def __init__(self, network_type, iterations, window, input_size, conv, nn,
filter_type, number_of_classifiers):
VARS = CNN_STATIC_VARIABLES.CNN_STATIC_VARS()
subject_set = VARS.get_subject_set_SS(False)
output = 10
remove_activities = VARS.CONVERTION_STATIC_DYNAMIC_INVERSE
keep_activities = VARS.CONVERTION_STATIC_DYNAMIC
self.config = VARS.get_config(input_size, output, iterations, 100,
network_type, conv, nn, filter_type)
print 'Creating data set'
self.data_set = data_generator.read_data_sets_without_activity(
subject_set, output, remove_activities, None, keep_activities,
window)
networks = []
for i in range(0, number_of_classifiers):
#self.config['model_name'] = self.config['model_name'] + "_" + str(i)
cnn = CNN_MOD_4.CNN_FILTER(self.config)
cnn.load_model('models/' + self.config['model_name'])
cnn.set_data_set(self.data_set)
#cnn.train_network()
cnn.test_network_stepwise()
networks.append(cnn)
ss_iterator = 0
num_samples = 0
while ss_iterator < 3:
prediction_steps = 10
test_set_length = len(self.data_set.test._labels)
threshold = 0.99
# Returns an n-long array with random integer
# integer range, length of array
pool_size = 1000
test_indecies = np.random.choice(test_set_length,
pool_size,
replace=False)
number_of_samples = 40
#print 'Predicting'
prediction_indices = np.zeros([pool_size, 3])
final_prediction_indices = np.zeros([number_of_samples * 10, 3])
for i in range(0, len(test_indecies)):
# Get the data instance
data = self.data_set.test._data[test_indecies[i]]
# Predict the class label
predictions = np.zeros((number_of_classifiers, output))
# Add the different prediction vectors to a list
for j in range(0, number_of_classifiers):
prediction = networks[j].run_network_return_probability(
[[data]])[0]
predictions[j] = prediction
activity = np.argmax(predictions)
confidens = prediction[activity]
prediction_indices[i] = [test_indecies[i], activity, confidens]
final_prediction_indices = []
equal_pool_size = False
threshold_subset = False
highest_confident = True
self_learning = False
if equal_pool_size:
''' Select the N most confident samples from each class '''
activity_list = [0, 1, 2, 3, 4, 5, 6, 7]
# Sort
prediction_indices = prediction_indices[
prediction_indices[:, 2].argsort()]
for i in range(0, len(activity_list)):
#print prediction_indices[prediction_indices[:,1] == activity]
top_predictions = prediction_indices[
prediction_indices[:, 1] ==
activity_list[i]][-number_of_samples:]
for item in top_predictions:
final_prediction_indices.append(item)
if threshold_subset:
''' Select random subsample with confidens over threshold'''
prediction_indices = prediction_indices[
prediction_indices[:, 2] >= 0.8]
prediction_indices = prediction_indices[
prediction_indices[:, 2] >= 0.9]
print 'Number of prediction instances', len(prediction_indices)
subset = np.random.choice(len(prediction_indices),
number_of_samples * 10,
replace=False)
final_prediction_indices = prediction_indices[subset]
if highest_confident:
''' Select the top n samples'''
# Sort
prediction_indices = prediction_indices[
prediction_indices[:, 2].argsort()]
final_prediction_indices = prediction_indices[
-number_of_samples * 10:]
print final_prediction_indices[0]
if self_learning:
prediction_indices = prediction_indices[
prediction_indices[:, 2].argsort()]
final_prediction_indices = prediction_indices[:
number_of_samples
* 10]
print 'Number of new instances', len(final_prediction_indices)
num_samples += len(final_prediction_indices)
self.data_set = data_generator.move_data_from_test_to_train(
final_prediction_indices, self.data_set)
activity_accuracy = np.zeros(
len(self.data_set.validation.labels[0]))
for cnn in networks:
cnn.set_data_set(self.data_set)
train_iterations = 800
print 'Number of training iterations', train_iterations
cnn.continue_training(train_iterations)
cnn.test_network_stepwise()
ss_iterator += 1
print 'Number of transfered instances', num_samples
def __init__(self, network_type, iterations, window, input_size, conv_f_1, conv_f_2, nn_1, filter_type, number_of_classifiers):
self.VARS = CNN_STATIC_VARIABLES.CNN_STATIC_VARS()
subject_set = self.VARS.get_subject_set_SS(True)
if network_type == "sd":
self.output = 2
self.config = self.VARS.get_config(input_size, self.output, iterations, 100, network_type, conv_f_1, conv_f_2, nn_1, filter_type)
convertion = self.VARS.CONVERTION_STATIC_DYNAMIC
if network_type == "original":
self.output = 17
convertion = self.VARS.CONVERTION_ORIGINAL
self.config = self.VARS.get_config(input_size, self.output, iterations, 100, network_type, conv_f_1, conv_f_2, nn_1, filter_type)
print 'Creating data set'
self.data_set = data_generator.read_SS_data_set(subject_set, self.output, convertion, window)
networks = []
filter_types = ["VALID","SAME","VALID"]
old_activity_accuracy = np.zeros(len(self.data_set.validation.labels[0]))
for i in range(0,number_of_classifiers):
self.config['model_name'] = self.config['model_name'] + "_" + str(i)
#self.config['filter_type'] = filter_types[i]
cnn = CNN_MOD_2.CNN_MOD(self.config)
cnn.set_data_set(self.data_set)
old_activity_accuracy += cnn.train_network()
networks.append(cnn)
old_activity_accuracy = old_activity_accuracy/number_of_classifiers
ss_iterator = 0
num_samples = 0
while ss_iterator < 5:
prediction_steps = 10
