공개 정적 최종 클래스 RunMetadata.Builder
Metadata output (i.e., non-Tensor) for a single Run() call.Protobuf 유형
tensorflow.RunMetadata 공개 방법
| RunMetadata.Builder | addAllFunctionGraphs (Iterable<? 확장 RunMetadata.FunctionGraphs > 값) This is only populated for graphs that are run as functions in TensorFlow V2. |
| RunMetadata.Builder | |
| RunMetadata.Builder | addFunctionGraphs (int 인덱스, RunMetadata.FunctionGraphs 값) This is only populated for graphs that are run as functions in TensorFlow V2. |
| RunMetadata.Builder | addFunctionGraphs (int 인덱스, RunMetadata.FunctionGraphs.Builder builderForValue) This is only populated for graphs that are run as functions in TensorFlow V2. |
| RunMetadata.Builder | addFunctionGraphs ( RunMetadata.FunctionGraphs 값) This is only populated for graphs that are run as functions in TensorFlow V2. |
| RunMetadata.Builder | addFunctionGraphs ( RunMetadata.FunctionGraphs.Builder builderForValue) This is only populated for graphs that are run as functions in TensorFlow V2. |
| RunMetadata.FunctionGraphs.Builder | addFunctionGraphsBuilder (정수 인덱스) This is only populated for graphs that are run as functions in TensorFlow V2. |
| RunMetadata.FunctionGraphs.Builder | addFunctionGraphsBuilder () This is only populated for graphs that are run as functions in TensorFlow V2. |
| RunMetadata.Builder | |
| RunMetadata.Builder | addPartitionGraphs ( GraphDef.Builder builderForValue) Graphs of the partitions executed by executors. |
| RunMetadata.Builder | |
| RunMetadata.Builder | addPartitionGraphs (int 인덱스, GraphDef.Builder builderForValue) Graphs of the partitions executed by executors. |
| GraphDef.Builder | addPartitionGraphsBuilder (정수 인덱스) Graphs of the partitions executed by executors. |
| GraphDef.Builder | addPartitionGraphsBuilder () Graphs of the partitions executed by executors. |
| RunMetadata.Builder | addRepeatedField (com.google.protobuf.Descriptors.FieldDescriptor 필드, 개체 값) |
| 메타데이터 실행 | 짓다 () |
| 메타데이터 실행 | 빌드부분 () |
| RunMetadata.Builder | 분명한 () |
| RunMetadata.Builder | 클리어비용그래프 () The cost graph for the computation defined by the run call. |
| RunMetadata.Builder | ClearField (com.google.protobuf.Descriptors.FieldDescriptor 필드) |
| RunMetadata.Builder | 클리어함수그래프 () This is only populated for graphs that are run as functions in TensorFlow V2. |
| RunMetadata.Builder | ClearOneof (com.google.protobuf.Descriptors.OneofDescriptor oneof) |
| RunMetadata.Builder | 클리어파티션그래프 () Graphs of the partitions executed by executors. |
| RunMetadata.Builder | 클리어스텝통계 () Statistics traced for this step. |
| RunMetadata.Builder | 클론 () |
| 비용 그래프 정의 | getCostGraph () The cost graph for the computation defined by the run call. |
| CostGraphDef.Builder | getCostGraphBuilder () The cost graph for the computation defined by the run call. |
| CostGraphDefOrBuilder | getCostGraphOrBuilder () The cost graph for the computation defined by the run call. |
| 메타데이터 실행 | |
| 최종 정적 com.google.protobuf.Descriptors.Descriptor | |
| com.google.protobuf.Descriptors.Descriptor | |
| RunMetadata.FunctionGraphs | getFunctionGraphs (정수 인덱스) This is only populated for graphs that are run as functions in TensorFlow V2. |
| RunMetadata.FunctionGraphs.Builder | getFunctionGraphsBuilder (정수 인덱스) This is only populated for graphs that are run as functions in TensorFlow V2. |
| 목록< RunMetadata.FunctionGraphs.Builder > | getFunctionGraphsBuilderList () This is only populated for graphs that are run as functions in TensorFlow V2. |
| 정수 | getFunctionGraphsCount () This is only populated for graphs that are run as functions in TensorFlow V2. |
| 목록< RunMetadata.FunctionGraphs > | getFunctionGraphsList () This is only populated for graphs that are run as functions in TensorFlow V2. |
