Class List

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

[detail level 123]

▼Nsinga	Licensed to the Apache Software Foundation (ASF) under one or more contributor license agreements
▼Ninit
CConstant
CGaussian
CMSRA	Ref: [He, Zhang, Ren and Sun 2015]: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
CUniform
CXavier	Ref: [Bengio and Glorot 2010] Understanding the difficulty of training deep feedforward neural networks
▼Nio
CBinFileReader	Binfilereader reads tuples from binary file with key-value pairs
CBinFileWriter	BinFile stores training/validation/test tuples
CReader	General Reader that provides functions for reading tuples
CTextFileReader	TextFileReader reads tuples from CSV file
CTextFileWriter	TextFileWriter write training/validation/test tuples in CSV file
CWriter	General Writer that provides functions for writing tuples
▼Nlang
C_Cpp	To implemente functions using cpp libraries
C_Cuda	To implemente functions using cuda libraries
C_Opencl	To implement function using opencl libraries
▼Nlogging
CCheckOpMessageBuilder
CCheckOpString
CLogMessage
CLogMessageFatal
C_Context
CAccuracy	Compute the accuray of the prediction, which is matched against the ground truth labels
CAdaGrad
CBlock	Block represent a chunk of memory (on device or host)
CChannel	Channel for appending metrics or other information into files or screen
CChannelManager
CConstraint	Apply constraints for parameters (gradient)
CCppCPU	Represent a CPU device which may have multiple threads/executors
CCSVDecoder	Decode the string of csv formated data into data tensor (dtype is kFloat32) and optionally a label tensor (dtype is kInt)
CCSVEncoder	Convert values from tensors into a csv formated string
CDecoder	The base decoder that converts a string into a set of tensors
CDevice	Allocate memory and execute Tensor operations
CDeviceMemPool
CEncoder	Base encoder class that convert a set of tensors into string for storage
CFeedForwardNet	The feed-forward neural net
CImageTransformer
CInitializer
CLayer	The base layer class
CLocalUpdater	LocalUpdater do gradient aggregation and update gradient calling the wrapped Optimizer on a specific device (i.e., CPU or GPU)
CLoss	The base loss class, which declares the APIs for computing the objective score (loss) for a pair of prediction (from the model) and the target (i.e
CMetric	The base metric class, which declares the APIs for computing the performance evaluation metrics given the prediction of the model and the ground truth, i.e., the target
CMSE	MSE is for mean squared error or squared euclidean distance
CNesterov
COptimizer	The base class for gradient descent algorithms used to update the model parameters in order to optimize the objective (loss) function
CPlatform	This class queries all available calculating devices on a given machine grouped according to manufacturer or device drivers
CRegularizer	Apply regularization for parameters (gradient), e.g., L1 norm and L2 norm
CRMSProp
CScheduler	Scheduling Tensor operations with dependency detection
CSGD
CSnapshot	The snapshot management
CSoftmaxCrossEntropy	Softmax + cross entropy for multi-category classification
CTensor	A Tensor instance is a multi-dimensional array resident on a Device (default device is the host CPU)
CTimer	For benchmarking the time cost of operations
CTokenizer	Tokenize a string
CTransformer	Base apply class that does data transformations in pre-processing stage
CUpdater	Basic Updater class just forward all the method function call to the wrapped Optimizer
CVirtualMemory	Manage device memory pool including garbage collection, memory opt
CFactory	Factory template to generate class (or a sub-class) object based on id
CPriorityQueue	Thread safe priority queue
CRegistra
CSafeQueue	Thread-safe queue
CSingleton	Thread-safe implementation for C++11 according to
Ctinydir_dir
Ctinydir_file	Defined(_TINYDIR_MALLOC)