SoftMax Derivative Formula Python

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$\frac{\delta{g_{i}}}{\delta{x_{j}}} = \left \{ \begin{aligned} &e^{x_{j}}, && \text{if}\ i=j \\ &0, && \text{otherwise} \end{aligned} \right.$

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$\begin{array}{rcl} \frac{\delta{\frac{e^{x_{i}}} {\sum_{k=1}^N e^{x_{k}}}}}{\delta{x_{j}}} & = & \frac{e^{x_{j}}\left(\sum_{k=1}^N e^{x_{k}}-e^{x_{i}}\right)}{\left[\sum_{k=1}^N e^{x_{k}}\right]^{2}}\\ &=&\frac{e^{x_{j}}}{\sum_{k=1}^N e^{x_{k}}} \dot{}\frac{\sum_{k=1}^N e^{x_{k}}-e^{x_{i}}}{\sum_{k=1}^N e^{x_{k}}}\\ &=&\frac{e^{x_{j}}}{\sum_{k=1}^N e^{x_{k}}} \left(\frac{\sum_{k=1}^N e^{x_{k}}}{\sum_{k=1}^N e^{x_{k}}}-\frac{e^{x_{i}}}{\sum_{k=1}^N e^{x_{k}}}\right)\\ &=&\frac{e^{x_{j}}}{\sum_{k=1}^N e^{x_{k}}} \left(1-\frac{e^{x_{i}}}{\sum_{k=1}^N e^{x_{k}}}\right)\\ &=&\sigma(x_{j})(1-\sigma(x_{i})) \end{array}$

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$\frac{\delta{S}}{\delta{x}} = \left [ \begin{array}{ccc} \sigma(x_{1}) - \sigma(x_{1})\sigma(x_{1}) & \ldots & 0-\sigma(x_{1})\sigma(x_{N}) \\ \ldots & \sigma(x_{j}) - \sigma(x_{j})\sigma(x_{i}) & \ldots \\ 0-\sigma(x_{N})\sigma(x_{1}) & \ldots & \sigma(x_{N}) - \sigma(x_{N})\sigma(x_{N}) \end{array} \right ]$

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$\frac{\partial S\left(z_{i}\right)}{\partial z_{j}}= \begin{cases}S\left(z_{i}\right) \times\left(1-S\left(z_{i}\right)\right) & \text { if } i=j \\ -S\left(z_{i}\right) \times S\left(z_{j}\right) & \text { if } i \neq j\end{cases}$

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SoftMax Derivative Formula Python

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