trainDetector

Train signal anomaly detector

Since R2023a

Syntax

trainDetector(d,data)

trainDetector(d,data,op)

Description

trainDetector(d,data) trains the detector object d on the training data set data.

trainDetector(d,data,op) specifies training options for the detector.

Examples

Train Convolutional Signal Anomaly Detector

Load the file sineWaveAnomalyData.mat, which contains two sets of synthetic three-channel sinusoidal signals.

sineWaveNormal contains 10 sinusoids of stable frequency and amplitude. Each signal has a series of small-amplitude impact-like imperfections. The signals have different lengths and initial phases. Plot the first three normal signals.

load sineWaveAnomalyData
sl = 3;

tiledlayout("vertical")
ax = zeros(sl,1);
for kj = 1:sl
    ax(kj) = nexttile;
    plot(sineWaveNormal{kj})
    title("Normal Signal")
end

sineWaveAbnormal contains three signals, all of the same length. Each signal in the set has an anomaly.

• All channels of the first signal have an abrupt change in frequency that lasts for a finite time.

• The second signal has a finite-duration amplitude change in one of its channels.

• The third signal has spikes at random times in all channels.

Plot the three signals with anomalies.

for kj = 1:sl
    plot(ax(kj),sineWaveAbnormal{kj})
    title(ax(kj),"Signal with Anomalies")
end

Create a convolutional autoencoder object to detect the anomalies in the abnormal signals. Specify that the signals used for both training and testing all have three channels.

D = deepSignalAnomalyDetector(sl);

Use the trainDetector function to train the convolutional neural network using the anomaly-free data.

• Use the training options for the adaptive moment estimation (Adam) optimizer. For more information, see trainingOptions (Deep Learning Toolbox).

• Use the CPU to train the network.

• Specify a maximum number of 100 epochs to use for training.

• Use a mini-batch size of 64.

• Display the training progress as a plot.

opts = trainingOptions("adam",ExecutionEnvironment="cpu", ...
    MaxEpochs=100,MiniBatchSize=64, ...
    Verbose=false,Plots="training-progress");

trainDetector(D,sineWaveNormal,opts)

Input Arguments

d — Anomaly detector

deepSignalAnomalyDetectorCNN object | deepSignalAnomalyDetectorLSTM object | deepSignalAnomalyDetectorLSTMForecaster object

Anomaly detector, specified as a deepSignalAnomalyDetectorCNN object, a deepSignalAnomalyDetectorLSTM object, or a deepSignalAnomalyDetectorLSTMForecaster object. Use the deepSignalAnomalyDetector function to create d.

data — Training data set

N_c-column matrix | M-element cell array | timetable | datastore

Training data set, specified as one of these:

• N_c-column matrix — A single multichannel signal observation (M = 1), where N_c is equal to the value of the NumChannels property of the detector.

• M-element cell array — M multichannel signal observations, where each cell contains an N_c-column matrix.

• Timetable — A single multichannel signal observation, contained in a MATLAB^® timetable. The timetable must contain increasing, uniformly-sampled, and finite values. The timetable can have:

  • A single variable containing an N_c-column matrix, where each column corresponds to a signal channel.

  • N_c variables, where each variable contains a vector that corresponds to a signal channel.

• Datastore — A signalDatastore, audioDatastore (Audio Toolbox), or arrayDatastore object. The detector uses the readall function to read all the signal observations contained in the datastore at once. You can also use a CombinedDatastore or TransformedDatastore object containing any of the supported datastores.

op — Training options

TrainingOptionsADAM object (default) | TrainingOptionsSGDM object | TrainingOptionsRMSProp object

Training options, specified as a TrainingOptionsADAM, TrainingOptionsSGDM, or TrainingOptionsRMSProp object. Use trainingOptions (Deep Learning Toolbox) to create the object.

• If the ModelType property of the detector is specified as "convautoencoder" or "lstmforecaster", the training options default to a TrainingOptionsADAM (Deep Learning Toolbox) object.

• If the ModelType property of the detector is specified as "lstmautoencoder", the training options default to a TrainingOptionsADAM (Deep Learning Toolbox) object with SequenceLength equal to 128.

Extended Capabilities

GPU Arrays

Accelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox™.

The trainDetector function supports GPU array input with these usage notes and limitations:

The ExecutionEnvironment option must be "gpu" or "auto" when the input data is:

• A gpuArray

• A cell array containing gpuArray objects

• A datastore that outputs cell arrays containing gpuArray objects

For more information, see Run MATLAB Functions on a GPU (Parallel Computing Toolbox).

Version History

Introduced in R2023a

R2024a: trainDetector automatically converts validation data to TCB format

The trainDetector function automatically converts both training data and validation data into TCB (time, channel, batch) format. In earlier releases, only training data was converted automatically. Validation data had to be formatted manually as "TCB" to work with trainDetector.