def __init__(self, network_type, iterations, window, input_size):
self.VARS = CNN_STATIC_VARIABLES.CNN_STATIC_VARS()
subject_set = self.VARS.get_subject_set()
if network_type == 'sd':
self.config = self.VARS.get_config(input_size, 2, iterations, 100, network_type)
convertion = self.VARS.CONVERTION_STATIC_DYNAMIC
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':
self.config = self.VARS.get_config(input_size, 17, iterations, 100, network_type)
convertion = self.VARS.CONVERTION_ORIGINAL
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 == 'static':
self.config = self.VARS.get_config(input_size, 5, iterations, 100, network_type)
remove_activities = self.VARS.REMOVE_DYNAMIC_ACTIVITIES
keep_activities = self.VARS.CONVERTION_STATIC
self.data_set = input_data_window_large.read_data_sets_without_activity(subject_set, len(keep_activities), remove_activities, None, keep_activities, window)
if network_type == 'dynamic':
remove_activities = self.VARS.CONVERTION_STATIC
keep_activities = self.VARS.CONVERTION_DYNAMIC
self.config = self.VARS.get_config(input_size, len(keep_activities), iterations, 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)
if network_type == 'walk_stairs':
remove_activities = self.VARS.CONVERTION_WALK_STAIRS_REMOVE
keep_activities = self.VARS.CONVERTION_WALK_STAIRS
self.config = self.VARS.get_config(input_size, len(keep_activities), iterations, 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)
if network_type == 'shuf_stand':
remove_activities = self.VARS.CONVERTION_SHUF_STAND_INVERSE
keep_activities = self.VARS.CONVERTION_SHUF_STAND
self.config = self.VARS.get_config(input_size, len(keep_activities), iterations, 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)
if network_type == 'stand_sit':
self.config = self.VARS.get_config(input_size, 3, iterations, 100, network_type)
convertion = self.VARS.CONVERTION_STAND_SIT
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 == 'lying':
self.config = self.VARS.get_config(input_size, 2, iterations, 100, network_type)
convertion = self.VARS.CONVERTION_LYING
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)
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))
def __init__(self, network_type, iterations, window, input_size):
self.VARS = CNN_STATIC_VARIABLES.CNN_STATIC_VARS()
subject_set = self.VARS.get_subject_set()
if network_type == 'sd':
self.config = self.VARS.get_config(input_size, 2, iterations, 100,
network_type)
convertion = self.VARS.CONVERTION_STATIC_DYNAMIC
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':
self.config = self.VARS.get_config(input_size, 17, iterations, 100,
network_type)
convertion = self.VARS.CONVERTION_ORIGINAL
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 == 'static':
self.config = self.VARS.get_config(input_size, 5, iterations, 100,
network_type)
remove_activities = self.VARS.REMOVE_DYNAMIC_ACTIVITIES
keep_activities = self.VARS.CONVERTION_STATIC
self.data_set = input_data_window_large.read_data_sets_without_activity(
subject_set, len(keep_activities), remove_activities, None,
keep_activities, window)
if network_type == 'dynamic':
remove_activities = self.VARS.CONVERTION_STATIC
keep_activities = self.VARS.CONVERTION_DYNAMIC
self.config = self.VARS.get_config(input_size,
len(keep_activities),
iterations, 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)
if network_type == 'shuf_stand':
remove_activities = self.VARS.CONVERTION_SHUF_STAND_INVERSE
keep_activities = self.VARS.CONVERTION_SHUF_STAND
self.config = self.VARS.get_config(input_size,
len(keep_activities),
iterations, 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(self.config)
self.cnn.set_data_set(self.data_set)
self.cnn.train_network()
self.cnn.save_model('models/' + network_type + '_' + str(input_size))
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)
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:
