---
name: matlab/matlab-train-network
source: https://app.decimal.ai/s/matlab-matlab-train-network@1/SKILL.md
source_sha256: d708987545b9
---

# 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.

| Criterion | fitcnet/fitrnet | trainnet |
|-----------|----------------|----------|
| Ease of use | Simplest — one function call | Requires network definition + trainingOptions |
| Solver | L-BFGS | Adam, SGDM, RMSProp, L-BFGS, LM (R2024b+) |
| Loss functions | MSE and cross-entropy only | Any built-in or custom (pass function handle) |
| Multiple input/output branches | No | Yes |
| Custom architecture | Via `Network` argument (R2025a+) | Yes |
| Data type | Tabular data only (a table or a numeric matrix) | Tabular data plus everything else (sequences, images, multi-input) |

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).

```matlab
% 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:

| Input layer | Expected data shape |
|-------------|-------------------|
| `featureInputLayer(C)` | observations×channels (e.g., 150×4) |
| `imageInputLayer([H W C])` | height×width×channels×observations (e.g., 28×28×1×5000) |
| `sequenceInputLayer(C)` | timesteps×channels×observations, or an observations×1 cell array where each element is a timesteps×channels time series |

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

Custom training loops (`dlfeval`/`dlgradient`/update functions) are appropriate
when the workflow requires customizations impossible via `trainingOptions` or
the specific workflow — multi-model adversarial training, alternating updates,
or custom weight update rules. Note that `trainingOptions` supports L-BFGS (R2023b+)
and Levenberg-Marquardt `"lm"` (R2024b+).

When a user has a working custom training loop and asks to speed it up, apply
`dlaccelerate` directly. Mention that their workflow may also be expressible
with `trainnet` (which handles acceleration internally), but do not push the
conversion — focus on accelerating the code they have.

### 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.

| Legacy or discouraged API | Recommended replacement |
|-----------|-------------------|
| `trainNetwork` | `trainnet` |
| `patternnet` | `fitcnet` (preferred), or `dlnetwork` + `trainnet` |
| `fitnet` | `fitrnet` (preferred), or `dlnetwork` + `trainnet` |
| `feedforwardnet` | `dlnetwork` + `trainnet` |
| `narxnet`, `timedelaynet` | `nlarx` (preferred), or `dlnetwork` + `trainnet` |
| `train()` (shallow `network` object) | `trainnet` |
| `classify` | `minibatchpredict` + `scores2label` |
| `activations` | `minibatchpredict(net,data,Outputs=layer)` |
| `predictAndUpdateState`, `classifyAndUpdateState` | `[Y,state] = predict(net,X); net.State = state;` |
| `classificationLayer` | Not required — use `trainnet` with `"crossentropy"` as the loss |
| `regressionLayer` | Not required — use `trainnet` with `"mse"` as the loss |
| `DAGNetwork`, `SeriesNetwork`, `layerGraph` | `dlnetwork` — supports `addLayers`, `connectLayers`, and `replaceLayer` for multi-branch architectures, anything `layerGraph` can do, `dlnetwork` can do directly |
| `resnet18`, `googlenet`, `squeezenet`, etc. (pretrained network functions that return `DAGNetwork`) | `imagePretrainedNetwork("resnet18", ...)` — returns a `dlnetwork` and handles head replacement automatically |
| Manually converting network scores to labels (e.g., `[~,idx] = max(scores)`) | `scores2label` |
| `plotconfusion` | `confusionchart` |
| `gensim` | `exportNetworkToSimulink` (preferred), or Predict block |
| `preparets` | `nlarx` (preferred, handles delays internally), or `dlnetwork` with `sequenceInputLayer(C, MinLength=numDelays)` + `convolution1dLayer(numDelays, ..., Padding="causal")` |
| `closeloop` | `forecast` (preferred, with `nlarx`), or iterative `predict` loop feeding previous predictions back as input |

See `references/legacy-api-redirects.md` for before/after code examples.

### Inference — use minibatchpredict (or predict)

If you see a for-loop calling `predict` or `forward` on batches for inference,
replace the entire loop with `minibatchpredict`. It handles batching, GPU
transfer, dlarray conversion, and acceleration automatically. Output format
(numeric array, table, or cell array) depends on the input type and network.

**Exception:** If the loop includes custom pre- or postprocessing around the
`predict` call that cannot be separated from it, `minibatchpredict` cannot
replicate the full pipeline. In that case, wrap the entire custom function
with `dlaccelerate` instead (see `references/dlaccelerate-workflow.md`).
Do not split the function into a `minibatchpredict` call plus separate
accelerated pre/postprocessing — a single `dlaccelerate` boundary around the
full function produces one unified trace.

- 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

```matlab
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

```matlab
% 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.

```matlab
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`.

Since the multi-output loss is a function handle, it can be accelerated using
`dlaccelerate`. **Always inform the user** that their custom loss function handle
can be accelerated with `dlaccelerate` for faster training — even if they did not
ask to speed up training. See `references/dlaccelerate-trainnet-custom-loss.md`
for the verification and production workflow.

---

## 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.

---

## Workflow: Deep Learning Code Acceleration with dlaccelerate — Diagnose, Fix, or Apply

**Does NOT apply to:**
- Simulink export
- MEX/Coder compilation
- `fitcnet`/`fitrnet`/`nlarx` workflows

**Applies when:**
- User has a custom training loop (`dlfeval`/`dlgradient`)
- User has a function that supports dlarray input and is long running
- User calls `trainnet` with a custom loss function handle (R2026a+)

Always attempt to dlaccelerate functions that:
- Are long-running.
- Have dlarray objects, structures of dlarray objects, or dlnetwork objects as inputs.
- Only perform operations that affect the output variables. Operations that do not affect the output variables include plotting, displaying output, and writing to files.

