パブリック静的最終クラスRunMetadata.Builder
Metadata output (i.e., non-Tensor) for a single Run() call.
tensorflow.RunMetadata パブリックメソッド
| メタデータビルダーの実行 | addAllFunctionGraphs (Iterable<? extends RunMetadata.FunctionGraphs > 値) This is only populated for graphs that are run as functions in TensorFlow V2. |
| メタデータビルダーの実行 | addAllPartitionGraphs (Iterable<? extends GraphDef > 値) Graphs of the partitions executed by executors. |
| メタデータビルダーの実行 | addFunctionGraphs (int インデックス、 RunMetadata.FunctionGraphs値) This is only populated for graphs that are run as functions in TensorFlow V2. |
| メタデータビルダーの実行 | addFunctionGraphs (int インデックス、 RunMetadata.FunctionGraphs.Builder builderForValue) This is only populated for graphs that are run as functions in TensorFlow V2. |
| メタデータビルダーの実行 | addFunctionGraphs ( RunMetadata.FunctionGraphs値) This is only populated for graphs that are run as functions in TensorFlow V2. |
| メタデータビルダーの実行 | addFunctionGraphs ( RunMetadata.FunctionGraphs.Builder builderForValue) This is only populated for graphs that are run as functions in TensorFlow V2. |
| RunMetadata.FunctionGraphs.Builder | addFunctionGraphsBuilder (int インデックス) 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. |
| メタデータビルダーの実行 | |
| メタデータビルダーの実行 | addPartitionGraphs ( GraphDef.Builder builderForValue) Graphs of the partitions executed by executors. |
| メタデータビルダーの実行 | |
| メタデータビルダーの実行 | addPartitionGraphs (int インデックス、 GraphDef.Builder builderForValue) Graphs of the partitions executed by executors. |
| グラフ定義ビルダー | addPartitionGraphsBuilder (int インデックス) Graphs of the partitions executed by executors. |
| グラフ定義ビルダー | addPartitionGraphsBuilder () Graphs of the partitions executed by executors. |
| メタデータビルダーの実行 | addRepeatedField (com.google.protobuf.Descriptors.FieldDescriptor フィールド、オブジェクト値) |
| メタデータの実行 | 建てる（） |
| メタデータの実行 | buildPartial () |
| メタデータビルダーの実行 | クリア（） |
| メタデータビルダーの実行 | クリアコストグラフ() The cost graph for the computation defined by the run call. |
| メタデータビルダーの実行 | clearField (com.google.protobuf.Descriptors.FieldDescriptor フィールド) |
| メタデータビルダーの実行 | クリア関数グラフ() This is only populated for graphs that are run as functions in TensorFlow V2. |
| メタデータビルダーの実行 | clearOneof (com.google.protobuf.Descriptors.OneofDescriptor oneof) |
| メタデータビルダーの実行 | ClearPartitionGraphs () Graphs of the partitions executed by executors. |
| メタデータビルダーの実行 | クリアステップ統計() Statistics traced for this step. |
| メタデータビルダーの実行 | クローン() |
| コストグラフ定義 | getコストグラフ() 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. |
| コストグラフ定義またはビルダー | getCostGraphOrBuilder () The cost graph for the computation defined by the run call. |
| メタデータの実行 | |
| 最終的な静的 com.google.protobuf.Descriptors.Descriptor | |
| com.google.protobuf.Descriptors.Descriptor | |
| RunMetadata.FunctionGraphs | getFunctionGraphs (int インデックス) This is only populated for graphs that are run as functions in TensorFlow V2. |
| RunMetadata.FunctionGraphs.Builder | getFunctionGraphsBuilder (int インデックス) This is only populated for graphs that are run as functions in TensorFlow V2. |
| リスト< RunMetadata.FunctionGraphs.Builder > | getFunctionGraphsBuilderList () This is only populated for graphs that are run as functions in TensorFlow V2. |
| 整数 | getFunctionGraphsCount () This is only populated for graphs that are run as functions in TensorFlow V2. |
| リスト< RunMetadata.FunctionGraphs > | getFunctionGraphsList () This is only populated for graphs that are run as functions in TensorFlow V2. |
| RunMetadata.FunctionGraphsOrBuilder | getFunctionGraphsOrBuilder (int インデックス) This is only populated for graphs that are run as functions in TensorFlow V2. |
| リスト<? RunMetadata.FunctionGraphsOrBuilderを拡張 > | getFunctionGraphsOrBuilderList () This is only populated for graphs that are run as functions in TensorFlow V2. |
| グラフ定義 | getPartitionGraphs (int インデックス) Graphs of the partitions executed by executors. |
| グラフ定義ビルダー | getPartitionGraphsBuilder (int インデックス) Graphs of the partitions executed by executors. |
| リスト< GraphDef.Builder > | getPartitionGraphsBuilderList () Graphs of the partitions executed by executors. |