data = self.data_set.test.next_data_label(index)
print np.argmax(data[1])+1, self.cnn.run_network(data)
def __init__(self, window, input_size):
self.input_size = input_size
self.window = window
self.VARS = CNN_STATIC_VARIABLES.CNN_STATIC_VARS()
subject_set = self.VARS.get_subject_set()
print 'Creating ORIGINAL data set'
keep_activities = self.VARS.CONVERTION_ORIGINAL
remove_activities = { 2:2, 3:3, 6:6, 7:7, 8:8, 9:9, 10:10, 11:11, 12:12, 13:13, 14:14, 15:15, 16:16, 17:17}
self.data_set_ORIGINAL = input_data_window_large.read_data_sets_without_activity(subject_set, len(keep_activities), remove_activities, None, keep_activities, window)
def __init__(self, network_type, iterations, models): self.VARS = CNN_STATIC_VARIABLES.CNN_STATIC_VARS() subject_set = self.VARS.get_subject_set(False) remove_activities = self.VARS.CONVERTION_ORIGINAL_INVERSE keep_activities = self.VARS.CONVERTION_ORIGINAL raw_signal_labels = get_raw_signal_labels(subject_set) output = 10 window = "1.0" for i in range(0,len(models)): print "model: " print i window_size = models[i][1]/6 self.data_set = input_data_window_large.read_data_sets_without_activity(subject_set, output, remove_activities, None, keep_activities, models[i][5]) self.config = self.VARS.get_config(window_size, output, iterations, window_size, network_type, models[i][2][0], models[i][2][1], models[i][3][0], models[i][4])
def __init__(self, network_type, iterations, window, input_size, conv_layers, neural_layers, filter_type):
self.VARS = CNN_STATIC_VARIABLES.CNN_STATIC_VARS()
subject_set = self.VARS.get_subject_set(False)
if network_type=='original':
remove_activities = self.VARS.CONVERTION_ORIGINAL_INVERSE
keep_activities = self.VARS.CONVERTION_ORIGINAL
output = 10
self.config = self.VARS.get_config(input_size, 10, iterations, 100, network_type, conv_f_1, conv_f_2, nn, filter_type)
self.data_set = input_data_window_large.read_data_sets_without_activity(subject_set, 10, remove_activities, None, keep_activities, window)
if network_type=='sd':
remove_activities = self.VARS.CONVERTION_STATIC_DYNAMIC_INVERSE
keep_activities = self.VARS.CONVERTION_STATIC_DYNAMIC
output = 10
self.config = self.VARS.get_config(input_size, output, iterations, 100, network_type, conv_layers, neural_layers, filter_type)
self.data_set = input_data_window_large.read_data_sets_without_activity(subject_set, output, remove_activities, None, keep_activities, window)
if network_type=='stand-sit':
remove_activities = self.VARS.CONVERTION_STAND_SIT_INVERSE
keep_activities = self.VARS.CONVERTION_STAND_SIT
self.config = self.VARS.get_config(input_size, 2, iterations, 100, network_type, conv_f_1, conv_f_2, nn, filter_type)
self.data_set = input_data_window_large.read_data_sets_without_activity(subject_set, 2, remove_activities, None, keep_activities, window)
if network_type=='stairs':
remove_activities = self.VARS.CONVERTION_STAIRS_INVERSE
keep_activities = self.VARS.CONVERTION_STAIRS
self.config = self.VARS.get_config(input_size, len(keep_activities), iterations, 100, network_type, conv_f_1, conv_f_2, nn, filter_type)
self.data_set = input_data_window_large.read_data_sets_without_activity(subject_set, len(keep_activities), remove_activities, None, keep_activities, window)
if network_type=='stairs-walk':
remove_activities = self.VARS.CONVERTION_STAIRS_WALK_INVERSE
keep_activities = self.VARS.CONVERTION_STAIRS_WALK
self.config = self.VARS.get_config(input_size, len(keep_activities), iterations, 100, network_type, conv_f_1, conv_f_2, nn, filter_type)
self.data_set = input_data_window_large.read_data_sets_without_activity(subject_set, len(keep_activities), remove_activities, None, keep_activities, window)
if network_type=='cycling-sitting':
remove_activities = self.VARS.CONVERTION_CYCLING_SITTING_INVERSE
keep_activities = self.VARS.CONVERTION_CYCLING_SITTING
