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After taking a look at your code, it seems that you've not employed any kind of regularization. You may want to use dropout. Moreover, in convolutional autoencoders, in the decoder part, there is a well-known artifact called checkerboard. I don't know how this can be a problem for your task since you're using one-dimensional convolution in the decoder. By ...
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No, you don't average across all feature maps.
When the input has multiple channels, you need your convolution filter to have the same number of channels. Therefore, the filter "covers" the full depth of the input. Then, you simply perform the element-wise multiplication of the filter with the overlapping region in the input and add all the ...
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To answer your last question - think of the model as your brain trying to give a maths test. Training data is what you encountered during homework/exercise and validating/testing data is what you encounter in the final examinations(most likely unseen data). To think of yourself as proficient in mathematics, you'd want your brain to be able to perform best on ...
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The purpose of max pooling operation is to decrease the spatial dimensions of the input while also being robust by only considering the maximum values.
Generally, as you might have noticed, most CNNs aim at decreasing the spatial dimension of the input while increasing its depth. Very broadly speaking, you can think of this as trying to encode the ...
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