Class List

Here are the classes, structs, unions and interfaces with brief descriptions:

[detail level 12345]

►Ndmlc | Namespace for dmlc |

►Nlua_stack | |

CHandler | |

►Nparameter | |

CFieldEntry< mxnet::TShape > | |

►Nserializer | Internal namespace for serializers |

CHandler | Generic serialization handler |

Carray_view | Read only data structure to reference continuous memory region of array. Provide unified view for vector, array and C style array. This data structure do not guarantee aliveness of referenced array |

CBlockingQueueThread | Blocking queue thread class |

CConcurrentBlockingQueue | Cocurrent blocking queue |

►CConfig | Class for config parser |

CConfigIterator | Iterator class |

CDataIter | Data iterator interface this is not a C++ style iterator, but nice for data pulling:) This interface is used to pull in the data The system can do some useful tricks for you like pre-fetching from disk and pre-computation |

CFunctionRegEntryBase | Common base class for function registry |

Chas_saveload | Whether a type have save/load function |

CIfThenElseType | Template to select type based on condition For example, IfThenElseType<true, int, float>::Type will give int |

►CInputSplit | Input split creates that allows reading of records from split of data, independent part that covers all the dataset |

CBlob | Blob of memory region |

CInputSplitShuffle | Class to construct input split with global shuffling |

Cis_arithmetic | Whether a type is arithemetic type |

Cis_floating_point | Whether a type is floating point type |

Cis_integral | Whether a type is integer type |

Cis_pod | Whether a type is pod type |

Cistream | Std::istream class that can can wrap Stream objects, can use istream with that output to underlying Stream |

CJSONObjectReadHelper | Helper class to read JSON into a class or struct object |

CJSONReader | Lightweight JSON Reader to read any STL compositions and structs. The user need to know the schema of the |

CJSONWriter | Lightweight json to write any STL compositions |

CLuaRef | Reference to lua object |

CLuaState | A Lua state |

CManualEvent | Simple manual-reset event gate which remains open after signalled |

CMemoryFixedSizeStream | A Stream that operates on fixed region of memory This class allows us to read/write from/to a fixed memory region |

CMemoryPool | A memory pool that allocate memory of fixed size and alignment |

CMemoryStringStream | A in memory stream that is backed by std::string. This class allows us to read/write from/to a std::string |

Cnullopt_t | Dummy type for assign null to optional |

COMPException | OMP Exception class catches, saves and rethrows exception from OMP blocks |

Coptional | C++17 compatible optional class |

Costream | Std::ostream class that can can wrap Stream objects, can use ostream with that output to underlying Stream |

CParser | Parser interface that parses input data used to load dmlc data format into your own data format Difference between RowBlockIter and Parser: RowBlockIter caches the data internally that can be used to iterate the dataset multiple times, Parser holds very limited internal state and was usually used to read data only once |

CParserFactoryReg | Registry entry of parser factory |

CRecordIOChunkReader | Reader of binary recordio from Blob returned by InputSplit This class divides the blob into several independent parts specified by caller, and read from one segment. The part reading can be used together with InputSplit::NextChunk for multi-threaded parsing(each thread take a RecordIOChunkReader) |

CRecordIOReader | Reader of binary recordio to reads in record from stream |

CRecordIOWriter | Writer of binary recordio binary format for recordio recordio format: magic lrecord data pad |

CRegistry | Registry class. Registry can be used to register global singletons. The most commonly use case are factory functions |

CRow | One row of training instance |

CRowBlock | Block of data, containing several rows in sparse matrix This is useful for (streaming-sxtyle) algorithms that scans through rows of data examples include: SGD, GD, L-BFGS, kmeans |

CRowBlockIter | Data structure that holds the data Row block iterator interface that gets RowBlocks Difference between RowBlockIter and Parser: RowBlockIter caches the data internally that can be used to iterate the dataset multiple times, Parser holds very limited internal state and was usually used to read data only once |

CSeekStream | Interface of i/o stream that support seek |

CSerializable | Interface for serializable objects |

CSpinlock | Simple userspace spinlock implementation |

CStream | Interface of stream I/O for serialization |

►CThreadedIter | Iterator that was backed by a thread to pull data eagerly from a single producer into a bounded buffer the consumer can pull the data at its own rate |

