def __init__(self, network_type, iterations, window, input_size):
self.VARS = CNN_STATIC_VARIABLES.CNN_STATIC_VARS()
subject_set = self.VARS.get_subject_set()
transition_remove_activties = {1:1, 2:2, 3:3, 4:4, 5:5, 6:6, 7:7, 8:8, 10:10, 11:11, 12:12, 13:13, 14:14, 15:15, 16:16, 17:17}
train_remove_activities = {9:9}
if network_type == 'sd':
self.config = self.VARS.get_config(input_size, 2, iterations, 100, network_type)
train_convert = self.VARS.CONVERTION_STATIC_DYNAMIC
print 'Creating data set'
self.data_set = input_data_window_large.read_EM_data_set(subject_set, 2, train_remove_activities, train_convert, transition_remove_activties, window)
if network_type == 'original':
train_convert = self.VARS.CONVERTION_ORIGINAL
self.config = self.VARS.get_config(input_size, 17, iterations, 100, network_type)
print 'Creating data set'
self.data_set = input_data_window_large.read_EM_data_set(subject_set, 17, train_remove_activities, train_convert, transition_remove_activties, window)
#TRAIN CNN MODEL WITH DATASET.TRAIN
self.cnn = CNN.CNN_TWO_LAYERS(self.config)
self.cnn.set_data_set(self.data_set)
self.cnn.train_network()
self.cnn.save_model('models/' + network_type +'_'+ str(input_size) + '_Without_T')
continue_EM=1
threshold = 0.9
while continue_EM < 4:
print "hei"
above_threshold = []
for i in range(0,len(self.data_set.transition._data)):
''' Get the transitions data point'''
data_batch = self.data_set.transition._data[i]
prediction = self.cnn.run_network_return_probability([[data_batch]])[0]
activity = np.argmax(prediction)
if prediction[activity] >= threshold:
above_threshold.append([i,activity])
''' Set the newly shuffled data set '''
self.data_set = input_data_window_large.shuffle_data(above_threshold, self.data_set)
self.cnn.set_data_set(self.data_set)
self.cnn.train_network()
continue_EM+=1
if len(above_threshold) == 0:
continue_EM = 4
self.cnn.save_model('models/' + network_type +'_'+ str(input_size) +'_EM')
def __init__(self, network_type, index, complete_set, window, input_size):
self.VARS = CNN_STATIC_VARIABLES.CNN_STATIC_VARS()
subject_set = self.VARS.get_subject_set()
if network_type == 'sd':
convertion = self.VARS.CONVERTION_STATIC_DYNAMIC
config = self.VARS.get_config(input_size, 2, index, 100, network_type)
print 'Creating data set'
self.data_set = input_data_window_large.read_data_sets(subject_set, self.VARS.len_convertion_list(convertion), convertion, None, window)
if network_type == 'original':
convertion = self.VARS.CONVERTION_ORIGINAL
config = self.VARS.get_config(input_size, 17, index, 100, network_type)
print 'Creating data set'
#self.data_set = input_data_window_large.read_data_sets(subject_set, self.VARS.len_convertion_list(convertion), convertion, None, window)
transition_remove_activties = {1:1, 2:2, 3:3, 4:4, 5:5, 6:6, 7:7, 8:8, 10:10, 11:11, 12:12, 13:13, 14:14, 15:15, 16:16, 17:17}
train_remove_activities = {9:9}
self.data_set = input_data_window_large.read_EM_data_set(subject_set, 17, train_remove_activities, convertion, transition_remove_activties, window)
if network_type == 'static':
remove_activities = self.VARS.REMOVE_DYNAMIC_ACTIVITIES
keep_activities = self.VARS.CONVERTION_STATIC
config = self.VARS.get_config(input_size, 5, index, 100, network_type)
self.data_set = input_data_window_large.read_data_sets_without_activity(subject_set, self.VARS.len_convertion_list(keep_activities), remove_activities, None, keep_activities, window)
if network_type == 'dynamic':
remove_activities = self.VARS.CONVERTION_STATIC
keep_activities = self.VARS.CONVERTION_DYNAMIC
config = self.VARS.get_config(input_size, 12, index, 100, network_type)
self.data_set = input_data_window_large.read_data_sets_without_activity(subject_set, self.VARS.len_convertion_list(keep_activities), remove_activities, None, keep_activities, window)
if network_type == 'shuf_stand':
remove_activities = self.VARS.CONVERTION_SHUF_STAND_INVERSE
keep_activities = self.VARS.CONVERTION_SHUF_STAND
config = self.VARS.get_config(input_size, 3, index, 100, network_type)
self.data_set = input_data_window_large.read_data_sets_without_activity(subject_set, len(keep_activities), remove_activities, None, keep_activities, window)
self.cnn = CNN.CNN_TWO_LAYERS(config)
self.cnn.set_data_set(self.data_set)
self.cnn.load_model('models/' + network_type + '_' + str(input_size))
if complete_set:
print self.cnn.test_network()
else:
for i in range(0,100):
#print self.data_set.test.next_data_label(i)[1]
data = self.data_set.test.next_data_label(i)
print np.argmax(data[1])+1, self.cnn.run_network(data)
