1 from sklearn.externals import joblib
2 from sklearn import svm
3 X = [[0, 0], [1, 1]]
4 y = [0, 1]
5 clf = svm.SVC()
6 clf.fit(X, y)
7 joblib.dump(clf, "train_model.m")

1 clf = joblib.load("train_model.m")
2 clf.predit([0,0]) #此处test_X为特征集

 1 import tensorflow as tf
 2 import numpy as np
 3
 4 W = tf.Variable([[1,1,1],[2,2,2]],dtype = tf.float32,name='w')
 5 b = tf.Variable([[0,1,2]],dtype = tf.float32,name='b')
 6
 7 init = tf.initialize_all_variables()
 8 saver = tf.train.Saver()
 9 with tf.Session() as sess:
10          sess.run(init)
11          save_path = saver.save(sess,"save/model.ckpt")  

1 import tensorflow as tf
2 import numpy as np
3
4 W = tf.Variable(tf.truncated_normal(shape=(2,3)),dtype = tf.float32,name='w')
5 b = tf.Variable(tf.truncated_normal(shape=(1,3)),dtype = tf.float32,name='b')
6
7 saver = tf.train.Saver()
8 with tf.Session() as sess:
9       saver.restore(sess,"save/model.ckpt")  

 1 import tensorflow as tf
 2
 3 # First, you design your mathematical operations
 4 # We are the default graph scope
 5
 6 # Let's design a variable
 7 v1 = tf.Variable(1. , name="v1")
 8 v2 = tf.Variable(2. , name="v2")
 9 # Let's design an operation
10 a = tf.add(v1, v2)
11
12 # Let's create a Saver object
13 # By default, the Saver handles every Variables related to the default graph
14 all_saver = tf.train.Saver()
15 # But you can precise which vars you want to save under which name
16 v2_saver = tf.train.Saver({"v2": v2})
17
18 # By default the Session handles the default graph and all its included variables
19 with tf.Session() as sess:
20   # Init v and v2
21   sess.run(tf.global_variables_initializer())
22   # Now v1 holds the value 1.0 and v2 holds the value 2.0
23   # We can now save all those values
24   all_saver.save(sess, 'data.chkp')
25   # or saves only v2
26   v2_saver.save(sess, 'data-v2.chkp')
27 模型的权重是保存在 .chkp 文件中，模型的图是保存在 .chkp.meta 文件中。

 1 import tensorflow as tf
 2
 3 # Let's laod a previous meta graph in the current graph in use: usually the default graph
 4 # This actions returns a Saver
 5 saver = tf.train.import_meta_graph('results/model.ckpt-1000.meta')
 6
 7 # We can now access the default graph where all our metadata has been loaded
 8 graph = tf.get_default_graph()
 9
10 # Finally we can retrieve tensors, operations, etc.
11 global_step_tensor = graph.get_tensor_by_name('loss/global_step:0')
12 train_op = graph.get_operation_by_name('loss/train_op')
13 hyperparameters = tf.get_collection('hyperparameters')
14
15 恢复权重
16
17 请记住，在实际的环境中，真实的权重只能存在于一个会话中。也就是说，restore 这个操作必须在一个会话中启动，然后将数据权重导入到图中。理解恢复操作的最好方法是将它简单的看做是一种数据初始化操作。
18 with tf.Session() as sess:
19     # To initialize values with saved data
20     saver.restore(sess, 'results/model.ckpt-1000-00000-of-00001')
21     print(sess.run(global_step_tensor)) # returns 1000

1 model.save('my_model.h5')
2 model = load_model('my_model.h5')