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Creating CNN architecture for binary classification

6 次查看（过去 30 天）
Reem
Reem 2025-7-28
关闭： John D'Errico 2025-7-29
I’m reaching out to kindly ask if somone could review the CNN architecture I’ve implemented in MATLAB. The code is running as expected, but I’d appreciate your expert opinion to confirm whether the structure is sound and appropriate for the task.
Below is a snippet of the architecture and training configuration:
matlab
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%% === CNN Architecture ===
layers = [
sequenceInputLayer(1, 'Name', 'input', 'MinLength', minTrainLen)
convolution1dLayer(5, 32, 'Padding', 'same', 'Name', 'conv1')
batchNormalizationLayer('Name', 'bn1')
reluLayer('Name', 'relu1')
maxPooling1dLayer(2, 'Stride', 2, 'Name', 'pool1')
convolution1dLayer(3, 64, 'Padding', 'same', 'Name', 'conv2')
batchNormalizationLayer('Name', 'bn2')
reluLayer('Name', 'relu2')
dropoutLayer(0.3, 'Name', 'dropout1')
globalAveragePooling1dLayer('Name', 'gap')
fullyConnectedLayer(32, 'Name', 'fc1')
reluLayer('Name', 'relu3')
fullyConnectedLayer(2, 'Name', 'fc_output')
softmaxLayer('Name', 'softmax')
classificationLayer('Name', 'output')
];
%% === Training Options ===
options = trainingOptions('adam', ...
'InitialLearnRate', 1e-3, ...
'MaxEpochs', 30, ...
'MiniBatchSize', max(1, min(64, numel(XTrainFinal))), ...
'Shuffle', 'every-epoch', ...
'ValidationData', {XVal, YVal}, ...
'ValidationFrequency', 5, ...
'ValidationPatience', 2, ...
'Verbose', false, ...
'Plots', 'none', ...
'ExecutionEnvironment', 'auto');
  1 个评论
Matt J
Matt J 2025-7-28
编辑：Matt J 2025-7-28
I believe you already asked that here,
https://www.mathworks.com/matlabcentral/answers/2178755-is-my-cnn-correct?s_tid=srchtitle
The statement of the question hasn't changed much, so I don't think you will get very different answers.

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