莫烦keras学习自修第四天【分类问题】
1.代码实战
#!/usr/bin/env python
#! _*_ coding:UTF-8 _*_
# 导入numpy
import numpy as np
np.random.seed(1337)
# 导入验证码图片数据集
from keras.datasets import mnist
from keras.utils import np_utils
# 导入kearas的模型
from keras.models import Sequential
# 导入keras的层和激励函数
from keras.layers import Dense, Activation
# 导入keras的优化器
from keras.optimizers import RMSprop
(X_train, y_train), (X_test, y_test) = mnist.load_data()
# 生成训练数据和测试数据
X_train = X_train.reshape(X_train.shape[0], -1) / 255.
X_test = X_test.reshape(X_test.shape[0], -1) / 255.
y_train = np_utils.to_categorical(y_train, num_classes=10)
y_test = np_utils.to_categorical(y_test, num_classes=10)
# 生成训练模型，传入每个层及激励函数构造训练模型
model = Sequential([
Dense(32, input_dim=784),
Activation('relu'),
Dense(10),
Activation('softmax'),
])
# 自定义优化器
rmsprop = RMSprop(lr=0.001, rho=0.9, epsilon=1e-08, decay=0.0)
# 使用优化器，和误差函数等编译训练模型
model.compile(optimizer=rmsprop,
loss='categorical_crossentropy',
metrics=['accuracy'])
# 开始训练神经网络
model.fit(X_train, y_train, epochs=2, batch_size=32)
# 开始测试神经网络
loss, accuracy = model.evaluate(X_test, y_test)
print('test loss: ', loss)
print('test accuracy: ', accuracy)
结果：
/Users/liudaoqiang/PycharmProjects/numpy/venv/bin/python /Users/liudaoqiang/Project/python_project/keras_day03/classifier.py
Using Theano backend.
Downloading data from https://s3.amazonaws.com/img-datasets/mnist.npz
16384/11490434 [..............................] - ETA: 0s
24576/11490434 [..............................] - ETA: 9:33
40960/11490434 [..............................] - ETA: 10:31
57344/11490434 [..............................] - ETA: 10:12
73728/11490434 [..............................] - ETA: 10:02
90112/11490434 [..............................] - ETA: 9:04
106496/11490434 [..............................] - ETA: 9:07
122880/11490434 [..............................] - ETA: 8:33
139264/11490434 [..............................] - ETA: 8:37
163840/11490434 [..............................] - ETA: 7:47
180224/11490434 [..............................] - ETA: 7:22
196608/11490434 [..............................] - ETA: 7:00
212992/11490434 [..............................] - ETA: 6:46
229376/11490434 [..............................] - ETA: 6:30
245760/11490434 [..............................] - ETA: 6:16
262144/11490434 [..............................] - ETA: 6:03
278528/11490434 [..............................] - ETA: 5:52
303104/11490434 [..............................] - ETA: 5:32
319488/11490434 [..............................] - ETA: 5:22
335872/11490434 [..............................] - ETA: 5:14
352256/11490434 [..............................] - ETA: 5:02
368640/11490434 [..............................] - ETA: 4:54
385024/11490434 [>.............................] - ETA: 4:43
401408/11490434 [>.............................] - ETA: 4:36
417792/11490434 [>.............................] - ETA: 4:27
442368/11490434 [>.............................] - ETA: 4:16
458752/11490434 [>.............................] - ETA: 4:08
475136/11490434 [>.............................] - ETA: 4:01
491520/11490434 [>.............................] - ETA: 3:55
524288/11490434 [>.............................] - ETA: 3:41
540672/11490434 [>.............................] - ETA: 3:36
557056/11490434 [>.............................] - ETA: 3:31
598016/11490434 [>.............................] - ETA: 3:17
614400/11490434 [>.............................] - ETA: 3:13
630784/11490434 [>.............................] - ETA: 3:09
647168/11490434 [>.............................] - ETA: 3:05
679936/11490434 [>.............................] - ETA: 2:56
720896/11490434 [>.............................] - ETA: 2:47
753664/11490434 [>.............................] - ETA: 2:41
786432/11490434 [=>............................] - ETA: 2:35
819200/11490434 [=>............................] - ETA: 2:29
851968/11490434 [=>............................] - ETA: 2:24
