卷积神经网络的应用实例-LeNet5网络结构

#encoding:utf-8
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
import time
#获取mnist数据
mnist_data_set = input_data.read_data_sets('MNIST_data', one_hot=True)


#声明输入图片数据，类别
x = tf.placeholder('float',[None,784])
y_ = tf.placeholder('float',[None, 10])
x_image = tf.reshape(x, [-1, 28, 28, 1])

#第一层卷积层，初始化卷积核参数，偏置值，该卷积层5*5大小，一个通道，共有6个不同卷积核
filter1 = tf.Variable(tf.truncated_normal([5,5,1,6]))
bias1 = tf.Variable(tf.truncated_normal([6]))
conv1 = tf.nn.conv2d(x_image, filter1, strides=[1,1,1,1], padding="SAME")
h_conv1 = tf.nn.sigmoid(conv1 + bias1)

maxpool2 = tf.nn.max_pool(h_conv1, ksize=[1,2,2,1], strides=[1,2,2,1],padding='SAME')

filter2 = tf.Variable(tf.truncated_normal([5,5,6,16]))
bias2 = tf.Variable(tf.truncated_normal([16]))
conv2 = tf.nn.conv2d(maxpool2, filter2, strides=[1,1,1,1,], padding='SAME')
h_conv2 = tf.nn.sigmoid(conv2 + bias2)

maxpool3 = tf.nn.max_pool(h_conv2,ksize=[1,2,2,1], strides=[1,2,2,1],padding='SAME')

filter3 = tf.Variable(tf.truncated_normal([5,5,16,120]))
bias3 = tf.Variable(tf.truncated_normal([120]))
conv3 = tf.nn.conv2d(maxpool3,filter3,strides=[1,1,1,1],padding='SAME')
h_conv3 = tf.nn.sigmoid(conv3+bias3)


#全连接层
#权值参数
W_fcl = tf.Variable(tf.truncated_normal([7*7*120,80]))
#偏置值
b_fcl = tf.Variable(tf.truncated_normal([80]))
#将卷积的输出展开
h_pool2_flat = tf.reshape(h_conv3, [-1,7*7*120])
#神经网络计算，并添加sigmoid激活函数
h_fcl = tf.nn.sigmoid(tf.matmul(h_pool2_flat,W_fcl) + b_fcl)

#输出层
W_fc2 = tf.Variable(tf.truncated_normal([80,10]))
b_fc2 = tf.Variable(tf.truncated_normal([10]))
y_conv = tf.nn.softmax(tf.matmul(h_fcl,W_fc2) + b_fc2)
#损失函数
cross_entropy = -tf.reduce_sum(y_ * tf.log(y_conv))
#使用GDO优化算法来调整参数
train_step = tf.train.GradientDescentOptimizer(0.001).minimize(cross_entropy)

sess = tf.InteractiveSession()
#测试正确率
correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction,"float"))

sess.run(tf.initialize_all_variables())



#进行训练
start_time = time.time()
for i in range(20000):
    batch_xs, batch_ys = mnist_data_set.train.next_batch(200)
    #每迭代100个batch，对当前训练数据进行测试，输出当前预测准确率
    if i % 2 == 0:
        train_accuracy = accuracy.eval(feed_dict={x:batch_xs,y_:batch_ys})
        print("step %d, training accuracy %g" %(i, train_accuracy))
        #计算间隔时间
        end_time = time.time()
        print("time",(end_time - start_time))
        start_time = end_time

    #训练数据
    train_step.run(feed_dict={x:batch_xs,y_:batch_ys})
sess.close()