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I am reading a paper explaining the derivations of the back-propagation equations in RNNs. There I read 'Note that the Weight Matrix remains the same across all time sequence so we can differentiate to it at each time step and sum all together.'

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My question is why this statement is correct. What is its mathematical derivation?

Your advice will be appreciated.

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  • $\begingroup$ If your objective function is $|f(t_{end})-y|^2$ where $x$ is the input, $f_t(x)$ the output at time $t$, and $y$ the desired output then it means you don't care of the output at $t < t_{end}$, but you do care of the partial derivative $ \frac{\partial f(t_{end})}{\partial z_{j,t}}$ where $z_{j,t}$ is output of $j$-th neuron at time $t$. Also your RNN will be quite similar to a $t_{end}$-layer non-recurrent NN, and it is not really possible to train a NN with too many layers. The alternative is to choose objective function $\sum_t|f_t(x)-y_t|^2$ possibly with constant $y_t = y_0$. $\endgroup$ – reuns Apr 20 at 8:46
  • $\begingroup$ The weights don't depend on $t$ so you'll have $\frac{\partial E}{\partial w_{i,j}} = \sum_t \sum_{t'\le t} \frac{\partial |f_t(x)-y_t|^2 }{\partial z_{j,t'}(x)}$ $\endgroup$ – reuns Apr 20 at 8:49
  • $\begingroup$ Thank you this helps. Two questions: 1) In your second comment you meant to write w rather than z ? (the derivative is w.r.t. W). 2) What if we have a RNN modeling a many to one sequence (e.g., sentiment analysis task) where the error is calculated in the end only? $\endgroup$ – AlK Apr 20 at 16:07

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