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import tensorflow as tfimport numpy as npimport matplotlib.pyplot as pltfrom tensorflow.examples.tutorials.mnist import input_datatrainimg = mnist.train.imagestrainlabel = mnist.train.labelstestimg =  mnist.test.imagestestlabel =  mnist.test.labelsprint(trainlabel[0]) #输出第一行label值

运行结果：

#初始化变量x = tf.placeholder("float",[None,784])  #placeholder（先占位，不复制）,[None,784]样本的个数（无限大），每个样本的特征（784个像素点）y = tf.placeholder("float",[None,10])#样本的类别（10个）W = tf.Variable(tf.zeros([784,10]))#每个特征（784个像素点）对应输出10个分类值b = tf.Variable(tf.zeros([10]))#逻辑回归模型（softmax完成多分类任务）actv = tf.nn.softmax(tf.matmul(x,W)+b)#计算属于正确类别的概率值#计算损失值（预测值与真实值间的均方差）cost = tf.reduce_mean(-tf.reduce_sum(y*tf.log(actv),reduction_indices=1))#采用梯度下降优化参数（W，b）,最小化损失值learning_rate=0.01                    optm = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost) #学习率为0.01#optimizer = tf.train.GradientDescentOptimizer(0.01)#学习率为0.01#最小化损失值#optm = optimizer.minimize(cost)#预测值，equal返回的值是布尔类型#argmax返回矩阵中最大元素的索引，0，代表列方向；1代表行方向                     pred =  tf.equal(tf.argmax(actv,1),tf.argmax(y,1)) #准确率accr =   tf.reduce_mean(tf.cast(pred,"float"))  #cast进行类型转化 （true为1，false为0）                 init_op = tf.global_variables_initializer() #定义全局变量training_epochs = 50 #将所有样本迭代50次batch_size = 100      #每次迭代选择样本的个数                display_step =5       #每进行5个epoch进行一次展示               with tf.Session() as sess:    sess.run(init_op)    for epoch in range(training_epochs):        avg_cost =0.0 # 初始化损失值        num_batch = int(mnist.train.num_examples/batch_size)         for i in range(num_batch):            batch_xs, batch_ys = mnist.train.next_batch(batch_size) #以batch为单位逐次进行            sess.run(optm,feed_dict={x: batch_xs,y: batch_ys}) #给x，y赋值            feeds={x: batch_xs,y: batch_ys}              avg_cost +=sess.run(cost,feed_dict= feeds)/num_batch       #显示        if epoch %  display_step == 0:            feeds_train = {x: batch_xs,y: batch_ys}                    feeds_test  = {x:mnist.test.images,y: mnist.test.labels}            train_acc   =  sess.run(accr,feed_dict= feeds_train)            test_acc    =  sess.run(accr,feed_dict= feeds_test)                     print("Epoch: %03d/%03d cost:%.9f trian_acc: %.3f test_acc: %.3f"                 % (epoch,training_epochs,avg_cost,train_acc,test_acc))         print("Done")

运行结果：

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