(self, ntoken, ninp, nhead, nhid, nlayers, dropout=0.5)
| 108 | """Container module with an encoder, a recurrent or transformer module, and a decoder.""" |
| 109 | |
| 110 | def __init__(self, ntoken, ninp, nhead, nhid, nlayers, dropout=0.5): |
| 111 | super(TransformerModel, self).__init__(d_model=ninp, nhead=nhead, dim_feedforward=nhid, num_encoder_layers=nlayers) |
| 112 | self.model_type = 'Transformer' |
| 113 | self.src_mask = None |
| 114 | self.pos_encoder = PositionalEncoding(ninp, dropout) |
| 115 | |
| 116 | self.input_emb = nn.Embedding(ntoken, ninp) |
| 117 | self.ninp = ninp |
| 118 | self.decoder = nn.Linear(ninp, ntoken) |
| 119 | |
| 120 | self.init_weights() |
| 121 | |
| 122 | def _generate_square_subsequent_mask(self, sz): |
| 123 | return torch.log(torch.tril(torch.ones(sz,sz))) |
nothing calls this directly
no test coverage detected