self.config = self.VARS.get_config(input_size, len(keep_activities), iterations, 100, network_type, conv_f_1, conv_f_2, nn, filter_type)
self.data_set = input_data_window_large.read_data_sets_without_activity(subject_set, len(keep_activities), remove_activities, None, keep_activities, window)
if network_type=='cycling-sitting-lying':
remove_activities = self.VARS.CONVERTION_CYCLING_SITTING_LYING_INVERSE
keep_activities = self.VARS.CONVERTION_CYCLING_SITTING_LYING
self.config = self.VARS.get_config(input_size, len(keep_activities), iterations, 100, network_type, conv_f_1, conv_f_2, nn, filter_type)
self.data_set = input_data_window_large.read_data_sets_without_activity(subject_set, len(keep_activities), remove_activities, None, keep_activities, window)
if network_type=='stand-nonvig-shuf':
remove_activities = self.VARS.CONVERTION_STAND_NONVIG_SHUF_INVERSE
keep_activities = self.VARS.CONVERTION_STAND_NONVIG_SHUF
self.config = self.VARS.get_config(input_size, len(keep_activities), iterations, 100, network_type, conv_f_1, conv_f_2, nn, filter_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_MOD_4.CNN_FILTER(self.config)
self.data_set.train.shuffle_data_set()
self.cnn.set_data_set(self.data_set)
self.cnn.train_network()
self.cnn.save_model('models/'+self.config['model_name'])
self.cnn.test_network()
# self.x: self._data_set.test.data, self.y_: self._data_set.test.labels, self.keep_prob: 1.0}))
if __name__ == "__main__":
test = True
VARS = CNN_STATIC_VARIABLES.CNN_STATIC_VARS()
subject_set = VARS.get_subject_set(False)
remove_activities = VARS.CONVERTION_STATIC_DYNAMIC_INVERSE
keep_activities = VARS.CONVERTION_STATIC_DYNAMIC
output = 10
config = VARS.get_config(600, output, 20000, 100, 'sd', [20, 40], [1500],
"VALID")
if test:
data_set = input_data_window_large.read_data_sets_without_activity(
[['01A'], subject_set[1]], output, remove_activities, None,
keep_activities, "1.0")
model = config['model_name']
print model
cnn = CNN_FILTER(config)
cnn.set_data_set(data_set)
cnn.load_model('models/' + model)
cnn.test_network_stepwise()
else:
data_set = input_data_window_large.read_data_sets_without_activity(
subject_set, output, remove_activities, None, keep_activities,
"1.0")
data_set.train.shuffle_data_set()
model = config['model_name']
cnn = CNN_FILTER(config)
def __init__(self, network_type, index, complete_set, window, input_size,
conv_f_1, conv_f_2, nn, filter_type):
self.VARS = CNN_STATIC_VARIABLES.CNN_STATIC_VARS()
subject_set = self.VARS.get_subject_set(False)
if network_type == 'original':
output = 10
remove_activities = self.VARS.CONVERTION_ORIGINAL_INVERSE
keep_activities = self.VARS.CONVERTION_ORIGINAL
self.config = self.VARS.get_config(input_size, 10, index, 100,
network_type, conv_f_1,
conv_f_2, nn, filter_type)
self.data_set = input_data_window_large.read_data_sets_without_activity(
subject_set, 10, remove_activities, True, keep_activities,
window)
if network_type == 'sd':
remove_activities = self.VARS.CONVERTION_STATIC_DYNAMIC_INVERSE
keep_activities = self.VARS.CONVERTION_STATIC_DYNAMIC
output = 2
self.config = self.VARS.get_config(input_size, output, index, 100,
network_type, conv_f_1,
conv_f_2, nn, filter_type)
self.data_set = input_data_window_large.read_data_sets_without_activity(
subject_set, output, remove_activities, True, keep_activities,
window)
if network_type == 'stand-up':
remove_activities = self.VARS.CONVERTION_STAND_UP_INVERSE
keep_activities = self.VARS.CONVERTION_STAND_UP
self.config = self.VARS.get_config(input_size,
len(keep_activities), index,
100, network_type, conv_f_1,
conv_f_2, nn_1, filter_type)
self.data_set = input_data_window_large.read_data_sets_without_activity(
subject_set, len(keep_activities), remove_activities, True,
keep_activities, window)
if network_type == 'stairs-walk':
remove_activities = self.VARS.CONVERTION_STAIRS_WALK_INVERSE
keep_activities = self.VARS.CONVERTION_STAIRS_WALK
