TF——线性回归

注释很清楚：
 1 import tensorflow as tf
 2 import os
 3 import numpy as np
 4 import matplotlib.pyplot as plt
 5 os.environ["CUDA_VISIBLE_DEVICES"]="0"
 6 learning_rate=0.01
 7 training_epochs=1000
 8 display_step=50
 9 if __name__ =='__main__':
10    train_X = np.asarray(
11        [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])
12    train_Y = np.asarray(
13        [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])
14    n_sample=train_X.shape[0]#维度——几行
15    print(n_sample)
16    '''变量'''
17    X=tf.placeholder("float")
18    Y=tf.placeholder("float")
19    '''模型'''
20    W=tf.Variable(np.random.randn(),name="weight")#标准正态分布
21    b = tf.Variable(np.random.randn(), name="bias")
22    '''线性回归模型
23     pred=x*k+b
24    '''
25    mul=tf.multiply(X,W)
26    pred=tf.add(mul,b)
27 
28    '''标准方差：
29    z=(Y-y)^2+...../n
30    '''
31    print(2*n_sample)
32    cost=tf.reduce_sum(tf.pow(pred-Y,2))/(2*n_sample)
33 
34    '''梯度下降法 去最小值，获得最有解'''
35    optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)
36    init = tf.global_variables_initializer()
37    with tf.Session() as sess:
38        sess.run(init)
39        '''训练模型'''
40        for epoch in range(training_epochs):
41            for(x,y) in zip(train_X,train_Y):
42                sess.run(optimizer,feed_dict={X:x,Y:y})
43 
44                if(epoch+1)%display_step==0:
45                    c=sess.run(cost,feed_dict={X:train_X,Y:train_Y})
46                    print("训练次数:", '%04d' % (epoch + 1),
47                          "标准差=", "{:.9f}".format(c),
48                          "斜率=", sess.run(W),
49                          "截距=",sess.run(b))
50        print("PPPPPPPPPPPPPPPPPPPPPPPPP")
51        training_cost = sess.run(cost, feed_dict={X: train_X, Y: train_Y})
52        print("标准差=", training_cost, "斜率=", sess.run(W), "截距=", sess.run(b))
53        plt.plot(train_X,train_Y,'ro',label="DATA")
54        plt.plot(train_X, sess.run(W)*train_X+sess.run(b), label="Line",color="blue")
55        plt.legend()
56        plt.show()