tensorflow::Input count, ::tensorflow::Input |
2757 | seed_generator, const DataTypeSlice& output_types, |
2758 | const gtl::ArraySlice<PartialTensorShape>& |
2759 | output_shapes); |
2760 | ShuffleAndRepeatDatasetV2(const ::tensorflow::Scope& scope, ::tensorflow::Input |
2761 | input_dataset, ::tensorflow::Input buffer_size, |
2762 | ::tensorflow::Input seed, ::tensorflow::Input seed2, |
2763 | ::tensorflow::Input count, ::tensorflow::Input |
2764 | seed_generator, const DataTypeSlice& output_types, |
2765 | const gtl::ArraySlice<PartialTensorShape>& |
2766 | output_shapes, const |
2767 | ShuffleAndRepeatDatasetV2::Attrs& attrs); |
2768 | operator ::tensorflow::Output() const { return handle; } |
2769 | operator ::tensorflow::Input() const { return handle; } |
2770 | ::tensorflow::Node* node() const { return handle.node(); } |
2771 | |
2772 | static Attrs ReshuffleEachIteration(bool x) { |
2773 | return Attrs().ReshuffleEachIteration(x); |
2774 | } |
2775 | static Attrs Metadata(StringPiece x) { |
2776 | return Attrs().Metadata(x); |
2777 | } |
2778 | |
2779 | Operation operation; |
2780 | ::tensorflow::Output handle; |
2781 | }; |
2782 | |
2783 | /// Creates a dataset that shuffles elements from `input_dataset` pseudorandomly. |
2784 | /// |
2785 | /// Args: |
2786 | /// * scope: A Scope object |
2787 | /// * buffer_size: The number of output elements to buffer in an iterator over |
2788 | /// this dataset. Compare with the `min_after_dequeue` attr when creating a |
2789 | /// `RandomShuffleQueue`. |
2790 | /// * seed: A scalar seed for the random number generator. If either `seed` or |
2791 | /// `seed2` is set to be non-zero, the random number generator is seeded |
2792 | /// by the given seed. Otherwise, a random seed is used. |
2793 | /// * seed2: A second scalar seed to avoid seed collision. |
2794 | /// |
2795 | /// Optional attributes (see `Attrs`): |
2796 | /// * reshuffle_each_iteration: If true, each iterator over this dataset will be given |
2797 | /// a different pseudorandomly generated seed, based on a sequence seeded by the |
2798 | /// `seed` and `seed2` inputs. If false, each iterator will be given the same |
2799 | /// seed, and repeated iteration over this dataset will yield the exact same |
2800 | /// sequence of results. |
2801 | /// |
2802 | /// Returns: |
2803 | /// * `Output`: The handle tensor. |
2804 | class ShuffleDataset { |
2805 | public: |
2806 | /// Optional attribute setters for ShuffleDataset |
2807 | struct Attrs { |
2808 | /// If true, each iterator over this dataset will be given |
2809 | /// a different pseudorandomly generated seed, based on a sequence seeded by the |
2810 | /// `seed` and `seed2` inputs. If false, each iterator will be given the same |
2811 | /// seed, and repeated iteration over this dataset will yield the exact same |
2812 | /// sequence of results. |
2813 | /// |
2814 | /// Defaults to true |
2815 | TF_MUST_USE_RESULT Attrs ReshuffleEachIteration(bool x) { |
2816 | Attrs ret = *this; |
2817 | ret.reshuffle_each_iteration_ = x; |
2818 | return ret; |
2819 | } |
2820 | |
2821 | /// Defaults to "" |
2822 | TF_MUST_USE_RESULT Attrs Metadata(StringPiece x) { |
2823 | Attrs ret = *this; |
2824 | ret.metadata_ = x; |
2825 | return ret; |
2826 | } |
2827 | |
2828 | bool reshuffle_each_iteration_ = true; |
2829 | StringPiece metadata_ = "" ; |
2830 | }; |
2831 | ShuffleDataset(const ::tensorflow::Scope& scope, ::tensorflow::Input |
2832 | input_dataset, ::tensorflow::Input buffer_size, |
