tensorflow基础使用5

MNIST数据集分类简单版本

# coding: utf-8# In[2]:import tensorflow as tffrom tensorflow.examples.tutorials.mnist import input_data# In[3]:#载入数据集mnist = input_data.read_data_sets("MNIST_data",one_hot=True)#每个批次的大小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loss = tf.reduce_mean(tf.square(y-prediction))#使用梯度下降法train_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))with tf.Session() as sess:    sess.run(init)    for epoch in range(21):        for batch in range(n_batch):            batch_xs,batch_ys =  mnist.train.next_batch(batch_size)            sess.run(train_step,feed_dict={x:batch_xs,y:batch_ys})                acc = sess.run(accuracy,feed_dict={x:mnist.test.images,y:mnist.test.labels})        print("Iter " + str(epoch) + ",Testing Accuracy " + str(acc))