from util.train import execute_train, build_arg_parser
# Training settings
parser = build_arg_parser()
parser.add_argument('--nheads', type=int, default=4, help='Number of attentions heads.')
parser.add_argument('--alpha', type=float, default=0.2, help='Alpha for the leaky_relu.')
args = parser.parse_args()
execute_train(gnn_args=dict(nfeat=None,
nhid=args.hidden,
nodes_out=None,
graph_out=None,
dropout=args.dropout,
device=None,
first_conv_descr=dict(layer_type=GATLayer,
args=dict(
nheads=args.nheads,
alpha=args.alpha
)),
middle_conv_descr=dict(layer_type=GATLayer,
args=dict(
nheads=args.nheads,
alpha=args.alpha
)),
fc_layers=args.fc_layers,
conv_layers=args.conv_layers,
skip=args.skip,
gru=args.gru,
fixed=args.fixed,
variable=args.variable), args=args)
execute_train(gnn_args=dict(nfeat=None,
nhid=args.hidden,
nodes_out=None,
graph_out=None,
dropout=args.dropout,
device=None,
first_conv_descr=dict(layer_type=PNALayer,
args=dict(
aggregators=args.aggregators.split(),
scalers=args.scalers.split(), avg_d=None,
towers=args.towers,
self_loop=args.self_loop,
divide_input=False,
pretrans_layers=args.pretrans_layers,
posttrans_layers=args.posttrans_layers
)),
middle_conv_descr=dict(layer_type=PNALayer,
args=dict(
aggregators=args.aggregators.split(),
scalers=args.scalers.split(),
avg_d=None, towers=args.towers,
self_loop=args.self_loop,
divide_input=True,
pretrans_layers=args.pretrans_layers,
posttrans_layers=args.posttrans_layers
)),
fc_layers=args.fc_layers,
conv_layers=args.conv_layers,
skip=args.skip,
gru=args.gru,
fixed=args.fixed,
variable=args.variable), args=args)
'--gin_fc_layers',
type=int,
default=2,
help='Number of fully connected layers after the aggregation.')
parser.add_argument('--aggregation',
type=str,
default='mean',
help='Type of LAF aggregation')
args = parser.parse_args()
execute_train(gnn_args=dict(
nfeat=None,
nhid=args.hidden,
nodes_out=None,
graph_out=None,
dropout=args.dropout,
device=None,
first_conv_descr=dict(layer_type=GINLafLayer,
args=dict(function=args.aggregation,
fc_layers=args.gin_fc_layers)),
middle_conv_descr=dict(layer_type=GINLafLayer,
args=dict(function=args.aggregation,
fc_layers=args.gin_fc_layers)),
fc_layers=args.fc_layers,
conv_layers=args.conv_layers,
skip=args.skip,
gru=args.gru,
fixed=args.fixed,
variable=args.variable),
args=args)