Check `references/dlaccelerate-workflow.md` Step 0 to determine whether the
function is acceleratable before recommending.

Follow `references/dlaccelerate-workflow.md` for the full diagnostic/fix/improve
process. Entry points:

- **User says "make faster" or "speed up" and the code has a custom training loop:** start at Step 0.
- **Code already uses `dlaccelerate` with problems:** start at Step 1 (identify
  antipatterns, apply fixes, verify).
- **Custom training loop without `dlaccelerate`:** start at Step 0 (requirements check,
  wrap, verify).
- **Any function with dlarray input that is called repeatedly:** start at Step 0.
  This includes custom model functions used for prediction.
- **Custom inference function called repeatedly:** same Step 0 applies.
  `dlaccelerate` is not training-specific — any repeatedly-called dlarray
  function benefits. Use `minibatchpredict` when the loop only calls `predict`
  with no custom pre- or postprocessing; use `dlaccelerate` when custom
  operations surround the predict call.
- **`trainnet` + custom loss function handle (R2026a+):** wrap the loss with
  `dlaccelerate` and pass the AcceleratedFunction to trainnet. See
  `references/dlaccelerate-trainnet-custom-loss.md`.

For antipatterns that break tracing and their fixes, see
`references/dlaccelerate-antipatterns.md`.

### dlaccelerate References

- `references/dlaccelerate-workflow.md` — full diagnostic/fix/improve process
- `references/dlaccelerate-antipatterns.md` — pattern catalog with BAD/GOOD examples
- `references/dlaccelerate-custom-training-loop.md` — acceleration levels, L2, clipping
- `references/dlaccelerate-trainnet-custom-loss.md` — trainnet + custom loss (R2026a+)
- `references/dlaccelerate-variable-length-sequences.md` — padding/bucketing strategies
- `references/dlaccelerate-custom-layers.md` — Acceleratable custom layers (`nnet.layer.Acceleratable`)
- `references/dlaccelerate-measure-speedup.md` — benchmarking methodology

---

## Key Functions

| Function | Purpose |
|----------|---------|
| `fitcnet` | Train neural network classifier for tabular data (Statistics and Machine Learning Toolbox) |
| `fitrnet` | Train neural network for regression on tabular data (Statistics and Machine Learning Toolbox) |
| `nlarx` | Nonlinear ARX model for NARX / time-delay time series (System Identification Toolbox) |
| `trainnet` | Train any `dlnetwork` with built-in or custom loss |
| `dlnetwork` | Modern network object (replaces DAGNetwork/SeriesNetwork/LayerGraph) |
| `trainingOptions` | Configure solver, epochs, validation, metrics |
| `minibatchpredict` | Batch inference (handles batching automatically) |
| `scores2label` | Convert score matrix to categorical labels |
| `testnet` | Evaluate network with metrics on a dataset (handles batching automatically)|
| `predict` | Single-batch inference on `dlnetwork`, `ClassificationNeuralNetwork`, `RegressionNeuralNetwork` |
| `imagePretrainedNetwork` | Load pretrained model with automatic head replacement |
| `exportNetworkToSimulink` | Export `dlnetwork` to Simulink as layer blocks |
| `analyzeNetwork` | Inspect network: `info = analyzeNetwork(net)` returns layer info, parameter counts, and architecture issues |
| `minibatchqueue` | Manage mini-batches with custom per-batch preprocessing |
| `dlaccelerate` | Accelerate a deep learning function by tracing and caching its execution graph |

---

## Common Mistakes

| What the agent might try | Why it's wrong | Do this instead |
|--------------------------|---------------|-----------------|
| `predict(net,dlarray(X,"TCB"))` | Unnecessary — `predict` on a `dlnetwork` accepts plain arrays | `predict(net,X)` |
| Manual accuracy/RMSE after training | Covered by existing functionality | `testnet(net,XTest,TTest,"accuracy")` |
| `squeezenet` + `layerGraph` + `replaceLayer` | Discouraged manual layer surgery for transfer learning | `imagePretrainedNetwork("squeezenet",NumClasses=N)` |
| Custom training loop for multi-output | Unnecessary complexity | `trainnet` with function handle loss |
| Transposing data to match the default layout (e.g., `cellfun(@transpose,...)`) | Unnecessary complexity | `InputDataFormats`, `TargetDataFormats` — arrange letters to match your data's actual dimension order |
| `testnet(net,ds,labels,"accuracy")` | `testnet` does not accept separate targets with datastores | `testnet(net,ds,"accuracy")` |
| `trainnet` for tabular data | Unnecessary complexity when using MSE/cross-entropy loss and LBFGS solver | `fitrnet` or `fitcnet` |
| `analyzeNetwork(net)` without capturing output | Loses programmatic access to layer info, parameter counts, and issues | `info = analyzeNetwork(net)` |
| Manually encoding categorical columns before passing to `trainnet`/`fitcnet`/`fitrnet` | Unnecessary complexity when these functions encode categorical data automatically | Pass categorical data directly |
| Manual for-loop batching for inference | Unnecessary complexity | `minibatchpredict(net,X)` — handles batching, GPU transfer, and acceleration automatically |
| Manual for-loop batching for custom preprocessing | Unnecessary complexity | `minibatchqueue` — handles batching, GPU transfer, dlarray conversion, and custom transforms |

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Copyright 2026 The MathWorks, Inc.

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