Source code for graphwar.nn.models.tagcn

import torch
import torch.nn as nn

from graphwar.nn.layers import TAGConv, Sequential, activations
from graphwar.utils import wrapper


[docs]class TAGCN(nn.Module): r"""Topological adaptive graph convolution network (TAGCN) from the `"Topological Adaptive Graph Convolutional Networks" <https://arxiv.org/abs/1806.03536>`_ paper (arXiv'17) Parameters ---------- in_channels : int, the input dimensions of model out_channels : int, the output dimensions of model hids : list, optional the number of hidden units for each hidden layer, by default [16] acts : list, optional the activation function for each hidden layer, by default ['relu'] K : int the number of propagation steps, by default 2 dropout : float, optional the dropout ratio of model, by default 0.5 bias : bool, optional whether to use bias in the layers, by default True normalize : bool, optional whether to compute symmetric normalization coefficients on the fly, by default False bn: bool, optional whether to use :class:`BatchNorm1d` after the convolution layer, by default False Note ---- It is convenient to extend the number of layers with different or the same hidden units (activation functions) using :meth:`graphwar.utils.wrapper`. See Examples below: Examples -------- >>> # TAGCN with one hidden layer >>> model = TAGCN(100, 10) >>> # TAGCN with two hidden layers >>> model = TAGCN(100, 10, hids=[32, 16], acts=['relu', 'elu']) >>> # TAGCN with two hidden layers, without activation at the first layer >>> model = TAGCN(100, 10, hids=[32, 16], acts=[None, 'relu']) >>> # TAGCN with very deep architectures, each layer has elu as activation function >>> model = TAGCN(100, 10, hids=[16]*8, acts=['elu']) See also -------- :class:`graphwar.nn.layers.TAGCNConv` """ @wrapper def __init__(self, in_channels: int, out_channels: int, hids: list = [16], acts: list = ['relu'], K: int = 2, dropout: float = 0.5, bias: bool = True, normalize: bool = True, bn: bool = False): super().__init__() conv = [] assert len(hids) == len(acts) for hid, act in zip(hids, acts): conv.append(TAGConv(in_channels, hid, K=K, bias=bias, normalize=normalize)) if bn: conv.append(nn.BatchNorm1d(hid)) conv.append(activations.get(act)) conv.append(nn.Dropout(dropout)) in_channels = hid conv.append(TAGConv(in_channels, out_channels, K=K, bias=bias, normalize=normalize)) self.conv = Sequential(*conv)
[docs] def reset_parameters(self): self.conv.reset_parameters()
[docs] def forward(self, x, edge_index, edge_weight=None): return self.conv(x, edge_index, edge_weight)