| RunMetadata.FunctionGraphsOrBuilder | getFunctionGraphsOrBuilder (정수 인덱스) This is only populated for graphs that are run as functions in TensorFlow V2. |
| 목록<? RunMetadata.FunctionGraphsOrBuilder 확장 > | getFunctionGraphsOrBuilderList () This is only populated for graphs that are run as functions in TensorFlow V2. |
| 그래프 정의 | getPartitionGraphs (정수 인덱스) Graphs of the partitions executed by executors. |
| GraphDef.Builder | getPartitionGraphsBuilder (정수 인덱스) Graphs of the partitions executed by executors. |
| 목록< GraphDef.Builder > | getPartitionGraphsBuilderList () Graphs of the partitions executed by executors. |
| 정수 | getPartitionGraphsCount () Graphs of the partitions executed by executors. |
| 목록< GraphDef > | getPartitionGraphsList () Graphs of the partitions executed by executors. |
| GraphDefOrBuilder | getPartitionGraphsOrBuilder (정수 인덱스) Graphs of the partitions executed by executors. |
| 목록<? GraphDefOrBuilder 확장 > | getPartitionGraphsOrBuilderList () Graphs of the partitions executed by executors. |
| 단계 통계 | getStepStats () Statistics traced for this step. |
| StepStats.Builder | getStepStatsBuilder () Statistics traced for this step. |
| StepStatsOrBuilder | getStepStatsOrBuilder () Statistics traced for this step. |
| 부울 | hasCost그래프 () The cost graph for the computation defined by the run call. |
| 부울 | hasStepStats () Statistics traced for this step. |
| 최종 부울 | 초기화됨 () |
| RunMetadata.Builder | |
| RunMetadata.Builder | mergeFrom (com.google.protobuf.다른 메시지 보내기) |
| RunMetadata.Builder | mergeFrom (com.google.protobuf.CodedInputStream 입력, com.google.protobuf.ExtensionRegistryLite ExtensionRegistry) |
| RunMetadata.Builder | |
| 최종 RunMetadata.Builder | mergeUnknownFields (com.google.protobuf.UnknownFieldSet 알려지지 않은Fields) |
| RunMetadata.Builder | RemoveFunctionGraphs (int 인덱스) This is only populated for graphs that are run as functions in TensorFlow V2. |
| RunMetadata.Builder | RemovePartitionGraphs (int 인덱스) Graphs of the partitions executed by executors. |
| RunMetadata.Builder | |
| RunMetadata.Builder | setCostGraph ( CostGraphDef.Builder builderForValue) The cost graph for the computation defined by the run call. |
| RunMetadata.Builder | setField (com.google.protobuf.Descriptors.FieldDescriptor 필드, 개체 값) |
| RunMetadata.Builder | setFunctionGraphs (int 인덱스, RunMetadata.FunctionGraphs 값) This is only populated for graphs that are run as functions in TensorFlow V2. |
| RunMetadata.Builder | setFunctionGraphs (int 인덱스, RunMetadata.FunctionGraphs.Builder builderForValue) This is only populated for graphs that are run as functions in TensorFlow V2. |
| RunMetadata.Builder | setPartitionGraphs (int 인덱스, GraphDef.Builder builderForValue) Graphs of the partitions executed by executors. |
| RunMetadata.Builder | |
| RunMetadata.Builder | setRepeatedField (com.google.protobuf.Descriptors.FieldDescriptor 필드, int 인덱스, 개체 값) |
| RunMetadata.Builder | |
| RunMetadata.Builder | |
| 최종 RunMetadata.Builder | setUnknownFields (com.google.protobuf.UnknownFieldSet UnknownFields) |
상속된 메서드
공개 방법
공개 RunMetadata.Builder addAllFunctionGraphs (Iterable<? 확장 RunMetadata.FunctionGraphs > 값)
This is only populated for graphs that are run as functions in TensorFlow V2. There will be an entry below for each function that is traced. The main use cases of the post_optimization_graph and the partition_graphs is to give the caller insight into the graphs that were actually run by the runtime. Additional information (such as those in step_stats) will match these graphs. We also include the pre_optimization_graph since it is usually easier to read, and is helpful in situations where the caller wants to get a high level idea of what the built graph looks like (since the various graph optimization passes might change the structure of the graph significantly).
