本小節主要是構建卷積神經網路，本小節構建的卷積網路過程如下：

       原圖片->第一層非線性卷積->第一層池化->第二層非線性卷積->第二層池化->第一層全連線->第二層全連線

      程式碼如下所示，上面有註釋，就不詳細再解釋。

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data',one_hot=True)

#每個批次的大小
batch_size = 100
#計算一共有多少個批次
n_batch = mnist.train.num_examples // batch_size

#引數概要
def variable_summaries(var):
    with tf.name_scope('summaries'):
        mean = tf.reduce_mean(var)
        tf.summary.scalar('mean', mean)#平均值
        with tf.name_scope('stddev'):
            stddev = tf.sqrt(tf.reduce_mean(tf.square(var - mean)))
        tf.summary.scalar('stddev', stddev)#標準差
        tf.summary.scalar('max', tf.reduce_max(var))#最大值
        tf.summary.scalar('min', tf.reduce_min(var))#最小值
        tf.summary.histogram('histogram', var)#直方圖

#初始化權值
def weight_variable(shape,name):
    initial = tf.truncated_normal(shape,stddev=0.1)#生成一個截斷的正態分佈
    return tf.Variable(initial,name=name)

#初始化偏置
def bias_variable(shape,name):
    initial = tf.constant(0.1,shape=shape)
    return tf.Variable(initial,name=name)

#卷積層
def conv2d(x,W):
    return tf.nn.conv2d(x,W,strides=[1,1,1,1],padding='SAME')
#strides[0]代表x方向的步長，strides[1]、strides[2]代表padding視窗大小，strides[3]代表y方向的步長，這裡採用same padding，
#same padding採取在圖片外圍補0的方式，使得進行卷積以後的圖片大小與原圖片大小相同，從strides[1]=strides[2]=1可知，補一圈0
#池化層
def max_pool_2x2(x):
    #ksize [1,x,y,1]
    return tf.nn.max_pool(x,ksize=[1,2,2,1],strides=[1,2,2,1],padding='SAME')
#ksize[0]、ksize[3]分別表示pooling視窗在x、y方向的步長，ksize[1]、ksize[2]表示pooling視窗的大小，strides含義跟上面相同。
#名稱空間
with tf.name_scope('input'):
    #定義兩個placeholder
    x = tf.placeholder(tf.float32,[None,784],name='x-input')
    y = tf.placeholder(tf.float32,[None,10],name='y-input')
    with tf.name_scope('x_image'):
        #改變x的格式轉為4D的向量[batch, in_height, in_width, in_channels]`
        x_image = tf.reshape(x,[-1,28,28,1],name='x_image')#將784畫素的圖片轉為28*28畫素


with tf.name_scope('Conv1'):
    #初始化第一個卷積層的權值和偏置
    with tf.name_scope('W_conv1'):
        W_conv1 = weight_variable([5,5,1,32],name='W_conv1')#5*5的取樣視窗，32個卷積核從1個平面抽取特徵
    with tf.name_scope('b_conv1'):  
        b_conv1 = bias_variable([32],name='b_conv1')#每一個卷積核一個偏置值

    #把x_image和權值向量進行卷積，再加上偏置值，然後應用於relu啟用函式
    with tf.name_scope('conv2d_1'):
        conv2d_1 = conv2d(x_image,W_conv1) + b_conv1
    with tf.name_scope('relu'):
        h_conv1 = tf.nn.relu(conv2d_1)
    with tf.name_scope('h_pool1'):
        h_pool1 = max_pool_2x2(h_conv1)#進行max-pooling

with tf.name_scope('Conv2'):
    #初始化第二個卷積層的權值和偏置
    with tf.name_scope('W_conv2'):
        W_conv2 = weight_variable([5,5,32,64],name='W_conv2')#5*5的取樣視窗，64個卷積核從32個平面抽取特徵
    with tf.name_scope('b_conv2'):  
        b_conv2 = bias_variable([64],name='b_conv2')#每一個卷積核一個偏置值

    #把h_pool1和權值向量進行卷積，再加上偏置值，然後應用於relu啟用函式
    with tf.name_scope('conv2d_2'):
        conv2d_2 = conv2d(h_pool1,W_conv2) + b_conv2
    with tf.name_scope('relu'):
        h_conv2 = tf.nn.relu(conv2d_2)
    with tf.name_scope('h_pool2'):
        h_pool2 = max_pool_2x2(h_conv2)#進行max-pooling

