Module tfrecord::protobuf

ProtocolBuffer types compiled from TensorFlow.

The types are provided by ProtocolBuffer documents from TensorFlow repository. They are used internally for {,de}serialization.

Modules

• api_def
  Nested message and enum types in ApiDef.
• attr_value
  Nested message and enum types in AttrValue.
• cost_graph_def
  Nested message and enum types in CostGraphDef.
• event
  Nested message and enum types in Event.
• feature
  Nested message and enum types in Feature.
• feature_configuration
  Nested message and enum types in FeatureConfiguration.
• full_type_def
  Nested message and enum types in FullTypeDef.
• function_def
  Nested message and enum types in FunctionDef.
• graph_debug_info
  Nested message and enum types in GraphDebugInfo.
• graph_transfer_info
  Nested message and enum types in GraphTransferInfo.
• kernel_def
  Nested message and enum types in KernelDef.
• log_message
  Nested message and enum types in LogMessage.
• node_def
  Nested message and enum types in NodeDef.
• op_def
  Nested message and enum types in OpDef.
• optimized_function_graph
  Nested message and enum types in OptimizedFunctionGraph.
• resource_handle_proto
  Nested message and enum types in ResourceHandleProto.
• session_log
  Nested message and enum types in SessionLog.
• summary
  Nested message and enum types in Summary.
• summary_metadata
  Nested message and enum types in SummaryMetadata.
• tensor_shape_proto
  Nested message and enum types in TensorShapeProto.
• tensor_slice_proto
  Nested message and enum types in TensorSliceProto.

Structs

• AllocationDescription
• AllocationRecord
  An allocation/de-allocation operation performed by the allocator.
• AllocatorMemoryUsed
• ApiDef
  Used to specify and override the default API & behavior in the generated code for client languages, from what you would get from the OpDef alone. There will be a set of ApiDefs that are common to all client languages, and another set per client language. The per-client-language ApiDefs will inherit values from the common ApiDefs which it can either replace or modify.
• ApiDefs
• AttrValue
  Protocol buffer representing the value for an attr used to configure an Op. Comment indicates the corresponding attr type. Only the field matching the attr type may be filled.
• BytesList
  LINT.IfChange Containers to hold repeated fundamental values.
• CostGraphDef
• DeviceAttributes
• DeviceLocality
• DeviceStepStats
• Event
  Protocol buffer representing an event that happened during the execution of a Brain model.
• Example
• ExampleParserConfiguration
• Feature
  Containers for non-sequential data.
• FeatureConfiguration
• FeatureList
  Containers for sequential data.
• FeatureLists
• Features
• FixedLenFeatureProto
• FloatList
• FullTypeDef
  Highly experimental and very likely to change. This encoding uses tags instead of dedicated messages for regularity. In particular the encoding imposes no restrictions on what the parameters of any type should be, which in particular needs to be true for type symbols.
• FunctionDef
  A function can be instantiated when the runtime can bind every attr with a value. When a GraphDef has a call to a function, it must have binding for every attr defined in the signature.
• FunctionDefLibrary
  A library is a set of named functions.
• GradientDef
  GradientDef defines the gradient function of a function defined in a function library.
• GraphDebugInfo
• GraphDef
  Represents the graph of operations
• GraphTransferConstNodeInfo
• GraphTransferGraphInputNodeInfo
• GraphTransferGraphOutputNodeInfo
• GraphTransferInfo
  Protocol buffer representing a handle to a tensorflow resource. Handles are not valid across executions, but can be serialized back and forth from within a single run.
• GraphTransferNodeInfo
• GraphTransferNodeInput
• GraphTransferNodeInputInfo
• GraphTransferNodeOutputInfo
• HistogramProto
  Serialization format for histogram module in tsl/lib/histogram/histogram.h
• Int64List
• InterconnectLink
• KernelDef
• KernelList
  A collection of KernelDefs
• LocalLinks
• LogMessage
  Protocol buffer used for logging messages to the events file.
• MemoryLogRawAllocation
• MemoryLogRawDeallocation
• MemoryLogStep
• MemoryLogTensorAllocation
• MemoryLogTensorDeallocation
• MemoryLogTensorOutput
• MemoryStats
  For memory tracking.
• NameAttrList
  A list of attr names and their values. The whole list is attached with a string name. E.g., MatMul[T=float].
• NodeDef
• NodeExecStats
  Time/size stats recorded for a single execution of a graph node.
• NodeOutput
  Output sizes recorded for a single execution of a graph node.
• OpDef
  Defines an operation. A NodeDef in a GraphDef specifies an Op by using the “op” field which should match the name of a OpDef. LINT.IfChange
• OpDeprecation
  Information about version-dependent deprecation of an op
• OpList
  A collection of OpDefs
• OptimizedFunctionGraph
  Optimized function graph after instantiation-related graph optimization passes (up till before graph partitioning). The first half of the proto is representing a GraphDef and the rest of the fields are extra information from graph optimizations.
• ReaderBaseState
  For serializing and restoring the state of ReaderBase, see reader_base.h for details.
• RegisteredGradient
  RegisteredGradient stores a gradient function that is registered in the gradients library and used in the ops of a function in the function library. Unlike GradientDef, these gradients are identified by op type, and not directly linked to any function.
• RequestedExitCode
• ResourceHandleProto
  Protocol buffer representing a handle to a tensorflow resource. Handles are not valid across executions, but can be serialized back and forth from within a single run.
• SaveSliceInfoDef
• SequenceExample
• SerializedDType
  Represents a serialized tf.dtypes.Dtype
• SessionLog
  Protocol buffer used for logging session state.
• SourceMetadata
  Holds the information of the source that writes the events.
• StepStats
• Summary
  A Summary is a set of named values to be displayed by the visualizer.
• SummaryDescription
  Metadata associated with a series of Summary data
• SummaryMetadata
  A SummaryMetadata encapsulates information on which plugins are able to make use of a certain summary value.
• TaggedRunMetadata
  For logging the metadata output for a single session.run() call.
• TensorDescription
• TensorProto
  Protocol buffer representing a tensor.
• TensorShapeProto
  Dimensions of a tensor.
• TensorSliceProto
  Can only be interpreted if you know the corresponding TensorShape.
• VarLenFeatureProto
• VariableDef
  Protocol buffer representing a Variable.
• VariantTensorDataProto
  Protocol buffer representing the serialization format of DT_VARIANT tensors.
• VersionDef
  Version information for a piece of serialized data
• WatchdogConfig
• WorkerHeartbeatRequest
• WorkerHeartbeatResponse

Enums

• DataClass
• DataType
  (== suppress_warning documentation-presence ==) LINT.IfChange
• FullTypeId
  LINT.IfChange Experimental. Represents the complete type information of a TensorFlow value.
• VariableAggregation
  Indicates how a distributed variable will be aggregated.
• VariableSynchronization
  Indicates when a distributed variable will be synced.
• WorkerHealth
  Current health status of a worker.
• WorkerShutdownMode
  Indicates the behavior of the worker when an internal error or shutdown signal is received.