Answers.Body: <p>The problem can be solved using the following line of code:</p>
  <pre><code>trainPred = classify(net, Signals,'SequenceLength','longest');
  </code></pre>
  
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Body: <p><a href="https://i.sstatic.net/vkDm6.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/vkDm6.png" alt="enter image description here" /></a></p>
  <p>I’m trying to classifiy ECG signals using LSTM and MATLAB, the above plot shows that the training accuracy of the system is 100% but when I apply this code to calculate and get the accuracy I get only 20%</p>
  <pre><code>LSTMAccuracy = sum(trainPred == Labels)/numel(Labels)*100
  </code></pre>
  <p><a href="https://i.sstatic.net/9UZLB.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/9UZLB.png" alt="enter image description here" /></a></p>
  <p>Am I missing something here? Or there something wrong I did in my code?</p>
  <p>Here is the configuration and the training code:</p>
  <pre><code>layers = [ ...
      sequenceInputLayer(1)
      bilstmLayer(100,'OutputMode','last') 
      fullyConnectedLayer(5)
      softmaxLayer
      classificationLayer
      ]
  
  options = trainingOptions('adam', ...
      'MaxEpochs',1750, ...
      'MiniBatchSize', 150, ...
      'InitialLearnRate', 0.0001, ...
      'ExecutionEnvironment',&quot;auto&quot;,...
      'plots','training-progress', ...
      'Verbose',false);
  
  net = trainNetwork(Signals, Labels, layers, options);
  
  trainPred = classify(net, Signals,'SequenceLength',1000);
  
  LSTMAccuracy = sum(trainPred == Labels)/numel(Labels)*100
  figure
  confusionchart(Labels,trainPred,'ColumnSummary','column-normalized',...
                'RowSummary','row-normalized','Title','Confusion Chart for LSTM');
  </code></pre>
  
Tags: <machine-learning><deep-learning><long-short-term-memory><signal-processing><matlab>
Title: What is the training accuracy of this model?
UserDisplayName: LinkToPhD
ThreadText: # What is the training accuracy of this model?
  
  (Asked by: username_0 on 2022-07-31)
  
  I’m trying to classifiy ECG signals using LSTM and MATLAB, the above plot shows that the training accuracy of the system is 100% but when I apply this code to calculate and get the accuracy I get only 20%
  LSTMAccuracy = sum(trainPred == Labels)/numel(Labels)*100
  
  
  Am I missing something here? Or there something wrong I did in my code?
  Here is the configuration and the training code:
  layers = [ ...
      sequenceInputLayer(1)
      bilstmLayer(100,'OutputMode','last') 
      fullyConnectedLayer(5)
      softmaxLayer
      classificationLayer
      ]
  
  options = trainingOptions('adam', ...
      'MaxEpochs',1750, ...
      'MiniBatchSize', 150, ...
      'InitialLearnRate', 0.0001, ...
      'ExecutionEnvironment',"auto",...
      'plots','training-progress', ...
      'Verbose',false);
  
  net = trainNetwork(Signals, Labels, layers, options);
  
  trainPred = classify(net, Signals,'SequenceLength',1000);
  
  LSTMAccuracy = sum(trainPred == Labels)/numel(Labels)*100
  figure
  confusionchart(Labels,trainPred,'ColumnSummary','column-normalized',...
                'RowSummary','row-normalized','Title','Confusion Chart for LSTM');
  
  **Tags:** machine-learning, deep-learning, long-short-term-memory, signal-processing, matlab
  
  ---
  
  ## 1 answers
  
  ### Answer by username_0 on 2022-08-03. Score: 0 | Accepted answer
  
  The problem can be solved using the following line of code:
  trainPred = classify(net, Signals,'SequenceLength','longest');
  
  > **Comments:**
  > **username_1**: Your answer could be improved with additional supporting information. Please [edit] to add further details, such as citations or documentation, so that others can confirm that your answer is correct. You can find more information on how to write good answers [in the help center](/help/how-to-answer).  
  
  ---
  
  