rmseMetric
Description
Use a RMSEMetric object to track the root mean squared error
(RMSE) when you train or test a deep neural network.
To specify which metrics to use during training, specify the Metrics option of the trainingOptions function. You can
use this option only when you train a network using the trainnet
function.
To plot the metrics during training, in the training options, specify Plots as "training-progress". If you specify the
ValidationData training option, then the software also plots and records the
metric values for the validation data. To output the metric values to the Command Window
during training, in the training options, set Verbose to true.
You can also access the metrics after training using the
TrainingHistory and ValidationHistory fields from
the second output of the trainnet function.
To specify which metrics to use when you test a neural network, use the metrics
argument of the testnet
function.
Creation
Description
metric = rmseMetric(PropertyName=Value)Name, NetworkOutput, and NormalizationFactor properties using name-value arguments.
Properties
Object Functions
trainingOptions | Options for training deep learning neural network |
trainnet | Train deep learning neural network |
testnet | Test deep learning neural network |
Examples
Plot and record the training and validation RMSE when you train a deep neural network.
Load the training and test data from the DigitsDataTrain and DigitsDataTest MAT files, respectively. To access this data, open the example as a live script. The data set contains synthetic images of handwritten digits and the corresponding angles (in degrees) by which each image is rotated. The anglesTrain and anglesTest variables are the rotation angles in degrees.
load DigitsDataTrain.mat load DigitsDataTest.mat
You can train a deep learning network to predict the rotation angle of the digit.
Create an image regression network.
layers = [
imageInputLayer([28 28 1])
convolution2dLayer(3,8,Padding="same")
batchNormalizationLayer
reluLayer
averagePooling2dLayer(2,'Stride',2)
convolution2dLayer(3,16,Padding="same")
batchNormalizationLayer
reluLayer
averagePooling2dLayer(2,'Stride',2)
convolution2dLayer(3,32,Padding="same")
batchNormalizationLayer
reluLayer
convolution2dLayer(3,32,Padding="same")
batchNormalizationLayer
reluLayer
dropoutLayer(0.2)
fullyConnectedLayer(1)];Create an RMSEMetric object and set the NormalizationFactor property to "batch-size". You can use this object to record and plot the training and validation RMSE.
metric = rmseMetric(NormalizationFactor="batch-size")metric =
RMSEMetric with properties:
Name: "RMSE"
NormalizationFactor: "batch-size"
NetworkOutput: []
Maximize: 0
Specify the RMSE metric in the training options. To plot the RMSE during training, set Plots to "training-progress". To output the values during training, set Verbose to true.
options = trainingOptions("adam", ... MaxEpochs=5, ... Metrics=metric, ... ValidationData={XTest,anglesTest}, ... ValidationFrequency=50, ... Plots="training-progress", ... Verbose=true);
Train the network using the trainnet function.
[net,info] = trainnet(XTrain,anglesTrain,layers,"mse",options); Iteration Epoch TimeElapsed LearnRate TrainingLoss ValidationLoss TrainingRMSE ValidationRMSE
_________ _____ ___________ _________ ____________ ______________ ____________ ______________
0 0 00:00:01 0.001 688.66 26.242
1 1 00:00:01 0.001 648.74 25.47
50 2 00:00:08 0.001 210.71 335.2 14.516 18.309
100 3 00:00:13 0.001 130.55 131.78 11.426 11.479
150 4 00:00:18 0.001 89.997 126.48 9.4867 11.246
195 5 00:00:23 0.001 88.085 93.085 9.3854 9.648
Training stopped: Max epochs completed
Access the loss and RMSE values for the validation data.
info.ValidationHistory
ans=5×3 table
Iteration Loss RMSE
_________ ______ ______
0 688.66 26.242
50 335.2 18.309
100 131.78 11.479
150 126.48 11.246
195 93.085 9.648
More About
Version History
Introduced in R2023b
