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'] )