Why is there so much difference in my cnn regression between loss of train and loss of validation?
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I'm doing position prediction with 1D deep learning using EMG signals. In other words, if I say angle estimation, I would have expressed it better. There are signs and angle information I get from 2 simultaneous muscles. In other words, my inputs are 2 channels and the target is angle values.
As with all classical deep learning methods (for regression), I prepared my data first ( like normalization, reshape). Then I give it to the deep learning network I created and provide the training.
here is my cnn model:
![](https://www.mathworks.com/matlabcentral/answers/uploaded_files/328469/image.png)
But in the results of the train and test (i.e. when I follow the training process: 'Plots', 'training-progress') I encounter something like this: There is a lot of difference between training loss and validation loss.
![](https://www.mathworks.com/matlabcentral/answers/uploaded_files/328472/image.png)
I can say that network has always learned the train data. So it memorized.
does not look like test data at all.
![](https://www.mathworks.com/matlabcentral/answers/uploaded_files/328475/image.jpeg)
I felt it was overfitting. And I tried the following:
1-I tried with sgdm and adam and it didn't make much difference
2- I reduced the learning rate.(current learning rate 1e-2)
3- I reduced the size of the data, and I made a downsample to avoid over-memorizing it (because the angles had the following values: for example, 178.50 178.52 178.52 177.88 177.87 )
When the size decreased, yes difference between training loss and validation loss decreased. .still there is difference.it didn't solve my problem. I can't find where I'm mistake.
If anyone could give some insight on this I would greatly appreciate it.
Thank you.
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