| /external/tensorflow/tensorflow/python/ops/ |
| D | metrics_impl.py | 53 def _remove_squeezable_dimensions(predictions, labels, weights): argument
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, weights
126 return predictions, labels, weights
129 def _maybe_expand_labels(labels, predictions): argument
145 with ops.name_scope(None, 'expand_labels', (labels, predictions)) as scope:
[all …]
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| D | confusion_matrix.py | 38 labels, predictions, expected_rank_diff=0, name=None): argument
64 [labels, predictions]):
66 labels = ops.convert_to_tensor(labels)
69 labels_shape = labels.get_shape()
77 labels = array_ops.squeeze(labels, [-1])
78 return labels, predictions
81 rank_diff = array_ops.rank(predictions) - array_ops.rank(labels)
90 labels = control_flow_ops.cond(
92 lambda: array_ops.squeeze(labels, [-1]),
93 lambda: labels)
[all …]
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| /external/autotest/utils/ |
| 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 = ['class:protecta', 'method:metafalica', 'exec:chronicle_key']
[all …]
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| /external/tensorflow/tensorflow/contrib/learn/python/learn/estimators/ |
| D | head.py | 142 labels=None, argument
542 def _mean_squared_loss(labels, logits, weights=None): argument
543 with ops.name_scope(None, "mean_squared_loss", (logits, labels)) as name:
545 labels = ops.convert_to_tensor(labels)
547 if len(labels.get_shape()) == 1:
548 labels = array_ops.expand_dims(labels, dim=(1,))
552 logits.get_shape().assert_is_compatible_with(labels.get_shape())
553 loss = math_ops.square(logits - math_ops.to_float(labels), name=name)
557 def _poisson_loss(labels, logits, weights=None): argument
559 with ops.name_scope(None, "_poisson_loss", (logits, labels)) as name:
[all …]
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| /external/tensorflow/tensorflow/contrib/metrics/python/ops/ |
| D | metric_ops.py | 67 labels, argument
103 labels=labels,
111 labels, argument
147 labels=labels,
155 labels, argument
191 labels=labels,
199 labels, argument
234 labels=labels,
347 labels, argument
396 labels=labels,
[all …]
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| D | metric_ops_test.py | 52 def _binary_2d_label_to_sparse_value(labels): argument
67 for row in labels:
79 shape = [len(labels), len(labels[0])]
85 def _binary_2d_label_to_sparse(labels): argument
98 _binary_2d_label_to_sparse_value(labels))
101 def _binary_3d_label_to_sparse_value(labels): argument
115 for d0, labels_d0 in enumerate(labels):
125 shape = [len(labels), len(labels[0]), len(labels[0][0])]
131 def _binary_3d_label_to_sparse(labels): argument
144 _binary_3d_label_to_sparse_value(labels))
[all …]
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| /external/tensorflow/tensorflow/python/ops/losses/ |
| D | losses_impl.py | 219 labels, predictions, weights=1.0, scope=None, argument
251 if labels is None:
256 (predictions, labels, weights)) as scope:
258 labels = math_ops.to_float(labels)
259 predictions.get_shape().assert_is_compatible_with(labels.get_shape())
260 losses = math_ops.abs(math_ops.subtract(predictions, labels))
268 labels, predictions, axis=None, weights=1.0, scope=None, argument
303 if labels is None:
308 (predictions, labels, weights)) as scope:
310 labels = math_ops.to_float(labels)
[all …]
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| /external/tensorflow/tensorflow/contrib/boosted_trees/python/utils/ |
| D | losses.py | 28 def per_example_logistic_loss(labels, weights, predictions): argument
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): argument
68 labels = math_ops.to_int64(labels)
70 labels_shape = labels.get_shape()
72 labels = array_ops.expand_dims(labels, 1)
74 target_one_hot = array_ops.one_hot(indices=labels, depth=num_classes)
75 labels = math_ops.reduce_sum(
77 labels = math_ops.to_float(labels)
[all …]
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| /external/tensorflow/tensorflow/contrib/kernel_methods/python/ |
| D | losses_test.py | 37 labels = constant_op.constant([0, 1])
39 _ = losses.sparse_multiclass_hinge_loss(labels, logits)
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)
62 labels = constant_op.constant([1, 0], dtype=dtypes.float32)
64 _ = losses.sparse_multiclass_hinge_loss(labels, logits)
70 labels = constant_op.constant([1, 0])
72 _ = losses.sparse_multiclass_hinge_loss(labels, logits, weights=None)