2833 | ::tensorflow::Input seed, ::tensorflow::Input seed2, const |
2834 | DataTypeSlice& output_types, const |
2835 | gtl::ArraySlice<PartialTensorShape>& output_shapes); |
2836 | ShuffleDataset(const ::tensorflow::Scope& scope, ::tensorflow::Input |
2837 | input_dataset, ::tensorflow::Input buffer_size, |
2838 | ::tensorflow::Input seed, ::tensorflow::Input seed2, const |
2839 | DataTypeSlice& output_types, const |
2840 | gtl::ArraySlice<PartialTensorShape>& output_shapes, const |
2841 | ShuffleDataset::Attrs& attrs); |
2842 | operator ::tensorflow::Output() const { return handle; } |
2843 | operator ::tensorflow::Input() const { return handle; } |
2844 | ::tensorflow::Node* node() const { return handle.node(); } |
2845 | |
2846 | static Attrs ReshuffleEachIteration(bool x) { |
2847 | return Attrs().ReshuffleEachIteration(x); |
2848 | } |
2849 | static Attrs Metadata(StringPiece x) { |
2850 | return Attrs().Metadata(x); |
2851 | } |
2852 | |
2853 | Operation operation; |
2854 | ::tensorflow::Output handle; |
2855 | }; |
2856 | |
2857 | /// TODO: add doc. |
2858 | /// |
2859 | /// Args: |
2860 | /// * scope: A Scope object |
2861 | /// |
2862 | /// Returns: |
2863 | /// * `Output`: The handle tensor. |
2864 | class ShuffleDatasetV2 { |
2865 | public: |
2866 | /// Optional attribute setters for ShuffleDatasetV2 |
2867 | struct Attrs { |
2868 | /// Defaults to "" |
2869 | TF_MUST_USE_RESULT Attrs Metadata(StringPiece x) { |
2870 | Attrs ret = *this; |
2871 | ret.metadata_ = x; |
2872 | return ret; |
2873 | } |
2874 | |
2875 | StringPiece metadata_ = "" ; |
2876 | }; |
2877 | ShuffleDatasetV2(const ::tensorflow::Scope& scope, ::tensorflow::Input |
2878 | input_dataset, ::tensorflow::Input buffer_size, |
2879 | ::tensorflow::Input seed_generator, const DataTypeSlice& |
2880 | output_types, const gtl::ArraySlice<PartialTensorShape>& |
2881 | output_shapes); |
2882 | ShuffleDatasetV2(const ::tensorflow::Scope& scope, ::tensorflow::Input |
2883 | input_dataset, ::tensorflow::Input buffer_size, |
2884 | ::tensorflow::Input seed_generator, const DataTypeSlice& |
2885 | output_types, const gtl::ArraySlice<PartialTensorShape>& |
2886 | output_shapes, const ShuffleDatasetV2::Attrs& attrs); |
2887 | operator ::tensorflow::Output() const { return handle; } |
2888 | operator ::tensorflow::Input() const { return handle; } |
2889 | ::tensorflow::Node* node() const { return handle.node(); } |
2890 | |
2891 | static Attrs Metadata(StringPiece x) { |
2892 | return Attrs().Metadata(x); |
2893 | } |
2894 | |
2895 | Operation operation; |
2896 | ::tensorflow::Output handle; |
2897 | }; |
2898 | |
2899 | /// TODO: add doc. |
2900 | /// |
2901 | /// Args: |
2902 | /// * scope: A Scope object |
2903 | /// |
2904 | /// Returns: |
2905 | /// * `Output`: The handle tensor. |
2906 | class ShuffleDatasetV3 { |
2907 | public: |
2908 | /// Optional attribute setters for ShuffleDatasetV3 |
2909 | struct Attrs { |
2910 | /// Defaults to true |
2911 | TF_MUST_USE_RESULT Attrs ReshuffleEachIteration(bool x) { |
2912 | Attrs ret = *this; |
2913 | ret.reshuffle_each_iteration_ = x; |
2914 | return ret; |
2915 | } |
2916 | |
2917 | /// Defaults to "" |
2918 | TF_MUST_USE_RESULT Attrs Metadata(StringPiece x) { |
2919 | Attrs ret = *this; |
2920 | ret.metadata_ = x; |
2921 | return ret; |
2922 | } |
2923 | |
2924 | bool reshuffle_each_iteration_ = true; |
2925 | StringPiece metadata_ = "" ; |
2926 | }; |
2927 | ShuffleDatasetV3(const ::tensorflow::Scope& scope, ::tensorflow::Input |
