def handleFileSelectionResponce(self, fileList, recognizer):
"""
@param fileList:
@return
"""
if len(fileList) == 0:
self.lblSelectedDir.configure(
text="Dataset directory not yet specified")
return
if len(fileList) > 1:
self.lblSelectedDir.configure(
text="Multiple Directories selected - data is of type %s" %
(str(recognizer)))
else:
self.lblSelectedDir.configure(
text="Single Dataset Selected - data is of type %s" %
(str(recognizer)))
self.dataset = DatasetFactory.buildMultiset()
with concurrent.futures.ThreadPoolExecutor() as tpe:
dsFutures = []
for i in fileList.keys():
dsFutures.append(
tpe.submit(
lambda x: DatasetFactory.buildDataset(
fileList[x], ast.literal_eval(x), hint=recognizer),
i))
for ff in dsFutures:
self.dataset.addDataset(ff.result())
self.plotter = Plotter.Plotter(self.dataset)
def handleFileSelectionResponce(self, fileList, recognizer):
"""
@param fileList:
@return
"""
if len(fileList) == 0:
self.lblSelectedDir.configure(text="Dataset directory not yet specified")
return
if len(fileList) > 1:
self.lblSelectedDir.configure(text="Multiple Directories selected - data is of type %s" % (str(recognizer)))
else:
self.lblSelectedDir.configure(text="Single Dataset Selected - data is of type %s" % (str(recognizer)))
self.dataset = DatasetFactory.buildMultiset()
with concurrent.futures.ThreadPoolExecutor() as tpe:
dsFutures = []
for i in fileList.keys():
dsFutures.append(tpe.submit(lambda x: DatasetFactory.buildDataset(fileList[x],
ast.literal_eval(x),
hint=recognizer), i))
for ff in dsFutures:
self.dataset.addDataset(ff.result())
self.plotter = Plotter.Plotter(self.dataset)
def _setDirectory(self):
"""
@return
"""
selected = filedialog.askdirectory()
self.selectedDir.configure(text=selected)
self.dataset = DatasetFactory.buildDataset(selected + '/')
self.plotter = Plotter.Plotter(self.dataset)
self._plotDataset()
def _setDirectory(self):
"""
@return
"""
selected = filedialog.askdirectory()
self.selectedDir.configure(text=selected)
self.dataset = DatasetFactory.buildDataset(selected + '/')
self.plotter = Plotter.Plotter(self.dataset)
self._plotDataset()
def execute_training_and_evaluation():
training_data_x, training_labels_y = DatasetFactory.load_training_data_and_its_labels(
)
evaluation_data_x, evaluation_labels_y = DatasetFactory.load_evaluation_data_and_its_labels(
)
network_model = ModelFactory.create_fpool3_model()
ModelService.compile_and_fit_the_model(network_model, training_data_x,
training_labels_y)
accuracy = ModelService.evaluate_the_model(network_model,
evaluation_data_x,
evaluation_labels_y)
file_name = ModelService.save_model_and_configuration(
network_model, accuracy)
print(
'Jarvis hot word detector training has completed. File stored @ ' +
file_name)
