def full_distributed(model_name, big_X_train, big_y_train, big_X_test, big_y_test,
class_names=class_names, ch_num=25, dr=0.1, addon=''):
features_train = [None] * len(big_X_train)
labels_train = [None] * len(big_y_train)
for i, (X_user, y_user) in enumerate(zip(big_X_train, big_y_train)):
temp = [item for sublist in X_user for item in sublist]
temp = np.asanyarray(temp)
temp = np.swapaxes(temp, 1, 2)
features_train[i] = temp.reshape(temp.shape[0], 1, ch_num, 1125)
lab = [item for sublist in y_user for item in sublist]
# encode class values as integers
encoder = LabelEncoder()
encoder.fit(lab)
encoded_Y = encoder.transform(lab)
# convert integers to dummy variables (i.e. one hot encoded)
labels_train[i] = np_utils.to_categorical(encoded_Y)
features_test = [None] * len(big_X_test)
labels_test = [None] * len(big_y_test)
for i, (X_user, y_user) in enumerate(zip(big_X_test, big_y_test)):
temp = [item for sublist in X_user for item in sublist]
temp = np.asanyarray(temp)
temp = np.swapaxes(temp, 1, 2)
features_test[i] = temp.reshape(temp.shape[0], 1, ch_num, 1125)
lab = [item for sublist in y_user for item in sublist]
# encode class values as integers
encoder = LabelEncoder()
encoder.fit(lab)
encoded_Y = encoder.transform(lab)
# convert integers to dummy variables (i.e. one hot encoded)
labels_test[i] = np_utils.to_categorical(encoded_Y)
# Flatten the data for training
full_features = np.vstack(features_train)
full_labels = np.vstack(labels_train)
metrics = np.zeros(((len(big_y_train)), 4))
for i in range(len(big_X_train)):
model = EEGNet_org(nb_classes=len(class_names), Chans=ch_num, Samples=1125, dropoutRate=0.2)
model.compile(loss=categorical_crossentropy,
optimizer=Adam(), metrics=['accuracy'])
filename_ = '{0}{1}{2}'.format(model_name, 'Distributed_{}'.format(i + 1), addon)
metrics[i] = distributed_unit(model, full_features, full_labels,
features_test[i], labels_test[i],
filename_, class_names)
metrics_to_csv(metrics, '{}_Distributed_Learning_{}'.format(model_name, filename_))
def standard_from_2mvt(model_name, model_file, big_X_train, big_y_train, big_X_test, big_y_test,
class_names=class_names, ch_num=25, ep=50, dr=0.1, addon=''):
features_train = [None] * len(big_X_train)
labels_train = [None] * len(big_y_train)
for i, (X_user, y_user) in enumerate(zip(big_X_train, big_y_train)):
temp = [item for sublist in X_user for item in sublist]
temp = np.asanyarray(temp)
temp = np.swapaxes(temp, 1, 2)
features_train[i] = temp.reshape(temp.shape[0], 1, ch_num, 1125)
lab = [item for sublist in y_user for item in sublist]
# encode class values as integers
encoder = LabelEncoder()
encoder.fit(lab)
encoded_Y = encoder.transform(lab)
# convert integers to dummy variables (i.e. one hot encoded)
labels_train[i] = np_utils.to_categorical(encoded_Y)
features_test = [None] * len(big_X_test)
labels_test = [None] * len(big_y_test)
for i, (X_user, y_user) in enumerate(zip(big_X_test, big_y_test)):
temp = [item for sublist in X_user for item in sublist]
temp = np.asanyarray(temp)
temp = np.swapaxes(temp, 1, 2)
features_test[i] = temp.reshape(temp.shape[0], 1, ch_num, 1125)
lab = [item for sublist in y_user for item in sublist]
# encode class values as integers
encoder = LabelEncoder()
encoder.fit(lab)
encoded_Y = encoder.transform(lab)
# convert integers to dummy variables (i.e. one hot encoded)
labels_test[i] = np_utils.to_categorical(encoded_Y)
metrics = np.zeros(((len(big_y_train)), 4))
for i in range(len(big_X_train)):
model_ = EEGNet_var(model_file, 2, 4, Chans=ch_num, dropoutRate=dr, Samples=1125)
model_.compile(loss=categorical_crossentropy,
optimizer=Adam(), metrics=['accuracy'])
filename_ = '{0}{1}{2}'.format(model_name, '_transfer_standard_learning_{}_'.format(i + 1), addon)
metrics[i] = transfer_unit(model_, features_train[i], labels_train[i],
features_test[i], labels_test[i], filename_,
class_names)
metrics_to_csv(metrics, '{}_transfer_standard_learning_{}'.format(model_name, addon))
def splited_with_model(model_name, big_X_train, big_y_train, big_X_test, big_y_test,
class_names=class_names, ch_num=25, ep=50, addon=''):
###
### First create the sequence of training users and single test user
###
list_element = []
my_list = [0, 1, 2, 3, 4, 5, 6, 7]
series = []
for idx in range(72):
if idx % 9 != 0:
list_element.append(my_list[idx % len(my_list)])
elif idx % 9 == 0:
series.append(list_element)
list_element = []
series[0] = list_element
metrics = np.zeros(((len(my_list)), 4))
###
### Iterate trough the sequences
###
r_series = reversed(series)
for user_list in r_series:
print('Starting Splited learning for user {}'.format(user_list[-1]))
###
### Only create the second training data (of the specific user)
###
temp = [item for sublist in big_X_train[user_list[-1]] for item in sublist]
temp = np.asanyarray(temp)
temp = np.swapaxes(temp, 1, 2)
features_train_2 = temp.reshape(temp.shape[0], 1, ch_num, 1125)
