# Compute layer style cost error

You shared no week and assignment number, so, how could mentors know which specific assignment you’re referring to? Though I know correctly, I request that you provide these details before I assist you.

PS: When I face any issue while doing any course, my procedure for asking help is: respect mentors’ time and make their job easy by giving them complete and accurate information.

knowing the topic in the header I assume that the mentors know what i am asking, this is the last assignment of week 4, neural style transfer. you should also respect learners

It is very helpful to the mentors if you give the week number and assignment number. This lets us go directly to the appropriate course notebook, without having to research the names of every assignment in every week of every course.

Next time I will comply

Franklin Viloria

It looks like you instrumented your `gram_matrix` function to print GA, so I added that along with some other instrumentation in `compute_style_layer_cost` and here’s what I see:

``````type(a_G) <class 'tensorflow.python.framework.ops.EagerTensor'>
m is 1
shape(a_S) [1 4 4 3]
shape(a_G) [1 4 4 3]
after reshape+T: shape(a_S) [ 3 16]
after reshape+T: shape(a_G) [ 3 16]
a_S [[ 2.6123514   6.3462267  -2.0568852   1.0848972  -0.34144378  5.9450154
-0.68249106  6.6380787   4.405425    3.2136106  -0.88850987  8.216282
1.1940846  -3.8442307   4.486284   -3.2315984 ]
[-3.3520837   3.8470404  -3.1489944  -1.2055032  -3.17067    -1.7347562
-3.1652112  -0.90944517  0.31713337  0.23800504 -0.10706711  0.6901974
1.7071393   6.2297974  -2.2124014  -5.5964684 ]
[ 0.74761856 -0.95714605 -4.0077353  -5.972679    5.036553    3.6944358
-1.7189786   9.18924     2.566379    1.4399388  -1.7099016   3.6196625
-0.9568796   2.8206615  -4.4811783   3.4338741 ]]
GA =
[[277.3386      1.9207706  84.06223  ]
[  1.9207706 139.91864    -1.1867776]
[ 84.06223    -1.1867776 244.99716  ]]
GA =
[[277.3386      1.9207706  84.06223  ]
[  1.9207706 139.91864    -1.1867776]
[ 84.06223    -1.1867776 244.99716  ]]
J_style_layer 0.0
type(a_G) <class 'tensorflow.python.framework.ops.EagerTensor'>
m is 1
shape(a_S) [1 4 4 3]
shape(a_G) [1 4 4 3]
after reshape+T: shape(a_S) [ 3 16]
after reshape+T: shape(a_G) [ 3 16]
a_S [[-3.404881   -2.5186315   1.3512313  -1.8821764  -0.39341784  5.434381
-0.2787553   3.5750656  -2.616547    1.2345984   0.25895953 -2.933355
-5.2402277   0.19823879  1.3253293  -0.41526246]
[ 7.183007   -3.8986888   0.18695849 -1.5039697  -0.34587932  6.118635
2.493302    9.585233    6.579515   -0.9685255  -0.5711074   2.555351
0.36834985  7.452428    6.3523054   0.583993  ]
[ 2.534576   -2.9244845  -1.2326248  -1.8601038   1.730303    0.91409665
2.0111642  -2.3005989   5.8995004  -2.2799122  -1.6340904  -3.1489797
-0.42677724  3.718691    5.739221   -2.0045857 ]]
GA =
[[112.29061    37.05909    -1.9554844]
[ 37.05909   352.1803    116.86712  ]
[ -1.9554844 116.86712   136.64961  ]]
GA =
[[277.3386      1.9207706  84.06223  ]
[  1.9207706 139.91864    -1.1867776]
[ 84.06223    -1.1867776 244.99716  ]]
J_style_layer 14.01648998260498
J_style_layer = tf.Tensor(14.01649, shape=(), dtype=float32)
All tests passed
``````

You can see that the GA values I get are significantly different than yours. One possible mistake is to directly reshape to the final shape you want for `a_S` and `a_G` instead of including a transpose to preserve the channels dimension.