| /external/tensorflow/tensorflow/core/kernels/data/ |
| 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...] |
| skip_dataset_op.cc | 17 #include "tensorflow/core/kernels/data/dataset.h"
33 // Create a new RepeatDatasetOp::Dataset, and return it as the output.
37 *output = new Dataset(ctx, count, input);
41 class Dataset : public GraphDatasetBase {
43 Dataset(OpKernelContext* ctx, int64 count, const DatasetBase* input)
48 ~Dataset() override { input_->Unref(); }
71 string DebugString() override { return "SkipDatasetOp::Dataset"; }
86 class EmptyIterator : public DatasetIterator<Dataset> {
89 : DatasetIterator<Dataset>(params) {}
108 class FiniteIterator : public DatasetIterator<Dataset> {
[all...] |
| take_dataset_op.cc | 17 #include "tensorflow/core/kernels/data/dataset.h"
34 // Create a new TakeDatasetOp::Dataset, and return it as the output.
37 *output = new Dataset(ctx, count, input);
41 class Dataset : public GraphDatasetBase {
43 Dataset(OpKernelContext* ctx, int64 count, const DatasetBase* input)
48 ~Dataset() override { input_->Unref(); }
72 string DebugString() override { return "TakeDatasetOp::Dataset"; }
87 class EmptyIterator : public DatasetIterator<Dataset> {
90 : DatasetIterator<Dataset>(params) {}
109 class FiniteIterator : public DatasetIterator<Dataset> {
[all...] |
| tensor_slice_dataset_op.cc | 18 #include "tensorflow/core/kernels/data/dataset.h"
33 // Create a new TensorDatasetOp::Dataset, insert it in the step
53 *output = new Dataset(ctx, std::move(components));
57 class Dataset : public GraphDatasetBase {
59 explicit Dataset(OpKernelContext* ctx, std::vector<Tensor> tensors)
84 string DebugString() override { return "TensorSliceDatasetOp::Dataset"; }
104 class Iterator : public DatasetIterator<Dataset> {
107 : DatasetIterator<Dataset>(params),
109 n_(params.dataset->tensors_[0].dim_size(0)) {}
117 out_tensors->reserve(dataset()->tensors_.size())
[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
|
| batch_dataset_op.cc | 18 #include "tensorflow/core/kernels/data/dataset.h"
41 *output = new Dataset(ctx, batch_size, input);
45 class Dataset : public GraphDatasetBase {
47 Dataset(OpKernelContext* ctx, int64 batch_size, const DatasetBase* input)
52 // we could tell statically that the input dataset is infinite,
62 ~Dataset() override { input_->Unref(); }
79 return strings::StrCat("BatchDatasetOp(", batch_size_, ")::Dataset");
95 class Iterator : public DatasetIterator<Dataset> {
98 : DatasetIterator<Dataset>(params),
99 input_impl_(params.dataset->input_->MakeIterator(params.prefix)) {
[all...] |
| dataset_utils.h | 20 #include "tensorflow/core/kernels/data/dataset.h"
24 namespace dataset { namespace in namespace:tensorflow
31 } // namespace dataset
|
| zip_dataset_op.cc | 17 #include "tensorflow/core/kernels/data/dataset.h"
37 *output = new Dataset(ctx, inputs);
41 class Dataset : public GraphDatasetBase {
43 explicit Dataset(OpKernelContext* ctx,
57 ~Dataset() override {
77 string DebugString() override { return "ZipDatasetOp::Dataset"; }
95 class Iterator : public DatasetIterator<Dataset> {
98 : DatasetIterator<Dataset>(params) {
99 input_impls_.reserve(params.dataset->inputs_.size());
101 for (const auto& input : params.dataset->inputs_)
[all...] |
| /developers/build/prebuilts/gradle/AutofillFramework/kotlinApp/Application/src/main/java/com/example/android/autofillframework/multidatasetservice/ |
| AutofillHelper.kt | 19 import android.service.autofill.Dataset
38 * Wraps autofill data in a [Dataset] object which can then be sent back to the
43 datasetAuth: Boolean): Dataset? {
45 val datasetBuilder: Dataset.Builder
47 datasetBuilder = Dataset.Builder(newRemoteViews(context.packageName, datasetName,
52 datasetBuilder = Dataset.Builder(newRemoteViews(context.packageName, datasetName,
83 val dataset = newDataset(context, autofillFieldMetadata, clientFormData, datasetAuth)
84 dataset?.let(responseBuilder::addDataset)
|
| /developers/samples/android/input/autofill/AutofillFramework/kotlinApp/Application/src/main/java/com/example/android/autofillframework/multidatasetservice/ |
| AutofillHelper.kt | 19 import android.service.autofill.Dataset
38 * Wraps autofill data in a [Dataset] object which can then be sent back to the
43 datasetAuth: Boolean): Dataset? {
45 val datasetBuilder: Dataset.Builder
47 datasetBuilder = Dataset.Builder(newRemoteViews(context.packageName, datasetName,
52 datasetBuilder = Dataset.Builder(newRemoteViews(context.packageName, datasetName,
83 val dataset = newDataset(context, autofillFieldMetadata, clientFormData, datasetAuth)
84 dataset?.let(responseBuilder::addDataset)
|
| /external/tensorflow/tensorflow/core/api_def/base_api/ |
| api_def_MapAndBatchDataset.pbtxt | 19 summary: "Creates a dataset that applies `f` to the outputs of `input_dataset` and then"
23 Unlike a "MapDataset", which applies `f` sequentially, this dataset invokes up
|
| api_def_RandomDataset.pbtxt | 17 summary: "Creates a Dataset that returns pseudorandom numbers."
