ba164c0dbb3d8171004380956a88431f4e8248ba,onmt/Models.py,Embeddings,make_positional_encodings,#Embeddings#Any#Any#,51

Before Change

        pe = torch.zeros(max_len, 1, dim)
        for i in range(dim):
            for j in range(max_len):
                k = float(j) / (10000.0 ** (2.0*i / float(dim)))
                pe[j, 0, i] = math.cos(k) if i % 2 == 1 else math.sin(k)
        return pe

After Change

            return self.word_lut.embedding_dim

    def make_positional_encodings(self, dim, max_len):
        pe = torch.arange(0, max_len).unsqueeze(1).expand(max_len, dim)
        div_term = 1 / torch.pow(10000, torch.arange(0, dim * 2, 2) / dim)
        pe = pe * div_term.expand_as(pe)
        pe[:, 0::2] = torch.sin(pe[:, 0::2])
        pe[:, 1::2] = torch.cos(pe[:, 1::2])
        return pe.unsqueeze(1)

    def load_pretrained_vectors(self, emb_file):

Instances