repeated .tensorflow.RunMetadata.FunctionGraphs function_graphs = 4; 공개 RunMetadata.Builder addAllPartitionGraphs (Iterable<? 확장 GraphDef > 값)
Graphs of the partitions executed by executors.
repeated .tensorflow.GraphDef partition_graphs = 3; 공개 RunMetadata.Builder addFunctionGraphs (int 인덱스, RunMetadata.FunctionGraphs 값)
This is only populated for graphs that are run as functions in TensorFlow V2. There will be an entry below for each function that is traced. The main use cases of the post_optimization_graph and the partition_graphs is to give the caller insight into the graphs that were actually run by the runtime. Additional information (such as those in step_stats) will match these graphs. We also include the pre_optimization_graph since it is usually easier to read, and is helpful in situations where the caller wants to get a high level idea of what the built graph looks like (since the various graph optimization passes might change the structure of the graph significantly).
repeated .tensorflow.RunMetadata.FunctionGraphs function_graphs = 4; 공개 RunMetadata.Builder addFunctionGraphs (int 인덱스, RunMetadata.FunctionGraphs.Builder builderForValue)
This is only populated for graphs that are run as functions in TensorFlow V2. There will be an entry below for each function that is traced. The main use cases of the post_optimization_graph and the partition_graphs is to give the caller insight into the graphs that were actually run by the runtime. Additional information (such as those in step_stats) will match these graphs. We also include the pre_optimization_graph since it is usually easier to read, and is helpful in situations where the caller wants to get a high level idea of what the built graph looks like (since the various graph optimization passes might change the structure of the graph significantly).
repeated .tensorflow.RunMetadata.FunctionGraphs function_graphs = 4; 공개 RunMetadata.Builder addFunctionGraphs ( RunMetadata.FunctionGraphs 값)
This is only populated for graphs that are run as functions in TensorFlow V2. There will be an entry below for each function that is traced. The main use cases of the post_optimization_graph and the partition_graphs is to give the caller insight into the graphs that were actually run by the runtime. Additional information (such as those in step_stats) will match these graphs. We also include the pre_optimization_graph since it is usually easier to read, and is helpful in situations where the caller wants to get a high level idea of what the built graph looks like (since the various graph optimization passes might change the structure of the graph significantly).
repeated .tensorflow.RunMetadata.FunctionGraphs function_graphs = 4; 공개 RunMetadata.Builder addFunctionGraphs ( RunMetadata.FunctionGraphs.Builder builderForValue)
This is only populated for graphs that are run as functions in TensorFlow V2. There will be an entry below for each function that is traced. The main use cases of the post_optimization_graph and the partition_graphs is to give the caller insight into the graphs that were actually run by the runtime. Additional information (such as those in step_stats) will match these graphs. We also include the pre_optimization_graph since it is usually easier to read, and is helpful in situations where the caller wants to get a high level idea of what the built graph looks like (since the various graph optimization passes might change the structure of the graph significantly).