lab = [item for sublist in big_y_train[user_list[-1]] for item in sublist]
# encode class values as integers
encoder = LabelEncoder()
encoder.fit(lab)
encoded_Y = encoder.transform(lab)
# convert integers to dummy variables (i.e. one hot encoded)
labels_train_2 = np_utils.to_categorical(encoded_Y)
###
### Create the testing data (of the specific user)
###
temp = [item for sublist in big_X_test[user_list[-1]] for item in sublist]
temp = np.asanyarray(temp)
temp = np.swapaxes(temp, 1, 2)
features_test_2 = temp.reshape(temp.shape[0], 1, ch_num, 1125)
lab = [item for sublist in big_y_test[user_list[-1]] for item in sublist]
# encode class values as integers
encoder = LabelEncoder()
encoder.fit(lab)
encoded_Y = encoder.transform(lab)
# convert integers to dummy variables (i.e. one hot encoded)
labels_test_2 = np_utils.to_categorical(encoded_Y)
filename_ = '{0}{1}{2}{3}'.format(model_name, '_Split_{}'.format(user_list[-1] + 1), addon,
'_{}_Epochs'.format(str(ep)))
model_file = 'models/{0}{1}_50_Epochs{2}.h5'.format(model_name, 'Freezing_{}'.format(user_list[-1] + 1),
'_-2_Frozen')
metrics[user_list[-1]] = split_unit_with_model(model_file, features_train_2, features_test_2,
labels_train_2, labels_test_2,
filename_, class_names, ep)
del features_train_2, features_test_2, \
labels_train_2, labels_test_2
metrics_to_csv(metrics, '{}_Split_Learning_{}'.format(model_name, filename_))
def splitted_layers(model_name, big_X_train, big_y_train, big_X_test, big_y_test,
class_names=class_names, ch_num=25, dr=0.1, addon=''):
###
### First create the sequence of training users and single test user
###
list_element = []
my_list = [0, 1, 2, 3, 4, 5, 6, 7]
series = []
for idx in range(72):
if idx % 9 != 0:
list_element.append(my_list[idx % len(my_list)])
elif idx % 9 == 0:
series.append(list_element)
list_element = []
series[0] = list_element
features_train = []
labels_train = []
metrics = np.zeros(((len(my_list)), 4))
###
### Iterate trough the sequences
###
for user_list in series:
print('Starting Splitted learning for user {}'.format(user_list[-1]))
model = EEGNet_org(nb_classes=4, Chans=ch_num, Samples=1125, dropoutRate=dr)
model.compile(loss=categorical_crossentropy,
optimizer=Adam(), metrics=['accuracy'])
# Create the first training data
for i in user_list[:-1]:
temp = [item for sublist in big_X_train[i] for item in sublist]
temp = np.asanyarray(temp)
temp = np.swapaxes(temp, 1, 2)
features_train.append(temp.reshape(temp.shape[0], 1, ch_num, 1125))
lab = [item for sublist in big_y_train[i] for item in sublist]
# encode class values as integers
encoder = LabelEncoder()
encoder.fit(lab)
encoded_Y = encoder.transform(lab)
# convert integers to dummy variables (i.e. one hot encoded)
labels_train.append(np_utils.to_categorical(encoded_Y))
# Also add the testing data to increase the size of training data
temp = [item for sublist in big_X_test[i] for item in sublist]
temp = np.asanyarray(temp)
temp = np.swapaxes(temp, 1, 2)
features_train.append(temp.reshape(temp.shape[0], 1, ch_num, 1125))
lab = [item for sublist in big_y_test[i] for item in sublist]
# encode class values as integers
encoder = LabelEncoder()
encoder.fit(lab)
encoded_Y = encoder.transform(lab)
# convert integers to dummy variables (i.e. one hot encoded)
labels_train.append(np_utils.to_categorical(encoded_Y))
###
### Create the second training data (of the specific user)
###
temp = [item for sublist in big_X_train[user_list[-1]] for item in sublist]
temp = np.asanyarray(temp)
temp = np.swapaxes(temp, 1, 2)
features_train_2 = temp.reshape(temp.shape[0], 1, ch_num, 1125)
lab = [item for sublist in big_y_train[user_list[-1]] for item in sublist]
# encode class values as integers
encoder = LabelEncoder()
encoder.fit(lab)
encoded_Y = encoder.transform(lab)
# convert integers to dummy variables (i.e. one hot encoded)
labels_train_2 = np_utils.to_categorical(encoded_Y)
# Flatten the data for training
full_features = np.vstack(features_train)
full_labels = np.vstack(labels_train)
###
### Create the testing data (of the specific user)
###
temp = [item for sublist in big_X_test[user_list[-1]] for item in sublist]
temp = np.asanyarray(temp)
temp = np.swapaxes(temp, 1, 2)
features_test_2 = temp.reshape(temp.shape[0], 1, ch_num, 1125)
lab = [item for sublist in big_y_test[user_list[-1]] for item in sublist]
# encode class values as integers
encoder = LabelEncoder()
encoder.fit(lab)
encoded_Y = encoder.transform(lab)
# convert integers to dummy variables (i.e. one hot encoded)
labels_test_2 = np_utils.to_categorical(encoded_Y)
filename_ = '{0}{1}{2}'.format(model_name, 'Splitted_{}'.format(user_list[-1] + 1), addon)
metrics[user_list[-1]] = freezing_unit(model, full_features, full_labels,
features_train_2, features_test_2,
labels_train_2, labels_test_2,
filename_, class_names)
metrics_to_csv(metrics, '{}_Splitted_Learning_{}'.format(model_name, filename_))