package tensorflow

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Used in: SummaryMetadata

• DATA_CLASS_UNKNOWN = 0

  Unknown data class, used (implicitly) for legacy data. Will not be processed by data ingestion pipelines.

• DATA_CLASS_SCALAR = 1

  Scalar time series. Each `Value` for the corresponding tag must have `tensor` set to a rank-0 tensor of floating-point dtype, which will be converted to float64.

• DATA_CLASS_TENSOR = 2

  Tensor time series. Each `Value` for the corresponding tag must have `tensor` set. The tensor value is arbitrary, but should be small to accommodate direct storage in database backends: an upper bound of a few kilobytes is a reasonable rule of thumb.

• DATA_CLASS_BLOB_SEQUENCE = 3

  Blob sequence time series. Each `Value` for the corresponding tag must have `tensor` set to a rank-1 tensor of bytestring dtype.

(== suppress_warning documentation-presence ==) LINT.IfChange

Used in: ResourceHandleProto.DtypeAndShape, TensorProto

• DT_INVALID = 0

  Not a legal value for DataType. Used to indicate a DataType field has not been set.

• DT_FLOAT = 1

  Data types that all computation devices are expected to be capable to support.

• DT_DOUBLE = 2

• DT_INT32 = 3

• DT_UINT8 = 4

• DT_INT16 = 5

• DT_INT8 = 6

• DT_STRING = 7

• DT_COMPLEX64 = 8

  Single-precision complex

• DT_INT64 = 9

• DT_BOOL = 10

• DT_QINT8 = 11

  Quantized int8

• DT_QUINT8 = 12

  Quantized uint8

• DT_QINT32 = 13

  Quantized int32

• DT_BFLOAT16 = 14

  Float32 truncated to 16 bits. Only for cast ops.

• DT_QINT16 = 15

  Quantized int16

• DT_QUINT16 = 16

  Quantized uint16

• DT_UINT16 = 17

• DT_COMPLEX128 = 18

  Double-precision complex

• DT_HALF = 19

• DT_RESOURCE = 20

• DT_VARIANT = 21

  Arbitrary C++ data types

• DT_UINT32 = 22

• DT_UINT64 = 23

• DT_FLOAT_REF = 101

  Do not use! These are only for parameters. Every enum above should have a corresponding value below (verified by types_test).

• DT_DOUBLE_REF = 102

• DT_INT32_REF = 103

• DT_UINT8_REF = 104

• DT_INT16_REF = 105

• DT_INT8_REF = 106

• DT_STRING_REF = 107

• DT_COMPLEX64_REF = 108

• DT_INT64_REF = 109

• DT_BOOL_REF = 110

• DT_QINT8_REF = 111

• DT_QUINT8_REF = 112

• DT_QINT32_REF = 113

• DT_BFLOAT16_REF = 114

• DT_QINT16_REF = 115

• DT_QUINT16_REF = 116

• DT_UINT16_REF = 117

• DT_COMPLEX128_REF = 118

• DT_HALF_REF = 119

• DT_RESOURCE_REF = 120

• DT_VARIANT_REF = 121

• DT_UINT32_REF = 122

• DT_UINT64_REF = 123

Used in: ProjectorConfig

• string tensor_name = 1

• string metadata_path = 2

• string bookmarks_path = 3

• repeated uint32 tensor_shape = 4

  Shape of the 2D tensor [N x D]. If missing, it will be inferred from the model checkpoint.

• optional SpriteMetadata sprite = 5

• string tensor_path = 6

  Path to the TSV file holding the tensor values. If missing, the tensor is assumed to be stored in the model checkpoint.

Protocol buffer representing an event that happened during the execution of a Brain model.

Used in: WorkerHeartbeatResponse

• double wall_time = 1

  Timestamp of the event.

• int64 step = 2

  Global step of the event.

• oneof what

  • string file_version = 3

    An event file was started, with the specified version. This is use to identify the contents of the record IO files easily. Current version is "brain.Event:2". All versions start with "brain.Event:".

  • bytes graph_def = 4

    An encoded version of a GraphDef.

  • Summary summary = 5

    A summary was generated.

  • LogMessage log_message = 6

    The user output a log message. Not all messages are logged, only ones generated via the Python tensorboard_logging module.

  • SessionLog session_log = 7

    The state of the session which can be used for restarting after crashes.

  • TaggedRunMetadata tagged_run_metadata = 8

    The metadata returned by running a session.run() call.

  • bytes meta_graph_def = 9

    An encoded version of a MetaGraphDef.

Serialization format for histogram module in core/lib/histogram/histogram.h

Used in: Summary.Value

• double min = 1

• double max = 2

• double num = 3

• double sum = 4

• double sum_squares = 5

• repeated double bucket_limit = 6

  Parallel arrays encoding the bucket boundaries and the bucket values. bucket(i) is the count for the bucket i. The range for a bucket is: i == 0: -DBL_MAX .. bucket_limit(0) i != 0: bucket_limit(i-1) .. bucket_limit(i)

• repeated double bucket = 7

Protocol buffer used for logging messages to the events file.

