TensorShapeProto.Builder

TensorShapeProto.Builder kelas akhir statis publik

 Dimensions of a tensor.
Protobuf tipe tensorflow.TensorShapeProto

Metode Publik

TensorShapeProto.Builder
addAllDim (Nilai Iterable<? extends TensorShapeProto.Dim >)
 Dimensions of the tensor, such as {"input", 30}, {"output", 40}
 for a 30 x 40 2D tensor.
TensorShapeProto.Builder
addDim ( TensorShapeProto.Dim.Pembuat pembangunForValue)
 Dimensions of the tensor, such as {"input", 30}, {"output", 40}
 for a 30 x 40 2D tensor.
TensorShapeProto.Builder
addDim (indeks 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 (indeks int, nilai TensorShapeProto.Dim )
 Dimensions of the tensor, such as {"input", 30}, {"output", 40}
 for a 30 x 40 2D tensor.
TensorShapeProto.Builder
addDim (nilai TensorShapeProto.Dim )
 Dimensions of the tensor, such as {"input", 30}, {"output", 40}
 for a 30 x 40 2D tensor.
TensorShapeProto.Dim.Builder
tambahkanDimBuilder ()
 Dimensions of the tensor, such as {"input", 30}, {"output", 40}
 for a 30 x 40 2D tensor.
TensorShapeProto.Dim.Builder
addDimBuilder (indeks int)
 Dimensions of the tensor, such as {"input", 30}, {"output", 40}
 for a 30 x 40 2D tensor.
TensorShapeProto.Builder
addRepeatedField (bidang com.google.protobuf.Descriptors.FieldDescriptor, Nilai objek)
TensorBentukProto
membangun ()
TensorBentukProto
membangunPartial ()
TensorShapeProto.Builder
jernih ()
TensorShapeProto.Builder
jelas Redup ()
 Dimensions of the tensor, such as {"input", 30}, {"output", 40}
 for a 30 x 40 2D tensor.
TensorShapeProto.Builder
clearField (bidang com.google.protobuf.Descriptors.FieldDescriptor)
TensorShapeProto.Builder
clearOneof (com.google.protobuf.Descriptors.OneofDescriptor salah satu)
TensorShapeProto.Builder
jelasTidak DiketahuiPeringkat ()
 If true, the number of dimensions in the shape is unknown.
TensorShapeProto.Builder
klon ()
TensorBentukProto
dapatkanDefaultInstanceForType ()
com.google.protobuf.Descriptors.Descriptor statis terakhir
dapatkanDescriptor ()
com.google.protobuf.Descriptors.Descriptor
dapatkanDescriptorForType ()
TensorShapeProto.Dim
getDim (indeks int)
 Dimensions of the tensor, such as {"input", 30}, {"output", 40}
 for a 30 x 40 2D tensor.
TensorShapeProto.Dim.Builder
getDimBuilder (indeks int)
 Dimensions of the tensor, such as {"input", 30}, {"output", 40}
 for a 30 x 40 2D tensor.
Daftar< TensorShapeProto.Dim.Builder >
dapatkanDimBuilderList ()
 Dimensions of the tensor, such as {"input", 30}, {"output", 40}
 for a 30 x 40 2D tensor.
ke dalam
dapatkan DimCount ()
 Dimensions of the tensor, such as {"input", 30}, {"output", 40}
 for a 30 x 40 2D tensor.
Daftar< TensorShapeProto.Dim >
dapatkanDimList ()
 Dimensions of the tensor, such as {"input", 30}, {"output", 40}
 for a 30 x 40 2D tensor.
TensorShapeProto.DimOrBuilder
getDimOrBuilder (indeks int)
 Dimensions of the tensor, such as {"input", 30}, {"output", 40}
 for a 30 x 40 2D tensor.
Daftar<? memperluas TensorShapeProto.DimOrBuilder >
dapatkanDimOrBuilderList ()
 Dimensions of the tensor, such as {"input", 30}, {"output", 40}
 for a 30 x 40 2D tensor.
boolean
dapatkanPeringkatTidak Diketahui ()
 If true, the number of dimensions in the shape is unknown.
boolean terakhir
diinisialisasi ()
TensorShapeProto.Builder
mergeFrom (com.google.protobuf.CodedInputStream masukan, com.google.protobuf.ExtensionRegistryLite extensionRegistry)
TensorShapeProto.Builder
mergeFrom (com.google.protobuf.Pesan lainnya)
TensorShapeProto.Builder terakhir
mergeUnknownFields (com.google.protobuf.UnknownFieldSet unknownFields)
TensorShapeProto.Builder
hapusDim (indeks int)
 Dimensions of the tensor, such as {"input", 30}, {"output", 40}
 for a 30 x 40 2D tensor.
TensorShapeProto.Builder
setDim (indeks int, nilai TensorShapeProto.Dim )
 Dimensions of the tensor, such as {"input", 30}, {"output", 40}
 for a 30 x 40 2D tensor.
TensorShapeProto.Builder
setDim (indeks 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 (bidang com.google.protobuf.Descriptors.FieldDescriptor, Nilai objek)
TensorShapeProto.Builder
setRepeatedField (bidang com.google.protobuf.Descriptors.FieldDescriptor, indeks int, Nilai objek)
TensorShapeProto.Builder terakhir
setUnknownFields (com.google.protobuf.UnknownFieldSet unknownFields)
TensorShapeProto.Builder
setUnknownRank (nilai boolean)
 If true, the number of dimensions in the shape is unknown.

