90 lines
3.0 KiB
Python
90 lines
3.0 KiB
Python
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import os
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import argparse
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import numpy as np
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from torch.utils.tensorboard import SummaryWriter
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from torchvision.utils import make_grid
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from torch.optim import lr_scheduler
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from model import *
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from loss import *
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from dataloader import *
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def get_opt():
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parser = argparse.ArgumentParser()
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parser.add_argument('-n', '--num-workers', type=int, default=4)
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parser.add_argument('-e', '--epoch', type=int, default=10)
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parser.add_argument('-b', '--batch-size', type=int, default=128)
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parser.add_argument('-t', '--test-batch-size', type=int, default=1)
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parser.add_argument('-d', '--display-step', type=int, default=600)
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opt = parser.parse_args()
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return opt
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def train(opt):
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model = PLA_Attention_Model(100, 100, 50).cuda()
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# model.load_state_dict(torch.load('checkpoint.pt'))
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model.train()
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train_dataset = NetworkFlowDataset('Thursday-WorkingHours.pcap',
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'Thursday-WorkingHours-Morning-WebAttacks.pcap_ISCX.csv')
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train_data_loader = NetworkFlowDataloader(opt, train_dataset)
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test_dataset = NetworkFlowDataset('Friday-WorkingHours.pcap', 'Friday-WorkingHours-Morning.pcap_ISCX.csv')
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test_data_loader = NetworkFlowDataloader(opt, test_dataset)
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optim = torch.optim.Adam(model.parameters(), lr=0.001)
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criterion = Loss()
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writer = SummaryWriter()
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for epoch in range(opt.epoch):
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for i in range(len(train_data_loader.data_loader)):
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step = epoch * len(train_data_loader.data_loader) + i + 1
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# load data
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flow, label = train_data_loader.next_batch()
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flow = flow.cuda()
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label = label.cuda()
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# train model
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optim.zero_grad() # 梯度清零，不加则会累加梯度
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result = model(flow)
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loss = criterion(result, label)
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loss.backward()
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optim.step()
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writer.add_scalar('loss', loss, step)
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writer.close()
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if step % opt.display_step == 0:
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_, predicted = torch.max(result, 1)
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total = label.size(0)
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correct = (predicted == label).sum().item()
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total_test = 0
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correct_test = 0
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for i in range(len(test_data_loader.data_loader)):
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flow, label = test_data_loader.next_batch()
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flow = flow.cuda()
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label = label.cuda()
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result = model(flow)
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_, predicted = torch.max(result, 1)
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total_test += label.size(0)
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correct_test += (predicted == label).sum().item()
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print(
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'[Epoch {}] Loss : {:.2}, train_acc : {:.2}, test_acc : {:.2}'.format(epoch, loss, correct / total,
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correct_test / total_test))
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torch.save(model.state_dict(), 'checkpoint.pt')
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if __name__ == '__main__':
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opt = get_opt()
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train(opt)
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