| /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...] |
| /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/autotest/contrib/ |
| print_host_labels.py | 16 labels = host.get_labels() variable
17 print 'Labels:'
18 print labels
|
| /external/tensorflow/tensorflow/contrib/boosted_trees/python/utils/ |
| losses.py | 30 def per_example_squared_hinge_loss(labels, weights, predictions):
31 loss = losses.hinge_loss(labels=labels, logits=predictions, weights=weights)
35 def per_example_logistic_loss(labels, weights, predictions):
36 """Logistic loss given labels, example weights and predictions.
39 labels: Rank 2 (N, 1) tensor of per-example labels.
47 labels = math_ops.cast(labels, dtypes.float32)
49 labels=labels, logits=predictions
[all...] |
| /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...] |
| /external/autotest/server/hosts/ |
| afe_store.py | 54 return host_info.HostInfo(host.labels, host.attributes)
64 # copy of HostInfo from the AFE and then add/remove labels / attribtes
66 # parallel, we'll end up with corrupted labels / attributes.
69 list(set(old_info.labels) - set(new_info.labels)))
71 list(set(new_info.labels) - set(old_info.labels)))
75 def _remove_labels_on_afe(self, labels):
76 """Requests the AFE to remove the given labels.
78 @param labels: Remove these
[all...] |
| /external/tensorflow/tensorflow/python/ops/ |
| confusion_matrix.py | 34 labels, predictions, expected_rank_diff=0, name=None):
41 But, for example, if `labels` contains class IDs and `predictions` contains 1
43 `labels`, so `expected_rank_diff` would be 1. In this case, we'd squeeze
44 `labels` if `rank(predictions) - rank(labels) == 0`, and
45 `predictions` if `rank(predictions) - rank(labels) == 2`.
51 labels: Label values, a `Tensor` whose dimensions match `predictions`.
53 expected_rank_diff: Expected result of `rank(predictions) - rank(labels)`.
57 Tuple of `labels` and `predictions`, possibly with last dim squeezed.
60 [labels, predictions])
[all...] |
| metrics_impl.py | 88 def _remove_squeezable_dimensions(predictions, labels, weights):
91 Squeezes last dim of `predictions` or `labels` if their rank differs by 1
103 labels: Optional label `Tensor` whose dimensions match `predictions`.
108 Tuple of `predictions`, `labels` and `weights`. Each of them possibly has
112 if labels is not None:
113 labels, predictions = confusion_matrix.remove_squeezable_dimensions(
114 labels, predictions)
115 predictions.get_shape().assert_is_compatible_with(labels.get_shape())
118 return predictions, labels, None
124 return predictions, labels, weight
[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/sparsemax/python/ops/ |
| sparsemax_loss.py | 28 def sparsemax_loss(logits, sparsemax, labels, name=None):
37 labels: A `Tensor`. Must have the same type as `logits`.
45 [logits, sparsemax, labels]) as name:
48 labels = ops.convert_to_tensor(labels, name="labels")
65 q_part = labels * (0.5 * labels - z)
66 # Fix the case where labels = 0 and z = -inf, where q_part would
73 math_ops.logical_and(math_ops.equal(labels, 0), math_ops.is_inf(z))
[all...] |
| /external/tensorflow/tensorflow/contrib/losses/python/losses/ |
| loss_ops.py | 240 def absolute_difference(predictions, labels=None, weights=1.0, scope=None):
253 labels: The ground truth output tensor, same dimensions as 'predictions'.
262 ValueError: If the shape of `predictions` doesn't match that of `labels` or
266 [predictions, labels, weights]) as scope:
267 predictions.get_shape().assert_is_compatible_with(labels.get_shape())
269 labels = math_ops.cast(labels, dtypes.float32)
270 losses = math_ops.abs(math_ops.subtract(predictions, labels))
276 "of the predictions and labels arguments has been changed.")
289 If `label_smoothing` is nonzero, smooth the labels towards 1/2
[all...] |
| /external/tensorflow/tensorflow/python/ops/losses/ |
| losses_impl.py | 213 labels, predictions, weights=1.0, scope=None,
227 labels: The ground truth output tensor, same dimensions as 'predictions'.
230 `labels`, and must be broadcastable to `labels` (i.e., all dimensions must
238 shape as `labels`; otherwise, it is scalar.
