C4 W2 A1: Help! grader problem

Course Q&A Deep Learning Specialization Convolutional Neural Networks
1

i have finished assignment 1 of week 2 course 4 residual network, all test passed but when i submit i get 66/100 score how could that be? how to solve this problem.

training=False

[[[ 0. 0. 0. 0. ]
[ 0. 0. 0. 0. ]]

[[192.71234 192.71234 192.71234 96.85617]
[ 96.85617 96.85617 96.85617 48.92808]]

[[578.1371 578.1371 578.1371 290.5685 ]
[290.5685 290.5685 290.5685 146.78426]]]
96.85617

With training=True

[[[0. 0. 0. 0. ]
[0. 0. 0. 0. ]]

[[0.40739 0.40739 0.40739 0.40739]
[0.40739 0.40739 0.40739 0.40739]]

[[4.99991 4.99991 4.99991 3.25948]
[3.25948 3.25948 3.25948 2.40739]]]
All tests passed!
Expected value

With training=False

[[[ 0. 0. 0. 0. ]
[ 0. 0. 0. 0. ]]

[[192.71234 192.71234 192.71234 96.85617]
[ 96.85617 96.85617 96.85617 48.92808]]

[[578.1371 578.1371 578.1371 290.5685 ]
[290.5685 290.5685 290.5685 146.78426]]]
96.85617

With training=True

[[[0. 0. 0. 0. ]
[0. 0. 0. 0. ]]

[[0.40739 0.40739 0.40739 0.40739]
[0.40739 0.40739 0.40739 0.40739]]

[[4.99991 4.99991 4.99991 3.25948]
[3.25948 3.25948 3.25948 2.40739]]]
Code Cell UNQ_C1: Function ‘identity_block’ is incorrect. Check implementation.
Code Cell UNQ_C2: Function ‘convolutional_block’ is correct.
Code Cell UNQ_C3: Function ‘ResNet50’ is correct.
If you see many functions being marked as incorrect, try to trace back your steps & identify if there is an incorrect function that is being used in other steps.
This dependency may be the cause of the errors.

2

Sorry that this question did not get answered when you originally asked it. Your results for the identity_block do look correct. The only thing I can guess is that perhaps you “hard-coded” something, e.g. used 2 explicitly for the f value.

Did you find a solution to this or did you just move on?