从输出预测神经网络输入 [英] Predicting Neural Network Input From it's Output

问题描述

假设您有一个神经网络，没有激活功能，只有已知的偏差，权重和输出.假设有可能(我认为没有可能)，第一步是从神经网络的输出中减去偏差，然后，您必须使用某种方法来获取输出而无需偏差和权重以找到隐藏层"的值.
在纸上，您可以使用替代来找到隐藏层"的值，但我想不出一种在代码中轻松实现此功能的方法.有没有更简单的方法可以做到这一点?

解决方案

通过实现代数设置，在神经网络的结构和权重的前提下，我已经能够成功地预测出什么输入将导致特定的输出.在将我们要计算的神经元作为方程中的变量之前，我已经把所有的神经元都放在了这一层，并且我使用自定义算法来求解这些变量.

Assume you have a Neural Network, with no activation function, only known biases, weights, and an Output. Assuming it is possible, which I see no reason it wouldn't be, the first step you would do would be to subtract the biases from the Neural Network's Output, and after that, you would have to use some method to take the Outputs without the biases and with the weights to find the values of the Hidden Layer.
On paper, you could use substitution to find the values of the Hidden Layer, but I can't think of a way to easily implement this in code. Are there any simpler ways to do this?

解决方案

I have sucessfully been able to predict what Input would result in a certain Output given the structure and weights of a Neural Network by implementing an Algebraic Setup. I have all of the Neurons in the layer before the one we are calculating for as variables in an equation and I use a custom algorithm to solve for those variables.

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