/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},
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/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: [
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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: [
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/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: [
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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: [
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/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
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/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);
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/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 ();
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/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));
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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."""
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/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);
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/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)
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/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];
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/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 "
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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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