Hello! I hope you are doing well.
I read all the posts on this topic (Exercise 9  L_model_backward) but didn’t get my answer.
A hint to solve this Ex is given in the attached file but I am not able to understand it.

First, what is the current cache?

I used linear_activation_backward function to determine dA_prev_temp, dW_temp, db_temp.

As given that grads[“dW” + str(l)] = dW^{[l]}] therefore, for sigmoid, I used:
Code removed
and for relu, I used:
code removed
Where am I making mistakes? Kindly guide me, I will be very thankful to you.
PS: After solving this, I will remove my code.
Regards,
Saif Ur Rehman.
Hi @saifkhanengr ,
First lets review the flow of this function: In the L_model_backward you will go from layer L to layer 1, and for each you will calculate the backprop value. So on L layers, L1 is your starting point, arraywise.
Second, lets keep in mind that "caches – list of caches containing:… " so we have a list of all the saved caches here.
For your #1: What is the current_cache? Current cache would be the cacheth entry in caches for each layer you are processing, starting with L1th layer and reverse until 0.
For your #2: You used linear_activation_backward to determine some values that, by the way, I don’t see you using moving forward. Make sure you call linear_activation_backward using the right dA and the right activation. The instructions give clear hints about this.
For your #3: Here’s where you may want to review the values you use to update the grads: as mentioned earlier, I don’t see that you are using the values returned by linear_activation_backwards.
Hope these hints help.
Juan
PS: IMPORTANT: Please remove your code as it goes against the Honor Code.
1 Like
Thank you so much sir @Juan_Olano. I understood and cleared the assignment.
Yes, I removed it.