clase final estática pública RunMetadata.Builder
Metadata output (i.e., non-Tensor) for a single Run() call.tipo
tensorflow.RunMetadata Métodos públicos
| RunMetadata.Builder | addAllFunctionGraphs (Iterable <? extiende RunMetadata.FunctionGraphs > valores) This is only populated for graphs that are run as functions in TensorFlow V2. |
| RunMetadata.Builder | addAllPartitionGraphs (Iterable <? extiende los valores de GraphDef >) Graphs of the partitions executed by executors. |
| RunMetadata.Builder | addFunctionGraphs (índice int, valor RunMetadata.FunctionGraphs ) This is only populated for graphs that are run as functions in TensorFlow V2. |
| RunMetadata.Builder | addFunctionGraphs (índice int, RunMetadata.FunctionGraphs.Builder builderForValue) This is only populated for graphs that are run as functions in TensorFlow V2. |
| RunMetadata.Builder | addFunctionGraphs (valor de RunMetadata.FunctionGraphs ) This is only populated for graphs that are run as functions in TensorFlow V2. |
| RunMetadata.Builder | addFunctionGraphs ( RunMetadata.FunctionGraphs.Builder builderForValue) This is only populated for graphs that are run as functions in TensorFlow V2. |
| RunMetadata.FunctionGraphs.Builder | addFunctionGraphsBuilder (índice int) This is only populated for graphs that are run as functions in TensorFlow V2. |
| RunMetadata.FunctionGraphs.Builder | addFunctionGraphsBuilder () This is only populated for graphs that are run as functions in TensorFlow V2. |
| RunMetadata.Builder | |
| RunMetadata.Builder | addPartitionGraphs ( GraphDef.Builder builderForValue) Graphs of the partitions executed by executors. |
| RunMetadata.Builder | |
| RunMetadata.Builder | addPartitionGraphs (índice int, GraphDef.Builder builderForValue) Graphs of the partitions executed by executors. |
| GraphDef.Builder | addPartitionGraphsBuilder (índice int) Graphs of the partitions executed by executors. |
| GraphDef.Builder | addPartitionGraphsBuilder () Graphs of the partitions executed by executors. |
| RunMetadata.Builder | addRepeatedField (campo com.google.protobuf.Descriptors.FieldDescriptor, valor del objeto) |
| RunMetadata | construir () |
| RunMetadata | buildPartial () |
| RunMetadata.Builder | claro () |
| RunMetadata.Builder | clearCostGraph () The cost graph for the computation defined by the run call. |
| RunMetadata.Builder | clearField (campo com.google.protobuf.Descriptors.FieldDescriptor) |
| RunMetadata.Builder | clearFunctionGraphs () This is only populated for graphs that are run as functions in TensorFlow V2. |
| RunMetadata.Builder | clearOneof (com.google.protobuf.Descriptors.OneofDescriptor oneof) |
| RunMetadata.Builder | clearPartitionGraphs () Graphs of the partitions executed by executors. |
| RunMetadata.Builder | clearStepStats () Statistics traced for this step. |
| RunMetadata.Builder | clonar () |
| CostGraphDef | getCostGraph () The cost graph for the computation defined by the run call. |
| CostGraphDef.Builder | getCostGraphBuilder () The cost graph for the computation defined by the run call. |
| CostGraphDefOrBuilder | getCostGraphOrBuilder () The cost graph for the computation defined by the run call. |
| RunMetadata | |
| com.google.protobuf.Descriptors.Descriptor estático final | |
| com.google.protobuf.Descriptors.Descriptor | |
| RunMetadata.FunctionGraphs | getFunctionGraphs (índice int) This is only populated for graphs that are run as functions in TensorFlow V2. |
| RunMetadata.FunctionGraphs.Builder | getFunctionGraphsBuilder (índice int) This is only populated for graphs that are run as functions in TensorFlow V2. |
| Lista < RunMetadata.FunctionGraphs.Builder > | getFunctionGraphsBuilderList () This is only populated for graphs that are run as functions in TensorFlow V2. |
| En t | getFunctionGraphsCount () This is only populated for graphs that are run as functions in TensorFlow V2. |
