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 Damien
Project: https://github.com/aymericdamien/TensorFlow-Examples/
'''

# Import MINST data
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)

import tensorflow as tf

# Parameters
learning_rate = 0.001
batch_size = 100
display_step = 1
model_path = "/home/lei/TensorFlow-Examples-master/examples/4_Utils/model.ckpt"

# Network Parameters
n_hidden_1 = 256 # 1st layer number of features
n_hidden_2 = 256 # 2nd layer number of features
n_input = 784 # MNIST data input (img shape: 28*28)
n_classes = 10 # MNIST total classes (0-9 digits)

# tf Graph input
x = tf.placeholder("float", [None, n_input])
y = tf.placeholder("float", [None, n_classes])

# Create model
def 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 & bias
weights = {
    '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 model
pred = multilayer_perceptron(x, weights, biases)

# Define loss and optimizer
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, y))
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)

# Initializing the variables
init = tf.initialize_all_variables()

# 'Saver' op to save and restore all the variables
saver = tf.train.Saver()

# Running first session
print "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 session
print "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