op.h File Reference

definition of all the operators More...

#include <string>
#include <vector>
#include "mxnet-cpp/base.h"
#include "mxnet-cpp/shape.h"
#include "mxnet-cpp/op_util.h"
#include "mxnet-cpp/operator.h"
#include "dmlc/optional.h"
#include "nnvm/tuple.h"

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Namespaces
	mxnet
	namespace of mxnet
	mxnet::cpp

Enumerations
enum	mxnet::cpp::PickMode { mxnet::cpp::PickMode::kClip = 0, mxnet::cpp::PickMode::kWrap = 1 }
	Specify how out-of-bound indices behave. Default is "clip". "clip" means clip to the range. So, if all indices mentioned are too large, they are replaced by the index that addresses the last element along an axis. "wrap" means to wrap. More...
enum	mxnet::cpp::DotForwardStype { mxnet::cpp::DotForwardStype::kNone = 0, mxnet::cpp::DotForwardStype::kCsr = 1, mxnet::cpp::DotForwardStype::kDefault = 2, mxnet::cpp::DotForwardStype::kRow_sparse = 3 }
	The desired storage type of the forward output given by user, if thecombination of input storage types and this hint does not matchany implemented ones, the dot operator will perform fallback operationand still produce an output of the. More...
enum	mxnet::cpp::Batch_dotForwardStype { mxnet::cpp::Batch_dotForwardStype::kNone = 0, mxnet::cpp::Batch_dotForwardStype::kCsr = 1, mxnet::cpp::Batch_dotForwardStype::kDefault = 2, mxnet::cpp::Batch_dotForwardStype::kRow_sparse = 3 }
	The desired storage type of the forward output given by user, if thecombination of input storage types and this hint does not matchany implemented ones, the dot operator will perform fallback operationand still produce an output of the. More...
enum	mxnet::cpp::CastDtype {   mxnet::cpp::CastDtype::kFloat16 = 0, mxnet::cpp::CastDtype::kFloat32 = 1, mxnet::cpp::CastDtype::kFloat64 = 2, mxnet::cpp::CastDtype::kInt32 = 3,   mxnet::cpp::CastDtype::kInt64 = 4, mxnet::cpp::CastDtype::kInt8 = 5, mxnet::cpp::CastDtype::kUint8 = 6 }
	Output data type. More...
enum	mxnet::cpp::Amp_castDtype {   mxnet::cpp::Amp_castDtype::kFloat16 = 0, mxnet::cpp::Amp_castDtype::kFloat32 = 1, mxnet::cpp::Amp_castDtype::kFloat64 = 2, mxnet::cpp::Amp_castDtype::kInt32 = 3,   mxnet::cpp::Amp_castDtype::kInt64 = 4, mxnet::cpp::Amp_castDtype::kInt8 = 5, mxnet::cpp::Amp_castDtype::kUint8 = 6 }
	Output data type. More...
enum	mxnet::cpp::TopkRetTyp { mxnet::cpp::TopkRetTyp::kBoth = 0, mxnet::cpp::TopkRetTyp::kIndices = 1, mxnet::cpp::TopkRetTyp::kMask = 2, mxnet::cpp::TopkRetTyp::kValue = 3 }
	The return type. "value" means to return the top k values, "indices" means to return the indices of the top k values, "mask" means to return a mask array containing 0 and 1. 1 means the top k values. "both" means to return a list of both values and. More...
enum	mxnet::cpp::TopkDtype {   mxnet::cpp::TopkDtype::kFloat16 = 0, mxnet::cpp::TopkDtype::kFloat32 = 1, mxnet::cpp::TopkDtype::kFloat64 = 2, mxnet::cpp::TopkDtype::kInt32 = 3,   mxnet::cpp::TopkDtype::kUint8 = 4 }
	DType of the output indices when ret_typ is "indices" or "both". An error will. More...
enum	mxnet::cpp::ArgsortDtype {   mxnet::cpp::ArgsortDtype::kFloat16 = 0, mxnet::cpp::ArgsortDtype::kFloat32 = 1, mxnet::cpp::ArgsortDtype::kFloat64 = 2, mxnet::cpp::ArgsortDtype::kInt32 = 3,   mxnet::cpp::ArgsortDtype::kUint8 = 4 }
	DType of the output indices. It is only valid when ret_typ is "indices" or "both". An error will be raised if the selected data type cannot precisely. More...
enum	mxnet::cpp::EmbeddingDtype {   mxnet::cpp::EmbeddingDtype::kFloat16 = 0, mxnet::cpp::EmbeddingDtype::kFloat32 = 1, mxnet::cpp::EmbeddingDtype::kFloat64 = 2, mxnet::cpp::EmbeddingDtype::kInt32 = 3,   mxnet::cpp::EmbeddingDtype::kInt64 = 4, mxnet::cpp::EmbeddingDtype::kInt8 = 5, mxnet::cpp::EmbeddingDtype::kUint8 = 6 }
	Data type of weight. More...
enum	mxnet::cpp::TakeMode { mxnet::cpp::TakeMode::kClip = 0, mxnet::cpp::TakeMode::kRaise = 1, mxnet::cpp::TakeMode::kWrap = 2 }
	Specify how out-of-bound indices bahave. Default is "clip". "clip" means clip to the range. So, if all indices mentioned are too large, they are replaced by the index that addresses the last element along an axis. "wrap" means to wrap. More...
enum	mxnet::cpp::One_hotDtype {   mxnet::cpp::One_hotDtype::kFloat16 = 0, mxnet::cpp::One_hotDtype::kFloat32 = 1, mxnet::cpp::One_hotDtype::kFloat64 = 2, mxnet::cpp::One_hotDtype::kInt32 = 3,   mxnet::cpp::One_hotDtype::kInt64 = 4, mxnet::cpp::One_hotDtype::kInt8 = 5, mxnet::cpp::One_hotDtype::kUint8 = 6 }
	DType of the output. More...
enum	mxnet::cpp::Cast_storageStype { mxnet::cpp::Cast_storageStype::kCsr = 0, mxnet::cpp::Cast_storageStype::kDefault = 1, mxnet::cpp::Cast_storageStype::kRow_sparse = 2 }
	Output storage type. More...
enum	mxnet::cpp::NormOutDtype {   mxnet::cpp::NormOutDtype::kNone = 0, mxnet::cpp::NormOutDtype::kFloat16 = 1, mxnet::cpp::NormOutDtype::kFloat32 = 2, mxnet::cpp::NormOutDtype::kFloat64 = 3,   mxnet::cpp::NormOutDtype::kInt32 = 4, mxnet::cpp::NormOutDtype::kInt64 = 5, mxnet::cpp::NormOutDtype::kInt8 = 6 }
	The data type of the output. More...
enum	mxnet::cpp::PoolingPoolType { mxnet::cpp::PoolingPoolType::kAvg = 0, mxnet::cpp::PoolingPoolType::kLp = 1, mxnet::cpp::PoolingPoolType::kMax = 2, mxnet::cpp::PoolingPoolType::kSum = 3 }
	Pooling type to be applied. More...
enum	mxnet::cpp::PoolingPoolingConvention { mxnet::cpp::PoolingPoolingConvention::kFull = 0, mxnet::cpp::PoolingPoolingConvention::kSame = 1, mxnet::cpp::PoolingPoolingConvention::kValid = 2 }
	Pooling convention to be applied. More...
enum	mxnet::cpp::PoolingLayout {   mxnet::cpp::PoolingLayout::kNone = 0, mxnet::cpp::PoolingLayout::kNCDHW = 1, mxnet::cpp::PoolingLayout::kNCHW = 2, mxnet::cpp::PoolingLayout::kNCW = 3,   mxnet::cpp::PoolingLayout::kNDHWC = 4, mxnet::cpp::PoolingLayout::kNHWC = 5, mxnet::cpp::PoolingLayout::kNWC = 6 }
	Set layout for input and output. Empty for default layout: NCW for 1d, NCHW for 2d and NCDHW for 3d. More...
enum	mxnet::cpp::SoftmaxDtype { mxnet::cpp::SoftmaxDtype::kNone = 0, mxnet::cpp::SoftmaxDtype::kFloat16 = 1, mxnet::cpp::SoftmaxDtype::kFloat32 = 2, mxnet::cpp::SoftmaxDtype::kFloat64 = 3 }
	DType of the output in case this can't be inferred. Defaults to the same as. More...
enum	mxnet::cpp::SoftminDtype { mxnet::cpp::SoftminDtype::kNone = 0, mxnet::cpp::SoftminDtype::kFloat16 = 1, mxnet::cpp::SoftminDtype::kFloat32 = 2, mxnet::cpp::SoftminDtype::kFloat64 = 3 }
	DType of the output in case this can't be inferred. Defaults to the same as. More...
enum	mxnet::cpp::Log_softmaxDtype { mxnet::cpp::Log_softmaxDtype::kNone = 0, mxnet::cpp::Log_softmaxDtype::kFloat16 = 1, mxnet::cpp::Log_softmaxDtype::kFloat32 = 2, mxnet::cpp::Log_softmaxDtype::kFloat64 = 3 }
	DType of the output in case this can't be inferred. Defaults to the same as. More...
enum	mxnet::cpp::DeconvolutionCudnnTune { mxnet::cpp::DeconvolutionCudnnTune::kNone = 0, mxnet::cpp::DeconvolutionCudnnTune::kFastest = 1, mxnet::cpp::DeconvolutionCudnnTune::kLimited_workspace = 2, mxnet::cpp::DeconvolutionCudnnTune::kOff = 3 }
	Whether to pick convolution algorithm by running performance test. More...
enum	mxnet::cpp::DeconvolutionLayout {   mxnet::cpp::DeconvolutionLayout::kNone = 0, mxnet::cpp::DeconvolutionLayout::kNCDHW = 1, mxnet::cpp::DeconvolutionLayout::kNCHW = 2, mxnet::cpp::DeconvolutionLayout::kNCW = 3,   mxnet::cpp::DeconvolutionLayout::kNDHWC = 4, mxnet::cpp::DeconvolutionLayout::kNHWC = 5 }
	Set layout for input, output and weight. Empty for default layout, NCW for 1d,. More...
enum	mxnet::cpp::ActivationActType {   mxnet::cpp::ActivationActType::kRelu = 0, mxnet::cpp::ActivationActType::kSigmoid = 1, mxnet::cpp::ActivationActType::kSoftrelu = 2, mxnet::cpp::ActivationActType::kSoftsign = 3,   mxnet::cpp::ActivationActType::kTanh = 4 }
	Activation function to be applied. More...
enum	mxnet::cpp::CTCLossBlankLabel { mxnet::cpp::CTCLossBlankLabel::kFirst = 0, mxnet::cpp::CTCLossBlankLabel::kLast = 1 }
	Set the label that is reserved for blank label.If "first", 0-th label is reserved, and label values for tokens in the vocabulary are between 1 and alphabet_size-1, and the padding mask is -1. If "last", last label value alphabet_size-1 is reserved for blank label instead, and label values for tokens in the vocabulary are between 0 and alphabet_size-2, and the. More...
enum	mxnet::cpp::ConvolutionCudnnTune { mxnet::cpp::ConvolutionCudnnTune::kNone = 0, mxnet::cpp::ConvolutionCudnnTune::kFastest = 1, mxnet::cpp::ConvolutionCudnnTune::kLimited_workspace = 2, mxnet::cpp::ConvolutionCudnnTune::kOff = 3 }
	Whether to pick convolution algo by running performance test. More...
enum	mxnet::cpp::ConvolutionLayout {   mxnet::cpp::ConvolutionLayout::kNone = 0, mxnet::cpp::ConvolutionLayout::kNCDHW = 1, mxnet::cpp::ConvolutionLayout::kNCHW = 2, mxnet::cpp::ConvolutionLayout::kNCW = 3,   mxnet::cpp::ConvolutionLayout::kNDHWC = 4, mxnet::cpp::ConvolutionLayout::kNHWC = 5 }
	Set layout for input, output and weight. Empty for default layout: NCW for 1d, NCHW for 2d and NCDHW for 3d.NHWC and NDHWC are. More...
enum	mxnet::cpp::UpSamplingSampleType { mxnet::cpp::UpSamplingSampleType::kBilinear = 0, mxnet::cpp::UpSamplingSampleType::kNearest = 1 }
	upsampling method More...
enum	mxnet::cpp::UpSamplingMultiInputMode { mxnet::cpp::UpSamplingMultiInputMode::kConcat = 0, mxnet::cpp::UpSamplingMultiInputMode::kSum = 1 }
	How to handle multiple input. concat means concatenate upsampled images along the channel dimension. sum means add all images together, only available for. More...
