| /external/protobuf/benchmarks/ |
| generate_datasets.cc | 43 const char *file_prefix = "dataset.";
60 std::cerr << "For dataset " << name << ", no such message: "
69 std::cerr << "For dataset " << name << ", payload[" << i << "] fails "
75 BenchmarkDataset dataset; local
76 dataset.set_name(name);
77 dataset.set_message_name(message_name);
79 dataset.add_payload()->assign(payload[i]);
85 dataset.SerializeToOstream(&writer);
88 std::cerr << "Wrote dataset: " << fname << "\n";
|
| /external/tensorflow/tensorflow/contrib/data/python/ops/ |
| interleave_ops.py | 15 """Non-deterministic dataset transformations."""
31 class ParallelInterleaveDataset(dataset_ops.Dataset):
32 """A `Dataset` that maps a function over its input and flattens the result."""
56 dataset = map_func(*nested_args)
58 dataset = map_func(nested_args)
60 if not isinstance(dataset, dataset_ops.Dataset):
61 raise TypeError("`map_func` must return a `Dataset` object.")
63 self._output_classes = dataset.output_classes
64 self._output_types = dataset.output_type
[all...] |
| dataset_ops.py | 31 class Dataset(dataset_ops.Dataset):
34 A `Dataset` can be used to represent an input pipeline as a
39 def __init__(self, dataset):
40 super(Dataset, self).__init__()
41 self._dataset = dataset
63 @deprecation.deprecated(None, "Use `tf.data.Dataset.from_tensors()`.")
65 """Creates a `Dataset` with a single element, comprising the given tensors.
71 A `Dataset`.
73 return Dataset(dataset_ops.TensorDataset(tensors)
[all...] |
| stats_ops.py | 33 in this module when defining your @{tf.data.Dataset}. All statistics will be
36 produced by iterating over a dataset:
39 dataset = ...
40 dataset = dataset.apply(stats_ops.bytes_produced_stats("total_bytes"))
47 dataset = ...
48 iterator = dataset.make_one_shot_iterator()
110 """Records the number of bytes produced by each element of the input dataset.
113 over the output dataset.
120 A `Dataset` transformation function, which can be passed t
[all...] |
| 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...] |
| unique.py | 15 """Unique element dataset transformations."""
28 """Creates a `Dataset` from another `Dataset`, discarding duplicates.
30 Use this transformation to produce a dataset that contains one instance of
34 dataset = tf.data.Dataset.from_tensor_slices([1, 37, 2, 37, 2, 1])
37 dataset = dataset.apply(tf.contrib.data.unique()) # ==> { 1, 37, 2 }
41 A `Dataset` transformation function, which can be passed to
42 @{tf.data.Dataset.apply}
[all...] |
| /external/tensorflow/tensorflow/contrib/eager/python/examples/linear_regression/ |
| linear_regression_test.py | 53 dataset = linear_regression.synthetic_dataset(true_w, true_b, noise_level,
56 it = tfe.Iterator(dataset)
71 dataset = linear_regression.synthetic_dataset(
76 linear_regression.fit(model, dataset, optimizer, logdir=self._tmp_logdir)
89 dataset = linear_regression.synthetic_dataset(
95 burn_in_dataset = dataset.take(10)
107 linear_regression.fit(model, dataset, optimizer)
|
| /external/tensorflow/tensorflow/python/data/ops/ |
| iterator_ops.py | 54 """Represents the state of iterating through a `Dataset`."""
61 instead use `Dataset.make_initializable_iterator()` or
62 `Dataset.make_one_shot_iterator()`.
70 each component of an element of this dataset.
72 corresponding to each component of an element of this dataset.
96 The returned iterator is not bound to a particular dataset, and it has
98 `Iterator.make_initializer(dataset)`.
105 dataset_range = Dataset.range(10)
138 each component of an element of this dataset.