| 整数 | getPartitionGraphsCount () Graphs of the partitions executed by executors. |
| リスト< GraphDef > | getPartitionGraphsList () Graphs of the partitions executed by executors. |
| グラフ定義またはビルダー | getPartitionGraphsOrBuilder (int インデックス) Graphs of the partitions executed by executors. |
| リスト<? GraphDefOrBuilder を拡張 > | getPartitionGraphsOrBuilderList () Graphs of the partitions executed by executors. |
| ステップ統計 | getStepStats () Statistics traced for this step. |
| StepStats.Builder | getStepStatsBuilder () Statistics traced for this step. |
| StepStatsOrBuilder | getStepStatsOrBuilder () Statistics traced for this step. |
| ブール値 | hasCostGraph () The cost graph for the computation defined by the run call. |
| ブール値 | hasStepStats () Statistics traced for this step. |
| 最終ブール値 | |
| メタデータビルダーの実行 | |
| メタデータビルダーの実行 | mergeFrom (com.google.protobuf.Message other) |
| メタデータビルダーの実行 | mergeFrom (com.google.protobuf.CodedInputStream 入力、com.google.protobuf.ExtensionRegistryLite extensionRegistry) |
| メタデータビルダーの実行 | |
| 最終的なRunMetadata.Builder | mergeUnknownFields (com.google.protobuf.UnknownFieldSet 不明フィールド) |
| メタデータビルダーの実行 | RemoveFunctionGraphs (int インデックス) This is only populated for graphs that are run as functions in TensorFlow V2. |
| メタデータビルダーの実行 | RemovePartitionGraphs (int インデックス) Graphs of the partitions executed by executors. |
| メタデータビルダーの実行 | |
| メタデータビルダーの実行 | setCostGraph ( CostGraphDef.Builder builderForValue) The cost graph for the computation defined by the run call. |
| メタデータビルダーの実行 | setField (com.google.protobuf.Descriptors.FieldDescriptor フィールド、オブジェクト値) |
| メタデータビルダーの実行 | setFunctionGraphs (int インデックス、 RunMetadata.FunctionGraphs値) This is only populated for graphs that are run as functions in TensorFlow V2. |
| メタデータビルダーの実行 | setFunctionGraphs (int インデックス、 RunMetadata.FunctionGraphs.Builder builderForValue) This is only populated for graphs that are run as functions in TensorFlow V2. |
| メタデータビルダーの実行 | setPartitionGraphs (int インデックス、 GraphDef.Builder builderForValue) Graphs of the partitions executed by executors. |
| メタデータビルダーの実行 | |
| メタデータビルダーの実行 | setRepeatedField (com.google.protobuf.Descriptors.FieldDescriptor フィールド、int インデックス、オブジェクト値) |
| メタデータビルダーの実行 | |
| メタデータビルダーの実行 | |
| 最終的なRunMetadata.Builder | setUnknownFields (com.google.protobuf.UnknownFieldSetknownFields) |
継承されたメソッド
パブリックメソッド
public RunMetadata.Builder addAllFunctionGraphs (Iterable<? extends RunMetadata.FunctionGraphs > 値)
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<? extends GraphDef > 値)
Graphs of the partitions executed by executors.
repeated .tensorflow.GraphDef partition_graphs = 3; public RunMetadata.Builder addFunctionGraphs (int インデックス、 RunMetadata.FunctionGraphs値)
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 (int インデックス、 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 ( RunMetadata.FunctionGraphs値)
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 ( 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 (int インデックス)
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 ()
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 (int インデックス、 GraphDef値)
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 ( GraphDef値)
Graphs of the partitions executed by executors.
repeated .tensorflow.GraphDef partition_graphs = 3; public RunMetadata.Builder addPartitionGraphs (int インデックス、 GraphDef.Builder builderForValue)
Graphs of the partitions executed by executors.
repeated .tensorflow.GraphDef partition_graphs = 3; public GraphDef.Builder addPartitionGraphsBuilder (int インデックス)
Graphs of the partitions executed by executors.
repeated .tensorflow.GraphDef partition_graphs = 3; public GraphDef.Builder addPartitionGraphsBuilder ()
Graphs of the partitions executed by executors.
repeated .tensorflow.GraphDef partition_graphs = 3; public RunMetadata.Builder addRepeatedField (com.google.protobuf.Descriptors.FieldDescriptor フィールド、オブジェクト値)
public RunMetadata.Builder clearCostGraph ()
The cost graph for the computation defined by the run call.
