- Use a wide range of image-oriented layer types to implement cutting-edge computer-vision algorithms. »
- Define network topologies with multiple inputs, outputs, and arbitrary directed acyclic graph connectivity structure. »
- Work with image, categorical, and numeric inputs and outputs. »
- Define networks with multiple loss functions to perform multitask learning. »
- Easily evaluate trained networks using a variety of built-in classifier metrics. »
- Train on out-of-core image datasets. »
- Train networks on either CPUs or NVIDIA GPUs. »
- Take advantage of the NVIDIA CUDA Deep Neural Network library (cuDNN) for optimal GPU performance. »
- Import and export trained networks as "WLNet" files. »
- Employ automatic tensor shape inference to write succinct network definitions. »
Related Examples
Related Functions
- BatchSize
- NetChain
- NetTrain
- NetExtract
- NetInitialize
- NetGraph
- NetDecoder
- NetEncoder
- BatchNormalizationLayer
- MeanSquaredLossLayer
- MeanAbsoluteLossLayer
- ConvolutionLayer
- DeconvolutionLayer
- CrossEntropyLossLayer
- DotPlusLayer
- DropoutLayer
- ElementwiseLayer
- EmbeddingLayer
- FlattenLayer
- CatenateLayer
- PoolingLayer
- SoftmaxLayer
- SummationLayer
- TotalLayer
- MaxTrainingRounds
- NetPort
- ReshapeLayer