Apache Singa
A General Distributed Deep Learning Library
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Class List
Here are the classes, structs, unions and interfaces with brief descriptions:
[detail level 123]
oNsingaLicensed to the Apache Software Foundation (ASF) under one or more contributor license agreements
|oNinit
|oNio
|oNlang
|oNlogging
|oCBlockBlock represent a chunk of memory (on device or host)
|oC_Context
|oCDeviceAllocate memory and execute Tensor operations
|oCCppCPURepresent a CPU device which may have multiple threads/executors
|oCPlatformThis class queries all available calculating devices on a given machine grouped according to manufacturer or device drivers
|oCVirtualMemoryManage device memory pool including garbage collection, memory opt
|oCDeviceMemPool
|oCSchedulerScheduling Tensor operations with dependency detection
|oCTensorA Tensor instance is a multi-dimensional array resident on a Device (default device is the host CPU)
|oCDecoderThe base decoder that converts a string into a set of tensors
|oCCSVDecoderDecode the string of csv formated data into data tensor (dtype is kFloat32) and optionally a label tensor (dtype is kInt)
|oCEncoderBase encoder class that convert a set of tensors into string for storage
|oCCSVEncoderConvert values from tensors into a csv formated string
|oCSnapshotThe snapshot management
|oCTransformerBase apply class that does data transformations in pre-processing stage
|oCImageTransformer
|oCFeedForwardNetThe feed-forward neural net
|oCInitializer
|oCLayerThe base layer class
|oCLossThe 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
|oCMSEMSE is for mean squared error or squared euclidean distance
|oCSoftmaxCrossEntropySoftmax + cross entropy for multi-category classification
|oCMetricThe 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
|oCAccuracyCompute the accuray of the prediction, which is matched against the ground truth labels
|oCOptimizerThe base class for gradient descent algorithms used to update the model parameters in order to optimize the objective (loss) function
|oCConstraintApply constraints for parameters (gradient)
|oCRegularizerApply regularization for parameters (gradient), e.g., L1 norm and L2 norm
|oCSGD
|oCNesterov
|oCAdaGrad
|oCRMSProp
|oCUpdaterBasic Updater class just forward all the method function call to the wrapped Optimizer
|oCLocalUpdaterLocalUpdater do gradient aggregation and update gradient calling the wrapped Optimizer on a specific device (i.e., CPU or GPU)
|oCChannelChannel for appending metrics or other information into files or screen
|oCChannelManager
|oCTokenizerTokenize a string
|oCTimerFor benchmarking the time cost of operations
|oCMemPoolConf
|oCTensorProto
|oCEncoderConf
|oCDecoderConf
|oCTransformerConf
|oCImageRecord
|oCBlobShape
|oCBlobProto
|oCFillerConf
|oCOptimizerConf
|oCConstraintConf
|oCRegularizerConf
|oCParamSpec
|oCLayerConf
|oCLossConf
|oCMetricConf
|oCArgMaxConf
|oCConcatConf
|oCContrastiveLossConf
|oCConvolutionConf
|oCRNNConf
|oCDropoutConf
|oCDummyDataConf
|oCEltwiseConf
|oCEmbedConf
|oCExpConf
|oCFlattenConf
|oCHingeLossConf
|oCInfogainLossConf
|oCInnerProductConf
|oCDenseConf
|oCLogConf
|oCLRNConf
|oCMemoryDataConf
|oCMVNConf
|oCPoolingConf
|oCPowerConf
|oCReductionConf
|oCReLUConf
|oCReshapeConf
|oCSigmoidConf
|oCSliceConf
|oCSoftmaxConf
|oCTanHConf
|oCTileConf
|oCThresholdConf
|oCSPPConf
|oCPReLUConf
|oCBatchNormConf
|\CSplitConf
oCFactoryFactory template to generate class (or a sub-class) object based on id
oCPriorityQueueThread safe priority queue
oCRegistra
oCSafeQueueThread-safe queue
oCSingletonThread-safe implementation for C++11 according to
oCtinydir_dir
\Ctinydir_fileDefined(_TINYDIR_MALLOC)