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,
conv_f_1, conv_f_2, nn_1, filter_type):
self.VARS = CNN_STATIC_VARIABLES.CNN_STATIC_VARS()
subject_set = self.VARS.get_subject_set()
convertion = self.VARS.CONVERTION_STATIC_DYNAMIC
config = self.VARS.get_config(input_size, 2, 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(
subject_set, self.VARS.len_convertion_list(convertion), convertion,
None, window)
self.cnn = CNN_MOD.CNN_MOD(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_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)
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, subject_set, config):
self._info = "Convolutional neural network with two convolutional layers and a fully connected network"
'''Config'''
self._input_size = config['input_size']
self._output_size = config['output_size']
self._iteration_size = config['iteration_size']
self._batch_size = config['batch_size']
self._save_model_name = config['model_name']
self._load_model = config['model_name_load']
self._input_size_sqrt = int(self._input_size**0.5)
'''Data set'''
self._data_set = input_data_window_large.read_data_sets(
subject_set, self._output_size)
'''Default values'''
# Conv1
self._bias_conv1 = 32
self._weight_conv1 = 32
# Conv2
self._bias_conv2 = 64
self._weight_conv2 = 64
# Neural network
self._bias_neural1 = 1024
self._weight_neural1 = 1024
self._downsampled_twice = self._input_size_sqrt / 4 + 1
self._weight_neural_input1 = self._downsampled_twice * self._downsampled_twice * self._weight_conv2
self._weight_neural_input2 = 1024
'''Placeholders for input and output'''
self.x = tf.placeholder("float", shape=[None, self._input_size])
self.y_ = tf.placeholder("float", shape=[None, self._output_size])
self.sess = None
def __init__(self, network_type, window, input_size, conv_f_1, conv_f_2, nn_1, filter_type):
''' CNN INIT'''
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, 1, 100, network_type, conv_f_1, conv_f_2, nn_1, filter_type)
self.data_set = input_data_window_large.read_data_sets(subject_set, self.VARS.len_convertion_list(convertion), convertion, None, window)
self.cnn = CNN_MOD.CNN_MOD(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_1) + '_' + filter_type)
''' Segmenting part '''
# Get the predictions and save them
window_list = []
data_length = self.data_set.test._num_examples
for i in range(0,data_length):
data = self.data_set.test.next_data_label(i)
prediction = self.cnn.run_network([data[0]])
window_list.append(prediction)
print 'Prediction finished'
window_size = input_size / 6
# After an activity, add a timestamp
segment_list = [0]
for i in range(0, len(window_list)-1):
if window_list[i] != window_list[i+1]:
segment_list.append((i+1)*window_size)
segment_list.append(len(window_list)*window_size)
print segment_list
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' convertion = self.VARS.CONVERTION_ORIGINAL self.data_set_ORIGINAL = input_data_window_large.read_data_sets(subject_set, self.VARS.len_convertion_list(convertion), convertion, None, window)
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' convertion = self.VARS.CONVERTION_ORIGINAL self.data_set_ORIGINAL = input_data_window_large.read_data_sets(subject_set, self.VARS.len_convertion_list(convertion), convertion, None, window)
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, network_type, index, models):
self.VARS = CNN_STATIC_VARIABLES.CNN_STATIC_VARS()
subject_set = self.VARS.get_subject_set()
convertion = self.VARS.CONVERTION_STATIC_DYNAMIC
raw_signal_labels = get_raw_signal_labels(subject_set, convertion)
df_results = pd.DataFrame(index=np.arange(len(models)),
columns=np.arange(len(raw_signal_labels)))
for i in range(0, len(models)):
window_size = models[i][1] / 6
