TensorShapeProto.Builder

classe final estática pública TensorShapeProto.Builder

 Dimensions of a tensor.
 

Protobuf tipo tensorflow.TensorShapeProto

Métodos Públicos

TensorShapeProto.Builder	addAllDim (Iterable<? estende TensorShapeProto.Dim > valores) Dimensions of the tensor, such as {"input", 30}, {"output", 40} for a 30 x 40 2D tensor.
TensorShapeProto.Builder	addDim ( TensorShapeProto.Dim.Builder construtorForValue) Dimensions of the tensor, such as {"input", 30}, {"output", 40} for a 30 x 40 2D tensor.
TensorShapeProto.Builder	addDim (índice int, TensorShapeProto.Dim.Builder builderForValue) Dimensions of the tensor, such as {"input", 30}, {"output", 40} for a 30 x 40 2D tensor.
TensorShapeProto.Builder	addDim (índice int, valor TensorShapeProto.Dim ) Dimensions of the tensor, such as {"input", 30}, {"output", 40} for a 30 x 40 2D tensor.
TensorShapeProto.Builder	addDim (valor TensorShapeProto.Dim ) Dimensions of the tensor, such as {"input", 30}, {"output", 40} for a 30 x 40 2D tensor.
TensorShapeProto.Dim.Builder	addDimBuilder () Dimensions of the tensor, such as {"input", 30}, {"output", 40} for a 30 x 40 2D tensor.
TensorShapeProto.Dim.Builder	addDimBuilder (índice interno) Dimensions of the tensor, such as {"input", 30}, {"output", 40} for a 30 x 40 2D tensor.
TensorShapeProto.Builder	addRepeatedField (campo com.google.protobuf.Descriptors.FieldDescriptor, valor do objeto)
TensorShapeProto	construir ()
TensorShapeProto	construirPartial ()
TensorShapeProto.Builder	claro ()
TensorShapeProto.Builder	limparDim () Dimensions of the tensor, such as {"input", 30}, {"output", 40} for a 30 x 40 2D tensor.
TensorShapeProto.Builder	clearField (campo com.google.protobuf.Descriptors.FieldDescriptor)
TensorShapeProto.Builder	clearOneof (com.google.protobuf.Descriptors.OneofDescriptor umof)
TensorShapeProto.Builder	limparUnknownRank () If true, the number of dimensions in the shape is unknown.
TensorShapeProto.Builder	clonar ()
TensorShapeProto	getDefaultInstanceForType ()
final estático com.google.protobuf.Descriptors.Descriptor	getDescriptor ()
com.google.protobuf.Descriptors.Descriptor	getDescriptorForType ()
TensorShapeProto.Dim	getDim (índice interno) Dimensions of the tensor, such as {"input", 30}, {"output", 40} for a 30 x 40 2D tensor.
TensorShapeProto.Dim.Builder	getDimBuilder (índice interno) Dimensions of the tensor, such as {"input", 30}, {"output", 40} for a 30 x 40 2D tensor.
Lista< TensorShapeProto.Dim.Builder >	getDimBuilderList () Dimensions of the tensor, such as {"input", 30}, {"output", 40} for a 30 x 40 2D tensor.
interno	getDimCount () Dimensions of the tensor, such as {"input", 30}, {"output", 40} for a 30 x 40 2D tensor.
Lista< TensorShapeProto.Dim >	getDimList () Dimensions of the tensor, such as {"input", 30}, {"output", 40} for a 30 x 40 2D tensor.
TensorShapeProto.DimOrBuilder	getDimOrBuilder (índice interno) Dimensions of the tensor, such as {"input", 30}, {"output", 40} for a 30 x 40 2D tensor.
Lista<? estende TensorShapeProto.DimOrBuilder >	getDimOrBuilderList () Dimensions of the tensor, such as {"input", 30}, {"output", 40} for a 30 x 40 2D tensor.
boleano	getUnknownRank () If true, the number of dimensions in the shape is unknown.
booleano final	éinicializado ()
TensorShapeProto.Builder	mergeFrom (entrada com.google.protobuf.CodedInputStream, com.google.protobuf.ExtensionRegistryLite extensionRegistry)
TensorShapeProto.Builder	mergeFrom (com.google.protobuf.Message outro)
TensorShapeProto.Builder final	mesclarUnknownFields (com.google.protobuf.UnknownFieldSet desconhecidoFields)
TensorShapeProto.Builder	removeDim (índice interno) Dimensions of the tensor, such as {"input", 30}, {"output", 40} for a 30 x 40 2D tensor.
TensorShapeProto.Builder	setDim (índice int, valor TensorShapeProto.Dim ) Dimensions of the tensor, such as {"input", 30}, {"output", 40} for a 30 x 40 2D tensor.
TensorShapeProto.Builder	setDim (índice int, TensorShapeProto.Dim.Builder builderForValue) Dimensions of the tensor, such as {"input", 30}, {"output", 40} for a 30 x 40 2D tensor.
TensorShapeProto.Builder	setField (campo com.google.protobuf.Descriptors.FieldDescriptor, valor do objeto)
TensorShapeProto.Builder	setRepeatedField (campo com.google.protobuf.Descriptors.FieldDescriptor, índice int, valor do objeto)
TensorShapeProto.Builder final	setUnknownFields (com.google.protobuf.UnknownFieldSet desconhecidoFields)
TensorShapeProto.Builder	setUnknownRank (valor booleano) If true, the number of dimensions in the shape is unknown.

