今日内容
-
Jupyter
在此处摁下“m"键就会编程markdown模式
摁”b“键是在下方创建代码
-
%matplotlib inline
作用：%matplotlib inline这一句是IPython的魔法函数，可以在IPython编译器里直接使用，作用是内嵌画图，省略掉plt.show()这一步，直接显示图像。
如果不加这一句的话，我们在画图结束之后需要加上plt.show()才可以显示图像。不然就只有<matplotlib.collections.PathCollection at 0x2aa7c1ae490>这行话
一定导入的是matplotlib.pyplot 不然里面没有scatter方法，
scatter()方法的参数含义参考：https://blog.csdn.net/m0_37393514/article/details/81298503
- 具体代码实现如下
import pandas as pd
data = pd.read_csv("C:\\Users\\94823\\Desktop\\aaa.csv")
data
| aaaIncome | Education | Income | |
|---|---|---|---|
| 0 | 1 | 10.000000 | 26.658839 |
| 1 | 2 | 10.401338 | 27.306435 |
| 2 | 3 | 10.842809 | 22.132410 |
| 3 | 4 | 11.244147 | 21.169841 |
| 4 | 5 | 11.645449 | 15.192634 |
| 5 | 6 | 12.086957 | 26.398951 |
| 6 | 7 | 12.048829 | 17.435307 |
| 7 | 8 | 12.889632 | 25.507885 |
| 8 | 9 | 13.290970 | 36.884595 |
| 9 | 10 | 13.732441 | 39.666109 |
| 10 | 11 | 14.133779 | 34.396281 |
| 11 | 12 | 14.635117 | 41.497994 |
| 12 | 13 | 14.978589 | 44.981575 |
| 13 | 14 | 15.377926 | 47.039595 |
| 14 | 15 | 15.779264 | 48.252578 |
| 15 | 16 | 16.220736 | 57.034251 |
| 16 | 17 | 16.622074 | 51.490919 |
| 17 | 18 | 17.023411 | 51.336621 |
| 18 | 19 | 17.464883 | 57.681998 |
| 19 | 20 | 17.866221 | 68.553714 |
| 20 | 21 | 18.267559 | 64.310925 |
| 21 | 22 | 18.709030 | 68.959009 |
| 22 | 23 | 19.110368 | 74.614639 |
| 23 | 24 | 19.511706 | 71.867195 |
| 24 | 25 | 19.913043 | 76.098135 |
| 25 | 26 | 20.354515 | 75.775216 |
| 26 | 27 | 20.755853 | 72.486055 |
| 27 | 28 | 21.167191 | 77.355021 |
| 28 | 29 | 21.598662 | 72.118790 |
| 29 | 30 | 22.000000 | 80.260571 |
import matplotlib.pyplot as plt
%matplotlib inline
plt.scatter(data.Education,data.Income)
<matplotlib.collections.PathCollection at 0x2aa7c1ae490>
x = data.Education # 定义横纵坐标
y = data.Income
import tensorflow as tf
model = tf.keras.Sequential() # 建立一个输入一个输出的模型。比如图中的线性模型y=ax+b,x为一个输入，y为一个输出
model.add(tf.keras.layers.Dense(1,input_shape=(1,)))
# 前面的1对应的是units这个参数，表示该层有多少个神经单元。
# 同时也是与上一层连接的W的列数。
# input_shape的第一维一般表示一个batch的大小，
# 即有多少个样本。后面表示每个样本应该有多少特征，也是多少个列。如果不写会根据输入的X自动推断。
# 具体参考https://blog.csdn.net/weixin_42055967/article/details/104420985
model.summary() # ax+b param中的两个参数就是a与b
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense (Dense) (None, 1) 2
=================================================================
Total params: 2
Trainable params: 2
Non-trainable params: 0
_________________________________________________________________
model.compile(
optimizer = 'adam',
loss = 'mse'
) # 减少误差优化的方法是梯度下降法：简写‘adam’，优化的损失函数是均方差‘mse’，
# 通过这种方法编译
history = model.fit(x, y, epochs=5000) # 把所有数据训练5000次
Epoch 4992/5000
1/1 [==============================] - 0s 3ms/step - loss: 287.1974
Epoch 4993/5000
1/1 [==============================] - 0s 2ms/step - loss: 286.9698
Epoch 4994/5000
1/1 [==============================] - 0s 3ms/step - loss: 286.7423
Epoch 4995/5000
1/1 [==============================] - 0s 3ms/step - loss: 286.5152
Epoch 4996/5000
1/1 [==============================] - 0s 2ms/step - loss: 286.2881
Epoch 4997/5000
1/1 [==============================] - 0s 2ms/step - loss: 286.0614
Epoch 4998/5000
1/1 [==============================] - 0s 3ms/step - loss: 285.8348
Epoch 4999/5000
1/1 [==============================] - 0s 3ms/step - loss: 285.6085
Epoch 5000/5000
1/1 [==============================] - 0s 3ms/step - loss: 285.3824
model.predict(x) # 通过x预测
array([[25.934877],
[26.824005],
[27.802044],
[28.691174],
[29.58022 ],
[30.558342],
[30.473873],
[32.336597],
[33.225723],
[34.203762],
[35.092888],
[36.20356 ],
[36.96449 ],
[37.849186],
[38.73831 ],
[39.716354],
[40.60548 ],
[41.494606],
[42.47265 ],
[43.361774],
[44.250904],
[45.228943],
[46.118073],
[47.007195],
[47.89632 ],
[48.874363],
[49.763493],
[50.674774],
[51.630657],
[52.519787]], dtype=float32)
model.predict(pd.Series([20]))
array([[48.088966]], dtype=float32)
明日计划
- 继续学习tensorflow