config = self.VARS.get_config(models[i][1], 2, index, 100,
models[i][0], models[i][2],
models[i][3], models[i][4],
models[i][5])
print 'Creating data set'
self.data_set = input_data_window_large.read_data_sets(
subject_set, self.VARS.len_convertion_list(convertion),
convertion, None, models[i][6])
self.cnn = CNN_MOD.CNN_MOD(config)
self.cnn.set_data_set(self.data_set)
self.cnn.load_model('models/' + models[i][0] + '_' +
str(models[i][1]) + '_' + str(models[i][2]) +
'_' + str(models[i][3]) + '_' +
str(models[i][4]) + '_' + models[i][5])
print "hei"
print len(self.data_set.test._data)
print models[i][1] / 6
for j in range(0, len(self.data_set.test._data)):
data = [self.data_set.test._data[j]]
prediction = self.cnn.sess.run(self.cnn.y_conv,
feed_dict={
self.cnn.x: data,
self.cnn.keep_prob: 1.0
})
prediction = np.argmax(prediction[0]) + 1
index = j * window_size
df_results.iloc[i][index:index + window_size] = prediction
print df_results
df_max = df_results.mode()
correct_instances = 0.0
for i in range(0, len(df_max.columns)):
if df_max[i][0] == int(raw_signal_labels[i]):
correct_instances += 1
print correct_instances
print correct_instances / len(df_max.columns)
def __init__(self, network_type, iterations, window):
self.VARS = CNN_STATIC_VARIABLES.CNN_STATIC_VARS()
subject_set = self.VARS.get_subject_set()
self.config = self.VARS.get_config(576, 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, 17, convertion, None, window)
self.cnn = CNN_MOD.CNN_MOD(self.config)
self.cnn.set_data_set(self.data_set)
self.cnn.train_network()
self.cnn.save_model('models/' + network_type)
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, models):
self.VARS = CNN_STATIC_VARIABLES.CNN_STATIC_VARS()
subject_set = self.VARS.get_subject_set()
convertion = self.VARS.CONVERTION_ORIGINAL
raw_signal_labels = get_raw_signal_labels(subject_set,convertion)
df_results = pd.DataFrame(index = np.arange(len(raw_signal_labels)) ,columns=np.arange(len(models)*2+1))
for i in range(0,len(raw_signal_labels)):
df_results[10][i] = raw_signal_labels[i];
for i in range(0,len(models)):
window_size = models[i][1]/6
config = self.VARS.get_config(models[i][1], self.VARS.len_convertion_list(convertion), index, 100, models[i][0], models[i][2], models[i][3], models[i][4], models[i][5])
print 'Creating data set'
self.data_set = input_data_window_large.read_data_sets(subject_set, self.VARS.len_convertion_list(convertion), convertion, None, models[i][6])
self.cnn = CNN_MOD.CNN_MOD(config)
self.cnn.set_data_set(self.data_set)
self.cnn.load_model('models/' + models[i][0]+ '_' + str(models[i][1]) + '_' + str(models[i][2]) + '_' + str(models[i][3]) + '_' + str(models[i][4]) + '_' + models[i][5])
print "hei"
print len(self.data_set.test._data)
print models[i][1]/6
for j in range(0,len(self.data_set.test._data)):
data = [self.data_set.test._data[j]]
prediction = self.cnn.sess.run(self.cnn.y_conv, feed_dict={self.cnn.x: data ,self.cnn.keep_prob:1.0})
prob = np.max(prediction[0])
prediction = np.argmax(prediction[0])+1
index=j*window_size
df_results[i*2][index:index+window_size] = prob
df_results[i*2+1][index:index+window_size] = prediction
print df_results
np.savetxt('original_5_classifiers.csv', df_results, fmt='%s',delimiter=",")
correct_instances = 0.0;
for i in range(0,len(df_results.index)):
highestProb=0
for j in range(0,len(df_results.columns)/2):
if df_results[j*2][i]>highestProb:
highestProb=df_results[j*2][i]
prediction = df_results[j*2+1][i]
if prediction == int(raw_signal_labels[i]):
correct_instances += 1
print correct_instances
print correct_instances / len(df_results.index)
def __init__(self, network_type, index, complete_set, window):
self.VARS = CNN_STATIC_VARIABLES.CNN_STATIC_VARS()
subject_set = self.VARS.get_subject_set()
