antarmuka publik RunMetadataOrBuilder
| Subkelas Tidak Langsung yang Diketahui |
Metode Publik
| abstrak CostGraphDef | dapatkanCostGraph () The cost graph for the computation defined by the run call. |
| abstrak CostGraphDefOrBuilder | dapatkanCostGraphOrBuilder () The cost graph for the computation defined by the run call. |
| abstrak RunMetadata.FunctionGraphs | getFunctionGraphs (indeks int) This is only populated for graphs that are run as functions in TensorFlow V2. |
| abstrak ke dalam | dapatkanFunctionGraphsCount () This is only populated for graphs that are run as functions in TensorFlow V2. |
| Daftar abstrak< RunMetadata.FunctionGraphs > | dapatkanFunctionGraphsList () This is only populated for graphs that are run as functions in TensorFlow V2. |
| abstrak RunMetadata.FunctionGraphsOrBuilder | getFunctionGraphsOrBuilder (indeks int) This is only populated for graphs that are run as functions in TensorFlow V2. |
| Daftar abstrak<? memperluas RunMetadata.FunctionGraphsOrBuilder > | dapatkanFunctionGraphsOrBuilderList () This is only populated for graphs that are run as functions in TensorFlow V2. |
| abstrak GraphDef | getPartitionGraphs (indeks int) Graphs of the partitions executed by executors. |
| abstrak ke dalam | dapatkanPartitionGraphsCount () Graphs of the partitions executed by executors. |
| Daftar abstrak< GraphDef > | dapatkanPartitionGraphsList () Graphs of the partitions executed by executors. |
| abstrak GraphDefOrBuilder | getPartitionGraphsOrBuilder (indeks int) Graphs of the partitions executed by executors. |
| Daftar abstrak<? memperluas GraphDefOrBuilder > | getPartitionGraphsOrBuilderList () Graphs of the partitions executed by executors. |
| StepStats abstrak | dapatkanStepStats () Statistics traced for this step. |
| abstrak StepStatsOrBuilder | dapatkanStepStatsOrBuilder () Statistics traced for this step. |
| boolean abstrak | hasCostGraph () The cost graph for the computation defined by the run call. |
| boolean abstrak | hasStepStats () Statistics traced for this step. |
Metode Publik
abstrak publik CostGraphDef getCostGraph ()
The cost graph for the computation defined by the run call.
.tensorflow.CostGraphDef cost_graph = 2; abstrak publik CostGraphDefOrBuilder getCostGraphOrBuilder ()
The cost graph for the computation defined by the run call.
.tensorflow.CostGraphDef cost_graph = 2; abstrak publik RunMetadata.FunctionGraphs getFunctionGraphs (int indeks)
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; abstrak publik 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; Daftar abstrak publik< 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; abstrak publik RunMetadata.FunctionGraphsOrBuilder getFunctionGraphsOrBuilder (int indeks)
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; Daftar abstrak publik<? memperluas 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; abstrak publik GraphDef getPartitionGraphs (indeks int)
Graphs of the partitions executed by executors.
repeated .tensorflow.GraphDef partition_graphs = 3; abstrak publik int getPartitionGraphsCount ()
Graphs of the partitions executed by executors.
repeated .tensorflow.GraphDef partition_graphs = 3; Daftar abstrak publik< GraphDef > getPartitionGraphsList ()
Graphs of the partitions executed by executors.
repeated .tensorflow.GraphDef partition_graphs = 3; abstrak publik GraphDefOrBuilder getPartitionGraphsOrBuilder (int indeks)
Graphs of the partitions executed by executors.
repeated .tensorflow.GraphDef partition_graphs = 3; Daftar abstrak publik<? memperluas GraphDefOrBuilder > getPartitionGraphsOrBuilderList ()
Graphs of the partitions executed by executors.
repeated .tensorflow.GraphDef partition_graphs = 3; StepStats abstrak publik 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; abstrak publik 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; boolean abstrak publik hasCostGraph ()
The cost graph for the computation defined by the run call.
.tensorflow.CostGraphDef cost_graph = 2; boolean abstrak publik 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;