from tensorflow.examples.tutorials.mnist import input_data 
import tensorflow as tf 
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True) 
sess = tf.InteractiveSession(）

def weight_variable(shape): 
 initial = tf.truncated_normal(shape, stddev=0.1)
#给权重制造一些随机的噪声来打破完全对称， 
 return tf.Variable(initial) 
#使用relu，给偏置增加一些小正值0.1，用来避免死亡节点 
def bias_variable(shape): 
 initial = tf.constant(0.1, shape=shape) 
 return tf.Variable(initial) 

def conv2d(x,W):#2维卷积函数，x输入，w是卷积的参数，strides代表卷积模板移动步长 
 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') 

x = tf.placeholder(tf.float32, [None, 784])# x 时特征 
y_ = tf.placeholder(tf.float32, [None, 10])# y_时真实的label 
x_image = tf.reshape(x, [-1, 28, 28,1]) 

w_conv1 = weight_variable([5, 5, 1, 32])
#代表卷积核尺寸为5X5，1个颜色通道，32个不同的卷积核，使用conv2d函数进行卷积操作， 
b_conv1 = bias_variable([32]) 
h_conv1 = tf.nn.relu(conv2d(x_image, w_conv1) + b_conv1) 
h_pool1 = max_pool_2x2(h_conv1) 

w_conv2 = weight_variable([5, 5, 32, 64])#这层提取64种特征 
b_conv2 = bias_variable([64]) 
h_conv2 = tf.nn.relu(conv2d(h_pool1, w_conv2) + b_conv2) 
h_pool2 = max_pool_2x2(h_conv2) 

w_fc1 = weight_variable([7*7*64, 1024]) 
b_fc1 = bias_variable([1024]) 
h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64]) 
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, w_fc1) + b_fc1) 

#为了减轻过拟合，使用一个Dropout层 
keep_prob = tf.placeholder(tf.float32) 
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob) 
 
#dropout层的输出连接一个softmax层，得到最后的概率输出 
w_fc2 = weight_variable([1024, 10]) 
b_fc2 = bias_variable([10]) 
y_conv = tf.nn.softmax(tf.matmul(h_fc1_drop, w_fc2) + b_fc2) 

#损失函数，并且定义优化器为Adam 
cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y_conv), 
            reduction_indices=[1])) 
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy) 
 
correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1)) 
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) 

#初始化所有参数 
tf.global_variables_initializer().run() 
for i in range (20000): 
 batch = mnist.train.next_batch(50) 
 if i%100 == 0: 
  train_accuracy = accuracy.eval(feed_dict={x:batch[0], y_: batch[1], 
             keep_prob: 1.0}) 
  print("step %d, training accuracy %g"%(i, train_accuracy)) 
 train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5}) 

print("test accuracy %g" %accuracy.eval(feed_dict={ 
 x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}))