def display_history(arguments, set_vars):
"""
Display history of current user on screen
@param: arguments: history command's arguments
@param: set_vars: dictionary of variables
"""
if not arguments:
show_history(arguments, set_vars)
set_vars['exit_status'] = 0
elif len(arguments) == 1 and arguments[0].isnumeric():
show_history(arguments, set_vars)
set_vars['exit_status'] = 0
else:
if not arguments[0].isnumeric():
print('intek-sh: history: ' + arguments[0] +
': numeric argument required')
else:
print('intek-sh: history: too many arguments')
set_vars['exit_status'] = 1
def showRawValues(self):
try:
self._dl = fn.qt.QDialogWidget(buttons=True)
qc = Qt.QComboBox()
qc.addItems(sorted(self.last_args.keys()))
self._dl.setWidget(qc)
#dates = []
#try:
#self.warning('-------------------')
#dates.append(self.trend.axisScaleDiv(self.trend.xBottom).lowerBound())
#dates.append(self.trend.axisScaleDiv(self.trend.xBottom).upperBound())
#self.warning(dates)
#except: self.warning(traceback.format_exc())
self._dl.setAccept(lambda q=qc:show_history(parseTrendModel(str(q.currentText()))[1])) #,dates=dates))
self._dl.show()
except:
self.warning(traceback.format_exc())
def showRawValues(self):
try:
self._dl = fn.qt.QDialogWidget(buttons=True)
qc = Qt.QComboBox()
qc.addItems(sorted(self.last_args.keys()))
self._dl.setWidget(qc)
#dates = []
#try:
#self.warning('-------------------')
#dates.append(self.trend.axisScaleDiv(self.trend.xBottom).lowerBound())
#dates.append(self.trend.axisScaleDiv(self.trend.xBottom).upperBound())
#self.warning(dates)
#except: self.warning(traceback.format_exc())
self._dl.setAccept(lambda q=qc: show_history(
parseTrendModel(str(q.currentText()))[1])) #,dates=dates))
self._dl.show()
except:
self.warning(traceback.format_exc())
epochs = 200
samples = 20000
size = 64
input_size = (size, size, 3)
x_train, y_train = load_regulized_train_dataset(samples, size)
y_train = to_categorical(y_train)
x_train, x_validation, y_train, y_validation = train_test_split(
x_train, y_train, test_size=0.2, stratify=y_train)
model = cnn_model(input_size)
callbacks = [
ModelCheckpoint(filepath="./models/model_{epoch:02d}.h5"),
TensorBoard(log_dir="./logs")
]
model_history = model.fit(x_train,
y_train,
epochs=epochs,
callbacks=callbacks,
validation_data=(x_validation, y_validation))
model.save("./models/model_final.h5")
history = model_history.history
with open("./history/model_history.pkl", "wb") as f:
pickle.dump(history, f)
show_history(history)