repeated .tensorflow.RunMetadata.FunctionGraphs function_graphs = 4; 공개 RunMetadata.FunctionGraphs.Builder addFunctionGraphsBuilder (int 인덱스)
This is only populated for graphs that are run as functions in TensorFlow V2. There will be an entry below for each function that is traced. The main use cases of the post_optimization_graph and the partition_graphs is to give the caller insight into the graphs that were actually run by the runtime. Additional information (such as those in step_stats) will match these graphs. We also include the pre_optimization_graph since it is usually easier to read, and is helpful in situations where the caller wants to get a high level idea of what the built graph looks like (since the various graph optimization passes might change the structure of the graph significantly).
repeated .tensorflow.RunMetadata.FunctionGraphs function_graphs = 4; 공개 RunMetadata.FunctionGraphs.Builder addFunctionGraphsBuilder ()
This is only populated for graphs that are run as functions in TensorFlow V2. There will be an entry below for each function that is traced. The main use cases of the post_optimization_graph and the partition_graphs is to give the caller insight into the graphs that were actually run by the runtime. Additional information (such as those in step_stats) will match these graphs. We also include the pre_optimization_graph since it is usually easier to read, and is helpful in situations where the caller wants to get a high level idea of what the built graph looks like (since the various graph optimization passes might change the structure of the graph significantly).
repeated .tensorflow.RunMetadata.FunctionGraphs function_graphs = 4; 공개 RunMetadata.Builder addPartitionGraphs (int 인덱스, GraphDef 값)
Graphs of the partitions executed by executors.
repeated .tensorflow.GraphDef partition_graphs = 3;공개 RunMetadata.Builder addPartitionGraphs ( GraphDef.Builder builderForValue)
Graphs of the partitions executed by executors.
repeated .tensorflow.GraphDef partition_graphs = 3; 공개 RunMetadata.Builder addPartitionGraphs ( GraphDef 값)
Graphs of the partitions executed by executors.
repeated .tensorflow.GraphDef partition_graphs = 3; 공개 RunMetadata.Builder addPartitionGraphs (int 인덱스, GraphDef.Builder builderForValue)
Graphs of the partitions executed by executors.
repeated .tensorflow.GraphDef partition_graphs = 3; 공개 GraphDef.Builder addPartitionGraphsBuilder (int 인덱스)
Graphs of the partitions executed by executors.
repeated .tensorflow.GraphDef partition_graphs = 3; 공개 GraphDef.Builder addPartitionGraphsBuilder ()
Graphs of the partitions executed by executors.
repeated .tensorflow.GraphDef partition_graphs = 3; 공개 RunMetadata.Builder ClearCostGraph ()
The cost graph for the computation defined by the run call.
.tensorflow.CostGraphDef cost_graph = 2; 공개 RunMetadata.Builder 클리어FunctionGraphs ()
This is only populated for graphs that are run as functions in TensorFlow V2. There will be an entry below for each function that is traced. The main use cases of the post_optimization_graph and the partition_graphs is to give the caller insight into the graphs that were actually run by the runtime. Additional information (such as those in step_stats) will match these graphs. We also include the pre_optimization_graph since it is usually easier to read, and is helpful in situations where the caller wants to get a high level idea of what the built graph looks like (since the various graph optimization passes might change the structure of the graph significantly).
repeated .tensorflow.RunMetadata.FunctionGraphs function_graphs = 4; 공개 RunMetadata.Builder ClearPartitionGraphs ()
Graphs of the partitions executed by executors.
repeated .tensorflow.GraphDef partition_graphs = 3; 공개 RunMetadata.Builder ClearStepStats ()
Statistics traced for this step. Populated if tracing is turned on via the "RunOptions" proto. EXPERIMENTAL: The format and set of events may change in future versions.
.tensorflow.StepStats step_stats = 1; 공개 CostGraphDef getCostGraph ()
The cost graph for the computation defined by the run call.
.tensorflow.CostGraphDef cost_graph = 2; 공개 CostGraphDef.Builder getCostGraphBuilder ()
The cost graph for the computation defined by the run call.
