| /external/libtextclassifier/util/math/ |
| softmax.h | 24 // Computes probability of a softmax label. Parameter "scores" is the vector of
26 // scores.size()).
27 float ComputeSoftmaxProbability(const std::vector<float> &scores, int label);
30 // "scores" is the vector of softmax logits.
31 std::vector<float> ComputeSoftmax(const std::vector<float> &scores);
34 std::vector<float> ComputeSoftmax(const float *scores, int scores_size);
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| softmax.cc | 26 float ComputeSoftmaxProbability(const std::vector<float> &scores, int label) {
27 if ((label < 0) || (label >= scores.size())) {
29 << "[0, " << scores.size() << ")";
35 // exp(scores[label]) / sum_i exp(scores[i])
39 // 1 / (1 + sum_{i != label} exp(scores[i] - scores[label]))
42 const float label_score = scores[label];
44 for (int i = 0; i < scores.size(); ++i) {
46 const float delta_score = scores[i] - label_score
[all...] |
| /frameworks/base/core/java/android/os/ |
| IProcessInfoService.aidl | 34 void getProcessStatesAndOomScoresFromPids(in int[] pids, out int[] states, out int[] scores);
|
| /frameworks/base/core/java/android/service/autofill/ |
| AutofillFieldClassificationService.java | 40 * A service that calculates field classification scores.
49 * when calculating the scores).
80 public static final String EXTRA_SCORES = "scores";
87 final float[][] scores = onGetScores(algorithmName, algorithmArgs, actualValues, local
89 if (scores != null) {
90 data.putParcelable(EXTRA_SCORES, new Scores(scores));
114 * Calculates field classification scores in a batch.
123 * specified by the caller when calculating the scores).
164 * @param algorithm name of the algorithm to be used to calculate the scores. If invalid o
202 public final float[][] scores; field in class:AutofillFieldClassificationService.Scores
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| /external/tensorflow/tensorflow/contrib/metrics/python/ops/ |
| histogram_ops.py | 40 scores,
49 histograms of the scores associated with `True` and `False` labels. By
55 to finite number of bins. If scores are concentrated in a fewer bins,
62 scores: 1-D numeric `Tensor`, same shape as boolean_labels.
63 score_range: `Tensor` of shape `[2]`, same dtype as `scores`. The min/max
64 values of score that we expect. Scores outside range will be clipped.
69 check_shape: Boolean. If `True`, do a runtime shape check on the scores
81 name, 'auc_using_histogram', [boolean_labels, scores, score_range]):
82 scores, boolean_labels = tensor_util.remove_squeezable_dimensions(
83 scores, boolean_labels
[all...] |
| /frameworks/native/libs/binder/include/binder/ |
| ProcessInfoService.h | 41 /*out*/ int32_t* states, /*out*/ int32_t *scores);
66 * exists. OoM scores will also be written in the "scores" output array.
73 /*out*/ int32_t* states, /*out*/ int32_t *scores) {
75 length, /*in*/ pids, /*out*/ states, /*out*/ scores);
|
| IProcessInfoService.h | 39 /*out*/ int32_t* scores) = 0;
|
| /external/tensorflow/tensorflow/core/util/ctc/ |
| ctc_decoder.h | 54 // - scores(b, i) - b = 0 to batch_size; i = 0 to output.size()
57 std::vector<Output>* output, ScoreOutput* scores) = 0;
79 CTCDecoder::ScoreOutput* scores) override {
84 if (scores->rows() < batch_size_ || scores->cols() == 0) {
86 "scores needs to be of size at least (batch_size, 1).");
95 (*scores)(b, 0) = 0;
99 (*scores)(b, 0) += -row.maxCoeff(&max_class_ix);
|
| ctc_beam_search_test.cc | 158 // Prepare containers for output and scores.
164 Eigen::Map<Eigen::MatrixXf> scores(&score[0][0], batch_size, top_paths);
166 EXPECT_TRUE(decoder.Decode(seq_len, inputs, &outputs, &scores).ok());
177 dictionary_decoder.Decode(seq_len, inputs, &dict_outputs, &scores).ok());
208 // Prepare containers for output and scores.
214 Eigen::Map<Eigen::MatrixXf> scores(&score[0][0], batch_size, top_paths);
216 EXPECT_TRUE(decoder.Decode(seq_len, inputs, &outputs, &scores).ok());
217 // Make sure all scores are finite.
303 // Prepare containers for output and scores.
