| /external/tensorflow/tensorflow/contrib/learn/python/learn/ops/ |
| seq2seq_ops_test.py | 38 labels = [array_ops.placeholder(dtypes.float32, [2, 2]) for _ in range(3)]
42 predictions, loss = ops.sequence_classifier(decoding, labels,
50 labels[0].name: [[1, 0], [0, 1]],
51 labels[1].name: [[1, 0], [0, 1]],
52 labels[2].name: [[1, 0], [0, 1]],
|
| /external/tensorflow/tensorflow/examples/learn/ |
| iris_custom_decay_dnn.py | 30 def my_model(features, labels, mode):
50 loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits)
65 labels=labels, predictions=predicted_classes)
|
| iris_custom_model.py | 29 def my_model(features, labels, mode):
51 loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits)
62 labels=labels, predictions=predicted_classes)
|
| /external/tensorflow/tensorflow/lite/tools/accuracy/ilsvrc/ |
| README.md | 5 The binary takes the path to validation images and labels as inputs. It outputs the accuracy after running the TFLite model on the validation sets.
7 To run the binary download the ILSVRC 2012 devkit [see instructions](#downloading-ilsvrc) and run the [`generate_validation_ground_truth` script](#ground-truth-label-generation) to generate the ground truth labels.
19 Path to ground truth labels file. This file should contain the same number
20 of labels as the number images in the ground truth directory. The labels are
23 more information about how to generate labels for images.
26 Path to the file containing labels, that is used to interpret the output of
57 The accuracy binary however expects the ground truth labels to contain the actual name of
59 category labels. The `validation_ground_truth.txt` can be converted by the following steps:
114 (6) Push the generated validation ground labels to device
[all...] |
| /external/tensorflow/tensorflow/python/keras/datasets/ |
| imdb.py | 101 labels = np.concatenate([labels_train, labels_test])
109 xs, labels = _remove_long_seq(maxlen, xs, labels)
128 x_train, y_train = np.array(xs[:idx]), np.array(labels[:idx])
129 x_test, y_test = np.array(xs[idx:]), np.array(labels[idx:])
|
| /external/tensorflow/tensorflow/contrib/learn/python/learn/estimators/ |
| head_test.py | 107 def _log_poisson_loss(self, logits, labels):
109 z = np.array([f[0] for f in labels])
117 labels = ((0.,), (1.,), (1.,))
122 labels=labels,
129 loss = self._log_poisson_loss(logits, labels)
148 labels=((0.,), (1.,), (1.,)),
166 labels=((0.,), (1.,), (1.,)),
182 labels=((0.,), (1.,), (1.,)),
192 labels=((0.,), (1.,), (1.,))
[all...] |
| state_saving_rnn_estimator.py | 93 activations, labels, sequence_length, target_column, features):
99 labels: A `Tensor` with length `[batch_size, padded_length]`.
111 activations, labels, sequence_length)
132 def _prepare_features_for_sqss(features, labels, mode,
140 - Adds the labels tensor to the sequence features dict.
145 labels: An iterable of `Tensor`.
185 # Add labels to the resulting sequence features dict.
187 sequence_features[rnn_common.RNNKeys.LABELS_KEY] = labels
242 labels,
257 labels: An iterable of `Tensor`, the `labels` argument of
[all...] |
| /external/autotest/server/hosts/ |
| afe_store_unittest.py | 23 def _create_mock_host(self, labels, attributes):
24 """Create a mock frontend.Host with the given labels and attributes.
26 @param labels: The labels to set on the host.
31 mock_host.labels = labels
41 self.assertListEqual(info.labels, ['label1'])
58 self.assertListEqual(info.labels, ['label1'])
63 """Tests that labels are updated correctly on commit."""
71 labels=['label1'])
[all...] |
| /external/cldr/tools/java/org/unicode/cldr/icu/ |
| IcuTextWriter.java | 118 String[] labels = path.split("/", -1); // Don't discard trailing slashes. local
119 int common = getCommon(lastLabels, labels);
128 for (int i = common + 1; i < labels.length; ++i) {
131 String label = labels[i];
136 if (i != labels.length - 1) {
142 wasSingular = appendValues(name, path, values, labels.length, out);
147 lastLabels = labels;
333 * find the initial labels (from a path) that are identical.
