TensorFlowモデルのロードと保存

私たちはよく訓練時間が長く、WeightとBiasを使っています.では、どのようにして訓練とテストを別々に操作しますか?
TFはモデルのロードと保存操作を提供して、ネット上を見てすべてとても簡単な使用を見て、ここで1つのニューラルネットワークの小さいプログラムを提供してテストします.
このブログでは、Titanicのデータを使用して操作します.
Train.Py

 1 import numpy as np
 2 import pandas as pd
 3 import tensorflow as tf
 4 from sklearn.model_selection import train_test_split
 5 
 6 ################################
 7 # Preparing Data
 8 ################################
 9 
10 # read data from file
11 data = pd.read_csv('data/train.csv')
12 
13 # fill nan values with 0
14 data = data.fillna(0)
15 # convert ['male', 'female'] values of Sex to [1, 0]
16 data['Sex'] = data['Sex'].apply(lambda s: 1 if s == 'male' else 0)
17 # 'Survived' is the label of one class,
18 # add 'Deceased' as the other class
19 data['Deceased'] = data['Survived'].apply(lambda s: 1 - s)
20 
21 # select features and labels for training
22 dataset_X = data[['Sex', 'Age', 'Pclass', 'SibSp', 'Parch', 'Fare']].as_matrix()
23 dataset_Y = data[['Deceased', 'Survived']].as_matrix()
24 
25 # split training data and validation set data
26 X_train, X_val, y_train, y_val = train_test_split(dataset_X, dataset_Y,
27                                                   test_size=0.2,
28                                                   random_state=42)
29 
30 ################################
31 # Constructing Dataflow Graph
32 ################################
33 
34 # create symbolic variables
35 X = tf.placeholder(tf.float32, shape=[None, 6])
36 y = tf.placeholder(tf.float32, shape=[None, 2])
37 
38 # weights and bias are the variables to be trained
39 weights = tf.Variable(tf.random_normal([6, 2]), name='weights')
40 bias = tf.Variable(tf.zeros([2]), name='bias')
41 y_pred = tf.nn.softmax(tf.matmul(X, weights) + bias)
42 
43 # Minimise cost using cross entropy
44 # NOTE: add a epsilon(1e-10) when calculate log(y_pred),
45 # otherwise the result will be -inf
46 cross_entropy = - tf.reduce_sum(y * tf.log(y_pred + 1e-10),
47                                 reduction_indices=1)
48 cost = tf.reduce_mean(cross_entropy)
49 
50 # use gradient descent optimizer to minimize cost
51 train_op = tf.train.GradientDescentOptimizer(0.001).minimize(cost)
52 
53 # calculate accuracy
54 correct_pred = tf.equal(tf.argmax(y, 1), tf.argmax(y_pred, 1))
55 acc_op = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
56 
57 ################################
58 # Training and Evaluating the model
59 ################################
60 saver = tf.train.Saver()
61 # use session to run the calculation
62 with tf.Session() as sess:
63     # variables have to be initialized at the first place
64     tf.global_variables_initializer().run()
65     # training loop
66     for epoch in range(10):
67         total_loss = 0.
68         for i in range(len(X_train)):
69             # prepare feed data and run
70             feed_dict = {X: [X_train[i]], y: [y_train[i]]}
71             _, loss = sess.run([train_op, cost], feed_dict=feed_dict)
72             total_loss += loss
73         # display loss per epoch
74         print('Epoch: %04d, total loss=%.9f' % (epoch + 1, total_loss))
75     saver_path = saver.save(sess,"wjy_data/model.ckpt")
76     # Accuracy calculated by TensorFlow
77     accuracy = sess.run(acc_op, feed_dict={X: X_val, y: y_val})
78     print("Accuracy on validation set: %.9f" % accuracy)
79 
80     # Accuracy calculated by NumPy
81     pred = sess.run(y_pred, feed_dict={X: X_val})
82     correct = np.equal(np.argmax(pred, 1), np.argmax(y_val, 1))
83     numpy_accuracy = np.mean(correct.astype(np.float32))
84     print("Accuracy on validation set (numpy): %.9f" % numpy_accuracy)
85 
86     # predict on test data
87     testdata = pd.read_csv('data/test.csv')
88     testdata = testdata.fillna(0)
89     # convert ['male', 'female'] values of Sex to [1, 0]
90     testdata['Sex'] = testdata['Sex'].apply(lambda s: 1 if s == 'male' else 0)
91     X_test = testdata[['Sex', 'Age', 'Pclass', 'SibSp', 'Parch', 'Fare']]
92     predictions = np.argmax(sess.run(y_pred, feed_dict={X: X_test}), 1)
93     submission = pd.DataFrame({
94         "PassengerId": testdata["PassengerId"],
95         "Survived": predictions
96     })
97 
98     submission.to_csv("titanic-submission.csv", index=False)

注意:

　　saver_path = saver.save(sess,"wjy_data/model.ckpt")
　　 wjy_data ， ！！！

Test.Py

 1 import numpy as np
 2 import pandas as pd
 3 import tensorflow as tf
 4 from sklearn.model_selection import train_test_split
 5 
 6 # create symbolic variables
 7 X = tf.placeholder(tf.float32, shape=[None, 6])
 8 y = tf.placeholder(tf.float32, shape=[None, 2])
 9 
10 # weights and bias are the variables to be trained
11 weights = tf.Variable(tf.random_normal([6, 2]), name='weights')
12 bias = tf.Variable(tf.zeros([2]), name='bias')
13 y_pred = tf.nn.softmax(tf.matmul(X, weights) + bias)
14 
15 # predict on test data
16 testdata = pd.read_csv('data/test.csv')
17 testdata = testdata.fillna(0)
18 # convert ['male', 'female'] values of Sex to [1, 0]
19 testdata['Sex'] = testdata['Sex'].apply(lambda s: 1 if s == 'male' else 0)
20 X_test = testdata[['Sex', 'Age', 'Pclass', 'SibSp', 'Parch', 'Fare']]
21 ################################
22 # Training and Evaluating the model
23 ################################
24 saver = tf.train.Saver()
25 # use session to run the calculation
26 with tf.Session() as sess:
27     # variables have to be initialized at the first place
28     tf.global_variables_initializer().run()
29     #save_path = saver.save(sess,"Saved_model/model.ckpt")
30     saver.restore(sess,"wjy_data/model.ckpt")# 
31     predictions = np.argmax(sess.run(y_pred, feed_dict={X: X_test}), 1)
32     submission = pd.DataFrame({
33         "PassengerId": testdata["PassengerId"],
34         "Survived": predictions
35     })
36     #saver = tf.train.Saver()
37     submission.to_csv("titanic-submission.csv", index=False)

モデルを保存する方法でデータをテストしたり訓練したりするのが便利です.そうしないとどうしますか.
参照先:
『深さ学習原理とTensorFlow実戦』
　　https://blog.csdn.net/lujiandong1/article/details/53301994