общедоступный статический конечный класс RunMetadata.Builder
Metadata output (i.e., non-Tensor) for a single Run() call.Тип Protobuf
tensorflow.RunMetadata Публичные методы
| ЗапуститьМетаданные.Builder | addAllFunctionGraphs (Iterable<? расширяет значения RunMetadata.FunctionGraphs >) This is only populated for graphs that are run as functions in TensorFlow V2. |
| ЗапуститьМетаданные.Builder | addAllPartitionGraphs (Iterable<? расширяет значения GraphDef >) Graphs of the partitions executed by executors. |
| ЗапуститьМетаданные.Builder | addFunctionGraphs (индекс int, значение RunMetadata.FunctionGraphs ) This is only populated for graphs that are run as functions in TensorFlow V2. |
| ЗапуститьМетаданные.Builder | addFunctionGraphs (индекс int, RunMetadata.FunctionGraphs.Builder builderForValue) This is only populated for graphs that are run as functions in TensorFlow V2. |
| ЗапуститьМетаданные.Builder | addFunctionGraphs (значение RunMetadata.FunctionGraphs ) This is only populated for graphs that are run as functions in TensorFlow V2. |
| ЗапуститьМетаданные.Builder | addFunctionGraphs ( RunMetadata.FunctionGraphs.Builder builderForValue) This is only populated for graphs that are run as functions in TensorFlow V2. |
| RunMetadata.FunctionGraphs.Builder | addFunctionGraphsBuilder (индекс int) 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. |
| ЗапуститьМетаданные.Builder | |
| ЗапуститьМетаданные.Builder | addPartitionGraphs ( GraphDef.Builder builderForValue) Graphs of the partitions executed by executors. |
| ЗапуститьМетаданные.Builder | |
| ЗапуститьМетаданные.Builder | addPartitionGraphs (индекс int, GraphDef.Builder builderForValue) Graphs of the partitions executed by executors. |
| GraphDef.Builder | addPartitionGraphsBuilder (индекс int) Graphs of the partitions executed by executors. |
| GraphDef.Builder | addPartitionGraphsBuilder () Graphs of the partitions executed by executors. |
| ЗапуститьМетаданные.Builder | addRepeatedField (поле com.google.protobuf.Descriptors.FieldDescriptor, значение объекта) |
| Запустить метаданные | строить () |
| Запустить метаданные | |
| ЗапуститьМетаданные.Builder | прозрачный () |
| ЗапуститьМетаданные.Builder | ClearCostGraph () The cost graph for the computation defined by the run call. |
| ЗапуститьМетаданные.Builder | ClearField (поле com.google.protobuf.Descriptors.FieldDescriptor) |
| ЗапуститьМетаданные.Builder | ОчиститьФункционГрафс () This is only populated for graphs that are run as functions in TensorFlow V2. |
| ЗапуститьМетаданные.Builder | ClearOneof (com.google.protobuf.Descriptors.OneofDescriptor oneof) |
| ЗапуститьМетаданные.Builder | ОчиститьПартиционГрафс () Graphs of the partitions executed by executors. |
| ЗапуститьМетаданные.Builder | очиститьстепстатс () Statistics traced for this step. |
| ЗапуститьМетаданные.Builder | клон () |
| СтоимостьGraphDef | 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. |
| СтоимостьGraphDefOrBuilder | getCostGraphOrBuilder () The cost graph for the computation defined by the run call. |
| Запустить метаданные | |
| окончательный статический com.google.protobuf.Descriptors.Descriptor | |
| com.google.protobuf.Descriptors.Descriptor | |
| RunMetadata.FunctionGraphs | getFunctionGraphs (индекс целого числа) This is only populated for graphs that are run as functions in TensorFlow V2. |
| RunMetadata.FunctionGraphs.Builder | getFunctionGraphsBuilder (индекс int) This is only populated for graphs that are run as functions in TensorFlow V2. |
| Список < RunMetadata.FunctionGraphs.Builder > | getFunctionGraphsBuilderList () This is only populated for graphs that are run as functions in TensorFlow V2. |
| интервал | getFunctionGraphsCount () This is only populated for graphs that are run as functions in TensorFlow V2. |
