complex_models
Module Contents
Classes
Base class for all neural network modules. |
|
Convolutional Complex Knowledge Graph Embeddings |
Attributes
- complex_models.seed = 1
- class complex_models.Complex(param)
Bases:
torch.nn.Module
Base class for all neural network modules.
Your models should also subclass this class.
Modules can also contain other Modules, allowing to nest them in a tree structure. You can assign the submodules as regular attributes:
import torch.nn as nn import torch.nn.functional as F class Model(nn.Module): def __init__(self): super().__init__() self.conv1 = nn.Conv2d(1, 20, 5) self.conv2 = nn.Conv2d(20, 20, 5) def forward(self, x): x = F.relu(self.conv1(x)) return F.relu(self.conv2(x))
Submodules assigned in this way will be registered, and will have their parameters converted too when you call
to()
, etc.Note
As per the example above, an
__init__()
call to the parent class must be made before assignment on the child.- Variables:
training (bool) – Boolean represents whether this module is in training or evaluation mode.
- init()
- forward_head_batch(e1_idx, rel_idx)
- forward_head_and_loss(e1_idx, rel_idx, targets)
- get_embeddings()
- abstract forward_triples(*args, **kwargs)
- abstract forward_triples_and_loss(*args, **kwargs)
- class complex_models.ConEx(params=None)
Bases:
torch.nn.Module
Convolutional Complex Knowledge Graph Embeddings
- residual_convolution(C_1, C_2)
- forward_head_batch(*, e1_idx, rel_idx)
Given a head entity and a relation (h,r), we compute scores for all entities. [score(h,r,x)|x in Entities] => [0.0,0.1,…,0.8], shape=> (1, |Entities|) Given a batch of head entities and relations => shape (size of batch,| Entities|)
- forward_head_and_loss(e1_idx, rel_idx, targets)
- init()
- get_embeddings()
- forward_triples(*, e1_idx, rel_idx, e2_idx)
- forward_triples_and_loss(e1_idx, rel_idx, e2_idx, targets)