if currentType in sourceFiles:
print("U [%s:%s]" % (currentType, currentPath))
sourceFiles[currentType].append(currentPath)
else:
print("I [%s:%s]" % (currentType, currentPath))
sourceFiles[currentType] = [currentPath]
if len(sourceFiles) == 0:
print("No files were added. Exiting.")
sys.exit(1)
nn = NN(Payload.vectorSize(), OutputMapper.vectorSize)
if os.path.isfile(weightsPath):
nn.load(weightsPath)
samples = TrainingSample.TrainingSampleBatch()
for trafficType in sourceFiles.keys():
for filePath in sourceFiles[trafficType]:
cap = pyshark.FileCapture(filePath)
for packet in cap:
try:
sample = TrainingSample.TrainingSample(
Payload.fromPySharkCapture(packet), trafficType)
samples.add(sample)
except AttributeError as ae:
print(ae)
samples.shuffle()
vectors = samples.toVectors()
# train tuning
m = y.shape[1]
m_train = int(0.7 * m)
x_train = x[:, 0:m_train]
y_train = y[:, 0:m_train]
x_val = x[:, m_train:]
y_val = y[:, m_train:]
Layers = (17, 8, 8, 4, 1)
maxIteration = 5000
alpha = 0.03
reg = 0.0
batch_size = 200
nn = NN(Layers, alpha, reg)
nn.load('config')
n_batch = m_train // batch_size
for epoch in range(0, maxIteration):
for i in range(0, n_batch):
start = i * batch_size
end = np.minimum(start + batch_size, m_train)
nn.train_once(x_train[:, start:end], y_train[:, start:end])
print(epoch, " train cost J: ", np.asscalar(nn.J(nn.predict(x), y)))
print(epoch, " test cost J: ", np.asscalar(nn.J(nn.predict(x_val), y_val)))
nn.test(x_val, y_val)
print(" accuracy: ", nn.accuracy)
print(" precision: ", nn.precision)
print(" recall: ", nn.recall)
print(" fScore: ", nn.fscore)
if __name__ == '__main__':
with open('../NN_config.yaml') as f:
NN_config = yaml.load(f)
nn = NN(
inputsLength=NN_config['lengths']['inputs'],
outputsLength=NN_config['lengths']['outputs'],
hiddenLayersLength=NN_config['lengths']['hiddenLayers'],
thicknessLength=NN_config['lengths']['neuronsPerHiddenLayer'],
activation=getattr(af, NN_config['activationFunction']),
learningRate=NN_config['learningRate']
)
if not '--reset' in sys.argv:
nn.load('../brain.json')
print('Fetching digits data...')
epoches = int(sys.argv[1])
digits = {
'train': get_digits(epoches, 'train'),
'test': get_digits(1000, 'test')
}
print('Done fetching! Time for training.')
print('Training...')
losses = np.array([], dtype='float64')
for n, dig in enumerate(digits['train']):
inputs = np.array(dig['pixels']).flatten() * 1.0
inputs /= 255.0
target = np.arange(10)*0.0
