| /external/tensorflow/tensorflow/contrib/data/python/ops/ |
| get_single_element.py | 25 def get_single_element(dataset):
26 """Returns the single element in `dataset` as a nested structure of tensors.
28 This function enables you to use a @{tf.data.Dataset} in a stateless
31 as a `Dataset`, and you want to use the transformation at serving time.
41 dataset = (tf.data.Dataset.from_tensor_slices(input_batch)
45 image_batch, label_batch = tf.contrib.data.get_single_element(dataset)
49 dataset: A @{tf.data.Dataset} object containing a single element.
53 element of `dataset`
[all...] |
| readers.py | 84 dataset to combine in a single batch.
88 and (optional) `reader_args` and returns a `Dataset` of Examples.
92 dataset. If None, cycles through the dataset forever.
101 dataset = reader(filenames, *reader_args)
103 dataset = reader(filenames)
104 if dataset.output_types == (dtypes.string, dtypes.string):
105 dataset = dataset.map(lambda _, v: v)
107 dataset = dataset.repeat(num_epochs
[all...] |
| batching.py | 15 """Batching dataset transformations."""
36 Like `Dataset.padded_batch()`, this transformation combines multiple
37 consecutive elements of the dataset, which might have different
47 # contents of a dataset.
63 number of consecutive elements of this dataset to combine in a
67 resulting `tf.SparseTensor`. Each element of this dataset must
72 A `Dataset` transformation function, which can be passed to
73 @{tf.data.Dataset.apply}.
76 def _apply_fn(dataset):
77 return DenseToSparseBatchDataset(dataset, batch_size, row_shape
[all...] |
| enumerate_ops.py | 15 """Enumerate dataset transformations."""
27 """A transformation that enumerate the elements of a dataset.
34 # contents of a dataset.
39 # structure of elements in the resulting dataset.
49 A `Dataset` transformation function, which can be passed to
50 @{tf.data.Dataset.apply}.
53 def _apply_fn(dataset):
55 return dataset_ops.Dataset.zip((dataset_ops.Dataset.range(start, max_value),
56 dataset))
[all...] |
| /external/tensorflow/tensorflow/contrib/tensor_forest/kernels/v4/ |
| decision_node_evaluator_test.cc | 37 std::unique_ptr<tensorflow::tensorforest::TensorDataSet> dataset(
41 ASSERT_EQ(eval->Decide(dataset, 2), 0);
42 ASSERT_EQ(eval->Decide(dataset, 3), 0);
43 ASSERT_EQ(eval->Decide(dataset, 4), 1);
54 std::unique_ptr<tensorflow::tensorforest::TensorDataSet> dataset(
58 ASSERT_EQ(eval->Decide(dataset, 2), 0);
59 ASSERT_EQ(eval->Decide(dataset, 3), 1);
60 ASSERT_EQ(eval->Decide(dataset, 4), 1);
72 std::unique_ptr<tensorflow::tensorforest::TensorDataSet> dataset(
76 ASSERT_EQ(eval->Decide(dataset, 2), 1)
[all...] |
| decision_node_evaluator.h | 32 virtual int32 Decide(const std::unique_ptr<TensorDataSet>& dataset,
52 int32 Decide(const std::unique_ptr<TensorDataSet>& dataset,
70 int32 Decide(const std::unique_ptr<TensorDataSet>& dataset,
85 int32 Decide(const std::unique_ptr<TensorDataSet>& dataset,
|
| grow_stats_test.cc | 56 std::unique_ptr<tensorflow::tensorforest::TensorDataSet> dataset(
60 stats->AddSplit(MakeSplit("0", 10.0), dataset, target, 0);
61 stats->AddSplit(MakeSplit("1", 4.0), dataset, target, 0);
64 stats->AddExample(dataset, target, i);
180 std::unique_ptr<tensorflow::tensorforest::TensorDataSet> dataset(
191 stat->AddExample(dataset, target.get(), 0);
198 stat->AddExample(dataset, target.get(), 0);
225 std::unique_ptr<tensorflow::tensorforest::TensorDataSet> dataset(
231 stats.AddSplit(MakeSplit("0", 0.0), dataset, &target, 0);
232 stats.AddSplit(MakeSplit("1", 0.0), dataset, &target, 0)
[all...] |
| /system/extras/tests/sdcard/ |
| plot_sdcard.py | 47 class DataSet(object):
48 """Dataset holds the summary and data (time,value pairs)."""