self.config = self.VARS.get_config(input_size,
len(keep_activities), index,
100, network_type, conv_f_1,
conv_f_2, nn_1, filter_type)
self.data_set = input_data_window_large.read_data_sets_without_activity(
subject_set, len(keep_activities), remove_activities, True,
keep_activities, window)
if network_type == 'stairs':
remove_activities = self.VARS.CONVERTION_STAIRS_INVERSE
keep_activities = self.VARS.CONVERTION_STAIRS
self.config = self.VARS.get_config(input_size,
len(keep_activities), index,
100, network_type, conv_f_1,
conv_f_2, nn_1, filter_type)
self.data_set = input_data_window_large.read_data_sets_without_activity(
subject_set, len(keep_activities), remove_activities, True,
keep_activities, window)
if network_type == 'cycling-sitting':
remove_activities = self.VARS.CONVERTION_CYCLING_SITTING_INVERSE
keep_activities = self.VARS.CONVERTION_CYCLING_SITTING
self.config = self.VARS.get_config(input_size,
len(keep_activities), index,
100, network_type, conv_f_1,
conv_f_2, nn_1, filter_type)
self.data_set = input_data_window_large.read_data_sets_without_activity(
subject_set, len(keep_activities), remove_activities, True,
keep_activities, window)
if network_type == 'stand-nonvig-shuf':
remove_activities = self.VARS.CONVERTION_STAND_NONVIG_SHUF_INVERSE
keep_activities = self.VARS.CONVERTION_STAND_NONVIG_SHUF
self.config = self.VARS.get_config(input_size,
len(keep_activities), index,
100, network_type, conv_f_1,
conv_f_2, nn_1, filter_type)
self.data_set = input_data_window_large.read_data_sets_without_activity(
subject_set, len(keep_activities), remove_activities, True,
keep_activities, window)
self.cnn = CNN_MOD_3.CNN_MOD(self.config)
self.cnn.set_data_set(self.data_set)
self.cnn.load_model('models/' + network_type + '_' + str(input_size) +
'_' + str(conv_f_1) + '_' + str(conv_f_2) + '_' +
str(nn[0]) + '_' + str(nn[1]) + '_' + filter_type)
if complete_set == 1:
print self.cnn.test_network()
elif complete_set == 2:
print self.cnn.test_real_accuracy_on_network(
self.data_set.test, window, input_size / 6, convertion)
elif complete_set == 3:
print "Activity accuracy"
''' Get the original data set - 3ee what activities fails '''
remove_activities = self.VARS.CONVERTION_ORIGINAL_INVERSE
keep_activities = self.VARS.CONVERTION_ORIGINAL
config = self.VARS.get_config(input_size, 10, index, 100,
network_type, conv_f_1, conv_f_2, nn,
filter_type)
original_data_set = input_data_window_large.read_data_sets_without_activity(
subject_set, 10, remove_activities, True, keep_activities,
window)
''' Get the act'''
activity_accuracy = self.cnn.get_activity_list_accuracy(
original_data_set, self.data_set)
for i in range(0, len(activity_accuracy)):
print str(activity_accuracy[i]).replace(".", ",")
else:
data = self.data_set.test.next_data_label(index)
print data
print np.argmax(data[1]) + 1, self.cnn.run_network(data)
#print(self.sess.run(self.accuracy,feed_dict={
# self.x: self._data_set.test.data, self.y_: self._data_set.test.labels, self.keep_prob: 1.0}))
if __name__ == "__main__":
test = True
VARS = CNN_STATIC_VARIABLES.CNN_STATIC_VARS()
subject_set = VARS.get_subject_set(False)
remove_activities = VARS.CONVERTION_STATIC_DYNAMIC_INVERSE
keep_activities = VARS.CONVERTION_STATIC_DYNAMIC
output = 10
config = VARS.get_config(600, output, 20000, 100, 'sd',[20,40], [1500], "VALID")
if test:
data_set = input_data_window_large.read_data_sets_without_activity([['01A'],subject_set[1]], output, remove_activities, None, keep_activities, "1.0")
model = config['model_name']
print model
cnn = CNN_FILTER(config)
cnn.set_data_set(data_set)
cnn.load_model('models/' + model)
cnn.test_network_stepwise()
else:
data_set = input_data_window_large.read_data_sets_without_activity(subject_set, output, remove_activities, None, keep_activities, "1.0")
#data_set.train.shuffle_data_set()
model = config['model_name']
cnn = CNN_FILTER(config)
cnn.set_data_set(data_set)
