| /external/tensorflow/tensorflow/core/api_def/base_api/ |
| D | api_def_SparseMatrixSparseCholesky.pbtxt | 14 description: "The sparse Cholesky decompsition of `input`."
16 summary: "Computes the sparse Cholesky decomposition of `input`."
18 Computes the Sparse Cholesky decomposition of a sparse matrix, with the given
21 The input sparse matrix and the fill-in reducing permutation `permutation` must
22 have compatible shapes. If the sparse matrix has rank 3; with the batch
28 the number of rows of each component of the sparse matrix.
30 Each component of the input sparse matrix must represent a symmetric positive
35 The returned sparse matrix has the same dense shape as the input sparse matrix.
36 For each component `A` of the input sparse matrix, the corresponding output
37 sparse matrix represents `L`, the lower triangular Cholesky factor satisfying
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| D | api_def_SparseMatrixOrderingAMD.pbtxt | 14 Computes the Approximate Minimum Degree (AMD) ordering for a sparse matrix.
17 given sparse matrix. This typically results in permuted sparse matrix's sparse
21 The input sparse matrix may have rank 2 or rank 3. The output Tensor,
25 Each component of the input sparse matrix must represent a square symmetric
27 sparse matrix does not affect the returned permutation, only the sparsity
28 pattern of the sparse matrix is used. Hence, a single AMD ordering may be
29 reused for the Cholesky decompositions of sparse matrices with the same sparsity
33 elements, where the input sparse matrix components each have `N` rows. That is,
40 from tensorflow.python.ops.linalg.sparse import sparse_csr_matrix_ops
48 a_st = tf.sparse.SparseTensor(a_indices, a_values, a_dense_shape)
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| D | api_def_SparseCountSparseOutput.pbtxt | 7 Tensor containing the indices of the sparse tensor to count.
13 Tensor containing values of the sparse tensor to count.
19 Tensor containing the dense shape of the sparse tensor to count.
32 Indices tensor for the resulting sparse tensor object.
38 Values tensor for the resulting sparse tensor object.
44 Shape tensor for the resulting sparse tensor object.
77 summary: "Performs sparse-output bin counting for a sparse tensor input."
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| D | api_def_SparseSlice.pbtxt | 6 2-D tensor represents the indices of the sparse tensor.
12 1-D tensor represents the values of the sparse tensor.
18 1-D. tensor represents the shape of the sparse tensor.
32 sparse tensors.
38 A list of 1-D tensors represents the values of the output sparse
45 A list of 1-D tensors represents the shape of the output sparse
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| D | api_def_SparseSplit.pbtxt | 13 2-D tensor represents the indices of the sparse tensor.
19 1-D tensor represents the values of the sparse tensor.
25 1-D. tensor represents the shape of the sparse tensor.
27 sparse tensors.
33 A list of 1-D tensors represents the values of the output sparse
40 A list of 1-D tensors represents the shape of the output sparse
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| D | api_def_BoostedTreesSparseAggregateStats.pbtxt | 25 int32; Rank 2 indices of feature sparse Tensors (shape=[number of sparse entries, 2]).
26 Number of sparse entries across all instances from the batch. The first value is
34 int32; Rank 1 values of feature sparse Tensors (shape=[number of sparse entries]).
35 Number of sparse entries across all instances from the batch. The first value is
42 int32; Rank 1 dense shape of feature sparse Tensors (shape=[2]).
49 int32; Rank 2 indices of summary sparse Tensors (shape=[number of non zero statistics, 4])
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| D | api_def_SparseFillEmptyRows.pbtxt | 6 2-D. the indices of the sparse tensor.
12 1-D. the values of the sparse tensor.
18 1-D. the shape of the sparse tensor.
25 for rows missing from the input sparse tensor.
26 output indices: 2-D. the indices of the filled sparse tensor.
32 1-D. the values of the filled sparse tensor.
39 input sparse tensor.
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| D | api_def_RaggedCountSparseOutput.pbtxt | 13 Tensor containing values of the sparse tensor to count.
26 Indices tensor for the resulting sparse tensor object.
32 Values tensor for the resulting sparse tensor object.
38 Shape tensor for the resulting sparse tensor object.
71 summary: "Performs sparse-output bin counting for a ragged tensor input."
