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#!/usr/bin/env python
# -*- coding: utf-8 -*-
import keras_metrics as km
from keras import backend as K
import random
from keras.utils import np_utils
from keras.optimizers import Adamax
import numpy as np
import os
import sys
import pickle
from keras.layers.core import Activation, Flatten, Dense, Dropout
from keras.initializers import glorot_uniform
from keras import Model
from keras.models import load_model
from sklearn.metrics import confusion_matrix,classification_report
def load(fname):
f=np.load(fname,allow_pickle=True)
x_test=np.array(f["obfs4"])
x2_test=np.array(f["bk"])
y_test=np.array([1 for i in range(x_test.shape[0])])
y2_test=np.array([0 for i in range(x2_test.shape[0])])
print "obfs4数据: ", x_test.shape[0]
print "非obfs4数据:", x2_test.shape[0]
return x_test, y_test, x2_test, y2_test
def action(filepath,modelpath):
# Testing the DF model
BATCH_SIZE = 128 # Batch size
VERBOSE = 2 # Output display mode
NB_CLASSES = 2 # number of outputs = number of classes
print ("开始进行obfs4模型的测试，测试数据集路径：")
print(filepath)
print("模型路径：")
print(h5model)
K.set_image_dim_ordering("tf") # tf is tensorflow
# Convert data as float32 type
X_test,y_test,X2_test,y2_test=load(filepath)
X_test = X_test.astype('float32')
y_test = y_test.astype('float32')
# we need a [Length x 1] x n shape as input to the DF CNN (Tensorflow)
X_test = X_test[:, :,np.newaxis]
# Convert class vectors to categorical classes matrices
y_test = np_utils.to_categorical(y_test, NB_CLASSES)
X2_test = X2_test.astype('float32')
y2_test = y2_test.astype('float32')
# we need a [Length x 1] x n shape as input to the DF CNN (Tensorflow)
X2_test = X2_test[:, :,np.newaxis]
# Convert class vectors to categorical classes matrices
y2_test = np_utils.to_categorical(y2_test, NB_CLASSES)
# Building and training model
#print ("loading DF model")
model=load_model(h5model,custom_objects={'binary_precision':km.binary_precision(), 'binary_recall':km.binary_recall()})
print "模型加载成功"
print "开始对obfs4测试集进行识别"
score_test = model.evaluate(X_test, y_test, verbose=VERBOSE)
predictions=model.predict(X_test, batch_size=BATCH_SIZE, verbose=VERBOSE)
predictions = np.argmax(predictions, axis=1)
num=0
for item in list(predictions):
if item == 1:
num+=1
print "识别结果：识别成功的数量：",str(num)
print "识别失败的数量：",str(int(X_test.shape[0])-num)
print "识别准确率为:", score_test[1]
print "开始对非obfs4测试集进行识别"
score_test = model.evaluate(X2_test, y2_test, verbose=VERBOSE)
predictions=model.predict(X2_test, batch_size=BATCH_SIZE, verbose=VERBOSE)
predictions = np.argmax(predictions, axis=1)
knum=0
for item in list(predictions):
if item == 0:
knum+=1
print "识别结果：识别成功的数量：",str(knum)
print "识别失败的数量：",str(int(X2_test.shape[0])-knum)
print "识别准确率为:", score_test[1]
print "总识别准确率为:", 1.0*(num+knum)/(int(X_test.shape[0])+int(X2_test.shape[0]))
if __name__ == "__main__":
reload(sys)
sys.setdefaultencoding('utf-8')
random.seed(0)
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
# Use only CPU
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" # see issue #152
os.environ["CUDA_VISIBLE_DEVICES"] = ""
filepath=sys.argv[1]
filename=filepath.split('/')[-1]
h5model=sys.argv[2]
action(filepath, h5model)
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