Building_a_Recurrent_Neural_Network_Step_by_Step-Exercise 6 - rnn_backward

Hi I am getting wrong answers ,can please help to get correct answers

There aren’t that many moving parts here, but the trickiest part is computing the da_next value that you pass to rnn_cell_backward. It’s not just da[:,:,t].

dear sir ,
i could not able to find out ,where i am making mistake.
getting currect shape of values ,but not values.


thank you

Please have a look at this diagram from the notebook and consider the contents of the green rectangle that I highlighted:

The implication is that the input for the da_next argument to rnn_cell_backward is the sum of two gradients: the one from the path out to \hat{y}^{<t>} and the one coming from the previous step (of course it’s the next step in forward propagation with t + 1, but we’re going backwards here).

Just to complete the picture, look at the left side of the diagram and notice the arrow pointing left there labeled da_{prev}. That is the same as the error pointing left into that + sign in the green rectangle at the right side, but that one is from step t + 1. The picture repeats for each time step, but going backwards for back prop.