RunMetadata.Builder

RunMetadata.Builder คลาสสุดท้ายแบบคงที่สาธารณะ

 Metadata output (i.e., non-Tensor) for a single Run() call.
 
Protobuf ประเภท tensorflow.RunMetadata

วิธีการสาธารณะ

RunMetadata.Builder
addAllFunctionGraphs (Iterable<? ขยาย RunMetadata.FunctionGraphs > ค่า)
 This is only populated for graphs that are run as functions in TensorFlow
 V2.
RunMetadata.Builder
addAllPartitionGraphs (Iterable<? ขยาย GraphDef > ค่า)
 Graphs of the partitions executed by executors.
RunMetadata.Builder
addFunctionGraphs (ดัชนี int, ค่า RunMetadata.FunctionGraphs )
 This is only populated for graphs that are run as functions in TensorFlow
 V2.
RunMetadata.Builder
addFunctionGraphs (ดัชนี int, RunMetadata.FunctionGraphs.Builder builderForValue)
 This is only populated for graphs that are run as functions in TensorFlow
 V2.
RunMetadata.Builder
addFunctionGraphs (ค่า RunMetadata.FunctionGraphs )
 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 (ดัชนี 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.
RunMetadata.Builder
addPartitionGraphs (ดัชนี int, ค่า GraphDef )
 Graphs of the partitions executed by executors.
RunMetadata.Builder
addPartitionGraphs ( GraphDef.Builder builderForValue)
 Graphs of the partitions executed by executors.
RunMetadata.Builder
addPartitionGraphs (ค่า GraphDef )
 Graphs of the partitions executed by executors.
RunMetadata.Builder
addPartitionGraphs (ดัชนี int, GraphDef.Builder builderForValue)
 Graphs of the partitions executed by executors.
GraphDef.Builder
addPartitionGraphsBuilder (ดัชนี int)
 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 ช่อง ค่าอ็อบเจ็กต์)
เรียกใช้ Metadata
เรียกใช้ Metadata
RunMetadata.Builder
RunMetadata.Builder
ล้างกราฟต้นทุน ()
 The cost graph for the computation defined by the run call.
RunMetadata.Builder
clearField (ฟิลด์ com.google.protobuf.Descriptors.FieldDescriptor)
RunMetadata.Builder
clearFunctionGraphs ()
 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
clearPartitionGraphs ()
 Graphs of the partitions executed by executors.
RunMetadata.Builder
clearStepStats ()
 Statistics traced for this step.
RunMetadata.Builder
กราฟต้นทุนDef
รับต้นทุนกราฟ ()
 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.
ราคากราฟDefOrBuilder
getCostGraphOrBuilder ()
 The cost graph for the computation defined by the run call.
เรียกใช้ Metadata
com.google.protobuf.Descriptors.Descriptor แบบคงที่ขั้นสุดท้าย
com.google.protobuf.Descriptors.Descriptor
RunMetadata.FunctionGraphs
getFunctionGraphs (ดัชนี int)
 This is only populated for graphs that are run as functions in TensorFlow
 V2.
RunMetadata.FunctionGraphs.Builder
getFunctionGraphsBuilder (ดัชนี int)
 This is only populated for graphs that are run as functions in TensorFlow
 V2.
รายการ < RunMetadata.FunctionGraphs.Builder >
getFunctionGraphsBuilderList ()
 This is only populated for graphs that are run as functions in TensorFlow
 V2.
ภายใน
getFunctionGraphsCount ()
 This is only populated for graphs that are run as functions in TensorFlow
 V2.
รายการ < RunMetadata.FunctionGraphs >
getFunctionGraphsList ()
 This is only populated for graphs that are run as functions in TensorFlow
 V2.
RunMetadata.FunctionGraphsOrBuilder
getFunctionGraphsOrBuilder (ดัชนี int)
 This is only populated for graphs that are run as functions in TensorFlow
 V2.