enum	mxnet::cpp::DropoutMode { mxnet::cpp::DropoutMode::kAlways = 0, mxnet::cpp::DropoutMode::kTraining = 1 }
	Whether to only turn on dropout during training or to also turn on for. More...
enum	mxnet::cpp::SoftmaxActivationMode { mxnet::cpp::SoftmaxActivationMode::kChannel = 0, mxnet::cpp::SoftmaxActivationMode::kInstance = 1 }
	Specifies how to compute the softmax. If set to instance, it computes softmax for each instance. If set to channel, It computes cross channel. More...
enum	mxnet::cpp::LeakyReLUActType {   mxnet::cpp::LeakyReLUActType::kElu = 0, mxnet::cpp::LeakyReLUActType::kGelu = 1, mxnet::cpp::LeakyReLUActType::kLeaky = 2, mxnet::cpp::LeakyReLUActType::kPrelu = 3,   mxnet::cpp::LeakyReLUActType::kRrelu = 4, mxnet::cpp::LeakyReLUActType::kSelu = 5 }
	Activation function to be applied. More...
enum	mxnet::cpp::RNNMode { mxnet::cpp::RNNMode::kGru = 0, mxnet::cpp::RNNMode::kLstm = 1, mxnet::cpp::RNNMode::kRnn_relu = 2, mxnet::cpp::RNNMode::kRnn_tanh = 3 }
	the type of RNN to compute More...
enum	mxnet::cpp::SoftmaxOutputNormalization { mxnet::cpp::SoftmaxOutputNormalization::kBatch = 0, mxnet::cpp::SoftmaxOutputNormalization::kNull = 1, mxnet::cpp::SoftmaxOutputNormalization::kValid = 2 }
	Normalizes the gradient. More...
enum	mxnet::cpp::PadMode { mxnet::cpp::PadMode::kConstant = 0, mxnet::cpp::PadMode::kEdge = 1, mxnet::cpp::PadMode::kReflect = 2 }
	Padding type to use. "constant" pads with constant_value "edge" pads using the edge values of the input array "reflect" pads by reflecting values with. More...
enum	mxnet::cpp::GridGeneratorTransformType { mxnet::cpp::GridGeneratorTransformType::kAffine = 0, mxnet::cpp::GridGeneratorTransformType::kWarp = 1 }
	The type of transformation. For affine, input data should be an affine matrix of size (batch, 6). For warp, input data should be an optical flow of size. More...
enum	mxnet::cpp::Pooling_v1PoolType { mxnet::cpp::Pooling_v1PoolType::kAvg = 0, mxnet::cpp::Pooling_v1PoolType::kMax = 1, mxnet::cpp::Pooling_v1PoolType::kSum = 2 }
	Pooling type to be applied. More...
enum	mxnet::cpp::Pooling_v1PoolingConvention { mxnet::cpp::Pooling_v1PoolingConvention::kFull = 0, mxnet::cpp::Pooling_v1PoolingConvention::kValid = 1 }
	Pooling convention to be applied. More...
enum	mxnet::cpp::Convolution_v1CudnnTune { mxnet::cpp::Convolution_v1CudnnTune::kNone = 0, mxnet::cpp::Convolution_v1CudnnTune::kFastest = 1, mxnet::cpp::Convolution_v1CudnnTune::kLimited_workspace = 2, mxnet::cpp::Convolution_v1CudnnTune::kOff = 3 }
	Whether to pick convolution algo by running performance test. Leads to higher startup time but may give faster speed. Options are: 'off': no tuning 'limited_workspace': run test and pick the fastest algorithm that doesn't 'fastest': pick the fastest algorithm and ignore workspace limit. If set to None (default), behavior is determined by environment variable MXNET_CUDNN_AUTOTUNE_DEFAULT: 0 for off, 1 for limited workspace (default), 2 for fastest. More...
enum	mxnet::cpp::Convolution_v1Layout {   mxnet::cpp::Convolution_v1Layout::kNone = 0, mxnet::cpp::Convolution_v1Layout::kNCDHW = 1, mxnet::cpp::Convolution_v1Layout::kNCHW = 2, mxnet::cpp::Convolution_v1Layout::kNDHWC = 3,   mxnet::cpp::Convolution_v1Layout::kNHWC = 4 }
	Set layout for input, output and weight. Empty for default layout: NCHW for 2d and NCDHW for 3d. More...
enum	mxnet::cpp::SpatialTransformerTransformType { mxnet::cpp::SpatialTransformerTransformType::kAffine = 0 }
	transformation type More...
enum	mxnet::cpp::SpatialTransformerSamplerType { mxnet::cpp::SpatialTransformerSamplerType::kBilinear = 0 }
	sampling type More...
enum	mxnet::cpp::L2NormalizationMode { mxnet::cpp::L2NormalizationMode::kChannel = 0, mxnet::cpp::L2NormalizationMode::kInstance = 1, mxnet::cpp::L2NormalizationMode::kSpatial = 2 }
	Specify the dimension along which to compute L2 norm. More...
enum	mxnet::cpp::MakeLossNormalization { mxnet::cpp::MakeLossNormalization::kBatch = 0, mxnet::cpp::MakeLossNormalization::kNull = 1, mxnet::cpp::MakeLossNormalization::kValid = 2 }
	If this is set to null, the output gradient will not be normalized. If this is set to batch, the output gradient will be divided by the batch size. If this is set to valid, the output gradient will be divided by the number of valid input. More...

Functions
Symbol	mxnet::cpp::khatri_rao (const std::string &symbol_name, const std::vector< Symbol > &args)
	Computes the Khatri-Rao product of the input matrices. More...
Symbol	mxnet::cpp::all_finite (const std::string &symbol_name, Symbol data, bool init_output=true)
	Check if all the float numbers in the array are finite (used for AMP) More...
Symbol	mxnet::cpp::multi_all_finite (const std::string &symbol_name, const std::vector< Symbol > &data, int num_arrays=1, bool init_output=true)
	Check if all the float numbers in all the arrays are finite (used for AMP) More...
Symbol	mxnet::cpp::Custom (const std::string &symbol_name, const std::vector< Symbol > &data, const std::string &op_type)
	Apply a custom operator implemented in a frontend language (like Python). More...
Symbol	mxnet::cpp::broadcast_power (const std::string &symbol_name, Symbol lhs, Symbol rhs)
	Returns result of first array elements raised to powers from second array,. More...
Symbol	mxnet::cpp::broadcast_maximum (const std::string &symbol_name, Symbol lhs, Symbol rhs)
	Returns element-wise maximum of the input arrays with broadcasting. More...
Symbol	mxnet::cpp::broadcast_minimum (const std::string &symbol_name, Symbol lhs, Symbol rhs)
	Returns element-wise minimum of the input arrays with broadcasting. More...
Symbol	mxnet::cpp::broadcast_hypot (const std::string &symbol_name, Symbol lhs, Symbol rhs)
	Returns the hypotenuse of a right angled triangle, given its "legs" with broadcasting. More...
Symbol	mxnet::cpp::Reshape (const std::string &symbol_name, Symbol data, Shape shape=Shape(), bool reverse=false, Shape target_shape=Shape(), bool keep_highest=false)
	Reshapes the input array. More...
Symbol	mxnet::cpp::Flatten (const std::string &symbol_name, Symbol data)
	Flattens the input array into a 2-D array by collapsing the higher dimensions. More...
Symbol	mxnet::cpp::transpose (const std::string &symbol_name, Symbol data, Shape axes=Shape())
	Permutes the dimensions of an array. More...
Symbol	mxnet::cpp::expand_dims (const std::string &symbol_name, Symbol data, int axis)
	Inserts a new axis of size 1 into the array shape. More...
Symbol	mxnet::cpp::slice (const std::string &symbol_name, Symbol data, Shape begin, Shape end, Shape step=Shape())
	Slices a region of the array. More...
Symbol	mxnet::cpp::slice_axis (const std::string &symbol_name, Symbol data, int axis, int begin, dmlc::optional< int > end)
	Slices along a given axis. More...
Symbol	mxnet::cpp::slice_like (const std::string &symbol_name, Symbol data, Symbol shape_like, Shape axes=Shape())
	Slices a region of the array like the shape of another array. More...
Symbol	mxnet::cpp::clip (const std::string &symbol_name, Symbol data, mx_float a_min, mx_float a_max)
	Clips (limits) the values in an array. More...
Symbol	mxnet::cpp::repeat (const std::string &symbol_name, Symbol data, int repeats, dmlc::optional< int > axis=dmlc::optional< int >())
	Repeats elements of an array. More...
Symbol	mxnet::cpp::tile (const std::string &symbol_name, Symbol data, Shape reps)
	Repeats the whole array multiple times. More...
Symbol	mxnet::cpp::reverse (const std::string &symbol_name, Symbol data, Shape axis)
	Reverses the order of elements along given axis while preserving array shape. More...
Symbol	mxnet::cpp::stack (const std::string &symbol_name, const std::vector< Symbol > &data, int num_args, int axis=0)
	Join a sequence of arrays along a new axis. More...
Symbol	mxnet::cpp::squeeze (const std::string &symbol_name, const std::vector< Symbol > &data, dmlc::optional< Shape > axis=dmlc::optional< Shape >())
	Remove single-dimensional entries from the shape of an array. Same behavior of defining the output tensor shape as numpy.squeeze for the most See the following note for exception. More...
Symbol	mxnet::cpp::depth_to_space (const std::string &symbol_name, Symbol data, int block_size)
	Rearranges(permutes) data from depth into blocks of spatial data. Similar to ONNX DepthToSpace operator: https://github.com/onnx/onnx/blob/master/docs/Operators.md#DepthToSpace. The output is a new tensor where the values from depth dimension are moved in to height and width dimension. The reverse of this operation is. More...
Symbol	mxnet::cpp::space_to_depth (const std::string &symbol_name, Symbol data, int block_size)
	Rearranges(permutes) blocks of spatial data into depth. Similar to ONNX SpaceToDepth operator: https://github.com/onnx/onnx/blob/master/docs/Operators.md#SpaceToDepth. More...
Symbol	mxnet::cpp::zeros_like (const std::string &symbol_name, Symbol data)
	Return an array of zeros with the same shape, type and storage type as the input array. More...
Symbol	mxnet::cpp::ones_like (const std::string &symbol_name, Symbol data)
	Return an array of ones with the same shape and type as the input array. More...
Symbol	mxnet::cpp::add_n (const std::string &symbol_name, const std::vector< Symbol > &args)
	Adds all input arguments element-wise. More...
Symbol	mxnet::cpp::argmax (const std::string &symbol_name, Symbol data, dmlc::optional< int > axis=dmlc::optional< int >(), bool keepdims=false)
	Returns indices of the maximum values along an axis. More...
Symbol	mxnet::cpp::argmin (const std::string &symbol_name, Symbol data, dmlc::optional< int > axis=dmlc::optional< int >(), bool keepdims=false)
	Returns indices of the minimum values along an axis. More...
Symbol	mxnet::cpp::argmax_channel (const std::string &symbol_name, Symbol data)
	Returns argmax indices of each channel from the input array. More...
Symbol	mxnet::cpp::pick (const std::string &symbol_name, Symbol data, Symbol index, dmlc::optional< int > axis=dmlc::optional< int >(-1), bool keepdims=false, PickMode mode=PickMode::kClip)
	Picks elements from an input array according to the input indices along the. More...
Symbol	mxnet::cpp::dot (const std::string &symbol_name, Symbol lhs, Symbol rhs, bool transpose_a=false, bool transpose_b=false, DotForwardStype forward_stype=DotForwardStype::kNone)
	Dot product of two arrays. More...
Symbol	mxnet::cpp::batch_dot (const std::string &symbol_name, Symbol lhs, Symbol rhs, bool transpose_a=false, bool transpose_b=false, Batch_dotForwardStype forward_stype=Batch_dotForwardStype::kNone)
	Batchwise dot product. More...
Symbol	mxnet::cpp::broadcast_add (const std::string &symbol_name, Symbol lhs, Symbol rhs)
	Returns element-wise sum of the input arrays with broadcasting. More...
Symbol	mxnet::cpp::broadcast_sub (const std::string &symbol_name, Symbol lhs, Symbol rhs)
	Returns element-wise difference of the input arrays with broadcasting. More...
Symbol	mxnet::cpp::broadcast_mul (const std::string &symbol_name, Symbol lhs, Symbol rhs)
	Returns element-wise product of the input arrays with broadcasting. More...
Symbol	mxnet::cpp::broadcast_div (const std::string &symbol_name, Symbol lhs, Symbol rhs)
	Returns element-wise division of the input arrays with broadcasting. More...
Symbol	mxnet::cpp::broadcast_mod (const std::string &symbol_name, Symbol lhs, Symbol rhs)
	Returns element-wise modulo of the input arrays with broadcasting. More...
