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Built-In Training

Train deep learning networks using built-in training functions

After defining the network architecture, you can define training parameters using the trainingOptions function. You can then train the network using the trainnet function. Use the trained network to predict class labels or numeric responses.


Deep Network DesignerDesign and visualize deep learning networks


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dlnetworkDeep learning neural network (Seit R2019b)
trainingOptionsOptions for training deep learning neural network
trainnetTrain deep learning neural network (Seit R2023b)
TrainingInfoNeural network training information (Seit R2023b)
showShow training information plot (Seit R2023b)
closeClose training information plot (Seit R2023b)
accuracyMetricDeep learning accuracy metric (Seit R2023b)
aucMetricDeep learning area under ROC curve (AUC) metric (Seit R2023b)
fScoreMetricDeep learning F-score metric (Seit R2023b)
precisionMetricDeep learning precision metric (Seit R2023b)
recallMetricDeep learning recall metric (Seit R2023b)
rmseMetricDeep learning root mean squared error metric (Seit R2023b)
predictCompute deep learning network output for inference (Seit R2019b)
minibatchpredictMini-batched neural network prediction (Seit R2024a)
scores2labelConvert prediction scores to labels (Seit R2024a)
confusionchartCreate confusion matrix chart for classification problem
sortClassesSort classes of confusion matrix chart
classifyAndUpdateState(Not recommended) Classify data using a trained recurrent neural network and update the network state