809d0e5b52561fefd3b0d98c08d3cbb175d7e706,onmt/Models.py,Encoder,forward,#Encoder#Any#Any#Any#,125

Before Change

        if self.encoder_layer == "mean":
            // Take the mean of vectors.
            mean = pre_emb.mean(0).view(1, pre_emb.size(1), pre_emb.size(2)) \
                   .expand(self.layers, pre_emb.size(1), pre_emb.size(2))
            return (mean, mean), pre_emb

        elif self.encoder_layer == "transformer":
            // Self-attention tranformer.

After Change

            out = emb.transpose(0, 1).contiguous()
            for i in range(self.layers):
                out = self.transformer[i](out, input[:, :, 0])
            return Variable(emb.data), out.transpose(0, 1).contiguous()

        else:
            // Standard RNN encoder.

Instances