| /external/tensorflow/tensorflow/core/kernels/data/ |
| map_dataset_op.cc | 19 #include "tensorflow/core/kernels/data/dataset.h"
53 *output = new Dataset(ctx, input, func_, std::move(captured_func),
58 class Dataset : public GraphDatasetBase {
60 Dataset(OpKernelContext* ctx, const DatasetBase* input,
74 ~Dataset() override { input_->Unref(); }
89 string DebugString() override { return "MapDatasetOp::Dataset"; }
123 class Iterator : public DatasetIterator<Dataset> {
126 : DatasetIterator<Dataset>(params),
127 input_impl_(params.dataset->input_->MakeIterator(params.prefix)) {}
146 dataset()->captured_func_->Run(ctx, std::move(args), out_tensors)
[all...] |
| unique_dataset_op.cc | 17 #include "tensorflow/core/kernels/data/dataset.h"
46 *output = new Dataset(ctx, input);
50 class Dataset : public GraphDatasetBase {
52 Dataset(OpKernelContext* ctx, const DatasetBase* input)
57 ~Dataset() override { input_->Unref(); }
74 return strings::StrCat("UniqueDatasetOp::Dataset");
87 class Iterator : public DatasetIterator<Dataset> {
90 : DatasetIterator<Dataset>(params),
91 input_impl_(params.dataset->input_->MakeIterator(params.prefix)) {}
|
| flat_map_dataset_op.cc | 19 #include "tensorflow/core/kernels/data/dataset.h"
54 *output = new Dataset(ctx, input, func_, std::move(captured_func),
59 class Dataset : public GraphDatasetBase {
61 Dataset(OpKernelContext* ctx, const DatasetBase* input,
75 ~Dataset() override { input_->Unref(); }
91 string DebugString() override { return "FlatMapDatasetOp::Dataset"; }
125 class Iterator : public DatasetIterator<Dataset> {
128 : DatasetIterator<Dataset>(params),
129 input_impl_(params.dataset->input_->MakeIterator(params.prefix)) {}
157 // Get the next element from the input dataset
[all...] |
| prefetch_dataset_op.cc | 19 #include "tensorflow/core/kernels/data/dataset.h"
43 *output = new Dataset(ctx, input, buffer_size);
47 class Dataset : public GraphDatasetBase {
49 Dataset(OpKernelContext* ctx, const DatasetBase* input, int64 buffer_size)
54 ~Dataset() override { input_->Unref(); }
69 string DebugString() override { return "PrefetchDatasetOp::Dataset"; }
84 class Iterator : public DatasetIterator<Dataset> {
87 : DatasetIterator<Dataset>(params),
88 input_impl_(params.dataset->input_->MakeIterator(params.prefix)) {}
121 "PrefetchDatasetOp::Dataset::Iterator::GetNext")
[all...] |
| scan_dataset_op.cc | 22 #include "tensorflow/core/kernels/data/dataset.h"
66 *output = new Dataset(ctx, input, func_, std::move(initial_state),
72 class Dataset : public GraphDatasetBase {
74 Dataset(OpKernelContext* ctx, const DatasetBase* input,
91 ~Dataset() override { input_->Unref(); }
106 string DebugString() override { return "ScanDatasetOp::Dataset"; }
148 class Iterator : public DatasetIterator<Dataset> {
151 : DatasetIterator<Dataset>(params),
152 input_impl_(params.dataset->input_->MakeIterator(params.prefix)),
153 state_(params.dataset->initial_state_) {
[all...] |
| /external/tensorflow/tensorflow/python/data/kernel_tests/ |
| interleave_dataset_op_test.py | 69 # Cycle length 1 acts like `Dataset.flat_map()`.
107 dataset = (
108 dataset_ops.Dataset.from_tensor_slices(input_values)
110 .interleave(lambda x: dataset_ops.Dataset.from_tensors(x).repeat(x),
112 iterator = dataset.make_initializable_iterator()
117 # Cycle length 1 acts like `Dataset.flat_map()`.
186 return dataset_ops.Dataset.from_tensor_slices(
190 dataset_ops.Dataset.range(10).map(_map_fn).interleave(
206 dataset_ops.Dataset.from_tensor_slices([])
208 .interleave(dataset_ops.Dataset.from_tensors, cycle_length=2
[all...] |
| list_files_dataset_op_test.py | 46 dataset = dataset_ops.Dataset.list_files(path.join(self.tmp_dir, '*'))
48 itr = dataset.make_one_shot_iterator()
56 dataset = dataset_ops.Dataset.list_files(path.join(self.tmp_dir, '*'))
58 itr = dataset.make_one_shot_iterator()
72 dataset = dataset_ops.Dataset.list_files(filename_placeholder)
75 itr = dataset.make_initializable_iterator()
88 dataset = dataset_ops.Dataset.list_files(filename_placeholder
[all...] |
| iterator_ops_test.py | 55 dataset = dataset_ops.Dataset.from_tensor_slices(component).map(add)
56 value = dataset.make_one_shot_iterator().get_next()
66 dataset = (dataset_ops.Dataset.from_tensor_slices([0.0, 1.0, 2.0])
69 ValueError, r"`Dataset.make_one_shot_iterator\(\)` does not support "
71 dataset.make_one_shot_iterator()
81 iterator = (dataset_ops.Dataset.from_tensor_slices(components).map(_map_fn)
106 iterator = (dataset_ops.Dataset.from_tensor_slices(tensor_components)
130 iterator = (dataset_ops.Dataset.from_tensor_slices(components
[all...] |
| map_dataset_op_test.py | 51 return (dataset_ops.Dataset.from_tensor_slices(components).map(_map_fn)
55 """Test an dataset that maps a TF function across its input elements."""
