TensorShapeProtoOrBuilder

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public interface TensorShapeProtoOrBuilder
Known Indirect Subclasses

Public Methods

abstract TensorShapeProto.Dim
getDim(int index)
 Dimensions of the tensor, such as {"input", 30}, {"output", 40}
 for a 30 x 40 2D tensor.
abstract int
getDimCount()
 Dimensions of the tensor, such as {"input", 30}, {"output", 40}
 for a 30 x 40 2D tensor.
abstract List<TensorShapeProto.Dim>
getDimList()
 Dimensions of the tensor, such as {"input", 30}, {"output", 40}
 for a 30 x 40 2D tensor.
abstract TensorShapeProto.DimOrBuilder
getDimOrBuilder(int index)
 Dimensions of the tensor, such as {"input", 30}, {"output", 40}
 for a 30 x 40 2D tensor.
abstract List<? extends TensorShapeProto.DimOrBuilder>
getDimOrBuilderList()
 Dimensions of the tensor, such as {"input", 30}, {"output", 40}
 for a 30 x 40 2D tensor.
abstract boolean
getUnknownRank()
 If true, the number of dimensions in the shape is unknown.

Public Methods

public abstract TensorShapeProto.Dim getDim (int index)

 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 abstract 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;

public abstract List<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;

public abstract TensorShapeProto.DimOrBuilder getDimOrBuilder (int index)

 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 abstract List<? extends 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;

public abstract boolean getUnknownRank ()

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