package tensorflow

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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.

Used in: NameAttrList, NodeDef

• oneof value

  • bytes s = 2

    "string"

  • int64 i = 3

    "int"

  • float f = 4

    "float"

  • bool b = 5

    "bool"

  • DataType type = 6

    "type"

  • TensorShapeProto shape = 7

    "shape"

  • TensorProto tensor = 8

    "tensor"

  • AttrValue.ListValue list = 1

    any "list(...)"

  • NameAttrList func = 10

    "func" represents a function. func.name is a function's name or a primitive op's name. func.attr.first is the name of an attr defined for that function. func.attr.second is the value for that attr in the instantiation.

  • string placeholder = 9

    This is a placeholder only used in nodes defined inside a function. It indicates the attr value will be supplied when the function is instantiated. For example, let us suppose a node "N" in function "FN". "N" has an attr "A" with value placeholder = "foo". When FN is instantiated with attr "foo" set to "bar", the instantiated node N's attr A will have been given the value "bar".

LINT.IfChange

Used in: AttrValue

• repeated bytes s = 2

  "list(string)"

• repeated int64 i = 3

  "list(int)"

• repeated float f = 4

  "list(float)"

• repeated bool b = 5

  "list(bool)"

• repeated DataType type = 6

  "list(type)"

• repeated TensorShapeProto shape = 7

  "list(shape)"

• repeated TensorProto tensor = 8

  "list(tensor)"

• repeated NameAttrList func = 9

  "list(attr)"

LINT.IfChange

Used in: AttrValue, AttrValue.ListValue, 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_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

Represents the graph of operations

• repeated NodeDef node = 1

• optional VersionDef versions = 4

  Compatibility versions of the graph. See core/public/version.h for version history. The GraphDef version is distinct from the TensorFlow version, and each release of TensorFlow will support a range of GraphDef versions.

• int32 version = 3

  Deprecated single version field; use versions above instead. Since all GraphDef changes before "versions" was introduced were forward compatible, this field is entirely ignored.

A list of attr names and their values. The whole list is attached with a string name. E.g., MatMul[T=float].

Used in: AttrValue, AttrValue.ListValue

• string name = 1

• map<string, AttrValue> attr = 2

Used in: GraphDef

• string name = 1

  The name given to this operator. Used for naming inputs, logging, visualization, etc. Unique within a single GraphDef. Must match the regexp "[A-Za-z0-9.][A-Za-z0-9_./]*".

• string op = 2

  The operation name. There may be custom parameters in attrs. Op names starting with an underscore are reserved for internal use.

• repeated string input = 3

  Each input is "node:src_output" with "node" being a string name and "src_output" indicating which output tensor to use from "node". If "src_output" is 0 the ":0" suffix can be omitted. Regular inputs may optionally be followed by control inputs that have the format "^node".

• string device = 4

  A (possibly partial) specification for the device on which this node should be placed. The expected syntax for this string is as follows: DEVICE_SPEC ::= PARTIAL_SPEC PARTIAL_SPEC ::= ("/" CONSTRAINT) * CONSTRAINT ::= ("job:" JOB_NAME) | ("replica:" [1-9][0-9]*) | ("task:" [1-9][0-9]*) | ( ("gpu" | "cpu") ":" ([1-9][0-9]* | "*") ) Valid values for this string include: * "/job:worker/replica:0/task:1/gpu:3" (full specification) * "/job:worker/gpu:3" (partial specification) * "" (no specification) If the constraints do not resolve to a single device (or if this field is empty or not present), the runtime will attempt to choose a device automatically.

• map<string, AttrValue> attr = 5

  Operation-specific graph-construction-time configuration. Note that this should include all attrs defined in the corresponding OpDef, including those with a value matching the default -- this allows the default to change and makes NodeDefs easier to interpret on their own. However, if an attr with a default is not specified in this list, the default will be used. The "names" (keys) must match the regexp "[a-z][a-z0-9_]+" (and one of the names from the corresponding OpDef's attr field). The values must have a type matching the corresponding OpDef attr's type field. TODO(josh11b): Add some examples here showing best practices.

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.

Protocol buffer representing a tensor.

Used in: AttrValue, AttrValue.ListValue

• 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. 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 ResourceHandle resource_handle_val = 14

  DT_RESOURCE

Dimensions of a tensor.

Used in: AttrValue, AttrValue.ListValue, 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.

Version information for a piece of serialized data There are different types of versions for each type of data (GraphDef, etc.), but they all have the same common shape described here. Each consumer has "consumer" and "min_producer" versions (specified elsewhere). A consumer is allowed to consume this data if producer >= min_producer consumer >= min_consumer consumer not in bad_consumers

Used in: GraphDef

• int32 producer = 1

  The version of the code that produced this data.

• int32 min_consumer = 2

  Any consumer below this version is not allowed to consume this data.

• repeated int32 bad_consumers = 3

  Specific consumer versions which are disallowed (e.g. due to bugs).