/external/annotation-tools/asmx/src/org/objectweb/asm/tree/ |
H A D | 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 77 TableSwitchInsnNode( final int min, final int max, final Label dflt, final Label[] labels) argument [all...] |
H A D | 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)); 71 LookupSwitchInsnNode( final Label dflt, final int[] keys, final Label[] labels) argument [all...] |
/external/tensorflow/tensorflow/contrib/nn/python/ops/ |
H A D | 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/tensorflow/tensorflow/contrib/boosted_trees/python/utils/ |
H A D | 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 [all...] |
/external/tensorflow/tensorflow/python/ops/ |
H A D | 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, prediction [all...] |
H A D | metrics_impl.py | 53 def _remove_squeezable_dimensions(predictions, labels, weights): 56 Squeezes last dim of `predictions` or `labels` if their rank differs by 1 68 labels: Optional label `Tensor` whose dimensions match `predictions`. 73 Tuple of `predictions`, `labels` and `weights`. Each of them possibly has 77 if labels is not None: 78 labels, predictions = confusion_matrix.remove_squeezable_dimensions( 79 labels, predictions) 80 predictions.get_shape().assert_is_compatible_with(labels.get_shape()) 83 return predictions, labels, None 89 return predictions, labels, weight [all...] |
/external/tensorflow/tensorflow/contrib/libsvm/python/kernel_tests/ |
H A D | 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, [[ [all...] |
/external/toolchain-utils/crosperf/ |
H A D | 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[ [all...] |
/external/tensorflow/tensorflow/contrib/kernel_methods/python/ |
H A D | 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, logit [all...] |
H A D | losses.py | 31 labels, 52 labels: `Tensor` of shape [batch_size] or [batch_size, 1]. Corresponds to 64 shape as `labels`; otherwise, it is a scalar. 67 ValueError: If `logits`, `labels` or `weights` have invalid or inconsistent 69 ValueError: If `labels` tensor has invalid dtype. 73 labels)) as scope: 85 # Check labels have valid type. 86 if labels.dtype != dtypes.int32 and labels.dtype != dtypes.int64: 88 'Invalid dtype for labels [all...] |
/external/tensorflow/tensorflow/contrib/sparsemax/python/ops/ |
H A D | 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") 58 q_part = labels * (0.5 * labels - shifted_logits)
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/external/tensorflow/tensorflow/contrib/metrics/python/ops/ |
H A D | 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,
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/external/autotest/utils/ |
H A D | 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 [all...] |
/external/tensorflow/tensorflow/contrib/metrics/python/metrics/ |
H A D | 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 [all...] |
H A D | 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)): 51 labels.dtype) 52 if not labels.dtype.is_compatible_with(predictions.dtype): 53 raise ValueError('Dtypes of predictions and labels should match. ' 54 'Given: predictions (%r) and labels ( [all...] |
/external/autotest/server/hosts/ |
H A D | 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 [all...] |
/external/autotest/contrib/ |
H A D | print_host_labels.py | 16 labels = host.get_labels() variable 18 print labels
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/external/tensorflow/tensorflow/contrib/learn/python/learn/learn_io/ |
H A D | pandas_io.py | 114 def extract_pandas_labels(labels): 115 """Extract data from pandas.DataFrame for labels. 118 labels: `pandas.DataFrame` or `pandas.Series` containing one column of 119 labels to be extracted. 122 A numpy `ndarray` of labels from the DataFrame. 128 if isinstance(labels, 130 if len(labels.columns) > 1: 131 raise ValueError('Only one column for labels is allowed.') 133 bad_data = [column for column in labels 134 if labels[colum [all...] |
/external/tensorflow/tensorflow/contrib/learn/python/learn/estimators/ |
H A D | logistic_regressor.py | 38 `(features, labels, mode) -> (predictions, loss, train_op)`. 45 def _model_fn(features, labels, mode, params): 49 predictions, loss, train_op = model_fn(features, labels, mode) 52 labels=labels, 100 `(features, labels, mode) -> (predictions, loss, train_op)`. 109 labels which are the output of `input_fn` and 110 returns features and labels which will be fed 124 def _make_logistic_eval_metric_ops(labels, predictions, thresholds): 128 labels [all...] |
H A D | head.py | 69 def _my_dnn_model_fn(features, labels, mode, params, config=None): 83 labels=labels, 97 labels=labels, 111 labels=labels, 142 labels=None, 156 labels: Labels `Tensor`, or `dict` of same. 194 label_dimension: Number of regression labels pe [all...] |
/external/python/cpython3/Lib/encodings/ |
H A D | 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 an [all...] |
/external/tensorflow/tensorflow/contrib/learn/python/learn/ops/ |
H A D | losses_ops.py | 32 def mean_squared_error_regressor(tensor_in, labels, weights, biases, name=None): 35 [tensor_in, labels]): 37 if len(labels.get_shape()) == 1 and len(predictions.get_shape()) == 2: 39 return predictions, losses.mean_squared_error(labels, predictions) 45 labels, 55 This function requires labels to be passed in one-hot encoding. 59 labels: Tensor, [batch_size, n_classes], one-hot labels of the output 71 with ops.name_scope(name, 'softmax_classifier', [tensor_in, labels]): 75 return nn.softmax(logits), losses.softmax_cross_entropy(labels, logit [all...] |
/external/tensorflow/tensorflow/contrib/learn/python/learn/ |
H A D | metric_spec_test.py | 35 def _fn0(predictions, labels, weights=None): 37 self.assertEqual("l1_value", labels) 93 def _fn(labels): 94 self.assertEqual(labels_, labels) 106 def _fn(labels, **kwargs): 107 self.assertEqual(labels_, labels) 120 def _fn(labels, predictions_by_another_name): 122 self.assertEqual(labels_, labels) 135 def _fn(predictions_by_another_name, labels): 137 self.assertEqual(labels_, labels) [all...] |
/external/tensorflow/tensorflow/contrib/losses/python/losses/ |
H A D | 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 toward [all...] |
/external/tensorflow/tensorflow/python/ops/losses/ |
H A D | 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, weight [all...] |