from typing import List
import torch.nn as nn
from greatx.nn.layers import Sequential, TAGConv, activations
from greatx.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[int], optional
the number of hidden units for each hidden layer, by default [16]
acts : List[str], 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
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 first activation
>>> model = TAGCN(100, 10, hids=[32, 16], acts=[None, 'relu'])
>>> # TAGCN with deep architectures, each layer has elu activation
>>> model = TAGCN(100, 10, hids=[16]*8, acts=['elu'])
See also
--------
:class:`greatx.nn.layers.TAGCNConv`
"""
@wrapper
def __init__(self, in_channels: int, out_channels: int,
hids: List[int] = [16], acts: List[str] = ['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)