epoch: 20 batch_id: 83 Batch loss 0.5640518069267273
……
epoch: 90 batch_id: 203 Batch loss 0.6403363943099976
epoch: 90 batch_id: 204 Batch loss 0.45315566658973694
epoch: 90 batch_id: 205 Batch loss 0.5528439879417419
epoch: 90 batch_id: 206 Batch loss 0.386596143245697

1. import tensorflow as tf
2. import numpy as np 
3. from sklearn.datasets import fetch_california_housing 
4. from sklearn.preprocessing import StandardScaler 
5. 
6. scaler = StandardScaler()  #将特征进行标准归一化
7. #获取房价数据
8. housing = fetch_california_housing() 
9. m,n = housing.data.shape 
10. print (housing.keys())        #输出房价的key
11. print (housing.feature_names) #输出房价的特征：
12. print (housing.target)  
13. print (housing.DESCR)  
14. 
15. 
16. housing_data_plus_bias = np.c_[np.ones((m,1)), housing.data] 
17. scaled_data = scaler. fit_transform(housing.data) 
18. data = np.c_[np.ones((m,1)),scaled_data] 
19. 
20. # #T1、传统方式
21. # A = tf.placeholder(tf.float32,shape=(None,3)) 
22. # B = A + 5 
23. # with tf.Session() as sess: 
24. #     test_b_l = B.eval(feed_dict={A:[[1,2,3]]}) 
25. #     test_b_2 = B.eval(feed_dict={A:[[4,5,6],[7,8,9]]}) 
26. #     print(test_b_1) 
27. #     print(test_b_2) 
28. 
29. #T2、采用mini-batch方式
30. X = tf.placeholder(tf.float32, shape=(None, n + 1), name="X") 
31. y = tf.placeholder(tf.float32, shape=(None, 1), name="y") 
32. #采用optimizer计算梯度,设置参数
33. n_epochs = 100
34. learning_rate = 0.01
35. batch_size=100
36. n_batches = int(np.ceil(m / batch_size)) 
37. theta = tf.Variable(tf.random_uniform([n + 1, 1], -1.0, 1.0, seed=42), name="theta")
38. y_pred = tf.matmul(X, theta, name="predictions") 
39. error = y_pred - y 
40. mse = tf.reduce_mean(tf.square(error), name="mse") 
41. optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate) 
42. training_op = optimizer.minimize(mse) 
43. init = tf.global_variables_initializer() 
44. 
45. #定义mini-batch取数据方式
46. def fetch_batch(epoch, batch_index, batch_size): 
47.     np.random.seed(epoch * n_batches + batch_index) 
48.     indices = np.random.randint(m, size=batch_size)
49.     X_batch  = data[indices] 
50.     y_batch = housing.target.reshape(-1, 1)[indices] 
51. return X_batch, y_batch
52. #mini-batch计算过程
53. with tf.Session() as sess: 
54.     sess.run(init) 
55. for epoch in range(n_epochs):#/gfeMat 
56.         avg_cost = 0.
57. for batch_index in range(n_batches): 
58.             X_batch, y_batch = fetch_batch(epoch, batch_index, batch_size) 
59.             sess.run(training_op, feed_dict={X: X_batch, y: y_batch}) 
60. 
61. if epoch % 10 == 0: 
62.                 total_loss = 0
63.                 acc_train = mse.eval(feed_dict={X: X_batch, y: y_batch}) 
64.                 total_loss += acc_train 
65. #print(acc_train, total_loss)
66. print("epoch:",epoch, "batch_id:",batch_index, "Batch loss", total_loss) 
67.