matlab-train-network

Train, evaluate, and export neural networks to Simulink in MATLAB using the recommended dlnetwork-based API (trainnet, dlnetwork, minibatchpredict, scores2label, testnet, imagePretrainedNetwork) or, for tabular data, the Statistics and Machine Learning Toolbox functions fitcnet and fitrnet.

When to Use

Activate this skill when a user asks to:

• Train any neural network (classifier, regression, multi-output, LSTM, CNN, etc.)
• Fine-tune or use a pretrained model for transfer learning
• Evaluate a trained network on test data
• Run inference / predict with a trained network
• Export a trained network to Simulink
• Migrate existing legacy (patternnet, fitnet, narxnet, gensim) or discouraged (trainNetwork, DAGNetwork, classify) code to recommended APIs
• Create a "pattern recognition network", "function fitting network", "NARX network", or any task historically associated with the Neural Network Toolbox shallow nets API
• Speed up or optimize any deep learning code (even without mentioning dlaccelerate by name)
• Make existing deep learning code faster using dlaccelerate
• Diagnose and fix dlaccelerate issues (low HitRate, retracing, code is slower after using dlaccelerate)
• Accelerate custom training (code that uses dlfeval/dlgradient)
• Accelerate a function that supports dlarray input and is long running
• Accelerate a custom loss function passed to trainnet (R2026a+)

When NOT to Use

• Importing/exporting models (importNetworkFromPyTorch, exportONNXNetwork)
• Data loading and preprocessing (imageDatastore, transforms, augmentation)
• Network architecture design decisions (choosing CNN vs LSTM vs transformer)
• Reinforcement learning workflows (use Reinforcement Learning Toolbox)
• Object detection (use specialized detector training functions in Computer Vision Toolbox)

Decision: fitrnet/fitcnet or trainnet

Apply this check before starting any training workflow below.