| /external/tensorflow/tensorflow/core/kernels/ |
| D | batch_matmul_op_impl.h | 62 Eigen::IndexPair<Eigen::DenseIndex> ContractionDims(bool adj_x, bool adj_y) { in ContractionDims() argument
63 return Eigen::IndexPair<Eigen::DenseIndex>(adj_x ? 0 : 1, adj_y ? 1 : 0); in ContractionDims()
77 const Tensor in_y, bool adj_x, bool adj_y, Tensor* out, in Run()
89 contract_pairs[0] = ContractionDims(adj_x, adj_y); in Run()
94 if (adj_x != adj_y) { in Run()
113 const Tensor& in_y, bool adj_x, bool adj_y, Tensor* out,
119 contract_pairs[0] = ContractionDims(adj_x, adj_y);
133 static void Multiply(bool adj_x, bool adj_y, Tx x, Ty y, Tz z) {
135 if (!adj_y) {
141 if (!adj_y) {
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| /external/tensorflow/tensorflow/core/api_def/base_api/ |
| D | api_def_BatchMatMul.pbtxt | 28 name: "adj_y"
40 the `adj_x` or `adj_y` flag to `True`, which are by default `False`.
48 c_o = r_y if adj_y else c_y
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| /external/tensorflow/tensorflow/contrib/timeseries/python/timeseries/ |
| D | math_utils_test.py | 105 adj_y=True).eval())
109 adj_x=True, adj_y=True).eval())
128 adj_y=True).eval())
132 adj_x=True, adj_y=True).eval())
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| D | math_utils.py | 270 def batch_times_matrix(batch, matrix, adj_x=False, adj_y=False): argument
302 if adj_y:
311 math_ops.matmul(batch_reshaped, matrix, adjoint_b=adj_y), result_shape)
314 def matrix_times_batch(matrix, batch, adj_x=False, adj_y=False): argument
318 batch=batch, matrix=matrix, adj_x=not adj_y, adj_y=not adj_x),
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| /external/tensorflow/tensorflow/core/ops/ |
| D | math_ops_test.cc | 392 auto set_adj = [&op](bool adj_x, bool adj_y) { in TEST() argument
397 .Attr("adj_y", adj_y) in TEST()
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| D | math_ops.cc | 135 bool adj_y; in __anonb22bfa860202() local
137 TF_RETURN_IF_ERROR(c->GetAttr("adj_y", &adj_y)); in __anonb22bfa860202()
139 DimensionHandle output_cols = c->Dim(b_shape, adj_y ? -2 : -1); in __anonb22bfa860202()
152 c->Dim(b_shape, adj_y ? -1 : -2), &unused)); in __anonb22bfa860202()
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| D | ops.pbtxt | 3344 name: "adj_y"
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| /external/tensorflow/tensorflow/core/graph/ |
| D | testlib.h | 85 Node* BatchMatmul(Graph* g, Node* in0, Node* in1, bool adj_x, bool adj_y);
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| D | testlib.cc | 171 Node* BatchMatmul(Graph* g, Node* in0, Node* in1, bool adj_x, bool adj_y) { in BatchMatmul() argument
177 .Attr("adj_y", adj_y) in BatchMatmul()
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| /external/tensorflow/tensorflow/lite/toco/graph_transformations/ |
| D | unroll_batch_matmul.cc | 242 if (batch_op->adj_y) { in Run()
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| /external/tensorflow/tensorflow/python/ops/ |
| D | math_grad.py | 1250 adj_y = op.get_attr("adj_y")
1253 if not adj_y:
1260 if not adj_y:
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| D | math_ops.py | 2542 a, b, adj_x=adjoint_a, adj_y=adjoint_b, name=name)
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| /external/tensorflow/tensorflow/lite/toco/ |
| D | model.h | 984 bool adj_y = false;
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| D | import_tensorflow.cc | 1101 batch_matmul->adj_y = GetBoolAttr(node, "adj_y"); in ConvertBatchMatMulOperator()
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| /external/tensorflow/tensorflow/compiler/tests/ |
| D | randomized_tests.cc | 1211 bool adj_y = random_bool(generator()); in TEST_F() local
1215 if (adj_y) { in TEST_F()
1224 .Attr("adj_y", adj_y)); in TEST_F()
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| /external/tensorflow/tensorflow/python/ops/parallel_for/ |
| D | pfor.py | 1977 adj_y = pfor_input.get_attr("adj_y")
1981 output = math_ops.matmul(x, y, adjoint_a=adj_x, adjoint_b=adj_y)
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| /external/tensorflow/tensorflow/python/keras/ |
| D | backend.py | 1560 adj_y = True if axes[1] == ndim(y) - 1 else None
1561 out = math_ops.matmul(x, y, adjoint_a=adj_x, adjoint_b=adj_y)
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| /external/tensorflow/tensorflow/core/ops/compat/ |
| D | ops_history.v0.pbtxt | 4643 name: "adj_y"
4684 name: "adj_y"
4726 name: "adj_y"
4769 name: "adj_y"
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| D | ops_history.v2.pbtxt | 9257 name: "adj_y"
9301 name: "adj_y"
9345 name: "adj_y"
9390 name: "adj_y"
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| D | ops_history.v1.pbtxt | 9257 name: "adj_y"
9301 name: "adj_y"
9345 name: "adj_y"
9390 name: "adj_y"
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| /external/tensorflow/tensorflow/tools/api/golden/v1/ |
| D | tensorflow.raw_ops.pbtxt | 317 …argspec: "args=[\'x\', \'y\', \'adj_x\', \'adj_y\', \'name\'], varargs=None, keywords=None, defaul…
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| /external/tensorflow/tensorflow/tools/api/golden/v2/ |
| D | tensorflow.raw_ops.pbtxt | 317 …argspec: "args=[\'x\', \'y\', \'adj_x\', \'adj_y\', \'name\'], varargs=None, keywords=None, defaul…
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