models.emb.sdne
¶
Module Contents¶
Classes¶
The SDNE model from the `”Structural Deep Network Embedding” |
-
class
models.emb.sdne.
SDNE_layer
(num_node, hidden_size1, hidden_size2, droput, alpha, beta, nu1, nu2)[source]¶ Bases:
torch.nn.Module
-
class
models.emb.sdne.
SDNE
(hidden_size1, hidden_size2, droput, alpha, beta, nu1, nu2, max_epoch, lr, cpu)[source]¶ Bases:
models.BaseModel
The SDNE model from the “Structural Deep Network Embedding” paper
- Args:
hidden_size1 (int) : The size of the first hidden layer. hidden_size2 (int) : The size of the second hidden layer. droput (float) : Droput rate. alpha (float) : Trade-off parameter between 1-st and 2-nd order objective function in SDNE. beta (float) : Parameter of 2-nd order objective function in SDNE. nu1 (float) : Parameter of l1 normlization in SDNE. nu2 (float) : Parameter of l2 normlization in SDNE. max_epoch (int) : The max epoches in training step. lr (float) : Learning rate in SDNE. cpu (bool) : Use CPU or GPU to train hin2vec.