| /external/tensorflow/tensorflow/python/tools/ |
| D | print_selective_registration_header_test.py | 87 def WriteGraphFiles(self, graphs): argument
89 for i, graph in enumerate(graphs):
104 graphs = [
110 'rawproto', self.WriteGraphFiles(graphs), default_ops)
126 graphs[0].node[0].ClearField('device')
127 graphs[0].node[2].ClearField('device')
129 'rawproto', self.WriteGraphFiles(graphs), default_ops)
198 graphs = [
203 'rawproto', self.WriteGraphFiles(graphs), default_ops)
222 self.WriteGraphFiles(graphs), 'rawproto', default_ops).split('\n'))
[all …]
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| D | print_selective_registration_header.py | 48 graphs = FLAGS.graphs.split(',')
50 selective_registration_header_lib.get_header(graphs,
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| D | selective_registration_header_lib.py | 194 def get_header(graphs, argument
211 ops_and_kernels = get_ops_and_kernels(proto_fileformat, graphs, default_ops)
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| /external/tensorflow/tensorflow/python/training/ |
| D | sync_replicas_optimizer_test.py | 37 graphs = []
81 graphs.append(graph)
84 return sessions, graphs, train_ops
101 sessions, graphs, train_ops = get_workers(num_workers,
105 var_0_g_0 = graphs[0].get_tensor_by_name("v0:0")
106 var_1_g_0 = graphs[0].get_tensor_by_name("v1:0")
107 local_step_0 = graphs[0].get_tensor_by_name("sync_rep_local_step:0")
113 var_0_g_1 = graphs[1].get_tensor_by_name("v0:0")
114 var_1_g_1 = graphs[1].get_tensor_by_name("v1:0")
115 var_sparse_g_1 = graphs[1].get_tensor_by_name("v_sparse:0")
[all …]
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| /external/llvm-project/libcxx/utils/ |
| D | graph_header_deps.py | 118 graphs = []
124 graphs += [g]
126 return graphs
128 def build_canonical_names(graphs): argument
131 for g in graphs:
142 def __init__(self, graphs): argument
143 self.graphs = list(graphs)
144 self.canonical_names = build_canonical_names(graphs)
151 for g in self.graphs:
184 graphs = post_process_outputs(outputs, args.libcxx_only)
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| /external/tensorflow/tensorflow/python/grappler/ |
| D | arithmetic_optimizer_test.py | 41 with context.collect_graphs(optimized=True) as graphs:
43 self.assertLen(graphs, 1)
44 self.assertLen(graphs[0].node, 4)
45 self.assertEqual(graphs[0].node[2].name,
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| D | constant_folding_test.py | 95 with context.collect_graphs(optimized=True) as graphs:
97 self.assertLen(graphs, 1)
99 for node in graphs[0].node:
107 self.assertLen(graphs[0].node, 11)
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| /external/tensorflow/tensorflow/python/autograph/pyct/static_analysis/ |
| D | reaching_fndefs.py | 122 def __init__(self, source_info, graphs): argument
124 self.graphs = graphs
130 subgraph = self.graphs[node]
170 def resolve(node, source_info, graphs): argument
180 visitor = TreeAnnotator(source_info, graphs)
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| D | liveness.py | 114 def __init__(self, source_info, graphs, include_annotations): argument
118 self.graphs = graphs
134 analyzer = Analyzer(self.graphs[node], self.include_annotations)
206 def resolve(node, source_info, graphs, include_annotations=True): argument
218 node = TreeAnnotator(source_info, graphs, include_annotations).visit(node)
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| D | reaching_fndefs_test.py | 48 graphs = cfg.build(node)
49 node = reaching_definitions.resolve(node, ctx, graphs)
50 node = reaching_fndefs.resolve(node, ctx, graphs)
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| D | reaching_definitions.py | 181 def __init__(self, source_info, graphs, definition_factory): argument
185 self.graphs = graphs
191 subgraph = self.graphs[node]
279 def resolve(node, source_info, graphs, definition_factory=Definition): argument
290 visitor = TreeAnnotator(source_info, graphs, definition_factory)
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| D | type_inference.py | 594 def __init__(self, source_info, graphs, resolver): argument
596 self.graphs = graphs
600 subgraph = self.graphs[node]
614 def resolve(node, source_info, graphs, resolver): argument
626 visitor = FunctionVisitor(source_info, graphs, resolver)
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| /external/tensorflow/tensorflow/python/eager/ |
| D | context_test.py | 85 with context.collect_graphs() as graphs:
88 self.assertLen(graphs, 1)
89 graph, = graphs
100 with context.collect_graphs() as graphs:
104 self.assertLen(graphs, 1)
