Hi AI community

I am working on the last programming assignment Gradient_Checking and I have been stuck for hours on the last jupyter cell. I have implemented everything as described but can not get the right result. Any ideas? I am getting this:

Thank you so much for your help!

Please click my name and message your notebook as an attachment.

Mistakes:

- In
`backward_propagation_n`

, `dW2`

and `db1`

are incorrect.
- In
`gradient_check_n`

, `theta_minus[i]`

is assigned incorrect value. (See the word **minus**)

Hereâ€™s some text from the markdown youâ€™ll find useful:

seems that there were errors in the `backward_propagation_n`

code! Good thing youâ€™ve implemented the gradient check. Go back to `backward_propagation_n`

and try to find/correct the errors *(Hint: check dW2 and db1)*. Rerun the gradient check when you think youâ€™ve fixed it. Remember, youâ€™ll need to re-execute the cell defining `backward_propagation_n()`

if you modify the code.

Hi and thank you for your feedback

can you please show me what the correct assignment for theta_minus[I] should look like? I donâ€™t see any problem there.

Sure. See this markdown text:

- To compute
`J_plus[i]`

:
- Set \theta^{+} to
`np.copy(parameters_values)`

- Set \theta^{+}_i to \theta^{+}_i + \varepsilon
- Calculate J^{+}_i using to
`forward_propagation_n(x, y, vector_to_dictionary(`

\theta^{+} `))`

.

- To compute
`J_minus[i]`

: do the same thing with \theta^{-}

The last point asks the learner to follow a similar line of thought for `theta_minus`

and fix the sign of `epsilon`

accordingly.

ohh, I see it now, I did not put the minus before epsilon thank you so much for your help. It is correct now