| /external/libtextclassifier/lang_id/ |
| lang-id_jni.cc | 48 const std::vector<std::pair<std::string, float>>& predictions = local
49 lang_id_result.predictions;
54 env->NewObjectArray(predictions.size(), result_class.get(), nullptr);
55 for (int i = 0; i < predictions.size(); i++) {
58 env->NewStringUTF(predictions[i].first.c_str()),
59 static_cast<jfloat>(predictions[i].second)));
|
| lang-id.h | 48 std::vector<std::pair<string, float>> predictions; member in struct:libtextclassifier3::mobile::lang_id::LangIdResult
112 // perform predictions. For more info, see doc for LangId
|
| /external/tensorflow/tensorflow/core/kernels/ |
| in_topk_op.cc | 56 errors::InvalidArgument("predictions must be 2-dimensional"));
60 errors::InvalidArgument("First dimension of predictions ",
64 const auto& predictions = predictions_in.matrix<T>(); variable
74 const auto num_classes = predictions.dimension(1);
79 T target_prediction = predictions(b, target);
84 T pred = predictions(b, i);
103 .HostMemory("predictions")
110 .HostMemory("predictions")
118 .HostMemory("predictions")
126 .HostMemory("predictions")
[all...] |
| /cts/tests/tests/gesture/src/android/gesture/cts/ |
| GestureStorageTester.java | 73 ArrayList<Prediction> predictions = mFixture.recognize(newLineGesture); local
74 assertEquals(1, predictions.size());
75 assertEquals(TEST_GESTURE_NAME, predictions.get(0).name);
|
| /external/tensorflow/tensorflow/contrib/linear_optimizer/python/ops/ |
| sdca_ops.py | 77 predictions = lr.predictions(examples)
307 """Returns predictions of the form w*x."""
331 def predictions(self, examples): member in class:SdcaModel
332 """Add operations to compute predictions by the model.
335 If poisson_loss is being used, predictions are exponentiated.
336 Otherwise, (raw) linear predictions (w*x) are returned.
339 examples: Examples to compute predictions on.
342 An Operation that computes the predictions for examples.
353 # Convert logits to probability for logistic loss predictions
[all...] |
| /external/tensorflow/tensorflow/lite/models/smartreply/ |
| predictor_test.cc | 71 std::vector<PredictorResponse> predictions; local
73 GetSegmentPredictions({"Welcome"}, *model_, /*config=*/{{}}, &predictions);
74 EXPECT_GT(predictions.size(), 0);
77 for (const auto &item : predictions) {
85 &predictions,
90 std::vector<PredictorResponse> predictions; local
93 &predictions);
94 EXPECT_GT(predictions.size(), 0);
97 for (const auto &item : predictions) {
104 EXPECT_THAT(&predictions, IncludeAnyResponesIn(std::unordered_set<string>
109 std::vector<PredictorResponse> predictions; local
137 std::vector<PredictorResponse> predictions; local
[all...] |
| /external/tensorflow/tensorflow/python/saved_model/model_utils/ |
| export_output.py | 216 predictions.
240 PREDICTIONS_NAME = 'predictions'
250 def __init__(self, loss=None, predictions=None, metrics=None):
255 predictions: dict of Tensors or single Tensor representing model
256 predictions.
272 if predictions is not None:
274 predictions, self.PREDICTIONS_NAME)
368 def predictions(self): member in class:_SupervisedOutput
383 receiver_tensors, self.loss, self.predictions, self.metrics)
390 training output by type-checking and wrapping loss, predictions, and metric
[all...] |
| /external/tensorflow/tensorflow/python/feature_column/ |
| feature_column_test.py | 360 predictions = fc.linear_model(features, [price])
366 self.assertAllClose([[0.], [0.]], self.evaluate(predictions))
368 self.assertAllClose([[10.], [50.]], self.evaluate(predictions))
375 predictions = get_keras_linear_model_predictions(features, [price])
381 self.assertAllClose([[0.], [0.]], self.evaluate(predictions))
383 self.assertAllClose([[10.], [50.]], self.evaluate(predictions))
560 predictions = fc.linear_model(features, [bucketized_price])
569 self.evaluate(predictions))
577 self.evaluate(predictions))
580 self.evaluate(predictions))
1473 predictions = fc.linear_model(features, [dense_and_sparse_column]) variable in class:LinearModelTest.test_dense_and_sparse_column._DenseAndSparseColumn
2158 predictions = get_keras_linear_model_predictions( variable in class:_LinearModelTest.test_dense_and_sparse_column._DenseAndSparseColumn
[all...] |
| feature_column_v2_test.py | 424 predictions = model(features)
429 self.assertAllClose([[0.], [0.]], self.evaluate(predictions))
431 self.assertAllClose([[10.], [50.]], self.evaluate(predictions))
437 predictions = fc_old.linear_model(features, [price])
443 self.assertAllClose([[0.], [0.]], self.evaluate(predictions))
445 self.assertAllClose([[10.], [50.]], self.evaluate(predictions))
670 predictions = model(features)
678 self.evaluate(predictions))
686 self.evaluate(predictions))
689 self.evaluate(predictions))
1745 predictions = model(features) variable in class:LinearModelTest.test_dense_and_sparse_column._DenseAndSparseColumn
2475 predictions = fc_old.linear_model(features, [dense_and_sparse_column]) variable in class:OldLinearModelTest.test_dense_and_sparse_column._DenseAndSparseColumn
[all...] |