.tensorflow.CostGraphDef cost_graph = 2; 공개 CostGraphDefOrBuilder getCostGraphOrBuilder ()
The cost graph for the computation defined by the run call.
.tensorflow.CostGraphDef cost_graph = 2; 공개 정적 최종 com.google.protobuf.Descriptors.Descriptor getDescriptor ()
공개 com.google.protobuf.Descriptors.Descriptor getDescriptorForType ()
공개 RunMetadata.FunctionGraphs getFunctionGraphs (int 인덱스)
This is only populated for graphs that are run as functions in TensorFlow V2. There will be an entry below for each function that is traced. The main use cases of the post_optimization_graph and the partition_graphs is to give the caller insight into the graphs that were actually run by the runtime. Additional information (such as those in step_stats) will match these graphs. We also include the pre_optimization_graph since it is usually easier to read, and is helpful in situations where the caller wants to get a high level idea of what the built graph looks like (since the various graph optimization passes might change the structure of the graph significantly).
repeated .tensorflow.RunMetadata.FunctionGraphs function_graphs = 4; 공개 RunMetadata.FunctionGraphs.Builder getFunctionGraphsBuilder (int 인덱스)
This is only populated for graphs that are run as functions in TensorFlow V2. There will be an entry below for each function that is traced. The main use cases of the post_optimization_graph and the partition_graphs is to give the caller insight into the graphs that were actually run by the runtime. Additional information (such as those in step_stats) will match these graphs. We also include the pre_optimization_graph since it is usually easier to read, and is helpful in situations where the caller wants to get a high level idea of what the built graph looks like (since the various graph optimization passes might change the structure of the graph significantly).
repeated .tensorflow.RunMetadata.FunctionGraphs function_graphs = 4; 공개 목록< RunMetadata.FunctionGraphs.Builder > getFunctionGraphsBuilderList ()
This is only populated for graphs that are run as functions in TensorFlow V2. There will be an entry below for each function that is traced. The main use cases of the post_optimization_graph and the partition_graphs is to give the caller insight into the graphs that were actually run by the runtime. Additional information (such as those in step_stats) will match these graphs. We also include the pre_optimization_graph since it is usually easier to read, and is helpful in situations where the caller wants to get a high level idea of what the built graph looks like (since the various graph optimization passes might change the structure of the graph significantly).
repeated .tensorflow.RunMetadata.FunctionGraphs function_graphs = 4; 공개 int getFunctionGraphsCount ()
This is only populated for graphs that are run as functions in TensorFlow V2. There will be an entry below for each function that is traced. The main use cases of the post_optimization_graph and the partition_graphs is to give the caller insight into the graphs that were actually run by the runtime. Additional information (such as those in step_stats) will match these graphs. We also include the pre_optimization_graph since it is usually easier to read, and is helpful in situations where the caller wants to get a high level idea of what the built graph looks like (since the various graph optimization passes might change the structure of the graph significantly).
repeated .tensorflow.RunMetadata.FunctionGraphs function_graphs = 4; 공개 목록< RunMetadata.FunctionGraphs > getFunctionGraphsList ()
This is only populated for graphs that are run as functions in TensorFlow V2. There will be an entry below for each function that is traced. The main use cases of the post_optimization_graph and the partition_graphs is to give the caller insight into the graphs that were actually run by the runtime. Additional information (such as those in step_stats) will match these graphs. We also include the pre_optimization_graph since it is usually easier to read, and is helpful in situations where the caller wants to get a high level idea of what the built graph looks like (since the various graph optimization passes might change the structure of the graph significantly).
repeated .tensorflow.RunMetadata.FunctionGraphs function_graphs = 4; 공개 RunMetadata.FunctionGraphsOrBuilder getFunctionGraphsOrBuilder (int 인덱스)
This is only populated for graphs that are run as functions in TensorFlow V2. There will be an entry below for each function that is traced. The main use cases of the post_optimization_graph and the partition_graphs is to give the caller insight into the graphs that were actually run by the runtime. Additional information (such as those in step_stats) will match these graphs. We also include the pre_optimization_graph since it is usually easier to read, and is helpful in situations where the caller wants to get a high level idea of what the built graph looks like (since the various graph optimization passes might change the structure of the graph significantly).
repeated .tensorflow.RunMetadata.FunctionGraphs function_graphs = 4; 공개 목록<? RunMetadata.FunctionGraphsOrBuilder > getFunctionGraphsOrBuilderList () 를 확장합니다.