| Lista < RunMetadata.FunctionGraphs > | getFunctionGraphsList () This is only populated for graphs that are run as functions in TensorFlow V2. |
| RunMetadata.FunctionGraphsOrBuilder | getFunctionGraphsOrBuilder (índice int) This is only populated for graphs that are run as functions in TensorFlow V2. |
| Lista <? extiende RunMetadata.FunctionGraphsOrBuilder > | getFunctionGraphsOrBuilderList () This is only populated for graphs that are run as functions in TensorFlow V2. |
| GraphDef | getPartitionGraphs (índice int) Graphs of the partitions executed by executors. |
| GraphDef.Builder | getPartitionGraphsBuilder (índice int) Graphs of the partitions executed by executors. |
| Lista < GraphDef.Builder > | getPartitionGraphsBuilderList () Graphs of the partitions executed by executors. |
| En t | getPartitionGraphsCount () Graphs of the partitions executed by executors. |
| Lista < GraphDef > | getPartitionGraphsList () Graphs of the partitions executed by executors. |
| GraphDefOrBuilder | getPartitionGraphsOrBuilder (índice int) Graphs of the partitions executed by executors. |
| Lista <? extiende GraphDefOrBuilder > | getPartitionGraphsOrBuilderList () Graphs of the partitions executed by executors. |
| StepStats | getStepStats () Statistics traced for this step. |
| StepStats.Builder | getStepStatsBuilder () Statistics traced for this step. |
| StepStatsOrBuilder | getStepStatsOrBuilder () Statistics traced for this step. |
| booleano | hasCostGraph () The cost graph for the computation defined by the run call. |
| booleano | hasStepStats () Statistics traced for this step. |
| booleano final | |
| RunMetadata.Builder | |
| RunMetadata.Builder | mergeFrom (com.google.protobuf.Message otro) |
| RunMetadata.Builder | mergeFrom (entrada com.google.protobuf.CodedInputStream, com.google.protobuf.ExtensionRegistryLite extensionRegistry) |
| RunMetadata.Builder | |
| final RunMetadata.Builder | mergeUnknownFields (com.google.protobuf.UnknownFieldSet unknownFields) |
| RunMetadata.Builder | removeFunctionGraphs (índice int) This is only populated for graphs that are run as functions in TensorFlow V2. |
| RunMetadata.Builder | removePartitionGraphs (índice int) Graphs of the partitions executed by executors. |
| RunMetadata.Builder | |
| RunMetadata.Builder | setCostGraph ( CostGraphDef.Builder builderForValue) The cost graph for the computation defined by the run call. |
| RunMetadata.Builder | setField (campo com.google.protobuf.Descriptors.FieldDescriptor, valor del objeto) |
| RunMetadata.Builder | setFunctionGraphs (índice int, valor RunMetadata.FunctionGraphs ) This is only populated for graphs that are run as functions in TensorFlow V2. |
| RunMetadata.Builder | setFunctionGraphs (índice int, RunMetadata.FunctionGraphs.Builder builderForValue) This is only populated for graphs that are run as functions in TensorFlow V2. |
| RunMetadata.Builder | setPartitionGraphs (índice int, GraphDef.Builder builderForValue) Graphs of the partitions executed by executors. |
| RunMetadata.Builder | |
| RunMetadata.Builder | setRepeatedField (campo com.google.protobuf.Descriptors.FieldDescriptor, índice int, valor del objeto) |
| RunMetadata.Builder | |
| RunMetadata.Builder | |
| final RunMetadata.Builder | setUnknownFields (com.google.protobuf.UnknownFieldSet unknownFields) |
Métodos heredados
Métodos públicos
public RunMetadata.Builder addAllFunctionGraphs (Iterable <? extiende los valores de RunMetadata.FunctionGraphs >)
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)..tensorflow.RunMetadata.FunctionGraphs
repeated .tensorflow.RunMetadata.FunctionGraphs function_graphs = 4; public RunMetadata.Builder addAllPartitionGraphs (Iterable <? extiende los valores de GraphDef >)
Graphs of the partitions executed by executors.
