last = i + T
ts.append(x[i:last])
return np.array(ts)
def gen_multi_feature(x, T):
ret = []
for i in range(x.shape[0]):
ts = slide_window(x[i], T)
ret.append(ts)
return np.array(ret)
if __name__ == '__main__':
path = './data/train-test-0.4.npz'
train_data, test_data, train_label, test_label = load_train_test(path)
T = 10
train_data = gen_multi_feature(train_data, T)
test_data = gen_multi_feature(test_data, T)
TEST = False
model_save_path = './models/cnn_lstm.model'
tmp_save_path = './models/cnn_lstm_%s.model'
batch_size = 512
time_step = train_data.shape[1]
input_size = train_data.shape[2]
n_filters = 64
hidden_size = 64
EPOCH = 100
lr = 1e-2
print('anomaly size, ratio:', anomaly_size, anomaly_ratio)
split_ratio = 0.4
save_path = './data/train-test-%s' % split_ratio
if not os.path.exists(save_path + '.npz'):
print('split train test...')
train_data, test_data, train_label, test_label = split_train_test(
data, label, split_ratio)
np.savez(save_path,
train_data=train_data,
test_data=test_data,
train_label=train_label,
test_label=test_label)
else:
print('read train test...')
train_data, test_data, train_label, test_label = load_train_test(
save_path + '.npz')
print(train_data.shape, test_data.shape, sum(train_label), sum(test_label))
batch_size = 512
EPOCH = 500
lr = 0.001
model_save_path = './models/nn.model'
TEST = True
x = torch.from_numpy(train_data).float()
y = torch.from_numpy(train_label).long()
model = NN().to(DEVICE)
optimizer = torch.optim.Adam(model.parameters(), lr=lr)
loss_func = torch.nn.CrossEntropyLoss()
if not TEST:
for epoch in range(EPOCH):