| /external/tensorflow/tensorflow/contrib/learn/python/learn/ops/ |
| ops_test.py | 38 labels = array_ops.placeholder(dtypes.float32, [None, 2])
42 prediction, loss = ops.softmax_classifier(features, labels, weights,
46 value = session.run(loss, {features: [[0.2, 0.3, 0.2]], labels: [[0, 1]]})
|
| seq2seq_ops.py | 38 def sequence_classifier(decoding, labels, sampling_decoding=None, name=None):
43 labels: List of Tensors with labels.
52 with ops.name_scope(name, "sequence_classifier", [decoding, labels]):
56 labels=labels[i], logits=pred,
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| /external/tensorflow/tensorflow/core/lib/monitoring/ |
| counter.h | 70 // metric). Each value is identified by a tuple of labels. The class allows the
95 // Retrieves the cell for the specified labels, creating it on demand if
97 template <typename... Labels>
98 CounterCell* GetCell(const Labels&... labels) LOCKS_EXCLUDED(mu_);
149 template <typename... Labels>
150 CounterCell* Counter<NumLabels>::GetCell(const Labels&... labels)
154 static_assert(sizeof...(Labels) == NumLabels,
155 "Mismatch between Counter<NumLabels> and number of labels "
[all...] |
| sampler.h | 100 // Each histogram is identified by a tuple of labels. The class allows the
126 // Retrieves the cell for the specified labels, creating it on demand if
128 template <typename... Labels>
129 SamplerCell* GetCell(const Labels&... labels) LOCKS_EXCLUDED(mu_);
192 template <typename... Labels>
193 SamplerCell* Sampler<NumLabels>::GetCell(const Labels&... labels)
197 static_assert(sizeof...(Labels) == NumLabels,
198 "Mismatch between Sampler<NumLabels> and number of labels "
[all...] |
| collected_metrics.h | 42 // Metrics may optionally have labels, which are additional dimensions used to
44 // might have two labels named "rpc_service" and "rpc_method".
51 // a counter and that it has two labels named "rpc_service" and "rpc_method").
53 // value) and specific values for each of the labels.
75 // Usually a Point should provide a |label| field for each of the labels
78 // or fewer labels than those that appear in the MetricDescriptor.
85 std::vector<Label> labels; member in struct:tensorflow::monitoring::Point
140 // No two Points in the same PointSet should have the same set of labels.
|
| /external/tensorflow/tensorflow/examples/how_tos/reading_data/ |
| convert_to_records.py | 43 labels = data_set.labels
64 'label': _int64_feature(int(labels[index])),
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| /external/tensorflow/tensorflow/examples/speech_commands/ |
| recognize_commands.cc | 20 RecognizeCommands::RecognizeCommands(const std::vector<string>& labels,
24 : labels_(labels),
29 labels_count_ = labels.size();
|
| /external/tensorflow/tensorflow/examples/tutorials/word2vec/ |
| word2vec_basic.py | 116 labels = np.ndarray(shape=(batch_size, 1), dtype=np.int32)
128 labels[i * num_skips + j, 0] = buffer[context_word]
137 return batch, labels
139 batch, labels = generate_batch(batch_size=8, num_skips=2, skip_window=1)
141 print(batch[i], reverse_dictionary[batch[i]], '->', labels[i, 0],
142 reverse_dictionary[labels[i, 0]])
187 # tf.nce_loss automatically draws a new sample of the negative labels each
196 labels=train_labels,
284 # Write corresponding labels for the embeddings.
292 # Create a configuration for visualizing embeddings with the labels i
[all...] |
| /external/antlr/tool/src/main/java/org/antlr/analysis/ |
| DecisionProbe.java | 155 /** Used while finding a path through an NFA whose edge labels match
301 List<Label> labels = new ArrayList<Label>(); // may access ith element; use array local
303 return labels;
308 labels);
309 return labels;
316 public String getInputSequenceDisplay(List<? extends Label> labels) {
319 for (Iterator<? extends Label> it = labels.iterator(); it.hasNext();) {
330 * find the path of NFA states associated with the labels sequence.
350 * The NFA path matching the sample input sequence (labels) is computed
358 List<? extends Label> labels)
[all...] |
| /external/autotest/frontend/afe/ |
| rpc_interface.py | 96 # labels
196 Yet another method to create labels.
304 labels = models.Label.query_objects(filter_data)
306 labels = labels.exclude(**exclude_filter)
309 return rpc_utils.prepare_rows_as_nested_dicts(labels, ())
315 non_static_lists = rpc_utils.prepare_rows_as_nested_dicts(labels, ())
318 label_ids = [label.id for label in labels]
321 replaced_label_names = {l.name for l in labels if l.id in replaced_ids}
470 def add_labels_to_host(id, labels)
[all...] |
| /external/autotest/cli/ |
| host_unittest.py | 37 labels = ['l0', 'l1', 'l2', 'p0', 'l3']
40 hh._cleanup_labels(labels, 'p0'))
44 labels = ['l0', 'l1', 'l2', 'l3']
47 hh._cleanup_labels(labels))
51 labels = ['l0', 'l1', 'l2', 'l3']
54 hh._cleanup_labels(labels, 'p0'))
81 self.assertEqual(['label0'], hl.labels)
89 self.assertEqualNoOrder(['label0', 'label2'], hl.labels)
97 self.assertEqual(['label,0'], hl.labels)
105 self.assertEqualNoOrder(['label,0', 'label,2'], hl.labels)
[all...] |
| /external/antlr/tool/src/main/java/org/antlr/codegen/ |
| ACyclicDFACodeGenerator.java | 115 if ( edgeST.impl.formalArguments.get("labels")!=null ) {
116 List<Integer> labels = edge.label.getSet().toList(); local
117 List<String> targetLabels = new ArrayList<String>(labels.size());
118 for (int j = 0; j < labels.size(); j++) {
119 Integer vI = labels.get(j);
124 edgeST.add("labels", targetLabels);
|
| /external/gemmlowp/profiling/ |
| profiler.h | 22 // pseudo-stack "labels", see ScopedProfilingLabel.
