The Wolfram Language has state-of-the-art capabilities for the construction, training and deployment of neural network machine learning systems. Many standard layer types are available and are assembled symbolically into a network, which can then immediately be trained and deployed on available CPUs and GPUs.
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Automated Machine Learning
Classify-- automatic training and classification using neural networks and other methods
Predict-- automatic training and data prediction
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FeatureExtraction-- automatic feature extraction from image, text, numeric, etc. data
LearnDistribution-- automatic learning of data distribution
ImageIdentify-- fully trained image identification for common objects
Prebuilt Material
NetModel-- complete pre-trained net models
ResourceData-- access to training data, networks, etc.
Net Representation
NetGraph-- symbolic representation of trained or untrained net graphs to be applied to data
NetChain-- symbolic representation of a simple chain of net layers
NetPort-- symbolic representation of a named input or output port for a layer
NetExtract-- extract properties and weights etc. from nets
NetInformation-- give summary and detailed information about any network
Net Operations
NetTrain-- train parameters in any net from examples
NetInitialize-- randomly initialize parameters for a network
NetPortGradient-- differentiate a net with respect to a port
NetStateObject-- store and reuse recurrent state in a net
NetTrainResultsObject-- represent what happened in net training
NetMeasurements-- measure the performance of a net on test data
TrainingProgressMeasurements-- measure performance metrics during training
LearningRate ▪ TrainingStoppingCriterion
Basic Layers
LinearLayer-- trainable layer with dense connections computing
ElementwiseLayer-- apply a specified function to each element in a tensor
SoftmaxLayer-- layer globally normalizing elements to the unit interval
![]() Loss Layers
MeanSquaredLossLayer ▪ MeanAbsoluteLossLayer ▪ CrossEntropyLossLayer ▪ ContrastiveLossLayer ▪ CTCLossLayer
Elementwise Computation Layers
ElementwiseLayer ▪ ThreadingLayer ▪ ConstantTimesLayer ▪ ConstantPlusLayer
Structure Manipulation Layers
CatenateLayer ▪ PrependLayer ▪ AppendLayer ▪ FlattenLayer ▪ ReshapeLayer ▪ ReplicateLayer ▪ PaddingLayer ▪ PartLayer ▪ TransposeLayer ▪ ExtractLayer
Array Operation Layers
ConstantArrayLayer-- embed a learned constant array into a NetGraph
SummationLayer ▪ TotalLayer ▪ AggregationLayer ▪ DotLayer ▪ OrderingLayer
Convolutional and Filtering Layers
Free pdf editor. ConvolutionLayer ▪ DeconvolutionLayer ▪ PoolingLayer ▪ ResizeLayer ▪ SpatialTransformationLayer
Recurrent Layers
BasicRecurrentLayer ▪ GatedRecurrentLayer ▪ LongShortTermMemoryLayer
Sequence-Handling Layers
EmbeddingLayer-- trainable layer for embedding integers into continuous vector spaces
SequenceLastLayer ▪ SequenceReverseLayer ▪ SequenceMostLayer ▪ SequenceRestLayer ▪ UnitVectorLayer
AttentionLayer-- trainable layer for finding parts of a sequence to attend to
Training Optimization Layers
ImageAugmentationLayer ▪ BatchNormalizationLayer ▪ DropoutLayer ▪ LocalResponseNormalizationLayer ▪ NormalizationLayer
Higher-Order Network Construction
NetMapOperator-- define a network that maps over a sequence
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NetMapThreadOperator-- define a network that maps over multiple sequences
NetFoldOperator-- define a recurrent network that folds in elements of a sequence
NetPairEmbeddingOperator ▪ NetNestOperator ▪ NetBidirectionalOperator
Network Surgery
NetDrop ▪ NetTake ▪ NetAppend ▪ NetPrepend ▪ NetJoin
NetDelete ▪ NetInsert ▪ NetReplace ▪ NetReplacePart
NetFlatten ▪ NetRename
Weight Sharing
NetSharedArray-- represent an array shared between several layers
NetInsertSharedArrays-- convert all arrays in a net into shared arrays
Encoding & Decoding
NetEncoder-- convert images, categories, etc. to net-compatible numerical arrays
'Audio' ▪ 'AudioMelSpectrogram' ▪ 'AudioMFCC' ▪ 'AudioSpectrogram' ▪ 'AudioSTFT' ▪ 'Boolean' ▪ 'Characters' ▪ 'Class' ▪ 'Function' ▪ 'Image' ▪ 'Image3D' ▪ 'Tokens'▫'BPESubwordTokens'▫'UTF8'
NetDecoder-- interpret net-generated numerical arrays as images, probabilities, etc.
'Boolean' ▪ 'Characters' ▪ 'Class' ▪ 'CTCBeamSearch' ▪ 'Image' ▪ 'Function' ▪ 'Image3D' ▪ 'Tokens'▫'BPESubwordTokens'
Activation Functions
Ramp-- rectified linear (ReLU)
Tanh ▪ LogisticSigmoid ▪ Exp ▪ Log ▪ Sin ▪ Cos ▪ Sqrt ▪ Abs
Importing & Exporting
'WLNet'-- Wolfram Language Net representation format
Simulating Neural Networks With Mathematica
'MXNet'-- MXNet net representation format
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Import ▪ Export
Managing Data & Training
ClassifierMeasurements-- measure accuracy, recall, etc. of a classifier net
DeleteMissing-- remove missing data before training
LossFunction ▪ TargetDevice ▪ ValidationSet ▪ TrainingProgressFunction ▪ TrainingProgressCheckpointing ▪ TrainingProgressReporting ▪ TrainingStoppingCriterion ▪ TrainingProgressMeasurements ▪ LearningRate ▪ LearningRateMultipliers ▪ NetEvaluationMode
Reinforcement Learning Environments
'OpenAIGym', .. -- access to video games and many other test environments
Mathematica Neural Network Example
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