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| ... | |
| def call(self, x, encoder_output, lookahead_mask, padding_mask, training): | |
| # Multi-head attention layer | |
| multihead_output1 = self.multihead_attention1(x, x, x, lookahead_mask) | |
| # Expected output shape = (batch_size, sequence_length, d_model) | |
| # Add in a dropout layer | |
| multihead_output1 = self.dropout1(multihead_output1, training=training) | |
| # Followed by an Add & Norm layer | |
| addnorm_output1 = self.add_norm1(x, multihead_output1) | |
| # Expected output shape = (batch_size, sequence_length, d_model) | |
| # Followed by another multi-head attention layer | |
| multihead_output2 = self.multihead_attention2(addnorm_output1, encoder_output, encoder_output, padding_mask) | |
| # Add in another dropout layer | |
| multihead_output2 = self.dropout2(multihead_output2, training=training) | |
| # Followed by another Add & Norm layer | |
| addnorm_output2 = self.add_norm1(addnorm_output1, multihead_output2) | |
| # Followed by a fully connected layer | |
| feedforward_output = self.feed_forward(addnorm_output2) | |
| # Expected output shape = (batch_size, sequence_length, d_model) | |
| # Add in another dropout layer | |
| feedforward_output = self.dropout3(feedforward_output, training=training) | |
| # Followed by another Add & Norm layer | |
| return self.add_norm3(addnorm_output2, feedforward_output) |
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