Tensor of contexts has shape: (64, 14, 256)

Tensor of right-shifted translations has shape: (64, 15)

Tensor of logits has shape: (64, 12000)

**Expected Output**

```
Tensor of contexts has shape: (64, 14, 256)
Tensor of right-shifted translations has shape: (64, 15)
Tensor of logits has shape: (64, 15, 12000)
```

I am unable to debug why I am getting a logit shape of (64, 12000). Where could it have gone wrong?

The difference between a 2D tensor and a 3D tensor is pretty fundamental, right? So you must be misinterpreting the operations that the math formulas are telling you to do. One way to debug this is to add print statements in the relevant parts of the code to print the shapes of all the objects. That should at least let you narrow it down to the line of code that is incorrect.

Iâ€™m not familiar with the NLP C4 material, but have done the DLS equivalent in DLS C5 W4. One thing to be careful about is the notational conventions for the difference between dot product style multiply and elementwise multiply. In DLS Prof Ng is consistent in that he *always* and *only* uses â€ś`*`

â€ť as the operator to signify â€śelementwiseâ€ť multiply. If he does not write an explicit operator between two tensors or array objects, then the operation is â€śdot product multiplyâ€ť.

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Thank you for your response. My actual confusion is in the C4W1_assignmentâ€™s decoderâ€™s layers. I am guessing I have misplaced some parameters of the decoderâ€™s layers as a result the tensor has a different dimension than expected and I have been trying to figure it out.

I figured it out. Thanks.

Nice work! Thanks for confirming.

Iâ€™ve had the same problem, can you tell me how I can solve it ? Thank you so much !!!

same here I get

â€śTensor of logits has shape: (64, 12000)â€ť

and I have no idea how to fix it

finally I figured out, in the instructions it says:

" Post-attention LSTM. Another LSTM layer. For this one you donâ€™t need it to return the state."

but I missinterpreted it as return_sequences=False when it should be return_sequences=True

hope this helps

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In my case, I was not returning the sequences in the post attention rnn. please check that.

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