מחלקה סופית סטטית ציבורית RunMetadata.Builder
Metadata output (i.e., non-Tensor) for a single Run() call.
tensorflow.RunMetadata שיטות ציבוריות
| RunMetadata.Builder | addAllFunctionGraphs (Iterable<? מרחיב את RunMetadata.FunctionGraphs > ערכים) This is only populated for graphs that are run as functions in TensorFlow V2. |
| RunMetadata.Builder | addAllPartitionGraphs (Iterable<? מרחיב את הערכים של GraphDef >) Graphs of the partitions executed by executors. |
| RunMetadata.Builder | addFunctionGraphs (int index, ערך RunMetadata.FunctionGraphs ) This is only populated for graphs that are run as functions in TensorFlow V2. |
| RunMetadata.Builder | addFunctionGraphs (int index, RunMetadata.FunctionGraphs.Builder builderForValue) This is only populated for graphs that are run as functions in TensorFlow V2. |
| RunMetadata.Builder | addFunctionGraphs (ערך 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 (int index) 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 (int index, GraphDef.Builder builderForValue) Graphs of the partitions executed by executors. |
| GraphDef.Builder | addPartitionGraphsBuilder (int index) Graphs of the partitions executed by executors. |
| GraphDef.Builder | addPartitionGraphsBuilder () Graphs of the partitions executed by executors. |
| RunMetadata.Builder | addRepeatedField (שדה com.google.protobuf.Descriptors.FieldDescriptor, ערך אובייקט) |
| RunMetadata | לבנות () |
| RunMetadata | buildPartial () |
| RunMetadata.Builder | ברור () |
| RunMetadata.Builder | clearCostGraph () The cost graph for the computation defined by the run call. |
| RunMetadata.Builder | clearField (שדה 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 | שיבוט () |
| 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 | |
| final static com.google.protobuf.Descriptors.Descriptor | |
| com.google.protobuf.Descriptors.Descriptor | |
| RunMetadata.FunctionGraphs | getFunctionGraphs (int index) This is only populated for graphs that are run as functions in TensorFlow V2. |
| RunMetadata.FunctionGraphs.Builder | getFunctionGraphsBuilder (int index) This is only populated for graphs that are run as functions in TensorFlow V2. |
| רשימה< RunMetadata.FunctionGraphs.Builder > | getFunctionGraphsBuilderList () This is only populated for graphs that are run as functions in TensorFlow V2. |
| int | getFunctionGraphsCount () This is only populated for graphs that are run as functions in TensorFlow V2. |
| רשימה< RunMetadata.FunctionGraphs > | getFunctionGraphsList () This is only populated for graphs that are run as functions in TensorFlow V2. |
| RunMetadata.FunctionGraphsOrBuilder | getFunctionGraphsOrBuilder (int index) This is only populated for graphs that are run as functions in TensorFlow V2. |
| רשימה<? מרחיב את RunMetadata.FunctionGraphsOrBuilder > | getFunctionGraphsOrBuilderList () This is only populated for graphs that are run as functions in TensorFlow V2. |
| GraphDef | getPartitionGraphs (int index) Graphs of the partitions executed by executors. |
| GraphDef.Builder | getPartitionGraphsBuilder (int index) Graphs of the partitions executed by executors. |
| רשימה< GraphDef.Builder > | getPartitionGraphsBuilderList () Graphs of the partitions executed by executors. |
| int | getPartitionGraphsCount () Graphs of the partitions executed by executors. |
| רשימה< GraphDef > | getPartitionGraphsList () Graphs of the partitions executed by executors. |
| GraphDefOrBuilder | getPartitionGraphsOrBuilder (int index) Graphs of the partitions executed by executors. |
| רשימה<? מרחיב את 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. |
| בוליאני | hasCostGraph () The cost graph for the computation defined by the run call. |
| בוליאני | hasStepStats () Statistics traced for this step. |
| בוליאנית סופית | |
| RunMetadata.Builder | |
| RunMetadata.Builder | mergeFrom (com.google.protobuf.Message אחר) |
| RunMetadata.Builder | mergeFrom (קלט com.google.protobuf.CodedInputStream, com.google.protobuf.ExtensionRegistryLite extensionRegistry) |
| RunMetadata.Builder | |
| Final RunMetadata.Builder | mergeUnknownFields (com.google.protobuf.UnknownFieldSet unknownFields) |
| RunMetadata.Builder | removeFunctionGraphs (int index) This is only populated for graphs that are run as functions in TensorFlow V2. |
| RunMetadata.Builder | removePartitionGraphs (int index) 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 (שדה com.google.protobuf.Descriptors.FieldDescriptor, ערך אובייקט) |
| RunMetadata.Builder | setFunctionGraphs (int index, ערך RunMetadata.FunctionGraphs ) This is only populated for graphs that are run as functions in TensorFlow V2. |
| RunMetadata.Builder | setFunctionGraphs (int index, RunMetadata.FunctionGraphs.Builder builderForValue) This is only populated for graphs that are run as functions in TensorFlow V2. |
| RunMetadata.Builder | setPartitionGraphs (int index, GraphDef.Builder builderForValue) Graphs of the partitions executed by executors. |
| RunMetadata.Builder | |
| RunMetadata.Builder | setRepeatedField (שדה com.google.protobuf.Descriptors.FieldDescriptor, אינדקס אינט, ערך אובייקט) |
| RunMetadata.Builder | |
| RunMetadata.Builder | |
| Final RunMetadata.Builder | setUnknownFields (com.google.protobuf.UnknownFieldSet unknownFields) |
שיטות בירושה
שיטות ציבוריות
Public RunMetadata.Builder addAllFunctionGraphs (Iterable<? מרחיב את RunMetadata.FunctionGraphs > ערכים)
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 RunMetadata.Builder addAllPartitionGraphs (Iterable<? מרחיב את הערכים של GraphDef >)
Graphs of the partitions executed by executors.