140 corresponding to each component of an element of this dataset. I
[all...] |
| /external/tensorflow/tensorflow/contrib/data/python/kernel_tests/ |
| stats_dataset_ops_test.py | 53 dataset = dataset_ops.Dataset.range(100).map(
56 iterator = dataset.make_initializable_iterator()
79 dataset = dataset_ops.Dataset.range(100).apply(
81 iterator = dataset.make_initializable_iterator()
98 dataset = dataset_ops.Dataset.range(100).apply(
100 iterator = dataset.make_initializable_iterator()
120 dataset = dataset_ops.Dataset.range(100).apply
[all...] |
| scan_dataset_op_test.py | 37 return dataset_ops.Dataset.from_tensors(0).repeat(None).apply(
61 iterator = dataset_ops.Dataset.from_tensors(1).repeat(None).apply(
85 dataset = dataset_ops.Dataset.from_tensors(0).repeat(5).apply(
87 self.assertEqual([None], dataset.output_shapes[0][0].as_list())
88 self.assertIs(None, dataset.output_shapes[0][1].ndims)
89 self.assertEqual([], dataset.output_shapes[1].as_list())
91 iterator = dataset.make_one_shot_iterator()
107 dataset = dataset_ops.Dataset.range(10
[all...] |
| /external/tensorflow/tensorflow/core/framework/ |
| dataset.h | 71 // Wrapper around GraphDefBuilder. Used to serialize Dataset graph.
122 Status AddDataset(const GraphDatasetBase* dataset,
124 return AddDataset(dataset, inputs, {}, output);
135 Status AddDataset(const GraphDatasetBase* dataset,
143 return AddDataset(dataset, enumerated_inputs, {}, attrs, output);
147 const GraphDatasetBase* dataset,
181 // TODO(b/65524810): Hack to allow functions to capture Dataset op
199 // Uses a heuristic to whitelist source dataset ops which have been
204 return (StringPiece(op_def->name()).ends_with("Dataset") &&
207 dataset::WhitelistedStatefulOpRegistry::Global()->Contains
488 const DatasetType* dataset; member in struct:tensorflow::DatasetIterator::Params
501 const DatasetType* dataset() const { return params_.dataset; } function in class:tensorflow::DatasetIterator
[all...] |
| dataset.cc | 15 #include "tensorflow/core/framework/dataset.h"
37 // Transfers ownership of `dataset` to `*this`.
38 explicit DatasetVariantWrapper(DatasetBase* dataset) : dataset_(dataset) {}
76 const GraphDatasetBase* dataset,
81 const string& op_type_name = dataset->op_name();
90 opts->WithAttr("output_shapes", dataset->output_shapes())));
94 opts->WithAttr("output_types", dataset->output_dtypes())));
217 "Dataset tensor must be a scalar of dtype DT_VARIANT.");
222 return errors::InvalidArgument("Tensor must be a Dataset object.")
242 DatasetBase* dataset = nullptr; local
[all...] |
| /external/tensorflow/tensorflow/contrib/tensor_forest/kernels/v4/ |
| decision_node_evaluator.cc | 61 const std::unique_ptr<TensorDataSet>& dataset, int example) const {
62 const float val = dataset->GetExampleValue(example, feature_num_);
83 const std::unique_ptr<TensorDataSet>& dataset, int example) const {
87 dataset->GetExampleValue(example, feature_num_[i]);
108 const std::unique_ptr<TensorDataSet>& dataset, int example) const {
109 const float val = dataset->GetExampleValue(example, feature_num_);
|
| /external/tensorflow/tensorflow/core/kernels/data/ |
| range_dataset_op.cc | 17 #include "tensorflow/core/kernels/data/dataset.h"
42 *output = new Dataset(ctx, start, stop, step);
46 class Dataset : public GraphDatasetBase {
48 Dataset(OpKernelContext* ctx, int64 start, int64 stop, int64 step)
70 step_, ")::Dataset");
87 class Iterator : public DatasetIterator<Dataset> {
90 : DatasetIterator<Dataset>(params) {
91 next_ = params.dataset->start_;
98 if ((dataset()->step_ > 0 && next_ >= dataset()->stop_) |
[all...] |
| dataset_utils.cc | 20 namespace dataset { namespace in namespace:tensorflow
37 // Retrieve the dataset that was created in `f`.
42 // Create an iterator for the dataset that was returned by `f`.
