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第826行：
第826行： − − 其中，[math]m[/math]为宏观态维度，[math]\sigma'_i[/math]为第i个宏观维度的平均平方误差（MSE），这一误差可以通过反向传播算法计算的宏观态[math]X_i[/math]的梯度计算得出。 − − 注意，上述结论都要求：<math>\partial_{x}f(x)</math>不为0，而对于所有的<math>x</math>，<math>\partial_{x}f(x)</math>处处为0时，我们得到： <math>\begin{gathered}EI(f)\approx\end{gathered}0</math>。对于更一般的情形，则需要考虑矩阵不满秩的情况，请参考[[神经网络的有效信息]]。 − +
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\end{gathered}
\end{gathered}
</math>
</math>
Among them, [math]m[/math] is the macroscopic state dimension, and [math]\sigma'_i[/math] is the mean squared error (MSE) of the i-th macroscopic dimension, which can be calculated by the gradient of macroscopic state [math]X_i[/math] under the backpropagation algorithm.
Among them, [math]m[/math] is the macroscopic state dimension, and [math]\sigma'_i[/math] is the mean squared error (MSE) of the i-th macroscopic dimension, which can be calculated by the gradient of macroscopic state [math]X_i[/math] under the backpropagation algorithm.
Note that the above conclusions all require: <math>\partial_{x}f(x)</math> is not 0, but for all [math] \ display style x [/math], [math] \ display style partial_ {x}f (x) When [/math] is 0 everywhere, we obtain: [math] \ display style {\ start {gathered}EI (f)\approx\end {gathered}0 }[/math]. For more general cases, it is necessary to consider the situation where the matrix is not of rank, please refer to the effective information of neural networks.
Note that the above conclusions all require: <math>\partial_{x}f(x)</math> is not 0, but for all <math>x</math>, when <math>\partial_{x}f(x)</math> is 0 everywhere, we obtain: <math>\begin{gathered}EI(f)\approx\end{gathered}0</math>. For more general cases, it is necessary to consider the situation where the matrix is not of rank, please refer to the effective information of neural networks.
==连续系统EI的源代码==
==连续系统EI的源代码==