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If you use a single layer CNN, then each vector in the resulting activation maps would be related to the original 3x3 block. However, if you stack multiple CNN layers, you increase the receptive field of each resulting vector, as shown in the image below (taken from here):
After the CNNs, you can certainly compute an LSTM. There are, however, some design ...
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The final dense layer's units should be equal to the number of features in your y_train.
Suppose your y_train has shape (11784,5) then dense layer's units should be 5 or if y_train has shape (11784,1), then units should be 1. Model expects final dense layer's units equal to the number of output features.
You have to identify which features you need in input ...
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Is it reasonable to use a CNN instead of an LSTM, even though it is a time series?
Yes, it is. Convolutional Neural Networks are applied to any kind of data in which neighboring information is supposedly relevant for the analysis of the data.
CNN are very popular with images, where data is correlated in space, and in video, where correlation happens both in ...
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If you know that your trajectory has a certain parametric form then you can use methods that explore the parameter space for that form. Examples of such methods are Hough transform and custom-built moments.
Hough transform maps a point in a real space into a manifold in the parameter space, and vice-versa, it maps a point in the parameter space into a line ...
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