cnn.train_network()
cnn.save_model('models/' + model)
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, conv_layers, neural_layers, filter_type):
self.VARS = CNN_STATIC_VARIABLES.CNN_STATIC_VARS()
subject_set = self.VARS.get_subject_set(False)
if network_type=='original':
output = 10
remove_activities = self.VARS.CONVERTION_ORIGINAL_INVERSE
keep_activities = self.VARS.CONVERTION_ORIGINAL
self.config = self.VARS.get_config(input_size, 10, index, 100, network_type, conv_f_1, conv_f_2, nn, filter_type)
self.data_set = input_data_window_large.read_data_sets_without_activity(subject_set, 10, remove_activities, True, keep_activities, window)
if network_type=='sd':
remove_activities = self.VARS.CONVERTION_STATIC_DYNAMIC_INVERSE
keep_activities = self.VARS.CONVERTION_STATIC_DYNAMIC
output = 10
self.config = self.VARS.get_config(input_size, output, index, 100, network_type, conv_layers, neural_layers, filter_type)
self.data_set = input_data_window_large.read_data_sets_without_activity(subject_set, output, remove_activities, True, keep_activities, window)
if network_type=='stand-up':
remove_activities = self.VARS.CONVERTION_STAND_UP_INVERSE
keep_activities = self.VARS.CONVERTION_STAND_UP
self.config = self.VARS.get_config(input_size, len(keep_activities), index, 100, network_type, conv_f_1, conv_f_2, nn_1, filter_type)
self.data_set = input_data_window_large.read_data_sets_without_activity(subject_set, len(keep_activities), remove_activities, True, keep_activities, window)
if network_type=='stairs-walk':
remove_activities = self.VARS.CONVERTION_STAIRS_WALK_INVERSE
keep_activities = self.VARS.CONVERTION_STAIRS_WALK
self.config = self.VARS.get_config(input_size, len(keep_activities), index, 100, network_type, conv_f_1, conv_f_2, nn_1, filter_type)
self.data_set = input_data_window_large.read_data_sets_without_activity(subject_set, len(keep_activities), remove_activities, True, keep_activities, window)
if network_type=='stairs':
remove_activities = self.VARS.CONVERTION_STAIRS_INVERSE
keep_activities = self.VARS.CONVERTION_STAIRS
self.config = self.VARS.get_config(input_size, len(keep_activities), index, 100, network_type, conv_f_1, conv_f_2, nn_1, filter_type)
self.data_set = input_data_window_large.read_data_sets_without_activity(subject_set, len(keep_activities), remove_activities, True, keep_activities, window)
if network_type=='cycling-sitting':
remove_activities = self.VARS.CONVERTION_CYCLING_SITTING_INVERSE
keep_activities = self.VARS.CONVERTION_CYCLING_SITTING
self.config = self.VARS.get_config(input_size, len(keep_activities), index, 100, network_type, conv_f_1, conv_f_2, nn_1, filter_type)
self.data_set = input_data_window_large.read_data_sets_without_activity(subject_set, len(keep_activities), remove_activities, True, keep_activities, window)
if network_type=='stand-nonvig-shuf':
remove_activities = self.VARS.CONVERTION_STAND_NONVIG_SHUF_INVERSE
keep_activities = self.VARS.CONVERTION_STAND_NONVIG_SHUF
self.config = self.VARS.get_config(input_size, len(keep_activities), index, 100, network_type, conv_f_1, conv_f_2, nn_1, filter_type)
self.data_set = input_data_window_large.read_data_sets_without_activity(subject_set, len(keep_activities), remove_activities, True, keep_activities, window)
self.cnn = CNN_MOD_4.CNN_FILTER(self.config)
self.cnn.set_data_set(self.data_set)
self.cnn.load_model('models/' + self.config['model_name'])
if complete_set==1:
print self.cnn.test_network()
elif complete_set==2:
print self.cnn.test_real_accuracy_on_network(self.data_set.test,window,input_size/6,convertion)
elif complete_set == 3:
print "Activity accuracy"
''' Get the original data set - 3ee what activities fails '''
remove_activities = self.VARS.CONVERTION_ORIGINAL_INVERSE
keep_activities = self.VARS.CONVERTION_ORIGINAL
config = self.VARS.get_config(input_size, 10, index, 100, network_type, conv_f_1, conv_f_2, nn, filter_type)