860160/11490434 [=>............................] - ETA: 2:23
892928/11490434 [=>............................] - ETA: 2:18
942080/11490434 [=>............................] - ETA: 2:11
958464/11490434 [=>............................] - ETA: 2:09
999424/11490434 [=>............................] - ETA: 2:04
1015808/11490434 [=>............................] - ETA: 2:03
1081344/11490434 [=>............................] - ETA: 1:55
1114112/11490434 [=>............................] - ETA: 1:52
1138688/11490434 [=>............................] - ETA: 1:51
1204224/11490434 [==>...........................] - ETA: 1:44
1236992/11490434 [==>...........................] - ETA: 1:42
1277952/11490434 [==>...........................] - ETA: 1:39
1294336/11490434 [==>...........................] - ETA: 1:38
1327104/11490434 [==>...........................] - ETA: 1:36
1392640/11490434 [==>...........................] - ETA: 1:31
1433600/11490434 [==>...........................] - ETA: 1:29
1466368/11490434 [==>...........................] - ETA: 1:27
1572864/11490434 [===>..........................] - ETA: 1:21
1589248/11490434 [===>..........................] - ETA: 1:20
1622016/11490434 [===>..........................] - ETA: 1:19
1695744/11490434 [===>..........................] - ETA: 1:15
1744896/11490434 [===>..........................] - ETA: 1:13
1761280/11490434 [===>..........................] - ETA: 1:13
1810432/11490434 [===>..........................] - ETA: 1:11
1900544/11490434 [===>..........................] - ETA: 1:07
1974272/11490434 [====>.........................] - ETA: 1:05
2007040/11490434 [====>.........................] - ETA: 1:04
2113536/11490434 [====>.........................] - ETA: 1:00
2179072/11490434 [====>.........................] - ETA: 58s
2195456/11490434 [====>.........................] - ETA: 58s
2244608/11490434 [====>.........................] - ETA: 57s
2301952/11490434 [=====>........................] - ETA: 55s
2424832/11490434 [=====>........................] - ETA: 52s
2441216/11490434 [=====>........................] - ETA: 52s
2490368/11490434 [=====>........................] - ETA: 51s
2564096/11490434 [=====>........................] - ETA: 49s
2613248/11490434 [=====>........................] - ETA: 48s
2703360/11490434 [======>.......................] - ETA: 46s
2752512/11490434 [======>.......................] - ETA: 45s
2826240/11490434 [======>.......................] - ETA: 44s
2875392/11490434 [======>.......................] - ETA: 43s
2949120/11490434 [======>.......................] - ETA: 42s
3014656/11490434 [======>.......................] - ETA: 41s
3121152/11490434 [=======>......................] - ETA: 39s
3137536/11490434 [=======>......................] - ETA: 39s
3186688/11490434 [=======>......................] - ETA: 38s
3276800/11490434 [=======>......................] - ETA: 37s
3383296/11490434 [=======>......................] - ETA: 36s
3448832/11490434 [========>.....................] - ETA: 35s
3522560/11490434 [========>.....................] - ETA: 34s
3588096/11490434 [========>.....................] - ETA: 33s
3645440/11490434 [========>.....................] - ETA: 33s
3710976/11490434 [========>.....................] - ETA: 32s
3801088/11490434 [========>.....................] - ETA: 31s
3883008/11490434 [=========>....................] - ETA: 30s
3956736/11490434 [=========>....................] - ETA: 29s
4038656/11490434 [=========>....................] - ETA: 29s
4145152/11490434 [=========>....................] - ETA: 28s
4202496/11490434 [=========>....................] - ETA: 27s
4284416/11490434 [==========>...................] - ETA: 27s
4390912/11490434 [==========>...................] - ETA: 26s
4440064/11490434 [==========>...................] - ETA: 25s
4530176/11490434 [==========>...................] - ETA: 25s
4636672/11490434 [===========>..................] - ETA: 24s
4685824/11490434 [===========>..................] - ETA: 23s
