一、sklearn模型保存與讀取?
1、保存

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")

2、讀取

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

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二、TensorFlow模型保存與讀取（該方式tensorflow只能保存變量而不是保存整個(gè)網(wǎng)絡(luò)，所以在提取模型時(shí)，我們還需要重新第一網(wǎng)絡(luò)結(jié)構(gòu)。）?
1、保存

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")

2、加載

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")

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三、TensorFlow模型保存與讀取（該方式tensorflow保存整個(gè)網(wǎng)絡(luò)）?
1、保存

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 模型的權(quán)重是保存在 .chkp 文件中，模型的圖是保存在 .chkp.meta 文件中。

2、加載

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 恢復(fù)權(quán)重 16 17 請(qǐng)記住，在實(shí)際的環(huán)境中，真實(shí)的權(quán)重只能存在于一個(gè)會(huì)話中。也就是說(shuō)，restore 這個(gè)操作必須在一個(gè)會(huì)話中啟動(dòng)，然后將數(shù)據(jù)權(quán)重導(dǎo)入到圖中。理解恢復(fù)操作的最好方法是將它簡(jiǎn)單的看做是一種數(shù)據(jù)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

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四、keras模型保存和加載

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

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轉(zhuǎn)載于:https://www.cnblogs.com/tectal/p/9053205.html

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