242 `labels` or if the shape of `weights` is invalid or if `labels`
250 if labels is None:
251 raise ValueError("labels must not be None.")
255 (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,
|
| /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/grpc-grpc/tools/run_tests/sanity/ |
| check_test_filtering.py | 40 def test_filtering(self, changed_files=[], labels=_LIST_OF_LANGUAGE_LABELS):
43 default labels should be able to match all jobs
45 :param labels: list of job labels that should be skipped
61 if "sanity" in job.labels:
63 all_jobs = [job for job in all_jobs if "sanity" not in job.labels]
65 if "sanity" in job.labels:
68 job for job in filtered_jobs if "sanity" not in job.labels
73 for label in labels:
75 self.assertNotIn(label, job.labels)
[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...] |
| /external/tensorflow/tensorflow/contrib/metrics/python/metrics/ |
| classification_test.py | 39 labels = array_ops.placeholder(dtypes.int32, shape=[None])
40 acc = classification.accuracy(pred, labels)
43 labels: [1, 1, 0, 0]})
49 labels = array_ops.placeholder(dtypes.bool, shape=[None])
50 acc = classification.accuracy(pred, labels)
53 labels: [1, 1, 0, 0]})
59 labels = array_ops.placeholder(dtypes.int64, shape=[None])
60 acc = classification.accuracy(pred, labels)
63 labels: [1, 1, 0, 0]})
69 labels = array_ops.placeholder(dtypes.string, shape=[None]
[all...] |
| /external/autotest/frontend/client/src/autotest/common/table/ |
| MultipleListFilter.java | 22 JSONArray labels = new JSONArray(); local
26 labels.set(labels.size(),
30 return labels;
|
| /external/tensorflow/tensorflow/compiler/tests/ |
| dense_layer_test.py | 35 """Returns all labels in run_metadata."""
36 labels = []
39 labels.append(node_stats.timeline_label)
40 return labels
43 def InLabels(labels, substr):
44 """Returns true iff one of the labels contains substr."""
45 return any(substr in x for x in labels)
50 def countXlaOps(self, labels):
51 """Count how many XlaCompile/XlaRun labels are present."""
52 xla_compile_count = sum("XlaCompile(" in x for x in labels)
[all...] |
| /external/tensorflow/tensorflow/core/kernels/ |
| sparse_xent_op.cc | 33 Status CheckInvalidLabelIndex(const Tensor& labels, int64 max_index) {
34 if (labels.NumElements() == 0) return Status::OK();
35 const auto label_values = labels.vec<Index>();
45 "). Label values: ", labels.SummarizeValue(labels.NumElements()));
58 const Tensor& labels = context->input(1); variable
62 OP_REQUIRES(context, TensorShapeUtils::IsVector(labels.shape()),
63 errors::InvalidArgument("labels must be 1-D, but got shape ",
64 labels.shape().DebugString()));
65 OP_REQUIRES(context, logits.dim_size(0) == labels.dim_size(0)
[all...] |
| /external/tensorflow/tensorflow/contrib/learn/python/learn/learn_io/ |
| dask_io.py | 93 def extract_dask_labels(labels):
94 """Extract data from dask.Series or dask.DataFrame for labels.
101 labels: A distributed dask.DataFrame or dask.Series with exactly one
114 if isinstance(labels, dd.DataFrame):
115 ncol = labels.columns
116 elif isinstance(labels, dd.Series):
117 ncol = labels.name
118 if isinstance(labels, allowed_classes):
120 raise ValueError('Only one column for labels is allowed.')
121 return _construct_dask_df_with_divisions(labels)
[all...] |
| /external/tensorflow/tensorflow/contrib/layers/python/layers/ |
| target_column_test.py | 36 labels = constant_op.constant([[0.], [1.], [1.]])
38 5. / 3, sess.run(target_column.loss(prediction, labels, {})))
46 labels = constant_op.constant([[0.], [1.], [1.]])
49 sess.run(target_column.loss(prediction, labels, features)),
53 sess.run(target_column.training_loss(prediction, labels, features)),
63 labels = constant_op.constant([[1.], [0.]])
68 sess.run(target_column.loss(logits, labels, {})),
77 labels = constant_op.constant([[1.], [0.]])
82 sess.run(target_column.loss(logits, labels, features)),
89 labels = constant_op.constant([[1.], [0.], [1.]]
[all...] |
