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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: NodeDef

• oneof value

  • AttrValue.ListValue list = 1

    any "list(...)"

  • bytes s = 2

    "string"

  • int64 i = 3

    "int"

  • float f = 4

    "float"

  • bool b = 5

    "bool"

  • DataType type = 6

    "type"

  • TensorShape shape = 7

    "shape"

  • LiteralTensor tensor = 8

    "tensor"

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 TensorShape shape = 7

  "list(shape)"

• repeated LiteralTensor tensor = 8

  "list(tensor)"

Align with Numpy at https://docs.scipy.org/doc/numpy/user/basics.types.html. No TensorFlow quantized data types.

Used in: AttrValue, AttrValue.ListValue, LiteralTensor

• DT_UNDEFINED = 0

• DT_INT8 = 1

• DT_INT16 = 2

• DT_INT32 = 3

• DT_INT64 = 4

• DT_UINT8 = 5

• DT_UINT16 = 6

• DT_UINT32 = 7

• DT_UINT64 = 8

• DT_FLOAT16 = 9

• DT_FLOAT32 = 10

• DT_FLOAT64 = 11

• DT_COMPLEX64 = 12

• DT_COMPLEX128 = 13

• DT_BOOL = 14

• DT_STRING = 15

• repeated NodeDef node = 1

• int32 version = 2

Protocol buffer representing a literal tensor value. As data types cross languages and toolkits differ, we can only cover the shared ones. Then each toolkit converts literal values to final ones according to type.

Used in: AttrValue, AttrValue.ListValue

• DataType dtype = 1

• optional TensorShape tensor_shape = 2

  Shape of the tensor.

• 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 int_val = 5

  DT_INT32, DT_INT16, DT_INT8.

• repeated int32 uint_val = 6

  DT_UINT32, DT_UINT16, DT_UINT8.

• repeated int64 int64_val = 7

  DT_INT64

• repeated int64 uint64_val = 8

  DT_UINT64

• repeated float float_val = 9

  DT_FLOAT16, DT_FLOAT32.

• repeated double double_val = 10

  DT_FLOAT64, DT_COMPLEX64, DT_COMPLEX128 (may be truncated)

• repeated bool bool_val = 11

  DT_BOOL

• repeated bytes string_val = 12

  DT_STRING

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". Regular inputs may optionally be followed by control inputs that have the format "node".

• map<string, AttrValue> attr = 4

  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.

Dimensions of a tensor.

Used in: AttrValue, AttrValue.ListValue, LiteralTensor

• repeated TensorShape.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: TensorShape

• 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 TensorShape may fail at runtime when deserializing a TensorShape containing a dim value of -1.

• string name = 2

  Optional name of the tensor dimension.