├── .idea
├── AnomalyDetection.iml
├── misc.xml
├── modules.xml
├── vcs.xml
└── workspace.xml
├── LSTM_model.py
├── README.md
├── Untitled Diagram.drawio
├── _1in_out_merge.py
├── _2npy_filter.py
├── _3draw_the_first_month.py
├── _3generate_data_csv.py
├── _4train_model.py
├── _5draw_train_loss.py
├── _5predict_by_model.py
├── _6predict_the_5th_week.py
├── __pycache__
├── LSTM_model.cpython-36.pyc
├── _5predict_by_model.cpython-36.pyc
└── config.cpython-36.pyc
├── config.py
├── imgs
├── Fig1.png
├── Fig2.png
├── Fig3.jpg
├── Fig4.jpg
├── Fig5.png
└── Fig6.jpg
├── model
├── checkpoint
├── final_state.npy
├── model.ckpt.data-00000-of-00001
├── model.ckpt.index
└── model.ckpt.meta
└── temp_data
├── cost.npy
├── cost.png
├── data.csv
└── npy.txt
/.idea/AnomalyDetection.iml:
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120 | cell_final_state
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/LSTM_model.py:
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1 | # -*- coding: utf-8 -*-
2 | """
3 | Created on 2018/11/16 9:58
4 | @author: Eric
5 | @email: qian.dong.2018@gmail.com
6 | """
7 | import tensorflow as tf
8 | from config import *
9 | class LSTMRNN(object):
10 | def __init__(self, n_steps, input_size, output_size, cell_size, batch_size):
11 | self.n_steps = n_steps
12 | self.input_size = input_size
13 | self.output_size = output_size
14 | self.cell_size = cell_size
15 | self.batch_size = batch_size
16 | with tf.name_scope('inputs'):
17 | self.xs = tf.placeholder(tf.float32, [None, n_steps, input_size], name='xs')
18 | self.ys = tf.placeholder(tf.float32, [None, output_size], name='ys')
19 | print("xs, ys:", self.xs,self.ys)
20 | with tf.variable_scope('in_hidden'):
21 | self.add_input_layer()
22 | with tf.variable_scope('LSTM_cell'):
23 | self.add_cell()
24 | with tf.variable_scope('out_hidden'):
25 | self.add_output_layer()
26 | with tf.name_scope('cost'):
27 | self.compute_cost()
28 | with tf.name_scope('train'):
29 | self.train_op = tf.train.AdamOptimizer(LR).minimize(self.cost)
30 | print("train_op:", self.train_op)
31 |
32 | def add_input_layer(self, ):
33 | l_in_x = tf.reshape(self.xs, [-1, self.input_size], name='2_2D') # (batch*n_step, in_size)
34 | # Ws (in_size, cell_size)
35 | Ws_in = self._weight_variable([self.input_size, self.cell_size])
36 | # bs (cell_size, )
37 | bs_in = self._bias_variable([self.cell_size, ])
38 | # l_in_y = (batch * n_steps, cell_size)
39 | with tf.name_scope('Wx_plus_b'):
40 | l_in_y = tf.matmul(l_in_x, Ws_in) + bs_in
41 | # reshape l_in_y ==> (batch, n_steps, cell_size)
42 | self.l_in_y = tf.reshape(l_in_y, [-1, self.n_steps, self.cell_size], name='2_3D')
43 |
44 | def add_cell(self):
45 | lstm_cell = tf.contrib.rnn.BasicLSTMCell(self.cell_size, forget_bias=0.3, state_is_tuple=False)
46 | with tf.name_scope('initial_state'):
47 | self.cell_init_state = lstm_cell.zero_state(self.batch_size, dtype=tf.float32)
48 | print("cell_init_state:", self.cell_init_state)
49 | self.cell_outputs, self.cell_final_state = tf.nn.dynamic_rnn(
50 | lstm_cell, self.l_in_y, initial_state=self.cell_init_state, time_major=False)
51 | print("cell_final_state:",self.cell_final_state)
52 |
53 | def add_output_layer(self):
54 | # shape = (batch * steps, cell_size)
55 | print("cell_outputs:", self.cell_outputs) # 50 20 10
56 | l_out_x = tf.reshape(self.cell_outputs, [self.batch_size, -1], name='2_2D')
