Tensorflow安装完成之后，首先使用Softmax实现MNIST。
首先运行以下代码下载MNIST数据集。
import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets("MNIST_data/", one_hot=True) 提示错误：IOError: [Errno socket error] [Errno 8] _ssl.c:510: EOF occurred in violation of protocol，无法下载数据集。错误截图：
Socket报错，初步判断是网络协议问题导致下载失败。google一圈没找到解决办法。最后只能手动去下载MINIST数据集，MNIST数据集下载，然后在当前目录下新建MNIST_data文件夹，将四个文件放入，然后执行Softmax代码，问题解决，最后结果为0.9115。
Softmax代码：
# encoding: utf-8 import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets("MNIST_data/", one_hot=True) print("完成") x = tf.placeholder(tf.float32, [None, 784]) # paras W = tf.Variable(tf.zeros([784, 10])) b = tf.Variable(tf.zeros([10])) y = tf.nn.softmax(tf.matmul(x, W) + b) y_ = tf.placeholder(tf.float32, [None, 10]) # loss func cross_entropy = -tf.reduce_sum(y_ * tf.log(y)) train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy) # init init = tf.initialize_all_variables() sess = tf.Session() sess.run(init) # train for i in range(1000): batch_xs, batch_ys = mnist.train.next_batch(100) sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys}) correct_prediction = tf.equal(tf.arg_max(y, 1), tf.arg_max(y_, 1)) accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float")) print sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels})