Used in: Event

• LogMessage.Level level = 1

• string message = 2

Used in: LogMessage

• UNKNOWN = 0

• DEBUGGING = 10

  Note: The logging level 10 cannot be named DEBUG. Some software projects compile their C/C++ code with -DDEBUG in debug builds. So the C++ code generated from this file should not have an identifier named DEBUG.

• INFO = 20

• WARN = 30

• ERROR = 40

• FATAL = 50

• string model_checkpoint_path = 1

  Path to the checkpoint file. Use either this or model_checkpoint_dir.

• repeated EmbeddingInfo embeddings = 2

• string model_checkpoint_dir = 3

  Path to the checkpoint directory. The directory will be scanned for the latest checkpoint file.

Used in: WorkerHeartbeatRequest

• int32 exit_code = 1

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.

Used in: TensorProto

• string device = 1

  Unique name for the device containing the resource.

• string container = 2

  Container in which this resource is placed.

• string name = 3

  Unique name of this resource.

• uint64 hash_code = 4

  Hash code for the type of the resource. Is only valid in the same device and in the same execution.

• string maybe_type_name = 5

  For debug-only, the name of the type pointed to by this handle, if available.

• repeated ResourceHandleProto.DtypeAndShape dtypes_and_shapes = 6

  Data types and shapes for the underlying resource.

• repeated string allowed_devices = 7

  A set of devices containing the resource. If empty, the resource only exists on `device`.

Protocol buffer representing a pair of (data type, tensor shape).

Used in: ResourceHandleProto

• DataType dtype = 1

• optional TensorShapeProto shape = 2

Protocol buffer used for logging session state.

Used in: Event

• SessionLog.SessionStatus status = 1

• string checkpoint_path = 2

  This checkpoint_path contains both the path and filename.

• string msg = 3

Used in: SessionLog

• STATUS_UNSPECIFIED = 0

• START = 1

• STOP = 2

• CHECKPOINT = 3

Used in: EmbeddingInfo

• string image_path = 1

• repeated uint32 single_image_dim = 2

  [width, height] of a single image in the sprite.

A Summary is a set of named values to be displayed by the visualizer. Summaries are produced regularly during training, as controlled by the "summary_interval_secs" attribute of the training operation. Summaries are also produced at the end of an evaluation.

Used in: Event

• repeated Summary.Value value = 1

  Set of values for the summary.

Used in: Value

• float sample_rate = 1

  Sample rate of the audio in Hz.

• int64 num_channels = 2

  Number of channels of audio.

• int64 length_frames = 3

  Length of the audio in frames (samples per channel).

• bytes encoded_audio_string = 4

  Encoded audio data and its associated RFC 2045 content type (e.g. "audio/wav").

• string content_type = 5

Used in: Value

• int32 height = 1

  Dimensions of the image.

• int32 width = 2

• int32 colorspace = 3

  Valid colorspace values are 1 - grayscale 2 - grayscale + alpha 3 - RGB 4 - RGBA 5 - DIGITAL_YUV 6 - BGRA

• bytes encoded_image_string = 4

  Image data in encoded format. All image formats supported by image_codec::CoderUtil can be stored here.

Used in: Summary

• string node_name = 7

  This field is deprecated and will not be set.

• string tag = 1

  Tag name for the data. Used by TensorBoard plugins to organize data. Tags are often organized by scope (which contains slashes to convey hierarchy). For example: foo/bar/0

• optional SummaryMetadata metadata = 9

  Contains metadata on the summary value such as which plugins may use it. Take note that many summary values may lack a metadata field. This is because the FileWriter only keeps a metadata object on the first summary value with a certain tag for each tag. TensorBoard then remembers which tags are associated with which plugins. This saves space.

• oneof value

  Value associated with the tag.

  • float simple_value = 2

  • bytes obsolete_old_style_histogram = 3

  • Image image = 4

  • HistogramProto histo = 5

  • Audio audio = 6

  • TensorProto tensor = 8

Metadata associated with a series of Summary data

• string type_hint = 1

  Hint on how plugins should process the data in this series. Supported values include "scalar", "histogram", "image", "audio"

A SummaryMetadata encapsulates information on which plugins are able to make use of a certain summary value.

Used in: Summary.Value

• optional SummaryMetadata.PluginData plugin_data = 1

  Data that associates a summary with a certain plugin.

• string display_name = 2

  Display name for viewing in TensorBoard.

• string summary_description = 3

  Longform readable description of the summary sequence. Markdown supported.

• DataClass data_class = 4

  Class of data stored in this time series. Required for compatibility with TensorBoard's generic data facilities (`DataProvider`, et al.). This value imposes constraints on the dtype and shape of the corresponding tensor values. See `DataClass` docs for details.

