version: "1.0.0" name: matlab-train-network description: > Train, evaluate, and export neural networks to Simulink in MATLAB. Migrate legacy (fitnet, patternnet) and discouraged (trainNetwork, DAGNetwork) code to modern, recommended R2024a+ APIs (trainnet, dlnetwork, testnet, imagePretrainedNetwork). Use when training, fine-tuning, evaluating, running inference, exporting to Simulink, or converting old training scripts. license: MathWorks BSD-3-Clause metadata: author: MathWorks version: "1.1"

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

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.

Pass tables directly to trainnet, fitcnet, and fitrnet. If inputs have categorical columns, pass them directly — they are encoded automatically (fitcnet/fitrnet always; trainnet/minibatchpredict/testnet from R2025a).

% Classification
mdl = fitcnet(tbl,responseName,LayerSizes=20);
[labels,score] = predict(mdl,tblTest);
L = loss(mdl,tblTest);
 
% Regression
mdl = fitrnet(tbl,responseName,LayerSizes=[20 20]);
Y = predict(mdl,tblTest);
L = loss(mdl,tblTest);
 
% Tabular data with trainnet (when fitcnet/fitrnet can't be used)
net = trainnet(tbl,net,"crossentropy",options);
accuracy = testnet(net,tblTest,"accuracy");
scores = minibatchpredict(net,tblPredictors);

• From R2024b, fitrnet supports multi-response variables.
• From R2025a, for custom architectures beyond LayerSizes, Activations, LayerWeightsInitializer, and LayerBiasesInitializer, pass a dlnetwork via the Network name-value argument.

Conventions

Training with trainnet + dlnetwork

Data formats

trainnet expects data in specific orientations by default:

If your data has a different layout, use InputDataFormats and/or TargetDataFormats in trainingOptions instead of transposing the data manually. The format string describes your data's current layout — one letter per dimension, not the desired layout. MATLAB handles the remapping internally. For cell arrays, add "B" (batch) to the format string — e.g., InputDataFormats="CTB" for cells of C×T matrices. Do not specify these options when data already matches the input layer's default.

What trainnet supports

Use trainnet and dlnetwork for all Deep Learning Toolbox training. This includes:

• Standard classification and regression
• Transfer learning
• Multi-input or multi-output networks
• Custom loss functions (pass a function handle to trainnet)
• Custom loss function backward passes via DifferentiableFunction
• Custom metrics (string, function handle, or deep.Metric subclass)
• Custom stopping criteria via OutputFcn in trainingOptions
• Custom layers

Only use a custom training loop (dlfeval/dlgradient/update functions) when a customization is impossible via trainingOptions — for example, a custom weight update rule. Note that trainingOptions supports L-BFGS (R2023b+) and Levenberg-Marquardt "lm" (R2024b+).

NEVER use these legacy or discouraged APIs

If the user has existing code using these APIs, migrate it to the recommended replacement and briefly explain which APIs were replaced and what the modern equivalents are. If the user asks for a legacy or discouraged API by name, acknowledge their request and explain that the function has been replaced with a recommended alternative before providing the solution.

Inference — use minibatchpredict (or predict)

• For classification: use minibatchpredict (or predict) + scores2label.
• For regression or when you need raw scores: use minibatchpredict or predict.
• predict is for single-batch/small-batch use and accepts plain numeric

arrays directly — do not wrap inputs in dlarray or call extractdata on outputs.

Evaluation — use testnet

• Use testnet to calculate post-training metrics on a test dataset instead

of doing it manually.

• For single-output networks, use string metrics: "accuracy", "rmse".
• trainnet and testnet accept targets as a separate argument only for

in-memory data (testnet(net,XTest,TTest,"accuracy")). When passing a datastore, targets must already be embedded in it (e.g., labeled imageDatastore or combined datastore with targets in a second column).

• For multi-output networks or advanced metric customization, see

references/metrics-guidance.md.

Transfer learning — use imagePretrainedNetwork

net = imagePretrainedNetwork("squeezenet",NumClasses=5);
 
options = trainingOptions("adam", ...
    MaxEpochs=10, ...
    MiniBatchSize=16, ...
    InitialLearnRate=1e-4, ...
    ValidationData=imdsVal, ...
    Metrics="accuracy", ...
    Plots="training-progress");
 
net = trainnet(augimdsTrain,net,"crossentropy",options);
 
% Inference — class names come from training data, not the pretrained net
classNames = categories(imdsTrain.Labels);
scores = minibatchpredict(net,imdsTest);
labels = scores2label(scores,classNames);

imagePretrainedNetwork returns class names only when both NumClasses and NumResponses are unset (pretrained mode, no transfer learning).

Workflow: Training

Check the Decision section above first — tabular data goes to fitrnet/fitcnet unless you need a non-LBFGS solver or a non-MSE/cross-entropy loss.

Standard training

% Define network
numChannels = 3;
numClasses = 5;
layers = [
    sequenceInputLayer(numChannels,Normalization="zscore")
    lstmLayer(100,OutputMode="last")
    fullyConnectedLayer(numClasses)
    softmaxLayer];
 
% Training options
options = trainingOptions("adam", ...
    MaxEpochs=30, ...
    MiniBatchSize=128, ...
    ValidationData={XVal,TVal}, ...
    Metrics="accuracy", ...
    Plots="training-progress");
 
% Train
net = trainnet(XTrain,TTrain,layers,"crossentropy",options);

Always normalize inputs. Set Normalization on the input layer (see example above). For regression, also normalize targets:

• R2026a+: append inverseNormalizationLayer to the last layer and set

NormalizeTargets=true in trainingOptions

• Pre-R2026a: manually z-score targets before training and denormalize

predictions at inference

See references/normalization.md for both workflows.

Custom loss function for multi-output

The function handle receives network outputs then targets, in order. Pass categorical targets directly — trainnet encodes them automatically.

lossFcn = @(Y1,Y2,T1,T2) crossentropy(Y1,T1) + mse(Y2,T2);
 
net = trainnet(ds,net,lossFcn,options);

For the full multi-output recipe (OutputNames alignment, combined datastores, testnet evaluation), see references/multi-output-training.md.

Workflow: Simulink Export

• `dlnetwork` (small, all layers supported): use exportNetworkToSimulink
• `dlnetwork` (large, or has layers unsupported by `exportNetworkToSimulink`): use the Predict block at library path deeplib/Predict
• `fitcnet`/`fitrnet` models: use the ClassificationNeuralNetwork Predict or RegressionNeuralNetwork Predict blocks from statsLibrary/

See references/simulink-export.md for details.

Key Functions

Common Mistakes

See also:

• references/legacy-api-redirects.md — legacy and discouraged API mapping

and before/after code examples

• references/metrics-guidance.md — when to use string vs object vs function

vs deep.Metric subclass

• references/multi-output-training.md — end-to-end multi-output recipe:

OutputNames alignment, combined datastores, loss function ordering

• references/normalization.md — how to normalize inputs and targets for

training

• references/simulink-export.md — exportNetworkToSimulink vs Predict block

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