.tensorflow.CostGraphDef cost_graph = 2; public 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; public RunMetadata.Builder clearPartitionGraphs ()
Graphs of the partitions executed by executors.
repeated .tensorflow.GraphDef partition_graphs = 3; public 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; public CostGraphDef getCostGraph ()
The cost graph for the computation defined by the run call.
.tensorflow.CostGraphDef cost_graph = 2; public CostGraphDef.Builder getCostGraphBuilder ()
The cost graph for the computation defined by the run call.
.tensorflow.CostGraphDef cost_graph = 2; public CostGraphDefOrBuilder getCostGraphOrBuilder ()
The cost graph for the computation defined by the run call.
.tensorflow.CostGraphDef cost_graph = 2; public static Final com.google.protobuf.Descriptors.Descriptor getDescriptor ()
public com.google.protobuf.Descriptors.Descriptor getDescriptorForType ()
public RunMetadata.FunctionGraphs getFunctionGraphs (int インデックス)
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 (int インデックス)
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 List< 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; public List< 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 (int インデックス)
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; 公開リスト<? extends 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 (int インデックス)
Graphs of the partitions executed by executors.
repeated .tensorflow.GraphDef partition_graphs = 3; public GraphDef.Builder getPartitionGraphsBuilder (int インデックス)
Graphs of the partitions executed by executors.
repeated .tensorflow.GraphDef partition_graphs = 3; public List< 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; public List< GraphDef > getPartitionGraphsList ()
Graphs of the partitions executed by executors.
repeated .tensorflow.GraphDef partition_graphs = 3; public GraphDefOrBuilder getPartitionGraphsOrBuilder (int インデックス)
Graphs of the partitions executed by executors.
repeated .tensorflow.GraphDef partition_graphs = 3; 公開リスト<? extends GraphDefOrBuilder > getPartitionGraphsOrBuilderList ()
Graphs of the partitions executed by executors.
repeated .tensorflow.GraphDef partition_graphs = 3; public 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; public 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; public 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; public boolean hasCostGraph ()
The cost graph for the computation defined by the run call.
.tensorflow.CostGraphDef cost_graph = 2; public boolean 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; パブリック最終ブール値isInitialized ()
public RunMetadata.Builder mergeCostGraph ( CostGraphDef値)
The cost graph for the computation defined by the run call.
.tensorflow.CostGraphDef cost_graph = 2; public RunMetadata.Builder mergeFrom (com.google.protobuf.CodedInputStream 入力、com.google.protobuf.ExtensionRegistryLite extensionRegistry)
投げる
| IO例外 |
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public RunMetadata.Builder mergeStepStats ( StepStats値)
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 Final RunMetadata.Builder mergeUnknownFields (com.google.protobuf.UnknownFieldSet knownFields)
public RunMetadata.Builder RemoveFunctionGraphs (int インデックス)
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 (int インデックス)
Graphs of the partitions executed by executors.
repeated .tensorflow.GraphDef partition_graphs = 3; public RunMetadata.Builder setCostGraph ( CostGraphDef値)
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 setFunctionGraphs (int インデックス、 RunMetadata.FunctionGraphs値)
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 setFunctionGraphs (int インデックス、 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 (int インデックス、 GraphDef.Builder builderForValue)
Graphs of the partitions executed by executors.
repeated .tensorflow.GraphDef partition_graphs = 3; public RunMetadata.Builder setPartitionGraphs (int インデックス、 GraphDef値)
Graphs of the partitions executed by executors.
repeated .tensorflow.GraphDef partition_graphs = 3; public RunMetadata.Builder setRepeatedField (com.google.protobuf.Descriptors.FieldDescriptor フィールド、int インデックス、オブジェクト値)
public 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 ( StepStats値)
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;