2928 | input_dataset, ::tensorflow::Input buffer_size, |
2929 | ::tensorflow::Input seed, ::tensorflow::Input seed2, |
2930 | ::tensorflow::Input seed_generator, const DataTypeSlice& |
2931 | output_types, const gtl::ArraySlice<PartialTensorShape>& |
2932 | output_shapes); |
2933 | ShuffleDatasetV3(const ::tensorflow::Scope& scope, ::tensorflow::Input |
2934 | input_dataset, ::tensorflow::Input buffer_size, |
2935 | ::tensorflow::Input seed, ::tensorflow::Input seed2, |
2936 | ::tensorflow::Input seed_generator, const DataTypeSlice& |
2937 | output_types, const gtl::ArraySlice<PartialTensorShape>& |
2938 | output_shapes, const ShuffleDatasetV3::Attrs& attrs); |
2939 | operator ::tensorflow::Output() const { return handle; } |
2940 | operator ::tensorflow::Input() const { return handle; } |
2941 | ::tensorflow::Node* node() const { return handle.node(); } |
2942 | |
2943 | static Attrs ReshuffleEachIteration(bool x) { |
2944 | return Attrs().ReshuffleEachIteration(x); |
2945 | } |
2946 | static Attrs Metadata(StringPiece x) { |
2947 | return Attrs().Metadata(x); |
2948 | } |
2949 | |
2950 | Operation operation; |
2951 | ::tensorflow::Output handle; |
2952 | }; |
2953 | |
2954 | /// Creates a dataset that skips `count` elements from the `input_dataset`. |
2955 | /// |
2956 | /// Args: |
2957 | /// * scope: A Scope object |
2958 | /// * count: A scalar representing the number of elements from the `input_dataset` |
2959 | /// that should be skipped. If count is -1, skips everything. |
2960 | /// |
2961 | /// Returns: |
2962 | /// * `Output`: The handle tensor. |
2963 | class SkipDataset { |
2964 | public: |
2965 | /// Optional attribute setters for SkipDataset |
2966 | struct Attrs { |
2967 | /// Defaults to "" |
2968 | TF_MUST_USE_RESULT Attrs Metadata(StringPiece x) { |
2969 | Attrs ret = *this; |
2970 | ret.metadata_ = x; |
2971 | return ret; |
2972 | } |
2973 | |
2974 | StringPiece metadata_ = "" ; |
2975 | }; |
2976 | SkipDataset(const ::tensorflow::Scope& scope, ::tensorflow::Input |
2977 | input_dataset, ::tensorflow::Input count, const DataTypeSlice& |
2978 | output_types, const gtl::ArraySlice<PartialTensorShape>& |
2979 | output_shapes); |
2980 | SkipDataset(const ::tensorflow::Scope& scope, ::tensorflow::Input |
2981 | input_dataset, ::tensorflow::Input count, const DataTypeSlice& |
2982 | output_types, const gtl::ArraySlice<PartialTensorShape>& |
2983 | output_shapes, const SkipDataset::Attrs& attrs); |
2984 | operator ::tensorflow::Output() const { return handle; } |
2985 | operator ::tensorflow::Input() const { return handle; } |
2986 | ::tensorflow::Node* node() const { return handle.node(); } |
2987 | |
2988 | static Attrs Metadata(StringPiece x) { |
2989 | return Attrs().Metadata(x); |
2990 | } |
2991 | |
2992 | Operation operation; |
2993 | ::tensorflow::Output handle; |
2994 | }; |
2995 | |
2996 | /// Creates a dataset that splits a SparseTensor into elements row-wise. |
2997 | /// |
2998 | /// Args: |
2999 | /// * scope: A Scope object |
3000 | /// |
3001 | /// Returns: |
3002 | /// * `Output`: The handle tensor. |
3003 | class SparseTensorSliceDataset { |
3004 | public: |
3005 | SparseTensorSliceDataset(const ::tensorflow::Scope& scope, ::tensorflow::Input |
3006 | indices, ::tensorflow::Input values, |
3007 | ::tensorflow::Input dense_shape); |
3008 | operator ::tensorflow::Output() const { return handle; } |
3009 | operator ::tensorflow::Input() const { return handle; } |
