MNIST分类

import tensorflow as tffrom tensorflow.examples.tutorials.mnist import input_data#载入数据集mnist = input_data.read_data_sets("MNIST_data",one_hot=True)#每个批次100张照片batch_size = 100#计算一共有多少个批次n_batch = mnist.train.num_examples // batch_size#定义两个placeholderx = tf.placeholder(tf.float32,[None,784])y = tf.placeholder(tf.float32,[None,10])#创建一个简单的神经网络，输入层784个神经元，输出层10个神经元W = tf.Variable(tf.zeros([784,10]))b = tf.Variable(tf.zeros([10]))prediction = tf.nn.softmax(tf.matmul(x,W)+b)#二次代价函数#square是求平方#reduce_mean是求平均值loss = tf.reduce_mean(tf.square(y-prediction))#使用梯度下降法来最小化loss，学习率是0.2train_step = tf.train.GradientDescentOptimizer(0.2).minimize(loss)#初始化变量init = tf.global_variables_initializer()#结果存放在一个布尔型列表中correct_prediction = tf.equal(tf.argmax(y,1),tf.argmax(prediction,1))#argmax返回一维张量中最大的值所在的位置#求准确率accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))#cast是进行数据格式转换，把布尔型转为float32类型with tf.Session() as sess:    #执行初始化    sess.run(init)    #迭代21个周期    for epoch in range(21):        #每个周期迭代n_batch个batch，每个batch为100        for batch in range(n_batch):            #获得一个batch的数据和标签            batch_xs,batch_ys =  mnist.train.next_batch(batch_size)            #通过feed喂到模型中进行训练            sess.run(train_step,feed_dict={x:batch_xs,y:batch_ys})                #计算准确率        acc = sess.run(accuracy,feed_dict={x:mnist.test.images,y:mnist.test.labels})        print("Iter " + str(epoch) + ",Testing Accuracy " + str(acc))