| /external/annotation-tools/asmx/src/org/objectweb/asm/tree/ |
| TableSwitchInsnNode.java | 66 public List labels; field in class:TableSwitchInsnNode
74 * @param labels beginnings of the handler blocks. <tt>labels[i]</tt> is
81 final Label[] labels)
87 this.labels = new ArrayList();
88 if (labels != null) {
89 this.labels.addAll(Arrays.asList(labels));
94 Label[] labels = new Label[this.labels.size()] local
[all...] |
| LookupSwitchInsnNode.java | 61 public List labels; field in class:LookupSwitchInsnNode
68 * @param labels beginnings of the handler blocks. <tt>labels[i]</tt> is
74 final Label[] labels)
79 this.labels = new ArrayList(labels == null ? 0 : labels.length);
85 if (labels != null) {
86 this.labels.addAll(Arrays.asList(labels));
95 Label[] labels = new Label[this.labels.size()]; local
[all...] |
| /toolchain/binutils/binutils-2.27/gas/testsuite/gas/cris/ |
| labfloat.s | 1 ; Check if labels are mistaken for floats.
|
| /external/tensorflow/tensorflow/contrib/nn/python/ops/ |
| cross_entropy.py | 29 labels,
32 """Computes softmax cross entropy between `logits` and `labels`.
43 need not be. All that is required is that each row of `labels` is
47 If using exclusive `labels` (wherein one and only
54 `logits` and `labels` must have the same shape `[batch_size, num_classes]`
59 labels: Each row `labels[i]` must be a valid probability distribution.
68 labels=labels, logits=logits, dim=dim, name=name)
76 labels,
[all...] |
| /external/toolchain-utils/crosperf/ |
| results_organizer_unittest.py | 8 We create some labels, benchmark_runs and then create a ResultsOrganizer,
137 labels = [mock_instance.label1, mock_instance.label2]
140 benchmark_runs[0] = BenchmarkRun('b1', benchmarks[0], labels[0], 1, '', '',
142 benchmark_runs[1] = BenchmarkRun('b2', benchmarks[0], labels[0], 2, '', '',
144 benchmark_runs[2] = BenchmarkRun('b3', benchmarks[0], labels[1], 1, '', '',
146 benchmark_runs[3] = BenchmarkRun('b4', benchmarks[0], labels[1], 2, '', '',
148 benchmark_runs[4] = BenchmarkRun('b5', benchmarks[1], labels[0], 1, '', '',
150 benchmark_runs[5] = BenchmarkRun('b6', benchmarks[1], labels[0], 2, '', '',
152 benchmark_runs[6] = BenchmarkRun('b7', benchmarks[1], labels[1], 1, '', '',
154 benchmark_runs[7] = BenchmarkRun('b8', benchmarks[1], labels[1], 2, '', ''
[all...] |
| /toolchain/binutils/binutils-2.27/gas/testsuite/gas/mmix/ |
| locall1.s | 1 % Get rid of labels that look compiler-generated, matching: "L.*:[0-9]+".
|
| fb-1.s | 1 # FB-labels are valid in GREG definitions.
|
| /external/autotest/utils/ |
| labellib_unittest.py | 44 labels = ['webcam', 'pool:suites']
45 mapping = labellib.LabelsMapping(labels)
46 self.assertEqual(mapping.getlabels(), labels)
49 labels = ['webcam', 'pool:suites', 'pool:party']
50 mapping = labellib.LabelsMapping(labels)
54 labels = ['ohse:tsubame', 'webcam']
55 mapping = labellib.LabelsMapping(labels)
59 labels = ['webcam', 'exec', 'method']
60 mapping = labellib.LabelsMapping(labels)
64 labels = ['class:protecta', 'method:metafalica', 'exec:chronicle_key'
[all...] |
| /external/tensorflow/tensorflow/contrib/metrics/python/metrics/ |
| classification_test.py | 32 labels = array_ops.placeholder(dtypes.int32, shape=[None])
33 acc = classification.accuracy(pred, labels)
36 labels: [1, 1, 0, 0]})
42 labels = array_ops.placeholder(dtypes.bool, shape=[None])
43 acc = classification.accuracy(pred, labels)
46 labels: [1, 1, 0, 0]})
52 labels = array_ops.placeholder(dtypes.int64, shape=[None])
53 acc = classification.accuracy(pred, labels)
56 labels: [1, 1, 0, 0]})
62 labels = array_ops.placeholder(dtypes.string, shape=[None]
[all...] |
| classification.py | 29 def accuracy(predictions, labels, weights=None, name=None):
30 """Computes the percentage of times that predictions matches labels.