57 | Ws_out = self._weight_variable([self.n_steps*self.cell_size, self.output_size])
58 | bs_out = self._bias_variable([self.output_size, ])
59 | # shape = (batch * steps, output_size)
60 | with tf.name_scope('Wx_plus_b'):
61 | self.pred = tf.matmul(l_out_x, Ws_out) + bs_out
62 | self.predict = tf.maximum(tf.rint(tf.matmul(l_out_x, Ws_out) + bs_out), tf.zeros([self.batch_size,1], tf.float32))
63 | print("predict:",self.predict)
64 |
65 | def compute_cost(self):
66 | print("pred:", self.pred)
67 | losses = self.ms_error(self.pred, self.ys)
68 | with tf.name_scope('average_cost'):
69 | self.cost = tf.div(
70 | tf.reduce_sum(losses, name='losses_sum'),
71 | self.batch_size,
72 | name='average_cost')
73 | print("cost:", self.cost)
74 | # tf.summary.scalar('cost', self.cost)
75 |
76 | def ms_error(self, labels, logits):
77 | return tf.square(tf.subtract(labels, logits))
78 |
79 | def _weight_variable(self, shape, name='weights'):
80 | initializer = tf.random_normal_initializer(mean=0., stddev=1., )
81 | return tf.get_variable(shape=shape, initializer=initializer, name=name)
82 |
83 | def _bias_variable(self, shape, name='biases'):
84 | initializer = tf.constant_initializer(0.1)
85 | return tf.get_variable(name=name, shape=shape, initializer=initializer)
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/README.md:
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1 | # AnomalyDetection
2 | ## LSTM对时间序列的数据做预测，通过偏离值的大小达到异常检测的目的
3 | ### 1.思路
4 | 用一个网格7x24个小时的数据预测该网格第7x24+1个小时的in out，如果这个预测in out与真实值的差距大于一个阈值（该阈值最终取3,见Fig3），就把这小时对应的网格当做一个异常的网格，可以达到实时检测的效果
5 | 
Fig1.某网格7*24小时（一周）的时序图
8 |
9 |
10 |
11 | #### Fig2.某网格相邻四周的时序图
12 |
13 |
14 |
15 | #### Fig3_1.训练loss与epoch的箱型图
16 |
17 |
18 |
19 | #### Fig3_2.最后一个epoch的loss箱型图
20 |
21 | #### （异常的阈值取3因为最后一个epoch的均方差loss基本小于9）
22 |
23 |
24 |
25 | #### Fig4.训练loss与每个step的曲线图
26 |
27 |
28 |
29 | #### Fig5.某网格第五周的预测图
30 |
31 | #### Fig5中红线是真实值±3，代表异常的阈值曲线，绿线是预测值曲线，蓝线是真实值的曲线，绿线超出红线即视为异常
32 |
33 | ### 2.流程
34 |
35 | >1)过滤掉稀疏的网格，一个网格用一个向量存储2088个小时的in/out，如果2088个值里边有超过三分之一的值为0，则过滤掉，不用该网格的数据进行模型训练。原因：一天24小时，可以有三分之一的时间比如0a.m.-8a.m. 没有in/out，其他时间段有in/out，比较符合该数据合理的直觉，共1317个符合条件的向量，原始数据有104000*2个向量
36 |
37 | >2）最终训练数据生成：将符合条件的向量用一个24*7的窗口划分
38 |
39 | >>A.为了减少模型过拟合，窗口之间不重合
40 |
41 | >>B.为了增加模型的适用性，划分窗口时加一个offset，使得模型从一周的随便一个小时起开始预测都可以，
42 | 最终有大约1317*(2088/(24*7))=16368个训练样本
43 |
44 |
45 | [in out and some temp images zip file](https://pan.baidu.com/s/1xtjGd2vXjHrTTpBo_UntvQ)
46 |
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/Untitled Diagram.drawio:
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/_1in_out_merge.py:
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1 | # -*- coding: utf-8 -*-
2 | """
3 | Created on 2018/11/15 20:04
4 | @author: Eric
5 | @email: qian.dong.2018@gmail.com
6 | """
7 | import os
8 | import numpy as np
9 |
10 |
11 | def main():
12 | if not os.path.exists("./in"):
13 | os.makedirs("./in")
14 | if not os.path.exists("./out"):
15 | os.makedirs("./out")
16 | gridID_in_hourly = {}
17 | gridID_out_hourly = {}
18 | for i in range(104000):
19 | gridID_in_hourly[i] = [0] * 2088
20 | gridID_out_hourly[i] = [0] * 2088
21 | root_path = "/datahouse/tripflow/ano_detect/200/bj-byhour-io/"
22 | for io_file in os.listdir(root_path):
23 | hour = int(io_file.split('-')[-1])
24 | io_file = os.path.join(root_path, io_file)
25 | print(io_file)
