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multihead_attention #17

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multihead_attention #17

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5 changes: 4 additions & 1 deletion modules.py
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Original file line number Diff line number Diff line change
Expand Up @@ -247,7 +247,10 @@ def multihead_attention(queries,

# Restore shape
outputs = tf.concat(tf.split(outputs, num_heads, axis=0), axis=2 ) # (N, T_q, C)


# Linear projections
outputs = tf.layers.dense(outputs, num_units, activation=tf.nn.relu) # (N, T_q, C)
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I think you are right about an extra projection, but can I ask about the activation function here? seems in the original paper there is no bias and activation, only a plain "Concat(head1, ..., headh)W_O"


# Residual connection
outputs += queries

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