Symbol	mxnet::cpp::relu (const std::string &symbol_name, Symbol data)
	Computes rectified linear activation. More...
Symbol	mxnet::cpp::sigmoid (const std::string &symbol_name, Symbol data)
	Computes sigmoid of x element-wise. More...
Symbol	mxnet::cpp::hard_sigmoid (const std::string &symbol_name, Symbol data, mx_float alpha=0.200000003, mx_float beta=0.5)
	Computes hard sigmoid of x element-wise. More...
Symbol	mxnet::cpp::softsign (const std::string &symbol_name, Symbol data)
	Computes softsign of x element-wise. More...
Symbol	mxnet::cpp::BlockGrad (const std::string &symbol_name, Symbol data)
	Stops gradient computation. More...
Symbol	mxnet::cpp::make_loss (const std::string &symbol_name, Symbol data)
	Make your own loss function in network construction. More...
Symbol	mxnet::cpp::reshape_like (const std::string &symbol_name, Symbol lhs, Symbol rhs)
	Reshape some or all dimensions of lhs to have the same shape as some or all. More...
Symbol	mxnet::cpp::shape_array (const std::string &symbol_name, Symbol data, dmlc::optional< int > lhs_begin=dmlc::optional< int >(), dmlc::optional< int > lhs_end=dmlc::optional< int >(), dmlc::optional< int > rhs_begin=dmlc::optional< int >(), dmlc::optional< int > rhs_end=dmlc::optional< int >())
	Returns a 1D int64 array containing the shape of data. More...
Symbol	mxnet::cpp::size_array (const std::string &symbol_name, Symbol data)
	Returns a 1D int64 array containing the size of data. More...
Symbol	mxnet::cpp::Cast (const std::string &symbol_name, Symbol data, CastDtype dtype)
	Casts all elements of the input to a new type. More...
Symbol	mxnet::cpp::negative (const std::string &symbol_name, Symbol data)
	Numerical negative of the argument, element-wise. More...
Symbol	mxnet::cpp::reciprocal (const std::string &symbol_name, Symbol data)
	Returns the reciprocal of the argument, element-wise. More...
Symbol	mxnet::cpp::abs (const std::string &symbol_name, Symbol data)
	Returns element-wise absolute value of the input. More...
Symbol	mxnet::cpp::sign (const std::string &symbol_name, Symbol data)
	Returns element-wise sign of the input. More...
Symbol	mxnet::cpp::round (const std::string &symbol_name, Symbol data)
	Returns element-wise rounded value to the nearest integer of the input. More...
Symbol	mxnet::cpp::rint (const std::string &symbol_name, Symbol data)
	Returns element-wise rounded value to the nearest integer of the input. More...
Symbol	mxnet::cpp::ceil (const std::string &symbol_name, Symbol data)
	Returns element-wise ceiling of the input. More...
Symbol	mxnet::cpp::floor (const std::string &symbol_name, Symbol data)
	Returns element-wise floor of the input. More...
Symbol	mxnet::cpp::trunc (const std::string &symbol_name, Symbol data)
	Return the element-wise truncated value of the input. More...
Symbol	mxnet::cpp::fix (const std::string &symbol_name, Symbol data)
	Returns element-wise rounded value to the nearest \ integer towards zero of the input. More...
Symbol	mxnet::cpp::square (const std::string &symbol_name, Symbol data)
	Returns element-wise squared value of the input. More...
Symbol	mxnet::cpp::sqrt (const std::string &symbol_name, Symbol data)
	Returns element-wise square-root value of the input. More...
Symbol	mxnet::cpp::rsqrt (const std::string &symbol_name, Symbol data)
	Returns element-wise inverse square-root value of the input. More...
Symbol	mxnet::cpp::cbrt (const std::string &symbol_name, Symbol data)
	Returns element-wise cube-root value of the input. More...
Symbol	mxnet::cpp::erf (const std::string &symbol_name, Symbol data)
	Returns element-wise gauss error function of the input. More...
Symbol	mxnet::cpp::erfinv (const std::string &symbol_name, Symbol data)
	Returns element-wise inverse gauss error function of the input. More...
Symbol	mxnet::cpp::rcbrt (const std::string &symbol_name, Symbol data)
	Returns element-wise inverse cube-root value of the input. More...
Symbol	mxnet::cpp::exp (const std::string &symbol_name, Symbol data)
	Returns element-wise exponential value of the input. More...
Symbol	mxnet::cpp::log (const std::string &symbol_name, Symbol data)
	Returns element-wise Natural logarithmic value of the input. More...
Symbol	mxnet::cpp::log10 (const std::string &symbol_name, Symbol data)
	Returns element-wise Base-10 logarithmic value of the input. More...
Symbol	mxnet::cpp::log2 (const std::string &symbol_name, Symbol data)
	Returns element-wise Base-2 logarithmic value of the input. More...
Symbol	mxnet::cpp::log1p (const std::string &symbol_name, Symbol data)
	Returns element-wise log(1 + x) value of the input. More...
Symbol	mxnet::cpp::expm1 (const std::string &symbol_name, Symbol data)
	Returns exp(x) - 1 computed element-wise on the input. More...
Symbol	mxnet::cpp::gamma (const std::string &symbol_name, Symbol data)
	Returns the gamma function (extension of the factorial function \ to the reals), computed element-wise on the input array. More...
Symbol	mxnet::cpp::gammaln (const std::string &symbol_name, Symbol data)
	Returns element-wise log of the absolute value of the gamma function \ of the input. More...
Symbol	mxnet::cpp::logical_not (const std::string &symbol_name, Symbol data)
	Returns the result of logical NOT (!) function. More...
Symbol	mxnet::cpp::amp_cast (const std::string &symbol_name, Symbol data, Amp_castDtype dtype)
	Cast function between low precision float/FP32 used by AMP. More...
Symbol	mxnet::cpp::amp_multicast (const std::string &symbol_name, const std::vector< Symbol > &data, int num_outputs)
	Cast function used by AMP, that casts its inputs to the common widest type. More...
Symbol	mxnet::cpp::topk (const std::string &symbol_name, Symbol data, dmlc::optional< int > axis=dmlc::optional< int >(-1), int k=1, TopkRetTyp ret_typ=TopkRetTyp::kIndices, bool is_ascend=false, TopkDtype dtype=TopkDtype::kFloat32)
	Returns the top k elements in an input array along the given axis. The returned elements will be sorted. More...
Symbol	mxnet::cpp::sort (const std::string &symbol_name, Symbol data, dmlc::optional< int > axis=dmlc::optional< int >(-1), bool is_ascend=true)
	Returns a sorted copy of an input array along the given axis. More...
Symbol	mxnet::cpp::argsort (const std::string &symbol_name, Symbol data, dmlc::optional< int > axis=dmlc::optional< int >(-1), bool is_ascend=true, ArgsortDtype dtype=ArgsortDtype::kFloat32)
	Returns the indices that would sort an input array along the given axis. More...
Symbol	mxnet::cpp::elemwise_add (const std::string &symbol_name, Symbol lhs, Symbol rhs)
	Adds arguments element-wise. More...
Symbol	mxnet::cpp::elemwise_sub (const std::string &symbol_name, Symbol lhs, Symbol rhs)
	Subtracts arguments element-wise. More...
Symbol	mxnet::cpp::elemwise_mul (const std::string &symbol_name, Symbol lhs, Symbol rhs)
	Multiplies arguments element-wise. More...
Symbol	mxnet::cpp::elemwise_div (const std::string &symbol_name, Symbol lhs, Symbol rhs)
	Divides arguments element-wise. More...
Symbol	mxnet::cpp::Embedding (const std::string &symbol_name, Symbol data, Symbol weight, int input_dim, int output_dim, EmbeddingDtype dtype=EmbeddingDtype::kFloat32, bool sparse_grad=false)
	Maps integer indices to vector representations (embeddings). More...
Symbol	mxnet::cpp::take (const std::string &symbol_name, Symbol a, Symbol indices, int axis=0, TakeMode mode=TakeMode::kClip)
	Takes elements from an input array along the given axis. More...
Symbol	mxnet::cpp::batch_take (const std::string &symbol_name, Symbol a, Symbol indices)
	Takes elements from a data batch. More...
Symbol	mxnet::cpp::one_hot (const std::string &symbol_name, Symbol indices, int depth, double on_value=1, double off_value=0, One_hotDtype dtype=One_hotDtype::kFloat32)
	Returns a one-hot array. More...
Symbol	mxnet::cpp::gather_nd (const std::string &symbol_name, Symbol data, Symbol indices)
	Gather elements or slices from data and store to a tensor whose shape is defined by indices. More...
Symbol	mxnet::cpp::scatter_nd (const std::string &symbol_name, Symbol data, Symbol indices, Shape shape)
	Scatters data into a new tensor according to indices. More...
Symbol	mxnet::cpp::broadcast_equal (const std::string &symbol_name, Symbol lhs, Symbol rhs)
	Returns the result of element-wise equal to (==) comparison operation with. More...
Symbol	mxnet::cpp::broadcast_not_equal (const std::string &symbol_name, Symbol lhs, Symbol rhs)
	Returns the result of element-wise not equal to (!=) comparison operation. More...
Symbol	mxnet::cpp::broadcast_greater (const std::string &symbol_name, Symbol lhs, Symbol rhs)
	Returns the result of element-wise greater than (>) comparison operation. More...
Symbol	mxnet::cpp::broadcast_greater_equal (const std::string &symbol_name, Symbol lhs, Symbol rhs)
	Returns the result of element-wise greater than or equal to (>=) comparison. More...
Symbol	mxnet::cpp::broadcast_lesser (const std::string &symbol_name, Symbol lhs, Symbol rhs)
	Returns the result of element-wise lesser than (<) comparison operation. More...
Symbol	mxnet::cpp::broadcast_lesser_equal (const std::string &symbol_name, Symbol lhs, Symbol rhs)
	Returns the result of element-wise lesser than or equal to (<=) comparison. More...
Symbol	mxnet::cpp::broadcast_logical_and (const std::string &symbol_name, Symbol lhs, Symbol rhs)
	Returns the result of element-wise logical and with broadcasting. More...
Symbol	mxnet::cpp::broadcast_logical_or (const std::string &symbol_name, Symbol lhs, Symbol rhs)
	Returns the result of element-wise logical or with broadcasting. More...
Symbol	mxnet::cpp::broadcast_logical_xor (const std::string &symbol_name, Symbol lhs, Symbol rhs)
	Returns the result of element-wise logical xor with broadcasting. More...
Symbol	mxnet::cpp::diag (const std::string &symbol_name, Symbol data, int k=0, int axis1=0, int axis2=1)
	Extracts a diagonal or constructs a diagonal array. More...
Symbol	mxnet::cpp::where (const std::string &symbol_name, Symbol condition, Symbol x, Symbol y)
	Return the elements, either from x or y, depending on the condition. More...
Symbol	mxnet::cpp::smooth_l1 (const std::string &symbol_name, Symbol data, mx_float scalar)
	Calculate Smooth L1 Loss(lhs, scalar) by summing. More...
Symbol	mxnet::cpp::cast_storage (const std::string &symbol_name, Symbol data, Cast_storageStype stype)
	Casts tensor storage type to the new type. More...
Symbol	mxnet::cpp::sum (const std::string &symbol_name, Symbol data, dmlc::optional< Shape > axis=dmlc::optional< Shape >(), bool keepdims=false, bool exclude=false)
	Computes the sum of array elements over given axes. More...
Symbol	mxnet::cpp::mean (const std::string &symbol_name, Symbol data, dmlc::optional< Shape > axis=dmlc::optional< Shape >(), bool keepdims=false, bool exclude=false)
	Computes the mean of array elements over given axes. More...
Symbol	mxnet::cpp::prod (const std::string &symbol_name, Symbol data, dmlc::optional< Shape > axis=dmlc::optional< Shape >(), bool keepdims=false, bool exclude=false)
	Computes the product of array elements over given axes. More...
Symbol	mxnet::cpp::nansum (const std::string &symbol_name, Symbol data, dmlc::optional< Shape > axis=dmlc::optional< Shape >(), bool keepdims=false, bool exclude=false)
	Computes the sum of array elements over given axes treating Not a Numbers. More...
Symbol	mxnet::cpp::nanprod (const std::string &symbol_name, Symbol data, dmlc::optional< Shape > axis=dmlc::optional< Shape >(), bool keepdims=false, bool exclude=false)
	Computes the product of array elements over given axes treating Not a Numbers. More...
Symbol	mxnet::cpp::max (const std::string &symbol_name, Symbol data, dmlc::optional< Shape > axis=dmlc::optional< Shape >(), bool keepdims=false, bool exclude=false)
	Computes the max of array elements over given axes. More...