original_data_set = input_data_window_large.read_data_sets_without_activity(subject_set, 10, remove_activities, True, keep_activities, window)
''' Get the act'''
activity_accuracy = self.cnn.get_activity_list_accuracy(original_data_set, self.data_set)
for i in range(0, len(activity_accuracy)):
print str(activity_accuracy[i]).replace(".",",")
else:
data = self.data_set.test.next_data_label(index)
print data
print np.argmax(data[1])+1, self.cnn.run_network(data)
def __init__(self, network_type, index, window, input_size, conv_layers,
neural_layers, filter_type):
self.VARS = CNN_STATIC_VARIABLES.CNN_STATIC_VARS()
subject_set = self.VARS.get_subject_set(False)
if network_type == 'original':
remove_activities = self.VARS.CONVERTION_ORIGINAL_INVERSE
keep_activities = self.VARS.CONVERTION_ORIGINAL
self.config = self.VARS.get_config(input_size, 10, index, 100,
network_type, conv_f_1,
conv_f_2, nn_1, filter_type)
self.data_set = input_data_window_large.read_data_sets_without_activity(
subject_set, 10, remove_activities, None, keep_activities,
window)
if network_type == 'sd':
remove_activities = self.VARS.CONVERTION_STATIC_DYNAMIC_INVERSE
keep_activities = self.VARS.CONVERTION_STATIC_DYNAMIC
self.config = self.VARS.get_config(input_size, 10, index, 100,
network_type, conv_layers,
neural_layers, filter_type)
print 'Creating data set'
self.data_set = input_data_window_large.read_data_sets_without_activity(
subject_set, 13, remove_activities, None, keep_activities,
window)
if network_type == 'stand-sit':
remove_activities = self.VARS.CONVERTION_STAND_SIT_INVERSE
keep_activities = self.VARS.CONVERTION_STAND_SIT
self.config = self.VARS.get_config(input_size, 2, index, 100,
network_type, conv_f_1,
conv_f_2, nn_1, filter_type)
self.data_set = input_data_window_large.read_data_sets_without_activity(
subject_set, 2, remove_activities, None, keep_activities,
window)
if network_type == 'stairs':
remove_activities = self.VARS.CONVERTION_STAIRS_INVERSE
keep_activities = self.VARS.CONVERTION_STAIRS
self.config = self.VARS.get_config(input_size,
len(keep_activities), index,
100, network_type, conv_f_1,
conv_f_2, nn_1, filter_type)
self.data_set = input_data_window_large.read_data_sets_without_activity(
subject_set, len(keep_activities), remove_activities, None,
keep_activities, window)
if network_type == 'stairs-walk':
remove_activities = self.VARS.CONVERTION_STAIRS_WALK_INVERSE
keep_activities = self.VARS.CONVERTION_STAIRS_WALK
self.config = self.VARS.get_config(input_size,
len(keep_activities), index,
100, network_type, conv_f_1,
conv_f_2, nn_1, filter_type)
self.data_set = input_data_window_large.read_data_sets_without_activity(
subject_set, len(keep_activities), remove_activities, None,
keep_activities, window)
if network_type == 'stand-nonvig-shuf':
remove_activities = self.VARS.CONVERTION_STAND_NONVIG_SHUF_INVERSE
keep_activities = self.VARS.CONVERTION_STAND_NONVIG_SHUF
self.config = self.VARS.get_config(input_size,
len(keep_activities), index,
100, network_type, conv_f_1,
conv_f_2, nn_1, filter_type)
self.data_set = input_data_window_large.read_data_sets_without_activity(
subject_set, len(keep_activities), remove_activities, None,
keep_activities, window)
if network_type == 'cycling-sitting':
remove_activities = self.VARS.CONVERTION_CYCLING_SITTING_INVERSE
keep_activities = self.VARS.CONVERTION_CYCLING_SITTING
self.config = self.VARS.get_config(input_size, 3, index, 100,
network_type, conv_f_1,
conv_f_2, nn_1, filter_type)
self.data_set = input_data_window_large.read_data_sets_without_activity(
subject_set, 3, remove_activities, None, keep_activities,
window)
if network_type == 'stand-up':
remove_activities = self.VARS.CONVERTION_STAND_UP_INVERSE
keep_activities = self.VARS.CONVERTION_STAND_UP
config = self.VARS.get_config(input_size, len(keep_activities),
index, 100, network_type, conv_f_1,