4775936/11490434 [===========>..................] - ETA: 23s
4792320/11490434 [===========>..................] - ETA: 23s
4898816/11490434 [===========>..................] - ETA: 22s
4931584/11490434 [===========>..................] - ETA: 22s
4997120/11490434 [============>.................] - ETA: 21s
5038080/11490434 [============>.................] - ETA: 21s
5103616/11490434 [============>.................] - ETA: 21s
5177344/11490434 [============>.................] - ETA: 20s
5242880/11490434 [============>.................] - ETA: 20s
5292032/11490434 [============>.................] - ETA: 20s
5398528/11490434 [=============>................] - ETA: 19s
5455872/11490434 [=============>................] - ETA: 19s
5505024/11490434 [=============>................] - ETA: 18s
5554176/11490434 [=============>................] - ETA: 18s
5660672/11490434 [=============>................] - ETA: 18s
5726208/11490434 [=============>................] - ETA: 17s
5767168/11490434 [==============>...............] - ETA: 17s
5816320/11490434 [==============>...............] - ETA: 17s
5922816/11490434 [==============>...............] - ETA: 16s
5971968/11490434 [==============>...............] - ETA: 16s
6029312/11490434 [==============>...............] - ETA: 16s
6062080/11490434 [==============>...............] - ETA: 16s
6094848/11490434 [==============>...............] - ETA: 15s
6111232/11490434 [==============>...............] - ETA: 15s
6266880/11490434 [===============>..............] - ETA: 15s
6324224/11490434 [===============>..............] - ETA: 14s
6340608/11490434 [===============>..............] - ETA: 14s
6512640/11490434 [================>.............] - ETA: 14s
6545408/11490434 [================>.............] - ETA: 13s
6586368/11490434 [================>.............] - ETA: 13s
6602752/11490434 [================>.............] - ETA: 13s
6758400/11490434 [================>.............] - ETA: 13s
6807552/11490434 [================>.............] - ETA: 12s
6848512/11490434 [================>.............] - ETA: 12s
6864896/11490434 [================>.............] - ETA: 12s
6930432/11490434 [=================>............] - ETA: 12s
7086080/11490434 [=================>............] - ETA: 11s
7127040/11490434 [=================>............] - ETA: 11s
7143424/11490434 [=================>............] - ETA: 11s
7225344/11490434 [=================>............] - ETA: 11s
7380992/11490434 [==================>...........] - ETA: 10s
7421952/11490434 [==================>...........] - ETA: 10s
7438336/11490434 [==================>...........] - ETA: 10s
7544832/11490434 [==================>...........] - ETA: 10s
7659520/11490434 [==================>...........] - ETA: 9s
7733248/11490434 [===================>..........] - ETA: 9s
7766016/11490434 [===================>..........] - ETA: 9s
7823360/11490434 [===================>..........] - ETA: 9s
7921664/11490434 [===================>..........] - ETA: 8s
8077312/11490434 [====================>.........] - ETA: 8s
8118272/11490434 [====================>.........] - ETA: 8s
8167424/11490434 [====================>.........] - ETA: 8s
8290304/11490434 [====================>.........] - ETA: 7s
8429568/11490434 [=====================>........] - ETA: 7s
8478720/11490434 [=====================>........] - ETA: 7s
8536064/11490434 [=====================>........] - ETA: 7s
8675328/11490434 [=====================>........] - ETA: 6s
8814592/11490434 [======================>.......] - ETA: 6s
8863744/11490434 [======================>.......] - ETA: 6s
8970240/11490434 [======================>.......] - ETA: 5s
9003008/11490434 [======================>.......] - ETA: 5s
9216000/11490434 [=======================>......] - ETA: 5s
9265152/11490434 [=======================>......] - ETA: 5s
9748480/11490434 [========================>.....] - ETA: 3s
9822208/11490434 [========================>.....] - ETA: 3s
