线性回归模型练习

 

单线性回归

y=w*x+b

import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt

# 随机生成1000个点，围绕在y=0.1x+0.3的直线周围
num_points = 1000
vectors_set = []
for i in range(num_points):
    x1 = np.random.normal(0.0, 0.55)
    y1 = x1 * 0.1 + 0.3 + np.random.normal(0.0, 0.03)
    vectors_set.append([x1, y1])

# 生成一些样本
x_data = [v[0] for v in vectors_set]
y_data = [v[1] for v in vectors_set]

plt.scatter(x_data,y_data,c='r')
plt.show()

tf.square()函数是求平方；tf.reduce_mean()是求均值

# 生成1维的W矩阵，取值是[-1,1]之间的随机数
with tf.name_scope('weight'):
    W = tf.Variable(tf.random_uniform([1], -1.0, 1.0), name='W')
# 生成1维的b矩阵，初始值是0
with tf.name_scope('bias'):
    b = tf.Variable(tf.zeros([1]), name='b')
# 经过计算得出预估值y
with tf.name_scope('y'):
    y = W * x_data + b

# 以预估值y和实际值y_data之间的均方误差作为损失
with tf.name_scope('loss'):
    loss = tf.reduce_mean(tf.square(y - y_data), name='loss')
# 采用梯度下降法来优化参数,这里0.5是指定的学习率
optimizer = tf.train.GradientDescentOptimizer(0.5)
# 训练的过程就是最小化这个误差值
train = optimizer.minimize(loss, name='train')

sess = tf.Session()

init = tf.global_variables_initializer()
sess.run(init)

# 初始化的W和b是多少
print ("W =", sess.run(W), "b =", sess.run(b), "loss =", sess.run(loss))
# 执行20次训练
for step in range(20):
    sess.run(train)
    # 输出训练好的W和b
    print ("W =", sess.run(W), "b =", sess.run(b), "loss =", sess.run(loss))
writer = tf.summary.FileWriter("logs/", sess.graph)

plt.scatter(x_data,y_data,c='r')
plt.plot(x_data,sess.run(W)*x_data+sess.run(b))
plt.show()

import tensorflow.compat.v1 as tf
import numpy as np
import matplotlib.pyplot as plt
import os
tf.disable_eager_execution() #保证sess.run()能够正常运行
os.environ["CUDA_VISIBLE_DEVICES"]="0"
#Parameters  
learning_rate=0.01
training_epochs=1000
display_step=50
#training Data  
train_X=np.asarray([3.3,4.4,5.5,6.71,6.93,4.168,9.779,6.182,7.59,2.167,
7.042,10.791,5.313,7.997,5.654,9.27,3.1])
train_Y=np.asarray([1.7,2.76,2.09,3.19,1.694,1.573,3.366,2.596,2.53,1.221,
2.827,3.465,1.65,2.904,2.42,2.94,1.3])
n_samples=train_X.shape[0]
#tf Graph Input  
X=tf.placeholder("float")
Y=tf.placeholder("float")
#Set model weights  
W=tf.Variable(np.random.randn(),name="weight")
b=tf.Variable(np.random.randn(),name='bias')
#Construct a linear model  
pred=tf.add(tf.multiply(X,W),b)
#Mean squared error  
cost=tf.reduce_sum(tf.pow(pred-Y,2))/(2*n_samples)
# Gradient descent  
optimizer=tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)
#Initialize the variables  
init =tf.global_variables_initializer()
#Start training  
with tf.Session() as sess:
     sess.run(init)
#Fit all training data  
     for epoch in range(training_epochs):
         for (x,y) in zip(train_X,train_Y):
             sess.run(optimizer,feed_dict={X:x,Y:y})
#Display logs per epoch step  
         if (epoch+1) % display_step==0:
            c=sess.run(cost,feed_dict={X:train_X,Y:train_Y})
            print("Epoch:" ,'%04d' %(epoch+1),"cost=","{:.9f}".format(c),"W=",sess.run(W),"b=",sess.run(b))
     print("Optimization Finished!")
     training_cost=sess.run(cost,feed_dict={X:train_X,Y:train_Y})
     print("Train cost=",training_cost,"W=",sess.run(W),"b=",sess.run(b))
#Graphic display  
     plt.plot(train_X,train_Y,'ro',label='Original data')
     plt.plot(train_X,sess.run(W)*train_X+sess.run(b),label="Fitting line")
     plt.legend()
     plt.show()