Exercice 6 AssertionError: Wrong values in attn_w_b2. Check the call to self.mha2

Course Q&A Deep Learning Specialization Sequence Models
1

Hi,

I don’t understand why this error appears.
AssertionError: Wrong values in attn_w_b2. Check the call to self.mha2
why is it wrong ?

    # BLOCK 2
    # calculate self-attention using the Q from the first block and K and V from the encoder output. 
    # Dropout will be applied during training
    # Return attention scores as attn_weights_block2 (~1 line) 
    mult_attn_out2, attn_weights_block2 = self.mha2(Q1, enc_output, enc_output, attention_mask=padding_mask, return_attention_scores=True)  # (batch_size, target_seq_len, embedding_dim)

Thanks for helps

2

Hi @CyrilleB

Ensure you are passing the training parameter correctly to handle dropout (training=training). If the issue persists, ensure padding_mask is correctly broadcastable to the shapes required by self.mha2 and Q1 and enc_output have correct shapes.

Hope this help, feel free to ask if you nees further assistance!

3

Hi @Alireza_Saei
Q1 and enc_ouput have the same shape :
(1, 3, 4)
(1, 3, 4)
In Block2, should dropout be used ?

I can read : ’ # Dropout will be applied during training’
Should I fix Dropout variable in self.mha2?

4

Yes, in Block 2, dropout should be applied during training. The corresponding comment line suggests that you need to pass the training argument correctly so that dropout can be applied.

5

I don’t find what’s wrong in my code…

6

You don’t need to do anything regarding dropout in Block 2. The default values set in the __init__() constructor are sufficient.

You only need to call self.mha2(…) and then self.layernorm2(…). With the appropriate arguments.

7

Yes,
I’ve finally found the mistake. The error comes from parameters in self.mha1 and not in self.mha2 such as the error looks like to indicate.
Instead of (x,x,x,…), I had written (x, enc_ouput,enc_output, …) on mha1…

Thank you for your helps.

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