309 Eigen::Map<Eigen::MatrixXf> scores(&score[0][0], batch_size, top_paths)
[all...] |
| /external/mesa3d/src/gallium/state_trackers/wgl/ |
| stw_ext_pixelformat.c | 339 struct stw_pixelformat_score *scores,
374 scores[index].points = 0;
383 scores[index].points = 0;
385 scores[index].points -= (actual_value - expected_value) * ami->weight;
402 struct stw_pixelformat_score *scores; local
408 * have higher scores. Start with a high score and take out penalty
413 scores = (struct stw_pixelformat_score *) MALLOC( count * sizeof( struct stw_pixelformat_score ) );
414 if (scores == NULL)
417 scores[i].points = 0x7fffffff;
418 scores[i].index = i
[all...] |
| /external/tensorflow/tensorflow/core/kernels/ |
| non_max_suppression_op.h | 29 typename TTypes<float, 1>::ConstTensor scores,
|
| non_max_suppression_op.cc | 42 const Tensor& scores, int* num_boxes) {
51 // The shape of 'scores' is [num_boxes]
52 OP_REQUIRES(context, scores.dims() == 1,
53 errors::InvalidArgument("scores must be 1-D",
54 scores.shape().DebugString()));
55 OP_REQUIRES(context, scores.dim_size(0) == *num_boxes,
56 errors::InvalidArgument("scores has incompatible shape"));
96 const Tensor& scores, const Tensor& max_output_size,
102 ParseAndCheckBoxSizes(context, boxes, scores, &num_boxes);
111 std::copy_n(scores.flat<float>().data(), num_boxes, scores_data.begin())
159 const Tensor& scores = context->input(1); variable
185 const Tensor& scores = context->input(1); variable
[all...] |
| multinomial_op_gpu.cu.cc | 39 // scores: [B, S, C]; maxima: [B, S]; output: [B, S].
42 const int32 num_samples, const float* scores,
46 if (ldg(maxima + maxima_idx) == ldg(scores + index)) {
59 typename TTypes<float>::Flat scores,
89 // Calculates "scores = logits - log(-log(noises))"; B*C*S elements.
96 To32Bit(scores).device(d) =
101 To32Bit(maxima).device(d) = To32Bit(scores).reshape(bsc).maximum(kTwo);
110 num_samples, scores.data(), maxima.data(),
|
| /external/tensorflow/tensorflow/python/estimator/export/ |
| export_output.py | 59 Either classes or scores or both must be set.
66 If only scores is set, it is interpreted as providing a score for every class
69 If both classes and scores are set, they are interpreted as zipped, so each
74 def __init__(self, scores=None, classes=None):
78 scores: A float `Tensor` giving scores (sometimes but not always
82 but only if `scores` is set. Interpretation varies-- see class doc.
85 ValueError: if neither classes nor scores is set, or one of them is not a
88 if (scores is not None
89 and not (isinstance(scores, ops.Tensor
105 def scores(self): member in class:ClassificationOutput
[all...] |
| export_output_test.py | 52 scores = array_ops.placeholder(dtypes.string, 1, name="output-tensor-1")
54 export_output_lib.ClassificationOutput(scores=scores)
55 self.assertEqual('Classification scores must be a float32 Tensor; got '
62 self.assertEqual("At least one of scores and classes must be set.",
127 """Tests multiple output tensors that include classes and scores."""
135 scores = array_ops.placeholder(dtypes.float32, 1,
136 name="output-tensor-scores")
139 scores=scores, classes=classes
[all...] |
| /external/tensorflow/tensorflow/examples/speech_commands/ |
| label_wav.cc | 71 // Analyzes the output of the graph to retrieve the highest scores and
77 std::vector<std::pair<int, float>> scores; local
78 scores.reserve(unsorted_scores_flat.size());
80 scores.push_back(std::pair<int, float>({i, unsorted_scores_flat(i)}));
82 std::sort(scores.begin(), scores.end(),
87 scores.resize(how_many_labels);
90 for (int i = 0; i < scores.size(); ++i) {
91 sorted_indices.flat<int>()(i) = scores[i].first;
92 sorted_scores.flat<float>()(i) = scores[i].second
164 Tensor scores; local
[all...] |
| /external/tensorflow/tensorflow/contrib/learn/python/learn/estimators/ |
| debug_test.py | 226 scores = classifier.evaluate(input_fn=input_fn, steps=1)
227 self._assertInRange(0.0, 1.0, scores['accuracy'])
228 self.assertIn('loss', scores)
243 scores = classifier.evaluate(input_fn=_input_fn, steps=1)
244 self.assertIn('loss', scores)
254 scores = classifier.evaluate(x=train_x, y=train_y, steps=1)
255 self._assertInRange(0.0, 1.0, scores['accuracy'])
292 scores = classifier.evaluate(input_fn=_input_fn, steps=1)
293 self._assertInRange(0.0, 1.0, scores['accuracy'])
294 self.assertIn('loss', scores)
[all...] |
