def task(self,myplugin,exp):
while self.tq.qsize() > 0:
target = self.tq.get_nowait()
self.pross = self.pross + 1
self.lock.acquire()
msg = '[*] %s|%s|%s' % (target,str(self.pross) + "/"+ str(self.maxpross),exp)
wi = self.console_width -len(msg)
if wi < 0:
msg = msg[0:self.console_width]
sys.stdout.write(msg + ' ' * (self.console_width -len(msg)) + '\r')
self.lock.release()
try:
p = myplugin(target,exp)
p.verify()
result = p.result
if result['status']:
self.lock.acquire()
msg = "[+] {target} | {file}".format(target=result['target'],file=exp)
color.cprint(msg + ' ' * (self.console_width -len(msg)),CYAN)
self.lock.release()
saveResult(self.filename,result)
except RequestException,e:
pass
except IOError,e:
pass
def task(self, myplugin, exp):
while self.tq.qsize() > 0:
target = self.tq.get_nowait()
self.pross = self.pross + 1
self.lock.acquire()
msg = '[*] %s|%s|%s' % (target, str(self.pross) + "/" +
str(self.maxpross), exp)
wi = self.console_width - len(msg)
if wi < 0:
msg = msg[0:self.console_width]
sys.stdout.write(msg + ' ' * (self.console_width - len(msg)) +
'\r')
self.lock.release()
try:
p = myplugin(target, exp)
p.verify()
result = p.result
if result['status']:
self.lock.acquire()
msg = "[+] {target} | {file}".format(
target=result['target'], file=exp)
color.cprint(msg + ' ' * (self.console_width - len(msg)),
CYAN)
self.lock.release()
saveResult(self.filename, result)
except RequestException, e:
pass
except IOError, e:
pass
def predict(testId, testX, model, stander):
'''
Do prediction
:param testId:
:param testX:
:param model: The chosen best model
:return: None
'''
ypred = model.predict(testX)
ypred = stander.inverse_log10_y(ypred)
# save prediction to csv
saveResult(testId, ypred)
def works(self):
while self.que.qsize() > 0:
exp = self.que.get_nowait()
m = __import__(exp[:-3])
myplugin = getattr(m, "Exploit")
#只剩一个插件,启动线程
if self.que.qsize() == 0:
for target in self.targets:
self.tq.put(target)
ts = []
for i in range(self.tnum):
t = threading.Thread(target=self.task,
args=(myplugin, exp))
ts.append(t)
t.start()
for t in ts:
t.join()
else:
for target in self.targets:
self.pross = self.pross + 1
self.lock.acquire()
msg = '[*] %s|%s|%s' % (target, str(self.pross) + "/" +
str(self.maxpross), exp)
wi = self.console_width - len(msg)
if wi < 0:
msg = msg[0:self.console_width]
sys.stdout.write(msg + ' ' *
(self.console_width - len(msg)) + '\r')
self.lock.release()
try:
p = myplugin(target, exp)
p.verify()
result = p.result
if result['status']:
self.lock.acquire()
msg = "[+] {target} | {file}".format(
target=result['target'], file=exp)
color.cprint(
msg + ' ' * (self.console_width - len(msg)),
CYAN)
self.lock.release()
saveResult(self.filename, result)
except RequestException, e:
pass
except IOError, e:
pass
except Exception, e:
if e.message <> "":
# print dir(e)
print "error:%s|%s|%s" % (e.message, exp, target)
pass
def works(self):
while self.que.qsize() > 0:
exp = self.que.get_nowait()
m = __import__(exp[:-3])
myplugin = getattr(m, "Exploit")
#只剩一个插件,启动线程
if self.que.qsize() == 0:
for target in self.targets:
self.tq.put(target)
ts = []
for i in range(self.tnum):
t = threading.Thread(target=self.task,args=(myplugin,exp))
ts.append(t)
t.start()
for t in ts:
t.join()