| Список < RunMetadata.FunctionGraphs > | getFunctionGraphsList () This is only populated for graphs that are run as functions in TensorFlow V2. |
| RunMetadata.FunctionGraphsOrBuilder | getFunctionGraphsOrBuilder (индекс int) This is only populated for graphs that are run as functions in TensorFlow V2. |
| Список<? расширяет RunMetadata.FunctionGraphsOrBuilder > | getFunctionGraphsOrBuilderList () This is only populated for graphs that are run as functions in TensorFlow V2. |
| ГрафDef | getPartitionGraphs (индекс int) Graphs of the partitions executed by executors. |
| GraphDef.Builder | getPartitionGraphsBuilder (индекс int) Graphs of the partitions executed by executors. |
| Список< GraphDef.Builder > | getPartitionGraphsBuilderList () Graphs of the partitions executed by executors. |
| интервал | getPartitionGraphsCount () Graphs of the partitions executed by executors. |
| Список <GraphDef> | getPartitionGraphsList () Graphs of the partitions executed by executors. |
| ГрафDefOrBuilder | getPartitionGraphsOrBuilder (индекс int) Graphs of the partitions executed by executors. |
| Список<? расширяет GraphDefOrBuilder > | getPartitionGraphsOrBuilderList () Graphs of the partitions executed by executors. |
| Степстатс | getStepStats () Statistics traced for this step. |
| StepStats.Builder | getStepStatsBuilder () Statistics traced for this step. |
| Стептстатсорбилдер | getStepStatsOrBuilder () Statistics traced for this step. |
| логическое значение | имеетCostGraph () The cost graph for the computation defined by the run call. |
| логическое значение | имеетСтепстатс () Statistics traced for this step. |
| последнее логическое значение | |
| ЗапуститьМетаданные.Builder | |
| ЗапуститьМетаданные.Builder | mergeFrom (com.google.protobuf.Message другое) |
| ЗапуститьМетаданные.Builder | mergeFrom (ввод com.google.protobuf.CodedInputStream, com.google.protobuf.ExtensionRegistryLite ExtensionRegistry) |
| ЗапуститьМетаданные.Builder | |
| окончательный RunMetadata.Builder | mergeUnknownFields (com.google.protobuf.UnknownFieldSetknownFields) |
| ЗапуститьМетаданные.Builder | RemoveFunctionGraphs (индекс int) This is only populated for graphs that are run as functions in TensorFlow V2. |
| ЗапуститьМетаданные.Builder | удалитьPartitionGraphs (индекс int) Graphs of the partitions executed by executors. |
| ЗапуститьМетаданные.Builder | |
| ЗапуститьМетаданные.Builder | setCostGraph ( CostGraphDef.Builder builderForValue) The cost graph for the computation defined by the run call. |
| ЗапуститьМетаданные.Builder | setField (поле com.google.protobuf.Descriptors.FieldDescriptor, значение объекта) |
| ЗапуститьМетаданные.Builder | setFunctionGraphs (индекс int, значение RunMetadata.FunctionGraphs ) This is only populated for graphs that are run as functions in TensorFlow V2. |
| ЗапуститьМетаданные.Builder | setFunctionGraphs (индекс int, RunMetadata.FunctionGraphs.Builder builderForValue) This is only populated for graphs that are run as functions in TensorFlow V2. |
| ЗапуститьМетаданные.Builder | setPartitionGraphs (индекс int, GraphDef.Builder builderForValue) Graphs of the partitions executed by executors. |
| ЗапуститьМетаданные.Builder | |
| ЗапуститьМетаданные.Builder | setRepeatedField (поле com.google.protobuf.Descriptors.FieldDescriptor, индекс int, значение объекта) |
| ЗапуститьМетаданные.Builder | |
| ЗапуститьМетаданные.Builder | |
| окончательный RunMetadata.Builder | setUnknownFields (com.google.protobuf.UnknownFieldSetknownFields) |
Унаследованные методы
Публичные методы
public RunMetadata.Builder addAllFunctionGraphs (Iterable<? расширяет значения RunMetadata.FunctionGraphs >)
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; public RunMetadata.Builder addAllPartitionGraphs (Iterable<? расширяет значения GraphDef >)
Graphs of the partitions executed by executors.
