def main():
try:
# get parameters from tuner
RECEIVED_PARAMS = nni.get_next_parameter()
LOG.debug(RECEIVED_PARAMS)
PARAMS = default_params()
PARAMS.update(RECEIVED_PARAMS)
LOG.debug(PARAMS)
method = sys.argv[1]
path = sys.argv[2]
r = sys.argv[3]
eps_heavy = str(float(sys.argv[4]))
eps_light = str(float(sys.argv[5]))
# s_path = '/home/haipeng/Documents/dataset/selected/'+method + '/' + method + '_video_bin_'+path+'_kb.csv'
# print('score_path: '+ s_path)
# selected_f = pd.read_csv(s_path,header=None)
# score_list = selected_f.sort_values(selected_f.columns[1], ascending=False)
# selected_col = score_list.iloc[:PARAMS['data_dim'],0]
# selected_col = sorted(selected_col)
# cols = [i for i in range(500,3000)]
# X_train, y_train, X_test, y_test, num_classes = su.load_data('/home/haipeng/Documents/dataset/Video_dataset/KB/video_bin_'+path+'_kb.csv', PARAMS['data_dim'])
root_p = '/home/haipeng/Documents/dataset/Video_dataset/'
X_train, y_train, X_test, y_test, num_classes = su.load_data(root_p + 'KB/lap_weight/' + method + '_' + str(path) +'_video_bin_dp_' + r + '_' + eps_heavy +'_'+eps_light+ '.csv', PARAMS['data_dim'])
print('num_classes is ' + str(num_classes))
X_train=np.expand_dims(X_train, axis=2)
X_test=np.expand_dims(X_test, axis=2)
model=built_and_compile(PARAMS, num_classes)
m=train(model, PARAMS, X_train, y_train, X_test, y_test)
test(PARAMS, m, X_test, y_test, num_classes, path, method, r, eps_heavy, eps_light)
except Exception as exception:
LOG.exception(exception)
raise
def main(step, dim):
selected_col = [i for i in range(1, 401)]
data_path = sys.argv[3]
model_path = sys.argv[4]
X_train, y_train, X_test, y_test, num_classes = su.load_data(
'/home/haipeng/Documents/dataset/Video_dataset/KB/video_bin_' + path +
'_kb.csv', dim)
permutation(X_test, y_test, step, dim, model_path)
def main():
# method = sys.argv[1]
method = 'mrmr'
model_path = '/home/lhp/PycharmProjects/dl_models/cnn_video_bin.h5'
model = load_model(model_path)
# path = '/home/lhp/PycharmProjects/dataset/Alexa_dataset/numeric_class.csv'
# selected_f = pd.read_csv(path,header=None)
# selected_col = selected_f.iloc[:,0]
# selected_col = sorted(selected_col)
selected_col = [i for i in range(1, 721)]
X_train, y_train, X_test, y_test, num_classes = su.load_data(
'video_bin_dp_5e-5_720.csv', selected_col)
# PARAMS = default_params()
# test(PARAMS, X_test, y_test, num_classes)
X_test = np.expand_dims(X_test, axis=2)
score, acc = model.evaluate(X_test, y_test, batch_size=100)
print(f"Model Performance: {score, acc}")
def main():
try:
# get parameters from tuner
RECEIVED_PARAMS = nni.get_next_parameter()
LOG.debug(RECEIVED_PARAMS)
PARAMS = default_params()
PARAMS.update(RECEIVED_PARAMS)
LOG.debug(PARAMS)
# cols = [i for i in range(500,3000)]
X_train, y_train, X_test, y_test, num_classes = su.load_data(
'/home/haipeng/Documents/dataset/cache_dataset/linux_tor.csv',
PARAMS['data_dim'])
#X_train, y_train, X_test, y_test, num_classes = su.load_sel_data(
# '/home/haipeng/Documents/dataset/'+path, cols)
print('num_classes is ' + str(num_classes))
X_train = np.expand_dims(X_train, axis=2)
X_test = np.expand_dims(X_test, axis=2)
model = built_and_compile(PARAMS, num_classes)
m = train(model, PARAMS, X_train, y_train, X_test, y_test)
test(PARAMS, m, X_test, y_test, num_classes)
except Exception as exception:
LOG.exception(exception)
raise
def main():
selected_col = [i for i in range(1, 401)]
X_train, y_train, X_test, y_test, num_classes = su.load_data(
'/home/lhp/PycharmProjects/dataset/Alexa_dataset/generic_class.csv',
selected_col)
permutation(X_test, y_test)