| /external/tensorflow/tensorflow/contrib/metrics/python/metrics/ |
| classification.py | 32 def accuracy(predictions, labels, weights=None, name=None):
33 """Computes the percentage of times that predictions matches labels.
37 matches 'labels'.
38 labels: the ground truth values, a `Tensor` of any shape and
50 if not (labels.dtype.is_integer or
51 labels.dtype in (dtypes.bool, dtypes.string)):
53 'Labels should have bool, integer, or string dtype, not %r' %
54 labels.dtype)
55 if not labels.dtype.is_compatible_with(predictions.dtype):
56 raise ValueError('Dtypes of predictions and labels should match.
[all...] |
| /external/minijail/ |
| bpf.c | 294 int bpf_resolve_jumps(struct bpf_labels *labels, struct sock_filter *filter,
314 if (instr->k >= labels->count) {
318 if (labels->labels[instr->k].location == 0xffffffff) {
320 labels->labels[instr->k].label);
324 labels->labels[instr->k].location - (offset + 1);
329 if (labels->labels[instr->k].location != 0xffffffff)
[all...] |
| /external/tensorflow/tensorflow/contrib/learn/python/learn/ops/ |
| losses_ops.py | 37 def mean_squared_error_regressor(tensor_in, labels, weights, biases, name=None):
40 [tensor_in, labels]):
42 if len(labels.get_shape()) == 1 and len(predictions.get_shape()) == 2:
44 return predictions, losses.mean_squared_error(labels, predictions)
50 labels,
60 This function requires labels to be passed in one-hot encoding.
64 labels: Tensor, [batch_size, n_classes], one-hot labels of the output
76 with ops.name_scope(name, 'softmax_classifier', [tensor_in, labels]):
80 return nn.softmax(logits), losses.softmax_cross_entropy(labels, logits
[all...] |
| /external/tensorflow/tensorflow/contrib/kernel_methods/python/ |
| 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: {}. Acceptable dtypes: int32 and int64'
[all...] |
| /external/tensorflow/tensorflow/contrib/learn/python/learn/estimators/ |
| logistic_regressor.py | 43 `(features, labels, mode) -> (predictions, loss, train_op)`.
50 def _model_fn(features, labels, mode, params):
54 predictions, loss, train_op = model_fn(features, labels, mode)
57 labels=labels,
109 `(features, labels, mode) -> (predictions, loss, train_op)`.
118 labels which are the output of `input_fn` and
119 returns features and labels which will be fed
133 def _make_logistic_eval_metric_ops(labels, predictions, thresholds):
137 labels: The labels `Tensor`, or a dict with only one `Tensor` keyed by name
[all...] |
| head.py | 80 def _my_dnn_model_fn(features, labels, mode, params, config=None):
94 labels=labels,
108 labels=labels,
122 labels=labels,
152 labels=None,
166 labels: Labels `Tensor`, or `dict` of same
[all...] |
| /external/tensorflow/tensorflow/contrib/learn/python/learn/learn_io/ |
| io_test.py | 43 labels = pd.DataFrame(iris.target)
47 classifier.fit(data, labels, steps=100)
48 score = accuracy_score(labels[0], list(classifier.predict_classes(data)))
59 labels = pd.Series(iris.target)
63 classifier.fit(data, labels, steps=100)
64 score = accuracy_score(labels, list(classifier.predict_classes(data)))
94 labels = ddf["a"]
95 extracted_labels = extract_dask_labels(labels)
99 # labels should only have one column
113 labels = pd.DataFrame(iris.target
[all...] |
| pandas_io.py | 124 def extract_pandas_labels(labels):
125 """Extract data from pandas.DataFrame for labels.
128 labels: `pandas.DataFrame` or `pandas.Series` containing one column of
129 labels to be extracted.
132 A numpy `ndarray` of labels from the DataFrame.
138 if isinstance(labels,
140 if len(labels.columns) > 1:
141 raise ValueError('Only one column for labels is allowed.')