#28*28的圖片第一次卷積後還是28*28，第一次池化後變為14*14
#第二次卷積後為14*14，第二次池化後變為了7*7
#進過上面操作後得到64張7*7的平面

with tf.name_scope('fc1'):
    #初始化第一個全連線層的權值
    with tf.name_scope('W_fc1'):
        W_fc1 = weight_variable([7*7*64,1024],name='W_fc1')#上一層有7*7*64個神經元，全連線層有1024個神經元
    with tf.name_scope('b_fc1'):
        b_fc1 = bias_variable([1024],name='b_fc1')#1024個節點

    #把池化層2的輸出扁平化為1維
    with tf.name_scope('h_pool2_flat'):
        h_pool2_flat = tf.reshape(h_pool2,[-1,7*7*64],name='h_pool2_flat')
    #求第一個全連線層的輸出
    with tf.name_scope('wx_plus_b1'):
        wx_plus_b1 = tf.matmul(h_pool2_flat,W_fc1) + b_fc1
    with tf.name_scope('relu'):
        h_fc1 = tf.nn.relu(wx_plus_b1)

    #keep_prob用來表示神經元的輸出概率
    with tf.name_scope('keep_prob'):
        keep_prob = tf.placeholder(tf.float32,name='keep_prob')
    with tf.name_scope('h_fc1_drop'):
        h_fc1_drop = tf.nn.dropout(h_fc1,keep_prob,name='h_fc1_drop')

with tf.name_scope('fc2'):
    #初始化第二個全連線層
    with tf.name_scope('W_fc2'):
        W_fc2 = weight_variable([1024,10],name='W_fc2')
    with tf.name_scope('b_fc2'):    
        b_fc2 = bias_variable([10],name='b_fc2')
    with tf.name_scope('wx_plus_b2'):
        wx_plus_b2 = tf.matmul(h_fc1_drop,W_fc2) + b_fc2
    with tf.name_scope('softmax'):
        #計算輸出
        prediction = tf.nn.softmax(wx_plus_b2)

#交叉熵代價函式
with tf.name_scope('cross_entropy'):
    cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y,logits=prediction),name='cross_entropy')
    tf.summary.scalar('cross_entropy',cross_entropy)
    
#使用AdamOptimizer進行優化
with tf.name_scope('train'):
    train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)

#求準確率
with tf.name_scope('accuracy'):
    with tf.name_scope('correct_prediction'):
        #結果存放在一個布林列表中
        correct_prediction = tf.equal(tf.argmax(prediction,1),tf.argmax(y,1))#argmax返回一維張量中最大的值所在的位置
    with tf.name_scope('accuracy'):
        #求準確率
        accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))
        tf.summary.scalar('accuracy',accuracy)
        
#合併所有的summary
merged = tf.summary.merge_all()

with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    train_writer = tf.summary.FileWriter('logs/train',sess.graph)
    test_writer = tf.summary.FileWriter('logs/test',sess.graph)
    for i in range(1001):
        #訓練模型
        batch_xs,batch_ys =  mnist.train.next_batch(batch_size)
        sess.run(train_step,feed_dict={x:batch_xs,y:batch_ys,keep_prob:0.5})
        #記錄訓練集計算的引數
        summary = sess.run(merged,feed_dict={x:batch_xs,y:batch_ys,keep_prob:1.0})
        train_writer.add_summary(summary,i)
        #記錄測試集計算的引數
        batch_xs,batch_ys =  mnist.test.next_batch(batch_size)
        summary = sess.run(merged,feed_dict={x:batch_xs,y:batch_ys,keep_prob:1.0})
        test_writer.add_summary(summary,i)
    
        if i%100==0:
            test_acc = sess.run(accuracy,feed_dict={x:mnist.test.images,y:mnist.test.labels,keep_prob:1.0})
            train_acc = sess.run(accuracy,feed_dict={x:mnist.train.images[:10000],y:mnist.train.labels[:10000],keep_prob:1.0})
            print ("Iter " + str(i) + ", Testing Accuracy= " + str(test_acc) + ", Training Accuracy= " + str(train_acc))