repeated .tensorflow.GraphDef partition_graphs = 3; public RunMetadata.Builder addFunctionGraphs (índice int, valor RunMetadata.FunctionGraphs )
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)..tensorflow.RunMetadata.FunctionGraphs
repeated .tensorflow.RunMetadata.FunctionGraphs function_graphs = 4; public RunMetadata.Builder addFunctionGraphs (int index, RunMetadata.FunctionGraphs.Builder builderForValue)
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)..tensorflow.RunMetadata.FunctionGraphs
repeated .tensorflow.RunMetadata.FunctionGraphs function_graphs = 4; public RunMetadata.Builder addFunctionGraphs (valor de RunMetadata.FunctionGraphs )
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)..tensorflow.RunMetadata.FunctionGraphs
repeated .tensorflow.RunMetadata.FunctionGraphs function_graphs = 4; public RunMetadata.Builder addFunctionGraphs ( RunMetadata.FunctionGraphs.Builder builderForValue)
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)..tensorflow.RunMetadata.FunctionGraphs
repeated .tensorflow.RunMetadata.FunctionGraphs function_graphs = 4;public RunMetadata.FunctionGraphs.Builder addFunctionGraphsBuilder (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)..tensorflow.RunMetadata.FunctionGraphs
repeated .tensorflow.RunMetadata.FunctionGraphs function_graphs = 4;public RunMetadata.FunctionGraphs.Builder addFunctionGraphsBuilder ()
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)..tensorflow.RunMetadata.FunctionGraphs
repeated .tensorflow.RunMetadata.FunctionGraphs function_graphs = 4;public RunMetadata.Builder addPartitionGraphs (índice int, valor GraphDef )
Graphs of the partitions executed by executors.
repeated .tensorflow.GraphDef partition_graphs = 3;public RunMetadata.Builder addPartitionGraphs ( GraphDef.Builder builderForValue)
Graphs of the partitions executed by executors.
repeated .tensorflow.GraphDef partition_graphs = 3;public RunMetadata.Builder addPartitionGraphs (valor GraphDef )
Graphs of the partitions executed by executors.
repeated .tensorflow.GraphDef partition_graphs = 3; public RunMetadata.Builder addPartitionGraphs (int index, GraphDef.Builder builderForValue)
Graphs of the partitions executed by executors.
repeated .tensorflow.GraphDef partition_graphs = 3;public GraphDef.Builder addPartitionGraphsBuilder (índice int)
Graphs of the partitions executed by executors.
repeated .tensorflow.GraphDef partition_graphs = 3;public GraphDef.Builder addPartitionGraphsBuilder ()
Graphs of the partitions executed by executors.
repeated .tensorflow.GraphDef partition_graphs = 3; public RunMetadata.Builder addRepeatedField (campo com.google.protobuf.Descriptors.FieldDescriptor, valor del objeto)
public RunMetadata.Builder clearCostGraph ()
The cost graph for the computation defined by the run call.
.tensorflow.CostGraphDef cost_graph = 2;public RunMetadata.Builder clearFunctionGraphs ()
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)..tensorflow.RunMetadata.FunctionGraphs
repeated .tensorflow.RunMetadata.FunctionGraphs function_graphs = 4;public RunMetadata.Builder clearPartitionGraphs ()
Graphs of the partitions executed by executors.
repeated .tensorflow.GraphDef partition_graphs = 3;public RunMetadata.Builder clearStepStats ()
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 CostGraphDef getCostGraph ()
The cost graph for the computation defined by the run call.
.tensorflow.CostGraphDef cost_graph = 2;public CostGraphDef.Builder getCostGraphBuilder ()
The cost graph for the computation defined by the run call.
.tensorflow.CostGraphDef cost_graph = 2;public CostGraphDefOrBuilder getCostGraphOrBuilder ()
The cost graph for the computation defined by the run call.
.tensorflow.CostGraphDef cost_graph = 2;público estático final com.google.protobuf.Descriptors.Descriptor getDescriptor ()
public com.google.protobuf.Descriptors.Descriptor getDescriptorForType ()
public 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)..tensorflow.RunMetadata.FunctionGraphs
repeated .tensorflow.RunMetadata.FunctionGraphs function_graphs = 4;public RunMetadata.FunctionGraphs.Builder getFunctionGraphsBuilder (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)..tensorflow.RunMetadata.FunctionGraphs
repeated .tensorflow.RunMetadata.FunctionGraphs function_graphs = 4;Lista pública < RunMetadata.FunctionGraphs.Builder > getFunctionGraphsBuilderList ()
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)..tensorflow.RunMetadata.FunctionGraphs
repeated .tensorflow.RunMetadata.FunctionGraphs function_graphs = 4;public 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)..tensorflow.RunMetadata.FunctionGraphs
repeated .tensorflow.RunMetadata.FunctionGraphs function_graphs = 4;Lista 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)..tensorflow.RunMetadata.FunctionGraphs
repeated .tensorflow.RunMetadata.FunctionGraphs function_graphs = 4;public 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)..tensorflow.RunMetadata.FunctionGraphs
repeated .tensorflow.RunMetadata.FunctionGraphs function_graphs = 4;Lista pública <? 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)..tensorflow.RunMetadata.FunctionGraphs
repeated .tensorflow.RunMetadata.FunctionGraphs function_graphs = 4;public GraphDef getPartitionGraphs (índice int)
Graphs of the partitions executed by executors.
