tensorflow_example2.py

from tensorflow.examples.tutorials.mnist import input_data

import tensorflow as tf

 

#初始化权重函数

def weight_variable(shape):

    initial = tf.truncated_normal(shape,stddev=0.1);

    return tf.Variable(initial)

 

#初始化偏置项

def bias_variable(shape):

    initial = tf.constant(0.1,shape=shape)

    return tf.Variable(initial)

 

#定义卷积函数

def conv2d(x,w):

    return tf.nn.conv2d(x,w,strides=[1,1,1,1],padding='SAME')

 

#定义一个2*2的最大池化层

def max_pool_2_2(x):

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

 

if __name__ == "__main__":

    #定义输入变量

    x = tf.placeholder("float",shape=[None,784])

    #定义输出变量

    y_ = tf.placeholder("float",shape=[None,10])

    #初始化权重,第一层卷积，32的意思代表的是输出32个通道

    # 其实，也就是设置32个卷积，每一个卷积都会对图像进行卷积操作

    w_conv1 = weight_variable([5,5,1,32])

    #初始化偏置项

    b_conv1 = bias_variable([32])

    #将输入的x转成一个4D向量，第2、3维对应图片的宽高，最后一维代表图片的颜色通道数

    # 输入的图像为灰度图，所以通道数为1，如果是RGB图，通道数为3

    # tf.reshape(x,[-1,28,28,1])的意思是将x自动转换成28*28*1的数组

    # -1的意思是代表不知道x的shape，它会按照后面的设置进行转换

    x_image = tf.reshape(x,[-1,28,28,1])

    # 卷积并激活

    h_conv1 = tf.nn.relu(conv2d(x_image,w_conv1) + b_conv1)

    #池化

    h_pool1 = max_pool_2_2(h_conv1)

    #第二层卷积

    #初始权重

    w_conv2 = weight_variable([5,5,32,64])

    #初始化偏置项

    b_conv2 = bias_variable([64])

    #将第一层卷积池化后的结果作为第二层卷积的输入

    h_conv2 = tf.nn.relu(conv2d(h_pool1,w_conv2) + b_conv2)

    #池化

    h_pool2 = max_pool_2_2(h_conv2)

    # 设置全连接层的权重
    w_fc1 = weight_variable([7*7*64,1024])

    # 设置全连接层的偏置
    b_fc1 = bias_variable([1024])

    # 将第二层卷积池化后的结果，转成一个7*7*64的数组
    h_pool2_flat = tf.reshape(h_pool2,[-1,7*7*64])

    # 通过全连接之后并激活
    h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat,w_fc1) + b_fc1)

    # 防止过拟合
    keep_prob = tf.placeholder("float")
    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)
    #日志输出，每迭代100次输出一次日志

    #定义交叉熵为损失函数
    cross_entropy = -tf.reduce_sum(y_ * tf.log(y_conv))

    #最小化交叉熵
    train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)

    #计算准确率
    correct_prediction = tf.equal(tf.argmax(y_conv,1),tf.argmax(y_,1))
    accuracy = tf.reduce_mean(tf.cast(correct_prediction,"float"))
    sess = tf.Session()
    sess.run(tf.initialize_all_variables())
    
    # 下载minist的手写数字的数据集

    mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)

    for i in range(200):

        batch = mnist.train.next_batch(50)

        #if i % 100 == 0:

        train_accuracy = accuracy.eval(session=sess,feed_dict={x:batch[0],y_:batch[1],keep_prob:1.0})

        print("步 %d,训练精确率： %g"%(i,train_accuracy))

        train_step.run(session = sess,feed_dict={x:batch[0],y_:batch[1],keep_prob:0.5})

    print("测试精确率 %g" % accuracy.eval(session=sess,feed_dict={

        x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}))