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- #
- # This module implements a single layer of the Decagon
- # model. This is going to be already quite complex, as
- # we will be using all the graph convolutional building
- # blocks.
- #
- # h_{i}^(k+1) = ϕ(∑_r ∑_{j∈N{r}^{i}} c_{r}^{ij} * \
- # W_{r}^(k) h_{j}^{k} + c_{r}^{i} h_{i}^(k))
- #
- # N{r}^{i} - set of neighbors of node i under relation r
- # W_{r}^(k) - relation-type specific weight matrix
- # h_{i}^(k) - hidden state of node i in layer k
- # h_{i}^(k)∈R^{d(k)} where d(k) is the dimensionality
- # of the representation in k-th layer
- # ϕ - activation function
- # c_{r}^{ij} - normalization constants
- # c_{r}^{ij} = 1/sqrt(|N_{r}^{i}| |N_{r}^{j}|)
- # c_{r}^{i} - normalization constants
- # c_{r}^{i} = 1/|N_{r}^{i}|
- #
-
-
- import torch
-
-
- class InputLayer(torch.nn.Module):
- def __init__(self, data, dimensionality=32, **kwargs):
- super().__init__(**kwargs)
- self.data = data
- self.dimensionality = dimensionality
- self.node_reps = None
- self.build()
-
- def build(self):
- self.node_reps = []
- for i, nt in enumerate(self.data.node_types):
- reps = torch.rand(nt.count, self.dimensionality)
- reps = torch.nn.Parameter(reps)
- self.register_parameter('node_reps[%d]' % i, reps)
- self.node_reps.append(reps)
-
- def forward(self):
- return self.node_reps
-
- def __repr__(self):
- s = ''
- s += 'GNN input layer with dimensionality: %d\n' % self.dimensionality
- s += ' # of node types: %d\n' % len(self.data.node_types)
- for nt in self.data.node_types:
- s += ' - %s (%d)\n' % (nt.name, nt.count)
- return s.strip()
-
-
- class DecagonLayer(torch.nn.Module):
- def __init__(self, data, **kwargs):
- super().__init__(**kwargs)
- self.data = data
-
- def __call__(self, previous_layer):
- pass
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