else:
for target in self.targets:
self.pross = self.pross + 1
self.lock.acquire()
msg = '[*] %s|%s|%s' % (target,str(self.pross) + "/"+ str(self.maxpross),exp)
wi = self.console_width -len(msg)
if wi < 0:
msg = msg[0:self.console_width]
sys.stdout.write(msg + ' ' * (self.console_width -len(msg)) + '\r')
self.lock.release()
try:
p = myplugin(target,exp)
p.verify()
result = p.result
if result['status']:
self.lock.acquire()
msg = "[+] {target} | {file}".format(target=result['target'],file=exp)
color.cprint(msg + ' ' * (self.console_width -len(msg)),CYAN)
self.lock.release()
saveResult(self.filename,result)
except RequestException,e:
pass
except IOError,e:
pass
except Exception,e:
if e.message <> "":
# print dir(e)
print "error:%s|%s|%s" % (e.message,exp,target)
pass
def works(self):
while self.que.qsize() > 0:
exp = self.que.get()
m = __import__(exp[:-3])
myplugin = getattr(m, "Exploit")
for target in self.targets:
msg = 'Scaning target:%s' % target
sys.stdout.write(msg + ' ' * (self.console_width - len(msg)) +
'\r')
try:
p = myplugin(target, exp)
p.verify()
result = p.result
if result['status']:
self.lock.acquire()
color.cprint(
"[+] {target} | {file}".format(
target=result['target'], file=exp), CYAN)
self.lock.release()
saveResult(self.filename, result)
except Exception, e:
#print e
pass
def evaluate_lenet5(learning_rate=0.1, n_epoches=200,
nkerns=[20, 50], batch_size=500):
# load data from dataset
logging.info('... loading data')
trainData, trainLabel = util.load_total_data()
testData = util.loadTestData()
trainData = util.upToInt(trainData)
train_set_x = theano.shared(np.asarray(trainData,
dtype = theano.config.floatX),
borrow = True)
train_set_y = theano.shared(np.asarray(trainLabel,
dtype = theano.config.floatX),
borrow = True)
test_set_x = theano.shared(np.asarray(testData,
dtype = theano.config.floatX),
borrow = True)
train_set_y = T.cast(train_set_y, 'int32')
n_train_batches = train_set_x.get_value(borrow=True).shape[0]
n_valid_batches = train_set_x.get_value(borrow=True).shape[0]
n_test_batches = test_set_x.get_value(borrow=True).shape[0]
n_train_batches /= batch_size
n_valid_batches /= batch_size
n_test_batches /= batch_size
rng = np.random.RandomState(23455)
# allocate symbolic variables for the data
index = T.lscalar() # index to a [mini]batch
# start-snippet-1
x = T.matrix('x')
y = T.ivector('y')
logging.info('... building the model')
layer0_input = x.reshape((batch_size, 1, 28, 28))
# Construct the first convolutional pooling layer:
# filtering reduces the image size to (28-5+1 , 28-5+1) = (24, 24)
# maxpooling reduces this further to (24/2, 24/2) = (12, 12)
# 4D output tensor is thus of shape (batch_size, nkerns[0], 12, 12)
layer0 = LeNetConvPoolLayer(
rng,
input=layer0_input,
image_shape=(batch_size, 1, 28, 28),
filter_shape=(nkerns[0], 1, 5, 5),
poolsize=(2, 2)
)
# Construct the second convolutional pooling layer
# filtering reduces the image size to (12-5+1, 12-5+1) = (8, 8)
# maxpooling reduces this further to (8/2, 8/2) = (4, 4)
# 4D output tensor is thus of shape (batch_size, nkerns[1], 4, 4)
layer1 = LeNetConvPoolLayer(
rng,
input=layer0.output,
image_shape=(batch_size, nkerns[0], 12, 12),
filter_shape=(nkerns[1], nkerns[0], 5, 5),
poolsize=(2, 2)
)
