import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
from tensorflow.examples.tutorials.mnist import mnist
import tensorflow.contrib.slim as slim

mnist=input_data.read_data_sets('../share/MNIST_DATA',one_hot=True)
x=tf.placeholder("float",shape=[None,784])
y_=tf.placeholder("float",shape=[None,10])

#cast x to 3D
x_image=tf.reshape(x,[-1,28,28,1])#shape of x is [N,28,28,1]

#conv layer1
net=slim.conv2d(x_image,32,[5,5],scope='conv1')#shape of net is [N,28,28,32]
net=slim.max_pool2d(net,[2,2],scope='pool1')#shape of net is [N,14,14,32]

#conv layer2
net=slim.conv2d(net,64,[5,5],scope='conv2')#shape of net is [N,14,14,64]
net=slim.max_pool2d(net,[2,2],scope='pool2')#shape of net is [N,7,7,64]

#reshape for full connection
net=tf.reshape(net,[-1,7*7*64])#[N,7*7*64]

#fc1
net=slim.fully_connected(net,1024,scope='fc1')#shape of net is [N,1024]

#dropout layer
keep_prob=tf.placeholder('float')
net=tf.nn.dropout(net,keep_prob)
#fc2
net=slim.fully_connected(net,10,scope='fc2')#[N,10]
#softmax
y=tf.nn.softmax(net)#[N,10]

cross_entropy=-tf.reduce_sum(tf.multiply(y_,tf.log(y)))#y and _y have same shape.
train_step=tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
correct_prediction=tf.equal(tf.argmax(y,axis=1),tf.argmax(y_,axis=1))#shape of correct_prediction is [N]
accuracy=tf.reduce_mean(tf.cast(correct_prediction,'float'))

init=tf.global_variables_initializer()
with tf.Session() as sess:
    sess.run(init)
    for i in range(10000):
        batch=mnist.train.next_batch(50)
        if i%100==0:
            train_accuracy=sess.run(accuracy,feed_dict={x:batch[0],y_:batch[1],keep_prob:1.0})
            print('step %d,training accuracy  %g !!!!!!!'%(i,train_accuracy))
        sess.run(train_step,feed_dict={x:batch[0],y_:batch[1],keep_prob:0.5})

    total_accuracy=sess.run(accuracy,feed_dict={x:mnist.test.images,y_:mnist.test.labels,keep_prob:1.0})
    print('test_accuracy  %s!!!!!!!'%(total_accuracy))
 

直接貼程式碼，程式碼沒什麼好說的了，我都做了註釋了。主要是參考TensorFlow官網上的教程點選開啟連結，但是使用了slim模組（slim介紹參考我的這個部落格點選開啟連結），於是大大縮小了程式碼量，也提高了程式碼的可讀性，強烈推薦slim模組。當然如果對上述程式碼中的函式不熟悉的可直接去TensorFlow官網檢視API手冊，裡面介紹得非常詳盡。當然在除錯程式碼時最重要的還是關注tensor的shape，於是我在每個tensor變數後都註釋了shape，方便除錯，也能提高程式的可讀性。

後面得到的結果展示