CProducer | Producer class interface that threaditer used as source to preduce the content |

►CThreadGroup | Thread lifecycle management group |

CThread | Lifecycle-managed thread (used by ThreadGroup) |

CThreadlocalAllocator | A thread local allocator that get memory from a threadlocal memory pool. This is suitable to allocate objects that do not cross thread |

CThreadlocalSharedPtr | Shared pointer like type that allocate object from a threadlocal object pool. This object is not thread-safe but can be faster than shared_ptr in certain usecases |

CThreadLocalStore | A threadlocal store to store threadlocal variables. Will return a thread local singleton of type T |

CTimerThread | Managed timer thread |

Ctype_name_helper | Helper class to construct a string that represents type name |

Ctype_name_helper< nnvm::Tuple< T > > | |

►Nmshadow | Namespace for mshadow |

►Nexpr | Namespace for abstract expressions and expressions template, have no dependency on tensor.h, These data structure takes no charge in computations, they are only used to define operations and represent expression in a symbolic way |

CBinaryMapExp | Binary map expression lhs [op] rhs |

CBLASEngine | |

CBLASEngine< cpu, double > | |

CBLASEngine< cpu, float > | |

CBLASEngine< gpu, double > | |

CBLASEngine< gpu, float > | |

CBLASEngine< gpu, half::half_t > | |

CBroadcast1DExp | Broadcast Tensor1D into a higher dimension Tensor input: Tensor<Device,1>: ishape[0] output: Tensor<Device,dimdst> : oshape[dimcast] = ishape[0] |

CBroadcastScalarExp | Broadcast scalar into a higher dimension Tensor input: Tensor<Device,1>: ishape = {1} output: Tensor<Device, dimdst> : oshape[dimcast] = ishape[0] |

CBroadcastWithAxisExp | Broadcasting the tensor in the given axis. If keepdim is off, insert the broadcasting dim after axis. Otherwise broadcasting axis |

CBroadcastWithMultiAxesExp | Broadcasting the tensor in multiple axes. The dimension of the source tensor in the given axes must be 1 |

CChannelPoolingExp | Channel pooling expression, do reduction over (local nearby) channels, used to implement local response normalization |

CChannelUnpoolingExp | Channel pooling expression, do reduction over (local nearby) channels, used to implement local response normalization |

CComplexBinaryMapExp | Binary map expression lhs [op] rhs where lhs and rhs are complex tensors |

CComplexUnitaryExp | Compute conj(src) where src is a complex tensor |

CConcatExp | Concat expression, concat two tensor's channel |

CCroppingExp | Crop expression, cut off the boundary region, reverse operation of padding |

CDotEngine | |

CDotEngine< SV, xpu, 1, 1, 2, false, transpose_right, DType > | |

CDotEngine< SV, xpu, 2, 1, 1, true, false, DType > | |

CDotEngine< SV, xpu, 2, 2, 2, transpose_left, transpose_right, DType > | |

CDotExp | Matrix multiplication expression dot(lhs[.T], rhs[.T]) |

CExp | Defines how expression exp can be evaluated and stored into dst |

CExpComplexEngine | Some engine that evaluate complex expression |

CExpComplexEngine< SV, Tensor< Device, 1, DType >, ReduceTo1DExp< SrcExp, DType, Reducer, 1 >, DType > | |

CExpComplexEngine< SV, Tensor< Device, 1, DType >, ReduceTo1DExp< SrcExp, DType, Reducer, m_dimkeep >, DType > | |

CExpComplexEngine< SV, Tensor< Device, dim, DType >, DotExp< Tensor< Device, ldim, DType >, Tensor< Device, rdim, DType >, ltrans, rtrans, DType >, DType > | |

CExpEngine | Engine that dispatches simple operations |

CExpInfo | Static type inference template, used to get the dimension of each expression, if ExpInfo<E>::kDim == -1, this means here are mismatch in expression if (ExpInfo<E>::kDevMask & cpu::kDevMask) != 0, this means this expression can be assigned to cpu |

