공개 정적 최종 클래스 TensorShapeProto.Builder
Dimensions of a tensor.Protobuf 유형
tensorflow.TensorShapeProto 공개 방법
| TensorShapeProto.Builder | addAllDim (Iterable<? 확장 TensorShapeProto.Dim > 값)
Dimensions of the tensor, such as {"input", 30}, {"output", 40}
for a 30 x 40 2D tensor. |
| TensorShapeProto.Builder | addDim ( TensorShapeProto.Dim.Builder builderForValue)
Dimensions of the tensor, such as {"input", 30}, {"output", 40}
for a 30 x 40 2D tensor. |
| TensorShapeProto.Builder | addDim (int 인덱스, TensorShapeProto.Dim.Builder builderForValue)
Dimensions of the tensor, such as {"input", 30}, {"output", 40}
for a 30 x 40 2D tensor. |
| TensorShapeProto.Builder | addDim (int 인덱스, TensorShapeProto.Dim 값)
Dimensions of the tensor, such as {"input", 30}, {"output", 40}
for a 30 x 40 2D tensor. |
| TensorShapeProto.Builder | addDim ( TensorShapeProto.Dim 값)
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 (정수 인덱스)
Dimensions of the tensor, such as {"input", 30}, {"output", 40}
for a 30 x 40 2D tensor. |
| TensorShapeProto.Builder | addRepeatedField (com.google.protobuf.Descriptors.FieldDescriptor 필드, 개체 값) |
| TensorShapeProto | 짓다 () |
| TensorShapeProto | 빌드부분 () |
| TensorShapeProto.Builder | 분명한 () |
| TensorShapeProto.Builder | 클리어딤 ()
Dimensions of the tensor, such as {"input", 30}, {"output", 40}
for a 30 x 40 2D tensor. |
| TensorShapeProto.Builder | ClearField (com.google.protobuf.Descriptors.FieldDescriptor 필드) |
| TensorShapeProto.Builder | ClearOneof (com.google.protobuf.Descriptors.OneofDescriptor oneof) |
| TensorShapeProto.Builder | 클리어알 수 없는 순위 () If true, the number of dimensions in the shape is unknown. |
| TensorShapeProto.Builder | 클론 () |
| TensorShapeProto | |
| 최종 정적 com.google.protobuf.Descriptors.Descriptor | |
| com.google.protobuf.Descriptors.Descriptor | |
| TensorShapeProto.Dim | getDim (정수 인덱스)
Dimensions of the tensor, such as {"input", 30}, {"output", 40}
for a 30 x 40 2D tensor. |
| TensorShapeProto.Dim.Builder | getDimBuilder (정수 인덱스)
Dimensions of the tensor, such as {"input", 30}, {"output", 40}
for a 30 x 40 2D tensor. |
| 목록< TensorShapeProto.Dim.Builder > | getDimBuilderList ()
Dimensions of the tensor, such as {"input", 30}, {"output", 40}
for a 30 x 40 2D tensor. |
| 정수 | getDim카운트 ()
Dimensions of the tensor, such as {"input", 30}, {"output", 40}
for a 30 x 40 2D tensor. |
| 목록< TensorShapeProto.Dim > | getDimList ()
Dimensions of the tensor, such as {"input", 30}, {"output", 40}
for a 30 x 40 2D tensor. |
| TensorShapeProto.DimOrBuilder | getDimOrBuilder (정수 인덱스)
Dimensions of the tensor, such as {"input", 30}, {"output", 40}
for a 30 x 40 2D tensor. |
| 목록<? TensorShapeProto.DimOrBuilder 확장 > | getDimOrBuilderList ()
Dimensions of the tensor, such as {"input", 30}, {"output", 40}
for a 30 x 40 2D tensor. |
| 부울 | getUnknownRank () If true, the number of dimensions in the shape is unknown. |
| 최종 부울 | 초기화됨 () |
| TensorShapeProto.Builder | mergeFrom (com.google.protobuf.CodedInputStream 입력, com.google.protobuf.ExtensionRegistryLite ExtensionRegistry) |
| TensorShapeProto.Builder | mergeFrom (com.google.protobuf.다른 메시지 보내기) |
| 최종 TensorShapeProto.Builder | mergeUnknownFields (com.google.protobuf.UnknownFieldSet 알려지지 않은Fields) |
| TensorShapeProto.Builder | RemoveDim (정수 인덱스)
Dimensions of the tensor, such as {"input", 30}, {"output", 40}
for a 30 x 40 2D tensor. |
| TensorShapeProto.Builder | setDim (int 인덱스, TensorShapeProto.Dim 값)
Dimensions of the tensor, such as {"input", 30}, {"output", 40}