3010 | ::tensorflow::Node* node() const { return handle.node(); } |
3011 | |
3012 | Operation operation; |
3013 | ::tensorflow::Output handle; |
3014 | }; |
3015 | |
3016 | /// Creates a dataset that emits the records from one or more TFRecord files. |
3017 | /// |
3018 | /// Args: |
3019 | /// * scope: A Scope object |
3020 | /// * filenames: A scalar or vector containing the name(s) of the file(s) to be |
3021 | /// read. |
3022 | /// * compression_type: A scalar containing either (i) the empty string (no |
3023 | /// compression), (ii) "ZLIB", or (iii) "GZIP". |
3024 | /// * buffer_size: A scalar representing the number of bytes to buffer. A value of |
3025 | /// 0 means no buffering will be performed. |
3026 | /// |
3027 | /// Returns: |
3028 | /// * `Output`: The handle tensor. |
3029 | class TFRecordDataset { |
3030 | public: |
3031 | /// Optional attribute setters for TFRecordDataset |
3032 | struct Attrs { |
3033 | /// Defaults to "" |
3034 | TF_MUST_USE_RESULT Attrs Metadata(StringPiece x) { |
3035 | Attrs ret = *this; |
3036 | ret.metadata_ = x; |
3037 | return ret; |
3038 | } |
3039 | |
3040 | StringPiece metadata_ = "" ; |
3041 | }; |
3042 | TFRecordDataset(const ::tensorflow::Scope& scope, ::tensorflow::Input |
3043 | filenames, ::tensorflow::Input compression_type, |
3044 | ::tensorflow::Input buffer_size); |
3045 | TFRecordDataset(const ::tensorflow::Scope& scope, ::tensorflow::Input |
3046 | filenames, ::tensorflow::Input compression_type, |
3047 | ::tensorflow::Input buffer_size, const TFRecordDataset::Attrs& |
3048 | attrs); |
3049 | operator ::tensorflow::Output() const { return handle; } |
3050 | operator ::tensorflow::Input() const { return handle; } |
3051 | ::tensorflow::Node* node() const { return handle.node(); } |
3052 | |
3053 | static Attrs Metadata(StringPiece x) { |
3054 | return Attrs().Metadata(x); |
3055 | } |
3056 | |
3057 | Operation operation; |
3058 | ::tensorflow::Output handle; |
3059 | }; |
3060 | |
3061 | /// Creates a dataset that contains `count` elements from the `input_dataset`. |
3062 | /// |
3063 | /// Args: |
3064 | /// * scope: A Scope object |
3065 | /// * count: A scalar representing the number of elements from the `input_dataset` |
3066 | /// that should be taken. A value of `-1` indicates that all of `input_dataset` |
3067 | /// is taken. |
3068 | /// |
3069 | /// Returns: |
3070 | /// * `Output`: The handle tensor. |
3071 | class TakeDataset { |
3072 | public: |
3073 | /// Optional attribute setters for TakeDataset |
3074 | struct Attrs { |
3075 | /// Defaults to "" |
3076 | TF_MUST_USE_RESULT Attrs Metadata(StringPiece x) { |
3077 | Attrs ret = *this; |
3078 | ret.metadata_ = x; |
3079 | return ret; |
3080 | } |
3081 | |
3082 | StringPiece metadata_ = "" ; |
3083 | }; |
3084 | TakeDataset(const ::tensorflow::Scope& scope, ::tensorflow::Input |
3085 | input_dataset, ::tensorflow::Input count, const DataTypeSlice& |
3086 | output_types, const gtl::ArraySlice<PartialTensorShape>& |
3087 | output_shapes); |
3088 | TakeDataset(const ::tensorflow::Scope& scope, ::tensorflow::Input |
3089 | input_dataset, ::tensorflow::Input count, const DataTypeSlice& |
3090 | output_types, const gtl::ArraySlice<PartialTensorShape>& |
3091 | output_shapes, const TakeDataset::Attrs& attrs); |
3092 | operator ::tensorflow::Output() const { return handle; } |
3093 | operator ::tensorflow::Input() const { return handle; } |
3094 | ::tensorflow::Node* node() const { return handle.node(); } |
3095 | |
