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删除625字节 、 2024年9月10日 (星期二)
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\end{gathered}  
 
\end{gathered}  
 
</math>
 
</math>
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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。
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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]]。
      
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.
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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.
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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==
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