repeated .tensorflow.GraphDef partition_graphs = 3;public GraphDef.Builder getPartitionGraphsBuilder (índice int)
Graphs of the partitions executed by executors.
repeated .tensorflow.GraphDef partition_graphs = 3;Lista pública < GraphDef.Builder > getPartitionGraphsBuilderList ()
Graphs of the partitions executed by executors.
repeated .tensorflow.GraphDef partition_graphs = 3;public int getPartitionGraphsCount ()
Graphs of the partitions executed by executors.
repeated .tensorflow.GraphDef partition_graphs = 3;Lista pública < GraphDef > getPartitionGraphsList ()
Graphs of the partitions executed by executors.
repeated .tensorflow.GraphDef partition_graphs = 3;public GraphDefOrBuilder getPartitionGraphsOrBuilder (índice int)
Graphs of the partitions executed by executors.
repeated .tensorflow.GraphDef partition_graphs = 3;Lista pública <? extiende GraphDefOrBuilder > getPartitionGraphsOrBuilderList ()
Graphs of the partitions executed by executors.
repeated .tensorflow.GraphDef partition_graphs = 3;public 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 StepStats.Builder getStepStatsBuilder ()
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 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 boolean hasCostGraph ()
The cost graph for the computation defined by the run call.
.tensorflow.CostGraphDef cost_graph = 2;public 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;public final boolean isInitialized ()
public RunMetadata.Builder mergeCostGraph (valor de CostGraphDef )
The cost graph for the computation defined by the run call.
.tensorflow.CostGraphDef cost_graph = 2;public RunMetadata.Builder mergeFrom (entrada com.google.protobuf.CodedInputStream, com.google.protobuf.ExtensionRegistryLite extensionRegistry)
Lanza
| IOException |
|---|
public RunMetadata.Builder mergeStepStats (valor de StepStats )
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 final RunMetadata.Builder mergeUnknownFields (com.google.protobuf.UnknownFieldSet unknownFields)
public RunMetadata.Builder removeFunctionGraphs (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)..tensorflow.RunMetadata.FunctionGraphs
repeated .tensorflow.RunMetadata.FunctionGraphs function_graphs = 4;public RunMetadata.Builder removePartitionGraphs (índice int)
Graphs of the partitions executed by executors.
repeated .tensorflow.GraphDef partition_graphs = 3;public RunMetadata.Builder setCostGraph (valor de CostGraphDef )
The cost graph for the computation defined by the run call.
.tensorflow.CostGraphDef cost_graph = 2;public RunMetadata.Builder setCostGraph ( CostGraphDef.Builder builderForValue)
The cost graph for the computation defined by the run call.
.tensorflow.CostGraphDef cost_graph = 2; public RunMetadata.Builder setField (campo com.google.protobuf.Descriptors.FieldDescriptor, valor del objeto)
public RunMetadata.Builder setFunctionGraphs (índice int, valor RunMetadata.FunctionGraphs )
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)..tensorflow.RunMetadata.FunctionGraphs
repeated .tensorflow.RunMetadata.FunctionGraphs function_graphs = 4; public RunMetadata.Builder setFunctionGraphs (int index, RunMetadata.FunctionGraphs.Builder builderForValue)
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)..tensorflow.RunMetadata.FunctionGraphs
repeated .tensorflow.RunMetadata.FunctionGraphs function_graphs = 4; public RunMetadata.Builder setPartitionGraphs (int index, GraphDef.Builder builderForValue)
Graphs of the partitions executed by executors.
repeated .tensorflow.GraphDef partition_graphs = 3;public RunMetadata.Builder setPartitionGraphs (índice int, valor GraphDef )
Graphs of the partitions executed by executors.
repeated .tensorflow.GraphDef partition_graphs = 3; public RunMetadata.Builder setRepeatedField (campo com.google.protobuf.Descriptors.FieldDescriptor, índice int, valor del objeto)
public RunMetadata.Builder setStepStats ( StepStats.Builder builderForValue)
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 RunMetadata.Builder setStepStats (valor StepStats )
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;