def __generate_output_data(self): """ Generate the output data of the DBN so that it can be visualised. """ if not len(self.output_data) == 0: return try: self.output_data = s.load(open('output/output_data.p', 'rb')) self.class_indices = s.load(open('output/class_indices.p', 'rb')) if not self.classes_to_visualise == None: self.__filter_output_data(self.classes_to_visualise) except: self.output_data = generate_output_for_test_data( image_data=self.image_data, binary_output=self.binary_output ) if self.testing else generate_output_for_train_data( image_data=self.image_data, binary_output=self.binary_output) self.class_indices = get_all_class_indices( training=False) if self.testing else get_all_class_indices() if not self.classes_to_visualise == None: self.__filter_output_data(self.classes_to_visualise) s.dump([out.tolist() for out in self.output_data], open('output/output_data.p', 'wb')) s.dump(self.class_indices, open('output/class_indices.p', 'wb')) self.legend = get_class_names_for_class_indices( list(set(sorted(self.class_indices))))
def __generate_input_data(self): """ Generate the input data for the DBN so that it can be visualized. """ if not len(self.input_data) == 0: return try: self.input_data = s.load(open('output/input_data.p', 'rb')) self.class_indices = s.load(open('output/class_indices.p', 'rb')) if not self.classes_to_visualise == None: self.__filter_input_data(self.classes_to_visualise) except: self.input_data = generate_input_data_list( training=False) if self.testing else generate_input_data_list( ) self.class_indices = get_all_class_indices( training=False) if self.testing else get_all_class_indices() if not self.classes_to_visualise == None: self.__filter_input_data(self.classes_to_visualise) s.dump([input.tolist() for input in self.input_data], open('output/input_data.p', 'wb')) s.dump(self.class_indices, open('output/class_indices.p', 'wb')) self.legend = get_class_names_for_class_indices( list(set(sorted(self.class_indices))))
def __generate_input_data(self): """ Generate the input data for the DBN so that it can be visualized. """ if not len(self.input_data) == 0: return try: self.input_data = s.load(open('output/input_data.p', 'rb')) self.class_indices = s.load(open('output/class_indices.p', 'rb')) if not self.classes_to_visualise == None: self.__filter_input_data(self.classes_to_visualise) except: self.input_data = generate_input_data_list(training=False) if self.testing else generate_input_data_list() self.class_indices = get_all_class_indices(training=False) if self.testing else get_all_class_indices() if not self.classes_to_visualise == None: self.__filter_input_data(self.classes_to_visualise) s.dump([input.tolist() for input in self.input_data], open('output/input_data.p', 'wb')) s.dump(self.class_indices, open('output/class_indices.p', 'wb')) self.legend = get_class_names_for_class_indices(list(set(sorted(self.class_indices))))
def __generate_output_data(self): """ Generate the output data of the DBN so that it can be visualised. """ if not len(self.output_data) == 0: return try: self.output_data = s.load(open('output/output_data.p', 'rb')) self.class_indices = s.load(open('output/class_indices.p', 'rb')) if not self.classes_to_visualise == None: self.__filter_output_data(self.classes_to_visualise) except: self.output_data = generate_output_for_test_data(image_data=self.image_data, binary_output=self.binary_output) if self.testing else generate_output_for_train_data( image_data=self.image_data, binary_output=self.binary_output) self.class_indices = get_all_class_indices(training=False) if self.testing else get_all_class_indices() if not self.classes_to_visualise == None: self.__filter_output_data(self.classes_to_visualise) s.dump([out.tolist() for out in self.output_data], open('output/output_data.p', 'wb')) s.dump(self.class_indices, open('output/class_indices.p', 'wb')) self.legend = get_class_names_for_class_indices(list(set(sorted(self.class_indices))))