CExpInfo< BinaryMapExp< OP, TA, TB, DType, etype > > | |

CExpInfo< ComplexBinaryMapExp< calctype, OP, TA, TB, DType, etype > > | |

CExpInfo< ComplexUnitaryExp< calctype, OP, TA, DType, etype > > | |

CExpInfo< ConcatExp< LhsExp, RhsExp, Device, DType, srcdim, dimsrc_m_cat > > | |

CExpInfo< FlipExp< SrcExp, Device, DType, srcdim > > | |

CExpInfo< ImplicitGEMMExp< LhsExp, RhsExp, DType > > | |

CExpInfo< MakeTensorExp< T, SrcExp, dim, DType > > | |

CExpInfo< MaskExp< IndexExp, SrcExp, DType > > | |

CExpInfo< MatChooseRowElementExp< SrcExp, IndexExp, DType > > | |

CExpInfo< MatFillRowElementExp< SrcExp, ValExp, IndexExp, DType > > | |

CExpInfo< OneHotEncodeExp< IndexExp, DType > > | |

CExpInfo< RangeExp< DType > > | |

CExpInfo< ScalarExp< DType > > | |

CExpInfo< SliceExExp< SrcExp, Device, DType, srcdim > > | |

CExpInfo< SliceExp< SrcExp, Device, DType, srcdim, dimsrc_m_slice > > | |

CExpInfo< TakeExp< IndexExp, SrcExp, DType > > | |

CExpInfo< TakeGradExp< IndexExp, SrcExp, DType > > | |

CExpInfo< Tensor< Device, dim, DType > > | |

CExpInfo< TernaryMapExp< OP, TA, TB, TC, DType, etype > > | |

CExpInfo< TransposeExp< E, DType > > | |

CExpInfo< TransposeIndicesExp< SrcExp, DType, dimsrc, etype > > | |

CExpInfo< TypecastExp< DstDType, SrcDType, EType, etype > > | |

CExpInfo< UnaryMapExp< OP, TA, DType, etype > > | |

CFlipExp | Slice expression, slice a tensor's channel |

CImplicitGEMMExp | Matrix multiplication |

CMakeTensorExp | General class that allows extension that makes tensors of some shape |

CMaskExp | Broadcast a mask and do element-wise multiplication |

CMatChooseRowElementExp | Make a choice of index in the lowest changing dimension |

CMatFillRowElementExp | Set value of a specific element in each line of the data matrix |

CMirroringExp | Mirror expression, mirror a image in width |

COneHotEncodeExp | Create a one-hot indicator array |

CPackColToPatchXExp | Reverse operation of UnpackPatchToCol, used to backprop gradient back this is a version supporting multiple images |

CPacketAlignCheck | |

CPacketAlignCheck< dim, BinaryMapExp< OP, TA, TB, DType, etype >, Arch > | |

CPacketAlignCheck< dim, ScalarExp< DType >, Arch > | |

CPacketAlignCheck< dim, Tensor< cpu, dim, DType >, Arch > | |

CPacketAlignCheck< dim, UnaryMapExp< OP, TA, DType, etype >, Arch > | |

CPacketCheck | Static check packet enable |

CPacketCheck< BinaryMapExp< OP, TA, TB, DType, etype >, Arch > | |

CPacketCheck< double, Arch > | |

CPacketCheck< float, Arch > | |

CPacketCheck< ScalarExp< DType >, Arch > | |

CPacketCheck< Tensor< cpu, dim, DType >, Arch > | |

CPacketCheck< UnaryMapExp< OP, TA, DType, etype >, Arch > | |

CPacketPlan | |

CPacketPlan< BinaryMapExp< OP, TA, TB, DType, etype >, DType, Arch > | |

CPacketPlan< ScalarExp< DType >, DType, Arch > | |

CPacketPlan< Tensor< Device, dim, DType >, DType, Arch > | |

CPacketPlan< UnaryMapExp< OP, TA, DType, etype >, DType, Arch > | |

CPaddingExp | Padding expression, pad a image with zeros |

CPlan | |

CPlan< BinaryMapExp< OP, TA, TB, DType, etype >, DType > | |

CPlan< Broadcast1DExp< SrcExp, DType, dimdst, 1 >, DType > | Execution plan of Broadcast1DExp |

CPlan< Broadcast1DExp< SrcExp, DType, dimdst, dimdst_m_cast >, DType > | |

CPlan< BroadcastScalarExp< SrcExp, DType, dimdst >, DType > | Execution plan of Broadcast1DExp |