26 | with open(io_file) as f:
27 | lines = f.readlines()
28 | lines = [line.strip().split(',') for line in lines]
29 | lines = np.array(lines)
30 | lines = lines.astype(int)
31 | for line in lines:
32 | gridID_out_hourly[line[0]][hour] = line[1]
33 | gridID_in_hourly[line[0]][hour] = line[2]
34 | for ID in gridID_in_hourly:
35 | np.save("./in/%d.npy" % ID, gridID_in_hourly[ID])
36 | np.save("./out/%d.npy" % ID, gridID_out_hourly[ID])
37 |
38 |
39 | if __name__ == "__main__":
40 | main()
41 |
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/_2npy_filter.py:
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1 | # -*- coding: utf-8 -*-
2 | """
3 | Created on 2018/11/15 20:04
4 | @author: Eric
5 | @email: qian.dong.2018@gmail.com
6 | """
7 | import os
8 | import numpy as np
9 |
10 |
11 | def main():
12 | root_path = '../data/in_out/'
13 | save_list = []
14 | for root, dirs, files in os.walk(root_path):
15 | for npy in files:
16 | npy_ = os.path.join(root, npy)
17 | npy_ = np.load(npy_)
18 | pos_rate = 1 - sum(npy_ == 0) / len(npy_) # 非0值的比例
19 | if pos_rate > 0.667:
20 | print(os.path.join(root, npy))
21 | save_list.append(os.path.join(root, npy))
22 | with open('npy.txt', 'w')as f:
23 | for name in save_list:
24 | f.write(name + "\n")
25 |
26 |
27 | if __name__ == "__main__":
28 | main()
29 |
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/_3draw_the_first_month.py:
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1 | # -*- coding: utf-8 -*-
2 | """
3 | Created on 2018/11/16 12:02
4 | @author: Eric
5 | @email: qian.dong.2018@gmail.com
6 | """
7 | import matplotlib
8 | matplotlib.use("Agg")
9 |
10 | import matplotlib.pyplot as plt
11 | import numpy as np
12 | import os
13 |
14 |
15 | def main():
16 | f = open("./temp_data/npy.txt")
17 | lines = f.readlines()
18 | lines = [line.strip() for line in lines]
19 | for line in lines:
20 | print(line)
21 | f, axs = plt.subplots(2, 1, figsize=(15, 5))
22 | axs[0].plot(np.load(line)[0:24 * 7])
23 | axs[1].plot(np.load(line)[0:24 * 7])
24 | axs[1].plot(np.load(line)[24 * 7 * 1:24 * 7 * 2])
25 | axs[1].plot(np.load(line)[24 * 7 * 2:24 * 7 * 3])
26 | axs[1].plot(np.load(line)[24 * 7 * 3:24 * 7 * 4])
27 | plt.xlabel("hour")
28 | plt.ylabel("number of in/out")
29 | if not os.path.exists("../data/first_month_figures"):
30 | os.makedirs("../data/first_month_figures")
31 | plt.savefig("../data/first_month_figures/%d.png" % (int(os.path.basename(line).split('.')[0])))
32 | plt.show()
33 |
34 |
35 | if __name__ == "__main__":
36 | main()
37 |
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/_3generate_data_csv.py:
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1 | # -*- coding: utf-8 -*-
2 | """
3 | Created on 2018/11/16 11:35
4 | @author: Eric
5 | @email: qian.dong.2018@gmail.com
6 | """
7 | import numpy as np
8 | import pandas as pd
9 |
10 |
11 | def main():
12 | f = open('./temp_data/npy.txt')
13 | lines = f.readlines()
14 | lines = [line.strip() for line in lines]
15 | f.close()
16 | data = np.array([[0] * 169] * 15063)
17 | cnt = 0
18 | for idx, line in enumerate(lines):
19 | npy = np.load(line)
20 | offset = idx % (24 * 7)
21 | for i in range((2088 - offset - 1) // (24 * 7)):
22 | data[cnt] = (npy[i * 24 * 7 + offset:(i + 1) * 24 * 7 + offset + 1])
23 | cnt += 1
24 | data = pd.DataFrame(data)
25 | data.to_csv('./temp_data/data.csv', index=False)