conv_f_2, nn_1, filter_type)
print 'Creating data set'
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)
self.cnn = CNN_MOD_4.CNN_FILTER(self.config)
self.cnn.set_data_set(self.data_set)
self.cnn.load_model('models/' + self.config['model_name'])
def __init__(self, network_type, index, window, input_size, conv_layers, neural_layers, filter_type):
self.VARS = CNN_STATIC_VARIABLES.CNN_STATIC_VARS()
subject_set = self.VARS.get_subject_set(False)
if network_type == 'original':
remove_activities = self.VARS.CONVERTION_ORIGINAL_INVERSE
keep_activities = self.VARS.CONVERTION_ORIGINAL
self.config = self.VARS.get_config(input_size, 10, index, 100, network_type, conv_f_1, conv_f_2, nn_1, filter_type)
self.data_set = input_data_window_large.read_data_sets_without_activity(subject_set, 10, remove_activities, None, keep_activities, window)
if network_type == 'sd':
remove_activities = self.VARS.CONVERTION_STATIC_DYNAMIC_INVERSE
keep_activities = self.VARS.CONVERTION_STATIC_DYNAMIC
self.config = self.VARS.get_config(input_size, 10, index, 100, network_type, conv_layers, neural_layers, filter_type)
print 'Creating data set'
self.data_set = input_data_window_large.read_data_sets_without_activity(subject_set, 13, remove_activities, None, keep_activities, window)
if network_type == 'stand-sit':
remove_activities = self.VARS.CONVERTION_STAND_SIT_INVERSE
keep_activities = self.VARS.CONVERTION_STAND_SIT
self.config = self.VARS.get_config(input_size, 2, index, 100, network_type, conv_f_1, conv_f_2, nn_1, filter_type)
self.data_set = input_data_window_large.read_data_sets_without_activity(subject_set, 2, remove_activities, None, keep_activities, window)
if network_type =='stairs':
remove_activities = self.VARS.CONVERTION_STAIRS_INVERSE
keep_activities = self.VARS.CONVERTION_STAIRS
self.config = self.VARS.get_config(input_size, len(keep_activities), index, 100, network_type, conv_f_1, conv_f_2, nn_1, filter_type)
self.data_set = input_data_window_large.read_data_sets_without_activity(subject_set, len(keep_activities), remove_activities, None, keep_activities, window)
if network_type =='stairs-walk':
remove_activities = self.VARS.CONVERTION_STAIRS_WALK_INVERSE
keep_activities = self.VARS.CONVERTION_STAIRS_WALK
self.config = self.VARS.get_config(input_size, len(keep_activities), index, 100, network_type, conv_f_1, conv_f_2, nn_1, filter_type)
self.data_set = input_data_window_large.read_data_sets_without_activity(subject_set, len(keep_activities), remove_activities, None, keep_activities, window)
if network_type=='stand-nonvig-shuf':
remove_activities = self.VARS.CONVERTION_STAND_NONVIG_SHUF_INVERSE
keep_activities = self.VARS.CONVERTION_STAND_NONVIG_SHUF
self.config = self.VARS.get_config(input_size, len(keep_activities), index, 100, network_type, conv_f_1, conv_f_2, nn_1, filter_type)
self.data_set = input_data_window_large.read_data_sets_without_activity(subject_set, len(keep_activities), remove_activities, None, keep_activities, window)
if network_type=='cycling-sitting':
remove_activities = self.VARS.CONVERTION_CYCLING_SITTING_INVERSE
keep_activities = self.VARS.CONVERTION_CYCLING_SITTING
self.config = self.VARS.get_config(input_size, 3, index, 100, network_type, conv_f_1, conv_f_2, nn_1, filter_type)
self.data_set = input_data_window_large.read_data_sets_without_activity(subject_set, 3, remove_activities, None, keep_activities, window)
if network_type == 'stand-up':
remove_activities = self.VARS.CONVERTION_STAND_UP_INVERSE
keep_activities = self.VARS.CONVERTION_STAND_UP
config = self.VARS.get_config(input_size, len(keep_activities), index, 100, network_type, conv_f_1, conv_f_2, nn_1, filter_type)
print 'Creating data set'
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)
self.cnn = CNN_MOD_4.CNN_FILTER(self.config)
self.cnn.set_data_set(self.data_set)
self.cnn.load_model('models/' + self.config['model_name'] )
