output = Generate_dataset(GLOBAL_NUM_GRAPHS)
g_list, test_glist = load_graphs(output, GLOBAL_NUM_GRAPHS)
#base_classifier = load_base_model(label_map, g_list)
base_args = {
'gm': 'mean_field',
'feat_dim': 2,
'latent_dim': 10,
'out_dim': 20,
'max_lv': 2,
'hidden': 32
}
base_classifier = GraphClassifier(num_classes=20, **base_args)
env = GraphEdgeEnv(base_classifier)
print("len g_list:", len(g_list))
if cmd_args.frac_meta > 0:
num_train = int(len(g_list) * (1 - cmd_args.frac_meta))
agent = Agent(g_list, test_glist[num_train:], env)
else:
agent = Agent(g_list, None, env)
if GLOBAL_PHASE == 'train':
print("\n\nStarting Training Loop\n\n")
agent.train()
loss = F.mse_loss(q_sa, list_target)
optimizer.zero_grad()
loss.backward()
optimizer.step()
pbar.set_description('exp: %.5f, loss: %0.5f' % (self.eps, loss))
log_out.close()
if __name__ == '__main__':
random.seed(cmd_args.seed)
np.random.seed(cmd_args.seed)
torch.manual_seed(cmd_args.seed)
label_map, _, g_list = load_graphs()
random.shuffle(g_list)
base_classifier = load_base_model(label_map, g_list)
env = GraphEdgeEnv(base_classifier, n_edges=1)
if cmd_args.frac_meta > 0:
num_train = int(len(g_list) * (1 - cmd_args.frac_meta))
agent = Agent(g_list[:num_train], g_list[num_train:], env)
else:
agent = Agent(g_list, None, env)
agent.train()
agent.net.load_state_dict(
torch.load(cmd_args.save_dir + '/epoch-best.model'))
agent.eval()