可视化 LSTM 网络的激活值

打开实时脚本

此示例说明如何通过提取激活值来调查和可视化 LSTM 网络学习到的特征。

加载预训练网络。JapaneseVowelsNet 是基于日语元音数据集训练的预训练 LSTM 网络，如 [1] 和 [2] 中所述。它是基于按序列长度排序的序列训练的，小批量大小为 27。

load JapaneseVowelsNet

查看网络架构。

net.Layers
ans = 
  4×1 Layer array with layers:

     1   'sequenceinput'   Sequence Input    Sequence input with 12 dimensions
     2   'lstm'            LSTM              LSTM with 100 hidden units
     3   'fc'              Fully Connected   9 fully connected layer
     4   'softmax'         Softmax           softmax

加载测试数据。

load JapaneseVowelsTestData

在绘图中可视化第一个时间序列。每行对应一个特征。

X = XTest{1};

figure
plot(XTest{1}')
xlabel("Time Step")
title("Test Observation 1")
numFeatures = size(XTest{1},1);
legend("Feature " + string(1:numFeatures),'Location',"northeastoutside")

Figure contains an axes object. The axes object with title Test Observation 1, xlabel Time Step contains 12 objects of type line. These objects represent Feature 1, Feature 2, Feature 3, Feature 4, Feature 5, Feature 6, Feature 7, Feature 8, Feature 9, Feature 10, Feature 11, Feature 12.

对于序列的每个时间步，获取 LSTM 层（第 2 层）在该时间步的激活值输出，并更新网络状态。

sequenceLength = size(X,2);
idxLayer = 2;
outputSize = net.Layers(idxLayer).NumHiddenUnits;

for i = 1:sequenceLength
    [features(i,:),state] = predict(net,X(:,1)',Outputs="lstm");
    net.State = state;
end
features = features';

使用热图可视化前 10 个隐藏单元。

figure
heatmap(features(1:10,:));
xlabel("Time Step")
ylabel("Hidden Unit")
title("LSTM Activations")

Figure contains an object of type heatmap. The chart of type heatmap has title LSTM Activations.

该热图显示每个隐藏单元激活的强度，并突出显示激活值随时间的变化情况。

参考

[1] M. Kudo, J. Toyama, and M. Shimbo. "Multidimensional Curve Classification Using Passing-Through Regions." Pattern Recognition Letters. Vol. 20, No. 11–13, pages 1103–1111.

[2] UCI Machine Learning Repository: Japanese Vowels Dataset. https://archive.ics.uci.edu/ml/datasets/Japanese+Vowels

另请参阅

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