sklearn、TensorFlow、keras模型保存与读取

一、sklearn模型保存与读取 1、保存

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")

2、读取

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

二、TensorFlow模型保存与读取（该方式tensorflow只能保存变量而不是保存整个网络，所以在提取模型时，我们还需要重新第一网络结构。） 1、保存

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")

2、加载

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")

三、TensorFlow模型保存与读取（该方式tensorflow保存整个网络） 转载http://www.jianshu.com/p/8487db911d9a 1、保存

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。

2、加载

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

四、keras模型保存和加载

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