Symbol	mxnet::cpp::min (const std::string &symbol_name, Symbol data, dmlc::optional< Shape > axis=dmlc::optional< Shape >(), bool keepdims=false, bool exclude=false)
	Computes the min of array elements over given axes. More...
Symbol	mxnet::cpp::broadcast_axis (const std::string &symbol_name, Symbol data, Shape axis=Shape(), Shape size=Shape())
	Broadcasts the input array over particular axes. More...
Symbol	mxnet::cpp::broadcast_to (const std::string &symbol_name, Symbol data, Shape shape=Shape())
	Broadcasts the input array to a new shape. More...
Symbol	mxnet::cpp::broadcast_like (const std::string &symbol_name, Symbol lhs, Symbol rhs, dmlc::optional< Shape > lhs_axes=dmlc::optional< Shape >(), dmlc::optional< Shape > rhs_axes=dmlc::optional< Shape >())
	Broadcasts lhs to have the same shape as rhs. More...
Symbol	mxnet::cpp::norm (const std::string &symbol_name, Symbol data, int ord=2, dmlc::optional< Shape > axis=dmlc::optional< Shape >(), NormOutDtype out_dtype=NormOutDtype::kNone, bool keepdims=false)
	Computes the norm on an NDArray. More...
Symbol	mxnet::cpp::sin (const std::string &symbol_name, Symbol data)
	Computes the element-wise sine of the input array. More...
Symbol	mxnet::cpp::cos (const std::string &symbol_name, Symbol data)
	Computes the element-wise cosine of the input array. More...
Symbol	mxnet::cpp::tan (const std::string &symbol_name, Symbol data)
	Computes the element-wise tangent of the input array. More...
Symbol	mxnet::cpp::arcsin (const std::string &symbol_name, Symbol data)
	Returns element-wise inverse sine of the input array. More...
Symbol	mxnet::cpp::arccos (const std::string &symbol_name, Symbol data)
	Returns element-wise inverse cosine of the input array. More...
Symbol	mxnet::cpp::arctan (const std::string &symbol_name, Symbol data)
	Returns element-wise inverse tangent of the input array. More...
Symbol	mxnet::cpp::degrees (const std::string &symbol_name, Symbol data)
	Converts each element of the input array from radians to degrees. More...
Symbol	mxnet::cpp::radians (const std::string &symbol_name, Symbol data)
	Converts each element of the input array from degrees to radians. More...
Symbol	mxnet::cpp::sinh (const std::string &symbol_name, Symbol data)
	Returns the hyperbolic sine of the input array, computed element-wise. More...
Symbol	mxnet::cpp::cosh (const std::string &symbol_name, Symbol data)
	Returns the hyperbolic cosine of the input array, computed element-wise. More...
Symbol	mxnet::cpp::tanh (const std::string &symbol_name, Symbol data)
	Returns the hyperbolic tangent of the input array, computed element-wise. More...
Symbol	mxnet::cpp::arcsinh (const std::string &symbol_name, Symbol data)
	Returns the element-wise inverse hyperbolic sine of the input array, \ computed element-wise. More...
Symbol	mxnet::cpp::arccosh (const std::string &symbol_name, Symbol data)
	Returns the element-wise inverse hyperbolic cosine of the input array, \ computed element-wise. More...
Symbol	mxnet::cpp::arctanh (const std::string &symbol_name, Symbol data)
	Returns the element-wise inverse hyperbolic tangent of the input array, \ computed element-wise. More...
Symbol	mxnet::cpp::Pooling (const std::string &symbol_name, Symbol data, Shape kernel=Shape(), PoolingPoolType pool_type=PoolingPoolType::kMax, bool global_pool=false, bool cudnn_off=false, PoolingPoolingConvention pooling_convention=PoolingPoolingConvention::kValid, Shape stride=Shape(), Shape pad=Shape(), dmlc::optional< int > p_value=dmlc::optional< int >(), dmlc::optional< bool > count_include_pad=dmlc::optional< bool >(), PoolingLayout layout=PoolingLayout::kNone)
	Performs pooling on the input. More...
Symbol	mxnet::cpp::softmax (const std::string &symbol_name, Symbol data, int axis=-1, dmlc::optional< double > temperature=dmlc::optional< double >(), SoftmaxDtype dtype=SoftmaxDtype::kNone)
	Applies the softmax function. More...
Symbol	mxnet::cpp::softmin (const std::string &symbol_name, Symbol data, int axis=-1, dmlc::optional< double > temperature=dmlc::optional< double >(), SoftminDtype dtype=SoftminDtype::kNone)
	Applies the softmin function. More...
Symbol	mxnet::cpp::log_softmax (const std::string &symbol_name, Symbol data, int axis=-1, dmlc::optional< double > temperature=dmlc::optional< double >(), Log_softmaxDtype dtype=Log_softmaxDtype::kNone)
	Computes the log softmax of the input. This is equivalent to computing softmax followed by log. More...
Symbol	mxnet::cpp::Deconvolution (const std::string &symbol_name, Symbol data, Symbol weight, Symbol bias, Shape kernel, uint32_t num_filter, Shape stride=Shape(), Shape dilate=Shape(), Shape pad=Shape(), Shape adj=Shape(), Shape target_shape=Shape(), uint32_t num_group=1, uint64_t workspace=512, bool no_bias=true, DeconvolutionCudnnTune cudnn_tune=DeconvolutionCudnnTune::kNone, bool cudnn_off=false, DeconvolutionLayout layout=DeconvolutionLayout::kNone)
	Computes 1D or 2D transposed convolution (aka fractionally strided convolution) of the input tensor. This operation can be seen as the gradient of Convolution operation with respect to its input. Convolution usually reduces the size of the input. Transposed convolution works the other way, going from a smaller. More...
Symbol	mxnet::cpp::Activation (const std::string &symbol_name, Symbol data, ActivationActType act_type)
	Applies an activation function element-wise to the input. More...
Symbol	mxnet::cpp::BatchNorm (const std::string &symbol_name, Symbol data, Symbol gamma, Symbol beta, Symbol moving_mean, Symbol moving_var, double eps=0.0010000000474974513, mx_float momentum=0.899999976, bool fix_gamma=true, bool use_global_stats=false, bool output_mean_var=false, int axis=1, bool cudnn_off=false)
	Batch normalization. More...
Symbol	mxnet::cpp::CTCLoss (const std::string &symbol_name, Symbol data, Symbol label, Symbol data_lengths, Symbol label_lengths, bool use_data_lengths=false, bool use_label_lengths=false, CTCLossBlankLabel blank_label=CTCLossBlankLabel::kFirst)
	Connectionist Temporal Classification Loss. More...
Symbol	mxnet::cpp::FullyConnected (const std::string &symbol_name, Symbol data, Symbol weight, Symbol bias, int num_hidden, bool no_bias=false, bool flatten=true)
	Applies a linear transformation: :math:Y = XW^T + b. More...
Symbol	mxnet::cpp::Convolution (const std::string &symbol_name, Symbol data, Symbol weight, Symbol bias, Shape kernel, uint32_t num_filter, Shape stride=Shape(), Shape dilate=Shape(), Shape pad=Shape(), uint32_t num_group=1, uint64_t workspace=1024, bool no_bias=false, ConvolutionCudnnTune cudnn_tune=ConvolutionCudnnTune::kNone, bool cudnn_off=false, ConvolutionLayout layout=ConvolutionLayout::kNone)
	Compute N-D convolution on *(N+2)*-D input. More...
Symbol	mxnet::cpp::UpSampling (const std::string &symbol_name, const std::vector< Symbol > &data, int scale, UpSamplingSampleType sample_type, int num_args, int num_filter=0, UpSamplingMultiInputMode multi_input_mode=UpSamplingMultiInputMode::kConcat, uint64_t workspace=512)
	Upsamples the given input data. More...
Symbol	mxnet::cpp::Concat (const std::string &symbol_name, const std::vector< Symbol > &data, int num_args, int dim=1)
	Joins input arrays along a given axis. More...
Symbol	mxnet::cpp::LayerNorm (const std::string &symbol_name, Symbol data, Symbol gamma, Symbol beta, int axis=-1, mx_float eps=9.99999975e-06, bool output_mean_var=false)
	Layer normalization. More...
Symbol	mxnet::cpp::LRN (const std::string &symbol_name, Symbol data, uint32_t nsize, mx_float alpha=9.99999975e-05, mx_float beta=0.75, mx_float knorm=2)
	Applies local response normalization to the input. More...
Symbol	mxnet::cpp::Dropout (const std::string &symbol_name, Symbol data, mx_float p=0.5, DropoutMode mode=DropoutMode::kTraining, Shape axes=Shape(), dmlc::optional< bool > cudnn_off=dmlc::optional< bool >(0))
	Applies dropout operation to input array. More...
Symbol	mxnet::cpp::SoftmaxActivation (const std::string &symbol_name, Symbol data, SoftmaxActivationMode mode=SoftmaxActivationMode::kInstance)
	Applies softmax activation to input. This is intended for internal layers. More...
Symbol	mxnet::cpp::moments (const std::string &symbol_name, Symbol data, dmlc::optional< Shape > axes=dmlc::optional< Shape >(), bool keepdims=false)
	Calculate the mean and variance of data. More...
Symbol	mxnet::cpp::LeakyReLU (const std::string &symbol_name, Symbol data, Symbol gamma, LeakyReLUActType act_type=LeakyReLUActType::kLeaky, mx_float slope=0.25, mx_float lower_bound=0.125, mx_float upper_bound=0.333999991)
	Applies Leaky rectified linear unit activation element-wise to the input. More...
Symbol	mxnet::cpp::RNN (const std::string &symbol_name, Symbol data, Symbol parameters, Symbol state, Symbol state_cell, Symbol sequence_length, uint32_t state_size, uint32_t num_layers, RNNMode mode, bool bidirectional=false, mx_float p=0, bool state_outputs=false, dmlc::optional< int > projection_size=dmlc::optional< int >(), dmlc::optional< double > lstm_state_clip_min=dmlc::optional< double >(), dmlc::optional< double > lstm_state_clip_max=dmlc::optional< double >(), bool lstm_state_clip_nan=false, bool use_sequence_length=false)
	Applies recurrent layers to input data. Currently, vanilla RNN, LSTM and GRU are implemented, with both multi-layer and bidirectional support. More...
Symbol	mxnet::cpp::SoftmaxOutput (const std::string &symbol_name, Symbol data, Symbol label, mx_float grad_scale=1, mx_float ignore_label=-1, bool multi_output=false, bool use_ignore=false, bool preserve_shape=false, SoftmaxOutputNormalization normalization=SoftmaxOutputNormalization::kNull, bool out_grad=false, mx_float smooth_alpha=0)
	Computes the gradient of cross entropy loss with respect to softmax output. More...
Symbol	mxnet::cpp::SwapAxis (const std::string &symbol_name, Symbol data, uint32_t dim1=0, uint32_t dim2=0)
	Interchanges two axes of an array. More...
Symbol	mxnet::cpp::BatchNorm_v1 (const std::string &symbol_name, Symbol data, Symbol gamma, Symbol beta, mx_float eps=0.00100000005, mx_float momentum=0.899999976, bool fix_gamma=true, bool use_global_stats=false, bool output_mean_var=false)
	Batch normalization. More...
Symbol	mxnet::cpp::softmax_cross_entropy (const std::string &symbol_name, Symbol data, Symbol label)
	Calculate cross entropy of softmax output and one-hot label. More...
Symbol	mxnet::cpp::LinearRegressionOutput (const std::string &symbol_name, Symbol data, Symbol label, mx_float grad_scale=1)
	Computes and optimizes for squared loss during backward propagation. Just outputs data during forward propagation. More...
Symbol	mxnet::cpp::MAERegressionOutput (const std::string &symbol_name, Symbol data, Symbol label, mx_float grad_scale=1)
	Computes mean absolute error of the input. More...
Symbol	mxnet::cpp::LogisticRegressionOutput (const std::string &symbol_name, Symbol data, Symbol label, mx_float grad_scale=1)
	Applies a logistic function to the input. More...
Symbol	mxnet::cpp::IdentityAttachKLSparseReg (const std::string &symbol_name, Symbol data, mx_float sparseness_target=0.100000001, mx_float penalty=0.00100000005, mx_float momentum=0.899999976)
	Apply a sparse regularization to the output a sigmoid activation function. More...
Symbol	mxnet::cpp::signsgd_update (const std::string &symbol_name, Symbol weight, Symbol grad, mx_float lr, mx_float wd=0, mx_float rescale_grad=1, mx_float clip_gradient=-1)
	Update function for SignSGD optimizer. More...