105 graph, = graphs
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| D | gradient_input_output_exclusions.py | 208 graphs = cfg.build(node)
211 node = reaching_fndefs.resolve(node, ctx, graphs)
212 node = liveness.resolve(node, ctx, graphs)
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| /external/dagger2/java/dagger/internal/codegen/binding/ |
| D | LegacyBindingGraph.java | 71 ImmutableList<LegacyBindingGraph> graphs) { in checkForDuplicates() argument
74 Multimaps.index(graphs, graph -> graph.componentDescriptor().typeElement()).asMap(), in checkForDuplicates()
79 return graphs; in checkForDuplicates()
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| /external/conscrypt/ |
| D | settings.gradle | 8 include ":conscrypt-benchmark-graphs"
22 project(':conscrypt-benchmark-graphs').projectDir = "$rootDir/benchmark-graphs" as File
|
| /external/tensorflow/tensorflow/lite/toco/ |
| D | README.md | 3 The TensorFlow Lite Converter converts TensorFlow graphs into
4 TensorFlow Lite graphs. There are additional usages that are also detailed in
22 frozen graphs (models generated via
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| /external/tensorflow/tensorflow/python/autograph/pyct/ |
| D | cfg_test.py | 58 graphs, node = self._build_cfg(test_fn)
59 graph, = graphs.values()
80 graphs, node = self._build_cfg(test_fn)
81 graph, = graphs.values()
1035 graphs = self._build_cfg(test_fn)
1036 for k, v in graphs.items():
1064 graphs = self._build_cfg(test_fn)
1065 for k, v in graphs.items():
1093 graphs = self._build_cfg(test_fn)
1094 for k, v in graphs.items():
[all …]
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| /external/tensorflow/tensorflow/c/ |
| D | generate-pc.sh | 70 Description: Library for computation using data flow graphs for scalable machine learning
84 Description: Library for computation using data flow graphs for scalable machine learning
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| /external/tensorflow/tensorflow/python/ops/ |
| D | custom_gradient.py | 355 graphs = {getattr(o, "graph", None) for o in flat_result}
359 graphs.discard(None) # Discard non-graph outputs
360 if graphs:
361 if len(graphs) > 1:
364 output_graph = graphs.pop()
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| D | cond_v2.py | 764 def _check_same_outputs(op_type, graphs): argument
781 b0_out=graphs[0].structured_outputs,
782 bn_out=graphs[branch_idx].structured_outputs,
785 for b in range(1, len(graphs)):
788 graphs[0].structured_outputs,
789 graphs[b].structured_outputs,
795 if len(graphs[0].outputs) != len(graphs[b].outputs):
800 len_0=len(graphs[0].outputs),
802 len_b=len(graphs[b].outputs)))
803 for b0_out, bn_out in zip(graphs[0].outputs, graphs[b].outputs):
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| /external/perfetto/infra/perfetto.dev/src/assets/ |
| D | script.js | 219 const graphs = document.querySelectorAll('.mermaid');
222 if (!graphs.length)
246 for (const graph of graphs) {
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| /external/tensorflow/ |
| D | SECURITY.md | 14 [**graphs**](https://developers.google.com/machine-learning/glossary/#graph).
41 Python code that generates TensorFlow graphs.
51 It is easily possible to create computation graphs in which malicious
58 In other words, graphs can contain vulnerabilities of their own. To allow users
92 from anywhere, and executes the graphs it is sent without performing any checks.
108 graphs known to the `ModelServer`. This means that an attacker may run
109 graphs using untrusted inputs as described above, but they would not be able to
110 execute arbitrary graphs. It is possible to safely expose a `ModelServer`
111 directly to an untrusted network, **but only if the graphs it is configured to
131 Given TensorFlow's flexibility, it is possible to specify computation graphs
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| /external/llvm-project/llvm/docs/DependenceGraphs/ |
| D | index.rst | 10 Dependence graphs are useful tools in compilers for analyzing relationships
12 behind these graphs are described in papers [1]_ and [2]_.
71 The DDG and the PDG are both directed graphs and they extend the
98 Notice that the common code for building the two types of graphs are
113 - Builder allows us to create DDG and PDG as separate graphs.
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| /external/tensorflow/tensorflow/core/protobuf/ |
| D | worker_service.proto | 30 // graphs on a set of local devices, on behalf of a MasterService.
32 // A worker service keeps track of multiple "registered graphs". Each
|