trainDLCHOMP

Train deep-learning-based CHOMP optimizer

Since R2024a

Syntax

info = trainDLCHOMP(dlchomp,trainingData,lossFcn,options)

Description

info = trainDLCHOMP(dlchomp,trainingData,lossFcn,options) trains the neural network of the deep-learning-based CHOMP optimizer using the training data, loss function, and training options.

The trainDLCHOMP function requires the Deep Learning Toolbox™.

Input Arguments

dlchomp — Deep-learning-based CHOMP optimizer

dlCHOMP object

Deep-learning-based CHOMP optimizer, specified as an dlCHOMP object.

trainingData — Training data

dlCHOMPDatastore object

Training data, specified as a dlCHOMPDatastore object.

Use the generateSamples function to generate the training datastore.

lossFcn — Loss function

"crossentropy" | "binary-crossentropy" | "mse" | "mean-squared-error" | "l2loss" | "mae" | "mean-absolute-error" | "l1loss" | "huber" | function handle

Loss function to use for training, specified as one of these values:

• "crossentropy" — Cross-entropy loss for classification tasks.

• "binary-crossentropy" — Binary cross-entropy loss for binary and multilabel classification tasks.

• "mse" / "mean-squared-error" / "l2loss" — Mean squared error for regression tasks.

• "mae" / "mean-absolute-error" / "l1loss" — Mean absolute error for regression tasks.

• "huber" — Huber loss for regression tasks

• Function handle with the syntax loss = f(Y1,...,Yn,T1,...,Tm), where Y1,...,Yn are dlarray objects that correspond to the n network predictions and T1,...,Tm are dlarray objects that correspond to the m targets.

Tip

For weighted cross-entropy, use the function handle @(Y,T) crossentropy(Y,T,weights).

options — Training options

trainingOptions object

Training options, specified as an trainingOptions (Deep Learning Toolbox) object.

Output Arguments

info — Training information

structure

Training information, returned as a TrainingInfo object with these properties:

• TrainingHistory — Information about training iterations

• ValidationHistory — Information about validation iterations

• OutputNetworkIteration — Iteration that corresponds to trained network

• StopReason — Reason why training stopped

You can use info to open and close the training progress plot using the show and close functions.

Version History

Introduced in R2024a