Métodos herdados

Da classe java.lang.Object

boleano	é igual (objeto arg0)
aula final<?>	getClass ()
interno	código hash ()
vazio final	notificar ()
vazio final	notificar todos ()
Corda	para sequenciar ()
vazio final	espere (long arg0, int arg1)
vazio final	espere (arg0 longo)
vazio final	espere ()

Da interface org.tensorflow.proto.framework.TensorShapeProtoOrBuilder

TensorShapeProto.Dim abstrato	getDim (índice interno) Dimensions of the tensor, such as {"input", 30}, {"output", 40} for a 30 x 40 2D tensor.
abstrato int	getDimCount () Dimensions of the tensor, such as {"input", 30}, {"output", 40} for a 30 x 40 2D tensor.
Lista abstrata< TensorShapeProto.Dim >	getDimList () Dimensions of the tensor, such as {"input", 30}, {"output", 40} for a 30 x 40 2D tensor.
TensorShapeProto.DimOrBuilder abstrato	getDimOrBuilder (índice interno) Dimensions of the tensor, such as {"input", 30}, {"output", 40} for a 30 x 40 2D tensor.
lista abstrata<? estende TensorShapeProto.DimOrBuilder >	getDimOrBuilderList () Dimensions of the tensor, such as {"input", 30}, {"output", 40} for a 30 x 40 2D tensor.
booleano abstrato	getUnknownRank () If true, the number of dimensions in the shape is unknown.

Métodos Públicos

public TensorShapeProto.Builder addAllDim (Iterable<? estende TensorShapeProto.Dim > valores)

 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.
 

repeated .tensorflow.TensorShapeProto.Dim dim = 2;

public TensorShapeProto.Builder addDim ( TensorShapeProto.Dim.Builder builderForValue)

 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.
 

repeated .tensorflow.TensorShapeProto.Dim dim = 2;

público TensorShapeProto.Builder addDim (índice int, TensorShapeProto.Dim.Builder builderForValue)

 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.
 

repeated .tensorflow.TensorShapeProto.Dim dim = 2;

público TensorShapeProto.Builder addDim (índice int, valor TensorShapeProto.Dim )

 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.
 

repeated .tensorflow.TensorShapeProto.Dim dim = 2;

público TensorShapeProto.Builder addDim (valor TensorShapeProto.Dim )

 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.
 

repeated .tensorflow.TensorShapeProto.Dim dim = 2;

público TensorShapeProto.Dim.Builder addDimBuilder ()

