class Model():
def __init__(self):
###############################################################
#
# Sets up all default requirements
#
# - Helpers: Useful global functions
# - Data: Data functions
#
###############################################################
self.Helpers = Helpers()
self.confs = self.Helpers.loadConfigs()
self.Data = Data()
def createDNNLayers(self, x, y):
###############################################################
#
# Sets up the DNN layers, configuration in required/confs.json
#
###############################################################
net = tflearn.input_data(shape=[None, len(x[0])])
for i in range(self.confs["NLU"]['FcLayers']):
net = tflearn.fully_connected(net, self.confs["NLU"]['FcUnits'])
net = tflearn.fully_connected(net,
len(y[0]),
activation=str(
self.confs["NLU"]['Activation']))
if self.confs["NLU"]['Regression']:
net = tflearn.regression(net)
return net
def trainDNN(self, x, y, words, classes, intentMap):
###############################################################
#
# Trains the DNN, configuration in required/confs.json
#
###############################################################
tf.reset_default_graph()
tmodel = tflearn.DNN(
self.createDNNLayers(x, y),
tensorboard_dir=self.confs["NLU"]['TFLearn']['Logs'],
tensorboard_verbose=self.confs["NLU"]['TFLearn']['LogsLevel'])
tmodel.fit(x,
y,
n_epoch=self.confs["NLU"]['Epochs'],
batch_size=self.confs["NLU"]['BatchSize'],
show_metric=self.confs["NLU"]['ShowMetric'])
self.saveModelData(
self.confs["NLU"]['TFLearn']['Data'], {
'words': words,
'classes': classes,
'x': x,
'y': y,
'intentMap': [intentMap]
}, tmodel)
def saveModelData(self, path, data, tmodel):
###############################################################
#
# Saves the model data for TFLearn and the NLU engine,
# configuration in required/confs.json
#
###############################################################
tmodel.save(self.confs["NLU"]['TFLearn']['Path'])
with open(path, "w") as outfile:
json.dump(data, outfile)
def buildDNN(self, x, y):
###############################################################
#
# Loads the DNN model, configuration in required/confs.json
#
###############################################################
tf.reset_default_graph()
tmodel = tflearn.DNN(self.createDNNLayers(x, y))
tmodel.load(self.confs["NLU"]['TFLearn']['Path'])
return tmodel
def predict(self, tmodel, parsedSentence, trainedWords, trainedClasses):
###############################################################
#
# Makes a prediction against the trained model, checking the
# confidence and then logging the results.
#
###############################################################
predictions = [[index, confidence] for index, confidence in enumerate(
tmodel.predict(
[self.Data.makeBagOfWords(parsedSentence, trainedWords)])[0])]
predictions.sort(key=lambda x: x[1], reverse=True)
classification = []
for prediction in predictions:
classification.append(
(trainedClasses[prediction[0]], prediction[1]))
return classification
class Model():
""" ALL Detection System 2019 Model Class
Model class for the ALL Detection System 2019 Chatbot.
"""
def __init__(self):
""" Initializes the Model class. """
self.Helpers = Helpers()
self.Data = Data()
def createDNNLayers(self, x, y):
""" Sets up the DNN layers """
net = tflearn.input_data(shape=[None, len(x[0])])
for i in range(self.Helpers.confs["NLU"]['FcLayers']):
net = tflearn.fully_connected(net,
self.Helpers.confs["NLU"]['FcUnits'])
net = tflearn.fully_connected(
net,
len(y[0]),
activation=str(self.Helpers.confs["NLU"]['Activation']))
if self.Helpers.confs["NLU"]['Regression']:
net = tflearn.regression(net)
return net
def trainDNN(self, x, y, words, classes, intentMap):
""" Trains the DNN """
tf.reset_default_graph()
tmodel = tflearn.DNN(
self.createDNNLayers(x, y),
tensorboard_dir=self.Helpers.confs["NLU"]['TFLearn']['Logs'],
tensorboard_verbose=self.Helpers.confs["NLU"]['TFLearn']
['LogsLevel'])
tmodel.fit(x,
y,
n_epoch=self.Helpers.confs["NLU"]['Epochs'],
batch_size=self.Helpers.confs["NLU"]['BatchSize'],
show_metric=self.Helpers.confs["NLU"]['ShowMetric'])
self.saveModelData(
self.Helpers.confs["NLU"]['TFLearn']['Data'], {
'words': words,
'classes': classes,
'x': x,
'y': y,
'intentMap': [intentMap]
}, tmodel)
def saveModelData(self, path, data, tmodel):
""" Saves the model data """
tmodel.save(self.Helpers.confs["NLU"]['TFLearn']['Path'])
with open(path, "w") as outfile:
json.dump(data, outfile)
def buildDNN(self, x, y):
""" Loads the DNN model """
tf.reset_default_graph()
tmodel = tflearn.DNN(self.createDNNLayers(x, y))
tmodel.load(self.Helpers.confs["NLU"]['TFLearn']['Path'])
return tmodel
def predict(self, tmodel, parsedSentence, trainedWords, trainedClasses):
""" Makes a prediction """
predictions = [[index, confidence] for index, confidence in enumerate(
tmodel.predict(
[self.Data.makeBagOfWords(parsedSentence, trainedWords)])[0])]
predictions.sort(key=lambda x: x[1], reverse=True)
classification = []
for prediction in predictions:
classification.append(
(trainedClasses[prediction[0]], prediction[1]))
return classification
class Model():
""" Model Class
Model helper functions.