|
| api_def_RangeDataset.pbtxt | 21 summary: "Creates a dataset with a range of values. Corresponds to python\'s xrange."
|
| api_def_SqlDataset.pbtxt | 21 summary: "Creates a dataset that executes a SQL query and emits rows of the result set."
|
| /external/tensorflow/tensorflow/python/data/kernel_tests/ |
| 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...] |
| flat_map_dataset_op_test.py | 40 dataset_ops.Dataset.from_tensor_slices(components)
41 .flat_map(lambda x: dataset_ops.Dataset.from_tensors([x]).repeat(x))
58 dataset_ops.Dataset.from_tensor_slices(components)
59 .flat_map(lambda x: dataset_ops.Dataset.from_tensor_slices(x)
60 .flat_map(lambda y: dataset_ops.Dataset.from_tensors(y)
79 dataset_ops.Dataset.from_tensor_slices(components)
80 .flat_map(lambda x: dataset_ops.Dataset.from_tensor_slices(x)
81 .flat_map(lambda y: dataset_ops.Dataset.from_tensors(y)
108 iterator = (dataset_ops.Dataset.range(10)
110 .flat_map(lambda d: dataset_ops.Dataset.from_tensors(d["foo"]
[all...] |
| /packages/apps/Contacts/src/com/android/contacts/editor/ |
| RawContactDeltaComparator.java | 92 if (type1.dataSet != null && type2.dataSet == null) {
94 } else if (type1.dataSet == null && type2.dataSet != null) {
98 if (type1.dataSet != null && type2.dataSet != null) {
99 value = type1.dataSet.compareTo(type2.dataSet);
|
| /external/libxkbcommon/xkbcommon/ |
| README.md | 20 ## Dataset
22 libxkbcommon does not distribute a keymap dataset itself, other than for
23 testing purposes. The most common dataset is xkeyboard-config, which is used
28 The dataset for Compose is distributed in libX11, as part of the X locale
|
| /external/tensorflow/tensorflow/core/kernels/data/sql/ |
| BUILD | 36 "//tensorflow/core/kernels/data:dataset",
|
| /external/tensorflow/tensorflow/examples/udacity/ |
| 1_notmnist.ipynb | 29 "This notebook uses the [notMNIST](http://yaroslavvb.blogspot.com/2011/09/notmnist-dataset.html) dataset to be used with python experiments. This dataset is designed to look like the classic [MNIST](http://yann.lecun.com/exdb/mnist/) dataset, while looking a little more like real data: it's a harder task, and the data is a lot less 'clean' than MNIST."
73 "First, we'll download the dataset to our local machine. The data consists of characters rendered in a variety of fonts on a 28x28 image. The labels are limited to 'A' through 'J' (10 classes). The training set has about 500k and the testset 19000 labeled examples. Given these sizes, it should be possible to train models quickly on any machine."
170 "Extract the dataset from the compressed .tar.gz file.\n",
272 "Now let's load the data in a more manageable format. Since, depending on your computer setup you might not be able to fit it all in memory, we'll load each class into a separate dataset, store them on disk and curate them independently. Later we'll merge them into a single dataset of manageable size.\n",
274 "We'll convert the entire dataset into a 3D array (image index, x, y) of floating point values, normalized to have approximately zero mean and standard deviation ~0.5 to make training easier down the road. \n",
321 " dataset = np.ndarray(shape=(len(image_files), image_size, image_size),\n"
[all...] |
| /external/tensorflow/tensorflow/contrib/data/python/ops/ |
| resampling.py | 15 """Resampling dataset transformations."""
35 """A transformation that resamples a dataset to achieve a target distribution.
41 class_func: A function mapping an element of the input dataset to a scalar
50 A `Dataset` transformation function, which can be passed to
51 @{tf.data.Dataset.apply}.
54 def _apply_fn(dataset):
55 """Function from `Dataset` to `Dataset` that applies the transformation."""
58 class_values_ds = dataset.map(class_func)
63 initial_dist_ds = dataset_ops.Dataset.from_tensors
[all...] |
| /external/tensorflow/tensorflow/examples/how_tos/reading_data/ |
| fully_connected_reader.py | 100 over the dataset once. On the other hand there is no special initialization
111 dataset = tf.data.TFRecordDataset(filename)
112 dataset = dataset.repeat(num_epochs)
115 dataset = dataset.map(decode)
116 dataset = dataset.map(augment)
117 dataset = dataset.map(normalize
[all...] |
| /external/tensorflow/tensorflow/contrib/data/python/kernel_tests/ |
| get_single_element_test.py | 36 dataset = (dataset_ops.Dataset.range(100)
41 element = get_single_element.get_single_element(dataset)
49 "Dataset was empty."):
53 "Dataset had more than one element."):
|
| /external/tensorflow/tensorflow/contrib/eager/python/examples/mnist/ |
| mnist_test.py | 39 return tf.data.Dataset.from_tensors((images, labels))
47 dataset = random_dataset()
50 mnist.train_one_epoch(model, optimizer, dataset)
55 dataset = random_dataset()
60 mnist.test(model, dataset)
|
| /external/tensorflow/tensorflow/contrib/eager/python/examples/rnn_colorbot/ |
| rnn_colorbot_test.py | 44 return tf.data.Dataset.from_tensors((labels, chars, sequence_length))
55 dataset = random_dataset()
57 rnn_colorbot.train_one_epoch(model, optimizer, dataset)
64 dataset = random_dataset()
66 rnn_colorbot.test(model, dataset)
|