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| /external/deqp/doc/testspecs/VK/ |
| D | sparse_resources.txt | 27 The test creates two queues - one supporting sparse binding operations, the second one supporting c…
29 First queue is used to perform binding of device memory to sparse buffer. The binding operation sig…
32 The second queue is used to perform transfer operations. The test creates two non-sparse buffer obj…
33 one used as input and the second as output. The input buffer is used to transfer data to sparse buf…
34 transfered further from sparse buffer to output buffer. The transer queue waits on a semaphore, bef…
46 The test creates two queues - one supporting sparse binding operations, the second one supporting c…
48 First queue is used to perform binding of device memory to sparse image. The binding operation sign…
51 The second queue is used to perform transfer operations. The test creates two non-sparse buffer obj…
52 one used as input and the second as output. The input buffer is used to transfer data to sparse ima…
53 transfered further from sparse image to output buffer. The transfer queue waits on a semaphore, bef…
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| /external/tensorflow/tensorflow/python/data/experimental/kernel_tests/ |
| D | bucket_by_sequence_length_test.py | 46 def _format_record(array, sparse): argument
47 if sparse:
56 def _get_record_type(sparse): argument
57 if sparse:
66 def _get_record_shape(sparse): argument
67 if sparse:
114 def build_dataset(sparse): argument
127 yield (_format_record(el, sparse),)
130 _generator, (_get_record_type(sparse),), (_get_record_shape(sparse),))
131 if sparse:
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| /external/llvm-project/mlir/test/Dialect/Linalg/ |
| D | sparse_invalid.mlir | 8 sparse = [
16 // expected-error@+1 {{'linalg.generic' op expected sparse annotations on tensors only}}
34 sparse = [
60 sparse = [
85 sparse = [
106 sparse = [
115 // expected-error@+1 {{'linalg.generic' op expected one sparse annotation for each tensor}}
132 sparse = [ 1, 2 ],
137 // expected-error@+1 {{'linalg.generic' op expected sparse annotation array for tensor 0}}
154 sparse = [
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| /external/tensorflow/tensorflow/python/keras/engine/ |
| D | input_layer.py | 106 sparse=None, argument
114 self._init_sparse = sparse
138 if sparse and ragged:
156 self.sparse = True if sparse else False
182 self.sparse = True
202 sparse=sparse,
245 'sparse': self.sparse,
262 sparse=None, argument
357 if sparse and ragged:
361 input_layer_config = {'name': name, 'dtype': dtype, 'sparse': sparse,
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| /external/tensorflow/tensorflow/python/kernel_tests/linalg/sparse/ |
| D | BUILD | 1 # Tests of TensorFlow sparse linear algebra kernels using the Python API.
32 "//tensorflow/python/ops/linalg/sparse",
48 "//tensorflow/python/ops/linalg/sparse",
49 "//tensorflow/python/ops/linalg/sparse:gen_sparse_csr_matrix_ops",
60 "//tensorflow/python/ops/linalg/sparse",
71 "//tensorflow/python/ops/linalg/sparse",
82 "//tensorflow/python/ops/linalg/sparse",
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| /external/openscreen/build/code_coverage/ |
| D | merge_lib.py | 32 sparse=True): argument
56 if sparse:
91 sparse=True): argument
129 counter_overflows, profdata_tool_path, sparse))
144 profdata_tool_path, sparse=True): argument
152 if sparse:
232 sparse=True, argument
267 sparse=sparse))
292 sparse=sparse)
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| /external/tensorflow/tensorflow/python/data/util/ |
| D | sparse_test.py | 22 from tensorflow.python.data.util import sparse
72 sparse.any_sparse(test_case["classes"]), test_case["expected"])
157 sparse.as_dense_shapes(test_case["types"], test_case["classes"]),
231 sparse.as_dense_types(test_case["types"], test_case["classes"]),
290 sparse.get_classes(test_case["classes"]), test_case["expected"])
322 classes = sparse.get_classes(expected)
326 actual = sparse.deserialize_sparse_tensors(
327 sparse.serialize_sparse_tensors(expected), types, shapes,
328 sparse.get_classes(expected))
352 classes = sparse.get_classes(expected)
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| /external/tensorflow/tensorflow/core/util/ |
| D | strided_slice_op.cc | 81 const StridedSliceSparseSpec& sparse, StridedSliceDenseSpec* dense) { in BuildDenseSpec() argument
95 const T* const strides_flat = sparse.strides_tensor.vec<T>().data(); in BuildDenseSpec()
96 dense->begin_valid = sparse.begin_tensor != nullptr; in BuildDenseSpec()
97 dense->end_valid = sparse.end_tensor != nullptr; in BuildDenseSpec()
99 const T* const begin_flat = sparse.begin_tensor != nullptr in BuildDenseSpec()
100 ? sparse.begin_tensor->vec<T>().data() in BuildDenseSpec()
102 const T* const end_flat = sparse.end_tensor != nullptr in BuildDenseSpec()
103 ? sparse.end_tensor->vec<T>().data() in BuildDenseSpec()
106 for (int i = 0; i < sparse.dims; i++) { in BuildDenseSpec()
107 if ((1 << i) & sparse.ellipsis_mask) { in BuildDenseSpec()
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| /external/rust/crates/regex-automata/src/ |
| D | determinize.rs | 102 let mut sparse = self.new_sparse_set(); in build() localVariable
