interface pública RunMetadataOrBuilder
Subclasses indiretas conhecidas |
Métodos Públicos
abstrato CostGraphDef | getCostGraph () The cost graph for the computation defined by the run call. |
abstrato CostGraphDefOrBuilder | getCostGraphOrBuilder () The cost graph for the computation defined by the run call. |
abstrato RunMetadata.FunctionGraphs | getFunctionGraphs (int index) 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. |
abstrato RunMetadata.FunctionGraphsOrBuilder | getFunctionGraphsOrBuilder (índice int) 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. |
GraphDef abstrato | getPartitionGraphs (int index) 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. |
GraphDefOrBuilder abstrato | |
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
public abstract CostGraphDef getCostGraph ()
The cost graph for the computation defined by the run call.
.tensorflow.CostGraphDef cost_graph = 2;
public abstract CostGraphDefOrBuilder getCostGraphOrBuilder ()
The cost graph for the computation defined by the run call.
.tensorflow.CostGraphDef cost_graph = 2;
public abstract RunMetadata.FunctionGraphs getFunctionGraphs (int index)
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;
public abstract 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;
public abstract List < 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;
public abstract RunMetadata.FunctionGraphsOrBuilder getFunctionGraphsOrBuilder (índice interno )
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 resumos 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;
public abstract GraphDef getPartitionGraphs (int index)
Graphs of the partitions executed by executors.
repeated .tensorflow.GraphDef partition_graphs = 3;
public abstract int getPartitionGraphsCount ()
Graphs of the partitions executed by executors.
repeated .tensorflow.GraphDef partition_graphs = 3;
public abstract List < GraphDef > getPartitionGraphsList ()
Graphs of the partitions executed by executors.
repeated .tensorflow.GraphDef partition_graphs = 3;
public abstract GraphDefOrBuilder getPartitionGraphsOrBuilder (int index)
Graphs of the partitions executed by executors.
repeated .tensorflow.GraphDef partition_graphs = 3;
Lista de resumos pública <? estende GraphDefOrBuilder > getPartitionGraphsOrBuilderList ()
Graphs of the partitions executed by executors.
repeated .tensorflow.GraphDef partition_graphs = 3;
public abstract 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;
public abstract 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;
public abstract boolean hasCostGraph ()
The cost graph for the computation defined by the run call.
.tensorflow.CostGraphDef cost_graph = 2;
public abstract boolean 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;