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Old 17th November 2016, 19:39   #14  |  Link
LoRd_MuldeR
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Quote:
Originally Posted by smok3 View Post
Define "train".
You present your current network with an input/output pair, the training sample. In this case, the "input" would be a low-resolution version of the original image, and the "output" is the original high-resolution image. You let your network create its own (high-res) output image from the given (low-res) "input" image. And then you compare that against the given optimal "output" image (original). Of course, there will be some difference between the network's actual output and the optimal (desired) output - especially at the beginning of the training phase. This difference, or "error", will be used to update (improve) the network, so that the error is reduced. For example, one approach is to let the "error" propagate through the network in backwards direction and adjust the individual weights accordingly. You repeat this process with many training samples (input/output pairs). In the end, you get a network that (hopefully) produces good results, even for unknown inputs.
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Last edited by LoRd_MuldeR; 17th November 2016 at 19:46.
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