I am training a simple DL model for binary class classification, with 2 blocks of these layers, followed by a softmax layer:
featureInputLayer -> fullyConnectedLayer -> layerNormalizationLayer -> reluLayer
For a fixed train, validation and test set, I am getting diverse results in accuracy, ranging from 45-85%. Kindly suggest how to stabilise the results.
Here, training instances are 100 (50 per class), validation 24 (10 per class) and using
options = trainingOptions(‘adam’, …
‘MaxEpochs’, 100, … % Adjust based on dataset size
‘MiniBatchSize’, 16, … % Choose batch size based on memory
‘ValidationData’, valDS, … % Validation data
‘ValidationFrequency’, 5, … % Adjust based on dataset size
‘Verbose’, true, …
‘Plots’, ‘training-progress’, …
‘OutputNetwork’, ‘best-validation-loss’, … % Save best model
‘Shuffle’, ‘every-epoch’,…
‘LearnRateSchedule’, ‘cosine’);I am training a simple DL model for binary class classification, with 2 blocks of these layers, followed by a softmax layer:
featureInputLayer -> fullyConnectedLayer -> layerNormalizationLayer -> reluLayer
For a fixed train, validation and test set, I am getting diverse results in accuracy, ranging from 45-85%. Kindly suggest how to stabilise the results.
Here, training instances are 100 (50 per class), validation 24 (10 per class) and using
options = trainingOptions(‘adam’, …
‘MaxEpochs’, 100, … % Adjust based on dataset size
‘MiniBatchSize’, 16, … % Choose batch size based on memory
‘ValidationData’, valDS, … % Validation data
‘ValidationFrequency’, 5, … % Adjust based on dataset size
‘Verbose’, true, …
‘Plots’, ‘training-progress’, …
‘OutputNetwork’, ‘best-validation-loss’, … % Save best model
‘Shuffle’, ‘every-epoch’,…
‘LearnRateSchedule’, ‘cosine’); I am training a simple DL model for binary class classification, with 2 blocks of these layers, followed by a softmax layer:
featureInputLayer -> fullyConnectedLayer -> layerNormalizationLayer -> reluLayer
For a fixed train, validation and test set, I am getting diverse results in accuracy, ranging from 45-85%. Kindly suggest how to stabilise the results.
Here, training instances are 100 (50 per class), validation 24 (10 per class) and using
options = trainingOptions(‘adam’, …
‘MaxEpochs’, 100, … % Adjust based on dataset size
‘MiniBatchSize’, 16, … % Choose batch size based on memory
‘ValidationData’, valDS, … % Validation data
‘ValidationFrequency’, 5, … % Adjust based on dataset size
‘Verbose’, true, …
‘Plots’, ‘training-progress’, …
‘OutputNetwork’, ‘best-validation-loss’, … % Save best model
‘Shuffle’, ‘every-epoch’,…
‘LearnRateSchedule’, ‘cosine’); deep learning, softmax layer, stabilization MATLAB Answers — New Questions

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