clase final estática pública TensorShapeProto.Builder
Dimensions of a tensor.Protobuf tipo
tensorflow.TensorShapeProto
Métodos públicos
TensorShapeProto.Builder | addAllDim (Iterable<? extiende los valores de TensorShapeProto.Dim >) Dimensions of the tensor, such as {"input", 30}, {"output", 40} for a 30 x 40 2D tensor. |
TensorShapeProto.Builder | addDim ( TensorShapeProto.Dim.Builder constructorForValue) 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 | agregarDimBuilder () Dimensions of the tensor, such as {"input", 30}, {"output", 40} for a 30 x 40 2D tensor. |
TensorShapeProto.Dim.Builder | addDimBuilder (índice int) 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 del objeto) |
TensorShapeProto | construir () |
TensorShapeProto | |
TensorShapeProto.Builder | claro () |
TensorShapeProto.Builder | claroDim () 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 uno de) |
TensorShapeProto.Builder | borrar rango desconocido () If true, the number of dimensions in the shape is unknown. |
TensorShapeProto.Builder | clonar () |
TensorShapeProto | |
com.google.protobuf.Descriptors.Descriptor estático final | |
com.google.protobuf.Descriptors.Descriptor | |
TensorShapeProto.Dim | getDim (índice int) Dimensions of the tensor, such as {"input", 30}, {"output", 40} for a 30 x 40 2D tensor. |
TensorShapeProto.Dim.Builder | getDimBuilder (índice int) 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. |
En t | obtenerDimCount () Dimensions of the tensor, such as {"input", 30}, {"output", 40} for a 30 x 40 2D tensor. |
Lista< TensorShapeProto.Dim > | obtenerListaDim () Dimensions of the tensor, such as {"input", 30}, {"output", 40} for a 30 x 40 2D tensor. |
TensorShapeProto.DimOrBuilder | getDimOrBuilder (índice int) Dimensions of the tensor, such as {"input", 30}, {"output", 40} for a 30 x 40 2D tensor. |
Lista<? extiende TensorShapeProto.DimOrBuilder > | getDimOrBuilderList () Dimensions of the tensor, such as {"input", 30}, {"output", 40} for a 30 x 40 2D tensor. |
booleano | obtener rango desconocido () If true, the number of dimensions in the shape is unknown. |
booleano final | |
TensorShapeProto.Builder | mergeFrom (entrada com.google.protobuf.CodedInputStream, com.google.protobuf.ExtensionRegistryLite extensiónRegistry) |
TensorShapeProto.Builder | mergeFrom (com.google.protobuf.Message otro) |
TensorShapeProto.Builder final | mergeUnknownFields (com.google.protobuf.UnknownFieldSet desconocidoFields) |
TensorShapeProto.Builder | removeDim (índice int) 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 del objeto) |
TensorShapeProto.Builder | setRepeatedField (campo com.google.protobuf.Descriptors.FieldDescriptor, índice int, valor del objeto) |
TensorShapeProto.Builder final | setUnknownFields (com.google.protobuf.UnknownFieldSet desconocidoFields) |
TensorShapeProto.Builder | setUnknownRank (valor booleano) If true, the number of dimensions in the shape is unknown. |
Métodos heredados
Métodos públicos
público TensorShapeProto.Builder addAllDim (Iterable<? extiende los valores de 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 ( 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;
público TensorShapeProto.Builder addRepeatedField (campo com.google.protobuf.Descriptors.FieldDescriptor, valor del objeto)
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 clearUnknownRank ()
If true, the number of dimensions in the shape is unknown. If true, "dim.size()" must be 0.
bool unknown_rank = 3;
público estático final 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<? extiende 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 público booleano ()
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 ()
TensorShapeProto.Builder público mergeFrom (entrada com.google.protobuf.CodedInputStream, com.google.protobuf.ExtensionRegistryLite extensiónRegistry)
Lanza
IOExcepción |
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público final TensorShapeProto.Builder mergeUnknownFields (com.google.protobuf.UnknownFieldSet desconocidoFields)
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;
público TensorShapeProto.Builder setField (campo com.google.protobuf.Descriptors.FieldDescriptor, valor del objeto)
público TensorShapeProto.Builder setRepeatedField (campo com.google.protobuf.Descriptors.FieldDescriptor, índice int, valor del objeto)
público final TensorShapeProto.Builder setUnknownFields (com.google.protobuf.UnknownFieldSet desconocidoFields)
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;