# the HiddenLayer being fully-connected, it operates on 2D matrices of
# shape (batch_size, num_pixels) (i.e matrix of rasterized images).
# This will generate a matrix of shape (batch_size, nkerns[1] * 4 * 4),
# or (500, 50 * 4 * 4) = (500, 800) with the default values.
layer2_input = layer1.output.flatten(2)
# construct a fully-connected sigmoidal layer
layer2 = HiddenLayer(
rng,
input=layer2_input,
n_in=nkerns[1] * 4 * 4,
n_out=500,
activation=T.tanh
)
# classify the values of the fully-connected sigmoidal layer
layer3 = LogisticRegression(input=layer2.output, n_in=500, n_out=10)
# the cost we minimize during training is the NLL of the model
cost = layer3.negative_log_likelihood(y)
# create a function to compute the mistakes that are made by the model
validate_model = theano.function(
[index],
layer3.errors(y),
givens={
x: train_set_x[index * batch_size: (index + 1) * batch_size],
y: train_set_y[index * batch_size: (index + 1) * batch_size],
}
)
# create a list of all model parameters to be fit by gradient descent
params = layer3.params + layer2.params + layer1.params + layer0.params
# create a list of gradients for all model parameters
grads = T.grad(cost, params)
# train_model is a function that updates the model parameters by
# SGD Since this model has many parameters, it would be tedious to
# manually create an update rule for each model parameter. We thus
# create the updates list by automatically looping over all
# (params[i], grads[i]) pairs.
updates = [
(param_i, param_i - learning_rate * grad_i)
for param_i, grad_i in zip(params, grads)
]
train_model = theano.function(
[index],
cost,
updates=updates,
givens={
x: train_set_x[index * batch_size: (index + 1) * batch_size],
y: train_set_y[index * batch_size: (index + 1) * batch_size]
}
)
validation_model = theano.function(
[index],
layer3.errors(y),
givens={
x: train_set_x[index * batch_size: (index + 1) * batch_size],
y: train_set_y[index * batch_size: (index + 1) * batch_size]
}
)
logging.info('... training')
patience = 10000
patience_increase = 2
improvement_threshold = 0.995
validation_frequency = min(n_train_batches, patience / 2)
best_validation_loss = np.inf
best_iter = 0
test_score = 0.
start_time = time.clock()
epoch = 0
done_looping = False
while (epoch < n_epoches) and (not done_looping):
epoch = epoch + 1
for minibatch_index in xrange(n_train_batches):
iter = (epoch - 1) * n_train_batches + minibatch_index
if iter % 100 == 0:
logging.info('training @ iter = %d' % (iter))
cost_ij = train_model(minibatch_index)
if (iter + 1) % validation_frequency == 0:
# compute zero-one loss on validation set
validation_losses = [validation_model(i) for i
in xrange(n_valid_batches)]
this_validation_loss = np.mean(validation_losses)
print('epoch %i, minibatch %i/%i, validation error %f %%' %
(epoch, minibatch_index + 1, n_train_batches,
this_validation_loss * 100.))
