TensorFlow新手实例源码--mnist源码

环境：python3.5（今[2017.11.25]实测3.6版本无法安装tensorflow）
以下源码已测试通过，准确率约91%
代码如下：

import tensorflow.examples.tutorials.mnist.input_data as input_datamnist = input_data.read_data_sets("MNIST_data/", one_hot=True)import tensorflow as tfx = 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(tf.float32, [None,10])cross_entropy = -tf.reduce_sum(y_*tf.log(y))train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)with tf.Session() as sess:  sess.run(tf.global_variables_initializer())  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}))  print(sess.run(W))  print(sess.run(b))