9912320/11490434 [========================>.....] - ETA: 3s
9945088/11490434 [========================>.....] - ETA: 3s
10027008/11490434 [=========================>....] - ETA: 3s
10100736/11490434 [=========================>....] - ETA: 2s
10190848/11490434 [=========================>....] - ETA: 2s
10272768/11490434 [=========================>....] - ETA: 2s
10354688/11490434 [==========================>...] - ETA: 2s
10395648/11490434 [==========================>...] - ETA: 2s
10502144/11490434 [==========================>...] - ETA: 2s
10551296/11490434 [==========================>...] - ETA: 1s
10625024/11490434 [==========================>...] - ETA: 1s
10690560/11490434 [==========================>...] - ETA: 1s
10764288/11490434 [===========================>..] - ETA: 1s
10846208/11490434 [===========================>..] - ETA: 1s
10919936/11490434 [===========================>..] - ETA: 1s
11337728/11490434 [============================>.] - ETA: 0s
11403264/11490434 [============================>.] - ETA: 0s
11493376/11490434 [==============================] - 23s 2us/step
11501568/11490434 [==============================] - 23s 2us/step
Epoch 1/2
32/60000 [..............................] - ETA: 26s - loss: 2.4741 - acc: 0.0625
1568/60000 [..............................] - ETA: 2s - loss: 1.6006 - acc: 0.5504
3264/60000 [>.............................] - ETA: 1s - loss: 1.2046 - acc: 0.6863
4704/60000 [=>............................] - ETA: 1s - loss: 1.0104 - acc: 0.7428
6368/60000 [==>...........................] - ETA: 1s - loss: 0.8834 - acc: 0.7747
8000/60000 [===>..........................] - ETA: 1s - loss: 0.7862 - acc: 0.7997
9728/60000 [===>..........................] - ETA: 1s - loss: 0.7161 - acc: 0.8150
11488/60000 [====>.........................] - ETA: 1s - loss: 0.6655 - acc: 0.8258
13376/60000 [=====>........................] - ETA: 1s - loss: 0.6203 - acc: 0.8360
15040/60000 [======>.......................] - ETA: 1s - loss: 0.5930 - acc: 0.8415
16928/60000 [=======>......................] - ETA: 1s - loss: 0.5626 - acc: 0.8487
18880/60000 [========>.....................] - ETA: 1s - loss: 0.5363 - acc: 0.8555
20832/60000 [=========>....................] - ETA: 1s - loss: 0.5138 - acc: 0.8611
22368/60000 [==========>...................] - ETA: 1s - loss: 0.5025 - acc: 0.8642
24288/60000 [===========>..................] - ETA: 1s - loss: 0.4874 - acc: 0.8675
26208/60000 [============>.................] - ETA: 0s - loss: 0.4728 - acc: 0.8708
28128/60000 [=============>................] - ETA: 0s - loss: 0.4608 - acc: 0.8732
30016/60000 [==============>...............] - ETA: 0s - loss: 0.4503 - acc: 0.8757
31616/60000 [==============>...............] - ETA: 0s - loss: 0.4406 - acc: 0.8779
33504/60000 [===============>..............] - ETA: 0s - loss: 0.4309 - acc: 0.8807
35296/60000 [================>.............] - ETA: 0s - loss: 0.4251 - acc: 0.8816
37120/60000 [=================>............] - ETA: 0s - loss: 0.4171 - acc: 0.8838
38976/60000 [==================>...........] - ETA: 0s - loss: 0.4101 - acc: 0.8855
40416/60000 [===================>..........] - ETA: 0s - loss: 0.4060 - acc: 0.8868
42240/60000 [====================>.........] - ETA: 0s - loss: 0.4000 - acc: 0.8884
44064/60000 [=====================>........] - ETA: 0s - loss: 0.3941 - acc: 0.8900
45888/60000 [=====================>........] - ETA: 0s - loss: 0.3875 - acc: 0.8920
47680/60000 [======================>.......] - ETA: 0s - loss: 0.3813 - acc: 0.8935
49184/60000 [=======================>......] - ETA: 0s - loss: 0.3773 - acc: 0.8945
51008/60000 [========================>.....] - ETA: 0s - loss: 0.3723 - acc: 0.8959
52832/60000 [=========================>....] - ETA: 0s - loss: 0.3669 - acc: 0.8973
54656/60000 [==========================>...] - ETA: 0s - loss: 0.3621 - acc: 0.8986
56480/60000 [===========================>..] - ETA: 0s - loss: 0.3577 - acc: 0.8998
