Source code for graphwar.nn.models.sat

import torch
import torch.nn as nn

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


[docs]class SAT(nn.Module): r"""Graph Convolution Network with Spectral Adversarial Training (SAT) from the `"Spectral Adversarial Training for Robust Graph Neural Network" <https://arxiv.org>`_ paper (arXiv'22) 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'] dropout : float, optional the dropout ratio of model, by default 0.5 bias : bool, optional whether to use bias in the layers, by default False normalize : bool, optional whether to compute symmetric normalization coefficients on the fly, by default True 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 -------- >>> # SAT with one hidden layer >>> model = SAT(100, 10) >>> # SAT with two hidden layers >>> model = SAT(100, 10, hids=[32, 16], acts=['relu', 'elu']) >>> # SAT with two hidden layers, without activation at the first layer >>> model = SAT(100, 10, hids=[32, 16], acts=[None, 'relu']) >>> # SAT with very deep architectures, each layer has elu as activation function >>> model = SAT(100, 10, hids=[16]*8, acts=['elu']) See also -------- :class:`graphwar.nn.layers.SATConv` """ @wrapper def __init__(self, in_channels: int, out_channels: int, hids: list = [16], acts: list = ['relu'], dropout: float = 0.5, bias: bool = False, normalize: bool = True, bn: bool = False): super().__init__() conv = [] assert len(hids) == len(acts) for hid, act in zip(hids, acts): conv.append(SATConv(in_channels, hid, 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(SATConv(in_channels, out_channels, 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)
# class SAT(nn.Module): # @wrapper # def __init__(self, # in_channels, # out_channels, # hids: list = [], # acts: list = [], # dropout: float = 0.5, # K: int = 5, # alpha: float = 0.1, # normalize: bool = True, # bn: bool = False, # bias: bool = False): # super().__init__() # conv = [] # for i, (hid, act) in enumerate(zip(hids, acts)): # if i == 0: # conv.append(SATConv(in_channels, # hid, # bias=bias, # K=K, # normalize=normalize, # alpha=alpha)) # else: # conv.append(nn.Linear(in_channels, hid, bias=bias)) # if bn: # conv.append(nn.BatchNorm1d(hid)) # conv.append(activations.get(act)) # conv.append(nn.Dropout(dropout)) # in_channels = hid # if not hids: # conv.append(SATConv(in_channels, # out_channels, # bias=bias, # K=K, # normalize=normalize, # alpha=alpha)) # else: # conv.append(nn.Linear(in_channels, out_channels, bias=bias)) # self.conv = Sequential(*conv) # def reset_parameters(self): # self.conv.reset_parameters() # def forward(self, x, edge_index, edge_weight=None): # return self.conv(x, edge_index, edge_weight)