def train():
dataset = trainDataset()
model = MainModel(dataset.vocab_size()[0], dataset.vocab_size()[1])
#model.load_state_dict(torch.load("model2.pth"))
model = model.cuda()
optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)
dataloader = torch.utils.data.DataLoader(dataset,
batch_size=1,
pin_memory=True)
for epoch in range(EPOCHS):
print(f"EPOCH: {epoch + 1}/{EPOCHS}")
losses = []
for idx, data in tqdm(enumerate(dataloader)):
outputs = model(data["source"].cuda(), data["target"].cuda(),
data["alignment"].cuda())
loss = torch.nn.functional.binary_cross_entropy(
outputs.view(-1), data["predictions"].cuda().view(-1).float())
# print(loss.item())
optimizer.zero_grad()
loss.backward()
optimizer.step()
losses.append(loss.detach())
print(f"Mean Loss for Epoch: {epoch} is {sum(losses) / len(losses)}")
torch.save(model.state_dict(), f"model_lstm.pth")
print("g_loss: " + str(g_avg_loss))
logging.info("g_loss: " + str(g_avg_loss))
# Testing
network.cpu().eval()
p_1, r_1, f1_1 = evaluator.sparse_network_reconstruct(network, args.pos_size[0])
p_2, r_2, f1_2 = evaluator.sparse_network_reconstruct(network, args.pos_size[1])
p_3, r_3, f1_3 = evaluator.sparse_network_reconstruct(network, args.pos_size[2])
print(p_3, r_3, f1_3)
if f1_3 > max_f1:
max_f1 = f1_3
max_p = p_3
max_r = r_3
torch.save(network.state_dict(), "model/" + model_dir + ".pt")
network.cuda(args.cuda)
network.generator.sample_linear.cpu()
print("Network Reconstruction Results (K=" + str(args.pos_size[0]) + "): ")
print("Prec: " + str(p_1) + " Rec: " + str(r_1) + " F1: " + str(f1_1))
print("Network Reconstruction Results (K=" + str(args.pos_size[1]) + "): ")
print("Prec: " + str(p_2) + " Rec: " + str(r_2) + " F1: " + str(f1_2))
print("Network Reconstruction Results (K=" + str(args.pos_size[2]) + "): ")
print("Prec: " + str(max_p) + " Rec: " + str(max_r) + " F1: " + str(max_f1))
logging.info("K=" + str(args.pos_size[0]) + "Prec: " + str(p_1) + " Rec: " + str(r_1) + " F1: " + str(f1_1))
logging.info("K=" + str(args.pos_size[1]) + "Prec: " + str(p_2) + " Rec: " + str(r_2) + " F1: " + str(f1_2))
logging.info("K=" + str(args.pos_size[2]) + "Prec: " + str(max_p) + " Rec: " + str(max_r) + " F1: " + str(max_f1))
accu, mle = evaluator.sparse_seq_predict(network, test_marker, test_time, test_mask, adj_list, 1)
print("accu", accu)
print("mle", mle)
logging.info("accu: " + str(accu))
logging.info("mle: " + str(mle))