https://www.bilibili.com/video/av22530538/?p=27

##mnist_lenet5_forward.py
#encoding:utf-8
import tensorflow as tf
IMAGE_SIZE = 28
NUM_CHANNELS = 1
CONV1_SIZE = 5
CONV1_KERNEL_NUM = 32

CONV2_SIZE = 5
CONV2_KERNEL_NUM = 64
FC_SIZE = 512
OUTPUT_MODE = 10

def get_weight(shape, regularizer):
    w = tf.Variable(tf.truncated_normal(shape,stddev=0.1))
    if regularizer != None:
        tf.add_to_collection('losses',tf.contrib.layers.l2_regularizer(regularizer)(w))
        
        return w

def get_bias(shape):
    b = tf.Variable(tf.zeros(shape))
    return b

def conv2d(x,w):
    return tf.nn.conv2d(x,w, strides=[1,1,1,1],padding='SAME')

def max_pool_2x2(x):
    return tf.nn.max_pool(x, ksize=[1,2,2,1],strides=[1,2,2,1],padding='SAME')

def forward(x, train,regularizer):
    conv1_w = get_weight([CONV1_SIZE,CONV1_SIZE,NUM_CHANNELS,CONV1_KERNEL_NUM],regularizer)
    conv1_b = get_bias([CONV1_KERNEL_NUM])
    conv1 = conv2d(x,conv1_w)
    relu1 = tf.nn.relu(tf.nn.bias_add(conv1,conv1_b))
    pool1 = max_pool_2x2(relu1)
    
    conv2_w = get_weight([CONV2_SIZE,CONV2_SIZE,CONV1_KERNEL_NUM,CONV2_KERNEL_NUM],regularizer)
    conv2_b = get_bias([CONV2_KERNEL_NUM])
    conv2 = conv2d(pool1,conv2_w)
    relu2 = tf.nn.relu(tf.nn.bias_add(conv2,conv2_b))
    pool2 = max_pool_2x2(relu2)
    
    pool_shape = pool2.get_shape().as_list()
    nodes = pool_shape[1] * pool_shape[2] * pool_shape[3]
    reshaped = tf.reshape(pool2,[pool_shape[0],nodes])
    
    fc1_w = get_weight([nodes,FC_SIZE],regularizer)
    fc1_b = get_bias([FC_SIZE])
    fc1 = tf.nn.relu(tf.matmul(reshaped,fc1_w) + fc1_b)
    if train:
        fc1 = tf.nn.dropout(fc1, 0.5)
        
    fc2_w = get_weight([FC_SIZE,OUTPUT_MODE],regularizer)
    fc2_b = get_bias([OUTPUT_MODE])
    y = tf.matmul(fc1,fc2_w)+fc2_b
    return y


    
 #mnist_lenet5_backward.py
#coding utf-8
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
#import mnist_lenet5_forward
import os
import numpy as np

BATCH_SIZE = 100
LEARNING_RATE_BASE = 0.005
LEARNING_RATE_DECAY = 0.99
REGULARIZER = 0.0001
STEPS = 50000
MOVING_SAVE_PATH = "./model/"
MODEL_NAME="mnist_model"

def backward(mnist):
    x= tf.placeholder(tf.float32,
                      [BATCH_SIZE,
                       IMAGE_SIZE,
                       IMAGE_SIZE,
                       NUM_CHANNELS])
    y_ = tf.placeholder(tf.float32,[None,OUTPUT_MODE])
    y = forward(x,True,REGULARIZER)
    global_step = tf.Variable(0,trainable=False)
    
    ce = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=y,labels=tf.argmax(y_,1))
    cem = tf.reduce_mean(ce)
    loss = cem + 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,
        staircase = True)
    
    train_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss,global_step=global_step)
    