This is only populated for graphs that are run as functions in TensorFlow V2. There will be an entry below for each function that is traced. The main use cases of the post_optimization_graph and the partition_graphs is to give the caller insight into the graphs that were actually run by the runtime. Additional information (such as those in step_stats) will match these graphs. We also include the pre_optimization_graph since it is usually easier to read, and is helpful in situations where the caller wants to get a high level idea of what the built graph looks like (since the various graph optimization passes might change the structure of the graph significantly).
repeated .tensorflow.RunMetadata.FunctionGraphs function_graphs = 4; 공개 GraphDef getPartitionGraphs (int 인덱스)
Graphs of the partitions executed by executors.
repeated .tensorflow.GraphDef partition_graphs = 3; 공개 GraphDef.Builder getPartitionGraphsBuilder (int 인덱스)
Graphs of the partitions executed by executors.
repeated .tensorflow.GraphDef partition_graphs = 3; 공개 목록< GraphDef.Builder > getPartitionGraphsBuilderList ()
Graphs of the partitions executed by executors.
repeated .tensorflow.GraphDef partition_graphs = 3; 공개 int getPartitionGraphsCount ()
Graphs of the partitions executed by executors.
repeated .tensorflow.GraphDef partition_graphs = 3; 공개 목록< GraphDef > getPartitionGraphsList ()
Graphs of the partitions executed by executors.
repeated .tensorflow.GraphDef partition_graphs = 3; 공개 GraphDefOrBuilder getPartitionGraphsOrBuilder (int 인덱스)
Graphs of the partitions executed by executors.
repeated .tensorflow.GraphDef partition_graphs = 3; 공개 목록<? GraphDefOrBuilder > getPartitionGraphsOrBuilderList () 를 확장합니다.
Graphs of the partitions executed by executors.
repeated .tensorflow.GraphDef partition_graphs = 3; 공개 StepStats getStepStats ()
Statistics traced for this step. Populated if tracing is turned on via the "RunOptions" proto. EXPERIMENTAL: The format and set of events may change in future versions.
.tensorflow.StepStats step_stats = 1; 공개 StepStats.Builder getStepStatsBuilder ()
Statistics traced for this step. Populated if tracing is turned on via the "RunOptions" proto. EXPERIMENTAL: The format and set of events may change in future versions.
.tensorflow.StepStats step_stats = 1; 공개 StepStatsOrBuilder getStepStatsOrBuilder ()
Statistics traced for this step. Populated if tracing is turned on via the "RunOptions" proto. EXPERIMENTAL: The format and set of events may change in future versions.
.tensorflow.StepStats step_stats = 1; 공개 부울 hasCostGraph ()
The cost graph for the computation defined by the run call.
.tensorflow.CostGraphDef cost_graph = 2; 공개 부울 hasStepStats ()
Statistics traced for this step. Populated if tracing is turned on via the "RunOptions" proto. EXPERIMENTAL: The format and set of events may change in future versions.
.tensorflow.StepStats step_stats = 1; 공개 최종 부울 isInitialized ()
공개 RunMetadata.Builder mergeCostGraph ( CostGraphDef 값)
The cost graph for the computation defined by the run call.
.tensorflow.CostGraphDef cost_graph = 2; 공개 RunMetadata.Builder mergeFrom (com.google.protobuf.CodedInputStream 입력, com.google.protobuf.ExtensionRegistryLite ExtensionRegistry)
던지기
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공개 RunMetadata.Builder mergeStepStats ( StepStats 값)
Statistics traced for this step. Populated if tracing is turned on via the "RunOptions" proto. EXPERIMENTAL: The format and set of events may change in future versions.