repeated .tensorflow.GraphDef partition_graphs = 3; Public RunMetadata.Builder addFunctionGraphs (אינדקס int, ערך 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).
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).
repeated .tensorflow.RunMetadata.FunctionGraphs function_graphs = 4; Public RunMetadata.Builder addFunctionGraphs (ערך 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).
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).
repeated .tensorflow.RunMetadata.FunctionGraphs function_graphs = 4; public RunMetadata.FunctionGraphs.Builder addFunctionGraphsBuilder (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 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).
repeated .tensorflow.RunMetadata.FunctionGraphs function_graphs = 4; public RunMetadata.Builder addPartitionGraphs (int index, ערך 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 (ערך 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 (int index)
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 (שדה com.google.protobuf.Descriptors.FieldDescriptor, ערך אובייקט)
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).
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; public static final com.google.protobuf.Descriptors.Descriptor getDescriptor ()
public com.google.protobuf.Descriptors.Descriptor getDescriptorForType ()
public 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 RunMetadata.FunctionGraphs.Builder getFunctionGraphsBuilder (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; רשימה ציבורית< 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).
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).
repeated .tensorflow.RunMetadata.FunctionGraphs function_graphs = 4; רשימה ציבורית< 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 RunMetadata.FunctionGraphsOrBuilder getFunctionGraphsOrBuilder (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; רשימה ציבורית<? מרחיב את 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 GraphDef getPartitionGraphs (int index)
Graphs of the partitions executed by executors.
repeated .tensorflow.GraphDef partition_graphs = 3; public GraphDef.Builder getPartitionGraphsBuilder (int index)
Graphs of the partitions executed by executors.
repeated .tensorflow.GraphDef partition_graphs = 3; רשימה ציבורית< 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; רשימה ציבורית< GraphDef > getPartitionGraphsList ()
Graphs of the partitions executed by executors.
repeated .tensorflow.GraphDef partition_graphs = 3; public GraphDefOrBuilder getPartitionGraphsOrBuilder (int index)
Graphs of the partitions executed by executors.
repeated .tensorflow.GraphDef partition_graphs = 3; רשימה ציבורית<? מרחיב את GraphDefOrBuilder > getPartitionGraphsOrBuilderList ()
Graphs of the partitions executed by executors.
repeated .tensorflow.GraphDef partition_graphs = 3; StepStats ציבורי 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; hasCostGraph בוליאני ציבורי ()
The cost graph for the computation defined by the run call.
.tensorflow.CostGraphDef cost_graph = 2; hasStepStats בוליאני ציבורי ()
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; בוליאני הסופי הציבורי הוא אתחול ()
public RunMetadata.Builder mergeCostGraph (ערך CostGraphDef )
The cost graph for the computation defined by the run call.
.tensorflow.CostGraphDef cost_graph = 2; public RunMetadata.Builder mergeFrom (קלט com.google.protobuf.CodedInputStream, com.google.protobuf.ExtensionRegistryLite extensionRegistry)
זורק
| IOException |
|---|
public RunMetadata.Builder mergeStepStats (ערך 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 (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 RunMetadata.Builder removePartitionGraphs (int index)
Graphs of the partitions executed by executors.
repeated .tensorflow.GraphDef partition_graphs = 3; public RunMetadata.Builder setCostGraph (ערך 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 (שדה com.google.protobuf.Descriptors.FieldDescriptor, ערך אובייקט)
Public RunMetadata.Builder setFunctionGraphs (אינדקס int, ערך 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).
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).
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 (int index, ערך GraphDef )
Graphs of the partitions executed by executors.
repeated .tensorflow.GraphDef partition_graphs = 3; Public RunMetadata.Builder setRepeatedField (שדה com.google.protobuf.Descriptors.FieldDescriptor, אינדקס int, ערך אובייקט)
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 (ערך 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;