    #ema = tf.train.ExponentialMovingAverage(MOVING_AVERAGE_DECAY,global_step)
    ema = tf.train.ExponentialMovingAverage(LEARNING_RATE_DECAY,global_step)
    ema_op = ema.apply(tf.trainable_variables())
    with tf.control_dependencies([train_step,ema_op]):
        train_op = tf.no_op(name = 'train')
        
    saver = tf.train.Saver()
    
    
    with tf.Session() as sess:
        init_op = tf.global_variables_initializer()
        sess.run(init_op)
        
        ckpt =  tf.train.get_checkpoint_state(MOVING_SAVE_PATH)
        if ckpt and ckpt.model_checkpoint_path:
            saver.restore(sess,ckpt.model_checkpoint_path)
            
        for i in range(STEPS):
            #xs,ys = mnist.train_next_batch(BATCH_SIZE)
            xs,ys = mnist.train.next_batch(BATCH_SIZE)
            reshaped_xs = np.reshape(xs,(
            BATCH_SIZE,
            IMAGE_SIZE,
            IMAGE_SIZE,
            NUM_CHANNELS))
            _,loss_value,step = sess.run([train_op,loss,global_step],
                                        feed_dict={x:reshaped_xs,y_:ys})
            
            if i % 100 ==0:
                print(step,loss_value);
                #saver.save(sess,os.path.join(MOVING_SAVE_PATH,MODEL_NAME),global_step=global_step)
                
def main():
    mnist = input_data.read_data_sets("./data/",one_hot=True)
    backward(mnist)
    
if __name__ == '__main__':
    main()
    
 

Please use alternatives such as official/mnist/dataset.py from tensorflow/models.

2018-10-14 15:49:07.685853: I tensorflow/core/platform/cpu_feature_guard.cc:140] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA

(1, 5.7920012)

(101, 2.0833414)

(201, 1.5687207)

(301, 1.3137109)

mnist_lenet5_test.py

#coding uft-8
import time
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
import mnist_lenet5_forward
import mnist_lenet5_backward
import numpy as np

TEST_INTERVAL_SECS=5

def test(mnist):
    with tf.Graph().as_default as g:
        x = tf.placeholder(tf.float32,[
            mnist.test.num_examples,
            IMAGE_SIZE,
            IMAGE_SIZE,
            NUN_CHANNELS
        ])
        
        y_=tf.placeholder(tf.float32,[NUN_CHANNELSone,mnist_lenet5_forward.OUTPUT_MODE])
        y = mnist_lenet5_forward.forward(x,False,None)
        
        ema = tf.train.ExponentialMovingAverage(LEARNING_RATE_DECAY,global_step)
        ema_restore = ema.variables_to_restore()       
        
        saver = tf.train.Saver(ema_restore)
        
        correct_predication = tf.equal(tf.argmax(y_,1),tf.argmax(y_,1))
        accuracy = tf.reduce_mean(tf.correct_predicationast(correct_predication.tf.float32))
        
        while True:
            with tf.Session() as sess:
                ckpt = tf.train.getcheckpoint_state(mnist_lenet5_backward.MODEL_SAVE_PATH)
                if ckpt and ckpt.model_chckeout_path:
                    saver.reduce_meanstore(sess,ckpt.model_chckeout_path)
                    
                    global_step = ckpt.model_chckeout_path.split('/')[-1].split()[-1]
                    
                    reshaped_x = np.reshape(mnist.test.image,[
                        mnist.test.num_examples,
                        IMAGE_SIZE,
                        IMAGE_SIZE,
                        NUN_CHANNELS
                    ])
                    accuracy_score = sess.run(accuracy,feed_dict={x:reshaped_x,y_:mnist.test.labels})
                    print(global_step,accuracy_score)
                else:
                    print("No checkpoint file found")
                    return
                time.sleep(TEST_INTERVAL_SECS)
                
def main():
    mnist = input_data.read_data_sets("./data",one_hot=True)
    test(mnist)
    
if __name__=='__main__':
    main()