| /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
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| shuffle_ops.py | 30 class _ShuffleAndRepeatDataset(dataset_ops.Dataset):
31 """A `Dataset` that fuses `shuffle` and `repeat`."""
38 """See `Dataset.map()` for details."""
89 """Shuffles and repeats a Dataset returning a new permutation for each epoch.
91 `dataset.apply(tf.contrib.data.shuffle_and_repeat(buffer_size, count))`
95 `dataset.shuffle(buffer_size, reshuffle_each_iteration=True).repeat(count)`
97 The difference is that the latter dataset is not serializable. So,
105 number of times the dataset should be repeated. The default behavior
106 (if `count` is `None` or `-1`) is for the dataset be repeated
113 A `Dataset` transformation function, which can be passed t
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| /external/tensorflow/tensorflow/contrib/data/python/kernel_tests/ |
| 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(
|
| batch_dataset_op_test.py | 47 dataset_ops.Dataset.from_tensor_slices(components)
75 dataset_ops.Dataset.from_tensor_slices(components)
108 dataset_ops.Dataset.from_tensors(input_tensor).apply(
114 dataset_ops.Dataset.from_tensors(input_tensor).apply(
135 data = dataset_ops.Dataset.from_tensor_slices(data)
154 data = dataset_ops.Dataset.from_tensor_slices(data)
174 data = dataset_ops.Dataset.from_tensor_slices(data)
200 dataset_ops.Dataset.from_tensor_slices(components).apply(
225 iterator = dataset_ops.Dataset.range(12).map(_sparse).apply(
249 dataset = dataset_ops.Dataset.from_tensors(els[0]
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| /external/tensorflow/tensorflow/core/kernels/data/ |
| window_dataset.cc | 53 if (i_ == dataset()->elements_.size()) {
57 *out_tensors = dataset()->elements_[i_++];
|
| stats_dataset_ops.cc | 18 #include "tensorflow/core/kernels/data/dataset.h"
25 // This op defines a `Dataset` that passes through its input elements and
46 *output = new Dataset(ctx, input, std::move(tag));
50 class Dataset : public GraphDatasetBase {
52 explicit Dataset(OpKernelContext* ctx, const DatasetBase* input, string tag)
57 ~Dataset() override { input_->Unref(); }
72 string DebugString() override { return "LatencyStatsDatasetOp::Dataset"; }
86 class Iterator : public DatasetIterator<Dataset> {
89 : DatasetIterator<Dataset>(params),
90 input_impl_(params.dataset->input_->MakeIterator(params.prefix)) {
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| dense_to_sparse_batch_dataset_op.cc | 18 #include "tensorflow/core/kernels/data/dataset.h"
34 // Create a new DenseToSparseBatchDatasetOp::Dataset, insert it in the
61 *output = new Dataset<T>(ctx, batch_size, row_shape, input); \
79 class Dataset : public GraphDatasetBase {
81 Dataset(OpKernelContext* ctx, int64 batch_size,
95 ~Dataset() override { input_->Unref(); }
114 ")::Dataset");
137 class Iterator : public DatasetIterator<Dataset<T>> {
140 : DatasetIterator<Dataset<T>>(params),
141 input_impl_(params.dataset->input_->MakeIterator(params.prefix)) {
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| sparse_tensor_slice_dataset_op.cc | 20 #include "tensorflow/core/kernels/data/dataset.h"
31 class Dataset : public GraphDatasetBase {
33 explicit Dataset(OpKernelContext* ctx,
54 return "SparseTensorSliceDatasetOp::Dataset";
79 class Iterator : public DatasetIterator<Dataset<T>> {
82 : DatasetIterator<Dataset<T>>(params),
83 num_elements_(params.dataset->sparse_tensor_.shape()[0]),
84 dense_shape_(DT_INT64, {params.dataset->sparse_tensor_.dims() - 1}),
85 group_iterable_(params.dataset->sparse_tensor_.group({0})),
89 params.dataset->sparse_tensor_.shape()[i + 1]
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| padded_batch_dataset_op.cc | 19 #include "tensorflow/core/kernels/data/dataset.h"
52 "in the input dataset's elements (",
72 "dataset's elements (",
82 " and input dataset's component ", i, ": ",
88 *output = new Dataset(ctx, batch_size, std::move(padded_shapes),
93 class Dataset : public GraphDatasetBase {
95 Dataset(OpKernelContext* ctx, int64 batch_size,
106 // semantics. If we could tell statically that the input dataset
120 ~Dataset() override { input_->Unref(); }
138 ")::Dataset");
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| 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> {
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| 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> {
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| /frameworks/base/services/autofill/java/com/android/server/autofill/ |
| Session.java | 63 import android.service.autofill.Dataset;
212 * List of dataset ids selected by the user.
