You need to first calculate all your updates as if the wieghts weren't shared, but just store them, don't actually do any updating yet.

Let $w_k$ be some weight that appears at locations $I_k = \{(i,j) \colon w_{i,j} = w_k\}$ in your network and $\Delta w_{i,j} = -\eta \frac{\partial J}{\partial w_{i,j}} $ where $\eta$ is the learning rate and $J$ is your objective function. Note that at this point is you didn't have weight sharing you would just upade $w_{i,j}$ as $$ w_{i,j} = w_{i,j} + \Delta w_{i,j}. $$ To deal with the shared weights you need to sum up all the individual updates. So set $$ \Delta w_k = \sum_{(i,j) \in I_k} \Delta w_{i,j} $$ and then update $$ w_k = w_k + \Delta w_k. $$

You need to first calculate all your updates as if the wieghts weren't shared, but just store them, don't actually do any updating yet.

Let $w_k$ be some weight that appears at locations $I_k = \{(i,j) \colon w_{i,j} = w_k\}$ in your network and $\Delta w_{i,j} = -\eta \frac{\partial J}{\partial w_{i,j}} $ where $\eta$ is the learning rate and $J$ is your objective function. Note that at this point if you didn't have weight sharing you would just upade $w_{i,j}$ as $$ w_{i,j} = w_{i,j} + \Delta w_{i,j}. $$ To deal with the shared weights you need to sum up all the individual updates. So set $$ \Delta w_k = \sum_{(i,j) \in I_k} \Delta w_{i,j} $$ and then update $$ w_k = w_k + \Delta w_k. $$