Pytorch MLP backward

MLP backward

\[\begin{aligned} &For \space an \space output \space layer \space node \space k\in K\\ &\frac{\delta E}{\delta W_{jk}}=O_j\delta_k\\ &where\\ &\delta_k = O_k(1-O_k)(O_k-t_k)\\ &For \space a \space hidden \space layer \space node \space j\in J\\ &\frac{\delta E}{\delta W_{ij}}=O_i\delta_j\\ &where\\ &\delta_j=O_j(1-O_j)\sum_{k\in K}\delta_k W_{jk}\\ \end{aligned} \]