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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchA reliable MATLAB deep learning workflow is more than choosing a network and calling trainnet. Start with representative data, make preprocessing consistent, choose validation and training settings deliberately, profile before optimizing, and test the finished model in the system where it will run. This checklist follows MathWorks documentation, chiefly labeled R2026b; check the documentation for your MATLAB release because features and hardware requirements can vary.
1. Define the task and check the data first
Decide what the model must predict and what inputs it will receive in practice before selecting an architecture. Check that the examples and labels represent that problem, and that the labels are usable. MathWorks’ practical guide emphasizes the importance of quality labeled data and preparation; the appropriate architecture also depends on the task and the data available.
- Confirm that the labels match the intended output and that examples cover the cases the model is expected to encounter.
- Inspect predictors and targets for NaN values. MathWorks notes that NaNs commonly propagate through a network and can prevent training from converging.
- Check dimensions, types, and layouts before combining different kinds of data. Mixed-type inputs may need reshaping or reformatting to work with combination layers.
For regression, normalizing targets can help stabilize and speed training. Treat normalization as part of the model pipeline: preserve the parameters needed to return predictions to the original target scale.
2. Make preprocessing one explicit part of the pipeline
Preprocessing consists of deterministic operations that normalize or enhance relevant features—for example, scaling values to a fixed range or resizing images to the network’s expected input size. Decide what transformations the task needs, then apply the intended transformations consistently to training, validation, and inference data. A mismatch between training inputs and production inputs can undermine results even when training completes successfully.
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There are two practical ways to organize this work:
| Approach | How it works | Useful when |
|---|---|---|
| Preprocess and save data before training | Apply the deterministic transformations once, then train from the prepared data. | You run repeated trials against the same data and want to avoid repeating preprocessing work. |
| Transform data through a datastore | Use datastore transform and combine operations to prepare data during training. |
You want preprocessing to remain integrated with the data-loading workflow. |
Whichever route you use, keep the transformation logic and any learned preprocessing parameters available for validation and inference. Do not let a training-only operation silently define inputs differently from the inputs the deployed model will receive.
3. Choose a starting architecture—and decide whether to transfer-learn
Choose an architecture based on the task, data, and required output rather than assuming the largest or newest network is automatically best. For natural-image classification or regression, MathWorks suggests considering a pretrained network. Transfer learning reuses features learned previously and adapts the network to the new task; MathWorks describes using higher learning-rate factors for new layers and lower factors for transferred layers as a possible approach.
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This is task-dependent guidance, not a universal rule. Compare a suitable pretrained starting point with a task-appropriate alternative using the same data splits and evaluation method. If the task differs substantially from the pretraining domain or the available data is limited, inspect the results rather than assuming transfer learning will help.
4. Set training options and validation deliberately
For built-in training, the documented pattern is to configure parameters with trainingOptions and train with trainnet. Use a custom training loop when the built-in training options do not provide the flexibility the task requires.
| Training route | What it offers | Choose it when |
|---|---|---|
trainingOptions with trainnet |
A built-in training path with configurable options and validation monitoring. | The task fits the supported built-in workflow and options. |
| Custom training loop | A way to implement training behavior beyond the built-in options. | You need control that the built-in route does not provide. |
Validation data can provide loss and metric values during training and can drive stopping through ValidationPatience. Without validation data, the training function does not validate during training. Validation is useful only if it is adequately sized and representative: too little or unrepresentative data can make its metrics misleading, while a very large validation set can slow training.
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Keep a separate test dataset for final evaluation. Validation informs training choices; it is not a substitute for testing on unseen cases after those choices have been made.
5. Read learning curves as diagnostic evidence
Training and validation curves can point toward a problem, but no adjustment is guaranteed to fix it. Change one relevant factor at a time where practical, then check whether the result improves on validation data.
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- NaNs or large loss spikes: MathWorks suggests trying a lower initial learning rate or gradient clipping.
- Loss is still falling at the end: Training longer may be worth testing.
- Loss plateaus: Consider a learning-rate drop; if that does not help, assess whether model capacity is limiting performance.
- Validation loss is much higher than training loss: Try measures such as augmentation, dropout, or stronger L2 regularization to address overfitting.
These are troubleshooting leads, not proof of a particular cause. Check the data, preprocessing, and task setup as well as the training settings before attributing a symptom to the network alone.
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6. Profile before trying to make training faster
Use the MATLAB Profiler app to find the slow parts of the workflow before spending time optimizing. Training may not be the only bottleneck: data loading and preprocessing can also consume time. For a datastore with a ReadSize property, MathWorks documents matching that value with MiniBatchSize as a performance tip. Check whether it fits your input pipeline and measure the result in your own setup rather than assuming a change will help.
7. Choose CPU, GPU, or parallel execution with requirements in view
trainnet uses a GPU by default when one is available. GPU and parallel training require Parallel Computing Toolbox, and GPU training also requires a supported device. Custom training loops require data to be placed on the GPU; minibatchqueue can prepare mini-batches and convert data to dlarray and gpuArray. Remote cluster execution has additional MATLAB Parallel Server requirements.
| Execution choice | Requirements and considerations |
|---|---|
| CPU | A GPU is not required. Whether it is suitable depends on the workload and performance needs. |
| Single GPU | Requires a supported GPU device and Parallel Computing Toolbox. trainnet can select an available GPU automatically. |
| Parallel or remote cluster | Parallel training requires Parallel Computing Toolbox; remote cluster use has additional MATLAB Parallel Server requirements. Consider data movement and the execution environment as well as compute capacity. |
Exact device support and toolbox requirements depend on the MATLAB release and hardware. Verify them against the documentation for the release and machine you will use.
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8. Make reproducibility choices explicit
GPU training is not automatically repeatable in the strict sense. MathWorks’ official trainnet documentation states: “To provide the best performance, deep learning using a GPU in MATLAB is not guaranteed to be deterministic.”
Since R2024b, deep.gpu.deterministicAlgorithms can restrict GPU operations to deterministic algorithms, but doing so can slow computations. Deterministic algorithms alone do not control every source of randomness: use rng and, when relevant, gpurng to set seeds for other random operations. Background or parallel preprocessing can also make training nondeterministic, and results can vary across GPU hardware. Record the relevant release, hardware, seeds, and preprocessing approach when comparing runs.
9. Evaluate the finished model before deployment
A strong validation score does not establish how well a model performs across the unseen solution space. Evaluate the final network on held-out test data, then check how it interacts with the other components of the system where it will be used. MathWorks’ deployment guidance treats testing the model and its interactions with system components as part of the work before deployment.
Quick Recap
- Use the reserved test dataset to evaluate cases not used to make training choices.
- Verify that inference applies the intended preprocessing and input formatting.
- Test the network as part of the integrated system, not only as an isolated training artifact.
MATLAB deep learning workflow checklist
- Define the task and check data, labels, array layout, and NaNs.
- Specify deterministic preprocessing and apply it consistently to training, validation, and inference.
- Select an architecture appropriate to the task; assess transfer learning where relevant.
- Choose built-in
trainnettraining or a custom loop according to required flexibility. - Set up representative validation data and reserve a separate test set.
- Use learning curves to guide troubleshooting, then verify any change.
- Profile the workflow before optimizing its speed.
- Check toolbox, device, and cluster prerequisites before selecting an execution mode.
- Plan for the limits of GPU determinism and record choices needed to interpret runs.
- Test the model on held-out data and in its integrated system before deployment.
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