Class List

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

[detail level 12345]

►Ndmlc	Namespace for dmlc
►Nio
CFileInfo	Use to store file information
CFileSystem	File system system interface
CURI	Common data structure for URI
►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
CScopedThread	Wrapper class to manage std::thread; uses RAII pattern to automatically join std::thread upon destruction
CSeekStream	Interface of i/o stream that support seek
CSerializable	Interface for serializable objects
CSpinlock	Simple userspace spinlock implementation
CStr2T	Interface class that defines a single method get() to convert a string into type T. Define template specialization of this class to define the conversion method for a particular type
CStr2T< double >	Template specialization of Str2T<> interface for double type
CStr2T< float >	Template specialization of Str2T<> interface for float type
CStr2T< int32_t >	Template specialization of Str2T<> interface for signed 32-bit integer
CStr2T< int64_t >	Template specialization of Str2T<> interface for signed 64-bit integer
CStr2T< uint32_t >	Template specialization of Str2T<> interface for unsigned 32-bit integer
CStr2T< uint64_t >	Template specialization of Str2T<> interface for unsigned 64-bit integer
CStream	Interface of stream I/O for serialization
CTemporaryDirectory	Manager class for temporary directories. Whenever a new TemporaryDirectory object is constructed, a temporary directory is created. The directory is deleted when the object is deleted or goes out of scope. Note: no symbolic links are allowed inside the temporary directory
►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< mxnet::Tuple< T > >
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< bool >
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
CCublasType	Converts between C++ datatypes and enums/constants needed by cuBLAS
CCublasType< double >
CCublasType< float >
CCublasType< int32_t >
CCublasType< mshadow::half::half_t >
CCublasType< uint8_t >
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
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
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
►Ncpp
CAccuracy
CAdaDeltaOptimizer
CAdaGradOptimizer
CAdamOptimizer
CBilinear
CConstant
CContext	Context interface
CDataBatch	Default object for holding a mini-batch of data and related information
CDataIter
CEvalMetric
CExecutor	Executor interface
CFactorScheduler
CFeedForward
CFeedForwardConfig
CInitializer
CKVStore
CLogLoss
CLRScheduler	Lr scheduler interface
CMAE
CMonitor	Monitor interface
CMSE
CMSRAPrelu
CMXDataIter
CMXDataIterBlob
CMXDataIterMap
CNDArray	NDArray interface
CNDBlob	Struct to store NDArrayHandle
CNormal
COne
COperator	Operator interface
COpMap	OpMap instance holds a map of all the symbol creators so we can get symbol creators by name. This is used internally by Symbol and Operator
COptimizer	Optimizer interface
COptimizerRegistry
CPSNR
CRMSE
CRMSPropOptimizer
CSGDOptimizer
CShape	Dynamic shape class that can hold shape of arbirary dimension
CSignumOptimizer
CSymBlob	Struct to store SymbolHandle
CSymbol	Symbol interface
CUniform
CXavier
CZero
►Nengine	Namespace of engine internal types
CCallbackOnComplete	OnComplete Callback to the engine, called by AsyncFn when action completes
CVar	Base class of engine variables
►Nfeatures
CEnumNames
CLibInfo
►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
CGPUAuxStream	Holds an auxiliary mshadow gpu stream that can be synced with a primary stream
CIIterator	Iterator type
►CImperative	Runtime functions for NDArray
CAGInfo
CInspectorManager	This singleton struct mediates individual TensorInspector objects so that we can control the global behavior from each of them
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
CSyncedGPUAuxStream	Provides automatic coordination of an auxilary stream with a primary one. This object, upon construction, prepares an aux stream for use by syncing it with enqueued primary-stream work. Object destruction will sync again so future primary-stream work will wait on enqueued aux-stream work. If MXNET_GPU_WORKER_NSTREAMS == 1, then this defaults simply: the primary stream will equal the aux stream and the syncs will be executed as nops. See ./src/operator/cudnn/cudnn_convolution-inl.h for a usage example
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 doesn't involve any arithmetic operations, but it can be converted to tensor of fixed dimension for further operations
CTensorInspector	This class provides a unified interface to inspect the value of all data types including Tensor, TBlob, and NDArray. If the tensor resides on GPU, then it will be copied from GPU memory back to CPU memory to be operated on. Internally, all data types are stored as a TBlob object tb_
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
►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
CNonMaximumSuppressionParam
CPadParam
CPReLUParam
CReduceParam
CReshapeParam
CScalarParam
CSliceLikeParam
CSoftmaxParam
CSplitParam
CSqueezeParam
CStridedSliceParam
CTakeParam
CTransposeParam
CUpSamplingParam
CWinogradConv2DParam
CWinogradNNPACKWeightTransformParam
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< mxnet::TShape >	Hash function for TShape
Chash< mxnet::Tuple< T > >	Hash function for Tuple
Chash< nnvm::TShape >	Hash function for TShape
Chash< nnvm::Tuple< T > >	Hash function for Tuple
►Ntvm
►Nruntime
Cextension_type_info< nnvm::compiler::AttrDict >
Cextension_type_info< nnvm::Graph >
Cextension_type_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
CLibFeature
Cmkldnn_batch_normalization_desc_t	A descriptor of a Batch Normalization operation
Cmkldnn_blocking_desc_t
Cmkldnn_convolution_desc_t	A descriptor of a convolution operation
Cmkldnn_eltwise_desc_t	A descriptor of a element-wise operation
Cmkldnn_engine	An opaque structure to describe an engine
Cmkldnn_exec_arg_t
Cmkldnn_inner_product_desc_t	A descriptor of an inner product operation
Cmkldnn_lrn_desc_t	A descriptor of a Local Response Normalization (LRN) operation
Cmkldnn_memory
Cmkldnn_memory_desc_t
Cmkldnn_memory_extra_desc_t	Description of extra information stored in memory
Cmkldnn_pooling_desc_t	A descriptor of a pooling operation
Cmkldnn_post_ops	An opaque structure for a chain of post operations
Cmkldnn_primitive
Cmkldnn_primitive_attr	An opaque structure for primitive descriptor attributes
Cmkldnn_primitive_desc	An opaque structure to describe a primitive descriptor
Cmkldnn_primitive_desc_iterator	An opaque structure to describe a primitive descriptor iterator
Cmkldnn_rnn_desc_t	A descriptor for an RNN operation
Cmkldnn_rnn_packed_desc_t	Description of tensor of packed weights for rnn
Cmkldnn_shuffle_desc_t	A descriptor of a shuffle operation
Cmkldnn_softmax_desc_t	A descriptor of a Softmax operation
Cmkldnn_stream
Cmkldnn_version_t	Version type
Cmkldnn_wino_desc_t	Description of tensor of weights for winograd 2x3 convolution
CMXCallbackList
CNativeOpInfo
CNDArrayOpInfo