143 bad_data = [column for column in labels
144 if labels[column].dtype.name not in PANDAS_DTYPES
[all...] |
| /external/autotest/server/cros/network/ |
| rf_switch_controller.py | 41 self.rf_switch_labels = rf_switch_host.labels
42 # RF Switches are named as rf_switch_1, rf_switch_2 using labels.
44 labels = [
48 self.hosts = afe.get_hosts(label=labels[0])
52 if RF_SWITCH_APS in host.labels:
54 elif RF_SWITCH_CLIENT in host.labels:
|
| rf_switch_client_box.py | 34 for label in client_box_host.labels:
44 'Labels not found:: %s' % msg)
57 def get_devices_using_labels(self, labels):
58 """Returns all devices with the passed labels in the Client Box.
60 @params labels: List of host labels.
67 labels.append(self.rf_switch_label)
71 lambda x: x in host.labels, labels))
72 if len(labels) == len(labels_list)
[all...] |
| /external/eigen/bench/ |
| dense_solvers.cpp | 11 std::vector<std::string> labels; variable
89 labels.push_back("LLT");
90 labels.push_back("LDLT");
91 labels.push_back("PartialPivLU");
92 labels.push_back("FullPivLU");
93 labels.push_back("HouseholderQR");
94 labels.push_back("ColPivHouseholderQR");
95 labels.push_back("CompleteOrthogonalDecomposition");
96 labels.push_back("FullPivHouseholderQR");
97 labels.push_back("JacobiSVD")
135 cout.width(32); cout << labels[i]; cout << " "; local
157 cout << "><td>" << labels[i] << "<\/td>"; local
[all...] |
| /external/snakeyaml/src/test/java/org/yaml/snakeyaml/issues/issue73/ |
| Blog.java | 27 private TreeSet<String> labels = new TreeSet<String>(); field in class:Blog
58 return labels;
61 public void setLabels(TreeSet<String> labels) {
62 this.labels = labels;
|
| /external/tensorflow/tensorflow/contrib/boosted_trees/estimator_batch/ |
| custom_loss_head.py | 49 metrics_fn: a function that takes predictions dict, labels and weights and
53 def loss_wrapper(labels, logits, weight_tensor):
56 shape=[array_ops.shape(labels)[0], 1], dtype=dtypes.float32)
57 weighted_loss, _ = loss_fn(labels, weight_tensor, logits)
71 def _metrics(self, eval_loss, predictions, labels, weights):
73 return self._metrics_fn(predictions, labels, weights)
|
| /external/tensorflow/tensorflow/contrib/metrics/python/ops/ |
| metric_ops_test.py | 52 def _binary_2d_label_to_sparse_value(labels):
55 Only 1 values in `labels` are included in result.
58 labels: Dense 2D binary indicator tensor.
62 `labels`.
67 for row in labels:
79 shape = [len(labels), len(labels[0])]
85 def _binary_2d_label_to_sparse(labels):
88 Only 1 values in `labels` are included in result.
91 labels: Dense 2D binary indicator tensor
[all...] |
| metric_ops.py | 49 'order of the labels and predictions arguments has been switched.')
51 labels,
63 labels: The ground truth values, a `Tensor` whose dimensions must match
66 `labels`, and must be broadcastable to `labels` (i.e., all dimensions
67 must be either `1`, or the same as the corresponding `labels`
80 ValueError: If `predictions` and `labels` have mismatched shapes, or if
87 labels=labels,
95 'order of the labels and predictions arguments has been switched.'
[all...] |
| /external/tensorflow/tensorflow/contrib/learn/python/learn/ |
| 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/training/python/training/ |
| sampling_ops.py | 72 ValueError: enqueue_many is True and labels doesn't have a batch
73 dimension, or if enqueue_many is False and labels isn't a scalar.