[all …]
|
| /external/tensorflow/tensorflow/python/estimator/canned/ |
| D | head.py | 149 def create_loss(self, features, mode, logits, labels): argument
177 self, features, mode, logits, labels=None, train_op_fn=None, argument
204 labels, logits, expected_labels_dimension):
223 if labels is None:
229 with ops.name_scope(None, 'labels', (labels, logits)) as scope:
230 labels = sparse_tensor.convert_to_tensor_or_sparse_tensor(labels)
231 if isinstance(labels, sparse_tensor.SparseTensor):
243 if (labels.shape.ndims is not None and logits.shape.ndims is not None and
244 labels.shape.ndims == logits.shape.ndims - 1):
245 labels = array_ops.expand_dims(labels, -1)
[all …]
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| /external/tensorflow/tensorflow/contrib/losses/python/losses/ |
| D | loss_ops.py | 264 def absolute_difference(predictions, labels=None, weights=1.0, scope=None): argument
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))
345 labels=multi_class_labels, logits=logits, name="xentropy")
397 labels=onehot_labels, logits=logits, name="xentropy")
404 def sparse_softmax_cross_entropy(logits, labels, weights=1.0, scope=None): argument
428 [logits, labels, weights]) as scope:
429 labels = array_ops.reshape(labels, shape=[array_ops.shape(labels)[0]])
[all …]
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| D | loss_ops_test.py | 119 labels = constant_op.constant([[1, 0, 0],
124 loss_ops.softmax_cross_entropy(logits, labels, weights=None)
131 labels = constant_op.constant([[1, 0, 0],
134 loss = loss_ops.softmax_cross_entropy(logits, labels)
142 labels = constant_op.constant([[0, 0, 1],
147 loss = loss_ops.softmax_cross_entropy(logits, labels)
155 labels = constant_op.constant([[0, 0, 1],
160 loss = loss_ops.softmax_cross_entropy(logits, labels, weights)
167 labels = constant_op.constant([[0, 0, 1],
172 loss = loss_ops.softmax_cross_entropy(logits, labels,
[all …]
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| /external/tensorflow/tensorflow/python/kernel_tests/ |
| D | metrics_test.py | 54 def _binary_2d_label_to_2d_sparse_value(labels): argument
70 for row in labels:
82 shape = [len(labels), len(labels[0])]
88 def _binary_2d_label_to_1d_sparse_value(labels): argument
107 for row in labels:
119 if indices != [[i] for i in range(len(labels))]:
121 shape = [len(labels)]
127 def _binary_3d_label_to_sparse_value(labels): argument
141 for d0, labels_d0 in enumerate(labels):
151 shape = [len(labels), len(labels[0]), len(labels[0][0])]
[all …]
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| D | sparse_xent_op_test.py | 48 def _npXent(self, features, labels): argument
50 labels = np.reshape(labels, [-1])
59 labels_mat[np.arange(batch_size), labels] = 1.0
86 labels = [4, 3, 0, -1]
91 features, labels))
104 features, labels))
113 labels = [3, 0]
133 np_loss, np_backprop = self._npXent(np.array(features), np.array(labels))
146 labels=[[0, 2]], logits=[[0., 1.], [2., 3.], [2., 3.]])
152 labels=constant_op.constant(0), logits=constant_op.constant(1.0))
[all …]
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| D | losses_test.py | 112 labels = constant_op.constant([[1, 0, 0], [0, 1, 0], [0, 0, 1]])
115 losses.softmax_cross_entropy(labels, logits, weights=None)
121 labels = constant_op.constant([[1, 0, 0], [0, 1, 0], [0, 0, 1]])
122 loss = losses.softmax_cross_entropy(labels, logits)
129 labels = constant_op.constant([[0, 0, 1], [1, 0, 0], [0, 1, 0]])
132 loss = losses.softmax_cross_entropy(labels, logits)
139 labels = constant_op.constant([[0, 0, 1], [1, 0, 0], [0, 1, 0]])
142 loss = losses.softmax_cross_entropy(labels, logits, weights)
148 labels = constant_op.constant([[0, 0, 1], [1, 0, 0], [0, 1, 0]])
151 loss = losses.softmax_cross_entropy(labels, logits,
[all …]
|
| /external/tensorflow/tensorflow/contrib/learn/python/learn/ |
| D | metric_spec_test.py | 35 def _fn0(predictions, labels, weights=None): argument
37 self.assertEqual("l1_value", labels)
93 def _fn(labels): argument
94 self.assertEqual(labels_, labels)
106 def _fn(labels, **kwargs): argument
107 self.assertEqual(labels_, labels)
120 def _fn(labels, predictions_by_another_name): argument
122 self.assertEqual(labels_, labels)
135 def _fn(predictions_by_another_name, labels): argument
137 self.assertEqual(labels_, labels)
[all …]
|
| /external/python/cpython3/Lib/encodings/ |
| 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 and len(labels[-1]) == 0:
