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 __init__(self, testing=True, binary_output=False):
"""
@param testing: Should be True if test data is to be plottet. Otherwise False.
@param image_data: If the testing should be done on image data.
@param binary_output: If the output of the DBN must be binary.
"""
if not check_for_data:
print 'No DBN data or testing data.'
return
self.status = -1
self.output = []
self.testing = testing
self.binary_output = binary_output
try:
self.output_data = s.load(open('output/output_data.p', 'rb'))
self.class_indices = s.load(open('output/class_indices.p', 'rb'))
except:
self.output_data = generate_output_for_test_data(
binary_output=self.binary_output) if testing else generate_output_for_train_data(
binary_output=self.binary_output)
self.class_indices = get_all_class_indices(training=False) if testing else get_all_class_indices()
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.output_data = np.array(self.output_data)
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))))