26 |
27 |
28 | if __name__ == "__main__":
29 | main()
30 |
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/_4train_model.py:
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1 | # -*- coding: utf-8 -*-
2 | """
3 | Created on 2018/11/15 17:33
4 | @author: Eric
5 | @email: qian.dong.2018@gmail.com
6 | """
7 | import os
8 | import numpy as np
9 | import pandas as pd
10 | import tensorflow as tf
11 | from LSTM_model import LSTMRNN
12 | import matplotlib.pyplot as plt
13 | from config import *
14 |
15 | Data = pd.read_csv('./temp_data/data.csv')
16 | Indices = np.arange(Data.shape[0])
17 | np.random.shuffle(Indices)
18 | Data = np.array(Data)
19 |
20 |
21 | def get_batch():
22 | global BATCH_START, BATCH_SIZE
23 | data = Data[Indices[BATCH_START:BATCH_START + BATCH_SIZE]]
24 | seq = np.array([d[0:TIME_STEPS].tolist() for d in data])
25 | res = np.array([d[-1] for d in data]).reshape(-1, 1)
26 | BATCH_START += BATCH_SIZE
27 | return [seq[:, :, np.newaxis], res]
28 |
29 |
30 | if __name__ == '__main__':
31 | with tf.Session() as sess:
32 | model = LSTMRNN(TIME_STEPS, INPUT_SIZE, OUTPUT_SIZE, CELL_SIZE, BATCH_SIZE)
33 | sess.run(tf.global_variables_initializer())
34 | saver = tf.train.Saver()
35 | costs = []
36 | begin = True
37 | # 训练 10 次
38 | for epoch in range(100):
39 | BATCH_START = 0
40 | np.random.shuffle(Indices)
41 | for i in range(len(Indices) // BATCH_SIZE):
42 | seq, res = get_batch() # 提取 batch data
43 | if begin:
44 | begin = False
45 | # 初始化 data
46 | feed_dict = {
47 | model.xs: seq,
48 | model.ys: res,
49 | }
50 | else:
51 | feed_dict = {
52 | model.xs: seq,
53 | model.ys: res,
54 | model.cell_init_state: state # 保持 state 的连续性
55 | }
56 | # 训练
57 | _, cost, state, pred = sess.run(
58 | [model.train_op, model.cost, model.cell_final_state, model.pred],
59 | feed_dict=feed_dict)
60 | print('epoch:%d step:%d cost: ' % (epoch, i), round(cost, 4))
61 | costs.append(cost)
62 | # print(pred)
63 | np.save("./model/final_state.npy", state)
64 | np.save("./temp_data/cost.npy", costs)
65 | plt.plot(costs)
66 | plt.show()
67 | plt.savefig("./temp_data/cost.png")
68 | if not os.path.exists("model"):
69 | os.makedirs("model")
70 | saver.save(sess=sess, save_path=os.path.join(os.getcwd(), 'model', 'model.ckpt'))
71 |
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/_5draw_train_loss.py:
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1 | # -*- coding: utf-8 -*-
2 | """
3 | Created on 2018/11/16 11:15
4 | @author: Eric
5 | @email: qian.dong.2018@gmail.com
6 | """
7 | import numpy as np
8 | import matplotlib.pyplot as plt
9 |
10 |
11 | def main():
12 | costs = np.load('cost.npy')
13 | plt.plot(costs)
14 | plt.xlabel("step")
15 | plt.ylabel("mean squre error")
16 | plt.title("loss-step graph")
17 | plt.show()
18 | costs = costs.reshape(10, -1)
19 | f, axs = plt.subplots(1, 1, figsize=(15, 5))
20 | axs.boxplot([cost for cost in costs])
21 | plt.ylabel("mean square error")
22 | plt.xlabel("epoch")
23 | plt.title("loss-epoch box graph")
24 | plt.show()
25 |
26 |
27 | if __name__ == "__main__":
28 | main()
29 |
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/_5predict_by_model.py:
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1 | # -*- coding: utf-8 -*-
2 | """
3 | Created on 2018/11/16 9:14
4 | @author: Eric
5 | @email: qian.dong.2018@gmail.com
6 | """
7 | import numpy as np