103 let mut uncompiled = vec![self.add_start(&mut sparse)?]; in build()
107 self.cached_state(dfa_id, b, &mut sparse)?; in build()
141 sparse: &mut SparseSet, in cached_state()
143 sparse.clear(); in cached_state()
145 self.next(dfa_id, b, sparse); in cached_state()
147 let state = self.new_state(sparse); in cached_state()
222 fn add_start(&mut self, sparse: &mut SparseSet) -> Result<S> { in add_start()
223 sparse.clear(); in add_start()
224 self.epsilon_closure(self.nfa.start(), sparse); in add_start()
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| D | sparse_set.rs | 22 sparse: Box<[usize]>, field
29 sparse: vec![0; size].into_boxed_slice(), in new()
41 self.sparse[value] = i; in insert()
45 let i = self.sparse[value]; in contains()
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| /external/tensorflow/tensorflow/python/training/ |
| D | input_test.py | 776 sparse = sparse_tensor.SparseTensor(
778 self.assertAllEqual((1,), sparse.dense_shape.get_shape().as_list())
779 batched = inp.batch([sparse], batch_size=2)
784 sparse = sparse_tensor.SparseTensor(
786 self.assertAllEqual((1,), sparse.dense_shape.get_shape().as_list())
787 batched = inp.batch([sparse], batch_size=2, enqueue_many=True)
792 sparse = sparse_tensor.SparseTensor(
796 self.assertIs(None, sparse.dense_shape.get_shape().num_elements())
797 batched = inp.batch([sparse], batch_size=2)
802 sparse = sparse_tensor.SparseTensor(
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| /external/eigen/doc/ |
| D | TutorialSparse.dox | 7 Manipulating and solving sparse problems involves various modules which are summarized below:
11 …eMatrix and SparseVector classes, matrix assembly, basic sparse linear algebra (including sparse t…
12 … <Eigen/SparseCholesky>\endcode</td><td>Direct sparse LLT and LDLT Cholesky factorization to solve…
14 <td>%Sparse LU factorization to solve general square sparse systems</td></tr>
15 …de<Eigen/SparseQR>\endcode </td><td>%Sparse QR factorization for solving sparse linear least-squar…
22 …ized representation storing only the nonzero coefficients. Such a matrix is called a sparse matrix.
26 The class SparseMatrix is the main sparse matrix representation of Eigen's sparse module; it offers…
45 and one of its possible sparse, \b column \b major representation:
68 The results of %Eigen's operations always produces \b compressed sparse matrices.
97 In this example, we start by defining a column-major sparse matrix type of double \c SparseMatrix<d…
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| /external/rust/crates/bstr/scripts/ |
| D | generate-unicode-data | 34 --sparse --minimize --anchored --state-size 2 \
42 --sparse --minimize --anchored --state-size 2 \
53 --sparse --minimize --anchored --state-size 4 \
65 --sparse --anchored --state-size 4 \
87 --sparse --minimize --state-size 2 \
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| /external/tensorflow/tensorflow/python/keras/layers/preprocessing/ |
| D | category_encoding_test.py | 63 num_tokens=num_tokens, output_mode=category_encoding.COUNT, sparse=True)
75 sparse=False)
94 input_data = keras.Input(shape=(None,), dtype=dtypes.int64, sparse=True)
118 input_data = keras.Input(shape=(None,), dtype=dtypes.int64, sparse=True)
119 weight_data = keras.Input(shape=(None,), dtype=dtypes.float32, sparse=True)
136 input_data = keras.Input(shape=(None,), dtype=dtypes.int64, sparse=True)
148 num_tokens=num_tokens, output_mode=category_encoding.COUNT, sparse=True)
160 sparse=False)
172 input_data = keras.Input(shape=(None,), dtype=dtypes.int64, sparse=True)
175 weight_data = keras.Input(shape=(None,), dtype=dtypes.float32, sparse=True)
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| /external/rust/crates/regex/src/ |
| D | sparse.rs | 24 sparse: Box<[usize]>, field
31 sparse: vec![0; size].into_boxed_slice(), in new()
51 self.sparse[value] = i; in insert()
55 let i = self.sparse[value]; in contains()
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| /external/tensorflow/tensorflow/python/ops/linalg/sparse/ |
| D | sparse.py | 23 from tensorflow.python.ops.linalg.sparse.conjugate_gradient import conjugate_gradient
24 from tensorflow.python.ops.linalg.sparse.sparse_csr_matrix_grad import *
25 from tensorflow.python.ops.linalg.sparse.sparse_csr_matrix_ops import *
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| /external/tensorflow/tensorflow/core/protobuf/tpu/ |
| D | tpu_embedding_configuration.proto | 63 // This parameter determines if the execution of the sparse core will be
68 // false: The execution of the sparse core is not pipelined with that of the
69 // TensorCore. The forward pass of every step on the sparse core is executed
71 // backward pass on the sparse core is executed only after the embedding
74 // previous step on both the sparse core and the TensorCore.
76 // true: The execution of the sparse core is pipelined with that of the
77 // TensorCore. The forward pass of every step on the sparse core can be
79 // waiting for the backward pass. This improves the utilization of the sparse
82 // sparse core is executed directly after the forward pass for the next step
86 // since the embedding updates are sparse, this is generally not considered a
|