RunMetadataOrBuilder

공용 인터페이스 RunMetadataOrBuilder

알려진 간접 하위 클래스

공개 방법

추상 CostGraphDef	getCostGraph () The cost graph for the computation defined by the run call.
추상 CostGraphDefOrBuilder	getCostGraphOrBuilder () The cost graph for the computation defined by the run call.
추상 RunMetadata.FunctionGraphs	getFunctionGraphs (정수 인덱스) This is only populated for graphs that are run as functions in TensorFlow V2.
추상 정수	getFunctionGraphsCount () This is only populated for graphs that are run as functions in TensorFlow V2.
추상 목록< RunMetadata.FunctionGraphs >	getFunctionGraphsList () This is only populated for graphs that are run as functions in TensorFlow V2.
추상 RunMetadata.FunctionGraphsOrBuilder	getFunctionGraphsOrBuilder (정수 인덱스) This is only populated for graphs that are run as functions in TensorFlow V2.
추상 목록<? RunMetadata.FunctionGraphsOrBuilder 확장 >	getFunctionGraphsOrBuilderList () This is only populated for graphs that are run as functions in TensorFlow V2.
추상 GraphDef	getPartitionGraphs (정수 인덱스) Graphs of the partitions executed by executors.
추상 정수	getPartitionGraphsCount () Graphs of the partitions executed by executors.
추상 목록< GraphDef >	getPartitionGraphsList () Graphs of the partitions executed by executors.
추상 GraphDefOrBuilder	getPartitionGraphsOrBuilder (정수 인덱스) Graphs of the partitions executed by executors.
추상 목록<? GraphDefOrBuilder 확장 >	getPartitionGraphsOrBuilderList () Graphs of the partitions executed by executors.
추상적인 단계 통계	getStepStats () Statistics traced for this step.
추상 StepStatsOrBuilder	getStepStatsOrBuilder () Statistics traced for this step.
추상 부울	hasCost그래프 () The cost graph for the computation defined by the run call.
추상 부울	hasStepStats () Statistics traced for this step.

공개 방법

공개 추상 CostGraphDef getCostGraph ()

 The cost graph for the computation defined by the run call.

.tensorflow.CostGraphDef cost_graph = 2;

공개 추상 CostGraphDefOrBuilder getCostGraphOrBuilder ()

 The cost graph for the computation defined by the run call.

.tensorflow.CostGraphDef cost_graph = 2;

공개 추상 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 추상 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 추상 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 추상 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 요약 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;