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"
class NeuralNetworkLanguageModelServer:
def __init__(self):
self.lm = NeuralNetworkLanguageModel()
self.output = None
self.requestMax = 50
self.port = 12345
self.host = "0.0.0.0"
def predict(self, data, sess):
if self.output == None:
raise ValueError("Model is not loaded yet!")
# print data
vectors = self.lm.getLongVectors(data)
output = sess.run(self.output, feed_dict={self.lm.input_placeholder: vectors})
result = sess.run(tf.argmax(output, 1))
return result
def _runServer(self, sess):
# create a socket object
serversocket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
# get local machine name
# host = socket.gethostname()
# bind to the port
serversocket.bind((self.host, self.port))
# queue up to 5 requests
serversocket.listen(self.requestMax)
while True:
# establish a connection
clientsocket, addr = serversocket.accept()
print "Got a connection from %s" % str(addr)
data = ""
while True:
chunk = clientsocket.recv(1024)
# print chunk
chunk = chunk.decode("utf-8")
data += chunk
if data.endswith("<END>"):
break
lines = data.split("\n")[0:-1] # Remove <END>
# print lines
result = map(str, self.predict(lines, sess).tolist())
clientsocket.send(" ".join(result))
clientsocket.close()
def runServer(self):
# Assume that you have 12GB of GPU memory and want to allocate ~4GB:
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.25)
with tf.Session(config=tf.ConfigProto(gpu_options=gpu_options)) as sess:
with tf.device(self.lm.device):
self.output = self.lm.inference(self.lm.input_placeholder)
saver = tf.train.Saver()
# Restore variables from disk.
saver.restore(sess, "model")
print "Model restored."
self._runServer(sess)
def __init__(self):
self.lm = NeuralNetworkLanguageModel()
self.output = None
self.requestMax = 50
self.port = 12345
self.host = '0.0.0.0'
class NeuralNetworkLanguageModelServer:
def __init__(self):
self.lm = NeuralNetworkLanguageModel()
self.output = None
self.requestMax = 50
self.port = 12345
self.host = '0.0.0.0'
def predict(self, data, sess):
if self.output == None:
raise ValueError("Model is not loaded yet!")
# print data
vectors = self.lm.getLongVectors(data)
output = sess.run(self.output, feed_dict={self.lm.input_placeholder: vectors})
result = sess.run(tf.argmax(output,1))
return result
def _runServer(self, sess):
# create a socket object
serversocket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
# get local machine name
# host = socket.gethostname()
# bind to the port
serversocket.bind((self.host, self.port))
# queue up to 5 requests
serversocket.listen(self.requestMax)
while True:
# establish a connection
clientsocket, addr = serversocket.accept()
print "Got a connection from %s" % str(addr)
data = ""
while True:
chunk = clientsocket.recv(1024)
# print chunk
chunk = chunk.decode("utf-8")
data += chunk
if data.endswith("<END>"):
break
lines = data.split("\n")[0:-1] # Remove <END>
# print lines
result = map(str, self.predict(lines, sess).tolist())
clientsocket.send(" ".join(result))
clientsocket.close()
def runServer(self):
# Assume that you have 12GB of GPU memory and want to allocate ~4GB:
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.25)
with tf.Session(config=tf.ConfigProto(gpu_options=gpu_options)) as sess:
with tf.device(self.lm.device):
self.output = self.lm.inference(self.lm.input_placeholder)
saver = tf.train.Saver()
# Restore variables from disk.
saver.restore(sess, "model")
print "Model restored."
self._runServer(sess)