CPlan< BroadcastWithAxisExp< SrcExp, DType, dimsrc, dimdst >, DType > | |

CPlan< BroadcastWithMultiAxesExp< SrcExp, DType, dimsrc >, DType > | |

CPlan< ChannelPoolingExp< Reducer, SrcExp, DType, srcdim >, DType > | |

CPlan< ChannelUnpoolingExp< Reducer, SrcExp, DType, srcdim >, DType > | |

CPlan< ComplexBinaryMapExp< op::complex::kBinaryCC, OP, TA, TB, DType, etype >, DType > | |

CPlan< ComplexBinaryMapExp< op::complex::kBinaryCR, OP, TA, TB, DType, etype >, DType > | |

CPlan< ComplexBinaryMapExp< op::complex::kBinaryRC, OP, TA, TB, DType, etype >, DType > | |

CPlan< ComplexUnitaryExp< op::complex::kUnitaryC2C, OP, TA, DType, etype >, DType > | |

CPlan< ComplexUnitaryExp< op::complex::kUnitaryC2R, OP, TA, DType, etype >, DType > | |

CPlan< ComplexUnitaryExp< op::complex::kUnitaryR2C, OP, TA, DType, etype >, DType > | |

CPlan< ConcatExp< LhsExp, RhsExp, Device, DType, srcdim, 1 >, DType > | |

CPlan< ConcatExp< LhsExp, RhsExp, Device, DType, srcdim, dimsrc_m_cat >, DType > | |

CPlan< CroppingExp< SrcExp, DType, srcdim >, DType > | |

CPlan< FlipExp< SrcExp, Device, DType, srcdim >, DType > | |

CPlan< ImplicitGEMMExp< LhsExp, RhsExp, DType >, DType > | |

CPlan< MakeTensorExp< SubType, SrcExp, dim, DType >, DType > | |

CPlan< MaskExp< IndexExp, SrcExp, DType >, DType > | |

CPlan< MatChooseRowElementExp< SrcExp, IndexExp, DType >, DType > | |

CPlan< MatFillRowElementExp< SrcExp, ValExp, IndexExp, DType >, DType > | |

CPlan< MirroringExp< SrcExp, DType, srcdim >, DType > | |

CPlan< OneHotEncodeExp< IndexExp, DType >, DType > | |

CPlan< PackColToPatchXExp< SrcExp, DType, dstdim >, DType > | |

CPlan< PaddingExp< SrcExp, DType, srcdim >, DType > | |

CPlan< PoolingExp< Reducer, SrcExp, DType, srcdim >, DType > | |

CPlan< RangeExp< DType >, DType > | |

CPlan< ReduceWithAxisExp< Reducer, SrcExp, DType, dimsrc, mask, dimdst >, DType > | |

