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plotLoss

Plot window reconstruction loss

Since R2023a

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    Syntax

    plotLoss(d,data)
    plotLoss(d,data,Name=Value)
    f = plotLoss(___)

    Description

    plotLoss(d,data) plots the window reconstruction loss computed over the input signal data set data by the detector d.

    example

    plotLoss(d,data,Name=Value) specifies additional options using name-value arguments.

    f = plotLoss(___) returns the figure handle of the plot.

    Examples

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    This example uses:

    Open Live Script

    Load a convolutional anomaly detector trained with three-channel sinusoidal signals. Display the model, threshold, and window properties of the detector.

    load sineWaveAnomalyDetector
D
    D = 
  deepSignalAnomalyDetectorCNN with properties:

                IsTrained: 1
              NumChannels: 3

   Model Information
                ModelType: 'convautoencoder'
               FilterSize: 8
               NumFilters: 32
      NumDownsampleLayers: 2
         DownsampleFactor: 2
       DropoutProbability: 0.2000

   Threshold Information
                Threshold: 0.0510
          ThresholdMethod: 'contaminationFraction'
       ThresholdParameter: 0.0100

   Window Information
             WindowLength: 1
            OverlapLength: 'auto'
    WindowLossAggregation: 'mean'

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

    • sineWaveNormal contains the 10 sinusoids used to train the convolutional anomaly detector. Each signal has a series of small-amplitude impact-like imperfections but otherwise has stable amplitude and frequency.

    • sineWaveAbnormal contains three signals of similar length and amplitude to the training data. One of the signals has an abrupt, finite-time change in frequency. Another signal has a finite-duration amplitude change in one of its channels. A third has random spikes in each channel.

    Plot three normal signals and the three signals with anomalies.

    load sineWaveAnomalyData
 
tiledlayout(3,2,TileSpacing="compact",Padding="compact")
rnd = randperm(length(sineWaveNormal));
for kj = 1:length(sineWaveAbnormal)
    nexttile
    plot(sineWaveNormal{rnd(kj)})
    title("Normal Signal")
    nexttile
    plot(sineWaveAbnormal{kj})
    title("Signal with Anomalies")
end

    Figure contains 6 axes objects. Axes object 1 with title Normal Signal contains 3 objects of type line. Axes object 2 with title Signal with Anomalies contains 3 objects of type line. Axes object 3 with title Normal Signal contains 3 objects of type line. Axes object 4 with title Signal with Anomalies contains 3 objects of type line. Axes object 5 with title Normal Signal contains 3 objects of type line. Axes object 6 with title Signal with Anomalies contains 3 objects of type line.

    Use the plotLoss function to display the reconstruction loss computed by the trained anomaly detector for each abnormal signal. Show the loss next to the corresponding signal. Signal regions where the loss exceeds a specified threshold are categorized as anomalous.

    tiledlayout(3,2,TileSpacing="compact",Padding="compact")
for kj = 1:length(sineWaveAbnormal)
    nexttile
    plot(sineWaveAbnormal{kj})
    xlabel("Window Index")
    title("Signal No. " + kj)
    nexttile
    plotLoss(D,sineWaveAbnormal{kj})
end

    Figure contains 6 axes objects. Axes object 1 with title Signal No. 1, xlabel Window Index contains 3 objects of type line. Axes object 2 with title Reconstruction Loss, xlabel Window Index, ylabel Loss contains 2 objects of type line, constantline. Axes object 3 with title Signal No. 2, xlabel Window Index contains 3 objects of type line. Axes object 4 with title Reconstruction Loss, xlabel Window Index, ylabel Loss contains 2 objects of type line, constantline. Axes object 5 with title Signal No. 3, xlabel Window Index contains 3 objects of type line. Axes object 6 with title Reconstruction Loss, xlabel Window Index, ylabel Loss contains 2 objects of type line, constantline.

    Input Arguments

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

    Signal data set, specified as one of these:

    • Nc-column matrix — A single multichannel signal observation (M = 1), where Nc 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 Nc-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 Nc-column matrix, where each column corresponds to a signal channel.

      • Nc 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.

    Name-Value Arguments

    Specify optional pairs of arguments as Name1=Value1,...,NameN=ValueN, where Name is the argument name and Value is the corresponding value. Name-value arguments must appear after other arguments, but the order of the pairs does not matter.

    Example: MiniBatchSize=64,ExecutionEnvironment="cpu" instructs the function to use a mini-batch size of 64 and use the computer CPU to detect anomalies.

    Mini-batch size used by the network to compute reconstructed signals, specified as a positive integer scalar.

    Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64

    Execution environment used by the network, specified as one of these:

    • "auto" — If available, use the GPU. If the GPU is not available, use the CPU.

    • "gpu" — Use the GPU.

    • "cpu" — Use the CPU.

    Data Types: char | string

    Output Arguments

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    Figure handle of the plot, returned as a figure handle.

    Extended Capabilities

    Version History

    Introduced in R2023a

    See Also

    Objects

    Functions

    Topics