/external/tensorflow/tensorflow/core/util/ |
H A D | example_proto_fast_parsing.h | 60 std::vector<Dense> dense; member in struct:tensorflow::example::FastParseExampleConfig
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H A D | strided_slice_op.cc | 62 // The dense indexed shrink mask is which processing dimensions 73 const StridedSliceSparseSpec& sparse, StridedSliceDenseSpec* dense) { 76 dense->begin.resize(dense->dims); 77 dense->end.resize(dense->dims); 78 dense->strides.resize(dense->dims); 80 dense->begin_mask = 0; 81 dense 72 BuildDenseSpec( const StridedSliceSparseSpec& sparse, StridedSliceDenseSpec* dense) argument [all...] |
/external/eigen/Eigen/src/SparseLU/ |
H A D | SparseLU_copy_to_ucol.h | 43 * \param dense Store the full representation of the column 51 BlockIndexVector repfnz ,IndexVector& perm_r, BlockScalarVector dense, GlobalLU_t& glu) 89 glu.ucol(nextu) = dense(irow); 90 dense(irow) = Scalar(0.0); 50 copy_to_ucol(const Index jcol, const Index nseg, IndexVector& segrep, BlockIndexVector repfnz ,IndexVector& perm_r, BlockScalarVector dense, GlobalLU_t& glu) argument
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H A D | SparseLU_column_bmod.h | 42 * \param dense Store the full representation of the column 53 Index SparseLUImpl<Scalar,StorageIndex>::column_bmod(const Index jcol, const Index nseg, BlockScalarVector dense, ScalarVector& tempv, argument 103 // then scatter the result of sup-col update to dense 106 LU_kernel_bmod<1>::run(segsize, dense, tempv, glu.lusup, luptr, lda, nrow, glu.lsub, lptr, no_zeros); 108 LU_kernel_bmod<Dynamic>::run(segsize, dense, tempv, glu.lusup, luptr, lda, nrow, glu.lsub, lptr, no_zeros); 116 // copy the SPA dense into L\U[*,j] 131 glu.lusup(nextlu) = dense(irow); 132 dense(irow) = Scalar(0.0);
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H A D | SparseLU_kernel_bmod.h | 23 * \param[in,out] dense Packed values of the original matrix 33 static EIGEN_DONT_INLINE void run(const Index segsize, BlockScalarVector& dense, ScalarVector& tempv, ScalarVector& lusup, Index& luptr, const Index lda, 39 EIGEN_DONT_INLINE void LU_kernel_bmod<SegSizeAtCompileTime>::run(const Index segsize, BlockScalarVector& dense, ScalarVector& tempv, ScalarVector& lusup, Index& luptr, const Index lda, argument 43 // First, copy U[*,j] segment from dense(*) to tempv(*) 45 // The result of matric-vector update is in dense[*] 52 tempv(i) = dense(irow); 75 // Scatter tempv[] into SPA dense[] as a temporary storage 80 dense(irow) = tempv(i); 83 // Scatter l into SPA dense[] 87 dense(iro 100 run(const Index , BlockScalarVector& dense, ScalarVector& , ScalarVector& lusup, Index& luptr, const Index lda, const Index nrow, IndexVector& lsub, const Index lptr, const Index no_zeros) argument [all...] |
H A D | SparseLU_panel_bmod.h | 47 * \param dense Store the full representation of the panel 57 const Index nseg, ScalarVector& dense, ScalarVector& tempv, 113 VectorBlock<ScalarVector> dense_col(dense, nextl_col, m); // Scatter/gather entire matrix column from/to here 160 VectorBlock<ScalarVector> dense_col(dense, nextl_col, m); // Scatter/gather entire matrix column from/to here 178 // Scatter l into SPA dense[] 195 VectorBlock<ScalarVector> dense_col(dense, nextl_col, m); // Scatter/gather entire matrix column from/to here 207 // then scatter the result of sup-col update to dense[] 56 panel_bmod(const Index m, const Index w, const Index jcol, const Index nseg, ScalarVector& dense, ScalarVector& tempv, IndexVector& segrep, IndexVector& repfnz, GlobalLU_t& glu) argument
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H A D | SparseLU_panel_dfs.h | 206 * \param[out] dense Accumulate the column vectors of the panel 219 void SparseLUImpl<Scalar,StorageIndex>::panel_dfs(const Index m, const Index w, const Index jcol, MatrixType& A, IndexVector& perm_r, Index& nseg, ScalarVector& dense, IndexVector& panel_lsub, IndexVector& segrep, IndexVector& repfnz, IndexVector& xprune, IndexVector& marker, IndexVector& parent, IndexVector& xplore, GlobalLU_t& glu) argument 235 VectorBlock<ScalarVector> dense_col(dense,nextl_col, m); // Accumulate a column vector here