รายการ<? ขยาย RunMetadata.FunctionGraphsOrBuilder >
getFunctionGraphsOrBuilderList ()
 This is only populated for graphs that are run as functions in TensorFlow
 V2.
กราฟDef
getPartitionGraphs (ดัชนี int)
 Graphs of the partitions executed by executors.
GraphDef.Builder
getPartitionGraphsBuilder (ดัชนี int)
 Graphs of the partitions executed by executors.
รายการ < GraphDef.Builder >
getPartitionGraphsBuilderList ()
 Graphs of the partitions executed by executors.
ภายใน
getPartitionGraphsCount ()
 Graphs of the partitions executed by executors.
รายการ < GraphDef >
getPartitionGraphsList ()
 Graphs of the partitions executed by executors.
GraphDefOrBuilder
getPartitionGraphsOrBuilder (ดัชนี int)
 Graphs of the partitions executed by executors.
รายการ<? ขยาย GraphDefOrBuilder >
getPartitionGraphsOrBuilderList ()
 Graphs of the partitions executed by executors.
StepStats
getStepStats ()
 Statistics traced for this step.
StepStats.Builder
getStepStatsBuilder ()
 Statistics traced for this step.
StepStatsOrBuilder
getStepStatsOrBuilder ()
 Statistics traced for this step.
บูลีน
มีกราฟต้นทุน ()
 The cost graph for the computation defined by the run call.
บูลีน
hasStepStats ()
 Statistics traced for this step.
บูลีนสุดท้าย
RunMetadata.Builder
mergeCostGraph (ค่า CostGraphDef )
 The cost graph for the computation defined by the run call.
RunMetadata.Builder
mergeFrom (com.google.protobuf.ข้อความ อื่น ๆ )
RunMetadata.Builder
mergeFrom (com.google.protobuf.CodedInputStream อินพุต com.google.protobuf.ExtensionRegistryLite extensionRegistry)
RunMetadata.Builder
mergeStepStats (ค่า StepStats )
 Statistics traced for this step.
RunMetadata.Builder สุดท้าย
mergeUnknownFields (com.google.protobuf.UnknownFieldSetknownFields)
RunMetadata.Builder
RemoveFunctionGraphs (ดัชนี int)
 This is only populated for graphs that are run as functions in TensorFlow
 V2.
RunMetadata.Builder
RemovePartitionGraphs (ดัชนี int)
 Graphs of the partitions executed by executors.
RunMetadata.Builder
setCostGraph (ค่า CostGraphDef )
 The cost graph for the computation defined by the run call.
RunMetadata.Builder
setCostGraph ( CostGraphDef.Builder builderForValue)
 The cost graph for the computation defined by the run call.
RunMetadata.Builder
setField (ฟิลด์ com.google.protobuf.Descriptors.FieldDescriptor ค่าอ็อบเจ็กต์)
RunMetadata.Builder
setFunctionGraphs (ดัชนี int, ค่า RunMetadata.FunctionGraphs )
 This is only populated for graphs that are run as functions in TensorFlow
 V2.
RunMetadata.Builder
setFunctionGraphs (ดัชนี int, RunMetadata.FunctionGraphs.Builder builderForValue)
 This is only populated for graphs that are run as functions in TensorFlow
 V2.
RunMetadata.Builder
setPartitionGraphs (ดัชนี int, GraphDef.Builder builderForValue)
 Graphs of the partitions executed by executors.
RunMetadata.Builder
setPartitionGraphs (ดัชนี int, ค่า GraphDef )
 Graphs of the partitions executed by executors.
RunMetadata.Builder
setRepeatedField (ฟิลด์ com.google.protobuf.Descriptors.FieldDescriptor, ดัชนี int, ค่าอ็อบเจ็กต์)
RunMetadata.Builder
setStepStats ( StepStats.Builder builderForValue)
 Statistics traced for this step.
RunMetadata.Builder
setStepStats (ค่า StepStats )
 Statistics traced for this step.
RunMetadata.Builder สุดท้าย
setUnknownFields (com.google.protobuf.UnknownFieldSetknownFields)

