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
- day03-课堂笔记
# 数据类型"""# int# py2存放长整型:long# py3环境,所以整型均由int存储num1 = -10num2 = 10000000000000000000 ...
- 第一章 mysql的体系结构与存储引擎
数据库从逻辑上可以分为两部分,一部分负责存储即文件系统,这部分有个更时髦的名字叫存储引擎,存储引擎负责如何把数据以及索引相关的内容以合适的形式组织并存储到磁盘上.另一部分为server部分,负责和用户 ...
- 008_使用pyenv进行py开发环境管理
一. 安装Python 绝大多数Python入门书籍,都会介绍如何安装Python,然而,本书要介绍一种全新的安装方式.使用pyenv来安装并管理我们的环境. pyenv简介 由于Python的依赖是 ...
- 五、Oracle 分组查询、视图
一.分组函数1.avg:平均分2.sum:求和3.max:最大值4.min:最小值注意:前面四个必须针对数值字段,且参数只能是一个5.count:求个数 二.分组查询1.语法是 group by 分组 ...
- Springboot实现跨域请求
之所以需要用到跨域请求,目的在于现在的Java项目,几乎基本上都前后端分离,除一些较老的维护项目外(通常是单体或者是maven多模块形式,不过本质上还是将前端放在webapps下). SpringBo ...
- JVM调优常用参数和注意点备忘录
本文主要是工作过程中总结的一些jvm调优的参数和注意的地方,作为一个备忘录,先占个坑,有时间在来细化具体的实例. gc日志是覆盖的方式如果文件名字固定会导致上一次被覆盖可以采用这个-Xloggc:ba ...
- Luogu P3165 [CQOI2014]排序机械臂
先讲一下和这题一起四倍经验的题: Luogu P4402 [Cerc2007]robotic sort 机械排序 SP2059 CERC07S - Robotic Sort UVA1402 Robot ...
- 关于GitHub的Hello Word
最近GitHub一直是最火的配置库技术之一,各个技术大牛也都纷纷入驻GitHub 我每天都打交道的DITA-OT开源项目也宣布迁入GitHub. 那么GitHub到底有什么过人之处呢?给各位先扫个盲. ...
- JavaEE学习之JPA中配置文件persistence.xml
下面是我从网上找到的关于JPA配置文件persistence.xml的相关描述: <?xml version="1.0" encoding="UTF-8" ...
- Heroku + Node.js + HTTPS
昨天把 biz-to-me 升级到支持 HTTPS 了,为此研究了一下如何让 Heroku 上跑的 Node.js 应用支持 HTTPS.我发现并没有任何文章描述这个具体的流程,只有零碎的信息,所以在 ...