环境：
python 3.8
tensorflow 2.3
numpy 1.20.3
pandas 1.3.3
matplotlib 3.4.2
import tensorflow as tf
import pandas as pd
import matplotlib.pyplot as plt
if __name__ == '__main__':
print('Tensorflow Version: {}'.format(tf.__version__))#格式化输出tensorflow版本
data = pd.read_csv('./dataset/Income1.csv')
print(data)
x = data.Education
y = data.Income
plt.scatter(x,y)#绘制散点图
plt.show()#显示图像
# Sequential()方法是一个容器，描述了神经网络的网络结构，在Sequential()的输入参数中描述从输入层到输出层的网络结构
model = tf.keras.Sequential()
model.add(tf.keras.layers.Dense(1,input_shape=(1,)))
print(model.summary())#输出模型参数
model.compile(
optimizer='adam',#优化方法
loss='mse'#损失函数，均方差
)
history = model.fit(x,y,epochs=5000)#训练5000次
print(model.predict(pd.Series([20])))#Series（是一维数组,因为输入x是一维数组，这里也是一维数组形式
**
tf.keras.layers.Dense(
units, # 正整数，输出空间的维数，一层的神经元个数
activation=None, # 激活函数，不指定则没有
use_bias=True, # 布尔值，是否使用偏移向量
kernel_initializer='glorot_uniform', # 核权重矩阵的初始值设定项
bias_initializer='zeros', # 偏差向量的初始值设定项
kernel_regularizer=None, # 正则化函数应用于核权矩阵
bias_regularizer=None, # 应用于偏差向量的正则化函数
activity_regularizer=None, # Regularizer function applied to the output of the layer (its "activation")
kernel_constraint=None, # Constraint function applied to the kernel weights matrix.
bias_constraint=None, **kwargs # Constraint function applied to the bias vector
)
tf.keras.layers.Dense(1,input_shape=(1,))：1对应units。输入参数为元组。
数据集Unnamed: 0 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 11.645485 15.192634 5 6 12.086957 26.398951 6 7 12.488294 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 14.535117 41.497994 12 13 14.976589 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 61.336621 18 19 17.464883 57.581988 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 75.775218 26 27 20.755853 72.486055 27 28 21.157191 77.355021 28 29 21.598662 72.118790 29 30 22.000000 80.260571
绘制散点图
Model: "sequential" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= dense (Dense) (None, 1) 2 ================================================================= Total params: 2 Trainable params: 2 Non-trainable params: 0
数据集链接：https://pan.baidu.com/s/1SgK1gZdTEYkBtVboe8EBgA
密码：1234
