Hi,

I am currently doing assignment 1 - Convolutional_block exercise.

This is the error i am getting -

```
AssertionError Traceback (most recent call last)
<ipython-input-16-869d2d1d4c16> in <module>
12 assert type(A) == EagerTensor, "Use only tensorflow and keras functions"
13 assert tuple(tf.shape(A).numpy()) == (3, 2, 2, 6), "Wrong shape."
---> 14 assert np.allclose(A.numpy(), convolutional_block_output1), "Wrong values when training=False."
15 print(A[0])
16
AssertionError: Wrong values when training=False.
```

I am not sure why there is an assertion error here. I have used X_shortcut where needed as well after going through previous posts on this topic.

Could you please let me know where i am going wrong?

Thank you.

This whole assignment is an excruciating exercise in proof-reading: there are lots of details, any one of which can throw off your results. Are you sure you checked your â€śBatchNormâ€ť layers? They give you an example in the template code. Yours should look exactly the same, except that you may have different inputs in the â€śshortcutâ€ť section.

To my understanding, input for the BatchNorm will be from Conv2D layer in the shortcut path.

So, I have provided X_shortcut calculated from the previous layer (Conv2D) to the BatchNorm layer. Is this not correct?

Yes, that sounds correct. But I was more worried about the â€śaxisâ€ť parameter.

Okay, for axis i am using the 3 for channels as the feature axis.

Solved it just now. I was using the wrong stride for the third component of the main path. Feeling silly now

Thanks for your help!

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Itâ€™s great that you were able to find the solution! Thanks for confirming.