8 | import pandas as pd
9 | import tensorflow as tf
10 | from config import *
11 |
12 | Data = pd.read_csv('./temp_data/data.csv')
13 | Indices = np.arange(Data.shape[0]) # 随机打乱索引并切分训练集与测试集
14 | np.random.shuffle(Indices)
15 | Data = np.array(Data)
16 |
17 |
18 | def get_batch():
19 | global BATCH_START, BATCH_SIZE
20 | # xs shape (50batch, 20steps)
21 | data = Data[Indices[BATCH_START:BATCH_START + BATCH_SIZE]]
22 | seq = np.array([d[0:TIME_STEPS].tolist() for d in data])
23 | res = np.array([d[-1] for d in data]).reshape(-1, 1)
24 | BATCH_START += BATCH_SIZE
25 | return [seq[:, :, np.newaxis], res]
26 |
27 |
28 | def get_data(num):
29 | data = Data[num]
30 | seq = np.array(data[0:TIME_STEPS])
31 | res = data[-1]
32 | return [seq.reshape(-1, 1), res]
33 |
34 |
35 | def predict_by_model(seq): # seq shape为 [TIME_STEPS, 1] [24*7, 1]
36 | seq = np.array(seq)
37 | # reshape为[BATCH_SIZE, 24*7, 1]
38 | seq = np.array([seq.tolist() for _ in range(BATCH_SIZE)]).reshape(BATCH_SIZE, 24*7, 1)
39 | with tf.Session() as sess:
40 | new_saver = tf.train.import_meta_graph('./model/model.ckpt.meta')
41 | new_saver.restore(sess, tf.train.latest_checkpoint('./model'))
42 | graph = tf.get_default_graph()
43 | predict = graph.get_tensor_by_name("out_hidden/Wx_plus_b/Maximum:0")
44 | xs = graph.get_tensor_by_name("inputs/xs:0")
45 | cell_init_state = graph.get_tensor_by_name("LSTM_cell/initial_state/BasicLSTMCellZeroState/zeros:0")
46 | state = np.load("./model/final_state.npy")
47 | pred = sess.run(predict, feed_dict={xs: seq, cell_init_state: state})
48 | return pred[0][0]
49 |
50 |
51 |
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/_6predict_the_5th_week.py:
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1 | # -*- coding: utf-8 -*-
2 | """
3 | Created on 2018/11/16 12:21
4 | @author: Eric
5 | @email: qian.dong.2018@gmail.com
6 | """
7 | from _5predict_by_model import predict_by_model
8 | import matplotlib
9 | matplotlib.use("Agg")
10 | import matplotlib.pyplot as plt
11 | import numpy as np
12 | import os
13 |
14 |
15 | def predict_5th_week(npy):
16 | test = np.load(npy)
17 | predicts = []
18 | for i in range(24 * 7):
19 | print(i, npy)
20 | seq = test[24 * 7 * 3 + i:24 * 7 * 4 + i]
21 | predicts.append(predict_by_model(seq))
22 | return predicts
23 |
24 |
25 | def draw_predict(predicts, npy):
26 | test = np.load(npy)
27 | f, axs = plt.subplots(2, 1, figsize=(15, 5))
28 | axs[0].plot(test[24 * 7 * 4:24 * 7 * 5] - 3, 'r')
29 | axs[0].plot(test[24 * 7 * 4:24 * 7 * 5], 'b')
30 | axs[0].plot(test[24 * 7 * 4:24 * 7 * 5] + 3, 'r')
31 | axs[0].plot(predicts, 'g')
32 | axs[1].plot(test[24 * 7 * 4:24 * 7 * 5], 'b')
33 | axs[1].plot(predicts, 'g')
34 | plt.xlabel("hour")
35 | plt.ylabel("number of in/out")
36 | plt.savefig("../data/predict_result/%d.png"%(int(os.path.basename(npy).split('.')[0])))
37 | plt.show()
38 |
39 |
40 | def main():
41 | f = open("./temp_data/npy.txt")
42 | lines = f.readlines()
43 | npys = [line.strip() for line in lines]
44 | for npy in npys[0:10]:
45 | predict = predict_5th_week(npy)
46 | draw_predict(predict, npy)
47 |
48 |
49 | if __name__ == '__main__':
50 | main()
51 |
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/config.py:
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1 | # -*- coding: utf-8 -*-
2 | """
3 | Created on 2018/11/16 9:18
4 | @author: Eric
