TrainingInfo

Neural network training information

Since R2023b

Description

Neural network training information including validation and loss information.

Creation

Create a TrainingInfo object using the second output of the trainnet.

Properties

TrainingHistory — Information about training iterations

table

Information about training iterations, returned as a table.

The table contains these variables:

• Iteration — Iteration number

• Loss — Training loss

• Metrics specified by the Metrics training option.

If you train with a stochastic solver (SGDM, Adam, or RMSProp), then the table has these additional variables:

• Epoch — Epoch number

• LearnRate — Learning rate

If you train with the L-BFGS solver, then the table has these additional variables:

• GradientNorm — Norm of the gradients

• StepNorm — Norm of the steps

Data Types: table

ValidationHistory — Information about validation iterations

table

Information about validation iterations, returned as a table.

The table contains these variables:

• Iteration — Iteration number

• Loss — Training loss

• Metrics specified by the Metrics training option.

Data Types: table

OutputNetworkIteration — Iteration that corresponds to the returned network

positive integer

Iteration that corresponds to the returned network, returned as a positive integer.

Data Types: double

StopReason — Reason for stopping training

string scalar

Reason for stopping training, returned as one of these values:

• "Max epochs completed" — Training reached the number of epochs specified by the MaxEpochs training option.

• "Max iterations completed" — Training reached the number of iterations specified by the MaxIterations training option.

• "Stopped by OutputFcn" — Function specified by the OutputFcn training option returned 1 (true).

• "Met validation criterion" — Validation loss is not lowest for ValidationPatience times.

• "Stopped manually" — Stop button pressed.

• "Error occurred" — The software threw an error.

• "Training loss is NaN" — Training loss is NaN.

• "Gradient tolerance reached" — Norm of the gradients is lower than the GradientTolerance training option.

• "Step tolerance reached" — Norm of the steps is lower than the StepTolerance training option.

• "Suitable learning rate not found" — Line search unable to find suitable learning rate.

Data Types: string

Object Functions

show	Show training information plot
close	Close training information plot

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       00:00:03         0.01          2.7615              3.9062
           50        1       00:00:11         0.01         0.48724              84.375
          100        2       00:00:17         0.01         0.16261              96.094
          150        2       00:00:24         0.01         0.10018              96.094
          200        3       00:00:29         0.01        0.056833                 100
          250        4       00:00:34         0.01        0.031866                 100
          300        4       00:00:40         0.01        0.021502                 100
          312        4       00:00:42         0.01        0.029409              99.219
Training stopped: Max epochs completed

View the training information.

info

info = 
  TrainingInfo with properties:

           TrainingHistory: [312×5 table]
         ValidationHistory: [0×0 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)

Version History

Introduced in R2023b