(self, src_n_token, d_model=512, n_head=8, dim_feedforward=2048, n_layers=6)
| 263 | """ |
| 264 | |
| 265 | def __init__(self, src_n_token, d_model=512, n_head=8, dim_feedforward=2048, n_layers=6): |
| 266 | super(TransformerEncoder, self).__init__() |
| 267 | self.src_n_token = src_n_token |
| 268 | self.d_model = d_model |
| 269 | self.n_head = n_head |
| 270 | self.dim_feedforward = dim_feedforward |
| 271 | self.n_layers = n_layers |
| 272 | |
| 273 | # input_emb / pos_emb / n-encoder layers |
| 274 | self.input_emb = layer.Embedding(input_dim=src_n_token, output_dim=d_model) |
| 275 | self.pos_emb = layer.Embedding(input_dim=src_n_token, output_dim=d_model) |
| 276 | self.layers = [] |
| 277 | for _ in range(self.n_layers): |
| 278 | self.layers.append(TransformerEncoderLayer(d_model=d_model, n_head=n_head, dim_feedforward=dim_feedforward)) |
| 279 | |
| 280 | def forward(self, enc_inputs): |
| 281 | """Pass the input through the encoder in turn. |
nothing calls this directly
no test coverage detected