3096 | static Attrs Metadata(StringPiece x) { |
3097 | return Attrs().Metadata(x); |
3098 | } |
3099 | |
3100 | Operation operation; |
3101 | ::tensorflow::Output handle; |
3102 | }; |
3103 | |
3104 | /// Creates a dataset that emits `components` as a tuple of tensors once. |
3105 | /// |
3106 | /// Args: |
3107 | /// * scope: A Scope object |
3108 | /// |
3109 | /// Returns: |
3110 | /// * `Output`: The handle tensor. |
3111 | class TensorDataset { |
3112 | public: |
3113 | /// Optional attribute setters for TensorDataset |
3114 | struct Attrs { |
3115 | /// Defaults to "" |
3116 | TF_MUST_USE_RESULT Attrs Metadata(StringPiece x) { |
3117 | Attrs ret = *this; |
3118 | ret.metadata_ = x; |
3119 | return ret; |
3120 | } |
3121 | |
3122 | StringPiece metadata_ = "" ; |
3123 | }; |
3124 | TensorDataset(const ::tensorflow::Scope& scope, ::tensorflow::InputList |
3125 | components, const gtl::ArraySlice<PartialTensorShape>& |
3126 | output_shapes); |
3127 | TensorDataset(const ::tensorflow::Scope& scope, ::tensorflow::InputList |
3128 | components, const gtl::ArraySlice<PartialTensorShape>& |
3129 | output_shapes, const TensorDataset::Attrs& attrs); |
3130 | operator ::tensorflow::Output() const { return handle; } |
3131 | operator ::tensorflow::Input() const { return handle; } |
3132 | ::tensorflow::Node* node() const { return handle.node(); } |
3133 | |
3134 | static Attrs Metadata(StringPiece x) { |
3135 | return Attrs().Metadata(x); |
3136 | } |
3137 | |
3138 | Operation operation; |
3139 | ::tensorflow::Output handle; |
3140 | }; |
3141 | |
3142 | /// Creates a dataset that emits each dim-0 slice of `components` once. |
3143 | /// |
3144 | /// Args: |
3145 | /// * scope: A Scope object |
3146 | /// |
3147 | /// Returns: |
3148 | /// * `Output`: The handle tensor. |
3149 | class TensorSliceDataset { |
3150 | public: |
3151 | /// Optional attribute setters for TensorSliceDataset |
3152 | struct Attrs { |
3153 | /// Defaults to false |
3154 | TF_MUST_USE_RESULT Attrs IsFiles(bool x) { |
3155 | Attrs ret = *this; |
3156 | ret.is_files_ = x; |
3157 | return ret; |
3158 | } |
3159 | |
3160 | /// Defaults to "" |
3161 | TF_MUST_USE_RESULT Attrs Metadata(StringPiece x) { |
3162 | Attrs ret = *this; |
3163 | ret.metadata_ = x; |
3164 | return ret; |
3165 | } |
3166 | |
3167 | /// Defaults to false |
3168 | TF_MUST_USE_RESULT Attrs ReplicateOnSplit(bool x) { |
3169 | Attrs ret = *this; |
3170 | ret.replicate_on_split_ = x; |
3171 | return ret; |
3172 | } |
3173 | |
3174 | bool is_files_ = false; |
3175 | StringPiece metadata_ = "" ; |
3176 | bool replicate_on_split_ = false; |
3177 | }; |
3178 | TensorSliceDataset(const ::tensorflow::Scope& scope, ::tensorflow::InputList |
3179 | components, const gtl::ArraySlice<PartialTensorShape>& |
3180 | output_shapes); |
3181 | TensorSliceDataset(const ::tensorflow::Scope& scope, ::tensorflow::InputList |
3182 | components, const gtl::ArraySlice<PartialTensorShape>& |
3183 | output_shapes, const TensorSliceDataset::Attrs& attrs); |
3184 | operator ::tensorflow::Output() const { return handle; } |
3185 | operator ::tensorflow::Input() const { return handle; } |
3186 | ::tensorflow::Node* node() const { return handle.node(); } |
3187 | |
3188 | static Attrs IsFiles(bool x) { |
3189 | return Attrs().IsFiles(x); |
3190 | } |
3191 | static Attrs Metadata(StringPiece x) { |
3192 | return Attrs().Metadata(x); |
3193 | } |
3194 | static Attrs ReplicateOnSplit(bool x) { |
3195 | return Attrs().ReplicateOnSplit(x); |
3196 | } |