convertion = self.VARS.CONVERTION_ORIGINAL
config = self.VARS.get_config(576, 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)
self.cnn = CNN_MOD.CNN_MOD(config)
self.cnn.set_data_set(self.data_set)
self.cnn.load_model('models/' + network_type)
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, network_type, index, models):
self.VARS = CNN_STATIC_VARIABLES.CNN_STATIC_VARS()
subject_set = self.VARS.get_subject_set()
convertion = self.VARS.CONVERTION_STATIC_DYNAMIC
raw_signal_labels = get_raw_signal_labels(subject_set,convertion)
df_results = pd.DataFrame(index = np.arange(len(models)),columns=np.arange(len(raw_signal_labels)))
for i in range(0,len(models)):
window_size = models[i][1]/6
config = self.VARS.get_config(models[i][1], 2, index, 100, models[i][0], models[i][2], models[i][3], models[i][4], models[i][5])
print 'Creating data set'
self.data_set = input_data_window_large.read_data_sets(subject_set, self.VARS.len_convertion_list(convertion), convertion, None, models[i][6])
self.cnn = CNN_MOD.CNN_MOD(config)
self.cnn.set_data_set(self.data_set)
self.cnn.load_model('models/' + models[i][0]+ '_' + str(models[i][1]) + '_' + str(models[i][2]) + '_' + str(models[i][3]) + '_' + str(models[i][4]) + '_' + models[i][5])
print "hei"
print len(self.data_set.test._data)
print models[i][1]/6
for j in range(0,len(self.data_set.test._data)):
data = [self.data_set.test._data[j]]
prediction = self.cnn.sess.run(self.cnn.y_conv, feed_dict={self.cnn.x: data ,self.cnn.keep_prob:1.0})
prediction = np.argmax(prediction[0])+1
index=j*window_size
df_results.iloc[i][index:index+window_size] = prediction
print df_results
df_max = df_results.mode();
correct_instances = 0.0;
for i in range(0,len(df_max.columns)):
if df_max[i][0] == int(raw_signal_labels[i]):
correct_instances += 1
print correct_instances
print correct_instances / len(df_max.columns)
def __init__(self, network_type, index, complete_set, window, input_size, conv_f_1, conv_f_2, nn_1, filter_type):
self.VARS = CNN_STATIC_VARIABLES.CNN_STATIC_VARS()
subject_set = self.VARS.get_subject_set()
convertion = self.VARS.CONVERTION_STATIC_DYNAMIC
config = self.VARS.get_config(input_size, 2, 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(subject_set, self.VARS.len_convertion_list(convertion), convertion, None, window)
self.cnn = CNN_MOD.CNN_MOD(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_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)
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, window, input_size, conv_f_1, conv_f_2,
nn_1, filter_type):
''' CNN INIT'''
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, 1, 100, network_type,
conv_f_1, conv_f_2, nn_1,
filter_type)
self.data_set = input_data_window_large.read_data_sets(
subject_set, self.VARS.len_convertion_list(convertion),
convertion, None, window)
self.cnn = CNN_MOD.CNN_MOD(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_1) + '_' + filter_type)
''' Segmenting part '''
# Get the predictions and save them
window_list = []
data_length = self.data_set.test._num_examples
for i in range(0, data_length):
data = self.data_set.test.next_data_label(i)
prediction = self.cnn.run_network([data[0]])
window_list.append(prediction)
print 'Prediction finished'
window_size = input_size / 6
# After an activity, add a timestamp
segment_list = [0]
for i in range(0, len(window_list) - 1):
if window_list[i] != window_list[i + 1]:
segment_list.append((i + 1) * window_size)
segment_list.append(len(window_list) * window_size)
print segment_list
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, models):
self.VARS = CNN_STATIC_VARIABLES.CNN_STATIC_VARS()
subject_set = self.VARS.get_subject_set()
convertion = self.VARS.CONVERTION_ORIGINAL
raw_signal_labels = get_raw_signal_labels(subject_set, convertion)