208 del labels[-1]
[all …]
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| /external/tensorflow/tensorflow/contrib/metrics/python/metrics/ |
| 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 = array_ops.placeholder(dtypes.string, shape=[None])
[all …]
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| /external/tensorflow/tensorflow/contrib/estimator/python/estimator/ |
| D | head_test.py | 84 def _sigmoid_cross_entropy(labels, logits): argument
88 -labels * np.log(sigmoid_logits)
89 -(1 - labels) * np.log(1 - sigmoid_logits))
156 def _loss_fn(labels): argument
157 del labels # unused
165 def _loss_fn(labels, logits, features): argument
166 del labels, logits, features # Unused
170 def _loss_fn(labels, logits, name=None): argument
171 del labels, logits, name # Unused
273 labels = np.array([[1, 0], [1, 1]], dtype=np.int64)
[all …]
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| /external/python/cpython2/Lib/encodings/ |
| D | 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]
192 for label in labels:
206 labels = dots.split(input)
[all …]
|
| /external/toolchain-utils/crosperf/ |
| D | machine_image_manager_unittest.py | 61 labels = []
70 labels.append(l)
71 return labels, duts
79 labels, duts = self.create_labels_and_duts_from_pattern(inp)
80 mim = MachineImageManager(labels, duts)
86 labels = [MockLabel('l1'), MockLabel('l2'), MockLabel('l3')]
88 mim = MachineImageManager(labels, [dut])
93 labels = [MockLabel('l1')]
95 mim = MachineImageManager(labels, duts)
100 labels = [
[all …]
|
| /external/minijail/ |
| D | bpf.c | 204 int bpf_resolve_jumps(struct bpf_labels *labels, struct sock_filter *filter, in bpf_resolve_jumps() argument
224 if (instr->k >= labels->count) { in bpf_resolve_jumps()
228 if (labels->labels[instr->k].location == 0xffffffff) { in bpf_resolve_jumps()
230 labels->labels[instr->k].label); in bpf_resolve_jumps()
234 labels->labels[instr->k].location - (offset + 1); in bpf_resolve_jumps()
239 if (labels->labels[instr->k].location != 0xffffffff) { in bpf_resolve_jumps()
241 labels->labels[instr->k].label); in bpf_resolve_jumps()
244 labels->labels[instr->k].location = offset; in bpf_resolve_jumps()
255 int bpf_label_id(struct bpf_labels *labels, const char *label) in bpf_label_id() argument
257 struct __bpf_label *begin = labels->labels, *end; in bpf_label_id()
[all …]
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| /external/tensorflow/tensorflow/contrib/losses/python/metric_learning/ |
| D | metric_loss_ops.py | 88 def contrastive_loss(labels, embeddings_anchor, embeddings_positive, argument
117 math_ops.to_float(labels) * math_ops.square(distances) +
118 (1. - math_ops.to_float(labels)) *
161 def triplet_semihard_loss(labels, embeddings, margin=1.0): argument
182 lshape = array_ops.shape(labels)
184 labels = array_ops.reshape(labels, [lshape[0], 1])
189 adjacency = math_ops.equal(labels, array_ops.transpose(labels))
193 batch_size = array_ops.size(labels)
245 def npairs_loss(labels, embeddings_anchor, embeddings_positive, argument
287 lshape = array_ops.shape(labels)
[all …]
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| /external/tensorflow/tensorflow/contrib/training/python/training/ |
| D | sampling_ops.py | 135 labels, argument
192 with ops.name_scope(name, 'stratified_sample', list(tensors) + [labels]):
194 labels = ops.convert_to_tensor(labels)
199 labels = array_ops.expand_dims(labels, 0)
207 labels, target_probs.get_shape().num_elements())
212 tensor_list, labels, [init_probs, target_probs] = _verify_input(
213 tensor_list, labels, [init_probs, target_probs])
241 tensor_list + [labels],
260 def _estimate_data_distribution(labels, num_classes, smoothing_constant=10): argument
277 labels, num_classes, dtype=dtypes.int64), 0))
[all …]
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| /external/annotation-tools/asmx/src/org/objectweb/asm/tree/ |
| D | LookupSwitchInsnNode.java | 61 public List labels; field in LookupSwitchInsnNode
74 final Label[] labels) in LookupSwitchInsnNode() argument
79 this.labels = new ArrayList(labels == null ? 0 : labels.length); in LookupSwitchInsnNode()
85 if (labels != null) { in LookupSwitchInsnNode()
86 this.labels.addAll(Arrays.asList(labels)); in LookupSwitchInsnNode()
95 Label[] labels = new Label[this.labels.size()]; in accept() local
96 this.labels.toArray(labels); in accept()
97 mv.visitLookupSwitchInsn(dflt, keys, labels); in accept()
|