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  /frameworks/ml/nn/runtime/test/generated/models/
rnn_state_relaxed.model.cpp 11 auto weights = model->addOperand(&type1); local
21 model->addOperation(ANEURALNETWORKS_RNN, {input, weights, recurrent_weights, bias, hidden_state_in, activation_param}, {hidden_state_out, output});
24 {input, weights, recurrent_weights, bias, hidden_state_in},
  /frameworks/ml/nn/runtime/test/specs/V1_0/
rnn_state.mod.py 24 weights = Input("weights", "TENSOR_FLOAT32", "{%d, %d}" % (units, input_size)) variable
34 model = model.Operation("RNN", input, weights, recurrent_weights, bias, hidden_state_in,
38 weights: [
rnn.mod.py 24 weights = Input("weights", "TENSOR_FLOAT32", "{%d, %d}" % (units, input_size)) variable
34 model = model.Operation("RNN", input, weights, recurrent_weights, bias, hidden_state_in,
38 weights: [
  /frameworks/ml/nn/runtime/test/specs/V1_1/
rnn_state_relaxed.mod.py 24 weights = Input("weights", "TENSOR_FLOAT32", "{%d, %d}" % (units, input_size)) variable
34 model = model.Operation("RNN", input, weights, recurrent_weights, bias, hidden_state_in,
39 weights: [
rnn_relaxed.mod.py 24 weights = Input("weights", "TENSOR_FLOAT32", "{%d, %d}" % (units, input_size)) variable
34 model = model.Operation("RNN", input, weights, recurrent_weights, bias, hidden_state_in,
39 weights: [
  /external/apache-commons-math/src/main/java/org/apache/commons/math/optimization/
LeastSquaresConverter.java 49 * This class support combination of residuals with or without weights and correlations.
66 /** Optional weights for the residuals. */
67 private final double[] weights; field in class:LeastSquaresConverter
80 this.weights = null;
84 /** Build a simple converter for uncorrelated residuals with the specific weights.
92 * Weights can be used for example to combine residuals with different standard
96 * In this case, the weights array should be initialized with value
102 * weights array must have consistent sizes or a {@link FunctionEvaluationException} will be
107 * @param weights weights to apply to the residual
    [all...]
  /external/clang/test/Profile/
c-general.c 124 // Never reached -> no weights
141 // Never reached -> no weights
200 // never reached -> no weights
217 static int weights[] = {1, 2, 2, 3, 3, 3, 4, 4, 4, 4, 5, 5, 5, 5, 5}; local
219 // No cases -> no weights
220 switch (weights[0]) {
229 for (int i = 0, len = sizeof(weights) / sizeof(weights[0]); i < len; ++i) {
232 switch (i[weights]) {
279 // Never reached -> no weights
    [all...]
  /external/libopus/src/
mlp_train.h 80 double **weights; member in struct:__anon25625
  /external/tensorflow/tensorflow/compiler/xla/
reference_util_test.cc 216 Array3D<float> weights = {{{5, 6}}}; local
218 ReferenceUtil::ConvArray3D(input, weights, 1, Padding::kSame);
229 Array3D<float> weights = {{{5, 6}}}; local
231 ReferenceUtil::ConvArray3D(input, weights, 1, Padding::kValid);
250 Array4D<float> weights(1, 1, 2, 2);
252 weights.FillWithYX(Array2D<float>({
258 ReferenceUtil::ConvArray4D(input, weights, {1, 1}, Padding::kSame);
285 Array4D<float> weights(1, 1, 2, 2);
287 weights.FillWithYX(Array2D<float>({
293 ReferenceUtil::ConvArray4D(input, weights, {1, 1}, Padding::kValid)
    [all...]