57920/60000 [===========================>..] - ETA: 0s - loss: 0.3540 - acc: 0.9007
59744/60000 [============================>.] - ETA: 0s - loss: 0.3492 - acc: 0.9020
60000/60000 [==============================] - 2s 29us/step - loss: 0.3488 - acc: 0.9021
Epoch 2/2
32/60000 [..............................] - ETA: 6s - loss: 0.0511 - acc: 0.9688
1856/60000 [..............................] - ETA: 1s - loss: 0.2283 - acc: 0.9316
3584/60000 [>.............................] - ETA: 1s - loss: 0.2271 - acc: 0.9350
5216/60000 [=>............................] - ETA: 1s - loss: 0.2180 - acc: 0.9356
6752/60000 [==>...........................] - ETA: 1s - loss: 0.2226 - acc: 0.9347
8416/60000 [===>..........................] - ETA: 1s - loss: 0.2237 - acc: 0.9351
10080/60000 [====>.........................] - ETA: 1s - loss: 0.2214 - acc: 0.9350
11744/60000 [====>.........................] - ETA: 1s - loss: 0.2173 - acc: 0.9367
13248/60000 [=====>........................] - ETA: 1s - loss: 0.2177 - acc: 0.9372
14848/60000 [======>.......................] - ETA: 1s - loss: 0.2171 - acc: 0.9372
16640/60000 [=======>......................] - ETA: 1s - loss: 0.2116 - acc: 0.9391
18432/60000 [========>.....................] - ETA: 1s - loss: 0.2092 - acc: 0.9402
20032/60000 [=========>....................] - ETA: 1s - loss: 0.2078 - acc: 0.9406
21856/60000 [=========>....................] - ETA: 1s - loss: 0.2068 - acc: 0.9407
23584/60000 [==========>...................] - ETA: 1s - loss: 0.2067 - acc: 0.9407
25408/60000 [===========>..................] - ETA: 1s - loss: 0.2036 - acc: 0.9413
27200/60000 [============>.................] - ETA: 0s - loss: 0.2058 - acc: 0.9404
28800/60000 [=============>................] - ETA: 0s - loss: 0.2050 - acc: 0.9407
30592/60000 [==============>...............] - ETA: 0s - loss: 0.2046 - acc: 0.9407
32224/60000 [===============>..............] - ETA: 0s - loss: 0.2045 - acc: 0.9408
34016/60000 [================>.............] - ETA: 0s - loss: 0.2034 - acc: 0.9413
35744/60000 [================>.............] - ETA: 0s - loss: 0.2031 - acc: 0.9413
37248/60000 [=================>............] - ETA: 0s - loss: 0.2025 - acc: 0.9414
38880/60000 [==================>...........] - ETA: 0s - loss: 0.2007 - acc: 0.9419
40448/60000 [===================>..........] - ETA: 0s - loss: 0.1982 - acc: 0.9425
42080/60000 [====================>.........] - ETA: 0s - loss: 0.1987 - acc: 0.9428
43744/60000 [====================>.........] - ETA: 0s - loss: 0.1977 - acc: 0.9431
45216/60000 [=====================>........] - ETA: 0s - loss: 0.1973 - acc: 0.9433
46848/60000 [======================>.......] - ETA: 0s - loss: 0.1973 - acc: 0.9434
48320/60000 [=======================>......] - ETA: 0s - loss: 0.1968 - acc: 0.9434
49984/60000 [=======================>......] - ETA: 0s - loss: 0.1967 - acc: 0.9435
51712/60000 [========================>.....] - ETA: 0s - loss: 0.1961 - acc: 0.9438
53312/60000 [=========================>....] - ETA: 0s - loss: 0.1955 - acc: 0.9438
55072/60000 [==========================>...] - ETA: 0s - loss: 0.1954 - acc: 0.9438
56864/60000 [===========================>..] - ETA: 0s - loss: 0.1954 - acc: 0.9439
58688/60000 [============================>.] - ETA: 0s - loss: 0.1942 - acc: 0.9440
60000/60000 [==============================] - 2s 30us/step - loss: 0.1937 - acc: 0.9440
32/10000 [..............................] - ETA: 0s
6784/10000 [===================>..........] - ETA: 0s
10000/10000 [==============================] - 0s 7us/step
('test loss: ', 0.18316557179167867)
('test accuracy: ', 0.9466)
Process finished with exit code 0
莫烦keras学习自修第四天【分类问题】的更多相关文章
- 莫烦scikit-learn学习自修第四天【内置训练数据集】
1. 代码实战 #!/usr/bin/env python #!_*_ coding:UTF-8 _*_ from sklearn import datasets from sklearn.linea ...
- 莫烦keras学习自修第五天【CNN卷积神经网络】
1.代码实战 #!/usr/bin/env python #! _*_ coding:UTF-8 _*_ import numpy as np np.random.seed(1337) # for r ...
