Sorry if this question has been asked before. I’ve been working with the nengo_dl MNIST classification example. As discussed in the tutorial, I’ve been able to train a SoftLIF nengo_dl network, then switch the parameters to LIF, resulting in a ~2% error rate.
My question now is how can I pass a single image to this network? I would like to read a single image from my camera and make a prediction using the NengoDL. (This is not necessarily handwritten digits).
I have the simulator:
sim = nengo_dl.Simulator(self.net, minibatch_size=1, unroll_simulation=10) sim.load_params("./mnist_params")
I see that sim.run_steps is good for feeding minibatches of images into the network, but this doesn’t seem appropriate for a single image. How can I get this to predict from a single image? Sorry if this is covered someplace, I wasn’t able to find this!