RunMetadataOrBuilder

الواجهة العامة RunMetadataOrBuilder
الفئات الفرعية غير المباشرة المعروفة

الأساليب العامة

ملخص CostGraphDef
الحصول على التكلفة ()
 The cost graph for the computation defined by the run call.
مجردة CostGraphDefOrBuilder
getCostGraphOrBuilder ()
 The cost graph for the computation defined by the run call.
ملخص RunMetadata.FunctionGraphs
getFunctionGraphs (فهرس كثافة العمليات)
 This is only populated for graphs that are run as functions in TensorFlow
 V2.
كثافة العمليات مجردة
getFunctionGraphsCount ()
 This is only populated for graphs that are run as functions in TensorFlow
 V2.
قائمة مجردة< RunMetadata.FunctionGraphs >
getFunctionGraphsList ()
 This is only populated for graphs that are run as functions in TensorFlow
 V2.
مجردة RunMetadata.FunctionGraphsOrBuilder
getFunctionGraphsOrBuilder (فهرس كثافة العمليات)
 This is only populated for graphs that are run as functions in TensorFlow
 V2.
قائمة مجردة <؟ يمتد RunMetadata.FunctionGraphsOrBuilder >
getFunctionGraphsOrBuilderList ()
 This is only populated for graphs that are run as functions in TensorFlow
 V2.
GraphDef مجردة
getPartitionGraphs (فهرس كثافة العمليات)
 Graphs of the partitions executed by executors.
كثافة العمليات مجردة
الحصول علىPartitionGraphsCount ()
 Graphs of the partitions executed by executors.
قائمة مجردة <GraphDef>
قائمة getPartitionGraphs ()
 Graphs of the partitions executed by executors.
مجردة GraphDefOrBuilder
getPartitionGraphsOrBuilder (فهرس كثافة العمليات)
 Graphs of the partitions executed by executors.
قائمة مجردة <؟ يمتد GraphDefOrBuilder >
getPartitionGraphsOrBuilderList ()
 Graphs of the partitions executed by executors.
إحصائيات الخطوات المجردة
الحصول على ستيبستاتس ()
 Statistics traced for this step.
مجردة StepStatsOrBuilder
getStepStatsOrBuilder ()
 Statistics traced for this step.
منطقية مجردة
hasCostGraph ()
 The cost graph for the computation defined by the run call.
منطقية مجردة
هاستيبستاتس ()
 Statistics traced for this step.

الأساليب العامة

الملخص العام CostGraphDef getCostGraph ()

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

الملخص العام CostGraphDefOrBuilder getCostGraphOrBuilder ()

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

الملخص العام RunMetadata.FunctionGraphs getFunctionGraphs (مؤشر 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;

الملخص العام 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;

قائمة مجردة عامة < 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;

الملخص العام RunMetadata.FunctionGraphsOrBuilder getFunctionGraphsOrBuilder (فهرس كثافة العمليات)

 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;

قائمة الملخصات العامة<؟ يمتد 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;

الملخص العام GraphDef getPartitionGraphs (مؤشر int)

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

الملخص العام int getPartitionGraphsCount ()

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

قائمة مجردة عامة< GraphDef > getPartitionGraphsList ()

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

الملخص العام GraphDefOrBuilder getPartitionGraphsOrBuilder (مؤشر int)

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

قائمة الملخصات العامة<؟ يمتد GraphDefOrBuilder > getPartitionGraphsOrBuilderList ()

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

الملخص العام 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;

الملخص العام 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;

الملخص المنطقي العام hasCostGraph ()

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

الملخص المنطقي العام 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;