type)
# process_data_for_right = process_three_layer(m, n, k, datas1, datas2, datas3, type,save_path)
# result_for_right = np.hstack((result_for_right, process_data_for_right))
# process结束后
# 保存激活数据信息
# print(result_for_right.shape)
# np.save(os.path.join(save_path, str(layer_nums) + '_hidden_layers_coverage.npy'), result_for_right)
del right_datas
gc.collect()
# 错误数据的激活信息保存
for attack_type in [
'fgsm', 'gaussian_noise', 'saliency_map',
'uniform_noise'
]:
wrong_datas = data_provider.get_wrong_active_data_with_attack_type(
'../data/mnist', dataset, layer_nums, attack_type)
# process_wrong_datas and save coverage data
# 默认我们用相邻两层
result_for_wrong = np.empty((len(wrong_datas), 0))
for layer_num in range(layer_nums - 2):
layer1 = layer_num
layer2 = layer_num + 1
layer3 = layer_num + 2
datas1 = np.array(
[list(item) for item in wrong_datas[:, layer1]])
datas2 = np.array(
[list(item) for item in wrong_datas[:, layer2]])
datas3 = np.array(
[list(item) for item in wrong_datas[:, layer3]])
print(datas1.shape)
print(datas2.shape)
threshold = 0
m = 1
n = 1
for type in [0, 1, 2]:
for layer_num in [3, 5, 10]:
for attack_type in [
'fgsm', 'gaussian_noise', 'saliency_map', 'uniform_noise'
]:
now = time.time()
print(
str(layer_num) + ' hidden layers model process type' +
str(type) + '...wrong...' + attack_type)
plt.figure
datas = data_provider.get_wrong_active_data_with_attack_type(
'../data/mnist/', 'mnist', layer_num, type=attack_type)
datas = data_provider.shuffle_mnist_data(datas)
# datas = np.array(random.sample(list(datas), sample_nums))
total_data = []
process_data = []
layers = []
for j in range(layer_num - 1):
datas1 = np.array([list(item) for item in datas[:, j]])
datas2 = np.array([list(item) for item in datas[:, j + 1]])
datas1[datas1 > threshold] = 1
datas2[datas2 > threshold] = 1
datas1[datas1 <= threshold] = 0
datas2[datas2 <= threshold] = 0
total_comb_num, coverage_data = two_layer(m,
n,
datas1,