学习TensorFlow，保存学习到的网络结构参数并调用

在深度学习中，不管使用那种学习框架，我们会遇到一个很重要的问题，那就是在训练完之后，如何存储学习到的深度网络的参数？在测试时，如何调用这些网络参数？针对这两个问题，本篇博文主要探索TensorFlow如何解决他们？本篇博文分为三个部分，第一是讲解tensorflow相关的函数，第二是代码例程，第三是运行结果。

一 tensorflow相关的函数

我们说的这两个功能主要由一个类来完成，class tf.train.Saver

saver = tf.train.Saver()save_path = saver.save(sess, model_path)load_path = saver.restore(sess, model_path)

saver = tf.train.Saver() 由类创建对象saver，用于保存和调用学习到的网络参数，参数保存在checkpoints里

save_path = saver.save(sess, model_path) 保存学习到的网络参数到model_path路径中

load_path = saver.restore(sess, model_path) 调用model_path路径中的保存的网络参数到graph中

二 代码例程

'''Save and Restore a model using TensorFlow.This example is using the MNIST database of handwritten digits(http://yann.lecun.com/exdb/mnist/)Author: Aymeric DamienProject: https://github.com/aymericdamien/TensorFlow-Examples/'''# Import MINST datafrom tensorflow.examples.tutorials.mnist import input_datamnist = input_data.read_data_sets("/tmp/data/", one_hot=True)import tensorflow as tf# Parameterslearning_rate = 0.001batch_size = 100display_step = 1model_path = "/home/lei/TensorFlow-Examples-master/examples/4_Utils/model.ckpt"# Network Parametersn_hidden_1 = 256 # 1st layer number of featuresn_hidden_2 = 256 # 2nd layer number of featuresn_input = 784 # MNIST data input (img shape: 28*28)n_classes = 10 # MNIST total classes (0-9 digits)# tf Graph inputx = tf.placeholder("float", [None, n_input])y = tf.placeholder("float", [None, n_classes])# Create modeldef multilayer_perceptron(x, weights, biases):    # Hidden layer with RELU activation    layer_1 = tf.add(tf.matmul(x, weights['h1']), biases['b1'])    layer_1 = tf.nn.relu(layer_1)    # Hidden layer with RELU activation    layer_2 = tf.add(tf.matmul(layer_1, weights['h2']), biases['b2'])    layer_2 = tf.nn.relu(layer_2)    # Output layer with linear activation    out_layer = tf.matmul(layer_2, weights['out']) + biases['out']    return out_layer# Store layers weight & biasweights = {    'h1': tf.Variable(tf.random_normal([n_input, n_hidden_1])),    'h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])),    'out': tf.Variable(tf.random_normal([n_hidden_2, n_classes]))}biases = {    'b1': tf.Variable(tf.random_normal([n_hidden_1])),    'b2': tf.Variable(tf.random_normal([n_hidden_2])),    'out': tf.Variable(tf.random_normal([n_classes]))}# Construct modelpred = multilayer_perceptron(x, weights, biases)# Define loss and optimizercost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, y))optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)# Initializing the variablesinit = tf.initialize_all_variables()# 'Saver' op to save and restore all the variablessaver = tf.train.Saver()# Running first sessionprint "Starting 1st session..."with tf.Session() as sess:    # Initialize variables    sess.run(init)    # Training cycle    for epoch in range(3):        avg_cost = 0.        total_batch = int(mnist.train.num_examples/batch_size)        # Loop over all batches        for i in range(total_batch):            batch_x, batch_y = mnist.train.next_batch(batch_size)            # Run optimization op (backprop) and cost op (to get loss value)            _, c = sess.run([optimizer, cost], feed_dict={x: batch_x,                                                          y: batch_y})            # Compute average loss            avg_cost += c / total_batch        # Display logs per epoch step        if epoch % display_step == 0:            print "Epoch:", '%04d' % (epoch+1), "cost=", \                "{:.9f}".format(avg_cost)    print "First Optimization Finished!"    # Test model    correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))    # Calculate accuracy    accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))    print "Accuracy:", accuracy.eval({x: mnist.test.images, y: mnist.test.labels})    # Save model weights to disk    save_path = saver.save(sess, model_path)    print "Model saved in file: %s" % save_path# Running a new sessionprint "Starting 2nd session..."with tf.Session() as sess:    # Initialize variables    sess.run(init)    # Restore model weights from previously saved model    load_path = saver.restore(sess, model_path)    print "Model restored from file: %s" % save_path    # Resume training    for epoch in range(7):        avg_cost = 0.        total_batch = int(mnist.train.num_examples / batch_size)        # Loop over all batches        for i in range(total_batch):            batch_x, batch_y = mnist.train.next_batch(batch_size)            # Run optimization op (backprop) and cost op (to get loss value)            _, c = sess.run([optimizer, cost], feed_dict={x: batch_x,                                                          y: batch_y})            # Compute average loss            avg_cost += c / total_batch        # Display logs per epoch step        if epoch % display_step == 0:            print "Epoch:", '%04d' % (epoch + 1), "cost=", \                "{:.9f}".format(avg_cost)    print "Second Optimization Finished!"    # Test model    correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))    # Calculate accuracy    accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))    print "Accuracy:", accuracy.eval(        {x: mnist.test.images, y: mnist.test.labels})

三 运行结果

参考资料：

https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/4_Utils/save_restore_model.py

https://www.tensorflow.org/versions/r0.9/api_docs/python/state_ops.html#Saver