深度学习-Tensorflow2.2-深度学习基础和tf.keras{1}-线性回归tf.keras概述-02

线性回归原理

线性方程 y=kx+b

import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' # 修改警告级别，不显示警告
import tensorflow as tf
import pandas as pd
import matplotlib.pyplot as plt

data = pd.read_csv('A.csv')
print(data)
plt.scatter(data.Education,data.Income)
plt.show()

预测目标与损失函数

目标：预测函数f(x)与真实值之间的整体误差最小。
损失函数：使用均方误差作为成本函数，也就是预测值和真实值之间差的平方取均值。

import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' # 修改警告级别，不显示警告
import tensorflow as tf
import pandas as pd
import matplotlib.pyplot as plt

data = pd.read_csv('A.csv')
# print(data)
# plt.scatter(data.Education,data.Income)
# plt.show()
x = data.Education
y = data.Income

model = tf.keras.Sequential()
model.add(tf.keras.layers.Dense(1,input_shape=(1,)))
model.summary()

import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' # 修改警告级别，不显示警告
import tensorflow as tf
import pandas as pd
import matplotlib.pyplot as plt

data = pd.read_csv('A.csv')
# print(data)
# plt.scatter(data.Education,data.Income)
# plt.show()
x = data.Education
y = data.Income

model = tf.keras.Sequential()
model.add(tf.keras.layers.Dense(1,input_shape=(1,)))
# model.summary() # ax+b
model.compile(optimizer='adam',loss='mse')# 编译（优化方法：admaoptimizer='adam',损失函数：均方差loss='mse'）
history = model.fit(x,y,epochs=5000) # 训练x,y5000次寻找a和b的最小值
print(history)

import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' # 修改警告级别，不显示警告
import tensorflow as tf
import pandas as pd
import matplotlib.pyplot as plt

data = pd.read_csv('A.csv')
# print(data)
# plt.scatter(data.Education,data.Income)
# plt.show()
x = data.Education
y = data.Income

model = tf.keras.Sequential()
model.add(tf.keras.layers.Dense(1,input_shape=(1,)))
# model.summary() # ax+b
model.compile(optimizer='adam',loss='mse')# 编译（优化方法：admaoptimizer='adam',损失函数：均方差loss='mse'）
history = model.fit(x,y,epochs=5000) # 训练x,y5000次寻找a和b的最小值
print(history)
print(model.predict(x))  # 预测现有的x值收入
print("20年的预测收入为:",model.predict(pd.Series([20]))) # 预测20年的收入