 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.
 

repeated .tensorflow.TensorShapeProto.Dim dim = 2;

público TensorShapeProto.Dim.Builder addDimBuilder (índice int)

 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.
 

repeated .tensorflow.TensorShapeProto.Dim dim = 2;

public TensorShapeProto.Builder addRepeatedField (campo com.google.protobuf.Descriptors.FieldDescriptor, valor do objeto)

construção pública do TensorShapeProto ()

público TensorShapeProto buildPartial ()

público TensorShapeProto.Builder claro ()

público TensorShapeProto.Builder clearDim ()

 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.
 

repeated .tensorflow.TensorShapeProto.Dim dim = 2;

público TensorShapeProto.Builder clearField (campo com.google.protobuf.Descriptors.FieldDescriptor)

público TensorShapeProto.Builder clearOneof (com.google.protobuf.Descriptors.OneofDescriptor oneof)

público TensorShapeProto.Builder clearUnknownRank ()

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

bool unknown_rank = 3;

clone público do TensorShapeProto.Builder ()

público TensorShapeProto getDefaultInstanceForType ()

final estático público com.google.protobuf.Descriptors.Descriptor getDescriptor ()

público com.google.protobuf.Descriptors.Descriptor getDescriptorForType ()

público TensorShapeProto.Dim getDim (índice int)

 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.
 

repeated .tensorflow.TensorShapeProto.Dim dim = 2;

público TensorShapeProto.Dim.Builder getDimBuilder (índice int)

 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.
 

repeated .tensorflow.TensorShapeProto.Dim dim = 2;

Lista pública< TensorShapeProto.Dim.Builder > getDimBuilderList ()

 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.
 

repeated .tensorflow.TensorShapeProto.Dim dim = 2;

público int getDimCount ()

 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.
 

repeated .tensorflow.TensorShapeProto.Dim dim = 2;

Lista pública< TensorShapeProto.Dim > getDimList ()

 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.
 

repeated .tensorflow.TensorShapeProto.Dim dim = 2;

público TensorShapeProto.DimOrBuilder getDimOrBuilder (índice int)

 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.
 

repeated .tensorflow.TensorShapeProto.Dim dim = 2;

Lista pública<? estende TensorShapeProto.DimOrBuilder > getDimOrBuilderList ()

 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.
 

repeated .tensorflow.TensorShapeProto.Dim dim = 2;

getUnknownRank booleano público ()

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

bool unknown_rank = 3;

público final booleano isInitialized ()

public TensorShapeProto.Builder mergeFrom (entrada com.google.protobuf.CodedInputStream, com.google.protobuf.ExtensionRegistryLite extensionRegistry)

Lança

IOException

público TensorShapeProto.Builder mergeFrom (com.google.protobuf.Message outro)

final público TensorShapeProto.Builder mergeUnknownFields (com.google.protobuf.UnknownFieldSet desconhecidoFields)

público TensorShapeProto.Builder removeDim (índice int)

 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.
 

repeated .tensorflow.TensorShapeProto.Dim dim = 2;

público TensorShapeProto.Builder setDim (índice int, valor TensorShapeProto.Dim )

 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.
 

repeated .tensorflow.TensorShapeProto.Dim dim = 2;

público TensorShapeProto.Builder setDim (índice int, TensorShapeProto.Dim.Builder builderForValue)

 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.
 

repeated .tensorflow.TensorShapeProto.Dim dim = 2;

public TensorShapeProto.Builder setField (campo com.google.protobuf.Descriptors.FieldDescriptor, valor do objeto)

public TensorShapeProto.Builder setRepeatedField (campo com.google.protobuf.Descriptors.FieldDescriptor, índice int, valor do objeto)

final público TensorShapeProto.Builder setUnknownFields (com.google.protobuf.UnknownFieldSet desconhecidoFields)

público TensorShapeProto.Builder setUnknownRank (valor booleano)

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

bool unknown_rank = 3;