.tensorflow.StepStats step_stats = 1; 공개 RunMetadata.Builder RemoveFunctionGraphs (int 인덱스)
This is only populated for graphs that are run as functions in TensorFlow V2. There will be an entry below for each function that is traced. The main use cases of the post_optimization_graph and the partition_graphs is to give the caller insight into the graphs that were actually run by the runtime. Additional information (such as those in step_stats) will match these graphs. We also include the pre_optimization_graph since it is usually easier to read, and is helpful in situations where the caller wants to get a high level idea of what the built graph looks like (since the various graph optimization passes might change the structure of the graph significantly).
repeated .tensorflow.RunMetadata.FunctionGraphs function_graphs = 4; 공개 RunMetadata.Builder 제거PartitionGraphs (int 인덱스)
Graphs of the partitions executed by executors.
repeated .tensorflow.GraphDef partition_graphs = 3; 공개 RunMetadata.Builder setCostGraph ( CostGraphDef 값)
The cost graph for the computation defined by the run call.
.tensorflow.CostGraphDef cost_graph = 2; 공개 RunMetadata.Builder setCostGraph ( CostGraphDef.Builder builderForValue)
The cost graph for the computation defined by the run call.
.tensorflow.CostGraphDef cost_graph = 2; 공개 RunMetadata.Builder setFunctionGraphs (int 인덱스, RunMetadata.FunctionGraphs 값)
This is only populated for graphs that are run as functions in TensorFlow V2. There will be an entry below for each function that is traced. The main use cases of the post_optimization_graph and the partition_graphs is to give the caller insight into the graphs that were actually run by the runtime. Additional information (such as those in step_stats) will match these graphs. We also include the pre_optimization_graph since it is usually easier to read, and is helpful in situations where the caller wants to get a high level idea of what the built graph looks like (since the various graph optimization passes might change the structure of the graph significantly).
repeated .tensorflow.RunMetadata.FunctionGraphs function_graphs = 4; 공개 RunMetadata.Builder setFunctionGraphs (int 인덱스, RunMetadata.FunctionGraphs.Builder builderForValue)
This is only populated for graphs that are run as functions in TensorFlow V2. There will be an entry below for each function that is traced. The main use cases of the post_optimization_graph and the partition_graphs is to give the caller insight into the graphs that were actually run by the runtime. Additional information (such as those in step_stats) will match these graphs. We also include the pre_optimization_graph since it is usually easier to read, and is helpful in situations where the caller wants to get a high level idea of what the built graph looks like (since the various graph optimization passes might change the structure of the graph significantly).
repeated .tensorflow.RunMetadata.FunctionGraphs function_graphs = 4; 공개 RunMetadata.Builder setPartitionGraphs (int 인덱스, GraphDef.Builder builderForValue)
Graphs of the partitions executed by executors.
repeated .tensorflow.GraphDef partition_graphs = 3; 공개 RunMetadata.Builder setPartitionGraphs (int 인덱스, GraphDef 값)
Graphs of the partitions executed by executors.
repeated .tensorflow.GraphDef partition_graphs = 3; 공개 RunMetadata.Builder setRepeatedField (com.google.protobuf.Descriptors.FieldDescriptor 필드, int 인덱스, 개체 값)
공개 RunMetadata.Builder setStepStats ( StepStats.Builder builderForValue)
Statistics traced for this step. Populated if tracing is turned on via the "RunOptions" proto. EXPERIMENTAL: The format and set of events may change in future versions.
.tensorflow.StepStats step_stats = 1; 공개 RunMetadata.Builder setStepStats ( StepStats 값)
Statistics traced for this step. Populated if tracing is turned on via the "RunOptions" proto. EXPERIMENTAL: The format and set of events may change in future versions.
.tensorflow.StepStats step_stats = 1;