df_results = pd.DataFrame(index=np.arange(len(raw_signal_labels)),
columns=np.arange(len(models) * 2 + 1))
for i in range(0, len(raw_signal_labels)):
df_results[10][i] = raw_signal_labels[i]
for i in range(0, len(models)):
window_size = models[i][1] / 6
config = self.VARS.get_config(
models[i][1], self.VARS.len_convertion_list(convertion), index,
100, models[i][0], models[i][2], models[i][3], models[i][4],
models[i][5])
print 'Creating data set'
self.data_set = input_data_window_large.read_data_sets(
subject_set, self.VARS.len_convertion_list(convertion),
convertion, None, models[i][6])
self.cnn = CNN_MOD.CNN_MOD(config)
self.cnn.set_data_set(self.data_set)
self.cnn.load_model('models/' + models[i][0] + '_' +
str(models[i][1]) + '_' + str(models[i][2]) +
'_' + str(models[i][3]) + '_' +
str(models[i][4]) + '_' + models[i][5])
print "hei"
print len(self.data_set.test._data)
print models[i][1] / 6
for j in range(0, len(self.data_set.test._data)):
data = [self.data_set.test._data[j]]
prediction = self.cnn.sess.run(self.cnn.y_conv,
feed_dict={
self.cnn.x: data,
self.cnn.keep_prob: 1.0
})
prob = np.max(prediction[0])
prediction = np.argmax(prediction[0]) + 1
index = j * window_size
df_results[i * 2][index:index + window_size] = prob
df_results[i * 2 + 1][index:index + window_size] = prediction
print df_results
np.savetxt('original_5_classifiers.csv',
df_results,
fmt='%s',
delimiter=",")
correct_instances = 0.0
for i in range(0, len(df_results.index)):
highestProb = 0
for j in range(0, len(df_results.columns) / 2):
if df_results[j * 2][i] > highestProb:
highestProb = df_results[j * 2][i]
prediction = df_results[j * 2 + 1][i]
if prediction == int(raw_signal_labels[i]):
correct_instances += 1
print correct_instances
print correct_instances / len(df_results.index)
6: 2,
7: 2,
8: 2,
9: 1,
10: 1,
11: 1,
12: 1,
13: 1,
14: 1,
15: 1,
16: 2,
17: 2
}
number_of_labels = len(set(convertion.values()))
data_set_1 = input_data_window_large.read_data_sets(subject_set,
number_of_labels,
convertion, None)
#data_set_1 = input_data.read_data_sets("MNIST_data/", one_hot=True)
nn = Simple_network(data_set_1, 2, 'model_1')
#nn.train_network()
#nn.test_network()
#nn.save_variables("model_1")
nn.load_model('model_1')
nn.test_network()
convertion_2 = {
1: 1,
2: 2,
3: 3,
self.sess.run(self.init)
for i in range(1000):
batch_xs, batch_ys = self.data_set.train.next_batch(100)
self.sess.run(self.train_step, feed_dict={self.x: batch_xs, self.y_: batch_ys})
def test_network(self):
correct_prediction = tf.equal(tf.argmax(self.y,1), tf.argmax(self.y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
print(self.sess.run(accuracy, feed_dict={self.x: self.data_set.test.data, self.y_: self.data_set.test.labels}))
train_subjects = ["P03","P04"]#,"P06","P07","P08","P09","P10","P14","P15","P16","P17","P18","P19","P20","P21"]
test_subjects = ["P11"]#,"P04","P06","P07","P08","P09","P10","P14","P15","P16","P17","P18","P19","P20","P21"]
subject_set = [train_subjects, test_subjects]
convertion = {1:1, 2:1, 3:1, 4:1, 5:1, 6:2, 7:2, 8:2, 9:1, 10:1, 11:1, 12:1, 13:1, 14:1, 15:1, 16:2, 17:2}
number_of_labels = len(set(convertion.values()))
data_set_1 = input_data_window_large.read_data_sets(subject_set, number_of_labels, convertion, None)
#data_set_1 = input_data.read_data_sets("MNIST_data/", one_hot=True)
nn = Simple_network(data_set_1,2,'model_1')
#nn.train_network()
#nn.test_network()
#nn.save_variables("model_1")
nn.load_model('model_1')
nn.test_network()
convertion_2 = {1:1, 2:2, 3:3, 4:4, 5:5, 6:6, 7:7, 8:8, 9:9, 10:10, 11:11, 12:12, 13:13, 14:14, 15:15, 16:16, 17:17}
number_label_2 = len(set(convertion_2.values()))
data_set_2 = input_data_window_large.read_data_sets(subject_set, number_label_2, convertion_2, None)
