interface pública RunMetadataOrBuilder
| Subclasses indiretas conhecidas |
Métodos Públicos
| Resumo CostGraphDef | getCostGraph () The cost graph for the computation defined by the run call. |
| CostGraphDefOrBuilder abstrato | getCostGraphOrBuilder () The cost graph for the computation defined by the run call. |
| RunMetadata.FunctionGraphs abstrato | getFunctionGraphs (índice interno) This is only populated for graphs that are run as functions in TensorFlow V2. |
| abstrato int | getFunctionGraphsCount () This is only populated for graphs that are run as functions in TensorFlow V2. |
| Lista abstrata< RunMetadata.FunctionGraphs > | getFunctionGraphsList () This is only populated for graphs that are run as functions in TensorFlow V2. |
| resumo RunMetadata.FunctionGraphsOrBuilder | getFunctionGraphsOrBuilder (índice interno) This is only populated for graphs that are run as functions in TensorFlow V2. |
| lista abstrata<? estende RunMetadata.FunctionGraphsOrBuilder > | getFunctionGraphsOrBuilderList () This is only populated for graphs that are run as functions in TensorFlow V2. |
| resumo GraphDef | getPartitionGraphs (índice interno) Graphs of the partitions executed by executors. |
| abstrato int | getPartitionGraphsCount () Graphs of the partitions executed by executors. |
| Lista abstrata< GraphDef > | getPartitionGraphsList () Graphs of the partitions executed by executors. |
| resumo GraphDefOrBuilder | getPartitionGraphsOrBuilder (índice interno) Graphs of the partitions executed by executors. |
| lista abstrata<? estende GraphDefOrBuilder > | getPartitionGraphsOrBuilderList () Graphs of the partitions executed by executors. |
| StepStats abstratos | getStepStats () Statistics traced for this step. |
| StepStatsOrBuilder abstrato | getStepStatsOrBuilder () Statistics traced for this step. |
| booleano abstrato | hasCostGraph () The cost graph for the computation defined by the run call. |
| booleano abstrato | hasStepStats () Statistics traced for this step. |
Métodos Públicos
resumo público CostGraphDef getCostGraph ()
The cost graph for the computation defined by the run call.
.tensorflow.CostGraphDef cost_graph = 2; resumo público CostGraphDefOrBuilder getCostGraphOrBuilder ()
The cost graph for the computation defined by the run call.
.tensorflow.CostGraphDef cost_graph = 2; resumo público RunMetadata.FunctionGraphs getFunctionGraphs (índice 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; público abstrato 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; Lista abstrata pública< 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; resumo público RunMetadata.FunctionGraphsOrBuilder getFunctionGraphsOrBuilder (índice 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; lista abstrata pública<? estende 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; público abstrato GraphDef getPartitionGraphs (índice int)
Graphs of the partitions executed by executors.
repeated .tensorflow.GraphDef partition_graphs = 3; público abstrato int getPartitionGraphsCount ()
Graphs of the partitions executed by executors.
repeated .tensorflow.GraphDef partition_graphs = 3; Lista abstrata pública< GraphDef > getPartitionGraphsList ()
Graphs of the partitions executed by executors.
repeated .tensorflow.GraphDef partition_graphs = 3; resumo público GraphDefOrBuilder getPartitionGraphsOrBuilder (índice int)
Graphs of the partitions executed by executors.
repeated .tensorflow.GraphDef partition_graphs = 3; lista abstrata pública<? estende GraphDefOrBuilder > getPartitionGraphsOrBuilderList ()
Graphs of the partitions executed by executors.
repeated .tensorflow.GraphDef partition_graphs = 3; público abstrato 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; público abstrato 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; público abstrato booleano hasCostGraph ()
The cost graph for the computation defined by the run call.
.tensorflow.CostGraphDef cost_graph = 2; público abstrato booleano 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;