for a 30 x 40 2D tensor. |
| TensorShapeProto.Builder | setDim (int 인덱스, TensorShapeProto.Dim.Builder builderForValue)
Dimensions of the tensor, such as {"input", 30}, {"output", 40}
for a 30 x 40 2D tensor. |
| TensorShapeProto.Builder | setField (com.google.protobuf.Descriptors.FieldDescriptor 필드, 개체 값) |
| TensorShapeProto.Builder | setRepeatedField (com.google.protobuf.Descriptors.FieldDescriptor 필드, int 인덱스, 개체 값) |
| 최종 TensorShapeProto.Builder | setUnknownFields (com.google.protobuf.UnknownFieldSet UnknownFields) |
| TensorShapeProto.Builder | setUnknownRank (부울 값) If true, the number of dimensions in the shape is unknown. |
상속된 메서드
공개 방법
공개 TensorShapeProto.Builder addAllDim (Iterable<? 확장 TensorShapeProto.Dim > 값)
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;공개 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; 공개 TensorShapeProto.Builder addDim (int 인덱스, 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; 공개 TensorShapeProto.Builder addDim (int 인덱스, TensorShapeProto.Dim 값)
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; 공개 TensorShapeProto.Builder addDim ( TensorShapeProto.Dim 값)
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; 공개 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; 공개 TensorShapeProto.Dim.Builder addDimBuilder (int 인덱스)
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; 공개 TensorShapeProto.Builder addRepeatedField (com.google.protobuf.Descriptors.FieldDescriptor 필드, 개체 값)
공개 TensorShapeProto.BuilderclearDim ( )
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; 공개 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; 공개 정적 최종 com.google.protobuf.Descriptors.Descriptor getDescriptor ()
공개 com.google.protobuf.Descriptors.Descriptor getDescriptorForType ()
공개 TensorShapeProto.Dim getDim (int 인덱스)
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; 공개 TensorShapeProto.Dim.Builder getDimBuilder (int 인덱스)
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; 공개 목록< 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; 공개 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; 공개 목록< 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; 공개 TensorShapeProto.DimOrBuilder getDimOrBuilder (int 인덱스)
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; 공개 목록<? TensorShapeProto.DimOrBuilder > getDimOrBuilderList ()를 확장합니다.
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; 공개 부울 getUnknownRank ()
If true, the number of dimensions in the shape is unknown. If true, "dim.size()" must be 0.
bool unknown_rank = 3; 공개 최종 부울 isInitialized ()
공개 TensorShapeProto.Builder mergeFrom (com.google.protobuf.CodedInputStream 입력, com.google.protobuf.ExtensionRegistryLite ExtensionRegistry)
던지기
| IO예외 |
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공개 최종 TensorShapeProto.Builder mergeUnknownFields (com.google.protobuf.UnknownFieldSet UnknownFields)
공개 TensorShapeProto.Builder RemoveDim (int 인덱스)
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; 공개 TensorShapeProto.Builder setDim (int 인덱스, TensorShapeProto.Dim 값)
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; 공개 TensorShapeProto.Builder setDim (int 인덱스, 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; 공개 TensorShapeProto.Builder setRepeatedField (com.google.protobuf.Descriptors.FieldDescriptor 필드, int 인덱스, 객체 값)
공개 TensorShapeProto.Builder setUnknownRank (부울 값)
If true, the number of dimensions in the shape is unknown. If true, "dim.size()" must be 0.
bool unknown_rank = 3;