class COVID19DN():
""" COVID19DN Class
Core COVID-19 Tensorflow DenseNet Classifier wrapper class using Tensroflow 2.
"""
def __init__(self):
""" Initializes the class. """
self.Helpers = Helpers("Core")
self.Model = Model()
self.Helpers.logger.info(
"COVID19DN Tensorflow initialization complete.")
def do_data(self):
""" Sorts the training data. """
self.Data = Data()
self.Data.process_data(
self.Data.paths_n_labels())
def do_train(self):
""" Creates & trains the model. """
self.Model.do_model(self.Data)
self.Model.do_train()
self.Model.do_evaluate()
def do_load_model(self):
""" Loads the model """
self.Model.load_model_and_weights()
def do_classify(self):
""" Loads model and classifies test data """
self.do_load_model()
self.Model.test_classifier()
def do_server(self):
""" Loads the API server """
self.do_load_model()
self.Server = Server(self.Model)
self.Server.start()
def do_http_classify(self):
""" Loads model and classifies test data """
self.Model.test_http_classifier()
class IdcQnn():
""" IdcQnn
IdcQnn is a wrapper class that creates the Invasive Ductal Carcinoma
Tensorflow QNN (Quantum Neural Network).
"""
def __init__(self):
""" Initializes the class. """
self.Helpers = Helpers("Core")
self.QModel = QModel()
self.Helpers.logger.info("IdcQnn QNN initialization complete.")
def do_data(self):
""" Sorts the training data """
self.Data = Data()
self.Data.get_paths_n_labels()
self.Data.process_data()
def do_train(self):
""" Creates & trains the QNN.
CREDIT: https://www.tensorflow.org/quantum/tutorials/mnist
CREDIT: https://arxiv.org/pdf/1802.06002.pdf
"""
self.QMNIST = QMNIST()
# "Quantumize" the training data
X_train_bin, X_test_bin = self.QMNIST.encode_data_as_binary(
self.Data.X_train, self.Data.X_test)
X_train_circ, X_test_circ = self.QMNIST.do_circuit_conversion(
X_train_bin, X_test_bin)
x_train_tfcirc, x_test_tfcirc = self.QMNIST.convert_to_tensors(
X_train_circ, X_test_circ)
# Create the Quantum Neural Network
model_circuit, model_readout = self.QModel.create_quantum_model()
self.QModel.create_keras_model(model_circuit, model_readout)
# Train the Quantum Neural Network
self.QModel.train_model(x_train_tfcirc, x_test_tfcirc,
self.Data.y_train, self.Data.y_test)
self.QModel.do_evaluate(x_train_tfcirc, x_test_tfcirc,
self.Data.y_test)
class QMNISTCOVID19():
""" QMNISTCOVID19
QMNISTCOVID19 is a wrapper class that creates the COVID-19 Tensorflow QNN (Quantum Neural Network).
"""
def __init__(self):
""" Initializes the class. """
self.Helpers = Helpers("Core")
self.Helpers.logger.info("QMNISTCOVID19 QNN initialization complete.")
def do_data(self):
""" Sorts the training data """
self.Data = Data()
self.Data.get_paths_n_labels()
self.Data.process_data()
def do_train(self):
""" Creates & trains the QNN. """
self.QMNIST = QMNIST()
X_train_bin, X_test_bin = self.QMNIST.encode_as_binary(
self.Data.X_train, self.Data.X_test)
X_train_circ, X__test_circ = self.QMNIST.do_circuit_conversion(
X_train_bin, X_test_bin)
x_train_tfcirc, x_test_tfcirc = self.QMNIST.convert_to_tensors(
X_train_circ, X__test_circ)
def do_load_model(self):
""" Loads the trained model """
print("TODO")
def do_classify(self):
""" Classifies the test data """
print("TODO")
class COVID19DN():
""" COVID19DN Class
Core COVID-19 Tensorflow DenseNet Classifier wrapper class using Tensroflow 2.
"""
def __init__(self):
""" Initializes the class. """
self.Helpers = Helpers("Core")
# Initiates the iotJumpWay connection class
self.iotJumpWayDevice = iotJumpWay({
"host": self.Helpers.confs["iotJumpWay"]["host"],
"port": self.Helpers.confs["iotJumpWay"]["port"],
"lid": self.Helpers.confs["iotJumpWay"]["loc"],
"zid": self.Helpers.confs["iotJumpWay"]["zne"],
"did": self.Helpers.confs["iotJumpWay"]["id"],
"dn": self.Helpers.confs["iotJumpWay"]["name"],
"un": self.Helpers.confs["iotJumpWay"]["mqtt"]["username"],
"pw": self.Helpers.confs["iotJumpWay"]["mqtt"]["password"]
})
self.iotJumpWayDevice.connect()
self.Model = Model(self.iotJumpWayDevice)
self.Helpers.logger.info(
"COVID19DN Tensorflow initialization complete.")
def life(self):
""" Sends vital statistics to HIAS """
cpu = psutil.cpu_percent()
mem = psutil.virtual_memory()[2]
hdd = psutil.disk_usage('/').percent
tmp = psutil.sensors_temperatures()['coretemp'][0].current
r = requests.get('http://ipinfo.io/json?token=' + self.Helpers.confs["iotJumpWay"]["key"])
data = r.json()
location = data["loc"].split(',')
self.Helpers.logger.info("COVID19DN Life (TEMPERATURE): " + str(tmp) + "\u00b0")
self.Helpers.logger.info("COVID19DN Life (CPU): " + str(cpu) + "%")
self.Helpers.logger.info("COVID19DN Life (Memory): " + str(mem) + "%")
self.Helpers.logger.info("COVID19DN Life (HDD): " + str(hdd) + "%")
self.Helpers.logger.info("COVID19DN Life (LAT): " + str(location[0]))
self.Helpers.logger.info("COVID19DN Life (LNG): " + str(location[1]))
# Send iotJumpWay notification
self.iotJumpWayDevice.devicePub("Life", {
"CPU": cpu,
"Memory": mem,
"Diskspace": hdd,
"Temperature": tmp,
"Latitude": location[0],
"Longitude": location[1]
})
threading.Timer(60.0, self.life).start()
def threading(self):
""" Creates required module threads. """
# Life thread
Thread(target = self.life, args = ()).start()
threading.Timer(60.0, self.life).start()
def do_data(self):
""" Sorts the training data. """
self.Data = Data()
self.Data.process_data(
self.Data.paths_n_labels())
def do_train(self):
""" Creates & trains the model. """
self.Model.do_model(self.Data)
self.Model.do_train()
self.Model.do_evaluate()
def do_load_model(self):
""" Loads the model """
self.Model.load_model_and_weights()
def do_classify(self):
""" Loads model and classifies test data """
self.do_load_model()
self.Model.test_classifier()
def do_server(self):
""" Loads the API server """
self.do_load_model()
self.Server = Server(self.Model)
self.Server.start()
def do_http_classify(self):
""" Loads model and classifies test data """
self.Model.test_http_classifier()