| /external/autotest/site_utils/ |
| balance_pools.py | 77 _POOL_PREFIX = constants.Labels.POOL_PREFIX
157 """Information about a pool of DUTs matching given labels.
160 the given labels, and divides them into three categories:
180 @property labels Labels that constrain the DUTs to consider.
184 @property pool_labels A list of labels that identify a DUT as part
190 def __init__(self, afe, pool, labels, start_time, end_time):
192 self.labels = labellib.LabelsMapping(labels)
193 self.labels['pool'] = poo
[all...] |
| diagnosis_utils_unittest.py | 44 labels = ('board:test_board', 'pool:test_pool', 'model:test_model')
52 rpc_helper.check_dut_availability(labels,
60 rpc_helper.check_dut_availability(labels,
71 rpc_helper.check_dut_availability(labels,
79 rpc_helper.check_dut_availability(labels,
86 rpc_helper.check_dut_availability(labels,
96 rpc_helper.check_dut_availability(labels,
|
| add_detected_host_labels.py | 9 detected labels to host.
12 - Does not keep a count of how many labels were
36 # the given prefix, e.g., a dut can't have both labels of power:battery and
42 Queries the detectable labels supported by the given host,
43 and adds those labels to the host.
49 False on failure to fetch labels or to add any individual label.
54 labels = host.get_labels()
60 logging.warning('Unable to query labels on hostname %s. Skipping.',
69 label_matches = afe.get_labels(name__in=labels)
77 labels_to_delete = [l for l in afe_host.labels
[all...] |
| /external/tensorflow/tensorflow/python/kernel_tests/ |
| metrics_test.py | 55 def _binary_2d_label_to_2d_sparse_value(labels):
58 Only 1 values in `labels` are included in result.
61 labels: Dense 2D binary indicator, shape [batch_size, num_classes].
65 is the number of `1` values in each row of `labels`. Values are indices
66 of `1` values along the last dimension of `labels`.
71 for row in labels:
83 shape = [len(labels), len(labels[0])]
89 def _binary_2d_label_to_1d_sparse_value(labels):
92 Only 1 values in `labels` are included in result
[all...] |
| losses_test.py | 113 labels = constant_op.constant([1, 9, 2, -5, -2, 6], shape=(2, 3))
114 losses.absolute_difference(labels, predictions)
122 labels = constant_op.constant([[1, 0, 0], [0, 1, 0], [0, 0, 1]])
125 losses.softmax_cross_entropy(labels, logits, weights=None)
132 labels = constant_op.constant([[1, 0, 0], [0, 1, 0], [0, 0, 1]])
133 loss = losses.softmax_cross_entropy(labels, logits)
141 labels = constant_op.constant([[0, 0, 1], [1, 0, 0], [0, 1, 0]])
144 loss = losses.softmax_cross_entropy(labels, logits)
152 labels = constant_op.constant([[0, 0, 1], [1, 0, 0], [0, 1, 0]])
155 loss = losses.softmax_cross_entropy(labels, logits, weights
[all...] |
| /external/jacoco/org.jacoco.core/src/org/jacoco/core/internal/flow/ |
| MethodProbesAdapter.java | 144 final Label[] labels) {
145 if (markLabels(dflt, labels)) {
146 probesVisitor.visitLookupSwitchInsnWithProbes(dflt, keys, labels,
149 probesVisitor.visitLookupSwitchInsn(dflt, keys, labels);
155 final Label dflt, final Label... labels) {
156 if (markLabels(dflt, labels)) {
158 labels, frame(1));
160 probesVisitor.visitTableSwitchInsn(min, max, dflt, labels);
164 private boolean markLabels(final Label dflt, final Label[] labels) {
166 LabelInfo.resetDone(labels);
[all...] |
| /external/tensorflow/tensorflow/contrib/nn/python/ops/ |
| sampling_ops.py | 112 labels,
161 labels=labels,
168 labels_one_hot = tf.one_hot(labels, n_classes)
170 labels=labels_one_hot,
180 labels: A `Tensor` of type `int64` and shape `[batch_size,
182 the `labels` argument of `nn.softmax_cross_entropy_with_logits`.
219 weights, biases, labels, inputs, sampled_values, resampling_temperature
223 true_classes=labels,
236 labels=labels
[all...] |
| /external/tensorflow/tensorflow/contrib/learn/python/learn/estimators/ |
| estimators_test.py | 51 def feature_engineering_fn(features, labels):
52 _, _ = features, labels
59 def model_fn(features, labels):
62 _ = labels
78 "label": metric_spec.MetricSpec(lambda predictions, labels: labels)
80 # labels = transformed_y (99)
92 def feature_engineering_fn(features, labels):
96 labels["y"] = constant_op.constant([99.])