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H A D | SparseLU.h | 562 ScalarVector dense; local 563 dense.setZero(maxpanel); 612 Base::panel_dfs(m, panel_size, jcol, m_mat, m_perm_r.indices(), nseg1, dense, panel_lsub, segrep, repfnz, xprune, marker, parent, xplore, m_glu); 615 Base::panel_bmod(m, panel_size, jcol, nseg1, dense, tempv, segrep, repfnz, m_glu); 635 VectorBlock<ScalarVector> dense_k(dense, k, m);
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/external/tensorflow/tensorflow/contrib/tensor_forest/kernels/v4/ |
H A D | input_data.cc | 104 void TensorDataSet::set_input_tensors(const Tensor& dense, argument 108 if (dense.shape().dims() == 2) { 109 dense_data_.reset(new DenseStorageType(dense.tensor<float, 2>())); 118 original_dense_tensor_ = dense; 134 if (rand_feature < available_features_.size()) { // it's dense.
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H A D | candidate_graph_runner.cc | 119 auto* dense = stats->mutable_classification()->mutable_dense_counts(); local 121 dense->add_value()->set_float_value(counts(i)); 129 auto* dense = stats->mutable_classification()->mutable_dense_counts(); local 131 dense->add_value()->set_float_value(counts(i));
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/external/tensorflow/tensorflow/core/kernels/ |
H A D | sparse_dense_binary_op_shared_test.cc | 77 AddInputFromArray<float>(TensorShape({2, 1}), {17, 19}); // dense 79 ExpectHasSubstr(RunOpKernel().ToString(), "broadcasts dense to sparse only"); 90 ExpectHasSubstr(RunOpKernel().ToString(), "broadcasts dense to sparse only"); 96 // [2 ] cdiv [dense: same shape, all 1's] 104 // Tensor dense(DT_FLOAT, TensorShape({3, 1})); 105 Tensor dense(DT_FLOAT, TensorShape(shape)); 106 auto dense_flat = dense.flat<float>(); 125 // [2 ] cdiv [dense: shape [3,1], all 1's] 133 Tensor dense(DT_FLOAT, TensorShape({3, 1})); 134 auto dense_flat = dense 211 SparseMatCMulDenseMat(Graph* g, Node* sp_indices, Node* sp_vals, Node* sp_shape, Node* dense) argument [all...] |
/external/tensorflow/tensorflow/python/layers/ |
H A D | core.py | 179 @tf_export('layers.dense') 180 def dense( function 420 fully_connected = dense
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H A D | layers.py | 41 @@dense 78 from tensorflow.python.layers.core import dense namespace
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/external/tensorflow/tensorflow/contrib/tensor_forest/kernels/ |
H A D | data_spec.h | 31 // A feature could be dense or sparse, and be of any size. 67 // data set, which were flattened to a single dense float tensor and/or a 76 // dense_features_size: <size> dense: [{<col1>}{<col2>}] sparse: [{<col3>}] 94 const DataColumn& dense(int i) const { return dense_.at(i); } function in class:tensorflow::tensorforest::TensorForestDataSpec 133 // This map tracks features in the total dense feature space to their
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H A D | tree_utils.cc | 539 GetFeatureFnType GetDenseFunctor(const Tensor& dense) { argument 540 if (dense.shape().dims() == 2) { 541 const auto dense_features = dense.matrix<float>(); 549 LOG(ERROR) << "trying to access nonexistent dense features.";
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/external/opencv/cv/src/ |
H A D | cvhistogram.cpp | 1415 CvMatND dense; local 1481 dense = *(CvMatND*)hist->bins; 1482 dense.type = (dense.type & ~CV_MAT_TYPE_MASK) | CV_32SC1; 1491 CV_CALL( cvConvert( (CvMatND*)hist->bins, &dense )); 1529 CV_CALL( cvConvert( &dense, (CvMatND*)hist->bins )); 2242 CV_ERROR( CV_StsBadArg, "The function supports dense histograms only" );
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