CPlan< ReshapeExp< SrcExp, DType, dimdst, 1 >, DType > | |

CPlan< ReshapeExp< SrcExp, DType, dimdst, dimsrc >, DType > | |

CPlan< ScalarExp< DType >, DType > | |

CPlan< SliceExExp< SrcExp, Device, DType, srcdim >, DType > | |

CPlan< SliceExp< SrcExp, Device, DType, srcdim, 1 >, DType > | |

CPlan< SliceExp< SrcExp, Device, DType, srcdim, dimsrc_m_slice >, DType > | |

CPlan< SwapAxisExp< SrcExp, DType, dimsrc, 1, a2 >, DType > | |

CPlan< SwapAxisExp< SrcExp, DType, dimsrc, m_a1, a2 >, DType > | |

CPlan< TakeExp< IndexExp, SrcExp, DType >, DType > | |

CPlan< TakeGradExp< IndexExp, SrcExp, DType >, DType > | |

CPlan< Tensor< Device, 1, DType >, DType > | |

CPlan< Tensor< Device, dim, DType >, DType > | |

CPlan< TernaryMapExp< OP, TA, TB, TC, DType, etype >, DType > | |

CPlan< TransposeExExp< SrcExp, DType, dimsrc >, DType > | |

CPlan< TransposeExp< EType, DType >, DType > | |

CPlan< TransposeIndicesExp< SrcExp, DType, dimsrc, etype >, DType > | |

CPlan< TypecastExp< DstDType, SrcDType, EType, etype >, DstDType > | |

CPlan< UnaryMapExp< OP, TA, DType, etype >, DType > | |

CPlan< UnpackPatchToColXExp< SrcExp, DType, srcdim >, DType > | |

CPlan< UnPoolingExp< Reducer, SrcExp, DType, srcdim >, DType > | |

CPlan< UpSamplingNearestExp< SrcExp, DType, srcdim >, DType > | |

CPoolingExp | Pooling expression, do reduction over local patches of a image |

CRangeExp | Generate a range vector similar to python: range(start, stop[, step][, repeat]). If step is positive, the last element is the largest start + i * step less than stop If step is negative, the last element is the smallest start + i * step greater than stop. All elements are repeated for `repeat` times, e.g range(0, 4, 2, 3) –> 0, 0, 0, 2, 2, 2 |

CReduceTo1DExp | Reduction to 1 dimension tensor input: Tensor<Device,k>: ishape output: Tensor<Device,1> shape[0] = ishape[dimkeep]; |

CReduceWithAxisExp | Reduce out the dimension of src labeled by axis |

CReshapeExp | Reshape the content to another shape input: Tensor<Device,dimsrc>: ishape output: Tensor<Device,dimdst> ishape.Size() == oshape.Size() |

CRValueExp | Base class of all rvalues |

CScalarExp | Scalar expression |

CShapeCheck | Runtime shape checking template get the shape of an expression, report error if shape mismatch |

CShapeCheck< dim, BinaryMapExp< OP, TA, TB, DType, etype > > | |

CShapeCheck< dim, ComplexBinaryMapExp< calctype, OP, TA, TB, DType, etype > > | |

CShapeCheck< dim, ComplexUnitaryExp< calctype, OP, TA, DType, etype > > | |

CShapeCheck< dim, ImplicitGEMMExp< LhsExp, RhsExp, DType > > | |

CShapeCheck< dim, MakeTensorExp< T, SrcExp, dim, DType > > | |

CShapeCheck< dim, MaskExp< IndexExp, SrcExp, DType > > | |

CShapeCheck< dim, MatChooseRowElementExp< SrcExp, IndexExp, DType > > | |

CShapeCheck< dim, MatFillRowElementExp< SrcExp, ValExp, IndexExp, DType > > | |

CShapeCheck< dim, OneHotEncodeExp< IndexExp, DType > > | |

CShapeCheck< dim, RangeExp< DType > > | |

CShapeCheck< dim, ScalarExp< DType > > | |

CShapeCheck< dim, TakeExp< IndexExp, SrcExp, DType > > | |

CShapeCheck< dim, TakeGradExp< IndexExp, SrcExp, DType > > | |

CShapeCheck< dim, Tensor< Device, dim, DType > > | |

CShapeCheck< dim, TernaryMapExp< OP, TA, TB, TC, DType, etype > > | |

CShapeCheck< dim, TransposeExp< E, DType > > | |

CShapeCheck< dim, TransposeIndicesExp< SrcExp, DType, dimsrc, etype > > | |

CShapeCheck< dim, TypecastExp< DstDType, SrcDType, EType, etype > > | |

CShapeCheck< dim, UnaryMapExp< OP, TA, DType, etype > > | |

CShapeCheck< srcdim, ConcatExp< LhsExp, RhsExp, Device, DType, srcdim, dimsrc_m_cat > > | |

CShapeCheck< srcdim, FlipExp< SrcExp, Device, DType, srcdim > > | |

CShapeCheck< srcdim, SliceExExp< SrcExp, Device, DType, srcdim > > | |

CShapeCheck< srcdim, SliceExp< SrcExp, Device, DType, srcdim, dimsrc_m_slice > > | |

CSliceExExp | Slice expression, slice a tensor's channel |

CSliceExp | Slice expression, slice a tensor's channel |

CStreamInfo | |

CStreamInfo< Device, ConcatExp< LhsExp, RhsExp, Device, DType, srcdim, dimsrc_m_cat > > | |