1005 final Dataset dataset = authenticatedResponse.getDatasets().get(datasetIdx); local
1029 final Dataset dataset = (Dataset) result; local
1136 final Dataset dataset = datasets.get(j); local
1226 final Dataset dataset = datasets.get(k); local
1605 final Dataset dataset = datasets.get(i); local
2232 final Dataset dataset = datasets.get(i); local
2345 final Dataset dataset = datasets.get(i); local
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| /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/afservice/src/main/java/com/example/android/autofill/service/data/source/local/ |
| LocalAutofillDataSource.java | 20 import android.service.autofill.Dataset;
114 logw("More than 1 dataset with name %s", datasetName);
116 DatasetWithFilledAutofillFields dataset = autofillDatasetFields.get(0);
119 datasetsCallback.onLoaded(dataset)
205 DatasetWithFilledAutofillFields dataset =
208 callback.onLoaded(dataset);
242 * For simplicity, {@link Dataset}s will be named in the form {@code dataset-X.P} where
243 * {@code X} means this was the Xth group of datasets saved, and {@code P} refers to the dataset
251 * Every time a dataset is saved, this should be called to increment the dataset number
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| /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/examples/get_started/regression/ |
| dnn_regression.py | 32 (train, test) = imports85.dataset()
|
| linear_regression_categorical.py | 32 (train, test) = imports85.dataset()
|
| /frameworks/base/services/autofill/java/com/android/server/autofill/ui/ |
| FillUi.java | 36 import android.service.autofill.Dataset;
37 import android.service.autofill.Dataset.DatasetFieldFilter;
85 void onDatasetPicked(@NonNull Dataset dataset);
296 final Dataset dataset = response.getDatasets().get(i); local
297 final int index = dataset.getFieldIds().indexOf(focusedViewId);
299 final RemoteViews presentation = dataset.getFieldPresentation(index);
302 + "service didn't provide a presentation for it on " + dataset);
314 final DatasetFieldFilter filter = dataset.getFilter(index)
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| /developers/samples/android/input/autofill/AutofillFramework/afservice/src/main/java/com/example/android/autofill/service/ |
| AuthActivity.java | 25 import android.service.autofill.Dataset;
63 * It is launched when an Autofill Response or specific Dataset within the Response requires
68 // Unique id for dataset intents.
182 public void onLoaded(DatasetWithFilledAutofillFields dataset) {
183 String datasetName = dataset.autofillDataset.getDatasetName();
187 dataset, remoteViews));
224 private void setDatasetIntent(Dataset dataset) {
225 mReplyIntent.putExtra(EXTRA_AUTHENTICATION_RESULT, dataset);
|
| ManualActivity.java | 27 import android.service.autofill.Dataset;
70 * launched to allow the user to select the dataset.
76 // Unique id for dataset intents.
131 for (DatasetWithFilledAutofillFields dataset : mAllDatasets) {
132 String datasetName = dataset.autofillDataset.getDatasetName();
133 String datasetId = dataset.autofillDataset.getId();
135 for (FilledAutofillField filledAutofillField : dataset.filledAutofillFields) {
163 String datasetName = "dataset-manual";
232 // public void onLoaded(DatasetWithFilledAutofillFields dataset) {
233 // String datasetName = dataset.autofillDataset.getDatasetName()
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| /external/tensorflow/tensorflow/python/data/kernel_tests/ |
| 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...] |
| /developers/build/prebuilts/gradle/AutofillFramework/afservice/src/main/java/com/example/android/autofill/service/ |
| AuthActivity.java | 24 import android.service.autofill.Dataset;
49 * It is launched when an Autofill Response or specific Dataset within the Response requires
54 // Unique id for dataset intents.
148 private void setDatasetIntent(Dataset dataset) {
149 mReplyIntent.putExtra(EXTRA_AUTHENTICATION_RESULT, dataset);
|
| /external/tensorflow/tensorflow/contrib/training/python/training/ |
| tensor_queue_dataset.py | 31 class _PrependFromQueueAndPaddedBatchDataset(dataset_ops.Dataset):
32 """A `Dataset` that prepends a queue to another `Dataset`.
101 """A transformation that prepends a queue to a `Dataset` and batches results.
107 Below is an example of how this dataset might be used to split incoming
109 are re-enqueued back into the dataset. A more realistic example would
114 dataset = tf.data.Dataset.from_tensor_slices([2*x for x in range(10)])
115 # Make a dataset of variable-length vectors and their lengths.
116 dataset = dataset.map(lambda count: (count, tf.ones((count,)))
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| /external/tensorflow/tensorflow/go/op/ |
| op_test.go | 105 dataset = TensorDataset(s, []tf.Output{c}, shapes)
109 init = MakeIterator(s, dataset, iterator)
131 t.Errorf("Expected sess.Run() to fail since the iterator should have reached the end of the dataset")
|
| /external/tensorflow/tensorflow/python/keras/_impl/keras/utils/ |
| io_utils.py | 36 """Representation of HDF5 dataset to be used instead of a Numpy array.
45 Providing `start` and `end` allows use of a slice of the dataset.
52 dataset: string, name of the HDF5 dataset in the file specified
54 start: int, start of desired slice of the specified dataset
55 end: int, end of desired slice of the specified dataset
59 An array-like HDF5 dataset.
63 def __init__(self, datapath, dataset, start=0, end=None, normalizer=None):
73 self.data = f[dataset]
119 """Gets a numpy-style shape tuple giving the dataset dimensions
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