折腾了很久，终于运行成功。

才云科技的书不错，就是需要微调一二。

心得：1，记得activate tensorflow,然后再python

           2，Python的代码格式很重要，不要错误。

           3，还不清楚如何不跳出去就能用tensorflow的方法。

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from __future__ import absolute_import from __future__ import division from __future__ import print_function import argparse import sys import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data INPUT_NODE = 784 OUTPUT_NODE = 10 LAYER1_NODE = 500 BATCH_SIZE = 100 LEARNING_RATE_BASE = 0.8 LEARNING_RATE_DECAY = 0.99 REGULARIZATION_RATE = 0.001 TRAINING_STEPS = 30000 MOVING_AVERAGE_DECAY = 0.99 FLAGS=None

def inference(input_tensor,avg_class,weights1,biases1,weights2,biases2):     if avg_class == None:         layer1=tf.nn.relu(tf.matmul(input_tensor,weights1)+biases1)         return tf.matmul(layer1,weights2)+biases2     else:         layer1 = tf.nn.relu(             tf.matmul(input_tensor,avg_class.average(weights1))+avg_class.average(biases1))         return tf.matmul(layer1,avg_class.average(weights2))+avg_class.average(biases2)

def main(_):     mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True)     x = tf.placeholder(tf.float32,[None,INPUT_NODE],name='x-input')     y_= tf.placeholder(tf.float32,[None,OUTPUT_NODE],name='y-input')     weights1 = tf.Variable(tf.truncated_normal([INPUT_NODE,LAYER1_NODE],stddev=0.1))     biases1 = tf.Variable(tf.constant(0.1,shape=[LAYER1_NODE]))     weights2 = tf.Variable(tf.truncated_normal([LAYER1_NODE,OUTPUT_NODE],stddev=0.1))     biases2 = tf.Variable(tf.constant(0.1,shape=[OUTPUT_NODE]))     y=inference(x,None,weights1,biases1,weights2,biases2)     global_step =tf.Variable(0,trainable=False)     variable_averages = tf.train.ExponentialMovingAverage(MOVING_AVERAGE_DECAY,global_step)     variables_averages_op = variable_averages.apply(tf.trainable_variables())     average_y = inference(x,variable_averages,weights1,biases1,weights2,biases2)     cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=y, labels=tf.argmax(y_,1))     cross_entropy_mean = tf.reduce_mean(cross_entropy)     regularizer = tf.contrib.layers.l2_regularizer(REGULARIZATION_RATE)     regularization=regularizer(weights1)+regularizer(weights2)     loss = cross_entropy_mean + regularization     learning_rate = tf.train.exponential_decay(         LEARNING_RATE_BASE,global_step,mnist.train.num_examples/BATCH_SIZE,LEARNING_RATE_DECAY)     train_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss,global_step=global_step)     with tf.control_dependencies([train_step,variables_averages_op]):         train_op=tf.no_op(name='train')     correct_prediction = tf.equal(tf.argmax(average_y,1),tf.argmax(y_,1))     accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))     with tf.Session() as sess:         tf.global_variables_initializer().run()         validate_feed = {x:mnist.validation.images,y_:mnist.validation.labels}         test_feed = {x:mnist.test.images,y_:mnist.test.labels}         for i in range(TRAINING_STEPS):             if i % 1000 == 0:                 validate_acc = sess.run(accuracy,feed_dict = validate_feed)                 print("After %d training steps,validation accuracy " "using average model is %g" % (i, validate_acc))             xs,ys = mnist.train.next_batch(BATCH_SIZE)             sess.run(train_op,feed_dict = {x:xs,y_:ys})         test_acc = sess.run(accuracy,feed_dict=test_feed)         print("After %d training steps,test accuracy using average model is %g" % (TRAINING_STEPS,test_acc))

if __name__ == '__main__':     parser = argparse.ArgumentParser()     parser.add_argument('--data_dir', type=str, default='/tmp/tensorflow/mnist/input_data',                       help='Directory for storing input data')     FLAGS, unparsed = parser.parse_known_args()     tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)