34 matches 'labels'.
35 labels: the ground truth values, a `Tensor` of any shape and
47 if not (labels.dtype.is_integer or
48 labels.dtype in (dtypes.bool, dtypes.string)):
50 'Labels should have bool, integer, or string dtype, not %r' %
51 labels.dtype)
52 if not labels.dtype.is_compatible_with(predictions.dtype):
53 raise ValueError('Dtypes of predictions and labels should match.
[all...] |
| /external/tensorflow/tensorflow/contrib/boosted_trees/python/utils/ |
| losses.py | 28 def per_example_logistic_loss(labels, weights, predictions):
29 """Logistic loss given labels, example weights and predictions.
32 labels: Rank 2 (N, 1) tensor of per-example labels.
40 labels = math_ops.to_float(labels)
42 labels=labels, logits=predictions)
49 def per_example_maxent_loss(labels, weights, logits, num_classes, eps=1e-15):
56 labels: Rank 2 (N, 1) or Rank 1 (N) tensor of per-example labels
[all...] |
| /external/autotest/contrib/ |
| print_host_labels.py | 16 labels = host.get_labels() variable
17 print 'Labels:'
18 print labels
|
| /external/tensorflow/tensorflow/contrib/libsvm/python/kernel_tests/ |
| decode_libsvm_op_test.py | 37 sparse_features, labels = libsvm_ops.decode_libsvm(
42 self.assertAllEqual(labels.get_shape().as_list(), [3])
44 features, labels = sess.run([features, labels])
45 self.assertAllEqual(labels, [1, 1, 2])
55 sparse_features, labels = libsvm_ops.decode_libsvm(
60 self.assertAllEqual(labels.get_shape().as_list(), [3, 2])
62 features, labels = sess.run([features, labels])
63 self.assertAllEqual(labels, [[1, 1], [1, 1], [2, 2]]
[all...] |
| /external/tensorflow/tensorflow/contrib/kernel_methods/python/ |
| losses_test.py | 37 labels = constant_op.constant([0, 1])
39 _ = losses.sparse_multiclass_hinge_loss(labels, logits)
42 """An error is raised when labels have invalid shape."""
45 labels = constant_op.constant([1, 0], shape=(1, 1, 2))
47 _ = losses.sparse_multiclass_hinge_loss(labels, logits)
53 labels = constant_op.constant([1, 0], shape=(2,))
56 _ = losses.sparse_multiclass_hinge_loss(labels, logits, weights)
59 """An error is raised when labels have invalid shape."""
62 labels = constant_op.constant([1, 0], dtype=dtypes.float32)
64 _ = losses.sparse_multiclass_hinge_loss(labels, logits
[all...] |
| /test/vti/dashboard/src/test/java/com/android/vts/util/ |
| ProfilingPointSummaryTest.java | 37 private static String[] labels = new String[] {"label1", "label2", "label3"}; field in class:ProfilingPointSummaryTest
44 * @param labels The list of data labels.
45 * @param values The list of data values. Must be equal in size to the labels list.
50 String[] labels, long[] values, VtsProfilingRegressionMode regressionMode) {
51 List<String> labelList = Arrays.asList(labels);
54 for (int i = 0; i < labels.length; ++i) {
55 StatSummary stat = new StatSummary(labels[i], regressionMode);
57 labelStats.put(labels[i], stat);
76 ProfilingPointSummaryEntity pt = createProfilingReport(labels, values, mode)
[all...] |
| /external/python/cpython3/Lib/encodings/ |
| idna.py | 162 labels = result.split(b'.')
163 for label in labels[:-1]:
166 if len(labels[-1]) >= 64:
171 labels = dots.split(input)
172 if labels and not labels[-1]:
174 del labels[-1]
177 for label in labels:
204 labels = input.split(b".")