Symbol	mxnet::cpp::signum_update (const std::string &symbol_name, Symbol weight, Symbol grad, Symbol mom, mx_float lr, mx_float momentum=0, mx_float wd=0, mx_float rescale_grad=1, mx_float clip_gradient=-1, mx_float wd_lh=0)
	SIGN momentUM (Signum) optimizer. More...
Symbol	mxnet::cpp::multi_sgd_update (const std::string &symbol_name, const std::vector< Symbol > &data, nnvm::Tuple< mx_float > lrs, nnvm::Tuple< mx_float > wds, mx_float rescale_grad=1, mx_float clip_gradient=-1, int num_weights=1)
	Update function for Stochastic Gradient Descent (SDG) optimizer. More...
Symbol	mxnet::cpp::multi_sgd_mom_update (const std::string &symbol_name, const std::vector< Symbol > &data, nnvm::Tuple< mx_float > lrs, nnvm::Tuple< mx_float > wds, mx_float momentum=0, mx_float rescale_grad=1, mx_float clip_gradient=-1, int num_weights=1)
	Momentum update function for Stochastic Gradient Descent (SGD) optimizer. More...
Symbol	mxnet::cpp::multi_mp_sgd_update (const std::string &symbol_name, const std::vector< Symbol > &data, nnvm::Tuple< mx_float > lrs, nnvm::Tuple< mx_float > wds, mx_float rescale_grad=1, mx_float clip_gradient=-1, int num_weights=1)
	Update function for multi-precision Stochastic Gradient Descent (SDG) optimizer. More...
Symbol	mxnet::cpp::multi_mp_sgd_mom_update (const std::string &symbol_name, const std::vector< Symbol > &data, nnvm::Tuple< mx_float > lrs, nnvm::Tuple< mx_float > wds, mx_float momentum=0, mx_float rescale_grad=1, mx_float clip_gradient=-1, int num_weights=1)
	Momentum update function for multi-precision Stochastic Gradient Descent (SGD) More...
Symbol	mxnet::cpp::sgd_update (const std::string &symbol_name, Symbol weight, Symbol grad, mx_float lr, mx_float wd=0, mx_float rescale_grad=1, mx_float clip_gradient=-1, bool lazy_update=true)
	Update function for Stochastic Gradient Descent (SGD) optimizer. More...
Symbol	mxnet::cpp::sgd_mom_update (const std::string &symbol_name, Symbol weight, Symbol grad, Symbol mom, mx_float lr, mx_float momentum=0, mx_float wd=0, mx_float rescale_grad=1, mx_float clip_gradient=-1, bool lazy_update=true)
	Momentum update function for Stochastic Gradient Descent (SGD) optimizer. More...
Symbol	mxnet::cpp::mp_sgd_update (const std::string &symbol_name, Symbol weight, Symbol grad, Symbol weight32, mx_float lr, mx_float wd=0, mx_float rescale_grad=1, mx_float clip_gradient=-1, bool lazy_update=true)
	Updater function for multi-precision sgd optimizer. More...
Symbol	mxnet::cpp::mp_sgd_mom_update (const std::string &symbol_name, Symbol weight, Symbol grad, Symbol mom, Symbol weight32, mx_float lr, mx_float momentum=0, mx_float wd=0, mx_float rescale_grad=1, mx_float clip_gradient=-1, bool lazy_update=true)
	Updater function for multi-precision sgd optimizer. More...
Symbol	mxnet::cpp::ftml_update (const std::string &symbol_name, Symbol weight, Symbol grad, Symbol d, Symbol v, Symbol z, mx_float lr, int t, mx_float beta1=0.600000024, mx_float beta2=0.999000013, double epsilon=9.9999999392252903e-09, mx_float wd=0, mx_float rescale_grad=1, mx_float clip_grad=-1)
	The FTML optimizer described in FTML - Follow the Moving Leader in Deep Learning, available at http://proceedings.mlr.press/v70/zheng17a/zheng17a.pdf. More...
Symbol	mxnet::cpp::adam_update (const std::string &symbol_name, Symbol weight, Symbol grad, Symbol mean, Symbol var, mx_float lr, mx_float beta1=0.899999976, mx_float beta2=0.999000013, mx_float epsilon=9.99999994e-09, mx_float wd=0, mx_float rescale_grad=1, mx_float clip_gradient=-1, bool lazy_update=true)
	Update function for Adam optimizer. Adam is seen as a generalization of AdaGrad. More...
Symbol	mxnet::cpp::nag_mom_update (const std::string &symbol_name, Symbol weight, Symbol grad, Symbol mom, mx_float lr, mx_float momentum=0, mx_float wd=0, mx_float rescale_grad=1, mx_float clip_gradient=-1)
	Update function for Nesterov Accelerated Gradient( NAG) optimizer. It updates the weights using the following formula,. More...
Symbol	mxnet::cpp::mp_nag_mom_update (const std::string &symbol_name, Symbol weight, Symbol grad, Symbol mom, Symbol weight32, mx_float lr, mx_float momentum=0, mx_float wd=0, mx_float rescale_grad=1, mx_float clip_gradient=-1)
	Update function for multi-precision Nesterov Accelerated Gradient( NAG) More...
Symbol	mxnet::cpp::rmsprop_update (const std::string &symbol_name, Symbol weight, Symbol grad, Symbol n, mx_float lr, mx_float gamma1=0.949999988, mx_float epsilon=9.99999994e-09, mx_float wd=0, mx_float rescale_grad=1, mx_float clip_gradient=-1, mx_float clip_weights=-1)
	Update function for RMSProp optimizer. More...
Symbol	mxnet::cpp::rmspropalex_update (const std::string &symbol_name, Symbol weight, Symbol grad, Symbol n, Symbol g, Symbol delta, mx_float lr, mx_float gamma1=0.949999988, mx_float gamma2=0.899999976, mx_float epsilon=9.99999994e-09, mx_float wd=0, mx_float rescale_grad=1, mx_float clip_gradient=-1, mx_float clip_weights=-1)
	Update function for RMSPropAlex optimizer. More...
Symbol	mxnet::cpp::ftrl_update (const std::string &symbol_name, Symbol weight, Symbol grad, Symbol z, Symbol n, mx_float lr, mx_float lamda1=0.00999999978, mx_float beta=1, mx_float wd=0, mx_float rescale_grad=1, mx_float clip_gradient=-1)
	Update function for Ftrl optimizer. Referenced from Ad Click Prediction: a View from the Trenches, available at http://dl.acm.org/citation.cfm?id=2488200. More...
Symbol	mxnet::cpp::SliceChannel (const std::string &symbol_name, Symbol data, int num_outputs, int axis=1, bool squeeze_axis=false)
	Splits an array along a particular axis into multiple sub-arrays. More...
Symbol	mxnet::cpp::Pad (const std::string &symbol_name, Symbol data, PadMode mode, Shape pad_width, double constant_value=0)
	Pads an input array with a constant or edge values of the array. More...
Symbol	mxnet::cpp::InstanceNorm (const std::string &symbol_name, Symbol data, Symbol gamma, Symbol beta, mx_float eps=0.00100000005)
	Applies instance normalization to the n-dimensional input array. More...
Symbol	mxnet::cpp::GridGenerator (const std::string &symbol_name, Symbol data, GridGeneratorTransformType transform_type, Shape target_shape=Shape(0, 0))
	Generates 2D sampling grid for bilinear sampling. More...
Symbol	mxnet::cpp::Pooling_v1 (const std::string &symbol_name, Symbol data, Shape kernel=Shape(), Pooling_v1PoolType pool_type=Pooling_v1PoolType::kMax, bool global_pool=false, Pooling_v1PoolingConvention pooling_convention=Pooling_v1PoolingConvention::kValid, Shape stride=Shape(), Shape pad=Shape())
	This operator is DEPRECATED. Perform pooling on the input. More...
Symbol	mxnet::cpp::Convolution_v1 (const std::string &symbol_name, Symbol data, Symbol weight, Symbol bias, Shape kernel, uint32_t num_filter, Shape stride=Shape(), Shape dilate=Shape(), Shape pad=Shape(), uint32_t num_group=1, uint64_t workspace=1024, bool no_bias=false, Convolution_v1CudnnTune cudnn_tune=Convolution_v1CudnnTune::kNone, bool cudnn_off=false, Convolution_v1Layout layout=Convolution_v1Layout::kNone)
	This operator is DEPRECATED. Apply convolution to input then add a bias. More...
Symbol	mxnet::cpp::Crop (const std::string &symbol_name, const std::vector< Symbol > &data, int num_args, Shape offset=Shape(0, 0), Shape h_w=Shape(0, 0), bool center_crop=false)
	.. note:: Crop is deprecated. Use slice instead. More...
Symbol	mxnet::cpp::SequenceReverse (const std::string &symbol_name, Symbol data, Symbol sequence_length, bool use_sequence_length=false, int axis=0)
	Reverses the elements of each sequence. More...
Symbol	mxnet::cpp::SpatialTransformer (const std::string &symbol_name, Symbol data, Symbol loc, SpatialTransformerTransformType transform_type, SpatialTransformerSamplerType sampler_type, Shape target_shape=Shape(0, 0), dmlc::optional< bool > cudnn_off=dmlc::optional< bool >())
	Applies a spatial transformer to input feature map. More...
Symbol	mxnet::cpp::BilinearSampler (const std::string &symbol_name, Symbol data, Symbol grid, dmlc::optional< bool > cudnn_off=dmlc::optional< bool >())
	Applies bilinear sampling to input feature map. More...
Symbol	mxnet::cpp::ROIPooling (const std::string &symbol_name, Symbol data, Symbol rois, Shape pooled_size, mx_float spatial_scale)
	Performs region of interest(ROI) pooling on the input array. More...
Symbol	mxnet::cpp::SequenceLast (const std::string &symbol_name, Symbol data, Symbol sequence_length, bool use_sequence_length=false, int axis=0)
	Takes the last element of a sequence. More...
Symbol	mxnet::cpp::L2Normalization (const std::string &symbol_name, Symbol data, mx_float eps=1.00000001e-10, L2NormalizationMode mode=L2NormalizationMode::kInstance)
	Normalize the input array using the L2 norm. More...
Symbol	mxnet::cpp::MakeLoss (const std::string &symbol_name, Symbol data, mx_float grad_scale=1, mx_float valid_thresh=0, MakeLossNormalization normalization=MakeLossNormalization::kNull)
	Make your own loss function in network construction. More...
Symbol	mxnet::cpp::SVMOutput (const std::string &symbol_name, Symbol data, Symbol label, mx_float margin=1, mx_float regularization_coefficient=1, bool use_linear=false)
	Computes support vector machine based transformation of the input. More...
Symbol	mxnet::cpp::Correlation (const std::string &symbol_name, Symbol data1, Symbol data2, uint32_t kernel_size=1, uint32_t max_displacement=1, uint32_t stride1=1, uint32_t stride2=1, uint32_t pad_size=0, bool is_multiply=true)
	Applies correlation to inputs. More...
Symbol	mxnet::cpp::SequenceMask (const std::string &symbol_name, Symbol data, Symbol sequence_length, bool use_sequence_length=false, mx_float value=0, int axis=0)
	Sets all elements outside the sequence to a constant value. More...
Symbol	mxnet::cpp::fill_element_0index (const std::string &symbol_name, Symbol lhs, Symbol mhs, Symbol rhs)
	Fill one element of each line(row for python, column for R/Julia) in lhs according to index indicated by rhs and values indicated by mhs. This function. More...
Symbol	mxnet::cpp::khatri_rao (const std::vector< Symbol > &args)
	Computes the Khatri-Rao product of the input matrices. More...
Symbol	mxnet::cpp::all_finite (Symbol data, bool init_output=true)
	Check if all the float numbers in the array are finite (used for AMP) More...
Symbol	mxnet::cpp::multi_all_finite (const std::vector< Symbol > &data, int num_arrays=1, bool init_output=true)
	Check if all the float numbers in all the arrays are finite (used for AMP) More...
Symbol	mxnet::cpp::Custom (const std::vector< Symbol > &data, const std::string &op_type)
	Apply a custom operator implemented in a frontend language (like Python). More...
Symbol	mxnet::cpp::broadcast_power (Symbol lhs, Symbol rhs)
	Returns result of first array elements raised to powers from second array,. More...
Symbol	mxnet::cpp::broadcast_maximum (Symbol lhs, Symbol rhs)
	Returns element-wise maximum of the input arrays with broadcasting. More...
Symbol	mxnet::cpp::broadcast_minimum (Symbol lhs, Symbol rhs)
	Returns element-wise minimum of the input arrays with broadcasting. More...
