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Custom Training Loops

Train deep learning networks using custom training loops

If the trainingOptions function does not provide the training options that you need for your task, or you have a loss function that the trainnet function does not support, then you can define a custom training loop. For models that cannot be specified as networks of layers, you can define the model as a function. To learn more, see Define Custom Training Loops, Loss Functions, and Networks.


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dlnetworkDeep learning neural network (Since R2019b)
trainingProgressMonitorMonitor and plot training progress for deep learning custom training loops (Since R2022b)
minibatchqueueCreate mini-batches for deep learning (Since R2020b)
padsequencesPad or truncate sequence data to same length (Since R2021a)
dlarrayDeep learning array for customization (Since R2019b)
dlgradientCompute gradients for custom training loops using automatic differentiation (Since R2019b)
dlfevalEvaluate deep learning model for custom training loops (Since R2019b)
crossentropyCross-entropy loss for classification tasks (Since R2019b)
l1lossL1 loss for regression tasks (Since R2021b)
l2lossL2 loss for regression tasks (Since R2021b)
huberHuber loss for regression tasks (Since R2021a)
mseHalf mean squared error (Since R2019b)
ctcConnectionist temporal classification (CTC) loss for unaligned sequence classification (Since R2021a)