So I've just recently gotten into artificial neural networks, and I have a couple of questions that I can't seem to find addressed anywhere.
Firstly, this one is more specific to image recognition, is it necessary to have a separate output node for each possible outcome and then analyze the results, or is there one output neuron that just gives out the final result? e.g. one neuron that outputs the amount of "dog" in the picture, another that outputs the amount of "cat," and so on vs. a single neuron that determines whether it's a cat or a dog or something else based on the inputs.
Second, based on what I read, in order to do backpropagation you don't alter the weights on the input layer until you achieve a certain level of accuracy with the hidden and output layers. I was just wondering why this is, and what purpose it serves if the current weights already achieved a desired accuracy. Is it more fine-tuning the closer you get to the input?
Third, why is it that "any application for a nn can be done with only 3 layers"? This seems a bit off to me, since I saw some talks on image recognition and they usually have 5+ layers. A quick explanation for the reasoning behind this or a specific source would be great.
Thanks, and if I somehow asked a duplicate question, please let me know and direct me to the original.