How to remove the last fully connected layer from a CNN in fastai to stack more than one CNN into a FC-NN?

[hwasiti (Haider Alwasiti)]

@davecg

You would pool along the horizontal dimension to get a dimension of 1 horizontal, 7 vertical or whatever the output size of your network is.

You would then add a convolutional layer or two on what would now be 2x7 or 7x2 depending on your image orientation. (Would obviously not be able to use a 3x3 kernel.)

@davecg How about this scenario

Let’s say we have a big stadium. We put microphones distributed evenly in the area of the stadium. The spectrogram of each microphone output is an image of sound (frequency x time) . Of course,each microphone is related with all the others in such a way that, large volume of sounds are picked up by roughly all microphones. However small group chit-chats are only detected by nearby microphones.

So the microphone spectrograms for 1 sec will be a square image that is related somehow with the others especially nearby microphones.

So instead of the xray model where we have only to add a convolutional layer with input of (2x7) to capture the spatial information of roughly relation of the two views, here we should add 3d-CNN to capture the 2D spatial information of the microphone distribution on the stadium floor? If we have 6 x 8 microphones, the 3d-CNN input should be like 6 x 8 x 7, right? What do you think?

This is an interesting project that I remembered using micrphones in a forest:
The fight against illegal deforestation with TensorFlow

I think Jeremy mentioned that Sara Hooker was involved in this project which is one of the best fastai students. Here is her talk about the project

Thank you guys for the amazing insight and discussions!