Week 4 transformer assignment

Course Q&A Deep Learning Specialization Sequence Models
1 
  Returns:
            encoder_layer_out -- Tensor of shape (batch_size, input_seq_len, fully_connected_dim)
        """
        # START CODE HERE
       {mentor edit: code removed}
        # END CODE HERE
        
        return encoder_layer_out

AssertionError Traceback (most recent call last)
in
1 # UNIT TEST
----> 2 EncoderLayer_test(EncoderLayer)

~/work/W4A1/public_tests.py in EncoderLayer_test(target)
92 [[ 0.23017104, -0.98100424, -0.78707516, 1.5379084 ],
93 [-1.2280797 , 0.76477575, -0.7169283 , 1.1802323 ],
—> 94 [ 0.14880152, -0.48318022, -1.1908402 , 1.5252188 ]]), “Wrong values when training=True”
95
96 encoded = encoder_layer1(q, False, np.array([[1, 1, 0]]))

AssertionError: Wrong values when training=True

I am getting this error, if someone could point out the problem

2

There is a mistake in the last step

3

Your self.ffn() layer is using the wrong data argument.
Your self.layernorm2() layer is using the same wrong data argument.

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