  /external/apache-commons-math/src/main/java/org/apache/commons/math/optimization/fitting/
CurveFitter.java 131 double[] weights = new double[observations.size()]; local
135 weights[i] = point.getWeight();
141 optimizer.optimize(new TheoreticalValuesFunction(f), target, weights, initialGuess);
  /external/libxcam/modules/ocl/
cv_edgetaper.cpp 64 cv::Mat weights = expanded (cv::Rect (expanded.cols / 2 - image.cols / 2, expanded.rows / 2 - image.rows / 2, image.cols, image.rows)); local
65 coefficients = weights.clone ();
  /external/tensorflow/tensorflow/contrib/tensor_forest/kernels/v4/
grow_stats_test.cc 78 std::vector<float> weights = {2.3, 20.3, 1.1}; local
80 new TestableInputTarget(labels, weights, 1));
121 std::vector<float> weights = {2.3, 20.3, 1.1}; local
123 new TestableInputTarget(labels, weights, 1));
177 std::vector<float> weights = {1, 1, 1}; local
179 new TestableInputTarget(labels, weights, 1));
223 std::vector<float> weights = {1, 1}; local
224 TestableInputTarget target(labels, weights, 1);
331 std::vector<float> weights = {2.3, 20.3, 1.1}; local
333 new TestableInputTarget(labels, weights, 1))
400 std::vector<float> weights = {2.3, 20.3, 1.1}; local
    [all...]
  /external/tensorflow/tensorflow/core/kernels/
bincount_op.cc 40 const typename TTypes<T, 1>::ConstTensor& weights,
70 if (weights.size()) {
71 partial_bins(worker_id, value) += weights(i);
105 const auto weights = weights_t.flat<T>(); variable
111 ctx, arr, weights, output));
range_sampler_test.cc 205 std::vector<float> weights = {1, 2, 4, 8, 16, 32, 64, 128, 256}; local
206 sampler_.reset(new FixedUnigramSampler(9, weights, 0.8, 0, 1, 0));
213 std::vector<float> weights = {1, 2, 4, 8, 16, 32, 64, 128, 256}; local
214 sampler_.reset(new FixedUnigramSampler(9, weights, 0.8, 0, 1, 0));
218 std::vector<float> weights = {1, 2, 4, 8, 16, 32, 64, 128, 256}; local
219 sampler_.reset(new FixedUnigramSampler(9, weights, 0.8, 0, 1, 0));
223 std::vector<float> weights = {1, 2, 4, 8, 16, 32, 64, 128, 256}; local
224 sampler_.reset(new FixedUnigramSampler(10, weights, 0.8, 1, 1, 0));
232 std::vector<float> weights = {1, 2, 4, 8, 16, 32, 64, 128, 256}; local
233 sampler_.reset(new FixedUnigramSampler(11, weights, 0.8, 2, 1, 0))
    [all...]
  /external/tensorflow/tensorflow/core/lib/random/
weighted_picker_test.cc 34 static void TestPickAt(int items, const int32* weights);
64 VLOG(0) << "======= Grown picker with zero weights";
73 VLOG(0) << "======= Shrink picker and check weights";
95 VLOG(0) << "======= Check uniform with big weights";
103 static const int32 weights[] = {1, 0, 200, 5, 42}; local
104 TestPickAt(TF_ARRAYSIZE(weights), weights); local
131 // Create zero weights array
132 std::vector<int32> weights(size);
134 weights[elem] = 0
    [all...]
  /external/tensorflow/tensorflow/python/ops/
template.py 428 def weights(self): member in class:Template
429 """List of weights/variables created by the Template."""
434 """List of trainable weights/variables created by the Template."""
439 """List of non-trainable weights/variables created by the Template."""
  /frameworks/base/media/mca/filterpacks/java/android/filterpacks/imageproc/
SepiaFilter.java 103 float weights[] = { 805.0f / 2048.0f, 715.0f / 2048.0f, 557.0f / 2048.0f, local
106 mProgram.setHostValue("matrix", weights);
  /external/apache-commons-math/src/main/java/org/apache/commons/math/analysis/integration/
LegendreGaussIntegrator.java 45 * Legendre polynomial. The weights a<sub>i</sub> of the quadrature formula
62 /** Weights for the 2 points method. */
75 /** Weights for the 3 points method. */
90 /** Weights for the 4 points method. */
107 /** Weights for the 5 points method. */
119 /** Weights for the current method. */
120 private final double[] weights; field in class:LegendreGaussIntegrator
135 weights = WEIGHTS_2;
139 weights = WEIGHTS_3;
143 weights = WEIGHTS_4
    [all...]
