dnn.rar_DNN_神经网络、入侵检测、python_网络安全

from __future__ import print_function from sklearn.cross_validation import train_test_split import pandas as pd import numpy as np np.random.seed(1337) # for reproducibility from keras.preprocessing import sequence from keras.utils import np_utils from keras.models import Sequential from keras.layers import Dense, Dropout, Activation, Embedding from keras.layers import LSTM, SimpleRNN, GRU from keras.datasets import imdb from keras.utils.np_utils import to_categorical from sklearn.metrics import (precision_score, recall_score,f1_score, accuracy_score,mean_squared_error,mean_absolute_error) from sklearn import metrics from sklearn.preprocessing import Normalizer import h5py from keras import callbacks from keras.callbacks import ModelCheckpoint, EarlyStopping, ReduceLROnPlateau, CSVLogger traindata = pd.read_csv('kdd/multiclass/Training.csv', header=None) testdata = pd.read_csv('kdd/multiclass/Testing.csv', header=None) X = traindata.iloc[:,0:42] Y = traindata.iloc[:,42] C = testdata.iloc[:,42] T = testdata.iloc[:,0:42] scaler = Normalizer().fit(X) trainX = scaler.transform(X) scaler = Normalizer().fit(T) testT = scaler.transform(T) y_train1 = np.array(Y) y_test1 = np.array(C) y_train= to_categorical(y_train1) y_test= to_categorical(y_test1) X_train = np.array(trainX) X_test = np.array(testT) batch_size = 64 # 1. define the network model = Sequential() model.add(Dense(1024,input_dim=41,activation='relu')) model.add(Dropout(0.01)) model.add(Dense(768,activation='relu')) model.add(Dropout(0.01)) model.add(Dense(512,activation='relu')) model.add(Dropout(0.01)) model.add(Dense(256,activation='relu')) model.add(Dropout(0.01)) model.add(Dense(128,activation='relu')) model.add(Dropout(0.01)) model.add(Dense(5)) model.add(Activation('softmax')) # try using different optimizers and different optimizer configs model.compile(loss='categorical_crossentropy',optimizer='adam',metrics=['accuracy']) checkpointer = callbacks.ModelCheckpoint(filepath="kddresults/dnn5layer/checkpoint-{epoch:02d}.hdf5", verbose=1, save_best_only=True, monitor='loss') csv_logger = CSVLogger('kddresults/dnn5layer/training_set_dnnanalysis.csv',separator=',', append=False) model.fit(X_train, y_train, validation_data=(X_test, y_test),batch_size=batch_size, nb_epoch=1000, callbacks=[checkpointer,csv_logger]) model.save("kddresults/dnn5layer/dnn5layer_model.hdf5")