206 if labels and len(labels[-1]) == 0
[all...] |
| /external/tensorflow/tensorflow/contrib/losses/python/losses/ |
| loss_ops.py | 264 def absolute_difference(predictions, labels=None, weights=1.0, scope=None):
277 labels: The ground truth output tensor, same dimensions as 'predictions'.
286 ValueError: If the shape of `predictions` doesn't match that of `labels` or
290 [predictions, labels, weights]) as scope:
291 predictions.get_shape().assert_is_compatible_with(labels.get_shape())
293 labels = math_ops.to_float(labels)
294 losses = math_ops.abs(math_ops.subtract(predictions, labels))
300 "of the predictions and labels arguments has been changed.")
313 If `label_smoothing` is nonzero, smooth the labels towards 1/2
[all...] |
| /external/tensorflow/tensorflow/python/ops/losses/ |
| losses_impl.py | 219 labels, predictions, weights=1.0, scope=None,
233 labels: The ground truth output tensor, same dimensions as 'predictions'.
236 `labels`, and must be broadcastable to `labels` (i.e., all dimensions must
244 shape as `labels`; otherwise, it is scalar.
248 `labels` or if the shape of `weights` is invalid or if `labels`
251 if labels is None:
252 raise ValueError("labels must not be None.")
256 (predictions, labels, weights)) as scope
[all...] |
| /external/tensorflow/tensorflow/contrib/metrics/python/ops/ |
| confusion_matrix_ops.py | 25 def confusion_matrix(labels, predictions, num_classes=None, dtype=dtypes.int32,
28 return cm.confusion_matrix(labels=labels, predictions=predictions,
|
| /external/tensorflow/tensorflow/python/ops/ |
| confusion_matrix.py | 38 labels, predictions, expected_rank_diff=0, name=None):
45 But, for example, if `labels` contains class IDs and `predictions` contains 1
47 `labels`, so `expected_rank_diff` would be 1. In this case, we'd squeeze
48 `labels` if `rank(predictions) - rank(labels) == 0`, and
49 `predictions` if `rank(predictions) - rank(labels) == 2`.
55 labels: Label values, a `Tensor` whose dimensions match `predictions`.
57 expected_rank_diff: Expected result of `rank(predictions) - rank(labels)`.
61 Tuple of `labels` and `predictions`, possibly with last dim squeezed.
64 [labels, predictions])
[all...] |
| /device/linaro/bootloader/edk2/AppPkg/Applications/Python/Python-2.7.10/Lib/encodings/ |
| idna.py | 157 labels = dots.split(input)
158 if labels and len(labels[-1])==0:
160 del labels[-1]
163 for label in labels:
178 labels = dots.split(input)
183 labels = input.split(".")
185 if labels and len(labels[-1]) == 0:
187 del labels[-1]
[all...] |
| /device/linaro/bootloader/edk2/AppPkg/Applications/Python/Python-2.7.2/Lib/encodings/ |
| idna.py | 157 labels = dots.split(input)
158 if labels and len(labels[-1])==0:
160 del labels[-1]
163 for label in labels:
178 labels = dots.split(input)
183 labels = input.split(".")
185 if labels and len(labels[-1]) == 0:
187 del labels[-1]
[all...] |
| /external/python/cpython2/Lib/encodings/ |
| idna.py | 157 labels = dots.split(input)
158 if labels and len(labels[-1])==0:
160 del labels[-1]
163 for label in labels:
178 labels = dots.split(input)
183 labels = input.split(".")
185 if labels and len(labels[-1]) == 0:
187 del labels[-1
[all...] |
| /prebuilts/gdb/darwin-x86/lib/python2.7/encodings/ |
| idna.py | 157 labels = dots.split(input)
158 if labels and len(labels[-1])==0:
160 del labels[-1]
163 for label in labels:
178 labels = dots.split(input)
183 labels = input.split(".")
185 if labels and len(labels[-1]) == 0:
187 del labels[-1
[all...] |
| /prebuilts/gdb/linux-x86/lib/python2.7/encodings/ |
| idna.py | 157 labels = dots.split(input)
158 if labels and len(labels[-1])==0:
160 del labels[-1]
163 for label in labels:
178 labels = dots.split(input)
183 labels = input.split(".")
185 if labels and len(labels[-1]) == 0:
187 del labels[-1
[all...] |