3197 | |
3198 | Operation operation; |
3199 | ::tensorflow::Output handle; |
3200 | }; |
3201 | |
3202 | /// Creates a dataset that emits the lines of one or more text files. |
3203 | /// |
3204 | /// Args: |
3205 | /// * scope: A Scope object |
3206 | /// * filenames: A scalar or a vector containing the name(s) of the file(s) to be |
3207 | /// read. |
3208 | /// * compression_type: A scalar containing either (i) the empty string (no |
3209 | /// compression), (ii) "ZLIB", or (iii) "GZIP". |
3210 | /// * buffer_size: A scalar containing the number of bytes to buffer. |
3211 | /// |
3212 | /// Returns: |
3213 | /// * `Output`: The handle tensor. |
3214 | class TextLineDataset { |
3215 | public: |
3216 | /// Optional attribute setters for TextLineDataset |
3217 | struct Attrs { |
3218 | /// Defaults to "" |
3219 | TF_MUST_USE_RESULT Attrs Metadata(StringPiece x) { |
3220 | Attrs ret = *this; |
3221 | ret.metadata_ = x; |
3222 | return ret; |
3223 | } |
3224 | |
3225 | StringPiece metadata_ = "" ; |
3226 | }; |
3227 | TextLineDataset(const ::tensorflow::Scope& scope, ::tensorflow::Input |
3228 | filenames, ::tensorflow::Input compression_type, |
3229 | ::tensorflow::Input buffer_size); |
3230 | TextLineDataset(const ::tensorflow::Scope& scope, ::tensorflow::Input |
3231 | filenames, ::tensorflow::Input compression_type, |
3232 | ::tensorflow::Input buffer_size, const TextLineDataset::Attrs& |
3233 | attrs); |
3234 | operator ::tensorflow::Output() const { return handle; } |
3235 | operator ::tensorflow::Input() const { return handle; } |
3236 | ::tensorflow::Node* node() const { return handle.node(); } |
3237 | |
3238 | static Attrs Metadata(StringPiece x) { |
3239 | return Attrs().Metadata(x); |
3240 | } |
3241 | |
3242 | Operation operation; |
3243 | ::tensorflow::Output handle; |
3244 | }; |
3245 | |
3246 | /// TODO: add doc. |
3247 | /// |
3248 | /// Args: |
3249 | /// * scope: A Scope object |
3250 | /// |
3251 | /// Returns: |
3252 | /// * `Output`: The output_handle tensor. |
3253 | class UnwrapDatasetVariant { |
3254 | public: |
3255 | UnwrapDatasetVariant(const ::tensorflow::Scope& scope, ::tensorflow::Input |
3256 | input_handle); |
3257 | operator ::tensorflow::Output() const { return output_handle; } |
3258 | operator ::tensorflow::Input() const { return output_handle; } |
3259 | ::tensorflow::Node* node() const { return output_handle.node(); } |
3260 | |
3261 | Operation operation; |
3262 | ::tensorflow::Output output_handle; |
3263 | }; |
3264 | |
3265 | /// Combines (nests of) input elements into a dataset of (nests of) windows. |
3266 | /// |
3267 | /// A "window" is a finite dataset of flat elements of size `size` (or possibly |
3268 | /// fewer if there are not enough input elements to fill the window and |
3269 | /// `drop_remainder` evaluates to false). |
3270 | /// |
3271 | /// The `shift` argument determines the number of input elements by which |
3272 | /// the window moves on each iteration. The first element in the `k`th window |
3273 | /// will be element |
3274 | /// |
3275 | /// ``` |
3276 | /// 1 + (k-1) * shift |
3277 | /// ``` |
3278 | /// |
3279 | /// of the input dataset. In particular, the first element of the first window |
3280 | /// will always be the first element of the input dataset. |
3281 | /// |
3282 | /// If the `stride` parameter is greater than 1, then each window will skip |
3283 | /// `(stride - 1)` input elements between each element that appears in the |