97 return features, labels
[all...] |
| /external/tensorflow/tensorflow/contrib/distribute/python/ |
| metrics_v1_test.py | 33 # First four batches of x: labels, predictions -> (labels == predictions)
39 lambda x: {"labels": x % 5, "predictions": x % 3}).batch(
44 # First four batches of labels, predictions: {TP, FP, TN, FN}
51 "labels": [True, False, True, False],
57 # First four batches of labels, predictions: {TP, FP, TN, FN}
64 "labels": [True, False, True, False],
71 "labels": [1., .5, 1., 0.],
152 labels = x["labels"]
[all...] |
| /bionic/tools/bionicbb/ |
| gerrit.py | 57 """Returns labels attached to a revision.
73 labels = {'Code-Review': {}, 'Verified': {}}
74 for review in details['labels']['Code-Review']['all']:
76 labels['Code-Review'][review['email']] = int(review['value'])
77 for review in details['labels']['Verified']['all']:
79 labels['Verified'][review['email']] = int(review['value'])
80 return labels
|
| /external/autotest/frontend/client/src/autotest/afe/ |
| LabelFilter.java | 19 setMatchAllText("All labels");
48 JSONArray labels = super.getMatchValue().isArray(); local
54 labels.set(labels.size(), new JSONString(platformString));
57 return labels;
|
| /external/clang/test/SemaCXX/ |
| switch-implicit-fallthrough-blocks.cpp | 13 expected-warning{{unannotated fall-through between switch labels}} \
|
| /external/python/cpython3/Lib/idlelib/idle_test/ |
| test_statusbar.py | 25 self.assertEqual(bar.labels, {})
30 self.assertIn('left', bar.labels)
31 left = bar.labels['left']
37 self.assertEqual(bar.labels['right']['text'], 'correct text')
|
| /external/tensorflow/tensorflow/contrib/learn/python/learn/utils/ |
| input_fn_utils.py | 44 'labels',
52 Training and eval input_fn should return a `(features, labels)` tuple.
57 labels: A `Tensor`, `SparseTensor`, or a dict of string to `Tensor` or
58 `SparseTensor`, specifying labels for training or eval. For serving, set
59 `labels` to `None`.
75 for use at serving time, so the labels return value is always None.
92 labels = None # these are not known in serving!
93 return InputFnOps(features, labels, inputs)
103 This input_fn is for use at serving time, so the labels return value is always
124 labels = None # these are not known in serving
[all...] |
| /external/grpc-grpc/tools/run_tests/ |
| run_tests_matrix.py | 115 labels=[],
157 job.labels = [platform, config, language, iomgr_platform
158 ] + labels
170 labels=['basictests'],
179 labels=['basictests', 'multilang'],
188 labels=['basictests', 'corelang'],
197 labels=['basictests', 'multilang'],
206 labels=['basictests', 'multilang'],
215 labels=['basictests', 'corelang'],
224 labels=['basictests', 'multilang']
[all...] |
| /external/tensorflow/tensorflow/contrib/distribute/python/examples/ |
| simple_estimator_example.py | 34 def model_fn(features, labels, mode): # pylint: disable=unused-argument
51 acc_obj.update_state(labels, labels)
74 labels = tf.data.Dataset.from_tensors([1.]).repeat(10)
75 return tf.data.Dataset.zip((features, labels))
83 labels = tf.data.Dataset.from_tensors([1.]).repeat(10)
84 return tf.data.Dataset.zip((features, labels))
|
| /external/tensorflow/tensorflow/examples/tutorials/mnist/ |
| mnist.py | 86 def loss(logits, labels):
87 """Calculates the loss from the logits and the labels.
91 labels: Labels tensor, int32 - [batch_size].
96 labels = tf.to_int64(labels)
97 return tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits)
129 def evaluation(logits, labels):
134 labels: Labels tensor, int32 - [batch_size], with values in th
[all...] |
| /external/tensorflow/tensorflow/python/keras/datasets/ |
| reuters.py | 84 xs, labels = f['x'], f['y']
90 labels = labels[indices]
98 xs, labels = _remove_long_seq(maxlen, xs, labels)
112 x_train, y_train = np.array(xs[:idx]), np.array(labels[:idx])
113 x_test, y_test = np.array(xs[idx:]), np.array(labels[idx:])
|
| /external/owasp/sanitizer/tools/ |
| googlecode_upload.py | 15 # project. You can optionally provide a list of labels that apply to
59 def upload(file, project_name, user_name, password, summary, labels=None):
69 labels: an optional list of label strings with which to tag the file.