def __init__(self, network_type, index, complete_set, window, input_size):
self.VARS = CNN_STATIC_VARIABLES.CNN_STATIC_VARS()
subject_set = self.VARS.get_subject_set()
if network_type == 'sd':
convertion = self.VARS.CONVERTION_STATIC_DYNAMIC
config = self.VARS.get_config(input_size, 2, index, 100,
network_type)
print 'Creating data set'
self.data_set = input_data_window_large.read_data_sets(
subject_set, self.VARS.len_convertion_list(convertion),
convertion, None, window)
if network_type == 'original':
convertion = self.VARS.CONVERTION_ORIGINAL
config = self.VARS.get_config(input_size, 17, index, 100,
network_type)
print 'Creating data set'
#self.data_set = input_data_window_large.read_data_sets(subject_set, self.VARS.len_convertion_list(convertion), convertion, None, window)
transition_remove_activties = {
1: 1,
2: 2,
3: 3,
4: 4,
5: 5,
6: 6,
7: 7,
8: 8,
10: 10,
11: 11,
12: 12,
13: 13,
14: 14,
15: 15,
16: 16,
17: 17
}
train_remove_activities = {9: 9}
self.data_set = input_data_window_large.read_EM_data_set(
subject_set, 17, train_remove_activities, convertion,
transition_remove_activties, window)
if network_type == 'static':
remove_activities = self.VARS.REMOVE_DYNAMIC_ACTIVITIES
keep_activities = self.VARS.CONVERTION_STATIC
config = self.VARS.get_config(input_size, 5, index, 100,
network_type)
self.data_set = input_data_window_large.read_data_sets_without_activity(
subject_set, self.VARS.len_convertion_list(keep_activities),
remove_activities, None, keep_activities, window)
if network_type == 'dynamic':
remove_activities = self.VARS.CONVERTION_STATIC
keep_activities = self.VARS.CONVERTION_DYNAMIC
config = self.VARS.get_config(input_size, 12, index, 100,
network_type)
self.data_set = input_data_window_large.read_data_sets_without_activity(
subject_set, self.VARS.len_convertion_list(keep_activities),
remove_activities, None, keep_activities, window)
if network_type == 'shuf_stand':
remove_activities = self.VARS.CONVERTION_SHUF_STAND_INVERSE
keep_activities = self.VARS.CONVERTION_SHUF_STAND
config = self.VARS.get_config(input_size, 3, index, 100,
network_type)
self.data_set = input_data_window_large.read_data_sets_without_activity(
subject_set, len(keep_activities), remove_activities, None,
keep_activities, window)
self.cnn = CNN.CNN_TWO_LAYERS(config)
self.cnn.set_data_set(self.data_set)
self.cnn.load_model('models/' + network_type + '_' + str(input_size))
if complete_set:
print self.cnn.test_network()
else:
for i in range(0, 100):
#print self.data_set.test.next_data_label(i)[1]
data = self.data_set.test.next_data_label(i)
print np.argmax(data[1]) + 1, self.cnn.run_network(data)
def __init__(self, network_type, iterations, window, input_size):
self.VARS = CNN_STATIC_VARIABLES.CNN_STATIC_VARS()
subject_set = self.VARS.get_subject_set()
transition_remove_activties = {
1: 1,
2: 2,
3: 3,
4: 4,
5: 5,
6: 6,
7: 7,
8: 8,
10: 10,
11: 11,
12: 12,
13: 13,
14: 14,
15: 15,
16: 16,
17: 17
}
train_remove_activities = {9: 9}
if network_type == 'sd':
self.config = self.VARS.get_config(input_size, 2, iterations, 100,
network_type)
train_convert = self.VARS.CONVERTION_STATIC_DYNAMIC
print 'Creating data set'
self.data_set = input_data_window_large.read_EM_data_set(
subject_set, 2, train_remove_activities, train_convert,
transition_remove_activties, window)
if network_type == 'original':
train_convert = self.VARS.CONVERTION_ORIGINAL
self.config = self.VARS.get_config(input_size, 17, iterations, 100,
network_type)
print 'Creating data set'
self.data_set = input_data_window_large.read_EM_data_set(
subject_set, 17, train_remove_activities, train_convert,
transition_remove_activties, window)
#TRAIN CNN MODEL WITH DATASET.TRAIN
self.cnn = CNN.CNN_TWO_LAYERS(self.config)
self.cnn.set_data_set(self.data_set)
self.cnn.train_network()
self.cnn.save_model('models/' + network_type + '_' + str(input_size) +
'_Without_T')
continue_EM = 1
threshold = 0.9
while continue_EM < 4:
print "hei"
above_threshold = []
for i in range(0, len(self.data_set.transition._data)):
''' Get the transitions data point'''
data_batch = self.data_set.transition._data[i]
prediction = self.cnn.run_network_return_probability(
[[data_batch]])[0]
activity = np.argmax(prediction)
if prediction[activity] >= threshold:
above_threshold.append([i, activity])
''' Set the newly shuffled data set '''
self.data_set = input_data_window_large.shuffle_data(
above_threshold, self.data_set)
self.cnn.set_data_set(self.data_set)
self.cnn.train_network()
continue_EM += 1
if len(above_threshold) == 0:
continue_EM = 4
self.cnn.save_model('models/' + network_type + '_' + str(input_size) +
'_EM')