วิธีการสืบทอด

วิธีการสาธารณะ

RunMetadata.Builder สาธารณะ addAllFunctionGraphs (Iterable<? ขยาย RunMetadata.FunctionGraphs > ค่า)

 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;

RunMetadata.Builder สาธารณะ addAllPartitionGraphs (Iterable <? ขยาย GraphDef > ค่า)

 Graphs of the partitions executed by executors.
 
repeated .tensorflow.GraphDef partition_graphs = 3;

RunMetadata.Builder addFunctionGraphs สาธารณะ (ดัชนี int, ค่า 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;

RunMetadata.Builder addFunctionGraphs สาธารณะ (ดัชนี 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;

RunMetadata.Builder addFunctionGraphs สาธารณะ (ค่า 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;

RunMetadata.Builder addFunctionGraphs สาธารณะ ( 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;

RunMetadata.FunctionGraphs.Builder สาธารณะ addFunctionGraphsBuilder (ดัชนี 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;

RunMetadata.FunctionGraphs.Builder สาธารณะ 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;

RunMetadata.Builder addPartitionGraphs สาธารณะ (ดัชนี int, ค่า GraphDef )

 Graphs of the partitions executed by executors.
 
repeated .tensorflow.GraphDef partition_graphs = 3;

RunMetadata.Builder สาธารณะ addPartitionGraphs ( GraphDef.Builder builderForValue)

 Graphs of the partitions executed by executors.
 
repeated .tensorflow.GraphDef partition_graphs = 3;

RunMetadata.Builder addPartitionGraphs สาธารณะ (ค่า GraphDef )

 Graphs of the partitions executed by executors.
 
repeated .tensorflow.GraphDef partition_graphs = 3;

RunMetadata.Builder addPartitionGraphs สาธารณะ (ดัชนี int, GraphDef.Builder builderForValue)

 Graphs of the partitions executed by executors.
 
repeated .tensorflow.GraphDef partition_graphs = 3;

GraphDef.Builder สาธารณะ addPartitionGraphsBuilder (ดัชนี int)

 Graphs of the partitions executed by executors.
 
repeated .tensorflow.GraphDef partition_graphs = 3;

GraphDef.Builder สาธารณะ addPartitionGraphsBuilder ()

 Graphs of the partitions executed by executors.
 
repeated .tensorflow.GraphDef partition_graphs = 3;

RunMetadata.Builder addRepeatedField สาธารณะ (ฟิลด์ com.google.protobuf.Descriptors.FieldDescriptor ค่าอ็อบเจ็กต์)

การสร้าง RunMetadata สาธารณะ ()

สาธารณะ RunMetadata buildPartial ()

RunMetadata.Builder สาธารณะ ชัดเจน ()

RunMetadata.Builder สาธารณะ clearCostGraph ()

 The cost graph for the computation defined by the run call.
 
.tensorflow.CostGraphDef cost_graph = 2;

RunMetadata.Builder clearField สาธารณะ (ฟิลด์ com.google.protobuf.Descriptors.FieldDescriptor)

RunMetadata.Builder สาธารณะ 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;

RunMetadata.Builder สาธารณะ clearOneof (com.google.protobuf.Descriptors.OneofDescriptor oneof)

RunMetadata.Builder สาธารณะ clearPartitionGraphs ()

 Graphs of the partitions executed by executors.
 
repeated .tensorflow.GraphDef partition_graphs = 3;

RunMetadata.Builder สาธารณะ 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;

โคลน RunMetadata.Builder สาธารณะ ()

CostGraphDef สาธารณะ getCostGraph ()

 The cost graph for the computation defined by the run call.
 
.tensorflow.CostGraphDef cost_graph = 2;

CostGraphDef.Builder สาธารณะ getCostGraphBuilder ()

 The cost graph for the computation defined by the run call.
 
.tensorflow.CostGraphDef cost_graph = 2;

CostGraphDefOrBuilder สาธารณะ getCostGraphOrBuilder ()

 The cost graph for the computation defined by the run call.
 
.tensorflow.CostGraphDef cost_graph = 2;

RunMetadata สาธารณะ getDefaultInstanceForType ()

สาธารณะคงที่สุดท้าย com.google.protobuf.Descriptors.Descriptor getDescriptor ()

สาธารณะ com.google.protobuf.Descriptors.Descriptor getDescriptorForType ()

RunMetadata.FunctionGraphs สาธารณะ getFunctionGraphs (ดัชนี 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;

RunMetadata.FunctionGraphs.Builder สาธารณะ getFunctionGraphsBuilder (ดัชนี 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;

รายการสาธารณะ < 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;

สาธารณะ 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;

รายการสาธารณะ < 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;

RunMetadata.FunctionGraphsOrBuilder สาธารณะ getFunctionGraphsOrBuilder (ดัชนี 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;

รายการสาธารณะ<? ขยาย 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;

GraphDef getPartitionGraphs สาธารณะ (ดัชนี int)

 Graphs of the partitions executed by executors.
 
repeated .tensorflow.GraphDef partition_graphs = 3;

GraphDef.Builder สาธารณะ getPartitionGraphsBuilder (ดัชนี int)

 Graphs of the partitions executed by executors.
 
