class TrainingResult(Screen):
def __init__(self, **kwargs):
super(TrainingResult, self).__init__(**kwargs)
contents = Builder.load_string(kv)
self.add_widget(contents)
self.ids = contents.ids
# Bind the network
self.network = NeuralNetwork(App.get_running_app())
# Sample content
# grid = self.ids.result_grid
# for j in range(157):
# # First column: Actual image
# grid.add_widget(Image(source='face1.png'))
#
# # Second column: Network's image reconstruction
# grid.add_widget(Image(source='face1.png'))
#
# # Third column: Network Representation
# grid.add_widget(Label(text='mad', size_hint=(.5, .5)))
#
# # Fourth column: Actual representation
# grid.add_widget(Label(text='happy', size_hint=(.5, .5)))
#
# # Fifth column: correct/incorrect
# grid.add_widget(Label(text='1', size_hint=(.5, .5)))
# Bind the buttons
def back_pressed(instance):
self.pause_training(instance)
App.get_running_app().go_back()
self.ids.back_button.bind(on_press=back_pressed)
# Start the training thread when the screen is displayed
self.bind(on_enter=self.start_training)
# Manage network training
def start_training(self, instance):
Logger.info('Starting training')
self.network.reset_training()
self.resume_training(self)
def resume_training(self, instance):
Logger.info('Resuming training')
self.clear_results()
self.training_paused = False
pause_button = self.ids.pause_button
pause_button.text = 'Pause'
pause_button.unbind(on_press=self.resume_training)
pause_button.bind(on_press=self.pause_training)
# Make sure the thread stops on application exit
App.get_running_app().unbind(on_stop=self.pause_training)
App.get_running_app().bind(on_stop=self.pause_training)
threading.Thread(target=self._run_training).start()
def pause_training(self, instance):
self.training_paused = True
pause_button = self.ids.pause_button
pause_button.text = 'Resume'
pause_button.unbind(on_press=self.pause_training)
pause_button.bind(on_press=self.resume_training)
self.display_results()
def clear_results(self):
'''Clears all children of the grid, except for the header labels.'''
grid = self.ids.result_grid
grid.clear_widgets()
@mainthread
def display_results(self):
'''Shows the result of the training in the grid.'''
grid = self.ids.result_grid
predictions, reconstructions = self.network.predict_all()
predictions_correct = predictions == self.network.targets
for is_test, image, reconstruction, prediction, target, prediction_correct in \
zip(self.network.idx_test, self.network.app.dataset['images'], reconstructions, predictions,
self.network.targets, predictions_correct):
# First column: Actual image
grid.add_widget(ResultImage(image))
# Second column: Network's image reconstruction
grid.add_widget(ResultImage(reconstruction))
# Third column: Network Representation
grid.add_widget(Label(text=self.network.target_names[prediction + 1], size_hint=(.5, .5)))
# Fourth column: Actual representation
grid.add_widget(Label(text=self.network.target_names[target + 1], size_hint=(.5, .5)))
# Fifth column: correct/incorrect
grid.add_widget(Label(text='%d' % prediction_correct, size_hint=(.5, .5)))
self.ids.result_scrollview.disabled = False
self.ids.table_header.disabled = False
def _run_training(self):
graph = self.ids.training_graph
for epoch, epochs, rmse, cerr, is_last in self.network.resume_training():
if (epoch % 10) == 1 or is_last:
Logger.debug('epoch: %d, rmse shape: %s' % (epoch, str(rmse.shape)))
graph.plot(epoch, epochs, rmse, cerr, self.network.minimum_rmse)
if self.training_paused:
break
Logger.info('Exit training thread')
self.display_results()