repeated .tensorflow.GraphDef partition_graphs = 3; public RunMetadata.Builder addFunctionGraphs (индекс int, значение RunMetadata.FunctionGraphs )
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; public RunMetadata.Builder addFunctionGraphs (индекс int, 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; public RunMetadata.Builder addFunctionGraphs (значение RunMetadata.FunctionGraphs )
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; общедоступный 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; public RunMetadata.FunctionGraphs.Builder addFunctionGraphsBuilder (индекс int)
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; общедоступный 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; public RunMetadata.Builder addPartitionGraphs (индекс int, значение GraphDef )
Graphs of the partitions executed by executors.
repeated .tensorflow.GraphDef partition_graphs = 3;public RunMetadata.Builder addPartitionGraphs ( GraphDef.Builder builderForValue)
Graphs of the partitions executed by executors.
repeated .tensorflow.GraphDef partition_graphs = 3; public RunMetadata.Builder addPartitionGraphs (значение GraphDef )
Graphs of the partitions executed by executors.
repeated .tensorflow.GraphDef partition_graphs = 3; public RunMetadata.Builder addPartitionGraphs (индекс int, GraphDef.Builder builderForValue)
Graphs of the partitions executed by executors.
repeated .tensorflow.GraphDef partition_graphs = 3; public GraphDef.Builder addPartitionGraphsBuilder (индекс int)
Graphs of the partitions executed by executors.
repeated .tensorflow.GraphDef partition_graphs = 3; общедоступный GraphDef.Builder addPartitionGraphsBuilder ()
Graphs of the partitions executed by executors.
repeated .tensorflow.GraphDef partition_graphs = 3; public RunMetadata.Builder addRepeatedField (поле com.google.protobuf.Descriptors.FieldDescriptor, значение объекта)
общедоступный RunMetadata.Builder ClearCostGraph ()
The cost graph for the computation defined by the run call.
.tensorflow.CostGraphDef cost_graph = 2; общедоступный RunMetadata.Builder ClearFunctionGraphs ()
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; общедоступный RunMetadata.Builder ClearOneof (com.google.protobuf.Descriptors.OneofDescriptor oneof)
общедоступный RunMetadata.Builder ClearPartitionGraphs ()
Graphs of the partitions executed by executors.
repeated .tensorflow.GraphDef partition_graphs = 3; общедоступный 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; общественный CostGraphDef getCostGraph ()
The cost graph for the computation defined by the run call.
.tensorflow.CostGraphDef cost_graph = 2; общедоступный CostGraphDef.Builder getCostGraphBuilder ()
The cost graph for the computation defined by the run call.
.tensorflow.CostGraphDef cost_graph = 2; общедоступный CostGraphDefOrBuilder getCostGraphOrBuilder ()
The cost graph for the computation defined by the run call.