338 private static int getCommon(String[] lastLabels, String[] labels) {
339 int min = Math.min(lastLabels.length, labels.length)
[all...] |
| /external/tensorflow/tensorflow/compiler/tf2xla/kernels/ |
| softmax_op.cc | 93 xla::XlaOp logits, xla::XlaOp labels) {
123 // sum(-labels *
128 auto mul = xla::Mul(xla::Neg(labels), sub);
134 // backprop: prob - labels, where
138 xla::Sub(xla::Div(exp_shifted_logits, sum_exp, {kBatchDim}), labels);
152 "logits and labels must be same size: logits_size=",
158 // check that "labels" is a matrix too.
163 auto labels = ctx->Input(1); variable
167 CrossEntropyWithLogits(ctx, type, xla_type, logits, labels);
187 errors::InvalidArgument("labels must be 1-D, but got shape "
210 xla::XlaOp labels; variable
[all...] |
| /external/tensorflow/tensorflow/contrib/learn/python/learn/datasets/ |
| mnist.py | 77 """Convert class labels from scalars to one-hot vectors."""
87 """Extract the labels into a 1D uint8 numpy array [index].
95 labels: a 1D uint8 numpy array.
108 labels = numpy.frombuffer(buf, dtype=numpy.uint8)
110 return dense_to_one_hot(labels, num_classes)
111 return labels
126 labels,
148 assert images.shape[0] == labels.shape[0], (
149 'images.shape: %s labels.shape: %s' % (images.shape, labels.shape)
172 def labels(self): member in class:DataSet
[all...] |
| /external/tensorflow/tensorflow/contrib/nn/python/ops/ |
| sampling_ops_test.py | 136 name='labels',
156 labels=self._labels(),
175 labels=self._labels(),
193 labels=self._labels(),
209 labels=self._labels(),
222 labels=self._labels(),
276 labels = constant_op.constant([[0], [1]], dtype=dtypes.int64)
281 labels=labels,
294 labels=labels
[all...] |
| /external/antlr/runtime/CSharp2/Sources/Antlr3.Runtime/Antlr.Runtime.Tree/ |
| TreeWizard.cs | 70 void Visit(object t, object parent, int childIndex, IDictionary<string, object> labels);
74 public virtual void Visit(object t, object parent, int childIndex, IDictionary<string, object> labels) {
259 public void Visit(object t, object parent, int childIndex, IDictionary<string, object> labels) {
302 * version of the general visit(t, pattern) method. The labels arg
336 public VisitTreeWizardContextVisitor(TreeWizard outer, IContextVisitor visitor, IDictionary<string, object> labels, TreePattern tpattern) {
339 _labels = labels;
371 IDictionary<string, object> labels = new Dictionary<string, object>(); // reused for each _parse
373 Visit(t, rootTokenType, new VisitTreeWizardContextVisitor(this, visitor, labels, tpattern));
377 * Given a pattern like (ASSIGN %lhs:ID %rhs:.) with optional labels
379 * return true if the pattern matches and fill the labels Map wit
[all...] |
| /external/antlr/runtime/CSharp3/Sources/Antlr3.Runtime/Tree/ |
| TreeWizard.cs | 73 void Visit( object t, object parent, int childIndex, IDictionary<string, object> labels );
78 public virtual void Visit( object t, object parent, int childIndex, IDictionary<string, object> labels )
296 public void Visit( object t, object parent, int childIndex, IDictionary<string, object> labels )
346 * version of the general visit(t, pattern) method. The labels arg
387 public VisitTreeWizardContextVisitor( TreeWizard outer, IContextVisitor visitor, IDictionary<string, object> labels, TreePattern tpattern )
391 _labels = labels;
427 IDictionary<string, object> labels = new Dictionary<string, object>(); // reused for each _parse
429 Visit( t, rootTokenType, new VisitTreeWizardContextVisitor( this, visitor, labels, tpattern ) );
433 * Given a pattern like (ASSIGN %lhs:ID %rhs:.) with optional labels
435 * return true if the pattern matches and fill the labels Map wit
[all...] |
| /external/tensorflow/tensorflow/python/ops/ |
| nn_impl.py | 110 labels=None,
120 For brevity, let `x = logits`, `z = labels`. The logistic loss is
140 `logits` and `labels` must have the same type and shape.