5 | @email: qian.dong.2018@gmail.com
6 | """
7 | BATCH_START = 0 # 建立 batch data 时候的 index
8 | TIME_STEPS = 168 # 时序数据的size
9 | BATCH_SIZE = 256
10 | INPUT_SIZE = 1
11 | OUTPUT_SIZE = 1
12 | CELL_SIZE = 64 # RNN 的 hidden unit size
13 | LR = 1e-5 # learning rate
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1 | model_checkpoint_path: "D:\\work\\python_project\\AnomalyDetection\\model\\model.ckpt"
2 | all_model_checkpoint_paths: "D:\\work\\python_project\\AnomalyDetection\\model\\model.ckpt"
3 |
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/temp_data/npy.txt:
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1 | ../data/in_out/in\21590.npy
2 | ../data/in_out/in\22592.npy
3 | ../data/in_out/in\24522.npy
4 | ../data/in_out/in\24523.npy
5 | ../data/in_out/in\24834.npy
6 | ../data/in_out/in\24849.npy
7 | ../data/in_out/in\25164.npy
8 | ../data/in_out/in\25171.npy
9 | ../data/in_out/in\25172.npy
10 | ../data/in_out/in\25173.npy
11 | ../data/in_out/in\25174.npy
12 | ../data/in_out/in\25176.npy
13 | ../data/in_out/in\25179.npy
14 | ../data/in_out/in\25180.npy
15 | ../data/in_out/in\25181.npy
16 | ../data/in_out/in\25182.npy
17 | ../data/in_out/in\25184.npy
18 | ../data/in_out/in\25497.npy
19 | ../data/in_out/in\25499.npy
20 | ../data/in_out/in\25507.npy
21 | ../data/in_out/in\25821.npy
22 | ../data/in_out/in\25822.npy
23 | ../data/in_out/in\25824.npy
24 | ../data/in_out/in\25825.npy
25 | ../data/in_out/in\25826.npy
26 | ../data/in_out/in\25827.npy
27 | ../data/in_out/in\25828.npy
28 | ../data/in_out/in\25829.npy
29 | ../data/in_out/in\25830.npy
30 | ../data/in_out/in\25831.npy
31 | ../data/in_out/in\25832.npy
32 | ../data/in_out/in\25833.npy
33 | ../data/in_out/in\26154.npy
34 | ../data/in_out/in\26155.npy
35 | ../data/in_out/in\26156.npy
36 | ../data/in_out/in\26157.npy
37 | ../data/in_out/in\26158.npy
38 | ../data/in_out/in\26460.npy
39 | ../data/in_out/in\26461.npy
40 | ../data/in_out/in\26471.npy
41 | ../data/in_out/in\26472.npy
42 | ../data/in_out/in\26473.npy
43 | ../data/in_out/in\26474.npy
44 | ../data/in_out/in\26480.npy
45 | ../data/in_out/in\26481.npy
46 | ../data/in_out/in\26482.npy
47 | ../data/in_out/in\26483.npy
48 | ../data/in_out/in\26485.npy
49 | ../data/in_out/in\26486.npy
50 | ../data/in_out/in\26783.npy
51 | ../data/in_out/in\26785.npy
52 | ../data/in_out/in\26796.npy
53 | ../data/in_out/in\26797.npy
54 | ../data/in_out/in\26800.npy
55 | ../data/in_out/in\26807.npy
56 | ../data/in_out/in\26809.npy
57 | ../data/in_out/in\26810.npy
58 | ../data/in_out/in\26811.npy
59 | ../data/in_out/in\26812.npy
60 | ../data/in_out/in\26813.npy
61 | ../data/in_out/in\27118.npy
62 | ../data/in_out/in\27119.npy
63 | ../data/in_out/in\27122.npy
64 | ../data/in_out/in\27134.npy
65 | ../data/in_out/in\27135.npy
66 | ../data/in_out/in\27136.npy
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68 | ../data/in_out/in\27138.npy
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70 | ../data/in_out/in\27437.npy
71 | ../data/in_out/in\27438.npy
72 | ../data/in_out/in\27440.npy
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78 | ../data/in_out/in\27461.npy
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85 | ../data/in_out/in\27766.npy
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87 | ../data/in_out/in\27768.npy
88 | ../data/in_out/in\27770.npy
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