def test(modelPath, testDataPath):
testData = []
labels = []
with codecs.open(testDataPath, "r", "UTF-8") as f:
for line in f:
line = line.strip()
input, label = line.split("\t")
testData.append(input)
lm = NeuralNetworkLanguageModel()
print lm.predict(testData, modelPath)
def train(modelPath, trainingDataPath):
trainingData = []
labels = []
with codecs.open(trainingDataPath, "r", "UTF-8") as f:
for line in f:
line = line.strip()
input, label = line.split("\t")
trainingData.append(input)
labels.append(int(label))
labels = np.array(labels)
lm = NeuralNetworkLanguageModel()
lm.train(trainingData, labels, savePath = modelPath)
def __init__(self):
self.lm = NeuralNetworkLanguageModel()
self.output = None
self.requestMax = 50
self.port = 12345
self.host = '0.0.0.0'