63 dataset = self._buildMapDataset(components, count)
64 iterator = dataset.make_initializable_iterator()
112 return (dataset_ops.Dataset.from_tensor_slices(components)
118 """Test an dataset that maps a TF function across its input elements."""
128 dataset = self._buildParallelMapDataset(
130 iterator = dataset.make_initializable_iterator()
187 # Tests whether a parallel map dataset will be cleaned up correctly when
195 dataset = self._buildParallelMapDataset(components, 1000, 100, 100
[all...] |
| cache_dataset_op_test.py | 54 repeat_dataset = (dataset_ops.Dataset.from_tensor_slices(components)
80 # Assert that the cached dataset has the same elements as the
122 cache_dataset1 = (dataset_ops.Dataset.from_tensor_slices(components)
124 cache_dataset2 = (dataset_ops.Dataset.from_tensor_slices(components)
152 cache_dataset1 = (dataset_ops.Dataset.from_tensor_slices(components)
154 cache_dataset2 = (dataset_ops.Dataset.from_tensor_slices(components)
208 dataset = dataset_ops.Dataset.range(3).flat_map(
209 lambda x: dataset_ops.Dataset.from_tensors(x).repeat(repeat_count))
211 cached_dataset = dataset.cache().repeat(2
[all...] |
| filter_dataset_op_test.py | 48 dataset_ops.Dataset.from_tensor_slices(components).map(_map_fn)
74 # Test an empty dataset.
78 dataset = dataset_ops.Dataset.range(100).filter(
80 iterator = dataset.make_one_shot_iterator()
89 iterator = (dataset_ops.Dataset.range(10)
116 dataset_ops.Dataset.from_tensor_slices([[1, 2, 3], [4, 5, 6]])
145 dataset_ops.Dataset.range(10).map(_map_fn).filter(_filter_fn).map(
|
| dataset_constructor_op_test.py | 42 """Test a dataset that represents a single tuple of tensors."""
45 iterator = (dataset_ops.Dataset.from_tensors(components)
67 """Test a dataset that represents a single tuple of tensors."""
78 dataset_ops.Dataset.from_tensors(components)
96 """Test an dataset that represents a single tuple of tensors."""
108 dataset_ops.Dataset.from_tensors(components)
130 """Test a dataset that represents the slices from a tuple of tensors."""
137 iterator = (dataset_ops.Dataset.from_tensor_slices(components)
155 """Test a dataset that represents the slices from a tuple of tensors."""
166 dataset_ops.Dataset.from_tensor_slices(components
[all...] |
| /external/tensorflow/tensorflow/contrib/data/python/kernel_tests/ |
| bucketing_test.py | 41 dataset_ops.Dataset.from_tensor_slices(components).map(lambda x: x * x)
69 dataset_ops.Dataset.from_tensor_slices(components).repeat(-1).apply(
90 dataset_ops.Dataset.from_tensor_slices(components).apply(
117 dataset_ops.Dataset.from_tensor_slices(components)
135 return dataset_ops.Dataset.zip((
143 dataset_ops.Dataset.from_tensor_slices(components)
169 return dataset_ops.Dataset.from_tensor_slices(components).repeat(-1).apply(
203 return dataset_ops.Dataset.zip(
204 (dataset_ops.Dataset.from_tensors(bucket),
216 dataset_ops.Dataset.from_tensor_slices(math_ops.range(32)).map(_map_fn)
[all...] |
| sequence_dataset_op_test.py | 32 return dataset_ops.Dataset.from_tensor_slices(components).skip(count)
52 return dataset_ops.Dataset.from_tensor_slices(components).take(count)
74 return dataset_ops.Dataset.from_tensor_slices(components).take(
100 # Test repeat empty dataset
|
| unique_dataset_op_test.py | 44 dataset = dataset_ops.Dataset.from_generator(lambda: current_test_case,
46 iterator = dataset.make_initializable_iterator()
88 return dataset_ops.Dataset.range(num_elements).map(
|
| dataset_constructor_op_test.py | 38 dataset = dataset_ops.Dataset.from_tensors(components)
49 new = batching._RestructuredDataset(dataset, new_types, new_shape_lists)
69 new = batching._RestructuredDataset(dataset, new_types, new_shape_lists)
79 return dataset_ops.Dataset.from_tensors(components)
91 return dataset_ops.Dataset.from_tensor_slices(components)
118 return dataset_ops.Dataset.from_sparse_tensor_slices(sparse_components)
|
| shuffle_dataset_op_test.py | 41 return dataset_ops.Dataset.range(range_limit).shuffle(
79 return dataset_ops.Dataset.range(num_elements).apply(
142 ds = dataset_ops.Dataset.range(20).apply(
153 return dataset_ops.Dataset.range(20).apply(
|
| /external/tensorflow/tensorflow/contrib/training/python/training/ |