CStreamInfo< Device, FlipExp< SrcExp, Device, DType, srcdim > > | |

CStreamInfo< Device, SliceExExp< SrcExp, Device, DType, srcdim > > | |

CStreamInfo< Device, SliceExp< SrcExp, Device, DType, srcdim, dimsrc_m_slice > > | |

CStreamInfo< Device, Tensor< Device, dim, DType > > | |

CSwapAxisExp | Swap two axis of a tensor input: Tensor<Device,dim>: ishape output: Tensor<Device,dimdst> oshape[a1],oshape[a2] = ishape[a2],oshape[a1] |

CTakeExp | Take a column from a matrix |

CTakeGradExp | Calculate embedding gradient |

CTernaryMapExp | Ternary map expression |

CTransposeExExp | Transpose axes of a tensor input: Tensor<Device,dim>: ishape output: Tensor<Device,dimdst> oshape[a1],oshape[a2] = ishape[a2],oshape[a1] |

CTransposeExp | Represent a transpose expression of a container |

CTransposeIndicesExp | Transform contiguous indices of the source tensor to indices of the transposed tensor. input: Tensor<Device, k>: ishape output: Tensor<Device, k>: oshape = ishape |

CTypecastExp | Typecast expression, cast the type of elements |

CTypeCheck | Template to do type check |

CTypeCheckPass | Used to help static type check |

CTypeCheckPass< false > | |

CTypeCheckPass< true > | |

CUnaryMapExp | Unary map expression op(src) |

CUnpackPatchToColXExp | Unpack local (overlap) patches of image to column of mat, can be used to implement convolution, this expression allow unpack of a batch this is a version support unpacking multiple images after getting unpacked mat, we can use: output = dot(weight, mat) to get covolved results, the relations: |

CUnPoolingExp | Unpooling expr reverse operation of pooling, used to pass gradient back |

CUpSamplingNearestExp | Nearest neighboor upsampling out(x, y) = in(int(x / scale_x), int(y / scale_y)) |

►Nop | Namespace for operators |

►Ncomplex | |

Cabs_square | |

Cconjugate | |

Cdiv | |

Cexchange | |

Cmul | |

Cpad_imag | |

Csum_real_imag | |

Ctoreal | |

Cdiv | Divide operator |

Cidentity | Identity function that maps a real number to it self |

Cminus | Minus operator |

Cmul | Mul operator |

Cplus | Plus operator |

Cright | Get rhs |

►Npacket | Namespace of packet math |

CAlignBytes | |

CPacket | Generic packet type |

CPacket< double, kSSE2 > | Vector real type for float |

CPacket< DType, kPlain > | |

CPacket< float, kSSE2 > | |

CPacketOp | Generic Packet operator |

CPacketOp< op::div, DType, Arch > | |

CPacketOp< op::identity, DType, Arch > | |

CPacketOp< op::minus, DType, Arch > | |

CPacketOp< op::mul, DType, Arch > | |

CPacketOp< op::plus, DType, Arch > | |

CSaver | |

CSaver< sv::saveto, TFloat, Arch > | |

►Nred | Namespace for potential reducer operations |

Cmaximum | Maximum reducer |

Cminimum | Minimum reducer |

Csum | Sum reducer |

►Nsv | Namespace for savers |

Cdivto | Divide to saver: /= |

Cminusto | Minus to saver: -= |

Cmulto | Multiply to saver: *= |

Cplusto | Save to saver: += |

Csaveto | Save to saver: = |

►Nutils | |

CIStream | Interface of stream I/O, used to serialize data, mshadow does not restricted to only this interface in SaveBinary/LoadBinary mshadow accept all class that implements Read and Write |

Ccpu | Device name CPU |

CDataType | |

CDataType< double > | |

CDataType< float > | |

CDataType< half::half2_t > | |

CDataType< half::half_t > | |

CDataType< int32_t > | |

CDataType< int64_t > | |

CDataType< int8_t > | |

CDataType< uint8_t > | |

Cgpu | Device name GPU |

CLayoutType | |

CLayoutType< kNCDHW > | |

CLayoutType< kNCHW > | |

CLayoutType< kNDHWC > | |

CLayoutType< kNHWC > | |

CMapExpCPUEngine | |

CMapExpCPUEngine< true, SV, Tensor< cpu, dim, DType >, dim, DType, E, etype > | |