Symbol	mxnet::cpp::broadcast_hypot (Symbol lhs, Symbol rhs)
	Returns the hypotenuse of a right angled triangle, given its "legs" with broadcasting. More...
Symbol	mxnet::cpp::Reshape (Symbol data, Shape shape=Shape(), bool reverse=false, Shape target_shape=Shape(), bool keep_highest=false)
	Reshapes the input array. More...
Symbol	mxnet::cpp::Flatten (Symbol data)
	Flattens the input array into a 2-D array by collapsing the higher dimensions. More...
Symbol	mxnet::cpp::transpose (Symbol data, Shape axes=Shape())
	Permutes the dimensions of an array. More...
Symbol	mxnet::cpp::expand_dims (Symbol data, int axis)
	Inserts a new axis of size 1 into the array shape. More...
Symbol	mxnet::cpp::slice (Symbol data, Shape begin, Shape end, Shape step=Shape())
	Slices a region of the array. More...
Symbol	mxnet::cpp::slice_axis (Symbol data, int axis, int begin, dmlc::optional< int > end)
	Slices along a given axis. More...
Symbol	mxnet::cpp::slice_like (Symbol data, Symbol shape_like, Shape axes=Shape())
	Slices a region of the array like the shape of another array. More...
Symbol	mxnet::cpp::clip (Symbol data, mx_float a_min, mx_float a_max)
	Clips (limits) the values in an array. More...
Symbol	mxnet::cpp::repeat (Symbol data, int repeats, dmlc::optional< int > axis=dmlc::optional< int >())
	Repeats elements of an array. More...
Symbol	mxnet::cpp::tile (Symbol data, Shape reps)
	Repeats the whole array multiple times. More...
Symbol	mxnet::cpp::reverse (Symbol data, Shape axis)
	Reverses the order of elements along given axis while preserving array shape. More...
Symbol	mxnet::cpp::stack (const std::vector< Symbol > &data, int num_args, int axis=0)
	Join a sequence of arrays along a new axis. More...
Symbol	mxnet::cpp::squeeze (const std::vector< Symbol > &data, dmlc::optional< Shape > axis=dmlc::optional< Shape >())
	Remove single-dimensional entries from the shape of an array. Same behavior of defining the output tensor shape as numpy.squeeze for the most See the following note for exception. More...
Symbol	mxnet::cpp::depth_to_space (Symbol data, int block_size)
	Rearranges(permutes) data from depth into blocks of spatial data. Similar to ONNX DepthToSpace operator: https://github.com/onnx/onnx/blob/master/docs/Operators.md#DepthToSpace. The output is a new tensor where the values from depth dimension are moved in to height and width dimension. The reverse of this operation is. More...
Symbol	mxnet::cpp::space_to_depth (Symbol data, int block_size)
	Rearranges(permutes) blocks of spatial data into depth. Similar to ONNX SpaceToDepth operator: https://github.com/onnx/onnx/blob/master/docs/Operators.md#SpaceToDepth. More...
Symbol	mxnet::cpp::zeros_like (Symbol data)
	Return an array of zeros with the same shape, type and storage type as the input array. More...
Symbol	mxnet::cpp::ones_like (Symbol data)
	Return an array of ones with the same shape and type as the input array. More...
Symbol	mxnet::cpp::add_n (const std::vector< Symbol > &args)
	Adds all input arguments element-wise. More...
Symbol	mxnet::cpp::argmax (Symbol data, dmlc::optional< int > axis=dmlc::optional< int >(), bool keepdims=false)
	Returns indices of the maximum values along an axis. More...
Symbol	mxnet::cpp::argmin (Symbol data, dmlc::optional< int > axis=dmlc::optional< int >(), bool keepdims=false)
	Returns indices of the minimum values along an axis. More...
Symbol	mxnet::cpp::argmax_channel (Symbol data)
	Returns argmax indices of each channel from the input array. More...
Symbol	mxnet::cpp::pick (Symbol data, Symbol index, dmlc::optional< int > axis=dmlc::optional< int >(-1), bool keepdims=false, PickMode mode=PickMode::kClip)
	Picks elements from an input array according to the input indices along the. More...
Symbol	mxnet::cpp::dot (Symbol lhs, Symbol rhs, bool transpose_a=false, bool transpose_b=false, DotForwardStype forward_stype=DotForwardStype::kNone)
	Dot product of two arrays. More...
Symbol	mxnet::cpp::batch_dot (Symbol lhs, Symbol rhs, bool transpose_a=false, bool transpose_b=false, Batch_dotForwardStype forward_stype=Batch_dotForwardStype::kNone)
	Batchwise dot product. More...
Symbol	mxnet::cpp::broadcast_add (Symbol lhs, Symbol rhs)
	Returns element-wise sum of the input arrays with broadcasting. More...
Symbol	mxnet::cpp::broadcast_sub (Symbol lhs, Symbol rhs)
	Returns element-wise difference of the input arrays with broadcasting. More...
Symbol	mxnet::cpp::broadcast_mul (Symbol lhs, Symbol rhs)
	Returns element-wise product of the input arrays with broadcasting. More...
Symbol	mxnet::cpp::broadcast_div (Symbol lhs, Symbol rhs)
	Returns element-wise division of the input arrays with broadcasting. More...
Symbol	mxnet::cpp::broadcast_mod (Symbol lhs, Symbol rhs)
	Returns element-wise modulo of the input arrays with broadcasting. More...
Symbol	mxnet::cpp::relu (Symbol data)
	Computes rectified linear activation. More...
Symbol	mxnet::cpp::sigmoid (Symbol data)
	Computes sigmoid of x element-wise. More...
Symbol	mxnet::cpp::hard_sigmoid (Symbol data, mx_float alpha=0.200000003, mx_float beta=0.5)
	Computes hard sigmoid of x element-wise. More...
Symbol	mxnet::cpp::softsign (Symbol data)
	Computes softsign of x element-wise. More...
Symbol	mxnet::cpp::BlockGrad (Symbol data)
	Stops gradient computation. More...
Symbol	mxnet::cpp::make_loss (Symbol data)
	Make your own loss function in network construction. More...
Symbol	mxnet::cpp::reshape_like (Symbol lhs, Symbol rhs)
	Reshape some or all dimensions of lhs to have the same shape as some or all. More...
Symbol	mxnet::cpp::shape_array (Symbol data, dmlc::optional< int > lhs_begin=dmlc::optional< int >(), dmlc::optional< int > lhs_end=dmlc::optional< int >(), dmlc::optional< int > rhs_begin=dmlc::optional< int >(), dmlc::optional< int > rhs_end=dmlc::optional< int >())
	Returns a 1D int64 array containing the shape of data. More...
Symbol	mxnet::cpp::size_array (Symbol data)
	Returns a 1D int64 array containing the size of data. More...
Symbol	mxnet::cpp::Cast (Symbol data, CastDtype dtype)
	Casts all elements of the input to a new type. More...
Symbol	mxnet::cpp::negative (Symbol data)
	Numerical negative of the argument, element-wise. More...
Symbol	mxnet::cpp::reciprocal (Symbol data)
	Returns the reciprocal of the argument, element-wise. More...
Symbol	mxnet::cpp::abs (Symbol data)
	Returns element-wise absolute value of the input. More...
Symbol	mxnet::cpp::sign (Symbol data)
	Returns element-wise sign of the input. More...
Symbol	mxnet::cpp::round (Symbol data)
	Returns element-wise rounded value to the nearest integer of the input. More...
Symbol	mxnet::cpp::rint (Symbol data)
	Returns element-wise rounded value to the nearest integer of the input. More...
Symbol	mxnet::cpp::ceil (Symbol data)
	Returns element-wise ceiling of the input. More...
Symbol	mxnet::cpp::floor (Symbol data)
	Returns element-wise floor of the input. More...
Symbol	mxnet::cpp::trunc (Symbol data)
	Return the element-wise truncated value of the input. More...
Symbol	mxnet::cpp::fix (Symbol data)
	Returns element-wise rounded value to the nearest \ integer towards zero of the input. More...
Symbol	mxnet::cpp::square (Symbol data)
	Returns element-wise squared value of the input. More...
Symbol	mxnet::cpp::sqrt (Symbol data)
	Returns element-wise square-root value of the input. More...
Symbol	mxnet::cpp::rsqrt (Symbol data)
	Returns element-wise inverse square-root value of the input. More...
Symbol	mxnet::cpp::cbrt (Symbol data)
	Returns element-wise cube-root value of the input. More...
Symbol	mxnet::cpp::erf (Symbol data)
	Returns element-wise gauss error function of the input. More...
Symbol	mxnet::cpp::erfinv (Symbol data)
	Returns element-wise inverse gauss error function of the input. More...
Symbol	mxnet::cpp::rcbrt (Symbol data)
	Returns element-wise inverse cube-root value of the input. More...
Symbol	mxnet::cpp::exp (Symbol data)
	Returns element-wise exponential value of the input. More...
Symbol	mxnet::cpp::log (Symbol data)
	Returns element-wise Natural logarithmic value of the input. More...
Symbol	mxnet::cpp::log10 (Symbol data)
	Returns element-wise Base-10 logarithmic value of the input. More...
Symbol	mxnet::cpp::log2 (Symbol data)
	Returns element-wise Base-2 logarithmic value of the input. More...
Symbol	mxnet::cpp::log1p (Symbol data)
	Returns element-wise log(1 + x) value of the input. More...
Symbol	mxnet::cpp::expm1 (Symbol data)
	Returns exp(x) - 1 computed element-wise on the input. More...
Symbol	mxnet::cpp::gamma (Symbol data)
	Returns the gamma function (extension of the factorial function \ to the reals), computed element-wise on the input array. More...
Symbol	mxnet::cpp::gammaln (Symbol data)
	Returns element-wise log of the absolute value of the gamma function \ of the input. More...
Symbol	mxnet::cpp::logical_not (Symbol data)
	Returns the result of logical NOT (!) function. More...
Symbol	mxnet::cpp::amp_cast (Symbol data, Amp_castDtype dtype)
	Cast function between low precision float/FP32 used by AMP. More...
Symbol	mxnet::cpp::amp_multicast (const std::vector< Symbol > &data, int num_outputs)
	Cast function used by AMP, that casts its inputs to the common widest type. More...
Symbol	mxnet::cpp::topk (Symbol data, dmlc::optional< int > axis=dmlc::optional< int >(-1), int k=1, TopkRetTyp ret_typ=TopkRetTyp::kIndices, bool is_ascend=false, TopkDtype dtype=TopkDtype::kFloat32)
	Returns the top k elements in an input array along the given axis. The returned elements will be sorted. More...
Symbol	mxnet::cpp::sort (Symbol data, dmlc::optional< int > axis=dmlc::optional< int >(-1), bool is_ascend=true)
	Returns a sorted copy of an input array along the given axis. More...
Symbol	mxnet::cpp::argsort (Symbol data, dmlc::optional< int > axis=dmlc::optional< int >(-1), bool is_ascend=true, ArgsortDtype dtype=ArgsortDtype::kFloat32)
	Returns the indices that would sort an input array along the given axis. More...
Symbol	mxnet::cpp::elemwise_add (Symbol lhs, Symbol rhs)
	Adds arguments element-wise. More...
Symbol	mxnet::cpp::elemwise_sub (Symbol lhs, Symbol rhs)
	Subtracts arguments element-wise. More...
Symbol	mxnet::cpp::elemwise_mul (Symbol lhs, Symbol rhs)
	Multiplies arguments element-wise. More...
Symbol	mxnet::cpp::elemwise_div (Symbol lhs, Symbol rhs)
	Divides arguments element-wise. More...
Symbol	mxnet::cpp::Embedding (Symbol data, Symbol weight, int input_dim, int output_dim, EmbeddingDtype dtype=EmbeddingDtype::kFloat32, bool sparse_grad=false)
	Maps integer indices to vector representations (embeddings). More...
Symbol	mxnet::cpp::take (Symbol a, Symbol indices, int axis=0, TakeMode mode=TakeMode::kClip)
	Takes elements from an input array along the given axis. More...
Symbol	mxnet::cpp::batch_take (Symbol a, Symbol indices)
	Takes elements from a data batch. More...
Symbol	mxnet::cpp::one_hot (Symbol indices, int depth, double on_value=1, double off_value=0, One_hotDtype dtype=One_hotDtype::kFloat32)
	Returns a one-hot array. More...
Symbol	mxnet::cpp::gather_nd (Symbol data, Symbol indices)
	Gather elements or slices from data and store to a tensor whose shape is defined by indices. More...
Symbol	mxnet::cpp::scatter_nd (Symbol data, Symbol indices, Shape shape)
	Scatters data into a new tensor according to indices. More...
Symbol	mxnet::cpp::broadcast_equal (Symbol lhs, Symbol rhs)
	Returns the result of element-wise equal to (==) comparison operation with. More...
