interfaz pública RunMetadataOrBuilder
| Subclases indirectas conocidas |
Métodos públicos
| resumen CostGraphDef | getCostGraph () The cost graph for the computation defined by the run call. |
| abstracto CostGraphDefOrBuilder | getCostGraphOrBuilder () The cost graph for the computation defined by the run call. |
| resumen RunMetadata.FunctionGraphs | getFunctionGraphs (índice int) This is only populated for graphs that are run as functions in TensorFlow V2. |
| resumen entero | getFunctionGraphsCount () This is only populated for graphs that are run as functions in TensorFlow V2. |
| Lista abstracta < RunMetadata.FunctionGraphs > | getFunctionGraphsList () This is only populated for graphs that are run as functions in TensorFlow V2. |
| abstracto RunMetadata.FunctionGraphsOrBuilder | getFunctionGraphsOrBuilder (índice int) This is only populated for graphs that are run as functions in TensorFlow V2. |
| Lista abstracta<? extiende RunMetadata.FunctionGraphsOrBuilder > | getFunctionGraphsOrBuilderList () This is only populated for graphs that are run as functions in TensorFlow V2. |
| resumen GraphDef | getPartitionGraphs (índice int) Graphs of the partitions executed by executors. |
| resumen entero | getPartitionGraphsCount () Graphs of the partitions executed by executors. |
| Lista abstracta < GraphDef > | getPartitionGraphsList () Graphs of the partitions executed by executors. |
| abstracto GraphDefOrBuilder | getPartitionGraphsOrBuilder (índice int) Graphs of the partitions executed by executors. |
| Lista abstracta<? extiende GraphDefOrBuilder > | getPartitionGraphsOrBuilderList () Graphs of the partitions executed by executors. |
| estadísticas de pasos abstractas | obtenerStepStats () Statistics traced for this step. |
| resumen StepStatsOrBuilder | getStepStatsOrBuilder () Statistics traced for this step. |
| booleano abstracto | tiene CostGraph () The cost graph for the computation defined by the run call. |
| booleano abstracto | tieneStepStats () Statistics traced for this step. |
Métodos públicos
resumen público CostGraphDef getCostGraph ()
The cost graph for the computation defined by the run call.
.tensorflow.CostGraphDef cost_graph = 2; resumen público CostGraphDefOrBuilder getCostGraphOrBuilder ()
The cost graph for the computation defined by the run call.
.tensorflow.CostGraphDef cost_graph = 2; resumen 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; resumen público 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 abstracta 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; resumen 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 de resúmenes públicos <? extiende 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; resumen público GraphDef getPartitionGraphs (índice int)
Graphs of the partitions executed by executors.
repeated .tensorflow.GraphDef partition_graphs = 3; resumen público int getPartitionGraphsCount ()
Graphs of the partitions executed by executors.
repeated .tensorflow.GraphDef partition_graphs = 3; Lista abstracta pública < GraphDef > getPartitionGraphsList ()
Graphs of the partitions executed by executors.
repeated .tensorflow.GraphDef partition_graphs = 3; resumen público GraphDefOrBuilder getPartitionGraphsOrBuilder (índice int)
Graphs of the partitions executed by executors.
repeated .tensorflow.GraphDef partition_graphs = 3; Lista de resúmenes públicos <? extiende GraphDefOrBuilder > getPartitionGraphsOrBuilderList ()
Graphs of the partitions executed by executors.
repeated .tensorflow.GraphDef partition_graphs = 3; resumen público 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; resumen público 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; hasCostGraph booleano abstracto público ()
The cost graph for the computation defined by the run call.
.tensorflow.CostGraphDef cost_graph = 2; hasStepStats booleano abstracto público ()
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;