.tensorflow.CostGraphDef cost_graph = 2; общедоступный статический окончательный com.google.protobuf.Descriptors.Descriptor getDescriptor ()
общедоступный com.google.protobuf.Descriptors.Descriptor getDescriptorForType ()
public RunMetadata.FunctionGraphs getFunctionGraphs (индекс int)
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; public RunMetadata.FunctionGraphs.Builder getFunctionGraphsBuilder (индекс int)
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; общедоступный список < 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; public 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; общедоступный список < 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; public RunMetadata.FunctionGraphsOrBuilder getFunctionGraphsOrBuilder (индекс int)
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; общедоступный список<? расширяет RunMetadata.FunctionGraphsOrBuilder > getFunctionGraphsOrBuilderList ()
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; public GraphDef getPartitionGraphs (индекс int)
Graphs of the partitions executed by executors.
repeated .tensorflow.GraphDef partition_graphs = 3; public GraphDef.Builder getPartitionGraphsBuilder (индекс int)
Graphs of the partitions executed by executors.
repeated .tensorflow.GraphDef partition_graphs = 3; общедоступный список < GraphDef.Builder > getPartitionGraphsBuilderList ()
Graphs of the partitions executed by executors.
repeated .tensorflow.GraphDef partition_graphs = 3; public int getPartitionGraphsCount ()
Graphs of the partitions executed by executors.
repeated .tensorflow.GraphDef partition_graphs = 3; общедоступный список < GraphDef > getPartitionGraphsList ()
Graphs of the partitions executed by executors.
repeated .tensorflow.GraphDef partition_graphs = 3; public GraphDefOrBuilder getPartitionGraphsOrBuilder (индекс int)
Graphs of the partitions executed by executors.
repeated .tensorflow.GraphDef partition_graphs = 3; общедоступный список<? расширяет GraphDefOrBuilder > getPartitionGraphsOrBuilderList ()
Graphs of the partitions executed by executors.
repeated .tensorflow.GraphDef partition_graphs = 3; общедоступная 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; общедоступный 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; общедоступный 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; общедоступное логическое значение hasCostGraph ()
The cost graph for the computation defined by the run call.
.tensorflow.CostGraphDef cost_graph = 2; общедоступное логическое значение 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; публичное финальное логическое значение isInitialized ()
public RunMetadata.Builder mergeCostGraph (значение CostGraphDef )
The cost graph for the computation defined by the run call.
.tensorflow.CostGraphDef cost_graph = 2; общедоступный RunMetadata.Builder mergeFrom (вход com.google.protobuf.CodedInputStream, com.google.protobuf.ExtensionRegistryLite ExtensionRegistry)
Броски
| Исключение IO |
|---|
public RunMetadata.Builder mergeStepStats (значение StepStats )
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; публичный финал RunMetadata.Builder mergeUnknownFields (com.google.protobuf.UnknownFieldSetknownFields)
public RunMetadata.Builder RemoveFunctionGraphs (индекс int)
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; public RunMetadata.Builder RemovePartitionGraphs (индекс int)
Graphs of the partitions executed by executors.
repeated .tensorflow.GraphDef partition_graphs = 3; public RunMetadata.Builder setCostGraph (значение CostGraphDef )
The cost graph for the computation defined by the run call.
.tensorflow.CostGraphDef cost_graph = 2; public RunMetadata.Builder setCostGraph ( CostGraphDef.Builder builderForValue)
The cost graph for the computation defined by the run call.
.tensorflow.CostGraphDef cost_graph = 2; public RunMetadata.Builder setField (поле com.google.protobuf.Descriptors.FieldDescriptor, значение объекта)
public RunMetadata.Builder setFunctionGraphs (индекс int, значение RunMetadata.FunctionGraphs )
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; общедоступный RunMetadata.Builder setFunctionGraphs (индекс int, 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; public RunMetadata.Builder setPartitionGraphs (индекс int, GraphDef.Builder builderForValue)
Graphs of the partitions executed by executors.
repeated .tensorflow.GraphDef partition_graphs = 3; public RunMetadata.Builder setPartitionGraphs (индекс int, значение GraphDef )
Graphs of the partitions executed by executors.
repeated .tensorflow.GraphDef partition_graphs = 3; public RunMetadata.Builder setRepeatedField (поле com.google.protobuf.Descriptors.FieldDescriptor, индекс int, значение объекта)
общедоступный 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; public RunMetadata.Builder setStepStats (значение StepStats )
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;