Symbol	mxnet::cpp::broadcast_not_equal (Symbol lhs, Symbol rhs)
	Returns the result of element-wise not equal to (!=) comparison operation. More...
Symbol	mxnet::cpp::broadcast_greater (Symbol lhs, Symbol rhs)
	Returns the result of element-wise greater than (>) comparison operation. More...
Symbol	mxnet::cpp::broadcast_greater_equal (Symbol lhs, Symbol rhs)
	Returns the result of element-wise greater than or equal to (>=) comparison. More...
Symbol	mxnet::cpp::broadcast_lesser (Symbol lhs, Symbol rhs)
	Returns the result of element-wise lesser than (<) comparison operation. More...
Symbol	mxnet::cpp::broadcast_lesser_equal (Symbol lhs, Symbol rhs)
	Returns the result of element-wise lesser than or equal to (<=) comparison. More...
Symbol	mxnet::cpp::broadcast_logical_and (Symbol lhs, Symbol rhs)
	Returns the result of element-wise logical and with broadcasting. More...
Symbol	mxnet::cpp::broadcast_logical_or (Symbol lhs, Symbol rhs)
	Returns the result of element-wise logical or with broadcasting. More...
Symbol	mxnet::cpp::broadcast_logical_xor (Symbol lhs, Symbol rhs)
	Returns the result of element-wise logical xor with broadcasting. More...
Symbol	mxnet::cpp::diag (Symbol data, int k=0, int axis1=0, int axis2=1)
	Extracts a diagonal or constructs a diagonal array. More...
Symbol	mxnet::cpp::where (Symbol condition, Symbol x, Symbol y)
	Return the elements, either from x or y, depending on the condition. More...
Symbol	mxnet::cpp::smooth_l1 (Symbol data, mx_float scalar)
	Calculate Smooth L1 Loss(lhs, scalar) by summing. More...
Symbol	mxnet::cpp::cast_storage (Symbol data, Cast_storageStype stype)
	Casts tensor storage type to the new type. More...
Symbol	mxnet::cpp::sum (Symbol data, dmlc::optional< Shape > axis=dmlc::optional< Shape >(), bool keepdims=false, bool exclude=false)
	Computes the sum of array elements over given axes. More...
Symbol	mxnet::cpp::mean (Symbol data, dmlc::optional< Shape > axis=dmlc::optional< Shape >(), bool keepdims=false, bool exclude=false)
	Computes the mean of array elements over given axes. More...
Symbol	mxnet::cpp::prod (Symbol data, dmlc::optional< Shape > axis=dmlc::optional< Shape >(), bool keepdims=false, bool exclude=false)
	Computes the product of array elements over given axes. More...
Symbol	mxnet::cpp::nansum (Symbol data, dmlc::optional< Shape > axis=dmlc::optional< Shape >(), bool keepdims=false, bool exclude=false)
	Computes the sum of array elements over given axes treating Not a Numbers. More...
Symbol	mxnet::cpp::nanprod (Symbol data, dmlc::optional< Shape > axis=dmlc::optional< Shape >(), bool keepdims=false, bool exclude=false)
	Computes the product of array elements over given axes treating Not a Numbers. More...
Symbol	mxnet::cpp::max (Symbol data, dmlc::optional< Shape > axis=dmlc::optional< Shape >(), bool keepdims=false, bool exclude=false)
	Computes the max of array elements over given axes. More...
Symbol	mxnet::cpp::min (Symbol data, dmlc::optional< Shape > axis=dmlc::optional< Shape >(), bool keepdims=false, bool exclude=false)
	Computes the min of array elements over given axes. More...
Symbol	mxnet::cpp::broadcast_axis (Symbol data, Shape axis=Shape(), Shape size=Shape())
	Broadcasts the input array over particular axes. More...
Symbol	mxnet::cpp::broadcast_to (Symbol data, Shape shape=Shape())
	Broadcasts the input array to a new shape. More...
Symbol	mxnet::cpp::broadcast_like (Symbol lhs, Symbol rhs, dmlc::optional< Shape > lhs_axes=dmlc::optional< Shape >(), dmlc::optional< Shape > rhs_axes=dmlc::optional< Shape >())
	Broadcasts lhs to have the same shape as rhs. More...
Symbol	mxnet::cpp::norm (Symbol data, int ord=2, dmlc::optional< Shape > axis=dmlc::optional< Shape >(), NormOutDtype out_dtype=NormOutDtype::kNone, bool keepdims=false)
	Computes the norm on an NDArray. More...
Symbol	mxnet::cpp::sin (Symbol data)
	Computes the element-wise sine of the input array. More...
Symbol	mxnet::cpp::cos (Symbol data)
	Computes the element-wise cosine of the input array. More...
Symbol	mxnet::cpp::tan (Symbol data)
	Computes the element-wise tangent of the input array. More...
Symbol	mxnet::cpp::arcsin (Symbol data)
	Returns element-wise inverse sine of the input array. More...
Symbol	mxnet::cpp::arccos (Symbol data)
	Returns element-wise inverse cosine of the input array. More...
Symbol	mxnet::cpp::arctan (Symbol data)
	Returns element-wise inverse tangent of the input array. More...
Symbol	mxnet::cpp::degrees (Symbol data)
	Converts each element of the input array from radians to degrees. More...
Symbol	mxnet::cpp::radians (Symbol data)
	Converts each element of the input array from degrees to radians. More...
Symbol	mxnet::cpp::sinh (Symbol data)
	Returns the hyperbolic sine of the input array, computed element-wise. More...
Symbol	mxnet::cpp::cosh (Symbol data)
	Returns the hyperbolic cosine of the input array, computed element-wise. More...
Symbol	mxnet::cpp::tanh (Symbol data)
	Returns the hyperbolic tangent of the input array, computed element-wise. More...
Symbol	mxnet::cpp::arcsinh (Symbol data)
	Returns the element-wise inverse hyperbolic sine of the input array, \ computed element-wise. More...
Symbol	mxnet::cpp::arccosh (Symbol data)
	Returns the element-wise inverse hyperbolic cosine of the input array, \ computed element-wise. More...
Symbol	mxnet::cpp::arctanh (Symbol data)
	Returns the element-wise inverse hyperbolic tangent of the input array, \ computed element-wise. More...
Symbol	mxnet::cpp::Pooling (Symbol data, Shape kernel=Shape(), PoolingPoolType pool_type=PoolingPoolType::kMax, bool global_pool=false, bool cudnn_off=false, PoolingPoolingConvention pooling_convention=PoolingPoolingConvention::kValid, Shape stride=Shape(), Shape pad=Shape(), dmlc::optional< int > p_value=dmlc::optional< int >(), dmlc::optional< bool > count_include_pad=dmlc::optional< bool >(), PoolingLayout layout=PoolingLayout::kNone)
	Performs pooling on the input. More...
Symbol	mxnet::cpp::softmax (Symbol data, int axis=-1, dmlc::optional< double > temperature=dmlc::optional< double >(), SoftmaxDtype dtype=SoftmaxDtype::kNone)
	Applies the softmax function. More...
Symbol	mxnet::cpp::softmin (Symbol data, int axis=-1, dmlc::optional< double > temperature=dmlc::optional< double >(), SoftminDtype dtype=SoftminDtype::kNone)
	Applies the softmin function. More...
Symbol	mxnet::cpp::log_softmax (Symbol data, int axis=-1, dmlc::optional< double > temperature=dmlc::optional< double >(), Log_softmaxDtype dtype=Log_softmaxDtype::kNone)
	Computes the log softmax of the input. This is equivalent to computing softmax followed by log. More...
Symbol	mxnet::cpp::Deconvolution (Symbol data, Symbol weight, Symbol bias, Shape kernel, uint32_t num_filter, Shape stride=Shape(), Shape dilate=Shape(), Shape pad=Shape(), Shape adj=Shape(), Shape target_shape=Shape(), uint32_t num_group=1, uint64_t workspace=512, bool no_bias=true, DeconvolutionCudnnTune cudnn_tune=DeconvolutionCudnnTune::kNone, bool cudnn_off=false, DeconvolutionLayout layout=DeconvolutionLayout::kNone)
	Computes 1D or 2D transposed convolution (aka fractionally strided convolution) of the input tensor. This operation can be seen as the gradient of Convolution operation with respect to its input. Convolution usually reduces the size of the input. Transposed convolution works the other way, going from a smaller. More...
Symbol	mxnet::cpp::Activation (Symbol data, ActivationActType act_type)
	Applies an activation function element-wise to the input. More...
Symbol	mxnet::cpp::BatchNorm (Symbol data, Symbol gamma, Symbol beta, Symbol moving_mean, Symbol moving_var, double eps=0.0010000000474974513, mx_float momentum=0.899999976, bool fix_gamma=true, bool use_global_stats=false, bool output_mean_var=false, int axis=1, bool cudnn_off=false)
	Batch normalization. More...
Symbol	mxnet::cpp::CTCLoss (Symbol data, Symbol label, Symbol data_lengths, Symbol label_lengths, bool use_data_lengths=false, bool use_label_lengths=false, CTCLossBlankLabel blank_label=CTCLossBlankLabel::kFirst)
	Connectionist Temporal Classification Loss. More...
Symbol	mxnet::cpp::FullyConnected (Symbol data, Symbol weight, Symbol bias, int num_hidden, bool no_bias=false, bool flatten=true)
	Applies a linear transformation: :math:Y = XW^T + b. More...
Symbol	mxnet::cpp::Convolution (Symbol data, Symbol weight, Symbol bias, Shape kernel, uint32_t num_filter, Shape stride=Shape(), Shape dilate=Shape(), Shape pad=Shape(), uint32_t num_group=1, uint64_t workspace=1024, bool no_bias=false, ConvolutionCudnnTune cudnn_tune=ConvolutionCudnnTune::kNone, bool cudnn_off=false, ConvolutionLayout layout=ConvolutionLayout::kNone)
	Compute N-D convolution on *(N+2)*-D input. More...
Symbol	mxnet::cpp::UpSampling (const std::vector< Symbol > &data, int scale, UpSamplingSampleType sample_type, int num_args, int num_filter=0, UpSamplingMultiInputMode multi_input_mode=UpSamplingMultiInputMode::kConcat, uint64_t workspace=512)
	Upsamples the given input data. More...
Symbol	mxnet::cpp::Concat (const std::vector< Symbol > &data, int num_args, int dim=1)
	Joins input arrays along a given axis. More...
Symbol	mxnet::cpp::LayerNorm (Symbol data, Symbol gamma, Symbol beta, int axis=-1, mx_float eps=9.99999975e-06, bool output_mean_var=false)
	Layer normalization. More...
Symbol	mxnet::cpp::LRN (Symbol data, uint32_t nsize, mx_float alpha=9.99999975e-05, mx_float beta=0.75, mx_float knorm=2)
	Applies local response normalization to the input. More...
Symbol	mxnet::cpp::Dropout (Symbol data, mx_float p=0.5, DropoutMode mode=DropoutMode::kTraining, Shape axes=Shape(), dmlc::optional< bool > cudnn_off=dmlc::optional< bool >(0))
	Applies dropout operation to input array. More...
Symbol	mxnet::cpp::SoftmaxActivation (Symbol data, SoftmaxActivationMode mode=SoftmaxActivationMode::kInstance)
	Applies softmax activation to input. This is intended for internal layers. More...
Symbol	mxnet::cpp::moments (Symbol data, dmlc::optional< Shape > axes=dmlc::optional< Shape >(), bool keepdims=false)
	Calculate the mean and variance of data. More...
Symbol	mxnet::cpp::LeakyReLU (Symbol data, Symbol gamma, LeakyReLUActType act_type=LeakyReLUActType::kLeaky, mx_float slope=0.25, mx_float lower_bound=0.125, mx_float upper_bound=0.333999991)
	Applies Leaky rectified linear unit activation element-wise to the input. More...
Symbol	mxnet::cpp::RNN (Symbol data, Symbol parameters, Symbol state, Symbol state_cell, Symbol sequence_length, uint32_t state_size, uint32_t num_layers, RNNMode mode, bool bidirectional=false, mx_float p=0, bool state_outputs=false, dmlc::optional< int > projection_size=dmlc::optional< int >(), dmlc::optional< double > lstm_state_clip_min=dmlc::optional< double >(), dmlc::optional< double > lstm_state_clip_max=dmlc::optional< double >(), bool lstm_state_clip_nan=false, bool use_sequence_length=false)
	Applies recurrent layers to input data. Currently, vanilla RNN, LSTM and GRU are implemented, with both multi-layer and bidirectional support. More...
