tensorflow学习（一）

 1 import tensorflow as tf
 2 import numpy as np
 3 import math
 4 
 5 class Model:
 6     def __init__(self,w = np.empty(None),b = None):
 7         self.b = b
 8         self.w = w
 9 
10     def predict(self, input):
11         return np.dot(input,self.w) + self.b
12 
13 data = np.float32(np.random.rand(1000,2))
14 label = np.dot(data,np.array([[0.100],[0.200]])) + 0.3
15 m,n = data.shape
16 
17 num_train = int(m * 0.6)
18 num_validation = int(m * 0.2)
19 num_test = int(m * 0.2)
20 
21 data_train = data[:num_train,:]
22 data_validation = data[num_train:(num_train+num_validation),:]
23 data_test = data[(num_train+num_validation):,:]
24 
25 label_train = label[:num_train,:]
26 label_validation = label[num_train:(num_train+num_validation),:]
27 label_test = label[(num_train+num_validation):,:]
28 
29 w = tf.Variable(tf.random_uniform([2,1],-1.0,1.0))
30 b = tf.Variable(tf.zeros([1]))
31 
32 y_train = tf.matmul(data_train,w) + b
33 loss = tf.reduce_mean(tf.square(label_train - y_train))
34 '''
35 bestModel = Model()
36 minRMSE = (1 << 31) -1
37 alphas = [0.1,0.3,0.5];
38 iters = [100,150,200,250];
39 for iter in iters:
40     for alpha in alphas:
41         optimizer = tf.train.GradientDescentOptimizer(alpha)
42         train = optimizer.minimize(loss)
43         init_state = tf.global_variables_initializer()
44         with tf.Session() as sess:
45             sess.run(init_state)
46             for step in range(0,iter):
47                 sess.run(train)
48             model = Model(sess.run(w),sess.run(b))
49             p = model.predict(data_validation)
50             rmse = np.sqrt(np.mean(np.square(label_validation - p)))
51             if rmse < minRMSE:
52                 minRMSE = rmse
53                 bestModel = model
54 np.save("E:\Python\models\weights.npy",bestModel.w)
55 np.save("E:\Python\models\b.npy",bestModel.b)
56 '''
57 
58 weights = np.load("E:\Python\models\weights.npy")
59 b = np.load("E:\Python\models\b.npy")
60 
61 model = Model(weights,b)
62 predicts = model.predict(data_test)
63 print(predicts)
64 print(label_test)
65 print(label_test - predicts)