| tensor_queue_dataset_test.py | 37 dataset = dataset_ops.Dataset.from_tensor_slices([0, 1, 2])
38 dataset = dataset.apply(
40 self.assertEqual((dtypes.variant, dtypes.int32), dataset.output_types)
42 [x.as_list() for x in dataset.output_shapes])
43 iterator = dataset.make_one_shot_iterator()
52 dataset = dataset_ops.Dataset.from_tensor_slices([0, 1, 2])
53 dataset = dataset.apply
[all...] |
| /external/tensorflow/tensorflow/python/grappler/ |
| datasets_test.py | 50 dataset = dataset_ops.Dataset.from_tensors(test_case['tensor'])
51 iterator = dataset.make_one_shot_iterator()
75 dataset = dataset_ops.Dataset.from_tensor_slices(test_case['tensor'])
76 iterator = dataset.make_one_shot_iterator()
108 dataset = dataset_ops.Dataset.from_generator(
112 iterator = dataset.make_one_shot_iterator()
124 dataset = dataset_ops.Dataset.range(42
[all...] |
| /developers/samples/android/input/autofill/AutofillFramework/afservice/src/main/java/com/example/android/autofill/service/data/adapter/ |
| ResponseAdapter.java | 21 import android.service.autofill.Dataset;
57 Dataset dataset = mDatasetAdapter.buildDatasetForFocusedNode(field, fieldType, remoteViews); local
58 if (dataset != null) {
59 responseBuilder.addDataset(dataset);
76 Dataset dataset; local
84 dataset = mDatasetAdapter.buildDataset(fieldTypesByAutofillHint,
89 dataset = mDatasetAdapter.buildDataset(fieldTypesByAutofillHint,
92 if (dataset != null)
[all...] |
| /external/tensorflow/tensorflow/python/data/ops/ |
| dataset_ops.py | 47 @tf_export("data.Dataset")
48 class Dataset(object):
51 A `Dataset` can be used to represent an input pipeline as a
62 """Creates a scalar `tf.Tensor` of `tf.variant` representing this dataset.
65 A scalar `tf.Tensor` of `tf.variant` type, which represents this dataset.
67 raise NotImplementedError("Dataset._as_variant_tensor")
70 """Creates an `Iterator` for enumerating the elements of this dataset.
76 dataset = ...
77 iterator = dataset.make_initializable_iterator()
88 An `Iterator` over the elements of this dataset
[all...] |
| /developers/build/prebuilts/gradle/AutofillFramework/kotlinApp/Application/src/main/java/com/example/android/autofillframework/multidatasetservice/model/ |
| FilledAutofillFieldCollection.kt | 18 import android.service.autofill.Dataset
31 * dataset name associated with it.
49 * Populates a [Dataset.Builder] with appropriate values for each [AutofillId]
50 * in a `AutofillFieldMetadataCollection`. In other words, it builds an Autofill dataset
56 datasetBuilder: Dataset.Builder): Boolean {
|
| /developers/samples/android/input/autofill/AutofillFramework/kotlinApp/Application/src/main/java/com/example/android/autofillframework/multidatasetservice/model/ |
| FilledAutofillFieldCollection.kt | 18 import android.service.autofill.Dataset
31 * dataset name associated with it.
49 * Populates a [Dataset.Builder] with appropriate values for each [AutofillId]
50 * in a `AutofillFieldMetadataCollection`. In other words, it builds an Autofill dataset
56 datasetBuilder: Dataset.Builder): Boolean {
|
| /external/tensorflow/tensorflow/contrib/data/python/ops/ |
| error_ops.py | 15 """Ignore_errors dataset transformations."""
28 """Creates a `Dataset` from another `Dataset` and silently ignores any errors.
30 Use this transformation to produce a dataset that contains the same elements
35 dataset = tf.data.Dataset.from_tensor_slices([1., 2., 0., 4.])
38 dataset = dataset.map(lambda x: tf.check_numerics(1. / x, "error"))
41 dataset =
42 dataset.apply(tf.contrib.data.ignore_errors()) # ==> { 1., 0.5, 0.2
[all...] |
| grouping.py | 15 """Grouping dataset transformations."""
35 This transformation maps each consecutive element in a dataset to a key
48 reduce_func: A function mapping a key and a dataset of up to `window_size`
49 consecutive elements matching that key to another dataset.
60 A `Dataset` transformation function, which can be passed to
61 @{tf.data.Dataset.apply}.
80 def _apply_fn(dataset):
81 """Function from `Dataset` to `Dataset` that applies the transformation."""
82 return GroupByWindowDataset(dataset, key_func, reduce_func
[all...] |