117 def UpdateWith(self, dataset):
118 self.duration = max(self.duration, dataset.duration)
119 self.name = dataset.name
134 data = [] # array of dataset
135 dataset = None
149 if line.startswith('# StopWatch'): # Start of a new dataset
150 if dataset:
151 if dataset.summary
[all...] |
| /external/tensorflow/tensorflow/examples/learn/ |
| iris.py | 14 """Example of DNNClassifier for Iris plant dataset.
70 dataset = tf.data.TextLineDataset([file_name])
72 dataset = dataset.skip(1)
73 dataset = dataset.map(_parse_csv)
76 # For this small dataset, which can fit into memory, to achieve true
78 # elements in the dataset.
79 dataset = dataset.shuffle(num_data
[all...] |
| /external/tensorflow/tensorflow/contrib/slim/python/slim/data/ |
| dataset_data_provider.py | 15 """A DataProvider that provides data from a Dataset.
31 slim.datasets.pascal_voc.Dataset(),
54 dataset,
69 dataset: An instance of the Dataset class.
79 record_key: The item name to use for the dataset record keys in the
84 ValueError: If `record_key` matches one of the items in the dataset.
87 dataset.data_sources,
88 reader_class=dataset.reader,
98 items = dataset.decoder.list_items(
[all...] |
| /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/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...] |
| /libcore/benchmarks/src/benchmarks/ |
| XmlSerializeBenchmark.java | 36 double[] dataset; field in class:XmlSerializeBenchmark
42 double contChance = dataset[0];
43 double levelUpChance = dataset[1];
44 double levelDownChance = dataset[2];
45 double attributeChance = dataset[3];
46 double writeChance1 = dataset[4];
47 double writeChance2 = dataset[5];
89 dataset = new double[splitted.length];
91 dataset[i] = Double.parseDouble(splitted[i]);
|
| /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...] |
| /external/tensorflow/tensorflow/python/data/kernel_tests/ |
| 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...] |
| shard_dataset_op_test.py | 28 dataset = dataset_ops.Dataset.range(10).shard(5, 2)
29 iterator = dataset.make_one_shot_iterator()
38 dataset_a = dataset_ops.Dataset.range(10)
39 dataset_b = dataset_ops.Dataset.range(10, 0, -1)
40 dataset = dataset_ops.Dataset.zip((dataset_a, dataset_b)).shard(5, 2)
41 iterator = dataset.make_one_shot_iterator()
50 dataset = dataset_ops.Dataset.range(10).shard(5, 0
[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...] |
| /external/tensorflow/tensorflow/core/kernels/data/ |
| dataset_utils.h | 20 #include "tensorflow/core/kernels/data/dataset.h"
24 namespace dataset { namespace in namespace:tensorflow
31 } // namespace dataset
|
| cache_dataset_ops.cc | 17 #include "tensorflow/core/kernels/data/dataset.h"
109 input_impl_(params.dataset->input_->MakeIterator(params.prefix)),
110 writer_(params.dataset->env_, params.dataset->filename_),
111 lockfile_(strings::StrCat(params.dataset->filename_, ".lockfile")),
139 if (out_tensors->size() != dataset()->num_tensors_) {
142 dataset()->num_tensors_, " got: ", out_tensors->size());
146 DCHECK_LT(tensor_index, dataset()->num_tensors_);
147 string key = dataset()->FormatName(cur_index_, tensor_index++);
166 if (dataset()->env_->FileExists(lockfile_).ok())
[all...] |
| /developers/build/prebuilts/gradle/NavigationDrawer/kotlinApp/Application/src/main/java/com/example/android/navigationdrawer/ |
| PlanetAdapter.kt | 29 private val dataset: Array<String>,
52 textView.text = dataset[position]
57 override fun getItemCount() = dataset.size
|
| /developers/samples/android/ui/views/NavigationDrawer/kotlinApp/Application/src/main/java/com/example/android/navigationdrawer/ |
| PlanetAdapter.kt | 29 private val dataset: Array<String>,
52 textView.text = dataset[position]
57 override fun getItemCount() = dataset.size
|
| /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/core/framework/ |
| dataset_stateful_op_whitelist.h | 22 namespace dataset { namespace in namespace:tensorflow
23 // Registry for stateful ops that need to be used in dataset functions.
50 } // namespace dataset
72 ::tensorflow::dataset::WhitelistedStatefulOpRegistry::Global()->Add( \
|
| /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)
|
