TensorFlow模型加载与保存

我们经常遇到训练时间很长，使用起来就是Weight和Bias。那么如何将训练和测试分开操作呢？

TF给出了模型的加载与保存操作，看了网上都是很简单的使用了一下，这里给出一个神经网络的小程序去测试。

本博文使用了Titanic的数据进行操作：

Train.Py

 import numpy as np
 import pandas as pd
 import tensorflow as tf
 from sklearn.model_selection import train_test_split

 ################################
 # Preparing Data
 ################################

 # read data from file
 data = pd.read_csv('data/train.csv')

 # fill nan values with 0
 data = data.fillna(0)
 # convert ['male', 'female'] values of Sex to [1, 0]
 data['Sex'] = data['Sex'].apply(lambda s: 1 if s == 'male' else 0)
 # 'Survived' is the label of one class,
 # add 'Deceased' as the other class
 data['Deceased'] = data['Survived'].apply(lambda s: 1 - s)

 # select features and labels for training
 dataset_X = data[['Sex', 'Age', 'Pclass', 'SibSp', 'Parch', 'Fare']].as_matrix()
 dataset_Y = data[['Deceased', 'Survived']].as_matrix()

 # split training data and validation set data
 X_train, X_val, y_train, y_val = train_test_split(dataset_X, dataset_Y,
                                                   test_size=0.2,
                                                   random_state=42)

 ################################
 # Constructing Dataflow Graph
 ################################

 # create symbolic variables
 X = tf.placeholder(tf.float32, shape=[None, 6])
 y = tf.placeholder(tf.float32, shape=[None, 2])

 # weights and bias are the variables to be trained
 weights = tf.Variable(tf.random_normal([6, 2]), name='weights')
 bias = tf.Variable(tf.zeros([2]), name='bias')
 y_pred = tf.nn.softmax(tf.matmul(X, weights) + bias)

 # Minimise cost using cross entropy
 # NOTE: add a epsilon(1e-10) when calculate log(y_pred),
 # otherwise the result will be -inf
 cross_entropy = - tf.reduce_sum(y * tf.log(y_pred + 1e-10),
                                 reduction_indices=1)
 cost = tf.reduce_mean(cross_entropy)

 # use gradient descent optimizer to minimize cost
 train_op = tf.train.GradientDescentOptimizer(0.001).minimize(cost)

 # calculate accuracy
 correct_pred = tf.equal(tf.argmax(y, 1), tf.argmax(y_pred, 1))
 acc_op = tf.reduce_mean(tf.cast(correct_pred, tf.float32))

 ################################
 # Training and Evaluating the model
 ################################
 saver = tf.train.Saver()
 # use session to run the calculation
 with tf.Session() as sess:
     # variables have to be initialized at the first place
     tf.global_variables_initializer().run()
     # training loop
     for epoch in range(10):
         total_loss = 0.
         for i in range(len(X_train)):
             # prepare feed data and run
             feed_dict = {X: [X_train[i]], y: [y_train[i]]}
             _, loss = sess.run([train_op, cost], feed_dict=feed_dict)
             total_loss += loss
         # display loss per epoch
         print('Epoch: %04d, total loss=%.9f' % (epoch + 1, total_loss))
     saver_path = saver.save(sess,"wjy_data/model.ckpt")
     # Accuracy calculated by TensorFlow
     accuracy = sess.run(acc_op, feed_dict={X: X_val, y: y_val})
     print("Accuracy on validation set: %.9f" % accuracy)

     # Accuracy calculated by NumPy
     pred = sess.run(y_pred, feed_dict={X: X_val})
     correct = np.equal(np.argmax(pred, 1), np.argmax(y_val, 1))
     numpy_accuracy = np.mean(correct.astype(np.float32))
     print("Accuracy on validation set (numpy): %.9f" % numpy_accuracy)

     # predict on test data
     testdata = pd.read_csv('data/test.csv')
     testdata = testdata.fillna(0)
     # convert ['male', 'female'] values of Sex to [1, 0]
     testdata['Sex'] = testdata['Sex'].apply(lambda s: 1 if s == 'male' else 0)
     X_test = testdata[['Sex', 'Age', 'Pclass', 'SibSp', 'Parch', 'Fare']]
     predictions = np.argmax(sess.run(y_pred, feed_dict={X: X_test}), 1)
     submission = pd.DataFrame({
         "PassengerId": testdata["PassengerId"],
         "Survived": predictions
     })

     submission.to_csv("titanic-submission.csv", index=False)

注意：

　　saver_path = saver.save(sess,"wjy_data/model.ckpt")
　　项目目录下面必须新建一个wjy_data的文件夹，不然会报错！！！

Test.Py

 import numpy as np
 import pandas as pd
 import tensorflow as tf
 from sklearn.model_selection import train_test_split

 # create symbolic variables
 X = tf.placeholder(tf.float32, shape=[None, 6])
 y = tf.placeholder(tf.float32, shape=[None, 2])

 # weights and bias are the variables to be trained
 weights = tf.Variable(tf.random_normal([6, 2]), name='weights')
 bias = tf.Variable(tf.zeros([2]), name='bias')
 y_pred = tf.nn.softmax(tf.matmul(X, weights) + bias)

 # predict on test data
 testdata = pd.read_csv('data/test.csv')
 testdata = testdata.fillna(0)
 # convert ['male', 'female'] values of Sex to [1, 0]
 testdata['Sex'] = testdata['Sex'].apply(lambda s: 1 if s == 'male' else 0)
 X_test = testdata[['Sex', 'Age', 'Pclass', 'SibSp', 'Parch', 'Fare']]
 ################################
 # Training and Evaluating the model
 ################################
 saver = tf.train.Saver()
 # use session to run the calculation
 with tf.Session() as sess:
     # variables have to be initialized at the first place
     tf.global_variables_initializer().run()
     #save_path = saver.save(sess,"Saved_model/model.ckpt")
     saver.restore(sess,"wjy_data/model.ckpt")#加载模型
     predictions = np.argmax(sess.run(y_pred, feed_dict={X: X_test}), 1)
     submission = pd.DataFrame({
         "PassengerId": testdata["PassengerId"],
         "Survived": predictions
     })
     #saver = tf.train.Saver()
     submission.to_csv("titanic-submission.csv", index=False)

很方便的使用保存模型的方式去测试和训练数据，不然怎么办~~

参考：

　　《深度学习原理与TensorFlow实战》

　　https://blog.csdn.net/lujiandong1/article/details/53301994