cnn_mnist.txt

#CNN识别手写MNIST数字集0-9
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data  #加载测试数据
mnist=input_data.read_data_sets("MNIST_data/",one_hot=True)

sess=tf.InteractiveSession()  #以交互式方式启动session

#创建两个占位符，x为输入图像的网络，y_为输入网络的图像类别
x=tf.placeholder("float",shape=[None,784])
y_=tf.placeholder("float",shape=[None,10])


def weight_variable(shape):   #初始化权重函数
    initial=tf.truncated_normal(shape,stddev=0.1)
    return tf.Variable(initial)
def bias_variable(shape):  #初始化偏置项
    initial=tf.constant(0.1,shape=shape)
    return tf.Variable(initial)
def conv2d(x,w):   #定义卷积函数  
    return tf.nn.conv2d(x,w,strides=[1,1,1,1],padding='SAME')
def max_pool_2_2(x):   #定义池化层
    return tf.nn.max_pool(x,ksize=[1,2,2,1],strides=[1,2,2,1],padding='SAME')

#第一层卷积
w_conv1=weight_variable([5,5,1,32])
b_conv1=bias_variable([32])
x_image=tf.reshape(x,[-1,28,28,1])
h_conv1=tf.nn.relu(conv2d(x_image,w_conv1)+b_conv1)  #第一层卷积结果
h_pool1=max_pool_2_2(h_conv1)  #第一层池化结果
#第二层卷积
w_conv2=weight_variable([5,5,32,64])
b_conv2=bias_variable([64])
h_conv2=tf.nn.relu(conv2d(h_pool1,w_conv2)+b_conv2)
h_pool2=max_pool_2_2(h_conv2)
#全连接层
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) #全连接层输出
keep_prob=tf.placeholder("float")  #使用dropout减少过拟合
h_fc1_drop=tf.nn.dropout(h_fc1,keep_prob)
#输出层
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) #模型预测输出
cross_entropy=-tf.reduce_sum(y_*tf.log(y_conv))   #交叉熵损失
#模型训练，使用AdamOptimizer来做梯度最速下降
train_step=tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
#正确预测，得到true或false的list
correct_prediction=tf.equal(tf.argmax(y_,1),tf.argmax(y_conv,1))
#将布尔值转化为浮点数，取平均值作为精确度
accuracy=tf.reduce_mean(tf.cast(correct_prediction,"float"))
#在session中初始化变量才能在session中调用
sess.run(tf.global_variables_initializer())

for i in range(2000):   #迭代优化模型
    batch=mnist.train.next_batch(50)   #每次取50个样本进行训练
    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})  #训练权重和偏置项
print("test accuracy %g" % accuracy.eval(feed_dict={
        x:mnist.test.images,y_:mnist.test.labels,keep_prob:10.}))