74 ValueError: enqueue_many is True, and batch dimension on data and labels
136 labels,
153 labels: Tensor for label of data. Label is a single integer or a batch,
169 ValueError: `enqueue_many` is True and labels doesn't have a batch
170 dimension, or if `enqueue_many` is False and labels isn't a scalar.
171 ValueError: `enqueue_many` is True, and batch dimension on data and labels
176 TFAssertion: if labels aren't integers in [0, num classes).
187 [data_batch], labels = tf.contrib.training.stratified_sample
[all...] |
| /external/tensorflow/tensorflow/contrib/losses/python/losses/ |
| loss_ops_test.py | 117 labels = constant_op.constant([[1, 0, 0],
122 loss_ops.softmax_cross_entropy(logits, labels, weights=None)
129 labels = constant_op.constant([[1, 0, 0],
132 loss = loss_ops.softmax_cross_entropy(logits, labels)
140 labels = constant_op.constant([[0, 0, 1],
145 loss = loss_ops.softmax_cross_entropy(logits, labels)
153 labels = constant_op.constant([[0, 0, 1],
158 loss = loss_ops.softmax_cross_entropy(logits, labels, weights)
165 labels = constant_op.constant([[0, 0, 1],
170 loss = loss_ops.softmax_cross_entropy(logits, labels,
[all...] |
| /external/python/google-api-python-client/samples/searchforshopping/ |
| histograms.py | 43 labels = []
47 labels.append(bucket['value'].rjust(20))
52 for label, value in zip(labels, values):
|
| /external/tensorflow/tensorflow/lite/java/src/test/java/org/tensorflow/lite/ |
| InterpreterMobileNetTest.java | 45 float[][] labels = new float[1][1001]; local
48 interpreter.run(img, labels);
53 assertThat(labels[0])
|
| /external/tensorflow/tensorflow/python/kernel_tests/ |
| sparse_xent_op_test.py | 49 def _npXent(self, features, labels):
51 labels = np.reshape(labels, [-1])
60 labels_mat[np.arange(batch_size), labels] = 1.0
88 labels = [4, 3, 0, -1]
94 features, labels))
107 gen_nn_ops.sparse_softmax_cross_entropy_with_logits(features, labels))
116 labels = [3, 0]
136 np_loss, np_backprop = self._npXent(np.array(features), np.array(labels))
149 labels=[[0, 2]], logits=[[0., 1.], [2., 3.], [2., 3.]]
[all...] |
| ctc_loss_op_test.py | 62 def _ctc_loss_v2(labels, inputs, sequence_length,
72 labels=labels,
85 labels,
95 inputs=inputs_t, labels=labels, sequence_length=seq_lens)
226 labels = SimpleSparseTensorFrom([targets_0, targets_1])
243 self._testCTCLoss(inputs, seq_lens, labels, loss_truth, grad_truth)
254 labels = SimpleSparseTensorFrom([[0, 1], [1, 0]])
264 inputs=inputs_t, labels=labels, sequence_length=seq_lens
[all...] |
| /external/boringssl/src/util/fipstools/delocate/testdata/x86_64-LabelRewrite/ |
| in2.s | 1 # References to local labels are rewrittenn in subsequent files.
|
| /external/tensorflow/tensorflow/examples/speech_commands/ |
| label_wav.py | 17 The model, labels and .wav file specified in the arguments will be loaded, and
26 --labels=/tmp/speech_commands_train/conv_labels.txt \
55 """Read in labels, one label per line."""
59 def run_graph(wav_data, labels, input_layer_name, output_layer_name,
70 # Sort to show labels in order of confidence
73 human_string = labels[node_id]
80 def label_wav(wav, labels, graph, input_name, output_name, how_many_labels):
81 """Loads the model and labels, and runs the inference to print predictions."""
85 if not labels or not tf.gfile.Exists(labels)
[all...] |
| /external/toolchain-utils/crosperf/ |
| 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...] |
