I have been training a resnet34 Convolutional network using fastai which classifies images of rooms of a house as to ‘kitchen’,‘bathroom’,‘bedroom’ etc. I am trying to predict the real estate selling price of this property from the photos. I have a seperate NN which does reasonably well using other advertisement data, but I want to see how the photos can add to this.
Hence, I can provide the initial price estimate as a scalar value in parallel to the photo of the room, using my previously trained Convolution NN as a starting point (transfer learning)
I’m new to Pytorch and trying to get a better grasp at it as I go deeper than what fastai provides. How would I define my nn.Module class to receive an image and scalar value as the inputs, then forward the scalar value past the Convolutional resnet34 to be combined with it’s top layer for a final 1 or 2 layer before attempting an improved final price prediction scalar output?