title: Problem with Converging of the 1D CNN model
body: <p>I want to train a 1D CNN with a sequential data. I have two classes of data, two features and 1584 samples. Each element of my training array is a 2xn cell (n = number of frames in each sample, <a href="https://i.stack.imgur.com/q0mH7.png" rel="nofollow noreferrer">screenshot attached</a>). I have tried with the MATLAB example, but my model does not converge att all. it just stays at 51%.</p>
  <p>I don't know how to set filter number, size or layers.
  Can anyone help?</p>
  <p><a href="https://www.mathworks.com/help/deeplearning/ug/sequence-classification-using-1-d-convolutions.html#SequenceClassificationUsing1DConvolutionsExample-2" rel="nofollow noreferrer">https://www.mathworks.com/help/deeplearning/ug/sequence-classification-using-1-d-convolutions.html#SequenceClassificationUsing1DConvolutionsExample-2</a></p>
  <pre><code>filterSize = 2;
  numFilters = 16;
  Num_classes=2;
  numFeatures=2; 
  
  
  layers = [ ...
      sequenceInputLayer(numFeatures)
      convolution1dLayer(filterSize,numFilters,Padding=&quot;causal&quot;)
      reluLayer
      layerNormalizationLayer
      convolution1dLayer(filterSize,2*numFilters,Padding=&quot;causal&quot;)
      reluLayer
      layerNormalizationLayer
      globalAveragePooling1dLayer
      fullyConnectedLayer(Num_classes)
      softmaxLayer
      classificationLayer];
  miniBatchSize = 27;
  
  options = trainingOptions(&quot;adam&quot;, ...
      MiniBatchSize=miniBatchSize, ...
      MaxEpochs=15, ...
      SequencePaddingDirection=&quot;left&quot;, ...
      ValidationData={ValidationData,labelsValidation}, ...
      Plots=&quot;training-progress&quot;, ...
      Verbose=0);
  </code></pre>