test_set_length = len(self.data_set.test._labels)
threshold = 0.4
# Returns an n-long array with random integer
# integer range, length of array
test_indecies = np.random.choice(test_set_length, test_set_length, replace=False)
#print 'Predicting'
prediction_indices = []
for i in range(0, len(test_indecies)):
# Get the data instance
data = self.data_set.test._data[test_indecies[i]]
# Predict the class label
predictions = np.zeros((number_of_classifiers, self.output))
# Add the different prediction vectors to a list
for j in range(0, number_of_classifiers):
prediction = networks[j].run_network_return_probability([[data]])[0]
predictions[j] = prediction
# Check if the majority of the predictions are UNDER the threshold
if np.sum(predictions > threshold) < number_of_classifiers:
prediction_indices.append([test_indecies[i], predictions])
print 'Number of new instances',len(prediction_indices)
num_samples += len(prediction_indices)
self.data_set = data_generator.move_data_from_test_to_train(prediction_indices, self.data_set)
activity_accuracy = np.zeros(len(self.data_set.validation.labels[0]))
for cnn in networks:
cnn.set_data_set(self.data_set)
accuracy = cnn.train_network()
activity_accuracy += accuracy
activity_accuracy = activity_accuracy / len(networks)
print old_activity_accuracy - activity_accuracy
ss_iterator +=1
print 'Number of transfered instances', num_samples
def __init__(self, network_type, iterations, window, input_size, conv, nn, filter_type, number_of_classifiers):
VARS = CNN_STATIC_VARIABLES.CNN_STATIC_VARS()
subject_set = VARS.get_subject_set_SS(False)
output = 10
remove_activities = VARS.CONVERTION_STATIC_DYNAMIC_INVERSE
keep_activities = VARS.CONVERTION_STATIC_DYNAMIC
self.config = VARS.get_config(input_size, output, iterations, 100, network_type, conv, nn, filter_type)
print "Creating data set"
self.data_set = data_generator.read_data_sets_without_activity(
subject_set, output, remove_activities, None, keep_activities, window
)
networks = []
for i in range(0, number_of_classifiers):
# self.config['model_name'] = self.config['model_name'] + "_" + str(i)
cnn = CNN_MOD_4.CNN_FILTER(self.config)
cnn.load_model("models/" + self.config["model_name"])
cnn.set_data_set(self.data_set)
# cnn.train_network()
cnn.test_network_stepwise()
networks.append(cnn)
ss_iterator = 0
num_samples = 0
while ss_iterator < 3:
prediction_steps = 10
test_set_length = len(self.data_set.test._labels)
threshold = 0.99
# Returns an n-long array with random integer
# integer range, length of array
pool_size = 1000
test_indecies = np.random.choice(test_set_length, pool_size, replace=False)
number_of_samples = 40
# print 'Predicting'
prediction_indices = np.zeros([pool_size, 3])
final_prediction_indices = np.zeros([number_of_samples * 10, 3])
for i in range(0, len(test_indecies)):
# Get the data instance
data = self.data_set.test._data[test_indecies[i]]
# Predict the class label
predictions = np.zeros((number_of_classifiers, output))
# Add the different prediction vectors to a list
for j in range(0, number_of_classifiers):
prediction = networks[j].run_network_return_probability([[data]])[0]
predictions[j] = prediction
activity = np.argmax(predictions)
confidens = prediction[activity]
prediction_indices[i] = [test_indecies[i], activity, confidens]
final_prediction_indices = []
equal_pool_size = False
threshold_subset = False
highest_confident = True
self_learning = False
if equal_pool_size:
""" Select the N most confident samples from each class """
activity_list = [0, 1, 2, 3, 4, 5, 6, 7]
# Sort
prediction_indices = prediction_indices[prediction_indices[:, 2].argsort()]
for i in range(0, len(activity_list)):
# print prediction_indices[prediction_indices[:,1] == activity]
top_predictions = prediction_indices[prediction_indices[:, 1] == activity_list[i]][
-number_of_samples:
]
for item in top_predictions:
final_prediction_indices.append(item)
if threshold_subset:
""" Select random subsample with confidens over threshold"""
prediction_indices = prediction_indices[prediction_indices[:, 2] >= 0.8]
prediction_indices = prediction_indices[prediction_indices[:, 2] >= 0.9]
print "Number of prediction instances", len(prediction_indices)
subset = np.random.choice(len(prediction_indices), number_of_samples * 10, replace=False)
final_prediction_indices = prediction_indices[subset]
if highest_confident:
""" Select the top n samples"""
# Sort
prediction_indices = prediction_indices[prediction_indices[:, 2].argsort()]
final_prediction_indices = prediction_indices[-number_of_samples * 10 :]
print final_prediction_indices[0]
if self_learning:
prediction_indices = prediction_indices[prediction_indices[:, 2].argsort()]
final_prediction_indices = prediction_indices[: number_of_samples * 10]
print "Number of new instances", len(final_prediction_indices)
num_samples += len(final_prediction_indices)
self.data_set = data_generator.move_data_from_test_to_train(final_prediction_indices, self.data_set)
activity_accuracy = np.zeros(len(self.data_set.validation.labels[0]))
for cnn in networks:
cnn.set_data_set(self.data_set)
train_iterations = 800
print "Number of training iterations", train_iterations
cnn.continue_training(train_iterations)
cnn.test_network_stepwise()
ss_iterator += 1
print "Number of transfered instances", num_samples
