def __init__(self):
super(Acrobot, self).__init__()
self.G1 = nx.path_graph(2).to_directed()
self.node_feat_size = 2
self.edge_feat_size = 3
self.graph_feat_size = 10
self.gn = FFGN(self.graph_feat_size, self.node_feat_size,
self.edge_feat_size).cuda()
self.gn.load_state_dict(torch.load('model0.05.pth'))
normalizers = torch.load('normalized/acrobot0.05.pth')
self.in_normalizer = normalizers['in_normalizer']
self.out_normalizer = normalizers['out_normalizer']
use_cuda = True
dl = DataLoader(dset, batch_size=200, num_workers=0, drop_last=True)
dl_eval = DataLoader(dset_eval,
batch_size=200,
num_workers=0,
drop_last=True)
G1 = nx.path_graph(6).to_directed()
G_target = nx.path_graph(6).to_directed()
nx.draw(G1)
plt.show()
node_feat_size = 6
edge_feat_size = 3
graph_feat_size = 10
gn = FFGN(graph_feat_size, node_feat_size, edge_feat_size).cuda()
if opt.model != '':
gn.load_state_dict(torch.load(opt.model))
optimizer = optim.Adam(gn.parameters(), lr=1e-4)
schedular = optim.lr_scheduler.StepLR(optimizer, 5e4, gamma=0.975)
savedir = os.path.join('./logs', 'runs',
datetime.now().strftime('%B%d_%H:%M:%S'))
writer = SummaryWriter(savedir)
step = 0
normalizers = torch.load('normalize.pth')
in_normalizer = normalizers['in_normalizer']
out_normalizer = normalizers['out_normalizer']
std = in_normalizer.get_std()
for epoch in range(300):