标签：tail gradient sdn http dict ext amp art 方法
方法一：命令行跑程序 参考http://blog.csdn.net/zhuiqiuk/article/details/52993544
（我用的python3，注意print的语法）
方法二：用IDE跑
将下好的mnist数据集拷贝到pycharm中,文件夹命名为MNIST_data
再创建一个python脚本，代码如下
import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets("./MNIST_data/", one_hot=True) x = tf.placeholder(tf.float32, [None, 784]) 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("float", [None,10]) cross_entropy = -tf.reduce_sum(y_*tf.log(y)) train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy) init = tf.initialize_all_variables() sess = tf.Session() sess.run(init) 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.argmax(y,1), tf.argmax(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}))
更多问题，可以私信我新浪微博@嘤嘤要偷偷瘦下去
标签：tail gradient sdn http dict ext amp art 方法
原文地址：http://www.cnblogs.com/xiaoyingying/p/7683849.html
