import tensorflow as tf
import tensorflow.examples.tutorials.mnist.input_data as input_data#1 读取数据
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)#2 建立模型  使用卷积神经网络#2.1 输入图像与标签
x = tf.placeholder("float", shape = [None, 28,28,1])
y_ = tf.placeholder("float", shape = [None, 10]) #2.2 搭建卷积神经网络    卷积+卷积+池化+全连接#2.2.1 卷积层 
w1 = tf.Variable(tf.truncated_normal([3, 3, 1, 32])) #高宽3*3 单通道 32个filter 
b1 = tf.Variable(tf.zeros([32]))                     #每个filter初始bias为0
conv1 = tf.nn.conv2d(input=x, filter=w1, strides=[1, 1, 1, 1], padding='SAME') + b1
conv1 = tf.nn.relu(conv1)                            #进行卷积 非线性激活#2.2.2 卷积层
w2 = tf.Variable(tf.truncated_normal([3, 3, 32, 32])) #高宽3*3 32通道 32个filter 
b2 = tf.Variable(tf.zeros([32]))                      #每个filter初始bias为0
conv2 = tf.nn.conv2d(input=conv1, filter=w2, strides=[1, 1, 1, 1], padding='SAME') + b2
conv2 = tf.nn.relu(conv2)                             #进行卷积 非线性激活 #2.2.3 池化
pool = tf.nn.max_pool(conv2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')#2.2.4 全连接
w = tf.Variable(tf.truncated_normal([14 * 14 * 32, 10], stddev=0.1))
b = tf.Variable(tf.zeros([10]))
pool_reshape = tf.reshape(pool, [-1, 14*14*32])#2.2.5 输出
y = tf.matmul(pool_reshape, w) + b#3 损失函数 优化训练
cross_entropy = tf.nn.softmax_cross_entropy_with_logits(labels = y_, logits = y)
crossEntropyLoss = tf.reduce_mean(cross_entropy)
optimizer = tf.train.AdamOptimizer().minimize(crossEntropyLoss)#4 测试
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))#5 开启会话
with tf.Session() as sess:# 初始化sess.run(tf.global_variables_initializer())# 训练for i in range(501):batch_xs, batch_ys = mnist.train.next_batch(100)batch_xs = batch_xs.reshape([100,28,28,1])sess.run(optimizer, feed_dict={x: batch_xs, y_: batch_ys})# 测试模型#if i%100 == 0:#result = sess.run(accuracy, feed_dict={x: mnist.test.images.reshape([10000,28,28,1]), y_: mnist.test.labels}) * 100#print(result)# 测试模型sum = 0;for i in range(10000):sum += sess.run(accuracy, feed_dict={x: mnist.test.images[i].reshape([1,28,28,1]), y_: mnist.test.labels[i].reshape([1,10])})print(sum)print(sum/10000)

 结果：