Symbol	mxnet::cpp::SoftmaxOutput (Symbol data, Symbol label, mx_float grad_scale=1, mx_float ignore_label=-1, bool multi_output=false, bool use_ignore=false, bool preserve_shape=false, SoftmaxOutputNormalization normalization=SoftmaxOutputNormalization::kNull, bool out_grad=false, mx_float smooth_alpha=0)
	Computes the gradient of cross entropy loss with respect to softmax output. More...
Symbol	mxnet::cpp::SwapAxis (Symbol data, uint32_t dim1=0, uint32_t dim2=0)
	Interchanges two axes of an array. More...
Symbol	mxnet::cpp::BatchNorm_v1 (Symbol data, Symbol gamma, Symbol beta, mx_float eps=0.00100000005, mx_float momentum=0.899999976, bool fix_gamma=true, bool use_global_stats=false, bool output_mean_var=false)
	Batch normalization. More...
Symbol	mxnet::cpp::softmax_cross_entropy (Symbol data, Symbol label)
	Calculate cross entropy of softmax output and one-hot label. More...
Symbol	mxnet::cpp::LinearRegressionOutput (Symbol data, Symbol label, mx_float grad_scale=1)
	Computes and optimizes for squared loss during backward propagation. Just outputs data during forward propagation. More...
Symbol	mxnet::cpp::MAERegressionOutput (Symbol data, Symbol label, mx_float grad_scale=1)
	Computes mean absolute error of the input. More...
Symbol	mxnet::cpp::LogisticRegressionOutput (Symbol data, Symbol label, mx_float grad_scale=1)
	Applies a logistic function to the input. More...
Symbol	mxnet::cpp::IdentityAttachKLSparseReg (Symbol data, mx_float sparseness_target=0.100000001, mx_float penalty=0.00100000005, mx_float momentum=0.899999976)
	Apply a sparse regularization to the output a sigmoid activation function. More...
Symbol	mxnet::cpp::signsgd_update (Symbol weight, Symbol grad, mx_float lr, mx_float wd=0, mx_float rescale_grad=1, mx_float clip_gradient=-1)
	Update function for SignSGD optimizer. More...
Symbol	mxnet::cpp::signum_update (Symbol weight, Symbol grad, Symbol mom, mx_float lr, mx_float momentum=0, mx_float wd=0, mx_float rescale_grad=1, mx_float clip_gradient=-1, mx_float wd_lh=0)
	SIGN momentUM (Signum) optimizer. More...
Symbol	mxnet::cpp::multi_sgd_update (const std::vector< Symbol > &data, nnvm::Tuple< mx_float > lrs, nnvm::Tuple< mx_float > wds, mx_float rescale_grad=1, mx_float clip_gradient=-1, int num_weights=1)
	Update function for Stochastic Gradient Descent (SDG) optimizer. More...
Symbol	mxnet::cpp::multi_sgd_mom_update (const std::vector< Symbol > &data, nnvm::Tuple< mx_float > lrs, nnvm::Tuple< mx_float > wds, mx_float momentum=0, mx_float rescale_grad=1, mx_float clip_gradient=-1, int num_weights=1)
	Momentum update function for Stochastic Gradient Descent (SGD) optimizer. More...
Symbol	mxnet::cpp::multi_mp_sgd_update (const std::vector< Symbol > &data, nnvm::Tuple< mx_float > lrs, nnvm::Tuple< mx_float > wds, mx_float rescale_grad=1, mx_float clip_gradient=-1, int num_weights=1)
	Update function for multi-precision Stochastic Gradient Descent (SDG) optimizer. More...
Symbol	mxnet::cpp::multi_mp_sgd_mom_update (const std::vector< Symbol > &data, nnvm::Tuple< mx_float > lrs, nnvm::Tuple< mx_float > wds, mx_float momentum=0, mx_float rescale_grad=1, mx_float clip_gradient=-1, int num_weights=1)
	Momentum update function for multi-precision Stochastic Gradient Descent (SGD) More...
Symbol	mxnet::cpp::sgd_update (Symbol weight, Symbol grad, mx_float lr, mx_float wd=0, mx_float rescale_grad=1, mx_float clip_gradient=-1, bool lazy_update=true)
	Update function for Stochastic Gradient Descent (SGD) optimizer. More...
Symbol	mxnet::cpp::sgd_mom_update (Symbol weight, Symbol grad, Symbol mom, mx_float lr, mx_float momentum=0, mx_float wd=0, mx_float rescale_grad=1, mx_float clip_gradient=-1, bool lazy_update=true)
	Momentum update function for Stochastic Gradient Descent (SGD) optimizer. More...
Symbol	mxnet::cpp::mp_sgd_update (Symbol weight, Symbol grad, Symbol weight32, mx_float lr, mx_float wd=0, mx_float rescale_grad=1, mx_float clip_gradient=-1, bool lazy_update=true)
	Updater function for multi-precision sgd optimizer. More...
Symbol	mxnet::cpp::mp_sgd_mom_update (Symbol weight, Symbol grad, Symbol mom, Symbol weight32, mx_float lr, mx_float momentum=0, mx_float wd=0, mx_float rescale_grad=1, mx_float clip_gradient=-1, bool lazy_update=true)
	Updater function for multi-precision sgd optimizer. More...
Symbol	mxnet::cpp::ftml_update (Symbol weight, Symbol grad, Symbol d, Symbol v, Symbol z, mx_float lr, int t, mx_float beta1=0.600000024, mx_float beta2=0.999000013, double epsilon=9.9999999392252903e-09, mx_float wd=0, mx_float rescale_grad=1, mx_float clip_grad=-1)
	The FTML optimizer described in FTML - Follow the Moving Leader in Deep Learning, available at http://proceedings.mlr.press/v70/zheng17a/zheng17a.pdf. More...
Symbol	mxnet::cpp::adam_update (Symbol weight, Symbol grad, Symbol mean, Symbol var, mx_float lr, mx_float beta1=0.899999976, mx_float beta2=0.999000013, mx_float epsilon=9.99999994e-09, mx_float wd=0, mx_float rescale_grad=1, mx_float clip_gradient=-1, bool lazy_update=true)
	Update function for Adam optimizer. Adam is seen as a generalization of AdaGrad. More...
Symbol	mxnet::cpp::nag_mom_update (Symbol weight, Symbol grad, Symbol mom, mx_float lr, mx_float momentum=0, mx_float wd=0, mx_float rescale_grad=1, mx_float clip_gradient=-1)
	Update function for Nesterov Accelerated Gradient( NAG) optimizer. It updates the weights using the following formula,. More...
Symbol	mxnet::cpp::mp_nag_mom_update (Symbol weight, Symbol grad, Symbol mom, Symbol weight32, mx_float lr, mx_float momentum=0, mx_float wd=0, mx_float rescale_grad=1, mx_float clip_gradient=-1)
	Update function for multi-precision Nesterov Accelerated Gradient( NAG) More...
Symbol	mxnet::cpp::rmsprop_update (Symbol weight, Symbol grad, Symbol n, mx_float lr, mx_float gamma1=0.949999988, mx_float epsilon=9.99999994e-09, mx_float wd=0, mx_float rescale_grad=1, mx_float clip_gradient=-1, mx_float clip_weights=-1)
	Update function for RMSProp optimizer. More...
Symbol	mxnet::cpp::rmspropalex_update (Symbol weight, Symbol grad, Symbol n, Symbol g, Symbol delta, mx_float lr, mx_float gamma1=0.949999988, mx_float gamma2=0.899999976, mx_float epsilon=9.99999994e-09, mx_float wd=0, mx_float rescale_grad=1, mx_float clip_gradient=-1, mx_float clip_weights=-1)
	Update function for RMSPropAlex optimizer. More...
Symbol	mxnet::cpp::ftrl_update (Symbol weight, Symbol grad, Symbol z, Symbol n, mx_float lr, mx_float lamda1=0.00999999978, mx_float beta=1, mx_float wd=0, mx_float rescale_grad=1, mx_float clip_gradient=-1)
	Update function for Ftrl optimizer. Referenced from Ad Click Prediction: a View from the Trenches, available at http://dl.acm.org/citation.cfm?id=2488200. More...
Symbol	mxnet::cpp::SliceChannel (Symbol data, int num_outputs, int axis=1, bool squeeze_axis=false)
	Splits an array along a particular axis into multiple sub-arrays. More...
Symbol	mxnet::cpp::Pad (Symbol data, PadMode mode, Shape pad_width, double constant_value=0)
	Pads an input array with a constant or edge values of the array. More...
Symbol	mxnet::cpp::InstanceNorm (Symbol data, Symbol gamma, Symbol beta, mx_float eps=0.00100000005)
	Applies instance normalization to the n-dimensional input array. More...
Symbol	mxnet::cpp::GridGenerator (Symbol data, GridGeneratorTransformType transform_type, Shape target_shape=Shape(0, 0))
	Generates 2D sampling grid for bilinear sampling. More...
Symbol	mxnet::cpp::Pooling_v1 (Symbol data, Shape kernel=Shape(), Pooling_v1PoolType pool_type=Pooling_v1PoolType::kMax, bool global_pool=false, Pooling_v1PoolingConvention pooling_convention=Pooling_v1PoolingConvention::kValid, Shape stride=Shape(), Shape pad=Shape())
	This operator is DEPRECATED. Perform pooling on the input. More...
Symbol	mxnet::cpp::Convolution_v1 (Symbol data, Symbol weight, Symbol bias, Shape kernel, uint32_t num_filter, Shape stride=Shape(), Shape dilate=Shape(), Shape pad=Shape(), uint32_t num_group=1, uint64_t workspace=1024, bool no_bias=false, Convolution_v1CudnnTune cudnn_tune=Convolution_v1CudnnTune::kNone, bool cudnn_off=false, Convolution_v1Layout layout=Convolution_v1Layout::kNone)
	This operator is DEPRECATED. Apply convolution to input then add a bias. More...
Symbol	mxnet::cpp::Crop (const std::vector< Symbol > &data, int num_args, Shape offset=Shape(0, 0), Shape h_w=Shape(0, 0), bool center_crop=false)
	.. note:: Crop is deprecated. Use slice instead. More...
Symbol	mxnet::cpp::SequenceReverse (Symbol data, Symbol sequence_length, bool use_sequence_length=false, int axis=0)
	Reverses the elements of each sequence. More...
Symbol	mxnet::cpp::SpatialTransformer (Symbol data, Symbol loc, SpatialTransformerTransformType transform_type, SpatialTransformerSamplerType sampler_type, Shape target_shape=Shape(0, 0), dmlc::optional< bool > cudnn_off=dmlc::optional< bool >())
	Applies a spatial transformer to input feature map. More...
Symbol	mxnet::cpp::BilinearSampler (Symbol data, Symbol grid, dmlc::optional< bool > cudnn_off=dmlc::optional< bool >())
	Applies bilinear sampling to input feature map. More...
Symbol	mxnet::cpp::ROIPooling (Symbol data, Symbol rois, Shape pooled_size, mx_float spatial_scale)
	Performs region of interest(ROI) pooling on the input array. More...
Symbol	mxnet::cpp::SequenceLast (Symbol data, Symbol sequence_length, bool use_sequence_length=false, int axis=0)
	Takes the last element of a sequence. More...
Symbol	mxnet::cpp::L2Normalization (Symbol data, mx_float eps=1.00000001e-10, L2NormalizationMode mode=L2NormalizationMode::kInstance)
	Normalize the input array using the L2 norm. More...
Symbol	mxnet::cpp::MakeLoss (Symbol data, mx_float grad_scale=1, mx_float valid_thresh=0, MakeLossNormalization normalization=MakeLossNormalization::kNull)
	Make your own loss function in network construction. More...
Symbol	mxnet::cpp::SVMOutput (Symbol data, Symbol label, mx_float margin=1, mx_float regularization_coefficient=1, bool use_linear=false)
	Computes support vector machine based transformation of the input. More...
Symbol	mxnet::cpp::Correlation (Symbol data1, Symbol data2, uint32_t kernel_size=1, uint32_t max_displacement=1, uint32_t stride1=1, uint32_t stride2=1, uint32_t pad_size=0, bool is_multiply=true)
	Applies correlation to inputs. More...
Symbol	mxnet::cpp::SequenceMask (Symbol data, Symbol sequence_length, bool use_sequence_length=false, mx_float value=0, int axis=0)
	Sets all elements outside the sequence to a constant value. More...
Symbol	mxnet::cpp::fill_element_0index (Symbol lhs, Symbol mhs, Symbol rhs)
	Fill one element of each line(row for python, column for R/Julia) in lhs according to index indicated by rhs and values indicated by mhs. This function. More...

Detailed Description

definition of all the operators

Copyright (c) 2016 by Contributors

Author

Chuntao Hong, Xin Li