3284 | /// window. Output windows will still contain `size` elements regardless of |
3285 | /// the value of `stride`. |
3286 | /// |
3287 | /// The `stride` argument determines the stride of the input elements, and the |
3288 | /// `shift` argument determines the shift of the window. |
3289 | /// |
3290 | /// For example, letting `{...}` to represent a Dataset: |
3291 | /// |
3292 | /// - `tf.data.Dataset.range(7).window(2)` produces |
3293 | /// `{{0, 1}, {2, 3}, {4, 5}, {6}}` |
3294 | /// - `tf.data.Dataset.range(7).window(3, 2, 1, True)` produces |
3295 | /// `{{0, 1, 2}, {2, 3, 4}, {4, 5, 6}}` |
3296 | /// - `tf.data.Dataset.range(7).window(3, 1, 2, True)` produces |
3297 | /// `{{0, 2, 4}, {1, 3, 5}, {2, 4, 6}}` |
3298 | /// |
3299 | /// Note that when the `window` transformation is applied to a dataset of |
3300 | /// nested elements, it produces a dataset of nested windows. |
3301 | /// |
3302 | /// For example: |
3303 | /// |
3304 | /// - `tf.data.Dataset.from_tensor_slices((range(4), range(4))).window(2)` |
3305 | /// produces `{({0, 1}, {0, 1}), ({2, 3}, {2, 3})}` |
3306 | /// - `tf.data.Dataset.from_tensor_slices({"a": range(4)}).window(2)` |
3307 | /// produces `{{"a": {0, 1}}, {"a": {2, 3}}}` |
3308 | /// |
3309 | /// Args: |
3310 | /// * scope: A Scope object |
3311 | /// * size: An integer scalar, representing the number of elements |
3312 | /// of the input dataset to combine into a window. Must be positive. |
3313 | /// * shift: An integer scalar, representing the number of input elements |
3314 | /// by which the window moves in each iteration. Defaults to `size`. |
3315 | /// Must be positive. |
3316 | /// * stride: An integer scalar, representing the stride of the input elements |
3317 | /// in the sliding window. Must be positive. The default value of 1 means |
3318 | /// "retain every input element". |
3319 | /// * drop_remainder: A Boolean scalar, representing whether the last window should be |
3320 | /// dropped if its size is smaller than `window_size`. |
3321 | /// |
3322 | /// Returns: |
3323 | /// * `Output`: The handle tensor. |
3324 | class WindowDataset { |
3325 | public: |
3326 | /// Optional attribute setters for WindowDataset |
3327 | struct Attrs { |
3328 | /// Defaults to "" |
3329 | TF_MUST_USE_RESULT Attrs Metadata(StringPiece x) { |
3330 | Attrs ret = *this; |
3331 | ret.metadata_ = x; |
3332 | return ret; |
3333 | } |
3334 | |
3335 | StringPiece metadata_ = "" ; |
3336 | }; |
3337 | WindowDataset(const ::tensorflow::Scope& scope, ::tensorflow::Input |
3338 | input_dataset, ::tensorflow::Input size, ::tensorflow::Input |
3339 | shift, ::tensorflow::Input stride, ::tensorflow::Input |
3340 | drop_remainder, const DataTypeSlice& output_types, const |
3341 | gtl::ArraySlice<PartialTensorShape>& output_shapes); |
3342 | WindowDataset(const ::tensorflow::Scope& scope, ::tensorflow::Input |
3343 | input_dataset, ::tensorflow::Input size, ::tensorflow::Input |
3344 | shift, ::tensorflow::Input stride, ::tensorflow::Input |
3345 | drop_remainder, const DataTypeSlice& output_types, const |
3346 | gtl::ArraySlice<PartialTensorShape>& output_shapes, const |
3347 | WindowDataset::Attrs& attrs); |
3348 | operator ::tensorflow::Output() const { return handle; } |
3349 | operator ::tensorflow::Input() const { return handle; } |
3350 | ::tensorflow::Node* node() const { return handle.node(); } |
3351 | |
3352 | static Attrs Metadata(StringPiece x) { |
3353 | return Attrs().Metadata(x); |
3354 | } |
3355 | |
3356 | Operation operation; |