CRandom | Random number generator |

CRandom< cpu, DType > | CPU random number generator |

CRandom< gpu, DType > | GPU random number generator |

CShape | Shape of a tensor |

CStream | Computaion stream structure, used for asynchronous computations |

CStream< gpu > | |

CTensor | General tensor |

CTensor< Device, 1, DType > | |

CTensorContainer | Tensor container that does memory allocation and resize like STL, use it to save the lines of FreeSpace in class. Do not abuse it, efficiency can come from pre-allocation and no re-allocation |

CTRValue | Tensor RValue, this is the super type of all kinds of possible tensors |

►Nmxnet | Namespace of mxnet |

►Ncommon | |

►Ncuda | Common utils for cuda |

CDeviceStore | |

►Nhelper | Helper functions |

CUniqueIf | Helper for non-array type `T` |

CUniqueIf< T[]> | Helper for an array of unknown bound `T` |

CUniqueIf< T[kSize]> | Helper for an array of known bound `T` |

►Nrandom | |

CRandGenerator | |

►CRandGenerator< cpu, DType > | |

CImpl | |

►CRandGenerator< gpu, double > | |

CImpl | |

►CRandGenerator< gpu, DType > | |

CImpl | |

Ccsr_idx_check | Indices should be non-negative, less than the number of columns and in ascending order per row |

Ccsr_indptr_check | IndPtr should be non-negative, in non-decreasing order, start with 0 and end with value equal with size of indices |

Cdeserialize_tuple | |

Cdeserialize_tuple< 0 > | |

Cis_container | |

CLazyAllocArray | |

CObjectPool | Object pool for fast allocation and deallocation |

CObjectPoolAllocatable | Helper trait class for easy allocation and deallocation |

Crsp_idx_check | Indices of RSPNDArray should be non-negative, less than the size of first dimension and in ascending order |

Cserialize_tuple | |

Cserialize_tuple< 0 > | |

Cserialized_size_tuple | |

Cserialized_size_tuple< 0 > | |

CStaticArray | Static array. This code is borrowed from struct Shape<ndim>, except that users can specify the type of the elements of the statically allocated array. The object instance of the struct is copyable between CPU and GPU |

►Nengine | Namespace of engine internal types |

CCallbackOnComplete | OnComplete Callback to the engine, called by AsyncFn when action completes |

CVar | Base class of engine variables |

►Nop | Namespace of arguments |

CEnvArguments | Environment arguments that is used by the function. These can be things like scalar arguments when add a value with scalar |

CGradFunctionArgument | Super class of all gradient function argument |

CInput0 | First input to the function |

CInput1 | Second input to the function |

COutputGrad | Gradient of output value |

COutputValue | Ouput value of the function to the function |

CSimpleOpRegEntry | Registry entry to register simple operators via functions |

CSimpleOpRegistry | Registry for TBlob functions |

CContext | Context information about the execution environment |

CDataBatch | DataBatch of NDArray, returned by Iterator |

CDataInst | Single data instance |

CDataIteratorReg | Registry entry for DataIterator factory functions |

CEngine | Dependency engine that schedules operations |

CExecutor | Executor of a computation graph. Executor can be created by Binding a symbol |

CIIterator | Iterator type |

►CImperative | Runtime functions for NDArray |

CAGInfo | |

CKVStore | Distributed key-value store |

CNDArray | Ndarray interface |

CNDArrayFunctionReg | Registry entry for NDArrayFunction |

COpContext | All the possible information needed by Operator.Forward and Backward This is the superset of RunContext. We use this data structure to bookkeep everything needed by Forward and Backward |

COperator | Operator interface. Operator defines basic operation unit of optimized computation graph in mxnet. This interface relies on pre-allocated memory in TBlob, the caller need to set the memory region in TBlob correctly before calling Forward and Backward |

COperatorProperty | OperatorProperty is a object that stores all information about Operator. It also contains method to generate context(device) specific operators |

COperatorPropertyReg | Registry entry for OperatorProperty factory functions |

COpStatePtr | Operator state. This is a pointer type, its content is mutable even if OpStatePtr is const |

CResource | Resources used by mxnet operations. A resource is something special other than NDArray, but will still participate |