| dnn_test.py | 213 scores = dnn_estimator.evaluate(input_fn=_input_fn_eval, steps=1)
214 self._assertInRange(0.0, 1.0, scores['accuracy'])
305 scores = classifier.evaluate(input_fn=input_fn, steps=1)
306 self._assertInRange(0.0, 1.0, scores['accuracy'])
307 self.assertIn('loss', scores)
328 scores = classifier.evaluate(input_fn=_input_fn, steps=1)
329 self.assertIn('loss', scores)
343 scores = classifier.evaluate(x=train_x, y=train_y, steps=1)
344 self._assertInRange(0.0, 1.0, scores['accuracy'])
397 scores = classifier.evaluate(input_fn=_input_fn, steps=1
[all...] |
| linear_test.py | 138 scores = classifier.evaluate(
140 self.assertGreater(scores['accuracy'], 0.9)
160 scores = classifier.evaluate(input_fn=_input_fn, steps=1)
161 self.assertGreater(scores['accuracy'], 0.9)
173 scores = classifier.evaluate(x=train_x, y=train_y, steps=1)
174 self.assertGreater(scores['accuracy'], 0.9)
205 scores = classifier.evaluate(input_fn=_input_fn, steps=1)
206 self.assertGreater(scores['accuracy'], 0.9)
207 self.assertIn('loss', scores)
236 scores = classifier.evaluate(input_fn=_input_fn, steps=1
[all...] |
| dnn_linear_combined_test.py | 371 scores = classifier.evaluate(
373 _assert_metrics_in_range(('accuracy', 'auc'), scores)
421 scores = classifier.evaluate(input_fn=_input_fn, steps=100)
422 _assert_metrics_in_range(('accuracy', 'auc'), scores)
467 scores = classifier.evaluate(input_fn=_input_fn, steps=100)
468 _assert_metrics_in_range(('accuracy', 'auc'), scores)
515 scores = classifier.evaluate(input_fn=_input_fn, steps=1)
516 _assert_metrics_in_range(('accuracy', 'auc'), scores)
539 scores = classifier.evaluate(
541 _assert_metrics_in_range(('accuracy',), scores)
[all...] |
| estimator_input_test.py | 181 scores = est.evaluate(
198 self.assertAllClose(scores2['MSE'], scores['MSE'])
202 self.assertAllClose(other_score, scores['MSE'])
209 scores = est.score(
217 self.assertAllClose(scores['MSE'], other_score)
218 self.assertTrue('global_step' in scores)
219 self.assertEqual(100, scores['global_step'])
227 scores = est.evaluate(
235 self.assertAllClose(other_score, scores['MSE'])
236 self.assertTrue('global_step' in scores)
[all...] |
| /external/tensorflow/tensorflow/contrib/metrics/python/kernel_tests/ |
| histogram_ops_test.py | 63 scores = constant_op.constant([], shape=[0], dtype=dtypes.float32)
65 auc, update_op = histogram_ops.auc_using_histogram(labels, scores,
144 scores. Defaults to [0, 1.].
160 scores = array_ops.placeholder(dtypes.float32, shape=[num_records])
162 labels, scores, score_range, nbins=nbins)
168 update_op.run(feed_dict={labels: labels_a, scores: scores_a})
186 """Create synthetic boolean_labels and scores with adjustable auc.
190 score_range: 2-tuple, (low, high), giving the range of the resultant scores
199 scores: np.array, dtype=np.float32
208 # and corresponding scores drawn from
[all...] |
| /frameworks/native/libs/binder/ |
| IProcessInfoService.cpp | 53 /*in*/ int32_t* pids, /*out*/ int32_t* states, /*out*/ int32_t* scores)
80 scores, length * sizeof(*scores))) != NO_ERROR) {
|
| /external/tensorflow/tensorflow/contrib/pi_examples/label_image/ |
| label_image.cc | 228 // Analyzes the output of the Inception graph to retrieve the highest scores and
234 std::vector<std::pair<int, float>> scores; local
236 scores.push_back(std::pair<int, float>({i, unsorted_scores_flat(i)}));
238 std::sort(scores.begin(), scores.end(),
243 scores.resize(how_many_labels);
244 Tensor sorted_indices(tensorflow::DT_INT32, {scores.size()});
245 Tensor sorted_scores(tensorflow::DT_FLOAT, {scores.size()});
246 for (int i = 0; i < scores.size(); ++i) {
247 sorted_indices.flat<int>()(i) = scores[i].first
269 Tensor scores; local
287 Tensor scores; local
[all...] |
| /frameworks/opt/net/wifi/tests/wifitests/src/com/android/server/wifi/ |
| ScoredNetworkEvaluatorTest.java | 196 Integer[] scores = {120}; local
199 mScoreCache, scoredScanDetails, scores, meteredHints);
282 Integer[] scores = {null, 120}; local
288 scanDetails, scores, meteredHints);
319 Integer[] scores = {100, 120}; local
327 scanDetails, scores, meteredHints);
359 Integer[] scores = {null, 120}; local
365 scanDetails, scores, meteredHints);
392 Integer[] scores = {120}; local
403 scanDetails, scores, meteredHints)
424 Integer[] scores = {100, 120}; local
457 Integer[] scores = {120, 120}; local
498 Integer[] scores = {100, 120}; local
543 Integer[] scores = {null, null}; local
[all...] |
