之前通过CNN进行的MNIST训练识别成功率已经很高了，不过每次运行都需要消耗很多的时间。在实际使用的时候，每次都要选经过训练后在进行识别那就太不方便了。

所以我们学习一下如何将训练习得的参数保存起来，然后在需要用的时候直接使用这些参数进行快速的识别。

本章节代码来自《Tensorflow 实战Google深度学习框架》5.5 TensorFlow 最佳实践样例程序  针对书中的代码做了一点点的调整。

mnist_inference.py:

#coding=utf-8
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

INPUT_NODE = 784
OUTPUT_NODE = 10
LAYER1_NODE = 500

def get_weight_variable(shape, regularizer):
    weights = tf.get_variable("weights", shape, initializer = tf.truncated_normal_initializer(stddev=0.1))
    if regularizer != None:
        tf.add_to_collection('losses', regularizer(weights))
    return weights

def inference(input_tensor, regularizer):
    with tf.variable_scope('layer1'):
        weights = get_weight_variable([INPUT_NODE, LAYER1_NODE], regularizer)
        biases = tf.get_variable("biases", [LAYER1_NODE], initializer=tf.constant_initializer(0.0))
        layer1 = tf.nn.relu(tf.matmul(input_tensor, weights) + biases)

    with tf.variable_scope('layer2'):
        weights = get_weight_variable([LAYER1_NODE, OUTPUT_NODE], regularizer)
        biases = tf.get_variable("biases", [OUTPUT_NODE], initializer=tf.constant_initializer(0.0))
        layer2 = tf.matmul(layer1, weights) + biases

    return layer2

这里是向前传播的方法文件。这个方法在训练和测试的过程都需要用到，将它抽离出来既能使用起来更加方便，也能保证训练和测试时使用的方法保持一致。

共享变量  tf.variable_scope & get_variable 方法：

详细的使用方法和工作原理参见教程：共享变量

get_variable

 weights = tf.get_variable("weights", shape, initializer = tf.truncated_normal_initializer(stddev=0.1))

源代码第十行使用get_variable函数获取变量。

在训练网络是会创建这些变量；

在测试时会通过训练时保存的模型加载这些变量的值。

mnist_train.py:

#coding=utf-8
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

import mnist_inference

BATCH_SIZE = 100
LEARNING_RATE_BASE = 0.8
LEARNING_RATE_DECAY = 0.99
REGULARAZTION_RATE = 0.0001
TRAINING_STEPS = 30000
MOVING_AVERAGE_DECAY = 0.99

def train(mnist):
    x = tf.placeholder(tf.float32, [None, mnist_inference.INPUT_NODE], name="x-input")
    y_ = tf.placeholder(tf.float32, [None, mnist_inference.OUTPUT_NODE], name="y-input")

    regularizer = tf.contrib.layers.l2_regularizer(REGULARAZTION_RATE)
    y = mnist_inference.inference(x, regularizer)

    global_step = tf.Variable(0, trainable=False)

    variable_averages = tf.train.ExponentialMovingAverage(MOVING_AVERAGE_DECAY, global_step)
    variables_averages_op = variable_averages.apply(tf.trainable_variables())

    cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=y, labels=tf.argmax(y_, 1))
    cross_entropy_mean = tf.reduce_mean(cross_entropy)
    loss = cross_entropy_mean + tf.add_n(tf.get_collection('losses'))
    learning_rate = tf.train.exponential_decay(LEARNING_RATE_BASE, global_step, mnist.train.num_examples / BATCH_SIZE, LEARNING_RATE_DECAY)
    train_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss, global_step=global_step)
    with tf.control_dependencies([train_step, variables_averages_op]):
        train_op = tf.no_op(name='train')

    saver = tf.train.Saver()
    with tf.Session() as sess:
        init = tf.global_variables_initializer()
        sess.run(init)
        for i in range(TRAINING_STEPS):
            xs, ys = mnist.train.next_batch(BATCH_SIZE)
            _,  loss_value, step = sess.run([train_op, loss, global_step], feed_dict={x: xs, y_: ys})

            if i % 1000 == 0:
                print("After %d training step(s), loss on training batch is %g." % (step, loss_value))
                saver.save(sess, "./mnist_variables/trained_variables.ckpt",global_step=global_step)


def main(argv=None):
    mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
    train(mnist)

if __name__ == '__main__':
    tf.app.run()

 使用正则函数防止过拟合：

regularizer = tf.contrib.layers.l2_regularizer(REGULARAZTION_RATE)

 第25行和第29行代码：

global_step = tf.Variable(0, trainable=False)
。。。。。。。

variables_averages_op = variable_averages.apply(tf.trainable_variables())

tf.trinable_variables()不会获取到global_step 因为trainable设置为了False。

如果tf.trainable_variables() 换成 tf.all_variables()就能获取到global_step了。

mnist_eval.py：

#coding=utf-8
import time
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

import mnist_inference
import mnist_train

EVAL_INTERVAL_SECS = 10

def evaluate(mnist):
    with tf.Graph().as_default() as g:
        x = tf.placeholder(tf.float32, [None, mnist_inference.INPUT_NODE], name="x-input")
        y_ = tf.placeholder(tf.float32, [None, mnist_inference.OUTPUT_NODE], name="y-input")
        validate_feed = {x:mnist.test.images,y_:mnist.test.labels}

        y = mnist_inference.inference(x, None)
        correct_prediction = tf.equal(tf.argmax(y, 1),tf.argmax(y_, 1))
        accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

        variable_averages = tf.train.ExponentialMovingAverage(mnist_train.MOVING_AVERAGE_DECAY)
        variables_to_restore = variable_averages.variables_to_restore()
        saver = tf.train.Saver(variables_to_restore)

        while True:
            with tf.Session() as sess:
                ckpt = tf.train.get_checkpoint_state("./mnist_variables/")
                if ckpt and ckpt.model_checkpoint_path:
                    saver.restore(sess, ckpt.model_checkpoint_path)
                    global_step = ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1]
                    accuracy_score = sess.run(accuracy, feed_dict=validate_feed)
                    print("After %s training step(s), validation accuracy = %g " % (global_step, accuracy_score))
                else:
                     print('No checkpoint file found')
                     return
                time.sleep(EVAL_INTERVAL_SECS)

def main(argv=None):
    mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
    evaluate(mnist)

if __name__ == '__main__':
    tf.app.run()

（未完待续。。。。）