"""
def __init__(self):
""" Initializes the class. """
self.Helpers = Helpers("Model")
self.Data = Data()
self.Helpers.logger.info("Model class initialized.")
def createDNN(self, x, y):
""" Sets up the DNN layers """
tf_model = tf.keras.models.Sequential([
tf.keras.layers.Dense(self.Helpers.confs["NLU"]['FcUnits'],
activation='relu',
input_shape=[len(x[0])]),
tf.keras.layers.Dense(self.Helpers.confs["NLU"]['FcUnits'],
activation='relu'),
tf.keras.layers.Dense(self.Helpers.confs["NLU"]['FcUnits'],
activation='relu'),
tf.keras.layers.Dense(self.Helpers.confs["NLU"]['FcUnits'],
activation='relu'),
tf.keras.layers.Dense(
len(y[0]), activation=self.Helpers.confs["NLU"]['Activation'])
], "GeniSysAI")
tf_model.summary()
self.Helpers.logger.info("Network initialization complete.")
return tf_model
def trainDNN(self, x, y, words, classes, intentMap):
""" Trains the DNN """
tf_model = self.createDNN(x, y)
optimizer = tf.keras.optimizers.Adam(
lr=self.Helpers.confs["NLU"]["LR"],
decay=self.Helpers.confs["NLU"]["Decay"])
tf_model.compile(optimizer=optimizer,
loss='binary_crossentropy',
metrics=[
tf.keras.metrics.BinaryAccuracy(name='acc'),
tf.keras.metrics.Precision(name='precision'),
tf.keras.metrics.Recall(name='recall'),
tf.keras.metrics.AUC(name='auc')
])
tf_model.fit(x,
y,
epochs=self.Helpers.confs["NLU"]['Epochs'],
batch_size=self.Helpers.confs["NLU"]['BatchSize'])
self.saveModelData(
self.Helpers.confs["NLU"]['Model']['Data'], {
'words': words,
'classes': classes,
'x': x,
'y': y,
'intentMap': [intentMap]
}, tf_model)
def saveModelData(self, path, data, tmodel):
""" Saves the model data """
with open(self.Helpers.confs["NLU"]['Model']['Model'], "w") as file:
file.write(tmodel.to_json())
self.Helpers.logger.info("Model JSON saved " +
self.Helpers.confs["NLU"]['Model']['Model'])
with open(self.Helpers.confs["NLU"]['Model']['Data'], "w") as outfile:
json.dump(data, outfile)
tmodel.save_weights(self.Helpers.confs["NLU"]['Model']['Weights'])
self.Helpers.logger.info("Weights saved " +
self.Helpers.confs["NLU"]['Model']['Weights'])
def buildDNN(self, x, y):
""" Loads the DNN model """
with open(self.Helpers.confs["NLU"]['Model']['Model']) as file:
m_json = file.read()
tmodel = tf.keras.models.model_from_json(m_json)
tmodel.load_weights(self.Helpers.confs["NLU"]['Model']['Weights'])
self.Helpers.logger.info("Model loaded ")
return tmodel
def predict(self, tmodel, parsedSentence, trainedWords, trainedClasses):
""" Makes a prediction """
predictions = [[index, confidence] for index, confidence in enumerate(
tmodel.predict(
[[self.Data.makeBagOfWords(parsedSentence, trainedWords)]])[0])
]
predictions.sort(key=lambda x: x[1], reverse=True)
classification = []
for prediction in predictions:
classification.append(
(trainedClasses[prediction[0]], prediction[1]))
return classification