Metode Warisan

Metode Publik

public TensorShapeProto.Builder addAllDim (Nilai Iterable<? extends 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;

TensorShapeProto.Builder addDim publik ( 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;

TensorShapeProto.Builder addDim publik (indeks 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;

TensorShapeProto.Builder addDim publik (indeks int, nilai 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;

TensorShapeProto.Builder addDim publik (nilai 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;

TensorShapeProto.Dim.Builder publik 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;

TensorShapeProto.Dim.Builder publik addDimBuilder (indeks 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 (bidang com.google.protobuf.Descriptors.FieldDescriptor, Nilai objek)

build TensorShapeProto publik ()

Tensor publikBentukProto buildPartial ()

TensorShapeProto.Builder publik jelas ()

TensorShapeProto.Builder publik 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;

TensorShapeProto.Builder clearField publik (bidang com.google.protobuf.Descriptors.FieldDescriptor)

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

TensorShapeProto.Builder publik clearUnknownRank () 

 If true, the number of dimensions in the shape is unknown.
 If true, "dim.size()" must be 0.
bool unknown_rank = 3;

klon TensorShapeProto.Builder publik ()

TensorShapeProto publik getDefaultInstanceForType ()

public static final com.google.protobuf.Descriptors.Descriptor getDescriptor ()

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

TensorShapeProto.Dim getDim publik (indeks 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;

TensorShapeProto.Dim.Builder publik getDimBuilder (int indeks) 

 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;

Daftar publik< 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;

int publik 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;

Daftar publik< 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;

TensorShapeProto.DimOrBuilder publik getDimOrBuilder (int indeks) 

 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;

Daftar Publik<? memperluas 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;

boolean publik getUnknownRank () 

 If true, the number of dimensions in the shape is unknown.
 If true, "dim.size()" must be 0.
bool unknown_rank = 3;

boolean akhir publik diinisialisasi ()

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

Melempar
Pengecualian IO

TensorShapeProto.Builder mergeFrom publik (com.google.protobuf.Pesan lainnya)

TensorShapeProto.Builder final publik menggabungkanUnknownFields (com.google.protobuf.UnknownFieldSet unknownFields)

TensorShapeProto.Builder publik deleteDim (int indeks) 

 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;

TensorShapeProto.Builder setDim publik (indeks int, nilai 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;

TensorShapeProto.Builder setDim publik (indeks 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 (bidang com.google.protobuf.Descriptors.FieldDescriptor, Nilai objek)

public TensorShapeProto.Builder setRepeatedField (bidang com.google.protobuf.Descriptors.FieldDescriptor, indeks int, Nilai objek)

public final TensorShapeProto.Builder setUnknownFields (com.google.protobuf.UnknownFieldSet unknownFields)

TensorShapeProto.Builder setUnknownRank (nilai boolean) publik 

 If true, the number of dimensions in the shape is unknown.
 If true, "dim.size()" must be 0.
bool unknown_rank = 3;