3357 | ::tensorflow::Output handle; |
3358 | }; |
3359 | |
3360 | /// TODO: add doc. |
3361 | /// |
3362 | /// Args: |
3363 | /// * scope: A Scope object |
3364 | /// |
3365 | /// Returns: |
3366 | /// * `Output`: The handle tensor. |
3367 | class WindowOp { |
3368 | public: |
3369 | WindowOp(const ::tensorflow::Scope& scope, ::tensorflow::InputList inputs, |
3370 | const DataTypeSlice& output_types, const |
3371 | gtl::ArraySlice<PartialTensorShape>& output_shapes); |
3372 | operator ::tensorflow::Output() const { return handle; } |
3373 | operator ::tensorflow::Input() const { return handle; } |
3374 | ::tensorflow::Node* node() const { return handle.node(); } |
3375 | |
3376 | Operation operation; |
3377 | ::tensorflow::Output handle; |
3378 | }; |
3379 | |
3380 | /// TODO: add doc. |
3381 | /// |
3382 | /// Args: |
3383 | /// * scope: A Scope object |
3384 | /// |
3385 | /// Returns: |
3386 | /// * `Output`: The output_handle tensor. |
3387 | class WrapDatasetVariant { |
3388 | public: |
3389 | WrapDatasetVariant(const ::tensorflow::Scope& scope, ::tensorflow::Input |
3390 | input_handle); |
3391 | operator ::tensorflow::Output() const { return output_handle; } |
3392 | operator ::tensorflow::Input() const { return output_handle; } |
3393 | ::tensorflow::Node* node() const { return output_handle.node(); } |
3394 | |
3395 | Operation operation; |
3396 | ::tensorflow::Output output_handle; |
3397 | }; |
3398 | |
3399 | /// Creates a dataset that zips together `input_datasets`. |
3400 | /// |
3401 | /// The elements of the resulting dataset are created by zipping corresponding |
3402 | /// elements from each of the input datasets. |
3403 | /// |
3404 | /// The size of the resulting dataset will match the size of the smallest input |
3405 | /// dataset, and no error will be raised if input datasets have different sizes. |
3406 | /// |
3407 | /// Args: |
3408 | /// * scope: A Scope object |
3409 | /// * input_datasets: List of `N` variant Tensors representing datasets to be zipped together. |
3410 | /// |
3411 | /// Returns: |
3412 | /// * `Output`: The handle tensor. |
3413 | class ZipDataset { |
3414 | public: |
3415 | /// Optional attribute setters for ZipDataset |
3416 | struct Attrs { |
3417 | /// Defaults to "" |
3418 | TF_MUST_USE_RESULT Attrs Metadata(StringPiece x) { |
3419 | Attrs ret = *this; |
3420 | ret.metadata_ = x; |
3421 | return ret; |
3422 | } |
3423 | |
3424 | StringPiece metadata_ = "" ; |
3425 | }; |
3426 | ZipDataset(const ::tensorflow::Scope& scope, ::tensorflow::InputList |
3427 | input_datasets, const DataTypeSlice& output_types, const |
3428 | gtl::ArraySlice<PartialTensorShape>& output_shapes); |
3429 | ZipDataset(const ::tensorflow::Scope& scope, ::tensorflow::InputList |
3430 | input_datasets, const DataTypeSlice& output_types, const |
3431 | gtl::ArraySlice<PartialTensorShape>& output_shapes, const |
3432 | ZipDataset::Attrs& attrs); |
3433 | operator ::tensorflow::Output() const { return handle; } |
3434 | operator ::tensorflow::Input() const { return handle; } |
3435 | ::tensorflow::Node* node() const { return handle.node(); } |
3436 | |
3437 | static Attrs Metadata(StringPiece x) { |
3438 | return Attrs().Metadata(x); |
3439 | } |
3440 | |
3441 | Operation operation; |
3442 | ::tensorflow::Output handle; |
3443 | }; |
3444 | |
3445 | } // namespace internal |
3446 | } // namespace ops |
3447 | } // namespace tensorflow |
3448 | |
3449 | #endif // TENSORFLOW_CC_OPS_DATASET_OPS_INTERNAL_H_ |
3450 | |