144 labels: A `Tensor` of the same type and shape as `logits`.
153 ValueError: If `logits` and `labels` do not have the same shape.
157 labels, logits)
160 with ops.name_scope(name, "logistic_loss", [logits, labels]) as name:
162 labels = ops.convert_to_tensor(labels, name="labels")
[all...] |
| /external/tensorflow/tensorflow/contrib/eager/python/examples/resnet50/ |
| resnet50_graph_test.py | 44 labels = np.random.randint(
47 one_hot[np.arange(batch_size), labels] = 1.
73 labels = tf.placeholder(tf.float32, [None, 1000], name='labels')
84 logits=logits, onehot_labels=labels)
100 feed_dict={images: np_images, labels: np_labels})
145 images, labels = tf.compat.v1.data.make_one_shot_iterator(
151 logits=logits, onehot_labels=labels)
|
| /external/tensorflow/tensorflow/lite/examples/android/app/src/main/java/org/tensorflow/demo/ |
| TFLiteImageClassifier.java | 59 private Vector<String> labels = new Vector<String>(); field in class:TFLiteImageClassifier
91 Log.i(TAG, "Reading labels from: " + labelFilename);
97 c.labels.add(line);
116 Log.i(TAG, "Read " + c.labels.size() + " labels");
121 c.labelProb = new byte[1][c.labels.size()];
180 for (int i = 0; i < labels.size(); ++i) {
184 labels.size() > i ? labels.get(i) : "unknown",
|
| /external/tensorflow/tensorflow/contrib/model_pruning/examples/cifar10/ |
| cifar10_pruning.py | 19 # Compute input images and labels for training. If you would like to run
21 inputs, labels = distorted_inputs()
26 # Compute the total loss of the prediction with respect to the labels.
27 loss = loss(predictions, labels)
136 labels: Labels. 1D tensor of [batch_size] size.
144 images, labels = cifar10_input.distorted_inputs(
146 return images, labels
157 labels: Labels. 1D tensor of [batch_size] size
[all...] |
| /bionic/tools/bionicbb/ |
| bionicbb.py | 68 request_data['labels'] = {label: -1}
70 request_data['labels'] = {label: +1}
100 labels = gerrit.get_labels(change_id, patch_set)
101 if bb_email in labels['Verified']:
102 bb_review = labels['Verified'][bb_email]
112 request_data = {'labels': {'Verified': 0}}
|
| /external/antlr/runtime/Python/unittests/ |
| testtree.py | 920 labels = {}
924 labels)
926 node = labels.get("x")
939 labels = {}
943 labels)
945 node = labels.get("x")
[all...] |
| /external/antlr/runtime/Python3/unittests/ |
| testtree.py | 911 labels = {}
915 labels)
917 node = labels.get("x")
930 labels = {}
934 labels)
936 node = labels.get("x")
949 labels = {}
953 labels)
955 node = labels.get("x")
[all...] |
| /external/antlr/tool/src/main/java/org/antlr/tool/ |
| GrammarNonDeterminismMessage.java | 71 List<Label> labels = probe.getSampleNonDeterministicInputSequence(problemState); local
72 String input = probe.getInputSequenceDisplay(labels);
107 labels);
|
| /external/autotest/contrib/ |
| task_runner.sh | 16 # Invalidate (delete) the hosts/labels/shard instead of adding them.
60 labels="${2}"
71 $AT_DIR/cli/atest label delete $labels --web $AFE
74 $AT_DIR/cli/atest label add -m $hostnames $labels --web $AFE
|
| /external/autotest/server/site_tests/moblab_RunSuite/ |
| moblab_RunSuite.py | 60 labels = labellib.LabelsMapping(dut.labels)
61 board = labels['board']
|
| /external/tensorflow/tensorflow/contrib/boosted_trees/python/utils/ |
| losses_test.py | 59 # For positive labels, points <= 0.3 get max loss of e.
60 # For negative labels, these points have minimum loss of 1/e.
65 # loss value of 1/e. For negative labels, these points are wrongly
82 labels = np.array([[0.123], [224.2], [-3], [2], [.3]], dtype=np.float32)
88 loss_tensor, _ = losses.per_example_squared_loss(labels, weights,
93 np.square(labels[:5] - predictions[:5]), loss[:5], atol=1e-4)
|
