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base_model.py
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base_model.py
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from tensorflow_core import keras
from tensorflow_core import argmax
from tensorflow_core import newaxis
import pandas as pd
import matplotlib.pyplot as plt
import random
import numpy as np
intrusion_list_1 = ['normal.', # normal
'back.', 'land.', 'neptune.', 'pod.', 'smurf.', 'teardrop.', # DOS
'ipsweep.', 'nmap.', 'portsweep.', 'satan.', # PROBE
'ftp_write.', 'guess_passwd.', 'imap.', 'multihop.', 'phf.', 'spy.', 'warezclient.', 'warezmaster.', # R2L
'buffer_overflow.', 'loadmodule.', 'perl.', 'rootkit.'] # U2R
intrusion_list_2 = ['normal.', # normal
'back.', 'neptune.', 'teardrop.', # DOS
'ipsweep.', 'satan.', # PROBE
'warezclient.', 'guess_passwd.' # R2L
]
intrusion_list_3 = ['normal.', # normal
'back.', 'neptune.', 'pod.', 'smurf.', 'teardrop.', # DOS
'ipsweep.', 'nmap.', 'portsweep.', 'satan.' # PROBE
]
class BaseModel():
def __init__(self):
self.is_save_model = False # 是否保存训练模型
self.train_accuracy = 2.5
self.val_accuracy = 1.0
self.test_accuracy = 1.0
self.train_loss=1.0
self.val_loss=1.0
self.test_loss=1.0
self.train_time = 0
self.history = keras.callbacks.History
self.model = keras.models.Model
self.batch_size = 64 #批次大小
self.epochs=100 #迭代轮数
self.input_shape = ()
self.num_classes= -1 #最终分类数
self.model_name=""
self.data_mode = 2 # 选取数据集
def LoadData(self):
if(self.data_mode == 1):
# 1号数据集39个特征，23分类
self.input_shape = (39, 1)
self.num_classes = 23
self.train_data = pd.read_csv(
'.//dataset//train_data_1.csv', header=None).values
self.train_label = pd.read_csv(
'.//dataset//train_label_1.csv', header=None).values
self.val_data = pd.read_csv(
'.//dataset//val_data_1.csv', header=None).values
self.val_label = pd.read_csv(
'.//dataset//val_label_1.csv', header=None).values
self.test_data = pd.read_csv(
'.//dataset//test_data_1.csv', header=None).values
self.test_label = pd.read_csv(
'.//dataset//test_label_1.csv', header=None).values
elif(self.data_mode == 2):
# 2号数据集10个特征，10分类
self.input_shape = (12, 1)
self.num_classes = 8
self.train_data = pd.read_csv(
'.//dataset//train_data_2.csv', header=None).values
self.train_label = pd.read_csv(
'.//dataset//train_label_2.csv', header=None).values
self.val_data = pd.read_csv(
'.//dataset//val_data_2.csv', header=None).values
self.val_label = pd.read_csv(
'.//dataset//val_label_2.csv', header=None).values
self.test_data = pd.read_csv(
'.//dataset//test_data_2.csv', header=None).values
self.test_label = pd.read_csv(
'.//dataset//test_label_2.csv', header=None).values
elif(self.data_mode == 3):
# 3号数据集19个特征，10分类
self.input_shape = (19, 1)
self.num_classes = 10
self.train_data = pd.read_csv(
'.//dataset//train_data_3.csv', header=None).values
self.train_label = pd.read_csv(
'.//dataset//train_label_3.csv', header=None).values
self.val_data = pd.read_csv(
'.//dataset//val_data_3.csv', header=None).values
self.val_label = pd.read_csv(
'.//dataset//val_label_3.csv', header=None).values
self.test_data = pd.read_csv(
'.//dataset//test_data_3.csv', header=None).values
self.test_label = pd.read_csv(
'.//dataset//test_label_3.csv', header=None).values
