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Syntax
Description
Examples
Show Neural Network Training Information
Unzip the digit sample data and create an image datastore. The imageDatastore
function automatically labels the images based on folder names.
unzip("DigitsData.zip") imds = imageDatastore("DigitsData", ... IncludeSubfolders=true, ... LabelSource="foldernames");
Define the convolutional neural network architecture. Specify the size of the images in the input layer of the network and the number of classes in the final fully connected layer. Each image is 28-by-28-by-1 pixels.
inputSize = [28 28 1]; numClasses = numel(categories(imds.Labels)); layers = [ imageInputLayer(inputSize) convolution2dLayer(5,20) batchNormalizationLayer reluLayer fullyConnectedLayer(numClasses) softmaxLayer];
Specify the training options.
Train for four epochs using the SGDM solver.
Monitor the accuracy metric.
options = trainingOptions("sgdm", ... MaxEpochs=4, ... Metrics="accuracy");
Train the neural network. For classification, use cross-entropy loss. Return the trained network and the training information.
[net,info] = trainnet(imds,layers,"crossentropy",options);
Iteration Epoch TimeElapsed LearnRate TrainingLoss TrainingAccuracy _________ _____ ___________ _________ ____________ ________________ 1 1 00:00:01 0.01 2.7615 3.9062 50 1 00:00:08 0.01 0.48211 85.938 100 2 00:00:13 0.01 0.16471 96.094 150 2 00:00:17 0.01 0.10177 96.094 200 3 00:00:26 0.01 0.057216 100 250 4 00:00:36 0.01 0.031815 100 300 4 00:00:40 0.01 0.021116 100 312 4 00:00:41 0.01 0.029253 99.219 Training stopped: Max epochs completed
View the training information.
info
info = TrainingInfo with properties: TrainingHistory: [312x5 table] ValidationHistory: [0x0 table] OutputNetworkIteration: 312 StopReason: "Max epochs completed"
Display the training information in a plot using the show
function.
show(info)
Close the plot using the close
function.
close(info)
Input Arguments
info
— Training information
TrainingInfo
object
Training information, specified as a TrainingInfo
object.
Version History
Introduced in R2023b
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