48 } // namespace dataset
|
| map_and_batch_dataset_op.cc | 21 #include "tensorflow/core/kernels/data/dataset.h"
73 *output = new Dataset(input, batch_size, num_parallel_batches,
79 class Dataset : public DatasetBase {
81 Dataset(const DatasetBase* input, int64 batch_size,
96 ~Dataset() override { input_->Unref(); }
112 string DebugString() override { return "MapAndBatchDatasetOp::Dataset"; }
115 class Iterator : public DatasetIterator<Dataset> {
118 : DatasetIterator<Dataset>(params),
119 input_impl_(params.dataset->input_->MakeIterator(params.prefix)),
120 invocation_results_(params.dataset->batch_size_
[all...] |
| reader_dataset_ops.cc | 17 #include "tensorflow/core/kernels/data/dataset.h"
76 *output = new Dataset(ctx, std::move(filenames), compression_type,
81 class Dataset : public GraphDatasetBase {
83 Dataset(OpKernelContext* ctx, std::vector<string> filenames,
109 string DebugString() override { return "TextLineDatasetOp::Dataset"; }
127 class Iterator : public DatasetIterator<Dataset> {
130 : DatasetIterator<Dataset>(params) {}
160 if (current_file_index_ == dataset()->filenames_.size()) {
209 if (current_file_index_ >= dataset()->filenames_.size()) {
212 " >= filenames_.size():", dataset()->filenames_.size())
[all...] |
| /developers/samples/android/input/autofill/AutofillFramework/afservice/src/main/java/com/example/android/autofill/service/data/ |
| FakeAutofillDataBuilder.java | 47 "dataset-" + datasetNumber + "." + partition, mPackageName);
60 AutofillDataset dataset, int partition) {
63 datasetWithFilledAutofillFields.autofillDataset = dataset;
69 mSeed, dataset.getId());
|
| /packages/experimental/FillService/src/foo/bar/fill/ |
| AuthActivity.java | 9 import android.service.autofill.Dataset;
31 final Dataset dataset; local
41 .addDataset(new Dataset.Builder(presentation1)
47 .addDataset(new Dataset.Builder(presentation2)
54 dataset = null;
59 dataset = new Dataset.Builder(presentation)
74 result.putExtra(AutofillManager.EXTRA_AUTHENTICATION_RESULT, dataset);
|
| /cts/tests/autofillservice/src/android/autofillservice/cts/ |
| ManualAuthenticationActivity.java | 38 public static void setDataset(CannedFillResponse.CannedDataset dataset) {
39 sDataset = dataset;
61 throw new IllegalStateException("no dataset or response");
|
| /developers/build/prebuilts/gradle/SpeedTracker/Wearable/src/main/java/com/example/android/wearable/speedtracker/ui/ |
| SpeedPickerListAdapter.java | 38 public SpeedPickerListAdapter(Context context, int[] dataset) {
41 mDataSet = dataset;
|
| /developers/samples/android/wearable/wear/SpeedTracker/Wearable/src/main/java/com/example/android/wearable/speedtracker/ui/ |
| SpeedPickerListAdapter.java | 38 public SpeedPickerListAdapter(Context context, int[] dataset) {
41 mDataSet = dataset;
|
| /development/samples/browseable/SpeedTracker/Wearable/src/com.example.android.wearable.speedtracker/ui/ |
| SpeedPickerListAdapter.java | 38 public SpeedPickerListAdapter(Context context, int[] dataset) {
41 mDataSet = dataset;
|
| /external/tensorflow/tensorflow/contrib/eager/python/examples/mnist/ |
| mnist_graph_test.py | 43 # Create a model, optimizer, and dataset as would be done
47 dataset = tf.data.Dataset.from_tensors((images, labels))
51 (images, labels) = dataset.make_one_shot_iterator().get_next()
|
| /external/tensorflow/tensorflow/contrib/kfac/examples/ |
| mnist.py | 34 """Loads MNIST dataset into memory.
38 num_epochs: int. Number of passes to make over the dataset.
42 use_fake_data: bool. If True, generate a synthetic dataset rather than
66 dataset = tf.data.Dataset.from_tensor_slices((np.asarray(
68 return (dataset.repeat(num_epochs).shuffle(num_examples).batch(batch_size)
|