if (this_validation_loss * 100.) < 0.001:
done_looping = True
break
end_time = time.clock()
logging.info('The code for file ' +
os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
# make a prediction and save file
# make a prediction
predict_model = theano.function(
inputs=[index],
outputs= layer3.predict(),
givens={
x: test_set_x[index * batch_size: (index + 1) * batch_size]
}
)
# save the result file
testLabel = np.array([])
for test_index in range(n_test_batches):
tempLabel = predict_model(test_index)
testLabel = np.hstack((testLabel, tempLabel))
util.saveResult(testLabel, './result/cnn_result.csv')
def GaussianNBClassify(trainData, trainLabel, testData): nbClf = GaussianNB() nbClf.fit(trainData, np.ravel(trainLabel)) testLabel = nbClf.predict(testData) util.saveResult(testLabel, './result/gaussion_nb_result.csv') return testLabel
def knnClassify(trainData, trainLabel, testData): knnClf = KNeighborsClassifier() knnClf.fit(trainData, np.ravel(trainLabel)) testLabel = knnClf.predict(testData) util.saveResult(testLabel, './result/sklearn_knn_result.csv') return testLabel
done_looping = False
prev_cost = np.inf
start_time = time.clock()
while (epoch < n_epochs) and (not done_looping):
epoch = epoch + 1
cost = train_model()
impr = prev_cost - cost
logging.info('epoch %d, cost: %f, impr: %f' % (epoch+1, cost, impr))
if impr < improvement_threshold:
break
prev_cost = cost
end_time = time.clock()
print >> sys.stderr, ('The code for file ' +
os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
# make a prediction
predict_model = theano.function(
inputs=[],
outputs= classifier.predict(),
givens={
x: test_set_x
}
)
testLabel = predict_model()
util.saveResult(testLabel, './result/mlp_result.csv')
def treeClassify(trainData, trainLabel, testData):
treeClf = tree.DecisionTreeClassifier()
treeClf.fit(trainData, np.ravel(trainLabel))
testLabel = treeClf.predict(testData)
util.saveResult(testLabel, "./result/decision_tree_result.csv")
return testLabel
def rfClassify(trainData, trainLabel, testData): rfClf = RandomForestClassifier(n_estimators=100) rfClf.fit(trainData, np.ravel(trainLabel)) testLabel = rfClf.predict(testData) util.saveResult(testLabel, './result/random_forest_result.csv') return testLabel
def svcClassify(trainData, trainLabel, testData): svcClf = svm.SVC(C=5.0) svcClf.fit(trainData, np.ravel(trainLabel)) testLabel = svcClf.predict(testData) util.saveResult(testLabel, './result/svm_result.csv') return testLabel
def svmLinearClassify(trainData, trainLabel,testData): svcClf = svm.SVC(kernel='linear') svcClf.fit(trainData, np.ravel(trainLabel)) testLabel = svcClf.predict(testData) util.saveResult(testLabel, './result/svm_linear_result.csv') return testLabel
def baggingClassify(trainData, trainLabel, testData): baggingCif = BaggingClassifier(KNeighborsClassifier()) baggingCif.fit(trainData, np.ravel(trainLabel)) testLabel = baggingCif.predict(testData) util.saveResult(testLabel, './result/bagging_result.csv') return testLabel
def MultinomialNBClassify(trainData, trainLabel, testData): nbClf = MultinomialNB() nbClf.fit(trainData, np.ravel(trainLabel)) testLabel = nbClf.predict(testData) util.saveResult(testLabel, './result/multinomial_nb_result.csv') return testLabel
def lrClassify(trainData, trainLabel, testData): lrClf = linear_model.LogisticRegression(C=1e5) lrClf.fit(trainData, np.ravel(trainLabel)) testLabel = lrClf.predict(testData) util.saveResult(testLabel, './result/logistic_regression_result.csv') return testLabel
def adaboostClassify(trainData, trainLabel, testData):
abClf = AdaBoostClassifier(n_estimators=500)
abClf.fit(trainData, np.ravel(trainLabel))
testLabel = abClf.predict(testData)
util.saveResult(testLabel, "./result/adaboost_result.csv")
return testLabel
testData = testData.reshape(testData.shape[0], 1, 28, 28)
model = Sequential()
model.add(Convolution2D(32, 1, 3, 3, border_mode='full'))
model.add(Activation('relu'))
model.add(Convolution2D(32, 32, 3, 3))
model.add(Activation('relu'))
model.add(MaxPooling2D(poolsize=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(32*196, 128))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(128, nb_classes))
model.add(Activation('softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adadelta')
model.fit(trainData, trainLabel, batch_size=batch_size, nb_epoch=nb_epoch, show_accuracy=True)
logging.info("Training process finished!")
logging.info("Predict for testData...")
y_pred = model.predict(testData,)
testLabel = np.argmax(y_pred, axis=1)
logging.info("Save result...")
util.saveResult(testLabel, './result/keras_cnn_result.csv')