是否可以反向运行神经网络? [英] Is it possible to run a neural network in reverse?

问题描述

如果我们有一个神经网络，例如使用Sigmodial Logistic激活函数的多层感知器反向传播神经网络，是否可以提供网络输出并计算出一组输入?因为我们可以通过使用自然对数和逆运算来反转激活函数，直到我们得到一个由所有权重乘以它们的输入得出的和值，所以我认为至少有可能获得一组可能的输入，将生成指定的输出值.

If we have a neural network such as the multilayer perceptron back propagation neural network that uses sigmodial logistic activation functions is it possible to feed the network outputs and have it compute back a set of inputs? Since we can reverse the activation function by using the natural logarithm and inverse operations until we have a sum value that is made up of all the weights multiplied by their inputs i would think that it would be possible to at least get sets of possible inputs that will generate the specified output value.

推荐答案

由于将结果分配给进食神经元的可能性是无限的，所以只有在接收神经元的数量等于进食神经元的数量的每一层才有可能喂养的神经元.有一个简单的线性方程式可以归结为一个矩阵乘法/求逆问题，可以轻松解决.

As there is an infinite number of possibilities to distribute the result to the feeding neurons this is only possible for each layer where the number of receiving neurons equals the number of the feeding neurons. There it is a simple linear equation which boils down to a Matrix multiplication/inversion problem, that can easily be solved.

如果接收神经元的数量(n)小于进食神经元的数量(m)的神经元数量，则具有m-n维的流形作为解空间.

If the number of neurons where the number of the receiving neurons (n) is smaller than the number of the feeding neurons (m), you have as solution space a manifold with m - n dimensions.

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