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You seem to be attempting to train your second layer's single perceptron to produce an XOR of its inputs. This isn't possible; a single perceptron can only learn to classify inputs that are linearly separable. The usual solution to solving the XOR problem with perceptrons is to use a two-layer network with the back propagation algorithm, so that the hidden ...


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When you train a neural network, you need both positive and negative inputs. If you were to say, only indicate to a neural network that when given an image that it should return 1, when given an image which is not similar, it may return 1 all the same. And in this circumstance, while there is only 1 image you'd like other images to be similar to, there ...


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