# 调整数据输入形状
self.Reshpae()
def Reshpae(self):
#因为送入神经网络是成batch的
#所以要把数据变shape为（self.train_data.shape[0], 39, 1）
#一维卷积只能在列维度移动所以要（self.train_data.shape[0], 39, 1）
if(self.data_mode == 1):
self.train_data = self.train_data.reshape(
self.train_data.shape[0], 39, 1)
self.test_data = self.test_data.reshape(
self.test_data.shape[0], 39, 1)
self.val_data = self.val_data.reshape(
self.val_data.shape[0], 39, 1)
elif(self.data_mode == 2):
self.train_data = self.train_data.reshape(
self.train_data.shape[0], 12, 1)
self.test_data = self.test_data.reshape(
self.test_data.shape[0], 12, 1)
self.val_data = self.val_data.reshape(
self.val_data.shape[0], 12, 1)
elif(self.data_mode == 3):
self.train_data = self.train_data.reshape(
self.train_data.shape[0], 19, 1)
self.test_data = self.test_data.reshape(
self.test_data.shape[0], 19, 1)
self.val_data = self.val_data.reshape(
self.val_data.shape[0], 19, 1)
def LoadModle(self, path):
self.model = keras.models.load_model(path)
def RandomTest(self):
num = random.randint(0, len(self.test_data)-1)
# 变为模型能接受的形式
x_predict = self.test_data[num]
x_predict = x_predict[newaxis,...]
predict = self.model.predict(x_predict)
print(predict,self.test_label[num])
if(self.data_mode == 1):
real = intrusion_list_1[int(self.test_label[num])]
pred = intrusion_list_1[argmax(predict[0], axis=-1)]
elif(self.data_mode == 2):
real = intrusion_list_2[int(self.test_label[num])]
pred = intrusion_list_2[argmax(predict[0], axis=-1)]
elif(self.data_mode == 3):
real = intrusion_list_3[int(self.test_label[num])]
pred = intrusion_list_3[argmax(predict[0], axis=-1)]
print('真实值：', real)
print('检测值：', pred)
return real,pred
def Evaluate(self):
# 将测试集输入到训练好的模型中，查看测试集的误差
score = self.model.evaluate(self.test_data, self.test_label,
verbose=1, batch_size=64)
self.test_accuracy = score[1]
self.test_loss = score[0]
print('Test loss:', score[0])
print('Test accuracy: %.2f%%' % (score[1] * 100))
def SaveTrainProcess(self):
# 保存训练结果txt文件
with open(f".//mymodles//{self.model_name}_{self.data_mode}.txt", "w") as f:
f.writelines(line+'\n' for line in[str(round(self.train_loss,7)),str(self.train_accuracy),
str(round(self.val_loss, 7)), str(self.val_accuracy),str(self.train_time)])
# 保存训练过程图片
acc = self.history.history['sparse_categorical_accuracy']
val_acc = self.history.history['val_sparse_categorical_accuracy']
loss = self.history.history['loss']
val_loss = self.history.history['val_loss']
plt.subplot(2, 1, 1)
plt.plot(acc, label='Training Accuracy')
plt.plot(val_acc, label='Validation Accuracy')
plt.title('Training and Validation Accuracy of '+self.model_name)
plt.legend()
#设置坐标轴刻度
my_x_ticks = np.arange(0, self.epochs, 1)
my_y_ticks = np.arange(0.5, 1, 0.05)
# plt.xticks(my_x_ticks)
plt.yticks(my_y_ticks)
plt.subplot(2, 1, 2)
plt.plot(loss, label='Training Loss')
plt.plot(val_loss, label='Validation Loss')
plt.title('Training and Validation Loss of '+self.model_name)
plt.legend()
#设置坐标轴刻度
my_x_ticks = np.arange(0, self.epochs, 1)
my_y_ticks = np.arange(0, 2, 0.15)
# plt.xticks(my_x_ticks)
plt.yticks(my_y_ticks)
# 调整图片使不重叠
plt.tight_layout()
plt.savefig('.//mymodles//'+self.model_name+"_"+str(self.data_mode)+'.jpg')
plt.clf()
# 等待子类实现
def Train(self):
pass