  /external/fio/tools/hist/
fiologparser_hist.py 33 ws :: Array of weights for our corresponding values
37 vs, ws = vs[idx], ws[idx] # weights and values sorted by value
41 def weights(start_ts, end_ts, start, end): function
42 """ Calculate weights based on fraction of sample falling in the
43 given interval [start,end]. Weights computed using vector / array
53 return :: Array of weights
215 ws = hs * weights(s_ts, end_time, iStart, iEnd)
  /external/tensorflow/tensorflow/compiler/xla/tests/
convolution_variants_test.cc 1154 auto weights = builder.ConstantR4FromArray4D<float>( local
1173 auto weights = builder.ConstantR4FromArray4D<float>( local
1194 auto weights = builder.ConstantR4FromArray4D<float>( local
1215 auto weights = builder.ConstantR4FromArray4D<float>( local
1307 auto weights = local
1346 auto weights = builder.ConstantLiteral(*weights_literal); local
    [all...]
  /external/tensorflow/tensorflow/contrib/boosted_trees/lib/utils/
dropout_utils_test.cc 44 // Fill an weights.
296 std::vector<float> GetWeightsByIndex(const std::vector<float>& weights,
301 res.push_back(weights[index]);
306 void MergeLastElements(const int32 last_n, std::vector<float>* weights) {
309 sum += weights->back();
310 weights->pop_back();
312 weights->push_back(sum);
320 std::vector<float> weights = {1.0, 1.0, 1.0, 1.0, 1.0}; local
329 std::vector<float> current_weights = weights;
342 std::vector<float> current_weights = weights;
365 std::vector<float> weights = {1.1, 2.1, 3.1, 4.1, 5.1}; local
410 std::vector<float> weights = {1.0, 1.0, 1.0, 1.0, 1.0}; local
    [all...]
  /external/tensorflow/tensorflow/contrib/eager/python/
network.py 407 def weights(self): member in class:Network
411 weights = []
413 weights += layer.weights
414 return weights
418 weights = []
420 weights += layer.trainable_weights
421 return weights
425 weights = []
427 weights += layer.non_trainable_weight
    [all...]
  /external/tensorflow/tensorflow/contrib/lite/kernels/
embedding_lookup_sparse.cc 30 // squares of the weights.
36 // Tensor[3]: Weights to use for aggregation, float.
50 // For instance, if params is a 10x20 matrix and ids, weights are:
96 TfLiteTensor* weights = GetInput(context, node, 3); local
97 TF_LITE_ENSURE_EQ(context, NumDimensions(weights), 1);
98 TF_LITE_ENSURE_EQ(context, weights->type, kTfLiteFloat32);
103 SizeOfDimension(weights, 0));
145 TfLiteTensor* weights = GetInput(context, node, 3); local
222 const float w = weights->data.f[i];
  /external/tensorflow/tensorflow/contrib/lite/toco/graph_transformations/
fuse_binary_into_following_affine.cc 45 const auto& weights = model->GetArray(following_op->inputs[1]); local
70 const Shape& weights_shape = weights.shape();
72 const auto& weights_buffer = weights.GetBuffer<ArrayDataType::kFloat>();
125 auto& weights = model->GetArray(weights_name); local
138 weights.GetMutableBuffer<ArrayDataType::kFloat>().data.data();
139 const int weights_size = RequiredBufferSizeForShape(weights.shape());
244 const auto& weights = model->GetArray(following_op->inputs[1]); local
246 if (!weights.buffer || !bias.buffer) {
248 "Not fusing %s because the following %s has non-constant weights or "
fuse_binary_into_preceding_affine.cc 93 auto& weights = model->GetArray(weights_name); local
100 const Shape& weights_shape = weights.shape();
103 auto& weights_buffer = weights.GetMutableBuffer<ArrayDataType::kFloat>();
265 const auto& weights = model->GetArray(preceding_op->inputs[1]); local
277 if (!weights.buffer || !bias.buffer) {
279 "Not fusing %s because the preceding %s has non-constant weights or "

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