I'm trying to use a series of conv3d layers to reduce the third dimension of my data while maintaining the spatial information between them. I've been using the deep network designer and have attached a few pictures of whats going on here. The pictures are kind of large on the browser, so I've attached them rather than posting them to the text box.
I have a 16x16x480 array that I want to eventually reduce to 16x16x1. What I was planning on doing was using a series of 3d convolutions with a step of 1,1,2 to maintain the 16x16 portion while halving the third dimension with each iteration.However, I keep gettignerror messages saying that there is an input size mismatch. I read the documentation for convolution3dLayer and it says that the layer takes a 3d input, but the example on the documentation page uses a 28x28x28x3 input - 4 dimensions.
So I'm not really sure what to do here, and hte documentation seems to be self-contradicting.
Does anyone know how I might go about correctly using convolution3dLayers? And if these are not the appropriate method by which to thin layers in the third dimension while preserving some of the data from them, what other methods might I use?