CResourceManager | Global resource manager |

CResourceRequest | The resources that can be requested by Operator |

CRunContext | Execution time context. The information needed in runtime for actual execution |

►CStorage | Storage manager across multiple devices |

CHandle | Storage handle |

CTBlob | Tensor blob class that can be used to hold tensor of any dimension, any device and any data type, This is a weak type that can be used to transfer data through interface TBlob itself do not involve any arithmentic operations, but it can be converted to tensor of fixed dimension for further operations |

►Nnnvm | |

►Ntop | |

CAvgPool2DParam | |

CBatchNormParam | |

CBroadcastToParam | |

CCastParam | |

CClipParam | |

CConcatenateParam | |

CConv2DParam | |

CConv2DTransposeParam | |

CDenseParam | |

CDropoutParam | |

CElementWiseReduceParam | |

CExpandDimsParam | |

CFillValueParam | |

CFlipParam | |

CGlobalPool2DParam | |

CIndicatorParam | |

CInitOpParam | |

CInitOpWithScalarParam | |

CL2NormalizeParam | |

CLayoutTransformParam | |

CLeakyReLUParam | |

CLRNParam | |

CMatMulParam | |

CMaxPool2DParam | |

CMultiBoxPriorParam | |

CMultiBoxTransformLocParam | |

CNMSParam | |

CPadParam | |

CPReLUParam | |

CReduceParam | |

CReshapeParam | |

CScalarParam | |

CSliceLikeParam | |

CSoftmaxParam | |

CSplitParam | |

CSqueezeParam | |

CStridedSliceParam | |

CTakeParam | |

CTransposeParam | |

CUpSamplingParam | |

CWinogradConv2DParam | |

CWinogradWeightTransformParam | |

CGraph | Symbolic computation graph. This is the intermediate representation for optimization pass |

►CIndexedGraph | Auxiliary data structure to index a graph. It maps Nodes in the graph to consecutive integers node_id. It also maps IndexedGraph::NodeEntry to consecutive integer entry_id. This allows storing properties of Node and NodeEntry into compact vector and quickly access them without resorting to hashmap |

CNode | Node data structure in IndexedGraph |

CNodeEntry | Data in the graph |

CLayout | |

CNode | Node represents an operation in a computation graph |

CNodeAttrs | The attributes of the current operation node. Usually are additional parameters like axis, |

CNodeEntry | Entry that represents output data from a node |

CNodeEntryEqual | This lets you use a NodeEntry as a key in a unordered_map of the form unordered_map<NodeEntry, ValueType, NodeEntryHash, NodeEntryEqual> |

CNodeEntryHash | This lets you use a NodeEntry as a key in a unordered_map of the form unordered_map<NodeEntry, ValueType, NodeEntryHash, NodeEntryEqual> |

COp | Operator structure |

COpGroup | Auxiliary data structure used to set attributes to a group of operators |

COpMap | A map data structure that takes Op* as key and returns ValueType |

CPassFunctionReg | Registry entry for pass functions |

CSymbol | Symbol is help class used to represent the operator node in Graph |

CTShape | A Shape class that is used to represent shape of each tensor |

CTuple | A dynamic sized array data structure that is optimized for storing small number of elements with same type |

►Nstd | |

Chash< dmlc::optional< T > > | Std hash function for optional |

Chash< nnvm::TShape > | Hash function for TShape |

Chash< nnvm::Tuple< T > > | Hash function for Tuple |

►Ntvm | |

►Nruntime | |

Cextension_class_info< nnvm::compiler::AttrDict > | |

Cextension_class_info< nnvm::Graph > | |

Cextension_class_info< nnvm::Symbol > | |

CDLContext | A Device context for Tensor and operator |

CDLDataType | The data type the tensor can hold |

CDLManagedTensor | C Tensor object, manage memory of DLTensor. This data structure is intended to faciliate the borrowing of DLTensor by another framework. It is not meant to transfer the tensor. When the borrowing framework doesn't need the tensor, it should call the deleter to notify the host that the resource is no longer needed |

CDLTensor | Plain C Tensor object, does not manage memory |

CMXCallbackList | |

CNativeOpInfo | |

CNDArrayOpInfo |

Generated on Mon Dec 10 2018 12:20:31 for mxnet by 1.8.11