116 // to your own labels, you will also see 'other' nodes that collect
125 // This means that 20% of all labels were under Foo, of which 12%/20%==60%
196 if (child->label == stack.labels[level]) {
203 child_to_add_to->label = stack.labels[level];
298 // This is OK because we're looking at a pseudo-stack of labels,
307 // here is that pointers are changed atomically, and the labels
313 dst->labels[dst->size] = thread->stack.labels[dst->size];
|
| /external/owasp/sanitizer/tools/ |
| upload_jars_to_googlecode_downloads.sh | 83 --labels='Type-Archive,OpSys-All,Featured' \
|
| /external/python/cpython2/Python/ |
| makeopcodetargets.py | 3 (for compilers supporting computed gotos or "labels-as-values", such as gcc).
|
| /external/tensorflow/tensorflow/contrib/learn/python/learn/estimators/ |
| state_saving_rnn_estimator_test.py | 268 labels = constant_op.constant(5.0, shape=[sequence_length])
304 features, labels, mode, sequence_feature_columns,
335 labels = constant_op.constant([1., 0., 1.])
350 model_fn_ops = model_fn(features=features, labels=labels, mode=mode)
398 labels = array_ops.slice(random_sequence, [0], [sequence_length])
407 labels = None
408 return features, labels
453 labels = array_ops.slice(random_sequence, [0], [sequence_length])
457 return {'inputs': inputs}, labels
[all...] |
| /external/tensorflow/tensorflow/contrib/text/kernels/ |
| skip_gram_kernels.cc | 72 std::vector<T> labels; variable
81 // (token, label) pairs for all labels whose distances from the token are
91 labels.push_back(input(i + j));
103 "labels", TensorShape({static_cast<int>(labels.size())}),
117 labels_output->vec<T>()(i) = labels[i];
|
| /external/tensorflow/tensorflow/examples/ios/camera/ |
| CameraExampleViewController.h | 40 std::vector<std::string> labels; variable
|
| /external/tensorflow/tensorflow/examples/label_image/ |
| label_image.py | 90 parser.add_argument("--labels", help="name of file containing labels")
103 if args.labels:
104 label_file = args.labels
138 labels = load_labels(label_file) variable
140 print(labels[i], results[i])
|
| /external/tensorflow/tensorflow/lite/examples/ios/camera/ |
| CameraExampleViewController.h | 53 std::vector<std::string> labels; variable
|
| /external/cldr/tools/java/org/unicode/cldr/draft/ |
| Typology.java | 91 String[] labels = fullPath.split("/"); local
94 for (String item : labels) {
150 String[] labels = path2.split("/"); external variable declarations
152 for (int j = 0; j < labels.length; ++j) {
153 labelToPaths.put(labels[j], path2);
157 Map<String, UnicodeSet> map = label_parent_uset.get(labels[j]);
159 label_parent_uset.put(labels[j], map = new TreeMap<String, UnicodeSet>());
166 parent += labels[j] + "/";
175 // System.out.println("\nuset - path labels\t" + uset_path);
178 // System.out.println("\npath -uset labels\t" + path_uset)
[all...] |
| /external/grpc-grpc/tools/run_tests/artifacts/ |
| distribtest_targets.py | 90 self.labels = ['distribtest', 'csharp', platform, arch]
94 self.labels.append(docker_suffix)
98 self.labels.append('dotnetcli')
100 self.labels.append('olddotnet')
158 self.labels = ['distribtest', 'python', platform, arch, docker_suffix]
196 self.labels = ['distribtest', 'ruby', platform, arch, docker_suffix]
232 self.labels = ['distribtest', 'php', platform, arch, docker_suffix]
270 self.labels = [
|
| /external/tensorflow/tensorflow/contrib/eager/python/examples/rnn_colorbot/ |
| rnn_colorbot.py | 138 """Multi-layer (LSTM) RNN that regresses on real-valued vector labels.
147 label_dimension: the length of the labels on which to regress
208 def loss(labels, predictions):
210 return tf.reduce_mean(tf.squared_difference(predictions, labels))
216 for (labels, chars, sequence_length) in tfe.Iterator(eval_data):
218 avg_loss(loss(labels, predictions))
229 def model_loss(labels, chars, sequence_length):
231 loss_value = loss(labels, predictions)
235 for (batch, (labels, chars, sequence_length)) in enumerate(
238 batch_model_loss = functools.partial(model_loss, labels, chars
[all...] |
| rnn_colorbot_test.py | 43 labels = tf.random_normal([batch_size, LABEL_DIMENSION])
44 return tf.data.Dataset.from_tensors((labels, chars, sequence_length))
|
| /external/tensorflow/tensorflow/core/kernels/ |
| sparse_xent_op_gpu.cu.cc | 63 typename TTypes<Index>::ConstVec labels,
66 SparseXentEigenImpl<GPUDevice, T, Index>::Compute(ctx, logits, labels,
|