- 莫烦keras学习自修第三天【回归问题】
1. 代码实战 #!/usr/bin/env python #!_*_ coding:UTF-8 _*_ import numpy as np # 这句话不知道是什么意思 np.random.seed ...
- 莫烦keras学习自修第二天【backend配置】
keras的backend包括tensorflow和theano,tensorflow只能在macos和linux上运行,theano可以在windows,macos及linux上运行 1. 使用配置 ...
- 莫烦keras学习自修第一天【keras的安装】
1. 安装步骤 (1)确保已经安装了python2或者python3 (2)安装numpy,python2使用pip2 install numpy, python3则使用pip3 install nu ...
- 莫烦theano学习自修第四天【激励函数】
1. 定义 激励函数通常用于隐藏层,是将特征值进行过滤或者激活的算法 2.常见的激励函数 1. sigmoid (1)sigmoid() (2)ultra_fast_sigmoid() (3)hard ...
- 莫烦theano学习自修第八天【分类问题】
1. 代码实现 from __future__ import print_function import numpy as np import theano import theano.tensor ...
- 莫烦scikit-learn学习自修第一天【scikit-learn安装】
1. 机器学习的分类 (1)有监督学习(包括分类和回归) (2)无监督学习(包括聚类) (3)强化学习 2. 安装 (1)安装python (2)安装numpy >=1.6.1 (3)安装sci ...
- 莫烦theano学习自修第九天【过拟合问题与正规化】
如下图所示(回归的过拟合问题):如果机器学习得到的回归为下图中的直线则是比较好的结果,但是如果进一步控制减少误差,导致机器学习到了下图中的曲线,则100%正确的学习了训练数据,看似较好,但是如果换成另 ...
随机推荐
- 转://ASM与文件系统之间文件传输
熟悉数据库运维的程序猿都知道,数据的备份重于一切,随着业务的发展,数据量也会越来越大,有时候备份集会放在文件系统上面,有的备份集会放在asm存储上面,实现文件系统到文件系统之间的文件传输很简单,cp或 ...
- flask-sqlalchemy 一对一，一对多，多对多操作
先进行如下操作: from flask import Flask from flask.ext.sqlalchemy import SQLAlchemy app=Flask(__name__) db= ...
- pytorch visdom可视化工具学习—1—安装和使用
1.安装 安装命令: (deeplearning) userdeMBP:~ user$ pip install visdomCollecting visdom Downloading https:/ ...
- go struct{}的几种特殊用法
参考:https://blog.csdn.net/kturing/article/details/80557280 1.声明为声明为map[string]struct{} 由于struct{}是空,不 ...
- Java-Method类常用方法详解
一.Method类的定义Method类位于 java.lang.reflect 包中,主要用于在程序运行状态中,动态地获取方法信息二.Method类的常用方法 1.getAnnotatedRetur ...
- [MicroPython]TurniBit开发板DIY自动窗帘模拟系统
一.准备工作 üTurnipBit 开发板 一块 ü下载数据线 一条 ü微型步进电机(28BYJ-48) 一个 ü步进电机驱动板(ULN2003APG) 一块 ü光敏传感器 一个 üTurnipBit ...
- Spring Boot 2.0(八)：Spring Boot 集成 Memcached
Memcached 介绍 Memcached 是一个高性能的分布式内存对象缓存系统,用于动态Web应用以减轻数据库负载.它通过在内存中缓存数据和对象来减少读取数据库的次数,从而提高动态.数据库驱动网站 ...
- disconf原理 “入坑”指南
之前有了解过disconf,也知道它是基于zookeeper来做的,但是对于其运行原理不太了解,趁着周末,debug下源码,也算是不枉费周末大好时光哈 :) .关于这篇文章,笔者主要是参考discon ...
- 构建前端gulp自动化
看了很多关于Gulp自动化的相关教程,很感谢大神们的教程, 因为担心自己会忘记啥的,所以就把自己搭建gulp自动化的过程记录下来~~~ gulp是依赖于Nodejs的,所以最好是有点nodejs的基础 ...
- Python3 与 C# 基础语法对比（就当Python和C#基础的普及吧）
文章汇总:https://www.cnblogs.com/dotnetcrazy/p/9160514.html 多图旧排版:https://www.cnblogs.com/dunitian/p/9 ...