from sklearn.externals import joblib

 from sklearn import svm

 X = [[0, 0], [1, 1]]

 y = [0, 1]

 clf = svm.SVC()

 clf.fit(X, y)

 joblib.dump(clf, "train_model.m")

 clf = joblib.load("train_model.m")

 clf.predit([0,0]) #此处test_X为特征集

 import tensorflow as tf

 import numpy as np  

 W = tf.Variable([[1,1,1],[2,2,2]],dtype = tf.float32,name='w')

 b = tf.Variable([[0,1,2]],dtype = tf.float32,name='b')  

 init = tf.initialize_all_variables()

 saver = tf.train.Saver()

 with tf.Session() as sess:

          sess.run(init)

          save_path = saver.save(sess,"save/model.ckpt")  

 import tensorflow as tf

 import numpy as np  

 W = tf.Variable(tf.truncated_normal(shape=(2,3)),dtype = tf.float32,name='w')

 b = tf.Variable(tf.truncated_normal(shape=(1,3)),dtype = tf.float32,name='b')  

 saver = tf.train.Saver()

 with tf.Session() as sess:

       saver.restore(sess,"save/model.ckpt")  

 import tensorflow as tf

 # First, you design your mathematical operations

 # We are the default graph scope

 # Let's design a variable

 v1 = tf.Variable(1. , name="v1")

 v2 = tf.Variable(2. , name="v2")

 # Let's design an operation

 a = tf.add(v1, v2)

 # Let's create a Saver object

 # By default, the Saver handles every Variables related to the default graph

 all_saver = tf.train.Saver()

 # But you can precise which vars you want to save under which name

 v2_saver = tf.train.Saver({"v2": v2}) 

 # By default the Session handles the default graph and all its included variables

 with tf.Session() as sess:

   # Init v and v2

   sess.run(tf.global_variables_initializer())

   # Now v1 holds the value 1.0 and v2 holds the value 2.0

   # We can now save all those values

   all_saver.save(sess, 'data.chkp')

   # or saves only v2

   v2_saver.save(sess, 'data-v2.chkp')

 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。

 import tensorflow as tf

 # Let's laod a previous meta graph in the current graph in use: usually the default graph

 # This actions returns a Saver

 saver = tf.train.import_meta_graph('results/model.ckpt-1000.meta')

 # We can now access the default graph where all our metadata has been loaded

 graph = tf.get_default_graph()

 # Finally we can retrieve tensors, operations, etc.

 global_step_tensor = graph.get_tensor_by_name('loss/global_step:0')

 train_op = graph.get_operation_by_name('loss/train_op')

 hyperparameters = tf.get_collection('hyperparameters')

 恢复权重

 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。

 with tf.Session() as sess:

     # To initialize values with saved data

     saver.restore(sess, 'results/model.ckpt-1000-00000-of-00001')

     print(sess.run(global_step_tensor)) # returns 1000

 model.save('my_model.h5')

 model = load_model('my_model.h5')