final case class SparseTensorProto(values: Option[TensorProto] = _root_.scala.None, indices: Option[TensorProto] = _root_.scala.None, dims: Seq[Long] = _root_.scala.Seq.empty, unknownFields: UnknownFieldSet = _root_.scalapb.UnknownFieldSet.empty) extends GeneratedMessage with Updatable[SparseTensorProto] with Product with Serializable
A serialized sparse-tensor value
- values
The sequence of non-default values are encoded as a tensor of shape [NNZ]. The default-value is zero for numeric tensors, and empty-string for string tensors. values must have a non-empty name present which serves as a name for SparseTensorProto when used in sparse_initializer list.
- indices
The indices of the non-default values, which may be stored in one of two formats. (a) Indices can be a tensor of shape [NNZ, rank] with the [i,j]-th value corresponding to the j-th index of the i-th value (in the values tensor). (b) Indices can be a tensor of shape [NNZ], in which case the i-th value must be the linearized-index of the i-th value (in the values tensor). The linearized-index can be converted into an index tuple (k_1,...,k_rank) using the shape provided below. The indices must appear in ascending order without duplication. In the first format, the ordering is lexicographic-ordering: e.g., index-value [1,4] must appear before [2,1]
- dims
The shape of the underlying dense-tensor: [dim_1, dim_2, ... dim_rank]
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Instance Constructors
- new SparseTensorProto(values: Option[TensorProto] = _root_.scala.None, indices: Option[TensorProto] = _root_.scala.None, dims: Seq[Long] = _root_.scala.Seq.empty, unknownFields: UnknownFieldSet = _root_.scalapb.UnknownFieldSet.empty)
- values
The sequence of non-default values are encoded as a tensor of shape [NNZ]. The default-value is zero for numeric tensors, and empty-string for string tensors. values must have a non-empty name present which serves as a name for SparseTensorProto when used in sparse_initializer list.
- indices
The indices of the non-default values, which may be stored in one of two formats. (a) Indices can be a tensor of shape [NNZ, rank] with the [i,j]-th value corresponding to the j-th index of the i-th value (in the values tensor). (b) Indices can be a tensor of shape [NNZ], in which case the i-th value must be the linearized-index of the i-th value (in the values tensor). The linearized-index can be converted into an index tuple (k_1,...,k_rank) using the shape provided below. The indices must appear in ascending order without duplication. In the first format, the ordering is lexicographic-ordering: e.g., index-value [1,4] must appear before [2,1]
- dims
The shape of the underlying dense-tensor: [dim_1, dim_2, ... dim_rank]
Value Members
- final def !=(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
- final def ##: Int
- Definition Classes
- AnyRef → Any
- final def ==(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
- def addAllDims(__vs: Iterable[Long]): SparseTensorProto
- def addDims(__vs: Long*): SparseTensorProto
- final def asInstanceOf[T0]: T0
- Definition Classes
- Any
- def clearDims: SparseTensorProto
- def clearIndices: SparseTensorProto
- def clearValues: SparseTensorProto
- def clone(): AnyRef
- Attributes
- protected[lang]
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- @throws(classOf[java.lang.CloneNotSupportedException]) @native()
- def companion: SparseTensorProto.type
- Definition Classes
- SparseTensorProto → GeneratedMessage
- val dims: Seq[Long]
- def discardUnknownFields: SparseTensorProto
- final def eq(arg0: AnyRef): Boolean
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- def finalize(): Unit
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- def getField(__field: FieldDescriptor): PValue
- Definition Classes
- SparseTensorProto → GeneratedMessage
- def getFieldByNumber(__fieldNumber: Int): Any
- Definition Classes
- SparseTensorProto → GeneratedMessage
- def getIndices: TensorProto
- def getValues: TensorProto
- val indices: Option[TensorProto]
- final def isInstanceOf[T0]: Boolean
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- final def ne(arg0: AnyRef): Boolean
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- def productElementNames: Iterator[String]
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- def serializedSize: Int
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- SparseTensorProto → GeneratedMessage
- final def synchronized[T0](arg0: => T0): T0
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- final def toByteArray: Array[Byte]
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- GeneratedMessage
- final def toByteString: ByteString
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- final def toPMessage: PMessage
- Definition Classes
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- def toProtoString: String
- Definition Classes
- SparseTensorProto → GeneratedMessage
- val unknownFields: UnknownFieldSet
- def update(ms: (Lens[SparseTensorProto, SparseTensorProto]) => Mutation[SparseTensorProto]*): SparseTensorProto
- Definition Classes
- Updatable
- val values: Option[TensorProto]
- final def wait(): Unit
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- final def wait(arg0: Long, arg1: Int): Unit
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- final def wait(arg0: Long): Unit
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- @throws(classOf[java.lang.InterruptedException]) @native()
- def withDims(__v: Seq[Long]): SparseTensorProto
- def withIndices(__v: TensorProto): SparseTensorProto
- def withUnknownFields(__v: UnknownFieldSet): SparseTensorProto
- def withValues(__v: TensorProto): SparseTensorProto
- final def writeDelimitedTo(output: OutputStream): Unit
- Definition Classes
- GeneratedMessage
- def writeTo(_output__: CodedOutputStream): Unit
- Definition Classes
- SparseTensorProto → GeneratedMessage
- final def writeTo(output: OutputStream): Unit
- Definition Classes
- GeneratedMessage