repeated .tensorflow.GraphDef partition_graphs = 3;

รายการสาธารณะ < GraphDef.Builder > getPartitionGraphsBuilderList ()

 Graphs of the partitions executed by executors.
 
repeated .tensorflow.GraphDef partition_graphs = 3;

int สาธารณะ getPartitionGraphsCount ()

 Graphs of the partitions executed by executors.
 
repeated .tensorflow.GraphDef partition_graphs = 3;

รายการสาธารณะ < GraphDef > getPartitionGraphsList ()

 Graphs of the partitions executed by executors.
 
repeated .tensorflow.GraphDef partition_graphs = 3;

GraphDefOrBuilder สาธารณะ getPartitionGraphsOrBuilder (ดัชนี int)

 Graphs of the partitions executed by executors.
 
repeated .tensorflow.GraphDef partition_graphs = 3;

รายการสาธารณะ<? ขยาย GraphDefOrBuilder > getPartitionGraphsOrBuilderList ()

 Graphs of the partitions executed by executors.
 
repeated .tensorflow.GraphDef partition_graphs = 3;

StepStats สาธารณะ 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;

StepStats.Builder สาธารณะ 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;

StepStatsOrBuilder สาธารณะ 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;

hasCostGraph บูลีนสาธารณะ ()

 The cost graph for the computation defined by the run call.
 
.tensorflow.CostGraphDef cost_graph = 2;

hasStepStats บูลีนสาธารณะ ()

 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;

บูลีนสุดท้ายสาธารณะ isInitialized ()

RunMetadata.Builder สาธารณะ ผสานCostGraph (ค่า CostGraphDef )

 The cost graph for the computation defined by the run call.
 
.tensorflow.CostGraphDef cost_graph = 2;

RunMetadata.Builder สาธารณะ ผสานจาก (com.google.protobuf.Message อื่น ๆ )

RunMetadata.Builder สาธารณะ ผสานจาก (com.google.protobuf.CodedInputStream อินพุต com.google.protobuf.ExtensionRegistryLite extensionRegistry)

ขว้าง
IOข้อยกเว้น

RunMetadata.Builder สาธารณะ ผสาน StepStats (ค่า 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;

RunMetadata.Builder สาธารณะขั้นสุดท้าย ผสาน UnknownFields (com.google.protobuf.UnknownFieldSetknownFields)

RunMetadata.Builder สาธารณะ RemoveFunctionGraphs (ดัชนี 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;

RunMetadata.Builder สาธารณะ RemovePartitionGraphs (ดัชนี int)

 Graphs of the partitions executed by executors.
 
repeated .tensorflow.GraphDef partition_graphs = 3;

RunMetadata.Builder สาธารณะ setCostGraph (ค่า CostGraphDef )

 The cost graph for the computation defined by the run call.
 
.tensorflow.CostGraphDef cost_graph = 2;

RunMetadata.Builder สาธารณะ setCostGraph ( CostGraphDef.Builder builderForValue)

 The cost graph for the computation defined by the run call.
 
.tensorflow.CostGraphDef cost_graph = 2;

RunMetadata.Builder setField สาธารณะ (ฟิลด์ com.google.protobuf.Descriptors.FieldDescriptor ค่าอ็อบเจ็กต์)

RunMetadata.Builder setFunctionGraphs สาธารณะ (ดัชนี int, ค่า 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;

RunMetadata.Builder setFunctionGraphs สาธารณะ (ดัชนี 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;

RunMetadata.Builder สาธารณะ setPartitionGraphs (ดัชนี int, GraphDef.Builder builderForValue)

 Graphs of the partitions executed by executors.
 
repeated .tensorflow.GraphDef partition_graphs = 3;

RunMetadata.Builder setPartitionGraphs สาธารณะ (ดัชนี int, ค่า GraphDef )

 Graphs of the partitions executed by executors.
 
repeated .tensorflow.GraphDef partition_graphs = 3;

RunMetadata.Builder setRepeatedField สาธารณะ (ฟิลด์ com.google.protobuf.Descriptors.FieldDescriptor, ดัชนี int, ค่าอ็อบเจ็กต์)

RunMetadata.Builder setStepStats สาธารณะ ( 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;

RunMetadata.Builder setStepStats สาธารณะ (ค่า 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;

RunMetadata.Builder สุดท้ายสาธารณะ setUnknownFields (com.google.protobuf.UnknownFieldSetknownFields)