84 if labels is not None:
85 form_fields.extend([('label', l.strip()) for l in labels])
155 def upload_find_auth(file_path, project_name, summary, labels=None,
159 file_path, project_name, summary, and labels are passed as-is to upload.
165 labels: an optional list of label strings with which to tag the file.
195 summary, labels)
220 parser.add_option('-l', '--labels', dest='labels'
[all...] |
| /external/tensorflow/tensorflow/contrib/tensor_forest/hybrid/python/ |
| hybrid_model.py | 106 def training_graph(self, data, labels, data_spec=None, epoch=None):
108 return self.optimizer.minimize(self.training_loss(data, labels))
110 def loss(self, data, labels):
115 labels, dtypes.float32)
122 labels=array_ops.squeeze(math_ops.cast(labels, dtypes.int32)),
130 def training_loss(self, data, labels):
131 return self.loss(data, labels)
133 def validation_loss(self, data, labels):
134 return self.loss(data, labels)
[all...] |
| /external/jacoco/org.jacoco.core/src/org/jacoco/core/internal/instr/ |
| MethodInstrumenter.java | 112 final Label dflt, final Label[] labels, final IFrame frame) {
113 // 1. Calculate intermediate labels:
115 LabelInfo.resetDone(labels);
117 final Label[] newLabels = createIntermediates(labels);
121 insertIntermediateProbes(dflt, labels, frame);
126 final int[] keys, final Label[] labels, final IFrame frame) {
127 // 1. Calculate intermediate labels:
129 LabelInfo.resetDone(labels);
131 final Label[] newLabels = createIntermediates(labels);
135 insertIntermediateProbes(dflt, labels, frame)
[all...] |
| /external/tensorflow/tensorflow/contrib/losses/python/metric_learning/ |
| metric_loss_ops.py | 86 def contrastive_loss(labels, embeddings_anchor, embeddings_positive,
92 by the margin constant for the samples of different labels.
96 labels: 1-D tf.int32 `Tensor` with shape [batch_size] of
97 binary labels indicating positive vs negative pair.
116 math_ops.cast(labels, dtypes.float32) * math_ops.square(distances) +
117 (1. - math_ops.cast(labels, dtypes.float32)) *
160 def triplet_semihard_loss(labels, embeddings, margin=1.0):
164 the same labels) to be smaller than the minimum negative distance among
171 labels: 1-D tf.int32 `Tensor` with shape [batch_size] of
172 multiclass integer labels
[all...] |
| /external/tensorflow/tensorflow/contrib/eager/python/ |
| metrics_impl.py | 366 """Calculates how often `predictions` matches `labels`.
376 def call(self, labels, predictions, weights=None):
379 For example, if labels is [1, 2, 3, 4] and predictions is [0, 2, 3, 4]
383 `labels` and `predictions` should have the same shape and type.
386 labels: Tensor with the true labels for each example. One example
395 array_ops.shape(labels), array_ops.shape(predictions),
396 message="Shapes of labels and predictions are unequal")
397 matches = math_ops.equal(labels, predictions)
401 return labels, prediction
[all...] |
| /external/tensorflow/tensorflow/contrib/compiler/ |
| xla_test.py | 271 def _test_train_model_fn(features, labels, mode, params):
273 del features, labels, params
280 def decorated_model_fn(features, labels, mode, params):
281 return _test_train_model_fn(features, labels, mode, params)
314 features, labels = make_dummy_features_labels()
316 features=features, labels=labels, mode=_TRAIN, params=params or {})
338 features, labels = make_dummy_features_labels()
340 features=features, labels=labels, mode=_TRAIN, params=params or {}
[all...] |
| /external/autotest/server/hosts/ |
| cros_label_unittest.py | 75 def __init__(self, labels=[], attributes={}):
76 self.labels = labels
83 def __init__(self, labels, *args):
84 self._afe_host = MockAFEHost(labels)
86 info = host_info.HostInfo(labels=labels)
96 def __init__(self, labels, *args):
97 super(MockHostWithoutAFE, self).__init__(labels, *args)
|