Used in: SummaryMetadata

• string plugin_name = 1

  The name of the plugin this data pertains to.

• bytes content = 2

  The content to store for the plugin. The best practice is for this to be a binary serialized protocol buffer.

For logging the metadata output for a single session.run() call.

Used in: Event

• string tag = 1

  Tag name associated with this metadata.

• bytes run_metadata = 2

  Byte-encoded version of the `RunMetadata` proto in order to allow lazy deserialization.

Protocol buffer representing a tensor.

Used in: Summary.Value, VariantTensorDataProto

• DataType dtype = 1

• optional TensorShapeProto tensor_shape = 2

  Shape of the tensor. TODO(touts): sort out the 0-rank issues.

• int32 version_number = 3

  Version number. In version 0, if the "repeated xxx" representations contain only one element, that element is repeated to fill the shape. This makes it easy to represent a constant Tensor with a single value.

• bytes tensor_content = 4

  Serialized raw tensor content from either Tensor::AsProtoTensorContent or memcpy in tensorflow::grpc::EncodeTensorToByteBuffer. This representation can be used for all tensor types. The purpose of this representation is to reduce serialization overhead during RPC call by avoiding serialization of many repeated small items.

• repeated int32 half_val = 13

  DT_HALF, DT_BFLOAT16. Note that since protobuf has no int16 type, we'll have some pointless zero padding for each value here.

• repeated float float_val = 5

  DT_FLOAT.

• repeated double double_val = 6

  DT_DOUBLE.

• repeated int32 int_val = 7

  DT_INT32, DT_INT16, DT_INT8, DT_UINT8.

• repeated bytes string_val = 8

  DT_STRING

• repeated float scomplex_val = 9

  DT_COMPLEX64. scomplex_val(2*i) and scomplex_val(2*i+1) are real and imaginary parts of i-th single precision complex.

• repeated int64 int64_val = 10

  DT_INT64

• repeated bool bool_val = 11

  DT_BOOL

• repeated double dcomplex_val = 12

  DT_COMPLEX128. dcomplex_val(2*i) and dcomplex_val(2*i+1) are real and imaginary parts of i-th double precision complex.

• repeated ResourceHandleProto resource_handle_val = 14

  DT_RESOURCE

• repeated VariantTensorDataProto variant_val = 15

  DT_VARIANT

• repeated uint32 uint32_val = 16

  DT_UINT32

• repeated uint64 uint64_val = 17

  DT_UINT64

Dimensions of a tensor.

Used in: ResourceHandleProto.DtypeAndShape, TensorProto

• repeated TensorShapeProto.Dim dim = 2

  Dimensions of the tensor, such as {"input", 30}, {"output", 40} for a 30 x 40 2D tensor. If an entry has size -1, this corresponds to a dimension of unknown size. The names are optional. The order of entries in "dim" matters: It indicates the layout of the values in the tensor in-memory representation. The first entry in "dim" is the outermost dimension used to layout the values, the last entry is the innermost dimension. This matches the in-memory layout of RowMajor Eigen tensors. If "dim.size()" > 0, "unknown_rank" must be false.

• bool unknown_rank = 3

  If true, the number of dimensions in the shape is unknown. If true, "dim.size()" must be 0.

One dimension of the tensor.

Used in: TensorShapeProto

• int64 size = 1

  Size of the tensor in that dimension. This value must be >= -1, but values of -1 are reserved for "unknown" shapes (values of -1 mean "unknown" dimension). Certain wrappers that work with TensorShapeProto may fail at runtime when deserializing a TensorShapeProto containing a dim value of -1.

• string name = 2

  Optional name of the tensor dimension.

Protocol buffer representing the serialization format of DT_VARIANT tensors.

Used in: TensorProto

• string type_name = 1

  Name of the type of objects being serialized.

• bytes metadata = 2

  Portions of the object that are not Tensors.

• repeated TensorProto tensors = 3

  Tensors contained within objects being serialized.

Used in: WorkerHeartbeatRequest

• int64 timeout_ms = 1

Current health status of a worker.

Used in: WorkerHeartbeatResponse

• OK = 0

  By default a worker is healthy.

• RECEIVED_SHUTDOWN_SIGNAL = 1

• INTERNAL_ERROR = 2

• SHUTTING_DOWN = 3

  Worker has been instructed to shutdown after a timeout.

• WorkerShutdownMode shutdown_mode = 1

• optional WatchdogConfig watchdog_config = 2

• optional RequestedExitCode exit_code = 3

• WorkerHealth health_status = 1

• repeated Event worker_log = 2

• string hostname = 3

Indicates the behavior of the worker when an internal error or shutdown signal is received.

Used in: WorkerHeartbeatRequest

• DEFAULT = 0

• NOT_CONFIGURED = 1

• WAIT_FOR_COORDINATOR = 2

• SHUTDOWN_AFTER_TIMEOUT = 3